diff options
138 files changed, 1582 insertions, 2849 deletions
diff --git a/README.html b/README.html index 4d0fd270..c9f86c6b 100644 --- a/README.html +++ b/README.html @@ -20,8 +20,8 @@ <script src="data:application/x-javascript;base64,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"></script> <script src="data:application/x-javascript;base64,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"></script> <script src="data:application/x-javascript;base64,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"></script> -<link href="data:text/css;charset=utf-8,pre%20%2Eoperator%2C%0Apre%20%2Eparen%20%7B%0Acolor%3A%20rgb%28104%2C%20118%2C%20135%29%0A%7D%0Apre%20%2Eliteral%20%7B%0Acolor%3A%20%23990073%0A%7D%0Apre%20%2Enumber%20%7B%0Acolor%3A%20%23099%3B%0A%7D%0Apre%20%2Ecomment%20%7B%0Acolor%3A%20%23998%3B%0Afont%2Dstyle%3A%20italic%0A%7D%0Apre%20%2Ekeyword%20%7B%0Acolor%3A%20%23900%3B%0Afont%2Dweight%3A%20bold%0A%7D%0Apre%20%2Eidentifier%20%7B%0Acolor%3A%20rgb%280%2C%200%2C%200%29%3B%0A%7D%0Apre%20%2Estring%20%7B%0Acolor%3A%20%23d14%3B%0A%7D%0A" rel="stylesheet" /> -<script src="data:application/x-javascript;base64,var hljs=new function(){function m(p){return p.replace(/&/gm,"&amp;").replace(/</gm,"&lt;")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.bash=function(){var e={"true":1,"false":1};var b={cN:"variable",b:"\\$([a-zA-Z0-9_]+)\\b"};var a={cN:"variable",b:"\\$\\{(([^}])|(\\\\}))+\\}",c:[hljs.CNM]};var f={cN:"string",b:'"',e:'"',i:"\\n",c:[hljs.BE,b,a],r:0};var c={cN:"string",b:"'",e:"'",c:[{b:"''"}],r:0};var d={cN:"test_condition",b:"",e:"",c:[f,c,b,a,hljs.CNM],k:{literal:e},r:0};return{dM:{k:{keyword:{"if":1,then:1,"else":1,fi:1,"for":1,"break":1,"continue":1,"while":1,"in":1,"do":1,done:1,echo:1,exit:1,"return":1,set:1,declare:1},literal:e},c:[{cN:"shebang",b:"(#!\\/bin\\/bash)|(#!\\/bin\\/sh)",r:10},b,a,hljs.HCM,hljs.CNM,f,c,hljs.inherit(d,{b:"\\[ ",e:" \\]",r:0}),hljs.inherit(d,{b:"\\[\\[ ",e:" \\]\\]"})]}}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.css=function(){var a={cN:"function",b:hljs.IR+"\\(",e:"\\)",c:[{eW:true,eE:true,c:[hljs.NM,hljs.ASM,hljs.QSM]}]};return{cI:true,dM:{i:"[=/|']",c:[hljs.CBLCLM,{cN:"id",b:"\\#[A-Za-z0-9_-]+"},{cN:"class",b:"\\.[A-Za-z0-9_-]+",r:0},{cN:"attr_selector",b:"\\[",e:"\\]",i:"$"},{cN:"pseudo",b:":(:)?[a-zA-Z0-9\\_\\-\\+\\(\\)\\\"\\']+"},{cN:"at_rule",b:"@(font-face|page)",l:"[a-z-]+",k:{"font-face":1,page:1}},{cN:"at_rule",b:"@",e:"[{;]",eE:true,k:{"import":1,page:1,media:1,charset:1},c:[a,hljs.ASM,hljs.QSM,hljs.NM]},{cN:"tag",b:hljs.IR,r:0},{cN:"rules",b:"{",e:"}",i:"[^\\s]",r:0,c:[hljs.CBLCLM,{cN:"rule",b:"[^\\s]",rB:true,e:";",eW:true,c:[{cN:"attribute",b:"[A-Z\\_\\.\\-]+",e:":",eE:true,i:"[^\\s]",starts:{cN:"value",eW:true,eE:true,c:[a,hljs.NM,hljs.QSM,hljs.ASM,hljs.CBLCLM,{cN:"hexcolor",b:"\\#[0-9A-F]+"},{cN:"important",b:"!important"}]}}]}]}]}}}();hljs.LANGUAGES.ini={cI:true,dM:{i:"[^\\s]",c:[{cN:"comment",b:";",e:"$"},{cN:"title",b:"^\\[",e:"\\]"},{cN:"setting",b:"^[a-z0-9_\\[\\]]+[ \\t]*=[ \\t]*",e:"$",c:[{cN:"value",eW:true,k:{on:1,off:1,"true":1,"false":1,yes:1,no:1},c:[hljs.QSM,hljs.NM]}]}]}};hljs.LANGUAGES.perl=function(){var d={getpwent:1,getservent:1,quotemeta:1,msgrcv:1,scalar:1,kill:1,dbmclose:1,undef:1,lc:1,ma:1,syswrite:1,tr:1,send:1,umask:1,sysopen:1,shmwrite:1,vec:1,qx:1,utime:1,local:1,oct:1,semctl:1,localtime:1,readpipe:1,"do":1,"return":1,format:1,read:1,sprintf:1,dbmopen:1,pop:1,getpgrp:1,not:1,getpwnam:1,rewinddir:1,qq:1,fileno:1,qw:1,endprotoent:1,wait:1,sethostent:1,bless:1,s:0,opendir:1,"continue":1,each:1,sleep:1,endgrent:1,shutdown:1,dump:1,chomp:1,connect:1,getsockname:1,die:1,socketpair:1,close:1,flock:1,exists:1,index:1,shmget:1,sub:1,"for":1,endpwent:1,redo:1,lstat:1,msgctl:1,setpgrp:1,abs:1,exit:1,select:1,print:1,ref:1,gethostbyaddr:1,unshift:1,fcntl:1,syscall:1,"goto":1,getnetbyaddr:1,join:1,gmtime:1,symlink:1,semget:1,splice:1,x:0,getpeername:1,recv:1,log:1,setsockopt:1,cos:1,last:1,reverse:1,gethostbyname:1,getgrnam:1,study:1,formline:1,endhostent:1,times:1,chop:1,length:1,gethostent:1,getnetent:1,pack:1,getprotoent:1,getservbyname:1,rand:1,mkdir:1,pos:1,chmod:1,y:0,substr:1,endnetent:1,printf:1,next:1,open:1,msgsnd:1,readdir:1,use:1,unlink:1,getsockopt:1,getpriority:1,rindex:1,wantarray:1,hex:1,system:1,getservbyport:1,endservent:1,"int":1,chr:1,untie:1,rmdir:1,prototype:1,tell:1,listen:1,fork:1,shmread:1,ucfirst:1,setprotoent:1,"else":1,sysseek:1,link:1,getgrgid:1,shmctl:1,waitpid:1,unpack:1,getnetbyname:1,reset:1,chdir:1,grep:1,split:1,require:1,caller:1,lcfirst:1,until:1,warn:1,"while":1,values:1,shift:1,telldir:1,getpwuid:1,my:1,getprotobynumber:1,"delete":1,and:1,sort:1,uc:1,defined:1,srand:1,accept:1,"package":1,seekdir:1,getprotobyname:1,semop:1,our:1,rename:1,seek:1,"if":1,q:0,chroot:1,sysread:1,setpwent:1,no:1,crypt:1,getc:1,chown:1,sqrt:1,write:1,setnetent:1,setpriority:1,foreach:1,tie:1,sin:1,msgget:1,map:1,stat:1,getlogin:1,unless:1,elsif:1,truncate:1,exec:1,keys:1,glob:1,tied:1,closedir:1,ioctl:1,socket:1,readlink:1,"eval":1,xor:1,readline:1,binmode:1,setservent:1,eof:1,ord:1,bind:1,alarm:1,pipe:1,atan2:1,getgrent:1,exp:1,time:1,push:1,setgrent:1,gt:1,lt:1,or:1,ne:1,m:0};var f={cN:"subst",b:"[$@]\\{",e:"\\}",k:d,r:10};var c={cN:"variable",b:"\\$\\d"};var b={cN:"variable",b:"[\\$\\%\\@\\*](\\^\\w\\b|#\\w+(\\:\\:\\w+)*|[^\\s\\w{]|{\\w+}|\\w+(\\:\\:\\w*)*)"};var h=[hljs.BE,f,c,b];var g={b:"->",c:[{b:hljs.IR},{b:"{",e:"}"}]};var e={cN:"comment",b:"^(__END__|__DATA__)",e:"\\n$",r:5};var a=[c,b,hljs.HCM,e,g,{cN:"string",b:"q[qwxr]?\\s*\\(",e:"\\)",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\[",e:"\\]",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\{",e:"\\}",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\|",e:"\\|",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\<",e:"\\>",c:h,r:5},{cN:"string",b:"qw\\s+q",e:"q",c:h,r:5},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"string",b:'"',e:'"',c:h,r:0},{cN:"string",b:"`",e:"`",c:[hljs.BE]},{cN:"string",b:"{\\w+}",r:0},{cN:"string",b:"-?\\w+\\s*\\=\\>",r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"("+hljs.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:{split:1,"return":1,print:1,reverse:1,grep:1},r:0,c:[hljs.HCM,e,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[hljs.BE],r:0}]},{cN:"sub",b:"\\bsub\\b",e:"(\\s*\\(.*?\\))?[;{]",k:{sub:1},r:5},{cN:"operator",b:"-\\w\\b",r:0},{cN:"pod",b:"\\=\\w",e:"\\=cut"}];f.c=a;g.c[1].c=a;return{dM:{k:d,c:a}}}();hljs.LANGUAGES.python=function(){var b=[{cN:"string",b:"(u|b)?r?'''",e:"'''",r:10},{cN:"string",b:'(u|b)?r?"""',e:'"""',r:10},{cN:"string",b:"(u|r|ur)'",e:"'",c:[hljs.BE],r:10},{cN:"string",b:'(u|r|ur)"',e:'"',c:[hljs.BE],r:10},{cN:"string",b:"(b|br)'",e:"'",c:[hljs.BE]},{cN:"string",b:'(b|br)"',e:'"',c:[hljs.BE]}].concat([hljs.ASM,hljs.QSM]);var d={cN:"title",b:hljs.UIR};var c={cN:"params",b:"\\(",e:"\\)",c:b.concat([hljs.CNM])};var a={bWK:true,e:":",i:"[${]",c:[d,c],r:10};return{dM:{k:{keyword:{and:1,elif:1,is:1,global:1,as:1,"in":1,"if":1,from:1,raise:1,"for":1,except:1,"finally":1,print:1,"import":1,pass:1,"return":1,exec:1,"else":1,"break":1,not:1,"with":1,"class":1,assert:1,yield:1,"try":1,"while":1,"continue":1,del:1,or:1,def:1,lambda:1,nonlocal:10},built_in:{None:1,True:1,False:1,Ellipsis:1,NotImplemented:1}},i:"(</|->|\\?)",c:b.concat([hljs.HCM,hljs.inherit(a,{cN:"function",k:{def:1}}),hljs.inherit(a,{cN:"class",k:{"class":1}}),hljs.CNM,{cN:"decorator",b:"@",e:"$"}])}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};hljs.LANGUAGES.ruby=function(){var a="[a-zA-Z_][a-zA-Z0-9_]*(\\!|\\?)?";var j="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?";var f={keyword:{and:1,"false":1,then:1,defined:1,module:1,"in":1,"return":1,redo:1,"if":1,BEGIN:1,retry:1,end:1,"for":1,"true":1,self:1,when:1,next:1,until:1,"do":1,begin:1,unless:1,END:1,rescue:1,nil:1,"else":1,"break":1,undef:1,not:1,"super":1,"class":1,"case":1,require:1,yield:1,alias:1,"while":1,ensure:1,elsif:1,or:1,def:1},keymethods:{__id__:1,__send__:1,abort:1,abs:1,"all?":1,allocate:1,ancestors:1,"any?":1,arity:1,assoc:1,at:1,at_exit:1,autoload:1,"autoload?":1,"between?":1,binding:1,binmode:1,"block_given?":1,call:1,callcc:1,caller:1,capitalize:1,"capitalize!":1,casecmp:1,"catch":1,ceil:1,center:1,chomp:1,"chomp!":1,chop:1,"chop!":1,chr:1,"class":1,class_eval:1,"class_variable_defined?":1,class_variables:1,clear:1,clone:1,close:1,close_read:1,close_write:1,"closed?":1,coerce:1,collect:1,"collect!":1,compact:1,"compact!":1,concat:1,"const_defined?":1,const_get:1,const_missing:1,const_set:1,constants:1,count:1,crypt:1,"default":1,default_proc:1,"delete":1,"delete!":1,delete_at:1,delete_if:1,detect:1,display:1,div:1,divmod:1,downcase:1,"downcase!":1,downto:1,dump:1,dup:1,each:1,each_byte:1,each_index:1,each_key:1,each_line:1,each_pair:1,each_value:1,each_with_index:1,"empty?":1,entries:1,eof:1,"eof?":1,"eql?":1,"equal?":1,"eval":1,exec:1,exit:1,"exit!":1,extend:1,fail:1,fcntl:1,fetch:1,fileno:1,fill:1,find:1,find_all:1,first:1,flatten:1,"flatten!":1,floor:1,flush:1,for_fd:1,foreach:1,fork:1,format:1,freeze:1,"frozen?":1,fsync:1,getc:1,gets:1,global_variables:1,grep:1,gsub:1,"gsub!":1,"has_key?":1,"has_value?":1,hash:1,hex:1,id:1,include:1,"include?":1,included_modules:1,index:1,indexes:1,indices:1,induced_from:1,inject:1,insert:1,inspect:1,instance_eval:1,instance_method:1,instance_methods:1,"instance_of?":1,"instance_variable_defined?":1,instance_variable_get:1,instance_variable_set:1,instance_variables:1,"integer?":1,intern:1,invert:1,ioctl:1,"is_a?":1,isatty:1,"iterator?":1,join:1,"key?":1,keys:1,"kind_of?":1,lambda:1,last:1,length:1,lineno:1,ljust:1,load:1,local_variables:1,loop:1,lstrip:1,"lstrip!":1,map:1,"map!":1,match:1,max:1,"member?":1,merge:1,"merge!":1,method:1,"method_defined?":1,method_missing:1,methods:1,min:1,module_eval:1,modulo:1,name:1,nesting:1,"new":1,next:1,"next!":1,"nil?":1,nitems:1,"nonzero?":1,object_id:1,oct:1,open:1,pack:1,partition:1,pid:1,pipe:1,pop:1,popen:1,pos:1,prec:1,prec_f:1,prec_i:1,print:1,printf:1,private_class_method:1,private_instance_methods:1,"private_method_defined?":1,private_methods:1,proc:1,protected_instance_methods:1,"protected_method_defined?":1,protected_methods:1,public_class_method:1,public_instance_methods:1,"public_method_defined?":1,public_methods:1,push:1,putc:1,puts:1,quo:1,raise:1,rand:1,rassoc:1,read:1,read_nonblock:1,readchar:1,readline:1,readlines:1,readpartial:1,rehash:1,reject:1,"reject!":1,remainder:1,reopen:1,replace:1,require:1,"respond_to?":1,reverse:1,"reverse!":1,reverse_each:1,rewind:1,rindex:1,rjust:1,round:1,rstrip:1,"rstrip!":1,scan:1,seek:1,select:1,send:1,set_trace_func:1,shift:1,singleton_method_added:1,singleton_methods:1,size:1,sleep:1,slice:1,"slice!":1,sort:1,"sort!":1,sort_by:1,split:1,sprintf:1,squeeze:1,"squeeze!":1,srand:1,stat:1,step:1,store:1,strip:1,"strip!":1,sub:1,"sub!":1,succ:1,"succ!":1,sum:1,superclass:1,swapcase:1,"swapcase!":1,sync:1,syscall:1,sysopen:1,sysread:1,sysseek:1,system:1,syswrite:1,taint:1,"tainted?":1,tell:1,test:1,"throw":1,times:1,to_a:1,to_ary:1,to_f:1,to_hash:1,to_i:1,to_int:1,to_io:1,to_proc:1,to_s:1,to_str:1,to_sym:1,tr:1,"tr!":1,tr_s:1,"tr_s!":1,trace_var:1,transpose:1,trap:1,truncate:1,"tty?":1,type:1,ungetc:1,uniq:1,"uniq!":1,unpack:1,unshift:1,untaint:1,untrace_var:1,upcase:1,"upcase!":1,update:1,upto:1,"value?":1,values:1,values_at:1,warn:1,write:1,write_nonblock:1,"zero?":1,zip:1}};var c={cN:"yardoctag",b:"@[A-Za-z]+"};var k=[{cN:"comment",b:"#",e:"$",c:[c]},{cN:"comment",b:"^\\=begin",e:"^\\=end",c:[c],r:10},{cN:"comment",b:"^__END__",e:"\\n$"}];var d={cN:"subst",b:"#\\{",e:"}",l:a,k:f};var i=[hljs.BE,d];var b=[{cN:"string",b:"'",e:"'",c:i,r:0},{cN:"string",b:'"',e:'"',c:i,r:0},{cN:"string",b:"%[qw]?\\(",e:"\\)",c:i,r:10},{cN:"string",b:"%[qw]?\\[",e:"\\]",c:i,r:10},{cN:"string",b:"%[qw]?{",e:"}",c:i,r:10},{cN:"string",b:"%[qw]?<",e:">",c:i,r:10},{cN:"string",b:"%[qw]?/",e:"/",c:i,r:10},{cN:"string",b:"%[qw]?%",e:"%",c:i,r:10},{cN:"string",b:"%[qw]?-",e:"-",c:i,r:10},{cN:"string",b:"%[qw]?\\|",e:"\\|",c:i,r:10}];var h={cN:"function",b:"\\bdef\\s+",e:" |$|;",l:a,k:f,c:[{cN:"title",b:j,l:a,k:f},{cN:"params",b:"\\(",e:"\\)",l:a,k:f}].concat(k)};var g={cN:"identifier",b:a,l:a,k:f,r:0};var e=k.concat(b.concat([{cN:"class",b:"\\b(class|module)\\b",e:"$|;",k:{"class":1,module:1},c:[{cN:"title",b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?",r:0},{cN:"inheritance",b:"<\\s*",c:[{cN:"parent",b:"("+hljs.IR+"::)?"+hljs.IR}]}].concat(k)},h,{cN:"constant",b:"(::)?([A-Z]\\w*(::)?)+",r:0},{cN:"symbol",b:":",c:b.concat([g]),r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{cN:"number",b:"\\?\\w"},{cN:"variable",b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},g,{b:"("+hljs.RSR+")\\s*",c:k.concat([{cN:"regexp",b:"/",e:"/[a-z]*",i:"\\n",c:[hljs.BE]}]),r:0}]));d.c=e;h.c[1].c=e;return{dM:{l:a,k:f,c:e}}}();hljs.LANGUAGES.scala=function(){var b={cN:"annotation",b:"@[A-Za-z]+"};var a={cN:"string",b:'u?r?"""',e:'"""',r:10};return{dM:{k:{type:1,yield:1,lazy:1,override:1,def:1,"with":1,val:1,"var":1,"false":1,"true":1,sealed:1,"abstract":1,"private":1,trait:1,object:1,"null":1,"if":1,"for":1,"while":1,"throw":1,"finally":1,"protected":1,"extends":1,"import":1,"final":1,"return":1,"else":1,"break":1,"new":1,"catch":1,"super":1,"class":1,"case":1,"package":1,"default":1,"try":1,"this":1,match:1,"continue":1,"throws":1},c:[{cN:"javadoc",b:"/\\*\\*",e:"\\*/",c:[{cN:"javadoctag",b:"@[A-Za-z]+"}],r:10},hljs.CLCM,hljs.CBLCLM,hljs.ASM,hljs.QSM,a,{cN:"class",b:"((case )?class |object |trait )",e:"({|$)",i:":",k:{"case":1,"class":1,trait:1,object:1},c:[{bWK:true,k:{"extends":1,"with":1},r:10},{cN:"title",b:hljs.UIR},{cN:"params",b:"\\(",e:"\\)",c:[hljs.ASM,hljs.QSM,a,b]}]},hljs.CNM,b]}}}();hljs.LANGUAGES.sql={cI:true,dM:{i:"[^\\s]",c:[{cN:"operator",b:"(begin|start|commit|rollback|savepoint|lock|alter|create|drop|rename|call|delete|do|handler|insert|load|replace|select|truncate|update|set|show|pragma|grant)\\b",e:";|"+hljs.ER,k:{keyword:{all:1,partial:1,global:1,month:1,current_timestamp:1,using:1,go:1,revoke:1,smallint:1,indicator:1,"end-exec":1,disconnect:1,zone:1,"with":1,character:1,assertion:1,to:1,add:1,current_user:1,usage:1,input:1,local:1,alter:1,match:1,collate:1,real:1,then:1,rollback:1,get:1,read:1,timestamp:1,session_user:1,not:1,integer:1,bit:1,unique:1,day:1,minute:1,desc:1,insert:1,execute:1,like:1,ilike:2,level:1,decimal:1,drop:1,"continue":1,isolation:1,found:1,where:1,constraints:1,domain:1,right:1,national:1,some:1,module:1,transaction:1,relative:1,second:1,connect:1,escape:1,close:1,system_user:1,"for":1,deferred:1,section:1,cast:1,current:1,sqlstate:1,allocate:1,intersect:1,deallocate:1,numeric:1,"public":1,preserve:1,full:1,"goto":1,initially:1,asc:1,no:1,key:1,output:1,collation:1,group:1,by:1,union:1,session:1,both:1,last:1,language:1,constraint:1,column:1,of:1,space:1,foreign:1,deferrable:1,prior:1,connection:1,unknown:1,action:1,commit:1,view:1,or:1,first:1,into:1,"float":1,year:1,primary:1,cascaded:1,except:1,restrict:1,set:1,references:1,names:1,table:1,outer:1,open:1,select:1,size:1,are:1,rows:1,from:1,prepare:1,distinct:1,leading:1,create:1,only:1,next:1,inner:1,authorization:1,schema:1,corresponding:1,option:1,declare:1,precision:1,immediate:1,"else":1,timezone_minute:1,external:1,varying:1,translation:1,"true":1,"case":1,exception:1,join:1,hour:1,"default":1,"double":1,scroll:1,value:1,cursor:1,descriptor:1,values:1,dec:1,fetch:1,procedure:1,"delete":1,and:1,"false":1,"int":1,is:1,describe:1,"char":1,as:1,at:1,"in":1,varchar:1,"null":1,trailing:1,any:1,absolute:1,current_time:1,end:1,grant:1,privileges:1,when:1,cross:1,check:1,write:1,current_date:1,pad:1,begin:1,temporary:1,exec:1,time:1,update:1,catalog:1,user:1,sql:1,date:1,on:1,identity:1,timezone_hour:1,natural:1,whenever:1,interval:1,work:1,order:1,cascade:1,diagnostics:1,nchar:1,having:1,left:1,call:1,"do":1,handler:1,load:1,replace:1,truncate:1,start:1,lock:1,show:1,pragma:1},aggregate:{count:1,sum:1,min:1,max:1,avg:1}},c:[{cN:"string",b:"'",e:"'",c:[hljs.BE,{b:"''"}],r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE,{b:'""'}],r:0},{cN:"string",b:"`",e:"`",c:[hljs.BE]},hljs.CNM]},hljs.CBLCLM,{cN:"comment",b:"--",e:"$"}]}};hljs.LANGUAGES.stan={dM:{c:[hljs.HCM,hljs.CLCM,hljs.QSM,hljs.CNM,{cN:"operator",b:"(?:<-|~|\\|\\||&&|==|!=|<=?|>=?|\\+|-|\\.?/|\\\\|\\^|\\^|!|'|%|:|,|;|=)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0},{cN:"function",b:"(?:Phi|Phi_approx|abs|acos|acosh|append_col|append_row|asin|asinh|atan|atan2|atanh|bernoulli_ccdf_log|bernoulli_cdf|bernoulli_cdf_log|bernoulli_log|bernoulli_logit_log|bernoulli_rng|bessel_first_kind|bessel_second_kind|beta_binomial_ccdf_log|beta_binomial_cdf|beta_binomial_cdf_log|beta_binomial_log|beta_binomial_rng|beta_ccdf_log|beta_cdf|beta_cdf_log|beta_log|beta_rng|binary_log_loss|binomial_ccdf_log|binomial_cdf|binomial_cdf_log|binomial_coefficient_log|binomial_log|binomial_logit_log|binomial_rng|block|categorical_log|categorical_logit_log|categorical_rng|cauchy_ccdf_log|cauchy_cdf|cauchy_cdf_log|cauchy_log|cauchy_rng|cbrt|ceil|chi_square_ccdf_log|chi_square_cdf|chi_square_cdf_log|chi_square_log|chi_square_rng|cholesky_decompose|col|cols|columns_dot_product|columns_dot_self|cos|cosh|crossprod|csr_extract_u|csr_extract_v|csr_extract_w|csr_matrix_times_vector|csr_to_dense_matrix|cumulative_sum|determinant|diag_matrix|diag_post_multiply|diag_pre_multiply|diagonal|digamma|dims|dirichlet_log|dirichlet_rng|distance|dot_product|dot_self|double_exponential_ccdf_log|double_exponential_cdf|double_exponential_cdf_log|double_exponential_log|double_exponential_rng|e|eigenvalues_sym|eigenvectors_sym|erf|erfc|exp|exp2|exp_mod_normal_ccdf_log|exp_mod_normal_cdf|exp_mod_normal_cdf_log|exp_mod_normal_log|exp_mod_normal_rng|expm1|exponential_ccdf_log|exponential_cdf|exponential_cdf_log|exponential_log|exponential_rng|fabs|falling_factorial|fdim|floor|fma|fmax|fmin|fmod|frechet_ccdf_log|frechet_cdf|frechet_cdf_log|frechet_log|frechet_rng|gamma_ccdf_log|gamma_cdf|gamma_cdf_log|gamma_log|gamma_p|gamma_q|gamma_rng|gaussian_dlm_obs_log|get_lp|gumbel_ccdf_log|gumbel_cdf|gumbel_cdf_log|gumbel_log|gumbel_rng|head|hypergeometric_log|hypergeometric_rng|hypot|if_else|int_step|inv|inv_chi_square_ccdf_log|inv_chi_square_cdf|inv_chi_square_cdf_log|inv_chi_square_log|inv_chi_square_rng|inv_cloglog|inv_gamma_ccdf_log|inv_gamma_cdf|inv_gamma_cdf_log|inv_gamma_log|inv_gamma_rng|inv_logit|inv_phi|inv_sqrt|inv_square|inv_wishart_log|inv_wishart_rng|inverse|inverse_spd|is_inf|is_nan|lbeta|lgamma|lkj_corr_cholesky_log|lkj_corr_cholesky_rng|lkj_corr_log|lkj_corr_rng|lmgamma|log|log10|log1m|log1m_exp|log1m_inv_logit|log1p|log1p_exp|log2|log_determinant|log_diff_exp|log_falling_factorial|log_inv_logit|log_mix|log_rising_factorial|log_softmax|log_sum_exp|logistic_ccdf_log|logistic_cdf|logistic_cdf_log|logistic_log|logistic_rng|logit|lognormal_ccdf_log|lognormal_cdf|lognormal_cdf_log|lognormal_log|lognormal_rng|machine_precision|max|mdivide_left_tri_low|mdivide_right_tri_low|mean|min|modified_bessel_first_kind|modified_bessel_second_kind|multi_gp_cholesky_log|multi_gp_log|multi_normal_cholesky_log|multi_normal_cholesky_rng|multi_normal_log|multi_normal_prec_log|multi_normal_rng|multi_student_t_log|multi_student_t_rng|multinomial_log|multinomial_rng|multiply_log|multiply_lower_tri_self_transpose|neg_binomial_2_ccdf_log|neg_binomial_2_cdf|neg_binomial_2_cdf_log|neg_binomial_2_log|neg_binomial_2_log_log|neg_binomial_2_log_rng|neg_binomial_2_rng|neg_binomial_ccdf_log|neg_binomial_cdf|neg_binomial_cdf_log|neg_binomial_log|neg_binomial_rng|negative_infinity|normal_ccdf_log|normal_cdf|normal_cdf_log|normal_log|normal_rng|not_a_number|num_elements|ordered_logistic_log|ordered_logistic_rng|owens_t|pareto_ccdf_log|pareto_cdf|pareto_cdf_log|pareto_log|pareto_rng|pareto_type_2_ccdf_log|pareto_type_2_cdf|pareto_type_2_cdf_log|pareto_type_2_log|pareto_type_2_rng|pi|poisson_ccdf_log|poisson_cdf|poisson_cdf_log|poisson_log|poisson_log_log|poisson_log_rng|poisson_rng|positive_infinity|pow|prod|qr_Q|qr_R|quad_form|quad_form_diag|quad_form_sym|rank|rayleigh_ccdf_log|rayleigh_cdf|rayleigh_cdf_log|rayleigh_log|rayleigh_rng|rep_array|rep_matrix|rep_row_vector|rep_vector|rising_factorial|round|row|rows|rows_dot_product|rows_dot_self|scaled_inv_chi_square_ccdf_log|scaled_inv_chi_square_cdf|scaled_inv_chi_square_cdf_log|scaled_inv_chi_square_log|scaled_inv_chi_square_rng|sd|segment|sin|singular_values|sinh|size|skew_normal_ccdf_log|skew_normal_cdf|skew_normal_cdf_log|skew_normal_log|skew_normal_rng|softmax|sort_asc|sort_desc|sort_indices_asc|sort_indices_desc|sqrt|sqrt2|square|squared_distance|step|student_t_ccdf_log|student_t_cdf|student_t_cdf_log|student_t_log|student_t_rng|sub_col|sub_row|sum|tail|tan|tanh|tcrossprod|tgamma|to_array_1d|to_array_2d|to_matrix|to_row_vector|to_vector|trace|trace_gen_quad_form|trace_quad_form|trigamma|trunc|uniform_ccdf_log|uniform_cdf|uniform_cdf_log|uniform_log|uniform_rng|variance|von_mises_log|von_mises_rng|weibull_ccdf_log|weibull_cdf|weibull_cdf_log|weibull_log|weibull_rng|wiener_log|wishart_log|wishart_rng)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"function",b:"(?:bernoulli|bernoulli_logit|beta|beta_binomial|binomial|binomial_logit|categorical|categorical_logit|cauchy|chi_square|dirichlet|double_exponential|exp_mod_normal|exponential|frechet|gamma|gaussian_dlm_obs|gumbel|hypergeometric|inv_chi_square|inv_gamma|inv_wishart|lkj_corr|lkj_corr_cholesky|logistic|lognormal|multi_gp|multi_gp_cholesky|multi_normal|multi_normal_cholesky|multi_normal_prec|multi_student_t|multinomial|neg_binomial|neg_binomial_2|neg_binomial_2_log|normal|ordered_logistic|pareto|pareto_type_2|poisson|poisson_log|rayleigh|scaled_inv_chi_square|skew_normal|student_t|uniform|von_mises|weibull|wiener|wishart)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"(?:for|in|while|if|then|else|return|lower|upper|print|increment_log_prob|integrate_ode|reject)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"(?:int|real|vector|simplex|unit_vector|ordered|positive_ordered|row_vector|matrix|cholesky_factor_cov|cholesky_factor_corr|corr_matrix|cov_matrix|void)\\b",e:hljs.IMMEDIATE_RE,r:5},{cN:"keyword",b:"(?:functions|data|transformed\\s+data|parameters|transformed\\s+parameters|model|generated\\s+quantities)\\b",e:hljs.IMMEDIATE_RE,r:5}]}};hljs.LANGUAGES.xml=function(){var b="[A-Za-z0-9\\._:-]+";var a={eW:true,c:[{cN:"attribute",b:b,r:0},{b:'="',rB:true,e:'"',c:[{cN:"value",b:'"',eW:true}]},{b:"='",rB:true,e:"'",c:[{cN:"value",b:"'",eW:true}]},{b:"=",c:[{cN:"value",b:"[^\\s/>]+"}]}]};return{cI:true,dM:{c:[{cN:"pi",b:"<\\?",e:"\\?>",r:10},{cN:"doctype",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},{cN:"comment",b:"<!--",e:"-->",r:10},{cN:"cdata",b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{title:{style:1}},c:[a],starts:{cN:"css",e:"</style>",rE:true,sL:"css"}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{title:{script:1}},c:[a],starts:{cN:"javascript",e:"<\/script>",rE:true,sL:"javascript"}},{cN:"vbscript",b:"<%",e:"%>",sL:"vbscript"},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"title",b:"[^ />]+"},a]}]}}}();
hljs.initHighlightingOnLoad();

"></script> +<link href="data:text/css;charset=utf-8,%2Ehljs%2Dliteral%20%7B%0Acolor%3A%20%23990073%3B%0A%7D%0A%2Ehljs%2Dnumber%20%7B%0Acolor%3A%20%23099%3B%0A%7D%0A%2Ehljs%2Dcomment%20%7B%0Acolor%3A%20%23998%3B%0Afont%2Dstyle%3A%20italic%3B%0A%7D%0A%2Ehljs%2Dkeyword%20%7B%0Acolor%3A%20%23900%3B%0Afont%2Dweight%3A%20bold%3B%0A%7D%0A%2Ehljs%2Dstring%20%7B%0Acolor%3A%20%23d14%3B%0A%7D%0A" rel="stylesheet" /> +<script src="data:application/x-javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script> <style type="text/css">code{white-space: pre;}</style> <style type="text/css"> @@ -30,10 +30,12 @@ } </style> <script type="text/javascript"> -if (window.hljs && document.readyState && document.readyState === "complete") { - window.setTimeout(function() { - hljs.initHighlighting(); - }, 0); +if (window.hljs) { + hljs.configure({languages: []}); + hljs.initHighlightingOnLoad(); + if (document.readyState && document.readyState === "complete") { + window.setTimeout(function() { hljs.initHighlighting(); }, 0); + } } </script> @@ -134,7 +136,7 @@ $(document).ready(function () { </div> <div id="usage" class="section level2"> <h2>Usage</h2> -<p>For a start, have a look a the code examples provided for <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> and <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, and at the package vignettes <a href="http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_L.html"><code>FOCUS L</code></a> and <a href="http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p> +<p>For a start, have a look a the code examples provided for <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> and <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, and at the package vignettes <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html"><code>FOCUS L</code></a> and <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p> </div> <div id="documentation" class="section level2"> <h2>Documentation</h2> @@ -143,17 +145,17 @@ $(document).ready(function () { <div id="features" class="section level2"> <h2>Features</h2> <ul> -<li>Highly flexible model specification using <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li> -<li>As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> -<li>Model solution (forward modelling) in the function <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> -<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="http://kinfit.r-forge.r-project.org/mkin_static/articles/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li> -<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric logration transformation that is now used for the formation fractions.</li> +<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li> +<li>As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> +<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> +<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li> +<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric logration transformation that is now used for the formation fractions.</li> <li>A side effect of this is that when parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which can not occur in a single experiment with a single defined radiolabel position.</li> <li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li> <li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li> <li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li> <li>Iteratively reweighted least squares fitting is implemented in a similar way as in KinGUII and CAKE (see below). Simply add the argument <code>reweight.method = "obs"</code> to your call to <code>mkinfit</code> and a separate variance componenent for each of the observed variables will be optimised in a second stage after the primary optimisation algorithm has converged.</li> -<li>Iterative reweighting is also possible using the two-component error model for analytical data of <a href="http://kinfit.r-forge.r-project.org/mkin_static/reference/sigma_rl.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = "tc"</code>.</li> +<li>Iterative reweighting is also possible using the two-component error model for analytical data of <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_rl.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = "tc"</code>.</li> <li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</li> </ul> </div> @@ -27,12 +27,12 @@ detailed guidance and helpful tools have been developed as detailed in ## Usage For a start, have a look a the code examples provided for -[`plot.mkinfit`](http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mkinfit.html) +[`plot.mkinfit`](https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html) and -[`plot.mmkin`](http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html), and +[`plot.mmkin`](https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html), and at the package vignettes -[`FOCUS L`](http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_L.html) and -[`FOCUS D`](http://kinfit.r-forge.r-project.org/mkin_static/articles/FOCUS_D.html). +[`FOCUS L`](https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html) and +[`FOCUS D`](https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html). ## Documentation @@ -44,28 +44,28 @@ and at [R-Forge](http://kinfit.r-forge.r-project.org/mkin_static/index.html). ## Features * Highly flexible model specification using - [`mkinmod`](http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinmod.html), + [`mkinmod`](https://pkgdown.jrwb.de/mkin/reference/mkinmod.html), including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable. * As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example - [`plot.mmkin`](http://kinfit.r-forge.r-project.org/mkin_static/reference/plot.mmkin.html). + [`plot.mmkin`](https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html). * Model solution (forward modelling) in the function - [`mkinpredict`](http://kinfit.r-forge.r-project.org/mkin_static/reference/mkinpredict.html) + [`mkinpredict`](https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html) is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the `deSolve` package (default is `lsoda`). * If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see - [vignette `compiled_models`](http://kinfit.r-forge.r-project.org/mkin_static/articles/compiled_models.html). + [vignette `compiled_models`](https://pkgdown.jrwb.de/mkin/articles/compiled_models.html). The autogeneration of C code was inspired by the [`ccSolve`](https://github.com/karlines/ccSolve) package. Thanks to Karline Soetaert for her work on that. * By default, kinetic rate constants and kinetic formation fractions are transformed internally using - [`transform_odeparms`](http://kinfit.r-forge.r-project.org/mkin_static/reference/transform_odeparms.html) + [`transform_odeparms`](https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html) so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice @@ -90,8 +90,8 @@ and at [R-Forge](http://kinfit.r-forge.r-project.org/mkin_static/index.html). componenent for each of the observed variables will be optimised in a second stage after the primary optimisation algorithm has converged. * Iterative reweighting is also possible using the two-component error model - for analytical data of - [Rocke and Lorenzato](http://kinfit.r-forge.r-project.org/mkin_static/reference/sigma_rl.html) + for analytical data of + [Rocke and Lorenzato](https://pkgdown.jrwb.de/mkin/reference/sigma_rl.html) using the argument `reweight.method = "tc"`. * When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. diff --git a/docs/articles/FOCUS_D.R b/docs/articles/FOCUS_D.R deleted file mode 100644 index b831e14e..00000000 --- a/docs/articles/FOCUS_D.R +++ /dev/null @@ -1,24 +0,0 @@ -## ---- include = FALSE---------------------------------------------------- -library(knitr) -opts_chunk$set(tidy = FALSE, cache = TRUE) - -## ----data---------------------------------------------------------------- -library("mkin", quietly = TRUE) -print(FOCUS_2006_D) - -## ----model--------------------------------------------------------------- -SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")) -print(SFO_SFO$diffs) - -## ----fit----------------------------------------------------------------- -fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) - -## ----plot, fig.height = 6, fig.width = 8--------------------------------- -plot_sep(fit, lpos = c("topright", "bottomright")) - -## ----plot_2, fig.height = 4, fig.width = 8------------------------------- -mkinparplot(fit) - -## ------------------------------------------------------------------------ -summary(fit) - diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index d9dd8ad5..af04f755 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -8,8 +8,11 @@ <title>Example evaluation of FOCUS Example Dataset D • mkin</title> <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> -<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> -<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> +<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Example Dataset D"> +<meta property="og:description" content=""> +<meta name="twitter:card" content="summary"> +<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--> @@ -77,7 +80,7 @@ <h1>Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2018-01-16</h4> + <h4 class="date">2018-03-01</h4> </div> @@ -85,7 +88,7 @@ <div class="contents"> <p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>) -<span class="kw">print</span>(FOCUS_2006_D)</code></pre></div> +<span class="kw">print</span>(FOCUS_<span class="dv">2006</span>_D)</code></pre></div> <pre><code>## name time value ## 1 parent 0 99.46 ## 2 parent 0 102.04 @@ -135,13 +138,13 @@ <p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">parent =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>), <span class="dt">m1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(SFO_SFO$diffs)</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(SFO_SFO<span class="op">$</span>diffs)</code></pre></div> <pre><code>## parent ## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" ## m1 ## "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"</code></pre> <p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fit <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fit <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div> <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(fit, <span class="dt">lpos =</span> <span class="kw">c</span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>))</code></pre></div> <p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p> @@ -150,10 +153,10 @@ <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(fit)</code></pre></div> -<pre><code>## mkin version: 0.9.46.1 -## R version: 3.4.1 -## Date of fit: Thu Sep 14 12:15:01 2017 -## Date of summary: Thu Sep 14 12:15:02 2017 +<pre><code>## mkin version: 0.9.46.3 +## R version: 3.4.3 +## Date of fit: Thu Mar 1 14:17:55 2018 +## Date of summary: Thu Mar 1 14:17:55 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent @@ -161,7 +164,7 @@ ## ## Model predictions using solution type deSolve ## -## Fitted with method Port using 153 model solutions performed in 1.14 s +## Fitted with method Port using 153 model solutions performed in 0.993 s ## ## Weighting: none ## @@ -286,7 +289,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png Binary files differindex 75c4c299..b4fa2ff4 100644 --- a/docs/articles/FOCUS_D_files/figure-html/plot-1.png +++ b/docs/articles/FOCUS_D_files/figure-html/plot-1.png diff --git a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png Binary files differindex 94e7e2b3..ba06ce31 100644 --- a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png +++ b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index 42ec2df1..5de06ad5 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -8,8 +8,11 @@ <title>Example evaluation of FOCUS Laboratory Data L1 to L3 • mkin</title> <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> -<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> -<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> +<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS Laboratory Data L1 to L3"> +<meta property="og:description" content=""> +<meta name="twitter:card" content="summary"> +<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--> @@ -77,7 +80,7 @@ <h1>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2018-01-16</h4> + <h4 class="date">2018-03-01</h4> </div> @@ -88,27 +91,27 @@ <a href="#laboratory-data-l1" class="anchor"></a>Laboratory Data L1</h1> <p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>) -FOCUS_2006_L1 =<span class="st"> </span><span class="kw">data.frame</span>( +FOCUS_<span class="dv">2006</span>_L1 =<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">t =</span> <span class="kw">rep</span>(<span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="dt">each =</span> <span class="dv">2</span>), <span class="dt">parent =</span> <span class="kw">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>, <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>, <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>)) -FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L1)</code></pre></div> +FOCUS_<span class="dv">2006</span>_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L1)</code></pre></div> <p>Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.</p> <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L1.SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_2006_L1_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L1.SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_<span class="dv">2006</span>_L1_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw">summary</span>(m.L1.SFO)</code></pre></div> -<pre><code>## mkin version: 0.9.47.1 +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Tue Jan 16 06:11:06 2018 -## Date of summary: Tue Jan 16 06:11:06 2018 +## Date of fit: Thu Mar 1 14:31:57 2018 +## Date of summary: Thu Mar 1 14:31:57 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.245 s +## Fitted with method Port using 37 model solutions performed in 0.24 s ## ## Weighting: none ## @@ -185,28 +188,21 @@ FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../re <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/mkinresplot.html">mkinresplot</a></span>(m.L1.SFO, <span class="dt">ylab =</span> <span class="st">"Observed"</span>, <span class="dt">xlab =</span> <span class="st">"Time"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-5-1.png" width="576"></p> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L1.FOMC <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_2006_L1_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>)</code></pre></div> -<pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge. -## Convergence code is 1</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(m.L1.FOMC, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L1.FOMC <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_<span class="dv">2006</span>_L1_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>) +<span class="kw">plot</span>(m.L1.FOMC, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-6-1.png" width="576"></p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L1.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.47.1 +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Tue Jan 16 06:11:07 2018 -## Date of summary: Tue Jan 16 06:11:07 2018 -## -## -## Warning: Optimisation by method Port did not converge. -## Convergence code is 1 -## +## Date of fit: Thu Mar 1 14:31:59 2018 +## Date of summary: Thu Mar 1 14:32:00 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 155 model solutions performed in 0.424 s +## Fitted with method Port using 611 model solutions performed in 1.375 s ## ## Weighting: none ## @@ -226,16 +222,16 @@ FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../re ## None ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.47 1.449 89.38 95.56 -## log_alpha 11.35 435.800 -917.60 940.30 -## log_beta 13.70 435.800 -915.20 942.60 +## Estimate Std. Error Lower Upper +## parent_0 92.47 1.482 89.31 95.63 +## log_alpha 11.25 598.200 -1264.00 1286.00 +## log_beta 13.60 598.200 -1261.00 1289.00 ## ## Parameter correlation: ## parent_0 log_alpha log_beta -## parent_0 1.0000 0.2209 0.2208 -## log_alpha 0.2209 1.0000 1.0000 -## log_beta 0.2208 1.0000 1.0000 +## parent_0 1.0000 -0.3016 -0.3016 +## log_alpha -0.3016 1.0000 1.0000 +## log_beta -0.3016 1.0000 1.0000 ## ## Residual standard error: 3.045 on 15 degrees of freedom ## @@ -244,9 +240,9 @@ FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../re ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47 63.33000 6.183e-20 89.38 95.56 -## alpha 85190.00 0.03367 4.868e-01 0.00 Inf -## beta 891000.00 0.03367 4.868e-01 0.00 Inf +## parent_0 92.47 64.45000 4.757e-20 89.31 95.63 +## alpha 76830.00 0.02852 4.888e-01 0.00 Inf +## beta 803500.00 0.02852 4.888e-01 0.00 Inf ## ## Chi2 error levels in percent: ## err.min n.optim df @@ -264,17 +260,17 @@ FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../re <h1 class="hasAnchor"> <a href="#laboratory-data-l2" class="anchor"></a>Laboratory Data L2</h1> <p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_2006_L2 =<span class="st"> </span><span class="kw">data.frame</span>( +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_<span class="dv">2006</span>_L2 =<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">t =</span> <span class="kw">rep</span>(<span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="dt">each =</span> <span class="dv">2</span>), <span class="dt">parent =</span> <span class="kw">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>, <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>, <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>)) -FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L2)</code></pre></div> +FOCUS_<span class="dv">2006</span>_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L2)</code></pre></div> <div id="sfo-fit-for-l2" class="section level2"> <h2 class="hasAnchor"> <a href="#sfo-fit-for-l2" class="anchor"></a>SFO fit for L2</h2> <p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_2006_L2_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_<span class="dv">2006</span>_L2_mkin, <span class="dt">quiet=</span><span class="ot">TRUE</span>) <span class="kw">plot</span>(m.L2.SFO, <span class="dt">show_residuals =</span> <span class="ot">TRUE</span>, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L2 - SFO"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p> @@ -286,22 +282,22 @@ FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../re <h2 class="hasAnchor"> <a href="#fomc-fit-for-l2" class="anchor"></a>FOMC fit for L2</h2> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.FOMC <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_2006_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.FOMC <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"FOMC"</span>, FOCUS_<span class="dv">2006</span>_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw">plot</span>(m.L2.FOMC, <span class="dt">show_residuals =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.47.1 +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Tue Jan 16 06:11:08 2018 -## Date of summary: Tue Jan 16 06:11:08 2018 +## Date of fit: Thu Mar 1 14:32:00 2018 +## Date of summary: Thu Mar 1 14:32:00 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.168 s +## Fitted with method Port using 81 model solutions performed in 0.158 s ## ## Weighting: none ## @@ -357,15 +353,15 @@ FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../re <h2 class="hasAnchor"> <a href="#dfop-fit-for-l2" class="anchor"></a>DFOP fit for L2</h2> <p>Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.DFOP <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"DFOP"</span>, FOCUS_2006_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L2.DFOP <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"DFOP"</span>, FOCUS_<span class="dv">2006</span>_L2_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw">plot</span>(m.L2.DFOP, <span class="dt">show_residuals =</span> <span class="ot">TRUE</span>, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.DFOP, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.47.1 +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Tue Jan 16 06:11:09 2018 -## Date of summary: Tue Jan 16 06:11:09 2018 +## Date of fit: Thu Mar 1 14:32:01 2018 +## Date of summary: Thu Mar 1 14:32:01 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -374,7 +370,7 @@ FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../re ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 0.774 s +## Fitted with method Port using 336 model solutions performed in 0.708 s ## ## Weighting: none ## @@ -433,17 +429,17 @@ FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../re <h1 class="hasAnchor"> <a href="#laboratory-data-l3" class="anchor"></a>Laboratory Data L3</h1> <p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_2006_L3 =<span class="st"> </span><span class="kw">data.frame</span>( +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_<span class="dv">2006</span>_L3 =<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">t =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>), <span class="dt">parent =</span> <span class="kw">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>)) -FOCUS_2006_L3_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L3)</code></pre></div> +FOCUS_<span class="dv">2006</span>_L3_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L3)</code></pre></div> <div id="fit-multiple-models" class="section level2"> <h2 class="hasAnchor"> <a href="#fit-multiple-models" class="anchor"></a>Fit multiple models</h2> <p>As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Only use one core here, not to offend the CRAN checks</span> mm.L3 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin.html">mmkin</a></span>(<span class="kw">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>), <span class="dt">cores =</span> <span class="dv">1</span>, - <span class="kw">list</span>(<span class="st">"FOCUS L3"</span> =<span class="st"> </span>FOCUS_2006_L3_mkin), <span class="dt">quiet =</span> <span class="ot">TRUE</span>) + <span class="kw">list</span>(<span class="st">"FOCUS L3"</span> =<span class="st"> </span>FOCUS_<span class="dv">2006</span>_L3_mkin), <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw">plot</span>(mm.L3)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="672"></p> <p>The <span class="math inline">\(\chi^2\)</span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p> @@ -454,10 +450,10 @@ mm.L3 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</code></pre></div> -<pre><code>## mkin version: 0.9.47.1 +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Tue Jan 16 06:11:10 2018 -## Date of summary: Tue Jan 16 06:11:10 2018 +## Date of fit: Thu Mar 1 14:32:02 2018 +## Date of summary: Thu Mar 1 14:32:02 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -542,30 +538,30 @@ mm.L3 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin <h1 class="hasAnchor"> <a href="#laboratory-data-l4" class="anchor"></a>Laboratory Data L4</h1> <p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_2006_L4 =<span class="st"> </span><span class="kw">data.frame</span>( +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">FOCUS_<span class="dv">2006</span>_L4 =<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">t =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>), <span class="dt">parent =</span> <span class="kw">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>)) -FOCUS_2006_L4_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_L4)</code></pre></div> +FOCUS_<span class="dv">2006</span>_L4_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_L4)</code></pre></div> <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Only use one core here, not to offend the CRAN checks</span> mm.L4 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin.html">mmkin</a></span>(<span class="kw">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>), <span class="dt">cores =</span> <span class="dv">1</span>, - <span class="kw">list</span>(<span class="st">"FOCUS L4"</span> =<span class="st"> </span>FOCUS_2006_L4_mkin), + <span class="kw">list</span>(<span class="st">"FOCUS L4"</span> =<span class="st"> </span>FOCUS_<span class="dv">2006</span>_L4_mkin), <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw">plot</span>(mm.L4)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="672"></p> <p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.47.1 +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Tue Jan 16 06:11:10 2018 -## Date of summary: Tue Jan 16 06:11:10 2018 +## Date of fit: Thu Mar 1 14:32:03 2018 +## Date of summary: Thu Mar 1 14:32:03 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.094 s +## Fitted with method Port using 46 model solutions performed in 0.089 s ## ## Weighting: none ## @@ -615,17 +611,17 @@ mm.L4 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin ## DT50 DT90 ## parent 106 352</code></pre> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.47.1 +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Tue Jan 16 06:11:10 2018 -## Date of summary: Tue Jan 16 06:11:10 2018 +## Date of fit: Thu Mar 1 14:32:03 2018 +## Date of summary: Thu Mar 1 14:32:03 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.139 s +## Fitted with method Port using 66 model solutions performed in 0.134 s ## ## Weighting: none ## @@ -690,7 +686,8 @@ mm.L4 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <div id="tocnav"> - <h2>Contents</h2> + <h2 class="hasAnchor"> +<a href="#tocnav" class="anchor"></a>Contents</h2> <ul class="nav nav-pills nav-stacked"> <li><a href="#laboratory-data-l1">Laboratory Data L1</a></li> <li> @@ -720,7 +717,7 @@ mm.L4 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png Binary files differindex f5f45ac6..c9da66ac 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png Binary files differindex 22f3a530..aa728f0f 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png Binary files differindex 9eb0378f..4eb7f3b1 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png Binary files differindex 93966e70..56654730 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png Binary files differindex 1a9c8457..b143282b 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png Binary files differindex 12b4beea..a4753a7e 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png Binary files differindex 55e96a9e..50528c8a 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png Binary files differindex 7b8e7f95..95a5bc5c 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png Binary files differindex 49c48168..745477a3 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png diff --git a/docs/articles/FOCUS_Z.R b/docs/articles/FOCUS_Z.R deleted file mode 100644 index 4d2dffca..00000000 --- a/docs/articles/FOCUS_Z.R +++ /dev/null @@ -1,115 +0,0 @@ -## ---- include = FALSE---------------------------------------------------- -require(knitr) -options(digits = 5) -opts_chunk$set(engine='R', tidy = FALSE) - -## ---- echo = TRUE, fig = TRUE, fig.width = 8, fig.height = 7------------- -library(mkin, quietly = TRUE) -LOD = 0.5 -FOCUS_2006_Z = data.frame( - t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21, - 42, 61, 96, 124), - Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8, - 2.9, 3.5, 5.3, 4.4, 1.2, 0.7), - Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1, - 1.6, 0.6, 0.5 * LOD, NA, NA, NA), - Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5, - 0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA), - Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5, - 25.2, 17.2, 4.8, 4.5, 2.8, 4.4)) - -FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z) - -## ----FOCUS_2006_Z_fits_1, echo=TRUE, fig.height=6------------------------ -Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"), - Z1 = mkinsub("SFO")) -m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.2a) -summary(m.Z.2a, data = FALSE)$bpar - -## ----FOCUS_2006_Z_fits_2, echo=TRUE, fig.height=6------------------------ -Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"), - Z1 = mkinsub("SFO"), - use_of_ff = "max") - -m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.2a.ff) -summary(m.Z.2a.ff, data = FALSE)$bpar - -## ----FOCUS_2006_Z_fits_3, echo=TRUE, fig.height=6------------------------ -Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO"), use_of_ff = "max") -m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.3) -summary(m.Z.3, data = FALSE)$bpar - -## ----FOCUS_2006_Z_fits_5, echo=TRUE, fig.height=7------------------------ -Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO"), use_of_ff = "max") -m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.5) - -## ----FOCUS_2006_Z_fits_6, echo=TRUE, fig.height=8------------------------ -Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFO"), - use_of_ff = "max") -m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, - parms.ini = m.Z.5$bparms.ode, - quiet = TRUE) -plot_sep(m.Z.FOCUS) -summary(m.Z.FOCUS, data = FALSE)$bpar -endpoints(m.Z.FOCUS) - -## ----FOCUS_2006_Z_fits_7, echo=TRUE, fig.height=8------------------------ -Z.mkin.1 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFORB")) -m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.mkin.1) -summary(m.Z.mkin.1, data = FALSE)$cov.unscaled - -## ----FOCUS_2006_Z_fits_9, echo=TRUE, fig.height=8------------------------ -Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO")) -m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.mkin.3) - -## ----FOCUS_2006_Z_fits_10, echo=TRUE, fig.height=8----------------------- -Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFO")) -m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, - parms.ini = m.Z.mkin.3$bparms.ode, - quiet = TRUE) -plot_sep(m.Z.mkin.4) - -## ----FOCUS_2006_Z_fits_11, echo=TRUE, fig.height=8----------------------- -Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFORB")) -m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, - parms.ini = m.Z.mkin.4$bparms.ode[1:4], - quiet = TRUE) -plot_sep(m.Z.mkin.5) - -## ----FOCUS_2006_Z_fits_11a, echo=TRUE------------------------------------ -m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, - parms.ini = c(m.Z.mkin.5$bparms.ode[1:7], - k_Z3_bound_free = 0), - fixed_parms = "k_Z3_bound_free", - quiet = TRUE) -plot_sep(m.Z.mkin.5a) - -## ----FOCUS_2006_Z_fits_11b, echo=TRUE------------------------------------ -mkinparplot(m.Z.mkin.5a) - -## ----FOCUS_2006_Z_fits_11b_endpoints, echo=TRUE-------------------------- -endpoints(m.Z.mkin.5a) - diff --git a/docs/articles/FOCUS_Z.Rnw b/docs/articles/FOCUS_Z.Rnw deleted file mode 100644 index 5abda0e1..00000000 --- a/docs/articles/FOCUS_Z.Rnw +++ /dev/null @@ -1,274 +0,0 @@ -%\VignetteIndexEntry{Example evaluation of FOCUS dataset Z} -%\VignetteEngine{knitr::knitr} -\documentclass[12pt,a4paper]{article} -\usepackage{a4wide} -\input{header} -\hypersetup{ - pdftitle = {Example evaluation of FOCUS dataset Z}, - pdfsubject = {Manuscript}, - pdfauthor = {Johannes Ranke}, - colorlinks = {true}, - linkcolor = {blue}, - citecolor = {blue}, - urlcolor = {red}, - hyperindex = {true}, - linktocpage = {true}, -} - -\begin{document} - -<<include=FALSE>>= -require(knitr) -opts_chunk$set(engine='R', tidy = FALSE, cache = TRUE) -options(width=70) -@ - -\title{Example evaluation of FOCUS dataset Z} -\author{\textbf{Johannes Ranke} \\[0.5cm] -%EndAName -Wissenschaftlicher Berater\\ -Kronacher Str. 8, 79639 Grenzach-Wyhlen, Germany\\[0.5cm] -and\\[0.5cm] -University of Bremen\\ -} -\maketitle - -\thispagestyle{empty} \setcounter{page}{0} - -\clearpage - -\tableofcontents - -\textbf{Key words}: Kinetics, FOCUS, nonlinear optimisation - -\section{The data} - -The following code defines the example dataset from Appendix 7 to the FOCUS kinetics -report \citep{FOCUSkinetics2011}, p.350. - -<<FOCUS_2006_Z_data, echo=TRUE, eval=TRUE>>= -require(mkin) -LOD = 0.5 -FOCUS_2006_Z = data.frame( - t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21, - 42, 61, 96, 124), - Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8, - 2.9, 3.5, 5.3, 4.4, 1.2, 0.7), - Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1, - 1.6, 0.6, 0.5 * LOD, NA, NA, NA), - Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5, - 0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA), - Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5, - 25.2, 17.2, 4.8, 4.5, 2.8, 4.4)) - -FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z) -@ - -\section{Parent compound and one metabolite} - -The next step is to set up the models used for the kinetic analysis. As the -simultaneous fit of parent and the first metabolite is usually straightforward, -Step 1 (SFO for parent only) is skipped here. We start with the model 2a, -with formation and decline of metabolite Z1 and the pathway from parent -directly to sink included (default in mkin). - -<<FOCUS_2006_Z_fits_1, echo=TRUE, fig.height=6>>= -Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"), - Z1 = mkinsub("SFO")) -m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.2a) -summary(m.Z.2a, data = FALSE)$bpar -@ - -As obvious from the parameter summary (the \texttt{bpar} component of the -summary), the kinetic rate constant from parent compound Z to sink -is negligible. Accordingly, the exact magnitude of the fitted parameter -\texttt{log k\_Z0\_sink} is ill-defined and the covariance matrix is not -returned (not shown, would be visible in the complete summary). -This suggests, in agreement with the analysis in the FOCUS kinetics report, to -simplify the model by removing the pathway to sink. - -A similar result can be obtained when formation fractions are used in the model -formulation: - -<<FOCUS_2006_Z_fits_2, echo=TRUE, fig.height=6>>= -Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"), - Z1 = mkinsub("SFO"), - use_of_ff = "max") - -m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.2a.ff) -summary(m.Z.2a.ff, data = FALSE)$bpar -@ - -Here, the ilr transformed formation fraction fitted in the model takes a very -large value, and the backtransformed formation fraction from parent Z to Z1 is -practically unity. Again, the covariance matrix is not returned as the model is -overparameterised. - -The simplified model is obtained by setting the list component \texttt{sink} to -\texttt{FALSE}.\footnote{If the model formulation without formation fractions -is used, the same effect can be obtained by fixing the parameter \texttt{k\_Z\_sink} -to a value of zero.} - -In the following, we use the parameterisation with formation fractions in order -to be able to compare with the results in the FOCUS guidance, and as it -makes it easier to use parameters obtained in a previous fit when adding a further -metabolite. - -<<FOCUS_2006_Z_fits_3, echo=TRUE, fig.height=6>>= -Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO"), use_of_ff = "max") -m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.3) -summary(m.Z.3, data = FALSE)$bpar -@ - -As there is only one transformation product for Z0 and no pathway -to sink, the formation fraction is internally fixed to unity. - -\section{Including metabolites Z2 and Z3} - -As suggested in the FOCUS report, the pathway to sink was removed for metabolite Z1 as -well in the next step. While this step appears questionable on the basis of the above results, it -is followed here for the purpose of comparison. Also, in the FOCUS report, it is -assumed that there is additional empirical evidence that Z1 quickly and exclusively -hydrolyses to Z2. - -<<FOCUS_2006_Z_fits_5, echo=TRUE, fig.height=7>>= -Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO"), use_of_ff = "max") -m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.5) -@ - -Finally, metabolite Z3 is added to the model. We use the optimised -differential equation parameter values from the previous fit in order to -accelerate the optimization. - -<<FOCUS_2006_Z_fits_6, echo=TRUE, fig.height=8>>= -Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFO"), - use_of_ff = "max") -m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, - parms.ini = m.Z.5$bparms.ode, - quiet = TRUE) -plot_sep(m.Z.FOCUS) -summary(m.Z.FOCUS, data = FALSE)$bpar -endpoints(m.Z.FOCUS) -@ - -This fit corresponds to the final result chosen in Appendix 7 of the FOCUS -report. Confidence intervals returned by mkin are based on internally -transformed parameters, however. - -\section{Using the SFORB model for parent and metabolites} - -As the FOCUS report states, there is a certain tailing of the time course of metabolite -Z3. Also, the time course of the parent compound is not fitted very well using the -SFO model, as residues at a certain low level remain. - -Therefore, an additional model is offered here, using the single first-order -reversible binding (SFORB) model for metabolite Z3. As expected, the $\chi^2$ -error level is lower for metabolite Z3 using this model and the graphical -fit for Z3 is improved. However, the covariance matrix is not returned. - -<<FOCUS_2006_Z_fits_7, echo=TRUE, fig.height=8>>= -Z.mkin.1 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFORB")) -m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.mkin.1) -summary(m.Z.mkin.1, data = FALSE)$cov.unscaled -@ - -Therefore, a further stepwise model building is performed starting from the -stage of parent and two metabolites, starting from the assumption that the model -fit for the parent compound can be improved by using the SFORB model. - -<<FOCUS_2006_Z_fits_9, echo=TRUE, fig.height=8>>= -Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO")) -m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE) -plot_sep(m.Z.mkin.3) -@ - -This results in a much better representation of the behaviour of the parent -compound Z0. - -Finally, Z3 is added as well. These models appear overparameterised (no -covariance matrix returned) if the sink for Z1 is left in the models. - -<<FOCUS_2006_Z_fits_10, echo=TRUE, fig.height=8>>= -Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFO")) -m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, - parms.ini = m.Z.mkin.3$bparms.ode, - quiet = TRUE) -plot_sep(m.Z.mkin.4) -@ - -The error level of the fit, but especially of metabolite Z3, can be improved if -the SFORB model is chosen for this metabolite, as this model is capable of -representing the tailing of the metabolite decline phase. - -<<FOCUS_2006_Z_fits_11, echo=TRUE, fig.height=8>>= -Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), - Z1 = mkinsub("SFO", "Z2", sink = FALSE), - Z2 = mkinsub("SFO", "Z3"), - Z3 = mkinsub("SFORB")) -m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, - parms.ini = m.Z.mkin.4$bparms.ode[1:4], - quiet = TRUE) -plot_sep(m.Z.mkin.5) -@ - -The summary view of the backtransformed parameters shows that we get no -confidence intervals due to overparameterisation. As the optimized -\texttt{k\_Z3\_bound\_free} is excessively small, it seems reasonable to fix it to -zero. - -<<FOCUS_2006_Z_fits_11a, echo=TRUE>>= -m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, - parms.ini = c(m.Z.mkin.5$bparms.ode[1:7], - k_Z3_bound_free = 0), - fixed_parms = "k_Z3_bound_free", - quiet = TRUE) -plot_sep(m.Z.mkin.5a) -@ - -As expected, the residual plots for Z0 and Z3 are more random than in the case of the -all SFO model for which they were shown above. In conclusion, the model -\texttt{Z.mkin.5a} is proposed as the best-fit model for the dataset from -Appendix 7 of the FOCUS report. - -A graphical representation of the confidence intervals can finally be obtained. - -<<FOCUS_2006_Z_fits_11b, echo=TRUE>>= -mkinparplot(m.Z.mkin.5a) -@ - -The endpoints obtained with this model are - -<<FOCUS_2006_Z_fits_11b_endpoints, echo=TRUE>>= -endpoints(m.Z.mkin.5a) -@ - -It is clear the degradation rate of Z3 towards the end of the experiment -is very low as DT50\_Z3\_b2 (the second Eigenvalue of the system of two differential -equations representing the SFORB system for Z3, corresponding to the slower rate -constant of the DFOP model) is reported to be infinity. However, this appears -to be a feature of the data. - -\bibliographystyle{plainnat} -\bibliography{references} - -\end{document} -% vim: set foldmethod=syntax: diff --git a/docs/articles/FOCUS_Z.html b/docs/articles/FOCUS_Z.html index a5cfc616..52a1db77 100644 --- a/docs/articles/FOCUS_Z.html +++ b/docs/articles/FOCUS_Z.html @@ -8,8 +8,11 @@ <title>Example evaluation of FOCUS dataset Z • mkin</title> <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> -<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> -<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> +<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Example evaluation of FOCUS dataset Z"> +<meta property="og:description" content=""> +<meta name="twitter:card" content="summary"> +<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--> @@ -77,7 +80,7 @@ <h1>Example evaluation of FOCUS dataset Z</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2018-01-16</h4> + <h4 class="date">2018-03-01</h4> </div> @@ -90,64 +93,64 @@ <p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(mkin, <span class="dt">quietly =</span> <span class="ot">TRUE</span>) LOD =<span class="st"> </span><span class="fl">0.5</span> -FOCUS_2006_Z =<span class="st"> </span><span class="kw">data.frame</span>( +FOCUS_<span class="dv">2006</span>_Z =<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">t =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">4</span>, <span class="dv">7</span>, <span class="dv">10</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">42</span>, <span class="dv">61</span>, <span class="dv">96</span>, <span class="dv">124</span>), <span class="dt">Z0 =</span> <span class="kw">c</span>(<span class="dv">100</span>, <span class="fl">81.7</span>, <span class="fl">70.4</span>, <span class="fl">51.1</span>, <span class="fl">41.2</span>, <span class="fl">6.6</span>, <span class="fl">4.6</span>, <span class="fl">3.9</span>, <span class="fl">4.6</span>, <span class="fl">4.3</span>, <span class="fl">6.8</span>, <span class="fl">2.9</span>, <span class="fl">3.5</span>, <span class="fl">5.3</span>, <span class="fl">4.4</span>, <span class="fl">1.2</span>, <span class="fl">0.7</span>), <span class="dt">Z1 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">18.3</span>, <span class="fl">29.6</span>, <span class="fl">46.3</span>, <span class="fl">55.1</span>, <span class="fl">65.7</span>, <span class="fl">39.1</span>, <span class="dv">36</span>, <span class="fl">15.3</span>, <span class="fl">5.6</span>, <span class="fl">1.1</span>, - <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>), - <span class="dt">Z2 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>, - <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>), - <span class="dt">Z3 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> *<span class="st"> </span>LOD, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>, + <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>), + <span class="dt">Z2 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>, + <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>), + <span class="dt">Z3 =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="ot">NA</span>, <span class="fl">0.5</span> <span class="op">*</span><span class="st"> </span>LOD, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>, <span class="fl">25.2</span>, <span class="fl">17.2</span>, <span class="fl">4.8</span>, <span class="fl">4.5</span>, <span class="fl">2.8</span>, <span class="fl">4.4</span>)) -FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_2006_Z)</code></pre></div> +FOCUS_<span class="dv">2006</span>_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span>(FOCUS_<span class="dv">2006</span>_Z)</code></pre></div> </div> <div id="parent-and-one-metabolite" class="section level1"> <h1 class="hasAnchor"> <a href="#parent-and-one-metabolite" class="anchor"></a>Parent and one metabolite</h1> <p>The next step is to set up the models used for the kinetic analysis. As the simultaneous fit of parent and the first metabolite is usually straightforward, Step 1 (SFO for parent only) is skipped here. We start with the model 2a, with formation and decline of metabolite Z1 and the pathway from parent directly to sink included (default in mkin).</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z.2a <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>), +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z<span class="fl">.2</span>a <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>), <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.2a <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.2a, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) -<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.2a)</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.2</span>a <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.2</span>a, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.2</span>a)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png" width="672"></p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.2a, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 9.7015e+01 3.553135 2.7304e+01 1.6792e-21 91.4014 102.62838 -## k_Z0_sink 6.2135e-10 0.226894 2.7385e-09 5.0000e-01 0.0000 Inf -## k_Z0_Z1 2.2360e+00 0.165073 1.3546e+01 7.3939e-14 1.8374 2.72107 -## k_Z1_sink 4.8212e-01 0.065854 7.3212e+00 3.5520e-08 0.4006 0.58024</code></pre> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.2</span>a, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div> +<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper +## Z0_0 9.7015e+01 3.553140 2.7304e+01 1.6793e-21 NA NA +## k_Z0_sink 1.2790e-11 0.226895 5.6368e-11 5.0000e-01 NA NA +## k_Z0_Z1 2.2360e+00 0.165073 1.3546e+01 7.3938e-14 NA NA +## k_Z1_sink 4.8212e-01 0.065854 7.3212e+00 3.5520e-08 NA NA</code></pre> <p>As obvious from the parameter summary (the component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p> <p>A similar result can be obtained when formation fractions are used in the model formulation:</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z.2a.ff <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>), +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z<span class="fl">.2</span>a.ff <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>), <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.2a.ff <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.2a.ff, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) -<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.2a.ff)</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.2</span>a.ff <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.2</span>a.ff, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.2</span>a.ff)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png" width="672"></p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.2a.ff, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.2</span>a.ff, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 97.01488 3.553146 27.3039 1.6793e-21 NA NA -## k_Z0 2.23601 0.216847 10.3114 3.6617e-11 NA NA +## Z0_0 97.01488 3.553145 27.3039 1.6793e-21 NA NA +## k_Z0 2.23601 0.216849 10.3114 3.6623e-11 NA NA ## k_Z1 0.48212 0.065854 7.3211 3.5520e-08 NA NA -## f_Z0_to_Z1 1.00000 0.101473 9.8548 9.7071e-11 NA NA</code></pre> +## f_Z0_to_Z1 1.00000 0.101473 9.8548 9.7068e-11 NA NA</code></pre> <p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p> <p>A simplified model is obtained by removing the pathway to the sink. </p> <p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z<span class="fl">.3</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z1"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>), <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.3</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.3</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.3</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.3</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.3</span>)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png" width="672"></p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.3</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z<span class="fl">.3</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 97.01488 2.681771 36.176 2.3636e-25 91.52152 102.508 -## k_Z0 2.23601 0.146862 15.225 2.2470e-15 1.95453 2.558 +## Z0_0 97.01488 2.681772 36.176 2.3636e-25 91.52152 102.508 +## k_Z0 2.23601 0.146861 15.225 2.2464e-15 1.95453 2.558 ## k_Z1 0.48212 0.042687 11.294 3.0686e-12 0.40216 0.578</code></pre> <p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p> </div> @@ -159,7 +162,7 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z2"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>), <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.5</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.5</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z<span class="fl">.5</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z<span class="fl">.5</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z<span class="fl">.5</span>)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png" width="672"></p> <p>Finally, metabolite Z3 is added to the model. We use the optimised differential equation parameter values from the previous fit in order to accelerate the optimization.</p> @@ -169,25 +172,25 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="dt">use_of_ff =</span> <span class="st">"max"</span>)</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.FOCUS <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.FOCUS, FOCUS_2006_Z_mkin, - <span class="dt">parms.ini =</span> m.Z<span class="fl">.5</span>$bparms.ode, +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.FOCUS <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.FOCUS, FOCUS_<span class="dv">2006</span>_Z_mkin, + <span class="dt">parms.ini =</span> m.Z<span class="fl">.5</span><span class="op">$</span>bparms.ode, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</code></pre></div> <pre><code>## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation by method Port did not converge. ## Convergence code is 1</code></pre> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.FOCUS)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="672"></p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.FOCUS, <span class="dt">data =</span> <span class="ot">FALSE</span>)$bpar</code></pre></div> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 96.84024 2.058814 47.0369 5.5723e-44 92.706852 100.973637 -## k_Z0 2.21540 0.118128 18.7543 7.7369e-25 1.990504 2.465708 -## k_Z1 0.47836 0.029294 16.3298 3.3443e-22 0.423035 0.540918 -## k_Z2 0.45166 0.044186 10.2218 3.0364e-14 0.371065 0.549767 -## k_Z3 0.05869 0.014290 4.1072 7.2560e-05 0.035983 0.095725 -## f_Z2_to_Z3 0.47147 0.057027 8.2676 2.7790e-11 0.360295 0.585556</code></pre> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.FOCUS, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>bpar</code></pre></div> +<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper +## Z0_0 96.837112 2.058861 47.0343 5.5877e-44 92.703779 100.970445 +## k_Z0 2.215368 0.118098 18.7587 7.6563e-25 1.990525 2.465609 +## k_Z1 0.478302 0.029289 16.3302 3.3408e-22 0.422977 0.540864 +## k_Z2 0.451617 0.044214 10.2144 3.1133e-14 0.371034 0.549702 +## k_Z3 0.058693 0.014296 4.1056 7.2924e-05 0.035994 0.095705 +## f_Z2_to_Z3 0.471516 0.057057 8.2639 2.8156e-11 0.360381 0.585548</code></pre> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.FOCUS)</code></pre></div> <pre><code>## $ff ## Z2_Z3 Z2_sink -## 0.47147 0.52853 +## 0.47152 0.52848 ## ## $SFORB ## logical(0) @@ -195,9 +198,9 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref ## $distimes ## DT50 DT90 ## Z0 0.31288 1.0394 -## Z1 1.44901 4.8135 -## Z2 1.53466 5.0980 -## Z3 11.81037 39.2332</code></pre> +## Z1 1.44918 4.8141 +## Z2 1.53481 5.0985 +## Z3 11.80973 39.2311</code></pre> <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> </div> <div id="using-the-sforb-model" class="section level1"> @@ -210,17 +213,17 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z3"</span>), <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.1</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.1</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.1</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.1</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.1</span>)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png" width="672"></p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.mkin<span class="fl">.1</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)$cov.unscaled</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.Z.mkin<span class="fl">.1</span>, <span class="dt">data =</span> <span class="ot">FALSE</span>)<span class="op">$</span>cov.unscaled</code></pre></div> <pre><code>## NULL</code></pre> <p>Therefore, a further stepwise model building is performed starting from the stage of parent and two metabolites, starting from the assumption that the model fit for the parent compound can be improved by using the SFORB model.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Z.mkin<span class="fl">.3</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">Z0 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>), <span class="dt">Z1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z2"</span>, <span class="dt">sink =</span> <span class="ot">FALSE</span>), <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.3</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.3</span>, FOCUS_2006_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.3</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.3</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.3</span>)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png" width="672"></p> <p>This results in a much better representation of the behaviour of the parent compound Z0.</p> @@ -230,8 +233,8 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z3"</span>), <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.4</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.4</span>, FOCUS_2006_Z_mkin, - <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.3</span>$bparms.ode, +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.4</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.4</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, + <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.3</span><span class="op">$</span>bparms.ode, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.4</span>)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png" width="672"></p> @@ -241,36 +244,36 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref <span class="dt">Z2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z3"</span>), <span class="dt">Z3 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.5</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_2006_Z_mkin, - <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.4</span>$bparms.ode[<span class="dv">1</span>:<span class="dv">4</span>], +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.5</span> <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, + <span class="dt">parms.ini =</span> m.Z.mkin<span class="fl">.4</span><span class="op">$</span>bparms.ode[<span class="dv">1</span><span class="op">:</span><span class="dv">4</span>], <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.5</span>)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png" width="672"></p> <p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized is excessively small, it seems reasonable to fix it to zero.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin.5a <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_2006_Z_mkin, - <span class="dt">parms.ini =</span> <span class="kw">c</span>(m.Z.mkin<span class="fl">.5</span>$bparms.ode[<span class="dv">1</span>:<span class="dv">7</span>], +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.Z.mkin<span class="fl">.5</span>a <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(Z.mkin<span class="fl">.5</span>, FOCUS_<span class="dv">2006</span>_Z_mkin, + <span class="dt">parms.ini =</span> <span class="kw">c</span>(m.Z.mkin<span class="fl">.5</span><span class="op">$</span>bparms.ode[<span class="dv">1</span><span class="op">:</span><span class="dv">7</span>], <span class="dt">k_Z3_bound_free =</span> <span class="dv">0</span>), <span class="dt">fixed_parms =</span> <span class="st">"k_Z3_bound_free"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) -<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin.5a)</code></pre></div> +<span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(m.Z.mkin<span class="fl">.5</span>a)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png" width="672"></p> <p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p> <p>A graphical representation of the confidence intervals can finally be obtained.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/mkinparplot.html">mkinparplot</a></span>(m.Z.mkin.5a)</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/mkinparplot.html">mkinparplot</a></span>(m.Z.mkin<span class="fl">.5</span>a)</code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png" width="672"></p> <p>The endpoints obtained with this model are</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.mkin.5a)</code></pre></div> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/endpoints.html">endpoints</a></span>(m.Z.mkin<span class="fl">.5</span>a)</code></pre></div> <pre><code>## $ff ## Z0_free_Z1 Z1_Z2 Z2_sink Z2_Z3_free Z3_free_sink ## 1.00000 1.00000 0.46344 0.53656 1.00000 ## ## $SFORB ## Z0_b1 Z0_b2 Z3_b1 Z3_b2 -## 2.4471373 0.0075126 0.0800076 0.0000000 +## 2.4471382 0.0075127 0.0800075 0.0000000 ## ## $distimes ## DT50 DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 -## Z0 0.3043 1.1848 0.28325 92.265 NA NA +## Z0 0.3043 1.1848 0.28325 92.264 NA NA ## Z1 1.5148 5.0320 NA NA NA NA ## Z2 1.6414 5.4526 NA NA NA NA ## Z3 NA NA NA NA 8.6635 Inf</code></pre> @@ -291,7 +294,8 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <div id="tocnav"> - <h2>Contents</h2> + <h2 class="hasAnchor"> +<a href="#tocnav" class="anchor"></a>Contents</h2> <ul class="nav nav-pills nav-stacked"> <li><a href="#the-data">The data</a></li> <li><a href="#parent-and-one-metabolite">Parent and one metabolite</a></li> @@ -310,7 +314,7 @@ FOCUS_2006_Z_mkin <-<span class="st"> </span><span class="kw"><a href="../ref </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png Binary files differindex cb32b3c6..b66289d9 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png Binary files differindex 7ce8a39c..af26f416 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png Binary files differindex a7528057..d4e7a647 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png Binary files differindex 3fe80d42..8c6d81ef 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png Binary files differindex ff0dbbbd..fffb4892 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png Binary files differindex dfe447ed..0ac5b6ce 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png Binary files differindex 8f7102aa..87226454 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png Binary files differindex 74a82e2b..58241d1d 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png Binary files differindex 7042e390..381e7df5 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png Binary files differindex 29824228..19e73f1c 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png diff --git a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png Binary files differindex 79f1b87e..891d8d92 100644 --- a/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png +++ b/docs/articles/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png diff --git a/docs/articles/compiled_models.R b/docs/articles/compiled_models.R deleted file mode 100644 index c5b06c11..00000000 --- a/docs/articles/compiled_models.R +++ /dev/null @@ -1,55 +0,0 @@ -## ---- include = FALSE---------------------------------------------------- -library(knitr) -opts_chunk$set(tidy = FALSE, cache = FALSE) - -## ----check_gcc----------------------------------------------------------- -Sys.which("gcc") - -## ----create_SFO_SFO------------------------------------------------------ -library("mkin", quietly = TRUE) -SFO_SFO <- mkinmod( - parent = mkinsub("SFO", "m1"), - m1 = mkinsub("SFO")) - -## ----benchmark_SFO_SFO, fig.height = 3----------------------------------- -if (require(rbenchmark)) { - b.1 <- benchmark( - "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, - solution_type = "deSolve", - use_compiled = FALSE, quiet = TRUE), - "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D, - solution_type = "eigen", quiet = TRUE), - "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, - solution_type = "deSolve", quiet = TRUE), - replications = 3) - print(b.1) - factor_SFO_SFO <- round(b.1["1", "relative"]) -} else { - factor_SFO_SFO <- NA - print("R package benchmark is not available") -} - -## ----benchmark_FOMC_SFO, fig.height = 3---------------------------------- -if (require(rbenchmark)) { - FOMC_SFO <- mkinmod( - parent = mkinsub("FOMC", "m1"), - m1 = mkinsub( "SFO")) - - b.2 <- benchmark( - "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, - use_compiled = FALSE, quiet = TRUE), - "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE), - replications = 3) - print(b.2) - factor_FOMC_SFO <- round(b.2["1", "relative"]) -} else { - factor_FOMC_SFO <- NA - print("R package benchmark is not available") -} - -## ----sessionInfo, echo = FALSE------------------------------------------- -cat(capture.output(sessionInfo())[1:3], sep = "\n") -if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) { - cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]])) -} - diff --git a/docs/articles/compiled_models.html b/docs/articles/compiled_models.html index d5d29a1a..9f0b5708 100644 --- a/docs/articles/compiled_models.html +++ b/docs/articles/compiled_models.html @@ -8,8 +8,11 @@ <title>Performance benefit by using compiled model definitions in mkin • mkin</title> <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> -<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> -<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> +<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Performance benefit by using compiled model definitions in mkin"> +<meta property="og:description" content=""> +<meta name="twitter:card" content="summary"> +<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--> @@ -77,7 +80,7 @@ <h1>Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2018-01-16</h4> + <h4 class="date">2018-03-01</h4> </div> @@ -97,73 +100,66 @@ SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mki <span class="dt">m1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> <p>We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the benchmark package.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">if (<span class="kw">require</span>(rbenchmark)) { +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="cf">if</span> (<span class="kw">require</span>(rbenchmark)) { b<span class="fl">.1</span> <-<span class="st"> </span><span class="kw">benchmark</span>( - <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D, + <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">solution_type =</span> <span class="st">"deSolve"</span>, <span class="dt">use_compiled =</span> <span class="ot">FALSE</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>), - <span class="st">"Eigenvalue based"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D, + <span class="st">"Eigenvalue based"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">solution_type =</span> <span class="st">"eigen"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>), - <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_2006_D, + <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(SFO_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">solution_type =</span> <span class="st">"deSolve"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>), <span class="dt">replications =</span> <span class="dv">3</span>) <span class="kw">print</span>(b<span class="fl">.1</span>) factor_SFO_SFO <-<span class="st"> </span><span class="kw">round</span>(b<span class="fl">.1</span>[<span class="st">"1"</span>, <span class="st">"relative"</span>]) -} else { +} <span class="cf">else</span> { factor_SFO_SFO <-<span class="st"> </span><span class="ot">NA</span> <span class="kw">print</span>(<span class="st">"R package benchmark is not available"</span>) }</code></pre></div> <pre><code>## Lade nötiges Paket: rbenchmark</code></pre> -<pre><code>## test replications elapsed relative user.self sys.self -## 3 deSolve, compiled 3 1.940 1.000 1.940 0 -## 1 deSolve, not compiled 3 13.865 7.147 13.864 0 -## 2 Eigenvalue based 3 2.427 1.251 2.428 0 -## user.child sys.child -## 3 0 0 -## 1 0 0 -## 2 0 0</code></pre> -<p>We see that using the compiled model is by a factor of around 7 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.</p> +<pre><code>## Warning in library(package, lib.loc = lib.loc, character.only = TRUE, +## logical.return = TRUE, : es gibt kein Paket namens 'rbenchmark'</code></pre> +<pre><code>## [1] "R package benchmark is not available"</code></pre> +<p>We see that using the compiled model is by a factor of around NA faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs.</p> </div> <div id="model-that-can-not-be-solved-with-eigenvalues" class="section level2"> <h2 class="hasAnchor"> <a href="#model-that-can-not-be-solved-with-eigenvalues" class="anchor"></a>Model that can not be solved with Eigenvalues</h2> <p>This evaluation is also taken from the example section of mkinfit.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">if (<span class="kw">require</span>(rbenchmark)) { +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="cf">if</span> (<span class="kw">require</span>(rbenchmark)) { FOMC_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>( <span class="dt">parent =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"FOMC"</span>, <span class="st">"m1"</span>), <span class="dt">m1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>( <span class="st">"SFO"</span>)) b<span class="fl">.2</span> <-<span class="st"> </span><span class="kw">benchmark</span>( - <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_2006_D, + <span class="st">"deSolve, not compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">use_compiled =</span> <span class="ot">FALSE</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>), - <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_2006_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>), + <span class="st">"deSolve, compiled"</span> =<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(FOMC_SFO, FOCUS_<span class="dv">2006</span>_D, <span class="dt">quiet =</span> <span class="ot">TRUE</span>), <span class="dt">replications =</span> <span class="dv">3</span>) <span class="kw">print</span>(b<span class="fl">.2</span>) factor_FOMC_SFO <-<span class="st"> </span><span class="kw">round</span>(b<span class="fl">.2</span>[<span class="st">"1"</span>, <span class="st">"relative"</span>]) -} else { +} <span class="cf">else</span> { factor_FOMC_SFO <-<span class="st"> </span><span class="ot">NA</span> <span class="kw">print</span>(<span class="st">"R package benchmark is not available"</span>) }</code></pre></div> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<pre><code>## test replications elapsed relative user.self sys.self -## 2 deSolve, compiled 3 3.432 1.000 3.428 0 -## 1 deSolve, not compiled 3 28.844 8.404 28.840 0 -## user.child sys.child -## 2 0 0 -## 1 0 0</code></pre> -<p>Here we get a performance benefit of a factor of 8 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 0.9.47.1 on</p> +<pre><code>## Lade nötiges Paket: rbenchmark</code></pre> +<pre><code>## Warning in library(package, lib.loc = lib.loc, character.only = TRUE, +## logical.return = TRUE, : es gibt kein Paket namens 'rbenchmark'</code></pre> +<pre><code>## [1] "R package benchmark is not available"</code></pre> +<p>Here we get a performance benefit of a factor of NA using the version of the differential equation model compiled from C code!</p> +<p>This vignette was built with mkin 0.9.46.3 on</p> <pre><code>## R version 3.4.3 (2017-11-30) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Debian GNU/Linux 9 (stretch)</code></pre> -<pre><code>## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz</code></pre> +<pre><code>## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</code></pre> </div> </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <div id="tocnav"> - <h2>Contents</h2> + <h2 class="hasAnchor"> +<a href="#tocnav" class="anchor"></a>Contents</h2> <ul class="nav nav-pills nav-stacked"> <li><a href="#model-that-can-also-be-solved-with-eigenvalues">Model that can also be solved with Eigenvalues</a></li> <li><a href="#model-that-can-not-be-solved-with-eigenvalues">Model that can not be solved with Eigenvalues</a></li> @@ -179,7 +175,7 @@ SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mki </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/articles/compiled_models_files/figure-html/benchmark_FOMC_SFO-1.png b/docs/articles/compiled_models_files/figure-html/benchmark_FOMC_SFO-1.png Binary files differdeleted file mode 100644 index dae2c9d4..00000000 --- a/docs/articles/compiled_models_files/figure-html/benchmark_FOMC_SFO-1.png +++ /dev/null diff --git a/docs/articles/compiled_models_files/figure-html/benchmark_SFO_SFO-1.png b/docs/articles/compiled_models_files/figure-html/benchmark_SFO_SFO-1.png Binary files differdeleted file mode 100644 index 696236dc..00000000 --- a/docs/articles/compiled_models_files/figure-html/benchmark_SFO_SFO-1.png +++ /dev/null diff --git a/docs/articles/header.tex b/docs/articles/header.tex deleted file mode 100644 index b8644ae2..00000000 --- a/docs/articles/header.tex +++ /dev/null @@ -1,22 +0,0 @@ -\usepackage{booktabs} -\usepackage{amsfonts} -\usepackage{latexsym} -\usepackage{amsmath} -\usepackage{amssymb} -\usepackage{graphicx} -\usepackage{parskip} -\usepackage[round]{natbib} -\usepackage{amstext} -\usepackage{hyperref} - -\newcommand{\Rpackage}[1]{{\normalfont\fontseries{b}\selectfont #1}} -\newcommand{\Robject}[1]{\texttt{#1}} -\newcommand{\Rclass}[1]{\textit{#1}} -\newcommand{\Rcmd}[1]{\texttt{#1}} - -\newcommand{\RR}{\textsf{R}} - -\RequirePackage[T1]{fontenc} -\RequirePackage{graphicx,ae,fancyvrb} -\IfFileExists{upquote.sty}{\RequirePackage{upquote}}{} -\usepackage{relsize} diff --git a/docs/articles/index.html b/docs/articles/index.html index c1dc0b64..2b16580a 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -18,12 +18,16 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> - + + +<meta property="og:title" content="Articles" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -123,7 +127,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/articles/mkin.R b/docs/articles/mkin.R deleted file mode 100644 index 19e80322..00000000 --- a/docs/articles/mkin.R +++ /dev/null @@ -1,34 +0,0 @@ -## ---- include = FALSE---------------------------------------------------- -require(knitr) -opts_chunk$set(engine='R', tidy=FALSE) - -## ---- echo = TRUE, cache = TRUE, fig = TRUE, fig.width = 8, fig.height = 7---- -library("mkin", quietly = TRUE) -# Define the kinetic model -m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"), - M1 = mkinsub("SFO", "M2"), - M2 = mkinsub("SFO"), - use_of_ff = "max", quiet = TRUE) - - -# Produce model predictions using some arbitrary parameters -sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO, - c(k_parent = 0.03, - f_parent_to_M1 = 0.5, k_M1 = log(2)/100, - f_M1_to_M2 = 0.9, k_M2 = log(2)/50), - c(parent = 100, M1 = 0, M2 = 0), - sampling_times) - -# Generate a dataset by adding normally distributed errors with -# standard deviation 3, for two replicates at each sampling time -d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, reps = 2, - sdfunc = function(x) 3, - n = 1, seed = 123456789 ) - -# Fit the model to the dataset -f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE) - -# Plot the results separately for parent and metabolites -plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright")) - diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index b70918ab..a91da0a4 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -8,8 +8,11 @@ <title>Introduction to mkin • mkin</title> <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> -<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> -<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> +<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Introduction to mkin"> +<meta property="og:description" content=""> +<meta name="twitter:card" content="summary"> +<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--> @@ -77,7 +80,7 @@ <h1>Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2018-01-16</h4> + <h4 class="date">2018-03-01</h4> </div> @@ -100,15 +103,15 @@ m_SFO_SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../referen sampling_times =<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>) d_SFO_SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinpredict.html">mkinpredict</a></span>(m_SFO_SFO_SFO, <span class="kw">c</span>(<span class="dt">k_parent =</span> <span class="fl">0.03</span>, - <span class="dt">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="dt">k_M1 =</span> <span class="kw">log</span>(<span class="dv">2</span>)/<span class="dv">100</span>, - <span class="dt">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="dt">k_M2 =</span> <span class="kw">log</span>(<span class="dv">2</span>)/<span class="dv">50</span>), + <span class="dt">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="dt">k_M1 =</span> <span class="kw">log</span>(<span class="dv">2</span>)<span class="op">/</span><span class="dv">100</span>, + <span class="dt">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="dt">k_M2 =</span> <span class="kw">log</span>(<span class="dv">2</span>)<span class="op">/</span><span class="dv">50</span>), <span class="kw">c</span>(<span class="dt">parent =</span> <span class="dv">100</span>, <span class="dt">M1 =</span> <span class="dv">0</span>, <span class="dt">M2 =</span> <span class="dv">0</span>), sampling_times) <span class="co"># Generate a dataset by adding normally distributed errors with</span> <span class="co"># standard deviation 3, for two replicates at each sampling time</span> d_SFO_SFO_SFO_err <-<span class="st"> </span><span class="kw"><a href="../reference/add_err.html">add_err</a></span>(d_SFO_SFO_SFO, <span class="dt">reps =</span> <span class="dv">2</span>, - <span class="dt">sdfunc =</span> function(x) <span class="dv">3</span>, + <span class="dt">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>, <span class="dt">n =</span> <span class="dv">1</span>, <span class="dt">seed =</span> <span class="dv">123456789</span> ) <span class="co"># Fit the model to the dataset</span> @@ -208,7 +211,8 @@ f_SFO_SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../referen <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <div id="tocnav"> - <h2>Contents</h2> + <h2 class="hasAnchor"> +<a href="#tocnav" class="anchor"></a>Contents</h2> <ul class="nav nav-pills nav-stacked"> <li><a href="#abstract">Abstract</a></li> <li> @@ -236,7 +240,7 @@ f_SFO_SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../referen </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png Binary files differindex fafe8afd..5a3e3b6c 100644 --- a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png +++ b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/articles/twa.R b/docs/articles/twa.R deleted file mode 100644 index 8b137891..00000000 --- a/docs/articles/twa.R +++ /dev/null @@ -1 +0,0 @@ - diff --git a/docs/articles/twa.html b/docs/articles/twa.html index 086c8593..400b1383 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -8,8 +8,11 @@ <title>Calculation of time weighted average concentrations with mkin • mkin</title> <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> -<!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> -<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"> +<script src="../jquery.sticky-kit.min.js"></script><script src="../pkgdown.js"></script><meta property="og:title" content="Calculation of time weighted average concentrations with mkin"> +<meta property="og:description" content=""> +<meta name="twitter:card" content="summary"> +<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--> @@ -77,7 +80,7 @@ <h1>Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2018-01-16</h4> + <h4 class="date">2018-03-01</h4> </div> @@ -126,7 +129,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/authors.html b/docs/authors.html index 7f3918c8..7abb53de 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -18,12 +18,16 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="pkgdown.css" rel="stylesheet"> <script src="jquery.sticky-kit.min.js"></script> <script src="pkgdown.js"></script> - + + +<meta property="og:title" content="Authors" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -103,19 +107,19 @@ <ul class="list-unstyled"> <li> - <p><strong>Johannes Ranke</strong>. Author, maintainer, copyright holder. - <br /><small>0000-0003-4371-6538</small></p> + <p><strong>Johannes Ranke</strong>. Author, maintainer, copyright holder. <a href='https://orcid.org/0000-0003-4371-6538' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid'></a> + </p> </li> <li> - <p><strong>Katrin Lindenberger</strong>. Contributor. + <p><strong>Katrin Lindenberger</strong>. Contributor. </p> </li> <li> - <p><strong>René Lehmann</strong>. Contributor. + <p><strong>René Lehmann</strong>. Contributor. </p> </li> <li> - <p><strong>Eurofins Regulatory AG</strong>. Copyright holder. + <p><strong>Eurofins Regulatory AG</strong>. Copyright holder. </p> </li> </ul> @@ -131,7 +135,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/index.html b/docs/index.html index 2f46d730..c20d124b 100644 --- a/docs/index.html +++ b/docs/index.html @@ -8,8 +8,18 @@ <title>Kinetic Evaluation of Chemical Degradation Data • mkin</title> <!-- jquery --><script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script><!-- Font Awesome icons --><link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> -<!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"> -<script src="jquery.sticky-kit.min.js"></script><script src="pkgdown.js"></script><!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"> +<script src="jquery.sticky-kit.min.js"></script><script src="pkgdown.js"></script><meta property="og:title" content="Kinetic Evaluation of Chemical Degradation Data"> +<meta property="og:description" content="Calculation routines based on the FOCUS Kinetics Report (2006, + 2014). Includes a function for conveniently defining differential equation + models, model solution based on eigenvalues if possible or using numerical + solvers and a choice of the optimisation methods made available by the 'FME' + package. If a C compiler (on windows: 'Rtools') is installed, differential + equation models are solved using compiled C functions. Please note that no + warranty is implied for correctness of results or fitness for a particular + purpose."> +<meta name="twitter:card" content="summary"> +<!-- mathjax --><script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--> @@ -159,8 +169,7 @@ <p>GPL</p> <h2>Developers</h2> <ul class="list-unstyled"> -<li>Johannes Ranke <br><small class="roles"> Author, maintainer, copyright holder </small> <br><small>(0000-0003-4371-6538)</small> -</li> +<li>Johannes Ranke <br><small class="roles"> Author, maintainer, copyright holder </small> <a href="https://orcid.org/0000-0003-4371-6538" target="orcid.widget"><img src="https://members.orcid.org/sites/default/files/vector_iD_icon.svg" class="orcid"></a> </li> <li><a href="authors.html">All authors...</a></li> </ul> <h2>Dev status</h2> @@ -176,7 +185,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/news/index.html b/docs/news/index.html index 64e4e9bc..dfb70875 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -18,12 +18,16 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> - + + +<meta property="og:title" content="All news" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -629,7 +633,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/pkgdown.css b/docs/pkgdown.css index fd7b0ba4..181fe639 100644 --- a/docs/pkgdown.css +++ b/docs/pkgdown.css @@ -34,13 +34,14 @@ img.icon { float: right; } -/* Section anchors ---------------------------------*/ - -.hasAnchor { - margin-left: -30px; +img { + max-width: 100%; } +/* Section anchors ---------------------------------*/ + a.anchor { + margin-left: -30px; display:inline-block; width: 30px; height: 30px; @@ -56,6 +57,13 @@ a.anchor { visibility: visible; } +@media (max-width: 767px) { + .hasAnchor:hover a.anchor { + visibility: hidden; + } +} + + /* Fixes for fixed navbar --------------------------*/ .contents h1, .contents h2, .contents h3, .contents h4 { @@ -63,6 +71,17 @@ a.anchor { margin-top: -60px; } +/* Static header placement on mobile devices */ +@media (max-width: 767px) { + .navbar-fixed-top { + position: absolute; + } + .navbar { + padding: 0; + } +} + + /* Sidebar --------------------------*/ #sidebar { @@ -81,33 +100,95 @@ a.anchor { margin-bottom: 0.5em; } +.orcid { + height: 16px; + vertical-align: middle; +} + +/* Reference index & topics ----------------------------------------------- */ + +.ref-index th {font-weight: normal;} +.ref-index h2 {font-size: 20px;} + +.ref-index td {vertical-align: top;} +.ref-index .alias {width: 40%;} +.ref-index .title {width: 60%;} + +.ref-index .alias {width: 40%;} +.ref-index .title {width: 60%;} + +.ref-arguments th {text-align: right; padding-right: 10px;} +.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments .name {width: 20%;} +.ref-arguments .desc {width: 80%;} + +/* Nice scrolling for wide elements --------------------------------------- */ + +table { + display: block; + overflow: auto; +} + /* Syntax highlighting ---------------------------------------------------- */ -code { - background-color: #f7f7f7; - color: #333; +pre { + word-wrap: normal; + word-break: normal; + border: 1px solid #eee; } -code a { - color: #375f84; + +pre, code { + background-color: #f8f8f8; + color: #333; } -.warning { color: red; } -.message { font-weight: bolder; } -.error { color: red; font-weight: bolder; } +pre code { + overflow: auto; + word-wrap: normal; + white-space: pre; +} -.fl,.number {color:rgb(21,20,181);} -.fu,.functioncall {color:#264D66 ;} -.ch,.st,.string {color:#375D81 ;} -.kw,.keyword {color:black;} -.argument {color:#264D66 ;} -.co,.comment {color: #777;} -.formalargs {color: #264D66;} -.eqformalargs {color:#264D66;} -.slot {font-style:italic;} -.symbol {color:black ;} -.prompt {color:black ;} +pre .img { + margin: 5px 0; +} -pre img { +pre .img img { background-color: #fff; display: block; + height: auto; +} + +code a, pre a { + color: #375f84; +} + +a.sourceLine:hover { + text-decoration: none; +} + +.fl {color: #1514b5;} +.fu {color: #000000;} /* function */ +.ch,.st {color: #036a07;} /* string */ +.kw {color: #264D66;} /* keyword */ +.co {color: #888888;} /* comment */ + +.message { color: black; font-weight: bolder;} +.error { color: orange; font-weight: bolder;} +.warning { color: #6A0366; font-weight: bolder;} + +/* Clipboard --------------------------*/ + +.hasCopyButton { + position: relative; +} + +.btn-copy-ex { + position: absolute; + right: 0; + top: 0; + visibility: hidden; +} + +.hasCopyButton:hover button.btn-copy-ex { + visibility: visible; } diff --git a/docs/pkgdown.js b/docs/pkgdown.js index c8b38c49..64b20df4 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -1,8 +1,94 @@ $(function() { - $("#sidebar").stick_in_parent({offset_top: 40}); + + $("#sidebar") + .stick_in_parent({offset_top: 40}) + .on('sticky_kit:bottom', function(e) { + $(this).parent().css('position', 'static'); + }) + .on('sticky_kit:unbottom', function(e) { + $(this).parent().css('position', 'relative'); + }); + $('body').scrollspy({ target: '#sidebar', offset: 60 }); + var cur_path = paths(location.pathname); + $("#navbar ul li a").each(function(index, value) { + if (value.text == "Home") + return; + if (value.getAttribute("href") === "#") + return; + + var path = paths(value.pathname); + if (is_prefix(cur_path, path)) { + // Add class to parent <li>, and enclosing <li> if in dropdown + var menu_anchor = $(value); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); }); + +function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); +} + +function is_prefix(needle, haystack) { + if (needle.length > haystack.lengh) + return(false); + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(false); + } + + return(true); +} + +/* Clipboard --------------------------*/ + +function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); +} + +if(Clipboard.isSupported()) { + $(document).ready(function() { + var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-hidden='true' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy' aria-hidden='true'></i></button>"; + + $(".examples").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new Clipboard('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); +} + diff --git a/docs/reference/DFOP.solution-1.png b/docs/reference/DFOP.solution-1.png Binary files differnew file mode 100644 index 00000000..1549a73b --- /dev/null +++ b/docs/reference/DFOP.solution-1.png diff --git a/docs/reference/DFOP.solution-2.png b/docs/reference/DFOP.solution-2.png Binary files differdeleted file mode 100644 index 0902b9df..00000000 --- a/docs/reference/DFOP.solution-2.png +++ /dev/null diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html index 30b9d057..ff1ad823 100644 --- a/docs/reference/DFOP.solution.html +++ b/docs/reference/DFOP.solution.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Double First-Order in Parallel kinetics — DFOP.solution" /> +<meta property="og:description" content="Function describing decline from a defined starting value using the sum + of two exponential decline functions." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +78,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -146,11 +152,11 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>DFOP.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.3</span>), <span class='fl'>0</span>, <span class='fl'>4</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><img src='DFOP.solution-2.png' alt='' width='540' height='400' /></pre> + <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>DFOP.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.3</span>), <span class='fl'>0</span>, <span class='fl'>4</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='img'><img src='DFOP.solution-1.png' alt='' width='700' height='432.632880098887' /></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -173,7 +179,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index 5d4eca29..11738484 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Subsetting method for mmkin objects — [.mmkin" /> +<meta property="og:description" content="Subsetting method for mmkin objects." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +77,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -147,16 +152,16 @@ <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ]</div><div class='output co'>#> dataset #> model B C -#> FOMC List,42 List,42 +#> FOMC List,45 List,45 #> attr(,"class") #> [1] "mmkin"</div><div class='input'> <span class='no'>fits</span>[, <span class='st'>"B"</span>]</div><div class='output co'>#> dataset #> model B -#> SFO List,42 -#> FOMC List,42 +#> SFO List,45 +#> FOMC List,45 #> attr(,"class") #> [1] "mmkin"</div><div class='input'> <span class='no'>fits</span>[<span class='st'>"SFO"</span>, <span class='st'>"B"</span>]</div><div class='output co'>#> dataset #> model B -#> SFO List,42 +#> SFO List,45 #> attr(,"class") #> [1] "mmkin"</div><div class='input'> <span class='fu'>head</span>( @@ -164,7 +169,7 @@ <span class='no'>fits</span><span class='kw'>[[</span><span class='st'>"FOMC"</span>, <span class='st'>"B"</span>]] )</div><div class='output co'>#> $par #> parent_0 log_alpha log_beta -#> 99.666192 2.549849 5.050586 +#> 99.666193 2.549849 5.050586 #> #> $ssr #> [1] 28.58291 @@ -180,7 +185,7 @@ #> 25 78 #> #> $counts -#> [1] "both X-convergence and relative convergence (5)" +#> [1] "relative convergence (4)" #> </div><div class='input'> <span class='fu'>head</span>( <span class='co'># The same can be achieved by</span> @@ -258,7 +263,7 @@ #> #> $time #> user system elapsed -#> 0.064 0.000 0.063 +#> 0.058 0.000 0.057 #> #> $mkinmod #> <mkinmod> model generated with @@ -398,7 +403,7 @@ #> { #> assign("calls", calls + 1, inherits = TRUE) #> if (trace_parms) -#> cat(P, "\\n") +#> cat(P, "\n") #> if (length(state.ini.optim) > 0) { #> odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) #> names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) @@ -420,7 +425,7 @@ #> if (mC$model < cost.old) { #> if (!quiet) #> cat("Model cost at call ", calls, ": ", mC$model, -#> "\\n") +#> "\n") #> if (plot) { #> outtimes_plot = seq(min(observed$time), max(observed$time), #> length.out = 100) @@ -447,8 +452,8 @@ #> } #> return(mC) #> } -#> <bytecode: 0x560110508c60> -#> <environment: 0x56010f8d8c30> +#> <bytecode: 0x55555ad80908> +#> <environment: 0x55555b1b4b90> #> #> $cost_notrans #> function (P) @@ -470,8 +475,8 @@ #> scaleVar = scaleVar) #> return(mC) #> } -#> <bytecode: 0x56010fede550> -#> <environment: 0x56010f8d8c30> +#> <bytecode: 0x55555b174428> +#> <environment: 0x55555b1b4b90> #> #> $hessian_notrans #> parent_0 k_parent_sink @@ -512,6 +517,10 @@ #> $weight.ini #> [1] "none" #> +#> $tc.ini +#> sigma_low rsd_high +#> 0.50 0.07 +#> #> $reweight.tol #> [1] 1e-08 #> @@ -534,7 +543,13 @@ #> 99.17407 #> #> $date -#> [1] "Sat Jul 29 15:14:04 2017" +#> [1] "Thu Mar 1 14:26:09 2018" +#> +#> $version +#> [1] "0.9.47.1" +#> +#> $Rversion +#> [1] "3.4.3" #> #> attr(,"class") #> [1] "mkinfit" "modFit" @@ -563,7 +578,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html index d79aaff6..5fb3ccfe 100644 --- a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B" /> +<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +81,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -132,7 +141,7 @@ in this fit.</p> Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> @@ -158,7 +167,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html index e0da5a8d..c1665dee 100644 --- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F" /> +<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +81,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -131,7 +140,7 @@ in this fit.</p> Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> @@ -157,7 +166,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html index 720073f2..db3d228d 100644 --- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Results of fitting the HS model to Datasets A to F of FOCUS (2006) — FOCUS_2006_HS_ref_A_to_F" /> +<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +81,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -132,7 +141,7 @@ in this fit.</p> Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> @@ -158,7 +167,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html index b12abf8c..cc4fcb1d 100644 --- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Results of fitting the SFO model to Datasets A to F of FOCUS (2006) — FOCUS_2006_SFO_ref_A_to_F" /> +<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +81,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -130,7 +139,7 @@ in this fit.</p> Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> @@ -156,7 +165,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html index 65bc572a..4548c983 100644 --- a/docs/reference/FOCUS_2006_datasets.html +++ b/docs/reference/FOCUS_2006_datasets.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Datasets A to F from the FOCUS Kinetics report from 2006 — FOCUS_2006_datasets" /> +<meta property="og:description" content="Data taken from FOCUS (2006), p. 258." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +77,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -123,7 +128,7 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> @@ -158,7 +163,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/FOMC.solution-1.png b/docs/reference/FOMC.solution-1.png Binary files differnew file mode 100644 index 00000000..58178df5 --- /dev/null +++ b/docs/reference/FOMC.solution-1.png diff --git a/docs/reference/FOMC.solution-2.png b/docs/reference/FOMC.solution-2.png Binary files differdeleted file mode 100644 index a673bc0e..00000000 --- a/docs/reference/FOMC.solution-2.png +++ /dev/null diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html index 9af30b7a..810b0eba 100644 --- a/docs/reference/FOMC.solution.html +++ b/docs/reference/FOMC.solution.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> - + + +<meta property="og:title" content="First-Order Multi-Compartment kinetics — FOMC.solution" /> + +<meta property="og:description" content="Function describing exponential decline from a defined starting value, with + a decreasing rate constant. +The form given here differs slightly from the original reference by Gustafson + and Holden (1990). The parameter beta corresponds to 1/beta in the + original equation." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +81,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -152,14 +161,14 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <p>Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil: A new model based on spatial variability. <em>Environmental Science and Technology</em> <b>24</b>, 1032-1038</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>FOMC.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>10</span>, <span class='fl'>2</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><img src='FOMC.solution-2.png' alt='' width='540' height='400' /></pre> + <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>FOMC.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>10</span>, <span class='fl'>2</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='img'><img src='FOMC.solution-1.png' alt='' width='700' height='432.632880098887' /></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -184,7 +193,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/HS.solution-1.png b/docs/reference/HS.solution-1.png Binary files differnew file mode 100644 index 00000000..e259134e --- /dev/null +++ b/docs/reference/HS.solution-1.png diff --git a/docs/reference/HS.solution-2.png b/docs/reference/HS.solution-2.png Binary files differdeleted file mode 100644 index 2e516447..00000000 --- a/docs/reference/HS.solution-2.png +++ /dev/null diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html index 8f6bbbe2..79358efb 100644 --- a/docs/reference/HS.solution.html +++ b/docs/reference/HS.solution.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Hockey-Stick kinetics — HS.solution" /> +<meta property="og:description" content="Function describing two exponential decline functions with a break point + between them." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +78,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -147,11 +153,11 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>HS.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>2</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><img src='HS.solution-2.png' alt='' width='540' height='400' /></pre> + <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>HS.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>2</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, <span class='kw'>ylim</span><span class='kw'>=</span><span class='fu'>c</span>(<span class='fl'>0</span>,<span class='fl'>100</span>))</div><div class='img'><img src='HS.solution-1.png' alt='' width='700' height='432.632880098887' /></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -174,7 +180,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/IORE.solution-1.png b/docs/reference/IORE.solution-1.png Binary files differnew file mode 100644 index 00000000..674c25d3 --- /dev/null +++ b/docs/reference/IORE.solution-1.png diff --git a/docs/reference/IORE.solution-2.png b/docs/reference/IORE.solution-2.png Binary files differdeleted file mode 100644 index a83d49c7..00000000 --- a/docs/reference/IORE.solution-2.png +++ /dev/null diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html index 45e090c0..f705ab2f 100644 --- a/docs/reference/IORE.solution.html +++ b/docs/reference/IORE.solution.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Indeterminate order rate equation kinetics — IORE.solution" /> +<meta property="og:description" content="Function describing exponential decline from a defined starting value, with + a concentration dependent rate constant." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -154,7 +157,7 @@ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>IORE.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.2</span>, <span class='fl'>1.3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>, - <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><img src='IORE.solution-2.png' alt='' width='540' height='400' /><div class='input'> <span class='no'>fit.fomc</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) + <span class='kw'>ylim</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0</span>, <span class='fl'>100</span>))</div><div class='img'><img src='IORE.solution-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='no'>fit.fomc</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>fit.iore</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>fit.iore.deS</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"IORE"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) @@ -191,7 +194,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/SFO.solution-2.png b/docs/reference/SFO.solution-2.png Binary files differdeleted file mode 100644 index 9626091f..00000000 --- a/docs/reference/SFO.solution-2.png +++ /dev/null diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html index ef9b8eb7..a7934a35 100644 --- a/docs/reference/SFO.solution.html +++ b/docs/reference/SFO.solution.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Single First-Order kinetics — SFO.solution" /> +<meta property="og:description" content="Function describing exponential decline from a defined starting value." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +77,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -136,11 +141,13 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFO.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><img src='SFO.solution-2.png' alt='' width='540' height='400' /></pre> + <pre class="examples"><div class='input'> </div><span class='co'># NOT RUN {</span> +<span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFO.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>) +<span class='co'># }</span></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -163,7 +170,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/SFORB.solution-2.png b/docs/reference/SFORB.solution-2.png Binary files differdeleted file mode 100644 index 63a50bf9..00000000 --- a/docs/reference/SFORB.solution-2.png +++ /dev/null diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html index ebe67733..aaae7cdd 100644 --- a/docs/reference/SFORB.solution.html +++ b/docs/reference/SFORB.solution.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Single First-Order Reversible Binding kinetics — SFORB.solution" /> +<meta property="og:description" content="Function describing the solution of the differential equations describing + the kinetic model with first-order terms for a two-way transfer from a free + to a bound fraction, and a first-order degradation term for the free + fraction. The initial condition is a defined amount in the free fraction and + no substance in the bound fraction." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +81,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -149,11 +158,13 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFORB.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>)</div><img src='SFORB.solution-2.png' alt='' width='540' height='400' /></pre> + <pre class="examples"><div class='input'> </div><span class='co'># NOT RUN {</span> +<span class='fu'>plot</span>(<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'>SFORB.solution</span>(<span class='no'>x</span>, <span class='fl'>100</span>, <span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>0</span>, <span class='fl'>2</span>) +<span class='co'># }</span></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -176,7 +187,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/add_err-1.png b/docs/reference/add_err-1.png Binary files differnew file mode 100644 index 00000000..4f9b1534 --- /dev/null +++ b/docs/reference/add_err-1.png diff --git a/docs/reference/add_err-2.png b/docs/reference/add_err-2.png Binary files differnew file mode 100644 index 00000000..8fcf4625 --- /dev/null +++ b/docs/reference/add_err-2.png diff --git a/docs/reference/add_err-3.png b/docs/reference/add_err-3.png Binary files differnew file mode 100644 index 00000000..e44839a6 --- /dev/null +++ b/docs/reference/add_err-3.png diff --git a/docs/reference/add_err-4.png b/docs/reference/add_err-4.png Binary files differdeleted file mode 100644 index 8bbd1758..00000000 --- a/docs/reference/add_err-4.png +++ /dev/null diff --git a/docs/reference/add_err-6.png b/docs/reference/add_err-6.png Binary files differdeleted file mode 100644 index 2a4fe33f..00000000 --- a/docs/reference/add_err-6.png +++ /dev/null diff --git a/docs/reference/add_err-8.png b/docs/reference/add_err-8.png Binary files differdeleted file mode 100644 index 49c4a5f0..00000000 --- a/docs/reference/add_err-8.png +++ /dev/null diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index d56a8728..42bec993 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -18,12 +18,21 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> - + + +<meta property="og:title" content="Add normally distributed errors to simulated kinetic degradation data — add_err" /> + +<meta property="og:description" content="Normally distributed errors are added to data predicted for a specific + degradation model using mkinpredict. The variance of the error + may depend on the predicted value and is specified as a standard deviation." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -195,14 +204,14 @@ <span class='no'>d_SFO_SFO_err</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>) -<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>)</div><img src='add_err-4.png' alt='' width='540' height='400' /><div class='input'> +<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>)</div><div class='img'><img src='add_err-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='co'># We would like to inspect the fit for dataset 3 more closely</span> <span class='co'># Using double brackets makes the returned object an mkinfit object</span> <span class='co'># instead of a list of mkinfit objects, so plot.mkinfit is used</span> -<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span><span class='kw'>[[</span><span class='fl'>3</span>]], <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='add_err-6.png' alt='' width='540' height='400' /><div class='input'> +<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span><span class='kw'>[[</span><span class='fl'>3</span>]], <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='add_err-2.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='co'># If we use single brackets, we should give two indices (model and dataset),</span> <span class='co'># and plot.mmkin is used</span> -<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>[<span class='fl'>1</span>, <span class='fl'>3</span>])</div><img src='add_err-8.png' alt='' width='540' height='400' /><div class='input'> +<span class='fu'>plot</span>(<span class='no'>f_SFO_SFO</span>[<span class='fl'>1</span>, <span class='fl'>3</span>])</div><div class='img'><img src='add_err-3.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> @@ -230,7 +239,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html index ed235a47..c45a0b7c 100644 --- a/docs/reference/endpoints.html +++ b/docs/reference/endpoints.html @@ -18,12 +18,22 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Function to calculate endpoints for further use from kinetic models fitted with mkinfit — endpoints" /> +<meta property="og:description" content="This function calculates DT50 and DT90 values as well as formation fractions from kinetic models +fitted with mkinfit. If the SFORB model was specified for one of the parents or metabolites, +the Eigenvalues are returned. These are equivalent to the rate constantes of the DFOP model, but +with the advantage that the SFORB model can also be used for metabolites." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +96,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -171,7 +176,7 @@ with the advantage that the SFORB model can also be used for metabolites.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/geometric_mean.html b/docs/reference/geometric_mean.html index 4af50f48..2d46b4de 100644 --- a/docs/reference/geometric_mean.html +++ b/docs/reference/geometric_mean.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Calculate the geometric mean — geometric_mean" /> +<meta property="og:description" content="Function calculating the geometric mean of numeric vectors" /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -152,7 +154,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html index 6c3979c5..2c51001f 100644 --- a/docs/reference/ilr.html +++ b/docs/reference/ilr.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Function to perform isometric log-ratio transformation — ilr" /> +<meta property="og:description" content="This implementation is a special case of the class of isometric log-ratio transformations." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -176,7 +178,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/index.html b/docs/reference/index.html index 4675748c..218194ad 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -18,12 +18,16 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> - + + +<meta property="og:title" content="Function reference" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -442,7 +446,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html index 0e99e579..5d6baf6a 100644 --- a/docs/reference/max_twa_parent.html +++ b/docs/reference/max_twa_parent.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — max_twa_parent" /> +<meta property="og:description" content="This function calculates maximum moving window time weighted average concentrations +(TWAs) for kinetic models fitted with mkinfit. Currently, only +calculations for the parent are implemented for the SFO, FOMC and DFOP models, +using the analytical formulas given in the PEC soil section of the FOCUS +guidance." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -139,7 +145,7 @@ guidance.</p> Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> @@ -172,7 +178,7 @@ guidance.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html index 0111deb3..ec7f8ccd 100644 --- a/docs/reference/mccall81_245T.html +++ b/docs/reference/mccall81_245T.html @@ -18,12 +18,21 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T" /> +<meta property="og:description" content="Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding + 2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether + extracts." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +79,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +95,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -127,108 +134,25 @@ <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> <p>McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labeled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107 - <a href = 'http://dx.doi.org/10.1021/jf00103a026'>http://dx.doi.org/10.1021/jf00103a026</a></p> + <a href='http://dx.doi.org/10.1021/jf00103a026'>http://dx.doi.org/10.1021/jf00103a026</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'> <span class='no'>SFO_SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>T245</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"phenol"</span>), <span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"anisole"</span>), - <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> </div><span class='co'># NOT RUN {</span> <span class='no'>fit.1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'>summary</span>(<span class='no'>fit.1</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version: 0.9.46 -#> R version: 3.4.1 -#> Date of fit: Sat Jul 29 15:14:14 2017 -#> Date of summary: Sat Jul 29 15:14:14 2017 -#> -#> Equations: -#> d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245 -#> d_phenol/dt = + k_T245_phenol * T245 - k_phenol_sink * phenol - -#> k_phenol_anisole * phenol -#> d_anisole/dt = + k_phenol_anisole * phenol - k_anisole_sink * anisole -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 612 model solutions performed in 3.558 s -#> -#> Weighting: none -#> -#> Starting values for parameters to be optimised: -#> value type -#> T245_0 100.9000 state -#> k_T245_sink 0.1000 deparm -#> k_T245_phenol 0.1001 deparm -#> k_phenol_sink 0.1002 deparm -#> k_phenol_anisole 0.1003 deparm -#> k_anisole_sink 0.1004 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> T245_0 100.900000 -Inf Inf -#> log_k_T245_sink -2.302585 -Inf Inf -#> log_k_T245_phenol -2.301586 -Inf Inf -#> log_k_phenol_sink -2.300587 -Inf Inf -#> log_k_phenol_anisole -2.299590 -Inf Inf -#> log_k_anisole_sink -2.298593 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> phenol_0 0 state -#> anisole_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> T245_0 103.9000 NA NA NA -#> log_k_T245_sink -4.1130 NA NA NA -#> log_k_T245_phenol -3.6120 NA NA NA -#> log_k_phenol_sink -26.8400 NA NA NA -#> log_k_phenol_anisole -0.9037 NA NA NA -#> log_k_anisole_sink -5.0090 NA NA NA -#> -#> Parameter correlation:</div><div class='output co'>#> <span class='warning'>Warning: Could not estimate covariance matrix; singular system:</span></div><div class='output co'>#> Could not estimate covariance matrix; singular system: -#> -#> Residual standard error: 2.78 on 18 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> T245_0 1.039e+02 4.282e+01 7.236e-20 NA NA -#> k_T245_sink 1.636e-02 8.901e-01 1.926e-01 NA NA -#> k_T245_phenol 2.701e-02 1.504e+00 7.499e-02 NA NA -#> k_phenol_sink 2.212e-12 7.870e-12 5.000e-01 NA NA -#> k_phenol_anisole 4.051e-01 2.518e+00 1.075e-02 NA NA -#> k_anisole_sink 6.679e-03 8.146e+00 9.469e-08 NA NA -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 10.070 6 16 -#> T245 7.908 3 5 -#> phenol 106.445 2 5 -#> anisole 5.379 1 6 -#> -#> Resulting formation fractions: -#> ff -#> T245_sink 3.772e-01 -#> T245_phenol 6.228e-01 -#> phenol_sink 5.462e-12 -#> phenol_anisole 1.000e+00 -#> anisole_sink 1.000e+00 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> T245 15.982 53.091 -#> phenol 1.711 5.685 -#> anisole 103.784 344.763</div><div class='input'> - <span class='co'># No convergence, no covariance matrix ...</span> + <span class='fu'>summary</span>(<span class='no'>fit.1</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>) + +<span class='co'># }</span><div class='input'> <span class='co'># No convergence, no covariance matrix ...</span> <span class='co'># k_phenol_sink is really small, therefore fix it to zero</span> <span class='no'>fit.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='kw'>k_phenol_sink</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='st'>"k_phenol_sink"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version: 0.9.46 -#> R version: 3.4.1 -#> Date of fit: Sat Jul 29 15:14:15 2017 -#> Date of summary: Sat Jul 29 15:14:15 2017 + <span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version used for fitting: 0.9.47.1 +#> R version used for fitting: 3.4.3 +#> Date of fit: Thu Mar 1 14:26:15 2018 +#> Date of summary: Thu Mar 1 14:26:15 2018 #> #> Equations: #> d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245 @@ -238,7 +162,7 @@ #> #> Model predictions using solution type deSolve #> -#> Fitted with method Port using 246 model solutions performed in 1.431 s +#> Fitted with method Port using 246 model solutions performed in 1.359 s #> #> Weighting: none #> @@ -340,7 +264,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html index 042bdced..a5432dac 100644 --- a/docs/reference/mkin_long_to_wide.html +++ b/docs/reference/mkin_long_to_wide.html @@ -18,12 +18,21 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Convert a dataframe from long to wide format — mkin_long_to_wide" /> +<meta property="og:description" content="This function takes a dataframe in the long form as required by modCost + and converts it into a dataframe with one independent variable and several + dependent variables as columns." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +95,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -186,7 +190,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html index ddf28dd9..6798efa6 100644 --- a/docs/reference/mkin_wide_to_long.html +++ b/docs/reference/mkin_wide_to_long.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Convert a dataframe with observations over time into long format — mkin_wide_to_long" /> +<meta property="og:description" content="This function simply takes a dataframe with one independent variable and several + dependent variable and converts it into the long form as required by modCost." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -165,7 +168,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html index 2d8d64a5..ab06e903 100644 --- a/docs/reference/mkinds.html +++ b/docs/reference/mkinds.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="A dataset class for mkin — mkinds" /> +<meta property="og:description" content="A dataset class for mkin" /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -154,7 +156,7 @@ in order to be compatible with mkinfit</p></dd> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html index 67b7a35e..496cce15 100644 --- a/docs/reference/mkinerrmin.html +++ b/docs/reference/mkinerrmin.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Calculate the minimum error to assume in order to pass the variance test — mkinerrmin" /> +<meta property="og:description" content="This function finds the smallest relative error still resulting in passing the +chi-squared test as defined in the FOCUS kinetics report from 2006." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +78,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -146,7 +152,7 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.</p> Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> @@ -186,7 +192,7 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index 0102aecb..4fb5ef9a 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -18,12 +18,29 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Fit a kinetic model to data with one or more state variables — mkinfit" /> +<meta property="og:description" content="This function uses the Flexible Modelling Environment package + FME to create a function calculating the model cost, i.e. the + deviation between the kinetic model and the observed data. This model cost is + then minimised using the Port algorithm nlminb, + using the specified initial or fixed parameters and starting values. + Per default, parameters in the kinetic models are internally transformed in order + to better satisfy the assumption of a normal distribution of their estimators. + In each step of the optimsation, the kinetic model is solved using the + function mkinpredict. The variance of the residuals for each + observed variable can optionally be iteratively reweighted until convergence + using the argument reweight.method = "obs"." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -395,17 +412,17 @@ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='co'># Use shorthand notation for parent only degradation</span> <span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>fit</span>)</div><div class='output co'>#> mkin version: 0.9.47.1 -#> R version: 3.4.3 -#> Date of fit: Tue Jan 30 10:05:48 2018 -#> Date of summary: Tue Jan 30 10:05:48 2018 +<span class='fu'>summary</span>(<span class='no'>fit</span>)</div><div class='output co'>#> mkin version used for fitting: 0.9.47.1 +#> R version used for fitting: 3.4.3 +#> Date of fit: Thu Mar 1 14:26:18 2018 +#> Date of summary: Thu Mar 1 14:26:18 2018 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted with method Port using 64 model solutions performed in 0.31 s +#> Fitted with method Port using 64 model solutions performed in 0.135 s #> #> Weighting: none #> @@ -474,7 +491,7 @@ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> <span class='fu'>print</span>(<span class='fu'>system.time</span>(<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)))</div><div class='output co'>#> user system elapsed -#> 1.196 0.000 1.195 </div><div class='input'><span class='fu'>coef</span>(<span class='no'>fit</span>)</div><div class='output co'>#> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink +#> 0.84 0.00 0.84 </div><div class='input'><span class='fu'>coef</span>(<span class='no'>fit</span>)</div><div class='output co'>#> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink #> 99.59848 -3.03822 -2.98030 -5.24750 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> $ff #> parent_sink parent_m1 m1_sink #> 0.485524 0.514476 1.000000 @@ -486,92 +503,19 @@ #> DT50 DT90 #> parent 7.022929 23.32967 #> m1 131.760712 437.69961 -#> </div><div class='input'> +#> </div><span class='co'># NOT RUN {</span> <span class='co'># deSolve is slower when no C compiler (gcc) was available during model generation</span> <span class='fu'>print</span>(<span class='fu'>system.time</span>(<span class='no'>fit.deSolve</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, - <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)))</div><div class='output co'>#> Model cost at call 1 : 18915.53 -#> Model cost at call 2 : 18915.53 -#> Model cost at call 6 : 11424.02 -#> Model cost at call 10 : 11424 -#> Model cost at call 12 : 4094.396 -#> Model cost at call 16 : 4094.396 -#> Model cost at call 19 : 1340.595 -#> Model cost at call 20 : 1340.593 -#> Model cost at call 25 : 1072.239 -#> Model cost at call 28 : 1072.236 -#> Model cost at call 30 : 874.2614 -#> Model cost at call 33 : 874.2611 -#> Model cost at call 35 : 616.2379 -#> Model cost at call 37 : 616.2374 -#> Model cost at call 40 : 467.4387 -#> Model cost at call 42 : 467.4382 -#> Model cost at call 46 : 398.2913 -#> Model cost at call 48 : 398.2912 -#> Model cost at call 49 : 398.2911 -#> Model cost at call 51 : 395.0711 -#> Model cost at call 54 : 395.071 -#> Model cost at call 56 : 378.3298 -#> Model cost at call 59 : 378.3298 -#> Model cost at call 62 : 376.9812 -#> Model cost at call 64 : 376.9811 -#> Model cost at call 67 : 375.2085 -#> Model cost at call 69 : 375.2085 -#> Model cost at call 70 : 375.2085 -#> Model cost at call 71 : 375.2085 -#> Model cost at call 72 : 374.5723 -#> Model cost at call 74 : 374.5723 -#> Model cost at call 77 : 374.0075 -#> Model cost at call 79 : 374.0075 -#> Model cost at call 80 : 374.0075 -#> Model cost at call 82 : 373.1711 -#> Model cost at call 84 : 373.1711 -#> Model cost at call 87 : 372.6445 -#> Model cost at call 88 : 372.1614 -#> Model cost at call 90 : 372.1614 -#> Model cost at call 91 : 372.1614 -#> Model cost at call 94 : 371.6464 -#> Model cost at call 99 : 371.4299 -#> Model cost at call 101 : 371.4299 -#> Model cost at call 104 : 371.4071 -#> Model cost at call 106 : 371.4071 -#> Model cost at call 107 : 371.4071 -#> Model cost at call 109 : 371.2524 -#> Model cost at call 113 : 371.2524 -#> Model cost at call 114 : 371.2136 -#> Model cost at call 115 : 371.2136 -#> Model cost at call 116 : 371.2136 -#> Model cost at call 119 : 371.2134 -#> Model cost at call 120 : 371.2134 -#> Model cost at call 122 : 371.2134 -#> Model cost at call 123 : 371.2134 -#> Model cost at call 125 : 371.2134 -#> Model cost at call 126 : 371.2134 -#> Model cost at call 135 : 371.2134 -#> Model cost at call 147 : 371.2134 -#> Model cost at call 151 : 371.2134 -#> Model cost at call 152 : 371.2134 -#> Model cost at call 153 : 371.2134 -#> Optimisation by method Port successfully terminated. -#> user system elapsed -#> 1.008 0.000 1.006 </div><div class='input'><span class='fu'>coef</span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink -#> 99.59848 -3.03822 -2.98030 -5.24750 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#> $ff -#> parent_sink parent_m1 m1_sink -#> 0.485524 0.514476 1.000000 -#> -#> $SFORB -#> logical(0) -#> -#> $distimes -#> DT50 DT90 -#> parent 7.022929 23.32967 -#> m1 131.760712 437.69961 -#> </div><div class='input'> - -<span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC</span> - + <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>))) +<span class='fu'>coef</span>(<span class='no'>fit.deSolve</span>) +<span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>) +<span class='co'># }</span><div class='input'> +# Use stepwise fitting, using optimised parameters from parent only fit, FOMC +</div><span class='co'># NOT RUN {</span> <span class='no'>FOMC_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"FOMC"</span>, <span class='st'>"m1"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)) +<span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> <span class='no'>fit.FOMC_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># Use starting parameters from parent only FOMC fit</span> <span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) @@ -581,739 +525,40 @@ <span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, SFORB</span> <span class='no'>SFORB_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFORB"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>)) +<span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> <span class='no'>fit.SFORB_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>fit.SFORB_SFO.deSolve</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># Use starting parameters from parent only SFORB fit (not really needed in this case)</span> <span class='no'>fit.SFORB</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>fit.SFORB_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - - - +<span class='co'># }</span><div class='input'> +</div><span class='co'># NOT RUN {</span> <span class='co'># Weighted fits, including IRLS</span> <span class='no'>SFO_SFO.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#> mkin version: 0.9.47.1 -#> R version: 3.4.3 -#> Date of fit: Tue Jan 30 10:06:00 2018 -#> Date of summary: Tue Jan 30 10:06:00 2018 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 185 model solutions performed in 0.739 s -#> -#> Weighting: none -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.60000 1.61400 96.3300 102.9000 -#> log_k_parent -2.31600 0.04187 -2.4010 -2.2310 -#> log_k_m1 -5.24800 0.13610 -5.5230 -4.9720 -#> f_parent_ilr_1 0.04096 0.06477 -0.0904 0.1723 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 -#> parent_0 1.0000 0.5178 -0.1701 -0.5489 -#> log_k_parent 0.5178 1.0000 -0.3285 -0.5451 -#> log_k_m1 -0.1701 -0.3285 1.0000 0.7466 -#> f_parent_ilr_1 -0.5489 -0.5451 0.7466 1.0000 -#> -#> Residual standard error: 3.211 on 36 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.600000 61.720 2.024e-38 96.330000 1.029e+02 -#> k_parent 0.098700 23.880 5.701e-24 0.090660 1.074e-01 -#> k_m1 0.005261 7.349 5.758e-09 0.003992 6.933e-03 -#> f_parent_to_m1 0.514500 22.490 4.374e-23 0.468100 5.606e-01 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.398 4 15 -#> parent 6.459 2 7 -#> m1 4.690 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5145 -#> parent_sink 0.4855 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.023 23.33 -#> m1 131.761 437.70 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 99.59848 -1.385e-01 -#> 0 parent 102.04 99.59848 2.442e+00 -#> 1 parent 93.50 90.23787 3.262e+00 -#> 1 parent 92.50 90.23787 2.262e+00 -#> 3 parent 63.23 74.07319 -1.084e+01 -#> 3 parent 68.99 74.07319 -5.083e+00 -#> 7 parent 52.32 49.91206 2.408e+00 -#> 7 parent 55.13 49.91206 5.218e+00 -#> 14 parent 27.27 25.01257 2.257e+00 -#> 14 parent 26.64 25.01257 1.627e+00 -#> 21 parent 11.50 12.53462 -1.035e+00 -#> 21 parent 11.64 12.53462 -8.946e-01 -#> 35 parent 2.85 3.14787 -2.979e-01 -#> 35 parent 2.91 3.14787 -2.379e-01 -#> 50 parent 0.69 0.71624 -2.624e-02 -#> 50 parent 0.63 0.71624 -8.624e-02 -#> 75 parent 0.05 0.06074 -1.074e-02 -#> 75 parent 0.06 0.06074 -7.381e-04 -#> 0 m1 0.00 0.00000 0.000e+00 -#> 0 m1 0.00 0.00000 0.000e+00 -#> 1 m1 4.84 4.80296 3.704e-02 -#> 1 m1 5.64 4.80296 8.370e-01 -#> 3 m1 12.91 13.02400 -1.140e-01 -#> 3 m1 12.96 13.02400 -6.400e-02 -#> 7 m1 22.97 25.04476 -2.075e+00 -#> 7 m1 24.47 25.04476 -5.748e-01 -#> 14 m1 41.69 36.69002 5.000e+00 -#> 14 m1 33.21 36.69002 -3.480e+00 -#> 21 m1 44.37 41.65310 2.717e+00 -#> 21 m1 46.44 41.65310 4.787e+00 -#> 35 m1 41.22 43.31312 -2.093e+00 -#> 35 m1 37.95 43.31312 -5.363e+00 -#> 50 m1 41.19 41.21831 -2.831e-02 -#> 50 m1 40.01 41.21831 -1.208e+00 -#> 75 m1 40.09 36.44703 3.643e+00 -#> 75 m1 33.85 36.44703 -2.597e+00 -#> 100 m1 31.04 31.98163 -9.416e-01 -#> 100 m1 33.13 31.98163 1.148e+00 -#> 120 m1 25.15 28.78984 -3.640e+00 -#> 120 m1 33.31 28.78984 4.520e+00</div><div class='input'><span class='no'>f.irls</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>f.irls</span>)</div><div class='output co'>#> mkin version: 0.9.47.1 -#> R version: 3.4.3 -#> Date of fit: Tue Jan 30 10:06:02 2018 -#> Date of summary: Tue Jan 30 10:06:02 2018 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 523 model solutions performed in 2.151 s -#> -#> Weighting: none -#> -#> Iterative reweighting with method obs -#> Final mean squared residuals of observed variables: -#> parent m1 -#> 11.573408 7.407845 -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.67000 1.79200 96.04000 103.300 -#> log_k_parent -2.31200 0.04560 -2.40400 -2.219 -#> log_k_m1 -5.25100 0.12510 -5.50500 -4.998 -#> f_parent_ilr_1 0.03785 0.06318 -0.09027 0.166 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 -#> parent_0 1.0000 0.5083 -0.1979 -0.6148 -#> log_k_parent 0.5083 1.0000 -0.3894 -0.6062 -#> log_k_m1 -0.1979 -0.3894 1.0000 0.7417 -#> f_parent_ilr_1 -0.6148 -0.6062 0.7417 1.0000 -#> -#> Residual standard error: 1.054 on 36 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.67000 55.630 8.184e-37 96.040000 1.033e+02 -#> k_parent 0.09906 21.930 1.016e-22 0.090310 1.087e-01 -#> k_m1 0.00524 7.996 8.486e-10 0.004066 6.753e-03 -#> f_parent_to_m1 0.51340 23.000 2.038e-23 0.468100 5.584e-01 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.399 4 15 -#> parent 6.466 2 7 -#> m1 4.679 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5134 -#> parent_sink 0.4866 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 6.997 23.24 -#> m1 132.282 439.43 -#> -#> Data: -#> time variable observed predicted residual err -#> 0 parent 99.46 99.67218 -2.122e-01 3.402 -#> 0 parent 102.04 99.67218 2.368e+00 3.402 -#> 1 parent 93.50 90.27153 3.228e+00 3.402 -#> 1 parent 92.50 90.27153 2.228e+00 3.402 -#> 3 parent 63.23 74.04648 -1.082e+01 3.402 -#> 3 parent 68.99 74.04648 -5.056e+00 3.402 -#> 7 parent 52.32 49.82092 2.499e+00 3.402 -#> 7 parent 55.13 49.82092 5.309e+00 3.402 -#> 14 parent 27.27 24.90287 2.367e+00 3.402 -#> 14 parent 26.64 24.90287 1.737e+00 3.402 -#> 21 parent 11.50 12.44764 -9.476e-01 3.402 -#> 21 parent 11.64 12.44764 -8.076e-01 3.402 -#> 35 parent 2.85 3.11002 -2.600e-01 3.402 -#> 35 parent 2.91 3.11002 -2.000e-01 3.402 -#> 50 parent 0.69 0.70374 -1.374e-02 3.402 -#> 50 parent 0.63 0.70374 -7.374e-02 3.402 -#> 75 parent 0.05 0.05913 -9.134e-03 3.402 -#> 75 parent 0.06 0.05913 8.662e-04 3.402 -#> 0 m1 0.00 0.00000 0.000e+00 2.722 -#> 0 m1 0.00 0.00000 0.000e+00 2.722 -#> 1 m1 4.84 4.81328 2.672e-02 2.722 -#> 1 m1 5.64 4.81328 8.267e-01 2.722 -#> 3 m1 12.91 13.04779 -1.378e-01 2.722 -#> 3 m1 12.96 13.04779 -8.779e-02 2.722 -#> 7 m1 22.97 25.07615 -2.106e+00 2.722 -#> 7 m1 24.47 25.07615 -6.062e-01 2.722 -#> 14 m1 41.69 36.70729 4.983e+00 2.722 -#> 14 m1 33.21 36.70729 -3.497e+00 2.722 -#> 21 m1 44.37 41.65050 2.720e+00 2.722 -#> 21 m1 46.44 41.65050 4.790e+00 2.722 -#> 35 m1 41.22 43.28866 -2.069e+00 2.722 -#> 35 m1 37.95 43.28866 -5.339e+00 2.722 -#> 50 m1 41.19 41.19338 -3.383e-03 2.722 -#> 50 m1 40.01 41.19338 -1.183e+00 2.722 -#> 75 m1 40.09 36.43820 3.652e+00 2.722 -#> 75 m1 33.85 36.43820 -2.588e+00 2.722 -#> 100 m1 31.04 31.98971 -9.497e-01 2.722 -#> 100 m1 33.13 31.98971 1.140e+00 2.722 -#> 120 m1 25.15 28.80897 -3.659e+00 2.722 -#> 120 m1 33.31 28.80897 4.501e+00 2.722</div><div class='input'><span class='no'>f.w.mean</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>weight</span> <span class='kw'>=</span> <span class='st'>"mean"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>f.w.mean</span>)</div><div class='output co'>#> mkin version: 0.9.47.1 -#> R version: 3.4.3 -#> Date of fit: Tue Jan 30 10:06:03 2018 -#> Date of summary: Tue Jan 30 10:06:03 2018 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 155 model solutions performed in 0.675 s -#> -#> Weighting: mean -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.7300 1.93200 95.81000 103.6000 -#> log_k_parent -2.3090 0.04837 -2.40700 -2.2110 -#> log_k_m1 -5.2550 0.12070 -5.49900 -5.0100 -#> f_parent_ilr_1 0.0354 0.06344 -0.09327 0.1641 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 -#> parent_0 1.0000 0.5004 -0.2143 -0.6514 -#> log_k_parent 0.5004 1.0000 -0.4282 -0.6383 -#> log_k_m1 -0.2143 -0.4282 1.0000 0.7390 -#> f_parent_ilr_1 -0.6514 -0.6383 0.7390 1.0000 -#> -#> Residual standard error: 0.09829 on 36 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.730000 51.630 1.166e-35 95.81000 1.036e+02 -#> k_parent 0.099360 20.670 7.303e-22 0.09007 1.096e-01 -#> k_m1 0.005224 8.287 3.649e-10 0.00409 6.672e-03 -#> f_parent_to_m1 0.512500 22.860 2.497e-23 0.46710 5.578e-01 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.401 4 15 -#> parent 6.473 2 7 -#> m1 4.671 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5125 -#> parent_sink 0.4875 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 6.976 23.18 -#> m1 132.696 440.81 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 99.73057 -0.270570 -#> 0 parent 102.04 99.73057 2.309430 -#> 1 parent 93.50 90.29805 3.201945 -#> 1 parent 92.50 90.29805 2.201945 -#> 3 parent 63.23 74.02503 -10.795028 -#> 3 parent 68.99 74.02503 -5.035028 -#> 7 parent 52.32 49.74838 2.571618 -#> 7 parent 55.13 49.74838 5.381618 -#> 14 parent 27.27 24.81588 2.454124 -#> 14 parent 26.64 24.81588 1.824124 -#> 21 parent 11.50 12.37885 -0.878849 -#> 21 parent 11.64 12.37885 -0.738849 -#> 35 parent 2.85 3.08022 -0.230219 -#> 35 parent 2.91 3.08022 -0.170219 -#> 50 parent 0.69 0.69396 -0.003958 -#> 50 parent 0.63 0.69396 -0.063958 -#> 75 parent 0.05 0.05789 -0.007888 -#> 75 parent 0.06 0.05789 0.002112 -#> 0 m1 0.00 0.00000 0.000000 -#> 0 m1 0.00 0.00000 0.000000 -#> 1 m1 4.84 4.82149 0.018512 -#> 1 m1 5.64 4.82149 0.818512 -#> 3 m1 12.91 13.06669 -0.156692 -#> 3 m1 12.96 13.06669 -0.106692 -#> 7 m1 22.97 25.10106 -2.131058 -#> 7 m1 24.47 25.10106 -0.631058 -#> 14 m1 41.69 36.72092 4.969077 -#> 14 m1 33.21 36.72092 -3.510923 -#> 21 m1 44.37 41.64835 2.721647 -#> 21 m1 46.44 41.64835 4.791647 -#> 35 m1 41.22 43.26923 -2.049225 -#> 35 m1 37.95 43.26923 -5.319225 -#> 50 m1 41.19 41.17364 0.016361 -#> 50 m1 40.01 41.17364 -1.163639 -#> 75 m1 40.09 36.43122 3.658776 -#> 75 m1 33.85 36.43122 -2.581224 -#> 100 m1 31.04 31.99612 -0.956124 -#> 100 m1 33.13 31.99612 1.133876 -#> 120 m1 25.15 28.82413 -3.674128 -#> 120 m1 33.31 28.82413 4.485872</div><div class='input'><span class='no'>f.w.value</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'>subset</span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"value"</span>, + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>) +<span class='no'>f.noweight</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='fu'>summary</span>(<span class='no'>f.noweight</span>) +<span class='no'>f.irls</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='fu'>summary</span>(<span class='no'>f.irls</span>) +<span class='no'>f.w.mean</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>weight</span> <span class='kw'>=</span> <span class='st'>"mean"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='fu'>summary</span>(<span class='no'>f.w.mean</span>) +<span class='no'>f.w.value</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'>subset</span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"value"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>f.w.value</span>)</div><div class='output co'>#> mkin version: 0.9.47.1 -#> R version: 3.4.3 -#> Date of fit: Tue Jan 30 10:06:04 2018 -#> Date of summary: Tue Jan 30 10:06:04 2018 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 174 model solutions performed in 0.68 s -#> -#> Weighting: manual -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.6600 2.712000 94.14000 105.2000 -#> log_k_parent -2.2980 0.008118 -2.31500 -2.2820 -#> log_k_m1 -5.2410 0.096690 -5.43800 -5.0450 -#> f_parent_ilr_1 0.0231 0.057990 -0.09474 0.1409 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 -#> parent_0 1.00000 0.6844 -0.08687 -0.7564 -#> log_k_parent 0.68435 1.0000 -0.12694 -0.5812 -#> log_k_m1 -0.08687 -0.1269 1.00000 0.5195 -#> f_parent_ilr_1 -0.75644 -0.5812 0.51951 1.0000 -#> -#> Residual standard error: 0.08396 on 34 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.660000 36.75 2.957e-29 94.14000 1.052e+02 -#> k_parent 0.100400 123.20 5.927e-47 0.09878 1.021e-01 -#> k_m1 0.005295 10.34 2.447e-12 0.00435 6.444e-03 -#> f_parent_to_m1 0.508200 24.79 1.184e-23 0.46660 5.497e-01 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.461 4 15 -#> parent 6.520 2 7 -#> m1 4.744 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5082 -#> parent_sink 0.4918 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 6.902 22.93 -#> m1 130.916 434.89 -#> -#> Data: -#> time variable observed predicted residual err -#> 0 parent 99.46 99.65571 -0.195714 99.46 -#> 0 parent 102.04 99.65571 2.384286 102.04 -#> 1 parent 93.50 90.13383 3.366170 93.50 -#> 1 parent 92.50 90.13383 2.366170 92.50 -#> 3 parent 63.23 73.73252 -10.502518 63.23 -#> 3 parent 68.99 73.73252 -4.742518 68.99 -#> 7 parent 52.32 49.34027 2.979728 52.32 -#> 7 parent 55.13 49.34027 5.789728 55.13 -#> 14 parent 27.27 24.42873 2.841271 27.27 -#> 14 parent 26.64 24.42873 2.211271 26.64 -#> 21 parent 11.50 12.09484 -0.594842 11.50 -#> 21 parent 11.64 12.09484 -0.454842 11.64 -#> 35 parent 2.85 2.96482 -0.114824 2.85 -#> 35 parent 2.91 2.96482 -0.054824 2.91 -#> 50 parent 0.69 0.65733 0.032670 0.69 -#> 50 parent 0.63 0.65733 -0.027330 0.63 -#> 75 parent 0.05 0.05339 -0.003386 0.05 -#> 75 parent 0.06 0.05339 0.006614 0.06 -#> 1 m1 4.84 4.82570 0.014301 4.84 -#> 1 m1 5.64 4.82570 0.814301 5.64 -#> 3 m1 12.91 13.06402 -0.154020 12.91 -#> 3 m1 12.96 13.06402 -0.104020 12.96 -#> 7 m1 22.97 25.04656 -2.076564 22.97 -#> 7 m1 24.47 25.04656 -0.576564 24.47 -#> 14 m1 41.69 36.53601 5.153988 41.69 -#> 14 m1 33.21 36.53601 -3.326012 33.21 -#> 21 m1 44.37 41.34639 3.023609 44.37 -#> 21 m1 46.44 41.34639 5.093609 46.44 -#> 35 m1 41.22 42.82669 -1.606690 41.22 -#> 35 m1 37.95 42.82669 -4.876690 37.95 -#> 50 m1 41.19 40.67342 0.516578 41.19 -#> 50 m1 40.01 40.67342 -0.663422 40.01 -#> 75 m1 40.09 35.91105 4.178947 40.09 -#> 75 m1 33.85 35.91105 -2.061053 33.85 -#> 100 m1 31.04 31.48161 -0.441612 31.04 -#> 100 m1 33.13 31.48161 1.648388 33.13 -#> 120 m1 25.15 28.32018 -3.170181 25.15 -#> 120 m1 33.31 28.32018 4.989819 33.31</div><div class='input'> - - +<span class='fu'>summary</span>(<span class='no'>f.w.value</span>) +<span class='co'># }</span><div class='input'> +</div><span class='co'># NOT RUN {</span> <span class='co'># Manual weighting</span> <span class='no'>dw</span> <span class='kw'><-</span> <span class='no'>FOCUS_2006_D</span> <span class='no'>errors</span> <span class='kw'><-</span> <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>1</span>) <span class='no'>dw</span>$<span class='no'>err.man</span> <span class='kw'><-</span> <span class='no'>errors</span>[<span class='no'>FOCUS_2006_D</span>$<span class='no'>name</span>] <span class='no'>f.w.man</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>f.w.man</span>)</div><div class='output co'>#> mkin version: 0.9.47.1 -#> R version: 3.4.3 -#> Date of fit: Tue Jan 30 10:06:05 2018 -#> Date of summary: Tue Jan 30 10:06:05 2018 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 297 model solutions performed in 1.178 s -#> -#> Weighting: manual -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.49000 1.33200 96.7800 102.2000 -#> log_k_parent -2.32100 0.03550 -2.3930 -2.2490 -#> log_k_m1 -5.24100 0.21280 -5.6730 -4.8100 -#> f_parent_ilr_1 0.04571 0.08966 -0.1361 0.2275 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 -#> parent_0 1.00000 0.5312 -0.09456 -0.3351 -#> log_k_parent 0.53123 1.0000 -0.17800 -0.3360 -#> log_k_m1 -0.09456 -0.1780 1.00000 0.7616 -#> f_parent_ilr_1 -0.33514 -0.3360 0.76156 1.0000 -#> -#> Residual standard error: 2.628 on 36 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.490000 74.69 2.221e-41 96.780000 1.022e+02 -#> k_parent 0.098140 28.17 2.012e-26 0.091320 1.055e-01 -#> k_m1 0.005292 4.70 1.873e-05 0.003437 8.148e-03 -#> f_parent_to_m1 0.516200 16.30 1.686e-18 0.452000 5.798e-01 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.400 4 15 -#> parent 6.454 2 7 -#> m1 4.708 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5162 -#> parent_sink 0.4838 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.063 23.46 -#> m1 130.971 435.08 -#> -#> Data: -#> time variable observed predicted residual err -#> 0 parent 99.46 99.48598 -0.025976 1 -#> 0 parent 102.04 99.48598 2.554024 1 -#> 1 parent 93.50 90.18612 3.313883 1 -#> 1 parent 92.50 90.18612 2.313883 1 -#> 3 parent 63.23 74.11316 -10.883162 1 -#> 3 parent 68.99 74.11316 -5.123162 1 -#> 7 parent 52.32 50.05029 2.269705 1 -#> 7 parent 55.13 50.05029 5.079705 1 -#> 14 parent 27.27 25.17975 2.090250 1 -#> 14 parent 26.64 25.17975 1.460250 1 -#> 21 parent 11.50 12.66765 -1.167654 1 -#> 21 parent 11.64 12.66765 -1.027654 1 -#> 35 parent 2.85 3.20616 -0.356164 1 -#> 35 parent 2.91 3.20616 -0.296164 1 -#> 50 parent 0.69 0.73562 -0.045619 1 -#> 50 parent 0.63 0.73562 -0.105619 1 -#> 75 parent 0.05 0.06326 -0.013256 1 -#> 75 parent 0.06 0.06326 -0.003256 1 -#> 0 m1 0.00 0.00000 0.000000 2 -#> 0 m1 0.00 0.00000 0.000000 2 -#> 1 m1 4.84 4.78729 0.052713 2 -#> 1 m1 5.64 4.78729 0.852713 2 -#> 3 m1 12.91 12.98785 -0.077848 2 -#> 3 m1 12.96 12.98785 -0.027848 2 -#> 7 m1 22.97 24.99695 -2.026945 2 -#> 7 m1 24.47 24.99695 -0.526945 2 -#> 14 m1 41.69 36.66353 5.026473 2 -#> 14 m1 33.21 36.66353 -3.453527 2 -#> 21 m1 44.37 41.65681 2.713187 2 -#> 21 m1 46.44 41.65681 4.783187 2 -#> 35 m1 41.22 43.35031 -2.130312 2 -#> 35 m1 37.95 43.35031 -5.400312 2 -#> 50 m1 41.19 41.25637 -0.066365 2 -#> 50 m1 40.01 41.25637 -1.246365 2 -#> 75 m1 40.09 36.46057 3.629433 2 -#> 75 m1 33.85 36.46057 -2.610567 2 -#> 100 m1 31.04 31.96929 -0.929288 2 -#> 100 m1 33.13 31.96929 1.160712 2 -#> 120 m1 25.15 28.76062 -3.610616 2 -#> 120 m1 33.31 28.76062 4.549384 2</div><div class='input'><span class='no'>f.w.man.irls</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, +<span class='fu'>summary</span>(<span class='no'>f.w.man</span>) +<span class='no'>f.w.man.irls</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>dw</span>, <span class='kw'>err</span> <span class='kw'>=</span> <span class='st'>"err.man"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>reweight.method</span> <span class='kw'>=</span> <span class='st'>"obs"</span>) -<span class='fu'>summary</span>(<span class='no'>f.w.man.irls</span>)</div><div class='output co'>#> mkin version: 0.9.47.1 -#> R version: 3.4.3 -#> Date of fit: Tue Jan 30 10:06:08 2018 -#> Date of summary: Tue Jan 30 10:06:08 2018 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 692 model solutions performed in 2.733 s -#> -#> Weighting: manual -#> -#> Iterative reweighting with method obs -#> Final mean squared residuals of observed variables: -#> parent m1 -#> 11.573407 7.407845 -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.67000 1.79200 96.04000 103.300 -#> log_k_parent -2.31200 0.04560 -2.40400 -2.220 -#> log_k_m1 -5.25100 0.12510 -5.50500 -4.998 -#> f_parent_ilr_1 0.03785 0.06318 -0.09027 0.166 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 -#> parent_0 1.0000 0.5083 -0.1979 -0.6148 -#> log_k_parent 0.5083 1.0000 -0.3894 -0.6062 -#> log_k_m1 -0.1979 -0.3894 1.0000 0.7417 -#> f_parent_ilr_1 -0.6148 -0.6062 0.7417 1.0000 -#> -#> Residual standard error: 1.054 on 36 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.67000 55.630 8.184e-37 96.040000 1.033e+02 -#> k_parent 0.09906 21.930 1.016e-22 0.090310 1.087e-01 -#> k_m1 0.00524 7.996 8.486e-10 0.004066 6.753e-03 -#> f_parent_to_m1 0.51340 23.000 2.038e-23 0.468100 5.584e-01 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.399 4 15 -#> parent 6.466 2 7 -#> m1 4.679 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5134 -#> parent_sink 0.4866 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 6.997 23.24 -#> m1 132.282 439.43 -#> -#> Data: -#> time variable observed predicted residual err.ini err -#> 0 parent 99.46 99.67218 -2.122e-01 1 3.402 -#> 0 parent 102.04 99.67218 2.368e+00 1 3.402 -#> 1 parent 93.50 90.27153 3.228e+00 1 3.402 -#> 1 parent 92.50 90.27153 2.228e+00 1 3.402 -#> 3 parent 63.23 74.04648 -1.082e+01 1 3.402 -#> 3 parent 68.99 74.04648 -5.056e+00 1 3.402 -#> 7 parent 52.32 49.82092 2.499e+00 1 3.402 -#> 7 parent 55.13 49.82092 5.309e+00 1 3.402 -#> 14 parent 27.27 24.90288 2.367e+00 1 3.402 -#> 14 parent 26.64 24.90288 1.737e+00 1 3.402 -#> 21 parent 11.50 12.44765 -9.476e-01 1 3.402 -#> 21 parent 11.64 12.44765 -8.076e-01 1 3.402 -#> 35 parent 2.85 3.11002 -2.600e-01 1 3.402 -#> 35 parent 2.91 3.11002 -2.000e-01 1 3.402 -#> 50 parent 0.69 0.70375 -1.375e-02 1 3.402 -#> 50 parent 0.63 0.70375 -7.375e-02 1 3.402 -#> 75 parent 0.05 0.05913 -9.134e-03 1 3.402 -#> 75 parent 0.06 0.05913 8.662e-04 1 3.402 -#> 0 m1 0.00 0.00000 0.000e+00 2 2.722 -#> 0 m1 0.00 0.00000 0.000e+00 2 2.722 -#> 1 m1 4.84 4.81328 2.672e-02 2 2.722 -#> 1 m1 5.64 4.81328 8.267e-01 2 2.722 -#> 3 m1 12.91 13.04779 -1.378e-01 2 2.722 -#> 3 m1 12.96 13.04779 -8.779e-02 2 2.722 -#> 7 m1 22.97 25.07615 -2.106e+00 2 2.722 -#> 7 m1 24.47 25.07615 -6.062e-01 2 2.722 -#> 14 m1 41.69 36.70729 4.983e+00 2 2.722 -#> 14 m1 33.21 36.70729 -3.497e+00 2 2.722 -#> 21 m1 44.37 41.65050 2.720e+00 2 2.722 -#> 21 m1 46.44 41.65050 4.790e+00 2 2.722 -#> 35 m1 41.22 43.28866 -2.069e+00 2 2.722 -#> 35 m1 37.95 43.28866 -5.339e+00 2 2.722 -#> 50 m1 41.19 41.19339 -3.386e-03 2 2.722 -#> 50 m1 40.01 41.19339 -1.183e+00 2 2.722 -#> 75 m1 40.09 36.43820 3.652e+00 2 2.722 -#> 75 m1 33.85 36.43820 -2.588e+00 2 2.722 -#> 100 m1 31.04 31.98971 -9.497e-01 2 2.722 -#> 100 m1 33.13 31.98971 1.140e+00 2 2.722 -#> 120 m1 25.15 28.80897 -3.659e+00 2 2.722 -#> 120 m1 33.31 28.80897 4.501e+00 2 2.722</div><div class='input'> -</div></pre> +<span class='fu'>summary</span>(<span class='no'>f.w.man.irls</span>) +<span class='co'># }</span></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -1346,7 +591,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html index 5703b188..c1287905 100644 --- a/docs/reference/mkinmod.html +++ b/docs/reference/mkinmod.html @@ -18,12 +18,24 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Function to set up a kinetic model with one or more state variables — mkinmod" /> +<meta property="og:description" content="The function usually takes several expressions, each assigning a compound name to + a list, specifying the kinetic model type and reaction or transfer to other + observed compartments. Instead of specifying several expressions, a list + of lists can be given in the speclist argument. +For the definition of model types and their parameters, the equations given + in the FOCUS and NAFTA guidance documents are used." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +82,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +98,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -182,7 +192,7 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <p>NAFTA Technical Working Group on Pesticides (not dated) Guidance for Evaluating and Calculating Degradation Kinetics in Environmental Media</p> @@ -196,36 +206,17 @@ <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinmod</span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> - +</div><span class='co'># NOT RUN {</span> <span class='co'># The above model used to be specified like this, before the advent of mkinsub()</span> <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinmod</span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>)) + <span class='co'># Show details of creating the C function</span> <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinmod</span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> Compilation argument: -#> /usr/lib/R/bin/R CMD SHLIB file519167a14b3a.c 2> file519167a14b3a.c.err.txt -#> Program source: -#> 1: #include <R.h> -#> 2: -#> 3: -#> 4: static double parms [3]; -#> 5: #define k_parent_sink parms[0] -#> 6: #define k_parent_m1 parms[1] -#> 7: #define k_m1_sink parms[2] -#> 8: -#> 9: void initpar(void (* odeparms)(int *, double *)) { -#> 10: int N = 3; -#> 11: odeparms(&N, parms); -#> 12: } -#> 13: -#> 14: -#> 15: void func ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) { -#> 16: -#> 17: f[0] = - k_parent_sink * y[0] - k_parent_m1 * y[0]; -#> 18: f[1] = + k_parent_m1 * y[0] - k_m1_sink * y[1]; -#> 19: }</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) + <span class='co'># If we have several parallel metabolites </span> <span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span> <span class='no'>m_synth_DFOP_par</span> <span class='kw'><-</span> <span class='fu'>mkinmod</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"DFOP"</span>, <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)), @@ -235,7 +226,8 @@ <span class='no'>fit_DFOP_par_c</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_DFOP_par</span>, <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>12</span>]]$<span class='no'>data</span>, - <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div></pre> + <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='co'># }</span></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -264,7 +256,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png Binary files differnew file mode 100644 index 00000000..42811535 --- /dev/null +++ b/docs/reference/mkinparplot-1.png diff --git a/docs/reference/mkinparplot-4.png b/docs/reference/mkinparplot-4.png Binary files differdeleted file mode 100644 index c9f4aadb..00000000 --- a/docs/reference/mkinparplot-4.png +++ /dev/null diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html index 67ba6052..30954168 100644 --- a/docs/reference/mkinparplot.html +++ b/docs/reference/mkinparplot.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Function to plot the confidence intervals obtained using mkinfit — mkinparplot" /> +<meta property="og:description" content="This function plots the confidence intervals for the parameters + fitted using mkinfit." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -132,7 +135,7 @@ <span class='kw'>T245</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"phenol"</span>), <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>), <span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"anisole"</span>)), <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>mkinparplot</span>(<span class='no'>fit</span>)</div><img src='mkinparplot-4.png' alt='' width='540' height='400' /></pre> +<span class='fu'>mkinparplot</span>(<span class='no'>fit</span>)</div><div class='img'><img src='mkinparplot-1.png' alt='' width='700' height='432.632880098887' /></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -157,7 +160,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinplot.html b/docs/reference/mkinplot.html index 7c875ebe..5df3f872 100644 --- a/docs/reference/mkinplot.html +++ b/docs/reference/mkinplot.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — mkinplot" /> +<meta property="og:description" content="Deprecated function. It now only calls the plot method plot.mkinfit." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -152,7 +154,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index b3453e6c..df6316e1 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -18,12 +18,21 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Produce predictions from a kinetic model using specific parameters — mkinpredict" /> +<meta property="og:description" content="This function produces a time series for all the observed variables in a + kinetic model as specified by mkinmod, using a specific set of + kinetic parameters and initial values for the state variables." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +79,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +95,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -299,17 +306,17 @@ <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'>seq</span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>), <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#> time parent m1 #> 201 20 4.978707 27.46227</div><div class='output co'>#> user system elapsed -#> 0.004 0.000 0.003 </div><div class='input'> <span class='fu'>system.time</span>( +#> 0.003 0.000 0.003 </div><div class='input'> <span class='fu'>system.time</span>( <span class='fu'>print</span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'>c</span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>), <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'>seq</span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>), <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#> time parent m1 #> 201 20 4.978707 27.46227</div><div class='output co'>#> user system elapsed -#> 0.000 0.000 0.001 </div><div class='input'> <span class='fu'>system.time</span>( +#> 0.002 0.000 0.001 </div><div class='input'> <span class='fu'>system.time</span>( <span class='fu'>print</span>(<span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'>c</span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>), <span class='fu'>c</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'>seq</span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>), <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)[<span class='fl'>201</span>,]))</div><div class='output co'>#> time parent m1 #> 201 20 4.978707 27.46227</div><div class='output co'>#> user system elapsed -#> 0.032 0.000 0.032 </div></pre> +#> 0.031 0.000 0.031 </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -334,7 +341,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinresplot-1.png b/docs/reference/mkinresplot-1.png Binary files differnew file mode 100644 index 00000000..8636baf2 --- /dev/null +++ b/docs/reference/mkinresplot-1.png diff --git a/docs/reference/mkinresplot-4.png b/docs/reference/mkinresplot-4.png Binary files differdeleted file mode 100644 index 5f3a65e3..00000000 --- a/docs/reference/mkinresplot-4.png +++ /dev/null diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html index 5cb9fa96..036917f1 100644 --- a/docs/reference/mkinresplot.html +++ b/docs/reference/mkinresplot.html @@ -18,12 +18,22 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Function to plot residuals stored in an mkin object — mkinresplot" /> +<meta property="og:description" content="This function plots the residuals for the specified subset of the + observed variables from an mkinfit object. A combined plot of the fitted + model and the residuals can be obtained using plot.mkinfit + using the argument show_residuals = TRUE." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +96,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -175,7 +180,7 @@ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='no'>model</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><img src='mkinresplot-4.png' alt='' width='540' height='400' /></pre> +<span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><div class='img'><img src='mkinresplot-1.png' alt='' width='700' height='432.632880098887' /></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -202,7 +207,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mkinsub.html b/docs/reference/mkinsub.html index 257d3f89..7724220f 100644 --- a/docs/reference/mkinsub.html +++ b/docs/reference/mkinsub.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Function to set up a kinetic submodel for one state variable — mkinsub" /> +<meta property="og:description" content="This is a convenience function to set up the lists used as arguments for + mkinmod." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -182,7 +185,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/mmkin-12.png b/docs/reference/mmkin-12.png Binary files differdeleted file mode 100644 index 9e40d451..00000000 --- a/docs/reference/mmkin-12.png +++ /dev/null diff --git a/docs/reference/mmkin-14.png b/docs/reference/mmkin-14.png Binary files differdeleted file mode 100644 index 72cfc5e7..00000000 --- a/docs/reference/mmkin-14.png +++ /dev/null diff --git a/docs/reference/mmkin-15.png b/docs/reference/mmkin-15.png Binary files differdeleted file mode 100644 index e8a23a55..00000000 --- a/docs/reference/mmkin-15.png +++ /dev/null diff --git a/docs/reference/mmkin-16.png b/docs/reference/mmkin-16.png Binary files differdeleted file mode 100644 index 0b315b1a..00000000 --- a/docs/reference/mmkin-16.png +++ /dev/null diff --git a/docs/reference/mmkin-17.png b/docs/reference/mmkin-17.png Binary files differdeleted file mode 100644 index 01bb3ae3..00000000 --- a/docs/reference/mmkin-17.png +++ /dev/null diff --git a/docs/reference/mmkin-18.png b/docs/reference/mmkin-18.png Binary files differdeleted file mode 100644 index b98940ff..00000000 --- a/docs/reference/mmkin-18.png +++ /dev/null diff --git a/docs/reference/mmkin-19.png b/docs/reference/mmkin-19.png Binary files differdeleted file mode 100644 index b5ac70f7..00000000 --- a/docs/reference/mmkin-19.png +++ /dev/null diff --git a/docs/reference/mmkin-20.png b/docs/reference/mmkin-20.png Binary files differdeleted file mode 100644 index c2e9e5ae..00000000 --- a/docs/reference/mmkin-20.png +++ /dev/null diff --git a/docs/reference/mmkin-21.png b/docs/reference/mmkin-21.png Binary files differdeleted file mode 100644 index 7e15e1b3..00000000 --- a/docs/reference/mmkin-21.png +++ /dev/null diff --git a/docs/reference/mmkin-23.png b/docs/reference/mmkin-23.png Binary files differdeleted file mode 100644 index 45e8efc1..00000000 --- a/docs/reference/mmkin-23.png +++ /dev/null diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index d649563f..e97e3f81 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Fit one or more kinetic models with one or more state variables to one or more datasets — mmkin" /> +<meta property="og:description" content="This function calls mkinfit on all combinations of models and datasets + specified in its first two arguments." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +78,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +94,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -152,45 +158,39 @@ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> + <pre class="examples"><span class='co'># NOT RUN {</span> <span class='no'>m_synth_SFO_lin</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M1"</span>), <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M2"</span>), - <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>) + <span class='no'>m_synth_FOMC_lin</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"FOMC"</span>, <span class='st'>"M1"</span>), <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M2"</span>), - <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>) + <span class='no'>models</span> <span class='kw'><-</span> <span class='fu'>list</span>(<span class='kw'>SFO_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_SFO_lin</span>, <span class='kw'>FOMC_lin</span> <span class='kw'>=</span> <span class='no'>m_synth_FOMC_lin</span>) <span class='no'>datasets</span> <span class='kw'><-</span> <span class='fu'>lapply</span>(<span class='no'>synthetic_data_for_UBA_2014</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='no'>x</span>$<span class='no'>data</span>) <span class='fu'>names</span>(<span class='no'>datasets</span>) <span class='kw'><-</span> <span class='fu'>paste</span>(<span class='st'>"Dataset"</span>, <span class='fl'>1</span>:<span class='fl'>3</span>) <span class='no'>time_default</span> <span class='kw'><-</span> <span class='fu'>system.time</span>(<span class='no'>fits.0</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)) -<span class='no'>time_1</span> <span class='kw'><-</span> <span class='fu'>system.time</span>(<span class='no'>fits.4</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> <span class='warning'>Warning: Optimisation by method Port did not converge.</span> -#> <span class='warning'>Convergence code is 1</span></div><div class='output co'>#> <span class='warning'>Warning: Optimisation by method Port did not converge.</span> -#> <span class='warning'>Convergence code is 1</span></div><div class='input'> -<span class='no'>time_default</span></div><div class='output co'>#> user system elapsed -#> 15.992 0.188 11.440 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#> user system elapsed -#> 24.576 0.000 24.578 </div><div class='input'> -<span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span>]])</div><div class='output co'>#> $ff -#> parent_M1 parent_sink M1_M2 M1_sink -#> 0.7340479 0.2659521 0.7505687 0.2494313 -#> -#> $SFORB -#> logical(0) -#> -#> $distimes -#> DT50 DT90 -#> parent 0.8777689 2.915885 -#> M1 2.3257456 7.725960 -#> M2 33.7200862 112.015702 -#> </div><div class='input'> +<span class='no'>time_1</span> <span class='kw'><-</span> <span class='fu'>system.time</span>(<span class='no'>fits.4</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)) + +<span class='no'>time_default</span> +<span class='no'>time_1</span> + +<span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span>]]) + <span class='co'># plot.mkinfit handles rows or columns of mmkin result objects</span> -<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ])</div><img src='mmkin-15.png' alt='' width='540' height='400' /><div class='input'><span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ], <span class='kw'>obs_var</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>))</div><img src='mmkin-17.png' alt='' width='540' height='400' /><div class='input'><span class='fu'>plot</span>(<span class='no'>fits.0</span>[, <span class='fl'>1</span>])</div><img src='mmkin-19.png' alt='' width='540' height='400' /><div class='input'><span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span> +<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ]) +<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ], <span class='kw'>obs_var</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)) +<span class='fu'>plot</span>(<span class='no'>fits.0</span>[, <span class='fl'>1</span>]) +<span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span> <span class='co'># by plot.mkinfit and can be plotted using plot_sep</span> -<span class='fu'>plot</span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]], <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='mmkin-21.png' alt='' width='540' height='400' /><div class='input'><span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]]) +<span class='fu'>plot</span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]], <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span>]]) <span class='co'># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span> <span class='co'># allow to plot the observed variables separately</span> -<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, <span class='fl'>1</span>])</div><img src='mmkin-23.png' alt='' width='540' height='400' /><div class='input'> -</div></pre> +<span class='fu'>plot</span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, <span class='fl'>1</span>]) +<span class='co'># }</span></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -217,7 +217,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/plot.mkinfit-1.png b/docs/reference/plot.mkinfit-1.png Binary files differnew file mode 100644 index 00000000..2bb8f5dd --- /dev/null +++ b/docs/reference/plot.mkinfit-1.png diff --git a/docs/reference/plot.mkinfit-10.png b/docs/reference/plot.mkinfit-10.png Binary files differdeleted file mode 100644 index 48ab5271..00000000 --- a/docs/reference/plot.mkinfit-10.png +++ /dev/null diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png Binary files differnew file mode 100644 index 00000000..22a3f8b0 --- /dev/null +++ b/docs/reference/plot.mkinfit-2.png diff --git a/docs/reference/plot.mkinfit-3.png b/docs/reference/plot.mkinfit-3.png Binary files differnew file mode 100644 index 00000000..93e859c7 --- /dev/null +++ b/docs/reference/plot.mkinfit-3.png diff --git a/docs/reference/plot.mkinfit-4.png b/docs/reference/plot.mkinfit-4.png Binary files differindex cb52d4ac..27edd6f3 100644 --- a/docs/reference/plot.mkinfit-4.png +++ b/docs/reference/plot.mkinfit-4.png diff --git a/docs/reference/plot.mkinfit-6.png b/docs/reference/plot.mkinfit-6.png Binary files differdeleted file mode 100644 index 8e0faa21..00000000 --- a/docs/reference/plot.mkinfit-6.png +++ /dev/null diff --git a/docs/reference/plot.mkinfit-8.png b/docs/reference/plot.mkinfit-8.png Binary files differdeleted file mode 100644 index 129f1445..00000000 --- a/docs/reference/plot.mkinfit-8.png +++ /dev/null diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html index 0af2bbf6..4bdad93f 100644 --- a/docs/reference/plot.mkinfit.html +++ b/docs/reference/plot.mkinfit.html @@ -18,12 +18,24 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Plot the observed data and the fitted model of an mkinfit object — plot.mkinfit" /> +<meta property="og:description" content="Solves the differential equations with the optimised and fixed parameters + from a previous successful call to mkinfit and plots + the observed data together with the solution of the fitted model. +If the current plot device is a tikz device, + then latex is being used for the formatting of the chi2 error level, + if show_errmin = TRUE." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +98,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -237,12 +244,12 @@ plot_sep(fit, sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE, … <span class='co'># parent to sink included, use Levenberg-Marquardt for speed</span> <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Parent"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span> ))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>) -<span class='fu'>plot</span>(<span class='no'>fit</span>)</div><img src='plot.mkinfit-4.png' alt='' width='540' height='400' /><div class='input'><span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='plot.mkinfit-6.png' alt='' width='540' height='400' /><div class='input'> +<span class='fu'>plot</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'><span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-2.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='co'># Show the observed variables separately</span> -<span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><img src='plot.mkinfit-8.png' alt='' width='540' height='400' /><div class='input'> +<span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='img'><img src='plot.mkinfit-3.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='co'># Show the observed variables separately, with residuals</span> <span class='fu'>plot</span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>), - <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><img src='plot.mkinfit-10.png' alt='' width='540' height='400' /><div class='input'> + <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-4.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='co'># The same can be obtained with less typing, using the convenience function plot_sep</span> <span class='fu'>plot_sep</span>(<span class='no'>fit</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div></pre> </div> @@ -269,7 +276,7 @@ plot_sep(fit, sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE, … </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png Binary files differnew file mode 100644 index 00000000..2554b68b --- /dev/null +++ b/docs/reference/plot.mmkin-1.png diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png Binary files differindex 21af1e7b..9a66294f 100644 --- a/docs/reference/plot.mmkin-2.png +++ b/docs/reference/plot.mmkin-2.png diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png Binary files differnew file mode 100644 index 00000000..b0f7fa21 --- /dev/null +++ b/docs/reference/plot.mmkin-3.png diff --git a/docs/reference/plot.mmkin-4.png b/docs/reference/plot.mmkin-4.png Binary files differdeleted file mode 100644 index 3004f48f..00000000 --- a/docs/reference/plot.mmkin-4.png +++ /dev/null diff --git a/docs/reference/plot.mmkin-6.png b/docs/reference/plot.mmkin-6.png Binary files differdeleted file mode 100644 index 02ed2ab1..00000000 --- a/docs/reference/plot.mmkin-6.png +++ /dev/null diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html index aa3df77a..11da6685 100644 --- a/docs/reference/plot.mmkin.html +++ b/docs/reference/plot.mmkin.html @@ -18,12 +18,23 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object — plot.mmkin" /> +<meta property="og:description" content="When x is a row selected from an mmkin object ([.mmkin), the same model + fitted for at least one dataset is shown. When it is a column, the fit of at least one model + to the same dataset is shown. +If the current plot device is a tikz device, + then latex is being used for the formatting of the chi2 error level." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +97,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -165,11 +171,11 @@ <span class='no'>fits</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'>c</span>(<span class='st'>"FOMC"</span>, <span class='st'>"HS"</span>), <span class='fu'>list</span>(<span class='st'>"FOCUS B"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_B</span>, <span class='st'>"FOCUS C"</span> <span class='kw'>=</span> <span class='no'>FOCUS_2006_C</span>), <span class='co'># named list for titles</span> <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>method.modFit</span> <span class='kw'>=</span> <span class='st'>"Marq"</span>) - <span class='fu'>plot</span>(<span class='no'>fits</span>[, <span class='st'>"FOCUS C"</span>])</div><img src='plot.mmkin-2.png' alt='' width='540' height='400' /><div class='input'> <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ])</div><img src='plot.mmkin-4.png' alt='' width='540' height='400' /><div class='input'> + <span class='fu'>plot</span>(<span class='no'>fits</span>[, <span class='st'>"FOCUS C"</span>])</div><div class='img'><img src='plot.mmkin-1.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, ])</div><div class='img'><img src='plot.mmkin-2.png' alt='' width='700' height='432.632880098887' /></div><div class='input'> <span class='co'># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span> <span class='co'># height should be smaller than the plot width (this is not possible for the html pages</span> <span class='co'># generated by pkgdown, as far as I know).</span> - <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, <span class='st'>"FOCUS C"</span>]) <span class='co'># same as plot(fits[1, 2])</span></div><img src='plot.mmkin-6.png' alt='' width='540' height='400' /></pre> + <span class='fu'>plot</span>(<span class='no'>fits</span>[<span class='st'>"FOMC"</span>, <span class='st'>"FOCUS C"</span>]) <span class='co'># same as plot(fits[1, 2])</span></div><div class='img'><img src='plot.mmkin-3.png' alt='' width='700' height='432.632880098887' /></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -194,7 +200,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/print.mkinds.html b/docs/reference/print.mkinds.html index 14342f21..8e0d18b2 100644 --- a/docs/reference/print.mkinds.html +++ b/docs/reference/print.mkinds.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Print mkinds objects — print.mkinds" /> +<meta property="og:description" content="Print mkinds objects." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -143,7 +145,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/print.mkinmod.html b/docs/reference/print.mkinmod.html index 33782b7d..db15cc60 100644 --- a/docs/reference/print.mkinmod.html +++ b/docs/reference/print.mkinmod.html @@ -18,12 +18,19 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Print mkinmod objects — print.mkinmod" /> +<meta property="og:description" content="Print mkinmod objects in a way that the user finds his way to get to its components." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +93,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -164,7 +166,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/schaefer07_complex_case-4.png b/docs/reference/schaefer07_complex_case-4.png Binary files differdeleted file mode 100644 index b90185a1..00000000 --- a/docs/reference/schaefer07_complex_case-4.png +++ /dev/null diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html index 605f572e..6c53f805 100644 --- a/docs/reference/schaefer07_complex_case.html +++ b/docs/reference/schaefer07_complex_case.html @@ -18,12 +18,21 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case" /> +<meta property="og:description" content="This dataset was used for a comparison of KinGUI and ModelMaker to check the + software quality of KinGUI in the original publication (Schäfer et al., 2007). + The results from the fitting are also included." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -86,12 +95,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -140,24 +144,12 @@ <span class='kw'>A1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"A2"</span>), <span class='kw'>B1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>C1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), - <span class='kw'>A2</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>A2</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> </div><span class='co'># NOT RUN {</span> <span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'>plot</span>(<span class='no'>fit</span>)</div><img src='schaefer07_complex_case-4.png' alt='' width='540' height='400' /><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> $ff -#> parent_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink -#> 0.3809618 0.1954667 0.4235715 0.0000000 0.4479596 0.5520404 -#> -#> $SFORB -#> logical(0) -#> -#> $distimes -#> DT50 DT90 -#> parent 13.95078 46.34349 -#> A1 49.75345 165.27739 -#> B1 37.26908 123.80521 -#> C1 11.23130 37.30957 -#> A2 28.50652 94.69662 -#> </div><div class='input'> - <span class='co'># Compare with the results obtained in the original publication</span> + <span class='fu'>plot</span>(<span class='no'>fit</span>) + <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>) + +<span class='co'># }</span><div class='input'> <span class='co'># Compare with the results obtained in the original publication</span> <span class='fu'>print</span>(<span class='no'>schaefer07_complex_results</span>)</div><div class='output co'>#> compound parameter KinGUI ModelMaker deviation #> 1 parent degradation rate 0.0496 0.0506 2.0 #> 2 parent DT50 13.9900 13.6900 2.2 @@ -194,7 +186,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/sigma_rl.html b/docs/reference/sigma_rl.html index b7c93961..868c0d4f 100644 --- a/docs/reference/sigma_rl.html +++ b/docs/reference/sigma_rl.html @@ -18,12 +18,21 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> - + + +<meta property="og:title" content="Two component error model of Rocke and Lorenzato — sigma_rl" /> + +<meta property="og:description" content="Function describing the standard deviation of the measurement error + in dependence of the measured value \(y\): +$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$" /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -157,7 +166,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html index f20e3948..977ff8d8 100644 --- a/docs/reference/summary.mkinfit.html +++ b/docs/reference/summary.mkinfit.html @@ -18,12 +18,22 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Summary method for class "mkinfit" — summary.mkinfit" /> +<meta property="og:description" content="Lists model equations, the summary as returned by summary.modFit, + the chi2 error levels calculated according to FOCUS guidance (2006) as far + as defined therein, and optionally the data, consisting of observed, predicted + and residual values." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +80,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +96,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -177,21 +185,21 @@ Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='fu'>summary</span>(<span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)), <span class='no'>FOCUS_2006_A</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> mkin version: 0.9.46 -#> R version: 3.4.1 -#> Date of fit: Sat Jul 29 15:15:30 2017 -#> Date of summary: Sat Jul 29 15:15:30 2017 + <pre class="examples"><div class='input'> <span class='fu'>summary</span>(<span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)), <span class='no'>FOCUS_2006_A</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> mkin version used for fitting: 0.9.47.1 +#> R version used for fitting: 3.4.3 +#> Date of fit: Thu Mar 1 14:26:27 2018 +#> Date of summary: Thu Mar 1 14:26:27 2018 #> #> Equations: #> d_parent/dt = - k_parent_sink * parent #> #> Model predictions using solution type analytical #> -#> Fitted with method Port using 35 model solutions performed in 0.084 s +#> Fitted with method Port using 35 model solutions performed in 0.076 s #> #> Weighting: none #> @@ -277,7 +285,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/synthetic_data_for_UBA.html b/docs/reference/synthetic_data_for_UBA.html index f9603f34..192e8dc2 100644 --- a/docs/reference/synthetic_data_for_UBA.html +++ b/docs/reference/synthetic_data_for_UBA.html @@ -18,12 +18,31 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014" /> +<meta property="og:description" content="The 12 datasets were generated using four different models and three different + variance components. The four models are either the SFO or the DFOP model with either + two sequential or two parallel metabolites. +Variance component 'a' is based on a normal distribution with standard deviation of 3, + Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7. + Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the + minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07 + for the increase of the standard deviation with y. +Initial concentrations for metabolites and all values where adding the variance component resulted + in a value below the assumed limit of detection of 0.1 were set to NA. +As an example, the first dataset has the title SFO_lin_a and is based on the SFO model + with two sequential metabolites (linear pathway), with added variance component 'a'. +Compare also the code in the example section to see the degradation models." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -136,7 +155,7 @@ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> + <pre class="examples"># NOT RUN { # The data have been generated using the following kinetic models m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"), M1 = list(type = "SFO", to = "M2"), @@ -235,11 +254,8 @@ fit <- mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data, quiet = TRUE) plot_sep(fit) summary(fit) - -</div><div class='output co'>#> <span class='error'>Error: <text>:68:43: Unerwartete(s) SPECIAL</span> -#> <span class='error'>67: </span> -#> <span class='error'>68: d_rep[d_rep$time == 0 & d_rep$name <!-- %in%</span> -#> <span class='error'> ^</span></div></pre> +# } +</pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -261,7 +277,7 @@ summary(fit) </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/synthetic_data_for_UBA_2014-10.png b/docs/reference/synthetic_data_for_UBA_2014-10.png Binary files differdeleted file mode 100644 index 7e15e1b3..00000000 --- a/docs/reference/synthetic_data_for_UBA_2014-10.png +++ /dev/null diff --git a/docs/reference/test_data_from_UBA_2014-12.png b/docs/reference/test_data_from_UBA_2014-12.png Binary files differdeleted file mode 100644 index 6738f3a0..00000000 --- a/docs/reference/test_data_from_UBA_2014-12.png +++ /dev/null diff --git a/docs/reference/test_data_from_UBA_2014-16.png b/docs/reference/test_data_from_UBA_2014-16.png Binary files differdeleted file mode 100644 index 6738f3a0..00000000 --- a/docs/reference/test_data_from_UBA_2014-16.png +++ /dev/null diff --git a/docs/reference/test_data_from_UBA_2014-4.png b/docs/reference/test_data_from_UBA_2014-4.png Binary files differdeleted file mode 100644 index 8c65e604..00000000 --- a/docs/reference/test_data_from_UBA_2014-4.png +++ /dev/null diff --git a/docs/reference/test_data_from_UBA_2014-6.png b/docs/reference/test_data_from_UBA_2014-6.png Binary files differdeleted file mode 100644 index 8c65e604..00000000 --- a/docs/reference/test_data_from_UBA_2014-6.png +++ /dev/null diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html index ed2ccd9c..c4292d9c 100644 --- a/docs/reference/test_data_from_UBA_2014.html +++ b/docs/reference/test_data_from_UBA_2014.html @@ -18,12 +18,20 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Three experimental datasets from two water sediment systems and one soil — test_data_from_UBA_2014" /> +<meta property="og:description" content="The datasets were used for the comparative validation of several kinetic evaluation + software packages (Ranke, 2014)." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -123,7 +131,7 @@ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> + <pre class="examples"><div class='input'> </div><span class='co'># NOT RUN {</span> <span class='co'># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span> <span class='co'># guidance. Due to the strong correlation of the parameter estimates, the</span> <span class='co'># covariance matrix is not returned. Note that level P-II evaluations are</span> @@ -131,57 +139,27 @@ <span class='co'># large parameter correlations, among other reasons (e.g. the adequacy of the</span> <span class='co'># model).</span> <span class='no'>m_ws</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent_w</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_s"</span>), - <span class='kw'>parent_s</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_w"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> <span class='no'>f_river</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_ws</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_river</span>)</div><img src='test_data_from_UBA_2014-4.png' alt='' width='540' height='400' /><div class='input'> - <span class='fu'>summary</span>(<span class='no'>f_river</span>)$<span class='no'>bpar</span></div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> parent_w_0 9.598567e+01 2.33959810 4.102657e+01 9.568973e-19 NA -#> k_parent_w_sink 3.603743e-01 0.03497716 1.030313e+01 4.988281e-09 NA -#> k_parent_w_parent_s 6.031370e-02 0.01746026 3.454342e+00 1.514738e-03 NA -#> k_parent_s_sink 5.099834e-11 0.10381939 4.912217e-10 5.000000e-01 NA -#> k_parent_s_parent_w 7.419672e-02 0.11338174 6.543974e-01 2.608057e-01 NA -#> Upper -#> parent_w_0 NA -#> k_parent_w_sink NA -#> k_parent_w_parent_s NA -#> k_parent_s_sink NA -#> k_parent_s_parent_w NA</div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_river</span>)</div><div class='output co'>#> err.min n.optim df -#> All data 0.09246946 5 6 -#> parent_w 0.06377096 3 3 -#> parent_s 0.20882324 2 3</div><div class='input'> + <span class='kw'>parent_s</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"parent_w"</span>)) + <span class='no'>f_river</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_ws</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) + <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_river</span>) + + <span class='fu'>summary</span>(<span class='no'>f_river</span>)$<span class='no'>bpar</span> + <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_river</span>) + <span class='co'># This is the evaluation used for the validation of software packages</span> <span class='co'># in the expertise from 2014</span> <span class='no'>m_soil</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='fu'>c</span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)), <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M3"</span>), <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"M3"</span>), <span class='kw'>M3</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), - <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>) + <span class='no'>f_soil</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_soil</span>, <span class='no'>test_data_from_UBA_2014</span><span class='kw'>[[</span><span class='fl'>3</span>]]$<span class='no'>data</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_soil</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><img src='test_data_from_UBA_2014-12.png' alt='' width='540' height='400' /><div class='input'> <span class='fu'>summary</span>(<span class='no'>f_soil</span>)$<span class='no'>bpar</span></div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 76.55425583 0.943443834 81.1434164 4.422340e-30 74.602593306 -#> k_parent 0.12081956 0.004815515 25.0896457 1.639665e-18 0.111257526 -#> k_M1 0.84258650 0.930121206 0.9058889 1.871937e-01 0.085876305 -#> k_M2 0.04210878 0.013729902 3.0669396 2.729137e-03 0.021450631 -#> k_M3 0.01122919 0.008044866 1.3958205 8.804914e-02 0.002550985 -#> f_parent_to_M1 0.32240199 0.278620411 1.1571370 1.295466e-01 NA -#> f_parent_to_M2 0.16099854 0.030548889 5.2701930 1.196191e-05 NA -#> f_M1_to_M3 0.27921500 0.314732717 0.8871496 1.920907e-01 0.015016888 -#> f_M2_to_M3 0.55641332 0.650247079 0.8556952 2.004966e-01 0.005360551 -#> Upper -#> parent_0 78.50591836 -#> k_parent 0.13120340 -#> k_M1 8.26714671 -#> k_M2 0.08266187 -#> k_M3 0.04942980 -#> f_parent_to_M1 NA -#> f_parent_to_M2 NA -#> f_M1_to_M3 0.90777217 -#> f_M2_to_M3 0.99658634</div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_soil</span>)</div><div class='output co'>#> err.min n.optim df -#> All data 0.09649963 9 20 -#> parent 0.04721283 2 6 -#> M1 0.26551209 2 5 -#> M2 0.20327575 2 5 -#> M3 0.05196549 3 4</div><div class='input'> -</div></pre> + <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>f_soil</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>)) + <span class='fu'>summary</span>(<span class='no'>f_soil</span>)$<span class='no'>bpar</span> + <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span>(<span class='no'>f_soil</span>) + +<span class='co'># }</span></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -203,7 +181,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html index bbf15a41..630a5103 100644 --- a/docs/reference/transform_odeparms.html +++ b/docs/reference/transform_odeparms.html @@ -18,12 +18,27 @@ <!-- Font Awesome icons --> <link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> +<!-- clipboard.js --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.7.1/clipboard.min.js" integrity="sha384-cV+rhyOuRHc9Ub/91rihWcGmMmCXDeksTtCihMupQHSsi8GIIRDG0ThDc3HGQFJ3" crossorigin="anonymous"></script> <!-- pkgdown --> <link href="../pkgdown.css" rel="stylesheet"> <script src="../jquery.sticky-kit.min.js"></script> <script src="../pkgdown.js"></script> + + +<meta property="og:title" content="Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms" /> +<meta property="og:description" content="The transformations are intended to map parameters that should only take + on restricted values to the full scale of real numbers. For kinetic rate + constants and other paramters that can only take on positive values, a + simple log transformation is used. For compositional parameters, such as + the formations fractions that should always sum up to 1 and can not be + negative, the ilr transformation is used. +The transformation of sets of formation fractions is fragile, as it supposes + the same ordering of the components in forward and backward transformation. + This is no problem for the internal use in mkinfit." /> +<meta name="twitter:card" content="summary" /> <!-- mathjax --> <script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> @@ -70,6 +85,9 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> @@ -83,12 +101,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> + </ul> </div><!--/.nav-collapse --> </div><!--/.container --> @@ -170,10 +183,10 @@ <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> <span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>fit</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>) <span class='co'># See transformed and backtransformed parameters</span></div><div class='output co'>#> mkin version: 0.9.46 -#> R version: 3.4.1 -#> Date of fit: Sat Jul 29 15:15:35 2017 -#> Date of summary: Sat Jul 29 15:15:35 2017 +<span class='fu'>summary</span>(<span class='no'>fit</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>) <span class='co'># See transformed and backtransformed parameters</span></div><div class='output co'>#> mkin version used for fitting: 0.9.47.1 +#> R version used for fitting: 3.4.3 +#> Date of fit: Thu Mar 1 14:26:28 2018 +#> Date of summary: Thu Mar 1 14:26:28 2018 #> #> Equations: #> d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent @@ -181,7 +194,7 @@ #> #> Model predictions using solution type deSolve #> -#> Fitted with method Port using 153 model solutions performed in 0.608 s +#> Fitted with method Port using 153 model solutions performed in 0.571 s #> #> Weighting: none #> @@ -245,84 +258,10 @@ #> DT50 DT90 #> parent 7.023 23.33 #> m1 131.761 437.70</div><div class='input'> - +</div><span class='co'># NOT RUN {</span> <span class='no'>fit.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version: 0.9.46 -#> R version: 3.4.1 -#> Date of fit: Sat Jul 29 15:15:36 2017 -#> Date of summary: Sat Jul 29 15:15:36 2017 -#> -#> Equations: -#> d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent -#> d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 352 model solutions performed in 1.401 s -#> -#> Weighting: none -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent_sink 0.1000 deparm -#> k_parent_m1 0.1001 deparm -#> k_m1_sink 0.1002 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.7500 -Inf Inf -#> k_parent_sink 0.1000 0 Inf -#> k_parent_m1 0.1001 0 Inf -#> k_m1_sink 0.1002 0 Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.600000 1.6140000 96.330000 1.029e+02 -#> k_parent_sink 0.047920 0.0037500 0.040310 5.553e-02 -#> k_parent_m1 0.050780 0.0020940 0.046530 5.502e-02 -#> k_m1_sink 0.005261 0.0007159 0.003809 6.713e-03 -#> -#> Parameter correlation: -#> parent_0 k_parent_sink k_parent_m1 k_m1_sink -#> parent_0 1.00000 0.6075 -0.06625 -0.1701 -#> k_parent_sink 0.60752 1.0000 -0.08740 -0.6253 -#> k_parent_m1 -0.06625 -0.0874 1.00000 0.4716 -#> k_m1_sink -0.17006 -0.6253 0.47164 1.0000 -#> -#> Residual standard error: 3.211 on 36 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.600000 61.720 2.024e-38 96.330000 1.029e+02 -#> k_parent_sink 0.047920 12.780 3.050e-15 0.040310 5.553e-02 -#> k_parent_m1 0.050780 24.250 3.407e-24 0.046530 5.502e-02 -#> k_m1_sink 0.005261 7.349 5.758e-09 0.003809 6.713e-03 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.398 4 15 -#> parent 6.827 3 6 -#> m1 4.490 1 9 -#> -#> Resulting formation fractions: -#> ff -#> parent_sink 0.4855 -#> parent_m1 0.5145 -#> m1_sink 1.0000 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.023 23.33 -#> m1 131.761 437.70</div><div class='input'> - +<span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span><span class='kw'>=</span><span class='fl'>FALSE</span>) +<span class='co'># }</span><div class='input'> <span class='no'>initials</span> <span class='kw'><-</span> <span class='no'>fit</span>$<span class='no'>start</span>$<span class='no'>value</span> <span class='fu'>names</span>(<span class='no'>initials</span>) <span class='kw'><-</span> <span class='fu'>rownames</span>(<span class='no'>fit</span>$<span class='no'>start</span>) <span class='no'>transformed</span> <span class='kw'><-</span> <span class='no'>fit</span>$<span class='no'>start_transformed</span>$<span class='no'>value</span> @@ -330,162 +269,29 @@ <span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink #> 100.750000 -2.302585 -2.301586 -2.300587 </div><div class='input'><span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO</span>)</div><div class='output co'>#> parent_0 k_parent_sink k_parent_m1 k_m1_sink #> 100.7500 0.1000 0.1001 0.1002 </div><div class='input'> - +</div><span class='co'># NOT RUN {</span> <span class='co'># The case of formation fractions</span> <span class='no'>SFO_SFO.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), - <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>) + <span class='no'>fit.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>fit.ff</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version: 0.9.46 -#> R version: 3.4.1 -#> Date of fit: Sat Jul 29 15:15:37 2017 -#> Date of summary: Sat Jul 29 15:15:37 2017 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 185 model solutions performed in 0.772 s -#> -#> Weighting: none -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.60000 1.61400 96.3300 102.9000 -#> log_k_parent -2.31600 0.04187 -2.4010 -2.2310 -#> log_k_m1 -5.24800 0.13610 -5.5230 -4.9720 -#> f_parent_ilr_1 0.04096 0.06477 -0.0904 0.1723 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 -#> parent_0 1.0000 0.5178 -0.1701 -0.5489 -#> log_k_parent 0.5178 1.0000 -0.3285 -0.5451 -#> log_k_m1 -0.1701 -0.3285 1.0000 0.7466 -#> f_parent_ilr_1 -0.5489 -0.5451 0.7466 1.0000 -#> -#> Residual standard error: 3.211 on 36 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.600000 61.720 2.024e-38 96.330000 1.029e+02 -#> k_parent 0.098700 23.880 5.701e-24 0.090660 1.074e-01 -#> k_m1 0.005261 7.349 5.758e-09 0.003992 6.933e-03 -#> f_parent_to_m1 0.514500 22.490 4.374e-23 0.468100 5.606e-01 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.398 4 15 -#> parent 6.459 2 7 -#> m1 4.690 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5145 -#> parent_sink 0.4855 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.023 23.33 -#> m1 131.761 437.70</div><div class='input'><span class='no'>initials</span> <span class='kw'><-</span> <span class='fu'>c</span>(<span class='st'>"f_parent_to_m1"</span> <span class='kw'>=</span> <span class='fl'>0.5</span>) +<span class='fu'>summary</span>(<span class='no'>fit.ff</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>) +<span class='no'>initials</span> <span class='kw'><-</span> <span class='fu'>c</span>(<span class='st'>"f_parent_to_m1"</span> <span class='kw'>=</span> <span class='fl'>0.5</span>) <span class='no'>transformed</span> <span class='kw'><-</span> <span class='fu'>transform_odeparms</span>(<span class='no'>initials</span>, <span class='no'>SFO_SFO.ff</span>) -<span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO.ff</span>)</div><div class='output co'>#> f_parent_to_m1 -#> 0.5 </div><div class='input'> +<span class='fu'>backtransform_odeparms</span>(<span class='no'>transformed</span>, <span class='no'>SFO_SFO.ff</span>) + <span class='co'># And without sink</span> <span class='no'>SFO_SFO.ff.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), - <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> + <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>) + <span class='no'>fit.ff.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff.2</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>summary</span>(<span class='no'>fit.ff.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version: 0.9.46 -#> R version: 3.4.1 -#> Date of fit: Sat Jul 29 15:15:38 2017 -#> Date of summary: Sat Jul 29 15:15:38 2017 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted with method Port using 104 model solutions performed in 0.416 s -#> -#> Weighting: none -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 84.790 2.96500 78.78 90.800 -#> log_k_parent -2.756 0.08088 -2.92 -2.593 -#> log_k_m1 -4.214 0.11150 -4.44 -3.988 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 -#> parent_0 1.0000 0.11059 0.46156 -#> log_k_parent 0.1106 1.00000 0.06274 -#> log_k_m1 0.4616 0.06274 1.00000 -#> -#> Residual standard error: 8.333 on 37 degrees of freedom -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 84.79000 28.600 3.938e-27 78.78000 90.80000 -#> k_parent 0.06352 12.360 5.237e-15 0.05392 0.07483 -#> k_m1 0.01478 8.966 4.114e-11 0.01179 0.01853 -#> -#> Chi2 error levels in percent: -#> err.min n.optim df -#> All data 19.66 3 16 -#> parent 17.56 2 7 -#> m1 18.71 1 9 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 10.91 36.25 -#> m1 46.89 155.75</div><div class='input'> -</div></pre> +<span class='fu'>summary</span>(<span class='no'>fit.ff.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>) +<span class='co'># }</span></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> @@ -510,7 +316,7 @@ </div> <div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> + <p>Site built with <a href="http://pkgdown.r-lib.org/">pkgdown</a>.</p> </div> </footer> diff --git a/docs/reference/twa.html b/docs/reference/twa.html deleted file mode 100644 index be76b439..00000000 --- a/docs/reference/twa.html +++ /dev/null @@ -1,179 +0,0 @@ -<!-- Generated by pkgdown: do not edit by hand --> -<!DOCTYPE html> -<html> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit — twa • mkin</title> - -<!-- jquery --> -<script src="https://code.jquery.com/jquery-3.1.0.min.js" integrity="sha384-nrOSfDHtoPMzJHjVTdCopGqIqeYETSXhZDFyniQ8ZHcVy08QesyHcnOUpMpqnmWq" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BVYiiSIFeK1dGmJRAkycuHAHRg32OmUcww7on3RYdg4Va+PmSTsz/K68vbdEjh4u" crossorigin="anonymous"> -<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script> - -<!-- Font Awesome icons --> -<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" rel="stylesheet" integrity="sha384-T8Gy5hrqNKT+hzMclPo118YTQO6cYprQmhrYwIiQ/3axmI1hQomh7Ud2hPOy8SP1" crossorigin="anonymous"> - - -<!-- pkgdown --> -<link href="../pkgdown.css" rel="stylesheet"> -<script src="../jquery.sticky-kit.min.js"></script> -<script src="../pkgdown.js"></script> - -<!-- mathjax --> -<script src='https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> - -<!--[if lt IE 9]> -<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> -<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - </head> - - <body> - <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> - <div class="container"> - <div class="navbar-header"> - <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar"> - <span class="icon-bar"></span> - <span class="icon-bar"></span> - <span class="icon-bar"></span> - </button> - <a class="navbar-brand" href="../index.html">mkin</a> - </div> - <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> - <a href="../reference/index.html">Functions and data</a> -</li> -<li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> - Articles - - <span class="caret"></span> - </a> - <ul class="dropdown-menu" role="menu"> - <li> - <a href="../articles/mkin.html">Introduction to mkin</a> - </li> - <li> - <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> - </li> - <li> - <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> - </li> - <li> - <a href="../articles/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> - </li> - <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> - </li> - </ul> -</li> -<li> - <a href="../news/index.html">News</a> -</li> - </ul> - - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="http://github.com/jranke/mkin"> - <span class="fa fa-github fa-lg"></span> - - </a> -</li> - </ul> - </div><!--/.nav-collapse --> - </div><!--/.container --> -</div><!--/.navbar --> - - - </header> - - <div class="row"> - <div class="col-md-9 contents"> - <div class="page-header"> - <h1>Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit</h1> - </div> - - - <p>This function calculates maximum moving window time weighted average concentrations -(TWAs) for kinetic models fitted with <code><a href='mkinfit.html'>mkinfit</a></code>. Currently, only -calculations for the parent are implemented for the SFO, FOMC and DFOP models, -using the analytical formulas given in the PEC soil section of the FOCUS -guidance.</p> - - - <pre class="usage"><span class='fu'>twa</span>(<span class='no'>fit</span>, <span class='no'>windows</span>)</pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a> Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>fit</th> - <td><p>An object of class <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> - </tr> - <tr> - <th>windows</th> - <td><p>The width of the time windows for which the TWAs should be calculated.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>A numeric vector, named using the <code>windows</code> argument.</p> - - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> - - <p>FOCUS (2006) “Guidance Document on Estimating Persistence and - Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, - EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href = 'http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> - - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'>twa</span>(<span class='no'>fit</span>, <span class='fu'>c</span>(<span class='fl'>7</span>, <span class='fl'>21</span>))</div><div class='output co'>#> 7 21 -#> 34.71343 18.22124 </div></pre> - </div> - <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> - <h2>Contents</h2> - <ul class="nav nav-pills nav-stacked"> - <li><a href="#arguments">Arguments</a></li> - - <li><a href="#value">Value</a></li> - - <li><a href="#references">References</a></li> - - <li><a href="#examples">Examples</a></li> - </ul> - - <h2>Author</h2> - - Johannes Ranke - - </div> -</div> - - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> -</div> - -<div class="pkgdown"> - <p>Site built with <a href="http://hadley.github.io/pkgdown/">pkgdown</a>.</p> -</div> - - </footer> - </div> - - </body> -</html> diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index ccde0c82..9bdfb5c6 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -1,258 +1,248 @@ <!DOCTYPE html> - -<html xmlns="http://www.w3.org/1999/xhtml"> - +<html> <head> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/> -<meta charset="utf-8" /> -<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> -<meta name="generator" content="pandoc" /> +<title>Laboratory Data L1</title> +<script type="text/javascript"> +window.onload = function() { + var imgs = document.getElementsByTagName('img'), i, img; + for (i = 0; i < imgs.length; i++) { + img = imgs[i]; + // center an image if it is the only element of its parent + if (img.parentElement.childElementCount === 1) + img.parentElement.style.textAlign = 'center'; + } +}; +</script> -<meta name="author" content="Johannes Ranke" /> +<!-- Styles for R syntax highlighter --> +<style type="text/css"> + pre .operator, + pre .paren { + color: rgb(104, 118, 135) + } + + pre .literal { + color: #990073 + } + + pre .number { + color: #099; + } + + pre .comment { + color: #998; + font-style: italic + } + + pre .keyword { + color: #900; + font-weight: bold + } + + pre .identifier { + color: rgb(0, 0, 0); + } + + pre .string { + color: #d14; + } +</style> -<meta name="date" content="2018-01-14" /> +<!-- R syntax highlighter --> +<script type="text/javascript"> +var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/</gm,"<")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}}; +hljs.initHighlightingOnLoad(); +</script> -<title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> +<!-- MathJax scripts --> +<script type="text/javascript" src="https://cdn.bootcss.com/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML"> +</script> -<script src="data:application/x-javascript;base64,/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.3",m=function(a,b){return new m.fn.init(a,b)},n=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,o=/^-ms-/,p=/-([\da-z])/gi,q=function(a,b){return b.toUpperCase()};m.fn=m.prototype={jquery:l,constructor:m,selector:"",length:0,toArray:function(){return d.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:d.call(this)},pushStack:function(a){var b=m.merge(this.constructor(),a);return b.prevObject=this,b.context=this.context,b},each:function(a,b){return m.each(this,a,b)},map:function(a){return this.pushStack(m.map(this,function(b,c){return a.call(b,c,b)}))},slice:function(){return this.pushStack(d.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},eq:function(a){var b=this.length,c=+a+(0>a?b:0);return this.pushStack(c>=0&&b>c?[this[c]]:[])},end:function(){return this.prevObject||this.constructor(null)},push:f,sort:c.sort,splice:c.splice},m.extend=m.fn.extend=function(){var a,b,c,d,e,f,g=arguments[0]||{},h=1,i=arguments.length,j=!1;for("boolean"==typeof g&&(j=g,g=arguments[h]||{},h++),"object"==typeof g||m.isFunction(g)||(g={}),h===i&&(g=this,h--);i>h;h++)if(null!=(e=arguments[h]))for(d in e)a=g[d],c=e[d],g!==c&&(j&&c&&(m.isPlainObject(c)||(b=m.isArray(c)))?(b?(b=!1,f=a&&m.isArray(a)?a:[]):f=a&&m.isPlainObject(a)?a:{},g[d]=m.extend(j,f,c)):void 0!==c&&(g[d]=c));return g},m.extend({expando:"jQuery"+(l+Math.random()).replace(/\D/g,""),isReady:!0,error:function(a){throw new Error(a)},noop:function(){},isFunction:function(a){return"function"===m.type(a)},isArray:Array.isArray||function(a){return"array"===m.type(a)},isWindow:function(a){return null!=a&&a==a.window},isNumeric:function(a){return!m.isArray(a)&&a-parseFloat(a)+1>=0},isEmptyObject:function(a){var b;for(b in a)return!1;return!0},isPlainObject:function(a){var b;if(!a||"object"!==m.type(a)||a.nodeType||m.isWindow(a))return!1;try{if(a.constructor&&!j.call(a,"constructor")&&!j.call(a.constructor.prototype,"isPrototypeOf"))return!1}catch(c){return!1}if(k.ownLast)for(b in a)return j.call(a,b);for(b in a);return void 0===b||j.call(a,b)},type:function(a){return null==a?a+"":"object"==typeof a||"function"==typeof a?h[i.call(a)]||"object":typeof a},globalEval:function(b){b&&m.trim(b)&&(a.execScript||function(b){a.eval.call(a,b)})(b)},camelCase:function(a){return a.replace(o,"ms-").replace(p,q)},nodeName:function(a,b){return a.nodeName&&a.nodeName.toLowerCase()===b.toLowerCase()},each:function(a,b,c){var d,e=0,f=a.length,g=r(a);if(c){if(g){for(;f>e;e++)if(d=b.apply(a[e],c),d===!1)break}else for(e in a)if(d=b.apply(a[e],c),d===!1)break}else if(g){for(;f>e;e++)if(d=b.call(a[e],e,a[e]),d===!1)break}else for(e in a)if(d=b.call(a[e],e,a[e]),d===!1)break;return a},trim:function(a){return null==a?"":(a+"").replace(n,"")},makeArray:function(a,b){var c=b||[];return null!=a&&(r(Object(a))?m.merge(c,"string"==typeof a?[a]:a):f.call(c,a)),c},inArray:function(a,b,c){var d;if(b){if(g)return g.call(b,a,c);for(d=b.length,c=c?0>c?Math.max(0,d+c):c:0;d>c;c++)if(c in b&&b[c]===a)return c}return-1},merge:function(a,b){var c=+b.length,d=0,e=a.length;while(c>d)a[e++]=b[d++];if(c!==c)while(void 0!==b[d])a[e++]=b[d++];return a.length=e,a},grep:function(a,b,c){for(var d,e=[],f=0,g=a.length,h=!c;g>f;f++)d=!b(a[f],f),d!==h&&e.push(a[f]);return e},map:function(a,b,c){var d,f=0,g=a.length,h=r(a),i=[];if(h)for(;g>f;f++)d=b(a[f],f,c),null!=d&&i.push(d);else for(f in a)d=b(a[f],f,c),null!=d&&i.push(d);return e.apply([],i)},guid:1,proxy:function(a,b){var c,e,f;return"string"==typeof b&&(f=a[b],b=a,a=f),m.isFunction(a)?(c=d.call(arguments,2),e=function(){return a.apply(b||this,c.concat(d.call(arguments)))},e.guid=a.guid=a.guid||m.guid++,e):void 0},now:function(){return+new Date},support:k}),m.each("Boolean Number String Function Array Date RegExp Object Error".split(" "),function(a,b){h["[object "+b+"]"]=b.toLowerCase()});function r(a){var b="length"in a&&a.length,c=m.type(a);return"function"===c||m.isWindow(a)?!1:1===a.nodeType&&b?!0:"array"===c||0===b||"number"==typeof b&&b>0&&b-1 in a}var s=function(a){var b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u="sizzle"+1*new Date,v=a.document,w=0,x=0,y=ha(),z=ha(),A=ha(),B=function(a,b){return a===b&&(l=!0),0},C=1<<31,D={}.hasOwnProperty,E=[],F=E.pop,G=E.push,H=E.push,I=E.slice,J=function(a,b){for(var c=0,d=a.length;d>c;c++)if(a[c]===b)return c;return-1},K="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",L="[\\x20\\t\\r\\n\\f]",M="(?:\\\\.|[\\w-]|[^\\x00-\\xa0])+",N=M.replace("w","w#"),O="\\["+L+"*("+M+")(?:"+L+"*([*^$|!~]?=)"+L+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+N+"))|)"+L+"*\\]",P=":("+M+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+O+")*)|.*)\\)|)",Q=new RegExp(L+"+","g"),R=new RegExp("^"+L+"+|((?:^|[^\\\\])(?:\\\\.)*)"+L+"+$","g"),S=new RegExp("^"+L+"*,"+L+"*"),T=new RegExp("^"+L+"*([>+~]|"+L+")"+L+"*"),U=new RegExp("="+L+"*([^\\]'\"]*?)"+L+"*\\]","g"),V=new RegExp(P),W=new RegExp("^"+N+"$"),X={ID:new RegExp("^#("+M+")"),CLASS:new RegExp("^\\.("+M+")"),TAG:new RegExp("^("+M.replace("w","w*")+")"),ATTR:new RegExp("^"+O),PSEUDO:new RegExp("^"+P),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+L+"*(even|odd|(([+-]|)(\\d*)n|)"+L+"*(?:([+-]|)"+L+"*(\\d+)|))"+L+"*\\)|)","i"),bool:new RegExp("^(?:"+K+")$","i"),needsContext:new RegExp("^"+L+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+L+"*((?:-\\d)?\\d*)"+L+"*\\)|)(?=[^-]|$)","i")},Y=/^(?:input|select|textarea|button)$/i,Z=/^h\d$/i,$=/^[^{]+\{\s*\[native \w/,_=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,aa=/[+~]/,ba=/'|\\/g,ca=new RegExp("\\\\([\\da-f]{1,6}"+L+"?|("+L+")|.)","ig"),da=function(a,b,c){var d="0x"+b-65536;return d!==d||c?b:0>d?String.fromCharCode(d+65536):String.fromCharCode(d>>10|55296,1023&d|56320)},ea=function(){m()};try{H.apply(E=I.call(v.childNodes),v.childNodes),E[v.childNodes.length].nodeType}catch(fa){H={apply:E.length?function(a,b){G.apply(a,I.call(b))}:function(a,b){var c=a.length,d=0;while(a[c++]=b[d++]);a.length=c-1}}}function ga(a,b,d,e){var f,h,j,k,l,o,r,s,w,x;if((b?b.ownerDocument||b:v)!==n&&m(b),b=b||n,d=d||[],k=b.nodeType,"string"!=typeof a||!a||1!==k&&9!==k&&11!==k)return d;if(!e&&p){if(11!==k&&(f=_.exec(a)))if(j=f[1]){if(9===k){if(h=b.getElementById(j),!h||!h.parentNode)return d;if(h.id===j)return d.push(h),d}else if(b.ownerDocument&&(h=b.ownerDocument.getElementById(j))&&t(b,h)&&h.id===j)return d.push(h),d}else{if(f[2])return H.apply(d,b.getElementsByTagName(a)),d;if((j=f[3])&&c.getElementsByClassName)return H.apply(d,b.getElementsByClassName(j)),d}if(c.qsa&&(!q||!q.test(a))){if(s=r=u,w=b,x=1!==k&&a,1===k&&"object"!==b.nodeName.toLowerCase()){o=g(a),(r=b.getAttribute("id"))?s=r.replace(ba,"\\$&"):b.setAttribute("id",s),s="[id='"+s+"'] ",l=o.length;while(l--)o[l]=s+ra(o[l]);w=aa.test(a)&&pa(b.parentNode)||b,x=o.join(",")}if(x)try{return H.apply(d,w.querySelectorAll(x)),d}catch(y){}finally{r||b.removeAttribute("id")}}}return i(a.replace(R,"$1"),b,d,e)}function ha(){var a=[];function b(c,e){return a.push(c+" ")>d.cacheLength&&delete b[a.shift()],b[c+" "]=e}return b}function ia(a){return a[u]=!0,a}function ja(a){var b=n.createElement("div");try{return!!a(b)}catch(c){return!1}finally{b.parentNode&&b.parentNode.removeChild(b),b=null}}function ka(a,b){var c=a.split("|"),e=a.length;while(e--)d.attrHandle[c[e]]=b}function la(a,b){var c=b&&a,d=c&&1===a.nodeType&&1===b.nodeType&&(~b.sourceIndex||C)-(~a.sourceIndex||C);if(d)return d;if(c)while(c=c.nextSibling)if(c===b)return-1;return a?1:-1}function ma(a){return function(b){var c=b.nodeName.toLowerCase();return"input"===c&&b.type===a}}function na(a){return function(b){var c=b.nodeName.toLowerCase();return("input"===c||"button"===c)&&b.type===a}}function oa(a){return ia(function(b){return b=+b,ia(function(c,d){var e,f=a([],c.length,b),g=f.length;while(g--)c[e=f[g]]&&(c[e]=!(d[e]=c[e]))})})}function pa(a){return a&&"undefined"!=typeof a.getElementsByTagName&&a}c=ga.support={},f=ga.isXML=function(a){var b=a&&(a.ownerDocument||a).documentElement;return b?"HTML"!==b.nodeName:!1},m=ga.setDocument=function(a){var b,e,g=a?a.ownerDocument||a:v;return g!==n&&9===g.nodeType&&g.documentElement?(n=g,o=g.documentElement,e=g.defaultView,e&&e!==e.top&&(e.addEventListener?e.addEventListener("unload",ea,!1):e.attachEvent&&e.attachEvent("onunload",ea)),p=!f(g),c.attributes=ja(function(a){return a.className="i",!a.getAttribute("className")}),c.getElementsByTagName=ja(function(a){return a.appendChild(g.createComment("")),!a.getElementsByTagName("*").length}),c.getElementsByClassName=$.test(g.getElementsByClassName),c.getById=ja(function(a){return o.appendChild(a).id=u,!g.getElementsByName||!g.getElementsByName(u).length}),c.getById?(d.find.ID=function(a,b){if("undefined"!=typeof b.getElementById&&p){var c=b.getElementById(a);return c&&c.parentNode?[c]:[]}},d.filter.ID=function(a){var b=a.replace(ca,da);return function(a){return a.getAttribute("id")===b}}):(delete d.find.ID,d.filter.ID=function(a){var b=a.replace(ca,da);return function(a){var c="undefined"!=typeof a.getAttributeNode&&a.getAttributeNode("id");return c&&c.value===b}}),d.find.TAG=c.getElementsByTagName?function(a,b){return"undefined"!=typeof b.getElementsByTagName?b.getElementsByTagName(a):c.qsa?b.querySelectorAll(a):void 0}:function(a,b){var c,d=[],e=0,f=b.getElementsByTagName(a);if("*"===a){while(c=f[e++])1===c.nodeType&&d.push(c);return d}return f},d.find.CLASS=c.getElementsByClassName&&function(a,b){return p?b.getElementsByClassName(a):void 0},r=[],q=[],(c.qsa=$.test(g.querySelectorAll))&&(ja(function(a){o.appendChild(a).innerHTML="<a id='"+u+"'></a><select id='"+u+"-\f]' msallowcapture=''><option selected=''></option></select>",a.querySelectorAll("[msallowcapture^='']").length&&q.push("[*^$]="+L+"*(?:''|\"\")"),a.querySelectorAll("[selected]").length||q.push("\\["+L+"*(?:value|"+K+")"),a.querySelectorAll("[id~="+u+"-]").length||q.push("~="),a.querySelectorAll(":checked").length||q.push(":checked"),a.querySelectorAll("a#"+u+"+*").length||q.push(".#.+[+~]")}),ja(function(a){var b=g.createElement("input");b.setAttribute("type","hidden"),a.appendChild(b).setAttribute("name","D"),a.querySelectorAll("[name=d]").length&&q.push("name"+L+"*[*^$|!~]?="),a.querySelectorAll(":enabled").length||q.push(":enabled",":disabled"),a.querySelectorAll("*,:x"),q.push(",.*:")})),(c.matchesSelector=$.test(s=o.matches||o.webkitMatchesSelector||o.mozMatchesSelector||o.oMatchesSelector||o.msMatchesSelector))&&ja(function(a){c.disconnectedMatch=s.call(a,"div"),s.call(a,"[s!='']:x"),r.push("!=",P)}),q=q.length&&new RegExp(q.join("|")),r=r.length&&new RegExp(r.join("|")),b=$.test(o.compareDocumentPosition),t=b||$.test(o.contains)?function(a,b){var c=9===a.nodeType?a.documentElement:a,d=b&&b.parentNode;return a===d||!(!d||1!==d.nodeType||!(c.contains?c.contains(d):a.compareDocumentPosition&&16&a.compareDocumentPosition(d)))}:function(a,b){if(b)while(b=b.parentNode)if(b===a)return!0;return!1},B=b?function(a,b){if(a===b)return l=!0,0;var d=!a.compareDocumentPosition-!b.compareDocumentPosition;return d?d:(d=(a.ownerDocument||a)===(b.ownerDocument||b)?a.compareDocumentPosition(b):1,1&d||!c.sortDetached&&b.compareDocumentPosition(a)===d?a===g||a.ownerDocument===v&&t(v,a)?-1:b===g||b.ownerDocument===v&&t(v,b)?1:k?J(k,a)-J(k,b):0:4&d?-1:1)}:function(a,b){if(a===b)return l=!0,0;var c,d=0,e=a.parentNode,f=b.parentNode,h=[a],i=[b];if(!e||!f)return a===g?-1:b===g?1:e?-1:f?1:k?J(k,a)-J(k,b):0;if(e===f)return la(a,b);c=a;while(c=c.parentNode)h.unshift(c);c=b;while(c=c.parentNode)i.unshift(c);while(h[d]===i[d])d++;return d?la(h[d],i[d]):h[d]===v?-1:i[d]===v?1:0},g):n},ga.matches=function(a,b){return ga(a,null,null,b)},ga.matchesSelector=function(a,b){if((a.ownerDocument||a)!==n&&m(a),b=b.replace(U,"='$1']"),!(!c.matchesSelector||!p||r&&r.test(b)||q&&q.test(b)))try{var d=s.call(a,b);if(d||c.disconnectedMatch||a.document&&11!==a.document.nodeType)return d}catch(e){}return ga(b,n,null,[a]).length>0},ga.contains=function(a,b){return(a.ownerDocument||a)!==n&&m(a),t(a,b)},ga.attr=function(a,b){(a.ownerDocument||a)!==n&&m(a);var e=d.attrHandle[b.toLowerCase()],f=e&&D.call(d.attrHandle,b.toLowerCase())?e(a,b,!p):void 0;return void 0!==f?f:c.attributes||!p?a.getAttribute(b):(f=a.getAttributeNode(b))&&f.specified?f.value:null},ga.error=function(a){throw new Error("Syntax error, unrecognized expression: "+a)},ga.uniqueSort=function(a){var b,d=[],e=0,f=0;if(l=!c.detectDuplicates,k=!c.sortStable&&a.slice(0),a.sort(B),l){while(b=a[f++])b===a[f]&&(e=d.push(f));while(e--)a.splice(d[e],1)}return k=null,a},e=ga.getText=function(a){var b,c="",d=0,f=a.nodeType;if(f){if(1===f||9===f||11===f){if("string"==typeof a.textContent)return a.textContent;for(a=a.firstChild;a;a=a.nextSibling)c+=e(a)}else if(3===f||4===f)return a.nodeValue}else while(b=a[d++])c+=e(b);return c},d=ga.selectors={cacheLength:50,createPseudo:ia,match:X,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(a){return a[1]=a[1].replace(ca,da),a[3]=(a[3]||a[4]||a[5]||"").replace(ca,da),"~="===a[2]&&(a[3]=" "+a[3]+" "),a.slice(0,4)},CHILD:function(a){return a[1]=a[1].toLowerCase(),"nth"===a[1].slice(0,3)?(a[3]||ga.error(a[0]),a[4]=+(a[4]?a[5]+(a[6]||1):2*("even"===a[3]||"odd"===a[3])),a[5]=+(a[7]+a[8]||"odd"===a[3])):a[3]&&ga.error(a[0]),a},PSEUDO:function(a){var b,c=!a[6]&&a[2];return X.CHILD.test(a[0])?null:(a[3]?a[2]=a[4]||a[5]||"":c&&V.test(c)&&(b=g(c,!0))&&(b=c.indexOf(")",c.length-b)-c.length)&&(a[0]=a[0].slice(0,b),a[2]=c.slice(0,b)),a.slice(0,3))}},filter:{TAG:function(a){var b=a.replace(ca,da).toLowerCase();return"*"===a?function(){return!0}:function(a){return a.nodeName&&a.nodeName.toLowerCase()===b}},CLASS:function(a){var b=y[a+" "];return b||(b=new RegExp("(^|"+L+")"+a+"("+L+"|$)"))&&y(a,function(a){return b.test("string"==typeof a.className&&a.className||"undefined"!=typeof a.getAttribute&&a.getAttribute("class")||"")})},ATTR:function(a,b,c){return function(d){var e=ga.attr(d,a);return null==e?"!="===b:b?(e+="","="===b?e===c:"!="===b?e!==c:"^="===b?c&&0===e.indexOf(c):"*="===b?c&&e.indexOf(c)>-1:"$="===b?c&&e.slice(-c.length)===c:"~="===b?(" "+e.replace(Q," ")+" ").indexOf(c)>-1:"|="===b?e===c||e.slice(0,c.length+1)===c+"-":!1):!0}},CHILD:function(a,b,c,d,e){var f="nth"!==a.slice(0,3),g="last"!==a.slice(-4),h="of-type"===b;return 1===d&&0===e?function(a){return!!a.parentNode}:function(b,c,i){var j,k,l,m,n,o,p=f!==g?"nextSibling":"previousSibling",q=b.parentNode,r=h&&b.nodeName.toLowerCase(),s=!i&&!h;if(q){if(f){while(p){l=b;while(l=l[p])if(h?l.nodeName.toLowerCase()===r:1===l.nodeType)return!1;o=p="only"===a&&!o&&"nextSibling"}return!0}if(o=[g?q.firstChild:q.lastChild],g&&s){k=q[u]||(q[u]={}),j=k[a]||[],n=j[0]===w&&j[1],m=j[0]===w&&j[2],l=n&&q.childNodes[n];while(l=++n&&l&&l[p]||(m=n=0)||o.pop())if(1===l.nodeType&&++m&&l===b){k[a]=[w,n,m];break}}else if(s&&(j=(b[u]||(b[u]={}))[a])&&j[0]===w)m=j[1];else while(l=++n&&l&&l[p]||(m=n=0)||o.pop())if((h?l.nodeName.toLowerCase()===r:1===l.nodeType)&&++m&&(s&&((l[u]||(l[u]={}))[a]=[w,m]),l===b))break;return m-=e,m===d||m%d===0&&m/d>=0}}},PSEUDO:function(a,b){var c,e=d.pseudos[a]||d.setFilters[a.toLowerCase()]||ga.error("unsupported pseudo: "+a);return e[u]?e(b):e.length>1?(c=[a,a,"",b],d.setFilters.hasOwnProperty(a.toLowerCase())?ia(function(a,c){var d,f=e(a,b),g=f.length;while(g--)d=J(a,f[g]),a[d]=!(c[d]=f[g])}):function(a){return e(a,0,c)}):e}},pseudos:{not:ia(function(a){var b=[],c=[],d=h(a.replace(R,"$1"));return d[u]?ia(function(a,b,c,e){var f,g=d(a,null,e,[]),h=a.length;while(h--)(f=g[h])&&(a[h]=!(b[h]=f))}):function(a,e,f){return b[0]=a,d(b,null,f,c),b[0]=null,!c.pop()}}),has:ia(function(a){return function(b){return ga(a,b).length>0}}),contains:ia(function(a){return a=a.replace(ca,da),function(b){return(b.textContent||b.innerText||e(b)).indexOf(a)>-1}}),lang:ia(function(a){return W.test(a||"")||ga.error("unsupported lang: "+a),a=a.replace(ca,da).toLowerCase(),function(b){var c;do if(c=p?b.lang:b.getAttribute("xml:lang")||b.getAttribute("lang"))return c=c.toLowerCase(),c===a||0===c.indexOf(a+"-");while((b=b.parentNode)&&1===b.nodeType);return!1}}),target:function(b){var c=a.location&&a.location.hash;return c&&c.slice(1)===b.id},root:function(a){return a===o},focus:function(a){return a===n.activeElement&&(!n.hasFocus||n.hasFocus())&&!!(a.type||a.href||~a.tabIndex)},enabled:function(a){return a.disabled===!1},disabled:function(a){return a.disabled===!0},checked:function(a){var b=a.nodeName.toLowerCase();return"input"===b&&!!a.checked||"option"===b&&!!a.selected},selected:function(a){return a.parentNode&&a.parentNode.selectedIndex,a.selected===!0},empty:function(a){for(a=a.firstChild;a;a=a.nextSibling)if(a.nodeType<6)return!1;return!0},parent:function(a){return!d.pseudos.empty(a)},header:function(a){return Z.test(a.nodeName)},input:function(a){return Y.test(a.nodeName)},button:function(a){var b=a.nodeName.toLowerCase();return"input"===b&&"button"===a.type||"button"===b},text:function(a){var b;return"input"===a.nodeName.toLowerCase()&&"text"===a.type&&(null==(b=a.getAttribute("type"))||"text"===b.toLowerCase())},first:oa(function(){return[0]}),last:oa(function(a,b){return[b-1]}),eq:oa(function(a,b,c){return[0>c?c+b:c]}),even:oa(function(a,b){for(var c=0;b>c;c+=2)a.push(c);return a}),odd:oa(function(a,b){for(var c=1;b>c;c+=2)a.push(c);return a}),lt:oa(function(a,b,c){for(var d=0>c?c+b:c;--d>=0;)a.push(d);return a}),gt:oa(function(a,b,c){for(var d=0>c?c+b:c;++d<b;)a.push(d);return a})}},d.pseudos.nth=d.pseudos.eq;for(b in{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})d.pseudos[b]=ma(b);for(b in{submit:!0,reset:!0})d.pseudos[b]=na(b);function qa(){}qa.prototype=d.filters=d.pseudos,d.setFilters=new qa,g=ga.tokenize=function(a,b){var c,e,f,g,h,i,j,k=z[a+" "];if(k)return b?0:k.slice(0);h=a,i=[],j=d.preFilter;while(h){(!c||(e=S.exec(h)))&&(e&&(h=h.slice(e[0].length)||h),i.push(f=[])),c=!1,(e=T.exec(h))&&(c=e.shift(),f.push({value:c,type:e[0].replace(R," ")}),h=h.slice(c.length));for(g in d.filter)!(e=X[g].exec(h))||j[g]&&!(e=j[g](e))||(c=e.shift(),f.push({value:c,type:g,matches:e}),h=h.slice(c.length));if(!c)break}return b?h.length:h?ga.error(a):z(a,i).slice(0)};function ra(a){for(var b=0,c=a.length,d="";c>b;b++)d+=a[b].value;return d}function sa(a,b,c){var d=b.dir,e=c&&"parentNode"===d,f=x++;return b.first?function(b,c,f){while(b=b[d])if(1===b.nodeType||e)return a(b,c,f)}:function(b,c,g){var h,i,j=[w,f];if(g){while(b=b[d])if((1===b.nodeType||e)&&a(b,c,g))return!0}else while(b=b[d])if(1===b.nodeType||e){if(i=b[u]||(b[u]={}),(h=i[d])&&h[0]===w&&h[1]===f)return j[2]=h[2];if(i[d]=j,j[2]=a(b,c,g))return!0}}}function ta(a){return a.length>1?function(b,c,d){var e=a.length;while(e--)if(!a[e](b,c,d))return!1;return!0}:a[0]}function ua(a,b,c){for(var d=0,e=b.length;e>d;d++)ga(a,b[d],c);return c}function va(a,b,c,d,e){for(var f,g=[],h=0,i=a.length,j=null!=b;i>h;h++)(f=a[h])&&(!c||c(f,d,e))&&(g.push(f),j&&b.push(h));return g}function wa(a,b,c,d,e,f){return d&&!d[u]&&(d=wa(d)),e&&!e[u]&&(e=wa(e,f)),ia(function(f,g,h,i){var j,k,l,m=[],n=[],o=g.length,p=f||ua(b||"*",h.nodeType?[h]:h,[]),q=!a||!f&&b?p:va(p,m,a,h,i),r=c?e||(f?a:o||d)?[]:g:q;if(c&&c(q,r,h,i),d){j=va(r,n),d(j,[],h,i),k=j.length;while(k--)(l=j[k])&&(r[n[k]]=!(q[n[k]]=l))}if(f){if(e||a){if(e){j=[],k=r.length;while(k--)(l=r[k])&&j.push(q[k]=l);e(null,r=[],j,i)}k=r.length;while(k--)(l=r[k])&&(j=e?J(f,l):m[k])>-1&&(f[j]=!(g[j]=l))}}else r=va(r===g?r.splice(o,r.length):r),e?e(null,g,r,i):H.apply(g,r)})}function xa(a){for(var b,c,e,f=a.length,g=d.relative[a[0].type],h=g||d.relative[" "],i=g?1:0,k=sa(function(a){return a===b},h,!0),l=sa(function(a){return J(b,a)>-1},h,!0),m=[function(a,c,d){var e=!g&&(d||c!==j)||((b=c).nodeType?k(a,c,d):l(a,c,d));return b=null,e}];f>i;i++)if(c=d.relative[a[i].type])m=[sa(ta(m),c)];else{if(c=d.filter[a[i].type].apply(null,a[i].matches),c[u]){for(e=++i;f>e;e++)if(d.relative[a[e].type])break;return wa(i>1&&ta(m),i>1&&ra(a.slice(0,i-1).concat({value:" "===a[i-2].type?"*":""})).replace(R,"$1"),c,e>i&&xa(a.slice(i,e)),f>e&&xa(a=a.slice(e)),f>e&&ra(a))}m.push(c)}return ta(m)}function ya(a,b){var c=b.length>0,e=a.length>0,f=function(f,g,h,i,k){var l,m,o,p=0,q="0",r=f&&[],s=[],t=j,u=f||e&&d.find.TAG("*",k),v=w+=null==t?1:Math.random()||.1,x=u.length;for(k&&(j=g!==n&&g);q!==x&&null!=(l=u[q]);q++){if(e&&l){m=0;while(o=a[m++])if(o(l,g,h)){i.push(l);break}k&&(w=v)}c&&((l=!o&&l)&&p--,f&&r.push(l))}if(p+=q,c&&q!==p){m=0;while(o=b[m++])o(r,s,g,h);if(f){if(p>0)while(q--)r[q]||s[q]||(s[q]=F.call(i));s=va(s)}H.apply(i,s),k&&!f&&s.length>0&&p+b.length>1&&ga.uniqueSort(i)}return k&&(w=v,j=t),r};return c?ia(f):f}return h=ga.compile=function(a,b){var c,d=[],e=[],f=A[a+" "];if(!f){b||(b=g(a)),c=b.length;while(c--)f=xa(b[c]),f[u]?d.push(f):e.push(f);f=A(a,ya(e,d)),f.selector=a}return f},i=ga.select=function(a,b,e,f){var i,j,k,l,m,n="function"==typeof a&&a,o=!f&&g(a=n.selector||a);if(e=e||[],1===o.length){if(j=o[0]=o[0].slice(0),j.length>2&&"ID"===(k=j[0]).type&&c.getById&&9===b.nodeType&&p&&d.relative[j[1].type]){if(b=(d.find.ID(k.matches[0].replace(ca,da),b)||[])[0],!b)return e;n&&(b=b.parentNode),a=a.slice(j.shift().value.length)}i=X.needsContext.test(a)?0:j.length;while(i--){if(k=j[i],d.relative[l=k.type])break;if((m=d.find[l])&&(f=m(k.matches[0].replace(ca,da),aa.test(j[0].type)&&pa(b.parentNode)||b))){if(j.splice(i,1),a=f.length&&ra(j),!a)return H.apply(e,f),e;break}}}return(n||h(a,o))(f,b,!p,e,aa.test(a)&&pa(b.parentNode)||b),e},c.sortStable=u.split("").sort(B).join("")===u,c.detectDuplicates=!!l,m(),c.sortDetached=ja(function(a){return 1&a.compareDocumentPosition(n.createElement("div"))}),ja(function(a){return a.innerHTML="<a href='#'></a>","#"===a.firstChild.getAttribute("href")})||ka("type|href|height|width",function(a,b,c){return c?void 0:a.getAttribute(b,"type"===b.toLowerCase()?1:2)}),c.attributes&&ja(function(a){return a.innerHTML="<input/>",a.firstChild.setAttribute("value",""),""===a.firstChild.getAttribute("value")})||ka("value",function(a,b,c){return c||"input"!==a.nodeName.toLowerCase()?void 0:a.defaultValue}),ja(function(a){return null==a.getAttribute("disabled")})||ka(K,function(a,b,c){var d;return c?void 0:a[b]===!0?b.toLowerCase():(d=a.getAttributeNode(b))&&d.specified?d.value:null}),ga}(a);m.find=s,m.expr=s.selectors,m.expr[":"]=m.expr.pseudos,m.unique=s.uniqueSort,m.text=s.getText,m.isXMLDoc=s.isXML,m.contains=s.contains;var t=m.expr.match.needsContext,u=/^<(\w+)\s*\/?>(?:<\/\1>|)$/,v=/^.[^:#\[\.,]*$/;function w(a,b,c){if(m.isFunction(b))return m.grep(a,function(a,d){return!!b.call(a,d,a)!==c});if(b.nodeType)return m.grep(a,function(a){return a===b!==c});if("string"==typeof b){if(v.test(b))return m.filter(b,a,c);b=m.filter(b,a)}return m.grep(a,function(a){return m.inArray(a,b)>=0!==c})}m.filter=function(a,b,c){var d=b[0];return c&&(a=":not("+a+")"),1===b.length&&1===d.nodeType?m.find.matchesSelector(d,a)?[d]:[]:m.find.matches(a,m.grep(b,function(a){return 1===a.nodeType}))},m.fn.extend({find:function(a){var b,c=[],d=this,e=d.length;if("string"!=typeof a)return this.pushStack(m(a).filter(function(){for(b=0;e>b;b++)if(m.contains(d[b],this))return!0}));for(b=0;e>b;b++)m.find(a,d[b],c);return c=this.pushStack(e>1?m.unique(c):c),c.selector=this.selector?this.selector+" "+a:a,c},filter:function(a){return this.pushStack(w(this,a||[],!1))},not:function(a){return this.pushStack(w(this,a||[],!0))},is:function(a){return!!w(this,"string"==typeof a&&t.test(a)?m(a):a||[],!1).length}});var x,y=a.document,z=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]*))$/,A=m.fn.init=function(a,b){var c,d;if(!a)return this;if("string"==typeof a){if(c="<"===a.charAt(0)&&">"===a.charAt(a.length-1)&&a.length>=3?[null,a,null]:z.exec(a),!c||!c[1]&&b)return!b||b.jquery?(b||x).find(a):this.constructor(b).find(a);if(c[1]){if(b=b instanceof m?b[0]:b,m.merge(this,m.parseHTML(c[1],b&&b.nodeType?b.ownerDocument||b:y,!0)),u.test(c[1])&&m.isPlainObject(b))for(c in b)m.isFunction(this[c])?this[c](b[c]):this.attr(c,b[c]);return this}if(d=y.getElementById(c[2]),d&&d.parentNode){if(d.id!==c[2])return x.find(a);this.length=1,this[0]=d}return this.context=y,this.selector=a,this}return a.nodeType?(this.context=this[0]=a,this.length=1,this):m.isFunction(a)?"undefined"!=typeof x.ready?x.ready(a):a(m):(void 0!==a.selector&&(this.selector=a.selector,this.context=a.context),m.makeArray(a,this))};A.prototype=m.fn,x=m(y);var B=/^(?:parents|prev(?:Until|All))/,C={children:!0,contents:!0,next:!0,prev:!0};m.extend({dir:function(a,b,c){var d=[],e=a[b];while(e&&9!==e.nodeType&&(void 0===c||1!==e.nodeType||!m(e).is(c)))1===e.nodeType&&d.push(e),e=e[b];return d},sibling:function(a,b){for(var c=[];a;a=a.nextSibling)1===a.nodeType&&a!==b&&c.push(a);return c}}),m.fn.extend({has:function(a){var b,c=m(a,this),d=c.length;return this.filter(function(){for(b=0;d>b;b++)if(m.contains(this,c[b]))return!0})},closest:function(a,b){for(var c,d=0,e=this.length,f=[],g=t.test(a)||"string"!=typeof a?m(a,b||this.context):0;e>d;d++)for(c=this[d];c&&c!==b;c=c.parentNode)if(c.nodeType<11&&(g?g.index(c)>-1:1===c.nodeType&&m.find.matchesSelector(c,a))){f.push(c);break}return this.pushStack(f.length>1?m.unique(f):f)},index:function(a){return a?"string"==typeof a?m.inArray(this[0],m(a)):m.inArray(a.jquery?a[0]:a,this):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(a,b){return this.pushStack(m.unique(m.merge(this.get(),m(a,b))))},addBack:function(a){return this.add(null==a?this.prevObject:this.prevObject.filter(a))}});function D(a,b){do a=a[b];while(a&&1!==a.nodeType);return a}m.each({parent:function(a){var b=a.parentNode;return b&&11!==b.nodeType?b:null},parents:function(a){return m.dir(a,"parentNode")},parentsUntil:function(a,b,c){return m.dir(a,"parentNode",c)},next:function(a){return D(a,"nextSibling")},prev:function(a){return D(a,"previousSibling")},nextAll:function(a){return m.dir(a,"nextSibling")},prevAll:function(a){return m.dir(a,"previousSibling")},nextUntil:function(a,b,c){return m.dir(a,"nextSibling",c)},prevUntil:function(a,b,c){return m.dir(a,"previousSibling",c)},siblings:function(a){return m.sibling((a.parentNode||{}).firstChild,a)},children:function(a){return m.sibling(a.firstChild)},contents:function(a){return m.nodeName(a,"iframe")?a.contentDocument||a.contentWindow.document:m.merge([],a.childNodes)}},function(a,b){m.fn[a]=function(c,d){var e=m.map(this,b,c);return"Until"!==a.slice(-5)&&(d=c),d&&"string"==typeof d&&(e=m.filter(d,e)),this.length>1&&(C[a]||(e=m.unique(e)),B.test(a)&&(e=e.reverse())),this.pushStack(e)}});var E=/\S+/g,F={};function G(a){var b=F[a]={};return m.each(a.match(E)||[],function(a,c){b[c]=!0}),b}m.Callbacks=function(a){a="string"==typeof a?F[a]||G(a):m.extend({},a);var b,c,d,e,f,g,h=[],i=!a.once&&[],j=function(l){for(c=a.memory&&l,d=!0,f=g||0,g=0,e=h.length,b=!0;h&&e>f;f++)if(h[f].apply(l[0],l[1])===!1&&a.stopOnFalse){c=!1;break}b=!1,h&&(i?i.length&&j(i.shift()):c?h=[]:k.disable())},k={add:function(){if(h){var d=h.length;!function f(b){m.each(b,function(b,c){var d=m.type(c);"function"===d?a.unique&&k.has(c)||h.push(c):c&&c.length&&"string"!==d&&f(c)})}(arguments),b?e=h.length:c&&(g=d,j(c))}return this},remove:function(){return h&&m.each(arguments,function(a,c){var d;while((d=m.inArray(c,h,d))>-1)h.splice(d,1),b&&(e>=d&&e--,f>=d&&f--)}),this},has:function(a){return a?m.inArray(a,h)>-1:!(!h||!h.length)},empty:function(){return h=[],e=0,this},disable:function(){return h=i=c=void 0,this},disabled:function(){return!h},lock:function(){return i=void 0,c||k.disable(),this},locked:function(){return!i},fireWith:function(a,c){return!h||d&&!i||(c=c||[],c=[a,c.slice?c.slice():c],b?i.push(c):j(c)),this},fire:function(){return k.fireWith(this,arguments),this},fired:function(){return!!d}};return k},m.extend({Deferred:function(a){var b=[["resolve","done",m.Callbacks("once memory"),"resolved"],["reject","fail",m.Callbacks("once memory"),"rejected"],["notify","progress",m.Callbacks("memory")]],c="pending",d={state:function(){return c},always:function(){return e.done(arguments).fail(arguments),this},then:function(){var a=arguments;return m.Deferred(function(c){m.each(b,function(b,f){var g=m.isFunction(a[b])&&a[b];e[f[1]](function(){var a=g&&g.apply(this,arguments);a&&m.isFunction(a.promise)?a.promise().done(c.resolve).fail(c.reject).progress(c.notify):c[f[0]+"With"](this===d?c.promise():this,g?[a]:arguments)})}),a=null}).promise()},promise:function(a){return null!=a?m.extend(a,d):d}},e={};return d.pipe=d.then,m.each(b,function(a,f){var g=f[2],h=f[3];d[f[1]]=g.add,h&&g.add(function(){c=h},b[1^a][2].disable,b[2][2].lock),e[f[0]]=function(){return e[f[0]+"With"](this===e?d:this,arguments),this},e[f[0]+"With"]=g.fireWith}),d.promise(e),a&&a.call(e,e),e},when:function(a){var b=0,c=d.call(arguments),e=c.length,f=1!==e||a&&m.isFunction(a.promise)?e:0,g=1===f?a:m.Deferred(),h=function(a,b,c){return function(e){b[a]=this,c[a]=arguments.length>1?d.call(arguments):e,c===i?g.notifyWith(b,c):--f||g.resolveWith(b,c)}},i,j,k;if(e>1)for(i=new Array(e),j=new Array(e),k=new Array(e);e>b;b++)c[b]&&m.isFunction(c[b].promise)?c[b].promise().done(h(b,k,c)).fail(g.reject).progress(h(b,j,i)):--f;return f||g.resolveWith(k,c),g.promise()}});var H;m.fn.ready=function(a){return m.ready.promise().done(a),this},m.extend({isReady:!1,readyWait:1,holdReady:function(a){a?m.readyWait++:m.ready(!0)},ready:function(a){if(a===!0?!--m.readyWait:!m.isReady){if(!y.body)return setTimeout(m.ready);m.isReady=!0,a!==!0&&--m.readyWait>0||(H.resolveWith(y,[m]),m.fn.triggerHandler&&(m(y).triggerHandler("ready"),m(y).off("ready")))}}});function I(){y.addEventListener?(y.removeEventListener("DOMContentLoaded",J,!1),a.removeEventListener("load",J,!1)):(y.detachEvent("onreadystatechange",J),a.detachEvent("onload",J))}function J(){(y.addEventListener||"load"===event.type||"complete"===y.readyState)&&(I(),m.ready())}m.ready.promise=function(b){if(!H)if(H=m.Deferred(),"complete"===y.readyState)setTimeout(m.ready);else if(y.addEventListener)y.addEventListener("DOMContentLoaded",J,!1),a.addEventListener("load",J,!1);else{y.attachEvent("onreadystatechange",J),a.attachEvent("onload",J);var c=!1;try{c=null==a.frameElement&&y.documentElement}catch(d){}c&&c.doScroll&&!function e(){if(!m.isReady){try{c.doScroll("left")}catch(a){return setTimeout(e,50)}I(),m.ready()}}()}return H.promise(b)};var K="undefined",L;for(L in m(k))break;k.ownLast="0"!==L,k.inlineBlockNeedsLayout=!1,m(function(){var a,b,c,d;c=y.getElementsByTagName("body")[0],c&&c.style&&(b=y.createElement("div"),d=y.createElement("div"),d.style.cssText="position:absolute;border:0;width:0;height:0;top:0;left:-9999px",c.appendChild(d).appendChild(b),typeof b.style.zoom!==K&&(b.style.cssText="display:inline;margin:0;border:0;padding:1px;width:1px;zoom:1",k.inlineBlockNeedsLayout=a=3===b.offsetWidth,a&&(c.style.zoom=1)),c.removeChild(d))}),function(){var a=y.createElement("div");if(null==k.deleteExpando){k.deleteExpando=!0;try{delete a.test}catch(b){k.deleteExpando=!1}}a=null}(),m.acceptData=function(a){var b=m.noData[(a.nodeName+" ").toLowerCase()],c=+a.nodeType||1;return 1!==c&&9!==c?!1:!b||b!==!0&&a.getAttribute("classid")===b};var M=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,N=/([A-Z])/g;function O(a,b,c){if(void 0===c&&1===a.nodeType){var d="data-"+b.replace(N,"-$1").toLowerCase();if(c=a.getAttribute(d),"string"==typeof c){try{c="true"===c?!0:"false"===c?!1:"null"===c?null:+c+""===c?+c:M.test(c)?m.parseJSON(c):c}catch(e){}m.data(a,b,c)}else c=void 0}return c}function P(a){var b;for(b in a)if(("data"!==b||!m.isEmptyObject(a[b]))&&"toJSON"!==b)return!1;

return!0}function Q(a,b,d,e){if(m.acceptData(a)){var f,g,h=m.expando,i=a.nodeType,j=i?m.cache:a,k=i?a[h]:a[h]&&h;if(k&&j[k]&&(e||j[k].data)||void 0!==d||"string"!=typeof b)return k||(k=i?a[h]=c.pop()||m.guid++:h),j[k]||(j[k]=i?{}:{toJSON:m.noop}),("object"==typeof b||"function"==typeof b)&&(e?j[k]=m.extend(j[k],b):j[k].data=m.extend(j[k].data,b)),g=j[k],e||(g.data||(g.data={}),g=g.data),void 0!==d&&(g[m.camelCase(b)]=d),"string"==typeof b?(f=g[b],null==f&&(f=g[m.camelCase(b)])):f=g,f}}function R(a,b,c){if(m.acceptData(a)){var d,e,f=a.nodeType,g=f?m.cache:a,h=f?a[m.expando]:m.expando;if(g[h]){if(b&&(d=c?g[h]:g[h].data)){m.isArray(b)?b=b.concat(m.map(b,m.camelCase)):b in d?b=[b]:(b=m.camelCase(b),b=b in d?[b]:b.split(" ")),e=b.length;while(e--)delete d[b[e]];if(c?!P(d):!m.isEmptyObject(d))return}(c||(delete g[h].data,P(g[h])))&&(f?m.cleanData([a],!0):k.deleteExpando||g!=g.window?delete g[h]:g[h]=null)}}}m.extend({cache:{},noData:{"applet ":!0,"embed ":!0,"object ":"clsid:D27CDB6E-AE6D-11cf-96B8-444553540000"},hasData:function(a){return a=a.nodeType?m.cache[a[m.expando]]:a[m.expando],!!a&&!P(a)},data:function(a,b,c){return Q(a,b,c)},removeData:function(a,b){return R(a,b)},_data:function(a,b,c){return Q(a,b,c,!0)},_removeData:function(a,b){return R(a,b,!0)}}),m.fn.extend({data:function(a,b){var c,d,e,f=this[0],g=f&&f.attributes;if(void 0===a){if(this.length&&(e=m.data(f),1===f.nodeType&&!m._data(f,"parsedAttrs"))){c=g.length;while(c--)g[c]&&(d=g[c].name,0===d.indexOf("data-")&&(d=m.camelCase(d.slice(5)),O(f,d,e[d])));m._data(f,"parsedAttrs",!0)}return e}return"object"==typeof a?this.each(function(){m.data(this,a)}):arguments.length>1?this.each(function(){m.data(this,a,b)}):f?O(f,a,m.data(f,a)):void 0},removeData:function(a){return this.each(function(){m.removeData(this,a)})}}),m.extend({queue:function(a,b,c){var d;return a?(b=(b||"fx")+"queue",d=m._data(a,b),c&&(!d||m.isArray(c)?d=m._data(a,b,m.makeArray(c)):d.push(c)),d||[]):void 0},dequeue:function(a,b){b=b||"fx";var c=m.queue(a,b),d=c.length,e=c.shift(),f=m._queueHooks(a,b),g=function(){m.dequeue(a,b)};"inprogress"===e&&(e=c.shift(),d--),e&&("fx"===b&&c.unshift("inprogress"),delete f.stop,e.call(a,g,f)),!d&&f&&f.empty.fire()},_queueHooks:function(a,b){var c=b+"queueHooks";return m._data(a,c)||m._data(a,c,{empty:m.Callbacks("once memory").add(function(){m._removeData(a,b+"queue"),m._removeData(a,c)})})}}),m.fn.extend({queue:function(a,b){var c=2;return"string"!=typeof a&&(b=a,a="fx",c--),arguments.length<c?m.queue(this[0],a):void 0===b?this:this.each(function(){var c=m.queue(this,a,b);m._queueHooks(this,a),"fx"===a&&"inprogress"!==c[0]&&m.dequeue(this,a)})},dequeue:function(a){return this.each(function(){m.dequeue(this,a)})},clearQueue:function(a){return this.queue(a||"fx",[])},promise:function(a,b){var c,d=1,e=m.Deferred(),f=this,g=this.length,h=function(){--d||e.resolveWith(f,[f])};"string"!=typeof a&&(b=a,a=void 0),a=a||"fx";while(g--)c=m._data(f[g],a+"queueHooks"),c&&c.empty&&(d++,c.empty.add(h));return h(),e.promise(b)}});var S=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,T=["Top","Right","Bottom","Left"],U=function(a,b){return a=b||a,"none"===m.css(a,"display")||!m.contains(a.ownerDocument,a)},V=m.access=function(a,b,c,d,e,f,g){var h=0,i=a.length,j=null==c;if("object"===m.type(c)){e=!0;for(h in c)m.access(a,b,h,c[h],!0,f,g)}else if(void 0!==d&&(e=!0,m.isFunction(d)||(g=!0),j&&(g?(b.call(a,d),b=null):(j=b,b=function(a,b,c){return j.call(m(a),c)})),b))for(;i>h;h++)b(a[h],c,g?d:d.call(a[h],h,b(a[h],c)));return e?a:j?b.call(a):i?b(a[0],c):f},W=/^(?:checkbox|radio)$/i;!function(){var a=y.createElement("input"),b=y.createElement("div"),c=y.createDocumentFragment();if(b.innerHTML="  <link/><table></table><a href='/a'>a</a><input type='checkbox'/>",k.leadingWhitespace=3===b.firstChild.nodeType,k.tbody=!b.getElementsByTagName("tbody").length,k.htmlSerialize=!!b.getElementsByTagName("link").length,k.html5Clone="<:nav></:nav>"!==y.createElement("nav").cloneNode(!0).outerHTML,a.type="checkbox",a.checked=!0,c.appendChild(a),k.appendChecked=a.checked,b.innerHTML="<textarea>x</textarea>",k.noCloneChecked=!!b.cloneNode(!0).lastChild.defaultValue,c.appendChild(b),b.innerHTML="<input type='radio' checked='checked' name='t'/>",k.checkClone=b.cloneNode(!0).cloneNode(!0).lastChild.checked,k.noCloneEvent=!0,b.attachEvent&&(b.attachEvent("onclick",function(){k.noCloneEvent=!1}),b.cloneNode(!0).click()),null==k.deleteExpando){k.deleteExpando=!0;try{delete b.test}catch(d){k.deleteExpando=!1}}}(),function(){var b,c,d=y.createElement("div");for(b in{submit:!0,change:!0,focusin:!0})c="on"+b,(k[b+"Bubbles"]=c in a)||(d.setAttribute(c,"t"),k[b+"Bubbles"]=d.attributes[c].expando===!1);d=null}();var X=/^(?:input|select|textarea)$/i,Y=/^key/,Z=/^(?:mouse|pointer|contextmenu)|click/,$=/^(?:focusinfocus|focusoutblur)$/,_=/^([^.]*)(?:\.(.+)|)$/;function aa(){return!0}function ba(){return!1}function ca(){try{return y.activeElement}catch(a){}}m.event={global:{},add:function(a,b,c,d,e){var f,g,h,i,j,k,l,n,o,p,q,r=m._data(a);if(r){c.handler&&(i=c,c=i.handler,e=i.selector),c.guid||(c.guid=m.guid++),(g=r.events)||(g=r.events={}),(k=r.handle)||(k=r.handle=function(a){return typeof m===K||a&&m.event.triggered===a.type?void 0:m.event.dispatch.apply(k.elem,arguments)},k.elem=a),b=(b||"").match(E)||[""],h=b.length;while(h--)f=_.exec(b[h])||[],o=q=f[1],p=(f[2]||"").split(".").sort(),o&&(j=m.event.special[o]||{},o=(e?j.delegateType:j.bindType)||o,j=m.event.special[o]||{},l=m.extend({type:o,origType:q,data:d,handler:c,guid:c.guid,selector:e,needsContext:e&&m.expr.match.needsContext.test(e),namespace:p.join(".")},i),(n=g[o])||(n=g[o]=[],n.delegateCount=0,j.setup&&j.setup.call(a,d,p,k)!==!1||(a.addEventListener?a.addEventListener(o,k,!1):a.attachEvent&&a.attachEvent("on"+o,k))),j.add&&(j.add.call(a,l),l.handler.guid||(l.handler.guid=c.guid)),e?n.splice(n.delegateCount++,0,l):n.push(l),m.event.global[o]=!0);a=null}},remove:function(a,b,c,d,e){var f,g,h,i,j,k,l,n,o,p,q,r=m.hasData(a)&&m._data(a);if(r&&(k=r.events)){b=(b||"").match(E)||[""],j=b.length;while(j--)if(h=_.exec(b[j])||[],o=q=h[1],p=(h[2]||"").split(".").sort(),o){l=m.event.special[o]||{},o=(d?l.delegateType:l.bindType)||o,n=k[o]||[],h=h[2]&&new RegExp("(^|\\.)"+p.join("\\.(?:.*\\.|)")+"(\\.|$)"),i=f=n.length;while(f--)g=n[f],!e&&q!==g.origType||c&&c.guid!==g.guid||h&&!h.test(g.namespace)||d&&d!==g.selector&&("**"!==d||!g.selector)||(n.splice(f,1),g.selector&&n.delegateCount--,l.remove&&l.remove.call(a,g));i&&!n.length&&(l.teardown&&l.teardown.call(a,p,r.handle)!==!1||m.removeEvent(a,o,r.handle),delete k[o])}else for(o in k)m.event.remove(a,o+b[j],c,d,!0);m.isEmptyObject(k)&&(delete r.handle,m._removeData(a,"events"))}},trigger:function(b,c,d,e){var f,g,h,i,k,l,n,o=[d||y],p=j.call(b,"type")?b.type:b,q=j.call(b,"namespace")?b.namespace.split("."):[];if(h=l=d=d||y,3!==d.nodeType&&8!==d.nodeType&&!$.test(p+m.event.triggered)&&(p.indexOf(".")>=0&&(q=p.split("."),p=q.shift(),q.sort()),g=p.indexOf(":")<0&&"on"+p,b=b[m.expando]?b:new m.Event(p,"object"==typeof b&&b),b.isTrigger=e?2:3,b.namespace=q.join("."),b.namespace_re=b.namespace?new RegExp("(^|\\.)"+q.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,b.result=void 0,b.target||(b.target=d),c=null==c?[b]:m.makeArray(c,[b]),k=m.event.special[p]||{},e||!k.trigger||k.trigger.apply(d,c)!==!1)){if(!e&&!k.noBubble&&!m.isWindow(d)){for(i=k.delegateType||p,$.test(i+p)||(h=h.parentNode);h;h=h.parentNode)o.push(h),l=h;l===(d.ownerDocument||y)&&o.push(l.defaultView||l.parentWindow||a)}n=0;while((h=o[n++])&&!b.isPropagationStopped())b.type=n>1?i:k.bindType||p,f=(m._data(h,"events")||{})[b.type]&&m._data(h,"handle"),f&&f.apply(h,c),f=g&&h[g],f&&f.apply&&m.acceptData(h)&&(b.result=f.apply(h,c),b.result===!1&&b.preventDefault());if(b.type=p,!e&&!b.isDefaultPrevented()&&(!k._default||k._default.apply(o.pop(),c)===!1)&&m.acceptData(d)&&g&&d[p]&&!m.isWindow(d)){l=d[g],l&&(d[g]=null),m.event.triggered=p;try{d[p]()}catch(r){}m.event.triggered=void 0,l&&(d[g]=l)}return b.result}},dispatch:function(a){a=m.event.fix(a);var b,c,e,f,g,h=[],i=d.call(arguments),j=(m._data(this,"events")||{})[a.type]||[],k=m.event.special[a.type]||{};if(i[0]=a,a.delegateTarget=this,!k.preDispatch||k.preDispatch.call(this,a)!==!1){h=m.event.handlers.call(this,a,j),b=0;while((f=h[b++])&&!a.isPropagationStopped()){a.currentTarget=f.elem,g=0;while((e=f.handlers[g++])&&!a.isImmediatePropagationStopped())(!a.namespace_re||a.namespace_re.test(e.namespace))&&(a.handleObj=e,a.data=e.data,c=((m.event.special[e.origType]||{}).handle||e.handler).apply(f.elem,i),void 0!==c&&(a.result=c)===!1&&(a.preventDefault(),a.stopPropagation()))}return k.postDispatch&&k.postDispatch.call(this,a),a.result}},handlers:function(a,b){var c,d,e,f,g=[],h=b.delegateCount,i=a.target;if(h&&i.nodeType&&(!a.button||"click"!==a.type))for(;i!=this;i=i.parentNode||this)if(1===i.nodeType&&(i.disabled!==!0||"click"!==a.type)){for(e=[],f=0;h>f;f++)d=b[f],c=d.selector+" ",void 0===e[c]&&(e[c]=d.needsContext?m(c,this).index(i)>=0:m.find(c,this,null,[i]).length),e[c]&&e.push(d);e.length&&g.push({elem:i,handlers:e})}return h<b.length&&g.push({elem:this,handlers:b.slice(h)}),g},fix:function(a){if(a[m.expando])return a;var b,c,d,e=a.type,f=a,g=this.fixHooks[e];g||(this.fixHooks[e]=g=Z.test(e)?this.mouseHooks:Y.test(e)?this.keyHooks:{}),d=g.props?this.props.concat(g.props):this.props,a=new m.Event(f),b=d.length;while(b--)c=d[b],a[c]=f[c];return a.target||(a.target=f.srcElement||y),3===a.target.nodeType&&(a.target=a.target.parentNode),a.metaKey=!!a.metaKey,g.filter?g.filter(a,f):a},props:"altKey bubbles cancelable ctrlKey currentTarget eventPhase metaKey relatedTarget shiftKey target timeStamp view which".split(" "),fixHooks:{},keyHooks:{props:"char charCode key keyCode".split(" "),filter:function(a,b){return null==a.which&&(a.which=null!=b.charCode?b.charCode:b.keyCode),a}},mouseHooks:{props:"button buttons clientX clientY fromElement offsetX offsetY pageX pageY screenX screenY toElement".split(" "),filter:function(a,b){var c,d,e,f=b.button,g=b.fromElement;return null==a.pageX&&null!=b.clientX&&(d=a.target.ownerDocument||y,e=d.documentElement,c=d.body,a.pageX=b.clientX+(e&&e.scrollLeft||c&&c.scrollLeft||0)-(e&&e.clientLeft||c&&c.clientLeft||0),a.pageY=b.clientY+(e&&e.scrollTop||c&&c.scrollTop||0)-(e&&e.clientTop||c&&c.clientTop||0)),!a.relatedTarget&&g&&(a.relatedTarget=g===a.target?b.toElement:g),a.which||void 0===f||(a.which=1&f?1:2&f?3:4&f?2:0),a}},special:{load:{noBubble:!0},focus:{trigger:function(){if(this!==ca()&&this.focus)try{return this.focus(),!1}catch(a){}},delegateType:"focusin"},blur:{trigger:function(){return this===ca()&&this.blur?(this.blur(),!1):void 0},delegateType:"focusout"},click:{trigger:function(){return m.nodeName(this,"input")&&"checkbox"===this.type&&this.click?(this.click(),!1):void 0},_default:function(a){return m.nodeName(a.target,"a")}},beforeunload:{postDispatch:function(a){void 0!==a.result&&a.originalEvent&&(a.originalEvent.returnValue=a.result)}}},simulate:function(a,b,c,d){var e=m.extend(new m.Event,c,{type:a,isSimulated:!0,originalEvent:{}});d?m.event.trigger(e,null,b):m.event.dispatch.call(b,e),e.isDefaultPrevented()&&c.preventDefault()}},m.removeEvent=y.removeEventListener?function(a,b,c){a.removeEventListener&&a.removeEventListener(b,c,!1)}:function(a,b,c){var d="on"+b;a.detachEvent&&(typeof a[d]===K&&(a[d]=null),a.detachEvent(d,c))},m.Event=function(a,b){return this instanceof m.Event?(a&&a.type?(this.originalEvent=a,this.type=a.type,this.isDefaultPrevented=a.defaultPrevented||void 0===a.defaultPrevented&&a.returnValue===!1?aa:ba):this.type=a,b&&m.extend(this,b),this.timeStamp=a&&a.timeStamp||m.now(),void(this[m.expando]=!0)):new m.Event(a,b)},m.Event.prototype={isDefaultPrevented:ba,isPropagationStopped:ba,isImmediatePropagationStopped:ba,preventDefault:function(){var a=this.originalEvent;this.isDefaultPrevented=aa,a&&(a.preventDefault?a.preventDefault():a.returnValue=!1)},stopPropagation:function(){var a=this.originalEvent;this.isPropagationStopped=aa,a&&(a.stopPropagation&&a.stopPropagation(),a.cancelBubble=!0)},stopImmediatePropagation:function(){var a=this.originalEvent;this.isImmediatePropagationStopped=aa,a&&a.stopImmediatePropagation&&a.stopImmediatePropagation(),this.stopPropagation()}},m.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(a,b){m.event.special[a]={delegateType:b,bindType:b,handle:function(a){var c,d=this,e=a.relatedTarget,f=a.handleObj;return(!e||e!==d&&!m.contains(d,e))&&(a.type=f.origType,c=f.handler.apply(this,arguments),a.type=b),c}}}),k.submitBubbles||(m.event.special.submit={setup:function(){return m.nodeName(this,"form")?!1:void m.event.add(this,"click._submit keypress._submit",function(a){var b=a.target,c=m.nodeName(b,"input")||m.nodeName(b,"button")?b.form:void 0;c&&!m._data(c,"submitBubbles")&&(m.event.add(c,"submit._submit",function(a){a._submit_bubble=!0}),m._data(c,"submitBubbles",!0))})},postDispatch:function(a){a._submit_bubble&&(delete a._submit_bubble,this.parentNode&&!a.isTrigger&&m.event.simulate("submit",this.parentNode,a,!0))},teardown:function(){return m.nodeName(this,"form")?!1:void m.event.remove(this,"._submit")}}),k.changeBubbles||(m.event.special.change={setup:function(){return X.test(this.nodeName)?(("checkbox"===this.type||"radio"===this.type)&&(m.event.add(this,"propertychange._change",function(a){"checked"===a.originalEvent.propertyName&&(this._just_changed=!0)}),m.event.add(this,"click._change",function(a){this._just_changed&&!a.isTrigger&&(this._just_changed=!1),m.event.simulate("change",this,a,!0)})),!1):void m.event.add(this,"beforeactivate._change",function(a){var b=a.target;X.test(b.nodeName)&&!m._data(b,"changeBubbles")&&(m.event.add(b,"change._change",function(a){!this.parentNode||a.isSimulated||a.isTrigger||m.event.simulate("change",this.parentNode,a,!0)}),m._data(b,"changeBubbles",!0))})},handle:function(a){var b=a.target;return this!==b||a.isSimulated||a.isTrigger||"radio"!==b.type&&"checkbox"!==b.type?a.handleObj.handler.apply(this,arguments):void 0},teardown:function(){return m.event.remove(this,"._change"),!X.test(this.nodeName)}}),k.focusinBubbles||m.each({focus:"focusin",blur:"focusout"},function(a,b){var c=function(a){m.event.simulate(b,a.target,m.event.fix(a),!0)};m.event.special[b]={setup:function(){var d=this.ownerDocument||this,e=m._data(d,b);e||d.addEventListener(a,c,!0),m._data(d,b,(e||0)+1)},teardown:function(){var d=this.ownerDocument||this,e=m._data(d,b)-1;e?m._data(d,b,e):(d.removeEventListener(a,c,!0),m._removeData(d,b))}}}),m.fn.extend({on:function(a,b,c,d,e){var f,g;if("object"==typeof a){"string"!=typeof b&&(c=c||b,b=void 0);for(f in a)this.on(f,b,c,a[f],e);return this}if(null==c&&null==d?(d=b,c=b=void 0):null==d&&("string"==typeof b?(d=c,c=void 0):(d=c,c=b,b=void 0)),d===!1)d=ba;else if(!d)return this;return 1===e&&(g=d,d=function(a){return m().off(a),g.apply(this,arguments)},d.guid=g.guid||(g.guid=m.guid++)),this.each(function(){m.event.add(this,a,d,c,b)})},one:function(a,b,c,d){return this.on(a,b,c,d,1)},off:function(a,b,c){var d,e;if(a&&a.preventDefault&&a.handleObj)return d=a.handleObj,m(a.delegateTarget).off(d.namespace?d.origType+"."+d.namespace:d.origType,d.selector,d.handler),this;if("object"==typeof a){for(e in a)this.off(e,b,a[e]);return this}return(b===!1||"function"==typeof b)&&(c=b,b=void 0),c===!1&&(c=ba),this.each(function(){m.event.remove(this,a,c,b)})},trigger:function(a,b){return this.each(function(){m.event.trigger(a,b,this)})},triggerHandler:function(a,b){var c=this[0];return c?m.event.trigger(a,b,c,!0):void 0}});function da(a){var b=ea.split("|"),c=a.createDocumentFragment();if(c.createElement)while(b.length)c.createElement(b.pop());return c}var ea="abbr|article|aside|audio|bdi|canvas|data|datalist|details|figcaption|figure|footer|header|hgroup|mark|meter|nav|output|progress|section|summary|time|video",fa=/ jQuery\d+="(?:null|\d+)"/g,ga=new RegExp("<(?:"+ea+")[\\s/>]","i"),ha=/^\s+/,ia=/<(?!area|br|col|embed|hr|img|input|link|meta|param)(([\w:]+)[^>]*)\/>/gi,ja=/<([\w:]+)/,ka=/<tbody/i,la=/<|&#?\w+;/,ma=/<(?:script|style|link)/i,na=/checked\s*(?:[^=]|=\s*.checked.)/i,oa=/^$|\/(?:java|ecma)script/i,pa=/^true\/(.*)/,qa=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g,ra={option:[1,"<select multiple='multiple'>","</select>"],legend:[1,"<fieldset>","</fieldset>"],area:[1,"<map>","</map>"],param:[1,"<object>","</object>"],thead:[1,"<table>","</table>"],tr:[2,"<table><tbody>","</tbody></table>"],col:[2,"<table><tbody></tbody><colgroup>","</colgroup></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:k.htmlSerialize?[0,"",""]:[1,"X<div>","</div>"]},sa=da(y),ta=sa.appendChild(y.createElement("div"));ra.optgroup=ra.option,ra.tbody=ra.tfoot=ra.colgroup=ra.caption=ra.thead,ra.th=ra.td;function ua(a,b){var c,d,e=0,f=typeof a.getElementsByTagName!==K?a.getElementsByTagName(b||"*"):typeof a.querySelectorAll!==K?a.querySelectorAll(b||"*"):void 0;if(!f)for(f=[],c=a.childNodes||a;null!=(d=c[e]);e++)!b||m.nodeName(d,b)?f.push(d):m.merge(f,ua(d,b));return void 0===b||b&&m.nodeName(a,b)?m.merge([a],f):f}function va(a){W.test(a.type)&&(a.defaultChecked=a.checked)}function wa(a,b){return m.nodeName(a,"table")&&m.nodeName(11!==b.nodeType?b:b.firstChild,"tr")?a.getElementsByTagName("tbody")[0]||a.appendChild(a.ownerDocument.createElement("tbody")):a}function xa(a){return a.type=(null!==m.find.attr(a,"type"))+"/"+a.type,a}function ya(a){var b=pa.exec(a.type);return b?a.type=b[1]:a.removeAttribute("type"),a}function za(a,b){for(var c,d=0;null!=(c=a[d]);d++)m._data(c,"globalEval",!b||m._data(b[d],"globalEval"))}function Aa(a,b){if(1===b.nodeType&&m.hasData(a)){var c,d,e,f=m._data(a),g=m._data(b,f),h=f.events;if(h){delete g.handle,g.events={};for(c in h)for(d=0,e=h[c].length;e>d;d++)m.event.add(b,c,h[c][d])}g.data&&(g.data=m.extend({},g.data))}}function Ba(a,b){var c,d,e;if(1===b.nodeType){if(c=b.nodeName.toLowerCase(),!k.noCloneEvent&&b[m.expando]){e=m._data(b);for(d in e.events)m.removeEvent(b,d,e.handle);b.removeAttribute(m.expando)}"script"===c&&b.text!==a.text?(xa(b).text=a.text,ya(b)):"object"===c?(b.parentNode&&(b.outerHTML=a.outerHTML),k.html5Clone&&a.innerHTML&&!m.trim(b.innerHTML)&&(b.innerHTML=a.innerHTML)):"input"===c&&W.test(a.type)?(b.defaultChecked=b.checked=a.checked,b.value!==a.value&&(b.value=a.value)):"option"===c?b.defaultSelected=b.selected=a.defaultSelected:("input"===c||"textarea"===c)&&(b.defaultValue=a.defaultValue)}}m.extend({clone:function(a,b,c){var d,e,f,g,h,i=m.contains(a.ownerDocument,a);if(k.html5Clone||m.isXMLDoc(a)||!ga.test("<"+a.nodeName+">")?f=a.cloneNode(!0):(ta.innerHTML=a.outerHTML,ta.removeChild(f=ta.firstChild)),!(k.noCloneEvent&&k.noCloneChecked||1!==a.nodeType&&11!==a.nodeType||m.isXMLDoc(a)))for(d=ua(f),h=ua(a),g=0;null!=(e=h[g]);++g)d[g]&&Ba(e,d[g]);if(b)if(c)for(h=h||ua(a),d=d||ua(f),g=0;null!=(e=h[g]);g++)Aa(e,d[g]);else Aa(a,f);return d=ua(f,"script"),d.length>0&&za(d,!i&&ua(a,"script")),d=h=e=null,f},buildFragment:function(a,b,c,d){for(var e,f,g,h,i,j,l,n=a.length,o=da(b),p=[],q=0;n>q;q++)if(f=a[q],f||0===f)if("object"===m.type(f))m.merge(p,f.nodeType?[f]:f);else if(la.test(f)){h=h||o.appendChild(b.createElement("div")),i=(ja.exec(f)||["",""])[1].toLowerCase(),l=ra[i]||ra._default,h.innerHTML=l[1]+f.replace(ia,"<$1></$2>")+l[2],e=l[0];while(e--)h=h.lastChild;if(!k.leadingWhitespace&&ha.test(f)&&p.push(b.createTextNode(ha.exec(f)[0])),!k.tbody){f="table"!==i||ka.test(f)?"<table>"!==l[1]||ka.test(f)?0:h:h.firstChild,e=f&&f.childNodes.length;while(e--)m.nodeName(j=f.childNodes[e],"tbody")&&!j.childNodes.length&&f.removeChild(j)}m.merge(p,h.childNodes),h.textContent="";while(h.firstChild)h.removeChild(h.firstChild);h=o.lastChild}else p.push(b.createTextNode(f));h&&o.removeChild(h),k.appendChecked||m.grep(ua(p,"input"),va),q=0;while(f=p[q++])if((!d||-1===m.inArray(f,d))&&(g=m.contains(f.ownerDocument,f),h=ua(o.appendChild(f),"script"),g&&za(h),c)){e=0;while(f=h[e++])oa.test(f.type||"")&&c.push(f)}return h=null,o},cleanData:function(a,b){for(var d,e,f,g,h=0,i=m.expando,j=m.cache,l=k.deleteExpando,n=m.event.special;null!=(d=a[h]);h++)if((b||m.acceptData(d))&&(f=d[i],g=f&&j[f])){if(g.events)for(e in g.events)n[e]?m.event.remove(d,e):m.removeEvent(d,e,g.handle);j[f]&&(delete j[f],l?delete d[i]:typeof d.removeAttribute!==K?d.removeAttribute(i):d[i]=null,c.push(f))}}}),m.fn.extend({text:function(a){return V(this,function(a){return void 0===a?m.text(this):this.empty().append((this[0]&&this[0].ownerDocument||y).createTextNode(a))},null,a,arguments.length)},append:function(){return this.domManip(arguments,function(a){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var b=wa(this,a);b.appendChild(a)}})},prepend:function(){return this.domManip(arguments,function(a){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var b=wa(this,a);b.insertBefore(a,b.firstChild)}})},before:function(){return this.domManip(arguments,function(a){this.parentNode&&this.parentNode.insertBefore(a,this)})},after:function(){return this.domManip(arguments,function(a){this.parentNode&&this.parentNode.insertBefore(a,this.nextSibling)})},remove:function(a,b){for(var c,d=a?m.filter(a,this):this,e=0;null!=(c=d[e]);e++)b||1!==c.nodeType||m.cleanData(ua(c)),c.parentNode&&(b&&m.contains(c.ownerDocument,c)&&za(ua(c,"script")),c.parentNode.removeChild(c));return this},empty:function(){for(var a,b=0;null!=(a=this[b]);b++){1===a.nodeType&&m.cleanData(ua(a,!1));while(a.firstChild)a.removeChild(a.firstChild);a.options&&m.nodeName(a,"select")&&(a.options.length=0)}return this},clone:function(a,b){return a=null==a?!1:a,b=null==b?a:b,this.map(function(){return m.clone(this,a,b)})},html:function(a){return V(this,function(a){var b=this[0]||{},c=0,d=this.length;if(void 0===a)return 1===b.nodeType?b.innerHTML.replace(fa,""):void 0;if(!("string"!=typeof a||ma.test(a)||!k.htmlSerialize&&ga.test(a)||!k.leadingWhitespace&&ha.test(a)||ra[(ja.exec(a)||["",""])[1].toLowerCase()])){a=a.replace(ia,"<$1></$2>");try{for(;d>c;c++)b=this[c]||{},1===b.nodeType&&(m.cleanData(ua(b,!1)),b.innerHTML=a);b=0}catch(e){}}b&&this.empty().append(a)},null,a,arguments.length)},replaceWith:function(){var a=arguments[0];return this.domManip(arguments,function(b){a=this.parentNode,m.cleanData(ua(this)),a&&a.replaceChild(b,this)}),a&&(a.length||a.nodeType)?this:this.remove()},detach:function(a){return this.remove(a,!0)},domManip:function(a,b){a=e.apply([],a);var c,d,f,g,h,i,j=0,l=this.length,n=this,o=l-1,p=a[0],q=m.isFunction(p);if(q||l>1&&"string"==typeof p&&!k.checkClone&&na.test(p))return this.each(function(c){var d=n.eq(c);q&&(a[0]=p.call(this,c,d.html())),d.domManip(a,b)});if(l&&(i=m.buildFragment(a,this[0].ownerDocument,!1,this),c=i.firstChild,1===i.childNodes.length&&(i=c),c)){for(g=m.map(ua(i,"script"),xa),f=g.length;l>j;j++)d=i,j!==o&&(d=m.clone(d,!0,!0),f&&m.merge(g,ua(d,"script"))),b.call(this[j],d,j);if(f)for(h=g[g.length-1].ownerDocument,m.map(g,ya),j=0;f>j;j++)d=g[j],oa.test(d.type||"")&&!m._data(d,"globalEval")&&m.contains(h,d)&&(d.src?m._evalUrl&&m._evalUrl(d.src):m.globalEval((d.text||d.textContent||d.innerHTML||"").replace(qa,"")));i=c=null}return this}}),m.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(a,b){m.fn[a]=function(a){for(var c,d=0,e=[],g=m(a),h=g.length-1;h>=d;d++)c=d===h?this:this.clone(!0),m(g[d])[b](c),f.apply(e,c.get());return this.pushStack(e)}});var Ca,Da={};function Ea(b,c){var d,e=m(c.createElement(b)).appendTo(c.body),f=a.getDefaultComputedStyle&&(d=a.getDefaultComputedStyle(e[0]))?d.display:m.css(e[0],"display");return e.detach(),f}function Fa(a){var b=y,c=Da[a];return c||(c=Ea(a,b),"none"!==c&&c||(Ca=(Ca||m("<iframe frameborder='0' width='0' height='0'/>")).appendTo(b.documentElement),b=(Ca[0].contentWindow||Ca[0].contentDocument).document,b.write(),b.close(),c=Ea(a,b),Ca.detach()),Da[a]=c),c}!function(){var a;k.shrinkWrapBlocks=function(){if(null!=a)return a;a=!1;var b,c,d;return c=y.getElementsByTagName("body")[0],c&&c.style?(b=y.createElement("div"),d=y.createElement("div"),d.style.cssText="position:absolute;border:0;width:0;height:0;top:0;left:-9999px",c.appendChild(d).appendChild(b),typeof b.style.zoom!==K&&(b.style.cssText="-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;display:block;margin:0;border:0;padding:1px;width:1px;zoom:1",b.appendChild(y.createElement("div")).style.width="5px",a=3!==b.offsetWidth),c.removeChild(d),a):void 0}}();var Ga=/^margin/,Ha=new RegExp("^("+S+")(?!px)[a-z%]+$","i"),Ia,Ja,Ka=/^(top|right|bottom|left)$/;a.getComputedStyle?(Ia=function(b){return b.ownerDocument.defaultView.opener?b.ownerDocument.defaultView.getComputedStyle(b,null):a.getComputedStyle(b,null)},Ja=function(a,b,c){var d,e,f,g,h=a.style;return c=c||Ia(a),g=c?c.getPropertyValue(b)||c[b]:void 0,c&&(""!==g||m.contains(a.ownerDocument,a)||(g=m.style(a,b)),Ha.test(g)&&Ga.test(b)&&(d=h.width,e=h.minWidth,f=h.maxWidth,h.minWidth=h.maxWidth=h.width=g,g=c.width,h.width=d,h.minWidth=e,h.maxWidth=f)),void 0===g?g:g+""}):y.documentElement.currentStyle&&(Ia=function(a){return a.currentStyle},Ja=function(a,b,c){var d,e,f,g,h=a.style;return c=c||Ia(a),g=c?c[b]:void 0,null==g&&h&&h[b]&&(g=h[b]),Ha.test(g)&&!Ka.test(b)&&(d=h.left,e=a.runtimeStyle,f=e&&e.left,f&&(e.left=a.currentStyle.left),h.left="fontSize"===b?"1em":g,g=h.pixelLeft+"px",h.left=d,f&&(e.left=f)),void 0===g?g:g+""||"auto"});function La(a,b){return{get:function(){var c=a();if(null!=c)return c?void delete this.get:(this.get=b).apply(this,arguments)}}}!function(){var b,c,d,e,f,g,h;if(b=y.createElement("div"),b.innerHTML="  <link/><table></table><a href='/a'>a</a><input type='checkbox'/>",d=b.getElementsByTagName("a")[0],c=d&&d.style){c.cssText="float:left;opacity:.5",k.opacity="0.5"===c.opacity,k.cssFloat=!!c.cssFloat,b.style.backgroundClip="content-box",b.cloneNode(!0).style.backgroundClip="",k.clearCloneStyle="content-box"===b.style.backgroundClip,k.boxSizing=""===c.boxSizing||""===c.MozBoxSizing||""===c.WebkitBoxSizing,m.extend(k,{reliableHiddenOffsets:function(){return null==g&&i(),g},boxSizingReliable:function(){return null==f&&i(),f},pixelPosition:function(){return null==e&&i(),e},reliableMarginRight:function(){return null==h&&i(),h}});function i(){var b,c,d,i;c=y.getElementsByTagName("body")[0],c&&c.style&&(b=y.createElement("div"),d=y.createElement("div"),d.style.cssText="position:absolute;border:0;width:0;height:0;top:0;left:-9999px",c.appendChild(d).appendChild(b),b.style.cssText="-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;display:block;margin-top:1%;top:1%;border:1px;padding:1px;width:4px;position:absolute",e=f=!1,h=!0,a.getComputedStyle&&(e="1%"!==(a.getComputedStyle(b,null)||{}).top,f="4px"===(a.getComputedStyle(b,null)||{width:"4px"}).width,i=b.appendChild(y.createElement("div")),i.style.cssText=b.style.cssText="-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;display:block;margin:0;border:0;padding:0",i.style.marginRight=i.style.width="0",b.style.width="1px",h=!parseFloat((a.getComputedStyle(i,null)||{}).marginRight),b.removeChild(i)),b.innerHTML="<table><tr><td></td><td>t</td></tr></table>",i=b.getElementsByTagName("td"),i[0].style.cssText="margin:0;border:0;padding:0;display:none",g=0===i[0].offsetHeight,g&&(i[0].style.display="",i[1].style.display="none",g=0===i[0].offsetHeight),c.removeChild(d))}}}(),m.swap=function(a,b,c,d){var e,f,g={};for(f in b)g[f]=a.style[f],a.style[f]=b[f];e=c.apply(a,d||[]);for(f in b)a.style[f]=g[f];return e};var Ma=/alpha\([^)]*\)/i,Na=/opacity\s*=\s*([^)]*)/,Oa=/^(none|table(?!-c[ea]).+)/,Pa=new RegExp("^("+S+")(.*)$","i"),Qa=new RegExp("^([+-])=("+S+")","i"),Ra={position:"absolute",visibility:"hidden",display:"block"},Sa={letterSpacing:"0",fontWeight:"400"},Ta=["Webkit","O","Moz","ms"];function Ua(a,b){if(b in a)return b;var c=b.charAt(0).toUpperCase()+b.slice(1),d=b,e=Ta.length;while(e--)if(b=Ta[e]+c,b in a)return b;return d}function Va(a,b){for(var c,d,e,f=[],g=0,h=a.length;h>g;g++)d=a[g],d.style&&(f[g]=m._data(d,"olddisplay"),c=d.style.display,b?(f[g]||"none"!==c||(d.style.display=""),""===d.style.display&&U(d)&&(f[g]=m._data(d,"olddisplay",Fa(d.nodeName)))):(e=U(d),(c&&"none"!==c||!e)&&m._data(d,"olddisplay",e?c:m.css(d,"display"))));for(g=0;h>g;g++)d=a[g],d.style&&(b&&"none"!==d.style.display&&""!==d.style.display||(d.style.display=b?f[g]||"":"none"));return a}function Wa(a,b,c){var d=Pa.exec(b);return d?Math.max(0,d[1]-(c||0))+(d[2]||"px"):b}function Xa(a,b,c,d,e){for(var f=c===(d?"border":"content")?4:"width"===b?1:0,g=0;4>f;f+=2)"margin"===c&&(g+=m.css(a,c+T[f],!0,e)),d?("content"===c&&(g-=m.css(a,"padding"+T[f],!0,e)),"margin"!==c&&(g-=m.css(a,"border"+T[f]+"Width",!0,e))):(g+=m.css(a,"padding"+T[f],!0,e),"padding"!==c&&(g+=m.css(a,"border"+T[f]+"Width",!0,e)));return g}function Ya(a,b,c){var d=!0,e="width"===b?a.offsetWidth:a.offsetHeight,f=Ia(a),g=k.boxSizing&&"border-box"===m.css(a,"boxSizing",!1,f);if(0>=e||null==e){if(e=Ja(a,b,f),(0>e||null==e)&&(e=a.style[b]),Ha.test(e))return e;d=g&&(k.boxSizingReliable()||e===a.style[b]),e=parseFloat(e)||0}return e+Xa(a,b,c||(g?"border":"content"),d,f)+"px"}m.extend({cssHooks:{opacity:{get:function(a,b){if(b){var c=Ja(a,"opacity");return""===c?"1":c}}}},cssNumber:{columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{"float":k.cssFloat?"cssFloat":"styleFloat"},style:function(a,b,c,d){if(a&&3!==a.nodeType&&8!==a.nodeType&&a.style){var e,f,g,h=m.camelCase(b),i=a.style;if(b=m.cssProps[h]||(m.cssProps[h]=Ua(i,h)),g=m.cssHooks[b]||m.cssHooks[h],void 0===c)return g&&"get"in g&&void 0!==(e=g.get(a,!1,d))?e:i[b];if(f=typeof c,"string"===f&&(e=Qa.exec(c))&&(c=(e[1]+1)*e[2]+parseFloat(m.css(a,b)),f="number"),null!=c&&c===c&&("number"!==f||m.cssNumber[h]||(c+="px"),k.clearCloneStyle||""!==c||0!==b.indexOf("background")||(i[b]="inherit"),!(g&&"set"in g&&void 0===(c=g.set(a,c,d)))))try{i[b]=c}catch(j){}}},css:function(a,b,c,d){var e,f,g,h=m.camelCase(b);return b=m.cssProps[h]||(m.cssProps[h]=Ua(a.style,h)),g=m.cssHooks[b]||m.cssHooks[h],g&&"get"in g&&(f=g.get(a,!0,c)),void 0===f&&(f=Ja(a,b,d)),"normal"===f&&b in Sa&&(f=Sa[b]),""===c||c?(e=parseFloat(f),c===!0||m.isNumeric(e)?e||0:f):f}}),m.each(["height","width"],function(a,b){m.cssHooks[b]={get:function(a,c,d){return c?Oa.test(m.css(a,"display"))&&0===a.offsetWidth?m.swap(a,Ra,function(){return Ya(a,b,d)}):Ya(a,b,d):void 0},set:function(a,c,d){var e=d&&Ia(a);return Wa(a,c,d?Xa(a,b,d,k.boxSizing&&"border-box"===m.css(a,"boxSizing",!1,e),e):0)}}}),k.opacity||(m.cssHooks.opacity={get:function(a,b){return Na.test((b&&a.currentStyle?a.currentStyle.filter:a.style.filter)||"")?.01*parseFloat(RegExp.$1)+"":b?"1":""},set:function(a,b){var c=a.style,d=a.currentStyle,e=m.isNumeric(b)?"alpha(opacity="+100*b+")":"",f=d&&d.filter||c.filter||"";c.zoom=1,(b>=1||""===b)&&""===m.trim(f.replace(Ma,""))&&c.removeAttribute&&(c.removeAttribute("filter"),""===b||d&&!d.filter)||(c.filter=Ma.test(f)?f.replace(Ma,e):f+" "+e)}}),m.cssHooks.marginRight=La(k.reliableMarginRight,function(a,b){return b?m.swap(a,{display:"inline-block"},Ja,[a,"marginRight"]):void 0}),m.each({margin:"",padding:"",border:"Width"},function(a,b){m.cssHooks[a+b]={expand:function(c){for(var d=0,e={},f="string"==typeof c?c.split(" "):[c];4>d;d++)e[a+T[d]+b]=f[d]||f[d-2]||f[0];return e}},Ga.test(a)||(m.cssHooks[a+b].set=Wa)}),m.fn.extend({css:function(a,b){return V(this,function(a,b,c){var d,e,f={},g=0;if(m.isArray(b)){for(d=Ia(a),e=b.length;e>g;g++)f[b[g]]=m.css(a,b[g],!1,d);return f}return void 0!==c?m.style(a,b,c):m.css(a,b)},a,b,arguments.length>1)},show:function(){return Va(this,!0)},hide:function(){return Va(this)},toggle:function(a){return"boolean"==typeof a?a?this.show():this.hide():this.each(function(){U(this)?m(this).show():m(this).hide()})}});function Za(a,b,c,d,e){
return new Za.prototype.init(a,b,c,d,e)}m.Tween=Za,Za.prototype={constructor:Za,init:function(a,b,c,d,e,f){this.elem=a,this.prop=c,this.easing=e||"swing",this.options=b,this.start=this.now=this.cur(),this.end=d,this.unit=f||(m.cssNumber[c]?"":"px")},cur:function(){var a=Za.propHooks[this.prop];return a&&a.get?a.get(this):Za.propHooks._default.get(this)},run:function(a){var b,c=Za.propHooks[this.prop];return this.options.duration?this.pos=b=m.easing[this.easing](a,this.options.duration*a,0,1,this.options.duration):this.pos=b=a,this.now=(this.end-this.start)*b+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),c&&c.set?c.set(this):Za.propHooks._default.set(this),this}},Za.prototype.init.prototype=Za.prototype,Za.propHooks={_default:{get:function(a){var b;return null==a.elem[a.prop]||a.elem.style&&null!=a.elem.style[a.prop]?(b=m.css(a.elem,a.prop,""),b&&"auto"!==b?b:0):a.elem[a.prop]},set:function(a){m.fx.step[a.prop]?m.fx.step[a.prop](a):a.elem.style&&(null!=a.elem.style[m.cssProps[a.prop]]||m.cssHooks[a.prop])?m.style(a.elem,a.prop,a.now+a.unit):a.elem[a.prop]=a.now}}},Za.propHooks.scrollTop=Za.propHooks.scrollLeft={set:function(a){a.elem.nodeType&&a.elem.parentNode&&(a.elem[a.prop]=a.now)}},m.easing={linear:function(a){return a},swing:function(a){return.5-Math.cos(a*Math.PI)/2}},m.fx=Za.prototype.init,m.fx.step={};var $a,_a,ab=/^(?:toggle|show|hide)$/,bb=new RegExp("^(?:([+-])=|)("+S+")([a-z%]*)$","i"),cb=/queueHooks$/,db=[ib],eb={"*":[function(a,b){var c=this.createTween(a,b),d=c.cur(),e=bb.exec(b),f=e&&e[3]||(m.cssNumber[a]?"":"px"),g=(m.cssNumber[a]||"px"!==f&&+d)&&bb.exec(m.css(c.elem,a)),h=1,i=20;if(g&&g[3]!==f){f=f||g[3],e=e||[],g=+d||1;do h=h||".5",g/=h,m.style(c.elem,a,g+f);while(h!==(h=c.cur()/d)&&1!==h&&--i)}return e&&(g=c.start=+g||+d||0,c.unit=f,c.end=e[1]?g+(e[1]+1)*e[2]:+e[2]),c}]};function fb(){return setTimeout(function(){$a=void 0}),$a=m.now()}function gb(a,b){var c,d={height:a},e=0;for(b=b?1:0;4>e;e+=2-b)c=T[e],d["margin"+c]=d["padding"+c]=a;return b&&(d.opacity=d.width=a),d}function hb(a,b,c){for(var d,e=(eb[b]||[]).concat(eb["*"]),f=0,g=e.length;g>f;f++)if(d=e[f].call(c,b,a))return d}function ib(a,b,c){var d,e,f,g,h,i,j,l,n=this,o={},p=a.style,q=a.nodeType&&U(a),r=m._data(a,"fxshow");c.queue||(h=m._queueHooks(a,"fx"),null==h.unqueued&&(h.unqueued=0,i=h.empty.fire,h.empty.fire=function(){h.unqueued||i()}),h.unqueued++,n.always(function(){n.always(function(){h.unqueued--,m.queue(a,"fx").length||h.empty.fire()})})),1===a.nodeType&&("height"in b||"width"in b)&&(c.overflow=[p.overflow,p.overflowX,p.overflowY],j=m.css(a,"display"),l="none"===j?m._data(a,"olddisplay")||Fa(a.nodeName):j,"inline"===l&&"none"===m.css(a,"float")&&(k.inlineBlockNeedsLayout&&"inline"!==Fa(a.nodeName)?p.zoom=1:p.display="inline-block")),c.overflow&&(p.overflow="hidden",k.shrinkWrapBlocks()||n.always(function(){p.overflow=c.overflow[0],p.overflowX=c.overflow[1],p.overflowY=c.overflow[2]}));for(d in b)if(e=b[d],ab.exec(e)){if(delete b[d],f=f||"toggle"===e,e===(q?"hide":"show")){if("show"!==e||!r||void 0===r[d])continue;q=!0}o[d]=r&&r[d]||m.style(a,d)}else j=void 0;if(m.isEmptyObject(o))"inline"===("none"===j?Fa(a.nodeName):j)&&(p.display=j);else{r?"hidden"in r&&(q=r.hidden):r=m._data(a,"fxshow",{}),f&&(r.hidden=!q),q?m(a).show():n.done(function(){m(a).hide()}),n.done(function(){var b;m._removeData(a,"fxshow");for(b in o)m.style(a,b,o[b])});for(d in o)g=hb(q?r[d]:0,d,n),d in r||(r[d]=g.start,q&&(g.end=g.start,g.start="width"===d||"height"===d?1:0))}}function jb(a,b){var c,d,e,f,g;for(c in a)if(d=m.camelCase(c),e=b[d],f=a[c],m.isArray(f)&&(e=f[1],f=a[c]=f[0]),c!==d&&(a[d]=f,delete a[c]),g=m.cssHooks[d],g&&"expand"in g){f=g.expand(f),delete a[d];for(c in f)c in a||(a[c]=f[c],b[c]=e)}else b[d]=e}function kb(a,b,c){var d,e,f=0,g=db.length,h=m.Deferred().always(function(){delete i.elem}),i=function(){if(e)return!1;for(var b=$a||fb(),c=Math.max(0,j.startTime+j.duration-b),d=c/j.duration||0,f=1-d,g=0,i=j.tweens.length;i>g;g++)j.tweens[g].run(f);return h.notifyWith(a,[j,f,c]),1>f&&i?c:(h.resolveWith(a,[j]),!1)},j=h.promise({elem:a,props:m.extend({},b),opts:m.extend(!0,{specialEasing:{}},c),originalProperties:b,originalOptions:c,startTime:$a||fb(),duration:c.duration,tweens:[],createTween:function(b,c){var d=m.Tween(a,j.opts,b,c,j.opts.specialEasing[b]||j.opts.easing);return j.tweens.push(d),d},stop:function(b){var c=0,d=b?j.tweens.length:0;if(e)return this;for(e=!0;d>c;c++)j.tweens[c].run(1);return b?h.resolveWith(a,[j,b]):h.rejectWith(a,[j,b]),this}}),k=j.props;for(jb(k,j.opts.specialEasing);g>f;f++)if(d=db[f].call(j,a,k,j.opts))return d;return m.map(k,hb,j),m.isFunction(j.opts.start)&&j.opts.start.call(a,j),m.fx.timer(m.extend(i,{elem:a,anim:j,queue:j.opts.queue})),j.progress(j.opts.progress).done(j.opts.done,j.opts.complete).fail(j.opts.fail).always(j.opts.always)}m.Animation=m.extend(kb,{tweener:function(a,b){m.isFunction(a)?(b=a,a=["*"]):a=a.split(" ");for(var c,d=0,e=a.length;e>d;d++)c=a[d],eb[c]=eb[c]||[],eb[c].unshift(b)},prefilter:function(a,b){b?db.unshift(a):db.push(a)}}),m.speed=function(a,b,c){var d=a&&"object"==typeof a?m.extend({},a):{complete:c||!c&&b||m.isFunction(a)&&a,duration:a,easing:c&&b||b&&!m.isFunction(b)&&b};return d.duration=m.fx.off?0:"number"==typeof d.duration?d.duration:d.duration in m.fx.speeds?m.fx.speeds[d.duration]:m.fx.speeds._default,(null==d.queue||d.queue===!0)&&(d.queue="fx"),d.old=d.complete,d.complete=function(){m.isFunction(d.old)&&d.old.call(this),d.queue&&m.dequeue(this,d.queue)},d},m.fn.extend({fadeTo:function(a,b,c,d){return this.filter(U).css("opacity",0).show().end().animate({opacity:b},a,c,d)},animate:function(a,b,c,d){var e=m.isEmptyObject(a),f=m.speed(b,c,d),g=function(){var b=kb(this,m.extend({},a),f);(e||m._data(this,"finish"))&&b.stop(!0)};return g.finish=g,e||f.queue===!1?this.each(g):this.queue(f.queue,g)},stop:function(a,b,c){var d=function(a){var b=a.stop;delete a.stop,b(c)};return"string"!=typeof a&&(c=b,b=a,a=void 0),b&&a!==!1&&this.queue(a||"fx",[]),this.each(function(){var b=!0,e=null!=a&&a+"queueHooks",f=m.timers,g=m._data(this);if(e)g[e]&&g[e].stop&&d(g[e]);else for(e in g)g[e]&&g[e].stop&&cb.test(e)&&d(g[e]);for(e=f.length;e--;)f[e].elem!==this||null!=a&&f[e].queue!==a||(f[e].anim.stop(c),b=!1,f.splice(e,1));(b||!c)&&m.dequeue(this,a)})},finish:function(a){return a!==!1&&(a=a||"fx"),this.each(function(){var b,c=m._data(this),d=c[a+"queue"],e=c[a+"queueHooks"],f=m.timers,g=d?d.length:0;for(c.finish=!0,m.queue(this,a,[]),e&&e.stop&&e.stop.call(this,!0),b=f.length;b--;)f[b].elem===this&&f[b].queue===a&&(f[b].anim.stop(!0),f.splice(b,1));for(b=0;g>b;b++)d[b]&&d[b].finish&&d[b].finish.call(this);delete c.finish})}}),m.each(["toggle","show","hide"],function(a,b){var c=m.fn[b];m.fn[b]=function(a,d,e){return null==a||"boolean"==typeof a?c.apply(this,arguments):this.animate(gb(b,!0),a,d,e)}}),m.each({slideDown:gb("show"),slideUp:gb("hide"),slideToggle:gb("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(a,b){m.fn[a]=function(a,c,d){return this.animate(b,a,c,d)}}),m.timers=[],m.fx.tick=function(){var a,b=m.timers,c=0;for($a=m.now();c<b.length;c++)a=b[c],a()||b[c]!==a||b.splice(c--,1);b.length||m.fx.stop(),$a=void 0},m.fx.timer=function(a){m.timers.push(a),a()?m.fx.start():m.timers.pop()},m.fx.interval=13,m.fx.start=function(){_a||(_a=setInterval(m.fx.tick,m.fx.interval))},m.fx.stop=function(){clearInterval(_a),_a=null},m.fx.speeds={slow:600,fast:200,_default:400},m.fn.delay=function(a,b){return a=m.fx?m.fx.speeds[a]||a:a,b=b||"fx",this.queue(b,function(b,c){var d=setTimeout(b,a);c.stop=function(){clearTimeout(d)}})},function(){var a,b,c,d,e;b=y.createElement("div"),b.setAttribute("className","t"),b.innerHTML="  <link/><table></table><a href='/a'>a</a><input type='checkbox'/>",d=b.getElementsByTagName("a")[0],c=y.createElement("select"),e=c.appendChild(y.createElement("option")),a=b.getElementsByTagName("input")[0],d.style.cssText="top:1px",k.getSetAttribute="t"!==b.className,k.style=/top/.test(d.getAttribute("style")),k.hrefNormalized="/a"===d.getAttribute("href"),k.checkOn=!!a.value,k.optSelected=e.selected,k.enctype=!!y.createElement("form").enctype,c.disabled=!0,k.optDisabled=!e.disabled,a=y.createElement("input"),a.setAttribute("value",""),k.input=""===a.getAttribute("value"),a.value="t",a.setAttribute("type","radio"),k.radioValue="t"===a.value}();var lb=/\r/g;m.fn.extend({val:function(a){var b,c,d,e=this[0];{if(arguments.length)return d=m.isFunction(a),this.each(function(c){var e;1===this.nodeType&&(e=d?a.call(this,c,m(this).val()):a,null==e?e="":"number"==typeof e?e+="":m.isArray(e)&&(e=m.map(e,function(a){return null==a?"":a+""})),b=m.valHooks[this.type]||m.valHooks[this.nodeName.toLowerCase()],b&&"set"in b&&void 0!==b.set(this,e,"value")||(this.value=e))});if(e)return b=m.valHooks[e.type]||m.valHooks[e.nodeName.toLowerCase()],b&&"get"in b&&void 0!==(c=b.get(e,"value"))?c:(c=e.value,"string"==typeof c?c.replace(lb,""):null==c?"":c)}}}),m.extend({valHooks:{option:{get:function(a){var b=m.find.attr(a,"value");return null!=b?b:m.trim(m.text(a))}},select:{get:function(a){for(var b,c,d=a.options,e=a.selectedIndex,f="select-one"===a.type||0>e,g=f?null:[],h=f?e+1:d.length,i=0>e?h:f?e:0;h>i;i++)if(c=d[i],!(!c.selected&&i!==e||(k.optDisabled?c.disabled:null!==c.getAttribute("disabled"))||c.parentNode.disabled&&m.nodeName(c.parentNode,"optgroup"))){if(b=m(c).val(),f)return b;g.push(b)}return g},set:function(a,b){var c,d,e=a.options,f=m.makeArray(b),g=e.length;while(g--)if(d=e[g],m.inArray(m.valHooks.option.get(d),f)>=0)try{d.selected=c=!0}catch(h){d.scrollHeight}else d.selected=!1;return c||(a.selectedIndex=-1),e}}}}),m.each(["radio","checkbox"],function(){m.valHooks[this]={set:function(a,b){return m.isArray(b)?a.checked=m.inArray(m(a).val(),b)>=0:void 0}},k.checkOn||(m.valHooks[this].get=function(a){return null===a.getAttribute("value")?"on":a.value})});var mb,nb,ob=m.expr.attrHandle,pb=/^(?:checked|selected)$/i,qb=k.getSetAttribute,rb=k.input;m.fn.extend({attr:function(a,b){return V(this,m.attr,a,b,arguments.length>1)},removeAttr:function(a){return this.each(function(){m.removeAttr(this,a)})}}),m.extend({attr:function(a,b,c){var d,e,f=a.nodeType;if(a&&3!==f&&8!==f&&2!==f)return typeof a.getAttribute===K?m.prop(a,b,c):(1===f&&m.isXMLDoc(a)||(b=b.toLowerCase(),d=m.attrHooks[b]||(m.expr.match.bool.test(b)?nb:mb)),void 0===c?d&&"get"in d&&null!==(e=d.get(a,b))?e:(e=m.find.attr(a,b),null==e?void 0:e):null!==c?d&&"set"in d&&void 0!==(e=d.set(a,c,b))?e:(a.setAttribute(b,c+""),c):void m.removeAttr(a,b))},removeAttr:function(a,b){var c,d,e=0,f=b&&b.match(E);if(f&&1===a.nodeType)while(c=f[e++])d=m.propFix[c]||c,m.expr.match.bool.test(c)?rb&&qb||!pb.test(c)?a[d]=!1:a[m.camelCase("default-"+c)]=a[d]=!1:m.attr(a,c,""),a.removeAttribute(qb?c:d)},attrHooks:{type:{set:function(a,b){if(!k.radioValue&&"radio"===b&&m.nodeName(a,"input")){var c=a.value;return a.setAttribute("type",b),c&&(a.value=c),b}}}}}),nb={set:function(a,b,c){return b===!1?m.removeAttr(a,c):rb&&qb||!pb.test(c)?a.setAttribute(!qb&&m.propFix[c]||c,c):a[m.camelCase("default-"+c)]=a[c]=!0,c}},m.each(m.expr.match.bool.source.match(/\w+/g),function(a,b){var c=ob[b]||m.find.attr;ob[b]=rb&&qb||!pb.test(b)?function(a,b,d){var e,f;return d||(f=ob[b],ob[b]=e,e=null!=c(a,b,d)?b.toLowerCase():null,ob[b]=f),e}:function(a,b,c){return c?void 0:a[m.camelCase("default-"+b)]?b.toLowerCase():null}}),rb&&qb||(m.attrHooks.value={set:function(a,b,c){return m.nodeName(a,"input")?void(a.defaultValue=b):mb&&mb.set(a,b,c)}}),qb||(mb={set:function(a,b,c){var d=a.getAttributeNode(c);return d||a.setAttributeNode(d=a.ownerDocument.createAttribute(c)),d.value=b+="","value"===c||b===a.getAttribute(c)?b:void 0}},ob.id=ob.name=ob.coords=function(a,b,c){var d;return c?void 0:(d=a.getAttributeNode(b))&&""!==d.value?d.value:null},m.valHooks.button={get:function(a,b){var c=a.getAttributeNode(b);return c&&c.specified?c.value:void 0},set:mb.set},m.attrHooks.contenteditable={set:function(a,b,c){mb.set(a,""===b?!1:b,c)}},m.each(["width","height"],function(a,b){m.attrHooks[b]={set:function(a,c){return""===c?(a.setAttribute(b,"auto"),c):void 0}}})),k.style||(m.attrHooks.style={get:function(a){return a.style.cssText||void 0},set:function(a,b){return a.style.cssText=b+""}});var sb=/^(?:input|select|textarea|button|object)$/i,tb=/^(?:a|area)$/i;m.fn.extend({prop:function(a,b){return V(this,m.prop,a,b,arguments.length>1)},removeProp:function(a){return a=m.propFix[a]||a,this.each(function(){try{this[a]=void 0,delete this[a]}catch(b){}})}}),m.extend({propFix:{"for":"htmlFor","class":"className"},prop:function(a,b,c){var d,e,f,g=a.nodeType;if(a&&3!==g&&8!==g&&2!==g)return f=1!==g||!m.isXMLDoc(a),f&&(b=m.propFix[b]||b,e=m.propHooks[b]),void 0!==c?e&&"set"in e&&void 0!==(d=e.set(a,c,b))?d:a[b]=c:e&&"get"in e&&null!==(d=e.get(a,b))?d:a[b]},propHooks:{tabIndex:{get:function(a){var b=m.find.attr(a,"tabindex");return b?parseInt(b,10):sb.test(a.nodeName)||tb.test(a.nodeName)&&a.href?0:-1}}}}),k.hrefNormalized||m.each(["href","src"],function(a,b){m.propHooks[b]={get:function(a){return a.getAttribute(b,4)}}}),k.optSelected||(m.propHooks.selected={get:function(a){var b=a.parentNode;return b&&(b.selectedIndex,b.parentNode&&b.parentNode.selectedIndex),null}}),m.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){m.propFix[this.toLowerCase()]=this}),k.enctype||(m.propFix.enctype="encoding");var ub=/[\t\r\n\f]/g;m.fn.extend({addClass:function(a){var b,c,d,e,f,g,h=0,i=this.length,j="string"==typeof a&&a;if(m.isFunction(a))return this.each(function(b){m(this).addClass(a.call(this,b,this.className))});if(j)for(b=(a||"").match(E)||[];i>h;h++)if(c=this[h],d=1===c.nodeType&&(c.className?(" "+c.className+" ").replace(ub," "):" ")){f=0;while(e=b[f++])d.indexOf(" "+e+" ")<0&&(d+=e+" ");g=m.trim(d),c.className!==g&&(c.className=g)}return this},removeClass:function(a){var b,c,d,e,f,g,h=0,i=this.length,j=0===arguments.length||"string"==typeof a&&a;if(m.isFunction(a))return this.each(function(b){m(this).removeClass(a.call(this,b,this.className))});if(j)for(b=(a||"").match(E)||[];i>h;h++)if(c=this[h],d=1===c.nodeType&&(c.className?(" "+c.className+" ").replace(ub," "):"")){f=0;while(e=b[f++])while(d.indexOf(" "+e+" ")>=0)d=d.replace(" "+e+" "," ");g=a?m.trim(d):"",c.className!==g&&(c.className=g)}return this},toggleClass:function(a,b){var c=typeof a;return"boolean"==typeof b&&"string"===c?b?this.addClass(a):this.removeClass(a):this.each(m.isFunction(a)?function(c){m(this).toggleClass(a.call(this,c,this.className,b),b)}:function(){if("string"===c){var b,d=0,e=m(this),f=a.match(E)||[];while(b=f[d++])e.hasClass(b)?e.removeClass(b):e.addClass(b)}else(c===K||"boolean"===c)&&(this.className&&m._data(this,"__className__",this.className),this.className=this.className||a===!1?"":m._data(this,"__className__")||"")})},hasClass:function(a){for(var b=" "+a+" ",c=0,d=this.length;d>c;c++)if(1===this[c].nodeType&&(" "+this[c].className+" ").replace(ub," ").indexOf(b)>=0)return!0;return!1}}),m.each("blur focus focusin focusout load resize scroll unload click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup error contextmenu".split(" "),function(a,b){m.fn[b]=function(a,c){return arguments.length>0?this.on(b,null,a,c):this.trigger(b)}}),m.fn.extend({hover:function(a,b){return this.mouseenter(a).mouseleave(b||a)},bind:function(a,b,c){return this.on(a,null,b,c)},unbind:function(a,b){return this.off(a,null,b)},delegate:function(a,b,c,d){return this.on(b,a,c,d)},undelegate:function(a,b,c){return 1===arguments.length?this.off(a,"**"):this.off(b,a||"**",c)}});var vb=m.now(),wb=/\?/,xb=/(,)|(\[|{)|(}|])|"(?:[^"\\\r\n]|\\["\\\/bfnrt]|\\u[\da-fA-F]{4})*"\s*:?|true|false|null|-?(?!0\d)\d+(?:\.\d+|)(?:[eE][+-]?\d+|)/g;m.parseJSON=function(b){if(a.JSON&&a.JSON.parse)return a.JSON.parse(b+"");var c,d=null,e=m.trim(b+"");return e&&!m.trim(e.replace(xb,function(a,b,e,f){return c&&b&&(d=0),0===d?a:(c=e||b,d+=!f-!e,"")}))?Function("return "+e)():m.error("Invalid JSON: "+b)},m.parseXML=function(b){var c,d;if(!b||"string"!=typeof b)return null;try{a.DOMParser?(d=new DOMParser,c=d.parseFromString(b,"text/xml")):(c=new ActiveXObject("Microsoft.XMLDOM"),c.async="false",c.loadXML(b))}catch(e){c=void 0}return c&&c.documentElement&&!c.getElementsByTagName("parsererror").length||m.error("Invalid XML: "+b),c};var yb,zb,Ab=/#.*$/,Bb=/([?&])_=[^&]*/,Cb=/^(.*?):[ \t]*([^\r\n]*)\r?$/gm,Db=/^(?:about|app|app-storage|.+-extension|file|res|widget):$/,Eb=/^(?:GET|HEAD)$/,Fb=/^\/\//,Gb=/^([\w.+-]+:)(?:\/\/(?:[^\/?#]*@|)([^\/?#:]*)(?::(\d+)|)|)/,Hb={},Ib={},Jb="*/".concat("*");try{zb=location.href}catch(Kb){zb=y.createElement("a"),zb.href="",zb=zb.href}yb=Gb.exec(zb.toLowerCase())||[];function Lb(a){return function(b,c){"string"!=typeof b&&(c=b,b="*");var d,e=0,f=b.toLowerCase().match(E)||[];if(m.isFunction(c))while(d=f[e++])"+"===d.charAt(0)?(d=d.slice(1)||"*",(a[d]=a[d]||[]).unshift(c)):(a[d]=a[d]||[]).push(c)}}function Mb(a,b,c,d){var e={},f=a===Ib;function g(h){var i;return e[h]=!0,m.each(a[h]||[],function(a,h){var j=h(b,c,d);return"string"!=typeof j||f||e[j]?f?!(i=j):void 0:(b.dataTypes.unshift(j),g(j),!1)}),i}return g(b.dataTypes[0])||!e["*"]&&g("*")}function Nb(a,b){var c,d,e=m.ajaxSettings.flatOptions||{};for(d in b)void 0!==b[d]&&((e[d]?a:c||(c={}))[d]=b[d]);return c&&m.extend(!0,a,c),a}function Ob(a,b,c){var d,e,f,g,h=a.contents,i=a.dataTypes;while("*"===i[0])i.shift(),void 0===e&&(e=a.mimeType||b.getResponseHeader("Content-Type"));if(e)for(g in h)if(h[g]&&h[g].test(e)){i.unshift(g);break}if(i[0]in c)f=i[0];else{for(g in c){if(!i[0]||a.converters[g+" "+i[0]]){f=g;break}d||(d=g)}f=f||d}return f?(f!==i[0]&&i.unshift(f),c[f]):void 0}function Pb(a,b,c,d){var e,f,g,h,i,j={},k=a.dataTypes.slice();if(k[1])for(g in a.converters)j[g.toLowerCase()]=a.converters[g];f=k.shift();while(f)if(a.responseFields[f]&&(c[a.responseFields[f]]=b),!i&&d&&a.dataFilter&&(b=a.dataFilter(b,a.dataType)),i=f,f=k.shift())if("*"===f)f=i;else if("*"!==i&&i!==f){if(g=j[i+" "+f]||j["* "+f],!g)for(e in j)if(h=e.split(" "),h[1]===f&&(g=j[i+" "+h[0]]||j["* "+h[0]])){g===!0?g=j[e]:j[e]!==!0&&(f=h[0],k.unshift(h[1]));break}if(g!==!0)if(g&&a["throws"])b=g(b);else try{b=g(b)}catch(l){return{state:"parsererror",error:g?l:"No conversion from "+i+" to "+f}}}return{state:"success",data:b}}m.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:zb,type:"GET",isLocal:Db.test(yb[1]),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Jb,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/xml/,html:/html/,json:/json/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":m.parseJSON,"text xml":m.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(a,b){return b?Nb(Nb(a,m.ajaxSettings),b):Nb(m.ajaxSettings,a)},ajaxPrefilter:Lb(Hb),ajaxTransport:Lb(Ib),ajax:function(a,b){"object"==typeof a&&(b=a,a=void 0),b=b||{};var c,d,e,f,g,h,i,j,k=m.ajaxSetup({},b),l=k.context||k,n=k.context&&(l.nodeType||l.jquery)?m(l):m.event,o=m.Deferred(),p=m.Callbacks("once memory"),q=k.statusCode||{},r={},s={},t=0,u="canceled",v={readyState:0,getResponseHeader:function(a){var b;if(2===t){if(!j){j={};while(b=Cb.exec(f))j[b[1].toLowerCase()]=b[2]}b=j[a.toLowerCase()]}return null==b?null:b},getAllResponseHeaders:function(){return 2===t?f:null},setRequestHeader:function(a,b){var c=a.toLowerCase();return t||(a=s[c]=s[c]||a,r[a]=b),this},overrideMimeType:function(a){return t||(k.mimeType=a),this},statusCode:function(a){var b;if(a)if(2>t)for(b in a)q[b]=[q[b],a[b]];else v.always(a[v.status]);return this},abort:function(a){var b=a||u;return i&&i.abort(b),x(0,b),this}};if(o.promise(v).complete=p.add,v.success=v.done,v.error=v.fail,k.url=((a||k.url||zb)+"").replace(Ab,"").replace(Fb,yb[1]+"//"),k.type=b.method||b.type||k.method||k.type,k.dataTypes=m.trim(k.dataType||"*").toLowerCase().match(E)||[""],null==k.crossDomain&&(c=Gb.exec(k.url.toLowerCase()),k.crossDomain=!(!c||c[1]===yb[1]&&c[2]===yb[2]&&(c[3]||("http:"===c[1]?"80":"443"))===(yb[3]||("http:"===yb[1]?"80":"443")))),k.data&&k.processData&&"string"!=typeof k.data&&(k.data=m.param(k.data,k.traditional)),Mb(Hb,k,b,v),2===t)return v;h=m.event&&k.global,h&&0===m.active++&&m.event.trigger("ajaxStart"),k.type=k.type.toUpperCase(),k.hasContent=!Eb.test(k.type),e=k.url,k.hasContent||(k.data&&(e=k.url+=(wb.test(e)?"&":"?")+k.data,delete k.data),k.cache===!1&&(k.url=Bb.test(e)?e.replace(Bb,"$1_="+vb++):e+(wb.test(e)?"&":"?")+"_="+vb++)),k.ifModified&&(m.lastModified[e]&&v.setRequestHeader("If-Modified-Since",m.lastModified[e]),m.etag[e]&&v.setRequestHeader("If-None-Match",m.etag[e])),(k.data&&k.hasContent&&k.contentType!==!1||b.contentType)&&v.setRequestHeader("Content-Type",k.contentType),v.setRequestHeader("Accept",k.dataTypes[0]&&k.accepts[k.dataTypes[0]]?k.accepts[k.dataTypes[0]]+("*"!==k.dataTypes[0]?", "+Jb+"; q=0.01":""):k.accepts["*"]);for(d in k.headers)v.setRequestHeader(d,k.headers[d]);if(k.beforeSend&&(k.beforeSend.call(l,v,k)===!1||2===t))return v.abort();u="abort";for(d in{success:1,error:1,complete:1})v[d](k[d]);if(i=Mb(Ib,k,b,v)){v.readyState=1,h&&n.trigger("ajaxSend",[v,k]),k.async&&k.timeout>0&&(g=setTimeout(function(){v.abort("timeout")},k.timeout));try{t=1,i.send(r,x)}catch(w){if(!(2>t))throw w;x(-1,w)}}else x(-1,"No Transport");function x(a,b,c,d){var j,r,s,u,w,x=b;2!==t&&(t=2,g&&clearTimeout(g),i=void 0,f=d||"",v.readyState=a>0?4:0,j=a>=200&&300>a||304===a,c&&(u=Ob(k,v,c)),u=Pb(k,u,v,j),j?(k.ifModified&&(w=v.getResponseHeader("Last-Modified"),w&&(m.lastModified[e]=w),w=v.getResponseHeader("etag"),w&&(m.etag[e]=w)),204===a||"HEAD"===k.type?x="nocontent":304===a?x="notmodified":(x=u.state,r=u.data,s=u.error,j=!s)):(s=x,(a||!x)&&(x="error",0>a&&(a=0))),v.status=a,v.statusText=(b||x)+"",j?o.resolveWith(l,[r,x,v]):o.rejectWith(l,[v,x,s]),v.statusCode(q),q=void 0,h&&n.trigger(j?"ajaxSuccess":"ajaxError",[v,k,j?r:s]),p.fireWith(l,[v,x]),h&&(n.trigger("ajaxComplete",[v,k]),--m.active||m.event.trigger("ajaxStop")))}return v},getJSON:function(a,b,c){return m.get(a,b,c,"json")},getScript:function(a,b){return m.get(a,void 0,b,"script")}}),m.each(["get","post"],function(a,b){m[b]=function(a,c,d,e){return m.isFunction(c)&&(e=e||d,d=c,c=void 0),m.ajax({url:a,type:b,dataType:e,data:c,success:d})}}),m._evalUrl=function(a){return m.ajax({url:a,type:"GET",dataType:"script",async:!1,global:!1,"throws":!0})},m.fn.extend({wrapAll:function(a){if(m.isFunction(a))return this.each(function(b){m(this).wrapAll(a.call(this,b))});if(this[0]){var b=m(a,this[0].ownerDocument).eq(0).clone(!0);this[0].parentNode&&b.insertBefore(this[0]),b.map(function(){var a=this;while(a.firstChild&&1===a.firstChild.nodeType)a=a.firstChild;return a}).append(this)}return this},wrapInner:function(a){return this.each(m.isFunction(a)?function(b){m(this).wrapInner(a.call(this,b))}:function(){var b=m(this),c=b.contents();c.length?c.wrapAll(a):b.append(a)})},wrap:function(a){var b=m.isFunction(a);return this.each(function(c){m(this).wrapAll(b?a.call(this,c):a)})},unwrap:function(){return this.parent().each(function(){m.nodeName(this,"body")||m(this).replaceWith(this.childNodes)}).end()}}),m.expr.filters.hidden=function(a){return a.offsetWidth<=0&&a.offsetHeight<=0||!k.reliableHiddenOffsets()&&"none"===(a.style&&a.style.display||m.css(a,"display"))},m.expr.filters.visible=function(a){return!m.expr.filters.hidden(a)};var Qb=/%20/g,Rb=/\[\]$/,Sb=/\r?\n/g,Tb=/^(?:submit|button|image|reset|file)$/i,Ub=/^(?:input|select|textarea|keygen)/i;function Vb(a,b,c,d){var e;if(m.isArray(b))m.each(b,function(b,e){c||Rb.test(a)?d(a,e):Vb(a+"["+("object"==typeof e?b:"")+"]",e,c,d)});else if(c||"object"!==m.type(b))d(a,b);else for(e in b)Vb(a+"["+e+"]",b[e],c,d)}m.param=function(a,b){var c,d=[],e=function(a,b){b=m.isFunction(b)?b():null==b?"":b,d[d.length]=encodeURIComponent(a)+"="+encodeURIComponent(b)};if(void 0===b&&(b=m.ajaxSettings&&m.ajaxSettings.traditional),m.isArray(a)||a.jquery&&!m.isPlainObject(a))m.each(a,function(){e(this.name,this.value)});else for(c in a)Vb(c,a[c],b,e);return d.join("&").replace(Qb,"+")},m.fn.extend({serialize:function(){return m.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var a=m.prop(this,"elements");return a?m.makeArray(a):this}).filter(function(){var a=this.type;return this.name&&!m(this).is(":disabled")&&Ub.test(this.nodeName)&&!Tb.test(a)&&(this.checked||!W.test(a))}).map(function(a,b){var c=m(this).val();return null==c?null:m.isArray(c)?m.map(c,function(a){return{name:b.name,value:a.replace(Sb,"\r\n")}}):{name:b.name,value:c.replace(Sb,"\r\n")}}).get()}}),m.ajaxSettings.xhr=void 0!==a.ActiveXObject?function(){return!this.isLocal&&/^(get|post|head|put|delete|options)$/i.test(this.type)&&Zb()||$b()}:Zb;var Wb=0,Xb={},Yb=m.ajaxSettings.xhr();a.attachEvent&&a.attachEvent("onunload",function(){for(var a in Xb)Xb[a](void 0,!0)}),k.cors=!!Yb&&"withCredentials"in Yb,Yb=k.ajax=!!Yb,Yb&&m.ajaxTransport(function(a){if(!a.crossDomain||k.cors){var b;return{send:function(c,d){var e,f=a.xhr(),g=++Wb;if(f.open(a.type,a.url,a.async,a.username,a.password),a.xhrFields)for(e in a.xhrFields)f[e]=a.xhrFields[e];a.mimeType&&f.overrideMimeType&&f.overrideMimeType(a.mimeType),a.crossDomain||c["X-Requested-With"]||(c["X-Requested-With"]="XMLHttpRequest");for(e in c)void 0!==c[e]&&f.setRequestHeader(e,c[e]+"");f.send(a.hasContent&&a.data||null),b=function(c,e){var h,i,j;if(b&&(e||4===f.readyState))if(delete Xb[g],b=void 0,f.onreadystatechange=m.noop,e)4!==f.readyState&&f.abort();else{j={},h=f.status,"string"==typeof f.responseText&&(j.text=f.responseText);try{i=f.statusText}catch(k){i=""}h||!a.isLocal||a.crossDomain?1223===h&&(h=204):h=j.text?200:404}j&&d(h,i,j,f.getAllResponseHeaders())},a.async?4===f.readyState?setTimeout(b):f.onreadystatechange=Xb[g]=b:b()},abort:function(){b&&b(void 0,!0)}}}});function Zb(){try{return new a.XMLHttpRequest}catch(b){}}function $b(){try{return new a.ActiveXObject("Microsoft.XMLHTTP")}catch(b){}}m.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/(?:java|ecma)script/},converters:{"text script":function(a){return m.globalEval(a),a}}}),m.ajaxPrefilter("script",function(a){void 0===a.cache&&(a.cache=!1),a.crossDomain&&(a.type="GET",a.global=!1)}),m.ajaxTransport("script",function(a){if(a.crossDomain){var b,c=y.head||m("head")[0]||y.documentElement;return{send:function(d,e){b=y.createElement("script"),b.async=!0,a.scriptCharset&&(b.charset=a.scriptCharset),b.src=a.url,b.onload=b.onreadystatechange=function(a,c){(c||!b.readyState||/loaded|complete/.test(b.readyState))&&(b.onload=b.onreadystatechange=null,b.parentNode&&b.parentNode.removeChild(b),b=null,c||e(200,"success"))},c.insertBefore(b,c.firstChild)},abort:function(){b&&b.onload(void 0,!0)}}}});var _b=[],ac=/(=)\?(?=&|$)|\?\?/;m.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var a=_b.pop()||m.expando+"_"+vb++;return this[a]=!0,a}}),m.ajaxPrefilter("json jsonp",function(b,c,d){var e,f,g,h=b.jsonp!==!1&&(ac.test(b.url)?"url":"string"==typeof b.data&&!(b.contentType||"").indexOf("application/x-www-form-urlencoded")&&ac.test(b.data)&&"data");return h||"jsonp"===b.dataTypes[0]?(e=b.jsonpCallback=m.isFunction(b.jsonpCallback)?b.jsonpCallback():b.jsonpCallback,h?b[h]=b[h].replace(ac,"$1"+e):b.jsonp!==!1&&(b.url+=(wb.test(b.url)?"&":"?")+b.jsonp+"="+e),b.converters["script json"]=function(){return g||m.error(e+" was not called"),g[0]},b.dataTypes[0]="json",f=a[e],a[e]=function(){g=arguments},d.always(function(){a[e]=f,b[e]&&(b.jsonpCallback=c.jsonpCallback,_b.push(e)),g&&m.isFunction(f)&&f(g[0]),g=f=void 0}),"script"):void 0}),m.parseHTML=function(a,b,c){if(!a||"string"!=typeof a)return null;"boolean"==typeof b&&(c=b,b=!1),b=b||y;var d=u.exec(a),e=!c&&[];return d?[b.createElement(d[1])]:(d=m.buildFragment([a],b,e),e&&e.length&&m(e).remove(),m.merge([],d.childNodes))};var bc=m.fn.load;m.fn.load=function(a,b,c){if("string"!=typeof a&&bc)return bc.apply(this,arguments);var d,e,f,g=this,h=a.indexOf(" ");return h>=0&&(d=m.trim(a.slice(h,a.length)),a=a.slice(0,h)),m.isFunction(b)?(c=b,b=void 0):b&&"object"==typeof b&&(f="POST"),g.length>0&&m.ajax({url:a,type:f,dataType:"html",data:b}).done(function(a){e=arguments,g.html(d?m("<div>").append(m.parseHTML(a)).find(d):a)}).complete(c&&function(a,b){g.each(c,e||[a.responseText,b,a])}),this},m.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(a,b){m.fn[b]=function(a){return this.on(b,a)}}),m.expr.filters.animated=function(a){return m.grep(m.timers,function(b){return a===b.elem}).length};var cc=a.document.documentElement;function dc(a){return m.isWindow(a)?a:9===a.nodeType?a.defaultView||a.parentWindow:!1}m.offset={setOffset:function(a,b,c){var d,e,f,g,h,i,j,k=m.css(a,"position"),l=m(a),n={};"static"===k&&(a.style.position="relative"),h=l.offset(),f=m.css(a,"top"),i=m.css(a,"left"),j=("absolute"===k||"fixed"===k)&&m.inArray("auto",[f,i])>-1,j?(d=l.position(),g=d.top,e=d.left):(g=parseFloat(f)||0,e=parseFloat(i)||0),m.isFunction(b)&&(b=b.call(a,c,h)),null!=b.top&&(n.top=b.top-h.top+g),null!=b.left&&(n.left=b.left-h.left+e),"using"in b?b.using.call(a,n):l.css(n)}},m.fn.extend({offset:function(a){if(arguments.length)return void 0===a?this:this.each(function(b){m.offset.setOffset(this,a,b)});var b,c,d={top:0,left:0},e=this[0],f=e&&e.ownerDocument;if(f)return b=f.documentElement,m.contains(b,e)?(typeof e.getBoundingClientRect!==K&&(d=e.getBoundingClientRect()),c=dc(f),{top:d.top+(c.pageYOffset||b.scrollTop)-(b.clientTop||0),left:d.left+(c.pageXOffset||b.scrollLeft)-(b.clientLeft||0)}):d},position:function(){if(this[0]){var a,b,c={top:0,left:0},d=this[0];return"fixed"===m.css(d,"position")?b=d.getBoundingClientRect():(a=this.offsetParent(),b=this.offset(),m.nodeName(a[0],"html")||(c=a.offset()),c.top+=m.css(a[0],"borderTopWidth",!0),c.left+=m.css(a[0],"borderLeftWidth",!0)),{top:b.top-c.top-m.css(d,"marginTop",!0),left:b.left-c.left-m.css(d,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var a=this.offsetParent||cc;while(a&&!m.nodeName(a,"html")&&"static"===m.css(a,"position"))a=a.offsetParent;return a||cc})}}),m.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(a,b){var c=/Y/.test(b);m.fn[a]=function(d){return V(this,function(a,d,e){var f=dc(a);return void 0===e?f?b in f?f[b]:f.document.documentElement[d]:a[d]:void(f?f.scrollTo(c?m(f).scrollLeft():e,c?e:m(f).scrollTop()):a[d]=e)},a,d,arguments.length,null)}}),m.each(["top","left"],function(a,b){m.cssHooks[b]=La(k.pixelPosition,function(a,c){return c?(c=Ja(a,b),Ha.test(c)?m(a).position()[b]+"px":c):void 0})}),m.each({Height:"height",Width:"width"},function(a,b){m.each({padding:"inner"+a,content:b,"":"outer"+a},function(c,d){m.fn[d]=function(d,e){var f=arguments.length&&(c||"boolean"!=typeof d),g=c||(d===!0||e===!0?"margin":"border");return V(this,function(b,c,d){var e;return m.isWindow(b)?b.document.documentElement["client"+a]:9===b.nodeType?(e=b.documentElement,Math.max(b.body["scroll"+a],e["scroll"+a],b.body["offset"+a],e["offset"+a],e["client"+a])):void 0===d?m.css(b,c,g):m.style(b,c,d,g)},b,f?d:void 0,f,null)}})}),m.fn.size=function(){return this.length},m.fn.andSelf=m.fn.addBack,"function"==typeof define&&define.amd&&define("jquery",[],function(){return m});var ec=a.jQuery,fc=a.$;return m.noConflict=function(b){return a.$===m&&(a.$=fc),b&&a.jQuery===m&&(a.jQuery=ec),m},typeof b===K&&(a.jQuery=a.$=m),m});
"></script> -<meta name="viewport" content="width=device-width, initial-scale=1" /> -<link href="data:text/css;charset=utf-8,html%7Bfont%2Dfamily%3Asans%2Dserif%3B%2Dwebkit%2Dtext%2Dsize%2Dadjust%3A100%25%3B%2Dms%2Dtext%2Dsize%2Dadjust%3A100%25%7Dbody%7Bmargin%3A0%7Darticle%2Caside%2Cdetails%2Cfigcaption%2Cfigure%2Cfooter%2Cheader%2Chgroup%2Cmain%2Cmenu%2Cnav%2Csection%2Csummary%7Bdisplay%3Ablock%7Daudio%2Ccanvas%2Cprogress%2Cvideo%7Bdisplay%3Ainline%2Dblock%3Bvertical%2Dalign%3Abaseline%7Daudio%3Anot%28%5Bcontrols%5D%29%7Bdisplay%3Anone%3Bheight%3A0%7D%5Bhidden%5D%2Ctemplate%7Bdisplay%3Anone%7Da%7Bbackground%2Dcolor%3Atransparent%7Da%3Aactive%2Ca%3Ahover%7Boutline%3A0%7Dabbr%5Btitle%5D%7Bborder%2Dbottom%3A1px%20dotted%7Db%2Cstrong%7Bfont%2Dweight%3A700%7Ddfn%7Bfont%2Dstyle%3Aitalic%7Dh1%7Bmargin%3A%2E67em%200%3Bfont%2Dsize%3A2em%7Dmark%7Bcolor%3A%23000%3Bbackground%3A%23ff0%7Dsmall%7Bfont%2Dsize%3A80%25%7Dsub%2Csup%7Bposition%3Arelative%3Bfont%2Dsize%3A75%25%3Bline%2Dheight%3A0%3Bvertical%2Dalign%3Abaseline%7Dsup%7Btop%3A%2D%2E5em%7Dsub%7Bbottom%3A%2D%2E25em%7Dimg%7Bborder%3A0%7Dsvg%3Anot%28%3Aroot%29%7Boverflow%3Ahidden%7Dfigure%7Bmargin%3A1em%2040px%7Dhr%7Bheight%3A0%3B%2Dwebkit%2Dbox%2Dsizing%3Acontent%2Dbox%3B%2Dmoz%2Dbox%2Dsizing%3Acontent%2Dbox%3Bbox%2Dsizing%3Acontent%2Dbox%7Dpre%7Boverflow%3Aauto%7Dcode%2Ckbd%2Cpre%2Csamp%7Bfont%2Dfamily%3Amonospace%2Cmonospace%3Bfont%2Dsize%3A1em%7Dbutton%2Cinput%2Coptgroup%2Cselect%2Ctextarea%7Bmargin%3A0%3Bfont%3Ainherit%3Bcolor%3Ainherit%7Dbutton%7Boverflow%3Avisible%7Dbutton%2Cselect%7Btext%2Dtransform%3Anone%7Dbutton%2Chtml%20input%5Btype%3Dbutton%5D%2Cinput%5Btype%3Dreset%5D%2Cinput%5Btype%3Dsubmit%5D%7B%2Dwebkit%2Dappearance%3Abutton%3Bcursor%3Apointer%7Dbutton%5Bdisabled%5D%2Chtml%20input%5Bdisabled%5D%7Bcursor%3Adefault%7Dbutton%3A%3A%2Dmoz%2Dfocus%2Dinner%2Cinput%3A%3A%2Dmoz%2Dfocus%2Dinner%7Bpadding%3A0%3Bborder%3A0%7Dinput%7Bline%2Dheight%3Anormal%7Dinput%5Btype%3Dcheckbox%5D%2Cinput%5Btype%3Dradio%5D%7B%2Dwebkit%2Dbox%2Dsizing%3Aborder%2Dbox%3B%2Dmoz%2Dbox%2Dsizing%3Aborder%2Dbox%3Bbox%2Dsizing%3Aborder%2Dbox%3Bpadding%3A0%7Dinput%5Btype%3Dnumber%5D%3A%3A%2Dwebkit%2Dinner%2Dspin%2Dbutton%2Cinput%5Btype%3Dnumber%5D%3A%3A%2Dwebkit%2Douter%2Dspin%2Dbutton%7Bheight%3Aauto%7Dinput%5Btype%3Dsearch%5D%7B%2Dwebkit%2Dbox%2Dsizing%3Acontent%2Dbox%3B%2Dmoz%2Dbox%2Dsizing%3Acontent%2Dbox%3Bbox%2Dsizing%3Acontent%2Dbox%3B%2Dwebkit%2Dappearance%3Atextfield%7Dinput%5Btype%3Dsearch%5D%3A%3A%2Dwebkit%2Dsearch%2Dcancel%2Dbutton%2Cinput%5Btype%3Dsearch%5D%3A%3A%2Dwebkit%2Dsearch%2Ddecoration%7B%2Dwebkit%2Dappearance%3Anone%7Dfieldset%7Bpadding%3A%2E35em%20%2E625em%20%2E75em%3Bmargin%3A0%202px%3Bborder%3A1px%20solid%20silver%7Dlegend%7Bpadding%3A0%3Bborder%3A0%7Dtextarea%7Boverflow%3Aauto%7Doptgroup%7Bfont%2Dweight%3A700%7Dtable%7Bborder%2Dspacing%3A0%3Bborder%2Dcollapse%3Acollapse%7Dtd%2Cth%7Bpadding%3A0%7D%40media%20print%7B%2A%2C%3Aafter%2C%3Abefore%7Bcolor%3A%23000%21important%3Btext%2Dshadow%3Anone%21important%3Bbackground%3A0%200%21important%3B%2Dwebkit%2Dbox%2Dshadow%3Anone%21important%3Bbox%2Dshadow%3Anone%21important%7Da%2Ca%3Avisited%7Btext%2Ddecoration%3Aunderline%7Da%5Bhref%5D%3Aafter%7Bcontent%3A%22%20%28%22%20attr%28href%29%20%22%29%22%7Dabbr%5Btitle%5D%3Aafter%7Bcontent%3A%22%20%28%22%20attr%28title%29%20%22%29%22%7Da%5Bhref%5E%3D%22javascript%3A%22%5D%3Aafter%2Ca%5Bhref%5E%3D%22%23%22%5D%3Aafter%7Bcontent%3A%22%22%7Dblockquote%2Cpre%7Bborder%3A1px%20solid%20%23999%3Bpage%2Dbreak%2Dinside%3Aavoid%7Dthead%7Bdisplay%3Atable%2Dheader%2Dgroup%7Dimg%2Ctr%7Bpage%2Dbreak%2Dinside%3Aavoid%7Dimg%7Bmax%2Dwidth%3A100%25%21important%7Dh2%2Ch3%2Cp%7Borphans%3A3%3Bwidows%3A3%7Dh2%2Ch3%7Bpage%2Dbreak%2Dafter%3Aavoid%7D%2Enavbar%7Bdisplay%3Anone%7D%2Ebtn%3E%2Ecaret%2C%2Edropup%3E%2Ebtn%3E%2Ecaret%7Bborder%2Dtop%2Dcolor%3A%23000%21important%7D%2Elabel%7Bborder%3A1px%20solid%20%23000%7D%2Etable%7Bborder%2Dcollapse%3Acollapse%21important%7D%2Etable%20td%2C%2Etable%20th%7Bbackground%2Dcolor%3A%23fff%21important%7D%2Etable%2Dbordered%20td%2C%2Etable%2Dbordered%20th%7Bborder%3A1px%20solid%20%23ddd%21important%7D%7D%40font%2Dface%7Bfont%2Dfamily%3A%27Glyphicons%20Halflings%27%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3tRxAWwFs2tcuJRnI6xjmBdWjbgFNRHMHiF1uHYBfUR%2Fut5Ug2jXAaT96%2B9RH%2FFToRwIzGbKmVJ1AZQnoabSB1yyIg7ByAridHApPMjyw0OiV6RjSbCuzwLAvFizBliWJua1tsuAgvNPbmljYbpt8lkWam7b3XZiOiKJskMOtmfScnsbPW208knwjuXrXK4Q1iKIgNyYXXDVT9C2Ye%2F78GQ5BEEXfFdde2RwauOysdJNL5AzCy84ard%2FnGAVN8alecnFdgu5Gbd5DJTL%2BhHZK0vApVy3OfU8XTSJg1TlssivsPYUlIqvn66PzrVTymCc4wgF6SDNR0pDf%2B9Gp%2BVnsUH5WtpHYsuhOaey8zdwLN47V8MTbm78g687%2BP3cx6tcAeNpjYGRgYGBk8s0%2FzBIfz2%2FzlUGeZQNQhOFCWfF0GP0%2F8P8c1jusIkAuBwMTSBQAYwQM6HjaY2BkYGAV%2Bd8KJgP%2FXWG9wwAUQQGLAYqPBl942n1TvUoDQRCe1VM8kWARjNrZGIurBAsRBIuA2vkAFsJiKTYW4guIjT5ARMgTxCLoA1hcb5OgDyGHrY7f7M65e8fpLF%2B%2B2W%2FnZ2eTmGfaIJi5I0qGDlZZcD51QzTTJirZPAI9JIwVA%2BwT8L5nOdMaV0AuMJ%2BicRHq8of6LSD18fzq8ds7xjpwBnQiSI9V5QVl6NwPvgM15NXn%2FAtWZyj3W0HjEXitOc%2FdIdbetPdFTZ%2BP6t%2BX7xU0%2Fk6GJtOe1%2FB3arN0%2Fpmz1J4UZc%2BD6ExwjD7vioeGd5HvhvU%2BR%2BDZcGZ6YBPNfAi0G97iBPwFXqph2cW8%2BD7kjMfwtinHb6kLb6Wygk3cZytSEoptGrlScdHtLPeri1JKueACMZfU1ViJG1Sq5E43dIt7SZZFl1zuRhb%2FGOs44xFVDbrJzB5tYs35OmaXTrEmkv0DajnMWQB42mNgYNCCwk0MLxheMPrhgUuY2JiUmOqY2pjWMD1hdmPOY%2B5hPsLCwWLEksSyiOUOawzrLrYiti%2FsCuxJ7Kc45DiSOPZxmnG2cG7jvMelweXDNYXrEbcBdxf3KR4OngheLd443g18fHwZfFv4NfiX8T8TEBIIEZggsEpQS7BMcJsQl5CFUI3QAWEp4RLhCyJaIldEbURXiJ4RYxEzE0sQ2yD2TzxIfJkEk4SeRJbENIkNEg8k%2FklqSGZITpE8InlL8p2UmVSG1A6pb9Jx0ltkjGSmyDySlZF1kc2RnSK7R%2FaZnJ5cmdwB%2BST5SwpuCvsUjRTLFHcoOShNU9qhzKespGyhXKV8SPmBCpOKgUqcyjSVR6omqgmqe9RE1OrUnqkHqO9R%2F6FholGgsUZzgeYZLTUtL60WbS7tKh0OnQydXTpvdGV0O3S%2F6Gnopekt0ruhz6fvpl%2Bnv0n%2Fh4GdQYvBJUMhwwTDdYYvjFSM4oxmGd0zVjK2M84w3mYiYZJgssLkkqmO6TzTF2Z2ZjVmd8ylzP3MJ5lfsRCwcLJoszhhyWXpZdlhecZKxirHapbVPesF1ndsJGwCbBbZ%2FLA1sn1jZ2XXY3fFXsM%2Bz36V%2FS8HD4cGh2OOTI51ThJOK5zeOUs4OzmXOS9wPuUi4JLgss7lm2uU6zY3NrcSty1u39zN3Mvct7l%2F8xDzMPLw88jyaPM44ynkaeEZ59niucqLyUvPKwgAn3OqOQAAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAHjarZK9TgJBEMf%2Fd6CRaAyRhMLqCgsbL4ciglTGRPEjSiSKlnLycXJ86CEniU%2FhM9jYWPgIFkYfwd6nsDD%2Bd1mBIIUx3mZnfzs3MzszuwDCeIYG8UUwQxmAFgxxPeeuyxrmcaNYxzTuFAewi0fFQSTxqXgM11pC8TgS2oPiCUS1d8Uh8ofiSczpYcVT5LjiCPlY8Qui%2BncOr7D02y6%2FBTCrP%2Fm%2Bb5bdTrPi2I26Z9qNGtbRQBMdXMJBGRW0YOCecxEWYoiTCvxrYBunqHPdoX2bLOyrMKlZg8thDETw5K7Itci1TXlGy0124QRZZLDFU%2FexhxztMozlosTpMH6ZPge0L%2BOKGnFKjJ4WRwppHPL0PP3SI2P9jLQwFOu3GRhDfkeyDo%2F%2FG7IHgzllZQxLdquvrdCyBVvat3seJlYo06gxapUxhU2JWnFygR03sSxnEkvcpf5Y5eibGq315TDp7fKWm8zbUVl71Aqq%2FZtNnlkWmLnQtno9ycvXYbA6W2pF3aKfCayyC0Ja7Fr%2FPW70%2FHO4YM0OKxFvzf0C1MyPjwAAeNpt1VWUU2cYRuHsgxenQt1d8%2F3JOUnqAyR1d%2FcCLQVKO22pu7tQd3d3d3d3d3cXmGzumrWy3pWLs%2FNdPDMpZaWu1783l1Lpf14MnfzO6FbqVupfGkD30iR60JNe9KYP09CXfvRnAAMZxGCGMG3pW6ZjemZgKDMyEzMzC7MyG7MzB3MyF3MzD%2FMyH%2FOzAAuyEAuzCIuyGIuzBGWCRIUqOQU16jRYkqVYmmVYluVYng6GMZwRNGmxAiuyEiuzCquyGquzBmuyFmuzDuuyHuuzARuyERuzCZuyGZuzBVuyFVuzDduyHdszklGMZgd2ZAw7MZZxjGdnJrALu9LJbuzOHkxkT%2FZib%2FZhX%2FZjfw7gQA7iYA7hUA7jcI7gSI7iaI7hWI7jeE7gRE7iZE5hEqdyGqdzBmdyFmdzDudyHudzARdyERdzCZdyGZdzBVdyFVdzDddyHddzAzdyEzdzC7dyG7dzB3dyF3dzD%2FdyH%2FfzAA%2FyEA%2FzCI%2FyGI%2FzBE%2FyFE%2FzDM%2FyHM%2FzAi%2FyEi%2FzCq%2FyGq%2FzBm%2FyFm%2FzDu%2FyHu%2FzAR%2FyER%2FzCZ%2FyGZ%2FzBV%2FyFV%2FzDd%2FyHd%2FzAz%2FyEz%2FzC7%2FyG7%2FzB3%2FyF3%2FzD%2F9mpYwsy7pl3bMeWc%2BsV9Y765NNk%2FXN%2BmX9swHZwGxQNjgb0nPkmInjR0V7Uq%2FOsaPL5Y7ylE3l8tQNN7kVt%2BrmbuHW3LrbcDvam1rtzVvdm50TxrU%2FDBvRtZUY1rV5a3jXFn550Wo%2FXDNWK3dFmh7X9LimxzU9qulRTY9qelTTo5rlKLt2wk7YiaprL%2ByFvbAX9pK9ZC%2FZS%2FaSvWQv2Uv2kr1kr2KvYq9ir2KvYq9ir2KvYq9ir2Kvaq9qr2qvaq9qr2qvaq9qr2qvai%2B3l9vL7eX2cnu5vdxebi%2B3l9sr7BV2CjuFncJOYaewU9gp7NTs1LyrZq9mr2avZq9mr2avZq9mr26vbq9ur26vbq9ur26vbq9ur26vYa9hr2GvYa9hr2GvYa%2FR7oXuQ%2Feh%2B2j%2FUU7e3C3cqc%2FV3fYdof%2FQf%2Bg%2F9B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6D92H7kP3ofvQfeg%2BdB%2B6D92H7kP3ofvQfRT29B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6D%2F2H%2FkP%2Fof%2FQf%2Bg%2F9B%2F6j6nuG3Ya7U5q%2F0hN3nCTW3Grbu4Wrs%2FrP%2Bk%2F6T%2FpP%2Bk%2F6T%2FpP%2Bk%2B6T7pPek86TzpPOk86TzpOuk66TrpOuk66TrpOlWmPu%2F36zrpOuk66TrpOuk66TrpOvl%2FPek76TvpO%2Bk76TvpO%2Bk76TvpO%2Bk76TvpO7V9t%2BqtVs%2FOaOURU6bo6PgPt6rZbwAAAAABVFDDFwAA%29%20format%28%27woff%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dtruetype%3Bbase64%2CAAEAAAAPAIAAAwBwRkZUTW0ql9wAAAD8AAAAHEdERUYBRAAEAAABGAAAACBPUy8yZ7lriQAAATgAAABgY21hcNqt44EAAAGYAAAGcmN2dCAAKAL4AAAIDAAAAARnYXNw%2F%2F8AAwAACBAAAAAIZ2x5Zn1dwm8AAAgYAACUpGhlYWQFTS%2FYAACcvAAAADZoaGVhCkQEEQAAnPQAAAAkaG10eNLHIGAAAJ0YAAADdGxvY2Fv%2B5XOAACgjAAAAjBtYXhwAWoA2AAAorwAAAAgbmFtZbMsoJsAAKLcAAADonBvc3S6o%2BU1AACmgAAACtF3ZWJmwxhUUAAAsVQAAAAGAAAAAQAAAADMPaLPAAAAANB2gXUAAAAA0HZzlwABAAAADgAAABgAAAAAAAIAAQABARYAAQAEAAAAAgAAAAMEiwGQAAUABAMMAtAAAABaAwwC0AAAAaQAMgK4AAAAAAUAAAAAAAAAAAAAAAIAAAAAAAAAAAAAAFVLV04AQAAg%2F%2F8DwP8QAAAFFAB7AAAAAQAAAAAAAAAAAAAAIAABAAAABQAAAAMAAAAsAAAACgAAAdwAAQAAAAAEaAADAAEAAAAsAAMACgAAAdwABAGwAAAAaABAAAUAKAAgACsAoAClIAogLyBfIKwgvSISIxsl%2FCYBJvonCScP4APgCeAZ4CngOeBJ4FngYOBp4HngieCX4QnhGeEp4TnhRuFJ4VnhaeF54YnhleGZ4gbiCeIW4hniIeIn4jniSeJZ4mD4%2F%2F%2F%2FAAAAIAAqAKAApSAAIC8gXyCsIL0iEiMbJfwmASb6JwknD%2BAB4AXgEOAg4DDgQOBQ4GDgYuBw4IDgkOEB4RDhIOEw4UDhSOFQ4WDhcOGA4ZDhl%2BIA4gniEOIY4iHiI%2BIw4kDiUOJg%2BP%2F%2F%2F%2F%2Fj%2F9r%2FZv9i4Ajf5N%2B132nfWd4F3P3aHdoZ2SHZE9kOIB0gHCAWIBAgCiAEH%2F4f%2BB%2F3H%2FEf6x%2FlH3wfdh9wH2ofZB9jH10fVx9RH0sfRR9EHt4e3B7WHtUezh7NHsUevx65HrMIFQABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAAAAAACjAAAAAAAAAA1AAAAIAAAACAAAAADAAAAKgAAACsAAAAEAAAAoAAAAKAAAAAGAAAApQAAAKUAAAAHAAAgAAAAIAoAAAAIAAAgLwAAIC8AAAATAAAgXwAAIF8AAAAUAAAgrAAAIKwAAAAVAAAgvQAAIL0AAAAWAAAiEgAAIhIAAAAXAAAjGwAAIxsAAAAYAAAl%2FAAAJfwAAAAZAAAmAQAAJgEAAAAaAAAm%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%2FAADiUAAA4lkAAAEJAADiYAAA4mAAAAETAAD4%2FwAA%2BP8AAAEUAAH1EQAB9REAAAEVAAH2qgAB9qoAAAEWAAYCCgAAAAABAAABAAAAAAAAAAAAAAAAAAAAAQACAAAAAAAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAAAAAwAAAAAAAAAAAAAAAAAAAAAAAAAEAAUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKAL4AAAAAf%2F%2FAAIAAgAoAAABaAMgAAMABwAusQEALzyyBwQA7TKxBgXcPLIDAgDtMgCxAwAvPLIFBADtMrIHBgH8PLIBAgDtMjMRIRElMxEjKAFA%2Fujw8AMg%2FOAoAtAAAQBkAGQETARMAFsAAAEyFh8BHgEdATc%2BAR8BFgYPATMyFhcWFRQGDwEOASsBFx4BDwEGJi8BFRQGBwYjIiYvAS4BPQEHDgEvASY2PwEjIiYnJjU0Nj8BPgE7AScuAT8BNhYfATU0Njc2AlgPJgsLCg%2BeBxYIagcCB57gChECBgMCAQIRCuCeBwIHaggWB54PCikiDyYLCwoPngcWCGoHAgee4AoRAgYDAgECEQrgngcCB2oIFgeeDwopBEwDAgECEQrgngcCB2oIFgeeDwopIg8mCwsKD54HFghqBwIHnuAKEQIGAwIBAhEK4J4HAgdqCBYHng8KKSIPJgsLCg%2BeBxYIagcCB57gChECBgAAAAABAAAAAARMBEwAIwAAATMyFhURITIWHQEUBiMhERQGKwEiJjURISImPQE0NjMhETQ2AcLIFR0BXhUdHRX%2Boh0VyBUd%2FqIVHR0VAV4dBEwdFf6iHRXIFR3%2BohUdHRUBXh0VyBUdAV4VHQAAAAABAHAAAARABEwARQAAATMyFgcBBgchMhYPAQ4BKwEVITIWDwEOASsBFRQGKwEiJj0BISImPwE%2BATsBNSEiJj8BPgE7ASYnASY2OwEyHwEWMj8BNgM5%2BgoFCP6UBgUBDAoGBngGGAp9ARMKBgZ4BhgKfQ8LlAsP%2Fu0KBgZ4BhgKff7tCgYGeAYYCnYFBv6UCAUK%2BhkSpAgUCKQSBEwKCP6UBgwMCKAIDGQMCKAIDK4LDw8LrgwIoAgMZAwIoAgMDAYBbAgKEqQICKQSAAABAGQABQSMBK4AOwAAATIXFhcjNC4DIyIOAwchByEGFSEHIR4EMzI%2BAzUzBgcGIyInLgEnIzczNjcjNzM%2BATc2AujycDwGtSM0QDkXEys4MjAPAXtk%2FtQGAZZk%2FtQJMDlCNBUWOUA0I64eYmunznYkQgzZZHABBdpkhhQ%2BH3UErr1oaS1LMCEPCx4uTzJkMjJkSnRCKw8PIjBKK6trdZ4wqndkLzVkV4UljQAAAgB7AAAETASwAD4ARwAAASEyHgUVHAEVFA4FKwEHITIWDwEOASsBFRQGKwEiJj0BISImPwE%2BATsBNSEiJj8BPgE7ARE0NhcRMzI2NTQmIwGsAV5DakIwFgwBAQwWMEJqQ7ICASAKBgZ4BhgKigsKlQoP%2FvUKBgZ4BhgKdf71CgYGeAYYCnUPtstALS1ABLAaJD8yTyokCwsLJCpQMkAlGmQMCKAIDK8LDg8KrwwIoAgMZAwIoAgMAdsKD8j%2B1EJWVEAAAAEAyAGQBEwCvAAPAAATITIWHQEUBiMhIiY9ATQ2%2BgMgFR0dFfzgFR0dArwdFcgVHR0VyBUdAAAAAgDIAAAD6ASwACUAQQAAARUUBisBFRQGBx4BHQEzMhYdASE1NDY7ATU0NjcuAT0BIyImPQEXFRQWFx4BFAYHDgEdASE1NCYnLgE0Njc%2BAT0BA%2BgdFTJjUVFjMhUd%2FOAdFTJjUVFjMhUdyEE3HCAgHDdBAZBBNxwgIBw3QQSwlhUdZFuVIyOVW5YdFZaWFR2WW5UjI5VbZB0VlshkPGMYDDI8MgwYYzyWljxjGAwyPDIMGGM8ZAAAAAEAAAAAAAAAAAAAAAAxAAAB%2F%2FIBLATCBEEAFgAAATIWFzYzMhYVFAYjISImNTQ2NyY1NDYB9261LCwueKqqeP0ST3FVQgLYBEF3YQ6teHmtclBFaw4MGZnXAAAAAgAAAGQEsASvABoAHgAAAB4BDwEBMzIWHQEhNTQ2OwEBJyY%2BARYfATc2AyEnAwL2IAkKiAHTHhQe%2B1AeFB4B1IcKCSAkCm9wCXoBebbDBLMTIxC7%2FRYlFSoqFSUC6rcQJBQJEJSWEPwecAIWAAAAAAQAAABkBLAETAALABcAIwA3AAATITIWBwEGIicBJjYXARYUBwEGJjURNDYJATYWFREUBicBJjQHARYGIyEiJjcBNjIfARYyPwE2MhkEfgoFCP3MCBQI%2FcwIBQMBCAgI%2FvgICgoDjAEICAoKCP74CFwBbAgFCvuCCgUIAWwIFAikCBQIpAgUBEwKCP3JCAgCNwgK2v74CBQI%2FvgIBQoCJgoF%2FvABCAgFCv3aCgUIAQgIFID%2BlAgKCggBbAgIpAgIpAgAAAAD%2F%2FD%2F8AS6BLoACQANABAAAAAyHwEWFA8BJzcTAScJAQUTA%2BAmDpkNDWPWXyL9mdYCZv4f%2FrNuBLoNmQ4mDlzWYP50%2FZrWAmb8anABTwAAAAEAAAAABLAEsAAPAAABETMyFh0BITU0NjsBEQEhArz6FR384B0V%2Bv4MBLACiv3aHRUyMhUdAiYCJgAAAAEADgAIBEwEnAAfAAABJTYWFREUBgcGLgE2NzYXEQURFAYHBi4BNjc2FxE0NgFwAoUnMFNGT4gkV09IQv2oWEFPiCRXT0hCHQP5ow8eIvzBN1EXGSltchkYEAIJm%2F2iKmAVGilucRoYEQJ%2FJioAAAACAAn%2F%2BAS7BKcAHQApAAAAMh4CFQcXFAcBFgYPAQYiJwEGIycHIi4CND4BBCIOARQeATI%2BATQmAZDItoNOAQFOARMXARY7GikT%2Fu13jgUCZLaDTk6DAXKwlFZWlLCUVlYEp06DtmQCBY15%2Fu4aJRg6FBQBEk0BAU6Dtsi2g1tWlLCUVlaUsJQAAQBkAFgErwREABkAAAE%2BAh4CFRQOAwcuBDU0PgIeAQKJMHt4dVg2Q3mEqD4%2Bp4V4Qzhadnh5A7VESAUtU3ZAOXmAf7JVVbJ%2FgHk5QHZTLQVIAAAAAf%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAAgAAgAAAAAAAAABAAAFFP%2BFAAAFFP9R%2FtQF3AABAAAAAAAAAAAAAAAAAAAAowG4ACgAAAAAAZAAAASwAAAEsABkBLAAAASwAAAEsABwAooAAAUUAAACigAABRQAAAGxAAABRQAAANgAAADYAAAAogAAAQQAAABIAAABBAAAAUUAAASwAGQEsAB7BLAAyASwAMgB9AAABLD%2F8gSwAAAEsAAABLD%2F8ASwAAAEsAAOBLAACQSwAGQEsP%2FTBLD%2F0wSwAAAEsAAABLAAAASwAAAEsAAABLAAJgSwAG4EsAAXBLAAFwSwABcEsABkBLAAGgSwAGQEsAAMBLAAZASwABcEsP%2BcBLAAZASwABcEsAAXBLAAAASwABcEsAAXBLAAFwSwAGQEsAAABLAAZASwAAAEsAAABLAAAASwAAAEsAAABLAAAASwAAAEsAAABLAAZASwAMgEsAAABLAAAASwADUEsABkBLAAyASw%2F7UEsAAhBLAAAASwAAAEsAAABLAAAASwAAAEsP%2BcBLAAAASwAAAEsAAABLAA2wSwABcEsAB1BLAAAASwAAAEsAAABLAACgSwAMgEsAAABLAAnQSwAMgEsADIBLAAyASwAAAEsP%2F%2BBLABLASwAGQEsACIBLABOwSwABcEsAAXBLAAFwSwABcEsAAXBLAAFwSwAAAEsAAXBLAAFwSwABcEsAAXBLAAAASwALcEsAC3BLAAAASwAAAEsABJBLAAFwSwAAAEsAAABLAAXQSw%2F9wEsP%2FcBLD%2FnwSwAGQEsAAABLAAAASwAAAEsABkBLD%2F%2FwSwAAAEsP9RBLAABgSwAAAEsAAABLABRQSwAAEEsAAABLD%2FnASwAEoEsAAUBLAAAASwAAAEsAAABLD%2FnASwAGEEsP%2F9BLAAFgSwABYEsAAWBLAAFgSwABgEsAAABMQAAASwAGQAAAAAAAD%2F2ABkADkAyAAAAScAZAAZABkAGQAZABkAGQAZAAAAAAAAAAAAAADZAAAAAAAOAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAMAZABkAAAAEAAAAAAAZP%2Bc%2F5z%2FnP%2Bc%2F5z%2FnP%2Bc%2F5wACQAJ%2F%2FL%2F8gBkAHkAJwBkAGQAAAAAAGT%2FogAAAAAAAAAAAAAAAADIAGQAAAABAI8AAP%2Bc%2F5wAZAAEAMgAyAAAAGQBkABkAAAAZAEs%2F7UAAAAAAAAAAAAAAAAAAABkAAABLAFBADIAMgAIAAAAAADIAT4AZgBmANkAyADIAAAAKgAqACoAKgCyAOgA6AFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BpAIGAiICfgKGAqwC5ANGA24DjAPEBAgEMgRiBKIE3AVcBboGcgb0ByAHYgfKCB4IYgi%2BCTYJhAm2Cd4KKApMCpQK4gswC4oLygwIDFgNKg1eDbAODg5oDrQPKA%2BmD%2BYQEhBUEJAQqhEqEXYRthIKEjgSfBLAExoTdBPQFCoU1BU8FagVzBYEFjYWYBawFv4XUhemGAIYLhhqGJYYsBjgGP4ZKBloGZQZxBnaGe4aNhpoGrga9hteG7QcMhyUHOIdHB1EHWwdlB28HeYeLh52HsAfYh%2FSIEYgviEyIXYhuCJAIpYiuCMOIyIjOCN6I8Ij4CQCJDAkXiSWJOIlNCVgJbwmFCZ%2BJuYnUCe8J%2FgoNChwKKwpoCnMKiYqSiqEKworeiwILGgsuizsLRwtiC30LiguZi6iLtgvDi9GL34vsi%2F4MD4whDDSMRIxYDGuMegyJDJeMpoy3jMiMz4zaDO2NBg0YDSoNNI1LDWeNeg2PjZ8Ntw3GjdON5I31DgQOEI4hjjIOQo5SjmIOcw6HDpsOpo63jugO9w8GDxQPKI8%2BD0yPew%2BOj6MPtQ%2FKD9uP6o%2F%2BkBIQIBAxkECQX5CGEKoQu5DGENCQ3ZDoEPKRBBEYESuRPZFWkW2RgZGdEa0RvZHNkd2R7ZH9kgWSDJITkhqSIZIzEkSSThJXkmESapKAkouSlIAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAAAAABAAxgABAAAAAAATABIAAAADAAEECQAAAGoAEgADAAEECQABACgAfAADAAEECQACAA4ApAADAAEECQADAEwAsgADAAEECQAEADgA%2FgADAAEECQAFAHgBNgADAAEECQAGADYBrgADAAEECQAIABYB5AADAAEECQAJABYB%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%2FtQAyAAAAAAAAAAAAAAAAAAAAAAAAAAABFwAAAQIBAwADAA0ADgEEAJYBBQEGAQcBCAEJAQoBCwEMAQ0BDgEPARABEQESARMA7wEUARUBFgEXARgBGQEaARsBHAEdAR4BHwEgASEBIgEjASQBJQEmAScBKAEpASoBKwEsAS0BLgEvATABMQEyATMBNAE1ATYBNwE4ATkBOgE7ATwBPQE%2BAT8BQAFBAUIBQwFEAUUBRgFHAUgBSQFKAUsBTAFNAU4BTwFQAVEBUgFTAVQBVQFWAVcBWAFZAVoBWwFcAV0BXgFfAWABYQFiAWMBZAFlAWYBZwFoAWkBagFrAWwBbQFuAW8BcAFxAXIBcwF0AXUBdgF3AXgBeQF6AXsBfAF9AX4BfwGAAYEBggGDAYQBhQGGAYcBiAGJAYoBiwGMAY0BjgGPAZABkQGSAZMBlAGVAZYBlwGYAZkBmgGbAZwBnQGeAZ8BoAGhAaIBowGkAaUBpgGnAagBqQGqAasBrAGtAa4BrwGwAbEBsgGzAbQBtQG2AbcBuAG5AboBuwG8Ab0BvgG%2FAcABwQHCAcMBxAHFAcYBxwHIAckBygHLAcwBzQHOAc8B0AHRAdIB0wHUAdUB1gHXAdgB2QHaAdsB3AHdAd4B3wHgAeEB4gHjAeQB5QHmAecB6AHpAeoB6wHsAe0B7gHvAfAB8QHyAfMB9AH1AfYB9wH4AfkB%2BgH7AfwB%2FQH%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%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMyOyIgZD0iTTEyNSAxMjAwaDEwNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xMTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMTA1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djExNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNMTA3NSAxMDAwaC04NTBxLTEwIDAgLTE3LjUgLTcuNXQtNy41IC0xNy41di04NTBxMCAtMTAgNy41IC0xNy41dDE3LjUgLTcuNWg4NTBxMTAgMCAxNy41IDcuNXQ3LjUgMTcuNXY4NTAgcTAgMTAgLTcuNSAxNy41dC0xNy41IDcuNXpNMzI1IDkwMGg1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek01MjUgOTAwaDQ1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNDUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NTAgcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTMyNSA3MDBoNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di01MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNNTI1IDcwMGg0NTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di01MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTQ1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djUwIHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek0zMjUgNTAwaDUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC01MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTUyNSA1MDBoNDUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC00NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1MCBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXpNMzI1IDMwMGg1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek01MjUgMzAwaDQ1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNDUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2NTAgcTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzMzsiIGQ9Ik05MDAgODAwdjIwMHEwIDgzIC01OC41IDE0MS41dC0xNDEuNSA1OC41aC0zMDBxLTgyIDAgLTE0MSAtNTl0LTU5IC0xNDF2LTIwMGgtMTAwcS00MSAwIC03MC41IC0yOS41dC0yOS41IC03MC41di02MDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDkwMHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjV2NjAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41aC0xMDB6TTQwMCA4MDB2MTUwcTAgMjEgMTUgMzUuNXQzNSAxNC41aDIwMCBxMjAgMCAzNSAtMTQuNXQxNSAtMzUuNXYtMTUwaC0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNDsiIGQ9Ik0xMjUgMTEwMGg1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTEwNzVoLTEwMHYxMDc1cTAgMTAgNy41IDE3LjV0MTcuNSA3LjV6TTEwNzUgMTA1MnE0IDAgOSAtMnExNiAtNiAxNiAtMjN2LTQyMXEwIC02IC0zIC0xMnEtMzMgLTU5IC02Ni41IC05OXQtNjUuNSAtNTh0LTU2LjUgLTI0LjV0LTUyLjUgLTYuNXEtMjYgMCAtNTcuNSA2LjV0LTUyLjUgMTMuNXQtNjAgMjFxLTQxIDE1IC02MyAyMi41dC01Ny41IDE1dC02NS41IDcuNSBxLTg1IDAgLTE2MCAtNTdxLTcgLTUgLTE1IC01cS02IDAgLTExIDNxLTE0IDcgLTE0IDIydjQzOHEyMiA1NSA4MiA5OC41dDExOSA0Ni41cTIzIDIgNDMgMC41dDQzIC03dDMyLjUgLTguNXQzOCAtMTN0MzIuNSAtMTFxNDEgLTE0IDYzLjUgLTIxdDU3IC0xNHQ2My41IC03cTEwMyAwIDE4MyA4N3E3IDggMTggOHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQxOyIgZD0iTTEyMDAgOTAwaC01MHEwIDIxIC00IDM3dC05LjUgMjYuNXQtMTggMTcuNXQtMjIgMTF0LTI4LjUgNS41dC0zMSAydC0zNyAwLjVoLTIwMHYtODUwcTAgLTIyIDI1IC0zNC41dDUwIC0xMy41bDI1IC0ydi0xMDBoLTQwMHYxMDBxNCAwIDExIDAuNXQyNCAzdDMwIDd0MjQgMTV0MTEgMjQuNXY4NTBoLTIwMHEtMjUgMCAtMzcgLTAuNXQtMzEgLTJ0LTI4LjUgLTUuNXQtMjIgLTExdC0xOCAtMTcuNXQtOS41IC0yNi41dC00IC0zN2gtNTB2MzAwIGgxMDAwdi0zMDB6TTUwMCA0NTBoLTI1cTAgMTUgLTQgMjQuNXQtOSAxNC41dC0xNyA3LjV0LTIwIDN0LTI1IDAuNWgtMTAwdi00MjVxMCAtMTEgMTIuNSAtMTcuNXQyNS41IC03LjVoMTJ2LTUwaC0yMDB2NTBxNTAgMCA1MCAyNXY0MjVoLTEwMHEtMTcgMCAtMjUgLTAuNXQtMjAgLTN0LTE3IC03LjV0LTkgLTE0LjV0LTQgLTI0LjVoLTI1djE1MGg1MDB2LTE1MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQ0OyIgZD0iTTEyMDAgMTEwMGgtMTIwMHYxMDBoMTIwMHYtMTAwek01MCAxMDAwaDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtOTAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djkwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTY1MCAxMDAwaDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDAgcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek03MDAgOTAwdi0zMDBoMzAwdjMwMGgtMzAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNDU7IiBkPSJNNTAgMTIwMGg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTkwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY5MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek02NTAgNzAwaDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djQwMCBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek03MDAgNjAwdi0zMDBoMzAwdjMwMGgtMzAwek0xMjAwIDBoLTEyMDB2MTAwaDEyMDB2LTEwMHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yMiAtMTF0LTE4IC0xNy41dC05LjUgLTI2LjV0LTQgLTM3aC01MHYzMDAgaDgwMHYtMzAwek0xMTAwIDIwMGgtMjAwdi0xMDBoMjAwdi0xMDBoLTMwMHYzMDBoMjAwdjEwMGgtMjAwdjEwMGgzMDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU3OyIgZD0iTTcwMSAxMDk4aDE2MHExNiAwIDIxIC0xMXQtNyAtMjNsLTQ2NCAtNDY0bDQ2NCAtNDY0cTEyIC0xMiA3IC0yM3QtMjEgLTExaC0xNjBxLTEzIDAgLTIzIDlsLTQ3MSA0NzFxLTcgOCAtNyAxOHQ3IDE4bDQ3MSA0NzFxMTAgOSAyMyA5eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTg7IiBkPSJNMzM5IDEwOThoMTYwcTEzIDAgMjMgLTlsNDcxIC00NzFxNyAtOCA3IC0xOHQtNyAtMThsLTQ3MSAtNDcxcS0xMCAtOSAtMjMgLTloLTE2MHEtMTYgMCAtMjEgMTF0NyAyM2w0NjQgNDY0bC00NjQgNDY0cS0xMiAxMiAtNyAyM3QyMSAxMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU5OyIgZD0iTTEwODcgODgycTExIC01IDExIC0yMXYtMTYwcTAgLTEzIC05IC0yM2wtNDcxIC00NzFxLTggLTcgLTE4IC03dC0xOCA3bC00NzEgNDcxcS05IDEwIC05IDIzdjE2MHEwIDE2IDExIDIxdDIzIC03bDQ2NCAtNDY0bDQ2NCA0NjRxMTIgMTIgMjMgN3oiIC8%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%2BCjwvZGVmcz48L3N2Zz4g%29%20format%28%27svg%27%29%7D%2Eglyphicon%7Bposition%3Arelative%3Btop%3A1px%3Bdisplay%3Ainline%2Dblock%3Bfont%2Dfamily%3A%27Glyphicons%20Halflings%27%3Bfont%2Dstyle%3Anormal%3Bfont%2Dweight%3A400%3Bline%2Dheight%3A1%3B%2Dwebkit%2Dfont%2Dsmoothing%3Aantialiased%3B%2Dmoz%2Dosx%2Dfont%2Dsmoothing%3Agrayscale%7D%2Eglyphicon%2Dasterisk%3Abefore%7Bcontent%3A%22%5C2a%22%7D%2Eglyphicon%2Dplus%3Abefore%7Bcontent%3A%22%5C2b%22%7D%2Eglyphicon%2Deur%3Abefore%2C%2Eglyphicon%2Deuro%3Abefore%7Bcontent%3A%22%5C20ac%22%7D%2Eglyphicon%2Dminus%3Abefore%7Bcontent%3A%22%5C2212%22%7D%2Eglyphicon%2Dcloud%3Abefore%7Bcontent%3A%22%5C2601%22%7D%2Eglyphicon%2Denvelope%3Abefore%7Bcontent%3A%22%5C2709%22%7D%2Eglyphicon%2Dpencil%3Abefore%7Bcontent%3A%22%5C270f%22%7D%2Eglyphicon%2Dglass%3Abefore%7Bcontent%3A%22%5Ce001%22%7D%2Eglyphicon%2Dmusic%3Abefore%7Bcontent%3A%22%5Ce002%22%7D%2Eglyphicon%2Dsearch%3Abefore%7Bcontent%3A%22%5Ce003%22%7D%2Eglyphicon%2Dheart%3Abefore%7Bcontent%3A%22%5Ce005%22%7D%2Eglyphicon%2Dstar%3Abefore%7Bcontent%3A%22%5Ce006%22%7D%2Eglyphicon%2Dstar%2Dempty%3Abefore%7Bcontent%3A%22%5Ce007%22%7D%2Eglyphicon%2Duser%3Abefore%7Bcontent%3A%22%5Ce008%22%7D%2Eglyphicon%2Dfilm%3Abefore%7Bcontent%3A%22%5Ce009%22%7D%2Eglyphicon%2Dth%2Dlarge%3Abefore%7Bcontent%3A%22%5Ce010%22%7D%2Eglyphicon%2Dth%3Abefore%7Bcontent%3A%22%5Ce011%22%7D%2Eglyphicon%2Dth%2Dlist%3Abefore%7Bcontent%3A%22%5Ce012%22%7D%2Eglyphicon%2Dok%3Abefore%7Bcontent%3A%22%5Ce013%22%7D%2Eglyphicon%2Dremove%3Abefore%7Bcontent%3A%22%5Ce014%22%7D%2Eglyphicon%2Dzoom%2Din%3Abefore%7Bcontent%3A%22%5Ce015%22%7D%2Eglyphicon%2Dzoom%2Dout%3Abefore%7Bcontent%3A%22%5Ce016%22%7D%2Eglyphicon%2Doff%3Abefore%7Bcontent%3A%22%5Ce017%22%7D%2Eglyphicon%2Dsignal%3Abefore%7Bcontent%3A%22%5Ce018%22%7D%2Eglyphicon%2Dcog%3Abefore%7Bcontent%3A%22%5Ce019%22%7D%2Eglyphicon%2Dtrash%3Abefore%7Bcontent%3A%22%5Ce020%22%7D%2Eglyphicon%2Dhome%3Abefore%7Bcontent%3A%22%5Ce021%22%7D%2Eglyphicon%2Dfile%3Abefore%7Bcontent%3A%22%5Ce022%22%7D%2Eglyphicon%2Dtime%3Abefore%7Bcontent%3A%22%5Ce023%22%7D%2Eglyphicon%2Droad%3Abefore%7Bcontent%3A%22%5Ce024%22%7D%2Eglyphicon%2Ddownload%2Dalt%3Abefore%7Bcontent%3A%22%5Ce025%22%7D%2Eglyphicon%2Ddownload%3Abefore%7Bcontent%3A%22%5Ce026%22%7D%2Eglyphicon%2Dupload%3Abefore%7Bcontent%3A%22%5Ce027%22%7D%2Eglyphicon%2Dinbox%3Abefore%7Bcontent%3A%22%5Ce028%22%7D%2Eglyphicon%2Dplay%2Dcircle%3Abefore%7Bcontent%3A%22%5Ce029%22%7D%2Eglyphicon%2Drepeat%3Abefore%7Bcontent%3A%22%5Ce030%22%7D%2Eglyphicon%2Drefresh%3Abefore%7Bcontent%3A%22%5Ce031%22%7D%2Eglyphicon%2Dlist%2Dalt%3Abefore%7Bcontent%3A%22%5Ce032%22%7D%2Eglyphicon%2Dlock%3Abefore%7Bcontent%3A%22%5Ce033%22%7D%2Eglyphicon%2Dflag%3Abefore%7Bcontent%3A%22%5Ce034%22%7D%2Eglyphicon%2Dheadphones%3Abefore%7Bcontent%3A%22%5Ce035%22%7D%2Eglyphicon%2Dvolume%2Doff%3Abefore%7Bcontent%3A%22%5Ce036%22%7D%2Eglyphicon%2Dvolume%2Ddown%3Abefore%7Bcontent%3A%22%5Ce037%22%7D%2Eglyphicon%2Dvolume%2Dup%3Abefore%7Bcontent%3A%22%5Ce038%22%7D%2Eglyphicon%2Dqrcode%3Abefore%7Bcontent%3A%22%5Ce039%22%7D%2Eglyphicon%2Dbarcode%3Abefore%7Bcontent%3A%22%5Ce040%22%7D%2Eglyphicon%2Dtag%3Abefore%7Bcontent%3A%22%5Ce041%22%7D%2Eglyphicon%2Dtags%3Abefore%7Bcontent%3A%22%5Ce042%22%7D%2Eglyphicon%2Dbook%3Abefore%7Bcontent%3A%22%5Ce043%22%7D%2Eglyphicon%2Dbookmark%3Abefore%7Bcontent%3A%22%5Ce044%22%7D%2Eglyphicon%2Dprint%3Abefore%7Bcontent%3A%22%5Ce045%22%7D%2Eglyphicon%2Dcamera%3Abefore%7Bcontent%3A%22%5Ce046%22%7D%2Eglyphicon%2Dfont%3Abefore%7Bcontent%3A%22%5Ce047%22%7D%2Eglyphicon%2Dbold%3Abefore%7Bcontent%3A%22%5Ce048%22%7D%2Eglyphicon%2Ditalic%3Abefore%7Bcontent%3A%22%5Ce049%22%7D%2Eglyphicon%2Dtext%2Dheight%3Abefore%7Bcontent%3A%22%5Ce050%22%7D%2Eglyphicon%2Dtext%2Dwidth%3Abefore%7Bcontent%3A%22%5Ce051%22%7D%2Eglyphicon%2Dalign%2Dleft%3Abefore%7Bcontent%3A%22%5Ce052%22%7D%2Eglyphicon%2Dalign%2Dcenter%3Abefore%7Bcontent%3A%22%5Ce053%22%7D%2Eglyphicon%2Dalign%2Dright%3Abefore%7Bcontent%3A%22%5Ce054%22%7D%2Eglyphicon%2Dalign%2Djustify%3Abefore%7Bcontent%3A%22%5Ce055%22%7D%2Eglyphicon%2Dlist%3Abefore%7Bcontent%3A%22%5Ce056%22%7D%2Eglyphicon%2Dindent%2Dleft%3Abefore%7Bcontent%3A%22%5Ce057%22%7D%2Eglyphicon%2Dindent%2Dright%3Abefore%7Bcontent%3A%22%5Ce058%22%7D%2Eglyphicon%2Dfacetime%2Dvideo%3Abefore%7Bcontent%3A%22%5Ce059%22%7D%2Eglyphicon%2Dpicture%3Abefore%7Bcontent%3A%22%5Ce060%22%7D%2Eglyphicon%2Dmap%2Dmarker%3Abefore%7Bcontent%3A%22%5Ce062%22%7D%2Eglyphicon%2Dadjust%3Abefore%7Bcontent%3A%22%5Ce063%22%7D%2Eglyphicon%2Dtint%3Abefore%7Bcontent%3A%22%5Ce064%22%7D%2Eglyphicon%2Dedit%3Abefore%7Bcontent%3A%22%5Ce065%22%7D%2Eglyphicon%2Dshare%3Abefore%7Bcontent%3A%22%5Ce066%22%7D%2Eglyphicon%2Dcheck%3Abefore%7Bcontent%3A%22%5Ce067%22%7D%2Eglyphicon%2Dmove%3Abefore%7Bcontent%3A%22%5Ce068%22%7D%2Eglyphicon%2Dstep%2Dbackward%3Abefore%7Bcontent%3A%22%5Ce069%22%7D%2Eglyphicon%2Dfast%2Dbackward%3Abefore%7Bcontent%3A%22%5Ce070%22%7D%2Eglyphicon%2Dbackward%3Abefore%7Bcontent%3A%22%5Ce071%22%7D%2Eglyphicon%2Dplay%3Abefore%7Bcontent%3A%22%5Ce072%22%7D%2Eglyphicon%2Dpause%3Abefore%7Bcontent%3A%22%5Ce073%22%7D%2Eglyphicon%2Dstop%3Abefore%7Bcontent%3A%22%5Ce074%22%7D%2Eglyphicon%2Dforward%3Abefore%7Bcontent%3A%22%5Ce075%22%7D%2Eglyphicon%2Dfast%2Dforward%3Abefore%7Bcontent%3A%22%5Ce076%22%7D%2Eglyphicon%2Dstep%2Dforward%3Abefore%7Bcontent%3A%22%5Ce077%22%7D%2Eglyphicon%2Deject%3Abefore%7Bcontent%3A%22%5Ce078%22%7D%2Eglyphicon%2Dchevron%2Dleft%3Abefore%7Bcontent%3A%22%5Ce079%22%7D%2Eglyphicon%2Dchevron%2Dright%3Abefore%7Bcontent%3A%22%5Ce080%22%7D%2Eglyphicon%2Dplus%2Dsign%3Abefore%7Bcontent%3A%22%5Ce081%22%7D%2Eglyphicon%2Dminus%2Dsign%3Abefore%7Bcontent%3A%22%5Ce082%22%7D%2Eglyphicon%2Dremove%2Dsign%3Abefore%7Bcontent%3A%22%5Ce083%22%7D%2Eglyphicon%2Dok%2Dsign%3Abefore%7Bcontent%3A%22%5Ce084%22%7D%2Eglyphicon%2Dquestion%2Dsign%3Abefore%7Bcontent%3A%22%5Ce085%22%7D%2Eglyphicon%2Dinfo%2Dsign%3Abefore%7Bcontent%3A%22%5Ce086%22%7D%2Eglyphicon%2Dscreenshot%3Abefore%7Bcontent%3A%22%5Ce087%22%7D%2Eglyphicon%2Dremove%2Dcircle%3Abefore%7Bcontent%3A%22%5Ce088%22%7D%2Eglyphicon%2Dok%2Dcircle%3Abefore%7Bcontent%3A%22%5Ce089%22%7D%2Eglyphicon%2Dban%2Dcircle%3Abefore%7Bcontent%3A%22%5Ce090%22%7D%2Eglyphicon%2Darrow%2Dleft%3Abefore%7Bcontent%3A%22%5Ce091%22%7D%2Eglyphicon%2Darrow%2Dright%3Abefore%7Bcontent%3A%22%5Ce092%22%7D%2Eglyphicon%2Darrow%2Dup%3Abefore%7Bcontent%3A%22%5Ce093%22%7D%2Eglyphicon%2Darrow%2Ddown%3Abefore%7Bcontent%3A%22%5Ce094%22%7D%2Eglyphicon%2Dshare%2Dalt%3Abefore%7Bcontent%3A%22%5Ce095%22%7D%2Eglyphicon%2Dresize%2Dfull%3Abefore%7Bcontent%3A%22%5Ce096%22%7D%2Eglyphicon%2Dresize%2Dsmall%3Abefore%7Bcontent%3A%22%5Ce097%22%7D%2Eglyphicon%2Dexclamation%2Dsign%3Abefore%7Bcontent%3A%22%5Ce101%22%7D%2Eglyphicon%2Dgift%3Abefore%7Bcontent%3A%22%5Ce102%22%7D%2Eglyphicon%2Dleaf%3Abefore%7Bcontent%3A%22%5Ce103%22%7D%2Eglyphicon%2Dfire%3Abefore%7Bcontent%3A%22%5Ce104%22%7D%2Eglyphicon%2Deye%2Dopen%3Abefore%7Bcontent%3A%22%5Ce105%22%7D%2Eglyphicon%2Deye%2Dclose%3Abefore%7Bcontent%3A%22%5Ce106%22%7D%2Eglyphicon%2Dwarning%2Dsign%3Abefore%7Bcontent%3A%22%5Ce107%22%7D%2Eglyphicon%2Dplane%3Abefore%7Bcontent%3A%22%5Ce108%22%7D%2Eglyphicon%2Dcalendar%3Abefore%7Bcontent%3A%22%5Ce109%22%7D%2Eglyphicon%2Drandom%3Abefore%7Bcontent%3A%22%5Ce110%22%7D%2Eglyphicon%2Dcomment%3Abefore%7Bcontent%3A%22%5Ce111%22%7D%2Eglyphicon%2Dmagnet%3Abefore%7Bcontent%3A%22%5Ce112%22%7D%2Eglyphicon%2Dchevron%2Dup%3Abefore%7Bcontent%3A%22%5Ce113%22%7D%2Eglyphicon%2Dchevron%2Ddown%3Abefore%7Bcontent%3A%22%5Ce114%22%7D%2Eglyphicon%2Dretweet%3Abefore%7Bcontent%3A%22%5Ce115%22%7D%2Eglyphicon%2Dshopping%2Dcart%3Abefore%7Bcontent%3A%22%5Ce116%22%7D%2Eglyphicon%2Dfolder%2Dclose%3Abefore%7Bcontent%3A%22%5Ce117%22%7D%2Eglyphicon%2Dfolder%2Dopen%3Abefore%7Bcontent%3A%22%5Ce118%22%7D%2Eglyphicon%2Dresize%2Dvertical%3Abefore%7Bcontent%3A%22%5Ce119%22%7D%2Eglyphicon%2Dresize%2Dhorizontal%3Abefore%7Bcontent%3A%22%5Ce120%22%7D%2Eglyphicon%2Dhdd%3Abefore%7Bcontent%3A%22%5Ce121%22%7D%2Eglyphicon%2Dbullhorn%3Abefore%7Bcontent%3A%22%5Ce122%22%7D%2Eglyphicon%2Dbell%3Abefore%7Bcontent%3A%22%5Ce123%22%7D%2Eglyphicon%2Dcertificate%3Abefore%7Bcontent%3A%22%5Ce124%22%7D%2Eglyphicon%2Dthumbs%2Dup%3Abefore%7Bcontent%3A%22%5Ce125%22%7D%2Eglyphicon%2Dthumbs%2Ddown%3Abefore%7Bcontent%3A%22%5Ce126%22%7D%2Eglyphicon%2Dhand%2Dright%3Abefore%7Bcontent%3A%22%5Ce127%22%7D%2Eglyphicon%2Dhand%2Dleft%3Abefore%7Bcontent%3A%22%5Ce128%22%7D%2Eglyphicon%2Dhand%2Dup%3Abefore%7Bcontent%3A%22%5Ce129%22%7D%2Eglyphicon%2Dhand%2Ddown%3Abefore%7Bcontent%3A%22%5Ce130%22%7D%2Eglyphicon%2Dcircle%2Darrow%2Dright%3Abefore%7Bcontent%3A%22%5Ce131%22%7D%2Eglyphicon%2Dcircle%2Darrow%2Dleft%3Abefore%7Bcontent%3A%22%5Ce132%22%7D%2Eglyphicon%2Dcircle%2Darrow%2Dup%3Abefore%7Bcontent%3A%22%5Ce133%22%7D%2Eglyphicon%2Dcircle%2Darrow%2Ddown%3Abefore%7Bcontent%3A%22%5Ce134%22%7D%2Eglyphicon%2Dglobe%3Abefore%7Bcontent%3A%22%5Ce135%22%7D%2Eglyphicon%2Dwrench%3Abefore%7Bcontent%3A%22%5Ce136%22%7D%2Eglyphicon%2Dtasks%3Abefore%7Bcontent%3A%22%5Ce137%22%7D%2Eglyphicon%2Dfilter%3Abefore%7Bcontent%3A%22%5Ce138%22%7D%2Eglyphicon%2Dbriefcase%3Abefore%7Bcontent%3A%22%5Ce139%22%7D%2Eglyphicon%2Dfullscreen%3Abefore%7Bcontent%3A%22%5Ce140%22%7D%2Eglyphicon%2Ddashboard%3Abefore%7Bcontent%3A%22%5Ce141%22%7D%2Eglyphicon%2Dpaperclip%3Abefore%7Bcontent%3A%22%5Ce142%22%7D%2Eglyphicon%2Dheart%2Dempty%3Abefore%7Bcontent%3A%22%5Ce143%22%7D%2Eglyphicon%2Dlink%3Abefore%7Bcontent%3A%22%5Ce144%22%7D%2Eglyphicon%2Dphone%3Abefore%7Bcontent%3A%22%5Ce145%22%7D%2Eglyphicon%2Dpushpin%3Abefore%7Bcontent%3A%22%5Ce146%22%7D%2Eglyphicon%2Dusd%3Abefore%7Bcontent%3A%22%5Ce148%22%7D%2Eglyphicon%2Dgbp%3Abefore%7Bcontent%3A%22%5Ce149%22%7D%2Eglyphicon%2Dsort%3Abefore%7Bcontent%3A%22%5Ce150%22%7D%2Eglyphicon%2Dsort%2Dby%2Dalphabet%3Abefore%7Bcontent%3A%22%5Ce151%22%7D%2Eglyphicon%2Dsort%2Dby%2Dalphabet%2Dalt%3Abefore%7Bcontent%3A%22%5Ce152%22%7D%2Eglyphicon%2Dsort%2Dby%2Dorder%3Abefore%7Bcontent%3A%22%5Ce153%22%7D%2Eglyphicon%2Dsort%2Dby%2Dorder%2Dalt%3Abefore%7Bcontent%3A%22%5Ce154%22%7D%2Eglyphicon%2Dsort%2Dby%2Dattributes%3Abefore%7Bcontent%3A%22%5Ce155%22%7D%2Eglyphicon%2Dsort%2Dby%2Dattributes%2Dalt%3Abefore%7Bcontent%3A%22%5Ce156%22%7D%2Eglyphicon%2Dunchecked%3Abefore%7Bcontent%3A%22%5Ce157%22%7D%2Eglyphicon%2Dexpand%3Abefore%7Bcontent%3A%22%5Ce158%22%7D%2Eglyphicon%2Dcollapse%2Ddown%3Abefore%7Bcontent%3A%22%5Ce159%22%7D%2Eglyphicon%2Dcollapse%2Dup%3Abefore%7Bcontent%3A%22%5Ce160%22%7D%2Eglyphicon%2Dlog%2Din%3Abefore%7Bcontent%3A%22%5Ce161%22%7D%2Eglyphicon%2Dflash%3Abefore%7Bcontent%3A%22%5Ce162%22%7D%2Eglyphicon%2Dlog%2Dout%3Abefore%7Bcontent%3A%22%5Ce163%22%7D%2Eglyphicon%2Dnew%2Dwindow%3Abefore%7Bcontent%3A%22%5Ce164%22%7D%2Eglyphicon%2Drecord%3Abefore%7Bcontent%3A%22%5Ce165%22%7D%2Eglyphicon%2Dsave%3Abefore%7Bcontent%3A%22%5Ce166%22%7D%2Eglyphicon%2Dopen%3Abefore%7Bcontent%3A%22%5Ce167%22%7D%2Eglyphicon%2Dsaved%3Abefore%7Bcontent%3A%22%5Ce168%22%7D%2Eglyphicon%2Dimport%3Abefore%7Bcontent%3A%22%5Ce169%22%7D%2Eglyphicon%2Dexport%3Abefore%7Bcontent%3A%22%5Ce170%22%7D%2Eglyphicon%2Dsend%3Abefore%7Bcontent%3A%22%5Ce171%22%7D%2Eglyphicon%2Dfloppy%2Ddisk%3Abefore%7Bcontent%3A%22%5Ce172%22%7D%2Eglyphicon%2Dfloppy%2Dsaved%3Abefore%7Bcontent%3A%22%5Ce173%22%7D%2Eglyphicon%2Dfloppy%2Dremove%3Abefore%7Bcontent%3A%22%5Ce174%22%7D%2Eglyphicon%2Dfloppy%2Dsave%3Abefore%7Bcontent%3A%22%5Ce175%22%7D%2Eglyphicon%2Dfloppy%2Dopen%3Abefore%7Bcontent%3A%22%5Ce176%22%7D%2Eglyphicon%2Dcredit%2Dcard%3Abefore%7Bcontent%3A%22%5Ce177%22%7D%2Eglyphicon%2Dtransfer%3Abefore%7Bcontent%3A%22%5Ce178%22%7D%2Eglyphicon%2Dcutlery%3Abefore%7Bcontent%3A%22%5Ce179%22%7D%2Eglyphicon%2Dheader%3Abefore%7Bcontent%3A%22%5Ce180%22%7D%2Eglyphicon%2Dcompressed%3Abefore%7Bcontent%3A%22%5Ce181%22%7D%2Eglyphicon%2Dearphone%3Abefore%7Bcontent%3A%22%5Ce182%22%7D%2Eglyphicon%2Dphone%2Dalt%3Abefore%7Bcontent%3A%22%5Ce183%22%7D%2Eglyphicon%2Dtower%3Abefore%7Bcontent%3A%22%5Ce184%22%7D%2Eglyphicon%2Dstats%3Abefore%7Bcontent%3A%22%5Ce185%22%7D%2Eglyphicon%2Dsd%2Dvideo%3Abefore%7Bcontent%3A%22%5Ce186%22%7D%2Eglyphicon%2Dhd%2Dvideo%3Abefore%7Bcontent%3A%22%5Ce187%22%7D%2Eglyphicon%2Dsubtitles%3Abefore%7Bcontent%3A%22%5Ce188%22%7D%2Eglyphicon%2Dsound%2Dstereo%3Abefore%7Bcontent%3A%22%5Ce189%22%7D%2Eglyphicon%2Dsound%2Ddolby%3Abefore%7Bcontent%3A%22%5Ce190%22%7D%2Eglyphicon%2Dsound%2D5%2D1%3Abefore%7Bcontent%3A%22%5Ce191%22%7D%2Eglyphicon%2Dsound%2D6%2D1%3Abefore%7Bcontent%3A%22%5Ce192%22%7D%2Eglyphicon%2Dsound%2D7%2D1%3Abefore%7Bcontent%3A%22%5Ce193%22%7D%2Eglyphicon%2Dcopyright%2Dmark%3Abefore%7Bcontent%3A%22%5Ce194%22%7D%2Eglyphicon%2Dregistration%2Dmark%3Abefore%7Bcontent%3A%22%5Ce195%22%7D%2Eglyphicon%2Dcloud%2Ddownload%3Abefore%7Bcontent%3A%22%5Ce197%22%7D%2Eglyphicon%2Dcloud%2Dupload%3Abefore%7Bcontent%3A%22%5Ce198%22%7D%2Eglyphicon%2Dtree%2Dconifer%3Abefore%7Bcontent%3A%22%5Ce199%22%7D%2Eglyphicon%2Dtree%2Ddeciduous%3Abefore%7Bcontent%3A%22%5Ce200%22%7D%2Eglyphicon%2Dcd%3Abefore%7Bcontent%3A%22%5Ce201%22%7D%2Eglyphicon%2Dsave%2Dfile%3Abefore%7Bcontent%3A%22%5Ce202%22%7D%2Eglyphicon%2Dopen%2Dfile%3Abefore%7Bcontent%3A%22%5Ce203%22%7D%2Eglyphicon%2Dlevel%2Dup%3Abefore%7Bcontent%3A%22%5Ce204%22%7D%2Eglyphicon%2Dcopy%3Abefore%7Bcontent%3A%22%5Ce205%22%7D%2Eglyphicon%2Dpaste%3Abefore%7Bcontent%3A%22%5Ce206%22%7D%2Eglyphicon%2Dalert%3Abefore%7Bcontent%3A%22%5Ce209%22%7D%2Eglyphicon%2Dequalizer%3Abefore%7Bcontent%3A%22%5Ce210%22%7D%2Eglyphicon%2Dking%3Abefore%7Bcontent%3A%22%5Ce211%22%7D%2Eglyphicon%2Dqueen%3Abefore%7Bcontent%3A%22%5Ce212%22%7D%2Eglyphicon%2Dpawn%3Abefore%7Bcontent%3A%22%5Ce213%22%7D%2Eglyphicon%2Dbishop%3Abefore%7Bcontent%3A%22%5Ce214%22%7D%2Eglyphicon%2Dknight%3Abefore%7Bcontent%3A%22%5Ce215%22%7D%2Eglyphicon%2Dbaby%2Dformula%3Abefore%7Bcontent%3A%22%5Ce216%22%7D%2Eglyphicon%2Dtent%3Abefore%7Bcontent%3A%22%5C26fa%22%7D%2Eglyphicon%2Dblackboard%3Abefore%7Bcontent%3A%22%5Ce218%22%7D%2Eglyphicon%2Dbed%3Abefore%7Bcontent%3A%22%5Ce219%22%7D%2Eglyphicon%2Dapple%3Abefore%7Bcontent%3A%22%5Cf8ff%22%7D%2Eglyphicon%2Derase%3Abefore%7Bcontent%3A%22%5Ce221%22%7D%2Eglyphicon%2Dhourglass%3Abefore%7Bcontent%3A%22%5C231b%22%7D%2Eglyphicon%2Dlamp%3Abefore%7Bcontent%3A%22%5Ce223%22%7D%2Eglyphicon%2Dduplicate%3Abefore%7Bcontent%3A%22%5Ce224%22%7D%2Eglyphicon%2Dpiggy%2Dbank%3Abefore%7Bcontent%3A%22%5Ce225%22%7D%2Eglyphicon%2Dscissors%3Abefore%7Bcontent%3A%22%5Ce226%22%7D%2Eglyphicon%2Dbitcoin%3Abefore%7Bcontent%3A%22%5Ce227%22%7D%2Eglyphicon%2Dbtc%3Abefore%7Bcontent%3A%22%5Ce227%22%7D%2Eglyphicon%2Dxbt%3Abefore%7Bcontent%3A%22%5Ce227%22%7D%2Eglyphicon%2Dyen%3Abefore%7Bcontent%3A%22%5C00a5%22%7D%2Eglyphicon%2Djpy%3Abefore%7Bcontent%3A%22%5C00a5%22%7D%2Eglyphicon%2Druble%3Abefore%7Bcontent%3A%22%5C20bd%22%7D%2Eglyphicon%2Drub%3Abefore%7Bcontent%3A%22%5C20bd%22%7D%2Eglyphicon%2Dscale%3Abefore%7Bcontent%3A%22%5Ce230%22%7D%2Eglyphicon%2Dice%2Dlolly%3Abefore%7Bcontent%3A%22%5Ce231%22%7D%2Eglyphicon%2Dice%2Dlolly%2Dtasted%3Abefore%7Bcontent%3A%22%5Ce232%22%7D%2Eglyphicon%2Deducation%3Abefore%7Bcontent%3A%22%5Ce233%22%7D%2Eglyphicon%2Doption%2Dhorizontal%3Abefore%7Bcontent%3A%22%5Ce234%22%7D%2Eglyphicon%2Doption%2Dvertical%3Abefore%7Bcontent%3A%22%5Ce235%22%7D%2Eglyphicon%2Dmenu%2Dhamburger%3Abefore%7Bcontent%3A%22%5Ce236%22%7D%2Eglyphicon%2Dmodal%2Dwindow%3Abefore%7Bcontent%3A%22%5Ce237%22%7D%2Eglyphicon%2Doil%3Abefore%7Bcontent%3A%22%5Ce238%22%7D%2Eglyphicon%2Dgrain%3Abefore%7Bcontent%3A%22%5Ce239%22%7D%2Eglyphicon%2Dsunglasses%3Abefore%7Bcontent%3A%22%5Ce240%22%7D%2Eglyphicon%2Dtext%2Dsize%3Abefore%7Bcontent%3A%22%5Ce241%22%7D%2Eglyphicon%2Dtext%2Dcolor%3Abefore%7Bcontent%3A%22%5Ce242%22%7D%2Eglyphicon%2Dtext%2Dbackground%3Abefore%7Bcontent%3A%22%5Ce243%22%7D%2Eglyphicon%2Dobject%2Dalign%2Dtop%3Abefore%7Bcontent%3A%22%5Ce244%22%7D%2Eglyphicon%2Dobject%2Dalign%2Dbottom%3Abefore%7Bcontent%3A%22%5Ce245%22%7D%2Eglyphicon%2Dobject%2Dalign%2Dhorizontal%3Abefore%7Bcontent%3A%22%5Ce246%22%7D%2Eglyphicon%2Dobject%2Dalign%2Dleft%3Abefore%7Bcontent%3A%22%5Ce247%22%7D%2Eglyphicon%2Dobject%2Dalign%2Dvertical%3Abefore%7Bcontent%3A%22%5Ce248%22%7D%2Eglyphicon%2Dobject%2Dalign%2Dright%3Abefore%7Bcontent%3A%22%5Ce249%22%7D%2Eglyphicon%2Dtriangle%2Dright%3Abefore%7Bcontent%3A%22%5Ce250%22%7D%2Eglyphicon%2Dtriangle%2Dleft%3Abefore%7Bcontent%3A%22%5Ce251%22%7D%2Eglyphicon%2Dtriangle%2Dbottom%3Abefore%7Bcontent%3A%22%5Ce252%22%7D%2Eglyphicon%2Dtriangle%2Dtop%3Abefore%7Bcontent%3A%22%5Ce253%22%7D%2Eglyphicon%2Dconsole%3Abefore%7Bcontent%3A%22%5Ce254%22%7D%2Eglyphicon%2Dsuperscript%3Abefore%7Bcontent%3A%22%5Ce255%22%7D%2Eglyphicon%2Dsubscript%3Abefore%7Bcontent%3A%22%5Ce256%22%7D%2Eglyphicon%2Dmenu%2Dleft%3Abefore%7Bcontent%3A%22%5Ce257%22%7D%2Eglyphicon%2Dmenu%2Dright%3Abefore%7Bcontent%3A%22%5Ce258%22%7D%2Eglyphicon%2Dmenu%2Ddown%3Abefore%7Bcontent%3A%22%5Ce259%22%7D%2Eglyphicon%2Dmenu%2Dup%3Abefore%7Bcontent%3A%22%5Ce260%22%7D%2A%7B%2Dwebkit%2Dbox%2Dsizing%3Aborder%2Dbox%3B%2Dmoz%2Dbox%2Dsizing%3Aborder%2Dbox%3Bbox%2Dsizing%3Aborder%2Dbox%7D%3Aafter%2C%3Abefore%7B%2Dwebkit%2Dbox%2Dsizing%3Aborder%2Dbox%3B%2Dmoz%2Dbox%2Dsizing%3Aborder%2Dbox%3Bbox%2Dsizing%3Aborder%2Dbox%7Dhtml%7Bfont%2Dsize%3A10px%3B%2Dwebkit%2Dtap%2Dhighlight%2Dcolor%3Argba%280%2C0%2C0%2C0%29%7Dbody%7Bfont%2Dfamily%3A%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A14px%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23333%3Bbackground%2Dcolor%3A%23fff%7Dbutton%2Cinput%2Cselect%2Ctextarea%7Bfont%2Dfamily%3Ainherit%3Bfont%2Dsize%3Ainherit%3Bline%2Dheight%3Ainherit%7Da%7Bcolor%3A%23337ab7%3Btext%2Ddecoration%3Anone%7Da%3Afocus%2Ca%3Ahover%7Bcolor%3A%2323527c%3Btext%2Ddecoration%3Aunderline%7Da%3Afocus%7Boutline%3Athin%20dotted%3Boutline%3A5px%20auto%20%2Dwebkit%2Dfocus%2Dring%2Dcolor%3Boutline%2Doffset%3A%2D2px%7Dfigure%7Bmargin%3A0%7Dimg%7Bvertical%2Dalign%3Amiddle%7D%2Ecarousel%2Dinner%3E%2Eitem%3Ea%3Eimg%2C%2Ecarousel%2Dinner%3E%2Eitem%3Eimg%2C%2Eimg%2Dresponsive%2C%2Ethumbnail%20a%3Eimg%2C%2Ethumbnail%3Eimg%7Bdisplay%3Ablock%3Bmax%2Dwidth%3A100%25%3Bheight%3Aauto%7D%2Eimg%2Drounded%7Bborder%2Dradius%3A6px%7D%2Eimg%2Dthumbnail%7Bdisplay%3Ainline%2Dblock%3Bmax%2Dwidth%3A100%25%3Bheight%3Aauto%3Bpadding%3A4px%3Bline%2Dheight%3A1%2E42857143%3Bbackground%2Dcolor%3A%23fff%3Bborder%3A1px%20solid%20%23ddd%3Bborder%2Dradius%3A4px%3B%2Dwebkit%2Dtransition%3Aall%20%2E2s%20ease%2Din%2Dout%3B%2Do%2Dtransition%3Aall%20%2E2s%20ease%2Din%2Dout%3Btransition%3Aall%20%2E2s%20ease%2Din%2Dout%7D%2Eimg%2Dcircle%7Bborder%2Dradius%3A50%25%7Dhr%7Bmargin%2Dtop%3A20px%3Bmargin%2Dbottom%3A20px%3Bborder%3A0%3Bborder%2Dtop%3A1px%20solid%20%23eee%7D%2Esr%2Donly%7Bposition%3Aabsolute%3Bwidth%3A1px%3Bheight%3A1px%3Bpadding%3A0%3Bmargin%3A%2D1px%3Boverflow%3Ahidden%3Bclip%3Arect%280%2C0%2C0%2C0%29%3Bborder%3A0%7D%2Esr%2Donly%2Dfocusable%3Aactive%2C%2Esr%2Donly%2Dfocusable%3Afocus%7Bposition%3Astatic%3Bwidth%3Aauto%3Bheight%3Aauto%3Bmargin%3A0%3Boverflow%3Avisible%3Bclip%3Aauto%7D%5Brole%3Dbutton%5D%7Bcursor%3Apointer%7D%2Eh1%2C%2Eh2%2C%2Eh3%2C%2Eh4%2C%2Eh5%2C%2Eh6%2Ch1%2Ch2%2Ch3%2Ch4%2Ch5%2Ch6%7Bfont%2Dfamily%3Ainherit%3Bfont%2Dweight%3A500%3Bline%2Dheight%3A1%2E1%3Bcolor%3Ainherit%7D%2Eh1%20%2Esmall%2C%2Eh1%20small%2C%2Eh2%20%2Esmall%2C%2Eh2%20small%2C%2Eh3%20%2Esmall%2C%2Eh3%20small%2C%2Eh4%20%2Esmall%2C%2Eh4%20small%2C%2Eh5%20%2Esmall%2C%2Eh5%20small%2C%2Eh6%20%2Esmall%2C%2Eh6%20small%2Ch1%20%2Esmall%2Ch1%20small%2Ch2%20%2Esmall%2Ch2%20small%2Ch3%20%2Esmall%2Ch3%20small%2Ch4%20%2Esmall%2Ch4%20small%2Ch5%20%2Esmall%2Ch5%20small%2Ch6%20%2Esmall%2Ch6%20small%7Bfont%2Dweight%3A400%3Bline%2Dheight%3A1%3Bcolor%3A%23777%7D%2Eh1%2C%2Eh2%2C%2Eh3%2Ch1%2Ch2%2Ch3%7Bmargin%2Dtop%3A20px%3Bmargin%2Dbottom%3A10px%7D%2Eh1%20%2Esmall%2C%2Eh1%20small%2C%2Eh2%20%2Esmall%2C%2Eh2%20small%2C%2Eh3%20%2Esmall%2C%2Eh3%20small%2Ch1%20%2Esmall%2Ch1%20small%2Ch2%20%2Esmall%2Ch2%20small%2Ch3%20%2Esmall%2Ch3%20small%7Bfont%2Dsize%3A65%25%7D%2Eh4%2C%2Eh5%2C%2Eh6%2Ch4%2Ch5%2Ch6%7Bmargin%2Dtop%3A10px%3Bmargin%2Dbottom%3A10px%7D%2Eh4%20%2Esmall%2C%2Eh4%20small%2C%2Eh5%20%2Esmall%2C%2Eh5%20small%2C%2Eh6%20%2Esmall%2C%2Eh6%20small%2Ch4%20%2Esmall%2Ch4%20small%2Ch5%20%2Esmall%2Ch5%20small%2Ch6%20%2Esmall%2Ch6%20small%7Bfont%2Dsize%3A75%25%7D%2Eh1%2Ch1%7Bfont%2Dsize%3A36px%7D%2Eh2%2Ch2%7Bfont%2Dsize%3A30px%7D%2Eh3%2Ch3%7Bfont%2Dsize%3A24px%7D%2Eh4%2Ch4%7Bfont%2Dsize%3A18px%7D%2Eh5%2Ch5%7Bfont%2Dsize%3A14px%7D%2Eh6%2Ch6%7Bfont%2Dsize%3A12px%7Dp%7Bmargin%3A0%200%2010px%7D%2Elead%7Bmargin%2Dbottom%3A20px%3Bfont%2Dsize%3A16px%3Bfont%2Dweight%3A300%3Bline%2Dheight%3A1%2E4%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Elead%7Bfont%2Dsize%3A21px%7D%7D%2Esmall%2Csmall%7Bfont%2Dsize%3A85%25%7D%2Emark%2Cmark%7Bpadding%3A%2E2em%3Bbackground%2Dcolor%3A%23fcf8e3%7D%2Etext%2Dleft%7Btext%2Dalign%3Aleft%7D%2Etext%2Dright%7Btext%2Dalign%3Aright%7D%2Etext%2Dcenter%7Btext%2Dalign%3Acenter%7D%2Etext%2Djustify%7Btext%2Dalign%3Ajustify%7D%2Etext%2Dnowrap%7Bwhite%2Dspace%3Anowrap%7D%2Etext%2Dlowercase%7Btext%2Dtransform%3Alowercase%7D%2Etext%2Duppercase%7Btext%2Dtransform%3Auppercase%7D%2Etext%2Dcapitalize%7Btext%2Dtransform%3Acapitalize%7D%2Etext%2Dmuted%7Bcolor%3A%23777%7D%2Etext%2Dprimary%7Bcolor%3A%23337ab7%7Da%2Etext%2Dprimary%3Afocus%2Ca%2Etext%2Dprimary%3Ahover%7Bcolor%3A%23286090%7D%2Etext%2Dsuccess%7Bcolor%3A%233c763d%7Da%2Etext%2Dsuccess%3Afocus%2Ca%2Etext%2Dsuccess%3Ahover%7Bcolor%3A%232b542c%7D%2Etext%2Dinfo%7Bcolor%3A%2331708f%7Da%2Etext%2Dinfo%3Afocus%2Ca%2Etext%2Dinfo%3Ahover%7Bcolor%3A%23245269%7D%2Etext%2Dwarning%7Bcolor%3A%238a6d3b%7Da%2Etext%2Dwarning%3Afocus%2Ca%2Etext%2Dwarning%3Ahover%7Bcolor%3A%2366512c%7D%2Etext%2Ddanger%7Bcolor%3A%23a94442%7Da%2Etext%2Ddanger%3Afocus%2Ca%2Etext%2Ddanger%3Ahover%7Bcolor%3A%23843534%7D%2Ebg%2Dprimary%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23337ab7%7Da%2Ebg%2Dprimary%3Afocus%2Ca%2Ebg%2Dprimary%3Ahover%7Bbackground%2Dcolor%3A%23286090%7D%2Ebg%2Dsuccess%7Bbackground%2Dcolor%3A%23dff0d8%7Da%2Ebg%2Dsuccess%3Afocus%2Ca%2Ebg%2Dsuccess%3Ahover%7Bbackground%2Dcolor%3A%23c1e2b3%7D%2Ebg%2Dinfo%7Bbackground%2Dcolor%3A%23d9edf7%7Da%2Ebg%2Dinfo%3Afocus%2Ca%2Ebg%2Dinfo%3Ahover%7Bbackground%2Dcolor%3A%23afd9ee%7D%2Ebg%2Dwarning%7Bbackground%2Dcolor%3A%23fcf8e3%7Da%2Ebg%2Dwarning%3Afocus%2Ca%2Ebg%2Dwarning%3Ahover%7Bbackground%2Dcolor%3A%23f7ecb5%7D%2Ebg%2Ddanger%7Bbackground%2Dcolor%3A%23f2dede%7Da%2Ebg%2Ddanger%3Afocus%2Ca%2Ebg%2Ddanger%3Ahover%7Bbackground%2Dcolor%3A%23e4b9b9%7D%2Epage%2Dheader%7Bpadding%2Dbottom%3A9px%3Bmargin%3A40px%200%2020px%3Bborder%2Dbottom%3A1px%20solid%20%23eee%7Dol%2Cul%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A10px%7Dol%20ol%2Col%20ul%2Cul%20ol%2Cul%20ul%7Bmargin%2Dbottom%3A0%7D%2Elist%2Dunstyled%7Bpadding%2Dleft%3A0%3Blist%2Dstyle%3Anone%7D%2Elist%2Dinline%7Bpadding%2Dleft%3A0%3Bmargin%2Dleft%3A%2D5px%3Blist%2Dstyle%3Anone%7D%2Elist%2Dinline%3Eli%7Bdisplay%3Ainline%2Dblock%3Bpadding%2Dright%3A5px%3Bpadding%2Dleft%3A5px%7Ddl%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A20px%7Ddd%2Cdt%7Bline%2Dheight%3A1%2E42857143%7Ddt%7Bfont%2Dweight%3A700%7Ddd%7Bmargin%2Dleft%3A0%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Edl%2Dhorizontal%20dt%7Bfloat%3Aleft%3Bwidth%3A160px%3Boverflow%3Ahidden%3Bclear%3Aleft%3Btext%2Dalign%3Aright%3Btext%2Doverflow%3Aellipsis%3Bwhite%2Dspace%3Anowrap%7D%2Edl%2Dhorizontal%20dd%7Bmargin%2Dleft%3A180px%7D%7Dabbr%5Bdata%2Doriginal%2Dtitle%5D%2Cabbr%5Btitle%5D%7Bcursor%3Ahelp%3Bborder%2Dbottom%3A1px%20dotted%20%23777%7D%2Einitialism%7Bfont%2Dsize%3A90%25%3Btext%2Dtransform%3Auppercase%7Dblockquote%7Bpadding%3A10px%2020px%3Bmargin%3A0%200%2020px%3Bfont%2Dsize%3A17%2E5px%3Bborder%2Dleft%3A5px%20solid%20%23eee%7Dblockquote%20ol%3Alast%2Dchild%2Cblockquote%20p%3Alast%2Dchild%2Cblockquote%20ul%3Alast%2Dchild%7Bmargin%2Dbottom%3A0%7Dblockquote%20%2Esmall%2Cblockquote%20footer%2Cblockquote%20small%7Bdisplay%3Ablock%3Bfont%2Dsize%3A80%25%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23777%7Dblockquote%20%2Esmall%3Abefore%2Cblockquote%20footer%3Abefore%2Cblockquote%20small%3Abefore%7Bcontent%3A%27%5C2014%20%5C00A0%27%7D%2Eblockquote%2Dreverse%2Cblockquote%2Epull%2Dright%7Bpadding%2Dright%3A15px%3Bpadding%2Dleft%3A0%3Btext%2Dalign%3Aright%3Bborder%2Dright%3A5px%20solid%20%23eee%3Bborder%2Dleft%3A0%7D%2Eblockquote%2Dreverse%20%2Esmall%3Abefore%2C%2Eblockquote%2Dreverse%20footer%3Abefore%2C%2Eblockquote%2Dreverse%20small%3Abefore%2Cblockquote%2Epull%2Dright%20%2Esmall%3Abefore%2Cblockquote%2Epull%2Dright%20footer%3Abefore%2Cblockquote%2Epull%2Dright%20small%3Abefore%7Bcontent%3A%27%27%7D%2Eblockquote%2Dreverse%20%2Esmall%3Aafter%2C%2Eblockquote%2Dreverse%20footer%3Aafter%2C%2Eblockquote%2Dreverse%20small%3Aafter%2Cblockquote%2Epull%2Dright%20%2Esmall%3Aafter%2Cblockquote%2Epull%2Dright%20footer%3Aafter%2Cblockquote%2Epull%2Dright%20small%3Aafter%7Bcontent%3A%27%5C00A0%20%5C2014%27%7Daddress%7Bmargin%2Dbottom%3A20px%3Bfont%2Dstyle%3Anormal%3Bline%2Dheight%3A1%2E42857143%7Dcode%2Ckbd%2Cpre%2Csamp%7Bfont%2Dfamily%3Amonospace%7Dcode%7Bpadding%3A2px%204px%3Bfont%2Dsize%3A90%25%3Bcolor%3A%23c7254e%3Bbackground%2Dcolor%3A%23f9f2f4%3Bborder%2Dradius%3A4px%7Dkbd%7Bpadding%3A2px%204px%3Bfont%2Dsize%3A90%25%3Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23333%3Bborder%2Dradius%3A3px%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%20%2D1px%200%20rgba%280%2C0%2C0%2C%2E25%29%3Bbox%2Dshadow%3Ainset%200%20%2D1px%200%20rgba%280%2C0%2C0%2C%2E25%29%7Dkbd%20kbd%7Bpadding%3A0%3Bfont%2Dsize%3A100%25%3Bfont%2Dweight%3A700%3B%2Dwebkit%2Dbox%2Dshadow%3Anone%3Bbox%2Dshadow%3Anone%7Dpre%7Bdisplay%3Ablock%3Bpadding%3A9%2E5px%3Bmargin%3A0%200%2010px%3Bfont%2Dsize%3A13px%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23333%3Bword%2Dbreak%3Abreak%2Dall%3Bword%2Dwrap%3Abreak%2Dword%3Bbackground%2Dcolor%3A%23f5f5f5%3Bborder%3A1px%20solid%20%23ccc%3Bborder%2Dradius%3A4px%7Dpre%20code%7Bpadding%3A0%3Bfont%2Dsize%3Ainherit%3Bcolor%3Ainherit%3Bwhite%2Dspace%3Apre%2Dwrap%3Bbackground%2Dcolor%3Atransparent%3Bborder%2Dradius%3A0%7D%2Epre%2Dscrollable%7Bmax%2Dheight%3A340px%3Boverflow%2Dy%3Ascroll%7D%2Econtainer%7Bpadding%2Dright%3A15px%3Bpadding%2Dleft%3A15px%3Bmargin%2Dright%3Aauto%3Bmargin%2Dleft%3Aauto%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Econtainer%7Bwidth%3A750px%7D%7D%40media%20%28min%2Dwidth%3A992px%29%7B%2Econtainer%7Bwidth%3A970px%7D%7D%40media%20%28min%2Dwidth%3A1200px%29%7B%2Econtainer%7Bwidth%3A1170px%7D%7D%2Econtainer%2Dfluid%7Bpadding%2Dright%3A15px%3Bpadding%2Dleft%3A15px%3Bmargin%2Dright%3Aauto%3Bmargin%2Dleft%3Aauto%7D%2Erow%7Bmargin%2Dright%3A%2D15px%3Bmargin%2Dleft%3A%2D15px%7D%2Ecol%2Dlg%2D1%2C%2Ecol%2Dlg%2D10%2C%2Ecol%2Dlg%2D11%2C%2Ecol%2Dlg%2D12%2C%2Ecol%2Dlg%2D2%2C%2Ecol%2Dlg%2D3%2C%2Ecol%2Dlg%2D4%2C%2Ecol%2Dlg%2D5%2C%2Ecol%2Dlg%2D6%2C%2Ecol%2Dlg%2D7%2C%2Ecol%2Dlg%2D8%2C%2Ecol%2Dlg%2D9%2C%2Ecol%2Dmd%2D1%2C%2Ecol%2Dmd%2D10%2C%2Ecol%2Dmd%2D11%2C%2Ecol%2Dmd%2D12%2C%2Ecol%2Dmd%2D2%2C%2Ecol%2Dmd%2D3%2C%2Ecol%2Dmd%2D4%2C%2Ecol%2Dmd%2D5%2C%2Ecol%2Dmd%2D6%2C%2Ecol%2Dmd%2D7%2C%2Ecol%2Dmd%2D8%2C%2Ecol%2Dmd%2D9%2C%2Ecol%2Dsm%2D1%2C%2Ecol%2Dsm%2D10%2C%2Ecol%2Dsm%2D11%2C%2Ecol%2Dsm%2D12%2C%2Ecol%2Dsm%2D2%2C%2Ecol%2Dsm%2D3%2C%2Ecol%2Dsm%2D4%2C%2Ecol%2Dsm%2D5%2C%2Ecol%2Dsm%2D6%2C%2Ecol%2Dsm%2D7%2C%2Ecol%2Dsm%2D8%2C%2Ecol%2Dsm%2D9%2C%2Ecol%2Dxs%2D1%2C%2Ecol%2Dxs%2D10%2C%2Ecol%2Dxs%2D11%2C%2Ecol%2Dxs%2D12%2C%2Ecol%2Dxs%2D2%2C%2Ecol%2Dxs%2D3%2C%2Ecol%2Dxs%2D4%2C%2Ecol%2Dxs%2D5%2C%2Ecol%2Dxs%2D6%2C%2Ecol%2Dxs%2D7%2C%2Ecol%2Dxs%2D8%2C%2Ecol%2Dxs%2D9%7Bposition%3Arelative%3Bmin%2Dheight%3A1px%3Bpadding%2Dright%3A15px%3Bpadding%2Dleft%3A15px%7D%2Ecol%2Dxs%2D1%2C%2Ecol%2Dxs%2D10%2C%2Ecol%2Dxs%2D11%2C%2Ecol%2Dxs%2D12%2C%2Ecol%2Dxs%2D2%2C%2Ecol%2Dxs%2D3%2C%2Ecol%2Dxs%2D4%2C%2Ecol%2Dxs%2D5%2C%2Ecol%2Dxs%2D6%2C%2Ecol%2Dxs%2D7%2C%2Ecol%2Dxs%2D8%2C%2Ecol%2Dxs%2D9%7Bfloat%3Aleft%7D%2Ecol%2Dxs%2D12%7Bwidth%3A100%25%7D%2Ecol%2Dxs%2D11%7Bwidth%3A91%2E66666667%25%7D%2Ecol%2Dxs%2D10%7Bwidth%3A83%2E33333333%25%7D%2Ecol%2Dxs%2D9%7Bwidth%3A75%25%7D%2Ecol%2Dxs%2D8%7Bwidth%3A66%2E66666667%25%7D%2Ecol%2Dxs%2D7%7Bwidth%3A58%2E33333333%25%7D%2Ecol%2Dxs%2D6%7Bwidth%3A50%25%7D%2Ecol%2Dxs%2D5%7Bwidth%3A41%2E66666667%25%7D%2Ecol%2Dxs%2D4%7Bwidth%3A33%2E33333333%25%7D%2Ecol%2Dxs%2D3%7Bwidth%3A25%25%7D%2Ecol%2Dxs%2D2%7Bwidth%3A16%2E66666667%25%7D%2Ecol%2Dxs%2D1%7Bwidth%3A8%2E33333333%25%7D%2Ecol%2Dxs%2Dpull%2D12%7Bright%3A100%25%7D%2Ecol%2Dxs%2Dpull%2D11%7Bright%3A91%2E66666667%25%7D%2Ecol%2Dxs%2Dpull%2D10%7Bright%3A83%2E33333333%25%7D%2Ecol%2Dxs%2Dpull%2D9%7Bright%3A75%25%7D%2Ecol%2Dxs%2Dpull%2D8%7Bright%3A66%2E66666667%25%7D%2Ecol%2Dxs%2Dpull%2D7%7Bright%3A58%2E33333333%25%7D%2Ecol%2Dxs%2Dpull%2D6%7Bright%3A50%25%7D%2Ecol%2Dxs%2Dpull%2D5%7Bright%3A41%2E66666667%25%7D%2Ecol%2Dxs%2Dpull%2D4%7Bright%3A33%2E33333333%25%7D%2Ecol%2Dxs%2Dpull%2D3%7Bright%3A25%25%7D%2Ecol%2Dxs%2Dpull%2D2%7Bright%3A16%2E66666667%25%7D%2Ecol%2Dxs%2Dpull%2D1%7Bright%3A8%2E33333333%25%7D%2Ecol%2Dxs%2Dpull%2D0%7Bright%3Aauto%7D%2Ecol%2Dxs%2Dpush%2D12%7Bleft%3A100%25%7D%2Ecol%2Dxs%2Dpush%2D11%7Bleft%3A91%2E66666667%25%7D%2Ecol%2Dxs%2Dpush%2D10%7Bleft%3A83%2E33333333%25%7D%2Ecol%2Dxs%2Dpush%2D9%7Bleft%3A75%25%7D%2Ecol%2Dxs%2Dpush%2D8%7Bleft%3A66%2E66666667%25%7D%2Ecol%2Dxs%2Dpush%2D7%7Bleft%3A58%2E33333333%25%7D%2Ecol%2Dxs%2Dpush%2D6%7Bleft%3A50%25%7D%2Ecol%2Dxs%2Dpush%2D5%7Bleft%3A41%2E66666667%25%7D%2Ecol%2Dxs%2Dpush%2D4%7Bleft%3A33%2E33333333%25%7D%2Ecol%2Dxs%2Dpush%2D3%7Bleft%3A25%25%7D%2Ecol%2Dxs%2Dpush%2D2%7Bleft%3A16%2E66666667%25%7D%2Ecol%2Dxs%2Dpush%2D1%7Bleft%3A8%2E33333333%25%7D%2Ecol%2Dxs%2Dpush%2D0%7Bleft%3Aauto%7D%2Ecol%2Dxs%2Doffset%2D12%7Bmargin%2Dleft%3A100%25%7D%2Ecol%2Dxs%2Doffset%2D11%7Bmargin%2Dleft%3A91%2E66666667%25%7D%2Ecol%2Dxs%2Doffset%2D10%7Bmargin%2Dleft%3A83%2E33333333%25%7D%2Ecol%2Dxs%2Doffset%2D9%7Bmargin%2Dleft%3A75%25%7D%2Ecol%2Dxs%2Doffset%2D8%7Bmargin%2Dleft%3A66%2E66666667%25%7D%2Ecol%2Dxs%2Doffset%2D7%7Bmargin%2Dleft%3A58%2E33333333%25%7D%2Ecol%2Dxs%2Doffset%2D6%7Bmargin%2Dleft%3A50%25%7D%2Ecol%2Dxs%2Doffset%2D5%7Bmargin%2Dleft%3A41%2E66666667%25%7D%2Ecol%2Dxs%2Doffset%2D4%7Bmargin%2Dleft%3A33%2E33333333%25%7D%2Ecol%2Dxs%2Doffset%2D3%7Bmargin%2Dleft%3A25%25%7D%2Ecol%2Dxs%2Doffset%2D2%7Bmargin%2Dleft%3A16%2E66666667%25%7D%2Ecol%2Dxs%2Doffset%2D1%7Bmargin%2Dleft%3A8%2E33333333%25%7D%2Ecol%2Dxs%2Doffset%2D0%7Bmargin%2Dleft%3A0%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Ecol%2Dsm%2D1%2C%2Ecol%2Dsm%2D10%2C%2Ecol%2Dsm%2D11%2C%2Ecol%2Dsm%2D12%2C%2Ecol%2Dsm%2D2%2C%2Ecol%2Dsm%2D3%2C%2Ecol%2Dsm%2D4%2C%2Ecol%2Dsm%2D5%2C%2Ecol%2Dsm%2D6%2C%2Ecol%2Dsm%2D7%2C%2Ecol%2Dsm%2D8%2C%2Ecol%2Dsm%2D9%7Bfloat%3Aleft%7D%2Ecol%2Dsm%2D12%7Bwidth%3A100%25%7D%2Ecol%2Dsm%2D11%7Bwidth%3A91%2E66666667%25%7D%2Ecol%2Dsm%2D10%7Bwidth%3A83%2E33333333%25%7D%2Ecol%2Dsm%2D9%7Bwidth%3A75%25%7D%2Ecol%2Dsm%2D8%7Bwidth%3A66%2E66666667%25%7D%2Ecol%2Dsm%2D7%7Bwidth%3A58%2E33333333%25%7D%2Ecol%2Dsm%2D6%7Bwidth%3A50%25%7D%2Ecol%2Dsm%2D5%7Bwidth%3A41%2E66666667%25%7D%2Ecol%2Dsm%2D4%7Bwidth%3A33%2E33333333%25%7D%2Ecol%2Dsm%2D3%7Bwidth%3A25%25%7D%2Ecol%2Dsm%2D2%7Bwidth%3A16%2E66666667%25%7D%2Ecol%2Dsm%2D1%7Bwidth%3A8%2E33333333%25%7D%2Ecol%2Dsm%2Dpull%2D12%7Bright%3A100%25%7D%2Ecol%2Dsm%2Dpull%2D11%7Bright%3A91%2E66666667%25%7D%2Ecol%2Dsm%2Dpull%2D10%7Bright%3A83%2E33333333%25%7D%2Ecol%2Dsm%2Dpull%2D9%7Bright%3A75%25%7D%2Ecol%2Dsm%2Dpull%2D8%7Bright%3A66%2E66666667%25%7D%2Ecol%2Dsm%2Dpull%2D7%7Bright%3A58%2E33333333%25%7D%2Ecol%2Dsm%2Dpull%2D6%7Bright%3A50%25%7D%2Ecol%2Dsm%2Dpull%2D5%7Bright%3A41%2E66666667%25%7D%2Ecol%2Dsm%2Dpull%2D4%7Bright%3A33%2E33333333%25%7D%2Ecol%2Dsm%2Dpull%2D3%7Bright%3A25%25%7D%2Ecol%2Dsm%2Dpull%2D2%7Bright%3A16%2E66666667%25%7D%2Ecol%2Dsm%2Dpull%2D1%7Bright%3A8%2E33333333%25%7D%2Ecol%2Dsm%2Dpull%2D0%7Bright%3Aauto%7D%2Ecol%2Dsm%2Dpush%2D12%7Bleft%3A100%25%7D%2Ecol%2Dsm%2Dpush%2D11%7Bleft%3A91%2E66666667%25%7D%2Ecol%2Dsm%2Dpush%2D10%7Bleft%3A83%2E33333333%25%7D%2Ecol%2Dsm%2Dpush%2D9%7Bleft%3A75%25%7D%2Ecol%2Dsm%2Dpush%2D8%7Bleft%3A66%2E66666667%25%7D%2Ecol%2Dsm%2Dpush%2D7%7Bleft%3A58%2E33333333%25%7D%2Ecol%2Dsm%2Dpush%2D6%7Bleft%3A50%25%7D%2Ecol%2Dsm%2Dpush%2D5%7Bleft%3A41%2E66666667%25%7D%2Ecol%2Dsm%2Dpush%2D4%7Bleft%3A33%2E33333333%25%7D%2Ecol%2Dsm%2Dpush%2D3%7Bleft%3A25%25%7D%2Ecol%2Dsm%2Dpush%2D2%7Bleft%3A16%2E66666667%25%7D%2Ecol%2Dsm%2Dpush%2D1%7Bleft%3A8%2E33333333%25%7D%2Ecol%2Dsm%2Dpush%2D0%7Bleft%3Aauto%7D%2Ecol%2Dsm%2Doffset%2D12%7Bmargin%2Dleft%3A100%25%7D%2Ecol%2Dsm%2Doffset%2D11%7Bmargin%2Dleft%3A91%2E66666667%25%7D%2Ecol%2Dsm%2Doffset%2D10%7Bmargin%2Dleft%3A83%2E33333333%25%7D%2Ecol%2Dsm%2Doffset%2D9%7Bmargin%2Dleft%3A75%25%7D%2Ecol%2Dsm%2Doffset%2D8%7Bmargin%2Dleft%3A66%2E66666667%25%7D%2Ecol%2Dsm%2Doffset%2D7%7Bmargin%2Dleft%3A58%2E33333333%25%7D%2Ecol%2Dsm%2Doffset%2D6%7Bmargin%2Dleft%3A50%25%7D%2Ecol%2Dsm%2Doffset%2D5%7Bmargin%2Dleft%3A41%2E66666667%25%7D%2Ecol%2Dsm%2Doffset%2D4%7Bmargin%2Dleft%3A33%2E33333333%25%7D%2Ecol%2Dsm%2Doffset%2D3%7Bmargin%2Dleft%3A25%25%7D%2Ecol%2Dsm%2Doffset%2D2%7Bmargin%2Dleft%3A16%2E66666667%25%7D%2Ecol%2Dsm%2Doffset%2D1%7Bmargin%2Dleft%3A8%2E33333333%25%7D%2Ecol%2Dsm%2Doffset%2D0%7Bmargin%2Dleft%3A0%7D%7D%40media%20%28min%2Dwidth%3A992px%29%7B%2Ecol%2Dmd%2D1%2C%2Ecol%2Dmd%2D10%2C%2Ecol%2Dmd%2D11%2C%2Ecol%2Dmd%2D12%2C%2Ecol%2Dmd%2D2%2C%2Ecol%2Dmd%2D3%2C%2Ecol%2Dmd%2D4%2C%2Ecol%2Dmd%2D5%2C%2Ecol%2Dmd%2D6%2C%2Ecol%2Dmd%2D7%2C%2Ecol%2Dmd%2D8%2C%2Ecol%2Dmd%2D9%7Bfloat%3Aleft%7D%2Ecol%2Dmd%2D12%7Bwidth%3A100%25%7D%2Ecol%2Dmd%2D11%7Bwidth%3A91%2E66666667%25%7D%2Ecol%2Dmd%2D10%7Bwidth%3A83%2E33333333%25%7D%2Ecol%2Dmd%2D9%7Bwidth%3A75%25%7D%2Ecol%2Dmd%2D8%7Bwidth%3A66%2E66666667%25%7D%2Ecol%2Dmd%2D7%7Bwidth%3A58%2E33333333%25%7D%2Ecol%2Dmd%2D6%7Bwidth%3A50%25%7D%2Ecol%2Dmd%2D5%7Bwidth%3A41%2E66666667%25%7D%2Ecol%2Dmd%2D4%7Bwidth%3A33%2E33333333%25%7D%2Ecol%2Dmd%2D3%7Bwidth%3A25%25%7D%2Ecol%2Dmd%2D2%7Bwidth%3A16%2E66666667%25%7D%2Ecol%2Dmd%2D1%7Bwidth%3A8%2E33333333%25%7D%2Ecol%2Dmd%2Dpull%2D12%7Bright%3A100%25%7D%2Ecol%2Dmd%2Dpull%2D11%7Bright%3A91%2E66666667%25%7D%2Ecol%2Dmd%2Dpull%2D10%7Bright%3A83%2E33333333%25%7D%2Ecol%2Dmd%2Dpull%2D9%7Bright%3A75%25%7D%2Ecol%2Dmd%2Dpull%2D8%7Bright%3A66%2E66666667%25%7D%2Ecol%2Dmd%2Dpull%2D7%7Bright%3A58%2E33333333%25%7D%2Ecol%2Dmd%2Dpull%2D6%7Bright%3A50%25%7D%2Ecol%2Dmd%2Dpull%2D5%7Bright%3A41%2E66666667%25%7D%2Ecol%2Dmd%2Dpull%2D4%7Bright%3A33%2E33333333%25%7D%2Ecol%2Dmd%2Dpull%2D3%7Bright%3A25%25%7D%2Ecol%2Dmd%2Dpull%2D2%7Bright%3A16%2E66666667%25%7D%2Ecol%2Dmd%2Dpull%2D1%7Bright%3A8%2E33333333%25%7D%2Ecol%2Dmd%2Dpull%2D0%7Bright%3Aauto%7D%2Ecol%2Dmd%2Dpush%2D12%7Bleft%3A100%25%7D%2Ecol%2Dmd%2Dpush%2D11%7Bleft%3A91%2E66666667%25%7D%2Ecol%2Dmd%2Dpush%2D10%7Bleft%3A83%2E33333333%25%7D%2Ecol%2Dmd%2Dpush%2D9%7Bleft%3A75%25%7D%2Ecol%2Dmd%2Dpush%2D8%7Bleft%3A66%2E66666667%25%7D%2Ecol%2Dmd%2Dpush%2D7%7Bleft%3A58%2E33333333%25%7D%2Ecol%2Dmd%2Dpush%2D6%7Bleft%3A50%25%7D%2Ecol%2Dmd%2Dpush%2D5%7Bleft%3A41%2E66666667%25%7D%2Ecol%2Dmd%2Dpush%2D4%7Bleft%3A33%2E33333333%25%7D%2Ecol%2Dmd%2Dpush%2D3%7Bleft%3A25%25%7D%2Ecol%2Dmd%2Dpush%2D2%7Bleft%3A16%2E66666667%25%7D%2Ecol%2Dmd%2Dpush%2D1%7Bleft%3A8%2E33333333%25%7D%2Ecol%2Dmd%2Dpush%2D0%7Bleft%3Aauto%7D%2Ecol%2Dmd%2Doffset%2D12%7Bmargin%2Dleft%3A100%25%7D%2Ecol%2Dmd%2Doffset%2D11%7Bmargin%2Dleft%3A91%2E66666667%25%7D%2Ecol%2Dmd%2Doffset%2D10%7Bmargin%2Dleft%3A83%2E33333333%25%7D%2Ecol%2Dmd%2Doffset%2D9%7Bmargin%2Dleft%3A75%25%7D%2Ecol%2Dmd%2Doffset%2D8%7Bmargin%2Dleft%3A66%2E66666667%25%7D%2Ecol%2Dmd%2Doffset%2D7%7Bmargin%2Dleft%3A58%2E33333333%25%7D%2Ecol%2Dmd%2Doffset%2D6%7Bmargin%2Dleft%3A50%25%7D%2Ecol%2Dmd%2Doffset%2D5%7Bmargin%2Dleft%3A41%2E66666667%25%7D%2Ecol%2Dmd%2Doffset%2D4%7Bmargin%2Dleft%3A33%2E33333333%25%7D%2Ecol%2Dmd%2Doffset%2D3%7Bmargin%2Dleft%3A25%25%7D%2Ecol%2Dmd%2Doffset%2D2%7Bmargin%2Dleft%3A16%2E66666667%25%7D%2Ecol%2Dmd%2Doffset%2D1%7Bmargin%2Dleft%3A8%2E33333333%25%7D%2Ecol%2Dmd%2Doffset%2D0%7Bmargin%2Dleft%3A0%7D%7D%40media%20%28min%2Dwidth%3A1200px%29%7B%2Ecol%2Dlg%2D1%2C%2Ecol%2Dlg%2D10%2C%2Ecol%2Dlg%2D11%2C%2Ecol%2Dlg%2D12%2C%2Ecol%2Dlg%2D2%2C%2Ecol%2Dlg%2D3%2C%2Ecol%2Dlg%2D4%2C%2Ecol%2Dlg%2D5%2C%2Ecol%2Dlg%2D6%2C%2Ecol%2Dlg%2D7%2C%2Ecol%2Dlg%2D8%2C%2Ecol%2Dlg%2D9%7Bfloat%3Aleft%7D%2Ecol%2Dlg%2D12%7Bwidth%3A100%25%7D%2Ecol%2Dlg%2D11%7Bwidth%3A91%2E66666667%25%7D%2Ecol%2Dlg%2D10%7Bwidth%3A83%2E33333333%25%7D%2Ecol%2Dlg%2D9%7Bwidth%3A75%25%7D%2Ecol%2Dlg%2D8%7Bwidth%3A66%2E66666667%25%7D%2Ecol%2Dlg%2D7%7Bwidth%3A58%2E33333333%25%7D%2Ecol%2Dlg%2D6%7Bwidth%3A50%25%7D%2Ecol%2Dlg%2D5%7Bwidth%3A41%2E66666667%25%7D%2Ecol%2Dlg%2D4%7Bwidth%3A33%2E33333333%25%7D%2Ecol%2Dlg%2D3%7Bwidth%3A25%25%7D%2Ecol%2Dlg%2D2%7Bwidth%3A16%2E66666667%25%7D%2Ecol%2Dlg%2D1%7Bwidth%3A8%2E33333333%25%7D%2Ecol%2Dlg%2Dpull%2D12%7Bright%3A100%25%7D%2Ecol%2Dlg%2Dpull%2D11%7Bright%3A91%2E66666667%25%7D%2Ecol%2Dlg%2Dpull%2D10%7Bright%3A83%2E33333333%25%7D%2Ecol%2Dlg%2Dpull%2D9%7Bright%3A75%25%7D%2Ecol%2Dlg%2Dpull%2D8%7Bright%3A66%2E66666667%25%7D%2Ecol%2Dlg%2Dpull%2D7%7Bright%3A58%2E33333333%25%7D%2Ecol%2Dlg%2Dpull%2D6%7Bright%3A50%25%7D%2Ecol%2Dlg%2Dpull%2D5%7Bright%3A41%2E66666667%25%7D%2Ecol%2Dlg%2Dpull%2D4%7Bright%3A33%2E33333333%25%7D%2Ecol%2Dlg%2Dpull%2D3%7Bright%3A25%25%7D%2Ecol%2Dlg%2Dpull%2D2%7Bright%3A16%2E66666667%25%7D%2Ecol%2Dlg%2Dpull%2D1%7Bright%3A8%2E33333333%25%7D%2Ecol%2Dlg%2Dpull%2D0%7Bright%3Aauto%7D%2Ecol%2Dlg%2Dpush%2D12%7Bleft%3A100%25%7D%2Ecol%2Dlg%2Dpush%2D11%7Bleft%3A91%2E66666667%25%7D%2Ecol%2Dlg%2Dpush%2D10%7Bleft%3A83%2E33333333%25%7D%2Ecol%2Dlg%2Dpush%2D9%7Bleft%3A75%25%7D%2Ecol%2Dlg%2Dpush%2D8%7Bleft%3A66%2E66666667%25%7D%2Ecol%2Dlg%2Dpush%2D7%7Bleft%3A58%2E33333333%25%7D%2Ecol%2Dlg%2Dpush%2D6%7Bleft%3A50%25%7D%2Ecol%2Dlg%2Dpush%2D5%7Bleft%3A41%2E66666667%25%7D%2Ecol%2Dlg%2Dpush%2D4%7Bleft%3A33%2E33333333%25%7D%2Ecol%2Dlg%2Dpush%2D3%7Bleft%3A25%25%7D%2Ecol%2Dlg%2Dpush%2D2%7Bleft%3A16%2E66666667%25%7D%2Ecol%2Dlg%2Dpush%2D1%7Bleft%3A8%2E33333333%25%7D%2Ecol%2Dlg%2Dpush%2D0%7Bleft%3Aauto%7D%2Ecol%2Dlg%2Doffset%2D12%7Bmargin%2Dleft%3A100%25%7D%2Ecol%2Dlg%2Doffset%2D11%7Bmargin%2Dleft%3A91%2E66666667%25%7D%2Ecol%2Dlg%2Doffset%2D10%7Bmargin%2Dleft%3A83%2E33333333%25%7D%2Ecol%2Dlg%2Doffset%2D9%7Bmargin%2Dleft%3A75%25%7D%2Ecol%2Dlg%2Doffset%2D8%7Bmargin%2Dleft%3A66%2E66666667%25%7D%2Ecol%2Dlg%2Doffset%2D7%7Bmargin%2Dleft%3A58%2E33333333%25%7D%2Ecol%2Dlg%2Doffset%2D6%7Bmargin%2Dleft%3A50%25%7D%2Ecol%2Dlg%2Doffset%2D5%7Bmargin%2Dleft%3A41%2E66666667%25%7D%2Ecol%2Dlg%2Doffset%2D4%7Bmargin%2Dleft%3A33%2E33333333%25%7D%2Ecol%2Dlg%2Doffset%2D3%7Bmargin%2Dleft%3A25%25%7D%2Ecol%2Dlg%2Doffset%2D2%7Bmargin%2Dleft%3A16%2E66666667%25%7D%2Ecol%2Dlg%2Doffset%2D1%7Bmargin%2Dleft%3A8%2E33333333%25%7D%2Ecol%2Dlg%2Doffset%2D0%7Bmargin%2Dleft%3A0%7D%7Dtable%7Bbackground%2Dcolor%3Atransparent%7Dcaption%7Bpadding%2Dtop%3A8px%3Bpadding%2Dbottom%3A8px%3Bcolor%3A%23777%3Btext%2Dalign%3Aleft%7Dth%7B%7D%2Etable%7Bwidth%3A100%25%3Bmax%2Dwidth%3A100%25%3Bmargin%2Dbottom%3A20px%7D%2Etable%3Etbody%3Etr%3Etd%2C%2Etable%3Etbody%3Etr%3Eth%2C%2Etable%3Etfoot%3Etr%3Etd%2C%2Etable%3Etfoot%3Etr%3Eth%2C%2Etable%3Ethead%3Etr%3Etd%2C%2Etable%3Ethead%3Etr%3Eth%7Bpadding%3A8px%3Bline%2Dheight%3A1%2E42857143%3Bvertical%2Dalign%3Atop%3Bborder%2Dtop%3A1px%20solid%20%23ddd%7D%2Etable%3Ethead%3Etr%3Eth%7Bvertical%2Dalign%3Abottom%3Bborder%2Dbottom%3A2px%20solid%20%23ddd%7D%2Etable%3Ecaption%2Bthead%3Etr%3Afirst%2Dchild%3Etd%2C%2Etable%3Ecaption%2Bthead%3Etr%3Afirst%2Dchild%3Eth%2C%2Etable%3Ecolgroup%2Bthead%3Etr%3Afirst%2Dchild%3Etd%2C%2Etable%3Ecolgroup%2Bthead%3Etr%3Afirst%2Dchild%3Eth%2C%2Etable%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%3Etd%2C%2Etable%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%3Eth%7Bborder%2Dtop%3A0%7D%2Etable%3Etbody%2Btbody%7Bborder%2Dtop%3A2px%20solid%20%23ddd%7D%2Etable%20%2Etable%7Bbackground%2Dcolor%3A%23fff%7D%2Etable%2Dcondensed%3Etbody%3Etr%3Etd%2C%2Etable%2Dcondensed%3Etbody%3Etr%3Eth%2C%2Etable%2Dcondensed%3Etfoot%3Etr%3Etd%2C%2Etable%2Dcondensed%3Etfoot%3Etr%3Eth%2C%2Etable%2Dcondensed%3Ethead%3Etr%3Etd%2C%2Etable%2Dcondensed%3Ethead%3Etr%3Eth%7Bpadding%3A5px%7D%2Etable%2Dbordered%7Bborder%3A1px%20solid%20%23ddd%7D%2Etable%2Dbordered%3Etbody%3Etr%3Etd%2C%2Etable%2Dbordered%3Etbody%3Etr%3Eth%2C%2Etable%2Dbordered%3Etfoot%3Etr%3Etd%2C%2Etable%2Dbordered%3Etfoot%3Etr%3Eth%2C%2Etable%2Dbordered%3Ethead%3Etr%3Etd%2C%2Etable%2Dbordered%3Ethead%3Etr%3Eth%7Bborder%3A1px%20solid%20%23ddd%7D%2Etable%2Dbordered%3Ethead%3Etr%3Etd%2C%2Etable%2Dbordered%3Ethead%3Etr%3Eth%7Bborder%2Dbottom%2Dwidth%3A2px%7D%2Etable%2Dstriped%3Etbody%3Etr%3Anth%2Dof%2Dtype%28odd%29%7Bbackground%2Dcolor%3A%23f9f9f9%7D%2Etable%2Dhover%3Etbody%3Etr%3Ahover%7Bbackground%2Dcolor%3A%23f5f5f5%7Dtable%20col%5Bclass%2A%3Dcol%2D%5D%7Bposition%3Astatic%3Bdisplay%3Atable%2Dcolumn%3Bfloat%3Anone%7Dtable%20td%5Bclass%2A%3Dcol%2D%5D%2Ctable%20th%5Bclass%2A%3Dcol%2D%5D%7Bposition%3Astatic%3Bdisplay%3Atable%2Dcell%3Bfloat%3Anone%7D%2Etable%3Etbody%3Etr%2Eactive%3Etd%2C%2Etable%3Etbody%3Etr%2Eactive%3Eth%2C%2Etable%3Etbody%3Etr%3Etd%2Eactive%2C%2Etable%3Etbody%3Etr%3Eth%2Eactive%2C%2Etable%3Etfoot%3Etr%2Eactive%3Etd%2C%2Etable%3Etfoot%3Etr%2Eactive%3Eth%2C%2Etable%3Etfoot%3Etr%3Etd%2Eactive%2C%2Etable%3Etfoot%3Etr%3Eth%2Eactive%2C%2Etable%3Ethead%3Etr%2Eactive%3Etd%2C%2Etable%3Ethead%3Etr%2Eactive%3Eth%2C%2Etable%3Ethead%3Etr%3Etd%2Eactive%2C%2Etable%3Ethead%3Etr%3Eth%2Eactive%7Bbackground%2Dcolor%3A%23f5f5f5%7D%2Etable%2Dhover%3Etbody%3Etr%2Eactive%3Ahover%3Etd%2C%2Etable%2Dhover%3Etbody%3Etr%2Eactive%3Ahover%3Eth%2C%2Etable%2Dhover%3Etbody%3Etr%3Ahover%3E%2Eactive%2C%2Etable%2Dhover%3Etbody%3Etr%3Etd%2Eactive%3Ahover%2C%2Etable%2Dhover%3Etbody%3Etr%3Eth%2Eactive%3Ahover%7Bbackground%2Dcolor%3A%23e8e8e8%7D%2Etable%3Etbody%3Etr%2Esuccess%3Etd%2C%2Etable%3Etbody%3Etr%2Esuccess%3Eth%2C%2Etable%3Etbody%3Etr%3Etd%2Esuccess%2C%2Etable%3Etbody%3Etr%3Eth%2Esuccess%2C%2Etable%3Etfoot%3Etr%2Esuccess%3Etd%2C%2Etable%3Etfoot%3Etr%2Esuccess%3Eth%2C%2Etable%3Etfoot%3Etr%3Etd%2Esuccess%2C%2Etable%3Etfoot%3Etr%3Eth%2Esuccess%2C%2Etable%3Ethead%3Etr%2Esuccess%3Etd%2C%2Etable%3Ethead%3Etr%2Esuccess%3Eth%2C%2Etable%3Ethead%3Etr%3Etd%2Esuccess%2C%2Etable%3Ethead%3Etr%3Eth%2Esuccess%7Bbackground%2Dcolor%3A%23dff0d8%7D%2Etable%2Dhover%3Etbody%3Etr%2Esuccess%3Ahover%3Etd%2C%2Etable%2Dhover%3Etbody%3Etr%2Esuccess%3Ahover%3Eth%2C%2Etable%2Dhover%3Etbody%3Etr%3Ahover%3E%2Esuccess%2C%2Etable%2Dhover%3Etbody%3Etr%3Etd%2Esuccess%3Ahover%2C%2Etable%2Dhover%3Etbody%3Etr%3Eth%2Esuccess%3Ahover%7Bbackground%2Dcolor%3A%23d0e9c6%7D%2Etable%3Etbody%3Etr%2Einfo%3Etd%2C%2Etable%3Etbody%3Etr%2Einfo%3Eth%2C%2Etable%3Etbody%3Etr%3Etd%2Einfo%2C%2Etable%3Etbody%3Etr%3Eth%2Einfo%2C%2Etable%3Etfoot%3Etr%2Einfo%3Etd%2C%2Etable%3Etfoot%3Etr%2Einfo%3Eth%2C%2Etable%3Etfoot%3Etr%3Etd%2Einfo%2C%2Etable%3Etfoot%3Etr%3Eth%2Einfo%2C%2Etable%3Ethead%3Etr%2Einfo%3Etd%2C%2Etable%3Ethead%3Etr%2Einfo%3Eth%2C%2Etable%3Ethead%3Etr%3Etd%2Einfo%2C%2Etable%3Ethead%3Etr%3Eth%2Einfo%7Bbackground%2Dcolor%3A%23d9edf7%7D%2Etable%2Dhover%3Etbody%3Etr%2Einfo%3Ahover%3Etd%2C%2Etable%2Dhover%3Etbody%3Etr%2Einfo%3Ahover%3Eth%2C%2Etable%2Dhover%3Etbody%3Etr%3Ahover%3E%2Einfo%2C%2Etable%2Dhover%3Etbody%3Etr%3Etd%2Einfo%3Ahover%2C%2Etable%2Dhover%3Etbody%3Etr%3Eth%2Einfo%3Ahover%7Bbackground%2Dcolor%3A%23c4e3f3%7D%2Etable%3Etbody%3Etr%2Ewarning%3Etd%2C%2Etable%3Etbody%3Etr%2Ewarning%3Eth%2C%2Etable%3Etbody%3Etr%3Etd%2Ewarning%2C%2Etable%3Etbody%3Etr%3Eth%2Ewarning%2C%2Etable%3Etfoot%3Etr%2Ewarning%3Etd%2C%2Etable%3Etfoot%3Etr%2Ewarning%3Eth%2C%2Etable%3Etfoot%3Etr%3Etd%2Ewarning%2C%2Etable%3Etfoot%3Etr%3Eth%2Ewarning%2C%2Etable%3Ethead%3Etr%2Ewarning%3Etd%2C%2Etable%3Ethead%3Etr%2Ewarning%3Eth%2C%2Etable%3Ethead%3Etr%3Etd%2Ewarning%2C%2Etable%3Ethead%3Etr%3Eth%2Ewarning%7Bbackground%2Dcolor%3A%23fcf8e3%7D%2Etable%2Dhover%3Etbody%3Etr%2Ewarning%3Ahover%3Etd%2C%2Etable%2Dhover%3Etbody%3Etr%2Ewarning%3Ahover%3Eth%2C%2Etable%2Dhover%3Etbody%3Etr%3Ahover%3E%2Ewarning%2C%2Etable%2Dhover%3Etbody%3Etr%3Etd%2Ewarning%3Ahover%2C%2Etable%2Dhover%3Etbody%3Etr%3Eth%2Ewarning%3Ahover%7Bbackground%2Dcolor%3A%23faf2cc%7D%2Etable%3Etbody%3Etr%2Edanger%3Etd%2C%2Etable%3Etbody%3Etr%2Edanger%3Eth%2C%2Etable%3Etbody%3Etr%3Etd%2Edanger%2C%2Etable%3Etbody%3Etr%3Eth%2Edanger%2C%2Etable%3Etfoot%3Etr%2Edanger%3Etd%2C%2Etable%3Etfoot%3Etr%2Edanger%3Eth%2C%2Etable%3Etfoot%3Etr%3Etd%2Edanger%2C%2Etable%3Etfoot%3Etr%3Eth%2Edanger%2C%2Etable%3Ethead%3Etr%2Edanger%3Etd%2C%2Etable%3Ethead%3Etr%2Edanger%3Eth%2C%2Etable%3Ethead%3Etr%3Etd%2Edanger%2C%2Etable%3Ethead%3Etr%3Eth%2Edanger%7Bbackground%2Dcolor%3A%23f2dede%7D%2Etable%2Dhover%3Etbody%3Etr%2Edanger%3Ahover%3Etd%2C%2Etable%2Dhover%3Etbody%3Etr%2Edanger%3Ahover%3Eth%2C%2Etable%2Dhover%3Etbody%3Etr%3Ahover%3E%2Edanger%2C%2Etable%2Dhover%3Etbody%3Etr%3Etd%2Edanger%3Ahover%2C%2Etable%2Dhover%3Etbody%3Etr%3Eth%2Edanger%3Ahover%7Bbackground%2Dcolor%3A%23ebcccc%7D%2Etable%2Dresponsive%7Bmin%2Dheight%3A%2E01%25%3Boverflow%2Dx%3Aauto%7D%40media%20screen%20and%20%28max%2Dwidth%3A767px%29%7B%2Etable%2Dresponsive%7Bwidth%3A100%25%3Bmargin%2Dbottom%3A15px%3Boverflow%2Dy%3Ahidden%3B%2Dms%2Doverflow%2Dstyle%3A%2Dms%2Dautohiding%2Dscrollbar%3Bborder%3A1px%20solid%20%23ddd%7D%2Etable%2Dresponsive%3E%2Etable%7Bmargin%2Dbottom%3A0%7D%2Etable%2Dresponsive%3E%2Etable%3Etbody%3Etr%3Etd%2C%2Etable%2Dresponsive%3E%2Etable%3Etbody%3Etr%3Eth%2C%2Etable%2Dresponsive%3E%2Etable%3Etfoot%3Etr%3Etd%2C%2Etable%2Dresponsive%3E%2Etable%3Etfoot%3Etr%3Eth%2C%2Etable%2Dresponsive%3E%2Etable%3Ethead%3Etr%3Etd%2C%2Etable%2Dresponsive%3E%2Etable%3Ethead%3Etr%3Eth%7Bwhite%2Dspace%3Anowrap%7D%2Etable%2Dresponsive%3E%2Etable%2Dbordered%7Bborder%3A0%7D%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Etd%3Afirst%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Eth%3Afirst%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Etd%3Afirst%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Eth%3Afirst%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Etd%3Afirst%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Eth%3Afirst%2Dchild%7Bborder%2Dleft%3A0%7D%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Etd%3Alast%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Eth%3Alast%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Etd%3Alast%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Eth%3Alast%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Etd%3Alast%2Dchild%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Eth%3Alast%2Dchild%7Bborder%2Dright%3A0%7D%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Alast%2Dchild%3Etd%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Alast%2Dchild%3Eth%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Alast%2Dchild%3Etd%2C%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Alast%2Dchild%3Eth%7Bborder%2Dbottom%3A0%7D%7Dfieldset%7Bmin%2Dwidth%3A0%3Bpadding%3A0%3Bmargin%3A0%3Bborder%3A0%7Dlegend%7Bdisplay%3Ablock%3Bwidth%3A100%25%3Bpadding%3A0%3Bmargin%2Dbottom%3A20px%3Bfont%2Dsize%3A21px%3Bline%2Dheight%3Ainherit%3Bcolor%3A%23333%3Bborder%3A0%3Bborder%2Dbottom%3A1px%20solid%20%23e5e5e5%7Dlabel%7Bdisplay%3Ainline%2Dblock%3Bmax%2Dwidth%3A100%25%3Bmargin%2Dbottom%3A5px%3Bfont%2Dweight%3A700%7Dinput%5Btype%3Dsearch%5D%7B%2Dwebkit%2Dbox%2Dsizing%3Aborder%2Dbox%3B%2Dmoz%2Dbox%2Dsizing%3Aborder%2Dbox%3Bbox%2Dsizing%3Aborder%2Dbox%7Dinput%5Btype%3Dcheckbox%5D%2Cinput%5Btype%3Dradio%5D%7Bmargin%3A4px%200%200%3Bmargin%2Dtop%3A1px%5C9%3Bline%2Dheight%3Anormal%7Dinput%5Btype%3Dfile%5D%7Bdisplay%3Ablock%7Dinput%5Btype%3Drange%5D%7Bdisplay%3Ablock%3Bwidth%3A100%25%7Dselect%5Bmultiple%5D%2Cselect%5Bsize%5D%7Bheight%3Aauto%7Dinput%5Btype%3Dfile%5D%3Afocus%2Cinput%5Btype%3Dcheckbox%5D%3Afocus%2Cinput%5Btype%3Dradio%5D%3Afocus%7Boutline%3Athin%20dotted%3Boutline%3A5px%20auto%20%2Dwebkit%2Dfocus%2Dring%2Dcolor%3Boutline%2Doffset%3A%2D2px%7Doutput%7Bdisplay%3Ablock%3Bpadding%2Dtop%3A7px%3Bfont%2Dsize%3A14px%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23555%7D%2Eform%2Dcontrol%7Bdisplay%3Ablock%3Bwidth%3A100%25%3Bheight%3A34px%3Bpadding%3A6px%2012px%3Bfont%2Dsize%3A14px%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23555%3Bbackground%2Dcolor%3A%23fff%3Bbackground%2Dimage%3Anone%3Bborder%3A1px%20solid%20%23ccc%3Bborder%2Dradius%3A4px%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%3B%2Dwebkit%2Dtransition%3Aborder%2Dcolor%20ease%2Din%2Dout%20%2E15s%2C%2Dwebkit%2Dbox%2Dshadow%20ease%2Din%2Dout%20%2E15s%3B%2Do%2Dtransition%3Aborder%2Dcolor%20ease%2Din%2Dout%20%2E15s%2Cbox%2Dshadow%20ease%2Din%2Dout%20%2E15s%3Btransition%3Aborder%2Dcolor%20ease%2Din%2Dout%20%2E15s%2Cbox%2Dshadow%20ease%2Din%2Dout%20%2E15s%7D%2Eform%2Dcontrol%3Afocus%7Bborder%2Dcolor%3A%2366afe9%3Boutline%3A0%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%208px%20rgba%28102%2C175%2C233%2C%2E6%29%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%208px%20rgba%28102%2C175%2C233%2C%2E6%29%7D%2Eform%2Dcontrol%3A%3A%2Dmoz%2Dplaceholder%7Bcolor%3A%23999%3Bopacity%3A1%7D%2Eform%2Dcontrol%3A%2Dms%2Dinput%2Dplaceholder%7Bcolor%3A%23999%7D%2Eform%2Dcontrol%3A%3A%2Dwebkit%2Dinput%2Dplaceholder%7Bcolor%3A%23999%7D%2Eform%2Dcontrol%5Bdisabled%5D%2C%2Eform%2Dcontrol%5Breadonly%5D%2Cfieldset%5Bdisabled%5D%20%2Eform%2Dcontrol%7Bbackground%2Dcolor%3A%23eee%3Bopacity%3A1%7D%2Eform%2Dcontrol%5Bdisabled%5D%2Cfieldset%5Bdisabled%5D%20%2Eform%2Dcontrol%7Bcursor%3Anot%2Dallowed%7Dtextarea%2Eform%2Dcontrol%7Bheight%3Aauto%7Dinput%5Btype%3Dsearch%5D%7B%2Dwebkit%2Dappearance%3Anone%7D%40media%20screen%20and%20%28%2Dwebkit%2Dmin%2Ddevice%2Dpixel%2Dratio%3A0%29%7Binput%5Btype%3Ddate%5D%2Eform%2Dcontrol%2Cinput%5Btype%3Dtime%5D%2Eform%2Dcontrol%2Cinput%5Btype%3Ddatetime%2Dlocal%5D%2Eform%2Dcontrol%2Cinput%5Btype%3Dmonth%5D%2Eform%2Dcontrol%7Bline%2Dheight%3A34px%7D%2Einput%2Dgroup%2Dsm%20input%5Btype%3Ddate%5D%2C%2Einput%2Dgroup%2Dsm%20input%5Btype%3Dtime%5D%2C%2Einput%2Dgroup%2Dsm%20input%5Btype%3Ddatetime%2Dlocal%5D%2C%2Einput%2Dgroup%2Dsm%20input%5Btype%3Dmonth%5D%2Cinput%5Btype%3Ddate%5D%2Einput%2Dsm%2Cinput%5Btype%3Dtime%5D%2Einput%2Dsm%2Cinput%5Btype%3Ddatetime%2Dlocal%5D%2Einput%2Dsm%2Cinput%5Btype%3Dmonth%5D%2Einput%2Dsm%7Bline%2Dheight%3A30px%7D%2Einput%2Dgroup%2Dlg%20input%5Btype%3Ddate%5D%2C%2Einput%2Dgroup%2Dlg%20input%5Btype%3Dtime%5D%2C%2Einput%2Dgroup%2Dlg%20input%5Btype%3Ddatetime%2Dlocal%5D%2C%2Einput%2Dgroup%2Dlg%20input%5Btype%3Dmonth%5D%2Cinput%5Btype%3Ddate%5D%2Einput%2Dlg%2Cinput%5Btype%3Dtime%5D%2Einput%2Dlg%2Cinput%5Btype%3Ddatetime%2Dlocal%5D%2Einput%2Dlg%2Cinput%5Btype%3Dmonth%5D%2Einput%2Dlg%7Bline%2Dheight%3A46px%7D%7D%2Eform%2Dgroup%7Bmargin%2Dbottom%3A15px%7D%2Echeckbox%2C%2Eradio%7Bposition%3Arelative%3Bdisplay%3Ablock%3Bmargin%2Dtop%3A10px%3Bmargin%2Dbottom%3A10px%7D%2Echeckbox%20label%2C%2Eradio%20label%7Bmin%2Dheight%3A20px%3Bpadding%2Dleft%3A20px%3Bmargin%2Dbottom%3A0%3Bfont%2Dweight%3A400%3Bcursor%3Apointer%7D%2Echeckbox%20input%5Btype%3Dcheckbox%5D%2C%2Echeckbox%2Dinline%20input%5Btype%3Dcheckbox%5D%2C%2Eradio%20input%5Btype%3Dradio%5D%2C%2Eradio%2Dinline%20input%5Btype%3Dradio%5D%7Bposition%3Aabsolute%3Bmargin%2Dtop%3A4px%5C9%3Bmargin%2Dleft%3A%2D20px%7D%2Echeckbox%2B%2Echeckbox%2C%2Eradio%2B%2Eradio%7Bmargin%2Dtop%3A%2D5px%7D%2Echeckbox%2Dinline%2C%2Eradio%2Dinline%7Bposition%3Arelative%3Bdisplay%3Ainline%2Dblock%3Bpadding%2Dleft%3A20px%3Bmargin%2Dbottom%3A0%3Bfont%2Dweight%3A400%3Bvertical%2Dalign%3Amiddle%3Bcursor%3Apointer%7D%2Echeckbox%2Dinline%2B%2Echeckbox%2Dinline%2C%2Eradio%2Dinline%2B%2Eradio%2Dinline%7Bmargin%2Dtop%3A0%3Bmargin%2Dleft%3A10px%7Dfieldset%5Bdisabled%5D%20input%5Btype%3Dcheckbox%5D%2Cfieldset%5Bdisabled%5D%20input%5Btype%3Dradio%5D%2Cinput%5Btype%3Dcheckbox%5D%2Edisabled%2Cinput%5Btype%3Dcheckbox%5D%5Bdisabled%5D%2Cinput%5Btype%3Dradio%5D%2Edisabled%2Cinput%5Btype%3Dradio%5D%5Bdisabled%5D%7Bcursor%3Anot%2Dallowed%7D%2Echeckbox%2Dinline%2Edisabled%2C%2Eradio%2Dinline%2Edisabled%2Cfieldset%5Bdisabled%5D%20%2Echeckbox%2Dinline%2Cfieldset%5Bdisabled%5D%20%2Eradio%2Dinline%7Bcursor%3Anot%2Dallowed%7D%2Echeckbox%2Edisabled%20label%2C%2Eradio%2Edisabled%20label%2Cfieldset%5Bdisabled%5D%20%2Echeckbox%20label%2Cfieldset%5Bdisabled%5D%20%2Eradio%20label%7Bcursor%3Anot%2Dallowed%7D%2Eform%2Dcontrol%2Dstatic%7Bmin%2Dheight%3A34px%3Bpadding%2Dtop%3A7px%3Bpadding%2Dbottom%3A7px%3Bmargin%2Dbottom%3A0%7D%2Eform%2Dcontrol%2Dstatic%2Einput%2Dlg%2C%2Eform%2Dcontrol%2Dstatic%2Einput%2Dsm%7Bpadding%2Dright%3A0%3Bpadding%2Dleft%3A0%7D%2Einput%2Dsm%7Bheight%3A30px%3Bpadding%3A5px%2010px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E5%3Bborder%2Dradius%3A3px%7Dselect%2Einput%2Dsm%7Bheight%3A30px%3Bline%2Dheight%3A30px%7Dselect%5Bmultiple%5D%2Einput%2Dsm%2Ctextarea%2Einput%2Dsm%7Bheight%3Aauto%7D%2Eform%2Dgroup%2Dsm%20%2Eform%2Dcontrol%7Bheight%3A30px%3Bpadding%3A5px%2010px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E5%3Bborder%2Dradius%3A3px%7D%2Eform%2Dgroup%2Dsm%20select%2Eform%2Dcontrol%7Bheight%3A30px%3Bline%2Dheight%3A30px%7D%2Eform%2Dgroup%2Dsm%20select%5Bmultiple%5D%2Eform%2Dcontrol%2C%2Eform%2Dgroup%2Dsm%20textarea%2Eform%2Dcontrol%7Bheight%3Aauto%7D%2Eform%2Dgroup%2Dsm%20%2Eform%2Dcontrol%2Dstatic%7Bheight%3A30px%3Bmin%2Dheight%3A32px%3Bpadding%3A6px%2010px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E5%7D%2Einput%2Dlg%7Bheight%3A46px%3Bpadding%3A10px%2016px%3Bfont%2Dsize%3A18px%3Bline%2Dheight%3A1%2E3333333%3Bborder%2Dradius%3A6px%7Dselect%2Einput%2Dlg%7Bheight%3A46px%3Bline%2Dheight%3A46px%7Dselect%5Bmultiple%5D%2Einput%2Dlg%2Ctextarea%2Einput%2Dlg%7Bheight%3Aauto%7D%2Eform%2Dgroup%2Dlg%20%2Eform%2Dcontrol%7Bheight%3A46px%3Bpadding%3A10px%2016px%3Bfont%2Dsize%3A18px%3Bline%2Dheight%3A1%2E3333333%3Bborder%2Dradius%3A6px%7D%2Eform%2Dgroup%2Dlg%20select%2Eform%2Dcontrol%7Bheight%3A46px%3Bline%2Dheight%3A46px%7D%2Eform%2Dgroup%2Dlg%20select%5Bmultiple%5D%2Eform%2Dcontrol%2C%2Eform%2Dgroup%2Dlg%20textarea%2Eform%2Dcontrol%7Bheight%3Aauto%7D%2Eform%2Dgroup%2Dlg%20%2Eform%2Dcontrol%2Dstatic%7Bheight%3A46px%3Bmin%2Dheight%3A38px%3Bpadding%3A11px%2016px%3Bfont%2Dsize%3A18px%3Bline%2Dheight%3A1%2E3333333%7D%2Ehas%2Dfeedback%7Bposition%3Arelative%7D%2Ehas%2Dfeedback%20%2Eform%2Dcontrol%7Bpadding%2Dright%3A42%2E5px%7D%2Eform%2Dcontrol%2Dfeedback%7Bposition%3Aabsolute%3Btop%3A0%3Bright%3A0%3Bz%2Dindex%3A2%3Bdisplay%3Ablock%3Bwidth%3A34px%3Bheight%3A34px%3Bline%2Dheight%3A34px%3Btext%2Dalign%3Acenter%3Bpointer%2Devents%3Anone%7D%2Eform%2Dgroup%2Dlg%20%2Eform%2Dcontrol%2B%2Eform%2Dcontrol%2Dfeedback%2C%2Einput%2Dgroup%2Dlg%2B%2Eform%2Dcontrol%2Dfeedback%2C%2Einput%2Dlg%2B%2Eform%2Dcontrol%2Dfeedback%7Bwidth%3A46px%3Bheight%3A46px%3Bline%2Dheight%3A46px%7D%2Eform%2Dgroup%2Dsm%20%2Eform%2Dcontrol%2B%2Eform%2Dcontrol%2Dfeedback%2C%2Einput%2Dgroup%2Dsm%2B%2Eform%2Dcontrol%2Dfeedback%2C%2Einput%2Dsm%2B%2Eform%2Dcontrol%2Dfeedback%7Bwidth%3A30px%3Bheight%3A30px%3Bline%2Dheight%3A30px%7D%2Ehas%2Dsuccess%20%2Echeckbox%2C%2Ehas%2Dsuccess%20%2Echeckbox%2Dinline%2C%2Ehas%2Dsuccess%20%2Econtrol%2Dlabel%2C%2Ehas%2Dsuccess%20%2Ehelp%2Dblock%2C%2Ehas%2Dsuccess%20%2Eradio%2C%2Ehas%2Dsuccess%20%2Eradio%2Dinline%2C%2Ehas%2Dsuccess%2Echeckbox%20label%2C%2Ehas%2Dsuccess%2Echeckbox%2Dinline%20label%2C%2Ehas%2Dsuccess%2Eradio%20label%2C%2Ehas%2Dsuccess%2Eradio%2Dinline%20label%7Bcolor%3A%233c763d%7D%2Ehas%2Dsuccess%20%2Eform%2Dcontrol%7Bborder%2Dcolor%3A%233c763d%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%7D%2Ehas%2Dsuccess%20%2Eform%2Dcontrol%3Afocus%7Bborder%2Dcolor%3A%232b542c%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%206px%20%2367b168%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%206px%20%2367b168%7D%2Ehas%2Dsuccess%20%2Einput%2Dgroup%2Daddon%7Bcolor%3A%233c763d%3Bbackground%2Dcolor%3A%23dff0d8%3Bborder%2Dcolor%3A%233c763d%7D%2Ehas%2Dsuccess%20%2Eform%2Dcontrol%2Dfeedback%7Bcolor%3A%233c763d%7D%2Ehas%2Dwarning%20%2Echeckbox%2C%2Ehas%2Dwarning%20%2Echeckbox%2Dinline%2C%2Ehas%2Dwarning%20%2Econtrol%2Dlabel%2C%2Ehas%2Dwarning%20%2Ehelp%2Dblock%2C%2Ehas%2Dwarning%20%2Eradio%2C%2Ehas%2Dwarning%20%2Eradio%2Dinline%2C%2Ehas%2Dwarning%2Echeckbox%20label%2C%2Ehas%2Dwarning%2Echeckbox%2Dinline%20label%2C%2Ehas%2Dwarning%2Eradio%20label%2C%2Ehas%2Dwarning%2Eradio%2Dinline%20label%7Bcolor%3A%238a6d3b%7D%2Ehas%2Dwarning%20%2Eform%2Dcontrol%7Bborder%2Dcolor%3A%238a6d3b%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%7D%2Ehas%2Dwarning%20%2Eform%2Dcontrol%3Afocus%7Bborder%2Dcolor%3A%2366512c%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%206px%20%23c0a16b%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%206px%20%23c0a16b%7D%2Ehas%2Dwarning%20%2Einput%2Dgroup%2Daddon%7Bcolor%3A%238a6d3b%3Bbackground%2Dcolor%3A%23fcf8e3%3Bborder%2Dcolor%3A%238a6d3b%7D%2Ehas%2Dwarning%20%2Eform%2Dcontrol%2Dfeedback%7Bcolor%3A%238a6d3b%7D%2Ehas%2Derror%20%2Echeckbox%2C%2Ehas%2Derror%20%2Echeckbox%2Dinline%2C%2Ehas%2Derror%20%2Econtrol%2Dlabel%2C%2Ehas%2Derror%20%2Ehelp%2Dblock%2C%2Ehas%2Derror%20%2Eradio%2C%2Ehas%2Derror%20%2Eradio%2Dinline%2C%2Ehas%2Derror%2Echeckbox%20label%2C%2Ehas%2Derror%2Echeckbox%2Dinline%20label%2C%2Ehas%2Derror%2Eradio%20label%2C%2Ehas%2Derror%2Eradio%2Dinline%20label%7Bcolor%3A%23a94442%7D%2Ehas%2Derror%20%2Eform%2Dcontrol%7Bborder%2Dcolor%3A%23a94442%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%7D%2Ehas%2Derror%20%2Eform%2Dcontrol%3Afocus%7Bborder%2Dcolor%3A%23843534%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%206px%20%23ce8483%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E075%29%2C0%200%206px%20%23ce8483%7D%2Ehas%2Derror%20%2Einput%2Dgroup%2Daddon%7Bcolor%3A%23a94442%3Bbackground%2Dcolor%3A%23f2dede%3Bborder%2Dcolor%3A%23a94442%7D%2Ehas%2Derror%20%2Eform%2Dcontrol%2Dfeedback%7Bcolor%3A%23a94442%7D%2Ehas%2Dfeedback%20label%7E%2Eform%2Dcontrol%2Dfeedback%7Btop%3A25px%7D%2Ehas%2Dfeedback%20label%2Esr%2Donly%7E%2Eform%2Dcontrol%2Dfeedback%7Btop%3A0%7D%2Ehelp%2Dblock%7Bdisplay%3Ablock%3Bmargin%2Dtop%3A5px%3Bmargin%2Dbottom%3A10px%3Bcolor%3A%23737373%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Eform%2Dinline%20%2Eform%2Dgroup%7Bdisplay%3Ainline%2Dblock%3Bmargin%2Dbottom%3A0%3Bvertical%2Dalign%3Amiddle%7D%2Eform%2Dinline%20%2Eform%2Dcontrol%7Bdisplay%3Ainline%2Dblock%3Bwidth%3Aauto%3Bvertical%2Dalign%3Amiddle%7D%2Eform%2Dinline%20%2Eform%2Dcontrol%2Dstatic%7Bdisplay%3Ainline%2Dblock%7D%2Eform%2Dinline%20%2Einput%2Dgroup%7Bdisplay%3Ainline%2Dtable%3Bvertical%2Dalign%3Amiddle%7D%2Eform%2Dinline%20%2Einput%2Dgroup%20%2Eform%2Dcontrol%2C%2Eform%2Dinline%20%2Einput%2Dgroup%20%2Einput%2Dgroup%2Daddon%2C%2Eform%2Dinline%20%2Einput%2Dgroup%20%2Einput%2Dgroup%2Dbtn%7Bwidth%3Aauto%7D%2Eform%2Dinline%20%2Einput%2Dgroup%3E%2Eform%2Dcontrol%7Bwidth%3A100%25%7D%2Eform%2Dinline%20%2Econtrol%2Dlabel%7Bmargin%2Dbottom%3A0%3Bvertical%2Dalign%3Amiddle%7D%2Eform%2Dinline%20%2Echeckbox%2C%2Eform%2Dinline%20%2Eradio%7Bdisplay%3Ainline%2Dblock%3Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A0%3Bvertical%2Dalign%3Amiddle%7D%2Eform%2Dinline%20%2Echeckbox%20label%2C%2Eform%2Dinline%20%2Eradio%20label%7Bpadding%2Dleft%3A0%7D%2Eform%2Dinline%20%2Echeckbox%20input%5Btype%3Dcheckbox%5D%2C%2Eform%2Dinline%20%2Eradio%20input%5Btype%3Dradio%5D%7Bposition%3Arelative%3Bmargin%2Dleft%3A0%7D%2Eform%2Dinline%20%2Ehas%2Dfeedback%20%2Eform%2Dcontrol%2Dfeedback%7Btop%3A0%7D%7D%2Eform%2Dhorizontal%20%2Echeckbox%2C%2Eform%2Dhorizontal%20%2Echeckbox%2Dinline%2C%2Eform%2Dhorizontal%20%2Eradio%2C%2Eform%2Dhorizontal%20%2Eradio%2Dinline%7Bpadding%2Dtop%3A7px%3Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A0%7D%2Eform%2Dhorizontal%20%2Echeckbox%2C%2Eform%2Dhorizontal%20%2Eradio%7Bmin%2Dheight%3A27px%7D%2Eform%2Dhorizontal%20%2Eform%2Dgroup%7Bmargin%2Dright%3A%2D15px%3Bmargin%2Dleft%3A%2D15px%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Eform%2Dhorizontal%20%2Econtrol%2Dlabel%7Bpadding%2Dtop%3A7px%3Bmargin%2Dbottom%3A0%3Btext%2Dalign%3Aright%7D%7D%2Eform%2Dhorizontal%20%2Ehas%2Dfeedback%20%2Eform%2Dcontrol%2Dfeedback%7Bright%3A15px%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Eform%2Dhorizontal%20%2Eform%2Dgroup%2Dlg%20%2Econtrol%2Dlabel%7Bpadding%2Dtop%3A14%2E33px%3Bfont%2Dsize%3A18px%7D%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Eform%2Dhorizontal%20%2Eform%2Dgroup%2Dsm%20%2Econtrol%2Dlabel%7Bpadding%2Dtop%3A6px%3Bfont%2Dsize%3A12px%7D%7D%2Ebtn%7Bdisplay%3Ainline%2Dblock%3Bpadding%3A6px%2012px%3Bmargin%2Dbottom%3A0%3Bfont%2Dsize%3A14px%3Bfont%2Dweight%3A400%3Bline%2Dheight%3A1%2E42857143%3Btext%2Dalign%3Acenter%3Bwhite%2Dspace%3Anowrap%3Bvertical%2Dalign%3Amiddle%3B%2Dms%2Dtouch%2Daction%3Amanipulation%3Btouch%2Daction%3Amanipulation%3Bcursor%3Apointer%3B%2Dwebkit%2Duser%2Dselect%3Anone%3B%2Dmoz%2Duser%2Dselect%3Anone%3B%2Dms%2Duser%2Dselect%3Anone%3Buser%2Dselect%3Anone%3Bbackground%2Dimage%3Anone%3Bborder%3A1px%20solid%20transparent%3Bborder%2Dradius%3A4px%7D%2Ebtn%2Eactive%2Efocus%2C%2Ebtn%2Eactive%3Afocus%2C%2Ebtn%2Efocus%2C%2Ebtn%3Aactive%2Efocus%2C%2Ebtn%3Aactive%3Afocus%2C%2Ebtn%3Afocus%7Boutline%3Athin%20dotted%3Boutline%3A5px%20auto%20%2Dwebkit%2Dfocus%2Dring%2Dcolor%3Boutline%2Doffset%3A%2D2px%7D%2Ebtn%2Efocus%2C%2Ebtn%3Afocus%2C%2Ebtn%3Ahover%7Bcolor%3A%23333%3Btext%2Ddecoration%3Anone%7D%2Ebtn%2Eactive%2C%2Ebtn%3Aactive%7Bbackground%2Dimage%3Anone%3Boutline%3A0%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%203px%205px%20rgba%280%2C0%2C0%2C%2E125%29%3Bbox%2Dshadow%3Ainset%200%203px%205px%20rgba%280%2C0%2C0%2C%2E125%29%7D%2Ebtn%2Edisabled%2C%2Ebtn%5Bdisabled%5D%2Cfieldset%5Bdisabled%5D%20%2Ebtn%7Bcursor%3Anot%2Dallowed%3Bfilter%3Aalpha%28opacity%3D65%29%3B%2Dwebkit%2Dbox%2Dshadow%3Anone%3Bbox%2Dshadow%3Anone%3Bopacity%3A%2E65%7Da%2Ebtn%2Edisabled%2Cfieldset%5Bdisabled%5D%20a%2Ebtn%7Bpointer%2Devents%3Anone%7D%2Ebtn%2Ddefault%7Bcolor%3A%23333%3Bbackground%2Dcolor%3A%23fff%3Bborder%2Dcolor%3A%23ccc%7D%2Ebtn%2Ddefault%2Efocus%2C%2Ebtn%2Ddefault%3Afocus%7Bcolor%3A%23333%3Bbackground%2Dcolor%3A%23e6e6e6%3Bborder%2Dcolor%3A%238c8c8c%7D%2Ebtn%2Ddefault%3Ahover%7Bcolor%3A%23333%3Bbackground%2Dcolor%3A%23e6e6e6%3Bborder%2Dcolor%3A%23adadad%7D%2Ebtn%2Ddefault%2Eactive%2C%2Ebtn%2Ddefault%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddefault%7Bcolor%3A%23333%3Bbackground%2Dcolor%3A%23e6e6e6%3Bborder%2Dcolor%3A%23adadad%7D%2Ebtn%2Ddefault%2Eactive%2Efocus%2C%2Ebtn%2Ddefault%2Eactive%3Afocus%2C%2Ebtn%2Ddefault%2Eactive%3Ahover%2C%2Ebtn%2Ddefault%3Aactive%2Efocus%2C%2Ebtn%2Ddefault%3Aactive%3Afocus%2C%2Ebtn%2Ddefault%3Aactive%3Ahover%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddefault%2Efocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddefault%3Afocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddefault%3Ahover%7Bcolor%3A%23333%3Bbackground%2Dcolor%3A%23d4d4d4%3Bborder%2Dcolor%3A%238c8c8c%7D%2Ebtn%2Ddefault%2Eactive%2C%2Ebtn%2Ddefault%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddefault%7Bbackground%2Dimage%3Anone%7D%2Ebtn%2Ddefault%2Edisabled%2C%2Ebtn%2Ddefault%2Edisabled%2Eactive%2C%2Ebtn%2Ddefault%2Edisabled%2Efocus%2C%2Ebtn%2Ddefault%2Edisabled%3Aactive%2C%2Ebtn%2Ddefault%2Edisabled%3Afocus%2C%2Ebtn%2Ddefault%2Edisabled%3Ahover%2C%2Ebtn%2Ddefault%5Bdisabled%5D%2C%2Ebtn%2Ddefault%5Bdisabled%5D%2Eactive%2C%2Ebtn%2Ddefault%5Bdisabled%5D%2Efocus%2C%2Ebtn%2Ddefault%5Bdisabled%5D%3Aactive%2C%2Ebtn%2Ddefault%5Bdisabled%5D%3Afocus%2C%2Ebtn%2Ddefault%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddefault%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddefault%2Eactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddefault%2Efocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddefault%3Aactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddefault%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddefault%3Ahover%7Bbackground%2Dcolor%3A%23fff%3Bborder%2Dcolor%3A%23ccc%7D%2Ebtn%2Ddefault%20%2Ebadge%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23333%7D%2Ebtn%2Dprimary%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23337ab7%3Bborder%2Dcolor%3A%232e6da4%7D%2Ebtn%2Dprimary%2Efocus%2C%2Ebtn%2Dprimary%3Afocus%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23286090%3Bborder%2Dcolor%3A%23122b40%7D%2Ebtn%2Dprimary%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23286090%3Bborder%2Dcolor%3A%23204d74%7D%2Ebtn%2Dprimary%2Eactive%2C%2Ebtn%2Dprimary%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dprimary%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23286090%3Bborder%2Dcolor%3A%23204d74%7D%2Ebtn%2Dprimary%2Eactive%2Efocus%2C%2Ebtn%2Dprimary%2Eactive%3Afocus%2C%2Ebtn%2Dprimary%2Eactive%3Ahover%2C%2Ebtn%2Dprimary%3Aactive%2Efocus%2C%2Ebtn%2Dprimary%3Aactive%3Afocus%2C%2Ebtn%2Dprimary%3Aactive%3Ahover%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dprimary%2Efocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dprimary%3Afocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dprimary%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23204d74%3Bborder%2Dcolor%3A%23122b40%7D%2Ebtn%2Dprimary%2Eactive%2C%2Ebtn%2Dprimary%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dprimary%7Bbackground%2Dimage%3Anone%7D%2Ebtn%2Dprimary%2Edisabled%2C%2Ebtn%2Dprimary%2Edisabled%2Eactive%2C%2Ebtn%2Dprimary%2Edisabled%2Efocus%2C%2Ebtn%2Dprimary%2Edisabled%3Aactive%2C%2Ebtn%2Dprimary%2Edisabled%3Afocus%2C%2Ebtn%2Dprimary%2Edisabled%3Ahover%2C%2Ebtn%2Dprimary%5Bdisabled%5D%2C%2Ebtn%2Dprimary%5Bdisabled%5D%2Eactive%2C%2Ebtn%2Dprimary%5Bdisabled%5D%2Efocus%2C%2Ebtn%2Dprimary%5Bdisabled%5D%3Aactive%2C%2Ebtn%2Dprimary%5Bdisabled%5D%3Afocus%2C%2Ebtn%2Dprimary%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dprimary%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dprimary%2Eactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dprimary%2Efocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dprimary%3Aactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dprimary%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dprimary%3Ahover%7Bbackground%2Dcolor%3A%23337ab7%3Bborder%2Dcolor%3A%232e6da4%7D%2Ebtn%2Dprimary%20%2Ebadge%7Bcolor%3A%23337ab7%3Bbackground%2Dcolor%3A%23fff%7D%2Ebtn%2Dsuccess%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%235cb85c%3Bborder%2Dcolor%3A%234cae4c%7D%2Ebtn%2Dsuccess%2Efocus%2C%2Ebtn%2Dsuccess%3Afocus%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23449d44%3Bborder%2Dcolor%3A%23255625%7D%2Ebtn%2Dsuccess%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23449d44%3Bborder%2Dcolor%3A%23398439%7D%2Ebtn%2Dsuccess%2Eactive%2C%2Ebtn%2Dsuccess%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dsuccess%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23449d44%3Bborder%2Dcolor%3A%23398439%7D%2Ebtn%2Dsuccess%2Eactive%2Efocus%2C%2Ebtn%2Dsuccess%2Eactive%3Afocus%2C%2Ebtn%2Dsuccess%2Eactive%3Ahover%2C%2Ebtn%2Dsuccess%3Aactive%2Efocus%2C%2Ebtn%2Dsuccess%3Aactive%3Afocus%2C%2Ebtn%2Dsuccess%3Aactive%3Ahover%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dsuccess%2Efocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dsuccess%3Afocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dsuccess%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23398439%3Bborder%2Dcolor%3A%23255625%7D%2Ebtn%2Dsuccess%2Eactive%2C%2Ebtn%2Dsuccess%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dsuccess%7Bbackground%2Dimage%3Anone%7D%2Ebtn%2Dsuccess%2Edisabled%2C%2Ebtn%2Dsuccess%2Edisabled%2Eactive%2C%2Ebtn%2Dsuccess%2Edisabled%2Efocus%2C%2Ebtn%2Dsuccess%2Edisabled%3Aactive%2C%2Ebtn%2Dsuccess%2Edisabled%3Afocus%2C%2Ebtn%2Dsuccess%2Edisabled%3Ahover%2C%2Ebtn%2Dsuccess%5Bdisabled%5D%2C%2Ebtn%2Dsuccess%5Bdisabled%5D%2Eactive%2C%2Ebtn%2Dsuccess%5Bdisabled%5D%2Efocus%2C%2Ebtn%2Dsuccess%5Bdisabled%5D%3Aactive%2C%2Ebtn%2Dsuccess%5Bdisabled%5D%3Afocus%2C%2Ebtn%2Dsuccess%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dsuccess%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dsuccess%2Eactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dsuccess%2Efocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dsuccess%3Aactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dsuccess%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dsuccess%3Ahover%7Bbackground%2Dcolor%3A%235cb85c%3Bborder%2Dcolor%3A%234cae4c%7D%2Ebtn%2Dsuccess%20%2Ebadge%7Bcolor%3A%235cb85c%3Bbackground%2Dcolor%3A%23fff%7D%2Ebtn%2Dinfo%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%235bc0de%3Bborder%2Dcolor%3A%2346b8da%7D%2Ebtn%2Dinfo%2Efocus%2C%2Ebtn%2Dinfo%3Afocus%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%2331b0d5%3Bborder%2Dcolor%3A%231b6d85%7D%2Ebtn%2Dinfo%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%2331b0d5%3Bborder%2Dcolor%3A%23269abc%7D%2Ebtn%2Dinfo%2Eactive%2C%2Ebtn%2Dinfo%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dinfo%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%2331b0d5%3Bborder%2Dcolor%3A%23269abc%7D%2Ebtn%2Dinfo%2Eactive%2Efocus%2C%2Ebtn%2Dinfo%2Eactive%3Afocus%2C%2Ebtn%2Dinfo%2Eactive%3Ahover%2C%2Ebtn%2Dinfo%3Aactive%2Efocus%2C%2Ebtn%2Dinfo%3Aactive%3Afocus%2C%2Ebtn%2Dinfo%3Aactive%3Ahover%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dinfo%2Efocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dinfo%3Afocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dinfo%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23269abc%3Bborder%2Dcolor%3A%231b6d85%7D%2Ebtn%2Dinfo%2Eactive%2C%2Ebtn%2Dinfo%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dinfo%7Bbackground%2Dimage%3Anone%7D%2Ebtn%2Dinfo%2Edisabled%2C%2Ebtn%2Dinfo%2Edisabled%2Eactive%2C%2Ebtn%2Dinfo%2Edisabled%2Efocus%2C%2Ebtn%2Dinfo%2Edisabled%3Aactive%2C%2Ebtn%2Dinfo%2Edisabled%3Afocus%2C%2Ebtn%2Dinfo%2Edisabled%3Ahover%2C%2Ebtn%2Dinfo%5Bdisabled%5D%2C%2Ebtn%2Dinfo%5Bdisabled%5D%2Eactive%2C%2Ebtn%2Dinfo%5Bdisabled%5D%2Efocus%2C%2Ebtn%2Dinfo%5Bdisabled%5D%3Aactive%2C%2Ebtn%2Dinfo%5Bdisabled%5D%3Afocus%2C%2Ebtn%2Dinfo%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dinfo%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dinfo%2Eactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dinfo%2Efocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dinfo%3Aactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dinfo%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dinfo%3Ahover%7Bbackground%2Dcolor%3A%235bc0de%3Bborder%2Dcolor%3A%2346b8da%7D%2Ebtn%2Dinfo%20%2Ebadge%7Bcolor%3A%235bc0de%3Bbackground%2Dcolor%3A%23fff%7D%2Ebtn%2Dwarning%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23f0ad4e%3Bborder%2Dcolor%3A%23eea236%7D%2Ebtn%2Dwarning%2Efocus%2C%2Ebtn%2Dwarning%3Afocus%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23ec971f%3Bborder%2Dcolor%3A%23985f0d%7D%2Ebtn%2Dwarning%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23ec971f%3Bborder%2Dcolor%3A%23d58512%7D%2Ebtn%2Dwarning%2Eactive%2C%2Ebtn%2Dwarning%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dwarning%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23ec971f%3Bborder%2Dcolor%3A%23d58512%7D%2Ebtn%2Dwarning%2Eactive%2Efocus%2C%2Ebtn%2Dwarning%2Eactive%3Afocus%2C%2Ebtn%2Dwarning%2Eactive%3Ahover%2C%2Ebtn%2Dwarning%3Aactive%2Efocus%2C%2Ebtn%2Dwarning%3Aactive%3Afocus%2C%2Ebtn%2Dwarning%3Aactive%3Ahover%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dwarning%2Efocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dwarning%3Afocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dwarning%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23d58512%3Bborder%2Dcolor%3A%23985f0d%7D%2Ebtn%2Dwarning%2Eactive%2C%2Ebtn%2Dwarning%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Dwarning%7Bbackground%2Dimage%3Anone%7D%2Ebtn%2Dwarning%2Edisabled%2C%2Ebtn%2Dwarning%2Edisabled%2Eactive%2C%2Ebtn%2Dwarning%2Edisabled%2Efocus%2C%2Ebtn%2Dwarning%2Edisabled%3Aactive%2C%2Ebtn%2Dwarning%2Edisabled%3Afocus%2C%2Ebtn%2Dwarning%2Edisabled%3Ahover%2C%2Ebtn%2Dwarning%5Bdisabled%5D%2C%2Ebtn%2Dwarning%5Bdisabled%5D%2Eactive%2C%2Ebtn%2Dwarning%5Bdisabled%5D%2Efocus%2C%2Ebtn%2Dwarning%5Bdisabled%5D%3Aactive%2C%2Ebtn%2Dwarning%5Bdisabled%5D%3Afocus%2C%2Ebtn%2Dwarning%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dwarning%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dwarning%2Eactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dwarning%2Efocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dwarning%3Aactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dwarning%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dwarning%3Ahover%7Bbackground%2Dcolor%3A%23f0ad4e%3Bborder%2Dcolor%3A%23eea236%7D%2Ebtn%2Dwarning%20%2Ebadge%7Bcolor%3A%23f0ad4e%3Bbackground%2Dcolor%3A%23fff%7D%2Ebtn%2Ddanger%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23d9534f%3Bborder%2Dcolor%3A%23d43f3a%7D%2Ebtn%2Ddanger%2Efocus%2C%2Ebtn%2Ddanger%3Afocus%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23c9302c%3Bborder%2Dcolor%3A%23761c19%7D%2Ebtn%2Ddanger%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23c9302c%3Bborder%2Dcolor%3A%23ac2925%7D%2Ebtn%2Ddanger%2Eactive%2C%2Ebtn%2Ddanger%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddanger%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23c9302c%3Bborder%2Dcolor%3A%23ac2925%7D%2Ebtn%2Ddanger%2Eactive%2Efocus%2C%2Ebtn%2Ddanger%2Eactive%3Afocus%2C%2Ebtn%2Ddanger%2Eactive%3Ahover%2C%2Ebtn%2Ddanger%3Aactive%2Efocus%2C%2Ebtn%2Ddanger%3Aactive%3Afocus%2C%2Ebtn%2Ddanger%3Aactive%3Ahover%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddanger%2Efocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddanger%3Afocus%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddanger%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23ac2925%3Bborder%2Dcolor%3A%23761c19%7D%2Ebtn%2Ddanger%2Eactive%2C%2Ebtn%2Ddanger%3Aactive%2C%2Eopen%3E%2Edropdown%2Dtoggle%2Ebtn%2Ddanger%7Bbackground%2Dimage%3Anone%7D%2Ebtn%2Ddanger%2Edisabled%2C%2Ebtn%2Ddanger%2Edisabled%2Eactive%2C%2Ebtn%2Ddanger%2Edisabled%2Efocus%2C%2Ebtn%2Ddanger%2Edisabled%3Aactive%2C%2Ebtn%2Ddanger%2Edisabled%3Afocus%2C%2Ebtn%2Ddanger%2Edisabled%3Ahover%2C%2Ebtn%2Ddanger%5Bdisabled%5D%2C%2Ebtn%2Ddanger%5Bdisabled%5D%2Eactive%2C%2Ebtn%2Ddanger%5Bdisabled%5D%2Efocus%2C%2Ebtn%2Ddanger%5Bdisabled%5D%3Aactive%2C%2Ebtn%2Ddanger%5Bdisabled%5D%3Afocus%2C%2Ebtn%2Ddanger%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddanger%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddanger%2Eactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddanger%2Efocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddanger%3Aactive%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddanger%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Ddanger%3Ahover%7Bbackground%2Dcolor%3A%23d9534f%3Bborder%2Dcolor%3A%23d43f3a%7D%2Ebtn%2Ddanger%20%2Ebadge%7Bcolor%3A%23d9534f%3Bbackground%2Dcolor%3A%23fff%7D%2Ebtn%2Dlink%7Bfont%2Dweight%3A400%3Bcolor%3A%23337ab7%3Bborder%2Dradius%3A0%7D%2Ebtn%2Dlink%2C%2Ebtn%2Dlink%2Eactive%2C%2Ebtn%2Dlink%3Aactive%2C%2Ebtn%2Dlink%5Bdisabled%5D%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dlink%7Bbackground%2Dcolor%3Atransparent%3B%2Dwebkit%2Dbox%2Dshadow%3Anone%3Bbox%2Dshadow%3Anone%7D%2Ebtn%2Dlink%2C%2Ebtn%2Dlink%3Aactive%2C%2Ebtn%2Dlink%3Afocus%2C%2Ebtn%2Dlink%3Ahover%7Bborder%2Dcolor%3Atransparent%7D%2Ebtn%2Dlink%3Afocus%2C%2Ebtn%2Dlink%3Ahover%7Bcolor%3A%2323527c%3Btext%2Ddecoration%3Aunderline%3Bbackground%2Dcolor%3Atransparent%7D%2Ebtn%2Dlink%5Bdisabled%5D%3Afocus%2C%2Ebtn%2Dlink%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dlink%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Ebtn%2Dlink%3Ahover%7Bcolor%3A%23777%3Btext%2Ddecoration%3Anone%7D%2Ebtn%2Dgroup%2Dlg%3E%2Ebtn%2C%2Ebtn%2Dlg%7Bpadding%3A10px%2016px%3Bfont%2Dsize%3A18px%3Bline%2Dheight%3A1%2E3333333%3Bborder%2Dradius%3A6px%7D%2Ebtn%2Dgroup%2Dsm%3E%2Ebtn%2C%2Ebtn%2Dsm%7Bpadding%3A5px%2010px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E5%3Bborder%2Dradius%3A3px%7D%2Ebtn%2Dgroup%2Dxs%3E%2Ebtn%2C%2Ebtn%2Dxs%7Bpadding%3A1px%205px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E5%3Bborder%2Dradius%3A3px%7D%2Ebtn%2Dblock%7Bdisplay%3Ablock%3Bwidth%3A100%25%7D%2Ebtn%2Dblock%2B%2Ebtn%2Dblock%7Bmargin%2Dtop%3A5px%7Dinput%5Btype%3Dbutton%5D%2Ebtn%2Dblock%2Cinput%5Btype%3Dreset%5D%2Ebtn%2Dblock%2Cinput%5Btype%3Dsubmit%5D%2Ebtn%2Dblock%7Bwidth%3A100%25%7D%2Efade%7Bopacity%3A0%3B%2Dwebkit%2Dtransition%3Aopacity%20%2E15s%20linear%3B%2Do%2Dtransition%3Aopacity%20%2E15s%20linear%3Btransition%3Aopacity%20%2E15s%20linear%7D%2Efade%2Ein%7Bopacity%3A1%7D%2Ecollapse%7Bdisplay%3Anone%7D%2Ecollapse%2Ein%7Bdisplay%3Ablock%7Dtr%2Ecollapse%2Ein%7Bdisplay%3Atable%2Drow%7Dtbody%2Ecollapse%2Ein%7Bdisplay%3Atable%2Drow%2Dgroup%7D%2Ecollapsing%7Bposition%3Arelative%3Bheight%3A0%3Boverflow%3Ahidden%3B%2Dwebkit%2Dtransition%2Dtiming%2Dfunction%3Aease%3B%2Do%2Dtransition%2Dtiming%2Dfunction%3Aease%3Btransition%2Dtiming%2Dfunction%3Aease%3B%2Dwebkit%2Dtransition%2Dduration%3A%2E35s%3B%2Do%2Dtransition%2Dduration%3A%2E35s%3Btransition%2Dduration%3A%2E35s%3B%2Dwebkit%2Dtransition%2Dproperty%3Aheight%2Cvisibility%3B%2Do%2Dtransition%2Dproperty%3Aheight%2Cvisibility%3Btransition%2Dproperty%3Aheight%2Cvisibility%7D%2Ecaret%7Bdisplay%3Ainline%2Dblock%3Bwidth%3A0%3Bheight%3A0%3Bmargin%2Dleft%3A2px%3Bvertical%2Dalign%3Amiddle%3Bborder%2Dtop%3A4px%20dashed%3Bborder%2Dtop%3A4px%20solid%5C9%3Bborder%2Dright%3A4px%20solid%20transparent%3Bborder%2Dleft%3A4px%20solid%20transparent%7D%2Edropdown%2C%2Edropup%7Bposition%3Arelative%7D%2Edropdown%2Dtoggle%3Afocus%7Boutline%3A0%7D%2Edropdown%2Dmenu%7Bposition%3Aabsolute%3Btop%3A100%25%3Bleft%3A0%3Bz%2Dindex%3A1000%3Bdisplay%3Anone%3Bfloat%3Aleft%3Bmin%2Dwidth%3A160px%3Bpadding%3A5px%200%3Bmargin%3A2px%200%200%3Bfont%2Dsize%3A14px%3Btext%2Dalign%3Aleft%3Blist%2Dstyle%3Anone%3Bbackground%2Dcolor%3A%23fff%3B%2Dwebkit%2Dbackground%2Dclip%3Apadding%2Dbox%3Bbackground%2Dclip%3Apadding%2Dbox%3Bborder%3A1px%20solid%20%23ccc%3Bborder%3A1px%20solid%20rgba%280%2C0%2C0%2C%2E15%29%3Bborder%2Dradius%3A4px%3B%2Dwebkit%2Dbox%2Dshadow%3A0%206px%2012px%20rgba%280%2C0%2C0%2C%2E175%29%3Bbox%2Dshadow%3A0%206px%2012px%20rgba%280%2C0%2C0%2C%2E175%29%7D%2Edropdown%2Dmenu%2Epull%2Dright%7Bright%3A0%3Bleft%3Aauto%7D%2Edropdown%2Dmenu%20%2Edivider%7Bheight%3A1px%3Bmargin%3A9px%200%3Boverflow%3Ahidden%3Bbackground%2Dcolor%3A%23e5e5e5%7D%2Edropdown%2Dmenu%3Eli%3Ea%7Bdisplay%3Ablock%3Bpadding%3A3px%2020px%3Bclear%3Aboth%3Bfont%2Dweight%3A400%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23333%3Bwhite%2Dspace%3Anowrap%7D%2Edropdown%2Dmenu%3Eli%3Ea%3Afocus%2C%2Edropdown%2Dmenu%3Eli%3Ea%3Ahover%7Bcolor%3A%23262626%3Btext%2Ddecoration%3Anone%3Bbackground%2Dcolor%3A%23f5f5f5%7D%2Edropdown%2Dmenu%3E%2Eactive%3Ea%2C%2Edropdown%2Dmenu%3E%2Eactive%3Ea%3Afocus%2C%2Edropdown%2Dmenu%3E%2Eactive%3Ea%3Ahover%7Bcolor%3A%23fff%3Btext%2Ddecoration%3Anone%3Bbackground%2Dcolor%3A%23337ab7%3Boutline%3A0%7D%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%2C%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Afocus%2C%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Ahover%7Bcolor%3A%23777%7D%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Afocus%2C%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Ahover%7Btext%2Ddecoration%3Anone%3Bcursor%3Anot%2Dallowed%3Bbackground%2Dcolor%3Atransparent%3Bbackground%2Dimage%3Anone%3Bfilter%3Aprogid%3ADXImageTransform%2EMicrosoft%2Egradient%28enabled%3Dfalse%29%7D%2Eopen%3E%2Edropdown%2Dmenu%7Bdisplay%3Ablock%7D%2Eopen%3Ea%7Boutline%3A0%7D%2Edropdown%2Dmenu%2Dright%7Bright%3A0%3Bleft%3Aauto%7D%2Edropdown%2Dmenu%2Dleft%7Bright%3Aauto%3Bleft%3A0%7D%2Edropdown%2Dheader%7Bdisplay%3Ablock%3Bpadding%3A3px%2020px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23777%3Bwhite%2Dspace%3Anowrap%7D%2Edropdown%2Dbackdrop%7Bposition%3Afixed%3Btop%3A0%3Bright%3A0%3Bbottom%3A0%3Bleft%3A0%3Bz%2Dindex%3A990%7D%2Epull%2Dright%3E%2Edropdown%2Dmenu%7Bright%3A0%3Bleft%3Aauto%7D%2Edropup%20%2Ecaret%2C%2Enavbar%2Dfixed%2Dbottom%20%2Edropdown%20%2Ecaret%7Bcontent%3A%22%22%3Bborder%2Dtop%3A0%3Bborder%2Dbottom%3A4px%20dashed%3Bborder%2Dbottom%3A4px%20solid%5C9%7D%2Edropup%20%2Edropdown%2Dmenu%2C%2Enavbar%2Dfixed%2Dbottom%20%2Edropdown%20%2Edropdown%2Dmenu%7Btop%3Aauto%3Bbottom%3A100%25%3Bmargin%2Dbottom%3A2px%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dright%20%2Edropdown%2Dmenu%7Bright%3A0%3Bleft%3Aauto%7D%2Enavbar%2Dright%20%2Edropdown%2Dmenu%2Dleft%7Bright%3Aauto%3Bleft%3A0%7D%7D%2Ebtn%2Dgroup%2C%2Ebtn%2Dgroup%2Dvertical%7Bposition%3Arelative%3Bdisplay%3Ainline%2Dblock%3Bvertical%2Dalign%3Amiddle%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2C%2Ebtn%2Dgroup%3E%2Ebtn%7Bposition%3Arelative%3Bfloat%3Aleft%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Eactive%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%3Aactive%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%3Afocus%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%3Ahover%2C%2Ebtn%2Dgroup%3E%2Ebtn%2Eactive%2C%2Ebtn%2Dgroup%3E%2Ebtn%3Aactive%2C%2Ebtn%2Dgroup%3E%2Ebtn%3Afocus%2C%2Ebtn%2Dgroup%3E%2Ebtn%3Ahover%7Bz%2Dindex%3A2%7D%2Ebtn%2Dgroup%20%2Ebtn%2B%2Ebtn%2C%2Ebtn%2Dgroup%20%2Ebtn%2B%2Ebtn%2Dgroup%2C%2Ebtn%2Dgroup%20%2Ebtn%2Dgroup%2B%2Ebtn%2C%2Ebtn%2Dgroup%20%2Ebtn%2Dgroup%2B%2Ebtn%2Dgroup%7Bmargin%2Dleft%3A%2D1px%7D%2Ebtn%2Dtoolbar%7Bmargin%2Dleft%3A%2D5px%7D%2Ebtn%2Dtoolbar%20%2Ebtn%2C%2Ebtn%2Dtoolbar%20%2Ebtn%2Dgroup%2C%2Ebtn%2Dtoolbar%20%2Einput%2Dgroup%7Bfloat%3Aleft%7D%2Ebtn%2Dtoolbar%3E%2Ebtn%2C%2Ebtn%2Dtoolbar%3E%2Ebtn%2Dgroup%2C%2Ebtn%2Dtoolbar%3E%2Einput%2Dgroup%7Bmargin%2Dleft%3A5px%7D%2Ebtn%2Dgroup%3E%2Ebtn%3Anot%28%3Afirst%2Dchild%29%3Anot%28%3Alast%2Dchild%29%3Anot%28%2Edropdown%2Dtoggle%29%7Bborder%2Dradius%3A0%7D%2Ebtn%2Dgroup%3E%2Ebtn%3Afirst%2Dchild%7Bmargin%2Dleft%3A0%7D%2Ebtn%2Dgroup%3E%2Ebtn%3Afirst%2Dchild%3Anot%28%3Alast%2Dchild%29%3Anot%28%2Edropdown%2Dtoggle%29%7Bborder%2Dtop%2Dright%2Dradius%3A0%3Bborder%2Dbottom%2Dright%2Dradius%3A0%7D%2Ebtn%2Dgroup%3E%2Ebtn%3Alast%2Dchild%3Anot%28%3Afirst%2Dchild%29%2C%2Ebtn%2Dgroup%3E%2Edropdown%2Dtoggle%3Anot%28%3Afirst%2Dchild%29%7Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dbottom%2Dleft%2Dradius%3A0%7D%2Ebtn%2Dgroup%3E%2Ebtn%2Dgroup%7Bfloat%3Aleft%7D%2Ebtn%2Dgroup%3E%2Ebtn%2Dgroup%3Anot%28%3Afirst%2Dchild%29%3Anot%28%3Alast%2Dchild%29%3E%2Ebtn%7Bborder%2Dradius%3A0%7D%2Ebtn%2Dgroup%3E%2Ebtn%2Dgroup%3Afirst%2Dchild%3Anot%28%3Alast%2Dchild%29%3E%2Ebtn%3Alast%2Dchild%2C%2Ebtn%2Dgroup%3E%2Ebtn%2Dgroup%3Afirst%2Dchild%3Anot%28%3Alast%2Dchild%29%3E%2Edropdown%2Dtoggle%7Bborder%2Dtop%2Dright%2Dradius%3A0%3Bborder%2Dbottom%2Dright%2Dradius%3A0%7D%2Ebtn%2Dgroup%3E%2Ebtn%2Dgroup%3Alast%2Dchild%3Anot%28%3Afirst%2Dchild%29%3E%2Ebtn%3Afirst%2Dchild%7Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dbottom%2Dleft%2Dradius%3A0%7D%2Ebtn%2Dgroup%20%2Edropdown%2Dtoggle%3Aactive%2C%2Ebtn%2Dgroup%2Eopen%20%2Edropdown%2Dtoggle%7Boutline%3A0%7D%2Ebtn%2Dgroup%3E%2Ebtn%2B%2Edropdown%2Dtoggle%7Bpadding%2Dright%3A8px%3Bpadding%2Dleft%3A8px%7D%2Ebtn%2Dgroup%3E%2Ebtn%2Dlg%2B%2Edropdown%2Dtoggle%7Bpadding%2Dright%3A12px%3Bpadding%2Dleft%3A12px%7D%2Ebtn%2Dgroup%2Eopen%20%2Edropdown%2Dtoggle%7B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%203px%205px%20rgba%280%2C0%2C0%2C%2E125%29%3Bbox%2Dshadow%3Ainset%200%203px%205px%20rgba%280%2C0%2C0%2C%2E125%29%7D%2Ebtn%2Dgroup%2Eopen%20%2Edropdown%2Dtoggle%2Ebtn%2Dlink%7B%2Dwebkit%2Dbox%2Dshadow%3Anone%3Bbox%2Dshadow%3Anone%7D%2Ebtn%20%2Ecaret%7Bmargin%2Dleft%3A0%7D%2Ebtn%2Dlg%20%2Ecaret%7Bborder%2Dwidth%3A5px%205px%200%3Bborder%2Dbottom%2Dwidth%3A0%7D%2Edropup%20%2Ebtn%2Dlg%20%2Ecaret%7Bborder%2Dwidth%3A0%205px%205px%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3E%2Ebtn%7Bdisplay%3Ablock%3Bfloat%3Anone%3Bwidth%3A100%25%3Bmax%2Dwidth%3A100%25%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3E%2Ebtn%7Bfloat%3Anone%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2B%2Ebtn%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2B%2Ebtn%2Dgroup%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%2B%2Ebtn%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%2B%2Ebtn%2Dgroup%7Bmargin%2Dtop%3A%2D1px%3Bmargin%2Dleft%3A0%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%3Anot%28%3Afirst%2Dchild%29%3Anot%28%3Alast%2Dchild%29%7Bborder%2Dradius%3A0%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%3Afirst%2Dchild%3Anot%28%3Alast%2Dchild%29%7Bborder%2Dtop%2Dright%2Dradius%3A4px%3Bborder%2Dbottom%2Dright%2Dradius%3A0%3Bborder%2Dbottom%2Dleft%2Dradius%3A0%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%3Alast%2Dchild%3Anot%28%3Afirst%2Dchild%29%7Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dtop%2Dright%2Dradius%3A0%3Bborder%2Dbottom%2Dleft%2Dradius%3A4px%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3Anot%28%3Afirst%2Dchild%29%3Anot%28%3Alast%2Dchild%29%3E%2Ebtn%7Bborder%2Dradius%3A0%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3Afirst%2Dchild%3Anot%28%3Alast%2Dchild%29%3E%2Ebtn%3Alast%2Dchild%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3Afirst%2Dchild%3Anot%28%3Alast%2Dchild%29%3E%2Edropdown%2Dtoggle%7Bborder%2Dbottom%2Dright%2Dradius%3A0%3Bborder%2Dbottom%2Dleft%2Dradius%3A0%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3Alast%2Dchild%3Anot%28%3Afirst%2Dchild%29%3E%2Ebtn%3Afirst%2Dchild%7Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dtop%2Dright%2Dradius%3A0%7D%2Ebtn%2Dgroup%2Djustified%7Bdisplay%3Atable%3Bwidth%3A100%25%3Btable%2Dlayout%3Afixed%3Bborder%2Dcollapse%3Aseparate%7D%2Ebtn%2Dgroup%2Djustified%3E%2Ebtn%2C%2Ebtn%2Dgroup%2Djustified%3E%2Ebtn%2Dgroup%7Bdisplay%3Atable%2Dcell%3Bfloat%3Anone%3Bwidth%3A1%25%7D%2Ebtn%2Dgroup%2Djustified%3E%2Ebtn%2Dgroup%20%2Ebtn%7Bwidth%3A100%25%7D%2Ebtn%2Dgroup%2Djustified%3E%2Ebtn%2Dgroup%20%2Edropdown%2Dmenu%7Bleft%3Aauto%7D%5Bdata%2Dtoggle%3Dbuttons%5D%3E%2Ebtn%20input%5Btype%3Dcheckbox%5D%2C%5Bdata%2Dtoggle%3Dbuttons%5D%3E%2Ebtn%20input%5Btype%3Dradio%5D%2C%5Bdata%2Dtoggle%3Dbuttons%5D%3E%2Ebtn%2Dgroup%3E%2Ebtn%20input%5Btype%3Dcheckbox%5D%2C%5Bdata%2Dtoggle%3Dbuttons%5D%3E%2Ebtn%2Dgroup%3E%2Ebtn%20input%5Btype%3Dradio%5D%7Bposition%3Aabsolute%3Bclip%3Arect%280%2C0%2C0%2C0%29%3Bpointer%2Devents%3Anone%7D%2Einput%2Dgroup%7Bposition%3Arelative%3Bdisplay%3Atable%3Bborder%2Dcollapse%3Aseparate%7D%2Einput%2Dgroup%5Bclass%2A%3Dcol%2D%5D%7Bfloat%3Anone%3Bpadding%2Dright%3A0%3Bpadding%2Dleft%3A0%7D%2Einput%2Dgroup%20%2Eform%2Dcontrol%7Bposition%3Arelative%3Bz%2Dindex%3A2%3Bfloat%3Aleft%3Bwidth%3A100%25%3Bmargin%2Dbottom%3A0%7D%2Einput%2Dgroup%2Dlg%3E%2Eform%2Dcontrol%2C%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Daddon%2C%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%7Bheight%3A46px%3Bpadding%3A10px%2016px%3Bfont%2Dsize%3A18px%3Bline%2Dheight%3A1%2E3333333%3Bborder%2Dradius%3A6px%7Dselect%2Einput%2Dgroup%2Dlg%3E%2Eform%2Dcontrol%2Cselect%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Daddon%2Cselect%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%7Bheight%3A46px%3Bline%2Dheight%3A46px%7Dselect%5Bmultiple%5D%2Einput%2Dgroup%2Dlg%3E%2Eform%2Dcontrol%2Cselect%5Bmultiple%5D%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Daddon%2Cselect%5Bmultiple%5D%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%2Ctextarea%2Einput%2Dgroup%2Dlg%3E%2Eform%2Dcontrol%2Ctextarea%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Daddon%2Ctextarea%2Einput%2Dgroup%2Dlg%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%7Bheight%3Aauto%7D%2Einput%2Dgroup%2Dsm%3E%2Eform%2Dcontrol%2C%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Daddon%2C%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%7Bheight%3A30px%3Bpadding%3A5px%2010px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E5%3Bborder%2Dradius%3A3px%7Dselect%2Einput%2Dgroup%2Dsm%3E%2Eform%2Dcontrol%2Cselect%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Daddon%2Cselect%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%7Bheight%3A30px%3Bline%2Dheight%3A30px%7Dselect%5Bmultiple%5D%2Einput%2Dgroup%2Dsm%3E%2Eform%2Dcontrol%2Cselect%5Bmultiple%5D%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Daddon%2Cselect%5Bmultiple%5D%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%2Ctextarea%2Einput%2Dgroup%2Dsm%3E%2Eform%2Dcontrol%2Ctextarea%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Daddon%2Ctextarea%2Einput%2Dgroup%2Dsm%3E%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%7Bheight%3Aauto%7D%2Einput%2Dgroup%20%2Eform%2Dcontrol%2C%2Einput%2Dgroup%2Daddon%2C%2Einput%2Dgroup%2Dbtn%7Bdisplay%3Atable%2Dcell%7D%2Einput%2Dgroup%20%2Eform%2Dcontrol%3Anot%28%3Afirst%2Dchild%29%3Anot%28%3Alast%2Dchild%29%2C%2Einput%2Dgroup%2Daddon%3Anot%28%3Afirst%2Dchild%29%3Anot%28%3Alast%2Dchild%29%2C%2Einput%2Dgroup%2Dbtn%3Anot%28%3Afirst%2Dchild%29%3Anot%28%3Alast%2Dchild%29%7Bborder%2Dradius%3A0%7D%2Einput%2Dgroup%2Daddon%2C%2Einput%2Dgroup%2Dbtn%7Bwidth%3A1%25%3Bwhite%2Dspace%3Anowrap%3Bvertical%2Dalign%3Amiddle%7D%2Einput%2Dgroup%2Daddon%7Bpadding%3A6px%2012px%3Bfont%2Dsize%3A14px%3Bfont%2Dweight%3A400%3Bline%2Dheight%3A1%3Bcolor%3A%23555%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23eee%3Bborder%3A1px%20solid%20%23ccc%3Bborder%2Dradius%3A4px%7D%2Einput%2Dgroup%2Daddon%2Einput%2Dsm%7Bpadding%3A5px%2010px%3Bfont%2Dsize%3A12px%3Bborder%2Dradius%3A3px%7D%2Einput%2Dgroup%2Daddon%2Einput%2Dlg%7Bpadding%3A10px%2016px%3Bfont%2Dsize%3A18px%3Bborder%2Dradius%3A6px%7D%2Einput%2Dgroup%2Daddon%20input%5Btype%3Dcheckbox%5D%2C%2Einput%2Dgroup%2Daddon%20input%5Btype%3Dradio%5D%7Bmargin%2Dtop%3A0%7D%2Einput%2Dgroup%20%2Eform%2Dcontrol%3Afirst%2Dchild%2C%2Einput%2Dgroup%2Daddon%3Afirst%2Dchild%2C%2Einput%2Dgroup%2Dbtn%3Afirst%2Dchild%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Afirst%2Dchild%3E%2Ebtn%2Dgroup%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Afirst%2Dchild%3E%2Edropdown%2Dtoggle%2C%2Einput%2Dgroup%2Dbtn%3Alast%2Dchild%3E%2Ebtn%2Dgroup%3Anot%28%3Alast%2Dchild%29%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Alast%2Dchild%3E%2Ebtn%3Anot%28%3Alast%2Dchild%29%3Anot%28%2Edropdown%2Dtoggle%29%7Bborder%2Dtop%2Dright%2Dradius%3A0%3Bborder%2Dbottom%2Dright%2Dradius%3A0%7D%2Einput%2Dgroup%2Daddon%3Afirst%2Dchild%7Bborder%2Dright%3A0%7D%2Einput%2Dgroup%20%2Eform%2Dcontrol%3Alast%2Dchild%2C%2Einput%2Dgroup%2Daddon%3Alast%2Dchild%2C%2Einput%2Dgroup%2Dbtn%3Afirst%2Dchild%3E%2Ebtn%2Dgroup%3Anot%28%3Afirst%2Dchild%29%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Afirst%2Dchild%3E%2Ebtn%3Anot%28%3Afirst%2Dchild%29%2C%2Einput%2Dgroup%2Dbtn%3Alast%2Dchild%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Alast%2Dchild%3E%2Ebtn%2Dgroup%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Alast%2Dchild%3E%2Edropdown%2Dtoggle%7Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dbottom%2Dleft%2Dradius%3A0%7D%2Einput%2Dgroup%2Daddon%3Alast%2Dchild%7Bborder%2Dleft%3A0%7D%2Einput%2Dgroup%2Dbtn%7Bposition%3Arelative%3Bfont%2Dsize%3A0%3Bwhite%2Dspace%3Anowrap%7D%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%7Bposition%3Arelative%7D%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%2B%2Ebtn%7Bmargin%2Dleft%3A%2D1px%7D%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%3Aactive%2C%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%3Afocus%2C%2Einput%2Dgroup%2Dbtn%3E%2Ebtn%3Ahover%7Bz%2Dindex%3A2%7D%2Einput%2Dgroup%2Dbtn%3Afirst%2Dchild%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Afirst%2Dchild%3E%2Ebtn%2Dgroup%7Bmargin%2Dright%3A%2D1px%7D%2Einput%2Dgroup%2Dbtn%3Alast%2Dchild%3E%2Ebtn%2C%2Einput%2Dgroup%2Dbtn%3Alast%2Dchild%3E%2Ebtn%2Dgroup%7Bz%2Dindex%3A2%3Bmargin%2Dleft%3A%2D1px%7D%2Enav%7Bpadding%2Dleft%3A0%3Bmargin%2Dbottom%3A0%3Blist%2Dstyle%3Anone%7D%2Enav%3Eli%7Bposition%3Arelative%3Bdisplay%3Ablock%7D%2Enav%3Eli%3Ea%7Bposition%3Arelative%3Bdisplay%3Ablock%3Bpadding%3A10px%2015px%7D%2Enav%3Eli%3Ea%3Afocus%2C%2Enav%3Eli%3Ea%3Ahover%7Btext%2Ddecoration%3Anone%3Bbackground%2Dcolor%3A%23eee%7D%2Enav%3Eli%2Edisabled%3Ea%7Bcolor%3A%23777%7D%2Enav%3Eli%2Edisabled%3Ea%3Afocus%2C%2Enav%3Eli%2Edisabled%3Ea%3Ahover%7Bcolor%3A%23777%3Btext%2Ddecoration%3Anone%3Bcursor%3Anot%2Dallowed%3Bbackground%2Dcolor%3Atransparent%7D%2Enav%20%2Eopen%3Ea%2C%2Enav%20%2Eopen%3Ea%3Afocus%2C%2Enav%20%2Eopen%3Ea%3Ahover%7Bbackground%2Dcolor%3A%23eee%3Bborder%2Dcolor%3A%23337ab7%7D%2Enav%20%2Enav%2Ddivider%7Bheight%3A1px%3Bmargin%3A9px%200%3Boverflow%3Ahidden%3Bbackground%2Dcolor%3A%23e5e5e5%7D%2Enav%3Eli%3Ea%3Eimg%7Bmax%2Dwidth%3Anone%7D%2Enav%2Dtabs%7Bborder%2Dbottom%3A1px%20solid%20%23ddd%7D%2Enav%2Dtabs%3Eli%7Bfloat%3Aleft%3Bmargin%2Dbottom%3A%2D1px%7D%2Enav%2Dtabs%3Eli%3Ea%7Bmargin%2Dright%3A2px%3Bline%2Dheight%3A1%2E42857143%3Bborder%3A1px%20solid%20transparent%3Bborder%2Dradius%3A4px%204px%200%200%7D%2Enav%2Dtabs%3Eli%3Ea%3Ahover%7Bborder%2Dcolor%3A%23eee%20%23eee%20%23ddd%7D%2Enav%2Dtabs%3Eli%2Eactive%3Ea%2C%2Enav%2Dtabs%3Eli%2Eactive%3Ea%3Afocus%2C%2Enav%2Dtabs%3Eli%2Eactive%3Ea%3Ahover%7Bcolor%3A%23555%3Bcursor%3Adefault%3Bbackground%2Dcolor%3A%23fff%3Bborder%3A1px%20solid%20%23ddd%3Bborder%2Dbottom%2Dcolor%3Atransparent%7D%2Enav%2Dtabs%2Enav%2Djustified%7Bwidth%3A100%25%3Bborder%2Dbottom%3A0%7D%2Enav%2Dtabs%2Enav%2Djustified%3Eli%7Bfloat%3Anone%7D%2Enav%2Dtabs%2Enav%2Djustified%3Eli%3Ea%7Bmargin%2Dbottom%3A5px%3Btext%2Dalign%3Acenter%7D%2Enav%2Dtabs%2Enav%2Djustified%3E%2Edropdown%20%2Edropdown%2Dmenu%7Btop%3Aauto%3Bleft%3Aauto%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enav%2Dtabs%2Enav%2Djustified%3Eli%7Bdisplay%3Atable%2Dcell%3Bwidth%3A1%25%7D%2Enav%2Dtabs%2Enav%2Djustified%3Eli%3Ea%7Bmargin%2Dbottom%3A0%7D%7D%2Enav%2Dtabs%2Enav%2Djustified%3Eli%3Ea%7Bmargin%2Dright%3A0%3Bborder%2Dradius%3A4px%7D%2Enav%2Dtabs%2Enav%2Djustified%3E%2Eactive%3Ea%2C%2Enav%2Dtabs%2Enav%2Djustified%3E%2Eactive%3Ea%3Afocus%2C%2Enav%2Dtabs%2Enav%2Djustified%3E%2Eactive%3Ea%3Ahover%7Bborder%3A1px%20solid%20%23ddd%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enav%2Dtabs%2Enav%2Djustified%3Eli%3Ea%7Bborder%2Dbottom%3A1px%20solid%20%23ddd%3Bborder%2Dradius%3A4px%204px%200%200%7D%2Enav%2Dtabs%2Enav%2Djustified%3E%2Eactive%3Ea%2C%2Enav%2Dtabs%2Enav%2Djustified%3E%2Eactive%3Ea%3Afocus%2C%2Enav%2Dtabs%2Enav%2Djustified%3E%2Eactive%3Ea%3Ahover%7Bborder%2Dbottom%2Dcolor%3A%23fff%7D%7D%2Enav%2Dpills%3Eli%7Bfloat%3Aleft%7D%2Enav%2Dpills%3Eli%3Ea%7Bborder%2Dradius%3A4px%7D%2Enav%2Dpills%3Eli%2Bli%7Bmargin%2Dleft%3A2px%7D%2Enav%2Dpills%3Eli%2Eactive%3Ea%2C%2Enav%2Dpills%3Eli%2Eactive%3Ea%3Afocus%2C%2Enav%2Dpills%3Eli%2Eactive%3Ea%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23337ab7%7D%2Enav%2Dstacked%3Eli%7Bfloat%3Anone%7D%2Enav%2Dstacked%3Eli%2Bli%7Bmargin%2Dtop%3A2px%3Bmargin%2Dleft%3A0%7D%2Enav%2Djustified%7Bwidth%3A100%25%7D%2Enav%2Djustified%3Eli%7Bfloat%3Anone%7D%2Enav%2Djustified%3Eli%3Ea%7Bmargin%2Dbottom%3A5px%3Btext%2Dalign%3Acenter%7D%2Enav%2Djustified%3E%2Edropdown%20%2Edropdown%2Dmenu%7Btop%3Aauto%3Bleft%3Aauto%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enav%2Djustified%3Eli%7Bdisplay%3Atable%2Dcell%3Bwidth%3A1%25%7D%2Enav%2Djustified%3Eli%3Ea%7Bmargin%2Dbottom%3A0%7D%7D%2Enav%2Dtabs%2Djustified%7Bborder%2Dbottom%3A0%7D%2Enav%2Dtabs%2Djustified%3Eli%3Ea%7Bmargin%2Dright%3A0%3Bborder%2Dradius%3A4px%7D%2Enav%2Dtabs%2Djustified%3E%2Eactive%3Ea%2C%2Enav%2Dtabs%2Djustified%3E%2Eactive%3Ea%3Afocus%2C%2Enav%2Dtabs%2Djustified%3E%2Eactive%3Ea%3Ahover%7Bborder%3A1px%20solid%20%23ddd%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enav%2Dtabs%2Djustified%3Eli%3Ea%7Bborder%2Dbottom%3A1px%20solid%20%23ddd%3Bborder%2Dradius%3A4px%204px%200%200%7D%2Enav%2Dtabs%2Djustified%3E%2Eactive%3Ea%2C%2Enav%2Dtabs%2Djustified%3E%2Eactive%3Ea%3Afocus%2C%2Enav%2Dtabs%2Djustified%3E%2Eactive%3Ea%3Ahover%7Bborder%2Dbottom%2Dcolor%3A%23fff%7D%7D%2Etab%2Dcontent%3E%2Etab%2Dpane%7Bdisplay%3Anone%7D%2Etab%2Dcontent%3E%2Eactive%7Bdisplay%3Ablock%7D%2Enav%2Dtabs%20%2Edropdown%2Dmenu%7Bmargin%2Dtop%3A%2D1px%3Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dtop%2Dright%2Dradius%3A0%7D%2Enavbar%7Bposition%3Arelative%3Bmin%2Dheight%3A50px%3Bmargin%2Dbottom%3A20px%3Bborder%3A1px%20solid%20transparent%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%7Bborder%2Dradius%3A4px%7D%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dheader%7Bfloat%3Aleft%7D%7D%2Enavbar%2Dcollapse%7Bpadding%2Dright%3A15px%3Bpadding%2Dleft%3A15px%3Boverflow%2Dx%3Avisible%3B%2Dwebkit%2Doverflow%2Dscrolling%3Atouch%3Bborder%2Dtop%3A1px%20solid%20transparent%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%200%20rgba%28255%2C255%2C255%2C%2E1%29%3Bbox%2Dshadow%3Ainset%200%201px%200%20rgba%28255%2C255%2C255%2C%2E1%29%7D%2Enavbar%2Dcollapse%2Ein%7Boverflow%2Dy%3Aauto%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dcollapse%7Bwidth%3Aauto%3Bborder%2Dtop%3A0%3B%2Dwebkit%2Dbox%2Dshadow%3Anone%3Bbox%2Dshadow%3Anone%7D%2Enavbar%2Dcollapse%2Ecollapse%7Bdisplay%3Ablock%21important%3Bheight%3Aauto%21important%3Bpadding%2Dbottom%3A0%3Boverflow%3Avisible%21important%7D%2Enavbar%2Dcollapse%2Ein%7Boverflow%2Dy%3Avisible%7D%2Enavbar%2Dfixed%2Dbottom%20%2Enavbar%2Dcollapse%2C%2Enavbar%2Dfixed%2Dtop%20%2Enavbar%2Dcollapse%2C%2Enavbar%2Dstatic%2Dtop%20%2Enavbar%2Dcollapse%7Bpadding%2Dright%3A0%3Bpadding%2Dleft%3A0%7D%7D%2Enavbar%2Dfixed%2Dbottom%20%2Enavbar%2Dcollapse%2C%2Enavbar%2Dfixed%2Dtop%20%2Enavbar%2Dcollapse%7Bmax%2Dheight%3A340px%7D%40media%20%28max%2Ddevice%2Dwidth%3A480px%29%20and%20%28orientation%3Alandscape%29%7B%2Enavbar%2Dfixed%2Dbottom%20%2Enavbar%2Dcollapse%2C%2Enavbar%2Dfixed%2Dtop%20%2Enavbar%2Dcollapse%7Bmax%2Dheight%3A200px%7D%7D%2Econtainer%2Dfluid%3E%2Enavbar%2Dcollapse%2C%2Econtainer%2Dfluid%3E%2Enavbar%2Dheader%2C%2Econtainer%3E%2Enavbar%2Dcollapse%2C%2Econtainer%3E%2Enavbar%2Dheader%7Bmargin%2Dright%3A%2D15px%3Bmargin%2Dleft%3A%2D15px%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Econtainer%2Dfluid%3E%2Enavbar%2Dcollapse%2C%2Econtainer%2Dfluid%3E%2Enavbar%2Dheader%2C%2Econtainer%3E%2Enavbar%2Dcollapse%2C%2Econtainer%3E%2Enavbar%2Dheader%7Bmargin%2Dright%3A0%3Bmargin%2Dleft%3A0%7D%7D%2Enavbar%2Dstatic%2Dtop%7Bz%2Dindex%3A1000%3Bborder%2Dwidth%3A0%200%201px%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dstatic%2Dtop%7Bborder%2Dradius%3A0%7D%7D%2Enavbar%2Dfixed%2Dbottom%2C%2Enavbar%2Dfixed%2Dtop%7Bposition%3Afixed%3Bright%3A0%3Bleft%3A0%3Bz%2Dindex%3A1030%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dfixed%2Dbottom%2C%2Enavbar%2Dfixed%2Dtop%7Bborder%2Dradius%3A0%7D%7D%2Enavbar%2Dfixed%2Dtop%7Btop%3A0%3Bborder%2Dwidth%3A0%200%201px%7D%2Enavbar%2Dfixed%2Dbottom%7Bbottom%3A0%3Bmargin%2Dbottom%3A0%3Bborder%2Dwidth%3A1px%200%200%7D%2Enavbar%2Dbrand%7Bfloat%3Aleft%3Bheight%3A50px%3Bpadding%3A15px%2015px%3Bfont%2Dsize%3A18px%3Bline%2Dheight%3A20px%7D%2Enavbar%2Dbrand%3Afocus%2C%2Enavbar%2Dbrand%3Ahover%7Btext%2Ddecoration%3Anone%7D%2Enavbar%2Dbrand%3Eimg%7Bdisplay%3Ablock%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%3E%2Econtainer%20%2Enavbar%2Dbrand%2C%2Enavbar%3E%2Econtainer%2Dfluid%20%2Enavbar%2Dbrand%7Bmargin%2Dleft%3A%2D15px%7D%7D%2Enavbar%2Dtoggle%7Bposition%3Arelative%3Bfloat%3Aright%3Bpadding%3A9px%2010px%3Bmargin%2Dtop%3A8px%3Bmargin%2Dright%3A15px%3Bmargin%2Dbottom%3A8px%3Bbackground%2Dcolor%3Atransparent%3Bbackground%2Dimage%3Anone%3Bborder%3A1px%20solid%20transparent%3Bborder%2Dradius%3A4px%7D%2Enavbar%2Dtoggle%3Afocus%7Boutline%3A0%7D%2Enavbar%2Dtoggle%20%2Eicon%2Dbar%7Bdisplay%3Ablock%3Bwidth%3A22px%3Bheight%3A2px%3Bborder%2Dradius%3A1px%7D%2Enavbar%2Dtoggle%20%2Eicon%2Dbar%2B%2Eicon%2Dbar%7Bmargin%2Dtop%3A4px%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dtoggle%7Bdisplay%3Anone%7D%7D%2Enavbar%2Dnav%7Bmargin%3A7%2E5px%20%2D15px%7D%2Enavbar%2Dnav%3Eli%3Ea%7Bpadding%2Dtop%3A10px%3Bpadding%2Dbottom%3A10px%3Bline%2Dheight%3A20px%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%7Bposition%3Astatic%3Bfloat%3Anone%3Bwidth%3Aauto%3Bmargin%2Dtop%3A0%3Bbackground%2Dcolor%3Atransparent%3Bborder%3A0%3B%2Dwebkit%2Dbox%2Dshadow%3Anone%3Bbox%2Dshadow%3Anone%7D%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%20%2Edropdown%2Dheader%2C%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%7Bpadding%3A5px%2015px%205px%2025px%7D%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%7Bline%2Dheight%3A20px%7D%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%3Afocus%2C%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%3Ahover%7Bbackground%2Dimage%3Anone%7D%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dnav%7Bfloat%3Aleft%3Bmargin%3A0%7D%2Enavbar%2Dnav%3Eli%7Bfloat%3Aleft%7D%2Enavbar%2Dnav%3Eli%3Ea%7Bpadding%2Dtop%3A15px%3Bpadding%2Dbottom%3A15px%7D%7D%2Enavbar%2Dform%7Bpadding%3A10px%2015px%3Bmargin%2Dtop%3A8px%3Bmargin%2Dright%3A%2D15px%3Bmargin%2Dbottom%3A8px%3Bmargin%2Dleft%3A%2D15px%3Bborder%2Dtop%3A1px%20solid%20transparent%3Bborder%2Dbottom%3A1px%20solid%20transparent%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%200%20rgba%28255%2C255%2C255%2C%2E1%29%2C0%201px%200%20rgba%28255%2C255%2C255%2C%2E1%29%3Bbox%2Dshadow%3Ainset%200%201px%200%20rgba%28255%2C255%2C255%2C%2E1%29%2C0%201px%200%20rgba%28255%2C255%2C255%2C%2E1%29%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dform%20%2Eform%2Dgroup%7Bdisplay%3Ainline%2Dblock%3Bmargin%2Dbottom%3A0%3Bvertical%2Dalign%3Amiddle%7D%2Enavbar%2Dform%20%2Eform%2Dcontrol%7Bdisplay%3Ainline%2Dblock%3Bwidth%3Aauto%3Bvertical%2Dalign%3Amiddle%7D%2Enavbar%2Dform%20%2Eform%2Dcontrol%2Dstatic%7Bdisplay%3Ainline%2Dblock%7D%2Enavbar%2Dform%20%2Einput%2Dgroup%7Bdisplay%3Ainline%2Dtable%3Bvertical%2Dalign%3Amiddle%7D%2Enavbar%2Dform%20%2Einput%2Dgroup%20%2Eform%2Dcontrol%2C%2Enavbar%2Dform%20%2Einput%2Dgroup%20%2Einput%2Dgroup%2Daddon%2C%2Enavbar%2Dform%20%2Einput%2Dgroup%20%2Einput%2Dgroup%2Dbtn%7Bwidth%3Aauto%7D%2Enavbar%2Dform%20%2Einput%2Dgroup%3E%2Eform%2Dcontrol%7Bwidth%3A100%25%7D%2Enavbar%2Dform%20%2Econtrol%2Dlabel%7Bmargin%2Dbottom%3A0%3Bvertical%2Dalign%3Amiddle%7D%2Enavbar%2Dform%20%2Echeckbox%2C%2Enavbar%2Dform%20%2Eradio%7Bdisplay%3Ainline%2Dblock%3Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A0%3Bvertical%2Dalign%3Amiddle%7D%2Enavbar%2Dform%20%2Echeckbox%20label%2C%2Enavbar%2Dform%20%2Eradio%20label%7Bpadding%2Dleft%3A0%7D%2Enavbar%2Dform%20%2Echeckbox%20input%5Btype%3Dcheckbox%5D%2C%2Enavbar%2Dform%20%2Eradio%20input%5Btype%3Dradio%5D%7Bposition%3Arelative%3Bmargin%2Dleft%3A0%7D%2Enavbar%2Dform%20%2Ehas%2Dfeedback%20%2Eform%2Dcontrol%2Dfeedback%7Btop%3A0%7D%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Enavbar%2Dform%20%2Eform%2Dgroup%7Bmargin%2Dbottom%3A5px%7D%2Enavbar%2Dform%20%2Eform%2Dgroup%3Alast%2Dchild%7Bmargin%2Dbottom%3A0%7D%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dform%7Bwidth%3Aauto%3Bpadding%2Dtop%3A0%3Bpadding%2Dbottom%3A0%3Bmargin%2Dright%3A0%3Bmargin%2Dleft%3A0%3Bborder%3A0%3B%2Dwebkit%2Dbox%2Dshadow%3Anone%3Bbox%2Dshadow%3Anone%7D%7D%2Enavbar%2Dnav%3Eli%3E%2Edropdown%2Dmenu%7Bmargin%2Dtop%3A0%3Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dtop%2Dright%2Dradius%3A0%7D%2Enavbar%2Dfixed%2Dbottom%20%2Enavbar%2Dnav%3Eli%3E%2Edropdown%2Dmenu%7Bmargin%2Dbottom%3A0%3Bborder%2Dtop%2Dleft%2Dradius%3A4px%3Bborder%2Dtop%2Dright%2Dradius%3A4px%3Bborder%2Dbottom%2Dright%2Dradius%3A0%3Bborder%2Dbottom%2Dleft%2Dradius%3A0%7D%2Enavbar%2Dbtn%7Bmargin%2Dtop%3A8px%3Bmargin%2Dbottom%3A8px%7D%2Enavbar%2Dbtn%2Ebtn%2Dsm%7Bmargin%2Dtop%3A10px%3Bmargin%2Dbottom%3A10px%7D%2Enavbar%2Dbtn%2Ebtn%2Dxs%7Bmargin%2Dtop%3A14px%3Bmargin%2Dbottom%3A14px%7D%2Enavbar%2Dtext%7Bmargin%2Dtop%3A15px%3Bmargin%2Dbottom%3A15px%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dtext%7Bfloat%3Aleft%3Bmargin%2Dright%3A15px%3Bmargin%2Dleft%3A15px%7D%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Enavbar%2Dleft%7Bfloat%3Aleft%21important%7D%2Enavbar%2Dright%7Bfloat%3Aright%21important%3Bmargin%2Dright%3A%2D15px%7D%2Enavbar%2Dright%7E%2Enavbar%2Dright%7Bmargin%2Dright%3A0%7D%7D%2Enavbar%2Ddefault%7Bbackground%2Dcolor%3A%23f8f8f8%3Bborder%2Dcolor%3A%23e7e7e7%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dbrand%7Bcolor%3A%23777%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dbrand%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dbrand%3Ahover%7Bcolor%3A%235e5e5e%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dtext%7Bcolor%3A%23777%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3Eli%3Ea%7Bcolor%3A%23777%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3Eli%3Ea%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3Eli%3Ea%3Ahover%7Bcolor%3A%23333%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Eactive%3Ea%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Eactive%3Ea%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Eactive%3Ea%3Ahover%7Bcolor%3A%23555%3Bbackground%2Dcolor%3A%23e7e7e7%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Edisabled%3Ea%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Edisabled%3Ea%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Edisabled%3Ea%3Ahover%7Bcolor%3A%23ccc%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dtoggle%7Bborder%2Dcolor%3A%23ddd%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dtoggle%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dtoggle%3Ahover%7Bbackground%2Dcolor%3A%23ddd%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dtoggle%20%2Eicon%2Dbar%7Bbackground%2Dcolor%3A%23888%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dcollapse%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dform%7Bborder%2Dcolor%3A%23e7e7e7%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Eopen%3Ea%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Eopen%3Ea%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%3E%2Eopen%3Ea%3Ahover%7Bcolor%3A%23555%3Bbackground%2Dcolor%3A%23e7e7e7%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%7Bcolor%3A%23777%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%3Ahover%7Bcolor%3A%23333%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Eactive%3Ea%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Eactive%3Ea%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Eactive%3Ea%3Ahover%7Bcolor%3A%23555%3Bbackground%2Dcolor%3A%23e7e7e7%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Afocus%2C%2Enavbar%2Ddefault%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Ahover%7Bcolor%3A%23ccc%3Bbackground%2Dcolor%3Atransparent%7D%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dlink%7Bcolor%3A%23777%7D%2Enavbar%2Ddefault%20%2Enavbar%2Dlink%3Ahover%7Bcolor%3A%23333%7D%2Enavbar%2Ddefault%20%2Ebtn%2Dlink%7Bcolor%3A%23777%7D%2Enavbar%2Ddefault%20%2Ebtn%2Dlink%3Afocus%2C%2Enavbar%2Ddefault%20%2Ebtn%2Dlink%3Ahover%7Bcolor%3A%23333%7D%2Enavbar%2Ddefault%20%2Ebtn%2Dlink%5Bdisabled%5D%3Afocus%2C%2Enavbar%2Ddefault%20%2Ebtn%2Dlink%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Enavbar%2Ddefault%20%2Ebtn%2Dlink%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Enavbar%2Ddefault%20%2Ebtn%2Dlink%3Ahover%7Bcolor%3A%23ccc%7D%2Enavbar%2Dinverse%7Bbackground%2Dcolor%3A%23222%3Bborder%2Dcolor%3A%23080808%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dbrand%7Bcolor%3A%239d9d9d%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dbrand%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dbrand%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dtext%7Bcolor%3A%239d9d9d%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3Eli%3Ea%7Bcolor%3A%239d9d9d%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3Eli%3Ea%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3Eli%3Ea%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Eactive%3Ea%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Eactive%3Ea%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Eactive%3Ea%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23080808%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Edisabled%3Ea%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Edisabled%3Ea%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Edisabled%3Ea%3Ahover%7Bcolor%3A%23444%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dtoggle%7Bborder%2Dcolor%3A%23333%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dtoggle%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dtoggle%3Ahover%7Bbackground%2Dcolor%3A%23333%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dtoggle%20%2Eicon%2Dbar%7Bbackground%2Dcolor%3A%23fff%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dcollapse%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dform%7Bborder%2Dcolor%3A%23101010%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Eopen%3Ea%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Eopen%3Ea%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%3E%2Eopen%3Ea%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23080808%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Edropdown%2Dheader%7Bborder%2Dcolor%3A%23080808%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%20%2Edivider%7Bbackground%2Dcolor%3A%23080808%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%7Bcolor%3A%239d9d9d%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3Eli%3Ea%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3Atransparent%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Eactive%3Ea%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Eactive%3Ea%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Eactive%3Ea%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23080808%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Afocus%2C%2Enavbar%2Dinverse%20%2Enavbar%2Dnav%20%2Eopen%20%2Edropdown%2Dmenu%3E%2Edisabled%3Ea%3Ahover%7Bcolor%3A%23444%3Bbackground%2Dcolor%3Atransparent%7D%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dlink%7Bcolor%3A%239d9d9d%7D%2Enavbar%2Dinverse%20%2Enavbar%2Dlink%3Ahover%7Bcolor%3A%23fff%7D%2Enavbar%2Dinverse%20%2Ebtn%2Dlink%7Bcolor%3A%239d9d9d%7D%2Enavbar%2Dinverse%20%2Ebtn%2Dlink%3Afocus%2C%2Enavbar%2Dinverse%20%2Ebtn%2Dlink%3Ahover%7Bcolor%3A%23fff%7D%2Enavbar%2Dinverse%20%2Ebtn%2Dlink%5Bdisabled%5D%3Afocus%2C%2Enavbar%2Dinverse%20%2Ebtn%2Dlink%5Bdisabled%5D%3Ahover%2Cfieldset%5Bdisabled%5D%20%2Enavbar%2Dinverse%20%2Ebtn%2Dlink%3Afocus%2Cfieldset%5Bdisabled%5D%20%2Enavbar%2Dinverse%20%2Ebtn%2Dlink%3Ahover%7Bcolor%3A%23444%7D%2Ebreadcrumb%7Bpadding%3A8px%2015px%3Bmargin%2Dbottom%3A20px%3Blist%2Dstyle%3Anone%3Bbackground%2Dcolor%3A%23f5f5f5%3Bborder%2Dradius%3A4px%7D%2Ebreadcrumb%3Eli%7Bdisplay%3Ainline%2Dblock%7D%2Ebreadcrumb%3Eli%2Bli%3Abefore%7Bpadding%3A0%205px%3Bcolor%3A%23ccc%3Bcontent%3A%22%2F%5C00a0%22%7D%2Ebreadcrumb%3E%2Eactive%7Bcolor%3A%23777%7D%2Epagination%7Bdisplay%3Ainline%2Dblock%3Bpadding%2Dleft%3A0%3Bmargin%3A20px%200%3Bborder%2Dradius%3A4px%7D%2Epagination%3Eli%7Bdisplay%3Ainline%7D%2Epagination%3Eli%3Ea%2C%2Epagination%3Eli%3Espan%7Bposition%3Arelative%3Bfloat%3Aleft%3Bpadding%3A6px%2012px%3Bmargin%2Dleft%3A%2D1px%3Bline%2Dheight%3A1%2E42857143%3Bcolor%3A%23337ab7%3Btext%2Ddecoration%3Anone%3Bbackground%2Dcolor%3A%23fff%3Bborder%3A1px%20solid%20%23ddd%7D%2Epagination%3Eli%3Afirst%2Dchild%3Ea%2C%2Epagination%3Eli%3Afirst%2Dchild%3Espan%7Bmargin%2Dleft%3A0%3Bborder%2Dtop%2Dleft%2Dradius%3A4px%3Bborder%2Dbottom%2Dleft%2Dradius%3A4px%7D%2Epagination%3Eli%3Alast%2Dchild%3Ea%2C%2Epagination%3Eli%3Alast%2Dchild%3Espan%7Bborder%2Dtop%2Dright%2Dradius%3A4px%3Bborder%2Dbottom%2Dright%2Dradius%3A4px%7D%2Epagination%3Eli%3Ea%3Afocus%2C%2Epagination%3Eli%3Ea%3Ahover%2C%2Epagination%3Eli%3Espan%3Afocus%2C%2Epagination%3Eli%3Espan%3Ahover%7Bz%2Dindex%3A3%3Bcolor%3A%2323527c%3Bbackground%2Dcolor%3A%23eee%3Bborder%2Dcolor%3A%23ddd%7D%2Epagination%3E%2Eactive%3Ea%2C%2Epagination%3E%2Eactive%3Ea%3Afocus%2C%2Epagination%3E%2Eactive%3Ea%3Ahover%2C%2Epagination%3E%2Eactive%3Espan%2C%2Epagination%3E%2Eactive%3Espan%3Afocus%2C%2Epagination%3E%2Eactive%3Espan%3Ahover%7Bz%2Dindex%3A2%3Bcolor%3A%23fff%3Bcursor%3Adefault%3Bbackground%2Dcolor%3A%23337ab7%3Bborder%2Dcolor%3A%23337ab7%7D%2Epagination%3E%2Edisabled%3Ea%2C%2Epagination%3E%2Edisabled%3Ea%3Afocus%2C%2Epagination%3E%2Edisabled%3Ea%3Ahover%2C%2Epagination%3E%2Edisabled%3Espan%2C%2Epagination%3E%2Edisabled%3Espan%3Afocus%2C%2Epagination%3E%2Edisabled%3Espan%3Ahover%7Bcolor%3A%23777%3Bcursor%3Anot%2Dallowed%3Bbackground%2Dcolor%3A%23fff%3Bborder%2Dcolor%3A%23ddd%7D%2Epagination%2Dlg%3Eli%3Ea%2C%2Epagination%2Dlg%3Eli%3Espan%7Bpadding%3A10px%2016px%3Bfont%2Dsize%3A18px%3Bline%2Dheight%3A1%2E3333333%7D%2Epagination%2Dlg%3Eli%3Afirst%2Dchild%3Ea%2C%2Epagination%2Dlg%3Eli%3Afirst%2Dchild%3Espan%7Bborder%2Dtop%2Dleft%2Dradius%3A6px%3Bborder%2Dbottom%2Dleft%2Dradius%3A6px%7D%2Epagination%2Dlg%3Eli%3Alast%2Dchild%3Ea%2C%2Epagination%2Dlg%3Eli%3Alast%2Dchild%3Espan%7Bborder%2Dtop%2Dright%2Dradius%3A6px%3Bborder%2Dbottom%2Dright%2Dradius%3A6px%7D%2Epagination%2Dsm%3Eli%3Ea%2C%2Epagination%2Dsm%3Eli%3Espan%7Bpadding%3A5px%2010px%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A1%2E5%7D%2Epagination%2Dsm%3Eli%3Afirst%2Dchild%3Ea%2C%2Epagination%2Dsm%3Eli%3Afirst%2Dchild%3Espan%7Bborder%2Dtop%2Dleft%2Dradius%3A3px%3Bborder%2Dbottom%2Dleft%2Dradius%3A3px%7D%2Epagination%2Dsm%3Eli%3Alast%2Dchild%3Ea%2C%2Epagination%2Dsm%3Eli%3Alast%2Dchild%3Espan%7Bborder%2Dtop%2Dright%2Dradius%3A3px%3Bborder%2Dbottom%2Dright%2Dradius%3A3px%7D%2Epager%7Bpadding%2Dleft%3A0%3Bmargin%3A20px%200%3Btext%2Dalign%3Acenter%3Blist%2Dstyle%3Anone%7D%2Epager%20li%7Bdisplay%3Ainline%7D%2Epager%20li%3Ea%2C%2Epager%20li%3Espan%7Bdisplay%3Ainline%2Dblock%3Bpadding%3A5px%2014px%3Bbackground%2Dcolor%3A%23fff%3Bborder%3A1px%20solid%20%23ddd%3Bborder%2Dradius%3A15px%7D%2Epager%20li%3Ea%3Afocus%2C%2Epager%20li%3Ea%3Ahover%7Btext%2Ddecoration%3Anone%3Bbackground%2Dcolor%3A%23eee%7D%2Epager%20%2Enext%3Ea%2C%2Epager%20%2Enext%3Espan%7Bfloat%3Aright%7D%2Epager%20%2Eprevious%3Ea%2C%2Epager%20%2Eprevious%3Espan%7Bfloat%3Aleft%7D%2Epager%20%2Edisabled%3Ea%2C%2Epager%20%2Edisabled%3Ea%3Afocus%2C%2Epager%20%2Edisabled%3Ea%3Ahover%2C%2Epager%20%2Edisabled%3Espan%7Bcolor%3A%23777%3Bcursor%3Anot%2Dallowed%3Bbackground%2Dcolor%3A%23fff%7D%2Elabel%7Bdisplay%3Ainline%3Bpadding%3A%2E2em%20%2E6em%20%2E3em%3Bfont%2Dsize%3A75%25%3Bfont%2Dweight%3A700%3Bline%2Dheight%3A1%3Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bwhite%2Dspace%3Anowrap%3Bvertical%2Dalign%3Abaseline%3Bborder%2Dradius%3A%2E25em%7Da%2Elabel%3Afocus%2Ca%2Elabel%3Ahover%7Bcolor%3A%23fff%3Btext%2Ddecoration%3Anone%3Bcursor%3Apointer%7D%2Elabel%3Aempty%7Bdisplay%3Anone%7D%2Ebtn%20%2Elabel%7Bposition%3Arelative%3Btop%3A%2D1px%7D%2Elabel%2Ddefault%7Bbackground%2Dcolor%3A%23777%7D%2Elabel%2Ddefault%5Bhref%5D%3Afocus%2C%2Elabel%2Ddefault%5Bhref%5D%3Ahover%7Bbackground%2Dcolor%3A%235e5e5e%7D%2Elabel%2Dprimary%7Bbackground%2Dcolor%3A%23337ab7%7D%2Elabel%2Dprimary%5Bhref%5D%3Afocus%2C%2Elabel%2Dprimary%5Bhref%5D%3Ahover%7Bbackground%2Dcolor%3A%23286090%7D%2Elabel%2Dsuccess%7Bbackground%2Dcolor%3A%235cb85c%7D%2Elabel%2Dsuccess%5Bhref%5D%3Afocus%2C%2Elabel%2Dsuccess%5Bhref%5D%3Ahover%7Bbackground%2Dcolor%3A%23449d44%7D%2Elabel%2Dinfo%7Bbackground%2Dcolor%3A%235bc0de%7D%2Elabel%2Dinfo%5Bhref%5D%3Afocus%2C%2Elabel%2Dinfo%5Bhref%5D%3Ahover%7Bbackground%2Dcolor%3A%2331b0d5%7D%2Elabel%2Dwarning%7Bbackground%2Dcolor%3A%23f0ad4e%7D%2Elabel%2Dwarning%5Bhref%5D%3Afocus%2C%2Elabel%2Dwarning%5Bhref%5D%3Ahover%7Bbackground%2Dcolor%3A%23ec971f%7D%2Elabel%2Ddanger%7Bbackground%2Dcolor%3A%23d9534f%7D%2Elabel%2Ddanger%5Bhref%5D%3Afocus%2C%2Elabel%2Ddanger%5Bhref%5D%3Ahover%7Bbackground%2Dcolor%3A%23c9302c%7D%2Ebadge%7Bdisplay%3Ainline%2Dblock%3Bmin%2Dwidth%3A10px%3Bpadding%3A3px%207px%3Bfont%2Dsize%3A12px%3Bfont%2Dweight%3A700%3Bline%2Dheight%3A1%3Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bwhite%2Dspace%3Anowrap%3Bvertical%2Dalign%3Amiddle%3Bbackground%2Dcolor%3A%23777%3Bborder%2Dradius%3A10px%7D%2Ebadge%3Aempty%7Bdisplay%3Anone%7D%2Ebtn%20%2Ebadge%7Bposition%3Arelative%3Btop%3A%2D1px%7D%2Ebtn%2Dgroup%2Dxs%3E%2Ebtn%20%2Ebadge%2C%2Ebtn%2Dxs%20%2Ebadge%7Btop%3A0%3Bpadding%3A1px%205px%7Da%2Ebadge%3Afocus%2Ca%2Ebadge%3Ahover%7Bcolor%3A%23fff%3Btext%2Ddecoration%3Anone%3Bcursor%3Apointer%7D%2Elist%2Dgroup%2Ditem%2Eactive%3E%2Ebadge%2C%2Enav%2Dpills%3E%2Eactive%3Ea%3E%2Ebadge%7Bcolor%3A%23337ab7%3Bbackground%2Dcolor%3A%23fff%7D%2Elist%2Dgroup%2Ditem%3E%2Ebadge%7Bfloat%3Aright%7D%2Elist%2Dgroup%2Ditem%3E%2Ebadge%2B%2Ebadge%7Bmargin%2Dright%3A5px%7D%2Enav%2Dpills%3Eli%3Ea%3E%2Ebadge%7Bmargin%2Dleft%3A3px%7D%2Ejumbotron%7Bpadding%2Dtop%3A30px%3Bpadding%2Dbottom%3A30px%3Bmargin%2Dbottom%3A30px%3Bcolor%3Ainherit%3Bbackground%2Dcolor%3A%23eee%7D%2Ejumbotron%20%2Eh1%2C%2Ejumbotron%20h1%7Bcolor%3Ainherit%7D%2Ejumbotron%20p%7Bmargin%2Dbottom%3A15px%3Bfont%2Dsize%3A21px%3Bfont%2Dweight%3A200%7D%2Ejumbotron%3Ehr%7Bborder%2Dtop%2Dcolor%3A%23d5d5d5%7D%2Econtainer%20%2Ejumbotron%2C%2Econtainer%2Dfluid%20%2Ejumbotron%7Bborder%2Dradius%3A6px%7D%2Ejumbotron%20%2Econtainer%7Bmax%2Dwidth%3A100%25%7D%40media%20screen%20and%20%28min%2Dwidth%3A768px%29%7B%2Ejumbotron%7Bpadding%2Dtop%3A48px%3Bpadding%2Dbottom%3A48px%7D%2Econtainer%20%2Ejumbotron%2C%2Econtainer%2Dfluid%20%2Ejumbotron%7Bpadding%2Dright%3A60px%3Bpadding%2Dleft%3A60px%7D%2Ejumbotron%20%2Eh1%2C%2Ejumbotron%20h1%7Bfont%2Dsize%3A63px%7D%7D%2Ethumbnail%7Bdisplay%3Ablock%3Bpadding%3A4px%3Bmargin%2Dbottom%3A20px%3Bline%2Dheight%3A1%2E42857143%3Bbackground%2Dcolor%3A%23fff%3Bborder%3A1px%20solid%20%23ddd%3Bborder%2Dradius%3A4px%3B%2Dwebkit%2Dtransition%3Aborder%20%2E2s%20ease%2Din%2Dout%3B%2Do%2Dtransition%3Aborder%20%2E2s%20ease%2Din%2Dout%3Btransition%3Aborder%20%2E2s%20ease%2Din%2Dout%7D%2Ethumbnail%20a%3Eimg%2C%2Ethumbnail%3Eimg%7Bmargin%2Dright%3Aauto%3Bmargin%2Dleft%3Aauto%7Da%2Ethumbnail%2Eactive%2Ca%2Ethumbnail%3Afocus%2Ca%2Ethumbnail%3Ahover%7Bborder%2Dcolor%3A%23337ab7%7D%2Ethumbnail%20%2Ecaption%7Bpadding%3A9px%3Bcolor%3A%23333%7D%2Ealert%7Bpadding%3A15px%3Bmargin%2Dbottom%3A20px%3Bborder%3A1px%20solid%20transparent%3Bborder%2Dradius%3A4px%7D%2Ealert%20h4%7Bmargin%2Dtop%3A0%3Bcolor%3Ainherit%7D%2Ealert%20%2Ealert%2Dlink%7Bfont%2Dweight%3A700%7D%2Ealert%3Ep%2C%2Ealert%3Eul%7Bmargin%2Dbottom%3A0%7D%2Ealert%3Ep%2Bp%7Bmargin%2Dtop%3A5px%7D%2Ealert%2Ddismissable%2C%2Ealert%2Ddismissible%7Bpadding%2Dright%3A35px%7D%2Ealert%2Ddismissable%20%2Eclose%2C%2Ealert%2Ddismissible%20%2Eclose%7Bposition%3Arelative%3Btop%3A%2D2px%3Bright%3A%2D21px%3Bcolor%3Ainherit%7D%2Ealert%2Dsuccess%7Bcolor%3A%233c763d%3Bbackground%2Dcolor%3A%23dff0d8%3Bborder%2Dcolor%3A%23d6e9c6%7D%2Ealert%2Dsuccess%20hr%7Bborder%2Dtop%2Dcolor%3A%23c9e2b3%7D%2Ealert%2Dsuccess%20%2Ealert%2Dlink%7Bcolor%3A%232b542c%7D%2Ealert%2Dinfo%7Bcolor%3A%2331708f%3Bbackground%2Dcolor%3A%23d9edf7%3Bborder%2Dcolor%3A%23bce8f1%7D%2Ealert%2Dinfo%20hr%7Bborder%2Dtop%2Dcolor%3A%23a6e1ec%7D%2Ealert%2Dinfo%20%2Ealert%2Dlink%7Bcolor%3A%23245269%7D%2Ealert%2Dwarning%7Bcolor%3A%238a6d3b%3Bbackground%2Dcolor%3A%23fcf8e3%3Bborder%2Dcolor%3A%23faebcc%7D%2Ealert%2Dwarning%20hr%7Bborder%2Dtop%2Dcolor%3A%23f7e1b5%7D%2Ealert%2Dwarning%20%2Ealert%2Dlink%7Bcolor%3A%2366512c%7D%2Ealert%2Ddanger%7Bcolor%3A%23a94442%3Bbackground%2Dcolor%3A%23f2dede%3Bborder%2Dcolor%3A%23ebccd1%7D%2Ealert%2Ddanger%20hr%7Bborder%2Dtop%2Dcolor%3A%23e4b9c0%7D%2Ealert%2Ddanger%20%2Ealert%2Dlink%7Bcolor%3A%23843534%7D%40%2Dwebkit%2Dkeyframes%20progress%2Dbar%2Dstripes%7Bfrom%7Bbackground%2Dposition%3A40px%200%7Dto%7Bbackground%2Dposition%3A0%200%7D%7D%40%2Do%2Dkeyframes%20progress%2Dbar%2Dstripes%7Bfrom%7Bbackground%2Dposition%3A40px%200%7Dto%7Bbackground%2Dposition%3A0%200%7D%7D%40keyframes%20progress%2Dbar%2Dstripes%7Bfrom%7Bbackground%2Dposition%3A40px%200%7Dto%7Bbackground%2Dposition%3A0%200%7D%7D%2Eprogress%7Bheight%3A20px%3Bmargin%2Dbottom%3A20px%3Boverflow%3Ahidden%3Bbackground%2Dcolor%3A%23f5f5f5%3Bborder%2Dradius%3A4px%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%202px%20rgba%280%2C0%2C0%2C%2E1%29%3Bbox%2Dshadow%3Ainset%200%201px%202px%20rgba%280%2C0%2C0%2C%2E1%29%7D%2Eprogress%2Dbar%7Bfloat%3Aleft%3Bwidth%3A0%3Bheight%3A100%25%3Bfont%2Dsize%3A12px%3Bline%2Dheight%3A20px%3Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23337ab7%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%20%2D1px%200%20rgba%280%2C0%2C0%2C%2E15%29%3Bbox%2Dshadow%3Ainset%200%20%2D1px%200%20rgba%280%2C0%2C0%2C%2E15%29%3B%2Dwebkit%2Dtransition%3Awidth%20%2E6s%20ease%3B%2Do%2Dtransition%3Awidth%20%2E6s%20ease%3Btransition%3Awidth%20%2E6s%20ease%7D%2Eprogress%2Dbar%2Dstriped%2C%2Eprogress%2Dstriped%20%2Eprogress%2Dbar%7Bbackground%2Dimage%3A%2Dwebkit%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3A%2Do%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3Alinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3B%2Dwebkit%2Dbackground%2Dsize%3A40px%2040px%3Bbackground%2Dsize%3A40px%2040px%7D%2Eprogress%2Dbar%2Eactive%2C%2Eprogress%2Eactive%20%2Eprogress%2Dbar%7B%2Dwebkit%2Danimation%3Aprogress%2Dbar%2Dstripes%202s%20linear%20infinite%3B%2Do%2Danimation%3Aprogress%2Dbar%2Dstripes%202s%20linear%20infinite%3Banimation%3Aprogress%2Dbar%2Dstripes%202s%20linear%20infinite%7D%2Eprogress%2Dbar%2Dsuccess%7Bbackground%2Dcolor%3A%235cb85c%7D%2Eprogress%2Dstriped%20%2Eprogress%2Dbar%2Dsuccess%7Bbackground%2Dimage%3A%2Dwebkit%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3A%2Do%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3Alinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%7D%2Eprogress%2Dbar%2Dinfo%7Bbackground%2Dcolor%3A%235bc0de%7D%2Eprogress%2Dstriped%20%2Eprogress%2Dbar%2Dinfo%7Bbackground%2Dimage%3A%2Dwebkit%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3A%2Do%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3Alinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%7D%2Eprogress%2Dbar%2Dwarning%7Bbackground%2Dcolor%3A%23f0ad4e%7D%2Eprogress%2Dstriped%20%2Eprogress%2Dbar%2Dwarning%7Bbackground%2Dimage%3A%2Dwebkit%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3A%2Do%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3Alinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%7D%2Eprogress%2Dbar%2Ddanger%7Bbackground%2Dcolor%3A%23d9534f%7D%2Eprogress%2Dstriped%20%2Eprogress%2Dbar%2Ddanger%7Bbackground%2Dimage%3A%2Dwebkit%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3A%2Do%2Dlinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%3Bbackground%2Dimage%3Alinear%2Dgradient%2845deg%2Crgba%28255%2C255%2C255%2C%2E15%29%2025%25%2Ctransparent%2025%25%2Ctransparent%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2050%25%2Crgba%28255%2C255%2C255%2C%2E15%29%2075%25%2Ctransparent%2075%25%2Ctransparent%29%7D%2Emedia%7Bmargin%2Dtop%3A15px%7D%2Emedia%3Afirst%2Dchild%7Bmargin%2Dtop%3A0%7D%2Emedia%2C%2Emedia%2Dbody%7Boverflow%3Ahidden%3Bzoom%3A1%7D%2Emedia%2Dbody%7Bwidth%3A10000px%7D%2Emedia%2Dobject%7Bdisplay%3Ablock%7D%2Emedia%2Dobject%2Eimg%2Dthumbnail%7Bmax%2Dwidth%3Anone%7D%2Emedia%2Dright%2C%2Emedia%3E%2Epull%2Dright%7Bpadding%2Dleft%3A10px%7D%2Emedia%2Dleft%2C%2Emedia%3E%2Epull%2Dleft%7Bpadding%2Dright%3A10px%7D%2Emedia%2Dbody%2C%2Emedia%2Dleft%2C%2Emedia%2Dright%7Bdisplay%3Atable%2Dcell%3Bvertical%2Dalign%3Atop%7D%2Emedia%2Dmiddle%7Bvertical%2Dalign%3Amiddle%7D%2Emedia%2Dbottom%7Bvertical%2Dalign%3Abottom%7D%2Emedia%2Dheading%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A5px%7D%2Emedia%2Dlist%7Bpadding%2Dleft%3A0%3Blist%2Dstyle%3Anone%7D%2Elist%2Dgroup%7Bpadding%2Dleft%3A0%3Bmargin%2Dbottom%3A20px%7D%2Elist%2Dgroup%2Ditem%7Bposition%3Arelative%3Bdisplay%3Ablock%3Bpadding%3A10px%2015px%3Bmargin%2Dbottom%3A%2D1px%3Bbackground%2Dcolor%3A%23fff%3Bborder%3A1px%20solid%20%23ddd%7D%2Elist%2Dgroup%2Ditem%3Afirst%2Dchild%7Bborder%2Dtop%2Dleft%2Dradius%3A4px%3Bborder%2Dtop%2Dright%2Dradius%3A4px%7D%2Elist%2Dgroup%2Ditem%3Alast%2Dchild%7Bmargin%2Dbottom%3A0%3Bborder%2Dbottom%2Dright%2Dradius%3A4px%3Bborder%2Dbottom%2Dleft%2Dradius%3A4px%7Da%2Elist%2Dgroup%2Ditem%2Cbutton%2Elist%2Dgroup%2Ditem%7Bcolor%3A%23555%7Da%2Elist%2Dgroup%2Ditem%20%2Elist%2Dgroup%2Ditem%2Dheading%2Cbutton%2Elist%2Dgroup%2Ditem%20%2Elist%2Dgroup%2Ditem%2Dheading%7Bcolor%3A%23333%7Da%2Elist%2Dgroup%2Ditem%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%3Ahover%7Bcolor%3A%23555%3Btext%2Ddecoration%3Anone%3Bbackground%2Dcolor%3A%23f5f5f5%7Dbutton%2Elist%2Dgroup%2Ditem%7Bwidth%3A100%25%3Btext%2Dalign%3Aleft%7D%2Elist%2Dgroup%2Ditem%2Edisabled%2C%2Elist%2Dgroup%2Ditem%2Edisabled%3Afocus%2C%2Elist%2Dgroup%2Ditem%2Edisabled%3Ahover%7Bcolor%3A%23777%3Bcursor%3Anot%2Dallowed%3Bbackground%2Dcolor%3A%23eee%7D%2Elist%2Dgroup%2Ditem%2Edisabled%20%2Elist%2Dgroup%2Ditem%2Dheading%2C%2Elist%2Dgroup%2Ditem%2Edisabled%3Afocus%20%2Elist%2Dgroup%2Ditem%2Dheading%2C%2Elist%2Dgroup%2Ditem%2Edisabled%3Ahover%20%2Elist%2Dgroup%2Ditem%2Dheading%7Bcolor%3Ainherit%7D%2Elist%2Dgroup%2Ditem%2Edisabled%20%2Elist%2Dgroup%2Ditem%2Dtext%2C%2Elist%2Dgroup%2Ditem%2Edisabled%3Afocus%20%2Elist%2Dgroup%2Ditem%2Dtext%2C%2Elist%2Dgroup%2Ditem%2Edisabled%3Ahover%20%2Elist%2Dgroup%2Ditem%2Dtext%7Bcolor%3A%23777%7D%2Elist%2Dgroup%2Ditem%2Eactive%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Afocus%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Ahover%7Bz%2Dindex%3A2%3Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23337ab7%3Bborder%2Dcolor%3A%23337ab7%7D%2Elist%2Dgroup%2Ditem%2Eactive%20%2Elist%2Dgroup%2Ditem%2Dheading%2C%2Elist%2Dgroup%2Ditem%2Eactive%20%2Elist%2Dgroup%2Ditem%2Dheading%3E%2Esmall%2C%2Elist%2Dgroup%2Ditem%2Eactive%20%2Elist%2Dgroup%2Ditem%2Dheading%3Esmall%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Afocus%20%2Elist%2Dgroup%2Ditem%2Dheading%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Afocus%20%2Elist%2Dgroup%2Ditem%2Dheading%3E%2Esmall%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Afocus%20%2Elist%2Dgroup%2Ditem%2Dheading%3Esmall%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Ahover%20%2Elist%2Dgroup%2Ditem%2Dheading%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Ahover%20%2Elist%2Dgroup%2Ditem%2Dheading%3E%2Esmall%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Ahover%20%2Elist%2Dgroup%2Ditem%2Dheading%3Esmall%7Bcolor%3Ainherit%7D%2Elist%2Dgroup%2Ditem%2Eactive%20%2Elist%2Dgroup%2Ditem%2Dtext%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Afocus%20%2Elist%2Dgroup%2Ditem%2Dtext%2C%2Elist%2Dgroup%2Ditem%2Eactive%3Ahover%20%2Elist%2Dgroup%2Ditem%2Dtext%7Bcolor%3A%23c7ddef%7D%2Elist%2Dgroup%2Ditem%2Dsuccess%7Bcolor%3A%233c763d%3Bbackground%2Dcolor%3A%23dff0d8%7Da%2Elist%2Dgroup%2Ditem%2Dsuccess%2Cbutton%2Elist%2Dgroup%2Ditem%2Dsuccess%7Bcolor%3A%233c763d%7Da%2Elist%2Dgroup%2Ditem%2Dsuccess%20%2Elist%2Dgroup%2Ditem%2Dheading%2Cbutton%2Elist%2Dgroup%2Ditem%2Dsuccess%20%2Elist%2Dgroup%2Ditem%2Dheading%7Bcolor%3Ainherit%7Da%2Elist%2Dgroup%2Ditem%2Dsuccess%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Dsuccess%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Dsuccess%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Dsuccess%3Ahover%7Bcolor%3A%233c763d%3Bbackground%2Dcolor%3A%23d0e9c6%7Da%2Elist%2Dgroup%2Ditem%2Dsuccess%2Eactive%2Ca%2Elist%2Dgroup%2Ditem%2Dsuccess%2Eactive%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Dsuccess%2Eactive%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Dsuccess%2Eactive%2Cbutton%2Elist%2Dgroup%2Ditem%2Dsuccess%2Eactive%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Dsuccess%2Eactive%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%233c763d%3Bborder%2Dcolor%3A%233c763d%7D%2Elist%2Dgroup%2Ditem%2Dinfo%7Bcolor%3A%2331708f%3Bbackground%2Dcolor%3A%23d9edf7%7Da%2Elist%2Dgroup%2Ditem%2Dinfo%2Cbutton%2Elist%2Dgroup%2Ditem%2Dinfo%7Bcolor%3A%2331708f%7Da%2Elist%2Dgroup%2Ditem%2Dinfo%20%2Elist%2Dgroup%2Ditem%2Dheading%2Cbutton%2Elist%2Dgroup%2Ditem%2Dinfo%20%2Elist%2Dgroup%2Ditem%2Dheading%7Bcolor%3Ainherit%7Da%2Elist%2Dgroup%2Ditem%2Dinfo%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Dinfo%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Dinfo%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Dinfo%3Ahover%7Bcolor%3A%2331708f%3Bbackground%2Dcolor%3A%23c4e3f3%7Da%2Elist%2Dgroup%2Ditem%2Dinfo%2Eactive%2Ca%2Elist%2Dgroup%2Ditem%2Dinfo%2Eactive%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Dinfo%2Eactive%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Dinfo%2Eactive%2Cbutton%2Elist%2Dgroup%2Ditem%2Dinfo%2Eactive%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Dinfo%2Eactive%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%2331708f%3Bborder%2Dcolor%3A%2331708f%7D%2Elist%2Dgroup%2Ditem%2Dwarning%7Bcolor%3A%238a6d3b%3Bbackground%2Dcolor%3A%23fcf8e3%7Da%2Elist%2Dgroup%2Ditem%2Dwarning%2Cbutton%2Elist%2Dgroup%2Ditem%2Dwarning%7Bcolor%3A%238a6d3b%7Da%2Elist%2Dgroup%2Ditem%2Dwarning%20%2Elist%2Dgroup%2Ditem%2Dheading%2Cbutton%2Elist%2Dgroup%2Ditem%2Dwarning%20%2Elist%2Dgroup%2Ditem%2Dheading%7Bcolor%3Ainherit%7Da%2Elist%2Dgroup%2Ditem%2Dwarning%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Dwarning%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Dwarning%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Dwarning%3Ahover%7Bcolor%3A%238a6d3b%3Bbackground%2Dcolor%3A%23faf2cc%7Da%2Elist%2Dgroup%2Ditem%2Dwarning%2Eactive%2Ca%2Elist%2Dgroup%2Ditem%2Dwarning%2Eactive%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Dwarning%2Eactive%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Dwarning%2Eactive%2Cbutton%2Elist%2Dgroup%2Ditem%2Dwarning%2Eactive%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Dwarning%2Eactive%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%238a6d3b%3Bborder%2Dcolor%3A%238a6d3b%7D%2Elist%2Dgroup%2Ditem%2Ddanger%7Bcolor%3A%23a94442%3Bbackground%2Dcolor%3A%23f2dede%7Da%2Elist%2Dgroup%2Ditem%2Ddanger%2Cbutton%2Elist%2Dgroup%2Ditem%2Ddanger%7Bcolor%3A%23a94442%7Da%2Elist%2Dgroup%2Ditem%2Ddanger%20%2Elist%2Dgroup%2Ditem%2Dheading%2Cbutton%2Elist%2Dgroup%2Ditem%2Ddanger%20%2Elist%2Dgroup%2Ditem%2Dheading%7Bcolor%3Ainherit%7Da%2Elist%2Dgroup%2Ditem%2Ddanger%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Ddanger%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Ddanger%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Ddanger%3Ahover%7Bcolor%3A%23a94442%3Bbackground%2Dcolor%3A%23ebcccc%7Da%2Elist%2Dgroup%2Ditem%2Ddanger%2Eactive%2Ca%2Elist%2Dgroup%2Ditem%2Ddanger%2Eactive%3Afocus%2Ca%2Elist%2Dgroup%2Ditem%2Ddanger%2Eactive%3Ahover%2Cbutton%2Elist%2Dgroup%2Ditem%2Ddanger%2Eactive%2Cbutton%2Elist%2Dgroup%2Ditem%2Ddanger%2Eactive%3Afocus%2Cbutton%2Elist%2Dgroup%2Ditem%2Ddanger%2Eactive%3Ahover%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23a94442%3Bborder%2Dcolor%3A%23a94442%7D%2Elist%2Dgroup%2Ditem%2Dheading%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A5px%7D%2Elist%2Dgroup%2Ditem%2Dtext%7Bmargin%2Dbottom%3A0%3Bline%2Dheight%3A1%2E3%7D%2Epanel%7Bmargin%2Dbottom%3A20px%3Bbackground%2Dcolor%3A%23fff%3Bborder%3A1px%20solid%20transparent%3Bborder%2Dradius%3A4px%3B%2Dwebkit%2Dbox%2Dshadow%3A0%201px%201px%20rgba%280%2C0%2C0%2C%2E05%29%3Bbox%2Dshadow%3A0%201px%201px%20rgba%280%2C0%2C0%2C%2E05%29%7D%2Epanel%2Dbody%7Bpadding%3A15px%7D%2Epanel%2Dheading%7Bpadding%3A10px%2015px%3Bborder%2Dbottom%3A1px%20solid%20transparent%3Bborder%2Dtop%2Dleft%2Dradius%3A3px%3Bborder%2Dtop%2Dright%2Dradius%3A3px%7D%2Epanel%2Dheading%3E%2Edropdown%20%2Edropdown%2Dtoggle%7Bcolor%3Ainherit%7D%2Epanel%2Dtitle%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A0%3Bfont%2Dsize%3A16px%3Bcolor%3Ainherit%7D%2Epanel%2Dtitle%3E%2Esmall%2C%2Epanel%2Dtitle%3E%2Esmall%3Ea%2C%2Epanel%2Dtitle%3Ea%2C%2Epanel%2Dtitle%3Esmall%2C%2Epanel%2Dtitle%3Esmall%3Ea%7Bcolor%3Ainherit%7D%2Epanel%2Dfooter%7Bpadding%3A10px%2015px%3Bbackground%2Dcolor%3A%23f5f5f5%3Bborder%2Dtop%3A1px%20solid%20%23ddd%3Bborder%2Dbottom%2Dright%2Dradius%3A3px%3Bborder%2Dbottom%2Dleft%2Dradius%3A3px%7D%2Epanel%3E%2Elist%2Dgroup%2C%2Epanel%3E%2Epanel%2Dcollapse%3E%2Elist%2Dgroup%7Bmargin%2Dbottom%3A0%7D%2Epanel%3E%2Elist%2Dgroup%20%2Elist%2Dgroup%2Ditem%2C%2Epanel%3E%2Epanel%2Dcollapse%3E%2Elist%2Dgroup%20%2Elist%2Dgroup%2Ditem%7Bborder%2Dwidth%3A1px%200%3Bborder%2Dradius%3A0%7D%2Epanel%3E%2Elist%2Dgroup%3Afirst%2Dchild%20%2Elist%2Dgroup%2Ditem%3Afirst%2Dchild%2C%2Epanel%3E%2Epanel%2Dcollapse%3E%2Elist%2Dgroup%3Afirst%2Dchild%20%2Elist%2Dgroup%2Ditem%3Afirst%2Dchild%7Bborder%2Dtop%3A0%3Bborder%2Dtop%2Dleft%2Dradius%3A3px%3Bborder%2Dtop%2Dright%2Dradius%3A3px%7D%2Epanel%3E%2Elist%2Dgroup%3Alast%2Dchild%20%2Elist%2Dgroup%2Ditem%3Alast%2Dchild%2C%2Epanel%3E%2Epanel%2Dcollapse%3E%2Elist%2Dgroup%3Alast%2Dchild%20%2Elist%2Dgroup%2Ditem%3Alast%2Dchild%7Bborder%2Dbottom%3A0%3Bborder%2Dbottom%2Dright%2Dradius%3A3px%3Bborder%2Dbottom%2Dleft%2Dradius%3A3px%7D%2Epanel%3E%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Elist%2Dgroup%20%2Elist%2Dgroup%2Ditem%3Afirst%2Dchild%7Bborder%2Dtop%2Dleft%2Dradius%3A0%3Bborder%2Dtop%2Dright%2Dradius%3A0%7D%2Epanel%2Dheading%2B%2Elist%2Dgroup%20%2Elist%2Dgroup%2Ditem%3Afirst%2Dchild%7Bborder%2Dtop%2Dwidth%3A0%7D%2Elist%2Dgroup%2B%2Epanel%2Dfooter%7Bborder%2Dtop%2Dwidth%3A0%7D%2Epanel%3E%2Epanel%2Dcollapse%3E%2Etable%2C%2Epanel%3E%2Etable%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%7Bmargin%2Dbottom%3A0%7D%2Epanel%3E%2Epanel%2Dcollapse%3E%2Etable%20caption%2C%2Epanel%3E%2Etable%20caption%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%20caption%7Bpadding%2Dright%3A15px%3Bpadding%2Dleft%3A15px%7D%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%7Bborder%2Dtop%2Dleft%2Dradius%3A3px%3Bborder%2Dtop%2Dright%2Dradius%3A3px%7D%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%7Bborder%2Dtop%2Dleft%2Dradius%3A3px%3Bborder%2Dtop%2Dright%2Dradius%3A3px%7D%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Afirst%2Dchild%7Bborder%2Dtop%2Dleft%2Dradius%3A3px%7D%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Afirst%2Dchild%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Afirst%2Dchild%3Ethead%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%3Alast%2Dchild%7Bborder%2Dtop%2Dright%2Dradius%3A3px%7D%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%7Bborder%2Dbottom%2Dright%2Dradius%3A3px%3Bborder%2Dbottom%2Dleft%2Dradius%3A3px%7D%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%7Bborder%2Dbottom%2Dright%2Dradius%3A3px%3Bborder%2Dbottom%2Dleft%2Dradius%3A3px%7D%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Afirst%2Dchild%7Bborder%2Dbottom%2Dleft%2Dradius%3A3px%7D%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3Alast%2Dchild%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etbody%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20td%3Alast%2Dchild%2C%2Epanel%3E%2Etable%3Alast%2Dchild%3Etfoot%3Alast%2Dchild%3Etr%3Alast%2Dchild%20th%3Alast%2Dchild%7Bborder%2Dbottom%2Dright%2Dradius%3A3px%7D%2Epanel%3E%2Epanel%2Dbody%2B%2Etable%2C%2Epanel%3E%2Epanel%2Dbody%2B%2Etable%2Dresponsive%2C%2Epanel%3E%2Etable%2B%2Epanel%2Dbody%2C%2Epanel%3E%2Etable%2Dresponsive%2B%2Epanel%2Dbody%7Bborder%2Dtop%3A1px%20solid%20%23ddd%7D%2Epanel%3E%2Etable%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20td%2C%2Epanel%3E%2Etable%3Etbody%3Afirst%2Dchild%3Etr%3Afirst%2Dchild%20th%7Bborder%2Dtop%3A0%7D%2Epanel%3E%2Etable%2Dbordered%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%7Bborder%3A0%7D%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Etd%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Eth%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Etd%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Eth%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Ethead%3Etr%3Etd%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Ethead%3Etr%3Eth%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Etd%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Eth%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Etd%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Eth%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Etd%3Afirst%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Eth%3Afirst%2Dchild%7Bborder%2Dleft%3A0%7D%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Etd%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Eth%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Etd%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Eth%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Ethead%3Etr%3Etd%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dbordered%3Ethead%3Etr%3Eth%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Etd%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Eth%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Etd%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Eth%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Etd%3Alast%2Dchild%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Eth%3Alast%2Dchild%7Bborder%2Dright%3A0%7D%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Afirst%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Afirst%2Dchild%3Eth%2C%2Epanel%3E%2Etable%2Dbordered%3Ethead%3Etr%3Afirst%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dbordered%3Ethead%3Etr%3Afirst%2Dchild%3Eth%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Afirst%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Afirst%2Dchild%3Eth%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Afirst%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Ethead%3Etr%3Afirst%2Dchild%3Eth%7Bborder%2Dbottom%3A0%7D%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Alast%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dbordered%3Etbody%3Etr%3Alast%2Dchild%3Eth%2C%2Epanel%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Alast%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Alast%2Dchild%3Eth%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Alast%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etbody%3Etr%3Alast%2Dchild%3Eth%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Alast%2Dchild%3Etd%2C%2Epanel%3E%2Etable%2Dresponsive%3E%2Etable%2Dbordered%3Etfoot%3Etr%3Alast%2Dchild%3Eth%7Bborder%2Dbottom%3A0%7D%2Epanel%3E%2Etable%2Dresponsive%7Bmargin%2Dbottom%3A0%3Bborder%3A0%7D%2Epanel%2Dgroup%7Bmargin%2Dbottom%3A20px%7D%2Epanel%2Dgroup%20%2Epanel%7Bmargin%2Dbottom%3A0%3Bborder%2Dradius%3A4px%7D%2Epanel%2Dgroup%20%2Epanel%2B%2Epanel%7Bmargin%2Dtop%3A5px%7D%2Epanel%2Dgroup%20%2Epanel%2Dheading%7Bborder%2Dbottom%3A0%7D%2Epanel%2Dgroup%20%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Elist%2Dgroup%2C%2Epanel%2Dgroup%20%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dtop%3A1px%20solid%20%23ddd%7D%2Epanel%2Dgroup%20%2Epanel%2Dfooter%7Bborder%2Dtop%3A0%7D%2Epanel%2Dgroup%20%2Epanel%2Dfooter%2B%2Epanel%2Dcollapse%20%2Epanel%2Dbody%7Bborder%2Dbottom%3A1px%20solid%20%23ddd%7D%2Epanel%2Ddefault%7Bborder%2Dcolor%3A%23ddd%7D%2Epanel%2Ddefault%3E%2Epanel%2Dheading%7Bcolor%3A%23333%3Bbackground%2Dcolor%3A%23f5f5f5%3Bborder%2Dcolor%3A%23ddd%7D%2Epanel%2Ddefault%3E%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dtop%2Dcolor%3A%23ddd%7D%2Epanel%2Ddefault%3E%2Epanel%2Dheading%20%2Ebadge%7Bcolor%3A%23f5f5f5%3Bbackground%2Dcolor%3A%23333%7D%2Epanel%2Ddefault%3E%2Epanel%2Dfooter%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dbottom%2Dcolor%3A%23ddd%7D%2Epanel%2Dprimary%7Bborder%2Dcolor%3A%23337ab7%7D%2Epanel%2Dprimary%3E%2Epanel%2Dheading%7Bcolor%3A%23fff%3Bbackground%2Dcolor%3A%23337ab7%3Bborder%2Dcolor%3A%23337ab7%7D%2Epanel%2Dprimary%3E%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dtop%2Dcolor%3A%23337ab7%7D%2Epanel%2Dprimary%3E%2Epanel%2Dheading%20%2Ebadge%7Bcolor%3A%23337ab7%3Bbackground%2Dcolor%3A%23fff%7D%2Epanel%2Dprimary%3E%2Epanel%2Dfooter%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dbottom%2Dcolor%3A%23337ab7%7D%2Epanel%2Dsuccess%7Bborder%2Dcolor%3A%23d6e9c6%7D%2Epanel%2Dsuccess%3E%2Epanel%2Dheading%7Bcolor%3A%233c763d%3Bbackground%2Dcolor%3A%23dff0d8%3Bborder%2Dcolor%3A%23d6e9c6%7D%2Epanel%2Dsuccess%3E%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dtop%2Dcolor%3A%23d6e9c6%7D%2Epanel%2Dsuccess%3E%2Epanel%2Dheading%20%2Ebadge%7Bcolor%3A%23dff0d8%3Bbackground%2Dcolor%3A%233c763d%7D%2Epanel%2Dsuccess%3E%2Epanel%2Dfooter%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dbottom%2Dcolor%3A%23d6e9c6%7D%2Epanel%2Dinfo%7Bborder%2Dcolor%3A%23bce8f1%7D%2Epanel%2Dinfo%3E%2Epanel%2Dheading%7Bcolor%3A%2331708f%3Bbackground%2Dcolor%3A%23d9edf7%3Bborder%2Dcolor%3A%23bce8f1%7D%2Epanel%2Dinfo%3E%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dtop%2Dcolor%3A%23bce8f1%7D%2Epanel%2Dinfo%3E%2Epanel%2Dheading%20%2Ebadge%7Bcolor%3A%23d9edf7%3Bbackground%2Dcolor%3A%2331708f%7D%2Epanel%2Dinfo%3E%2Epanel%2Dfooter%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dbottom%2Dcolor%3A%23bce8f1%7D%2Epanel%2Dwarning%7Bborder%2Dcolor%3A%23faebcc%7D%2Epanel%2Dwarning%3E%2Epanel%2Dheading%7Bcolor%3A%238a6d3b%3Bbackground%2Dcolor%3A%23fcf8e3%3Bborder%2Dcolor%3A%23faebcc%7D%2Epanel%2Dwarning%3E%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dtop%2Dcolor%3A%23faebcc%7D%2Epanel%2Dwarning%3E%2Epanel%2Dheading%20%2Ebadge%7Bcolor%3A%23fcf8e3%3Bbackground%2Dcolor%3A%238a6d3b%7D%2Epanel%2Dwarning%3E%2Epanel%2Dfooter%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dbottom%2Dcolor%3A%23faebcc%7D%2Epanel%2Ddanger%7Bborder%2Dcolor%3A%23ebccd1%7D%2Epanel%2Ddanger%3E%2Epanel%2Dheading%7Bcolor%3A%23a94442%3Bbackground%2Dcolor%3A%23f2dede%3Bborder%2Dcolor%3A%23ebccd1%7D%2Epanel%2Ddanger%3E%2Epanel%2Dheading%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dtop%2Dcolor%3A%23ebccd1%7D%2Epanel%2Ddanger%3E%2Epanel%2Dheading%20%2Ebadge%7Bcolor%3A%23f2dede%3Bbackground%2Dcolor%3A%23a94442%7D%2Epanel%2Ddanger%3E%2Epanel%2Dfooter%2B%2Epanel%2Dcollapse%3E%2Epanel%2Dbody%7Bborder%2Dbottom%2Dcolor%3A%23ebccd1%7D%2Eembed%2Dresponsive%7Bposition%3Arelative%3Bdisplay%3Ablock%3Bheight%3A0%3Bpadding%3A0%3Boverflow%3Ahidden%7D%2Eembed%2Dresponsive%20%2Eembed%2Dresponsive%2Ditem%2C%2Eembed%2Dresponsive%20embed%2C%2Eembed%2Dresponsive%20iframe%2C%2Eembed%2Dresponsive%20object%2C%2Eembed%2Dresponsive%20video%7Bposition%3Aabsolute%3Btop%3A0%3Bbottom%3A0%3Bleft%3A0%3Bwidth%3A100%25%3Bheight%3A100%25%3Bborder%3A0%7D%2Eembed%2Dresponsive%2D16by9%7Bpadding%2Dbottom%3A56%2E25%25%7D%2Eembed%2Dresponsive%2D4by3%7Bpadding%2Dbottom%3A75%25%7D%2Ewell%7Bmin%2Dheight%3A20px%3Bpadding%3A19px%3Bmargin%2Dbottom%3A20px%3Bbackground%2Dcolor%3A%23f5f5f5%3Bborder%3A1px%20solid%20%23e3e3e3%3Bborder%2Dradius%3A4px%3B%2Dwebkit%2Dbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E05%29%3Bbox%2Dshadow%3Ainset%200%201px%201px%20rgba%280%2C0%2C0%2C%2E05%29%7D%2Ewell%20blockquote%7Bborder%2Dcolor%3A%23ddd%3Bborder%2Dcolor%3Argba%280%2C0%2C0%2C%2E15%29%7D%2Ewell%2Dlg%7Bpadding%3A24px%3Bborder%2Dradius%3A6px%7D%2Ewell%2Dsm%7Bpadding%3A9px%3Bborder%2Dradius%3A3px%7D%2Eclose%7Bfloat%3Aright%3Bfont%2Dsize%3A21px%3Bfont%2Dweight%3A700%3Bline%2Dheight%3A1%3Bcolor%3A%23000%3Btext%2Dshadow%3A0%201px%200%20%23fff%3Bfilter%3Aalpha%28opacity%3D20%29%3Bopacity%3A%2E2%7D%2Eclose%3Afocus%2C%2Eclose%3Ahover%7Bcolor%3A%23000%3Btext%2Ddecoration%3Anone%3Bcursor%3Apointer%3Bfilter%3Aalpha%28opacity%3D50%29%3Bopacity%3A%2E5%7Dbutton%2Eclose%7B%2Dwebkit%2Dappearance%3Anone%3Bpadding%3A0%3Bcursor%3Apointer%3Bbackground%3A0%200%3Bborder%3A0%7D%2Emodal%2Dopen%7Boverflow%3Ahidden%7D%2Emodal%7Bposition%3Afixed%3Btop%3A0%3Bright%3A0%3Bbottom%3A0%3Bleft%3A0%3Bz%2Dindex%3A1050%3Bdisplay%3Anone%3Boverflow%3Ahidden%3B%2Dwebkit%2Doverflow%2Dscrolling%3Atouch%3Boutline%3A0%7D%2Emodal%2Efade%20%2Emodal%2Ddialog%7B%2Dwebkit%2Dtransition%3A%2Dwebkit%2Dtransform%20%2E3s%20ease%2Dout%3B%2Do%2Dtransition%3A%2Do%2Dtransform%20%2E3s%20ease%2Dout%3Btransition%3Atransform%20%2E3s%20ease%2Dout%3B%2Dwebkit%2Dtransform%3Atranslate%280%2C%2D25%25%29%3B%2Dms%2Dtransform%3Atranslate%280%2C%2D25%25%29%3B%2Do%2Dtransform%3Atranslate%280%2C%2D25%25%29%3Btransform%3Atranslate%280%2C%2D25%25%29%7D%2Emodal%2Ein%20%2Emodal%2Ddialog%7B%2Dwebkit%2Dtransform%3Atranslate%280%2C0%29%3B%2Dms%2Dtransform%3Atranslate%280%2C0%29%3B%2Do%2Dtransform%3Atranslate%280%2C0%29%3Btransform%3Atranslate%280%2C0%29%7D%2Emodal%2Dopen%20%2Emodal%7Boverflow%2Dx%3Ahidden%3Boverflow%2Dy%3Aauto%7D%2Emodal%2Ddialog%7Bposition%3Arelative%3Bwidth%3Aauto%3Bmargin%3A10px%7D%2Emodal%2Dcontent%7Bposition%3Arelative%3Bbackground%2Dcolor%3A%23fff%3B%2Dwebkit%2Dbackground%2Dclip%3Apadding%2Dbox%3Bbackground%2Dclip%3Apadding%2Dbox%3Bborder%3A1px%20solid%20%23999%3Bborder%3A1px%20solid%20rgba%280%2C0%2C0%2C%2E2%29%3Bborder%2Dradius%3A6px%3Boutline%3A0%3B%2Dwebkit%2Dbox%2Dshadow%3A0%203px%209px%20rgba%280%2C0%2C0%2C%2E5%29%3Bbox%2Dshadow%3A0%203px%209px%20rgba%280%2C0%2C0%2C%2E5%29%7D%2Emodal%2Dbackdrop%7Bposition%3Afixed%3Btop%3A0%3Bright%3A0%3Bbottom%3A0%3Bleft%3A0%3Bz%2Dindex%3A1040%3Bbackground%2Dcolor%3A%23000%7D%2Emodal%2Dbackdrop%2Efade%7Bfilter%3Aalpha%28opacity%3D0%29%3Bopacity%3A0%7D%2Emodal%2Dbackdrop%2Ein%7Bfilter%3Aalpha%28opacity%3D50%29%3Bopacity%3A%2E5%7D%2Emodal%2Dheader%7Bmin%2Dheight%3A16%2E43px%3Bpadding%3A15px%3Bborder%2Dbottom%3A1px%20solid%20%23e5e5e5%7D%2Emodal%2Dheader%20%2Eclose%7Bmargin%2Dtop%3A%2D2px%7D%2Emodal%2Dtitle%7Bmargin%3A0%3Bline%2Dheight%3A1%2E42857143%7D%2Emodal%2Dbody%7Bposition%3Arelative%3Bpadding%3A15px%7D%2Emodal%2Dfooter%7Bpadding%3A15px%3Btext%2Dalign%3Aright%3Bborder%2Dtop%3A1px%20solid%20%23e5e5e5%7D%2Emodal%2Dfooter%20%2Ebtn%2B%2Ebtn%7Bmargin%2Dbottom%3A0%3Bmargin%2Dleft%3A5px%7D%2Emodal%2Dfooter%20%2Ebtn%2Dgroup%20%2Ebtn%2B%2Ebtn%7Bmargin%2Dleft%3A%2D1px%7D%2Emodal%2Dfooter%20%2Ebtn%2Dblock%2B%2Ebtn%2Dblock%7Bmargin%2Dleft%3A0%7D%2Emodal%2Dscrollbar%2Dmeasure%7Bposition%3Aabsolute%3Btop%3A%2D9999px%3Bwidth%3A50px%3Bheight%3A50px%3Boverflow%3Ascroll%7D%40media%20%28min%2Dwidth%3A768px%29%7B%2Emodal%2Ddialog%7Bwidth%3A600px%3Bmargin%3A30px%20auto%7D%2Emodal%2Dcontent%7B%2Dwebkit%2Dbox%2Dshadow%3A0%205px%2015px%20rgba%280%2C0%2C0%2C%2E5%29%3Bbox%2Dshadow%3A0%205px%2015px%20rgba%280%2C0%2C0%2C%2E5%29%7D%2Emodal%2Dsm%7Bwidth%3A300px%7D%7D%40media%20%28min%2Dwidth%3A992px%29%7B%2Emodal%2Dlg%7Bwidth%3A900px%7D%7D%2Etooltip%7Bposition%3Aabsolute%3Bz%2Dindex%3A1070%3Bdisplay%3Ablock%3Bfont%2Dfamily%3A%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A12px%3Bfont%2Dstyle%3Anormal%3Bfont%2Dweight%3A400%3Bline%2Dheight%3A1%2E42857143%3Btext%2Dalign%3Aleft%3Btext%2Dalign%3Astart%3Btext%2Ddecoration%3Anone%3Btext%2Dshadow%3Anone%3Btext%2Dtransform%3Anone%3Bletter%2Dspacing%3Anormal%3Bword%2Dbreak%3Anormal%3Bword%2Dspacing%3Anormal%3Bword%2Dwrap%3Anormal%3Bwhite%2Dspace%3Anormal%3Bfilter%3Aalpha%28opacity%3D0%29%3Bopacity%3A0%3Bline%2Dbreak%3Aauto%7D%2Etooltip%2Ein%7Bfilter%3Aalpha%28opacity%3D90%29%3Bopacity%3A%2E9%7D%2Etooltip%2Etop%7Bpadding%3A5px%200%3Bmargin%2Dtop%3A%2D3px%7D%2Etooltip%2Eright%7Bpadding%3A0%205px%3Bmargin%2Dleft%3A3px%7D%2Etooltip%2Ebottom%7Bpadding%3A5px%200%3Bmargin%2Dtop%3A3px%7D%2Etooltip%2Eleft%7Bpadding%3A0%205px%3Bmargin%2Dleft%3A%2D3px%7D%2Etooltip%2Dinner%7Bmax%2Dwidth%3A200px%3Bpadding%3A3px%208px%3Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23000%3Bborder%2Dradius%3A4px%7D%2Etooltip%2Darrow%7Bposition%3Aabsolute%3Bwidth%3A0%3Bheight%3A0%3Bborder%2Dcolor%3Atransparent%3Bborder%2Dstyle%3Asolid%7D%2Etooltip%2Etop%20%2Etooltip%2Darrow%7Bbottom%3A0%3Bleft%3A50%25%3Bmargin%2Dleft%3A%2D5px%3Bborder%2Dwidth%3A5px%205px%200%3Bborder%2Dtop%2Dcolor%3A%23000%7D%2Etooltip%2Etop%2Dleft%20%2Etooltip%2Darrow%7Bright%3A5px%3Bbottom%3A0%3Bmargin%2Dbottom%3A%2D5px%3Bborder%2Dwidth%3A5px%205px%200%3Bborder%2Dtop%2Dcolor%3A%23000%7D%2Etooltip%2Etop%2Dright%20%2Etooltip%2Darrow%7Bbottom%3A0%3Bleft%3A5px%3Bmargin%2Dbottom%3A%2D5px%3Bborder%2Dwidth%3A5px%205px%200%3Bborder%2Dtop%2Dcolor%3A%23000%7D%2Etooltip%2Eright%20%2Etooltip%2Darrow%7Btop%3A50%25%3Bleft%3A0%3Bmargin%2Dtop%3A%2D5px%3Bborder%2Dwidth%3A5px%205px%205px%200%3Bborder%2Dright%2Dcolor%3A%23000%7D%2Etooltip%2Eleft%20%2Etooltip%2Darrow%7Btop%3A50%25%3Bright%3A0%3Bmargin%2Dtop%3A%2D5px%3Bborder%2Dwidth%3A5px%200%205px%205px%3Bborder%2Dleft%2Dcolor%3A%23000%7D%2Etooltip%2Ebottom%20%2Etooltip%2Darrow%7Btop%3A0%3Bleft%3A50%25%3Bmargin%2Dleft%3A%2D5px%3Bborder%2Dwidth%3A0%205px%205px%3Bborder%2Dbottom%2Dcolor%3A%23000%7D%2Etooltip%2Ebottom%2Dleft%20%2Etooltip%2Darrow%7Btop%3A0%3Bright%3A5px%3Bmargin%2Dtop%3A%2D5px%3Bborder%2Dwidth%3A0%205px%205px%3Bborder%2Dbottom%2Dcolor%3A%23000%7D%2Etooltip%2Ebottom%2Dright%20%2Etooltip%2Darrow%7Btop%3A0%3Bleft%3A5px%3Bmargin%2Dtop%3A%2D5px%3Bborder%2Dwidth%3A0%205px%205px%3Bborder%2Dbottom%2Dcolor%3A%23000%7D%2Epopover%7Bposition%3Aabsolute%3Btop%3A0%3Bleft%3A0%3Bz%2Dindex%3A1060%3Bdisplay%3Anone%3Bmax%2Dwidth%3A276px%3Bpadding%3A1px%3Bfont%2Dfamily%3A%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A14px%3Bfont%2Dstyle%3Anormal%3Bfont%2Dweight%3A400%3Bline%2Dheight%3A1%2E42857143%3Btext%2Dalign%3Aleft%3Btext%2Dalign%3Astart%3Btext%2Ddecoration%3Anone%3Btext%2Dshadow%3Anone%3Btext%2Dtransform%3Anone%3Bletter%2Dspacing%3Anormal%3Bword%2Dbreak%3Anormal%3Bword%2Dspacing%3Anormal%3Bword%2Dwrap%3Anormal%3Bwhite%2Dspace%3Anormal%3Bbackground%2Dcolor%3A%23fff%3B%2Dwebkit%2Dbackground%2Dclip%3Apadding%2Dbox%3Bbackground%2Dclip%3Apadding%2Dbox%3Bborder%3A1px%20solid%20%23ccc%3Bborder%3A1px%20solid%20rgba%280%2C0%2C0%2C%2E2%29%3Bborder%2Dradius%3A6px%3B%2Dwebkit%2Dbox%2Dshadow%3A0%205px%2010px%20rgba%280%2C0%2C0%2C%2E2%29%3Bbox%2Dshadow%3A0%205px%2010px%20rgba%280%2C0%2C0%2C%2E2%29%3Bline%2Dbreak%3Aauto%7D%2Epopover%2Etop%7Bmargin%2Dtop%3A%2D10px%7D%2Epopover%2Eright%7Bmargin%2Dleft%3A10px%7D%2Epopover%2Ebottom%7Bmargin%2Dtop%3A10px%7D%2Epopover%2Eleft%7Bmargin%2Dleft%3A%2D10px%7D%2Epopover%2Dtitle%7Bpadding%3A8px%2014px%3Bmargin%3A0%3Bfont%2Dsize%3A14px%3Bbackground%2Dcolor%3A%23f7f7f7%3Bborder%2Dbottom%3A1px%20solid%20%23ebebeb%3Bborder%2Dradius%3A5px%205px%200%200%7D%2Epopover%2Dcontent%7Bpadding%3A9px%2014px%7D%2Epopover%3E%2Earrow%2C%2Epopover%3E%2Earrow%3Aafter%7Bposition%3Aabsolute%3Bdisplay%3Ablock%3Bwidth%3A0%3Bheight%3A0%3Bborder%2Dcolor%3Atransparent%3Bborder%2Dstyle%3Asolid%7D%2Epopover%3E%2Earrow%7Bborder%2Dwidth%3A11px%7D%2Epopover%3E%2Earrow%3Aafter%7Bcontent%3A%22%22%3Bborder%2Dwidth%3A10px%7D%2Epopover%2Etop%3E%2Earrow%7Bbottom%3A%2D11px%3Bleft%3A50%25%3Bmargin%2Dleft%3A%2D11px%3Bborder%2Dtop%2Dcolor%3A%23999%3Bborder%2Dtop%2Dcolor%3Argba%280%2C0%2C0%2C%2E25%29%3Bborder%2Dbottom%2Dwidth%3A0%7D%2Epopover%2Etop%3E%2Earrow%3Aafter%7Bbottom%3A1px%3Bmargin%2Dleft%3A%2D10px%3Bcontent%3A%22%20%22%3Bborder%2Dtop%2Dcolor%3A%23fff%3Bborder%2Dbottom%2Dwidth%3A0%7D%2Epopover%2Eright%3E%2Earrow%7Btop%3A50%25%3Bleft%3A%2D11px%3Bmargin%2Dtop%3A%2D11px%3Bborder%2Dright%2Dcolor%3A%23999%3Bborder%2Dright%2Dcolor%3Argba%280%2C0%2C0%2C%2E25%29%3Bborder%2Dleft%2Dwidth%3A0%7D%2Epopover%2Eright%3E%2Earrow%3Aafter%7Bbottom%3A%2D10px%3Bleft%3A1px%3Bcontent%3A%22%20%22%3Bborder%2Dright%2Dcolor%3A%23fff%3Bborder%2Dleft%2Dwidth%3A0%7D%2Epopover%2Ebottom%3E%2Earrow%7Btop%3A%2D11px%3Bleft%3A50%25%3Bmargin%2Dleft%3A%2D11px%3Bborder%2Dtop%2Dwidth%3A0%3Bborder%2Dbottom%2Dcolor%3A%23999%3Bborder%2Dbottom%2Dcolor%3Argba%280%2C0%2C0%2C%2E25%29%7D%2Epopover%2Ebottom%3E%2Earrow%3Aafter%7Btop%3A1px%3Bmargin%2Dleft%3A%2D10px%3Bcontent%3A%22%20%22%3Bborder%2Dtop%2Dwidth%3A0%3Bborder%2Dbottom%2Dcolor%3A%23fff%7D%2Epopover%2Eleft%3E%2Earrow%7Btop%3A50%25%3Bright%3A%2D11px%3Bmargin%2Dtop%3A%2D11px%3Bborder%2Dright%2Dwidth%3A0%3Bborder%2Dleft%2Dcolor%3A%23999%3Bborder%2Dleft%2Dcolor%3Argba%280%2C0%2C0%2C%2E25%29%7D%2Epopover%2Eleft%3E%2Earrow%3Aafter%7Bright%3A1px%3Bbottom%3A%2D10px%3Bcontent%3A%22%20%22%3Bborder%2Dright%2Dwidth%3A0%3Bborder%2Dleft%2Dcolor%3A%23fff%7D%2Ecarousel%7Bposition%3Arelative%7D%2Ecarousel%2Dinner%7Bposition%3Arelative%3Bwidth%3A100%25%3Boverflow%3Ahidden%7D%2Ecarousel%2Dinner%3E%2Eitem%7Bposition%3Arelative%3Bdisplay%3Anone%3B%2Dwebkit%2Dtransition%3A%2E6s%20ease%2Din%2Dout%20left%3B%2Do%2Dtransition%3A%2E6s%20ease%2Din%2Dout%20left%3Btransition%3A%2E6s%20ease%2Din%2Dout%20left%7D%2Ecarousel%2Dinner%3E%2Eitem%3Ea%3Eimg%2C%2Ecarousel%2Dinner%3E%2Eitem%3Eimg%7Bline%2Dheight%3A1%7D%40media%20all%20and%20%28transform%2D3d%29%2C%28%2Dwebkit%2Dtransform%2D3d%29%7B%2Ecarousel%2Dinner%3E%2Eitem%7B%2Dwebkit%2Dtransition%3A%2Dwebkit%2Dtransform%20%2E6s%20ease%2Din%2Dout%3B%2Do%2Dtransition%3A%2Do%2Dtransform%20%2E6s%20ease%2Din%2Dout%3Btransition%3Atransform%20%2E6s%20ease%2Din%2Dout%3B%2Dwebkit%2Dbackface%2Dvisibility%3Ahidden%3Bbackface%2Dvisibility%3Ahidden%3B%2Dwebkit%2Dperspective%3A1000px%3Bperspective%3A1000px%7D%2Ecarousel%2Dinner%3E%2Eitem%2Eactive%2Eright%2C%2Ecarousel%2Dinner%3E%2Eitem%2Enext%7Bleft%3A0%3B%2Dwebkit%2Dtransform%3Atranslate3d%28100%25%2C0%2C0%29%3Btransform%3Atranslate3d%28100%25%2C0%2C0%29%7D%2Ecarousel%2Dinner%3E%2Eitem%2Eactive%2Eleft%2C%2Ecarousel%2Dinner%3E%2Eitem%2Eprev%7Bleft%3A0%3B%2Dwebkit%2Dtransform%3Atranslate3d%28%2D100%25%2C0%2C0%29%3Btransform%3Atranslate3d%28%2D100%25%2C0%2C0%29%7D%2Ecarousel%2Dinner%3E%2Eitem%2Eactive%2C%2Ecarousel%2Dinner%3E%2Eitem%2Enext%2Eleft%2C%2Ecarousel%2Dinner%3E%2Eitem%2Eprev%2Eright%7Bleft%3A0%3B%2Dwebkit%2Dtransform%3Atranslate3d%280%2C0%2C0%29%3Btransform%3Atranslate3d%280%2C0%2C0%29%7D%7D%2Ecarousel%2Dinner%3E%2Eactive%2C%2Ecarousel%2Dinner%3E%2Enext%2C%2Ecarousel%2Dinner%3E%2Eprev%7Bdisplay%3Ablock%7D%2Ecarousel%2Dinner%3E%2Eactive%7Bleft%3A0%7D%2Ecarousel%2Dinner%3E%2Enext%2C%2Ecarousel%2Dinner%3E%2Eprev%7Bposition%3Aabsolute%3Btop%3A0%3Bwidth%3A100%25%7D%2Ecarousel%2Dinner%3E%2Enext%7Bleft%3A100%25%7D%2Ecarousel%2Dinner%3E%2Eprev%7Bleft%3A%2D100%25%7D%2Ecarousel%2Dinner%3E%2Enext%2Eleft%2C%2Ecarousel%2Dinner%3E%2Eprev%2Eright%7Bleft%3A0%7D%2Ecarousel%2Dinner%3E%2Eactive%2Eleft%7Bleft%3A%2D100%25%7D%2Ecarousel%2Dinner%3E%2Eactive%2Eright%7Bleft%3A100%25%7D%2Ecarousel%2Dcontrol%7Bposition%3Aabsolute%3Btop%3A0%3Bbottom%3A0%3Bleft%3A0%3Bwidth%3A15%25%3Bfont%2Dsize%3A20px%3Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Btext%2Dshadow%3A0%201px%202px%20rgba%280%2C0%2C0%2C%2E6%29%3Bfilter%3Aalpha%28opacity%3D50%29%3Bopacity%3A%2E5%7D%2Ecarousel%2Dcontrol%2Eleft%7Bbackground%2Dimage%3A%2Dwebkit%2Dlinear%2Dgradient%28left%2Crgba%280%2C0%2C0%2C%2E5%29%200%2Crgba%280%2C0%2C0%2C%2E0001%29%20100%25%29%3Bbackground%2Dimage%3A%2Do%2Dlinear%2Dgradient%28left%2Crgba%280%2C0%2C0%2C%2E5%29%200%2Crgba%280%2C0%2C0%2C%2E0001%29%20100%25%29%3Bbackground%2Dimage%3A%2Dwebkit%2Dgradient%28linear%2Cleft%20top%2Cright%20top%2Cfrom%28rgba%280%2C0%2C0%2C%2E5%29%29%2Cto%28rgba%280%2C0%2C0%2C%2E0001%29%29%29%3Bbackground%2Dimage%3Alinear%2Dgradient%28to%20right%2Crgba%280%2C0%2C0%2C%2E5%29%200%2Crgba%280%2C0%2C0%2C%2E0001%29%20100%25%29%3Bfilter%3Aprogid%3ADXImageTransform%2EMicrosoft%2Egradient%28startColorstr%3D%27%2380000000%27%2C%20endColorstr%3D%27%2300000000%27%2C%20GradientType%3D1%29%3Bbackground%2Drepeat%3Arepeat%2Dx%7D%2Ecarousel%2Dcontrol%2Eright%7Bright%3A0%3Bleft%3Aauto%3Bbackground%2Dimage%3A%2Dwebkit%2Dlinear%2Dgradient%28left%2Crgba%280%2C0%2C0%2C%2E0001%29%200%2Crgba%280%2C0%2C0%2C%2E5%29%20100%25%29%3Bbackground%2Dimage%3A%2Do%2Dlinear%2Dgradient%28left%2Crgba%280%2C0%2C0%2C%2E0001%29%200%2Crgba%280%2C0%2C0%2C%2E5%29%20100%25%29%3Bbackground%2Dimage%3A%2Dwebkit%2Dgradient%28linear%2Cleft%20top%2Cright%20top%2Cfrom%28rgba%280%2C0%2C0%2C%2E0001%29%29%2Cto%28rgba%280%2C0%2C0%2C%2E5%29%29%29%3Bbackground%2Dimage%3Alinear%2Dgradient%28to%20right%2Crgba%280%2C0%2C0%2C%2E0001%29%200%2Crgba%280%2C0%2C0%2C%2E5%29%20100%25%29%3Bfilter%3Aprogid%3ADXImageTransform%2EMicrosoft%2Egradient%28startColorstr%3D%27%2300000000%27%2C%20endColorstr%3D%27%2380000000%27%2C%20GradientType%3D1%29%3Bbackground%2Drepeat%3Arepeat%2Dx%7D%2Ecarousel%2Dcontrol%3Afocus%2C%2Ecarousel%2Dcontrol%3Ahover%7Bcolor%3A%23fff%3Btext%2Ddecoration%3Anone%3Bfilter%3Aalpha%28opacity%3D90%29%3Boutline%3A0%3Bopacity%3A%2E9%7D%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dleft%2C%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dright%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dnext%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dprev%7Bposition%3Aabsolute%3Btop%3A50%25%3Bz%2Dindex%3A5%3Bdisplay%3Ainline%2Dblock%3Bmargin%2Dtop%3A%2D10px%7D%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dleft%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dprev%7Bleft%3A50%25%3Bmargin%2Dleft%3A%2D10px%7D%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dright%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dnext%7Bright%3A50%25%3Bmargin%2Dright%3A%2D10px%7D%2Ecarousel%2Dcontrol%20%2Eicon%2Dnext%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dprev%7Bwidth%3A20px%3Bheight%3A20px%3Bfont%2Dfamily%3Aserif%3Bline%2Dheight%3A1%7D%2Ecarousel%2Dcontrol%20%2Eicon%2Dprev%3Abefore%7Bcontent%3A%27%5C2039%27%7D%2Ecarousel%2Dcontrol%20%2Eicon%2Dnext%3Abefore%7Bcontent%3A%27%5C203a%27%7D%2Ecarousel%2Dindicators%7Bposition%3Aabsolute%3Bbottom%3A10px%3Bleft%3A50%25%3Bz%2Dindex%3A15%3Bwidth%3A60%25%3Bpadding%2Dleft%3A0%3Bmargin%2Dleft%3A%2D30%25%3Btext%2Dalign%3Acenter%3Blist%2Dstyle%3Anone%7D%2Ecarousel%2Dindicators%20li%7Bdisplay%3Ainline%2Dblock%3Bwidth%3A10px%3Bheight%3A10px%3Bmargin%3A1px%3Btext%2Dindent%3A%2D999px%3Bcursor%3Apointer%3Bbackground%2Dcolor%3A%23000%5C9%3Bbackground%2Dcolor%3Argba%280%2C0%2C0%2C0%29%3Bborder%3A1px%20solid%20%23fff%3Bborder%2Dradius%3A10px%7D%2Ecarousel%2Dindicators%20%2Eactive%7Bwidth%3A12px%3Bheight%3A12px%3Bmargin%3A0%3Bbackground%2Dcolor%3A%23fff%7D%2Ecarousel%2Dcaption%7Bposition%3Aabsolute%3Bright%3A15%25%3Bbottom%3A20px%3Bleft%3A15%25%3Bz%2Dindex%3A10%3Bpadding%2Dtop%3A20px%3Bpadding%2Dbottom%3A20px%3Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Btext%2Dshadow%3A0%201px%202px%20rgba%280%2C0%2C0%2C%2E6%29%7D%2Ecarousel%2Dcaption%20%2Ebtn%7Btext%2Dshadow%3Anone%7D%40media%20screen%20and%20%28min%2Dwidth%3A768px%29%7B%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dleft%2C%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dright%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dnext%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dprev%7Bwidth%3A30px%3Bheight%3A30px%3Bmargin%2Dtop%3A%2D15px%3Bfont%2Dsize%3A30px%7D%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dleft%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dprev%7Bmargin%2Dleft%3A%2D15px%7D%2Ecarousel%2Dcontrol%20%2Eglyphicon%2Dchevron%2Dright%2C%2Ecarousel%2Dcontrol%20%2Eicon%2Dnext%7Bmargin%2Dright%3A%2D15px%7D%2Ecarousel%2Dcaption%7Bright%3A20%25%3Bleft%3A20%25%3Bpadding%2Dbottom%3A30px%7D%2Ecarousel%2Dindicators%7Bbottom%3A20px%7D%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3Aafter%2C%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3Abefore%2C%2Ebtn%2Dtoolbar%3Aafter%2C%2Ebtn%2Dtoolbar%3Abefore%2C%2Eclearfix%3Aafter%2C%2Eclearfix%3Abefore%2C%2Econtainer%2Dfluid%3Aafter%2C%2Econtainer%2Dfluid%3Abefore%2C%2Econtainer%3Aafter%2C%2Econtainer%3Abefore%2C%2Edl%2Dhorizontal%20dd%3Aafter%2C%2Edl%2Dhorizontal%20dd%3Abefore%2C%2Eform%2Dhorizontal%20%2Eform%2Dgroup%3Aafter%2C%2Eform%2Dhorizontal%20%2Eform%2Dgroup%3Abefore%2C%2Emodal%2Dfooter%3Aafter%2C%2Emodal%2Dfooter%3Abefore%2C%2Enav%3Aafter%2C%2Enav%3Abefore%2C%2Enavbar%2Dcollapse%3Aafter%2C%2Enavbar%2Dcollapse%3Abefore%2C%2Enavbar%2Dheader%3Aafter%2C%2Enavbar%2Dheader%3Abefore%2C%2Enavbar%3Aafter%2C%2Enavbar%3Abefore%2C%2Epager%3Aafter%2C%2Epager%3Abefore%2C%2Epanel%2Dbody%3Aafter%2C%2Epanel%2Dbody%3Abefore%2C%2Erow%3Aafter%2C%2Erow%3Abefore%7Bdisplay%3Atable%3Bcontent%3A%22%20%22%7D%2Ebtn%2Dgroup%2Dvertical%3E%2Ebtn%2Dgroup%3Aafter%2C%2Ebtn%2Dtoolbar%3Aafter%2C%2Eclearfix%3Aafter%2C%2Econtainer%2Dfluid%3Aafter%2C%2Econtainer%3Aafter%2C%2Edl%2Dhorizontal%20dd%3Aafter%2C%2Eform%2Dhorizontal%20%2Eform%2Dgroup%3Aafter%2C%2Emodal%2Dfooter%3Aafter%2C%2Enav%3Aafter%2C%2Enavbar%2Dcollapse%3Aafter%2C%2Enavbar%2Dheader%3Aafter%2C%2Enavbar%3Aafter%2C%2Epager%3Aafter%2C%2Epanel%2Dbody%3Aafter%2C%2Erow%3Aafter%7Bclear%3Aboth%7D%2Ecenter%2Dblock%7Bdisplay%3Ablock%3Bmargin%2Dright%3Aauto%3Bmargin%2Dleft%3Aauto%7D%2Epull%2Dright%7Bfloat%3Aright%21important%7D%2Epull%2Dleft%7Bfloat%3Aleft%21important%7D%2Ehide%7Bdisplay%3Anone%21important%7D%2Eshow%7Bdisplay%3Ablock%21important%7D%2Einvisible%7Bvisibility%3Ahidden%7D%2Etext%2Dhide%7Bfont%3A0%2F0%20a%3Bcolor%3Atransparent%3Btext%2Dshadow%3Anone%3Bbackground%2Dcolor%3Atransparent%3Bborder%3A0%7D%2Ehidden%7Bdisplay%3Anone%21important%7D%2Eaffix%7Bposition%3Afixed%7D%40%2Dms%2Dviewport%7Bwidth%3Adevice%2Dwidth%7D%2Evisible%2Dlg%2C%2Evisible%2Dmd%2C%2Evisible%2Dsm%2C%2Evisible%2Dxs%7Bdisplay%3Anone%21important%7D%2Evisible%2Dlg%2Dblock%2C%2Evisible%2Dlg%2Dinline%2C%2Evisible%2Dlg%2Dinline%2Dblock%2C%2Evisible%2Dmd%2Dblock%2C%2Evisible%2Dmd%2Dinline%2C%2Evisible%2Dmd%2Dinline%2Dblock%2C%2Evisible%2Dsm%2Dblock%2C%2Evisible%2Dsm%2Dinline%2C%2Evisible%2Dsm%2Dinline%2Dblock%2C%2Evisible%2Dxs%2Dblock%2C%2Evisible%2Dxs%2Dinline%2C%2Evisible%2Dxs%2Dinline%2Dblock%7Bdisplay%3Anone%21important%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Evisible%2Dxs%7Bdisplay%3Ablock%21important%7Dtable%2Evisible%2Dxs%7Bdisplay%3Atable%21important%7Dtr%2Evisible%2Dxs%7Bdisplay%3Atable%2Drow%21important%7Dtd%2Evisible%2Dxs%2Cth%2Evisible%2Dxs%7Bdisplay%3Atable%2Dcell%21important%7D%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Evisible%2Dxs%2Dblock%7Bdisplay%3Ablock%21important%7D%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Evisible%2Dxs%2Dinline%7Bdisplay%3Ainline%21important%7D%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Evisible%2Dxs%2Dinline%2Dblock%7Bdisplay%3Ainline%2Dblock%21important%7D%7D%40media%20%28min%2Dwidth%3A768px%29%20and%20%28max%2Dwidth%3A991px%29%7B%2Evisible%2Dsm%7Bdisplay%3Ablock%21important%7Dtable%2Evisible%2Dsm%7Bdisplay%3Atable%21important%7Dtr%2Evisible%2Dsm%7Bdisplay%3Atable%2Drow%21important%7Dtd%2Evisible%2Dsm%2Cth%2Evisible%2Dsm%7Bdisplay%3Atable%2Dcell%21important%7D%7D%40media%20%28min%2Dwidth%3A768px%29%20and%20%28max%2Dwidth%3A991px%29%7B%2Evisible%2Dsm%2Dblock%7Bdisplay%3Ablock%21important%7D%7D%40media%20%28min%2Dwidth%3A768px%29%20and%20%28max%2Dwidth%3A991px%29%7B%2Evisible%2Dsm%2Dinline%7Bdisplay%3Ainline%21important%7D%7D%40media%20%28min%2Dwidth%3A768px%29%20and%20%28max%2Dwidth%3A991px%29%7B%2Evisible%2Dsm%2Dinline%2Dblock%7Bdisplay%3Ainline%2Dblock%21important%7D%7D%40media%20%28min%2Dwidth%3A992px%29%20and%20%28max%2Dwidth%3A1199px%29%7B%2Evisible%2Dmd%7Bdisplay%3Ablock%21important%7Dtable%2Evisible%2Dmd%7Bdisplay%3Atable%21important%7Dtr%2Evisible%2Dmd%7Bdisplay%3Atable%2Drow%21important%7Dtd%2Evisible%2Dmd%2Cth%2Evisible%2Dmd%7Bdisplay%3Atable%2Dcell%21important%7D%7D%40media%20%28min%2Dwidth%3A992px%29%20and%20%28max%2Dwidth%3A1199px%29%7B%2Evisible%2Dmd%2Dblock%7Bdisplay%3Ablock%21important%7D%7D%40media%20%28min%2Dwidth%3A992px%29%20and%20%28max%2Dwidth%3A1199px%29%7B%2Evisible%2Dmd%2Dinline%7Bdisplay%3Ainline%21important%7D%7D%40media%20%28min%2Dwidth%3A992px%29%20and%20%28max%2Dwidth%3A1199px%29%7B%2Evisible%2Dmd%2Dinline%2Dblock%7Bdisplay%3Ainline%2Dblock%21important%7D%7D%40media%20%28min%2Dwidth%3A1200px%29%7B%2Evisible%2Dlg%7Bdisplay%3Ablock%21important%7Dtable%2Evisible%2Dlg%7Bdisplay%3Atable%21important%7Dtr%2Evisible%2Dlg%7Bdisplay%3Atable%2Drow%21important%7Dtd%2Evisible%2Dlg%2Cth%2Evisible%2Dlg%7Bdisplay%3Atable%2Dcell%21important%7D%7D%40media%20%28min%2Dwidth%3A1200px%29%7B%2Evisible%2Dlg%2Dblock%7Bdisplay%3Ablock%21important%7D%7D%40media%20%28min%2Dwidth%3A1200px%29%7B%2Evisible%2Dlg%2Dinline%7Bdisplay%3Ainline%21important%7D%7D%40media%20%28min%2Dwidth%3A1200px%29%7B%2Evisible%2Dlg%2Dinline%2Dblock%7Bdisplay%3Ainline%2Dblock%21important%7D%7D%40media%20%28max%2Dwidth%3A767px%29%7B%2Ehidden%2Dxs%7Bdisplay%3Anone%21important%7D%7D%40media%20%28min%2Dwidth%3A768px%29%20and%20%28max%2Dwidth%3A991px%29%7B%2Ehidden%2Dsm%7Bdisplay%3Anone%21important%7D%7D%40media%20%28min%2Dwidth%3A992px%29%20and%20%28max%2Dwidth%3A1199px%29%7B%2Ehidden%2Dmd%7Bdisplay%3Anone%21important%7D%7D%40media%20%28min%2Dwidth%3A1200px%29%7B%2Ehidden%2Dlg%7Bdisplay%3Anone%21important%7D%7D%2Evisible%2Dprint%7Bdisplay%3Anone%21important%7D%40media%20print%7B%2Evisible%2Dprint%7Bdisplay%3Ablock%21important%7Dtable%2Evisible%2Dprint%7Bdisplay%3Atable%21important%7Dtr%2Evisible%2Dprint%7Bdisplay%3Atable%2Drow%21important%7Dtd%2Evisible%2Dprint%2Cth%2Evisible%2Dprint%7Bdisplay%3Atable%2Dcell%21important%7D%7D%2Evisible%2Dprint%2Dblock%7Bdisplay%3Anone%21important%7D%40media%20print%7B%2Evisible%2Dprint%2Dblock%7Bdisplay%3Ablock%21important%7D%7D%2Evisible%2Dprint%2Dinline%7Bdisplay%3Anone%21important%7D%40media%20print%7B%2Evisible%2Dprint%2Dinline%7Bdisplay%3Ainline%21important%7D%7D%2Evisible%2Dprint%2Dinline%2Dblock%7Bdisplay%3Anone%21important%7D%40media%20print%7B%2Evisible%2Dprint%2Dinline%2Dblock%7Bdisplay%3Ainline%2Dblock%21important%7D%7D%40media%20print%7B%2Ehidden%2Dprint%7Bdisplay%3Anone%21important%7D%7D%0A" rel="stylesheet" /> -<script src="data:application/x-javascript;base64,/*!
 * Bootstrap v3.3.5 (http://getbootstrap.com)
 * Copyright 2011-2015 Twitter, Inc.
 * Licensed under the MIT license
 */
if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);"></script> -<script src="data:application/x-javascript;base64,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"></script> -<script src="data:application/x-javascript;base64,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"></script> -<script src="data:application/x-javascript;base64,/*! jQuery UI - v1.11.4 - 2016-01-05
* http://jqueryui.com
* Includes: core.js, widget.js, mouse.js, position.js, draggable.js, droppable.js, resizable.js, selectable.js, sortable.js, accordion.js, autocomplete.js, button.js, dialog.js, menu.js, progressbar.js, selectmenu.js, slider.js, spinner.js, tabs.js, tooltip.js, effect.js, effect-blind.js, effect-bounce.js, effect-clip.js, effect-drop.js, effect-explode.js, effect-fade.js, effect-fold.js, effect-highlight.js, effect-puff.js, effect-pulsate.js, effect-scale.js, effect-shake.js, effect-size.js, effect-slide.js, effect-transfer.js
* Copyright jQuery Foundation and other contributors; Licensed MIT */

(function(e){"function"==typeof define&&define.amd?define(["jquery"],e):e(jQuery)})(function(e){function t(t,s){var n,a,o,r=t.nodeName.toLowerCase();return"area"===r?(n=t.parentNode,a=n.name,t.href&&a&&"map"===n.nodeName.toLowerCase()?(o=e("img[usemap='#"+a+"']")[0],!!o&&i(o)):!1):(/^(input|select|textarea|button|object)$/.test(r)?!t.disabled:"a"===r?t.href||s:s)&&i(t)}function i(t){return e.expr.filters.visible(t)&&!e(t).parents().addBack().filter(function(){return"hidden"===e.css(this,"visibility")}).length}function s(e){return function(){var t=this.element.val();e.apply(this,arguments),this._refresh(),t!==this.element.val()&&this._trigger("change")}}e.ui=e.ui||{},e.extend(e.ui,{version:"1.11.4",keyCode:{BACKSPACE:8,COMMA:188,DELETE:46,DOWN:40,END:35,ENTER:13,ESCAPE:27,HOME:36,LEFT:37,PAGE_DOWN:34,PAGE_UP:33,PERIOD:190,RIGHT:39,SPACE:32,TAB:9,UP:38}}),e.fn.extend({scrollParent:function(t){var i=this.css("position"),s="absolute"===i,n=t?/(auto|scroll|hidden)/:/(auto|scroll)/,a=this.parents().filter(function(){var t=e(this);return s&&"static"===t.css("position")?!1:n.test(t.css("overflow")+t.css("overflow-y")+t.css("overflow-x"))}).eq(0);return"fixed"!==i&&a.length?a:e(this[0].ownerDocument||document)},uniqueId:function(){var e=0;return function(){return this.each(function(){this.id||(this.id="ui-id-"+ ++e)})}}(),removeUniqueId:function(){return this.each(function(){/^ui-id-\d+$/.test(this.id)&&e(this).removeAttr("id")})}}),e.extend(e.expr[":"],{data:e.expr.createPseudo?e.expr.createPseudo(function(t){return function(i){return!!e.data(i,t)}}):function(t,i,s){return!!e.data(t,s[3])},focusable:function(i){return t(i,!isNaN(e.attr(i,"tabindex")))},tabbable:function(i){var s=e.attr(i,"tabindex"),n=isNaN(s);return(n||s>=0)&&t(i,!n)}}),e("<a>").outerWidth(1).jquery||e.each(["Width","Height"],function(t,i){function s(t,i,s,a){return e.each(n,function(){i-=parseFloat(e.css(t,"padding"+this))||0,s&&(i-=parseFloat(e.css(t,"border"+this+"Width"))||0),a&&(i-=parseFloat(e.css(t,"margin"+this))||0)}),i}var n="Width"===i?["Left","Right"]:["Top","Bottom"],a=i.toLowerCase(),o={innerWidth:e.fn.innerWidth,innerHeight:e.fn.innerHeight,outerWidth:e.fn.outerWidth,outerHeight:e.fn.outerHeight};e.fn["inner"+i]=function(t){return void 0===t?o["inner"+i].call(this):this.each(function(){e(this).css(a,s(this,t)+"px")})},e.fn["outer"+i]=function(t,n){return"number"!=typeof t?o["outer"+i].call(this,t):this.each(function(){e(this).css(a,s(this,t,!0,n)+"px")})}}),e.fn.addBack||(e.fn.addBack=function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}),e("<a>").data("a-b","a").removeData("a-b").data("a-b")&&(e.fn.removeData=function(t){return function(i){return arguments.length?t.call(this,e.camelCase(i)):t.call(this)}}(e.fn.removeData)),e.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase()),e.fn.extend({focus:function(t){return function(i,s){return"number"==typeof i?this.each(function(){var t=this;setTimeout(function(){e(t).focus(),s&&s.call(t)},i)}):t.apply(this,arguments)}}(e.fn.focus),disableSelection:function(){var e="onselectstart"in document.createElement("div")?"selectstart":"mousedown";return function(){return this.bind(e+".ui-disableSelection",function(e){e.preventDefault()})}}(),enableSelection:function(){return this.unbind(".ui-disableSelection")},zIndex:function(t){if(void 0!==t)return this.css("zIndex",t);if(this.length)for(var i,s,n=e(this[0]);n.length&&n[0]!==document;){if(i=n.css("position"),("absolute"===i||"relative"===i||"fixed"===i)&&(s=parseInt(n.css("zIndex"),10),!isNaN(s)&&0!==s))return s;n=n.parent()}return 0}}),e.ui.plugin={add:function(t,i,s){var n,a=e.ui[t].prototype;for(n in s)a.plugins[n]=a.plugins[n]||[],a.plugins[n].push([i,s[n]])},call:function(e,t,i,s){var n,a=e.plugins[t];if(a&&(s||e.element[0].parentNode&&11!==e.element[0].parentNode.nodeType))for(n=0;a.length>n;n++)e.options[a[n][0]]&&a[n][1].apply(e.element,i)}};var n=0,a=Array.prototype.slice;e.cleanData=function(t){return function(i){var s,n,a;for(a=0;null!=(n=i[a]);a++)try{s=e._data(n,"events"),s&&s.remove&&e(n).triggerHandler("remove")}catch(o){}t(i)}}(e.cleanData),e.widget=function(t,i,s){var n,a,o,r,h={},l=t.split(".")[0];return t=t.split(".")[1],n=l+"-"+t,s||(s=i,i=e.Widget),e.expr[":"][n.toLowerCase()]=function(t){return!!e.data(t,n)},e[l]=e[l]||{},a=e[l][t],o=e[l][t]=function(e,t){return this._createWidget?(arguments.length&&this._createWidget(e,t),void 0):new o(e,t)},e.extend(o,a,{version:s.version,_proto:e.extend({},s),_childConstructors:[]}),r=new i,r.options=e.widget.extend({},r.options),e.each(s,function(t,s){return e.isFunction(s)?(h[t]=function(){var e=function(){return i.prototype[t].apply(this,arguments)},n=function(e){return i.prototype[t].apply(this,e)};return function(){var t,i=this._super,a=this._superApply;return this._super=e,this._superApply=n,t=s.apply(this,arguments),this._super=i,this._superApply=a,t}}(),void 0):(h[t]=s,void 0)}),o.prototype=e.widget.extend(r,{widgetEventPrefix:a?r.widgetEventPrefix||t:t},h,{constructor:o,namespace:l,widgetName:t,widgetFullName:n}),a?(e.each(a._childConstructors,function(t,i){var s=i.prototype;e.widget(s.namespace+"."+s.widgetName,o,i._proto)}),delete a._childConstructors):i._childConstructors.push(o),e.widget.bridge(t,o),o},e.widget.extend=function(t){for(var i,s,n=a.call(arguments,1),o=0,r=n.length;r>o;o++)for(i in n[o])s=n[o][i],n[o].hasOwnProperty(i)&&void 0!==s&&(t[i]=e.isPlainObject(s)?e.isPlainObject(t[i])?e.widget.extend({},t[i],s):e.widget.extend({},s):s);return t},e.widget.bridge=function(t,i){var s=i.prototype.widgetFullName||t;e.fn[t]=function(n){var o="string"==typeof n,r=a.call(arguments,1),h=this;return o?this.each(function(){var i,a=e.data(this,s);return"instance"===n?(h=a,!1):a?e.isFunction(a[n])&&"_"!==n.charAt(0)?(i=a[n].apply(a,r),i!==a&&void 0!==i?(h=i&&i.jquery?h.pushStack(i.get()):i,!1):void 0):e.error("no such method '"+n+"' for "+t+" widget instance"):e.error("cannot call methods on "+t+" prior to initialization; "+"attempted to call method '"+n+"'")}):(r.length&&(n=e.widget.extend.apply(null,[n].concat(r))),this.each(function(){var t=e.data(this,s);t?(t.option(n||{}),t._init&&t._init()):e.data(this,s,new i(n,this))})),h}},e.Widget=function(){},e.Widget._childConstructors=[],e.Widget.prototype={widgetName:"widget",widgetEventPrefix:"",defaultElement:"<div>",options:{disabled:!1,create:null},_createWidget:function(t,i){i=e(i||this.defaultElement||this)[0],this.element=e(i),this.uuid=n++,this.eventNamespace="."+this.widgetName+this.uuid,this.bindings=e(),this.hoverable=e(),this.focusable=e(),i!==this&&(e.data(i,this.widgetFullName,this),this._on(!0,this.element,{remove:function(e){e.target===i&&this.destroy()}}),this.document=e(i.style?i.ownerDocument:i.document||i),this.window=e(this.document[0].defaultView||this.document[0].parentWindow)),this.options=e.widget.extend({},this.options,this._getCreateOptions(),t),this._create(),this._trigger("create",null,this._getCreateEventData()),this._init()},_getCreateOptions:e.noop,_getCreateEventData:e.noop,_create:e.noop,_init:e.noop,destroy:function(){this._destroy(),this.element.unbind(this.eventNamespace).removeData(this.widgetFullName).removeData(e.camelCase(this.widgetFullName)),this.widget().unbind(this.eventNamespace).removeAttr("aria-disabled").removeClass(this.widgetFullName+"-disabled "+"ui-state-disabled"),this.bindings.unbind(this.eventNamespace),this.hoverable.removeClass("ui-state-hover"),this.focusable.removeClass("ui-state-focus")},_destroy:e.noop,widget:function(){return this.element},option:function(t,i){var s,n,a,o=t;if(0===arguments.length)return e.widget.extend({},this.options);if("string"==typeof t)if(o={},s=t.split("."),t=s.shift(),s.length){for(n=o[t]=e.widget.extend({},this.options[t]),a=0;s.length-1>a;a++)n[s[a]]=n[s[a]]||{},n=n[s[a]];if(t=s.pop(),1===arguments.length)return void 0===n[t]?null:n[t];n[t]=i}else{if(1===arguments.length)return void 0===this.options[t]?null:this.options[t];o[t]=i}return this._setOptions(o),this},_setOptions:function(e){var t;for(t in e)this._setOption(t,e[t]);return this},_setOption:function(e,t){return this.options[e]=t,"disabled"===e&&(this.widget().toggleClass(this.widgetFullName+"-disabled",!!t),t&&(this.hoverable.removeClass("ui-state-hover"),this.focusable.removeClass("ui-state-focus"))),this},enable:function(){return this._setOptions({disabled:!1})},disable:function(){return this._setOptions({disabled:!0})},_on:function(t,i,s){var n,a=this;"boolean"!=typeof t&&(s=i,i=t,t=!1),s?(i=n=e(i),this.bindings=this.bindings.add(i)):(s=i,i=this.element,n=this.widget()),e.each(s,function(s,o){function r(){return t||a.options.disabled!==!0&&!e(this).hasClass("ui-state-disabled")?("string"==typeof o?a[o]:o).apply(a,arguments):void 0}"string"!=typeof o&&(r.guid=o.guid=o.guid||r.guid||e.guid++);var h=s.match(/^([\w:-]*)\s*(.*)$/),l=h[1]+a.eventNamespace,u=h[2];u?n.delegate(u,l,r):i.bind(l,r)})},_off:function(t,i){i=(i||"").split(" ").join(this.eventNamespace+" ")+this.eventNamespace,t.unbind(i).undelegate(i),this.bindings=e(this.bindings.not(t).get()),this.focusable=e(this.focusable.not(t).get()),this.hoverable=e(this.hoverable.not(t).get())},_delay:function(e,t){function i(){return("string"==typeof e?s[e]:e).apply(s,arguments)}var s=this;return setTimeout(i,t||0)},_hoverable:function(t){this.hoverable=this.hoverable.add(t),this._on(t,{mouseenter:function(t){e(t.currentTarget).addClass("ui-state-hover")},mouseleave:function(t){e(t.currentTarget).removeClass("ui-state-hover")}})},_focusable:function(t){this.focusable=this.focusable.add(t),this._on(t,{focusin:function(t){e(t.currentTarget).addClass("ui-state-focus")},focusout:function(t){e(t.currentTarget).removeClass("ui-state-focus")}})},_trigger:function(t,i,s){var n,a,o=this.options[t];if(s=s||{},i=e.Event(i),i.type=(t===this.widgetEventPrefix?t:this.widgetEventPrefix+t).toLowerCase(),i.target=this.element[0],a=i.originalEvent)for(n in a)n in i||(i[n]=a[n]);return this.element.trigger(i,s),!(e.isFunction(o)&&o.apply(this.element[0],[i].concat(s))===!1||i.isDefaultPrevented())}},e.each({show:"fadeIn",hide:"fadeOut"},function(t,i){e.Widget.prototype["_"+t]=function(s,n,a){"string"==typeof n&&(n={effect:n});var o,r=n?n===!0||"number"==typeof n?i:n.effect||i:t;n=n||{},"number"==typeof n&&(n={duration:n}),o=!e.isEmptyObject(n),n.complete=a,n.delay&&s.delay(n.delay),o&&e.effects&&e.effects.effect[r]?s[t](n):r!==t&&s[r]?s[r](n.duration,n.easing,a):s.queue(function(i){e(this)[t](),a&&a.call(s[0]),i()})}}),e.widget;var o=!1;e(document).mouseup(function(){o=!1}),e.widget("ui.mouse",{version:"1.11.4",options:{cancel:"input,textarea,button,select,option",distance:1,delay:0},_mouseInit:function(){var t=this;this.element.bind("mousedown."+this.widgetName,function(e){return t._mouseDown(e)}).bind("click."+this.widgetName,function(i){return!0===e.data(i.target,t.widgetName+".preventClickEvent")?(e.removeData(i.target,t.widgetName+".preventClickEvent"),i.stopImmediatePropagation(),!1):void 0}),this.started=!1},_mouseDestroy:function(){this.element.unbind("."+this.widgetName),this._mouseMoveDelegate&&this.document.unbind("mousemove."+this.widgetName,this._mouseMoveDelegate).unbind("mouseup."+this.widgetName,this._mouseUpDelegate)},_mouseDown:function(t){if(!o){this._mouseMoved=!1,this._mouseStarted&&this._mouseUp(t),this._mouseDownEvent=t;var i=this,s=1===t.which,n="string"==typeof this.options.cancel&&t.target.nodeName?e(t.target).closest(this.options.cancel).length:!1;return s&&!n&&this._mouseCapture(t)?(this.mouseDelayMet=!this.options.delay,this.mouseDelayMet||(this._mouseDelayTimer=setTimeout(function(){i.mouseDelayMet=!0},this.options.delay)),this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=this._mouseStart(t)!==!1,!this._mouseStarted)?(t.preventDefault(),!0):(!0===e.data(t.target,this.widgetName+".preventClickEvent")&&e.removeData(t.target,this.widgetName+".preventClickEvent"),this._mouseMoveDelegate=function(e){return i._mouseMove(e)},this._mouseUpDelegate=function(e){return i._mouseUp(e)},this.document.bind("mousemove."+this.widgetName,this._mouseMoveDelegate).bind("mouseup."+this.widgetName,this._mouseUpDelegate),t.preventDefault(),o=!0,!0)):!0}},_mouseMove:function(t){if(this._mouseMoved){if(e.ui.ie&&(!document.documentMode||9>document.documentMode)&&!t.button)return this._mouseUp(t);if(!t.which)return this._mouseUp(t)}return(t.which||t.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=this._mouseStart(this._mouseDownEvent,t)!==!1,this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted)},_mouseUp:function(t){return this.document.unbind("mousemove."+this.widgetName,this._mouseMoveDelegate).unbind("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&e.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),o=!1,!1},_mouseDistanceMet:function(e){return Math.max(Math.abs(this._mouseDownEvent.pageX-e.pageX),Math.abs(this._mouseDownEvent.pageY-e.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),function(){function t(e,t,i){return[parseFloat(e[0])*(p.test(e[0])?t/100:1),parseFloat(e[1])*(p.test(e[1])?i/100:1)]}function i(t,i){return parseInt(e.css(t,i),10)||0}function s(t){var i=t[0];return 9===i.nodeType?{width:t.width(),height:t.height(),offset:{top:0,left:0}}:e.isWindow(i)?{width:t.width(),height:t.height(),offset:{top:t.scrollTop(),left:t.scrollLeft()}}:i.preventDefault?{width:0,height:0,offset:{top:i.pageY,left:i.pageX}}:{width:t.outerWidth(),height:t.outerHeight(),offset:t.offset()}}e.ui=e.ui||{};var n,a,o=Math.max,r=Math.abs,h=Math.round,l=/left|center|right/,u=/top|center|bottom/,d=/[\+\-]\d+(\.[\d]+)?%?/,c=/^\w+/,p=/%$/,f=e.fn.position;e.position={scrollbarWidth:function(){if(void 0!==n)return n;var t,i,s=e("<div style='display:block;position:absolute;width:50px;height:50px;overflow:hidden;'><div style='height:100px;width:auto;'></div></div>"),a=s.children()[0];return e("body").append(s),t=a.offsetWidth,s.css("overflow","scroll"),i=a.offsetWidth,t===i&&(i=s[0].clientWidth),s.remove(),n=t-i},getScrollInfo:function(t){var i=t.isWindow||t.isDocument?"":t.element.css("overflow-x"),s=t.isWindow||t.isDocument?"":t.element.css("overflow-y"),n="scroll"===i||"auto"===i&&t.width<t.element[0].scrollWidth,a="scroll"===s||"auto"===s&&t.height<t.element[0].scrollHeight;return{width:a?e.position.scrollbarWidth():0,height:n?e.position.scrollbarWidth():0}},getWithinInfo:function(t){var i=e(t||window),s=e.isWindow(i[0]),n=!!i[0]&&9===i[0].nodeType;return{element:i,isWindow:s,isDocument:n,offset:i.offset()||{left:0,top:0},scrollLeft:i.scrollLeft(),scrollTop:i.scrollTop(),width:s||n?i.width():i.outerWidth(),height:s||n?i.height():i.outerHeight()}}},e.fn.position=function(n){if(!n||!n.of)return f.apply(this,arguments);n=e.extend({},n);var p,m,g,v,y,b,_=e(n.of),x=e.position.getWithinInfo(n.within),w=e.position.getScrollInfo(x),k=(n.collision||"flip").split(" "),T={};return b=s(_),_[0].preventDefault&&(n.at="left top"),m=b.width,g=b.height,v=b.offset,y=e.extend({},v),e.each(["my","at"],function(){var e,t,i=(n[this]||"").split(" ");1===i.length&&(i=l.test(i[0])?i.concat(["center"]):u.test(i[0])?["center"].concat(i):["center","center"]),i[0]=l.test(i[0])?i[0]:"center",i[1]=u.test(i[1])?i[1]:"center",e=d.exec(i[0]),t=d.exec(i[1]),T[this]=[e?e[0]:0,t?t[0]:0],n[this]=[c.exec(i[0])[0],c.exec(i[1])[0]]}),1===k.length&&(k[1]=k[0]),"right"===n.at[0]?y.left+=m:"center"===n.at[0]&&(y.left+=m/2),"bottom"===n.at[1]?y.top+=g:"center"===n.at[1]&&(y.top+=g/2),p=t(T.at,m,g),y.left+=p[0],y.top+=p[1],this.each(function(){var s,l,u=e(this),d=u.outerWidth(),c=u.outerHeight(),f=i(this,"marginLeft"),b=i(this,"marginTop"),D=d+f+i(this,"marginRight")+w.width,S=c+b+i(this,"marginBottom")+w.height,N=e.extend({},y),M=t(T.my,u.outerWidth(),u.outerHeight());"right"===n.my[0]?N.left-=d:"center"===n.my[0]&&(N.left-=d/2),"bottom"===n.my[1]?N.top-=c:"center"===n.my[1]&&(N.top-=c/2),N.left+=M[0],N.top+=M[1],a||(N.left=h(N.left),N.top=h(N.top)),s={marginLeft:f,marginTop:b},e.each(["left","top"],function(t,i){e.ui.position[k[t]]&&e.ui.position[k[t]][i](N,{targetWidth:m,targetHeight:g,elemWidth:d,elemHeight:c,collisionPosition:s,collisionWidth:D,collisionHeight:S,offset:[p[0]+M[0],p[1]+M[1]],my:n.my,at:n.at,within:x,elem:u})}),n.using&&(l=function(e){var t=v.left-N.left,i=t+m-d,s=v.top-N.top,a=s+g-c,h={target:{element:_,left:v.left,top:v.top,width:m,height:g},element:{element:u,left:N.left,top:N.top,width:d,height:c},horizontal:0>i?"left":t>0?"right":"center",vertical:0>a?"top":s>0?"bottom":"middle"};d>m&&m>r(t+i)&&(h.horizontal="center"),c>g&&g>r(s+a)&&(h.vertical="middle"),h.important=o(r(t),r(i))>o(r(s),r(a))?"horizontal":"vertical",n.using.call(this,e,h)}),u.offset(e.extend(N,{using:l}))})},e.ui.position={fit:{left:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollLeft:s.offset.left,a=s.width,r=e.left-t.collisionPosition.marginLeft,h=n-r,l=r+t.collisionWidth-a-n;t.collisionWidth>a?h>0&&0>=l?(i=e.left+h+t.collisionWidth-a-n,e.left+=h-i):e.left=l>0&&0>=h?n:h>l?n+a-t.collisionWidth:n:h>0?e.left+=h:l>0?e.left-=l:e.left=o(e.left-r,e.left)},top:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollTop:s.offset.top,a=t.within.height,r=e.top-t.collisionPosition.marginTop,h=n-r,l=r+t.collisionHeight-a-n;t.collisionHeight>a?h>0&&0>=l?(i=e.top+h+t.collisionHeight-a-n,e.top+=h-i):e.top=l>0&&0>=h?n:h>l?n+a-t.collisionHeight:n:h>0?e.top+=h:l>0?e.top-=l:e.top=o(e.top-r,e.top)}},flip:{left:function(e,t){var i,s,n=t.within,a=n.offset.left+n.scrollLeft,o=n.width,h=n.isWindow?n.scrollLeft:n.offset.left,l=e.left-t.collisionPosition.marginLeft,u=l-h,d=l+t.collisionWidth-o-h,c="left"===t.my[0]?-t.elemWidth:"right"===t.my[0]?t.elemWidth:0,p="left"===t.at[0]?t.targetWidth:"right"===t.at[0]?-t.targetWidth:0,f=-2*t.offset[0];0>u?(i=e.left+c+p+f+t.collisionWidth-o-a,(0>i||r(u)>i)&&(e.left+=c+p+f)):d>0&&(s=e.left-t.collisionPosition.marginLeft+c+p+f-h,(s>0||d>r(s))&&(e.left+=c+p+f))},top:function(e,t){var i,s,n=t.within,a=n.offset.top+n.scrollTop,o=n.height,h=n.isWindow?n.scrollTop:n.offset.top,l=e.top-t.collisionPosition.marginTop,u=l-h,d=l+t.collisionHeight-o-h,c="top"===t.my[1],p=c?-t.elemHeight:"bottom"===t.my[1]?t.elemHeight:0,f="top"===t.at[1]?t.targetHeight:"bottom"===t.at[1]?-t.targetHeight:0,m=-2*t.offset[1];0>u?(s=e.top+p+f+m+t.collisionHeight-o-a,(0>s||r(u)>s)&&(e.top+=p+f+m)):d>0&&(i=e.top-t.collisionPosition.marginTop+p+f+m-h,(i>0||d>r(i))&&(e.top+=p+f+m))}},flipfit:{left:function(){e.ui.position.flip.left.apply(this,arguments),e.ui.position.fit.left.apply(this,arguments)},top:function(){e.ui.position.flip.top.apply(this,arguments),e.ui.position.fit.top.apply(this,arguments)}}},function(){var t,i,s,n,o,r=document.getElementsByTagName("body")[0],h=document.createElement("div");t=document.createElement(r?"div":"body"),s={visibility:"hidden",width:0,height:0,border:0,margin:0,background:"none"},r&&e.extend(s,{position:"absolute",left:"-1000px",top:"-1000px"});for(o in s)t.style[o]=s[o];t.appendChild(h),i=r||document.documentElement,i.insertBefore(t,i.firstChild),h.style.cssText="position: absolute; left: 10.7432222px;",n=e(h).offset().left,a=n>10&&11>n,t.innerHTML="",i.removeChild(t)}()}(),e.ui.position,e.widget("ui.draggable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this.element.addClass("ui-draggable"),this.options.disabled&&this.element.addClass("ui-draggable-disabled"),this._setHandleClassName(),this._mouseInit()},_setOption:function(e,t){this._super(e,t),"handle"===e&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(".ui-draggable-dragging")?(this.destroyOnClear=!0,void 0):(this.element.removeClass("ui-draggable ui-draggable-dragging ui-draggable-disabled"),this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(t){var i=this.options;return this._blurActiveElement(t),this.helper||i.disabled||e(t.target).closest(".ui-resizable-handle").length>0?!1:(this.handle=this._getHandle(t),this.handle?(this._blockFrames(i.iframeFix===!0?"iframe":i.iframeFix),!0):!1)},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=e(this);return e("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var i=this.document[0];if(this.handleElement.is(t.target))try{i.activeElement&&"body"!==i.activeElement.nodeName.toLowerCase()&&e(i.activeElement).blur()}catch(s){}},_mouseStart:function(t){var i=this.options;return this.helper=this._createHelper(t),this.helper.addClass("ui-draggable-dragging"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return"fixed"===e(this).css("position")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._normalizeRightBottom(),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(e){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:e.pageX-this.offset.left,top:e.pageY-this.offset.top}},_mouseDrag:function(t,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",t)!==!1&&i._clear()}):this._trigger("stop",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this.handleElement.addClass("ui-draggable-handle")},_removeHandleClassName:function(){this.handleElement.removeClass("ui-draggable-handle")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper),n=s?e(i.helper.apply(this.element[0],[t])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return n.parents("body").length||n.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css("position"))||n.css("position","absolute"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css("left"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relativeContainer=null,n.containment?"window"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t=/(scroll|auto)/.test(i.css("overflow")),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)},_convertPositionTo:function(e,t){t||(t=this.position);var i="absolute"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),e.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),e.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),"y"===o.axis&&(h=this.originalPageX),"x"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_normalizeRightBottom:function(){"y"!==this.options.axis&&"auto"!==this.helper.css("right")&&(this.helper.width(this.helper.width()),this.helper.css("right","auto")),"x"!==this.options.axis&&"auto"!==this.helper.css("bottom")&&(this.helper.height(this.helper.height()),this.helper.css("bottom","auto"))},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),s.offset=this.positionAbs),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,i,s){var n=e.extend({},i,{item:s.element});s.sortables=[],e(s.options.connectToSortable).each(function(){var i=e(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger("activate",t,n))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,e.each(s.sortables,function(){var e=this;e.isOver?(e.isOver=0,s.cancelHelperRemoval=!0,e.cancelHelperRemoval=!1,e._storedCSS={position:e.placeholder.css("position"),top:e.placeholder.css("top"),left:e.placeholder.css("left")},e._mouseStop(t),e.options.helper=e.options._helper):(e.cancelHelperRemoval=!0,e._trigger("deactivate",t,n))})},drag:function(t,i,s){e.each(s.sortables,function(){var n=!1,a=this;a.positionAbs=s.positionAbs,a.helperProportions=s.helperProportions,a.offset.click=s.offset.click,a._intersectsWith(a.containerCache)&&(n=!0,e.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==a&&this._intersectsWith(this.containerCache)&&e.contains(a.element[0],this.element[0])&&(n=!1),n
})),n?(a.isOver||(a.isOver=1,s._parent=i.helper.parent(),a.currentItem=i.helper.appendTo(a.element).data("ui-sortable-item",!0),a.options._helper=a.options.helper,a.options.helper=function(){return i.helper[0]},t.target=a.currentItem[0],a._mouseCapture(t,!0),a._mouseStart(t,!0,!0),a.offset.click.top=s.offset.click.top,a.offset.click.left=s.offset.click.left,a.offset.parent.left-=s.offset.parent.left-a.offset.parent.left,a.offset.parent.top-=s.offset.parent.top-a.offset.parent.top,s._trigger("toSortable",t),s.dropped=a.element,e.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,a.fromOutside=s),a.currentItem&&(a._mouseDrag(t),i.position=a.position)):a.isOver&&(a.isOver=0,a.cancelHelperRemoval=!0,a.options._revert=a.options.revert,a.options.revert=!1,a._trigger("out",t,a._uiHash(a)),a._mouseStop(t,!0),a.options.revert=a.options._revert,a.options.helper=a.options._helper,a.placeholder&&a.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(t),i.position=s._generatePosition(t,!0),s._trigger("fromSortable",t),s.dropped=!1,e.each(s.sortables,function(){this.refreshPositions()}))})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,i,s){var n=e("body"),a=s.options;n.css("cursor")&&(a._cursor=n.css("cursor")),n.css("cursor",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e("body").css("cursor",n._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("opacity")&&(a._opacity=n.css("opacity")),n.css("opacity",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css("opacity",n._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(e,t,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.scrollParentNotHidden[0],r=s.document[0];o!==r&&"HTML"!==o.tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+o.offsetHeight-t.pageY<n.scrollSensitivity?o.scrollTop=a=o.scrollTop+n.scrollSpeed:t.pageY-s.overflowOffset.top<n.scrollSensitivity&&(o.scrollTop=a=o.scrollTop-n.scrollSpeed)),n.axis&&"y"===n.axis||(s.overflowOffset.left+o.offsetWidth-t.pageX<n.scrollSensitivity?o.scrollLeft=a=o.scrollLeft+n.scrollSpeed:t.pageX-s.overflowOffset.left<n.scrollSensitivity&&(o.scrollLeft=a=o.scrollLeft-n.scrollSpeed))):(n.axis&&"x"===n.axis||(t.pageY-e(r).scrollTop()<n.scrollSensitivity?a=e(r).scrollTop(e(r).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(r).scrollTop())<n.scrollSensitivity&&(a=e(r).scrollTop(e(r).scrollTop()+n.scrollSpeed))),n.axis&&"y"===n.axis||(t.pageX-e(r).scrollLeft()<n.scrollSensitivity?a=e(r).scrollLeft(e(r).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(r).scrollLeft())<n.scrollSensitivity&&(a=e(r).scrollLeft(e(r).scrollLeft()+n.scrollSpeed)))),a!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(s,t)}}),e.ui.plugin.add("draggable","snap",{start:function(t,i,s){var n=s.options;s.snapElements=[],e(n.snap.constructor!==String?n.snap.items||":data(ui-draggable)":n.snap).each(function(){var t=e(this),i=t.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:i.top,left:i.left})})},drag:function(t,i,s){var n,a,o,r,h,l,u,d,c,p,f=s.options,m=f.snapTolerance,g=i.offset.left,v=g+s.helperProportions.width,y=i.offset.top,b=y+s.helperProportions.height;for(c=s.snapElements.length-1;c>=0;c--)h=s.snapElements[c].left-s.margins.left,l=h+s.snapElements[c].width,u=s.snapElements[c].top-s.margins.top,d=u+s.snapElements[c].height,h-m>v||g>l+m||u-m>b||y>d+m||!e.contains(s.snapElements[c].item.ownerDocument,s.snapElements[c].item)?(s.snapElements[c].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=!1):("inner"!==f.snapMode&&(n=m>=Math.abs(u-b),a=m>=Math.abs(d-y),o=m>=Math.abs(h-v),r=m>=Math.abs(l-g),n&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left)),p=n||a||o||r,"outer"!==f.snapMode&&(n=m>=Math.abs(u-y),a=m>=Math.abs(d-b),o=m>=Math.abs(h-g),r=m>=Math.abs(l-v),n&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d-s.helperProportions.height,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left)),!s.snapElements[c].snapping&&(n||a||o||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=n||a||o||r||p)}}),e.ui.plugin.add("draggable","stack",{start:function(t,i,s){var n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(i).css("zIndex"),10)||0)});o.length&&(n=parseInt(e(o[0]).css("zIndex"),10)||0,e(o).each(function(t){e(this).css("zIndex",n+t)}),this.css("zIndex",n+o.length))}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("zIndex")&&(a._zIndex=n.css("zIndex")),n.css("zIndex",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css("zIndex",n._zIndex)}}),e.ui.draggable,e.widget("ui.droppable",{version:"1.11.4",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass("ui-droppable")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var t=0;e.length>t;t++)e[t]===this&&e.splice(t,1)},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];this._splice(t),this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,i){if("accept"===t)this.accept=e.isFunction(i)?i:function(e){return e.is(i)};else if("scope"===t){var s=e.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(t,i)},_activate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),i&&this._trigger("activate",t,this.ui(i))},_deactivate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),i&&this._trigger("deactivate",t,this.ui(i))},_over:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(i)))},_out:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(i)))},_drop:function(t,i){var s=i||e.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var i=e(this).droppable("instance");return i.options.greedy&&!i.options.disabled&&i.options.scope===s.options.scope&&i.accept.call(i.element[0],s.currentItem||s.element)&&e.ui.intersect(s,e.extend(i,{offset:i.element.offset()}),i.options.tolerance,t)?(n=!0,!1):void 0}),n?!1:this.accept.call(this.element[0],s.currentItem||s.element)?(this.options.activeClass&&this.element.removeClass(this.options.activeClass),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("drop",t,this.ui(s)),this.element):!1):!1},ui:function(e){return{draggable:e.currentItem||e.element,helper:e.helper,position:e.position,offset:e.positionAbs}}}),e.ui.intersect=function(){function e(e,t,i){return e>=t&&t+i>e}return function(t,i,s,n){if(!i.offset)return!1;var a=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,r=a+t.helperProportions.width,h=o+t.helperProportions.height,l=i.offset.left,u=i.offset.top,d=l+i.proportions().width,c=u+i.proportions().height;switch(s){case"fit":return a>=l&&d>=r&&o>=u&&c>=h;case"intersect":return a+t.helperProportions.width/2>l&&d>r-t.helperProportions.width/2&&o+t.helperProportions.height/2>u&&c>h-t.helperProportions.height/2;case"pointer":return e(n.pageY,u,i.proportions().height)&&e(n.pageX,l,i.proportions().width);case"touch":return(o>=u&&c>=o||h>=u&&c>=h||u>o&&h>c)&&(a>=l&&d>=a||r>=l&&d>=r||l>a&&r>d);default:return!1}}}(),e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,i){var s,n,a=e.ui.ddmanager.droppables[t.options.scope]||[],o=i?i.type:null,r=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();e:for(s=0;a.length>s;s++)if(!(a[s].options.disabled||t&&!a[s].accept.call(a[s].element[0],t.currentItem||t.element))){for(n=0;r.length>n;n++)if(r[n]===a[s].element[0]){a[s].proportions().height=0;continue e}a[s].visible="none"!==a[s].element.css("display"),a[s].visible&&("mousedown"===o&&a[s]._activate.call(a[s],i),a[s].offset=a[s].element.offset(),a[s].proportions({width:a[s].element[0].offsetWidth,height:a[s].element[0].offsetHeight}))}},drop:function(t,i){var s=!1;return e.each((e.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance,i)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(t,i){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)})},drag:function(t,i){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,i),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,a,o=e.ui.intersect(t,this,this.options.tolerance,i),r=!o&&this.isover?"isout":o&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,a=this.element.parents(":data(ui-droppable)").filter(function(){return e(this).droppable("instance").options.scope===n}),a.length&&(s=e(a[0]).droppable("instance"),s.greedyChild="isover"===r)),s&&"isover"===r&&(s.isover=!1,s.isout=!0,s._out.call(s,i)),this[r]=!0,this["isout"===r?"isover":"isout"]=!1,this["isover"===r?"_over":"_out"].call(this,i),s&&"isout"===r&&(s.isout=!1,s.isover=!0,s._over.call(s,i)))}})},dragStop:function(t,i){t.element.parentsUntil("body").unbind("scroll.droppable"),t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)}},e.ui.droppable,e.widget("ui.resizable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if("hidden"===e(t).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(e("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=r.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=e(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),t=this.handles.split(","),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a="ui-resizable-"+s,n=e("<div class='ui-resizable-handle "+a+"'></div>"),n.css({zIndex:r.zIndex}),"se"===s&&n.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=e(this.handles[i]),this._on(this.handles[i],{mousedown:o._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),t.css(n,a),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:"se")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){r.disabled||(e(this).removeClass("ui-resizable-autohide"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass("ui-resizable-autohide"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css("position"),width:t.outerWidth(),height:t.outerHeight(),top:t.css("top"),left:t.css("left")}).insertAfter(t),t.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(".ui-resizable-"+this.axis).css("cursor"),e("body").css("cursor","auto"===n?this.axis+"-resize":n),o.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var i,s,n=this.originalMousePosition,a=this.axis,o=t.pageX-n.left||0,r=t.pageY-n.top||0,h=this._change[a];return this._updatePrevProperties(),h?(i=h.apply(this,[t,o,r]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate("resize",t),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css("left"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css("top"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var e={};return this.position.top!==this.prevPosition.top&&(e.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(e.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(e.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(e.height=this.size.height+"px"),this.helper.css(e),e},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),"sw"===s&&(e.left=t.left+(i.width-e.width),e.top=null),"nw"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidth<e.width,n=this._isNumber(e.height)&&t.maxHeight&&t.maxHeight<e.height,a=this._isNumber(e.width)&&t.minWidth&&t.minWidth>e.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_getPaddingPlusBorderDimensions:function(e){for(var t=0,i=[],s=[e.css("borderTopWidth"),e.css("borderRightWidth"),e.css("borderBottomWidth"),e.css("borderLeftWidth")],n=[e.css("paddingTop"),e.css("paddingRight"),e.css("paddingBottom"),e.css("paddingLeft")];4>t;t++)i[t]=parseInt(s[t],10)||0,i[t]+=parseInt(n[t],10)||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var e,t=0,i=this.helper||this.element;this._proportionallyResizeElements.length>t;t++)e=this._proportionallyResizeElements[t],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(e)),e.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e("<div style='overflow:hidden;'></div>"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),"resize"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add("resizable","animate",{stop:function(t){var i=e(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css("left"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css("top"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css("width"),10),height:parseInt(i.element.css("height"),10),top:parseInt(i.element.css("top"),10),left:parseInt(i.element.css("left"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable("instance"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e(["Top","Right","Left","Bottom"]).each(function(e,s){i[e]=h._num(t.css("padding"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,"left")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t){var i,s,n,a,o=e(this).resizable("instance"),r=o.options,h=o.containerOffset,l=o.position,u=o._aspectRatio||t.shiftKey,d={top:0,left:0},c=o.containerElement,p=!0;c[0]!==document&&/static/.test(c.css("position"))&&(d=h),l.left<(o._helper?h.left:0)&&(o.size.width=o.size.width+(o._helper?o.position.left-h.left:o.position.left-d.left),u&&(o.size.height=o.size.width/o.aspectRatio,p=!1),o.position.left=r.helper?h.left:0),l.top<(o._helper?h.top:0)&&(o.size.height=o.size.height+(o._helper?o.position.top-h.top:o.position.top),u&&(o.size.width=o.size.height*o.aspectRatio,p=!1),o.position.top=o._helper?h.top:0),n=o.containerElement.get(0)===o.element.parent().get(0),a=/relative|absolute/.test(o.containerElement.css("position")),n&&a?(o.offset.left=o.parentData.left+o.position.left,o.offset.top=o.parentData.top+o.position.top):(o.offset.left=o.element.offset().left,o.offset.top=o.element.offset().top),i=Math.abs(o.sizeDiff.width+(o._helper?o.offset.left-d.left:o.offset.left-h.left)),s=Math.abs(o.sizeDiff.height+(o._helper?o.offset.top-d.top:o.offset.top-h.top)),i+o.size.width>=o.parentData.width&&(o.size.width=o.parentData.width-i,u&&(o.size.height=o.size.width/o.aspectRatio,p=!1)),s+o.size.height>=o.parentData.height&&(o.size.height=o.parentData.height-s,u&&(o.size.width=o.size.height*o.aspectRatio,p=!1)),p||(o.position.left=o.prevPosition.left,o.position.top=o.prevPosition.top,o.size.width=o.prevSize.width,o.size.height=o.prevSize.height)},stop:function(){var t=e(this).resizable("instance"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add("resizable","alsoResize",{start:function(){var t=e(this).resizable("instance"),i=t.options;e(i.alsoResize).each(function(){var t=e(this);t.data("ui-resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})},resize:function(t,i){var s=e(this).resizable("instance"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0};e(n.alsoResize).each(function(){var t=e(this),s=e(this).data("ui-resizable-alsoresize"),n={},a=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(a,function(e,t){var i=(s[t]||0)+(r[t]||0);i&&i>=0&&(n[t]=i||null)}),t.css(n)})},stop:function(){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","ghost",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass("string"==typeof i.ghost?i.ghost:""),t.ghost.appendTo(t.helper)},resize:function(){var t=e(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=e(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(){var t,i=e(this).resizable("instance"),s=i.options,n=i.size,a=i.originalSize,o=i.originalPosition,r=i.axis,h="number"==typeof s.grid?[s.grid,s.grid]:s.grid,l=h[0]||1,u=h[1]||1,d=Math.round((n.width-a.width)/l)*l,c=Math.round((n.height-a.height)/u)*u,p=a.width+d,f=a.height+c,m=s.maxWidth&&p>s.maxWidth,g=s.maxHeight&&f>s.maxHeight,v=s.minWidth&&s.minWidth>p,y=s.minHeight&&s.minHeight>f;s.grid=h,v&&(p+=l),y&&(f+=u),m&&(p-=l),g&&(f-=u),/^(se|s|e)$/.test(r)?(i.size.width=p,i.size.height=f):/^(ne)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.top=o.top-c):/^(sw)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.left=o.left-d):((0>=f-u||0>=p-l)&&(t=i._getPaddingPlusBorderDimensions(this)),f-u>0?(i.size.height=f,i.position.top=o.top-c):(f=u-t.height,i.size.height=f,i.position.top=o.top+a.height-f),p-l>0?(i.size.width=p,i.position.left=o.left-d):(p=l-t.width,i.size.width=p,i.position.left=o.left+a.width-p))}}),e.ui.resizable,e.widget("ui.selectable",e.ui.mouse,{version:"1.11.4",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var t,i=this;this.element.addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){t=e(i.options.filter,i.element[0]),t.addClass("ui-selectee"),t.each(function(){var t=e(this),i=t.offset();e.data(this,"selectable-item",{element:this,$element:t,left:i.left,top:i.top,right:i.left+t.outerWidth(),bottom:i.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this.selectees=t.addClass("ui-selectee"),this._mouseInit(),this.helper=e("<div class='ui-selectable-helper'></div>")},_destroy:function(){this.selectees.removeClass("ui-selectee").removeData("selectable-item"),this.element.removeClass("ui-selectable ui-selectable-disabled"),this._mouseDestroy()},_mouseStart:function(t){var i=this,s=this.options;this.opos=[t.pageX,t.pageY],this.options.disabled||(this.selectees=e(s.filter,this.element[0]),this._trigger("start",t),e(s.appendTo).append(this.helper),this.helper.css({left:t.pageX,top:t.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var s=e.data(this,"selectable-item");s.startselected=!0,t.metaKey||t.ctrlKey||(s.$element.removeClass("ui-selected"),s.selected=!1,s.$element.addClass("ui-unselecting"),s.unselecting=!0,i._trigger("unselecting",t,{unselecting:s.element}))}),e(t.target).parents().addBack().each(function(){var s,n=e.data(this,"selectable-item");return n?(s=!t.metaKey&&!t.ctrlKey||!n.$element.hasClass("ui-selected"),n.$element.removeClass(s?"ui-unselecting":"ui-selected").addClass(s?"ui-selecting":"ui-unselecting"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger("selecting",t,{selecting:n.element}):i._trigger("unselecting",t,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(t){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,a=this.opos[0],o=this.opos[1],r=t.pageX,h=t.pageY;return a>r&&(i=r,r=a,a=i),o>h&&(i=h,h=o,o=i),this.helper.css({left:a,top:o,width:r-a,height:h-o}),this.selectees.each(function(){var i=e.data(this,"selectable-item"),l=!1;
i&&i.element!==s.element[0]&&("touch"===n.tolerance?l=!(i.left>r||a>i.right||i.top>h||o>i.bottom):"fit"===n.tolerance&&(l=i.left>a&&r>i.right&&i.top>o&&h>i.bottom),l?(i.selected&&(i.$element.removeClass("ui-selected"),i.selected=!1),i.unselecting&&(i.$element.removeClass("ui-unselecting"),i.unselecting=!1),i.selecting||(i.$element.addClass("ui-selecting"),i.selecting=!0,s._trigger("selecting",t,{selecting:i.element}))):(i.selecting&&((t.metaKey||t.ctrlKey)&&i.startselected?(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.$element.addClass("ui-selected"),i.selected=!0):(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.startselected&&(i.$element.addClass("ui-unselecting"),i.unselecting=!0),s._trigger("unselecting",t,{unselecting:i.element}))),i.selected&&(t.metaKey||t.ctrlKey||i.startselected||(i.$element.removeClass("ui-selected"),i.selected=!1,i.$element.addClass("ui-unselecting"),i.unselecting=!0,s._trigger("unselecting",t,{unselecting:i.element})))))}),!1}},_mouseStop:function(t){var i=this;return this.dragged=!1,e(".ui-unselecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-unselecting"),s.unselecting=!1,s.startselected=!1,i._trigger("unselected",t,{unselected:s.element})}),e(".ui-selecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-selecting").addClass("ui-selected"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger("selected",t,{selected:s.element})}),this._trigger("stop",t),this.helper.remove(),!1}}),e.widget("ui.sortable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css("float"))||/inline|table-cell/.test(e.css("display"))},_create:function(){this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),"handle"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(".ui-sortable-handle").removeClass("ui-sortable-handle"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass("ui-sortable-handle")})},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled").find(".ui-sortable-handle").removeClass("ui-sortable-handle"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+"-item")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+"-item")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find("*").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(a=this.document.find("body"),this.storedCursor=a.css("cursor"),a.css("cursor",o.cursor),this.storedStylesheet=e("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(a)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",t,this._uiHash(this));return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<o.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+o.scrollSpeed:t.pageY-this.overflowOffset.top<o.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-o.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<o.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+o.scrollSpeed:t.pageX-this.overflowOffset.left<o.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-o.scrollSpeed)):(t.pageY-this.document.scrollTop()<o.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-o.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<o.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+o.scrollSpeed)),t.pageX-this.document.scrollLeft()<o.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-o.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<o.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+o.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?"next":"prev"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&"x"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),a&&"y"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),"original"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[\-=_](.+)/);i&&s.push((t.key||i[1]+"[]")+"="+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+"="),s.join("&")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||"id")||"")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d="x"===this.options.axis||s+l>r&&h>s+l,c="y"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&"right"===a||"down"===n?2:1:n&&("down"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&t||"up"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s],this.document[0]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i],this.document[0]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+"-item",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.floating=this.items.length?"x"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e("<"+s+">",t.document[0]).addClass(i||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper");return"tbody"===s?t._createTrPlaceholder(t.currentItem.find("tr").eq(0),e("<tr>",t.document[0]).appendTo(n)):"tr"===s?t._createTrPlaceholder(t.currentItem,n):"img"===s&&n.attr("src",t.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_createTrPlaceholder:function(t,i){var s=this;t.children().each(function(){e("<td>&#160;</td>",s.document[0]).attr("colspan",e(this).attr("colspan")||1).appendTo(i)})},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?"left":"top",r=u?"width":"height",d=u?"clientX":"clientY",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger("change",t,this._uiHash()),this.containers[p]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||e("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===n.containment?this.document.width():this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s="hidden"!==e(t).css("overflow"),this.containment=[i.left+(parseInt(e(t).css("borderLeftWidth"),10)||0)+(parseInt(e(t).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css("borderTopWidth"),10)||0)+(parseInt(e(t).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css("borderLeftWidth"),10)||0)-(parseInt(e(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css("borderTopWidth"),10)||0)-(parseInt(e(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s="absolute"===t?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(a=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(o=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger("receive",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger("update",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger("remove",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,t||this._trigger("beforeStop",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget("ui.accordion",{version:"1.11.4",options:{active:0,animate:{},collapsible:!1,event:"click",header:"> li > :first-child,> :not(li):even",heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var t=this.options;this.prevShow=this.prevHide=e(),this.element.addClass("ui-accordion ui-widget ui-helper-reset").attr("role","tablist"),t.collapsible||t.active!==!1&&null!=t.active||(t.active=0),this._processPanels(),0>t.active&&(t.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():e()}},_createIcons:function(){var t=this.options.icons;t&&(e("<span>").addClass("ui-accordion-header-icon ui-icon "+t.header).prependTo(this.headers),this.active.children(".ui-accordion-header-icon").removeClass(t.header).addClass(t.activeHeader),this.headers.addClass("ui-accordion-icons"))},_destroyIcons:function(){this.headers.removeClass("ui-accordion-icons").children(".ui-accordion-header-icon").remove()},_destroy:function(){var e;this.element.removeClass("ui-accordion ui-widget ui-helper-reset").removeAttr("role"),this.headers.removeClass("ui-accordion-header ui-accordion-header-active ui-state-default ui-corner-all ui-state-active ui-state-disabled ui-corner-top").removeAttr("role").removeAttr("aria-expanded").removeAttr("aria-selected").removeAttr("aria-controls").removeAttr("tabIndex").removeUniqueId(),this._destroyIcons(),e=this.headers.next().removeClass("ui-helper-reset ui-widget-content ui-corner-bottom ui-accordion-content ui-accordion-content-active ui-state-disabled").css("display","").removeAttr("role").removeAttr("aria-hidden").removeAttr("aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&e.css("height","")},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):("event"===e&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(t)),this._super(e,t),"collapsible"!==e||t||this.options.active!==!1||this._activate(0),"icons"===e&&(this._destroyIcons(),t&&this._createIcons()),"disabled"===e&&(this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this.headers.add(this.headers.next()).toggleClass("ui-state-disabled",!!t)),void 0)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var i=e.ui.keyCode,s=this.headers.length,n=this.headers.index(t.target),a=!1;switch(t.keyCode){case i.RIGHT:case i.DOWN:a=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:a=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(t);break;case i.HOME:a=this.headers[0];break;case i.END:a=this.headers[s-1]}a&&(e(t.target).attr("tabIndex",-1),e(a).attr("tabIndex",0),a.focus(),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===e.ui.keyCode.UP&&t.ctrlKey&&e(t.currentTarget).prev().focus()},refresh:function(){var t=this.options;this._processPanels(),t.active===!1&&t.collapsible===!0||!this.headers.length?(t.active=!1,this.active=e()):t.active===!1?this._activate(0):this.active.length&&!e.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=e()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var e=this.headers,t=this.panels;this.headers=this.element.find(this.options.header).addClass("ui-accordion-header ui-state-default ui-corner-all"),this.panels=this.headers.next().addClass("ui-accordion-content ui-helper-reset ui-widget-content ui-corner-bottom").filter(":not(.ui-accordion-content-active)").hide(),t&&(this._off(e.not(this.headers)),this._off(t.not(this.panels)))},_refresh:function(){var t,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active).addClass("ui-accordion-header-active ui-state-active ui-corner-top").removeClass("ui-corner-all"),this.active.next().addClass("ui-accordion-content-active").show(),this.headers.attr("role","tab").each(function(){var t=e(this),i=t.uniqueId().attr("id"),s=t.next(),n=s.uniqueId().attr("id");t.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(t=n.height(),this.element.siblings(":visible").each(function(){var i=e(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(t-=i.outerHeight(!0))}),this.headers.each(function(){t-=e(this).outerHeight(!0)}),this.headers.next().each(function(){e(this).height(Math.max(0,t-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===s&&(t=0,this.headers.next().each(function(){t=Math.max(t,e(this).css("height","").height())}).height(t))},_activate:function(t){var i=this._findActive(t)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):e()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n[0]===s[0],o=a&&i.collapsible,r=o?e():n.next(),h=s.next(),l={oldHeader:s,oldPanel:h,newHeader:o?e():n,newPanel:r};
t.preventDefault(),a&&!i.collapsible||this._trigger("beforeActivate",t,l)===!1||(i.active=o?!1:this.headers.index(n),this.active=a?e():n,this._toggle(l),s.removeClass("ui-accordion-header-active ui-state-active"),i.icons&&s.children(".ui-accordion-header-icon").removeClass(i.icons.activeHeader).addClass(i.icons.header),a||(n.removeClass("ui-corner-all").addClass("ui-accordion-header-active ui-state-active ui-corner-top"),i.icons&&n.children(".ui-accordion-header-icon").removeClass(i.icons.header).addClass(i.icons.activeHeader),n.next().addClass("ui-accordion-content-active")))},_toggle:function(t){var i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(e(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.css("box-sizing"),l=e.length&&(!t.length||e.index()<t.index()),u=this.options.animate||{},d=l&&u.down||u,c=function(){o._toggleComplete(i)};return"number"==typeof d&&(a=d),"string"==typeof d&&(n=d),n=n||d.easing||u.easing,a=a||d.duration||u.duration,t.length?e.length?(s=e.show().outerHeight(),t.animate(this.hideProps,{duration:a,easing:n,step:function(e,t){t.now=Math.round(e)}}),e.hide().animate(this.showProps,{duration:a,easing:n,complete:c,step:function(e,i){i.now=Math.round(e),"height"!==i.prop?"content-box"===h&&(r+=i.now):"content"!==o.options.heightStyle&&(i.now=Math.round(s-t.outerHeight()-r),r=0)}}),void 0):t.animate(this.hideProps,a,n,c):e.animate(this.showProps,a,n,c)},_toggleComplete:function(e){var t=e.oldPanel;t.removeClass("ui-accordion-content-active").prev().removeClass("ui-corner-top").addClass("ui-corner-all"),t.length&&(t.parent()[0].className=t.parent()[0].className),this._trigger("activate",null,e)}}),e.widget("ui.menu",{version:"1.11.4",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-carat-1-e"},items:"> *",menus:"ul",position:{my:"left-1 top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.element.uniqueId().addClass("ui-menu ui-widget ui-widget-content").toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length).attr({role:this.options.role,tabIndex:0}),this.options.disabled&&this.element.addClass("ui-state-disabled").attr("aria-disabled","true"),this._on({"mousedown .ui-menu-item":function(e){e.preventDefault()},"click .ui-menu-item":function(t){var i=e(t.target);!this.mouseHandled&&i.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),i.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&e(this.document[0].activeElement).closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":function(t){if(!this.previousFilter){var i=e(t.currentTarget);i.siblings(".ui-state-active").removeClass("ui-state-active"),this.focus(t,i)}},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(e,t){var i=this.active||this.element.find(this.options.items).eq(0);t||this.focus(e,i)},blur:function(t){this._delay(function(){e.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(e){this._closeOnDocumentClick(e)&&this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeClass("ui-menu ui-widget ui-widget-content ui-menu-icons ui-front").removeAttr("role").removeAttr("tabIndex").removeAttr("aria-labelledby").removeAttr("aria-expanded").removeAttr("aria-hidden").removeAttr("aria-disabled").removeUniqueId().show(),this.element.find(".ui-menu-item").removeClass("ui-menu-item").removeAttr("role").removeAttr("aria-disabled").removeUniqueId().removeClass("ui-state-hover").removeAttr("tabIndex").removeAttr("role").removeAttr("aria-haspopup").children().each(function(){var t=e(this);t.data("ui-menu-submenu-carat")&&t.remove()}),this.element.find(".ui-menu-divider").removeClass("ui-menu-divider ui-widget-content")},_keydown:function(t){var i,s,n,a,o=!0;switch(t.keyCode){case e.ui.keyCode.PAGE_UP:this.previousPage(t);break;case e.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case e.ui.keyCode.HOME:this._move("first","first",t);break;case e.ui.keyCode.END:this._move("last","last",t);break;case e.ui.keyCode.UP:this.previous(t);break;case e.ui.keyCode.DOWN:this.next(t);break;case e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:o=!1,s=this.previousFilter||"",n=String.fromCharCode(t.keyCode),a=!1,clearTimeout(this.filterTimer),n===s?a=!0:n=s+n,i=this._filterMenuItems(n),i=a&&-1!==i.index(this.active.next())?this.active.nextAll(".ui-menu-item"):i,i.length||(n=String.fromCharCode(t.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(t,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}o&&t.preventDefault()},_activate:function(e){this.active.is(".ui-state-disabled")||(this.active.is("[aria-haspopup='true']")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length),a.filter(":not(.ui-menu)").addClass("ui-menu ui-widget ui-widget-content ui-front").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=e(this),i=t.parent(),s=e("<span>").addClass("ui-menu-icon ui-icon "+n).data("ui-menu-submenu-carat",!0);i.attr("aria-haspopup","true").prepend(s),t.attr("aria-labelledby",i.attr("id"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(".ui-menu-item").each(function(){var t=e(this);s._isDivider(t)&&t.addClass("ui-widget-content ui-menu-divider")}),i.not(".ui-menu-item, .ui-menu-divider").addClass("ui-menu-item").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),i.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!e.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(e,t){"icons"===e&&this.element.find(".ui-menu-icon").removeClass(this.options.icons.submenu).addClass(t.submenu),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},focus:function(e,t){var i,s;this.blur(e,e&&"focus"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass("ui-state-focus").removeClass("ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),this.active.parent().closest(".ui-menu-item").addClass("ui-state-active"),e&&"keydown"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(".ui-menu"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger("focus",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(e.css(this.activeMenu[0],"paddingTop"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass("ui-state-focus"),this.active=null,this._trigger("blur",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),"true"===e.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false").end().find(".ui-state-active").not(".ui-state-focus").removeClass("ui-state-active")},_closeOnDocumentClick:function(t){return!e(t.target).closest(".ui-menu").length},_isDivider:function(e){return!/[^\-\u2014\u2013\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(".ui-menu-item",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move("next","first",e)},previous:function(e){this._move("prev","last",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(e,t,i){var s;this.active&&(s="first"===e||"last"===e?this.active["first"===e?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[e+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||e(t.target).closest(".ui-menu-item");var i={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,i)},_filterMenuItems:function(t){var i=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),s=RegExp("^"+i,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return s.test(e.trim(e(this).text()))})}}),e.widget("ui.autocomplete",{version:"1.11.4",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a="textarea"===n,o="input"===n;this.isMultiLine=a?!0:o?!1:this.element.prop("isContentEditable"),this.valueMethod=this.element[a||o?"val":"text"],this.isNewMenu=!0,this.element.addClass("ui-autocomplete-input").attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case a.PAGE_UP:t=!0,this._move("previousPage",n);break;case a.PAGE_DOWN:t=!0,this._move("nextPage",n);break;case a.UP:t=!0,this._keyEvent("previous",n);break;case a.DOWN:t=!0,this._keyEvent("next",n);break;case a.ENTER:this.menu.active&&(t=!0,n.preventDefault(),this.menu.select(n));break;case a.TAB:this.menu.active&&this.menu.select(n);break;case a.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(t)return t=!1,(!this.isMultiLine||this.menu.element.is(":visible"))&&s.preventDefault(),void 0;if(!i){var n=e.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move("previousPage",s);break;case n.PAGE_DOWN:this._move("nextPage",s);break;case n.UP:this._keyEvent("previous",s);break;case n.DOWN:this._keyEvent("next",s)}}},input:function(e){return s?(s=!1,e.preventDefault(),void 0):(this._searchTimeout(e),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(e),this._change(e),void 0)}}),this._initSource(),this.menu=e("<ul>").addClass("ui-autocomplete ui-front").appendTo(this._appendTo()).menu({role:null}).hide().menu("instance"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault(),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur});var i=this.menu.element[0];e(t.target).closest(".ui-menu-item").length||this._delay(function(){var t=this;this.document.one("mousedown",function(s){s.target===t.element[0]||s.target===i||e.contains(i,s.target)||t.close()})})},menufocus:function(t,i){var s,n;return this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type))?(this.menu.blur(),this.document.one("mousemove",function(){e(t.target).trigger(t.originalEvent)}),void 0):(n=i.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:n})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(n.value),s=i.item.attr("aria-label")||n.value,s&&e.trim(s).length&&(this.liveRegion.children().hide(),e("<div>").text(s).appendTo(this.liveRegion)),void 0)},menuselect:function(e,t){var i=t.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==this.document[0].activeElement&&(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",e,{item:i})&&this._value(i.value),this.term=this._value(),this.close(e),this.selectedItem=i}}),this.liveRegion=e("<span>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeClass("ui-autocomplete-input").removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(e,t){this._super(e,t),"source"===e&&this._initSource(),"appendTo"===e&&this.menu.element.appendTo(this._appendTo()),"disabled"===e&&t&&this.xhr&&this.xhr.abort()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_initSource:function(){var t,i,s=this;e.isArray(this.options.source)?(t=this.options.source,this.source=function(i,s){s(e.ui.autocomplete.filter(t,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(t,n){s.xhr&&s.xhr.abort(),s.xhr=e.ajax({url:i,data:t,dataType:"json",success:function(e){n(e)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(e){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),i=this.menu.element.is(":visible"),s=e.altKey||e.ctrlKey||e.metaKey||e.shiftKey;(!t||t&&!i&&!s)&&(this.selectedItem=null,this.search(null,e))},this.options.delay)},search:function(e,t){return e=null!=e?e:this._value(),this.term=this._value(),e.length<this.options.minLength?this.close(t):this._trigger("search",t)!==!1?this._search(e):void 0},_search:function(e){this.pending++,this.element.addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:e},this._response())},_response:function(){var t=++this.requestIndex;return e.proxy(function(e){t===this.requestIndex&&this.__response(e),this.pending--,this.pending||this.element.removeClass("ui-autocomplete-loading")},this)},__response:function(e){e&&(e=this._normalize(e)),this._trigger("response",null,{content:e}),!this.options.disabled&&e&&e.length&&!this.cancelSearch?(this._suggest(e),this._trigger("open")):this._close()},close:function(e){this.cancelSearch=!0,this._close(e)},_close:function(e){this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",e))},_change:function(e){this.previous!==this._value()&&this._trigger("change",e,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:e.map(t,function(t){return"string"==typeof t?{label:t,value:t}:e.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var i=this.menu.element.empty();this._renderMenu(i,t),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(e.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next()},_resizeMenu:function(){var e=this.menu.element;e.outerWidth(Math.max(e.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(t,i){var s=this;e.each(i,function(e,i){s._renderItemData(t,i)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-autocomplete-item",t)},_renderItem:function(t,i){return e("<li>").text(i.label).appendTo(t)},_move:function(e,t){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(e)||this.menu.isLastItem()&&/^next/.test(e)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[e](t),void 0):(this.search(null,t),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(e,t){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(e,t),t.preventDefault())}}),e.extend(e.ui.autocomplete,{escapeRegex:function(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,i){var s=RegExp(e.ui.autocomplete.escapeRegex(i),"i");return e.grep(t,function(e){return s.test(e.label||e.value||e)})}}),e.widget("ui.autocomplete",e.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(e){return e+(e>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,this.liveRegion.children().hide(),e("<div>").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var r,h="ui-button ui-widget ui-state-default ui-corner-all",l="ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only",u=function(){var t=e(this);setTimeout(function(){t.find(":ui-button").button("refresh")},1)},d=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,"\\'"),n=s?e(s).find("[name='"+i+"'][type=radio]"):e("[name='"+i+"'][type=radio]",t.ownerDocument).filter(function(){return!this.form})),n};e.widget("ui.button",{version:"1.11.4",defaultElement:"<button>",options:{disabled:null,text:!0,label:null,icons:{primary:null,secondary:null}},_create:function(){this.element.closest("form").unbind("reset"+this.eventNamespace).bind("reset"+this.eventNamespace,u),"boolean"!=typeof this.options.disabled?this.options.disabled=!!this.element.prop("disabled"):this.element.prop("disabled",this.options.disabled),this._determineButtonType(),this.hasTitle=!!this.buttonElement.attr("title");var t=this,i=this.options,s="checkbox"===this.type||"radio"===this.type,n=s?"":"ui-state-active";null===i.label&&(i.label="input"===this.type?this.buttonElement.val():this.buttonElement.html()),this._hoverable(this.buttonElement),this.buttonElement.addClass(h).attr("role","button").bind("mouseenter"+this.eventNamespace,function(){i.disabled||this===r&&e(this).addClass("ui-state-active")}).bind("mouseleave"+this.eventNamespace,function(){i.disabled||e(this).removeClass(n)}).bind("click"+this.eventNamespace,function(e){i.disabled&&(e.preventDefault(),e.stopImmediatePropagation())}),this._on({focus:function(){this.buttonElement.addClass("ui-state-focus")},blur:function(){this.buttonElement.removeClass("ui-state-focus")}}),s&&this.element.bind("change"+this.eventNamespace,function(){t.refresh()}),"checkbox"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){return i.disabled?!1:void 0}):"radio"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){if(i.disabled)return!1;e(this).addClass("ui-state-active"),t.buttonElement.attr("aria-pressed","true");var s=t.element[0];d(s).not(s).map(function(){return e(this).button("widget")[0]}).removeClass("ui-state-active").attr("aria-pressed","false")}):(this.buttonElement.bind("mousedown"+this.eventNamespace,function(){return i.disabled?!1:(e(this).addClass("ui-state-active"),r=this,t.document.one("mouseup",function(){r=null}),void 0)}).bind("mouseup"+this.eventNamespace,function(){return i.disabled?!1:(e(this).removeClass("ui-state-active"),void 0)}).bind("keydown"+this.eventNamespace,function(t){return i.disabled?!1:((t.keyCode===e.ui.keyCode.SPACE||t.keyCode===e.ui.keyCode.ENTER)&&e(this).addClass("ui-state-active"),void 0)}).bind("keyup"+this.eventNamespace+" blur"+this.eventNamespace,function(){e(this).removeClass("ui-state-active")}),this.buttonElement.is("a")&&this.buttonElement.keyup(function(t){t.keyCode===e.ui.keyCode.SPACE&&e(this).click()})),this._setOption("disabled",i.disabled),this._resetButton()},_determineButtonType:function(){var e,t,i;this.type=this.element.is("[type=checkbox]")?"checkbox":this.element.is("[type=radio]")?"radio":this.element.is("input")?"input":"button","checkbox"===this.type||"radio"===this.type?(e=this.element.parents().last(),t="label[for='"+this.element.attr("id")+"']",this.buttonElement=e.find(t),this.buttonElement.length||(e=e.length?e.siblings():this.element.siblings(),this.buttonElement=e.filter(t),this.buttonElement.length||(this.buttonElement=e.find(t))),this.element.addClass("ui-helper-hidden-accessible"),i=this.element.is(":checked"),i&&this.buttonElement.addClass("ui-state-active"),this.buttonElement.prop("aria-pressed",i)):this.buttonElement=this.element},widget:function(){return this.buttonElement},_destroy:function(){this.element.removeClass("ui-helper-hidden-accessible"),this.buttonElement.removeClass(h+" ui-state-active "+l).removeAttr("role").removeAttr("aria-pressed").html(this.buttonElement.find(".ui-button-text").html()),this.hasTitle||this.buttonElement.removeAttr("title")},_setOption:function(e,t){return this._super(e,t),"disabled"===e?(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),t&&("checkbox"===this.type||"radio"===this.type?this.buttonElement.removeClass("ui-state-focus"):this.buttonElement.removeClass("ui-state-focus ui-state-active")),void 0):(this._resetButton(),void 0)},refresh:function(){var t=this.element.is("input, button")?this.element.is(":disabled"):this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOption("disabled",t),"radio"===this.type?d(this.element[0]).each(function(){e(this).is(":checked")?e(this).button("widget").addClass("ui-state-active").attr("aria-pressed","true"):e(this).button("widget").removeClass("ui-state-active").attr("aria-pressed","false")}):"checkbox"===this.type&&(this.element.is(":checked")?this.buttonElement.addClass("ui-state-active").attr("aria-pressed","true"):this.buttonElement.removeClass("ui-state-active").attr("aria-pressed","false"))},_resetButton:function(){if("input"===this.type)return this.options.label&&this.element.val(this.options.label),void 0;var t=this.buttonElement.removeClass(l),i=e("<span></span>",this.document[0]).addClass("ui-button-text").html(this.options.label).appendTo(t.empty()).text(),s=this.options.icons,n=s.primary&&s.secondary,a=[];s.primary||s.secondary?(this.options.text&&a.push("ui-button-text-icon"+(n?"s":s.primary?"-primary":"-secondary")),s.primary&&t.prepend("<span class='ui-button-icon-primary ui-icon "+s.primary+"'></span>"),s.secondary&&t.append("<span class='ui-button-icon-secondary ui-icon "+s.secondary+"'></span>"),this.options.text||(a.push(n?"ui-button-icons-only":"ui-button-icon-only"),this.hasTitle||t.attr("title",e.trim(i)))):a.push("ui-button-text-only"),t.addClass(a.join(" "))}}),e.widget("ui.buttonset",{version:"1.11.4",options:{items:"button, input[type=button], input[type=submit], input[type=reset], input[type=checkbox], input[type=radio], a, :data(ui-button)"},_create:function(){this.element.addClass("ui-buttonset")},_init:function(){this.refresh()},_setOption:function(e,t){"disabled"===e&&this.buttons.button("option",e,t),this._super(e,t)},refresh:function(){var t="rtl"===this.element.css("direction"),i=this.element.find(this.options.items),s=i.filter(":ui-button");i.not(":ui-button").button(),s.button("refresh"),this.buttons=i.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-all ui-corner-left ui-corner-right").filter(":first").addClass(t?"ui-corner-right":"ui-corner-left").end().filter(":last").addClass(t?"ui-corner-left":"ui-corner-right").end().end()},_destroy:function(){this.element.removeClass("ui-buttonset"),this.buttons.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-left ui-corner-right").end().button("destroy")}}),e.ui.button,e.widget("ui.dialog",{version:"1.11.4",options:{appendTo:"body",autoOpen:!0,buttons:[],closeOnEscape:!0,closeText:"Close",dialogClass:"",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var i=e(this).css(t).offset().top;0>i&&e(this).css("top",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr("title").addClass("ui-dialog-content ui-widget-content").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||"body").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().removeClass("ui-dialog-content ui-widget-content").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger("beforeClose",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(":focusable").focus().length)try{i=this.document[0].activeElement,i&&"body"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger("close",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+e(this).css("z-index")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",a+1),s=!0),s&&!i&&this._trigger("focus",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=e(this.document[0].activeElement),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"),void 0)},_focusTabbable:function(){var e=this._focusedElement;e||(e=this.element.find("[autofocus]")),e.length||(e=this.element.find(":tabbable")),e.length||(e=this.uiDialogButtonPane.find(":tabbable")),e.length||(e=this.uiDialogTitlebarClose.filter(":tabbable")),e.length||(e=this.uiDialog),e.eq(0).focus()},_keepFocus:function(t){function i(){var t=this.document[0].activeElement,i=this.uiDialog[0]===t||e.contains(this.uiDialog[0],t);i||this._focusTabbable()}t.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=e("<div>").addClass("ui-dialog ui-widget ui-widget-content ui-corner-all ui-front "+this.options.dialogClass).hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===e.ui.keyCode.ESCAPE)return t.preventDefault(),this.close(t),void 0;if(t.keyCode===e.ui.keyCode.TAB&&!t.isDefaultPrevented()){var i=this.uiDialog.find(":tabbable"),s=i.filter(":first"),n=i.filter(":last");t.target!==n[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==s[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){n.focus()}),t.preventDefault()):(this._delay(function(){s.focus()}),t.preventDefault())}},mousedown:function(e){this._moveToTop(e)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=e("<div>").addClass("ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix").prependTo(this.uiDialog),this._on(this.uiDialogTitlebar,{mousedown:function(t){e(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.focus()}}),this.uiDialogTitlebarClose=e("<button type='button'></button>").button({label:this.options.closeText,icons:{primary:"ui-icon-closethick"},text:!1}).addClass("ui-dialog-titlebar-close").appendTo(this.uiDialogTitlebar),this._on(this.uiDialogTitlebarClose,{click:function(e){e.preventDefault(),this.close(e)}}),t=e("<span>").uniqueId().addClass("ui-dialog-title").prependTo(this.uiDialogTitlebar),this._title(t),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(e){this.options.title||e.html("&#160;"),e.text(this.options.title)
},_createButtonPane:function(){this.uiDialogButtonPane=e("<div>").addClass("ui-dialog-buttonpane ui-widget-content ui-helper-clearfix"),this.uiButtonSet=e("<div>").addClass("ui-dialog-buttonset").appendTo(this.uiDialogButtonPane),this._createButtons()},_createButtons:function(){var t=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),e.isEmptyObject(i)||e.isArray(i)&&!i.length?(this.uiDialog.removeClass("ui-dialog-buttons"),void 0):(e.each(i,function(i,s){var n,a;s=e.isFunction(s)?{click:s,text:i}:s,s=e.extend({type:"button"},s),n=s.click,s.click=function(){n.apply(t.element[0],arguments)},a={icons:s.icons,text:s.showText},delete s.icons,delete s.showText,e("<button></button>",s).button(a).appendTo(t.uiButtonSet)}),this.uiDialog.addClass("ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function t(e){return{position:e.position,offset:e.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(s,n){e(this).addClass("ui-dialog-dragging"),i._blockFrames(),i._trigger("dragStart",s,t(n))},drag:function(e,s){i._trigger("drag",e,t(s))},stop:function(n,a){var o=a.offset.left-i.document.scrollLeft(),r=a.offset.top-i.document.scrollTop();s.position={my:"left top",at:"left"+(o>=0?"+":"")+o+" "+"top"+(r>=0?"+":"")+r,of:i.window},e(this).removeClass("ui-dialog-dragging"),i._unblockFrames(),i._trigger("dragStop",n,t(a))}})},_makeResizable:function(){function t(e){return{originalPosition:e.originalPosition,originalSize:e.originalSize,position:e.position,size:e.size}}var i=this,s=this.options,n=s.resizable,a=this.uiDialog.css("position"),o="string"==typeof n?n:"n,e,s,w,se,sw,ne,nw";this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:o,start:function(s,n){e(this).addClass("ui-dialog-resizing"),i._blockFrames(),i._trigger("resizeStart",s,t(n))},resize:function(e,s){i._trigger("resize",e,t(s))},stop:function(n,a){var o=i.uiDialog.offset(),r=o.left-i.document.scrollLeft(),h=o.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:"left top",at:"left"+(r>=0?"+":"")+r+" "+"top"+(h>=0?"+":"")+h,of:i.window},e(this).removeClass("ui-dialog-resizing"),i._unblockFrames(),i._trigger("resizeStop",n,t(a))}}).css("position",a)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=e(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),i=e.inArray(this,t);-1!==i&&t.splice(i,1)},_trackingInstances:function(){var e=this.document.data("ui-dialog-instances");return e||(e=[],this.document.data("ui-dialog-instances",e)),e},_minHeight:function(){var e=this.options;return"auto"===e.height?e.minHeight:Math.min(e.minHeight,e.height)},_position:function(){var e=this.uiDialog.is(":visible");e||this.uiDialog.show(),this.uiDialog.position(this.options.position),e||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};e.each(t,function(e,t){i._setOption(e,t),e in i.sizeRelatedOptions&&(s=!0),e in i.resizableRelatedOptions&&(n[e]=t)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(e,t){var i,s,n=this.uiDialog;"dialogClass"===e&&n.removeClass(this.options.dialogClass).addClass(t),"disabled"!==e&&(this._super(e,t),"appendTo"===e&&this.uiDialog.appendTo(this._appendTo()),"buttons"===e&&this._createButtons(),"closeText"===e&&this.uiDialogTitlebarClose.button({label:""+t}),"draggable"===e&&(i=n.is(":data(ui-draggable)"),i&&!t&&n.draggable("destroy"),!i&&t&&this._makeDraggable()),"position"===e&&this._position(),"resizable"===e&&(s=n.is(":data(ui-resizable)"),s&&!t&&n.resizable("destroy"),s&&"string"==typeof t&&n.resizable("option","handles",t),s||t===!1||this._makeResizable()),"title"===e&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var e,t,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),e=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),t=Math.max(0,s.minHeight-e),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-e):"none","auto"===s.height?this.element.css({minHeight:t,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-e)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=e(this);return e("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return e(t.target).closest(".ui-dialog").length?!0:!!e(t.target).closest(".ui-datepicker").length},_createOverlay:function(){if(this.options.modal){var t=!0;this._delay(function(){t=!1}),this.document.data("ui-dialog-overlays")||this._on(this.document,{focusin:function(e){t||this._allowInteraction(e)||(e.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=e("<div>").addClass("ui-widget-overlay ui-front").appendTo(this._appendTo()),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var e=this.document.data("ui-dialog-overlays")-1;e?this.document.data("ui-dialog-overlays",e):this.document.unbind("focusin").removeData("ui-dialog-overlays"),this.overlay.remove(),this.overlay=null}}}),e.widget("ui.progressbar",{version:"1.11.4",options:{max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.addClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").attr({role:"progressbar","aria-valuemin":this.min}),this.valueDiv=e("<div class='ui-progressbar-value ui-widget-header ui-corner-left'></div>").appendTo(this.element),this._refreshValue()},_destroy:function(){this.element.removeClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.valueDiv.remove()},value:function(e){return void 0===e?this.options.value:(this.options.value=this._constrainedValue(e),this._refreshValue(),void 0)},_constrainedValue:function(e){return void 0===e&&(e=this.options.value),this.indeterminate=e===!1,"number"!=typeof e&&(e=0),this.indeterminate?!1:Math.min(this.options.max,Math.max(this.min,e))},_setOptions:function(e){var t=e.value;delete e.value,this._super(e),this.options.value=this._constrainedValue(t),this._refreshValue()},_setOption:function(e,t){"max"===e&&(t=Math.max(this.min,t)),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,i=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).toggleClass("ui-corner-right",t===this.options.max).width(i.toFixed(0)+"%"),this.element.toggleClass("ui-progressbar-indeterminate",this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=e("<div class='ui-progressbar-overlay'></div>").appendTo(this.valueDiv))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),e.widget("ui.selectmenu",{version:"1.11.4",defaultElement:"<select>",options:{appendTo:null,disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:null,change:null,close:null,focus:null,open:null,select:null},_create:function(){var e=this.element.uniqueId().attr("id");this.ids={element:e,button:e+"-button",menu:e+"-menu"},this._drawButton(),this._drawMenu(),this.options.disabled&&this.disable()},_drawButton:function(){var t=this;this.label=e("label[for='"+this.ids.element+"']").attr("for",this.ids.button),this._on(this.label,{click:function(e){this.button.focus(),e.preventDefault()}}),this.element.hide(),this.button=e("<span>",{"class":"ui-selectmenu-button ui-widget ui-state-default ui-corner-all",tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true"}).insertAfter(this.element),e("<span>",{"class":"ui-icon "+this.options.icons.button}).prependTo(this.button),this.buttonText=e("<span>",{"class":"ui-selectmenu-text"}).appendTo(this.button),this._setText(this.buttonText,this.element.find("option:selected").text()),this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){t.menuItems||t._refreshMenu()}),this._hoverable(this.button),this._focusable(this.button)},_drawMenu:function(){var t=this;this.menu=e("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=e("<div>",{"class":"ui-selectmenu-menu ui-front"}).append(this.menu).appendTo(this._appendTo()),this.menuInstance=this.menu.menu({role:"listbox",select:function(e,i){e.preventDefault(),t._setSelection(),t._select(i.item.data("ui-selectmenu-item"),e)},focus:function(e,i){var s=i.item.data("ui-selectmenu-item");null!=t.focusIndex&&s.index!==t.focusIndex&&(t._trigger("focus",e,{item:s}),t.isOpen||t._select(s,e)),t.focusIndex=s.index,t.button.attr("aria-activedescendant",t.menuItems.eq(s.index).attr("id"))}}).menu("instance"),this.menu.addClass("ui-corner-bottom").removeClass("ui-corner-all"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this._setText(this.buttonText,this._getSelectedItem().text()),this.options.width||this._resizeButton()},_refreshMenu:function(){this.menu.empty();var e,t=this.element.find("option");t.length&&(this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup"),e=this._getSelectedItem(),this.menuInstance.focus(null,e),this._setAria(e.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(e){this.options.disabled||(this.menuItems?(this.menu.find(".ui-state-focus").removeClass("ui-state-focus"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",e))},_position:function(){this.menuWrap.position(e.extend({of:this.button},this.options.position))},close:function(e){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",e))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderMenu:function(t,i){var s=this,n="";e.each(i,function(i,a){a.optgroup!==n&&(e("<li>",{"class":"ui-selectmenu-optgroup ui-menu-divider"+(a.element.parent("optgroup").prop("disabled")?" ui-state-disabled":""),text:a.optgroup}).appendTo(t),n=a.optgroup),s._renderItemData(t,a)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-selectmenu-item",t)},_renderItem:function(t,i){var s=e("<li>");return i.disabled&&s.addClass("ui-state-disabled"),this._setText(s,i.label),s.appendTo(t)},_setText:function(e,t){t?e.text(t):e.html("&#160;")},_move:function(e,t){var i,s,n=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex):(i=this.menuItems.eq(this.element[0].selectedIndex),n+=":not(.ui-state-disabled)"),s="first"===e||"last"===e?i["first"===e?"prevAll":"nextAll"](n).eq(-1):i[e+"All"](n).eq(0),s.length&&this.menuInstance.focus(t,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex)},_toggle:function(e){this[this.isOpen?"close":"open"](e)},_setSelection:function(){var e;this.range&&(window.getSelection?(e=window.getSelection(),e.removeAllRanges(),e.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(t){this.isOpen&&(e(t.target).closest(".ui-selectmenu-menu, #"+this.ids.button).length||this.close(t))}},_buttonEvents:{mousedown:function(){var e;window.getSelection?(e=window.getSelection(),e.rangeCount&&(this.range=e.getRangeAt(0))):this.range=document.selection.createRange()},click:function(e){this._setSelection(),this._toggle(e)},keydown:function(t){var i=!0;switch(t.keyCode){case e.ui.keyCode.TAB:case e.ui.keyCode.ESCAPE:this.close(t),i=!1;break;case e.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case e.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case e.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case e.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case e.ui.keyCode.LEFT:this._move("prev",t);break;case e.ui.keyCode.RIGHT:this._move("next",t);break;case e.ui.keyCode.HOME:case e.ui.keyCode.PAGE_UP:this._move("first",t);break;case e.ui.keyCode.END:case e.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),i=!1}i&&t.preventDefault()}},_selectFocusedItem:function(e){var t=this.menuItems.eq(this.focusIndex);t.hasClass("ui-state-disabled")||this._select(t.data("ui-selectmenu-item"),e)},_select:function(e,t){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=e.index,this._setText(this.buttonText,e.label),this._setAria(e),this._trigger("select",t,{item:e}),e.index!==i&&this._trigger("change",t,{item:e}),this.close(t)},_setAria:function(e){var t=this.menuItems.eq(e.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(e,t){"icons"===e&&this.button.find("span.ui-icon").removeClass(this.options.icons.button).addClass(t.button),this._super(e,t),"appendTo"===e&&this.menuWrap.appendTo(this._appendTo()),"disabled"===e&&(this.menuInstance.option("disabled",t),this.button.toggleClass("ui-state-disabled",t).attr("aria-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)),"width"===e&&this._resizeButton()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_toggleAttr:function(){this.button.toggleClass("ui-corner-top",this.isOpen).toggleClass("ui-corner-all",!this.isOpen).attr("aria-expanded",this.isOpen),this.menuWrap.toggleClass("ui-selectmenu-open",this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var e=this.options.width;e||(e=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(e)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){return{disabled:this.element.prop("disabled")}},_parseOptions:function(t){var i=[];t.each(function(t,s){var n=e(s),a=n.parent("optgroup");i.push({element:n,index:t,value:n.val(),label:n.text(),optgroup:a.attr("label")||"",disabled:a.prop("disabled")||n.prop("disabled")})}),this.items=i},_destroy:function(){this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.label.attr("for",this.ids.element)}}),e.widget("ui.slider",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"slide",options:{animate:!1,distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this.element.addClass("ui-slider ui-slider-"+this.orientation+" ui-widget"+" ui-widget-content"+" ui-corner-all"),this._refresh(),this._setOption("disabled",this.options.disabled),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,i,s=this.options,n=this.element.find(".ui-slider-handle").addClass("ui-state-default ui-corner-all"),a="<span class='ui-slider-handle ui-state-default ui-corner-all' tabindex='0'></span>",o=[];for(i=s.values&&s.values.length||1,n.length>i&&(n.slice(i).remove(),n=n.slice(0,i)),t=n.length;i>t;t++)o.push(a);this.handles=n.add(e(o.join("")).appendTo(this.element)),this.handle=this.handles.eq(0),this.handles.each(function(t){e(this).data("ui-slider-handle-index",t)})},_createRange:function(){var t=this.options,i="";t.range?(t.range===!0&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:e.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass("ui-slider-range-min ui-slider-range-max").css({left:"",bottom:""}):(this.range=e("<div></div>").appendTo(this.element),i="ui-slider-range ui-widget-header ui-corner-all"),this.range.addClass(i+("min"===t.range||"max"===t.range?" ui-slider-range-"+t.range:""))):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all"),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,a,o,r,h,l,u=this,d=this.options;return d.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:t.pageX,y:t.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var i=Math.abs(s-u.values(t));(n>i||n===i&&(t===u._lastChangedValue||u.values(t)===d.min))&&(n=i,a=e(this),o=t)}),r=this._start(t,o),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,a.addClass("ui-state-active").focus(),h=a.offset(),l=!e(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=l?{left:0,top:0}:{left:t.pageX-h.left-a.width()/2,top:t.pageY-h.top-a.height()/2-(parseInt(a.css("borderTopWidth"),10)||0)-(parseInt(a.css("borderBottomWidth"),10)||0)+(parseInt(a.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(e){var t={x:e.pageX,y:e.pageY},i=this._normValueFromMouse(t);return this._slide(e,this._handleIndex,i),!1},_mouseStop:function(e){return this.handles.removeClass("ui-state-active"),this._mouseSliding=!1,this._stop(e,this._handleIndex),this._change(e,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(e){var t,i,s,n,a;return"horizontal"===this.orientation?(t=this.elementSize.width,i=e.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(t=this.elementSize.height,i=e.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/t,s>1&&(s=1),0>s&&(s=0),"vertical"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),a=this._valueMin()+s*n,this._trimAlignValue(a)},_start:function(e,t){var i={handle:this.handles[t],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("start",e,i)},_slide:function(e,t,i){var s,n,a;this.options.values&&this.options.values.length?(s=this.values(t?0:1),2===this.options.values.length&&this.options.range===!0&&(0===t&&i>s||1===t&&s>i)&&(i=s),i!==this.values(t)&&(n=this.values(),n[t]=i,a=this._trigger("slide",e,{handle:this.handles[t],value:i,values:n}),s=this.values(t?0:1),a!==!1&&this.values(t,i))):i!==this.value()&&(a=this._trigger("slide",e,{handle:this.handles[t],value:i}),a!==!1&&this.value(i))},_stop:function(e,t){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("stop",e,i)},_change:function(e,t){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._lastChangedValue=t,this._trigger("change",e,i)}},value:function(e){return arguments.length?(this.options.value=this._trimAlignValue(e),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(t,i){var s,n,a;if(arguments.length>1)return this.options.values[t]=this._trimAlignValue(i),this._refreshValue(),this._change(null,t),void 0;if(!arguments.length)return this._values();if(!e.isArray(arguments[0]))return this.options.values&&this.options.values.length?this._values(t):this.value();for(s=this.options.values,n=arguments[0],a=0;s.length>a;a+=1)s[a]=this._trimAlignValue(n[a]),this._change(null,a);this._refreshValue()},_setOption:function(t,i){var s,n=0;switch("range"===t&&this.options.range===!0&&("min"===i?(this.options.value=this._values(0),this.options.values=null):"max"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),e.isArray(this.options.values)&&(n=this.options.values.length),"disabled"===t&&this.element.toggleClass("ui-state-disabled",!!i),this._super(t,i),t){case"orientation":this._detectOrientation(),this.element.removeClass("ui-slider-horizontal ui-slider-vertical").addClass("ui-slider-"+this.orientation),this._refreshValue(),this.handles.css("horizontal"===i?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),s=0;n>s;s+=1)this._change(null,s);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_value:function(){var e=this.options.value;return e=this._trimAlignValue(e)},_values:function(e){var t,i,s;if(arguments.length)return t=this.options.values[e],t=this._trimAlignValue(t);if(this.options.values&&this.options.values.length){for(i=this.options.values.slice(),s=0;i.length>s;s+=1)i[s]=this._trimAlignValue(i[s]);return i}return[]},_trimAlignValue:function(e){if(this._valueMin()>=e)return this._valueMin();if(e>=this._valueMax())return this._valueMax();var t=this.options.step>0?this.options.step:1,i=(e-this._valueMin())%t,s=e-i;return 2*Math.abs(i)>=t&&(s+=i>0?t:-t),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var e=this.options.max,t=this._valueMin(),i=this.options.step,s=Math.floor(+(e-t).toFixed(this._precision())/i)*i;e=s+t,this.max=parseFloat(e.toFixed(this._precision()))},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshValue:function(){var t,i,s,n,a,o=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,u={};this.options.values&&this.options.values.length?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),u["horizontal"===h.orientation?"left":"bottom"]=i+"%",e(this).stop(1,1)[l?"animate":"css"](u,r.animate),h.options.range===!0&&("horizontal"===h.orientation?(0===s&&h.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({width:i-t+"%"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({height:i-t+"%"},{queue:!1,duration:r.animate}))),t=i}):(s=this.value(),n=this._valueMin(),a=this._valueMax(),i=a!==n?100*((s-n)/(a-n)):0,u["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](u,r.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},r.animate),"max"===o&&"horizontal"===this.orientation&&this.range[l?"animate":"css"]({width:100-i+"%"},{queue:!1,duration:r.animate}),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},r.animate),"max"===o&&"vertical"===this.orientation&&this.range[l?"animate":"css"]({height:100-i+"%"},{queue:!1,duration:r.animate}))},_handleEvents:{keydown:function(t){var i,s,n,a,o=e(t.target).data("ui-slider-handle-index");switch(t.keyCode){case e.ui.keyCode.HOME:case e.ui.keyCode.END:case e.ui.keyCode.PAGE_UP:case e.ui.keyCode.PAGE_DOWN:case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,e(t.target).addClass("ui-state-active"),i=this._start(t,o),i===!1))return}switch(a=this.options.step,s=n=this.options.values&&this.options.values.length?this.values(o):this.value(),t.keyCode){case e.ui.keyCode.HOME:n=this._valueMin();break;case e.ui.keyCode.END:n=this._valueMax();break;case e.ui.keyCode.PAGE_UP:n=this._trimAlignValue(s+(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.PAGE_DOWN:n=this._trimAlignValue(s-(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:if(s===this._valueMax())return;n=this._trimAlignValue(s+a);break;case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(s===this._valueMin())return;n=this._trimAlignValue(s-a)}this._slide(t,o,n)},keyup:function(t){var i=e(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,i),this._change(t,i),e(t.target).removeClass("ui-state-active"))}}}),e.widget("ui.spinner",{version:"1.11.4",defaultElement:"<input>",widgetEventPrefix:"spin",options:{culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var t={},i=this.element;return e.each(["min","max","step"],function(e,s){var n=i.attr(s);void 0!==n&&n.length&&(t[s]=n)}),t},_events:{keydown:function(e){this._start(e)&&this._keydown(e)&&e.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",e),void 0)},mousewheel:function(e,t){if(t){if(!this.spinning&&!this._start(e))return!1;this._spin((t>0?1:-1)*this.options.step,e),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(e)},100),e.preventDefault()}},"mousedown .ui-spinner-button":function(t){function i(){var e=this.element[0]===this.document[0].activeElement;e||(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===this.document[0].activeElement?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(t)!==!1&&this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){return e(t.currentTarget).hasClass("ui-state-active")?this._start(t)===!1?!1:(this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t),void 0):void 0},"mouseleave .ui-spinner-button":"_stop"},_draw:function(){var e=this.uiSpinner=this.element.addClass("ui-spinner-input").attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml());this.element.attr("role","spinbutton"),this.buttons=e.find(".ui-spinner-button").attr("tabIndex",-1).button().removeClass("ui-corner-all"),this.buttons.height()>Math.ceil(.5*e.height())&&e.height()>0&&e.height(e.height()),this.options.disabled&&this.disable()},_keydown:function(t){var i=this.options,s=e.ui.keyCode;switch(t.keyCode){case s.UP:return this._repeat(null,1,t),!0;case s.DOWN:return this._repeat(null,-1,t),!0;case s.PAGE_UP:return this._repeat(null,i.page,t),!0;case s.PAGE_DOWN:return this._repeat(null,-i.page,t),!0}return!1},_uiSpinnerHtml:function(){return"<span class='ui-spinner ui-widget ui-widget-content ui-corner-all'></span>"},_buttonHtml:function(){return"<a class='ui-spinner-button ui-spinner-up ui-corner-tr'><span class='ui-icon "+this.options.icons.up+"'>&#9650;</span>"+"</a>"+"<a class='ui-spinner-button ui-spinner-down ui-corner-br'>"+"<span class='ui-icon "+this.options.icons.down+"'>&#9660;</span>"+"</a>"},_start:function(e){return this.spinning||this._trigger("start",e)!==!1?(this.counter||(this.counter=1),this.spinning=!0,!0):!1},_repeat:function(e,t,i){e=e||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,t,i)},e),this._spin(t*this.options.step,i)},_spin:function(e,t){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+e*this._increment(this.counter)),this.spinning&&this._trigger("spin",t,{value:i})===!1||(this._value(i),this.counter++)},_increment:function(t){var i=this.options.incremental;return i?e.isFunction(i)?i(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_adjustValue:function(e){var t,i,s=this.options;return t=null!==s.min?s.min:0,i=e-t,i=Math.round(i/s.step)*s.step,e=t+i,e=parseFloat(e.toFixed(this._precision())),null!==s.max&&e>s.max?s.max:null!==s.min&&s.min>e?s.min:e},_stop:function(e){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",e))},_setOption:function(e,t){if("culture"===e||"numberFormat"===e){var i=this._parse(this.element.val());return this.options[e]=t,this.element.val(this._format(i)),void 0}("max"===e||"min"===e||"step"===e)&&"string"==typeof t&&(t=this._parse(t)),"icons"===e&&(this.buttons.first().find(".ui-icon").removeClass(this.options.icons.up).addClass(t.up),this.buttons.last().find(".ui-icon").removeClass(this.options.icons.down).addClass(t.down)),this._super(e,t),"disabled"===e&&(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable"))},_setOptions:s(function(e){this._super(e)}),_parse:function(e){return"string"==typeof e&&""!==e&&(e=window.Globalize&&this.options.numberFormat?Globalize.parseFloat(e,10,this.options.culture):+e),""===e||isNaN(e)?null:e
},_format:function(e){return""===e?"":window.Globalize&&this.options.numberFormat?Globalize.format(e,this.options.numberFormat,this.options.culture):e},_refresh:function(){this.element.attr({"aria-valuemin":this.options.min,"aria-valuemax":this.options.max,"aria-valuenow":this._parse(this.element.val())})},isValid:function(){var e=this.value();return null===e?!1:e===this._adjustValue(e)},_value:function(e,t){var i;""!==e&&(i=this._parse(e),null!==i&&(t||(i=this._adjustValue(i)),e=this._format(i))),this.element.val(e),this._refresh()},_destroy:function(){this.element.removeClass("ui-spinner-input").prop("disabled",!1).removeAttr("autocomplete").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:s(function(e){this._stepUp(e)}),_stepUp:function(e){this._start()&&(this._spin((e||1)*this.options.step),this._stop())},stepDown:s(function(e){this._stepDown(e)}),_stepDown:function(e){this._start()&&(this._spin((e||1)*-this.options.step),this._stop())},pageUp:s(function(e){this._stepUp((e||1)*this.options.page)}),pageDown:s(function(e){this._stepDown((e||1)*this.options.page)}),value:function(e){return arguments.length?(s(this._value).call(this,e),void 0):this._parse(this.element.val())},widget:function(){return this.uiSpinner}}),e.widget("ui.tabs",{version:"1.11.4",delay:300,options:{active:null,collapsible:!1,event:"click",heightStyle:"content",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:function(){var e=/#.*$/;return function(t){var i,s;t=t.cloneNode(!1),i=t.href.replace(e,""),s=location.href.replace(e,"");try{i=decodeURIComponent(i)}catch(n){}try{s=decodeURIComponent(s)}catch(n){}return t.hash.length>1&&i===s}}(),_create:function(){var t=this,i=this.options;this.running=!1,this.element.addClass("ui-tabs ui-widget ui-widget-content ui-corner-all").toggleClass("ui-tabs-collapsible",i.collapsible),this._processTabs(),i.active=this._initialActive(),e.isArray(i.disabled)&&(i.disabled=e.unique(i.disabled.concat(e.map(this.tabs.filter(".ui-state-disabled"),function(e){return t.tabs.index(e)}))).sort()),this.active=this.options.active!==!1&&this.anchors.length?this._findActive(i.active):e(),this._refresh(),this.active.length&&this.load(i.active)},_initialActive:function(){var t=this.options.active,i=this.options.collapsible,s=location.hash.substring(1);return null===t&&(s&&this.tabs.each(function(i,n){return e(n).attr("aria-controls")===s?(t=i,!1):void 0}),null===t&&(t=this.tabs.index(this.tabs.filter(".ui-tabs-active"))),(null===t||-1===t)&&(t=this.tabs.length?0:!1)),t!==!1&&(t=this.tabs.index(this.tabs.eq(t)),-1===t&&(t=i?!1:0)),!i&&t===!1&&this.anchors.length&&(t=0),t},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):e()}},_tabKeydown:function(t){var i=e(this.document[0].activeElement).closest("li"),s=this.tabs.index(i),n=!0;if(!this._handlePageNav(t)){switch(t.keyCode){case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:s++;break;case e.ui.keyCode.UP:case e.ui.keyCode.LEFT:n=!1,s--;break;case e.ui.keyCode.END:s=this.anchors.length-1;break;case e.ui.keyCode.HOME:s=0;break;case e.ui.keyCode.SPACE:return t.preventDefault(),clearTimeout(this.activating),this._activate(s),void 0;case e.ui.keyCode.ENTER:return t.preventDefault(),clearTimeout(this.activating),this._activate(s===this.options.active?!1:s),void 0;default:return}t.preventDefault(),clearTimeout(this.activating),s=this._focusNextTab(s,n),t.ctrlKey||t.metaKey||(i.attr("aria-selected","false"),this.tabs.eq(s).attr("aria-selected","true"),this.activating=this._delay(function(){this.option("active",s)},this.delay))}},_panelKeydown:function(t){this._handlePageNav(t)||t.ctrlKey&&t.keyCode===e.ui.keyCode.UP&&(t.preventDefault(),this.active.focus())},_handlePageNav:function(t){return t.altKey&&t.keyCode===e.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===e.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 0},_findNextTab:function(t,i){function s(){return t>n&&(t=0),0>t&&(t=n),t}for(var n=this.tabs.length-1;-1!==e.inArray(s(),this.options.disabled);)t=i?t+1:t-1;return t},_focusNextTab:function(e,t){return e=this._findNextTab(e,t),this.tabs.eq(e).focus(),e},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):"disabled"===e?(this._setupDisabled(t),void 0):(this._super(e,t),"collapsible"===e&&(this.element.toggleClass("ui-tabs-collapsible",t),t||this.options.active!==!1||this._activate(0)),"event"===e&&this._setupEvents(t),"heightStyle"===e&&this._setupHeightStyle(t),void 0)},_sanitizeSelector:function(e){return e?e.replace(/[!"$%&'()*+,.\/:;<=>?@\[\]\^`{|}~]/g,"\\$&"):""},refresh:function(){var t=this.options,i=this.tablist.children(":has(a[href])");t.disabled=e.map(i.filter(".ui-state-disabled"),function(e){return i.index(e)}),this._processTabs(),t.active!==!1&&this.anchors.length?this.active.length&&!e.contains(this.tablist[0],this.active[0])?this.tabs.length===t.disabled.length?(t.active=!1,this.active=e()):this._activate(this._findNextTab(Math.max(0,t.active-1),!1)):t.active=this.tabs.index(this.active):(t.active=!1,this.active=e()),this._refresh()},_refresh:function(){this._setupDisabled(this.options.disabled),this._setupEvents(this.options.event),this._setupHeightStyle(this.options.heightStyle),this.tabs.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}),this.panels.not(this._getPanelForTab(this.active)).hide().attr({"aria-hidden":"true"}),this.active.length?(this.active.addClass("ui-tabs-active ui-state-active").attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}),this._getPanelForTab(this.active).show().attr({"aria-hidden":"false"})):this.tabs.eq(0).attr("tabIndex",0)},_processTabs:function(){var t=this,i=this.tabs,s=this.anchors,n=this.panels;this.tablist=this._getList().addClass("ui-tabs-nav ui-helper-reset ui-helper-clearfix ui-widget-header ui-corner-all").attr("role","tablist").delegate("> li","mousedown"+this.eventNamespace,function(t){e(this).is(".ui-state-disabled")&&t.preventDefault()}).delegate(".ui-tabs-anchor","focus"+this.eventNamespace,function(){e(this).closest("li").is(".ui-state-disabled")&&this.blur()}),this.tabs=this.tablist.find("> li:has(a[href])").addClass("ui-state-default ui-corner-top").attr({role:"tab",tabIndex:-1}),this.anchors=this.tabs.map(function(){return e("a",this)[0]}).addClass("ui-tabs-anchor").attr({role:"presentation",tabIndex:-1}),this.panels=e(),this.anchors.each(function(i,s){var n,a,o,r=e(s).uniqueId().attr("id"),h=e(s).closest("li"),l=h.attr("aria-controls");t._isLocal(s)?(n=s.hash,o=n.substring(1),a=t.element.find(t._sanitizeSelector(n))):(o=h.attr("aria-controls")||e({}).uniqueId()[0].id,n="#"+o,a=t.element.find(n),a.length||(a=t._createPanel(o),a.insertAfter(t.panels[i-1]||t.tablist)),a.attr("aria-live","polite")),a.length&&(t.panels=t.panels.add(a)),l&&h.data("ui-tabs-aria-controls",l),h.attr({"aria-controls":o,"aria-labelledby":r}),a.attr("aria-labelledby",r)}),this.panels.addClass("ui-tabs-panel ui-widget-content ui-corner-bottom").attr("role","tabpanel"),i&&(this._off(i.not(this.tabs)),this._off(s.not(this.anchors)),this._off(n.not(this.panels)))},_getList:function(){return this.tablist||this.element.find("ol,ul").eq(0)},_createPanel:function(t){return e("<div>").attr("id",t).addClass("ui-tabs-panel ui-widget-content ui-corner-bottom").data("ui-tabs-destroy",!0)},_setupDisabled:function(t){e.isArray(t)&&(t.length?t.length===this.anchors.length&&(t=!0):t=!1);for(var i,s=0;i=this.tabs[s];s++)t===!0||-1!==e.inArray(s,t)?e(i).addClass("ui-state-disabled").attr("aria-disabled","true"):e(i).removeClass("ui-state-disabled").removeAttr("aria-disabled");this.options.disabled=t},_setupEvents:function(t){var i={};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.anchors.add(this.tabs).add(this.panels)),this._on(!0,this.anchors,{click:function(e){e.preventDefault()}}),this._on(this.anchors,i),this._on(this.tabs,{keydown:"_tabKeydown"}),this._on(this.panels,{keydown:"_panelKeydown"}),this._focusable(this.tabs),this._hoverable(this.tabs)},_setupHeightStyle:function(t){var i,s=this.element.parent();"fill"===t?(i=s.height(),i-=this.element.outerHeight()-this.element.height(),this.element.siblings(":visible").each(function(){var t=e(this),s=t.css("position");"absolute"!==s&&"fixed"!==s&&(i-=t.outerHeight(!0))}),this.element.children().not(this.panels).each(function(){i-=e(this).outerHeight(!0)}),this.panels.each(function(){e(this).height(Math.max(0,i-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===t&&(i=0,this.panels.each(function(){i=Math.max(i,e(this).height("").height())}).height(i))},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n.closest("li"),o=a[0]===s[0],r=o&&i.collapsible,h=r?e():this._getPanelForTab(a),l=s.length?this._getPanelForTab(s):e(),u={oldTab:s,oldPanel:l,newTab:r?e():a,newPanel:h};t.preventDefault(),a.hasClass("ui-state-disabled")||a.hasClass("ui-tabs-loading")||this.running||o&&!i.collapsible||this._trigger("beforeActivate",t,u)===!1||(i.active=r?!1:this.tabs.index(a),this.active=o?e():a,this.xhr&&this.xhr.abort(),l.length||h.length||e.error("jQuery UI Tabs: Mismatching fragment identifier."),h.length&&this.load(this.tabs.index(a),t),this._toggle(t,u))},_toggle:function(t,i){function s(){a.running=!1,a._trigger("activate",t,i)}function n(){i.newTab.closest("li").addClass("ui-tabs-active ui-state-active"),o.length&&a.options.show?a._show(o,a.options.show,s):(o.show(),s())}var a=this,o=i.newPanel,r=i.oldPanel;this.running=!0,r.length&&this.options.hide?this._hide(r,this.options.hide,function(){i.oldTab.closest("li").removeClass("ui-tabs-active ui-state-active"),n()}):(i.oldTab.closest("li").removeClass("ui-tabs-active ui-state-active"),r.hide(),n()),r.attr("aria-hidden","true"),i.oldTab.attr({"aria-selected":"false","aria-expanded":"false"}),o.length&&r.length?i.oldTab.attr("tabIndex",-1):o.length&&this.tabs.filter(function(){return 0===e(this).attr("tabIndex")}).attr("tabIndex",-1),o.attr("aria-hidden","false"),i.newTab.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_activate:function(t){var i,s=this._findActive(t);s[0]!==this.active[0]&&(s.length||(s=this.active),i=s.find(".ui-tabs-anchor")[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return t===!1?e():this.tabs.eq(t)},_getIndex:function(e){return"string"==typeof e&&(e=this.anchors.index(this.anchors.filter("[href$='"+e+"']"))),e},_destroy:function(){this.xhr&&this.xhr.abort(),this.element.removeClass("ui-tabs ui-widget ui-widget-content ui-corner-all ui-tabs-collapsible"),this.tablist.removeClass("ui-tabs-nav ui-helper-reset ui-helper-clearfix ui-widget-header ui-corner-all").removeAttr("role"),this.anchors.removeClass("ui-tabs-anchor").removeAttr("role").removeAttr("tabIndex").removeUniqueId(),this.tablist.unbind(this.eventNamespace),this.tabs.add(this.panels).each(function(){e.data(this,"ui-tabs-destroy")?e(this).remove():e(this).removeClass("ui-state-default ui-state-active ui-state-disabled ui-corner-top ui-corner-bottom ui-widget-content ui-tabs-active ui-tabs-panel").removeAttr("tabIndex").removeAttr("aria-live").removeAttr("aria-busy").removeAttr("aria-selected").removeAttr("aria-labelledby").removeAttr("aria-hidden").removeAttr("aria-expanded").removeAttr("role")}),this.tabs.each(function(){var t=e(this),i=t.data("ui-tabs-aria-controls");i?t.attr("aria-controls",i).removeData("ui-tabs-aria-controls"):t.removeAttr("aria-controls")}),this.panels.show(),"content"!==this.options.heightStyle&&this.panels.css("height","")},enable:function(t){var i=this.options.disabled;i!==!1&&(void 0===t?i=!1:(t=this._getIndex(t),i=e.isArray(i)?e.map(i,function(e){return e!==t?e:null}):e.map(this.tabs,function(e,i){return i!==t?i:null})),this._setupDisabled(i))},disable:function(t){var i=this.options.disabled;if(i!==!0){if(void 0===t)i=!0;else{if(t=this._getIndex(t),-1!==e.inArray(t,i))return;i=e.isArray(i)?e.merge([t],i).sort():[t]}this._setupDisabled(i)}},load:function(t,i){t=this._getIndex(t);var s=this,n=this.tabs.eq(t),a=n.find(".ui-tabs-anchor"),o=this._getPanelForTab(n),r={tab:n,panel:o},h=function(e,t){"abort"===t&&s.panels.stop(!1,!0),n.removeClass("ui-tabs-loading"),o.removeAttr("aria-busy"),e===s.xhr&&delete s.xhr};this._isLocal(a[0])||(this.xhr=e.ajax(this._ajaxSettings(a,i,r)),this.xhr&&"canceled"!==this.xhr.statusText&&(n.addClass("ui-tabs-loading"),o.attr("aria-busy","true"),this.xhr.done(function(e,t,n){setTimeout(function(){o.html(e),s._trigger("load",i,r),h(n,t)},1)}).fail(function(e,t){setTimeout(function(){h(e,t)},1)})))},_ajaxSettings:function(t,i,s){var n=this;return{url:t.attr("href"),beforeSend:function(t,a){return n._trigger("beforeLoad",i,e.extend({jqXHR:t,ajaxSettings:a},s))}}},_getPanelForTab:function(t){var i=e(t).attr("aria-controls");return this.element.find(this._sanitizeSelector("#"+i))}}),e.widget("ui.tooltip",{version:"1.11.4",options:{content:function(){var t=e(this).attr("title")||"";return e("<a>").text(t).html()},hide:!0,items:"[title]:not([disabled])",position:{my:"left top+15",at:"left bottom",collision:"flipfit flip"},show:!0,tooltipClass:null,track:!1,close:null,open:null},_addDescribedBy:function(t,i){var s=(t.attr("aria-describedby")||"").split(/\s+/);s.push(i),t.data("ui-tooltip-id",i).attr("aria-describedby",e.trim(s.join(" ")))},_removeDescribedBy:function(t){var i=t.data("ui-tooltip-id"),s=(t.attr("aria-describedby")||"").split(/\s+/),n=e.inArray(i,s);-1!==n&&s.splice(n,1),t.removeData("ui-tooltip-id"),s=e.trim(s.join(" ")),s?t.attr("aria-describedby",s):t.removeAttr("aria-describedby")},_create:function(){this._on({mouseover:"open",focusin:"open"}),this.tooltips={},this.parents={},this.options.disabled&&this._disable(),this.liveRegion=e("<div>").attr({role:"log","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body)},_setOption:function(t,i){var s=this;return"disabled"===t?(this[i?"_disable":"_enable"](),this.options[t]=i,void 0):(this._super(t,i),"content"===t&&e.each(this.tooltips,function(e,t){s._updateContent(t.element)}),void 0)},_disable:function(){var t=this;e.each(this.tooltips,function(i,s){var n=e.Event("blur");n.target=n.currentTarget=s.element[0],t.close(n,!0)}),this.element.find(this.options.items).addBack().each(function(){var t=e(this);t.is("[title]")&&t.data("ui-tooltip-title",t.attr("title")).removeAttr("title")})},_enable:function(){this.element.find(this.options.items).addBack().each(function(){var t=e(this);t.data("ui-tooltip-title")&&t.attr("title",t.data("ui-tooltip-title"))})},open:function(t){var i=this,s=e(t?t.target:this.element).closest(this.options.items);s.length&&!s.data("ui-tooltip-id")&&(s.attr("title")&&s.data("ui-tooltip-title",s.attr("title")),s.data("ui-tooltip-open",!0),t&&"mouseover"===t.type&&s.parents().each(function(){var t,s=e(this);s.data("ui-tooltip-open")&&(t=e.Event("blur"),t.target=t.currentTarget=this,i.close(t,!0)),s.attr("title")&&(s.uniqueId(),i.parents[this.id]={element:this,title:s.attr("title")},s.attr("title",""))}),this._registerCloseHandlers(t,s),this._updateContent(s,t))},_updateContent:function(e,t){var i,s=this.options.content,n=this,a=t?t.type:null;return"string"==typeof s?this._open(t,e,s):(i=s.call(e[0],function(i){n._delay(function(){e.data("ui-tooltip-open")&&(t&&(t.type=a),this._open(t,e,i))})}),i&&this._open(t,e,i),void 0)},_open:function(t,i,s){function n(e){l.of=e,o.is(":hidden")||o.position(l)}var a,o,r,h,l=e.extend({},this.options.position);if(s){if(a=this._find(i))return a.tooltip.find(".ui-tooltip-content").html(s),void 0;i.is("[title]")&&(t&&"mouseover"===t.type?i.attr("title",""):i.removeAttr("title")),a=this._tooltip(i),o=a.tooltip,this._addDescribedBy(i,o.attr("id")),o.find(".ui-tooltip-content").html(s),this.liveRegion.children().hide(),s.clone?(h=s.clone(),h.removeAttr("id").find("[id]").removeAttr("id")):h=s,e("<div>").html(h).appendTo(this.liveRegion),this.options.track&&t&&/^mouse/.test(t.type)?(this._on(this.document,{mousemove:n}),n(t)):o.position(e.extend({of:i},this.options.position)),o.hide(),this._show(o,this.options.show),this.options.show&&this.options.show.delay&&(r=this.delayedShow=setInterval(function(){o.is(":visible")&&(n(l.of),clearInterval(r))},e.fx.interval)),this._trigger("open",t,{tooltip:o})}},_registerCloseHandlers:function(t,i){var s={keyup:function(t){if(t.keyCode===e.ui.keyCode.ESCAPE){var s=e.Event(t);s.currentTarget=i[0],this.close(s,!0)}}};i[0]!==this.element[0]&&(s.remove=function(){this._removeTooltip(this._find(i).tooltip)}),t&&"mouseover"!==t.type||(s.mouseleave="close"),t&&"focusin"!==t.type||(s.focusout="close"),this._on(!0,i,s)},close:function(t){var i,s=this,n=e(t?t.currentTarget:this.element),a=this._find(n);return a?(i=a.tooltip,a.closing||(clearInterval(this.delayedShow),n.data("ui-tooltip-title")&&!n.attr("title")&&n.attr("title",n.data("ui-tooltip-title")),this._removeDescribedBy(n),a.hiding=!0,i.stop(!0),this._hide(i,this.options.hide,function(){s._removeTooltip(e(this))}),n.removeData("ui-tooltip-open"),this._off(n,"mouseleave focusout keyup"),n[0]!==this.element[0]&&this._off(n,"remove"),this._off(this.document,"mousemove"),t&&"mouseleave"===t.type&&e.each(this.parents,function(t,i){e(i.element).attr("title",i.title),delete s.parents[t]}),a.closing=!0,this._trigger("close",t,{tooltip:i}),a.hiding||(a.closing=!1)),void 0):(n.removeData("ui-tooltip-open"),void 0)},_tooltip:function(t){var i=e("<div>").attr("role","tooltip").addClass("ui-tooltip ui-widget ui-corner-all ui-widget-content "+(this.options.tooltipClass||"")),s=i.uniqueId().attr("id");return e("<div>").addClass("ui-tooltip-content").appendTo(i),i.appendTo(this.document[0].body),this.tooltips[s]={element:t,tooltip:i}},_find:function(e){var t=e.data("ui-tooltip-id");return t?this.tooltips[t]:null},_removeTooltip:function(e){e.remove(),delete this.tooltips[e.attr("id")]},_destroy:function(){var t=this;e.each(this.tooltips,function(i,s){var n=e.Event("blur"),a=s.element;n.target=n.currentTarget=a[0],t.close(n,!0),e("#"+i).remove(),a.data("ui-tooltip-title")&&(a.attr("title")||a.attr("title",a.data("ui-tooltip-title")),a.removeData("ui-tooltip-title"))}),this.liveRegion.remove()}});var c="ui-effects-",p=e;e.effects={effect:{}},function(e,t){function i(e,t,i){var s=d[t.type]||{};return null==e?i||!t.def?null:t.def:(e=s.floor?~~e:parseFloat(e),isNaN(e)?t.def:s.mod?(e+s.mod)%s.mod:0>e?0:e>s.max?s.max:e)}function s(i){var s=l(),n=s._rgba=[];return i=i.toLowerCase(),f(h,function(e,a){var o,r=a.re.exec(i),h=r&&a.parse(r),l=a.space||"rgba";return h?(o=s[l](h),s[u[l].cache]=o[u[l].cache],n=s._rgba=o._rgba,!1):t}),n.length?("0,0,0,0"===n.join()&&e.extend(n,a.transparent),s):a[i]}function n(e,t,i){return i=(i+1)%1,1>6*i?e+6*(t-e)*i:1>2*i?t:2>3*i?e+6*(t-e)*(2/3-i):e}var a,o="backgroundColor borderBottomColor borderLeftColor borderRightColor borderTopColor color columnRuleColor outlineColor textDecorationColor textEmphasisColor",r=/^([\-+])=\s*(\d+\.?\d*)/,h=[{re:/rgba?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(e){return[e[1],e[2],e[3],e[4]]}},{re:/rgba?\(\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(e){return[2.55*e[1],2.55*e[2],2.55*e[3],e[4]]}},{re:/#([a-f0-9]{2})([a-f0-9]{2})([a-f0-9]{2})/,parse:function(e){return[parseInt(e[1],16),parseInt(e[2],16),parseInt(e[3],16)]}},{re:/#([a-f0-9])([a-f0-9])([a-f0-9])/,parse:function(e){return[parseInt(e[1]+e[1],16),parseInt(e[2]+e[2],16),parseInt(e[3]+e[3],16)]}},{re:/hsla?\(\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,space:"hsla",parse:function(e){return[e[1],e[2]/100,e[3]/100,e[4]]}}],l=e.Color=function(t,i,s,n){return new e.Color.fn.parse(t,i,s,n)},u={rgba:{props:{red:{idx:0,type:"byte"},green:{idx:1,type:"byte"},blue:{idx:2,type:"byte"}}},hsla:{props:{hue:{idx:0,type:"degrees"},saturation:{idx:1,type:"percent"},lightness:{idx:2,type:"percent"}}}},d={"byte":{floor:!0,max:255},percent:{max:1},degrees:{mod:360,floor:!0}},c=l.support={},p=e("<p>")[0],f=e.each;p.style.cssText="background-color:rgba(1,1,1,.5)",c.rgba=p.style.backgroundColor.indexOf("rgba")>-1,f(u,function(e,t){t.cache="_"+e,t.props.alpha={idx:3,type:"percent",def:1}}),l.fn=e.extend(l.prototype,{parse:function(n,o,r,h){if(n===t)return this._rgba=[null,null,null,null],this;(n.jquery||n.nodeType)&&(n=e(n).css(o),o=t);var d=this,c=e.type(n),p=this._rgba=[];return o!==t&&(n=[n,o,r,h],c="array"),"string"===c?this.parse(s(n)||a._default):"array"===c?(f(u.rgba.props,function(e,t){p[t.idx]=i(n[t.idx],t)}),this):"object"===c?(n instanceof l?f(u,function(e,t){n[t.cache]&&(d[t.cache]=n[t.cache].slice())}):f(u,function(t,s){var a=s.cache;f(s.props,function(e,t){if(!d[a]&&s.to){if("alpha"===e||null==n[e])return;d[a]=s.to(d._rgba)}d[a][t.idx]=i(n[e],t,!0)}),d[a]&&0>e.inArray(null,d[a].slice(0,3))&&(d[a][3]=1,s.from&&(d._rgba=s.from(d[a])))}),this):t},is:function(e){var i=l(e),s=!0,n=this;return f(u,function(e,a){var o,r=i[a.cache];return r&&(o=n[a.cache]||a.to&&a.to(n._rgba)||[],f(a.props,function(e,i){return null!=r[i.idx]?s=r[i.idx]===o[i.idx]:t})),s}),s},_space:function(){var e=[],t=this;return f(u,function(i,s){t[s.cache]&&e.push(i)}),e.pop()},transition:function(e,t){var s=l(e),n=s._space(),a=u[n],o=0===this.alpha()?l("transparent"):this,r=o[a.cache]||a.to(o._rgba),h=r.slice();return s=s[a.cache],f(a.props,function(e,n){var a=n.idx,o=r[a],l=s[a],u=d[n.type]||{};null!==l&&(null===o?h[a]=l:(u.mod&&(l-o>u.mod/2?o+=u.mod:o-l>u.mod/2&&(o-=u.mod)),h[a]=i((l-o)*t+o,n)))}),this[n](h)},blend:function(t){if(1===this._rgba[3])return this;var i=this._rgba.slice(),s=i.pop(),n=l(t)._rgba;return l(e.map(i,function(e,t){return(1-s)*n[t]+s*e}))},toRgbaString:function(){var t="rgba(",i=e.map(this._rgba,function(e,t){return null==e?t>2?1:0:e});return 1===i[3]&&(i.pop(),t="rgb("),t+i.join()+")"},toHslaString:function(){var t="hsla(",i=e.map(this.hsla(),function(e,t){return null==e&&(e=t>2?1:0),t&&3>t&&(e=Math.round(100*e)+"%"),e});return 1===i[3]&&(i.pop(),t="hsl("),t+i.join()+")"},toHexString:function(t){var i=this._rgba.slice(),s=i.pop();return t&&i.push(~~(255*s)),"#"+e.map(i,function(e){return e=(e||0).toString(16),1===e.length?"0"+e:e}).join("")},toString:function(){return 0===this._rgba[3]?"transparent":this.toRgbaString()}}),l.fn.parse.prototype=l.fn,u.hsla.to=function(e){if(null==e[0]||null==e[1]||null==e[2])return[null,null,null,e[3]];var t,i,s=e[0]/255,n=e[1]/255,a=e[2]/255,o=e[3],r=Math.max(s,n,a),h=Math.min(s,n,a),l=r-h,u=r+h,d=.5*u;return t=h===r?0:s===r?60*(n-a)/l+360:n===r?60*(a-s)/l+120:60*(s-n)/l+240,i=0===l?0:.5>=d?l/u:l/(2-u),[Math.round(t)%360,i,d,null==o?1:o]},u.hsla.from=function(e){if(null==e[0]||null==e[1]||null==e[2])return[null,null,null,e[3]];var t=e[0]/360,i=e[1],s=e[2],a=e[3],o=.5>=s?s*(1+i):s+i-s*i,r=2*s-o;return[Math.round(255*n(r,o,t+1/3)),Math.round(255*n(r,o,t)),Math.round(255*n(r,o,t-1/3)),a]},f(u,function(s,n){var a=n.props,o=n.cache,h=n.to,u=n.from;l.fn[s]=function(s){if(h&&!this[o]&&(this[o]=h(this._rgba)),s===t)return this[o].slice();var n,r=e.type(s),d="array"===r||"object"===r?s:arguments,c=this[o].slice();return f(a,function(e,t){var s=d["object"===r?e:t.idx];null==s&&(s=c[t.idx]),c[t.idx]=i(s,t)}),u?(n=l(u(c)),n[o]=c,n):l(c)},f(a,function(t,i){l.fn[t]||(l.fn[t]=function(n){var a,o=e.type(n),h="alpha"===t?this._hsla?"hsla":"rgba":s,l=this[h](),u=l[i.idx];return"undefined"===o?u:("function"===o&&(n=n.call(this,u),o=e.type(n)),null==n&&i.empty?this:("string"===o&&(a=r.exec(n),a&&(n=u+parseFloat(a[2])*("+"===a[1]?1:-1))),l[i.idx]=n,this[h](l)))})})}),l.hook=function(t){var i=t.split(" ");f(i,function(t,i){e.cssHooks[i]={set:function(t,n){var a,o,r="";if("transparent"!==n&&("string"!==e.type(n)||(a=s(n)))){if(n=l(a||n),!c.rgba&&1!==n._rgba[3]){for(o="backgroundColor"===i?t.parentNode:t;(""===r||"transparent"===r)&&o&&o.style;)try{r=e.css(o,"backgroundColor"),o=o.parentNode}catch(h){}n=n.blend(r&&"transparent"!==r?r:"_default")}n=n.toRgbaString()}try{t.style[i]=n}catch(h){}}},e.fx.step[i]=function(t){t.colorInit||(t.start=l(t.elem,i),t.end=l(t.end),t.colorInit=!0),e.cssHooks[i].set(t.elem,t.start.transition(t.end,t.pos))}})},l.hook(o),e.cssHooks.borderColor={expand:function(e){var t={};return f(["Top","Right","Bottom","Left"],function(i,s){t["border"+s+"Color"]=e}),t}},a=e.Color.names={aqua:"#00ffff",black:"#000000",blue:"#0000ff",fuchsia:"#ff00ff",gray:"#808080",green:"#008000",lime:"#00ff00",maroon:"#800000",navy:"#000080",olive:"#808000",purple:"#800080",red:"#ff0000",silver:"#c0c0c0",teal:"#008080",white:"#ffffff",yellow:"#ffff00",transparent:[null,null,null,0],_default:"#ffffff"}}(p),function(){function t(t){var i,s,n=t.ownerDocument.defaultView?t.ownerDocument.defaultView.getComputedStyle(t,null):t.currentStyle,a={};if(n&&n.length&&n[0]&&n[n[0]])for(s=n.length;s--;)i=n[s],"string"==typeof n[i]&&(a[e.camelCase(i)]=n[i]);else for(i in n)"string"==typeof n[i]&&(a[i]=n[i]);return a}function i(t,i){var s,a,o={};for(s in i)a=i[s],t[s]!==a&&(n[s]||(e.fx.step[s]||!isNaN(parseFloat(a)))&&(o[s]=a));return o}var s=["add","remove","toggle"],n={border:1,borderBottom:1,borderColor:1,borderLeft:1,borderRight:1,borderTop:1,borderWidth:1,margin:1,padding:1};e.each(["borderLeftStyle","borderRightStyle","borderBottomStyle","borderTopStyle"],function(t,i){e.fx.step[i]=function(e){("none"!==e.end&&!e.setAttr||1===e.pos&&!e.setAttr)&&(p.style(e.elem,i,e.end),e.setAttr=!0)}}),e.fn.addBack||(e.fn.addBack=function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}),e.effects.animateClass=function(n,a,o,r){var h=e.speed(a,o,r);return this.queue(function(){var a,o=e(this),r=o.attr("class")||"",l=h.children?o.find("*").addBack():o;l=l.map(function(){var i=e(this);return{el:i,start:t(this)}}),a=function(){e.each(s,function(e,t){n[t]&&o[t+"Class"](n[t])})},a(),l=l.map(function(){return this.end=t(this.el[0]),this.diff=i(this.start,this.end),this}),o.attr("class",r),l=l.map(function(){var t=this,i=e.Deferred(),s=e.extend({},h,{queue:!1,complete:function(){i.resolve(t)}});return this.el.animate(this.diff,s),i.promise()}),e.when.apply(e,l.get()).done(function(){a(),e.each(arguments,function(){var t=this.el;e.each(this.diff,function(e){t.css(e,"")})}),h.complete.call(o[0])})})},e.fn.extend({addClass:function(t){return function(i,s,n,a){return s?e.effects.animateClass.call(this,{add:i},s,n,a):t.apply(this,arguments)}}(e.fn.addClass),removeClass:function(t){return function(i,s,n,a){return arguments.length>1?e.effects.animateClass.call(this,{remove:i},s,n,a):t.apply(this,arguments)}}(e.fn.removeClass),toggleClass:function(t){return function(i,s,n,a,o){return"boolean"==typeof s||void 0===s?n?e.effects.animateClass.call(this,s?{add:i}:{remove:i},n,a,o):t.apply(this,arguments):e.effects.animateClass.call(this,{toggle:i},s,n,a)}}(e.fn.toggleClass),switchClass:function(t,i,s,n,a){return e.effects.animateClass.call(this,{add:i,remove:t},s,n,a)}})}(),function(){function t(t,i,s,n){return e.isPlainObject(t)&&(i=t,t=t.effect),t={effect:t},null==i&&(i={}),e.isFunction(i)&&(n=i,s=null,i={}),("number"==typeof i||e.fx.speeds[i])&&(n=s,s=i,i={}),e.isFunction(s)&&(n=s,s=null),i&&e.extend(t,i),s=s||i.duration,t.duration=e.fx.off?0:"number"==typeof s?s:s in e.fx.speeds?e.fx.speeds[s]:e.fx.speeds._default,t.complete=n||i.complete,t}function i(t){return!t||"number"==typeof t||e.fx.speeds[t]?!0:"string"!=typeof t||e.effects.effect[t]?e.isFunction(t)?!0:"object"!=typeof t||t.effect?!1:!0:!0}e.extend(e.effects,{version:"1.11.4",save:function(e,t){for(var i=0;t.length>i;i++)null!==t[i]&&e.data(c+t[i],e[0].style[t[i]])},restore:function(e,t){var i,s;for(s=0;t.length>s;s++)null!==t[s]&&(i=e.data(c+t[s]),void 0===i&&(i=""),e.css(t[s],i))},setMode:function(e,t){return"toggle"===t&&(t=e.is(":hidden")?"show":"hide"),t},getBaseline:function(e,t){var i,s;switch(e[0]){case"top":i=0;break;case"middle":i=.5;break;case"bottom":i=1;break;default:i=e[0]/t.height}switch(e[1]){case"left":s=0;break;case"center":s=.5;break;case"right":s=1;break;default:s=e[1]/t.width}return{x:s,y:i}},createWrapper:function(t){if(t.parent().is(".ui-effects-wrapper"))return t.parent();var i={width:t.outerWidth(!0),height:t.outerHeight(!0),"float":t.css("float")},s=e("<div></div>").addClass("ui-effects-wrapper").css({fontSize:"100%",background:"transparent",border:"none",margin:0,padding:0}),n={width:t.width(),height:t.height()},a=document.activeElement;try{a.id}catch(o){a=document.body}return t.wrap(s),(t[0]===a||e.contains(t[0],a))&&e(a).focus(),s=t.parent(),"static"===t.css("position")?(s.css({position:"relative"}),t.css({position:"relative"})):(e.extend(i,{position:t.css("position"),zIndex:t.css("z-index")}),e.each(["top","left","bottom","right"],function(e,s){i[s]=t.css(s),isNaN(parseInt(i[s],10))&&(i[s]="auto")}),t.css({position:"relative",top:0,left:0,right:"auto",bottom:"auto"})),t.css(n),s.css(i).show()},removeWrapper:function(t){var i=document.activeElement;return t.parent().is(".ui-effects-wrapper")&&(t.parent().replaceWith(t),(t[0]===i||e.contains(t[0],i))&&e(i).focus()),t},setTransition:function(t,i,s,n){return n=n||{},e.each(i,function(e,i){var a=t.cssUnit(i);a[0]>0&&(n[i]=a[0]*s+a[1])}),n}}),e.fn.extend({effect:function(){function i(t){function i(){e.isFunction(a)&&a.call(n[0]),e.isFunction(t)&&t()}var n=e(this),a=s.complete,r=s.mode;(n.is(":hidden")?"hide"===r:"show"===r)?(n[r](),i()):o.call(n[0],s,i)}var s=t.apply(this,arguments),n=s.mode,a=s.queue,o=e.effects.effect[s.effect];return e.fx.off||!o?n?this[n](s.duration,s.complete):this.each(function(){s.complete&&s.complete.call(this)}):a===!1?this.each(i):this.queue(a||"fx",i)},show:function(e){return function(s){if(i(s))return e.apply(this,arguments);var n=t.apply(this,arguments);return n.mode="show",this.effect.call(this,n)}}(e.fn.show),hide:function(e){return function(s){if(i(s))return e.apply(this,arguments);var n=t.apply(this,arguments);return n.mode="hide",this.effect.call(this,n)}}(e.fn.hide),toggle:function(e){return function(s){if(i(s)||"boolean"==typeof s)return e.apply(this,arguments);var n=t.apply(this,arguments);return n.mode="toggle",this.effect.call(this,n)}}(e.fn.toggle),cssUnit:function(t){var i=this.css(t),s=[];return e.each(["em","px","%","pt"],function(e,t){i.indexOf(t)>0&&(s=[parseFloat(i),t])}),s}})}(),function(){var t={};e.each(["Quad","Cubic","Quart","Quint","Expo"],function(e,i){t[i]=function(t){return Math.pow(t,e+2)}}),e.extend(t,{Sine:function(e){return 1-Math.cos(e*Math.PI/2)},Circ:function(e){return 1-Math.sqrt(1-e*e)},Elastic:function(e){return 0===e||1===e?e:-Math.pow(2,8*(e-1))*Math.sin((80*(e-1)-7.5)*Math.PI/15)},Back:function(e){return e*e*(3*e-2)},Bounce:function(e){for(var t,i=4;((t=Math.pow(2,--i))-1)/11>e;);return 1/Math.pow(4,3-i)-7.5625*Math.pow((3*t-2)/22-e,2)}}),e.each(t,function(t,i){e.easing["easeIn"+t]=i,e.easing["easeOut"+t]=function(e){return 1-i(1-e)},e.easing["easeInOut"+t]=function(e){return.5>e?i(2*e)/2:1-i(-2*e+2)/2}})}(),e.effects,e.effects.effect.blind=function(t,i){var s,n,a,o=e(this),r=/up|down|vertical/,h=/up|left|vertical|horizontal/,l=["position","top","bottom","left","right","height","width"],u=e.effects.setMode(o,t.mode||"hide"),d=t.direction||"up",c=r.test(d),p=c?"height":"width",f=c?"top":"left",m=h.test(d),g={},v="show"===u;o.parent().is(".ui-effects-wrapper")?e.effects.save(o.parent(),l):e.effects.save(o,l),o.show(),s=e.effects.createWrapper(o).css({overflow:"hidden"}),n=s[p](),a=parseFloat(s.css(f))||0,g[p]=v?n:0,m||(o.css(c?"bottom":"right",0).css(c?"top":"left","auto").css({position:"absolute"}),g[f]=v?a:n+a),v&&(s.css(p,0),m||s.css(f,a+n)),s.animate(g,{duration:t.duration,easing:t.easing,queue:!1,complete:function(){"hide"===u&&o.hide(),e.effects.restore(o,l),e.effects.removeWrapper(o),i()
}})},e.effects.effect.bounce=function(t,i){var s,n,a,o=e(this),r=["position","top","bottom","left","right","height","width"],h=e.effects.setMode(o,t.mode||"effect"),l="hide"===h,u="show"===h,d=t.direction||"up",c=t.distance,p=t.times||5,f=2*p+(u||l?1:0),m=t.duration/f,g=t.easing,v="up"===d||"down"===d?"top":"left",y="up"===d||"left"===d,b=o.queue(),_=b.length;for((u||l)&&r.push("opacity"),e.effects.save(o,r),o.show(),e.effects.createWrapper(o),c||(c=o["top"===v?"outerHeight":"outerWidth"]()/3),u&&(a={opacity:1},a[v]=0,o.css("opacity",0).css(v,y?2*-c:2*c).animate(a,m,g)),l&&(c/=Math.pow(2,p-1)),a={},a[v]=0,s=0;p>s;s++)n={},n[v]=(y?"-=":"+=")+c,o.animate(n,m,g).animate(a,m,g),c=l?2*c:c/2;l&&(n={opacity:0},n[v]=(y?"-=":"+=")+c,o.animate(n,m,g)),o.queue(function(){l&&o.hide(),e.effects.restore(o,r),e.effects.removeWrapper(o),i()}),_>1&&b.splice.apply(b,[1,0].concat(b.splice(_,f+1))),o.dequeue()},e.effects.effect.clip=function(t,i){var s,n,a,o=e(this),r=["position","top","bottom","left","right","height","width"],h=e.effects.setMode(o,t.mode||"hide"),l="show"===h,u=t.direction||"vertical",d="vertical"===u,c=d?"height":"width",p=d?"top":"left",f={};e.effects.save(o,r),o.show(),s=e.effects.createWrapper(o).css({overflow:"hidden"}),n="IMG"===o[0].tagName?s:o,a=n[c](),l&&(n.css(c,0),n.css(p,a/2)),f[c]=l?a:0,f[p]=l?0:a/2,n.animate(f,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){l||o.hide(),e.effects.restore(o,r),e.effects.removeWrapper(o),i()}})},e.effects.effect.drop=function(t,i){var s,n=e(this),a=["position","top","bottom","left","right","opacity","height","width"],o=e.effects.setMode(n,t.mode||"hide"),r="show"===o,h=t.direction||"left",l="up"===h||"down"===h?"top":"left",u="up"===h||"left"===h?"pos":"neg",d={opacity:r?1:0};e.effects.save(n,a),n.show(),e.effects.createWrapper(n),s=t.distance||n["top"===l?"outerHeight":"outerWidth"](!0)/2,r&&n.css("opacity",0).css(l,"pos"===u?-s:s),d[l]=(r?"pos"===u?"+=":"-=":"pos"===u?"-=":"+=")+s,n.animate(d,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){"hide"===o&&n.hide(),e.effects.restore(n,a),e.effects.removeWrapper(n),i()}})},e.effects.effect.explode=function(t,i){function s(){b.push(this),b.length===d*c&&n()}function n(){p.css({visibility:"visible"}),e(b).remove(),m||p.hide(),i()}var a,o,r,h,l,u,d=t.pieces?Math.round(Math.sqrt(t.pieces)):3,c=d,p=e(this),f=e.effects.setMode(p,t.mode||"hide"),m="show"===f,g=p.show().css("visibility","hidden").offset(),v=Math.ceil(p.outerWidth()/c),y=Math.ceil(p.outerHeight()/d),b=[];for(a=0;d>a;a++)for(h=g.top+a*y,u=a-(d-1)/2,o=0;c>o;o++)r=g.left+o*v,l=o-(c-1)/2,p.clone().appendTo("body").wrap("<div></div>").css({position:"absolute",visibility:"visible",left:-o*v,top:-a*y}).parent().addClass("ui-effects-explode").css({position:"absolute",overflow:"hidden",width:v,height:y,left:r+(m?l*v:0),top:h+(m?u*y:0),opacity:m?0:1}).animate({left:r+(m?0:l*v),top:h+(m?0:u*y),opacity:m?1:0},t.duration||500,t.easing,s)},e.effects.effect.fade=function(t,i){var s=e(this),n=e.effects.setMode(s,t.mode||"toggle");s.animate({opacity:n},{queue:!1,duration:t.duration,easing:t.easing,complete:i})},e.effects.effect.fold=function(t,i){var s,n,a=e(this),o=["position","top","bottom","left","right","height","width"],r=e.effects.setMode(a,t.mode||"hide"),h="show"===r,l="hide"===r,u=t.size||15,d=/([0-9]+)%/.exec(u),c=!!t.horizFirst,p=h!==c,f=p?["width","height"]:["height","width"],m=t.duration/2,g={},v={};e.effects.save(a,o),a.show(),s=e.effects.createWrapper(a).css({overflow:"hidden"}),n=p?[s.width(),s.height()]:[s.height(),s.width()],d&&(u=parseInt(d[1],10)/100*n[l?0:1]),h&&s.css(c?{height:0,width:u}:{height:u,width:0}),g[f[0]]=h?n[0]:u,v[f[1]]=h?n[1]:0,s.animate(g,m,t.easing).animate(v,m,t.easing,function(){l&&a.hide(),e.effects.restore(a,o),e.effects.removeWrapper(a),i()})},e.effects.effect.highlight=function(t,i){var s=e(this),n=["backgroundImage","backgroundColor","opacity"],a=e.effects.setMode(s,t.mode||"show"),o={backgroundColor:s.css("backgroundColor")};"hide"===a&&(o.opacity=0),e.effects.save(s,n),s.show().css({backgroundImage:"none",backgroundColor:t.color||"#ffff99"}).animate(o,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){"hide"===a&&s.hide(),e.effects.restore(s,n),i()}})},e.effects.effect.size=function(t,i){var s,n,a,o=e(this),r=["position","top","bottom","left","right","width","height","overflow","opacity"],h=["position","top","bottom","left","right","overflow","opacity"],l=["width","height","overflow"],u=["fontSize"],d=["borderTopWidth","borderBottomWidth","paddingTop","paddingBottom"],c=["borderLeftWidth","borderRightWidth","paddingLeft","paddingRight"],p=e.effects.setMode(o,t.mode||"effect"),f=t.restore||"effect"!==p,m=t.scale||"both",g=t.origin||["middle","center"],v=o.css("position"),y=f?r:h,b={height:0,width:0,outerHeight:0,outerWidth:0};"show"===p&&o.show(),s={height:o.height(),width:o.width(),outerHeight:o.outerHeight(),outerWidth:o.outerWidth()},"toggle"===t.mode&&"show"===p?(o.from=t.to||b,o.to=t.from||s):(o.from=t.from||("show"===p?b:s),o.to=t.to||("hide"===p?b:s)),a={from:{y:o.from.height/s.height,x:o.from.width/s.width},to:{y:o.to.height/s.height,x:o.to.width/s.width}},("box"===m||"both"===m)&&(a.from.y!==a.to.y&&(y=y.concat(d),o.from=e.effects.setTransition(o,d,a.from.y,o.from),o.to=e.effects.setTransition(o,d,a.to.y,o.to)),a.from.x!==a.to.x&&(y=y.concat(c),o.from=e.effects.setTransition(o,c,a.from.x,o.from),o.to=e.effects.setTransition(o,c,a.to.x,o.to))),("content"===m||"both"===m)&&a.from.y!==a.to.y&&(y=y.concat(u).concat(l),o.from=e.effects.setTransition(o,u,a.from.y,o.from),o.to=e.effects.setTransition(o,u,a.to.y,o.to)),e.effects.save(o,y),o.show(),e.effects.createWrapper(o),o.css("overflow","hidden").css(o.from),g&&(n=e.effects.getBaseline(g,s),o.from.top=(s.outerHeight-o.outerHeight())*n.y,o.from.left=(s.outerWidth-o.outerWidth())*n.x,o.to.top=(s.outerHeight-o.to.outerHeight)*n.y,o.to.left=(s.outerWidth-o.to.outerWidth)*n.x),o.css(o.from),("content"===m||"both"===m)&&(d=d.concat(["marginTop","marginBottom"]).concat(u),c=c.concat(["marginLeft","marginRight"]),l=r.concat(d).concat(c),o.find("*[width]").each(function(){var i=e(this),s={height:i.height(),width:i.width(),outerHeight:i.outerHeight(),outerWidth:i.outerWidth()};f&&e.effects.save(i,l),i.from={height:s.height*a.from.y,width:s.width*a.from.x,outerHeight:s.outerHeight*a.from.y,outerWidth:s.outerWidth*a.from.x},i.to={height:s.height*a.to.y,width:s.width*a.to.x,outerHeight:s.height*a.to.y,outerWidth:s.width*a.to.x},a.from.y!==a.to.y&&(i.from=e.effects.setTransition(i,d,a.from.y,i.from),i.to=e.effects.setTransition(i,d,a.to.y,i.to)),a.from.x!==a.to.x&&(i.from=e.effects.setTransition(i,c,a.from.x,i.from),i.to=e.effects.setTransition(i,c,a.to.x,i.to)),i.css(i.from),i.animate(i.to,t.duration,t.easing,function(){f&&e.effects.restore(i,l)})})),o.animate(o.to,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){0===o.to.opacity&&o.css("opacity",o.from.opacity),"hide"===p&&o.hide(),e.effects.restore(o,y),f||("static"===v?o.css({position:"relative",top:o.to.top,left:o.to.left}):e.each(["top","left"],function(e,t){o.css(t,function(t,i){var s=parseInt(i,10),n=e?o.to.left:o.to.top;return"auto"===i?n+"px":s+n+"px"})})),e.effects.removeWrapper(o),i()}})},e.effects.effect.scale=function(t,i){var s=e(this),n=e.extend(!0,{},t),a=e.effects.setMode(s,t.mode||"effect"),o=parseInt(t.percent,10)||(0===parseInt(t.percent,10)?0:"hide"===a?0:100),r=t.direction||"both",h=t.origin,l={height:s.height(),width:s.width(),outerHeight:s.outerHeight(),outerWidth:s.outerWidth()},u={y:"horizontal"!==r?o/100:1,x:"vertical"!==r?o/100:1};n.effect="size",n.queue=!1,n.complete=i,"effect"!==a&&(n.origin=h||["middle","center"],n.restore=!0),n.from=t.from||("show"===a?{height:0,width:0,outerHeight:0,outerWidth:0}:l),n.to={height:l.height*u.y,width:l.width*u.x,outerHeight:l.outerHeight*u.y,outerWidth:l.outerWidth*u.x},n.fade&&("show"===a&&(n.from.opacity=0,n.to.opacity=1),"hide"===a&&(n.from.opacity=1,n.to.opacity=0)),s.effect(n)},e.effects.effect.puff=function(t,i){var s=e(this),n=e.effects.setMode(s,t.mode||"hide"),a="hide"===n,o=parseInt(t.percent,10)||150,r=o/100,h={height:s.height(),width:s.width(),outerHeight:s.outerHeight(),outerWidth:s.outerWidth()};e.extend(t,{effect:"scale",queue:!1,fade:!0,mode:n,complete:i,percent:a?o:100,from:a?h:{height:h.height*r,width:h.width*r,outerHeight:h.outerHeight*r,outerWidth:h.outerWidth*r}}),s.effect(t)},e.effects.effect.pulsate=function(t,i){var s,n=e(this),a=e.effects.setMode(n,t.mode||"show"),o="show"===a,r="hide"===a,h=o||"hide"===a,l=2*(t.times||5)+(h?1:0),u=t.duration/l,d=0,c=n.queue(),p=c.length;for((o||!n.is(":visible"))&&(n.css("opacity",0).show(),d=1),s=1;l>s;s++)n.animate({opacity:d},u,t.easing),d=1-d;n.animate({opacity:d},u,t.easing),n.queue(function(){r&&n.hide(),i()}),p>1&&c.splice.apply(c,[1,0].concat(c.splice(p,l+1))),n.dequeue()},e.effects.effect.shake=function(t,i){var s,n=e(this),a=["position","top","bottom","left","right","height","width"],o=e.effects.setMode(n,t.mode||"effect"),r=t.direction||"left",h=t.distance||20,l=t.times||3,u=2*l+1,d=Math.round(t.duration/u),c="up"===r||"down"===r?"top":"left",p="up"===r||"left"===r,f={},m={},g={},v=n.queue(),y=v.length;for(e.effects.save(n,a),n.show(),e.effects.createWrapper(n),f[c]=(p?"-=":"+=")+h,m[c]=(p?"+=":"-=")+2*h,g[c]=(p?"-=":"+=")+2*h,n.animate(f,d,t.easing),s=1;l>s;s++)n.animate(m,d,t.easing).animate(g,d,t.easing);n.animate(m,d,t.easing).animate(f,d/2,t.easing).queue(function(){"hide"===o&&n.hide(),e.effects.restore(n,a),e.effects.removeWrapper(n),i()}),y>1&&v.splice.apply(v,[1,0].concat(v.splice(y,u+1))),n.dequeue()},e.effects.effect.slide=function(t,i){var s,n=e(this),a=["position","top","bottom","left","right","width","height"],o=e.effects.setMode(n,t.mode||"show"),r="show"===o,h=t.direction||"left",l="up"===h||"down"===h?"top":"left",u="up"===h||"left"===h,d={};e.effects.save(n,a),n.show(),s=t.distance||n["top"===l?"outerHeight":"outerWidth"](!0),e.effects.createWrapper(n).css({overflow:"hidden"}),r&&n.css(l,u?isNaN(s)?"-"+s:-s:s),d[l]=(r?u?"+=":"-=":u?"-=":"+=")+s,n.animate(d,{queue:!1,duration:t.duration,easing:t.easing,complete:function(){"hide"===o&&n.hide(),e.effects.restore(n,a),e.effects.removeWrapper(n),i()}})},e.effects.effect.transfer=function(t,i){var s=e(this),n=e(t.to),a="fixed"===n.css("position"),o=e("body"),r=a?o.scrollTop():0,h=a?o.scrollLeft():0,l=n.offset(),u={top:l.top-r,left:l.left-h,height:n.innerHeight(),width:n.innerWidth()},d=s.offset(),c=e("<div class='ui-effects-transfer'></div>").appendTo(document.body).addClass(t.className).css({top:d.top-r,left:d.left-h,height:s.innerHeight(),width:s.innerWidth(),position:a?"fixed":"absolute"}).animate(u,t.duration,t.easing,function(){c.remove(),i()})}});"></script> -<link href="data:text/css;charset=utf-8,%0A%0A%2Etocify%20%7B%0Awidth%3A%2020%25%3B%0Amax%2Dheight%3A%2090%25%3B%0Aoverflow%3A%20auto%3B%0Amargin%2Dleft%3A%202%25%3B%0Aposition%3A%20fixed%3B%0Aborder%3A%201px%20solid%20%23ccc%3B%0Awebkit%2Dborder%2Dradius%3A%206px%3B%0Amoz%2Dborder%2Dradius%3A%206px%3B%0Aborder%2Dradius%3A%206px%3B%0A%7D%0A%0A%2Etocify%20ul%2C%20%2Etocify%20li%20%7B%0Alist%2Dstyle%3A%20none%3B%0Amargin%3A%200%3B%0Apadding%3A%200%3B%0Aborder%3A%20none%3B%0Aline%2Dheight%3A%2030px%3B%0A%7D%0A%0A%2Etocify%2Dheader%20%7B%0Atext%2Dindent%3A%2010px%3B%0A%7D%0A%0A%2Etocify%2Dsubheader%20%7B%0Atext%2Dindent%3A%2020px%3B%0Adisplay%3A%20none%3B%0A%7D%0A%0A%2Etocify%2Dsubheader%20li%20%7B%0Afont%2Dsize%3A%2012px%3B%0A%7D%0A%0A%2Etocify%2Dsubheader%20%2Etocify%2Dsubheader%20%7B%0Atext%2Dindent%3A%2030px%3B%0A%7D%0A%0A%2Etocify%2Dsubheader%20%2Etocify%2Dsubheader%20%2Etocify%2Dsubheader%20%7B%0Atext%2Dindent%3A%2040px%3B%0A%7D%0A%0A%2Etocify%20%2Etocify%2Ditem%20%3E%20a%2C%20%2Etocify%20%2Enav%2Dlist%20%2Enav%2Dheader%20%7B%0Amargin%3A%200px%3B%0A%7D%0A%0A%2Etocify%20%2Etocify%2Ditem%20a%2C%20%2Etocify%20%2Elist%2Dgroup%2Ditem%20%7B%0Apadding%3A%205px%3B%0A%7D%0A%2Etocify%20%2Enav%2Dpills%20%3E%20li%20%7B%0Afloat%3A%20none%3B%0A%7D%0A%0A%0A" rel="stylesheet" /> -<script src="data:application/x-javascript;base64,/* jquery Tocify - v1.9.1 - 2013-10-22
 * http://www.gregfranko.com/jquery.tocify.js/
 * Copyright (c) 2013 Greg Franko; Licensed MIT */

// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic.  I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE.
(function(tocify) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    // Calls the second IIFE and locally passes in the global jQuery, window, and document objects
    tocify(window.jQuery, window, document);

  }

  // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable.  The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set.  All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript.
  (function($, window, document, undefined) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    var tocClassName = "tocify",
      tocClass = "." + tocClassName,
      tocFocusClassName = "tocify-focus",
      tocHoverClassName = "tocify-hover",
      hideTocClassName = "tocify-hide",
      hideTocClass = "." + hideTocClassName,
      headerClassName = "tocify-header",
      headerClass = "." + headerClassName,
      subheaderClassName = "tocify-subheader",
      subheaderClass = "." + subheaderClassName,
      itemClassName = "tocify-item",
      itemClass = "." + itemClassName,
      extendPageClassName = "tocify-extend-page",
      extendPageClass = "." + extendPageClassName;

    // Calling the jQueryUI Widget Factory Method
    $.widget("toc.tocify", {

      //Plugin version
      version: "1.9.1",

      // These options will be used as defaults
      options: {

        // **context**: Accepts String: Any jQuery selector
        // The container element that holds all of the elements used to generate the table of contents
        context: "body",

        // **ignoreSelector**: Accepts String: Any jQuery selector
        // A selector to any element that would be matched by selectors that you wish to be ignored
        ignoreSelector: null,

        // **selectors**: Accepts an Array of Strings: Any jQuery selectors
        // The element's used to generate the table of contents.  The order is very important since it will determine the table of content's nesting structure
        selectors: "h1, h2, h3",

        // **showAndHide**: Accepts a boolean: true or false
        // Used to determine if elements should be shown and hidden
        showAndHide: true,

        // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown"
        // Used to display any of the table of contents nested items
        showEffect: "slideDown",

        // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the show animation
        showEffectSpeed: "medium",

        // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp"
        // Used to hide any of the table of contents nested items
        hideEffect: "slideUp",

        // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the hide animation
        hideEffectSpeed: "medium",

        // **smoothScroll**: Accepts a boolean: true or false
        // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page
        smoothScroll: true,

        // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the smoothScroll animation
        smoothScrollSpeed: "medium",

        // **scrollTo**: Accepts Number (pixels)
        // The amount of space between the top of page and the selected table of contents item after the page has been scrolled
        scrollTo: 0,

        // **showAndHideOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be shown and hidden while scrolling
        showAndHideOnScroll: true,

        // **highlightOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling
        highlightOnScroll: true,

        // **highlightOffset**: Accepts a number
        // The offset distance in pixels to trigger the next active table of contents item
        highlightOffset: 40,

        // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none"
        // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents
        theme: "bootstrap",

        // **extendPage**: Accepts a boolean: true or false
        // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased
        extendPage: true,

        // **extendPageOffset**: Accepts a number: pixels
        // How close to the bottom of the page a user must scroll before the page is extended
        extendPageOffset: 100,

        // **history**: Accepts a boolean: true or false
        // Adds a hash to the page url to maintain history
        history: true,

        // **scrollHistory**: Accepts a boolean: true or false
        // Adds a hash to the page url, to maintain history, when scrolling to a TOC item
        scrollHistory: false,

        // **hashGenerator**: How the hash value (the anchor segment of the URL, following the
        // # character) will be generated.
        //
        // "compact" (default) - #CompressesEverythingTogether
        // "pretty" - #looks-like-a-nice-url-and-is-easily-readable
        // function(text, element){} - Your own hash generation function that accepts the text as an
        // argument, and returns the hash value.
        hashGenerator: "compact",

        // **highlightDefault**: Accepts a boolean: true or false
        // Set's the first TOC item as active if no other TOC item is active.
        highlightDefault: true

      },

      // _Create
      // -------
      //      Constructs the plugin.  Only called once.
      _create: function() {

        var self = this;

        self.extendPageScroll = true;

        // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings)
        self.items = [];

        // Generates the HTML for the dynamic table of contents
        self._generateToc();

        // Adds CSS classes to the newly generated table of contents HTML
        self._addCSSClasses();

        self.webkit = (function() {

          for (var prop in window) {

            if (prop) {

              if (prop.toLowerCase().indexOf("webkit") !== -1) {

                return true;

              }

            }

          }

          return false;

        }());

        // Adds jQuery event handlers to the newly generated table of contents
        self._setEventHandlers();

        // Binding to the Window load event to make sure the correct scrollTop is calculated
        $(window).load(function() {

          // Sets the active TOC item
          self._setActiveElement(true);

          // Once all animations on the page are complete, this callback function will be called
          $("html, body").promise().done(function() {

            setTimeout(function() {

              self.extendPageScroll = false;

            }, 0);

          });

        });

      },

      // _generateToc
      // ------------
      //      Generates the HTML for the dynamic table of contents
      _generateToc: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above
          firstElem,

          // Instantiated variable that will store the top level newly created unordered list DOM element
          ul,
          ignoreSelector = self.options.ignoreSelector;


        // Determine the element to start the toc with
        // get all the top level selectors
        firstElem = [];
        var selectors = this.options.selectors.replace(/ /g, "").split(",");
        // find the first set that have at least one non-ignored element
        for(var i = 0; i < selectors.length; i++) {
          var foundSelectors = $(this.options.context).find(selectors[i]);
          for (var s = 0; s < foundSelectors.length; s++) {
            if (!$(foundSelectors[s]).is(ignoreSelector)) {
              firstElem = foundSelectors;
              break;
            }
          }
          if (firstElem.length> 0)
            break;
        }

        if (!firstElem.length) {

          self.element.addClass(hideTocClassName);

          return;

        }

        self.element.addClass(tocClassName);

        // Loops through each top level selector
        firstElem.each(function(index) {

          //If the element matches the ignoreSelector then we skip it
          if ($(this).is(ignoreSelector)) {
            return;
          }

          // Creates an unordered list HTML element and adds a dynamic ID and standard class name
          ul = $("<ul/>", {
            "id": headerClassName + index,
            "class": headerClassName
          }).

          // Appends a top level list item HTML element to the previously created HTML header
          append(self._nestElements($(this), index));

          // Add the created unordered list element to the HTML element calling the plugin
          self.element.append(ul);

          // Finds all of the HTML tags between the header and subheader elements
          $(this).nextUntil(this.nodeName.toLowerCase()).each(function() {

            // If there are no nested subheader elemements
            if ($(this).find(self.options.selectors).length === 0) {

              // Loops through all of the subheader elements
              $(this).filter(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

            // If there are nested subheader elements
            else {

              // Loops through all of the subheader elements
              $(this).find(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

          });

        });

      },

      _setActiveElement: function(pageload) {

        var self = this,

          hash = window.location.hash.substring(1),

          elem = self.element.find('li[data-unique="' + hash + '"]');

        if (hash.length) {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          elem.addClass(self.focusClass);

          // Triggers the click event on the currently focused TOC item
          elem.click();

        } else {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          if (!hash.length && pageload && self.options.highlightDefault) {

            // Highlights the first TOC item if no other items are highlighted
            self.element.find(itemClass).first().addClass(self.focusClass);

          }

        }

        return self;

      },

      // _nestElements
      // -------------
      //      Helps create the table of contents list by appending nested list items
      _nestElements: function(self, index) {

        var arr, item, hashValue;

        arr = $.grep(this.items, function(item) {

          return item === self.text();

        });

        // If there is already a duplicate TOC item
        if (arr.length) {

          // Adds the current TOC item text and index (for slight randomization) to the internal array
          this.items.push(self.text() + index);

        }

        // If there not a duplicate TOC item
        else {

          // Adds the current TOC item text to the internal array
          this.items.push(self.text());

        }

        hashValue = this._generateHashValue(arr, self, index);

        // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
        item = $("<li/>", {

          // Sets a common class name to the list item
          "class": itemClassName,

          "data-unique": hashValue

        });

        if (this.options.theme !== "bootstrap3") {

          item.append($("<a/>", {

            "text": self.text()

          }));

        } else {

          item.text(self.text());

        }

        // Adds an HTML anchor tag before the currently traversed HTML element
        self.before($("<div/>", {

          // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore)
          "name": hashValue,

          "data-unique": hashValue

        }));

        return item;

      },

      // _generateHashValue
      // ------------------
      //      Generates the hash value that will be used to refer to each item.
      _generateHashValue: function(arr, self, index) {

        var hashValue = "",
          hashGeneratorOption = this.options.hashGenerator;

        if (hashGeneratorOption === "pretty") {

          // prettify the text
          hashValue = self.text().toLowerCase().replace(/\s/g, "-");

          // fix double hyphens
          while (hashValue.indexOf("--") > -1) {
            hashValue = hashValue.replace(/--/g, "-");
          }

          // fix colon-space instances
          while (hashValue.indexOf(":-") > -1) {
            hashValue = hashValue.replace(/:-/g, "-");
          }

        } else if (typeof hashGeneratorOption === "function") {

          // call the function
          hashValue = hashGeneratorOption(self.text(), self);

        } else {

          // compact - the default
          hashValue = self.text().replace(/\s/g, "");

        }

        // add the index if we need to
        if (arr.length) {
          hashValue += "" + index;
        }

        // return the value
        return hashValue;

      },

      // _appendElements
      // ---------------
      //      Helps create the table of contents list by appending subheader elements

      _appendSubheaders: function(self, ul) {

        // The current element index
        var index = $(this).index(self.options.selectors),

          // Finds the previous header DOM element
          previousHeader = $(self.options.selectors).eq(index - 1),

          currentTagName = +$(this).prop("tagName").charAt(1),

          previousTagName = +previousHeader.prop("tagName").charAt(1),

          lastSubheader;

        // If the current header DOM element is smaller than the previous header DOM element or the first subheader
        if (currentTagName < previousTagName) {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index));

        }

        // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element
        else if (currentTagName === previousTagName) {

          ul.find(itemClass).last().after(self._nestElements($(this), index));

        } else {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          ul.find(itemClass).last().

          // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name
          after($("<ul/>", {

            "class": subheaderClassName,

            "data-tag": currentTagName

          })).next(subheaderClass).

          // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
          append(self._nestElements($(this), index));
        }

      },

      // _setEventHandlers
      // ----------------
      //      Adds jQuery event handlers to the newly generated table of contents
      _setEventHandlers: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // Instantiates a new variable that will be used to hold a specific element's context
          $self,

          // Instantiates a new variable that will be used to determine the smoothScroll animation time duration
          duration;

        // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin
        this.element.on("click.tocify", "li", function(event) {

          if (self.options.history) {

            window.location.hash = $(this).attr("data-unique");

          }

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          $(this).addClass(self.focusClass);

          // If the showAndHide option is true
          if (self.options.showAndHide) {

            var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]');

            self._triggerShow(elem);

          }

          self._scrollTo($(this));

        });

        // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin
        this.element.find("li").on({

          // Mouseenter event handler
          "mouseenter.tocify": function() {

            // Adds a hover CSS class to the current list item
            $(this).addClass(self.hoverClass);

            // Makes sure the cursor is set to the pointer icon
            $(this).css("cursor", "pointer");

          },

          // Mouseleave event handler
          "mouseleave.tocify": function() {

            if (self.options.theme !== "bootstrap") {

              // Removes the hover CSS class from the current list item
              $(this).removeClass(self.hoverClass);

            }

          }
        });

        // only attach handler if needed (expensive in IE)
        if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) {
          // Window scroll event handler
          $(window).on("scroll.tocify", function() {

            // Once all animations on the page are complete, this callback function will be called
            $("html, body").promise().done(function() {

              // Local variables

              // Stores how far the user has scrolled
              var winScrollTop = $(window).scrollTop(),

                // Stores the height of the window
                winHeight = $(window).height(),

                // Stores the height of the document
                docHeight = $(document).height(),

                scrollHeight = $("body")[0].scrollHeight,

                // Instantiates a variable that will be used to hold a selected HTML element
                elem,

                lastElem,

                lastElemOffset,

                currentElem;

              if (self.options.extendPage) {

                // If the user has scrolled to the bottom of the page and the last toc item is not focused
                if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) {

                  if (!$(extendPageClass).length) {

                    lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]');

                    if (!lastElem.length) return;

                    // Gets the top offset of the page header that is linked to the last toc item
                    lastElemOffset = lastElem.offset().top;

                    // Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset
                    $(self.options.context).append($("<div/>", {

                      "class": extendPageClassName,

                      "height": Math.abs(lastElemOffset - winScrollTop) + "px",

                      "data-unique": extendPageClassName

                    }));

                    if (self.extendPageScroll) {

                      currentElem = self.element.find('li.' + self.focusClass);

                      self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]'));

                    }

                  }

                }

              }

              // The zero timeout ensures the following code is run after the scroll events
              setTimeout(function() {

                // _Local variables_

                // Stores the distance to the closest anchor
                var closestAnchorDistance = null,

                  // Stores the index of the closest anchor
                  closestAnchorIdx = null,

                  // Keeps a reference to all anchors
                  anchors = $(self.options.context).find("div[data-unique]"),

                  anchorText;

                // Determines the index of the closest anchor
                anchors.each(function(idx) {
                  var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset);
                  if (closestAnchorDistance == null || distance < closestAnchorDistance) {
                    closestAnchorDistance = distance;
                    closestAnchorIdx = idx;
                  } else {
                    return false;
                  }
                });

                anchorText = $(anchors[closestAnchorIdx]).attr("data-unique");

                // Stores the list item HTML element that corresponds to the currently traversed anchor tag
                elem = $('li[data-unique="' + anchorText + '"]');

                // If the `highlightOnScroll` option is true and a next element is found
                if (self.options.highlightOnScroll && elem.length) {

                  // Removes highlighting from all of the list item's
                  self.element.find("." + self.focusClass).removeClass(self.focusClass);

                  // Highlights the corresponding list item
                  elem.addClass(self.focusClass);

                }

                if (self.options.scrollHistory) {

                  if (window.location.hash !== "#" + anchorText) {

                    window.location.replace("#" + anchorText);

                  }
                }

                // If the `showAndHideOnScroll` option is true
                if (self.options.showAndHideOnScroll && self.options.showAndHide) {

                  self._triggerShow(elem, true);

                }

              }, 0);

            });

          });
        }

      },

      // Show
      // ----
      //      Opens the current sub-header
      show: function(elem, scroll) {

        // Stores the plugin context in the `self` variable
        var self = this,
          element = elem;

        // If the sub-header is not already visible
        if (!elem.is(":visible")) {

          // If the current element does not have any nested subheaders, is not a header, and its parent is not visible
          if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) {

            // Sets the current element to all of the subheaders within the current header
            elem = elem.parents(subheaderClass).add(elem);

          }

          // If the current element does not have any nested subheaders and is not a header
          else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) {

            // Sets the current element to the closest subheader
            elem = elem.closest(subheaderClass);

          }

          //Determines what jQuery effect to use
          switch (self.options.showEffect) {

            //Uses `no effect`
            case "none":

              elem.show();

              break;

              //Uses the jQuery `show` special effect
            case "show":

              elem.show(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `slideDown` special effect
            case "slideDown":

              elem.slideDown(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `fadeIn` special effect
            case "fadeIn":

              elem.fadeIn(self.options.showEffectSpeed);

              break;

              //If none of the above options were passed, then a `jQueryUI show effect` is expected
            default:

              elem.show();

              break;

          }

        }

        // If the current subheader parent element is a header
        if (elem.parent().is(headerClass)) {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem));

        }

        // If the current subheader parent element is not a header
        else {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings())));

        }

        // Maintains chainablity
        return self;

      },

      // Hide
      // ----
      //      Closes the current sub-header
      hide: function(elem) {

        // Stores the plugin context in the `self` variable
        var self = this;

        //Determines what jQuery effect to use
        switch (self.options.hideEffect) {

          // Uses `no effect`
          case "none":

            elem.hide();

            break;

            // Uses the jQuery `hide` special effect
          case "hide":

            elem.hide(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `slideUp` special effect
          case "slideUp":

            elem.slideUp(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `fadeOut` special effect
          case "fadeOut":

            elem.fadeOut(self.options.hideEffectSpeed);

            break;

            // If none of the above options were passed, then a `jqueryUI hide effect` is expected
          default:

            elem.hide();

            break;

        }

        // Maintains chainablity
        return self;
      },

      // _triggerShow
      // ------------
      //      Determines what elements get shown on scroll and click
      _triggerShow: function(elem, scroll) {

        var self = this;

        // If the current element's parent is a header element or the next element is a nested subheader element
        if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) {

          // Shows the next sub-header element
          self.show(elem.next(subheaderClass), scroll);

        }

        // If the current element's parent is a subheader element
        else if (elem.parent().is(subheaderClass)) {

          // Shows the parent sub-header element
          self.show(elem.parent(), scroll);

        }

        // Maintains chainability
        return self;

      },

      // _addCSSClasses
      // --------------
      //      Adds CSS classes to the newly generated table of contents HTML
      _addCSSClasses: function() {

        // If the user wants a jqueryUI theme
        if (this.options.theme === "jqueryui") {

          this.focusClass = "ui-state-default";

          this.hoverClass = "ui-state-hover";

          //Adds the default styling to the dropdown list
          this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content");

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap") {

          this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list");

          this.focusClass = "active";

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap3") {

          this.element.find(headerClass + "," + subheaderClass).addClass("list-group");

          this.element.find(itemClass).addClass("list-group-item");

          this.focusClass = "active";

        }

        // If a user does not want a prebuilt theme
        else {

          // Adds more neutral classes (instead of jqueryui)

          this.focusClass = tocFocusClassName;

          this.hoverClass = tocHoverClassName;

        }

        //Maintains chainability
        return this;

      },

      // setOption
      // ---------
      //      Sets a single Tocify option after the plugin is invoked
      setOption: function() {

        // Calls the jQueryUI Widget Factory setOption method
        $.Widget.prototype._setOption.apply(this, arguments);

      },

      // setOptions
      // ----------
      //      Sets a single or multiple Tocify options after the plugin is invoked
      setOptions: function() {

        // Calls the jQueryUI Widget Factory setOptions method
        $.Widget.prototype._setOptions.apply(this, arguments);

      },

      // _scrollTo
      // ---------
      //      Scrolls to a specific element
      _scrollTo: function(elem) {

        var self = this,
          duration = self.options.smoothScroll || 0,
          scrollTo = self.options.scrollTo,
          currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]');

        if (!currentDiv.length) {

          return self;

        }

        // Once all animations on the page are complete, this callback function will be called
        $("html, body").promise().done(function() {

          // Animates the html and body element scrolltops
          $("html, body").animate({

            // Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag
            "scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px"

          }, {

            // Sets the smoothScroll animation time duration to the smoothScrollSpeed option
            "duration": duration

          });

        });

        // Maintains chainability
        return self;

      }

    });

  })); //end of plugin
"></script> -<script src="data:application/x-javascript;base64,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"></script> -<link href="data:text/css;charset=utf-8,pre%20%2Eoperator%2C%0Apre%20%2Eparen%20%7B%0Acolor%3A%20rgb%28104%2C%20118%2C%20135%29%0A%7D%0Apre%20%2Eliteral%20%7B%0Acolor%3A%20%23990073%0A%7D%0Apre%20%2Enumber%20%7B%0Acolor%3A%20%23099%3B%0A%7D%0Apre%20%2Ecomment%20%7B%0Acolor%3A%20%23998%3B%0Afont%2Dstyle%3A%20italic%0A%7D%0Apre%20%2Ekeyword%20%7B%0Acolor%3A%20%23900%3B%0Afont%2Dweight%3A%20bold%0A%7D%0Apre%20%2Eidentifier%20%7B%0Acolor%3A%20rgb%280%2C%200%2C%200%29%3B%0A%7D%0Apre%20%2Estring%20%7B%0Acolor%3A%20%23d14%3B%0A%7D%0A" rel="stylesheet" /> -<script src="data:application/x-javascript;base64,var hljs=new function(){function m(p){return p.replace(/&/gm,"&amp;").replace(/</gm,"&lt;")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.bash=function(){var e={"true":1,"false":1};var b={cN:"variable",b:"\\$([a-zA-Z0-9_]+)\\b"};var a={cN:"variable",b:"\\$\\{(([^}])|(\\\\}))+\\}",c:[hljs.CNM]};var f={cN:"string",b:'"',e:'"',i:"\\n",c:[hljs.BE,b,a],r:0};var c={cN:"string",b:"'",e:"'",c:[{b:"''"}],r:0};var d={cN:"test_condition",b:"",e:"",c:[f,c,b,a,hljs.CNM],k:{literal:e},r:0};return{dM:{k:{keyword:{"if":1,then:1,"else":1,fi:1,"for":1,"break":1,"continue":1,"while":1,"in":1,"do":1,done:1,echo:1,exit:1,"return":1,set:1,declare:1},literal:e},c:[{cN:"shebang",b:"(#!\\/bin\\/bash)|(#!\\/bin\\/sh)",r:10},b,a,hljs.HCM,hljs.CNM,f,c,hljs.inherit(d,{b:"\\[ ",e:" \\]",r:0}),hljs.inherit(d,{b:"\\[\\[ ",e:" \\]\\]"})]}}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.css=function(){var a={cN:"function",b:hljs.IR+"\\(",e:"\\)",c:[{eW:true,eE:true,c:[hljs.NM,hljs.ASM,hljs.QSM]}]};return{cI:true,dM:{i:"[=/|']",c:[hljs.CBLCLM,{cN:"id",b:"\\#[A-Za-z0-9_-]+"},{cN:"class",b:"\\.[A-Za-z0-9_-]+",r:0},{cN:"attr_selector",b:"\\[",e:"\\]",i:"$"},{cN:"pseudo",b:":(:)?[a-zA-Z0-9\\_\\-\\+\\(\\)\\\"\\']+"},{cN:"at_rule",b:"@(font-face|page)",l:"[a-z-]+",k:{"font-face":1,page:1}},{cN:"at_rule",b:"@",e:"[{;]",eE:true,k:{"import":1,page:1,media:1,charset:1},c:[a,hljs.ASM,hljs.QSM,hljs.NM]},{cN:"tag",b:hljs.IR,r:0},{cN:"rules",b:"{",e:"}",i:"[^\\s]",r:0,c:[hljs.CBLCLM,{cN:"rule",b:"[^\\s]",rB:true,e:";",eW:true,c:[{cN:"attribute",b:"[A-Z\\_\\.\\-]+",e:":",eE:true,i:"[^\\s]",starts:{cN:"value",eW:true,eE:true,c:[a,hljs.NM,hljs.QSM,hljs.ASM,hljs.CBLCLM,{cN:"hexcolor",b:"\\#[0-9A-F]+"},{cN:"important",b:"!important"}]}}]}]}]}}}();hljs.LANGUAGES.ini={cI:true,dM:{i:"[^\\s]",c:[{cN:"comment",b:";",e:"$"},{cN:"title",b:"^\\[",e:"\\]"},{cN:"setting",b:"^[a-z0-9_\\[\\]]+[ \\t]*=[ \\t]*",e:"$",c:[{cN:"value",eW:true,k:{on:1,off:1,"true":1,"false":1,yes:1,no:1},c:[hljs.QSM,hljs.NM]}]}]}};hljs.LANGUAGES.perl=function(){var d={getpwent:1,getservent:1,quotemeta:1,msgrcv:1,scalar:1,kill:1,dbmclose:1,undef:1,lc:1,ma:1,syswrite:1,tr:1,send:1,umask:1,sysopen:1,shmwrite:1,vec:1,qx:1,utime:1,local:1,oct:1,semctl:1,localtime:1,readpipe:1,"do":1,"return":1,format:1,read:1,sprintf:1,dbmopen:1,pop:1,getpgrp:1,not:1,getpwnam:1,rewinddir:1,qq:1,fileno:1,qw:1,endprotoent:1,wait:1,sethostent:1,bless:1,s:0,opendir:1,"continue":1,each:1,sleep:1,endgrent:1,shutdown:1,dump:1,chomp:1,connect:1,getsockname:1,die:1,socketpair:1,close:1,flock:1,exists:1,index:1,shmget:1,sub:1,"for":1,endpwent:1,redo:1,lstat:1,msgctl:1,setpgrp:1,abs:1,exit:1,select:1,print:1,ref:1,gethostbyaddr:1,unshift:1,fcntl:1,syscall:1,"goto":1,getnetbyaddr:1,join:1,gmtime:1,symlink:1,semget:1,splice:1,x:0,getpeername:1,recv:1,log:1,setsockopt:1,cos:1,last:1,reverse:1,gethostbyname:1,getgrnam:1,study:1,formline:1,endhostent:1,times:1,chop:1,length:1,gethostent:1,getnetent:1,pack:1,getprotoent:1,getservbyname:1,rand:1,mkdir:1,pos:1,chmod:1,y:0,substr:1,endnetent:1,printf:1,next:1,open:1,msgsnd:1,readdir:1,use:1,unlink:1,getsockopt:1,getpriority:1,rindex:1,wantarray:1,hex:1,system:1,getservbyport:1,endservent:1,"int":1,chr:1,untie:1,rmdir:1,prototype:1,tell:1,listen:1,fork:1,shmread:1,ucfirst:1,setprotoent:1,"else":1,sysseek:1,link:1,getgrgid:1,shmctl:1,waitpid:1,unpack:1,getnetbyname:1,reset:1,chdir:1,grep:1,split:1,require:1,caller:1,lcfirst:1,until:1,warn:1,"while":1,values:1,shift:1,telldir:1,getpwuid:1,my:1,getprotobynumber:1,"delete":1,and:1,sort:1,uc:1,defined:1,srand:1,accept:1,"package":1,seekdir:1,getprotobyname:1,semop:1,our:1,rename:1,seek:1,"if":1,q:0,chroot:1,sysread:1,setpwent:1,no:1,crypt:1,getc:1,chown:1,sqrt:1,write:1,setnetent:1,setpriority:1,foreach:1,tie:1,sin:1,msgget:1,map:1,stat:1,getlogin:1,unless:1,elsif:1,truncate:1,exec:1,keys:1,glob:1,tied:1,closedir:1,ioctl:1,socket:1,readlink:1,"eval":1,xor:1,readline:1,binmode:1,setservent:1,eof:1,ord:1,bind:1,alarm:1,pipe:1,atan2:1,getgrent:1,exp:1,time:1,push:1,setgrent:1,gt:1,lt:1,or:1,ne:1,m:0};var f={cN:"subst",b:"[$@]\\{",e:"\\}",k:d,r:10};var c={cN:"variable",b:"\\$\\d"};var b={cN:"variable",b:"[\\$\\%\\@\\*](\\^\\w\\b|#\\w+(\\:\\:\\w+)*|[^\\s\\w{]|{\\w+}|\\w+(\\:\\:\\w*)*)"};var h=[hljs.BE,f,c,b];var g={b:"->",c:[{b:hljs.IR},{b:"{",e:"}"}]};var e={cN:"comment",b:"^(__END__|__DATA__)",e:"\\n$",r:5};var a=[c,b,hljs.HCM,e,g,{cN:"string",b:"q[qwxr]?\\s*\\(",e:"\\)",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\[",e:"\\]",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\{",e:"\\}",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\|",e:"\\|",c:h,r:5},{cN:"string",b:"q[qwxr]?\\s*\\<",e:"\\>",c:h,r:5},{cN:"string",b:"qw\\s+q",e:"q",c:h,r:5},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"string",b:'"',e:'"',c:h,r:0},{cN:"string",b:"`",e:"`",c:[hljs.BE]},{cN:"string",b:"{\\w+}",r:0},{cN:"string",b:"-?\\w+\\s*\\=\\>",r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"("+hljs.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:{split:1,"return":1,print:1,reverse:1,grep:1},r:0,c:[hljs.HCM,e,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[hljs.BE],r:0}]},{cN:"sub",b:"\\bsub\\b",e:"(\\s*\\(.*?\\))?[;{]",k:{sub:1},r:5},{cN:"operator",b:"-\\w\\b",r:0},{cN:"pod",b:"\\=\\w",e:"\\=cut"}];f.c=a;g.c[1].c=a;return{dM:{k:d,c:a}}}();hljs.LANGUAGES.python=function(){var b=[{cN:"string",b:"(u|b)?r?'''",e:"'''",r:10},{cN:"string",b:'(u|b)?r?"""',e:'"""',r:10},{cN:"string",b:"(u|r|ur)'",e:"'",c:[hljs.BE],r:10},{cN:"string",b:'(u|r|ur)"',e:'"',c:[hljs.BE],r:10},{cN:"string",b:"(b|br)'",e:"'",c:[hljs.BE]},{cN:"string",b:'(b|br)"',e:'"',c:[hljs.BE]}].concat([hljs.ASM,hljs.QSM]);var d={cN:"title",b:hljs.UIR};var c={cN:"params",b:"\\(",e:"\\)",c:b.concat([hljs.CNM])};var a={bWK:true,e:":",i:"[${]",c:[d,c],r:10};return{dM:{k:{keyword:{and:1,elif:1,is:1,global:1,as:1,"in":1,"if":1,from:1,raise:1,"for":1,except:1,"finally":1,print:1,"import":1,pass:1,"return":1,exec:1,"else":1,"break":1,not:1,"with":1,"class":1,assert:1,yield:1,"try":1,"while":1,"continue":1,del:1,or:1,def:1,lambda:1,nonlocal:10},built_in:{None:1,True:1,False:1,Ellipsis:1,NotImplemented:1}},i:"(</|->|\\?)",c:b.concat([hljs.HCM,hljs.inherit(a,{cN:"function",k:{def:1}}),hljs.inherit(a,{cN:"class",k:{"class":1}}),hljs.CNM,{cN:"decorator",b:"@",e:"$"}])}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};hljs.LANGUAGES.ruby=function(){var a="[a-zA-Z_][a-zA-Z0-9_]*(\\!|\\?)?";var j="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?";var f={keyword:{and:1,"false":1,then:1,defined:1,module:1,"in":1,"return":1,redo:1,"if":1,BEGIN:1,retry:1,end:1,"for":1,"true":1,self:1,when:1,next:1,until:1,"do":1,begin:1,unless:1,END:1,rescue:1,nil:1,"else":1,"break":1,undef:1,not:1,"super":1,"class":1,"case":1,require:1,yield:1,alias:1,"while":1,ensure:1,elsif:1,or:1,def:1},keymethods:{__id__:1,__send__:1,abort:1,abs:1,"all?":1,allocate:1,ancestors:1,"any?":1,arity:1,assoc:1,at:1,at_exit:1,autoload:1,"autoload?":1,"between?":1,binding:1,binmode:1,"block_given?":1,call:1,callcc:1,caller:1,capitalize:1,"capitalize!":1,casecmp:1,"catch":1,ceil:1,center:1,chomp:1,"chomp!":1,chop:1,"chop!":1,chr:1,"class":1,class_eval:1,"class_variable_defined?":1,class_variables:1,clear:1,clone:1,close:1,close_read:1,close_write:1,"closed?":1,coerce:1,collect:1,"collect!":1,compact:1,"compact!":1,concat:1,"const_defined?":1,const_get:1,const_missing:1,const_set:1,constants:1,count:1,crypt:1,"default":1,default_proc:1,"delete":1,"delete!":1,delete_at:1,delete_if:1,detect:1,display:1,div:1,divmod:1,downcase:1,"downcase!":1,downto:1,dump:1,dup:1,each:1,each_byte:1,each_index:1,each_key:1,each_line:1,each_pair:1,each_value:1,each_with_index:1,"empty?":1,entries:1,eof:1,"eof?":1,"eql?":1,"equal?":1,"eval":1,exec:1,exit:1,"exit!":1,extend:1,fail:1,fcntl:1,fetch:1,fileno:1,fill:1,find:1,find_all:1,first:1,flatten:1,"flatten!":1,floor:1,flush:1,for_fd:1,foreach:1,fork:1,format:1,freeze:1,"frozen?":1,fsync:1,getc:1,gets:1,global_variables:1,grep:1,gsub:1,"gsub!":1,"has_key?":1,"has_value?":1,hash:1,hex:1,id:1,include:1,"include?":1,included_modules:1,index:1,indexes:1,indices:1,induced_from:1,inject:1,insert:1,inspect:1,instance_eval:1,instance_method:1,instance_methods:1,"instance_of?":1,"instance_variable_defined?":1,instance_variable_get:1,instance_variable_set:1,instance_variables:1,"integer?":1,intern:1,invert:1,ioctl:1,"is_a?":1,isatty:1,"iterator?":1,join:1,"key?":1,keys:1,"kind_of?":1,lambda:1,last:1,length:1,lineno:1,ljust:1,load:1,local_variables:1,loop:1,lstrip:1,"lstrip!":1,map:1,"map!":1,match:1,max:1,"member?":1,merge:1,"merge!":1,method:1,"method_defined?":1,method_missing:1,methods:1,min:1,module_eval:1,modulo:1,name:1,nesting:1,"new":1,next:1,"next!":1,"nil?":1,nitems:1,"nonzero?":1,object_id:1,oct:1,open:1,pack:1,partition:1,pid:1,pipe:1,pop:1,popen:1,pos:1,prec:1,prec_f:1,prec_i:1,print:1,printf:1,private_class_method:1,private_instance_methods:1,"private_method_defined?":1,private_methods:1,proc:1,protected_instance_methods:1,"protected_method_defined?":1,protected_methods:1,public_class_method:1,public_instance_methods:1,"public_method_defined?":1,public_methods:1,push:1,putc:1,puts:1,quo:1,raise:1,rand:1,rassoc:1,read:1,read_nonblock:1,readchar:1,readline:1,readlines:1,readpartial:1,rehash:1,reject:1,"reject!":1,remainder:1,reopen:1,replace:1,require:1,"respond_to?":1,reverse:1,"reverse!":1,reverse_each:1,rewind:1,rindex:1,rjust:1,round:1,rstrip:1,"rstrip!":1,scan:1,seek:1,select:1,send:1,set_trace_func:1,shift:1,singleton_method_added:1,singleton_methods:1,size:1,sleep:1,slice:1,"slice!":1,sort:1,"sort!":1,sort_by:1,split:1,sprintf:1,squeeze:1,"squeeze!":1,srand:1,stat:1,step:1,store:1,strip:1,"strip!":1,sub:1,"sub!":1,succ:1,"succ!":1,sum:1,superclass:1,swapcase:1,"swapcase!":1,sync:1,syscall:1,sysopen:1,sysread:1,sysseek:1,system:1,syswrite:1,taint:1,"tainted?":1,tell:1,test:1,"throw":1,times:1,to_a:1,to_ary:1,to_f:1,to_hash:1,to_i:1,to_int:1,to_io:1,to_proc:1,to_s:1,to_str:1,to_sym:1,tr:1,"tr!":1,tr_s:1,"tr_s!":1,trace_var:1,transpose:1,trap:1,truncate:1,"tty?":1,type:1,ungetc:1,uniq:1,"uniq!":1,unpack:1,unshift:1,untaint:1,untrace_var:1,upcase:1,"upcase!":1,update:1,upto:1,"value?":1,values:1,values_at:1,warn:1,write:1,write_nonblock:1,"zero?":1,zip:1}};var c={cN:"yardoctag",b:"@[A-Za-z]+"};var k=[{cN:"comment",b:"#",e:"$",c:[c]},{cN:"comment",b:"^\\=begin",e:"^\\=end",c:[c],r:10},{cN:"comment",b:"^__END__",e:"\\n$"}];var d={cN:"subst",b:"#\\{",e:"}",l:a,k:f};var i=[hljs.BE,d];var b=[{cN:"string",b:"'",e:"'",c:i,r:0},{cN:"string",b:'"',e:'"',c:i,r:0},{cN:"string",b:"%[qw]?\\(",e:"\\)",c:i,r:10},{cN:"string",b:"%[qw]?\\[",e:"\\]",c:i,r:10},{cN:"string",b:"%[qw]?{",e:"}",c:i,r:10},{cN:"string",b:"%[qw]?<",e:">",c:i,r:10},{cN:"string",b:"%[qw]?/",e:"/",c:i,r:10},{cN:"string",b:"%[qw]?%",e:"%",c:i,r:10},{cN:"string",b:"%[qw]?-",e:"-",c:i,r:10},{cN:"string",b:"%[qw]?\\|",e:"\\|",c:i,r:10}];var h={cN:"function",b:"\\bdef\\s+",e:" |$|;",l:a,k:f,c:[{cN:"title",b:j,l:a,k:f},{cN:"params",b:"\\(",e:"\\)",l:a,k:f}].concat(k)};var g={cN:"identifier",b:a,l:a,k:f,r:0};var e=k.concat(b.concat([{cN:"class",b:"\\b(class|module)\\b",e:"$|;",k:{"class":1,module:1},c:[{cN:"title",b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?",r:0},{cN:"inheritance",b:"<\\s*",c:[{cN:"parent",b:"("+hljs.IR+"::)?"+hljs.IR}]}].concat(k)},h,{cN:"constant",b:"(::)?([A-Z]\\w*(::)?)+",r:0},{cN:"symbol",b:":",c:b.concat([g]),r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{cN:"number",b:"\\?\\w"},{cN:"variable",b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},g,{b:"("+hljs.RSR+")\\s*",c:k.concat([{cN:"regexp",b:"/",e:"/[a-z]*",i:"\\n",c:[hljs.BE]}]),r:0}]));d.c=e;h.c[1].c=e;return{dM:{l:a,k:f,c:e}}}();hljs.LANGUAGES.scala=function(){var b={cN:"annotation",b:"@[A-Za-z]+"};var a={cN:"string",b:'u?r?"""',e:'"""',r:10};return{dM:{k:{type:1,yield:1,lazy:1,override:1,def:1,"with":1,val:1,"var":1,"false":1,"true":1,sealed:1,"abstract":1,"private":1,trait:1,object:1,"null":1,"if":1,"for":1,"while":1,"throw":1,"finally":1,"protected":1,"extends":1,"import":1,"final":1,"return":1,"else":1,"break":1,"new":1,"catch":1,"super":1,"class":1,"case":1,"package":1,"default":1,"try":1,"this":1,match:1,"continue":1,"throws":1},c:[{cN:"javadoc",b:"/\\*\\*",e:"\\*/",c:[{cN:"javadoctag",b:"@[A-Za-z]+"}],r:10},hljs.CLCM,hljs.CBLCLM,hljs.ASM,hljs.QSM,a,{cN:"class",b:"((case )?class |object |trait )",e:"({|$)",i:":",k:{"case":1,"class":1,trait:1,object:1},c:[{bWK:true,k:{"extends":1,"with":1},r:10},{cN:"title",b:hljs.UIR},{cN:"params",b:"\\(",e:"\\)",c:[hljs.ASM,hljs.QSM,a,b]}]},hljs.CNM,b]}}}();hljs.LANGUAGES.sql={cI:true,dM:{i:"[^\\s]",c:[{cN:"operator",b:"(begin|start|commit|rollback|savepoint|lock|alter|create|drop|rename|call|delete|do|handler|insert|load|replace|select|truncate|update|set|show|pragma|grant)\\b",e:";|"+hljs.ER,k:{keyword:{all:1,partial:1,global:1,month:1,current_timestamp:1,using:1,go:1,revoke:1,smallint:1,indicator:1,"end-exec":1,disconnect:1,zone:1,"with":1,character:1,assertion:1,to:1,add:1,current_user:1,usage:1,input:1,local:1,alter:1,match:1,collate:1,real:1,then:1,rollback:1,get:1,read:1,timestamp:1,session_user:1,not:1,integer:1,bit:1,unique:1,day:1,minute:1,desc:1,insert:1,execute:1,like:1,ilike:2,level:1,decimal:1,drop:1,"continue":1,isolation:1,found:1,where:1,constraints:1,domain:1,right:1,national:1,some:1,module:1,transaction:1,relative:1,second:1,connect:1,escape:1,close:1,system_user:1,"for":1,deferred:1,section:1,cast:1,current:1,sqlstate:1,allocate:1,intersect:1,deallocate:1,numeric:1,"public":1,preserve:1,full:1,"goto":1,initially:1,asc:1,no:1,key:1,output:1,collation:1,group:1,by:1,union:1,session:1,both:1,last:1,language:1,constraint:1,column:1,of:1,space:1,foreign:1,deferrable:1,prior:1,connection:1,unknown:1,action:1,commit:1,view:1,or:1,first:1,into:1,"float":1,year:1,primary:1,cascaded:1,except:1,restrict:1,set:1,references:1,names:1,table:1,outer:1,open:1,select:1,size:1,are:1,rows:1,from:1,prepare:1,distinct:1,leading:1,create:1,only:1,next:1,inner:1,authorization:1,schema:1,corresponding:1,option:1,declare:1,precision:1,immediate:1,"else":1,timezone_minute:1,external:1,varying:1,translation:1,"true":1,"case":1,exception:1,join:1,hour:1,"default":1,"double":1,scroll:1,value:1,cursor:1,descriptor:1,values:1,dec:1,fetch:1,procedure:1,"delete":1,and:1,"false":1,"int":1,is:1,describe:1,"char":1,as:1,at:1,"in":1,varchar:1,"null":1,trailing:1,any:1,absolute:1,current_time:1,end:1,grant:1,privileges:1,when:1,cross:1,check:1,write:1,current_date:1,pad:1,begin:1,temporary:1,exec:1,time:1,update:1,catalog:1,user:1,sql:1,date:1,on:1,identity:1,timezone_hour:1,natural:1,whenever:1,interval:1,work:1,order:1,cascade:1,diagnostics:1,nchar:1,having:1,left:1,call:1,"do":1,handler:1,load:1,replace:1,truncate:1,start:1,lock:1,show:1,pragma:1},aggregate:{count:1,sum:1,min:1,max:1,avg:1}},c:[{cN:"string",b:"'",e:"'",c:[hljs.BE,{b:"''"}],r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE,{b:'""'}],r:0},{cN:"string",b:"`",e:"`",c:[hljs.BE]},hljs.CNM]},hljs.CBLCLM,{cN:"comment",b:"--",e:"$"}]}};hljs.LANGUAGES.stan={dM:{c:[hljs.HCM,hljs.CLCM,hljs.QSM,hljs.CNM,{cN:"operator",b:"(?:<-|~|\\|\\||&&|==|!=|<=?|>=?|\\+|-|\\.?/|\\\\|\\^|\\^|!|'|%|:|,|;|=)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0},{cN:"function",b:"(?:Phi|Phi_approx|abs|acos|acosh|append_col|append_row|asin|asinh|atan|atan2|atanh|bernoulli_ccdf_log|bernoulli_cdf|bernoulli_cdf_log|bernoulli_log|bernoulli_logit_log|bernoulli_rng|bessel_first_kind|bessel_second_kind|beta_binomial_ccdf_log|beta_binomial_cdf|beta_binomial_cdf_log|beta_binomial_log|beta_binomial_rng|beta_ccdf_log|beta_cdf|beta_cdf_log|beta_log|beta_rng|binary_log_loss|binomial_ccdf_log|binomial_cdf|binomial_cdf_log|binomial_coefficient_log|binomial_log|binomial_logit_log|binomial_rng|block|categorical_log|categorical_logit_log|categorical_rng|cauchy_ccdf_log|cauchy_cdf|cauchy_cdf_log|cauchy_log|cauchy_rng|cbrt|ceil|chi_square_ccdf_log|chi_square_cdf|chi_square_cdf_log|chi_square_log|chi_square_rng|cholesky_decompose|col|cols|columns_dot_product|columns_dot_self|cos|cosh|crossprod|csr_extract_u|csr_extract_v|csr_extract_w|csr_matrix_times_vector|csr_to_dense_matrix|cumulative_sum|determinant|diag_matrix|diag_post_multiply|diag_pre_multiply|diagonal|digamma|dims|dirichlet_log|dirichlet_rng|distance|dot_product|dot_self|double_exponential_ccdf_log|double_exponential_cdf|double_exponential_cdf_log|double_exponential_log|double_exponential_rng|e|eigenvalues_sym|eigenvectors_sym|erf|erfc|exp|exp2|exp_mod_normal_ccdf_log|exp_mod_normal_cdf|exp_mod_normal_cdf_log|exp_mod_normal_log|exp_mod_normal_rng|expm1|exponential_ccdf_log|exponential_cdf|exponential_cdf_log|exponential_log|exponential_rng|fabs|falling_factorial|fdim|floor|fma|fmax|fmin|fmod|frechet_ccdf_log|frechet_cdf|frechet_cdf_log|frechet_log|frechet_rng|gamma_ccdf_log|gamma_cdf|gamma_cdf_log|gamma_log|gamma_p|gamma_q|gamma_rng|gaussian_dlm_obs_log|get_lp|gumbel_ccdf_log|gumbel_cdf|gumbel_cdf_log|gumbel_log|gumbel_rng|head|hypergeometric_log|hypergeometric_rng|hypot|if_else|int_step|inv|inv_chi_square_ccdf_log|inv_chi_square_cdf|inv_chi_square_cdf_log|inv_chi_square_log|inv_chi_square_rng|inv_cloglog|inv_gamma_ccdf_log|inv_gamma_cdf|inv_gamma_cdf_log|inv_gamma_log|inv_gamma_rng|inv_logit|inv_phi|inv_sqrt|inv_square|inv_wishart_log|inv_wishart_rng|inverse|inverse_spd|is_inf|is_nan|lbeta|lgamma|lkj_corr_cholesky_log|lkj_corr_cholesky_rng|lkj_corr_log|lkj_corr_rng|lmgamma|log|log10|log1m|log1m_exp|log1m_inv_logit|log1p|log1p_exp|log2|log_determinant|log_diff_exp|log_falling_factorial|log_inv_logit|log_mix|log_rising_factorial|log_softmax|log_sum_exp|logistic_ccdf_log|logistic_cdf|logistic_cdf_log|logistic_log|logistic_rng|logit|lognormal_ccdf_log|lognormal_cdf|lognormal_cdf_log|lognormal_log|lognormal_rng|machine_precision|max|mdivide_left_tri_low|mdivide_right_tri_low|mean|min|modified_bessel_first_kind|modified_bessel_second_kind|multi_gp_cholesky_log|multi_gp_log|multi_normal_cholesky_log|multi_normal_cholesky_rng|multi_normal_log|multi_normal_prec_log|multi_normal_rng|multi_student_t_log|multi_student_t_rng|multinomial_log|multinomial_rng|multiply_log|multiply_lower_tri_self_transpose|neg_binomial_2_ccdf_log|neg_binomial_2_cdf|neg_binomial_2_cdf_log|neg_binomial_2_log|neg_binomial_2_log_log|neg_binomial_2_log_rng|neg_binomial_2_rng|neg_binomial_ccdf_log|neg_binomial_cdf|neg_binomial_cdf_log|neg_binomial_log|neg_binomial_rng|negative_infinity|normal_ccdf_log|normal_cdf|normal_cdf_log|normal_log|normal_rng|not_a_number|num_elements|ordered_logistic_log|ordered_logistic_rng|owens_t|pareto_ccdf_log|pareto_cdf|pareto_cdf_log|pareto_log|pareto_rng|pareto_type_2_ccdf_log|pareto_type_2_cdf|pareto_type_2_cdf_log|pareto_type_2_log|pareto_type_2_rng|pi|poisson_ccdf_log|poisson_cdf|poisson_cdf_log|poisson_log|poisson_log_log|poisson_log_rng|poisson_rng|positive_infinity|pow|prod|qr_Q|qr_R|quad_form|quad_form_diag|quad_form_sym|rank|rayleigh_ccdf_log|rayleigh_cdf|rayleigh_cdf_log|rayleigh_log|rayleigh_rng|rep_array|rep_matrix|rep_row_vector|rep_vector|rising_factorial|round|row|rows|rows_dot_product|rows_dot_self|scaled_inv_chi_square_ccdf_log|scaled_inv_chi_square_cdf|scaled_inv_chi_square_cdf_log|scaled_inv_chi_square_log|scaled_inv_chi_square_rng|sd|segment|sin|singular_values|sinh|size|skew_normal_ccdf_log|skew_normal_cdf|skew_normal_cdf_log|skew_normal_log|skew_normal_rng|softmax|sort_asc|sort_desc|sort_indices_asc|sort_indices_desc|sqrt|sqrt2|square|squared_distance|step|student_t_ccdf_log|student_t_cdf|student_t_cdf_log|student_t_log|student_t_rng|sub_col|sub_row|sum|tail|tan|tanh|tcrossprod|tgamma|to_array_1d|to_array_2d|to_matrix|to_row_vector|to_vector|trace|trace_gen_quad_form|trace_quad_form|trigamma|trunc|uniform_ccdf_log|uniform_cdf|uniform_cdf_log|uniform_log|uniform_rng|variance|von_mises_log|von_mises_rng|weibull_ccdf_log|weibull_cdf|weibull_cdf_log|weibull_log|weibull_rng|wiener_log|wishart_log|wishart_rng)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"function",b:"(?:bernoulli|bernoulli_logit|beta|beta_binomial|binomial|binomial_logit|categorical|categorical_logit|cauchy|chi_square|dirichlet|double_exponential|exp_mod_normal|exponential|frechet|gamma|gaussian_dlm_obs|gumbel|hypergeometric|inv_chi_square|inv_gamma|inv_wishart|lkj_corr|lkj_corr_cholesky|logistic|lognormal|multi_gp|multi_gp_cholesky|multi_normal|multi_normal_cholesky|multi_normal_prec|multi_student_t|multinomial|neg_binomial|neg_binomial_2|neg_binomial_2_log|normal|ordered_logistic|pareto|pareto_type_2|poisson|poisson_log|rayleigh|scaled_inv_chi_square|skew_normal|student_t|uniform|von_mises|weibull|wiener|wishart)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"(?:for|in|while|if|then|else|return|lower|upper|print|increment_log_prob|integrate_ode|reject)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"(?:int|real|vector|simplex|unit_vector|ordered|positive_ordered|row_vector|matrix|cholesky_factor_cov|cholesky_factor_corr|corr_matrix|cov_matrix|void)\\b",e:hljs.IMMEDIATE_RE,r:5},{cN:"keyword",b:"(?:functions|data|transformed\\s+data|parameters|transformed\\s+parameters|model|generated\\s+quantities)\\b",e:hljs.IMMEDIATE_RE,r:5}]}};hljs.LANGUAGES.xml=function(){var b="[A-Za-z0-9\\._:-]+";var a={eW:true,c:[{cN:"attribute",b:b,r:0},{b:'="',rB:true,e:'"',c:[{cN:"value",b:'"',eW:true}]},{b:"='",rB:true,e:"'",c:[{cN:"value",b:"'",eW:true}]},{b:"=",c:[{cN:"value",b:"[^\\s/>]+"}]}]};return{cI:true,dM:{c:[{cN:"pi",b:"<\\?",e:"\\?>",r:10},{cN:"doctype",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},{cN:"comment",b:"<!--",e:"-->",r:10},{cN:"cdata",b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{title:{style:1}},c:[a],starts:{cN:"css",e:"</style>",rE:true,sL:"css"}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{title:{script:1}},c:[a],starts:{cN:"javascript",e:"<\/script>",rE:true,sL:"javascript"}},{cN:"vbscript",b:"<%",e:"%>",sL:"vbscript"},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"title",b:"[^ />]+"},a]}]}}}();
hljs.initHighlightingOnLoad();

"></script> -<style type="text/css">code{white-space: pre;}</style> <style type="text/css"> - pre:not([class]) { - background-color: white; - } -</style> -<script type="text/javascript"> -if (window.hljs && document.readyState && document.readyState === "complete") { - window.setTimeout(function() { - hljs.initHighlighting(); - }, 0); +body, td { + font-family: sans-serif; + background-color: white; + font-size: 13px; } -</script> +body { + max-width: 800px; + margin: auto; + padding: 1em; + line-height: 20px; +} +tt, code, pre { + font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; +} -<style type="text/css"> h1 { - font-size: 34px; -} -h1.title { - font-size: 38px; + font-size:2.2em; } + h2 { - font-size: 30px; + font-size:1.8em; } + h3 { - font-size: 24px; + font-size:1.4em; } + h4 { - font-size: 18px; + font-size:1.0em; } + h5 { - font-size: 16px; -} -h6 { - font-size: 12px; -} -.table th:not([align]) { - text-align: left; + font-size:0.9em; } -</style> - - -</head> - -<body> -<style type="text/css"> -.main-container { - max-width: 940px; - margin-left: auto; - margin-right: auto; -} -code { - color: inherit; - background-color: rgba(0, 0, 0, 0.04); -} -img { - max-width:100%; - height: auto; -} -.tabbed-pane { - padding-top: 12px; -} -button.code-folding-btn:focus { - outline: none; +h6 { + font-size:0.8em; } -</style> - - -<div class="container-fluid main-container"> - -<!-- tabsets --> -<script> -$(document).ready(function () { - window.buildTabsets("TOC"); -}); -</script> - -<!-- code folding --> - - - - -<script> -$(document).ready(function () { - - // move toc-ignore selectors from section div to header - $('div.section.toc-ignore') - .removeClass('toc-ignore') - .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); - - // establish options - var options = { - selectors: "h1,h2,h3", - theme: "bootstrap3", - context: '.toc-content', - hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); - }, - ignoreSelector: ".toc-ignore", - scrollTo: 0 - }; - options.showAndHide = false; - options.smoothScroll = true; - - // tocify - var toc = $("#TOC").tocify(options).data("toc-tocify"); -}); -</script> - -<style type="text/css"> - -#TOC { - margin: 25px 0px 20px 0px; -} -@media (max-width: 768px) { -#TOC { - position: relative; - width: 100%; +a:visited { + color: rgb(50%, 0%, 50%); } -} - -.toc-content { - padding-left: 30px; - padding-right: 40px; +pre, img { + max-width: 100%; } - -div.main-container { - max-width: 1200px; +pre { + overflow-x: auto; } - -div.tocify { - width: 20%; - max-width: 260px; - max-height: 85%; +pre code { + display: block; padding: 0.5em; } -@media (min-width: 768px) and (max-width: 991px) { - div.tocify { - width: 25%; - } +code { + font-size: 92%; + border: 1px solid #ccc; } -@media (max-width: 767px) { - div.tocify { - width: 100%; - max-width: none; - } +code[class] { + background-color: #F8F8F8; } -.tocify ul, .tocify li { - line-height: 20px; +table, td, th { + border: none; } -.tocify-subheader .tocify-item { - font-size: 0.90em; - padding-left: 25px; - text-indent: 0; +blockquote { + color:#666666; + margin:0; + padding-left: 1em; + border-left: 0.5em #EEE solid; } -.tocify .list-group-item { - border-radius: 0px; +hr { + height: 0px; + border-bottom: none; + border-top-width: thin; + border-top-style: dotted; + border-top-color: #999999; } -.tocify-subheader { - display: inline; +@media print { + * { + background: transparent !important; + color: black !important; + filter:none !important; + -ms-filter: none !important; + } + + body { + font-size:12pt; + max-width:100%; + } + + a, a:visited { + text-decoration: underline; + } + + hr { + visibility: hidden; + page-break-before: always; + } + + pre, blockquote { + padding-right: 1em; + page-break-inside: avoid; + } + + tr, img { + page-break-inside: avoid; + } + + img { + max-width: 100% !important; + } + + @page :left { + margin: 15mm 20mm 15mm 10mm; + } + + @page :right { + margin: 15mm 10mm 15mm 20mm; + } + + p, h2, h3 { + orphans: 3; widows: 3; + } + + h2, h3 { + page-break-after: avoid; + } } -.tocify-subheader .tocify-item { - font-size: 0.95em; -} - </style> -<!-- setup 3col/9col grid for toc_float and main content --> -<div class="row-fluid"> -<div class="col-xs-12 col-sm-4 col-md-3"> -<div id="TOC" class="tocify"> -</div> -</div> - -<div class="toc-content col-xs-12 col-sm-8 col-md-9"> - - - - -<div class="fluid-row" id="header"> +</head> -<h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> -<h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2018-01-14</em></h4> - -</div> +<body> +<h1>Laboratory Data L1</h1> +<p>The following code defines example dataset L1 from the FOCUS kinetics +report, p. 284:</p> -<div id="laboratory-data-l1" class="section level1"> -<h1>Laboratory Data L1</h1> -<p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p> -<pre class="r"><code>library("mkin", quietly = TRUE) +<pre><code class="r">library("mkin", quietly = TRUE) FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, 27.7, 27.3, 10.0, 10.4, 2.9, 4.0)) -FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> -<p>Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.</p> -<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> -<pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) -summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version: 0.9.47.1 +FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1) +</code></pre> + +<p>Here we use the assumptions of simple first order (SFO), the case of declining +rate constant over time (FOMC) and the case of two different phases of the +kinetics (DFOP). For a more detailed discussion of the models, please see the +FOCUS kinetics report.</p> + +<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> +for parent only degradation models. The following two lines fit the model and +produce the summary report of the model fit. This covers the numerical analysis +given in the FOCUS report.</p> + +<pre><code class="r">m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) +summary(m.L1.SFO) +</code></pre> + +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Sun Jan 14 17:50:05 2018 -## Date of summary: Sun Jan 14 17:50:05 2018 +## Date of fit: Thu Mar 1 14:24:54 2018 +## Date of summary: Thu Mar 1 14:24:54 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.242 s +## Fitted with method Port using 37 model solutions performed in 0.24 s ## ## Weighting: none ## @@ -321,36 +311,46 @@ summary(m.L1.SFO)</code></pre> ## 21 parent 10.0 12.416 -2.4163 ## 21 parent 10.4 12.416 -2.0163 ## 30 parent 2.9 5.251 -2.3513 -## 30 parent 4.0 5.251 -1.2513</code></pre> +## 30 parent 4.0 5.251 -1.2513 +</code></pre> + <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> -<pre class="r"><code>plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> + +<pre><code class="r">plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO") +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/></p> + <p>The residual plot can be easily obtained by</p> -<pre class="r"><code>mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> -<pre class="r"><code>m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre> -<pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge. -## Convergence code is 1</code></pre> -<pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.47.1 + +<pre><code class="r">mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time") +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/></p> + +<p>For comparison, the FOMC model is fitted as well, and the \(\chi^2\) error level +is checked.</p> + +<pre><code class="r">m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE) +plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC") +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-6"/></p> + +<pre><code class="r">summary(m.L1.FOMC, data = FALSE) +</code></pre> + +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Sun Jan 14 17:50:06 2018 -## Date of summary: Sun Jan 14 17:50:06 2018 -## -## -## Warning: Optimisation by method Port did not converge. -## Convergence code is 1 -## +## Date of fit: Thu Mar 1 14:24:56 2018 +## Date of summary: Thu Mar 1 14:24:57 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 155 model solutions performed in 0.432 s +## Fitted with method Port using 611 model solutions performed in 1.376 s ## ## Weighting: none ## @@ -370,16 +370,16 @@ summary(m.L1.SFO)</code></pre> ## None ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.47 1.449 89.38 95.56 -## log_alpha 11.35 435.800 -917.60 940.30 -## log_beta 13.70 435.800 -915.20 942.60 +## Estimate Std. Error Lower Upper +## parent_0 92.47 1.482 89.31 95.63 +## log_alpha 11.25 598.200 -1264.00 1286.00 +## log_beta 13.60 598.200 -1261.00 1289.00 ## ## Parameter correlation: ## parent_0 log_alpha log_beta -## parent_0 1.0000 0.2209 0.2208 -## log_alpha 0.2209 1.0000 1.0000 -## log_beta 0.2208 1.0000 1.0000 +## parent_0 1.0000 -0.3016 -0.3016 +## log_alpha -0.3016 1.0000 1.0000 +## log_beta -0.3016 1.0000 1.0000 ## ## Residual standard error: 3.045 on 15 degrees of freedom ## @@ -388,9 +388,9 @@ summary(m.L1.SFO)</code></pre> ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47 63.33000 6.183e-20 89.38 95.56 -## alpha 85190.00 0.03367 4.868e-01 0.00 Inf -## beta 891000.00 0.03367 4.868e-01 0.00 Inf +## parent_0 92.47 64.45000 4.757e-20 89.31 95.63 +## alpha 76830.00 0.02852 4.888e-01 0.00 Inf +## beta 803500.00 0.02852 4.888e-01 0.00 Inf ## ## Chi2 error levels in percent: ## err.min n.optim df @@ -399,50 +399,100 @@ summary(m.L1.SFO)</code></pre> ## ## Estimated disappearance times: ## DT50 DT90 DT50back -## parent 7.249 24.08 7.249</code></pre> -<p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p> -<p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline"><em>χ</em><sup>2</sup></span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline"><em>χ</em><sup>2</sup></span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> -</div> -<div id="laboratory-data-l2" class="section level1"> +## parent 7.249 24.08 7.249 +</code></pre> + +<p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which +is an indication that the model is overparameterised, <em>i.e.</em> contains too many +parameters that are ill-defined as a consequence.</p> + +<p>And in fact, due to the higher number of parameters, and the lower number of +degrees of freedom of the fit, the \(\chi^2\) error level is actually higher for +the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the +parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have +excessive confidence intervals, that span more than 25 orders of magnitude (!) +when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test +for significant difference from zero does not indicate such a significant difference, +with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> +and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> + +<p>The \(\chi^2\) error levels reported in Appendix 3 and Appendix 7 to the FOCUS +kinetics report are rounded to integer percentages and partly deviate by one +percentage point from the results calculated by mkin. The reason for +this is not known. However, mkin gives the same \(\chi^2\) error levels +as the kinfit package and the calculation routines of the kinfit package have +been extensively compared to the results obtained by the KinGUI +software, as documented in the kinfit package vignette. KinGUI was the first +widely used standard package in this field. Also, the calculation of +\(\chi^2\) error levels was compared with KinGUII, CAKE and DegKin manager in +a project sponsored by the German Umweltbundesamt [@ranke2014].</p> + <h1>Laboratory Data L2</h1> -<p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> -<pre class="r"><code>FOCUS_2006_L2 = data.frame( + +<p>The following code defines example dataset L2 from the FOCUS kinetics +report, p. 287:</p> + +<pre><code class="r">FOCUS_2006_L2 = data.frame( t = rep(c(0, 1, 3, 7, 14, 28), each = 2), parent = c(96.1, 91.8, 41.4, 38.7, 19.3, 22.3, 4.6, 4.6, 2.6, 1.2, 0.3, 0.6)) -FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> -<div id="sfo-fit-for-l2" class="section level2"> +FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) +</code></pre> + <h2>SFO fit for L2</h2> -<p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p> -<pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) + +<p>Again, the SFO model is fitted and the result is plotted. The residual plot +can be obtained simply by adding the argument <code>show_residuals</code> to the plot +command.</p> + +<pre><code class="r">m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, - main = "FOCUS L2 - SFO")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.</p> -<p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p> -<p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p> -</div> -<div id="fomc-fit-for-l2" class="section level2"> + main = "FOCUS L2 - SFO") +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-8"/></p> + +<p>The \(\chi^2\) error level of 14% suggests that the model does not fit very well. +This is also obvious from the plots of the fit, in which we have included +the residual plot.</p> + +<p>In the FOCUS kinetics report, it is stated that there is no apparent systematic +error observed from the residual plot up to the measured DT90 (approximately at +day 5), and there is an underestimation beyond that point.</p> + +<p>We may add that it is difficult to judge the random nature of the residuals just +from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a +priori</em> why a consistent underestimation after the approximate DT90 should be +irrelevant. However, this can be rationalised by the fact that the FOCUS fate +models generally only implement SFO kinetics.</p> + <h2>FOMC fit for L2</h2> -<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> -<pre class="r"><code>m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) + +<p>For comparison, the FOMC model is fitted as well, and the \(\chi^2\) error level +is checked.</p> + +<pre><code class="r">m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE, - main = "FOCUS L2 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.47.1 + main = "FOCUS L2 - FOMC") +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/></p> + +<pre><code class="r">summary(m.L2.FOMC, data = FALSE) +</code></pre> + +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Sun Jan 14 17:50:07 2018 -## Date of summary: Sun Jan 14 17:50:07 2018 +## Date of fit: Thu Mar 1 14:24:57 2018 +## Date of summary: Thu Mar 1 14:24:57 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.166 s +## Fitted with method Port using 81 model solutions performed in 0.169 s ## ## Weighting: none ## @@ -491,21 +541,31 @@ plot(m.L2.FOMC, show_residuals = TRUE, ## ## Estimated disappearance times: ## DT50 DT90 DT50back -## parent 0.8092 5.356 1.612</code></pre> -<p>The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is much lower in this case. Therefore, the FOMC model provides a better description of the data, as less experimental error has to be assumed in order to explain the data.</p> -</div> -<div id="dfop-fit-for-l2" class="section level2"> +## parent 0.8092 5.356 1.612 +</code></pre> + +<p>The error level at which the \(\chi^2\) test passes is much lower in this case. +Therefore, the FOMC model provides a better description of the data, as less +experimental error has to be assumed in order to explain the data.</p> + <h2>DFOP fit for L2</h2> -<p>Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level.</p> -<pre class="r"><code>m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) + +<p>Fitting the four parameter DFOP model further reduces the \(\chi^2\) error level.</p> + +<pre><code class="r">m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, - main = "FOCUS L2 - DFOP")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.47.1 + main = "FOCUS L2 - DFOP") +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/></p> + +<pre><code class="r">summary(m.L2.DFOP, data = FALSE) +</code></pre> + +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Sun Jan 14 17:50:08 2018 -## Date of summary: Sun Jan 14 17:50:08 2018 +## Date of fit: Thu Mar 1 14:24:58 2018 +## Date of summary: Thu Mar 1 14:24:58 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -514,7 +574,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 0.712 s +## Fitted with method Port using 336 model solutions performed in 0.721 s ## ## Weighting: none ## @@ -542,8 +602,12 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## log_k2 -1.0880 NA NA NA ## g_ilr -0.2821 NA NA NA ## -## Parameter correlation:</code></pre> -<pre><code>## Warning in print.summary.mkinfit(x): Could not estimate covariance matrix; singular system:</code></pre> +## Parameter correlation: +</code></pre> + +<pre><code>## Warning in print.summary.mkinfit(x): Could not estimate covariance matrix; singular system: +</code></pre> + <pre><code>## Could not estimate covariance matrix; singular system: ## ## Residual standard error: 1.732 on 8 degrees of freedom @@ -565,36 +629,62 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Estimated disappearance times: ## DT50 DT90 DT50_k1 DT50_k2 -## parent 0.5335 5.311 0.03009 2.058</code></pre> -<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> -</div> -</div> -<div id="laboratory-data-l3" class="section level1"> +## parent 0.5335 5.311 0.03009 2.058 +</code></pre> + +<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the +chi<sup>2</sup> error level criterion. However, the failure to calculate the covariance +matrix indicates that the parameter estimates correlate excessively. Therefore, +the FOMC model may be preferred for this dataset.</p> + <h1>Laboratory Data L3</h1> -<p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> -<pre class="r"><code>FOCUS_2006_L3 = data.frame( + +<p>The following code defines example dataset L3 from the FOCUS kinetics report, +p. 290.</p> + +<pre><code class="r">FOCUS_2006_L3 = data.frame( t = c(0, 3, 7, 14, 30, 60, 91, 120), parent = c(97.8, 60, 51, 43, 35, 22, 15, 12)) -FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)</code></pre> -<div id="fit-multiple-models" class="section level2"> +FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3) +</code></pre> + <h2>Fit multiple models</h2> -<p>As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p> -<pre class="r"><code># Only use one core here, not to offend the CRAN checks + +<p>As of mkin version 0.9-39 (June 2015), we can fit several models to +one or more datasets in one call to the function <code>mmkin</code>. The datasets +have to be passed in a list, in this case a named list holding only +the L3 dataset prepared above.</p> + +<pre><code class="r"># Only use one core here, not to offend the CRAN checks mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1, list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE) -plot(mm.L3)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level considerably.</p> -</div> -<div id="accessing-mmkin-objects" class="section level2"> +plot(mm.L3) +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/></p> + +<p>The \(\chi^2\) error level of 21% as well as the plot suggest that the SFO model +does not fit very well. The FOMC model performs better, with an +error level at which the \(\chi^2\) test passes of 7%. Fitting the four +parameter DFOP model further reduces the \(\chi^2\) error level +considerably.</p> + <h2>Accessing mmkin objects</h2> -<p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> -<p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> -<pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> -<pre><code>## mkin version: 0.9.47.1 + +<p>The objects returned by mmkin are arranged like a matrix, with +models as a row index and datasets as a column index.</p> + +<p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, +using square brackets for indexing which will result in the use of +the summary and plot functions working on mkinfit objects.</p> + +<pre><code class="r">summary(mm.L3[["DFOP", 1]]) +</code></pre> + +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Sun Jan 14 17:50:08 2018 -## Date of summary: Sun Jan 14 17:50:08 2018 +## Date of fit: Thu Mar 1 14:24:59 2018 +## Date of summary: Thu Mar 1 14:24:59 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -603,7 +693,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.291 s +## Fitted with method Port using 137 model solutions performed in 0.283 s ## ## Weighting: none ## @@ -668,40 +758,64 @@ plot(mm.L3)</code></pre> ## 30 parent 35.0 35.15 -0.14707 ## 60 parent 22.0 23.26 -1.25919 ## 91 parent 15.0 15.18 -0.18181 -## 120 parent 12.0 10.19 1.81395</code></pre> -<pre class="r"><code>plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the <span class="math inline"><em>χ</em><sup>2</sup></span> error level criterion for laboratory data L3.</p> -<p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p> -</div> -</div> -<div id="laboratory-data-l4" class="section level1"> +## 120 parent 12.0 10.19 1.81395 +</code></pre> + +<pre><code class="r">plot(mm.L3[["DFOP", 1]], show_errmin = TRUE) +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-13"/></p> + +<p>Here, a look to the model plot, the confidence intervals of the parameters +and the correlation matrix suggest that the parameter estimates are reliable, and +the DFOP model can be used as the best-fit model based on the \(\chi^2\) error +level criterion for laboratory data L3.</p> + +<p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is +misleading, as it tests for a significant difference from zero. In this case, +zero appears to be the correct value for this parameter, and the confidence +interval for the backtransformed parameter <code>g</code> is quite narrow.</p> + <h1>Laboratory Data L4</h1> -<p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> -<pre class="r"><code>FOCUS_2006_L4 = data.frame( + +<p>The following code defines example dataset L4 from the FOCUS kinetics +report, p. 293:</p> + +<pre><code class="r">FOCUS_2006_L4 = data.frame( t = c(0, 3, 7, 14, 30, 60, 91, 120), parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0)) -FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)</code></pre> +FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4) +</code></pre> + <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> -<pre class="r"><code># Only use one core here, not to offend the CRAN checks + +<pre><code class="r"># Only use one core here, not to offend the CRAN checks mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1, list("FOCUS L4" = FOCUS_2006_L4_mkin), quiet = TRUE) -plot(mm.L4)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> -<pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.47.1 +plot(mm.L4) +</code></pre> + +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-15"/></p> + +<p>The \(\chi^2\) error level of 3.3% as well as the plot suggest that the SFO model +fits very well. The error level at which the \(\chi^2\) test passes is slightly +lower for the FOMC model. However, the difference appears negligible.</p> + +<pre><code class="r">summary(mm.L4[["SFO", 1]], data = FALSE) +</code></pre> + +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Sun Jan 14 17:50:09 2018 -## Date of summary: Sun Jan 14 17:50:09 2018 +## Date of fit: Thu Mar 1 14:24:59 2018 +## Date of summary: Thu Mar 1 14:24:59 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.094 s +## Fitted with method Port using 46 model solutions performed in 0.098 s ## ## Weighting: none ## @@ -749,19 +863,23 @@ plot(mm.L4)</code></pre> ## ## Estimated disappearance times: ## DT50 DT90 -## parent 106 352</code></pre> -<pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.47.1 +## parent 106 352 +</code></pre> + +<pre><code class="r">summary(mm.L4[["FOMC", 1]], data = FALSE) +</code></pre> + +<pre><code>## mkin version: 0.9.46.3 ## R version: 3.4.3 -## Date of fit: Sun Jan 14 17:50:09 2018 -## Date of summary: Sun Jan 14 17:50:09 2018 +## Date of fit: Thu Mar 1 14:24:59 2018 +## Date of summary: Thu Mar 1 14:24:59 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.139 s +## Fitted with method Port using 66 model solutions performed in 0.134 s ## ## Weighting: none ## @@ -810,37 +928,11 @@ plot(mm.L4)</code></pre> ## ## Estimated disappearance times: ## DT50 DT90 DT50back -## parent 108.9 1644 494.9</code></pre> -</div> -<div id="references" class="section level1 unnumbered"> -<h1>References</h1> -<div id="refs" class="references"> -<div id="ref-ranke2014"> -<p>Ranke, Johannes. 2014. “Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0.” Umweltbundesamt Projektnummer 27452.</p> -</div> -</div> -</div> - - - -</div> -</div> - -</div> - -<script> - -// add bootstrap table styles to pandoc tables -function bootstrapStylePandocTables() { - $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); -} -$(document).ready(function () { - bootstrapStylePandocTables(); -}); - - -</script> +## parent 108.9 1644 494.9 +</code></pre> +<h1>References</h1> </body> + </html> diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd index e97876da..b16dfea6 100644 --- a/vignettes/compiled_models.Rmd +++ b/vignettes/compiled_models.Rmd @@ -92,10 +92,10 @@ Here we get a performance benefit of a factor of `r factor_FOMC_SFO`
using the version of the differential equation model compiled from C code!
-This vignette was built with mkin `r packageVersion("mkin")` on
+This vignette was built with mkin `r utils::packageVersion("mkin")` on
```{r sessionInfo, echo = FALSE}
-cat(capture.output(sessionInfo())[1:3], sep = "\n")
+cat(utils::capture.output(utils::sessionInfo())[1:3], sep = "\n")
if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
}
|
