sanity_checks_for_QSAR_explainability.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDKit: 2019.03.4\n",
      "SHAP: 0.33.0\n"
     ]
    }
   ],
   "source": [
    "import base64\n",
    "import functools\n",
    "from io import BytesIO\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "from ipywidgets import interact\n",
    "from matplotlib import cm\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import rdkit\n",
    "from rdkit import Chem, RDPaths\n",
    "from rdkit.Chem import AllChem,  DataStructs, Draw, rdBase, rdCoordGen, rdDepictor\n",
    "from rdkit.Chem.Draw import IPythonConsole, rdMolDraw2D, SimilarityMaps\n",
    "from rdkit.ML.Descriptors import MoleculeDescriptors\n",
    "print(f'RDKit: {rdBase.rdkitVersion}')\n",
    "import scipy\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import seaborn as sns\n",
    "import shap\n",
    "print(f'SHAP: {shap.__version__}')\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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     },
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    }
   ],
   "source": [
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from chainer-chemistry's visualizer_utils.py\n",
    "def red_blue_cmap(x):\n",
    "    \"\"\"Red to Blue color map\n",
    "    Args:\n",
    "        x (float): value between -1 ~ 1, represents normalized saliency score\n",
    "    Returns (tuple): tuple of 3 float values representing R, G, B.\n",
    "    \"\"\"\n",
    "    if x > 0:\n",
    "        # Red for positive value\n",
    "        # x=0 -> 1, 1, 1 (white)\n",
    "        # x=1 -> 1, 0, 0 (red)\n",
    "        return 1.0, 1.0 - x, 1.0 - x\n",
    "    else:\n",
    "        # Blue for negative value\n",
    "        x *= -1\n",
    "        return 1.0 - x, 1.0 - x, 1.0\n",
    "\n",
    "def is_visible(begin, end):\n",
    "    if begin <= 0 or end <= 0:\n",
    "        return 0\n",
    "    elif begin >= 1 or end >= 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return (begin + end) * 0.5\n",
    "    \n",
    "def color_bond(bond, saliency, color_fn):\n",
    "    begin = saliency[bond.GetBeginAtomIdx()]\n",
    "    end = saliency[bond.GetEndAtomIdx()]\n",
    "    return color_fn(is_visible(begin, end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moltopng(mol, atom_colors, bond_colors, molSize=(450,150),kekulize=True):\n",
    "    mc = Chem.Mol(mol.ToBinary())\n",
    "    if kekulize:\n",
    "        try:\n",
    "            Chem.Kekulize(mc)\n",
    "        except:\n",
    "            mc = Chem.Mol(mol.ToBinary())\n",
    "    if not mc.GetNumConformers():\n",
    "        rdDepictor.Compute2DCoords(mc)\n",
    "    drawer = rdMolDraw2D.MolDraw2DCairo(molSize[0],molSize[1])\n",
    "    drawer.drawOptions().useBWAtomPalette()\n",
    "    drawer.drawOptions().padding = .2\n",
    "    drawer.DrawMolecule(\n",
    "        mc,\n",
    "        highlightAtoms=[i for i in range(len(atom_colors))], \n",
    "        highlightAtomColors=atom_colors, \n",
    "        highlightBonds=[i for i in range(len(bond_colors))],\n",
    "        highlightBondColors=bond_colors,\n",
    "        highlightAtomRadii={i: .5 for i in range(len(atom_colors))}\n",
    "    )\n",
    "    drawer.FinishDrawing()\n",
    "    return drawer.GetDrawingText()\n",
    "\n",
    "def label_cat(label):\n",
    "    return '$+$' if bool(label!=0) else '$\\cdot$'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://iwatobipen.wordpress.com/2017/02/25/draw-molecule-with-atom-index-in-rdkit/\n",
    "def mol_with_atom_index(mol):\n",
    "    import copy\n",
    "    atoms = mol.GetNumAtoms()\n",
    "    molwithidx = copy.deepcopy(mol)\n",
    "    for idx in range( atoms ):\n",
    "        molwithidx.GetAtomWithIdx( idx ).SetProp( 'molAtomMapNumber', str( molwithidx.GetAtomWithIdx( idx ).GetIdx() ) )\n",
    "    return molwithidx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_mols_with_idx(mol, atom_colors, bond_colors):\n",
    "    images = []\n",
    "    _img = moltopng(mol, atom_colors, bond_colors, molSize=(600, 400))\n",
    "    output = BytesIO()\n",
    "    output.write(_img)\n",
    "    encoded_string = base64.b64encode(output.getvalue()).decode()\n",
    "    images.append(f\"<img style='width: 420px; margin: 0px; float: left; border: 0px solid black;' src='data:_img/png;base64,{encoded_string}' />\")\n",
    "    _img = moltopng(mol_with_atom_index(mol), {}, {}, molSize=(600, 400))\n",
    "    output = BytesIO()\n",
    "    output.write(_img)\n",
    "    encoded_string = base64.b64encode(output.getvalue()).decode()\n",
    "    images.append(f\"<img style='width: 420px; margin: 0px; float: left; border: 0px solid black;' src='data:_img/png;base64,{encoded_string}' />\")\n",
    "\n",
    "    images = ''.join(images)\n",
    "    display(HTML(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weights = saliency\n",
    "# add scaler argument\n",
    "def plot_mol_saliency(mol, saliency, scaler=None, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br']):\n",
    "    n_atom = mol.GetNumAtoms()\n",
    "    symbols = [f'{mol.GetAtomWithIdx(i).GetSymbol()}_{i}' for i in range(mol.GetNumAtoms())]\n",
    "    df = pd.DataFrame(columns=atoms)\n",
    "    saliency = saliency[:n_atom]\n",
    "    num_atoms = mol.GetNumAtoms()\n",
    "    if scaler is not None:\n",
    "        vmax = np.max(np.abs(saliency))\n",
    "        weights = scaler(saliency)\n",
    "    else:\n",
    "        weights, vmax = SimilarityMaps.GetStandardizedWeights(saliency)\n",
    "    \n",
    "    arr = np.zeros((num_atoms, len(atoms)))\n",
    "    for i in range(mol.GetNumAtoms()):\n",
    "        _a = mol.GetAtomWithIdx(i).GetSymbol()\n",
    "        arr[i,atoms.index(_a)] = weights[i]\n",
    "    df = pd.DataFrame(arr, index=symbols, columns=atoms)\n",
    "    \n",
    "    vmin = - vmax\n",
    "    atom_colors = {i: red_blue_cmap(e) for i, e in enumerate(weights)}\n",
    "    # bondlist = [bond.GetIdx() for bond in mol.GetBonds()]\n",
    "    bond_colors = {i: color_bond(bond, weights, red_blue_cmap) for i, bond in enumerate(mol.GetBonds())}\n",
    "    draw_mols_with_idx(mol, atom_colors, bond_colors)\n",
    "    \n",
    "    fig = plt.figure(figsize=(18, 9))\n",
    "    grid = plt.GridSpec(15, 10)\n",
    "    ax = fig.add_subplot(grid[1:, -1])\n",
    "    ax.barh(range(mol.GetNumAtoms()), np.maximum(0, df.values).sum(axis=1), color='C3')\n",
    "    ax.barh(range(mol.GetNumAtoms()), np.minimum(0, df.values).sum(axis=1), color='C0')\n",
    "    ax.set_yticks(range(mol.GetNumAtoms()))\n",
    "    ax.set_ylim(-.5, mol.GetNumAtoms()-.5)\n",
    "    ax.axvline(0, color='k', linestyle='-', linewidth=.5)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.tick_params(axis='both', which='both', left=False, labelleft=False)\n",
    "\n",
    "    ax = fig.add_subplot(grid[1:, :-1], sharey=ax)\n",
    "    im = ax.imshow(df.values, cmap='bwr', vmin=vmin, vmax=vmax, aspect='auto')\n",
    "    ax.set_yticks(range(mol.GetNumAtoms()))\n",
    "    ax.set_ylim(mol.GetNumAtoms() -.5, -.5)\n",
    "    symbols= {i: f'${mol.GetAtomWithIdx(i).GetSymbol()}_{{{i}}}$' for i in range(mol.GetNumAtoms())}\n",
    "    ax.set_yticklabels(symbols.values())\n",
    "    ax.set_xticks(range(len(atoms)))\n",
    "\n",
    "    ax.set_xlim(-.5, len(atoms) -.5)\n",
    "    ax.set_xticklabels(atoms, rotation=90)\n",
    "    ax.set_ylabel('Node')\n",
    "\n",
    "    for (j,i),label in np.ndenumerate(df.values):\n",
    "        ax.text(i,j, label_cat(label) ,ha='center',va='center')\n",
    "    ax.tick_params(axis='both', which='both', bottom=True, labelbottom=True, top=False, labeltop=False)\n",
    "    ax.grid(b=None)\n",
    "\n",
    "\n",
    "    ax = fig.add_subplot(grid[0, :-1])\n",
    "    fig.colorbar(mappable=im, cax=ax, orientation='horizontal')\n",
    "    ax.tick_params(axis='both', which='both', bottom=False, labelbottom=False, top=True, labeltop=True)\n",
    "\n",
    "    display(fig)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Random Forest/LightGBM Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1025, 257)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://iwatobipen.wordpress.com/2018/11/07/visualize-important-features-of-machine-leaning-rdkit/\n",
    "\n",
    "def mol2fp(mol,radius=2, nBits=1024):\n",
    "    bitInfo={}\n",
    "    fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=radius, nBits=nBits, bitInfo=bitInfo)\n",
    "    arr = np.zeros((1,))\n",
    "    DataStructs.ConvertToNumpyArray(fp, arr)\n",
    "    return arr, bitInfo\n",
    "\n",
    "\n",
    "train_path = os.path.join(RDPaths.RDDocsDir, 'Book/data/solubility.train.sdf')\n",
    "test_path = os.path.join(RDPaths.RDDocsDir, 'Book/data/solubility.test.sdf')\n",
    "train_mols = [m for m in Chem.SDMolSupplier(train_path) if m is not None]\n",
    "test_mols = [m for m in Chem.SDMolSupplier(test_path) if m is not None]\n",
    "len(train_mols), len(test_mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_pred_results(mol, model):\n",
    "    y_pred = model.predict(mol2fp(mol)[0].reshape((1,-1)))\n",
    "    sol_dict = {val: key for key, val in sol_classes.items()}\n",
    "    print(f\"True: {mol.GetProp('SOL_classification')} vs Predicted: {sol_dict[y_pred[0]]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1025, 1024) (1025,) (257, 1024) (257,)\n"
     ]
    }
   ],
   "source": [
    "sol_classes = {'(A) low': 0, '(B) medium': 1, '(C) high': 2}\n",
    "X_train = np.array([mol2fp(m)[0] for m in train_mols])\n",
    "y_train = np.array([sol_classes[m.GetProp('SOL_classification')] for m in train_mols], dtype=np.int) \n",
    "X_test = np.array([mol2fp(m)[0] for m in test_mols])\n",
    "y_test = np.array([sol_classes[m.GetProp('SOL_classification')] for m in test_mols], dtype=np.int)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 0.992, Test: 0.685\n",
      "Shuffuled Data: 0.985, True Train: 0.352, Test: 0.370\n",
      "Shuffuled Data: 0.985, True Train: 0.371, Test: 0.401\n",
      "Shuffuled Data: 0.985, True Train: 0.363, Test: 0.350\n",
      "Shuffuled Data: 0.980, True Train: 0.368, Test: 0.358\n",
      "Shuffuled Data: 0.981, True Train: 0.354, Test: 0.389\n",
      "Shuffuled Data: 0.983, True Train: 0.365, Test: 0.389\n",
      "Shuffuled Data: 0.984, True Train: 0.355, Test: 0.393\n",
      "Shuffuled Data: 0.982, True Train: 0.363, Test: 0.444\n",
      "Shuffuled Data: 0.986, True Train: 0.378, Test: 0.475\n",
      "Shuffuled Data: 0.980, True Train: 0.359, Test: 0.401\n"
     ]
    }
   ],
   "source": [
    "rf_models = []\n",
    "clf = RandomForestClassifier(random_state=20200119)\n",
    "clf.fit(X_train, y_train)\n",
    "print(f'Train: {accuracy_score(y_train, clf.predict(X_train)):.3f}, Test: {accuracy_score(y_test, clf.predict(X_test)):.3f}')\n",
    "rf_models.append(clf)\n",
    "for _ in range(10):\n",
    "    y_perm = np.random.permutation(y_train)\n",
    "    _clf = RandomForestClassifier(random_state=20200119)\n",
    "    _clf.fit(X_train, y_perm)\n",
    "    rf_models.append(_clf)\n",
    "    print(f'Shuffuled Data: {accuracy_score(y_perm, _clf.predict(X_train)):.3f}, True Train: {accuracy_score(y_train, _clf.predict(X_train)):.3f}, Test: {accuracy_score(y_test, _clf.predict(X_test)):.3f}')\n",
    "          \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_train != clf.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (B) medium vs Predicted: (A) low\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F71041A74A8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (B) medium vs Predicted: (A) low\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F71040C7FD0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (B) medium vs Predicted: (A) low\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F71040BCDA0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (B) medium vs Predicted: (A) low\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F711F5B4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (A) low vs Predicted: (B) medium\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F71040BCE80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (B) medium vs Predicted: (A) low\n"
     ]
    },
    {
     "data": {
      "image/png": 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gAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogqf8HHrvQSleaof0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F711F5B4C50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (B) medium vs Predicted: (A) low\n"
     ]
    },
    {
     "data": {
      "image/png": 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pgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiApgQZISqABkhJogKQEGiCp/wWzJMy2iugmFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F711F5B4D68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (A) low vs Predicted: (B) medium\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=360x360 at 0x7F71040C79B0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "incorrect_mols = [train_mols[i] for i, flg in enumerate(y_train != clf.predict(X_train)) if flg==True]\n",
    "for m in incorrect_mols:\n",
    "    show_pred_results(m, clf)\n",
    "    display(Draw.MolToImage(m, size=(360, 360)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 0.974, Test: 0.685\n",
      "Shuffuled Data: 0.854, True Train: 0.391, Test: 0.393\n",
      "Shuffuled Data: 0.860, True Train: 0.359, Test: 0.354\n",
      "Shuffuled Data: 0.869, True Train: 0.338, Test: 0.346\n",
      "Shuffuled Data: 0.859, True Train: 0.346, Test: 0.327\n",
      "Shuffuled Data: 0.869, True Train: 0.355, Test: 0.381\n",
      "Shuffuled Data: 0.871, True Train: 0.381, Test: 0.440\n",
      "Shuffuled Data: 0.868, True Train: 0.391, Test: 0.362\n",
      "Shuffuled Data: 0.877, True Train: 0.353, Test: 0.374\n",
      "Shuffuled Data: 0.868, True Train: 0.372, Test: 0.397\n",
      "Shuffuled Data: 0.872, True Train: 0.403, Test: 0.459\n"
     ]
    }
   ],
   "source": [
    "lgb_models = []\n",
    "import lightgbm as lgb\n",
    "_clf = lgb.LGBMClassifier()\n",
    "_clf.fit(X_train, y_train)\n",
    "print(f'Train: {accuracy_score(y_train, _clf.predict(X_train)):.3f}, Test: {accuracy_score(y_test, _clf.predict(X_test)):.3f}')\n",
    "lgb_models.append(_clf)\n",
    "    \n",
    "for _ in range(10):\n",
    "    y_perm = np.random.permutation(y_train)\n",
    "    _clf = lgb.LGBMClassifier()\n",
    "    _clf.fit(X_train, y_perm)\n",
    "    lgb_models.append(_clf)\n",
    "    print(f'Shuffuled Data: {accuracy_score(y_perm, _clf.predict(X_train)):.3f}, True Train: {accuracy_score(y_train, _clf.predict(X_train)):.3f}, Test: {accuracy_score(y_test, _clf.predict(X_test)):.3f}')\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function Setttings 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_proba(fp, proba_fn, class_id):\n",
    "    return proba_fn((fp,))[0][class_id]\n",
    "\n",
    "# デフォルトでGetMorganFingerprintの引数が2048ビットになっているため\n",
    "def fp_partial(nBits):\n",
    "    return functools.partial(SimilarityMaps.GetMorganFingerprint, nBits=nBits)\n",
    "\n",
    "def show_pred_results(mol, model):\n",
    "    y_pred = model.predict(mol2fp(mol)[0].reshape((1,-1)))\n",
    "    sol_dict = {val: key for key, val in sol_classes.items()}\n",
    "    print(f\"True: {mol.GetProp('SOL_classification')} vs Predicted: {sol_dict[y_pred[0]]}\")\n",
    "\n",
    "def weight_from_shap_values(mol, model, class_id=2, collided_bits='mean'):\n",
    "    import lightgbm\n",
    "    import sklearn\n",
    "    fp, bitinfo = mol2fp(mol)\n",
    "    weights = np.zeros(mol.GetNumAtoms(), ) \n",
    "    \n",
    "    if isinstance(model, sklearn.ensemble.RandomForestClassifier):\n",
    "    # TreeExplainerのインスタンスを立ち上げるときにclfのみを与えると後にエラーが出る\n",
    "        explainer = shap.TreeExplainer(model, data=X_train)\n",
    "    elif isinstance(model, lightgbm.sklearn.LGBMClassifier):\n",
    "        explainer = shap.TreeExplainer(model)\n",
    "    \n",
    "    shap_values = explainer.shap_values(fp.reshape((1,-1)))[class_id]\n",
    "    for bit, infos in bitinfo.items():\n",
    "        for atom_infos in infos:\n",
    "            if collided_bits == 'mean':            \n",
    "                weights[atom_infos[0]] += shap_values[0, bit]/ len(infos)\n",
    "            else:\n",
    "                weights[atom_infos[0]] += shap_values[0, bit]\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def variation_sanity_check(mol, saliency, randomized_saliency, scaler=None, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br', 'I']):\n",
    "    n_atom = mol.GetNumAtoms()\n",
    "    symbols = [f'{mol.GetAtomWithIdx(i).GetSymbol()}_{i}' for i in range(mol.GetNumAtoms())]\n",
    "    df = pd.DataFrame(columns=atoms)\n",
    "    saliency = saliency[:n_atom]\n",
    "    num_atoms = mol.GetNumAtoms()\n",
    "    if scaler is not None:\n",
    "        vmax = np.max(np.abs(saliency))\n",
    "        weights = scaler(saliency)\n",
    "    else:\n",
    "        weights, vmax = SimilarityMaps.GetStandardizedWeights(saliency)\n",
    "    \n",
    "    arr = np.zeros((num_atoms, len(atoms)))\n",
    "    for i in range(mol.GetNumAtoms()):\n",
    "        _a = mol.GetAtomWithIdx(i).GetSymbol()\n",
    "        # use saliency instead of scaled weights \n",
    "        arr[i,atoms.index(_a)] = saliency[i]\n",
    "    df = pd.DataFrame(arr, index=symbols, columns=atoms)\n",
    "    \n",
    "    vmin = - vmax\n",
    "    atom_colors = {i: red_blue_cmap(e) for i, e in enumerate(weights)}\n",
    "    # bondlist = [bond.GetIdx() for bond in mol.GetBonds()]\n",
    "    bond_colors = {i: color_bond(bond, weights, red_blue_cmap) for i, bond in enumerate(mol.GetBonds())}\n",
    "    draw_mols_with_idx(mol, atom_colors, bond_colors)\n",
    "    \n",
    "    fig = plt.figure(figsize=(18, 9))\n",
    "    grid = plt.GridSpec(15, 10)\n",
    "    ax = fig.add_subplot(grid[1:, -1])\n",
    "    ax.barh(range(1, mol.GetNumAtoms()+1), np.maximum(0, df.values).sum(axis=1), color='C3')\n",
    "    ax.barh(range(1, mol.GetNumAtoms()+1), np.minimum(0, df.values).sum(axis=1), color='C0')\n",
    "    ax.set_yticks(range(1, mol.GetNumAtoms()+1))\n",
    "    ax.set_ylim(0.5, mol.GetNumAtoms()+.5)\n",
    "    ax.axvline(0, color='k', linestyle='-', linewidth=.5)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.tick_params(axis='both', which='both', left=False, labelleft=False)\n",
    "\n",
    "    ax = fig.add_subplot(grid[1:, :-1], sharey=ax)\n",
    "    ax.boxplot(randomized_saliency, vert=False)\n",
    "    # use saliency instead of scaled weights \n",
    "    ax.plot(saliency, range(1, len(saliency)+1), linewidth=2, color='green')\n",
    "    ax.vlines([0], 0.5, mol.GetNumAtoms()+.5, \"black\", linestyles='solid', linewidth=3)\n",
    "    symbols= {i: f'${mol.GetAtomWithIdx(i).GetSymbol()}_{{{i}}}$' for i in range(mol.GetNumAtoms())}\n",
    "    ax.set_yticklabels(symbols.values())\n",
    "    ax.set_ylabel('Node')\n",
    "\n",
    "    display(fig)\n",
    "    plt.close(fig)\n",
    "\n",
    "def calc_saliency(mol, model, randomized_models, weight_fn='simmap'):\n",
    "    if weight_fn=='simmap':\n",
    "        saliency = SimilarityMaps.GetAtomicWeightsForModel(mol, fp_partial(1024), lambda x: get_proba(x, model.predict_proba, 2))\n",
    "    elif weight_fn=='shap':\n",
    "        saliency = weight_from_shap_values(mol, model, class_id=2, collided_bits='mean')\n",
    "    else:\n",
    "        raise NotImplementedError()\n",
    "    randomized_saliency = np.zeros((10, len(saliency)))\n",
    "    for i in range(len(randomized_models)):\n",
    "        _m = randomized_models[i]\n",
    "        if weight_fn=='simmap':\n",
    "            _w = SimilarityMaps.GetAtomicWeightsForModel(mol, fp_partial(1024), lambda x: get_proba(x, _m.predict_proba, 2))\n",
    "        elif weight_fn=='shap':\n",
    "            _w = weight_from_shap_values(mol, _m, class_id=2, collided_bits='mean')\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "\n",
    "        randomized_saliency[i, :] = np.array(_w)\n",
    "    return saliency, randomized_saliency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataset_cor_df(mols, models):\n",
    "    cor_dict = {\n",
    "        'SimMap_P': [],\n",
    "        'SimMap_S': [],\n",
    "        'SHAP_P': [],\n",
    "        'SHAP_S': [],\n",
    "    }\n",
    "    for mol in tqdm(mols):\n",
    "        saliency, randomized_saliency = calc_saliency(mol, models[0], models[1:], weight_fn='simmap')\n",
    "        for i in range(randomized_saliency.shape[0]):\n",
    "            _p = scipy.stats.pearsonr(saliency, randomized_saliency[i, :])[0]\n",
    "            _s = scipy.stats.spearmanr(saliency, randomized_saliency[i, :])[0]\n",
    "            # print(_p, _s)\n",
    "            cor_dict['SimMap_P'].append(_p)\n",
    "            cor_dict['SimMap_S'].append(_s)\n",
    "\n",
    "        saliency, randomized_saliency = calc_saliency(mol, models[0], models[1:], weight_fn='shap')\n",
    "        for i in range(randomized_saliency.shape[0]):\n",
    "            _p = scipy.stats.pearsonr(saliency, randomized_saliency[i, :])[0]\n",
    "            _s = scipy.stats.spearmanr(saliency, randomized_saliency[i, :])[0]\n",
    "            # print(_p, _s)\n",
    "            cor_dict['SHAP_P'].append(_p)\n",
    "            cor_dict['SHAP_S'].append(_s)\n",
    "\n",
    "    cor_df = pd.DataFrame(cor_dict)\n",
    "    cor_df = cor_df.stack()\n",
    "    cor_df = cor_df.reset_index(level=1, drop=False)\n",
    "    cor_df.columns = ['Correlation', 'value']\n",
    "    return cor_df\n",
    "\n",
    "def correlation_sanity_check(saliency, randomized_saliency):\n",
    "    saliency_dict = {\n",
    "        'Pearson': [],\n",
    "        'Spearman': [],\n",
    "    }\n",
    "    for i in range(randomized_saliency.shape[0]):\n",
    "        _p = scipy.stats.pearsonr(saliency, randomized_saliency[i, :])[0]\n",
    "        _s = scipy.stats.spearmanr(saliency, randomized_saliency[i, :])[0]\n",
    "        # print(_p, _s)\n",
    "        saliency_dict['Pearson'].append(_p)\n",
    "        saliency_dict['Spearman'].append(_s)\n",
    "    \n",
    "    df = pd.DataFrame(saliency_dict)\n",
    "    df = df.stack()\n",
    "    df = df.reset_index(level=1, drop=False)\n",
    "    df.columns = ['Correlation', 'value']\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
    "    ax = sns.boxplot(x=df['Correlation'], y=df['value'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_encoded_string(mol, saliency):\n",
    "    num_atoms = mol.GetNumAtoms()\n",
    "    saliency = saliency[:num_atoms]\n",
    "    scaler = None\n",
    "    if scaler is not None:\n",
    "        vmax = np.max(np.abs(saliency))\n",
    "        weights = scaler(saliency)\n",
    "    else:\n",
    "        weights, vmax = SimilarityMaps.GetStandardizedWeights(saliency)\n",
    "\n",
    "    vmin = - vmax\n",
    "    atom_colors = {i: red_blue_cmap(e) for i, e in enumerate(weights)}\n",
    "    bond_colors = {i: color_bond(bond, weights, red_blue_cmap) for i, bond in enumerate(mol.GetBonds())}\n",
    "    return moltopng(mol, atom_colors, bond_colors, molSize=(600, 400))\n",
    "\n",
    "def draw_visual_inspection(mol, saliency, randomized_saliency):\n",
    "    images = []\n",
    "    _img = get_encoded_string(mol, saliency)\n",
    "    output = BytesIO()\n",
    "    output.write(_img)\n",
    "    encoded_string = base64.b64encode(output.getvalue()).decode()\n",
    "    title = 'trained'\n",
    "    \n",
    "    images.append(f\"<p>Trained with True Data</p>\")\n",
    "    images.append(f\"<img width='240px' margin-right='auto' border='0px' title='{title}' src='data:_img/png;base64,{encoded_string}'/>\")\n",
    "    images.append(f\"<p>Trained with Randomized Data</p>\")\n",
    "    for i in range(len(randomized_saliency)):\n",
    "        _img = get_encoded_string(mol, randomized_saliency[i, :])\n",
    "        output = BytesIO()\n",
    "        output.write(_img)\n",
    "        encoded_string = base64.b64encode(output.getvalue()).decode()\n",
    "        title = f'randomized{i}'\n",
    "        images.append(f\"<img width='240px' margin='0px' style='float:left' border='0px' title='{title}' src='data:_img/png;base64,{encoded_string}'/>\")\n",
    "        \n",
    "    images = ''.join(images)\n",
    "    display(HTML(images))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True: (C) high vs Predicted: (C) high\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<p>Trained with True Data</p><img width='240px' margin-right='auto' border='0px' title='trained' src='data:_img/png;base64,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'/><p>Trained with Randomized Data</p><img width='240px' margin='0px' style='float:left' border='0px' title='randomized0' 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'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized1' 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v4lXnzUFAgWVcRqZSX4513uObeXZXtYRWVgpVsNmzbprifOiWzWJCcjG++wdmzsFrdTcGrRBEWC7KysGoVfvnFGzum/AaD0H/s3o2kJA9umXdHRQVmzOBrpVv27FFiCgIQRRQVITVV7jp8RGYmli3DsWNSdsK7XmUcnj2L5ctx6pT01/dLDEI/YbPhpZeUNSl6PbsdZ86w6Yzr8vKq7h6UyWrFvn0KfROmKCkpWLMGZWUe/1ba7TCbsXUrNm1S6PsnRWEQ+okff0R+vtxF1KmsDG++yY4zLtq9W7IJNA+x27F/v9xFKJjVirVrceCAV9/NWCw4cwbff89p0nowCP2B1YrEROXeDl5VXo6ffpK7CB+Uk6PER7/VVN70K/+HUBZmM378ERkZMrwRtFpRUIDvv2enp7owCP3Bhg2+MStVVoYlS5T+gq5ABw74xp20KOLYMbmLUJ7KFMzPl21m225HWRl++IFZWCsGoT/47DOUlspdhGOKi9mayzmlpbh8We4iHGOz4eRJpU/hepnNhtWrUVgo84M6UYTJhJUr5ew2pWQMQp9XeVacrygvx9dfy12ET0lLk7sCJ2VkyF2BkmzejLw8RSxXEUWUl+Pnn/lOpQYMQp+3bp0v/WSLIpKTfWMiVyE8etCg5CwWnD4tdxGKceSIstb62mzIz0dystx1KA+D0OetX+9cZ6Ya2e1lubnzioq+k6Kieuj1SEnxwjj+oLTU99b7XbrEx8AAUFCAHTsUlIKVrFacOOFLc0jewSD0bcXF7v9Mi0VF35w92yE3d/7lyzNE0e1QrU9ZGd+TOiorCxpf+x0VBFy5IncRchNF/PKLQm/lrVYkJfnG8iuv8bVfMrrRoUMIDHT9y02mfenpgzMzJ1gsFwIDe8bGfi0IBumqq5ndjh07PD2In8jM9L0XLJsNOTlyFyG348dRVKTcZxYWC9es3YBB6NuOHnVx55bNlpudPe3cud5lZclabaOYmISWLVOMxoFSF1izykbDVK/cXLkrcJ7djuxsuYuQldWKHTsU/Q7GasWxYygqkrsOxWAQ+rYjR5x+CCGKlvz8xLS0Nvn5HwiCJjx8aps2aeHh0wDvne+p13NtoUN8ZVdMNQpvcuRpSjshpEZ2O28Kr+ExTL7N2SMdSkuTsrOnms3HAQQHD4uOTgwI6OSRyuqk1SIjg4fY1aO8HIIgdxEuUXN/GZsNf/yhuDUyN7PbcfYsSksRHCx3KQrAO0Lflpfn6GeazakXLoy8cGG42XzcYIi75ZZ1zZptkiUFAdjtfIxUv/Jy31spU+nqmbEqdOaMzyyaFUUcPix3Ecrgm79n9CdH1tbbbPnZ2dPOnu1SWrpBqw2PinqrVatDISEjPV9draxWdnuqn9nsq3eEGo2in5B51MGDEmxn8g6bDceO+UxsexSnRn1b3TMwomgtLPwiJ2euzZYjCLqwsKejohZqtVGOXFkQDIKgLSr6LiioryAESFPun+x27qmvn4duqkRR3LFj2fnz+4uKsgcMeLxLl+GSDyEIKr0jLC/3sfVNNhsuXUJsrNx1yI1B6LfKyrZkZ8dXVBwGYDQOjYlZHBDQzfEvj439Ji2tZUHBJ2VlSdHR74WE3C9teT56r+Pr0tJ2L18+LS1ttyAIoiju3Pl1jx73PfLIopiYtnKX5g/OnYNW60tvAqxWnDnDIOTUqI/T62v4oNl8OiNjXHr6XRUVhw2Gtk2bft+8+WanUhCAVtvolltWBwR0NZtPX7z41/T0uyoqDklTNKDRwODx/Yo+TyvpMt6Cgsz//veZBQv6paXtDgtrMnnyRxMmLA4Karh//8+zZ3datmxaeXmhVGOJosTF+4ozZ3xmXrRS5eFZxDtC32Y03rDC3m4vzct798qVt0SxQqMJjoiY0ajRLEFwccu90Ti0Zct9lZOrZWVbzp7t0bDho9HRixycXK2DToeGDd28hv8zGKR5fmO1mrds+fjHH182mYp1OsOddz770EMLgoIaAOjff9KqVa9t3vzRpk0f7Ny5/P77Xx427AWNxt0Qs9tV+kbn0iW5K3BeWRkqKhAg8dMPHyOIfFTqyx54ACdOVP5RLCxcmpMz02rNAoSGDSdGRb2j0zWWZBSbLT83d15BwRJRtGq14RERMyMiprvTgyY0FP/6F3r3lqQ6v2Uy4ccf3Z1n279/zfLl8Tk5ZwB07z564sTEqKjW1T7n3Ll9X38dn5qaDKBly54TJiTExbnVWkGnw4QJ7lzAJ5WVYelSH9g4UU1AAO6+G7fcIncdsmIQ+rbp0/HLLzCZUrKzp5WX7wIQGHh7TExiUFBfyccym1MvX/57Scl6AAZDXHT0+y4vPQ0Kwpo1aNpU0vr80bJlrrfgycw8/vXX0w8f3gggNrbjhAnvd+16dx2fv3//mmXLpubmngPQvfvoSZM+jIxs6drQDRrgr3917Ut92IUL+OUXH5saBaDVol8/dHPuyYm/4dSolOx2lJaivBxm87U3hno9AgIQFASjUfoVIk2bZmRlzS4oWAaIOl3TqKg3GjacBHhkIUrl7sOrW/IvXhzl8pZ8mw1NmniiRn8TEuJKH6zS0ryffpq/efNHdrstODjir399xZEJzx497u3cedimTR/8/PPCAwfWHju2efjwqffdNzcwMMTZAsLDna7ZDyi5uWgdKs9mUjneEbrLZMLly8jORl4eKiqg0UAQqh7t2O3XNkSLIkQRQUFo1AgxMYiKcvchSnl5+QcffDB//sLy8hJBCIqImNqo0VyNxunXLBeIoqWgYElOzqt2e6Eg6MPCnouKek2jceKhX/v2WL3acwX6j//9z7mDeW02S3Lyf1esmFNcnKvV6gcOnDJmzOuhoZFODZqfn/HDD7N37FgmimJ4eNOxY9/o12+S4PCbOK0Wt92GDh2cGtMfpKRg716f3JbXvDnuvVfuImTFIHSR2Yz0dJw/j9JSaDTOPRjQ6WC3IywMLVuiaVNX1tetWbNm6tSp586dAxAaOjo6+kO9vqXTV3GMKFoLCj62WjOjot68/uM225Xc3Nfy8z8CbFpto8jIl8PDX3CkYalWi8mT8c9/eqhev3LmDHbtcvSn6+jRpOXL4zMyjgLo3HnYhAmLb7mlixtDpyxbNi0tbReAVq1unzgxsW1bh+bb9XqMGIGICJdH9lVbt+LYMbmLcElUFMaNk7sIWTEInVZaihMnkJEhwa5hnQ6iiFat0K6do6u29u3bFx8fn5ycDKBnz54JCQmLFg1MTXWrjDqUlm66fDm+ouIYoG3d+oTBUH23mcm07/Ll+LKyZACBgT2joxPqPcIiNBSJiegr/UNMP1RWhp9+qv/HLCvr5Dff/OPAgbUAYmLajRnzeu/eY90fXRTFHTuWfvfdzMLCLEEQ+vWbOH78Ow0b1rMCS6fDI4+ocZ/oxo04fdojV96//4fo6LimTT31HC8iAo884qFr+wYGoRNMJhw9isxM2O1SToBUzqa2bo24OOhqf2h75cqV11577aOPPrLZbI0aNXr55ZdfeOEFrVb7n//go4+kb9RiNp/OyXmpuPgHAAZD26ioN0JDa31tLSlZk5091WI5ByAkZHRMTF13qIGBSEmpeQck3WzVqroeE5aVFaxd+9bGjQlWa4XRGDZ69KwRI+J1OinXwldUlK5f/+66dW9bLKaAgOB77pkxevQsvb7WPTktWmDwYAnH9xm//OLcPLYjLl488OOP8adObW/duv/06cmOT1A7JTxcjat8r8cgdFRaGo4fh93uqefhWi20WvToUcMqEovFsmTJkldffbWwsFCv1z/33HOvvfZawz834l28iHvvhckkWSWubUYUxfK8vA+uXFlot9f1zFKjwYgReP99yar1e0eO4ODBGm4KRdG+Y8eyb799sajosiBo+vV7dPz4dxs2jPFQGdnZp1eseCkl5QcA0dFtxo59s8abTr0egwapdD1wUhIknJspLr68du3LO3d+brfbQkKiRo9e0L//0x4KwkaN8PDDnriwz2AQ1s9kQkoKCgu90TlJp0N0NHr2vHZrmJSUNHXq1OPHjwMYNmxYYmJip07VV2mOHYsjRyQZ393NiFZrRk7O7MLCWlexGo349FP06iVJtapQXo6VK6v/7J04sW358vj09IMAOnQY/OijCc2bd/dCMceObVm+PP7ixcMAOnUaOmHC4mbNbpivMxgwfrwa50UB/P47Dh6U4Do2myU5ecm6da+WlxdqtfqBA58bNWp+UFCYBJeuRWwsHnjAc5f3AQzCely5gl27YLN5b2G0RoPAQPTrh4yM1L///e/r168HEBcX9/77748cWfO+vY0bMWeOu4e43rgZsXdMTILLmxHr2NcYG4vNm92qU4W2bMHFi1V/zsu7sGLFnMolnRERt4wZ87pTSzrdZ7NZk5O/WLFibnFxTuWd6COPLAoNjQKg06FTJ3T3RiIr0YED2LnT3ReK1NSkFSviL106CiAubtiYMQlNmnSWpr7atW+P4dK3XvclDMK6pKfXPCvlaaWl+atWvf3zz4vNZnN4ePjMmTOnT59uqH2/hc2GO+90/YS/em/jXFLDzWXDho3nzlXjVms3XbmCjRtRVla2bt07lc/qDAbjyJEvjho102AIkqWk0tL8deve3rhxsdVqDg4OHzVq5ogR0wMDDQ89pN5mXWlp2LLF9Q31ly+fXLnyH0eOrAUQHd3+wQcXdekyWsr6aiEIuP123H67F4ZSLgZhrc6cwdGj3k5Bm826efMXy5fPLSrKEQTNmDGPfvTRoqio+nt7rl6N115z6HjC69ntZfn5H9b7YM9lfz5ufFsUTRpNcLNmM44enRUc7GLvUw/Jz0d+PkpLYbOhQQOEhCAqSlmTe6Iovvzyik8+mXHlSrogCLffPmb8+HcjI1vIXRcuXUr95pu/Hzy4HkCTJnEzZ74/bZqc51zKKy8PP/7oShCWlxf8+utbW7YsttnMQUFhf/nLrDvvlHjFUx0MBgwdijZtvDOaQjEIa3buHA4f9nYKHj685fPP48+fPwyga9ehTzyxuE2bbn37ItKB/dB2O0aORHq6E8tZnVrq6Y7rF6C2bdv2jTfeGDtWgsX9rrFYsGsXkpOxfTuOHUNWVtVRGFotBAFWK2w2mM2IjET79hg0CAMGYOBABAfLVS/27t07bdq0HTt2AGjVqtejjya0a9dftmpqcvRo0rJlUzMz63qMrQZ2O/79b+deNETRnpKybNWqF4uLLwuC5vbbH33ggfdCQ6M9VmMN9HqMH6/2JvgMwhpkZyMlxaspeOnS6WXLXtqx4wcAjRu3mTTpzX79qqJCp8OgQWjQoP6LHDiAJ55AeXn9n+nC5j836XRo0mRLVlb84cOHAQwdOnTx4sXdvNjfsKIC69bhs8+wdSsCAqra4NVLq0VICEwm9OyJp57CmDEOfSOkkpmZOX/+/M8++8xutzdp0uSxx+Z16fKUzabEo9M0GsuJE0v+9a+aFzarx3ffOXEw76lT21asiM/IOAigXbshY8YkNG16qweLq4Vej6ef9v6wysIgrK64GNu3e6+FvMlUumrVuytXvm2xmAIDg++/f8ZDD92wSUsQEBCAoUMdask2fz5WraprK4XNlpubu8CFdjBuMhqxZg2io61ffPHF3Llzc3JyNBrNo48+umiRQxO/7sjKwqJF+PRTCIIrfTuvCgmBzYZx4zB7NuLipKuvJmaz+eOPP3755ZeLi4sNBsOzzz67YMECo7HBqlVOz357gSAgMhL33FPrVle5C/Se3buxb1/962Xy8y+sWTMnJWUpgPDwZqNHL+zTZ7I36qtJy5YYNUquwZWCQXgDmw2bN6O83BsNA0VR3LZt6VdfzSwoyBIEYfDgiY899k5YWA17FTQaNGqE/g7Mh1VU4N57kZFRw6/inw1CX7Hbi1xrEOqyoCDMm4f77qv61/z8/Hnz5i1ZssRqtTqyFMhlV67glVfw3//Cbpes4YBOB70ed9+NRYvQqpU016xmzZo18fHxZ86cATB69OjExMTWrasOTsrNxcaNijsA3WDAfffBaKz6126wYAsAACAASURBVOubH/Xo0SMxMXHgQM/ONyjH5ctYvbquyQazuSwp6Z1Nm6pWPA0b9uLw4XV1J/A0vR6DB3v8jZ3yMQhvcOgQzp/3xgvNqVMpn38en5q6E0Dbtrc/9VRiXFxdexV0OnTpgpYt67/yuXMYM6b6VoqrR0YAcPnICNcEBeGee/D669U/nprq6OYQF4giPvkEM2fCYpGy1cBVlXEYH49XX5VykeTx48enT5++ceNGAB07dly8ePGIESOqfc6JE9i3T0GH3mm1GDq0hi4Q17fDHT169IcfftjSkR9f3/fFFzU/nhBF8cCBFStXzsjPTxcEoXv3MQ8++F54eHOvF3gDrRZTpqh3oe9VDMJrCgqQnOzxFLxyJWPZstnbty8TRbFRo6aPPvrGkCEO7QPT6TB8uEM/sjt24PnnqwJAwkMEXRAQgE6d8NVXtbaOc6RdgLMuXMDYsTh6FCUlbl6pHkYjoqKwapUEO+fy8vLmz59fOakYERHxyiuv1DGpuGMHzp1TRBbqdOjZs9aDJioPSFm4cGFJSUlQUNDUqVPnzJkTGhrq3Rq9bdcuHDhQ/WUkPX3vihXTzpzZAaB5815jxiS2bt1Pnvpu1KIFRntjj4bSMQiv2bIFxcUenBQ1m8vXrv3ghx8WmkwlBkPQ6NFTx4yZExTk6OuCRoPYWEd7sqxbh9mz8y9elPJYeWcZDGjRAt98U8+Sy7obyDlr40aMG4eyMi/lhCAgKAjvv49nnnHxCpX/+fPmzSsoKNDr9VOmTHn99dcj61woLIrYtg2XLsmchTodOnZEjx71fFpGRsbs2bOXLVsmimLTpk3feOONSZO82gHAy0pKsHz5tW9NYWHm+vXzd+z4TBTtDRvGjhz5ar9+TwmCIlY86fUYOVLtZ9NXYhBWuXQJe/d68HZwz541n3029fLlcwB69Rr9t799GB3d0tmLaLUYMgT1vqW2Wq1ffPHFP/85t7AwRxB0DRs+ERW1UKv17JqUagIC0LIlli6tv9pKkqyzSEzESy/JsJwkOBgTJ2LJkmvHTzooKSkpPj7+6NGjAIYNG7Z48eIuXRw6OEn2LHQwBa9KSUmJj4/fuXMngNtvvz0xMbGv/54/smEDzp6F1WpOTv547dqXTaZirdYwcOCzo0cvDAxU0A1xWBgefVTuIpSBQVglKclTM2lnzuz7/PP4Y8eSAbRu3eOJJxI6dx7k2qUEAY0bo0+fuj5ny5Yt8fFVuxR69hxaXr4Y6Oblb3JQELp1w8cfI8jJtic3HzLl+DqLOXOQmOhunzmXBQdj2DCsWFHX+SHXO3ny5D/+8Y+1a9cCaNeu3euvv+7s3kpRxO7dOHNGhizU6dCjBzp2dO6rRFFcunTpzJkzs7KyBEGYOHHiO++807ixE51sfUVeHl5+ec133027cuUsgC5dRo8ZkxgZ2Vruum6g12PYMLRWVlGyYRACQE5OVUNRyW3c+Mmnnz4vivawsJgJExYOG/aEm7MiWi2GDas5YE6fPv3SSy/98MMN+9ZPnsRTT6GoSPpzmmoTGIiHH8aMGa4cOFzJhXUW8qZgJaMRd92Fn36q5z+8oKDgrbfeSkhIqKioCAsLmzVrVnx8fICryxVOnsTevd7LQo0GGg2GDEFsrItXKC0tfffdd99++22TyRQcHDxjxoxZs2YFBiqr2ZA7Dhw4EB8fv337dgCNG3d86KHFHTtWX/EkO0FARITaT5y4HoMQAHbuRHa2R66cnX12+vRbhw6dMmHCa0ajBHsVtFq0bVv9zXjli8tbb71VUVFx84tLSQnmzEFyskN77d1hMCAwEO++i0Eu3vFec/M6i7lz54aE1Nz77cMPMXu2zClYKTgY48fj889r/lu73b5s2bIXX3zx8uXLldso33333ZgYdw9OysvD1q2oqPB4HOp0CAvD4MES9Nm5/k1bmzZt3nzzTRmbDUnl+hVP4eERw4e/MmDACxqNErdR6nR48EF4eAevL2EQwmLBL7948OlgWVmhJBF4lcGAqxsNHJ9u2rYNc+agvNwjcVi563/4cMyZI2WvJkfWWSQl4f77FbTNPDgYb7yBqVOrf3zbtm3x8fEHDx4EMHjw4ISEhO7SHdNgs+HgwarzMj3xC63RQKtFz54SbzjbsmXL9OnTDx06BODOO+9MSEjwZrMhCdW44ik7O/L33xWxuLcavR4dOkjwbtWfMAhx4QIOHlTiz2ttdDoMGICwMKSkpEybNm3Xrl1wbAGCyYQvv8R//gO7XbI4FAQEBiIuDnPnorNnjoup4z8zMxMdO7rVL8YTjEZs3Yrevav+9cKFC3PmzFm6dCmAW2655fXXX/fQssmSEuzdW9VOQapfa40GgoC2bdG9u0d2m1Uu7PJysyFpVVvxlJCQ0PnP34Sff0ZmprIaIAgCQkMxYYLrTy78EoPQg/OiHqLRoEGDjC++cHFJemkpvv0W//0vTCaUl7t+fFpgIEQRvXvj//7P40fQ1XjjGxPTeMAA7NkDi8WzoztLENCkCU6ehCCUvfPOO5XPw4xG44svvuiF52FFRTh8GOfOQaNx6/+MXg8A7dujUyenFz05Kz8//+2331682NFzxxTi+hVP7du3X7Ro0egbN+WZTPjmGy91qnKEKMJgwNixCA+XuxSFYRBi7Vpfuh2s3Iy4YsXC8vL6H57VwW7H7t348Uds2wYAVqtDq2kEAcHBMJvRogUeeAD33uvQyRhSqbbO4q67ZmzZMqukRInrLIKCxMGDVxw7NiM9PV0QhDFjxrz33nvNm3uvjYjFgvPnceoUrlyBTger1aF3PBoNdDrYbGjcGO3aoWlTr943eLTZkLQqVzxVJnfdK57y8rBihVLeq+l0uPtutJD//C7FUXsQlpZi61afCcLrNyNK1bbKbseJE9i7FykpSE3F5cuw22EwQBCg1Va9gJrNCA1F8+bo2RO9eqFXL4SFuf9f46Lr11kAbYE3AKWts9gLTAN2AOjVq1dCQkJ/RxrFeobViuxsXL6My5dRWIiKiqoDp66qnEcNDERYGGJiEBODyEinN0RKKCkpadq0aceOHYMiD3W6ecXTe++9Fx1d18FJGRlYuxYWi8yHXOp0GDgQSvp/qSBqD8LMTGV1bqzNjZsRez79dML//d9ADzWrKi1Ffj4qKmAyoUEDBAYiIkJxTxTGjt2ycmW83X4YADAUWAwoYZ1FJjAf+AywA7EdOrx69OhTGhlTpSYmEyyWqtdlnQ4BAQ4dbOJN0jYbktD1K56GDBmSkJBw660OHZyUno4NG+R8ndHr0bcvunaVrQCFU3sQnjiB1FQpZ/BFUUxK+vzXXz+9cOGYVqtr06bXAw/8s0cP1zcSFRdf+e671zZs+Mhut4WGNho37uWRI18wGLQ9e7q+l8vX5eaiRQuUlVmBL4C5QA6gAR4FFgFyrbMwAx8DLwPFgAF4FlgQHNxg2zZHu+JRNYo61On6FU/NmjVbuHChsyueMjOxbp0r59e7T6/HoEG1toQlMAj37sXFi1Je8MMPp2zd+uXQoY/37DnSaq3Ytm3p/v0b//a3D0eOfMHZS9lslg0blnzzzatlZYVarf7uu5+7uhlRENC5M9q2lbJyH/LKK3j33avHSuQDbwOLATMQDswEpgNevsdZA0wDzgIARgOJQGsAGg3uuQdr13q3Fv9S7VCnhISEQd5d+F9WJtmKp4ICrF4Nk8mrDRB0Oowapd43zQ5SexAmJ+PKFcmu9vvv3y5a9MiTTyaMHj3t6gcTEycnJ3+7ePGBZs2cmJ4/eDDps8+mXrx4HMCttw578snEal/eti0ca0vpb6xWREcjP7/ah1OBvwPrAQBxwPuAd9ZZHAemAxsBAB2BxcANd/+BgThzpoZTisgpshzqJIriihUrZsyQcsWTxYLNm5Ge7o3lM3o9IiJwzz0SNEDwe8p6euF90v44rlmTGBXV4p57nr/+g48++jqADRs+cvAiGRmpCxeOmjdv+MWLx5s2jZs7d928eZtuDlGvtUxTml9/rXFjVhywDtgEdARSgVHAcOCYJwvJA6YBXYGNQASQAByuloIABAFffeXJKtTh3nvvPXbs2FtvvRUSErJ27dpOnTrNmjWruLjYcyPu3bt3wIAB48aNS09P79WrV3Jy8vfff+/+ut/KU52HDUNgoAefu2u10Otxxx146CGmoEPUHoQu76K7mdlcfvr0nl69Rmm1N/Rdjoxs1qbNbUeP/lbvFUpK8pcunRUf3+2PP9aHhIRPmvRWQsKh226r+c5GUbt0vem//0XtL4DDgINAAtAQSAK6A9OAQqlLsACJQBvgA0ADPA2kAtOAGl7YysvxxRdSj69KQUFBM2fOPHHixKRJk0wm09tvv92xY8evvvpK8jmtzMzMZ555pk+fPjt27IiNjf300093794t7brf1q0xeTJuvRU6ncRxWDkX2qYNJk5Et24yr1P1IWoPQgl/UAoKsu12W2RkDe8Zo6NbXLlS16NIm83666//fv75uJUr37bZrEOGTProo9QHH5yp09X6rEthSxG9xG7HL7/UvbhJD0wD0oCpgB34AGgDJAJSvXFIAnoA8UABMAzYD3wK1LWbMj0dmZkSDa56TZs2/eqrr3bt2tW3b9+MjIzHHnusT58+lac7uc9sNicmJnbo0OHf//63TqebOnXqiRMnnn76aU+s+61cxjl5Mrp2hU5X1b7AzQtWNiJ++GEMHw6jUYoqVUOVr6bXkXx2oraFZHUsMDt8eMuMGbd9/PEzRUU5XbsOff/9/dOmfdWgQT2rH5W2n8E7Dhxw8B1AIyARSAEGAleAeKA3kOze4CeBe4HhwFGgHbAG2ATU31ZOr8fmze6NTDfq3bv3//73vy+//LJx48Z79uzp37//5MmTs7Ky3LnmmjVrOnToEB8fX1xcPHr06OPHjycmJoZ6aIvSn4KC0L8/nnwSgwYhJgZardNbWSrzLzwcffrg8ccxfLiUzX7Vw7HD0/yXhO0Tw8JiNBptTs75m//q8uXzERFNb/74pUunly17aceOHwA0btxm0qQ3+/VzdG+4p7teKdP//ufUY92ewG/AGmAqsA8YBIwGPgRaOjlsAfAWkABUAGHALCAecPRHp7gYSUmYNMnJMalOgiBMnjz5oYceqmw2tHTp0pUrV7p2qNPx48enT5++ceNGAB07dly8ePGIEV49OEmnQ4cO6NABJhMuXEB6Oi5dQnHxtdYHogi7varvqyBAFGG1IigITZqgeXM0b84Hge5S+6rRgwdx9qxkV5s5s29+/qWPPz59/WPC3NwLzz7b5i9/+dvTT9+wXubYseR584ZbLBVBQaFjxsy59954vd7R11atFl27wvPr5hRn7FisWOHC15UDHwALgRIgCJgKzAEcebNvB5YBLwKX/9yq+C7g9MFJ7dsjNdWFsskh1zcbat68+YIFCyZPnuzIF15/cFJERMQrr7wi41bFaux2FBejtBRlZddWxlWedBYSggYNVDon5CFqnxoNDZXyYdu9907LyTlfbYHo8uVzAFRbSgogLu6O6OhWQ4ZMWrLk5IMPznQ8BfFnz08VOnzYta8LAmYCR4GHARPwNtAZ2A4AEIHPgNuBYKAhcNefeyEApAI9gMeAy8AQYB/wlQspCOD8eaW0XfZLbdu2/f777zdv3tytW7f09PTHHnts6NChlac71cZisSQmJrZp0+aDDz7QaDRPP/10amrqtGnTFJKCADQaNGyI2Fi0bYvOnav+adcOzZohPJwpKDG13xHm5GD3bin3t1ZuqB8yZHLPnvdYLBXbty87eHBTbRvqKyrKAgJceait1WL4cPjRsd6OCg1FSYmb10gB4oE/gMNAe2AK8CXwODASqACWAhuBD4EXgGIgDtABC4FJgOsLq4KCcO4c6mxISRJw8FCnOg5OInVSexBaLNiwQcpNFJUt1jZu/OTChWM6nd79Fms10usxapS0l/QBZjOCgiT5ZtmBP4DbgW+BR4AEYNp1fzsZ+BY4AHQCDgJxgLvvOBo2xLZtHj+siirVcahTvQcnkTqpPQgB/Pqrgs43d1BMDOo8gtc/5eSgRQvJjhQGAPQFLgGnb1w1dgFoA/wNcLQHQr3CwvDTTxgyRKrrUf2qHeq0YMGCP/74IyEhoaKiou6Dk0iF1L5qFEDjxjh71pce4Wi1Ku0cWF4u7e7JcmAP8MxNvwXNgNuA+hsgOOXPzqjkJXFxcevWrVu9evWMGTNSU1MnTpxoNpu1Wu0zzzyzYMGCm+dLSc3UvlgG8Pbpo+4TRTRuLHcRMpG0U0Y2YANqbJrVApC0Fzt86Z2WP7n//vuPHDny7LPP2my26OjoP/7445NPPmEKUjUMQkRE+FiXlrAwKbc/+pDAQE80lqstWiVuTqXOfZ9KEBAQMH78eJvN1rFjRwePDyS18akE8AxBQKtWPnNTqNOhdWu5i5BJw4bSHucWA2iBGhogAOeBGhoguEwU2e+DSLkYhADQqpXcFThMEFT6gBBAQIC0O0aCgNuBdUC13TMXgD8AKQ+9M5t5EhORcjEIASAwEE2a+MAEqVaL9u19oE7PkTpOpgHnb1odOgcAUL0BgjtsNsS4shGfiLyBq0ardOqES5fkLqI+Go1650Urde2K06clvN7DwEZgOrAfuAeoAJYBm4APASdOUa5Xs2Y8EIdIuVR8c3EjoxEtWij6SaFWi86dFV2hFwwcKHk/nS+AfwNHgCnA/wfYgF+AGtoAuaNPH2mvR0RS4h3hNZ064eJFhR54W9lctEULueuQW//+MBik3ZMnAE8BT0l4xWpCQjB8uOcuT0Tu4h3hNTodbrsNOkW+N9Bq0bs3p9dw221SNob1DpsNd90ldxFEVDsG4Q1iYtCsmeKmHysnRUNC5K5DASq7jfvWG4LGjdGsmdxFEFHtGITVde0q8dlMbtJq0aSJL23w8LQpU+DhY8OlFBiIKVPkLoKI6qSY13vF0GjQrx8CAhRx26HVokED9Owpdx1Kcs89clfgpMcfl7sCIqoTg7AGBgMGDoTBIHMWajQIDkb//gq6PVUCgwHPPOMbZzEKAgYM4LwokdLxJbZmRiMGD5bzvlCrRWgoBg5U6OIdeU2froj79XoZjZg/X+4iiKg+DMJaGY24806EhsqwdkanQ2QkBg2CXu/toX1CkyZ4/HGldx7XaNCtG/r1k7sOIqoPg7AuAQEYPBhNmnj1tkyrRZs26NtXcYtXFeW115T+LiEwEEuWyF0EETmAQVgPrRa9eqF7d+h0Hn9Wp9UiIAD9+qFjR88O5AciI/HGGwgOlruOWgQG4uGH0b273HUQkQMYhA655RYMH47GjT11lyYI0GrRsiX+8hc0auSRIfzP88+jfXuF3jeHhmLxYrmLICLHcCWGowIC0Ls38vNx+DCKimCzSXPmuCBAo0FMDDp3Vu79jTJpNFi5El27oqRE7lJuZDTihx/QoIHcdRCRYxiEzgkPx6BBKCjAyZPIyoJG43rHL70edjuaN0fbtoxAF7VsiWXLMGECysrkLuVPwcGYPRuDB8tdBxE5jEHoirAw9O4NiwWZmbhwAXl50Gphs8Fur+cLtVoIAkQR0dFo1gwxMQqd2fMh99+Pl17Cm2+itFTuUgCjESNH4qWX5K6DiJzBIHSdXo8WLdCiBex2FBQgPx/5+SgqQkUFrNZrp1hotTAYEBCAhg0RHo7wcDRo4Bvb4HzFnDnIzMSXX8qchUFB6NMHy5fzm0vkYxiEEtBoEBGBiAi561Cxf/0LooivvpItC41G3HEH1q1T+qYOIroZV42SPxAELFmCGTNgNMowenAw7r0Xv/ziG43fiKgaBiH5j3nz8OWXCA72andWoxFz5+Lbb3kvSOSrGITkV8aMwf796NDBGwtxg4IQE4ONGzFrlsfHIiLPYRCSv2nXDgcP4qWXYDTCYPDIEBoNjEY89hhOn8aAAR4Zgoi8hkFIfkinw0sv4eRJjBmDoCApJy0rI3DQIKSk4OOPERIi2ZWJSC4MQvJbTZti+XIcOYJJkxAU5G5oBQUhMBAjR+K337B1Kzp3lqhKIpIbg5D8XOvW+PxzZGfjgw9wxx1VGzod7GMgCGjQAAEB6NwZCxbg3DmsWYPbbvNwxUTkXdxHSKoQGoopUzBlCkpKsG0btm/Hb7/h1CkUFSEw8IZjtmw2mEwIDETr1ujXD3feiTvvRFSUfKUTkYcxCEldQkIwejRGj67614oKZGUhN7eqGVDl/WJsrDz7EYlIFgxCUrWAgKo+eUSkWnxGSEREqsYgJCIiVWMQEhGRqjEIiYhI1RiERESkagxCIiJSNQYhERGpGoOQiIhUjUFIRESqxs4yROSHLBYUFKCkBGVlOHUKAEpKcPAgAgMREoIGDRAaKneJpBgMQiLyE+XlOH8e588jMxMmU1UvdbsdqakAUFSEnTurDh6x2yGKiIioarAXEwNBkLNykheDkIh8m9WKU6dw6BDy86HVwmyu+vjVP9jtVX+w2WCzXfvCnBxcuYJDhwCgXTt06YLISK9VTQrCICQiX1Vejn37cOQIBAEWC4Abcs4RdntVXh47hpMnERaGPn3YhF11GIRE5HusVuzdi4MHIYpOh1+NRBEWC3JysHEjGjTAkCFo3FiCy5JPYBASkY9JT0dSEiwWWK3SX9xiwZUr+PlntGqFwYNhMEg/BCkNg5CIfIbNhu3bcfp01USo51gsSEvDhQu4+27Exnp2LJId9xESkW8oLsa33+LUKY+nYCWbDeXlWLMG+/d7YziSEe8IicgH5OZi1SqYzRBFr45rtWLPHuTn4847ucXCb/GOkIiULjsbP/2Eigpvp2AliwWnT+OXX+QZnbyAQUhEipabi59/vrYpUBYWC9LTsWmTnDWQ5zAIiUi5SkqwerXMKVjJasW5c9i5U+46yAMYhESkUDYbVq9GRYXcdfzJYsHhwzh9Wu46SGoMQiJSqN9+Q0mJsp7MWSzYsgVFRXLXQZJiEBKREl24gJMnPbJl3k1WKzZsUFY8k5sYhESkOFYrkpKUmIIARBGFhThyRO46SDoMQiJSnH37FLFApjYWC3btUtDDS3ITg5CIlMVkwv79Cr0dvMpmwx9/yF0ESYRBSETK4hMtzWw2HD7Mm0I/wSAkIgWxWnH4sNJvB6/ik0L/wCAkIgVJS5O7AodZrVWn25OvYxASkYIcPuylwyUkYbHg0iW5iyC3MQiJSClMJuTmyl2EM2w2nDwpdxHkNgYhESnFhQvQauUuwhl2O86elbsIchuDkIiU4tw5RW8frFFFBUpL5S6C3MMgJCKlyMyUuwLnaTTIypK7CHIPg5CIFMFmQ1mZ3EU4z2JBTo7cRZB7GIREpAiFhdDp5C7CeaLIIPR5DEIiUoTiYrkrcIko8lQmn8cgJCJFKC+H3S53Ec4TBJhMchdB7mEQEpEi+Nx60atsNrkrIPf44JQ8Efkju91Td4RGY0THjn9p2vRWj1wdPnkjS9djEBKRIgiCp67ctGm355/f6Kmrk+/j1CgRKYJOB41vviD5Vjccuplv/twRkd8JDPTVIDQY5K6A3OObP3dE5HeCg+WuwFVGo9wVkHsYhESkCA0b+uryy0aN5K6A3MMgJCJFCAryyalRnQ7R0XIXQe7xwZ87IvJTkZFyV+A8jYZB6PMYhESkFC1a+N4KTJvNJ/ObrscgJCKlaN7c92ZHGzf2vZqpGn4DiUgpoqJ87I5Qr0dcnNxFkNsYhESkIB06+FIWiiJat5a7CHIbg5CIFKRzZw/2WpOWIKBZMwQEyF0HuY1BSEQKEhaGqCi5i3CMToeePeUugqTAICQiZbnjDuj1chfhgPBwNG4sdxEkBQYhESlLbCwaNVL6BKlOhwED5C6CJMIgJCLFGTJE0UtmNBo0bYomTeSugyTCICQixWnUCB06QKfU81K1WgwZIncRJB0GIREpUb9+Cl2Qqdejf3+EhMhdB0mHQUhESqTXY9Qoxd0UarWIjUXnznLXQZJiEBKRQkVFYeBABa0gFQQYjRgxQu46SGoMQiJSrk6d0KWLUu4LDQY88ICCgpmkwiAkIkXr1w/t2skfP5UpGBoqcxnkCYIoinLXQERUj23bcPIkLBYZhhYEGAx48EFERMgwOnkBg5CIfMPevdi3z9tZqNXCaOS9oJ9jEBKRzzh/Hr/+CqsVdrs3htPr0bQp/vIX+SdmyaMYhETkS0pLsXEjcnM9e2uo0UCrxYAB6NTJg6OQQjAIicj3pKYiORk2G6xWia8sCNDpcMstGDwYwcESX5yUiUFIRD7JYsGBA9i/H6IoTRwKArRaREaif38eK6EuDEIi8mEWC44fx4EDMJlgs7n47NBggN2Oli3Roweio6UukRSPQUhE/iAnB6dOIS0NJSXQ6epfUKPXQxBgtyM2Fu3bo1UrGAzeqpUUhkFIRH7FZEJ2NnJykJODoiKUl8NiqQpFvR4GA4KDERGBqChER/vAwYfkBQxCIiJSNbZYIyIiVWMQEhGRqjEIiYhI1RiERESkagxCIiJSNQYhERGpGoOQiIhUjUFIRESqxiAkIiJVYxASEZGqMQiJiEjVGIRERKRqDEIiIlI1BiEREakag5CIiFSNQUhERKrGICQiIlVjEBIRkaoxCImISNUYhEREpGoMQiIiUjUGIRERqRqDkIiIVI1BSEREqsYgJCIiVWMQEhGRqjEIiYhI1RiERESkagxCIiJSNQYhERGpGoOQiIhUjUFIRESqxiAkIiJVYxASEZGqMQiJiEjVGIRERKRqDEIiIlI1BiEREakag5CIiFSNQUhERKrGICQiIlVjEBIRkaoxCImISNUYhEREpGoMQiIiUjUGIRERqRqDkIiIVI1BSEREqsYgJCIiVWMQEhGRqjEIiYhI1RiERESkagxCIiJSNQYhERGpGoOQiIhUjUFIRESqxiAkIiJVYxASEZGqMQiJiEjVGIRERKRqDEIiIlI1BiEREakag5CIiFSNQUhERKrGICQiT2k0RgAAACxJREFUIlVjEBIRkaoxCImISNUYhEREpGoMQiIiUjUGIRERqRqDkIiIVO3/BympAOJ/VimPAAAAAElFTkSuQmCC'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized2' 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'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized3' 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'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized4' 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'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized5' 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'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized6' 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'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized7' 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4FadKkADaRkIXbjTlz5C5CwTweLF+O3bsbNJbcbuTmKvoWSiEYhJFAFGWeNegnr5eNhmBs2YK9e5X++TqdWLQIJ07IXYciuVz4/nscPy7D3arHg/JyLF1as8EW1YlBGAnKypR+lfTxenH8uNxFqNBrryn06eDZ3ntP7gqUx5eCZWWydVH69of57ruaXbjpbAzCSFBQoI4gxF+bVJD/8vOxebM6upQdDnz2WWCT4SKe14tly1BRIfODOt+M3u+/5+DtujEIVc/hUPrTo9N5POw9C8yiRepIQR9BwNq1chehJL/+itJSRQxX8Q1YW75cNTfNDYlBqHqlpWq6UAKorFRZwfJatEhNNzpVVfjf/+QuQjH27sXhwwoaxeb1oqwMGzbIXYfyMAhVT5IgtNttr7/+/A8/fCZFRefH3lE/5eejsFDuIgIhili3ThENINmVl2PTJgWloI/bjT//xNGjctehMAxCdfN4Qm0uiKL43Xef3HTTBW+8MXHmzDEuV9hbH6KIiopwnyRCrFunvr079Hrs2iV3EXITRaxZo7gU9PF48PPPfJR7BgahutlsIW3Csnv35gce6DNu3D35+Yc7dOg2derHRmPYV7BnEPpv3Tp5dhwMhdOJzZvlLkJu+/cr+kvudmPrVrmLUBIGobpVVwf56Lu09MS0aaPuuafX5s1rExKSn3wy6+OP13frdqXUBdZNRQ+95LV9u9wVBM7pxKZNchchK7cbmzcrtDno4/Fg3z4+oTiFu0+omy8IA9rtz+12ffbZvLlzn62qKjcYjHfd9cgjj0yKiYkPW411EAQ4ndw+7TxEEceOyV1EUPbulbsCWWVnq2Bkpihi61ZccYXcdSgDg1DdHI7AUvC335ZNmzbywIHdAC65pP/YsbNbt74wXMWdG4PQH0VFat18XF0DfKTl9WLHDkU3B328XuTmwmaD1Sp3KQrAIFQ3/4fn5eRkz5gx+uefvwPQqlX7MWNeufLKG8JYWb3OtQE3na6wEEajKmdA++a2avNGJzdX7goCsXcvunSRuwgFYBCqmz89MOXlJa+//vxnn83zeNxxcYn/+tfYe+8d3QCDYuohihxhf34VFWptEZpMqKxEUpLcdchh927VDMj0eLB3Lzp3DqxXKSIxCNWt/iD0eNyLFi147bXxJSWFer3httv+M2LElMTERv4c2Wg06fX677//rFOnS02mKGnKPY3yH6LIzuEI08oDYlnZQrt9i9t9PCHhn9HR10p+AkFQZUM2dHa7ypaV93pRWIjUVLnrkJs6bzjJD+vXr7jzzm6TJw8vKSns1avfp59umjBhvp8pCGDq1E+ioixffPHGrbdetHLlV2EtleoUjvv06urfc3IuPXr0vuLirPLyj3NzB+Tl3eR07pf+TJqUl6eyRrzbjUOH5C5CAVT1odFZ6rxW5ubuf+KJO4YNu2bfvh0tWrSZMePzt95a3q5dp4COnJCQPHv2V23bXpybuz8z8+Zhw67Zu1eysfyCoLLrhSykfcbmdh89dmx4Ts5l1dW/GwxNmjSZm5Y2S6eLr6j4+sCBC48fH+X1lkl1LlFElPSdCCpw+LBq+kV9fBt6kyBy2Uc1q/VAorq66r33ZixYMNXpdFgs0fffP+aBB8ZFRZmDPv7pnas6ne6GG4aMGTPT/2bluej1SE9HQkKIh4lwe/bg/vslmJctis6SktcLCyd4vRWCYEpMfKhRo8k6XRwAj6foxIlJxcVzAY9en5ySMiEpaQQQ6mI2Oh22bIHRGGrlqvPZZ+qbI6vT4fbbNTqy6SQGobrt3Vuz97QoikuXfpiVNfbEiXxBEAYNGjp69PSUlMaSnEXy4TZ6PVq2REyMJNVFrOJi9O8f6t7ilZVL8vMzXa4DAGJiBjduPNtobF3rNXb75uPHM222tQDM5m5paVlWa0hLK1it2LgxlAOoUnU1Fi1S3ygwoxF9+6KxNJcKtWIQqtuhQygrwx9/rJ82bdT27b8B6Nix59ixszt3vlTyc+XkZL/88mNr136LkCdg6HRo107rN6HnJYro0iX4rjaHY/fx46Orqn4AEBXVITX1lZiY6+p5fWXlkvz8kS5XDmoic47R2Cq4U7dqhW+/De6tKnbsGNasUV+LUK9Ht2644AK565AVg1BKbjcKC1FUhMrKU1uKR0cjLg5JSUhOln4B5e3bj4wf/9TSpQtFUUxNTR858sXBg+8VwjkaWpIp+YKAiy8OQ3ER54YbkJMT8Ls8nuITJyb+1eGZlJLyrJ8dnqJYXVz86okTU7zeSkGwJCWNTEkZr9MF3HIfOBCzZgVcttrt3YuNG9XXIgTQti0uuUTuImTFIAxVSQl27MC2bdi/H+XlMBqh09XMDfB4oNfXjGcRRbhcSElB+/a4+GJcdBGio0M6b3V19auvvjp58pSqqsqoKMuQISOHDRtvtTZEb6NvkbZ5856rrCwzGIx33PFwoIu0mc1o1y58BUaOp57CV4GM2BVFV1nZuwUFz3g8JwTBGB//r9TUF/T6lIBO6nYfKSh4qqxsISAaDOmpqS/Gx98L+Ht3FRWFMWMwZEhA54wE27Zhxw5V7rXZtCmuuUbuImTFIAxSZSV++QUrVtSsgxXQgxyLBS4XWrbENdegV69gegiXLFkycuTInJwcAH36DB43bk7Tpq0CPop/PB7355+/XlBwdNSol07/eWlp0fz5kz79dK7X60lISP7PfybcffcInc6vNm+jRmjSJDzlRpavv8bkyaiq8uvFVVXLjh/PdDh2AoiO7p+WNisq6qKgT11dvf748VHV1b8BsFh6pqXNtlj86m+Pjsb77+NCGVbuk9m6ddivznkoSUkYNEjuImTFIAzY8eNYtKhmLECIzwPMZgDo2xeDByMuzq+3bN68OTMzc+3atQC6deuWlZWVlnZliOMp6rFu3U/Tp2ceOLBLp9N/9dWeFi3a1HrB7t2bp0/P3Lx5LYAOHbo9+WTWebew0OnQqhVHyviloAADB55/crrTuff48ccrK5cCMJnaNmr0Qlzc7VKcXywr+7CgYKzbnQ8I8fFDU1OnGwz1DasQRURHY/16LU6PWbs2mH5sf6xf/0WTJu2bNw9sBpT/4uPx97+H6djqwCAMQFkZPvkEGzbA65XySYDBAJ0O/fvj5ptrorFORUVFkyZNmjt3rsfjSU5OnjBhwogRI/R6fUEBCgqkX6glN3f/nDlP//jjFwBatGjz6KMvDhhwzmvr6tVLpk4defRoDoCrrhr81FP1tVB1OnTsyFWd/FX/Y0KPp7SoaGpxcZYoOvT6hOTkcUlJmYIg5SQ+r7eqqGhGUdE0UbTrdNFJSWNSUsYJwjm/qQMGICtLwvOrxpo10k9Oz83dunBh5p49q9u2vXz8+LVhevwfF4ebbgrHgVVD//zzz8tdgwqIIn76CVlZyMuD2y3xYwBfrB46hGXL0Lgxmjat/QKXy/Xaa6/dcssta9asMRgMI0aMWLx4cZ8+fXQ6HQCjEUVFUpZUXV319tsvPvXUPXv3brdYoh988OmXXvq4ffvO9bylVav2t98+3GqN3bHjt/37//jyyzdttopOnS45e4qFICAujjMIA1BdjS1b6lyj3FtW9mFe3t+rqn4ExPj4oc2afR0TM1AQJF43URBM0dF94+Lu8njy7fatNtvq8vJPDYYmUVEdz35xTAxGjULLltKWoA55eVKur1ZeXvDxx4+9997DhYUHY2MbXXddZqtW3cMUhFar1p/Zs0V4fiUlmDMHhw83xPKJUVHo1AnDhp1qGi5btmzkyJG7d+8G0L9//9mzZ1941uOXfftODVINReiTEQsKjsye/dQ335xzFKtej1atQh0opCmFhRgwoPZ3z2Zbdfx4pt2+DYDV2ictLctsbohNBKqqVhw/nulw7AAQHd0vLW1WVNQZ/XXx8fj5Z+lHR6vCxo3YvVuC43g8rmXL5i1a9JzNVqbXG6+55uF//GOi1RrGm8e0NAwYEL7DqwCD8DyyszFrFhyOhhsVbTIhJgbjxqGsLPuxxx779ttvAbRv3/6VV1654Ya65+2VlSEvL9QKT5+MeNFFvZ58MivoyYj1zGs0mbQ+YykIDz+MtWtrer9drsOFhc/8NaSzWWrqCwEN6QydKLrLyhYUFIz3eAoBXXz8kLS0mXp9IwBmM/75T4wc2WC1KMuuXdiyJdSHFDt3Llu4MPPIkZ0AOnbsP3RoVnp6HS1vabVqhStDWkFB9RiE9fnlF7z3ngzr6LtcJXv2TNu5c5bL5UxMTBw7duzo0aNN5x5dKorYsyf4mdfnbcYFoc7GZVpa46ZNkZgYyoG1aOdO3HsvbDZbUdH0v57VWZOSnkhJGSsIFllK8nhKioqmFRfPEkWnXp+YnDw2MXG01Wpavly7n29uLtatC34AXX7+3o8/fnzr1qUAGjdud889M7t0GSxlfecgCOjUCZ3CNRBHHRiE5/TTTzKsHCiK7v37F2zdOt5uLxQE3aBBQxYsmNmo0fnX9iwpwZEjAd+N2u22jz+e89ZbU2y2sExG9K19+u670xwOu8US/cADY2bMGGexBL/2aVh4vRDFmqesgqDAFcFFUezX78tffhnjcuUCQlzcbampM4xG+R/EOZ3Zx48/Vln5LYCoqPb33PPKggWy7fZcJ98aF9XVKCmBxQKLBSkpiI0Ny7lKS/H998Hcj9pspUuXTv3++1lut9NqTRg8eNx112UaDA20bLnJhEsu0ehj3ZMYhHVbuRIffdTQKZifv2LDhszS0h0AGjfu16PHrMaNOz32GDp08Ovt2dmBNV4DGuoZitMHoLZp0+bFF1+8/XZJBvcHy+mEywWXC253TQqe3fwVBBgMMBphMsFolHGQ68aNG0eNGvXrr78CMJt7NG6cZbFcLlcxdaqqWnb8+EiHo77H2A2mqAhr1mDNGvz6K/bvR1kZTCbo9TAa4fHA64XDAZ0O6em4+GJcfTWuvBJdukhz5yOK+PjjwG5GRdH7yy8LP/30ifLyAkHQXXbZkLvvfjkurkG3BzQYMHhwuG4O1IJBWIdt2zBnToOmYEXF/i1bnj506AsAsbEZXbu+1LJlTVSYzZgwAc2bn/8gNhsOHPDr7zCIyX+h2759xfTpmTt27ADQr1+/WbNmdWrI7hhRhNOJ6mo4HBCEAEbZ+l5sNMJigdnckIl49OjRiRMnvv32216vt0mTJr16PZ+b+6DdrqzWqo/Z7OrSZd7//vdcWVmZ0Wh8+OGHJ02aFB8fwGJDIcrPx0cf4b33sG8foqJQWenXH4LZXLNFxo034oEHcPXVoSbi0qUBDBzdvXvVRx9l5uZuA9ChQ98hQ7JatKhvbHaYGI24666GP62yMAhrO3YMzz0X6pL//nO7q3bunLFz5zSPx24wRF944ZiLLhqn15/qPPTNN5g61a+RlkeOoKSkvktAaemJ+fMnB7ccTCh8q2zrdO4FCxaMHz++sLBQp9MNGTJk5ky/On5D4vXCZoPNBiCkWSa+CIyKQnQ0DBJPUajF6XS+/vrrEyZMqKioMJlMDz300OTJk43GuOuvR0FBWM8cDIMBF1+MhQtRXFz3VNewnn3dOkyZghUroNPVfMhB0OkQEwOzGY8/jocfDr55tHUrdu48fwYXFx/+4otnfvnlQwBJSc1vu23KFVfcF+QpQ9asGa6+Wq6TKwWD8AwuF556CoWFDbNgoHjgwIebN4+trs4HhNath3brNt1iqWOugsGA9u0xdqwfRxSRnQ2Xq476fQuEzp37bFVVeXALhAZNp0OzZqfmDpaUlDz//PPz5s1zu93+DAUKnteLqqqamSVSfaK+ODSZEBsbplkCS5YsyczMPHDgAIDBgwfPnj27deuajZN27MD99zfcXZqfYmKwdClS/+rPO33xo65du86ePfvK8AxJ3LQJjz6K7dths0n28UZHQ6fD2LF47DFYAh+HVFSEn36q7zGh02n75pvpS5dOc7nsJpN10KAnBg8eZzTK9tTcaESvXmhde2MuzWEQnuGDDxpoI5UTJ9Zv3JhZWLgOQHJyz549ZzdqVN9chagoDBmCvmCcVVQAACAASURBVH3Pf2SHA/v21b4nlWTLiODodEhIQLNmtX+ene3v5JAgVVfX7Gkbjm+4Lw6tVkRHS9hZunv37tGjR//www8AOnToMGvWrIEDB9Z6zcKFyMoKvukjObMZc+fi0rO+vKcvhzt48OA5c+a0atVKqpOWlODRR7FoEaqrw/LxRkcjOhrvvIPBgQ/b/OKLuu9URFHcsOHLTz4ZU1SUKwhCz5633X33y8nJLUKvNhTcldeHQXhKTg6mTAl7CtpsR7ZseerAgYWAaLWmd+36YuvWfs0Di4rCyy/Dn8culZU4eLDmAiHhJoJB0OlgNiMj45xh4c9yAQHzeFBWJv0KQGfzjS9NSAi9p7S4uHjixIm+TsWkpKRnn322nk7F8ePx3XfSLKEQIosFjz12zo0mfBukTJkypbKy0mKxjBw58plnnokNeVTGd9/hnntqnvaGVXQ0rr8e77zj7zrAPnX2jh48uHHhwlH79v0K4G9/6zF06Oy2bS+TtNggpaejXz+5i1AABmENUcT48cjLC+PF0+Op3rPn1e3bp7jdlXq95YILRl588TNGo7/XBYMBPXrg//7PrxeXlmLXrpK5c6XcVj5QgoCoKLRpc54BCC6Xa968ec89J9E4C6cTpaUNuheOICA2Nph+NAB//d9//vnnS0tLjUbjv/71rxdeeCElpb6NkzwejByJ336TOQutVtx7L0aNOs/Ljhw58tRTTy1cuFAUxfT09BdffPHee4Ocperx4Mkn8cYbDdcgNpuRlITvvw9g+8yqKnz11anVLUpKji5ePHHVqrdF0ZuY2PTmm5/r2/dBQVDEiCfuTX8Sg7DGpk2YPz+MT1/y8pZs2DCysjIHQLNmg3v2nBMT0yrQg5hMmDgR6enneZnb7V6wYMEzz4w/caJQrzfccssDI0ZMSUwM85iUM/lSMCPD3+do51pSPLCzVlWhqgpeb0PPdhAEmM2BNRwAAMuWLcvMzNy5cyeA/v37z5o166KL/No4yePBqFFYt062LPQzBU9av359ZmbmunXrAPTs2XP27NmXnt2dWq+qKvz97/j9d383pZKKIMBqxWefBbBR0apVyMuDy+Vcvvz1//53gt1eYTCY+vV76LbbppjNCpqmEBuLm2+WuwhlYBDWePJJ5OeH5cjFxZs3bMgsKFgLICmpa48eWWlpVwV3KJ0OXbue5+qzYsWKzMyaWQpXXdVv1KhZbdo09KIROh2sVrRqFfBg9LM3mQpgnEVFRbgeGflDEGAy+b+a+N69ex9//PGlS5cCaNu27QsvvBDo3EqvF5MmYckSGbLQbEZmJu4LcJyjKIoffvjh2LFj8/PzBUEYOnTo9OnTG/vXHiktRZ8+2LtXtoFCVivefNPf3YbLyjB58pIPPhhVWHgQQJcug4cOnZ2aqqwRKUYjLr/cr3lZWsAgBIBdu2oWFJXc3r1vrF//iCh6zea0rl2nZGQ8EGKviNGIGTOQlFTHr/bv3//0009/8cUZ89bt9prJhZLv03QugoCUFDRuHHyrLJhxFvKmoI9/WVhaWjp16tSsrCyHw5GQkDBu3LjMzMyoqCCXEfnsM0yf3nBZqNfDbMasWbjiiiCPUFVVNWPGjGnTptnt9ujo6DFjxowbN85cz/ZjQEUFLrsM+/bJsNjh6axWvPUW7rnnPC/bunVrZmbm6tWrATRt2mHIkFkXX1x7xJPsBAEJCcEMBYpUDEIAmDkT27eH5SpaWXlw6dLOGRn/6tJlktEowVwFoxHXXYdajQffxWXq1KkOh+Psi4vHg7w8lJeHPSZ0OggCWrSQYJWKs8dZjB8/PuZcm/nabKislDkFfertI/V6vQsXLnziiScKCgp80yhnzJiRlpYW4jn37MGIESgpCXscWq3IyMDs2RI8VTr9pi0jI+Oll146V4PY5ULfvti8WRGTRqxWLF6Ma6+t+7enj3hKTEy68cZn+/VriEm6QTAYMGAAkpPlrkMxGISw2fDoo8GvWH1eLleZJBF4Ulwc5sypaW/5391UXo68vDA2DXU6xMUhPV3KyXV+jbNo+NEx9RMExMTAaq3141WrVmVmZm7btg1Anz59srKyunSRbOMkpxPz5uGDD+B217lzYahMJhgMGDMGd94p5ePXFStWjB49evv27QCuvvrqrKyssxcb+ve/8emnCpouEhODTZtq795X54inkpKUTZvCeGEJmsGANm3Qs6fcdSiJIgYvyWvLlvCusSxtCgJwOmu2LF+/fv1ll112//335+fn9+zZ85dffvnggw/O9dAlLg4XXIDUVOh0Ev//9T0RzMhAixYSTzFPT0//4IMPfvvtt0suueTIkSP3339/7969fQMuani9ykpBAKKIysrTr3+HDx++7777rr766m3btjVr1uz9999fuXKlhCkIwGRCZia+/hp9+sBslvJTMJlgNuPWW7FsGe66S+JBSP369du0adP8+fMbNWq0cuXKrl273nfffYWFhSdfsHAhPvtMQSkIwGbD9def0TxdtmxZ165dMzMzS0tL+/fvv2XLlvnz56ekpLRti+RkxW3N6Ouz6NZN7joUhi1CzJyJbdvkLiIQej0uu+zI2rVBDkn3enHiBE6cOGPThSD4zhYTg7S0s9s/Ejtnw7e4uCHmCwZBp0NKiq26evr06b7nYVar9Yknnjjv87DQHTyIN9/E999Drw8pQqKjIYq4807861+odzaHBEpKSqZNmzZr1iyn89S+Y8eOmS66CJWV4T11EKxW3HcfXn/9jBFP7dq1mzlz5uAzH7s5HPj6azgcCvqGGgy44Qa/piNritaDUBQxfLgiHj/4yTcZ8Y8/pjidfjw8q1dlJYqLT62+4k+XqW8GuW8N6qQkJCaGe9HNM9QeZ5GZOW74cLMiV8UQRfHLH38cM2FCbm6uIAi33Xbbyy+/3KJFwy0jUlmJH3/El19i1y5ERcFm82vfZqMRUVFwudCrF267DX36NOiaI7UWGzIaX9mz54Zw9PSGzmIpvfXWqZ9/PsvpdNY/4qm0FN99F5b+6iDo9ejT5/zzrzRI60F4/DgmTFBNEJ4+GXHQoMGvvSbNslXV1aiqQmUl7PaaLj3frnz4a3kyrxd6PUymmqWnYmLk7PA5Y3Ds3/724tNP337jjbJVU5eN27aNGj/+1w0bAPTo0SMrK+vyy2XbOKm6Gps2YfNmbNiAnByUlsJohF5fc0MjinC54PUiORkZGejZEz16oHPnBr2/qWXZsmWjRo3atWsXAKA/MBuQbVOnuniBhcATQM2Ip5dffjk1tb6Nk/LzsXKl/FnoW5GjbVuZy1AmrQfhhg14+21FrFZVvzMnI3a74oqsefOuDNOdndd7ap8+vb5mYz759uOr24pvvsl84okdu3cD6HfFFbMmTeok3x54Jx3Nz584c+bbH33k9XqbNm783NixD44cqVPSNr+iiJKSmoUHdDpYLIiPD2IlgPCqrnY1aTKvrOw5oAwwAg8DkwAldOetAjKBbQD0+r7PP581frxfGycdPYrVq+XMQoMB3bqhfXvZClA4rQfh4sVYvFjagZTi/v3v7N07v6xslyAYkpN7dOz4ZNOmwU8kcjiKtm+flJ09VxQ9UVHJnTpNaN9+hNWqf/BBDY/78npx4oTb5VrwySfjp04tLCrS6XRDbr115vPPN5JpSLjT5Xr9vfcmTJtWUVlpMhofuv/+yWPHxsXFITGxZss78tv772PECFRWFgGTgLmAB0gGJgAjALn6Ig4DzwAfAgCaA1OAe5s0EQ4f9rd35PhxrFgBj0eG54V6PXr3RkZGQ59XRfTPP/+83DXIaeVKHD4s5QF//fWB7dunNG06sGPHJ9PTryst/WP79olmc0pKSq9AD+X1urKzX1u16paCgjU6naF9+xFXX704La2Pb0p+69Zo00bKytWkqgoul06n696584NDhwJYv3nzlh073v7oIwhCr65dw70HXi1Lfvxx8NChny5e7HQ6B1977dKFC+/5xz9qnhh5vQjz6JjIc8stvm0XrcD1wGBgN7AH+B5YAnQAWjZsOTbgBeAuYDNgBZ4GPgO6A4IgoH17+NkTERODli1x+HBNX0vD8K3x0K8fV5A5D623CF94AdnZkh0tJ+fTtWvv7tkz64ILTi2D9ssv9+XkfDp48Nb4+AD67o4dW7Zhw8iyst0AmjTp37Pn7FpvHzjQ3wWfIlBBQa1rSfaffz727LPfLl8OoH1GxiuTJt1wzTUNUMjufftGP/vsDytXAujQtu2sSZMG1trk1LfQjpJ6RxVu9WrceGPNGK7TLAFGAjkAgMHAHKBV+GsRgS+BMUAuIAC3AS8DZ4x46t4dGzcGcESXC+vW4ciRhugmNRoRH4++fYNeEF5DtP73Ke0Upd27Z0dHt2zf/pHTf9ilywsAsrPn+nmQ8vLsFSsGLVt2bVnZ7ri49v36fdO//09nh6gCh5U3kLoW2mqfkfHNRx/99PnnHdq2zf7zz0FDhlx7xx279u4NXxXFpaWjxo+/uG/fH1auTEpIyJo8eceqVbVT0Ef5j6CVZN68OpfVvhHYBUwFYoClwIXAOKB2WkpqI3AFcAeQC/QA1gKf10pBALt24eDBAA5qNOKqq3DFFTCZwjjiTKeD0YguXXDddUxBv2g9CCW8L/N4qouKNjRrNkgQzhhyFx3dPCmp+/Hja857BKezZPPmcUuWdDpy5FuTKbFbt6k33rg9Pb3u7QMbYPdghTr3mqL9r7pq24oVWZMnx8fFLVuzpss114waP76svFza87tcrtlvvZXRu/erb7+t0+n+c++92b/+OmrYsLr7Y0VRNYOSFcDpxNKl53pmbwHGAnuAewE7MA3oAHwASN6ndRQYDvQGfgWaAvOB34G6x/2KIj77LOATNG+OW2/FBRfAYJA4DnU6GAxo1Qo33YQLLlDcGDfF0noQSvhFqa4+Loqe6Og65orFxLS02fLqea8ouvfte/Orr9rv3DlNFN2tW997003ZHTuO1enOOY1Lu51t9d4CGI3GUcOG/fn77yMffNDr9b769tsZvXvPfustjz/T6PywbM2arv37Z06YUFpW1v+qq7YsWzZ/xoyUOhdBP8njabglz1VuzZrzDi1KBz4AfgMuBY4A9wO9gXX1v8dvTmA2cAHwJmAARgJ7gP/Uc5202/Hxx8GcyTeM85Zb0L49DAYJBlT5MrVVKwwejMsvZ0MwMJq9mtYIdtH/epwrWs8Zufn5K775pvtvvw232wsbN+43aNCWyy//wGw+z/aBGv2i+7d0Y3Ji4uwpU9Z///2VvXsXlZRkTpjQ67rr1v72Wyhn3vvnnzfee++1d9yxMzu7bevWSz788KfPP+/oz4B0QdBw+z0w33/vZ59/L+AX4H2gMbABuBy4DwhxH7UlwAVAJlDx1wid2cD514/Pzg7+OYXZjO7dcfvt6Nmz5lFyoDM4ffkXH48uXXDrrbj8cgmWvNcg+ebNKkNQS7LUzWJJEwR9VdWhs39VWXnIaq1j0l9Fxf4tW54+dOgLALGxGV27vtSypb+b0ml0kSSXy/8hd90uvnjNV18t+fHHkc88s3nHjqtuvnnwtdfOefHFVgEOoSstK5s6Z07Wm286nM6E+Phxjz6a+Z//RPm/5orXC4eDY0f98eOPfq2AAwAQgPuAW4EZwDTgQ+B/wBhgHBDoP/VuYDTwAwCgAzALCGC+k8WC339HKGOzDAZkZCAjAw4Hjh3D0aMoKEBl5al1DwB4PDW7uwgCRBFuNywWNGqE9HQ0aRL2NQ4jntZHjb7/PpYvl+xo3313aXX1sVtu2X/6Y8KqqsOLF2e0bTusV68zxssUFKxdtuxaj8dhNMZefPEzF1yQqdf72z6NisI996DOkRkRrqwsiEdu1Xb7q2+/PWXWrMqqKovZPPLBB5/JzIz14ybI6/Uu/PLLJyZNKjhxwjdVccazz6Y1Ok9jvQ4GA/e8OS+vF1ZrcJsO7geeBr4AALQAJgN+7hpcDEz8a6piEvBsEFMVTSZMnownnwy05vPwLd5us8FuP9Wh4FsDz2pFbKyGH46Egdb/LZs2lXK6c4cOo6qqDtUaILp16zMAag0lBZCScklMzN9at773ppv2duw41v8UBKDXo95FnSJXUKObLGbz2BEjdq5Zc9fNN9sdjmmvvdbxqqtWr1sHQBTFtz/6qOfAgdF/+1t827bX3Habby4EgOw//+zav//9I0cWnDjR97LLNv/00wdz5gSTgoD/zRwty80Nemm3NsDnwHKgE5AL3A/0A7bX+xYXMBvIAF4FdMB/gGxgVBAT9p3OwGZQ+EkQEBuLtDS0bIm2bWv+06oVmjRBfDxTUGJa/+dMT5cyCFu1uisj458bNoz+9dd/5uR8duDAB8uWDThw4MMePV45e/6DTme84YZNl1/+gcUS8D6nLpdWV84NIVFapKd/8sYbv3377aU9ehwvLGySmgrggczM/4wZc3GHDu/PmTNv6lSj0Xjd3Xe/tmABgKZpaYVFRc2bNn3/1VdX/Pe/nTt2DKlyjpc5n5ycEP8Y+wGbgPlAI2Al0BW4Dyis65XLgK5AJlAK9Ae2APOB4LfYCOc8HWoIWu8atdkwYoS0k1t9S6y9UVq6S6czhr7EWp2sVrzxhrSHVANR9K04EiKv17tp+/aeXbp8unjx3Q89lDV58qhhw07+9r5HH/108eKty5df2K7dtp0727dpYw59SJUgIClJzqWs1eCTTzB8+NlT6YNQAkwDZgFOIBEYC4wGfM909wKPA0sBAO2AmcDg+o7knyZNcPRo6Ich2Wg9CAE89hhOnJC7iAB17ozHH5e7iIbn20pRum/spYMGHTt+fP9vvxlOi6jDR49m9O49bMiQuVOnSnUi6HSIj2/QPY1UaO5cPPGEhMsPZAOPAd8CANoDk4FNQBbgABKAcUAmIM2o8ZgYSfKbZKP1rlEA3bqprMM9Kkqry21LetNWbbdv2Lp1UP/+hjMbas2bNu3eqdOa0OZa1Kb5201/OBzSPkttD3wDLAbaANnAUGAa4AaGA3uBsVKlIPyd1EPKpaoECI9evcIxmzCMvF509mvvF6rP8cJCj8fTolmzs3/VslmzvGPHJD4fs/B8wvMvdBPwB/AQ4AFSgU3AG0BQI54ocjEI0batnNvMBqFFC61OIgzDglHCOY4p8ZlO7nRM52Y2h+kvMQq4E/AAHYCw3EJyoy21YxBCEHD11ar5KpvNGDBA7iLkImmWpDVqpNfrD9W1C9ehvLz0Jk0kPBdEUWX973JISFDrcCIJ1+UgWfCPEwD695e7Ar/p9Vp9QAiJg9BiNvfs0uWbZcvcZw4aPnz06Kbt26+65BIJzwVoeXFYfzVporK+mZOCm1xKysE/TgBITET37ir4I4yKwqBBar1rloakH9KoYcMO5eXNfffd03/4zEsvAXjkgQckPBHAIDy/Vq0aYpe+cGjbVu4KKDRavqae4fbbsXmz0hcAMRg03C/qo9dLeLG86+abf1i5cvSzz27ZseP6a65xOBwL//vfn1avnvPiixe2ayfVWQCJ8ztStWqlysXJjUb06CF3ERQaBmGNRo3Qpw9Wr1bun6LZjLvu0vxUNJMJTqeE4wsXZGVd3qvXG++///nXXxuNxh6dO3//ySd1768bCk234v2l06FNG+zaJXcdAbJa0b273EVQaPj3ecrtt2PdOoUGoU6H1FRcdZXcdchO6kFNgiA8OGTIg0OGSHvYWufQ/P2Lv669FtnZSu+YqaW6Gr17y10EhYbPLU4xm/HQQwqdU2gw4JFHOAJftQPVGYT+ue469Y3AzMjQ6nSmCMIgPEOnTrjiCsVlYVQU7r4b0o7nVzHVhYpOx2eEfurbV6FdMucSFYW77pK7CAoZg7C2oUPRpImCnumYTOjaNaRtPyONxaKmEZiCwC15/Wc2Y+BANfV86PW4+265i6CQqeeC0lD0ejzxBGJjFXETbzSiWTP85z9y16EoJpPKliuzWOSuQE0eeURNvaMZGZw7EQkYhHWIjcWECbBaZW54GAxITcXYsQpqniqCIMBqVU2rwWhUxC2VelxzDeLi5C7CP7GxGDtW7iJICgzCuqWkYOJExMXJdhEzmZCejgkT2Jyoi9UqdwX+EQQ1tW6UQRDw7LPq+Gczm3HHHXIXQVJgEJ5TSgomT0aTJjIMzoiKwgUX1LRKqQ46HSwWFTQKDQa1DnOV1T//qYJvfkwMpkzhxxshGIT1iY/HxIno3r1Bs9BkwvXX4/HH1Tc6skFFR8tdwfkIgmr6+BTGZEJWltIbhcnJkHwZPpILg/A8jEY8/DD+/W9YLGF/VmcyIS4OTzyBf/xDBa0dmel0ir5S+gaL8ulusO66CxdcoNynq1Yr3n2XH2/kYBD65dJLMX06unZFVFRYIkqng8mEvn0xcybat5f++JHJaoXBoNBbBkFAbKzcRaiYIODjjxU68cRiwT/+AcmX4SMZCaK6RqLL7cABfPQRcnPhcsHrleCAvit55864806kpUlwQG3xeFBUpLjZFIKAhAR2bYfujTcwZgyqqkI/0irgaqAPsCrEAwkCmjbFnj2K7o+gQDEIg3HwIJYswbZt0Othtwd5EIsFXi8uuww33MAIDIHDgbIyBWWhb3YHL5MSueMOfPMNbLYQD7NKqiCMjsa6dbj44hAPQ8rCIAyezYaNG/Hzz/jzTxgMcLvhcp3nLSYT9Hp4vbjwQlxxBbp04agzKVRWwmZTRBb61tdOSJC7jshht+PSS7FrV4hLr62SJAitVnz0EW6+OZRjkBIxCCXgduPgQRw4gAMHkJeHsjLY7TV/t4IAoxFWKxIT0bw5MjKQkYHmzRX6YEvFKipQXS1zFvo+7MREOWuIRMXF6NWr5nlEsFaFHoRWK155BcOHB30AUi4Oe5KAwYC2bbnSkqx8I1NkzEJfCrItGAZJSVi3DpddhsOH4XDIU4PViunTmYIRi6NGKVLExsq29JogICoKiYls6YdJo0bYsAGdOskz0d5qxTvv4JFHZDg1NQwGIUWQmBjExzd0GgkCoqO5JV24JSTg559x550NupSCyYTkZKxYwb2WIhyDkCJLVBSSkxtofqEgQKdDYqIKlrmJCCYTFizAO+8gNrYhRplFR+Oqq7BnDzegj3wMQoo4ej2Sk8PeTepbOyYlhQN/G9iddyI7GwMGhPH2w2JBfDzeeAM//YSUlHCdhZSDQUgRKiYGKSkwm6WPQ98ciaQkxMXxoaAsmjTB0qVYuhQXXijxjE2zGVYr/u//kJuLoUOlPDIpGYOQIpdOh/h4JCfXbFURYmj5juAbFJOYyIUmZde3L3buxJdf4pJLYLEgKiqko8XFIS4Oo0cjJwcvv8z10rWF8whJG0QRdjuqq+FyQRACmGXhe7HBAIsFZrPMmzXTOfz5J959FwsXoqAAej0qK2v9flWd8wh9c3wdDvTti2HDMHgw18XTKAYhaYwowumEywWnE253zQ9PRuPpGenbTdBohMnE/FOLQ4ewciVWrMD69Th0CF4voqLg9a6qqrpar+8TE7PK5YLDgaQkdOiAvn3Rty8uuyzU1iSpHYOQtE0U4fWesYC6Tgedjg//IkNpKY4fx+rVq4YPv7pbtz7vvLMqJQVpaRzhRGfgcw7SNkGAXq/cje8oNAkJSEjAsWMAEBuLLl3kLogUiR0+RESkaQxCIiLSNAYhERFpGoOQiIg0jUFIRESaxiAkIiJNYxASEZGmMQiJiEjTGIRERKRpXFmGiCKQ243yclRVwW5HTg4AVFVhzx6YTLBaERvL3ZTpFAYhEUUIux1HjuDIERQUwOGoWTjP68X+/QBQWYlNm6DXQxDg9UIUER+P9HSkpyMlhYvLahqDkIjUze1GTg6ys1FWBkE4tafIyaXUT/8vp6+vXlyM0lJkZ0MU0aoV2rVDUlID1k2KwSAkIrWy27FzJ/buBXAq/wLi9cLpBID9+3HwIOLi0KUL0tOlLJKUj0FIROrjdmPHDuzZA1GExyPBAUURbjeKi7F2LWJicMklSEmR4LCkCgxCIlKZo0fxyy9wuaSJwFpcLpSU4Kef0Lw5evfmzoWawCAkItXwePD77zh0KMiOUP+53cjNxdGj6NsXqanhPRfJjvMIiUgdqqqwdClycsKegj4eDxwOLF+O3bsb4nQkI7YIiUgFiovx009wuSCKDXpetxtbt6K0FJdcwikWEYstQiJSuhMn8MMPcDobOgV9fNMz1qyR5+zUABiERKRovrZgw3SHnovbjaNH8fPPctZA4cMgJCLlqqqSPwV93G7k5WHzZrnroDBgEBKRQnk8WL5cESno43YjOxuHDsldB0mNQUhECrV+PSorz1gUTXZuN379FZWVctdBkmIQEpESHT2KgwfDMmU+RF4vVq/mwJmIwiAkIsXxePDLL0pMQQBeL8rLaxY4pcjAICQixfnjD7hcchdxbm43Nm+uWa2bIgCDkIiUxeHArl0KbQ6eJIrYsUPuIkgiDEIiUpadO1XwBM7jwd69bBRGCAYhESmIL2AU3hw8iU8KIwODkIgUREWz9Nxu7NkjdxEkBQYhESlIdraih8nU4najoEDuIihkDEIiUgqHAyUlchcRCN963KR2DEIiUopjx6BT1TVJFJGbK3cRFDJVfemIKKLl5ampX9TH6YTNJncRFBoGIREphRqft+l0KCyUuwgKDYOQiBTB41Fl08rtRnGx3EVQaBiERKQIFRUwGOQuInCiyCBUPQYhESlCVZXcFQSLuzKpHYOQiBTBblfBymp14kJrascgJCJFcLnUGoRut9wVUGhU2CVPRJHI6w1XEMbEJF100YAWLTqH5eiA1xumA1MDEUSV3oMRUWTZvRubN6syVPR63HOP3EVQCNg1SkSKoNdDEOQuIijqWg2HzsYPkIgUISpKrYliMsldAYVGnd87Ioo4VqtaW4RWq9wVUGgYhESkCLGxqtmPt5b4eLkroNAwCIlIEcxmVXaNGgxISpK7CAqNl0PMSgAABBpJREFUCr93RBShEhPlriBwgoCUFLmLoNAwCIlIKdLT1dco9HrZIlQ9tX3piChyNW0KvV7uIgKUkqLWMT50EoOQiJQiKUllLUKjERkZchdBIVPVl46IIl3r1mrKQq8XzZvLXQSFTD3fOCLSgHbt1BSETZtyNn0kUM83jog0IC5ONWNHDQZcdJHcRZAUGIREpCxdu6pjq/r4eE6ciBAMQiJSlrQ0JCYqfSimwYAePeQugiTCICQixendW9HzKHQ6pKUhNVXuOkgiDEIiUpzERLRurdws1OnQu7fcRZB0GIREpETduyMqSu4i6uLrFI2OlrsOkg6DkIiUyGBA376KGzWj1yMtDW3byl0HSYpBSEQKlZyMHj0UlIU6HSwWXHml3HWQ1BiERKRcbduiXTulZKHBgGuvhdEodx0kNQYhESla9+5o2VL++DGZMHAgYmJkLoPCQRBFUe4aiIjO47ffcPAg3G4ZTi0IMBoxcCASEmQ4OzUABiERqcOOHfjjj4bOQr0eFguuvZZtwUjGICQi1ThyBGvXwuOB19sQpzMakZqKK6+Uv2OWwopBSERqYrNh7VoUF4e3aajTQa9H9+6cKaEJDEIiUp8DB7BhA7zesMShwYDGjdG7N6xW6Q9OCsQgJCJVcruxaxd27YIoShOHggC9HgkJ6NmT20poC4OQiFTM7cb+/di1Cw4HvN4gnx0aDBBFNGuGjh2RnCx1iaR4DEIiigTFxTh4ELm5sNmg18PtRv3XNoMBggCvF6mpaN0azZtzRIx2MQiJKKI4HDhxAsXFKC5GeTmcTrhcNaGo18NkgtWKhAQkJSE5GQkJSt/4kBoAg5CIiDSNS6wREZGmMQiJiEjTGIRERKRpDEIiItI0BiEREWkag5CIiDSNQUhERJrGICQiIk1jEBIRkaYxCImISNMYhEREpGkMQiIi0jQGIRERaRqDkIiINI1BSEREmsYgJCIiTWMQEhGRpjEIiYhI0xiERESkaQxCIiLSNAYhERFpGoOQiIg0jUFIRESaxiAkIiJNYxASEZGmMQiJiEjTGIRERKRpDEIiItI0BiEREWkag5CIiDSNQUhERJrGICQiIk1jEBIRkaYxCImISNMYhEREpGkMQiIi0jQGIRERaRqDkIiINI1BSEREmsYgJCIiTWMQEhGRpjEIiYhI0xiERESkaQxCIiLSNAYhERFpGoOQiIg0jUFIRESaxiAkIiJNYxASEZGmMQiJiEjTGIRERKRpDEIiItI0BiEREWkag5CIiDSNQUhERJrGICQiIk1jEBIRkaYxCImISNMYhEREpGkMQiIi0jQGIRERaRqDkIiINI1BSEREmsYgJCIiTWMQEhGRpjEIiYhI0xiERESkaQxCIiLSNAYhERFpGoOQiIg0jUFIRESa9v/LeVlNgUmC7wAAAABJRU5ErkJggg=='/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized8' 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tsy5MLUcTPP+PTT1FY6PJZvxYLSkvx3Xf46itZzK2SNwahl0hIQEGB1EVUyWDA6tX8layloiK5x4wtC6lSJhP++U8kJrp14YvJhJQULF+OwkL3ndQDMQi9gcWCzz+/tXGgbJWWYutWqYvwQGazZ3T4NBhccqvPGxgMWLECZ85IMHRhW9IbH4+cHHef2nMwCL3Br79KvLzaTgYD1q2T+5WNDHnQXUfZ3pyXki0Fs7Ml+y21bdC1fDmzsDIMQm/w/fcecDloU1yMY8ekLsKjWK2e8SnHhkFYnsWC1atx/brEowJWK4qLsWIFO+NViEHo8bKykJUldRF2MxiwaZPURXiU0lIIgtRF1IQ85y1L5ptvcOWKLMbGRRFFRfj0U8+4z+5eDEKPl5joMffNAIgiDh3ie2UNyHmy6N1u9iAlAEhKwokTMvpxt1iQnY3166WuQ3YYhB7PKdPQLJbitLTZmZnrnFFRNTQaJCe74TzewGr1vOkn8nnbl9jVq9i4UXb/HCYTDh1CerrUdcgLg9CzFRfj0iUHjyFmZn61bVvbtLQ5J05MsVpd/ntrMODQIVefxEt40OjgTYIgixuBEhNFfPGFTP8hjEZ89RWv3G/HIPRsaWkO9TvOyzu0e3f/gwefLim5EBzcpUuXL1Uql7dPtlrZ78JeRqMn3Re1EUWZvv+71f79uH5dvi+eyYTNm6UuQkbYa9Sz/f57LW+9GI3X0tPfycj4SBQtOl3d1q1nRUWNFwQ3bWx26RJE0cPmgEjCQ6c1mEzw8ZG6CAmZTPjpJ9ndFL2dyYT9+9GvH+rUkboUWWAQerbTp2v86dtqNZ09u+LkybfM5nyVShsV9UqbNnO12mDXFFgxrRZXrqBRI3ee0yN53AChjYfmt9Ps3u0BF8UWC7ZswejRUtchCwxCz1bThRNXryYkJ08oKEgFUL/+4JiYZYGB97iksiqpVMjOZhBWw0NTEAoPQosFO3bI+nLQxmpFSgry8hDs1g/B8sQg9Gz5+fY+srAwLTl5Unb2TwACAtq0a/d+w4bDXFhZlaxW9rionucGIaDgW9/Hj3vMKyeKSErCkCFS1yE9BqFns6ehjMmUk5Y2OyNjhSiatdrQVq2mNm8+yQ2TYqpga/lEVZPtTItqCYKCg3D3bo+ZkGk2IykJDzyg1JfqFgahZ6v6HpQoms+fX5OaOtNovCoImqZNX4yOnqfT1bfnyCqVThDUWVnr6tTprVLpnVPuH6xWD7h15K1EUfz228+PHz+cnX1l9OixsbH3S12RFyks9KQ+TwAsFpw9i6goqeuQGIPQa127ti05OS4//ziAevUGxsQsDQrqYP/Tu3b9KiGh2dmzK69eTWjX7r1GjR52bnmK/wwqjYMHf5s+feLBg78JgiCK4n/+8+WDD458550lUVEtpS7NK6SmQq32gJkyN5lMOH6cQch1hJ5NU9EnmaKi0wcOPLFnz6D8/OP+/i27dfumT5+tNUpBADpd3R49/hcU1L6o6PS+faP27BmUn++0btlqNbRaZx3Mmznx48Lly1mvvfbS0KF9Dh78rWHDxosXfzRv3tKgoODNm9f36XPP9OkT8/PznHYyxUpO9pj7ojZWK1JTpS5CegxCz1ZutZbFUpSWNnv79pisrG/Vav82bd4eMOB4WNjjtTt4vXoD+/c/1LHjJzpd/WvXtu3c2fnQoT8bjVcdL1ulQkCA44fxcraRNscZjcZPPlnWq1fbf/97lUajefHFCUlJJ5977uVx4+IOHPj9xRcnWK3WVas+6NatxSefLLM4Y8anKEKlzLeWc+ekrqDm8vM9ZvMal1HmT6v3CAm5+VfxwoV/JyS0TEubY7UaIyKeHTz4dJs2s1UqhxY220YWBw1Ka958AqC6eHHt1q1tTp9e5GAnNpWKC3mr55Qs2bJlQ+/e0TNmxBUWFjzwwPC9e1MXLFgWGBhk+26dOnUXLFj2yy/7evXqd+PG9Rkz4u6/v0dSUqITTqxABQUe2RNPo/GwcU0XYBB6trAwAMjJ2ZeY2Ofw4b+Ull4OCener9/uLl3+rdc7bZmeVhsaE7NswIDkhg2HmUw5J05M27Gjw5Urtd9OyWxGgwbOqs5rORiE6empTzzx4DPPjDx37kzr1tHr1v305ZcbmjZtfvcjO3TosnHjri++WB8Z2ezYsUPDh9/39NMjzp8/W+tTq93UoUhmLl+ueKxC5iwWXL4sdRESE0TPnaMtP2YzLl/GtWsoKEBxcdkXAwIQHIx69VC/vvPfIFavznznnTfPn/8cEH18wqOjFzRp8izgwokoTlmSr9Xi2285X6Z6ubm1WZyek3Pj3XfnfPrpRxaLJTS0zpQpb/31r+PVdvzwGQwlq1Z9sGTJvKKiQh8f3xdfnDB58kx//xrfxdbpEBhY47I93m+/yXG7CXv06oVHHpG6CCkxCB11/TqOHMHBgzh5Erm50OmgUpUtqDWbyz4g2gZ7jEY0aIB77kGXLujUydFBspKSkg8++GDu3HnFxYVqtW9U1ITWrWdqNO4YebM1aUtLe9tkylOptM2avVzTJm3NmmHZMtcV6D0KC2s298JkMn311T/nz59x/fo1rVY7evRzM2bMr1u3Xo1OeulS5jvvvPntt5+Loti4cfjMmQueeOJZwe6PLYIAPz9F9hr9+Wds2+aRyz9bt8bzz0tdhJQYhLVUUIAdO/DTT7h6FWp1DQabBQE+PjCbERWFoUPRty/0NV+kt2HDhgkTJpw9exZAo0bDY2KW+/k1q/FR7COK5rNnPzYYsqKj/377143G6+npc2vRtlulwsiReO45F9XrVUpLUVRk71vrzp0JM2bEnTyZAqB//8Hz5i2Njo6p9akPHdo3ffrEAweSAHTu3H3BgmXdu/e254mCgKAgj7xH6Kj//Af79kldRK2Eh2PCBKmLkBKDsMYuXcLXX2PPHgiCozOlfX0hihgyBH/6k70N/w4dOhQXF5eYmAigS5cu8fHx333X7+xZh8qowtWrvyQnxxUUnBAE9cCBJ/39y682y8s7lJwcd/16IoDg4C4xMfF16/ar+ph+fpg2DR07uqpmb2JnL7rff0+fNWvyzz9vBNC8easZM+Y//HAtpwrfThTFb75ZO2fO1Ozsy4IgPP74mNmzFzdoUP3Yc926jp/cA335JY4edcWBv01La1OnTof6drXCqI2GDfHaa646uCdgENZATg4++wx798JsdmZbYa0WKhWGDcNTT1V1Q+n69etz58796KOPLBZL3bp1Z82aNX78eLVa/d13WLfO+QMTRUWnU1OnZ2V9C8Dfv2V09IIqlmFcvrwhOXlCcfFZAA0bDm/fvqorVJ0OX32lyCuGWql6mDAvL3fZsoUrV8YbjaXBwSETJ04bNy5Op3NmJ6Di4qIPP3x32bJFpaUGPz//8eOnTJw4Ta+v9CdVoQOEAL74AsecttbW5kh2dtzWrTsvXOgbHp74zDOuGlVv0ACTJ7vo2B5BPXv2bKlr8ACiiB9/xIIFOHsWJpOTRwGsVlgsOHMGP/6I8HBERJR/gMlk+vDDDx955JFdu3ZpNJrx48f/8MMP/fv3V6lUAOrWxaZNzgxmi6Xo1KkFBw8+nZ9/TK32b916eteuXwYFVXUFFxDQpmnTl7TawJycpIKC5HPnVpnNBaGhve7uaCoI6N0b/aq5aKRbRLHiOflWq/Wbb9Y+88zI7dt/FkXx8cfHfPHF+oEDh6jVTv6IodXq+vaNffTRp7KzLycnH9m9e+f333/dsGHjtm3b3f1g2wChQmeNpqbi0iVnHSy7uPi1bdte/vnnjLy8+n5+cd26dW3UyFVBGBiI3nbd9/ZWvCKs3o0bWLgQ587BYHD5ufR6dOuGV1+Fr2/ZVxISEiZMmJCamgpg8ODBy5Ytu+ee8rM0J0/G6dNOOb944cLaEyemlpZeBoSIiDHt2i2u0TIMgyHzxIk3L16sdBarry9mzUK7Ct5CqWIV3h3dvXvH9OlxKSlHAfTp03/BgviYmE5uKCYxcduMGXEnThwH0K/fwHnzlrZrd0fHIkFQ8ArRjRuR6IQlmCardcXhw2//+mteaalWpXq5c+c5994bUoupBPaLisK4cS48vuwxCKuRkoJ581Ba6r4t1rRahIRg7lwUFKS99tprmzZtAtCmTZv3339/2LCKN07aswcffOBod4icnH3JyRNzcpIAhIb2iImJDw2t5YfE2w8VEtK9fftlNw/VoAH+8Q+H6lSg/PxbF4WZmRfmz59hm9IZFhYxY8b8Gk3pdJzZbP7yyzXz58+8fv2qSqV67LFn3nlnSd269fHHRDA/P7fVIjOJidi82cFGownnzsVt3Zpy7RqAwU2bxg8a1K5ezSb91kanTgrfoZdBWJXt2/HxxxL0DjSbczIyFp0+vdRkMoaGhk6dOnXSpEk6XaUbJ1mteP553LhRy9NVexlXKxVcXIaENHrxRQwc6NiBlcdsRn4+iouLly9fbBur8/X1e/XV1ydMmOrj41v9810gNzfngw8WrVy51Gg0hoSETpgwddy4STqdrk4dBS8PPX4c331X6xtH6TduTN6+fePvvwNoXafOkgEDhrdo4dT6KqFSYdAgDB7sjnPJFYOwUj/+iH/9y90peOfGSaphw5755z+X1Ldjtti2bfjkkxr/DlosxRkZy9PT55nNLlmMaLEUnT797qlTi6xWg1rt3779lF9/nebvL68lZgYDDIayoV+dDjqd7K5pRFFcu/a7GTOmXLx4XhCEkSMfmz373SZNmkpdF06fTps587WEhE0AWrZss3jx+488ItluzxWzWJCfX7YMxfbqBgXdGnhwritX8NFHtXjLyC0tXZiUtPTAAaPFEqLXT+vVK65bN73bBlp9fPDYY2jf3k2nkyUGYcW2bMGnn7o7Be/eOKl+/Q6zZtn1IyqK+NvfcOlSDSby1GiqpyNun4DasmXLBQsWPP64Eyb3147ViqwsXLiAixdx7RqKiiAIZZM7bH0PrFZYrfDxQZ06aNIEERGIiJByr4wDBw5MnDhxz549ADp16jZ/fnzPnn0lq6YiO3cmvPnmhPT0qoax3aegACkpSE7GyZO4dAnFxWXTstXqspfWZIIgoF49REaiQwfccw+aN3fOZazVipkzazSIYhXFz1NSXt+xI7u4WCUIz9xzz3sDBjRw8wcxnQ5xcUpd8lKGQViBgwexcKFbOyXduVahRXT032+uVfDxweLFaGrHp/+TJ/HWW3aFdy0W/zlIrYav77bjx+OOHz8OYODAgUuXLu3QoWY7QznCYsHvv+PoUVy4ULZhnD3vV4IAnQ5mMxo1QocOaNMGld+fdr6srKw5c+asXr3aarU2btx4+vTZTz31V0GQY39gs9n0+ecr3nnn7by8PK1W+/LLL8+dOzfYzrWxTpGTg127kJCArCzodCgpsesjoU5X9iGoe3cMHowOHRxNxPh4+yeO7jh/Pm7btqPZ2QBiIyPjBw7sKEkHXr0ec+Yo+I42wCC828WLmDzZHRNEbcrdPGzZckqrVtNu3zJCEBASgg8/tKsl28cfY9u2qiLcaLyWnv5OLdrBOMjHB8uXo04d85o1a2bOnHn16lWVSvXMM88sWWLXjV9HFBVh/34cOeJoAwStFqKItm3Rq5fLJ0YajcaPP/541qxZBQUFOp1u3Lhx77zzTmBgUG5uWfc+WbFdUgcHV7rU1bWnP3kS33yDY8cgCLX/9KpSwccHWi1GjcLQobW8d3rjBv7xD3vG6i8UFMzYtWttSgqAJoGB8/r1+3NM7XsAOSo6GmPHSnZ2eWAQ3sFoxPjxuHrVPW839q5V0GjQrh3mzq3+iFXUb2sQevLkW2Zzfu0ahNaaXo+//Q2xsWX/mZOTM3v27BUrVpjNZnumAtVaSQl+/RXJyWUrNZ1CpYJKhagoDBxobzOgmtqwYUNcXNyZM2cADB8+fNmyZc2bl20ZYZs1I7dfWdtntZt7Zdze/Khz587Lli3r56J1o7//jlWrcPYsSkud9o+i10OlwqOPYtSoGlz+l5Zi82b89BNEEcHBVVxdFZtMi/ftWx2lUTcAACAASURBVPTbbwaz2U+rfb1Hj2k9e/pI2F1Cr8eoUejSRbIC5IFBeIdVq/DLL+64KZqTsy85OS4nZy/uWmBQIR8fPP88Hnig+iNnZWHSpPJXtE7ZMqJ29Hr064dXXy3/9bQ0exeH1M7hw9i5ExaLS9a92OKwWzf07evMxeOpqamTJk3asmULgOjo6KVLlw4ZMqTcYwwGFBfLKAsFAYGBFYyh3t4Od/jw4cuXL2/WrJnTzlpYiFWrkJQEo9El/xZ6PXx88Oqr6N69mkeKIg4exLp1ty4E69atMAhF4Lu0tCnbt5/PzxeAx9q0eW/AgMigIGeXXkMaDWbOdNXsIc/BILzl99/x5psunyBT67UKvr5YufL2nXgrdeQIFiwo+x8pLExLSXnNtndgQECbdu3eb9jQffP6tFq0aIEFCypNC3vaBdRUQQH+9z9cveryTVK1Wvj64tFHnbC34o0bN+bMmWO7qVinTp233nqripuKRUXOvARyRNUbTdg2SJk3b15hYaGvr++ECRNmzJgR6Hj7tYMHsWQJjEaXv8B6Pbp2xauvVjqNOCMDX39dvpmFvz98fMpl4YHLlydu3bonMxNAt0aNlg0a1Cc83FVl10jbtux/DwbhTaKIuDi4rns1AIulJCPjgzvXKszQaOx9X9Bq0asXpkyx68G7duH993OOH5+dkbFCFM1abWirVlObN590d88z19Fq0bgxFi+u5uOmyWRasWLF2287Z55FRgbWr4fJ5L6xNK0WAwagU237utj+92fPnp2bm6vVap977rn58+fXq24NdX4+zGbps9DHB/7+1TwmMzPzzTff/Pzzz0VRDA8PX7BgwbPP1rYDgNWKzz7D5s3um8+t0yEgALNnl5+ulpuL9euxa1cFr4FKhdDQm0GYVVg4Z/fu1ceOWUUxLCDg7b59/9qhg0omM1N0OowdC/esVpQ3BmGZpCQsXerCOTJOWaug12PJEjRpUs3DzGbzmjVrpk6dmZt7VRA0kZH/Fx09T6dz7ZyUcrRahIfj73+3d02eU+ZZHDiAxESXXyfcTatFu3a4//4az7xLSEiIi4tLSUkBMHjw4KVLl8bYPWmioMD5bW/tJwjQ66tPwZv27dsXFxe3d+9eAN27d1+2bFnvmja3NBgwfz7S0903k83G9r/6+uvo1g0AjEZs3YqNG6sqIzAQer3RYvn4yJFZiYkFRqNOrR7XqdO8fv0C3TntuFp16+KNN6QuQhYYhGVefhlZWS458p1rFTrHxMTXrXtf7Q6lUqF7d0yfXtVjtm3bFhdXtkqhe/eBoaFLfX07uPlF9vFB69aYMaPGu7PevcmU/fMsEhNx8KBk24NrtWjWDA8/fGvOSNXS09MnT568ceNGAK1atZo/f34t1lZKdY+0dlvviqK4du3aqVOnXr58WRCEMWPGLF68uFEj+zrZFhVh+nRkZkrwMcdGr8crryAkBF9+iWvXqnmwWr3h+vWJW7dm5OUBGN6ixbJBg5rbM6rhTjodnnqKbX9tGIQAcOwYFixwtFdnhc6eXXn8+CuiaNXrG7ZtOy8y8v8cXAem0+Hjj1HhnbPTp09Pnz7922/vWLd+7hxmz0ZhofsSQq/Hgw9i7Fh7I+FutZhnkZiIAwcke5O00WrRtCkeeaSa68Lc3NyFCxfGx8eXlpaGhIRMmzYtLi5OX9uWyu6fO1PZ7Bg7FRUVvfvuu4sWLTIYDP7+/lOmTJk2bZpP1aFaUoI33kBWloNtPB2lUsHfv9p9tI/cuBGXlLTz8mUA0XXrLh04cEhUlFvqq6HGjTFxosKXD97EbZgA4JNPcOGCS46s09U9f/7TZs3Gde/+fZ06fRxvjmxrkVFuV9uioqIFCxY8/fTTx44d8/f3nz59+pdfftmxY0cAISF44AFcvIjsbJf3DbdNHnn9dTz0kEO/X23atHnppZcCAwOTkpKSk5NXrVpVUFDQq1evypZYHDqEvXslTkEAVisKCpCbi1atKnuAde3atSNHjvz5559FURwzZsz69euHDBmicWD2vEYDrdZN/++CAI0GwcEO7SWp0+liY2Ofeuqpy5cvHzlyZOfOnV9//XXjxo3bVXZpYrHgrbdw/rzEKYg/NsTSaCqb+nWjtHTagQMv/PprRmFhHb3+7/37f/bQQ61CQ91cpl20WvzlL5B8zqps8IoQRUX4y19c+FZiMuU5d7lecDD+9a+ypLH/dtP+/Vi+HAaDS+YZ2Dqw9OqFF1+0a+G/neyZZ3HuHL7/XvoUvEmrxX33oWvX8l/fsWNHXFzc0aNHAfTv3z8+Pr5TrSfY3EUUUVKCkpKyLnGuULvboVXbtm3bpEmTjh07BmDAgAHx8fEVNBv68EPs2iVB5/vK2FZN3pmFJqt1RWrq7MOHc41GrUr1XKtW87t2rRcSAn9/OV5y6XTo1g0PPyx1HTLCIMT27fjkE5fcF3URX1/Mm4eWLbFv376JEycmJSXBvgkIRiN++AHffw+r1WlvLLaZBM2a4cUXXTX7rIr/zcJCCVrCVkujwejRaNy47D8vXLgwY8aMtWvXAoiIiJg/f37tp01WyWpFUVHZZwLn/lrbNldyxVu6bWJXpc2GduzAihWye4HvnBeakJUV99tvKTk5AAaHhcX37Nnu5lWg7fJZVlkoCAgNxeTJDl3Xex0GIebOxcGDUhdRE2o1Bg7M3LevllPSS0rw00/44QeYTDAYar/MQKeDKKJ9ezz5JNq2reVB7FTZhe8XX+DSJTl2HQsIwAsvwGQqXrx4sW08zM/P7/XXX69+PMxhFgtKSlBa6ujVoe2nyccHPj61H+61U05OzqJFi5YuXWo03rbvWG4uXn3V3XNE7WH79BcQkJ6XN3nfvo0XLgBoHRy8pEeP4eWmdNtSx9X/fDWi1+PVV+HivoYeR+lBKIoYPdqTLgdtixFPnZpnMpUtUp45c2ZAzW9HiiKOHUNCAvbvtx3Wrtk0ggBfXxiNCAvD4MG47z64cwSk3DyLZ5+d0qLFNKtVXps62Wg0Ym7ud//615Tz588LgvDYY4+99957kZGRbitAFGE0wmAoGxi2/7fcFp9abVnrTXdezJRvNtSx4zCTyX07YtdErtm88Ny5pampRqs1RKeb1qFDXLt2FW+cpFYjJEQuF4VaLcaMcfnnVg+k9CC8dAlxcXL80Fmh2xcjPvTQ8A8/dELbKlHEmTM4cQLHjuH8eVy7VvY+aOsiZrGUbVzj54fGjdG2LWJi0K4dHG8PUmu3T46tW7flgw8u6NBBsk2dKnTx4oH16yeeO7cHQLdu3eLj4/v2lWzjJFGE2QyTqWzDDau1/HuyKEIQyhpna7Vls28klJCQMHHixBMnTgAYXL/+spiYeyT8abuLVRQ/v3jx9RMnsktLVYLwTIsW7/Xo0aDqq3ytFkFB0mehVouRI9Gjh8RlyJLSg3DPHixfjuJiqeuoTrmNk7p2jf/nP/u56AKjpAT5+TAaYTSWTRcPCnJmR02neP/9be+9F3fp0nEALVoMHDFiaePG7tvUqTL5+VkJCXP27VstitagoLAnn3x75cq/qmR1ZwywWiGKZfkHQKWS/i26HJPBsCI29u1Dh/JMJq1K9XKzZnPbtAmWNp8BADuuXYtLTj6anw8gtl69+O7dO94cCq5Cmzbo3Rs//yzZKlcAWi2GDUOfPpIVIG9KD8J167BunXPvvojnzn167twnBQUnBEETEtKtZcs3GjQo3zrZfkbj9fT0ueU2TvL3V7/6qnJ/qktKsHIlSkvN+/ev2bJlZlHRVUFQde78zPDhS/z9pRn8sFiMe/d+/PPPs0pLC9RqXa9e4x544J3AwKDRo2HnknG6Zds2rFp1PS9vbnr6RxkZFlGsq9PNat16fFSUWqLQvlBSMiM1de3FiwCa+PrOa9v22SZNBJWqmh25QkPx6KPo3RuCgIwM/POfruoSXjWtFo8+yi0mqqD0IFyyBLt2OfOAhw8/d+HCvyIjxzZoMMxqLb14cW129pb27ZdHRY2v6aFsGyelpb1tMuWV2zhJo8Gf/6zc+c+//op9+8rWlZWU5OzYsSgxcanFYvT1DY2Nndqv3yS12q2NrFJTN6xfP/HGjQwA0dHDR45cVqdOcwCCgObN8ac/ubMWr/DCC8jOtv31UF5eXHJy4vXrADoHB8fHxNzn3r3Uiy2WxadPLzp1ymC1+qnVr7dsOa1VKx/bVb6tuUCFy1t1Ojz4IIYNu+NG87VrWLUKxcXuW+5ju989dizkuahfNpQehG++iRMnnHa0zMyvDx4cHRMT37z5xJtfPHToz1lZX/fvf6RGOx9Vu3HSyJF4/nlnFe5JrFYsX15+Rv3Vq2kbN7528uQmAPXrtxk+/P22bd2xyUZ2duqGDZPS07cAaNAgesSIpa1b33H1r9E4eW2l90tOxrx55Sawbbh8eUJy8tniYgDDGzZc3r59Mzub2DpABL7LypqSknK+pEQAHgsLe69du8hyLeQ1mvI7wggCunbFk09WfLFYWopvv8XJk+7IQr0eDRvi2We5cL5aSg/CiROdueNEYmJvg+HS4MGnBeHWGp2Skgtbt7aIjHyhQ4eP7DmInRsnxcZi0iRnFe5JzpzB+vUVj7acOpWwfv2E7OxUAK1aDR4xYlnDhq7adrG4+EZCwpy9ez+yWi1+fnUGDXqrT5/xKlX5oVSNBn37omdPF1Xhjd59F3v23L0mpsRi+SAjY156eqHZ7KtWT4iKmtG6daDLFsMdyM2dmJy858YNAN1CQuJjYvpWdhc0NPTWEHqzZhg9Gi1bVnP0lBR89x2MRle1y1GroVZj6NCyu7JUHaUH4d/+hsxM5xzKYinZtCmwadOX7g68xMTeZnPhgAHHqz6CyZRz6tSiM2eWWq3GajdO6ttXoY3jf/gB6emVftdiMe3du+KXX942GPLUam2vXi8/8MBcHx9ndvaxnSIhYXZJSa5are3W7bkhQ+b7+1e6cVJoKF54wYnn92pmM0aPrmJSSabB8OaJE59fvCgC4T4+C6Kjn23SxLnv9FkGw5y0tNXnz1tFMczH5+02bf4aGVnpxkm2FUV+fggJwciRuO8+e4PHaERCAvbsKZvX6yxqNVQqxMTgoYeknNvtaZTeXMCJE/pKS6+IosXPr4KpnH5+TbOzt1TxXFE0nz+/JjV1ptF4VRBUERHPxsQsqXrjJGV+zhNFZGRU9QC1WnvvvRO7dBmTkDB3796Pdu/+4PDhLwYNmlXh5VotnDqVsGFD3JUrKSi76Ixv2LCa/v0FBSgs5N1R+6SkQKOpIgjDfXz+3aXL+KiouOTkvTk5fzl8+MOMjGXt2/d2xoJWo9X68dmzs06eLDCbdSrVuKioeW3bVnPRaWtAOngwRo2q2T7vOh2GDcN992HHDvz2GwTB0QY6Wm1Zh4v774d7h1G9gNKDsLZN/6tQWUBVGlzXrm1LTp6Un38MQL16A2NilgYFVb8SoEa/dF4jO9uuTwB+fnVHjlzWtetfNmyIy8hI3LAh7tChf48YER8VZe+mTne7di1948bJqakbAdSr12r48Pejo4fb80SVCufOcbsb+xw6ZM+q3h6hobv79Vt74cLUEyf25+b2TUwcExGxuF27Rg78Pm+4fHlicnLGH8OQy9q3b27nMKQo4pFHatmGNSAAw4djyBAcO4a9e3HpEtTqmiWircNTaCh69kSXLvbu/0l3UnoQOvHmgV7fUBDUxcXn7v5WcfE5H5/wu79eVHQ6NXV6Vta3APz9W0RH/z0szK614bbOTQqUmVmDhmrh4V3GjduVmrrhf/+bkJl5aOXK+6Kjhz/88PLQ0GY1OmlJSe6OHQt//TXebC719Q2JjZ12771xGo2977lGI4PQbkeO2PkCC8CfmzT5U1jYu6dPLzp1au3Fi99fujTl9imddkstKJiUkrIlOxtAdGDg0nbthjRoUIPn63RISyu/I0yNaLXo2hVdu6K4GKdOIS0NGRnIyyvrU2obvbJYykYibV8xmxEQgMhItG2LVq0Q7Myb/wqk9CBs2NBph1KrfUNCul+58qMoLis3WSYv72BkZPlhouvXE/fuvd9qLdVoAlu3ntG8eZxKZe97q05X8ZaEXu/ChRqPp0RHj2jZcvDu3R9s3TovNXXj6dNb+/adMHDgDL2++g9Bomg9dOjzTZteLyzMFgRVly7PPvTQuwEBNf6hcdGez95GFGs6Yu+vVs9u02ZMRMT01NRvs7LmpKX98/z5d9q2/XO5np+VuGE0zvljqWIdne6t2i1VNBrx++8OBeFNfn7o2LHsUFYrcnKQl4fCwltzaG3tz4ODUbeu7JpceDKlB2GTJtDpnNbwoXnziQcPjs7I+Oj25ROpqTMAREW9Uu7BoaG9/PyiQkK6t2u3WK+v2aJrjUahy7SvXq3Ns7Ra39jYqZ06jd60aeqxY+t27Fh05MiXTz65tnnz/qIo7t//6W+/fXLlygm1WhMe3i029g3bEoirV9O++OKJS5eOAWjePHbkyPjGjWv5ZpefX7vnKczVq7UbtG/p7/9Nt27brl2blJx8LD//L4cPf3bhQnxMTIfKlw2YrNYVZ8/OTkvLNZm0KtXzkZHzo6PrVbLhZTXMZpw+XZsnVk2lQt26HO1zD6UHYWQktFqnBWF4+FPZ2VuSkyfl5R1u0GCo1Vp68eLnV6/+0r798rsXEapU2v79D6rVtbmnbzLBjQ2cZaSgoPbPDQmJfPrpr/r1m7RhQ9zFiweDghoD+O67/zt48F9du46NjZ1qNpceOrT2008ffPjh5X36jA8KCissvBoS0uSBB+Z16eLoxknFxRy+qU52NjSaWs8ZGViv3sH+/decPz8zNXX7tWudd+58Jjx8SUxM/bviLeHq1bjk5JSCAgCD69ePj4lp5+AYCS/5PZzSl0+4YFdeW4u1lQUFJwRB63iLtQoFBOCLL5x7SA9gseD9953QoEoUrZmZByMiuh89+vWXX44eMSL+3ntvXcGvW/fno0e/njjxSMOG91y6dLR+/TYajaMbXOh0ePpp1GjgSYl27cKKFY7vBZNjMi06dWrpmTNGqzVUq53aqtWk5s11KhWA9MLCySkpG69cAdA6IGBJu3bDnTI6EhqKzz5zwnFIIkq/IvT3R2jozXZOTiE0bfrXpk3/6sQj3q1NG5ceXqZKS6FWO2HNlSCoIiK6A/j112WhoU379LnjrvWQIfOPHv16796PRo36qNb3Qu86o8fscCKlwkKn7C0ZqtUuvOee5yIjX0tJ2XTlyrQTJ2wDhwdzc+PPnCm1WkO02mmtWsU1b6531vIpvroeTulBCKBnT/z4oxw3d62Mj49C222bzc5cPWkylVy8uL9nz5dUqjt+C0JCmoSHd83IcGoLWshzWz2Zcerug20CAn7s2fN/ly9PSUlJKywcc+iQ0WpVC8JLzZq907bt3fdLHeKiBjHkLvLaIEYSffrUcgmQVCwWdOsmdRGer7DwitVqCQmpYKw1NLRpXt5F555O2UMQ9nHBv9HDjRolDxgwrlkziyg20OsP9u+/skMHJ6cgeT4GIaKj4bKGhS7RvHn5Nr8KodE4/62y8ikwzuzcIwge9jMmDZ3Oma2e/qBXqZ4MC7OIYnRAQEcXtZ/mSgYPxyCEIOCBByTeldt+vr4Yblc/Ey+k1zvzDnZAQEOVSp2TU0EDhJycc8HBFTRAqDVRdEUPI6/j7++piaLMPk9ehEEIAMPcsWOPc6jVCh0gxB8t9Z1Fq/WNiOh+8uSPVusdAzy5uRcyMw9GRd3ntDMBFgt7jdohNNQVV4TuwH2OPJxn/tg5W9266NXLAz6M+vjg0UcVfZPNuXFy770Tc3LO7dlzx24hW7bMANC7d/kGCI4QRfj7O/F4XqpBA0+atHa7sDCpKyCHKPg99U7PPot9++Q+tU+jUe59UZv69ZGT47Sjdez4VHr6lo0bJ2VlHW7TZqjZXHr48OenTv3y8MPLnbuRIffDsUvDhu7but2JNJrqNyAkeWMQlmnYEPffj59/dlqXGafz8cHYsUofaoqIwJkzzpys/thja5o27ZuUtPLYsW/Uam14eLfnn99cbpd5xzVu7NzjeSlBQOPGuHBB6jpqSKdDixZSF0EOYRDeMmYMdu2SaRAKAho1wuDBUtchtfBw56ypv0kQhB49/tqjhwsbIGi1iIpy3eG9S6dONdthRA6MRoV2uPAiHCO8xdcXkybJdE2hXo833lDoZry3a9RI7rev7yaKaNpU6iI8RZcuMv0NrELjxmwj6+kYhHfo0gUDBsju9qPtpmi4M+fzeypB8Lyrq4AAjhHaLSbGw7q0aLXoV/vdnkkmGITlvfACIiJkNDNTr0ePHhg6VOo6ZCMmBh7UGESjQUyM1EV4EJ0OXbp40q0PQWAQegEGYXlqNWbPRnCwLFY0abVo2hQTJ1b/SOVo3tyT3icBtG8vdQWeZdgwT7o7GhbGtRNeQAZv9vITFISFCxEYKHEWarVo1Ahz5sjo8lQO1Gp07OgBiz4BCALCw3lftIY6dPCYITdfXzz6qNRFkBMwCCvWoAHeew8hIZKFkF6PJk2weLHHvCe4U7dunnFRqNHg3nulLsLjCAKefNIzLgq1Wr7A3oFBWKkGDbB0KcLCJBiR8vFBTAwWLWIKViwgAO3be8BFYYMGnOJUK4MGyW7G2t18fTFmjAf8FJIdGIRVCQnBkiXo1cutH091OowciVmzPGlKiPvde6/c34K0Wtx/v9RFeCiNBn/9q9wvCgMDubDXazAIq6HTYfJkvPIKfH1dvkOFXo+QEMyejWee8YxbfxLy9UW/fvLdM0SjQdu2aNBA6jo8V79+iIiQxYy1Cvn4YMIEuX8WI7vJ9edMZu67DytXont36PUuiSiVCjod7r8fn3yCdu2cf3yv1KUL6tSR6ScGrRaDBkldhEcTBEyeLNO7IjodevXibGBvwiC0V0gIpk7F/Plo0wY+Pk57/9VoylYKfvABXnhB7neDZEUQMGqUHC8KtVqMGiXT93BPEhaGsWNl9yshCAgMxMsvS10HORMn5tdMq1ZYtAinT+M//8GBA1CrUVJSy0P5+cFiQWwsHnmETZlrKTgYw4dj/XoZdSPR6dCzJ5o0kboO7zB0KI4fx/79MmoBrNPh7bdlF8/kGAZhbbRsialTUVSEvXuxbRvS06HVwmyu/rdVr4dKBasVHTpg4EB068brBke1bInevZGUJIsNfDQaREWhd2+p6/AmcXF44w1cvCiLF1ivx2uvsXWs92EQ1p6/PwYPxuDBMJtx+jROnUJ6Os6fR04OiovLfm0FATod/P1Rpw6aNkWbNmjdGs2ayXRky0P17o3CQiQnS/xWqdEgLAwjRkhZgxfS6TBvHiZPxrVrEl/46/X4v/9Dr15S1kCuwSB0AtsUwbZtpa5DwWwLFVJSJLuFptWicWM89ph85zl6sIAALF6MqVNx7ZpkH3b0eowdiwcflObs5GL8rSUvcf/96NZNmk5AWi1atMATT7AZnssEB2PJEjRtKs1Ce70er76KYcMkODW5BYOQvMe992L4cGi1br3zrNGgd2+MHMlrQRfz98eiRejXz60TVbRaBAZi3jxuMeHd+AmWvErr1qhfH//9L/LyXH4XTaOBTodRoxAR4doTURmNBq++ik6d8OGHMJtdPmTo44O2bTF5MoKCXHsikhqDkLxNaCjGjsVvvyEpCRYLrFbnn0IQoNGgXTvExnLer9v164eYGHz4IZKTYTC45BQ6HTQavPQSYmNdcnySGQYheSGVCr17o3177NiB9HRYrU6LQ0GAWo2wMAwejHr1nHNMqrHQUMyaheRkrFyJq1edGYc6HQQBQ4fiySfZ8145GITktQICMHw4cnOxdy9SUyEIDt0stU2EadoUffuiUSNn1UgOsF0XHj6Mr75CRgZE0aEX2NcXgoBhwzByJIKDnVcleQAGIXm5kBAMHYpBg5CWhiNHkJ0NtRomE0TRrqfrdLBYEBKCDh1wzz3w93dxuVRTnTujc2dcvoyEBOzciZycmsWhRgNBQEwMhgxB9+6c+KtMgmjn+wGRVzAaceFC2Z8bN2A0QqOBSgVRhCBAEGC1wmyGWo3QUISFITISkZG8SeY5kpKwfDlMJpjNsFptr+uOa9cG7N7dv169HffeC1GEKEKlgkoFvR5RUZg9W44ta8mN+PGHlEWnQ4sWaNGi7D8tFhQVobgYogirFWo19HoEBPCN0WOFhsLH59YSC9vrausIrFYjOBiCcMdKl+BgvtjEICRFU6sRFMTp8d7LNrvJtnGg7e9Ed+EaYCIiUjQGIRERKRqDkIiIFI1BSEREisYgJCIiRWMQEhGRojEIiYhI0RiERESkaAxCIiJSNHaWISJvdLOnjCBArwcAlQo+PmVN11y0UyV5JgYhEXmLwkKcPo3AQGi1EIRbX7e1HlWr4e9/q706ALMZxcU4dw5NmtzRgJQUhkFIRB7OZMKRI9izB9nZt67/KnQzAm00GpSUYM0aiCI6dUKvXggLc0O9JDcMQiLyWEVF2L4dv/0GQUBpaS0PYtvgfv9+HD6MevUwZAjatnVijSR/DEIi8kAmE7ZtQ2IiRBFmsxMOaLXCaERWFr78EnXq4NFHERnphMOSJ2AQEpGnSUvDunUwGmu2Gb2dSktx+TJWrUJMDEaNurW1IXkvBiEReQ6zGd9/j+PHYTS68CyiCJMJx48jPR3PPouoKBeei2SAE6WIyEPk5iI+HseOuTYFbzKbUVSETz/Frl3uOB1Jh1eEROQJsrLwj3/AYHD3+j+TCb/8guxs/OlPd8w4JS/CK0Iikr3z57FyJUpKpFkFbzTiyBF8/jnX4HsrBiERyVtWFlavRmkpRFGyGkwmpKfj66+lrIFchkFIRDKWm4t//KP2awSdyGhEaio2b5a6DnI+BiERyZXZjNWryxa8y4HRiD17cPSo1HWQkzEIiUiufvgBubnyGpkzGvHdd7hxQ+o6yJkYhEQkS+np9ikItAAABh1JREFUOHrUJUvmHWQyYe1aDhZ6EwYhEcmPyVTWO0aGRBHXriEpSeo6yGkYhEQkP9u3yzQFbYxG/PQTSkqkroOcg0FIRDJTVITERFkHIQCLBdu3S10EOQeDkIhkxranhMyZzdizhxeF3oFBSERyYjJh9245zpGpEEcKvQKDkIjk5Phxj2npacts+V+8UnUYhEQkJ3v3yqKPjJ2MRpw7J3UR5CgGIRHJRnExsrKkLqImjEYcPix1EeQoBiERyUZ6OtRqqYuoCVFESorURZCjGIREJBsnT8p91cTdSkqQlyd1EeQQBiERyUZGhufNPdFocP681EWQQxiERCQPZjMKCqQuouZKSz1sXJPuwiAkInm4fh0ajdRF1JwoIjNT6iLIIQxCIpKH3FyoPPMdibsyeTjP/LEjIu9TUACLReoiaqW4WOoKyCEMQiKSh9JSz5spY+MpDeGoEh54R56IvJLF4qLN6Ov4+j7QrFnHBg1ccXAAnnohS39gEBKRPAiCi7qMdqhff8sTT7jiyGU8dGiT/sDXj4jkQafzmHbb5XhWNxy6C4OQiOTBz89TE0Wvl7oCcgiDkIjkISjIU68Ig4OlroAcwiAkInmoW9dTp1+6bhoOuQWDkIjkISDAI2+NarWIiJC6CHIIg5CIZKNxY6krqDmVCk2aSF0EOYRBSESy0bat57UbtVgQFiZ1EeQQBiERyUabNp63Jq9pU8+rme7E14+IZKNxY2i1UhdREzodunSRughyFIOQiGRDENCliyddYIki2rWTughylOf8wBGREvTq5TFzRwUBrVrB11fqOshRDEIikpN69RAeLnUR9tFqMWCA1EWQEzAIiUhmhgyBTid1EdURBDRogMhIqesgJ2AQEpHMNG+Oxo3lPlKo1WLECKmLIOeQ948aESnTI4/IeqRQrUZUFJo1k7oOcg4GIRHJT+PG6NZNvksp1Go8+qjURZDTMAiJSJaGDZPphEydDsOHIyRE6jrIaRiERCRLOh3GjpXdrBmNBs2bo2dPqesgZ2IQEpFchYdjxAgZ3SBVqRAYiKeflroOcjIGIRHJWI8e6N1bLteFPj546SXuR+99GIREJG/DhqFDB+mz0JaCoaESl0EuIIiiKHUNRERVEkX89784fBhGowRnFwT4+uKll9CokQRnJ9djEBKRh9i+HVu3wmRy60m1WgQG4sUXeS3oxRiEROQ50tLwxRcwmWC1uuN0Oh1atMDo0RwX9G4MQiLyKHl5+PJLZGW59japWg21GiNGoEcPF56F5IFBSEQe6NAhbNgAk8kld0p1OrRsiVGjEBzs/IOT/DAIicgzGY3YtQs7d5b93XEqFTQaNGqEESO4rYSiMAiJyJMZjThwALt2oagIZnNtxg5FET4+sFrRti1iYxER4YIqSdYYhETkFbKycPQojh1DXh60WhiN1YSiTgeVChYLoqLQpQuio+Hj465aSV4YhETkXYqLcf48MjORmYkbN1BUBKMRFgsA6HTw8UFQEBo1QlgYmjRBw4Zy3/iQXI9BSEREisaPQkREpGgMQiIiUjQGIRERKRqDkIiIFI1BSEREisYgJCIiRWMQEhGRojEIiYhI0RiERESkaAxCIiJSNAYhEREpGoOQiIgUjUFIRESKxiAkIiJFYxASEZGiMQiJiEjRGIRERKRoDEIiIlI0BiERESkag5CIiBSNQUhERIrGICQiIkVjEBIRkaIxCImISNEYhEREpGgMQiIiUjQGIRERKRqDkIiIFI1BSEREisYgJCIiRWMQEhGRojEIiYhI0RiERESkaAxCIiJSNAYhEREpGoOQiIgUjUFIRESKxiAkIiJFYxASEZGiMQiJiEjRGIRERKRoDEIiIlI0BiERESkag5CIiBSNQUhERIrGICQiIkVjEBIRkaIxCImISNEYhEREpGgMQiIiUjQGIRERKRqDkIiIFI1BSEREisYgJCIiRWMQEhGRojEIiYhI0RiERESkaAxCIiJSNAYhEREpGoOQiIgUjUFIRESKxiAkIiJFYxASEZGiMQiJiEjRGIRERKRoDEIiIlI0BiERESkag5CIiBSNQUhERIrGICQiIkVjEBIRkaL9PzYUz/EgRHQTAAAAAElFTkSuQmCC'/><img width='240px' margin='0px' style='float:left' border='0px' title='randomized9' 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'/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 590\n",
    "mol = train_mols[i]\n",
    "show_pred_results(mol, rf_models[0])\n",
    "saliency, randomized_saliency = calc_saliency(mol, rf_models[0], rf_models[1:])\n",
    "draw_visual_inspection(mol, saliency, randomized_saliency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img style='width: 420px; margin: 0px; float: left; border: 0px solid black;' 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' 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variation_sanity_check(mol, saliency, randomized_saliency, scaler=None, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br', 'I'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_sanity_check(saliency, randomized_saliency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison: RF vs LGB, SimilarityMaps vs SHAP, and Traing vs Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7bcd2d583e944ccb654ab573be5d9ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1025), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 2h 58min 31s, sys: 8.89 s, total: 2h 58min 40s\n",
      "Wall time: 2h 58min 39s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf_cor_df = dataset_cor_df(train_mols, rf_models)\n",
    "rf_cor_df['algo'] = 'RF'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a642e9c30a1547148ed22f1dd3ab3708",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1025), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 3h 23s, sys: 11min 43s, total: 3h 12min 7s\n",
      "Wall time: 1h 34min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgb_cor_df = dataset_cor_df(train_mols, lgb_models)\n",
    "lgb_cor_df['algo'] = 'LGB'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor_merged = pd.concat([rf_cor_df, lgb_cor_df], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
    "ax = sns.boxplot(x=cor_merged['Correlation'], y=cor_merged['value'], hue=cor_merged['algo'])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78cdd63f25fc4420bf3db89b24384ba8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=257), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "579b2678ebf14690be774efe23ee1814",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=257), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPU times: user 1h 29min 37s, sys: 2min 59s, total: 1h 32min 36s\n",
      "Wall time: 1h 8min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf_cor_df_test = dataset_cor_df(test_mols, rf_models)\n",
    "rf_cor_df_test['algo'] = 'RF'\n",
    "lgb_cor_df_test = dataset_cor_df(test_mols, lgb_models)\n",
    "lgb_cor_df_test['algo'] = 'LGB'\n",
    "cor_merged_test = pd.concat([rf_cor_df_test, lgb_cor_df_test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
    "ax = sns.boxplot(x=cor_merged_test['Correlation'], y=cor_merged_test['value'], hue=cor_merged_test['algo'])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor_merged['data'] = 'train'\n",
    "cor_merged_test['data'] = 'test'\n",
    "cor_merged_all = pd.concat([cor_merged, cor_merged_test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_df = cor_merged_all[cor_merged_all.algo=='RF']\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
    "ax = sns.boxplot(x=_df['Correlation'], y=_df['value'], hue=_df['data'])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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enaampowdO7bbctOmTcu8efO6Xbdhw4bceOONufPOO1NXV5dZs2alqakpQ4cO3ZsvAQAAqHFV+4RnyZIlOeywwzJ69Og0NDRk+vTpWbx48du670MPPZTjjjsuBxxwQIYOHZrjjjsuP/nJT3p5YgAAYF9TtU94WlpaMmrUqM7LlUolS5Ys2W6573//+/mP//iPHHHEEfnLv/zLHHzwwTu8b0tLyy6fs729PUuXLu2ZF1AlbW1tSbLPv46eYF3UFtuji3VRe2yTLtZFbbE9ulgX9JaqBc/b8Ud/9Ec55ZRT0tDQkH/+53/OZz7zmdx66627/Xj9+/dPY2NjD0649w0aNChJ9vnX0ROsi9pie3SxLmqPbdLFuqgttkeXktaFaKstVTukrVKpZPXq1Z2XW1paUqlUui0zbNiwNDQ0JElOP/30/OIXv3jb9wUAAKha8EyYMCHLly/PihUrsnnz5jQ3N6epqanbMq2trZ0//+AHP8h73/veJMmkSZPy0EMPZePGjdm4cWMeeuihTJo0aa/ODwAA1L6qHdJWX1+fefPm5ZxzzsnWrVtz2mmnZdy4cZk/f37e//7358QTT8xtt92WH/zgB+nbt2+GDh2av/qrv0qSHHDAAfnUpz6V2bNnJ0k+/elP54ADDqjWSwEAAGpUVb/DM3ny5EyePLnbdRdeeGHnz3Pnzs3cuXN3eN/Zs2d3Bg8AAMCOVPUPjwIAAPQmwQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFKu+2gMAPeNv/uZvsmzZsqo9/5vPfeGFF1ZthiQZO3ZsLrjggqrOAADUDsEDhVi2bFme+vnSbB00vCrPX7f1jbeT//t8S1WeP0n6tq2r2nMDALVJ8EBBtg4antfeN63aY1TNwGfvrfYIAECN8R0eAACgWD7heQeq/R2JxPckAADgnRA870C1vyOR1M73JFatWiX+/pP4AwCoXYLnHdrfvyORvPE9iddeezm//MWTGfPurVWbY0hHXZKk/YXHqzbDi6/0rdpzAwCwa4KH3Tbm3Vvz2aM3VXuMqrrmiSHVHoEdqPbhpz59BIDaIXiA4ixbtqyqn0D69BEAaofgAYq0v38C6dNHAHiD01IDAADFEjwAAECxBA8AAFAs3+EBgL2k2mcQTJxFENj/CB4AepX/5HdZtWpV2ja0+htmziII7EWCB4BetWzZsjz186XZOmh41Wao2/rGP3f/9/mWqs3Qt21d3j2g335/BsHEWQSBvUvwANDrtg4antfeN63aY1TVwGfvTba9XO0xAPY7TloAAAAUS/AAAADFEjwAAECxfIcHAGA/52yKXZwyvTyCBwBgP7ds2bL88hdPOmW6U6YXSfAAAOCU6XHK9FL5Dg8AAFAswQMAABTLIW0AwH7JF/W7rFq1KgdWdQLoPYIHANgvLVu2LE/9fGm2DhpetRnqtr7xX7H/+3xL1Wbo27Yu7x7QL+lXtRGgVwkeAGC/tXXQ8Lz2vmnVHqOqBj57b7Lt5WqPAb3Gd3gAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKFZV/w7Pgw8+mKuvvjrbtm3L6aefnjlz5nS7/eabb87tt9+evn37Zvjw4bnmmmtyyCGHJEkaGxszfvz4JMnBBx+cr33ta3t9fgAAoLZVLXi2bt2aq666KjfffHMqlUpmz56dpqamjB07tnOZxsbG3HnnnRk4cGD+8R//MV/+8pdzww03JEkGDBiQe+65p1rjAwAA+4CqHdK2ZMmSHHbYYRk9enQaGhoyffr0LF68uNsyEydOzMCBA5MkRx11VFavXl2NUQEAgH1U1YKnpaUlo0aN6rxcqVTS0tKy0+XvuOOOnHDCCZ2X29vbM2vWrPzxH/9xHnjggV6dFQAA2DdV9Ts8b9c999yTn//85/nWt77Ved0Pf/jDVCqVrFixImeeeWbGjx+fMWPGvOXjtLe3Z+nSpbs9R1tb227ftzTbtm2r9gg1o62tbY/2q56cg6710LfKc9QC+2bt8d7ZpRb2T/tmF/tml1rYN+lZVQueSqXS7RC1lpaWVCqV7ZZ75JFH8rWvfS3f+ta30tDQ0O3+STJ69Ogcc8wxeeaZZ3YZPP37909jY+Nuzzxo0KAkL+/2/UvSp48T/L1p0KBBe7Rf9eQc9s8310PSXuU5aoF9s/Z47+xSC/unfbOLfbNLT+ybgqm2VG3vnjBhQpYvX54VK1Zk8+bNaW5uTlNTU7dlnnnmmcybNy833XRTRowY0Xn9xo0bs3nz5iTJunXr8sQTT3Q72QEAAEBSxU946uvrM2/evJxzzjnZunVrTjvttIwbNy7z58/P+9///px44on50pe+lLa2tlx44YVJuk4//dxzz+Xzn/986urq0tHRkXPPPVfwAAAA26nqd3gmT56cyZMnd7vuzbhJkltuuWWH9zv66KPz3e9+tzdHAwAACuCATQAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACjWLoNnzZo1+exnP5tzzjknSbJs2bLcfvvtvT4YAADAntpl8FxxxRWZNGlSWltbkySHH354br311l4fDAAAYE/tMnjWr1+fadOmpU+fNxatr6/v/BkAAKCW7bJcBg0alPXr16euri5J8tRTT2Xw4MG9PhgAAMCeqt/VAldccUXOO++8vPjii/nTP/3TrF+/PvPnz98bswEAAOyRXQbPkUcemW9961v51a9+lY6OjhxxxBHp16/f3pgNAIqyZcuWvPDbvrnmiSHVHqWqXni5bw5ct67aYwD7iV0Gz8KFC7tdfuaZZ5IkM2fO7J2JACjKunXr0rdtbQY+e2+1R6mqvm1rszXb0rfagwDsZ3YZPE8//XTnz+3t7fn3f//3HHnkkT0SPA8++GCuvvrqbNu2LaeffnrmzJnT7fbNmzfn8ssvzy9+8YsccMABuf7663PooYcmSb7+9a/njjvuSJ8+ffK//tf/yvHHH7/H8wBAb+rbt28OG/TbfPboTdUepaqueWJI+g8fXu0xgP3ELoPnc5/7XLfLmzZtysUXX7zHT7x169ZcddVVufnmm1OpVDJ79uw0NTVl7NixncvcfvvtGTJkSO6///40NzfnK1/5Sm644YYsW7Yszc3NaW5uTktLS84+++wsWrQoffv6vRlArRk+fHh+tWFLXnvftGqPUlUDn703/ba9XO0x+C98+viGvm1rs6VvXeIbCxTqHZ9feuDAgVm5cuUeP/GSJUty2GGHZfTo0WloaMj06dOzePHibsv84Ac/yEc/+tEkydSpU/Pv//7v6ejoyOLFizN9+vQ0NDRk9OjROeyww7JkyZI9ngkAACjLLj/h+R//4390/tzR0ZFly5bl5JNP3uMnbmlpyahRozovVyqV7aKlpaUlBx988BuD1tdn8ODBWb9+fVpaWvLf//t/73bflpaWPZ4J9mV+U/nGbynXrXvjV5RrXt6/vxjuS+Gwaz59fMPAZ+9N2tflhf38fTPx3lmqXQbPX/zFX3T+3Ldv3xxyyCHdQmVf0t7enqVLl+72/dva2npwmn3btm3bqj1CzWhra9uj/aqnbN68udoj1ATrocvmzZtrYt/03tnFe2eXWnjvtG926ejoqPYINaNW3jvpObsMnmOOOaZXnrhSqWT16tWdl1taWlKpVLZb5te//nVGjRqV119/PS+//HKGDRv2tu67I/37909jY+Nuzzxo0KAkjr9Okj593vHRkMUaNGjQHu1XPWXUqFFZ1Va3X/+mcuCz92bUqDfeC4a1r9yvvxh+zRND0n/UqJrYN713dvHe2aUW3jvtm10aGhoyul/bfv2+mfTce6dgqi07DZ4PfOADqaur2+76jo6O1NXV5YknntijJ54wYUKWL1+eFStWpFKppLm5Odddd123ZZqamnL33XfnAx/4QBYtWpSJEyemrq4uTU1NmTt3bs4+++y0tLRk+fLl+W//7b/t0TwAAEB5dho8Tz75ZO8+cX195s2bl3POOSdbt27NaaedlnHjxmX+/Pl5//vfnxNPPDGzZ8/OZZddlilTpmTo0KG5/vrrkyTjxo3LySefnGnTpqVv376ZN2/eXjlDm+9IvMHZXAAA2Ffs8pC2N61duzbt7e2dl3/nd35nj5988uTJmTx5crfrLrzwws6f+/fvn69+9as7vO95552X8847b49nAAAAyrXL4Fm8eHGuvfbatLa2Zvjw4XnppZfy3ve+N83NzXtjvpribC5v8LckAADYV+wyeObPn5/vfOc7Ofvss7Nw4cL89Kc/zb/+67/ujdmoYVu2bMkLv3X6SqevBACobbs8XUx9fX2GDRuWbdu2Zdu2bZk4cWJ+/vOf743ZAAAA9sguP+EZMmRIXn311fz+7/9+Lr300gwfPvw/T+PI/qxfv34ZPehVp698Ykj6Dx9e7TEAANiJXX7C88EPfjCvvPJKrrzyyhx//PEZM2ZMbrrppr0xGwAAwB7Z5Sc8W7duzV/8xV9k6NChmTZtWqZNm5Zhw4btjdkAAAD2yC4/4Tn//PPT3NycefPm5Te/+U3+7M/+LGedddZeGA0AAGDP7DJ43jRixIgceOCBOeCAA7J27drenAkAAKBH7PKQtm9/+9v5t3/7t6xbty4f/vCH87//9//O2LFj98ZsAAAAe2SXwbN69ep89rOfTWNj496YBwAAoMfsMnjmzp27N+YAAADocW/7OzwAAAD7GsEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABSrvhpPumHDhlx88cVZtWpVDjnkkNxwww0ZOnRot2WWLl2aL3zhC3nllVfSp0+fnHfeeZk2bVqS5Iorrshjjz2WwYMHJ0m++MUvprGxca+/DgAAoLZVJXgWLFiQY489NnPmzMmCBQuyYMGCXHbZZd2WGTBgQK699tocfvjhaWlpyWmnnZZJkyZlyJAhSZLLL788H/7wh6sxPgAAsI+oyiFtixcvzsyZM5MkM2fOzAMPPLDdMkcccUQOP/zwJEmlUsnw4cOzbt26vTkmAACwj6tK8KxduzYjR45Mkhx00EFZu3btWy6/ZMmSbNmyJWPGjOm87vrrr8+pp56aa665Jps3b+7VeQEAgH1Trx3SdtZZZ2XNmjXbXX/RRRd1u1xXV5e6urqdPk5ra2suu+yyXHvttenT540+u+SSS8o5sPQAABtTSURBVHLQQQdly5Yt+dznPpcFCxbk/PPP3+VM7e3tWbp06Tt8JV3a2tp2+76l2bZtW7VHqBltbW17tF/15Bx0rYe+VZ6jFtg3a4/3zi61sH/aN7vYN7vUwr5Jz+q14Lnlllt2etuIESPS2tqakSNHprW1NcOHD9/hcq+88ko++clP5uKLL85RRx3Vef2bnw41NDRk1qxZ+cY3vvG2Zurfv/8endxg0KBBSV7e7fuX5M345I39ohZOmmH/fMMb6yFpr/IctcC+WXu8d3aphf3TvtnFvtmlJ/ZNwVRbqrJ3NzU1ZeHChUmShQsX5sQTT9xumc2bN+fTn/50ZsyYsd3JCVpbW5MkHR0deeCBBzJu3LjeHxoAANjnVCV45syZk4cffjgnnXRSHnnkkcyZMydJ8vTTT+fKK69Mktx33315/PHHc/fdd2fGjBmZMWNGZy1feumlOfXUU3Pqqadm/fr1Oe+886rxMgAAgBpXldNSDxs2LN/85je3u37ChAmZMGFCknRGzo7ceuutvTofAABQhqoEDwAAteXFV/rmmieGVO35N25+4yRWQxs6qjbDi6/0jS9KlEfwAADs5wYOHJhDxo6t6gybli1Lkow8rHpzjEsytsrrgZ4neAAA9nOHHHJI5s+fX9UZLrzwwiSp+hyUR/AAAPutvm3rMvDZe6v2/HVbXkuSdPQbWLUZ+ratS1Kp2vNDbxM8AMB+qRYOXVr2n4dxjX1PNYOjUhPrAnqL4AEA9ksXXHBBtUdwGBfsBYIHgF7nsKH/PGxoQL+qPT/A/krwANCrauFQmVo5bGjVqlXJ6xuqOAPA/kfwANCrHDbUfY5f/qLV3zrxt06AvUjwAMBeUgufdvlbJ8D+RvAAwF7i0y6Ava9PtQcAAADoLYIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoVn21B2Df9eIrfXPNE0Oq9vwbN9clSYY2dFRthhdf6ZtxVXt2AAB2RfCwWwYOHJhDxo6t6gybli1Lkow8rHpzjEsytsrrAQCAnRM87JZDDjkk8+fPr+oMF154YZJUfQ4AAGqX7/AAAADFEjwAAECxHNL2DvVtW5eBz95bteev2/JakqSj38CqzdC3bV2SStWeHwAA3i7B8w7UwpfTl/3nF/XHvqeawVGpiXUBAAC7InjegQsuuKDaI/iiPgAAvAOCByhSNf9OlL8RBQC1Q/BAQar5HbNa+n5ZtQ+59DeiAKB2CB4oRLX/c1tL3y+r9uGnDj0FgNoheKAQ/pMPALA9f4cHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAACiW4AEAAIoleAAAgGIJHgAAoFiCBwAAKJbgAQAAiiV4AACAYgkeAACgWIIHAAAoluABAACKJXgAAIBiCR4AAKBYggcAAChWfTWedMOGDbn44ouzatWqHHLIIbnhhhsydOjQ7ZZrbGzM+PHjkyQHH3xwvva1ryVJVqxYkUsuuSQbNmzIkUcemS996UtpaGjYq68BAACofVX5hGfBggU59thj8/3vfz/HHntsFixYsMPlBgwYkHvuuSf33HNPZ+wkyVe+8pWcddZZuf/++zNkyJDccccde2t0AABgH1KV4Fm8eHFmzpyZJJk5c2YeeOCBt33fjo6O/PSnP83UqVOTJB/96EezePHiXpkTAADYt1XlkLa1a9dm5MiRSZKDDjooa9eu3eFy7e3tmTVrVurr6zNnzpx86EMfyvr16zNkyJDU178x+qhRo9LS0vK2nre9vT1Lly7tmRdRJW1tbUmyz7+OnmBd1Bbbo4t1UXtsky7WRW2xPbpYF/SWXgues846K2vWrNnu+osuuqjb5bq6utTV1e3wMX74wx+mUqlkxYoVOfPMMzN+/Pi8+93v3u2Z+vfvn8bGxt2+fy0YNGhQkuzzr6MnWBe1xfboYl3UHtuki3VRW2yPLiWtC9FWW3oteG655Zad3jZixIi0trZm5MiRaW1tzfDhw3e4XKVSSZKMHj06xxxzTJ555plMnTo1mzZtyuuvv576+vqsXr26czkAAID/qirf4WlqasrChQuTJAsXLsyJJ5643TIbN27M5s2bkyTr1q3LE088kbFjx6auri4f/OAHs2jRoiTJ3Xffnaampr03PAAAsM+oSvDMmTMnDz/8cE466aQ88sgjmTNnTpLk6aefzpVXXpkkee6553LaaaflIx/5SM4888yce+65GTt2bJLksssuy80335wpU6Zkw4YNOf3006vxMgAAgBpXlZMWDBs2LN/85je3u37ChAmZMGFCkuToo4/Od7/73R3ef/To0U5FDQAA7FJVPuEBAADYGwQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECx6qvxpBs2bMjFF1+cVatW5ZBDDskNN9yQoUOHdlvmpz/9af7qr/6q8/Lzzz+f66+/Ph/60IdyxRVX5LHHHsvgwYOTJF/84hfT2Ni4V18DAABQ+6oSPAsWLMixxx6bOXPmZMGCBVmwYEEuu+yybstMnDgx99xzT5I3Aumkk07Kcccd13n75Zdfng9/+MN7dW4AAGDfUpVD2hYvXpyZM2cmSWbOnJkHHnjgLZdftGhRjj/++AwcOHBvjAcAABSiKp/wrF27NiNHjkySHHTQQVm7du1bLt/c3Jyzzz6723XXX399/vZv/zbHHntsLr300jQ0NOzyedvb27N06dLdH7wGtLW1Jck+/zp6gnVRW2yPLtZF7bFNulgXtcX26GJd0Ft6LXjOOuusrFmzZrvrL7room6X6+rqUldXt9PHaW1tzf/7f/8vkyZN6rzukksuyUEHHZQtW7bkc5/7XBYsWJDzzz9/lzP1799/n/+uz6BBg5Jkn38dPcG6qC22RxfrovbYJl2si9pie3QpaV2IttrSa8Fzyy237PS2ESNGpLW1NSNHjkxra2uGDx++02Xvu+++TJkyJf369eu87s1PhxoaGjJr1qx84xvf6LG5AQCAclTlOzxNTU1ZuHBhkmThwoU58cQTd7psc3Nzpk+f3u261tbWJElHR0ceeOCBjBs3rveGBQAA9llVCZ45c+bk4YcfzkknnZRHHnkkc+bMSZI8/fTTufLKKzuXW7lyZX7961/nmGOO6Xb/Sy+9NKeeempOPfXUrF+/Puedd95enR8AANg3VOWkBcOGDcs3v/nN7a6fMGFCJkyY0Hn50EMPzU9+8pPtlrv11lt7dT4AAKAMVfmEBwAAYG8QPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxapK8Nx3332ZPn163ve+9+Xpp5/e6XIPPvhgpk6dmilTpmTBggWd169YsSKnn356pkyZkosuuiibN2/eG2MDAAD7mKoEz/jx4/M3f/M3+YM/+IOdLrN169ZcddVV+fu///s0Nzfne9/7XpYtW5Yk+cpXvpKzzjor999/f4YMGZI77rhjb40OAADsQ+o6Ojo6qvXkZ5xxRi6//PJMmDBhu9uefPLJ3HjjjfmHf/iHJMnXv/71JMmcOXMyceLEPPzww6mvr99uubeydOnSNDY29uyLeIcWLVqUe++9d7fv/2b0jR07drcfY9q0aZk6depu37+nWBe1pRa2R1Ib28S6qC17uj2Sct4vrIva4/2ii3XRpRb+z0mX+moPsDMtLS0ZNWpU5+VKpZIlS5Zk/fr1GTJkSOrr3xh91KhRaWlpeVuP2d7enqVLl/bKvG/XSy+9lLa2tt2+/7vf/e4k2aPHeOmll6q+Ht6cw7qoHbWwPd6co9rbxLqoLXu6PZJy3i+si9rj/aL7DNYFtajXguess87KmjVrtrv+oosuyoc+9KHeetq31L9//6rXdmNjY84+++yqzlArrIvaYnt0sS5qi+3RxbqoPbZJF+uii+CqLb0WPLfccsse3b9SqWT16tWdl1taWlKpVDJs2LBs2rQpr7/+eurr67N69epUKpU9nBYAAChRzZ6WesKECVm+fHlWrFiRzZs3p7m5OU1NTamrq8sHP/jBLFq0KEly9913p6mpqcrTAgAAtagqwXP//ffnhBNOyJNPPplPfvKT+cQnPpHkjU9xzj333CRJfX195s2bl3POOSfTpk3LySefnHHjxiVJLrvsstx8882ZMmVKNmzYkNNPP70aLwMAAKhxVT1L297mjBkAAPQ2/+esLTV7SBsAAMCeEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUS/AAAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEEDwAAUCzBAwAAFEvwAAAAxRI8AABAsQQPAABQLMEDAAAUq77aA+xN7e3tWbp0abXHAACgYO3t7dUegf+irqOjo6PaQwAAAPQGh7QBAADFEjwAAECxBA8AAFAswQMAABRL8AAAAMUSPAAAQLEET8FuuummTJ8+PaeeempmzJiRn/3sZ7nyyiuzbNmyt/0Yjz76aH73d383t99+e+d1S5cuze/+7u/mH/7hH3ps1kcffTS/93u/lxkzZuTkk0/OjTfe2GOPTe3Zl/bN1157LXPnzs2pp56aU045JR/72Mfy6quv9tjjs+/Y0X57xhln5Omnn+5cZuXKlTnllFO63e/qq6/O8ccfn23btnVed9ddd2XixImZMWNGpk2bln/5l3/Z6fO+k2XZP1Vr31yzZk0++clP5iMf+UimTZuWc889t+dfHPSA/eoPj+5PnnzyyfzoRz/K3XffnYaGhqxbty5btmzJ1Vdf/Y4fa/z48bnvvvty+umnJ0m+973v5X3ve19Pj5zf//3fz9e//vW0tbVl5syZ+aM/+qMceeSRPf48VNe+tm/eeuutOfDAA3PdddclSZ5//vn069evR5+D2rez/XZXtm3blgceeCAHH3xwHnvssUycOLHztmnTpmXevHlZu3Ztpk+fnqamphx44IE7fJx3siz7l2rum1/96lfzh3/4hznzzDOTJM8++2zPvTDoQT7hKdRvfvObDBs2LA0NDUmS4cOHp1KpdPuNzwc+8IFce+21mT59es4666wsWbIkZ5xxRk488cQsXry487F+53d+J+3t7VmzZk06Ojryk5/8JCeccELn7f/yL/+S0047LR/5yEdywQUX5LXXXkuSXHHFFZk3b15mzZqVqVOn5oc//OHbmn3QoEE58sgj88ILL/TU6qCG7Gv75m9+85tUKpXOy+95z3s6Z2f/sbP9dlceffTRjB07Nh/72MfS3Ny8w2VGjBiRMWPG5KWXXtrl472TZdk/VHPfbG1tzahRozov98YvQ6EnCJ5CHXfccfn1r3+dqVOn5gtf+EIee+yx7ZZpa2vLxIkT09zcnHe961254YYb8o1vfCN/+7d/m69+9avdlp06dWr+7d/+LU888USOPPLIbv/hmzJlSu68887867/+a97znvfkjjvu6Lxt1apVueOOO/L1r389n//859Pe3r7L2devX5+f/exnGTdu3B6sAWrVvrZvnnbaafm7v/u7/Mmf/Emuv/76LF++vGdWBPuUt9pvL7300syYMSMzZszInDlzut2vubk506dPz5QpU/KjH/1oh795X7FiRVasWJExY8bsco53siz7h2rumx//+Mdz5ZVX5owzzshNN92UlpaWnn1x0EMc0laod73rXbnrrrvy+OOP59FHH83FF1+cuXPndlumX79+nb8NHz9+fBoaGtKvX7+MHz8+q1at6rbsySefnIsvvjjPP/98pk+fnieffLLztl/+8pe54YYb8vLLL+fVV1/NpEmTut2vT58+OfzwwzN69Og8//zzaWxs3OHMjz/+eGbOnJk+ffrk3HPPFTyF2tf2zcbGxjzwwAN5+OGH88gjj2T27Nn5zne+k/e+9709uVqocW+1337lK1/JhAkTkvz/9u41pKk3jgP4d5u5vC0ZaWlK+KJYWirTMoioxLQLFokGFV2gRgWhWQgjX5gmSRFBVAZJreYrTdfVrFiCzm7oiliakDl09kYryJrivJz/C2m0v2V5qbHj9/Nqe85znvM7h9+L/c7znLOR5yQOHDgAAHA4HKitrYVWq4W/vz9iYmJQX1+PNWvWAADu378Ps9kMb29vFBQUIDAw8JfHH09fml7cmZsrV66E0WiEyWRCXV0dtmzZgnv37kGpVP6DMyf6cyx4REwmkyEhIQEJCQlYuHAhbt265bJ9xowZkEgkAACpVOq8My6VSjE0NOTSNygoCF5eXnjy5Alyc3NdflRqtVoUFxdDpVLBYDC43F36Pv6vvv/o+zM8JH6elpt+fn5ITk5GcnIypFIpamtrWfBMQ7/L2/+rr6/H169fsWnTJgAjL8CQy+XOH5Xfn5P4E+PpS9OPO3MzMDAQqampSE1Nxf79+9HQ0ICUlJTJnRDRFOOSNpFqa2tzWXrz9u1bhIaGTmrMzMxM5OTkQCaTubTb7XYEBQVhYGAAd+/eddn24MEDDA8Po6OjAzabDREREZOKgTyfp+Wm2WzGly9fAIzcFW1tbZ10vOR5JpK3VVVVKCwsRE1NDWpqavD48WM8ffrU+SwZ0VRwZ24+e/bMuc+3b9/Q0dGBkJCQcZ8D0d/GGR6R6u3tRWFhIXp6eiCTyTB//nwUFBQgKytrwmOq1eqftmdlZSEjIwNKpRIxMTEur+wNCQlBeno67HY78vPzIZfLJ3x8EgdPy02bzYbjx48DGHmr0apVq3j3choab9729fXBZDIhPz/f2ebr64u4uLg/foEL0Z9wZ242NTXhxIkTkMlkEAQBGRkZiI6OntT5EP0NEkEQBHcHQeKk1WqxevVqrFu3zt2hELlgbhIREU0fXNJGRERERESixRke+qdMJhPOnDnj0hYWFoaLFy+6KSKiEcxNcrfKykro9XqXNrVajby8PDdFRDSCuUmejgUPERERERGJFpe0ERERERGRaLHgISIiIiIi0WLBQ0Tkgbq7u5GdnY2kpCSkpaVBo9HAarVO+XF27twJi8UyZp9r1665/H+HRqNBT0/PlMdCREQ0ESx4iIg8jCAIOHToEJYtWwaj0QiDwYCjR4/i06dPv913cHBw1FjDw8OTikev17sUPCUlJVAoFJMak4iIaKrwj0eJiDzM8+fP4eXlhW3btjnbVCoVBEHAqVOnYDKZIJFIcPDgQWzYsAEvXrzAuXPnoFAoYLVaceXKFezduxcxMTFoamrC5cuXYbVacf78eTgcDoSHh6OoqAh+fn4ux83Ly4PFYkF/fz9SUlKQmZkJvV6Prq4u7N69G4GBgSgtLUViYiIqKiqgVCqh0+lQWVkJAEhPT8eePXvQ2dkJjUaDuLg4vHr1CnPmzEFxcTFmzpz5T68jERFND5zhISLyMO/evUNUVNSo9kePHqGlpQW3b9+GTqfD6dOn0dXVBQBobm5Gbm4uHj58CABob2/H9u3bUVVVBR8fH1y6dAk6nQ43b97E4sWLodPpRo2fnZ0Ng8GAO3fuoKGhAS0tLdi1axeCg4Nx/fp1lJaWuvR/8+YNDAYDysvLUVZWhhs3bqC5udl5/B07dqCqqgoBAQHOuIiIiKYaZ3iIiETCbDZj48aNkMlkmD17NpYuXQqLxQJ/f38sWbIE4eHhzr6hoaGIjY0FALx+/Rqtra3OGaOBgQHnth9VV1ejvLwcg4OD6O7uxvv376FSqcaMJykpCb6+vgCAtWvXorGxEYmJiQgLC8OiRYsAAFFRUfjw4cOUXQciIqIfseAhIvIwCxYsGPeMyPei42ffBUHAihUrcPbs2V/ub7PZcPXqVVRUVGDWrFnQarXo7+8fX+A/8Pb2dn6WyWSTGouIiGgsXNJGRORhli9fDofDgbKyMmdbS0sLFAoFqqurMTQ0hM+fP6OxsRHR0dG/HS82NhYvX75Ee3s7AKC3t3fUG9/sdjt8fHwQEBCAjx8/oq6uzrnNz88Pdrt91Ljx8fEwGo3o6+tDb28vjEYj4uPjJ3raREREE8IZHiIiDyORSHDhwgWcPHkSJSUlkMvlmDdvHo4dOwa73Y7NmzdDIpEgJycHQUFBaGtrG3M8pVKJoqIiHDlyBA6HAwBw+PBhREREOPuoVCpERkZi/fr1mDt3LtRqtXPb1q1bsW/fPgQHB7s8xxMVFYW0tDRkZGQAGHlpQWRkJDo7O6fychAREY1JIgiC4O4giIiIiIiI/gYuaSMiIiIiItFiwUNERERERKLFgoeIiIiIiESLBQ8REREREYkWCx4iIiIiIhItFjxERERERCRaLHiIiIiIiEi0WPAQEREREZFo/QeCmnnALWbiXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_df = cor_merged_all[cor_merged_all.algo=='LGB']\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
    "ax = sns.boxplot(x=_df['Correlation'], y=_df['value'], hue=_df['data'])\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_models = {\n",
    "    'rf': rf_models,\n",
    "    'lgb': lgb_models,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "mols = train_mols + test_mols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _sanity_check_fn(i, algo, weight_fn, method):\n",
    "    mol = mols[i]\n",
    "    models = all_models[algo]\n",
    "    saliency, randomized_saliency = calc_saliency(mol, models[0], models[1:], weight_fn=weight_fn)\n",
    "    show_pred_results(mol, models[0])\n",
    "    if method=='visual':\n",
    "        draw_visual_inspection(mol, saliency, randomized_saliency)\n",
    "    elif method=='variation':\n",
    "        variation_sanity_check(mol, saliency, randomized_saliency, scaler=None, atoms=['C', 'N', 'O', 'S', 'F', 'Cl', 'P', 'Br', 'I'])\n",
    "    elif method=='correlation':\n",
    "        correlation_sanity_check(saliency, randomized_saliency)\n",
    "    else:\n",
    "        raise NotImplementedError()\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbcbd9f83c8b4d8fb1a258f1eb3df1ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=640, description='i', max=1281), Dropdown(description='algo', options=('…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(\n",
    "    _sanity_check_fn, i=(0, len(mols) - 1, 1), \n",
    "    algo=['rf', 'lgb'], weight_fn=['simmap', 'shap'], method=['visual', 'variation', 'correlation']\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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