feature_explanation.ipynb

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      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n"
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},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?v.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?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_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=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}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(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[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===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);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&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,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(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_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(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.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=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[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&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.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=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,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(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},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(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[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 U(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\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},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(96),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+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({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});return i},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:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}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=s.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=f},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(158),c=(n(1),!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){var n=a.getNodeFromInstance(t);n.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,a=o+r;return{start:o,end:a}}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(392),l=n(168),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(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),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+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},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):void 0,8===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(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},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\"):void 0,Array.isArray(c)&&(c.length<=1?void 0: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",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0: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 t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import read_csv\n",
    "import numpy as np\n",
    "import shap\n",
    "import tensorflow.keras.backend \n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import deeplift\n",
    "\n",
    "# supress deprecation warnings from TF\n",
    "import tensorflow as tf\n",
    "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_4 (Conv1D)            (None, 25, 64)            128       \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 25, 128)           8320      \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 25, 256)           33024     \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, 25, 256)           65792     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 25, 256)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6400)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               819328    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 926,721\n",
      "Trainable params: 926,721\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import model_from_json\n",
    "\n",
    "keras_model_weights = \"final_model/keras2_conv1d.h5\"\n",
    "keras_model_json = \"final_model/keras2_conv1d.json\"\n",
    "\n",
    "keras_model = model_from_json(open(keras_model_json).read())\n",
    "keras_model = tensorflow.keras.models.load_model(keras_model_weights)\n",
    "keras_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set=read_csv('data/train_set.csv')\n",
    "val_set=read_csv('data/val_set.csv')\n",
    "test_set=read_csv('data/test_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainy=read_csv('data/trainy.csv')\n",
    "valy=read_csv('data/valy.csv')\n",
    "testy=read_csv('data/testy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_csv('data/orginal_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the range between 0 and 1\n",
    "column = list(train_set.columns.values)\n",
    "for col in column:\n",
    "    minimum = df[col].min()\n",
    "    maximum = df[col].max()\n",
    "    train_set[col] = (train_set[col] - minimum)/(maximum-minimum)\n",
    "    val_set[col] = (val_set[col] - minimum)/(maximum-minimum)\n",
    "    test_set[col] = (test_set[col] - minimum)/(maximum-minimum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = train_set.values\n",
    "val_set = val_set.values\n",
    "test_set = test_set.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = np.reshape(train_set, (train_set.shape[0],train_set.shape[1],1))\n",
    "val_set = np.reshape(val_set, (val_set.shape[0],val_set.shape[1],1))\n",
    "test_set = np.reshape(test_set, (test_set.shape[0],test_set.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nonlinear_mxts_mode is set to: DeepLIFT_GenomicsDefault\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For layer 0 the preceding linear layer is preact_0 of type Conv1D;\n",
      "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
      "For layer 1 the preceding linear layer is preact_1 of type Conv1D;\n",
      "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
      "For layer 2 the preceding linear layer is preact_2 of type Conv1D;\n",
      "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
      "For layer 3 the preceding linear layer is preact_3 of type Conv1D;\n",
      "In accordance with nonlinear_mxts_mode=DeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to Rescale\n",
      "For layer 6 the preceding linear layer is preact_6 of type Dense;\n",
      "In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n",
      "Heads-up: I assume sigmoid is the output layer, not an intermediate one; if it's an intermediate layer then please bug me and I will implement the grad func\n",
      "For layer 7 the preceding linear layer is preact_7 of type Dense;\n",
      "In accordance with nonlinear_mxts_modeDeepLIFT_GenomicsDefault we are setting the NonlinearMxtsMode to RevealCancel\n",
      "nonlinear_mxts_mode is set to: Rescale\n",
      "Heads-up: I assume sigmoid is the output layer, not an intermediate one; if it's an intermediate layer then please bug me and I will implement the grad func\n",
      "nonlinear_mxts_mode is set to: RevealCancel\n",
      "Heads-up: I assume sigmoid is the output layer, not an intermediate one; if it's an intermediate layer then please bug me and I will implement the grad func\n",
      "nonlinear_mxts_mode is set to: Gradient\n",
      "Heads-up: I assume sigmoid is the output layer, not an intermediate one; if it's an intermediate layer then please bug me and I will implement the grad func\n",
      "nonlinear_mxts_mode is set to: GuidedBackprop\n",
      "Heads-up: I assume sigmoid is the output layer, not an intermediate one; if it's an intermediate layer then please bug me and I will implement the grad func\n"
     ]
    }
   ],
   "source": [
    "from deeplift.layers import NonlinearMxtsMode\n",
    "import deeplift.conversion.kerasapi_conversion as kc\n",
    "from collections import OrderedDict\n",
    "\n",
    "method_to_model = OrderedDict()\n",
    "for method_name, nonlinear_mxts_mode in [\n",
    "    #The genomics default = rescale on conv layers, revealcance on fully-connected\n",
    "    ('rescale_conv_revealcancel_fc', NonlinearMxtsMode.DeepLIFT_GenomicsDefault),\n",
    "    ('rescale_all_layers', NonlinearMxtsMode.Rescale),\n",
    "    ('revealcancel_all_layers', NonlinearMxtsMode.RevealCancel),\n",
    "    ('grad_times_inp', NonlinearMxtsMode.Gradient),\n",
    "    ('guided_backprop', NonlinearMxtsMode.GuidedBackprop)]:\n",
    "    method_to_model[method_name] = kc.convert_model_from_saved_files(\n",
    "        h5_file=keras_model_weights,\n",
    "        json_file=keras_model_json,\n",
    "        nonlinear_mxts_mode=nonlinear_mxts_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum difference in predictions: 0.0\n"
     ]
    }
   ],
   "source": [
    "######### sanity checks ########\n",
    "from deeplift.util import compile_func\n",
    "model_to_test = method_to_model['revealcancel_all_layers']\n",
    "deeplift_prediction_func = compile_func([model_to_test.get_layers()[0].get_activation_vars()],\n",
    "                                         model_to_test.get_layers()[-1].get_activation_vars())\n",
    "original_model_predictions = keras_model.predict(test_set, batch_size=200)\n",
    "converted_model_predictions = deeplift.util.run_function_in_batches(\n",
    "                                input_data_list=[test_set],\n",
    "                                func=deeplift_prediction_func,\n",
    "                                batch_size=200,\n",
    "                                progress_update=None)\n",
    "print(\"maximum difference in predictions:\",np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)))\n",
    "assert np.max(np.array(converted_model_predictions)-np.array(original_model_predictions)) < 10**-5\n",
    "predictions = converted_model_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "revealcancel_model = method_to_model['revealcancel_all_layers']\n",
    "grad_model = method_to_model['grad_times_inp']\n",
    "guided_backprop_model = method_to_model['guided_backprop']\n",
    "rescale_conv = method_to_model['rescale_conv_revealcancel_fc']\n",
    "rescale_all = method_to_model['rescale_all_layers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<deeplift.layers.core.Input at 0x7ff2b95af4d0>,\n",
       " <deeplift.layers.convolutional.Conv1D at 0x7ff240318bd0>,\n",
       " <deeplift.layers.activations.ReLU at 0x7ff240318290>,\n",
       " <deeplift.layers.convolutional.Conv1D at 0x7ff2403188d0>,\n",
       " <deeplift.layers.activations.ReLU at 0x7ff2403185d0>,\n",
       " <deeplift.layers.convolutional.Conv1D at 0x7ff240318b90>,\n",
       " <deeplift.layers.activations.ReLU at 0x7ff240318990>,\n",
       " <deeplift.layers.convolutional.Conv1D at 0x7ff240318dd0>,\n",
       " <deeplift.layers.activations.ReLU at 0x7ff240318c10>,\n",
       " <deeplift.layers.core.NoOp at 0x7ff240318e10>,\n",
       " <deeplift.layers.core.Flatten at 0x7ff240318e90>,\n",
       " <deeplift.layers.core.Dense at 0x7ff240318f90>,\n",
       " <deeplift.layers.activations.ReLU at 0x7ff240318f10>,\n",
       " <deeplift.layers.core.Dense at 0x7ff24095d150>,\n",
       " <deeplift.layers.activations.Sigmoid at 0x7ff2a9a2ad50>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescale_all.get_layers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "######## compute importance scores ##########\n",
    "from keras import backend as K\n",
    "import deeplift\n",
    "from deeplift.util import get_integrated_gradients_function\n",
    "\n",
    "revealcancel_func = revealcancel_model.get_target_contribs_func(find_scores_layer_idx=0, target_layer_idx=-2)\n",
    "revealcancel_fc = rescale_conv.get_target_contribs_func(find_scores_layer_idx=0, target_layer_idx=-2)\n",
    "revealcancel_all = rescale_all.get_target_contribs_func(find_scores_layer_idx=0, target_layer_idx=-2)\n",
    "#rescale_multiplier = rescale_all.get_target_multipliers_func(find_scores_layer_idx=0, target_layer_idx=-1)\n",
    "\n",
    "grad_times_inp_func = grad_model.get_target_contribs_func(find_scores_layer_idx=0, target_layer_idx=-2)\n",
    "guided_backprop_times_inp_func = guided_backprop_model.get_target_contribs_func(find_scores_layer_idx=0, target_layer_idx=-2)\n",
    "\n",
    "\n",
    "#To get a function that just gives the gradients, we use the multipliers of the Gradient model\n",
    "gradient_func = grad_model.get_target_multipliers_func(find_scores_layer_idx=0, target_layer_idx=-2)\n",
    "\n",
    "#pure-gradients or pure-guidedbackprop perform rather poorly because they produce scores on pixels that are 0 (which are\n",
    "#the backround in MNIST). But we can give them a slight advantage by masking out positions that\n",
    "#are zero. Also, the method of simonyan et al uses the magnitude of the gradient.\n",
    "\n",
    "\n",
    "#prepare the integrated gradients scoring functions\n",
    "#heads-up: these take 5x and 10x longer to compute respectively!\n",
    "integrated_grads_5 = get_integrated_gradients_function(gradient_func, 5)\n",
    "integrated_grads_10 = get_integrated_gradients_function(gradient_func, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No reference provided - using zeros\n",
      "Done 0\n",
      "Done 10000\n"
     ]
    }
   ],
   "source": [
    "gradient_multiplier = np.array(gradient_func(task_idx=0,\n",
    "                                         input_data_list=[test_set],\n",
    "                                         batch_size=10,\n",
    "                                         progress_update=10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19584, 25, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient_multiplier.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HEADS UP! integrated_grads_5 and integrated_grads_10 take 5x and 10x longer to run respectively\n",
      "Consider leaving them out to get faster results\n",
      "Computing scores for: rescale_conv_revealcancel_fc\n",
      "\tComputing scores for task: 0\n",
      "Computing scores for: revealcancel\n",
      "\tComputing scores for task: 0\n",
      "Computing scores for: rescale_all_layers\n",
      "\tComputing scores for task: 0\n",
      "Computing scores for: guided_backprop_times_inp\n",
      "\tComputing scores for task: 0\n",
      "Computing scores for: grad_times_inp\n",
      "\tComputing scores for task: 0\n",
      "Computing scores for: integrated_grads_5\n",
      "\tComputing scores for task: 0\n",
      "Computing scores for: integrated_grads_10\n",
      "\tComputing scores for task: 0\n"
     ]
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "method_to_task_to_scores = OrderedDict()\n",
    "print(\"HEADS UP! integrated_grads_5 and integrated_grads_10 take 5x and 10x longer to run respectively\")\n",
    "print(\"Consider leaving them out to get faster results\")\n",
    "for method_name, score_func in [\n",
    "                               ('rescale_conv_revealcancel_fc', revealcancel_fc),\n",
    "                               ('revealcancel', revealcancel_func),\n",
    "                               ('rescale_all_layers',revealcancel_all),\n",
    "                               ('guided_backprop_times_inp', guided_backprop_times_inp_func),\n",
    "                               \n",
    "                               ('grad_times_inp', grad_times_inp_func),\n",
    "                               ('integrated_grads_5', integrated_grads_5),\n",
    "                               ('integrated_grads_10', integrated_grads_10)\n",
    "]:\n",
    "    print(\"Computing scores for:\",method_name)\n",
    "    method_to_task_to_scores[method_name] = {}\n",
    "    for task_idx in range(1):\n",
    "        print(\"\\tComputing scores for task: \"+str(task_idx))\n",
    "        scores = np.array(score_func(\n",
    "                    task_idx=task_idx,\n",
    "                    input_data_list=[test_set],\n",
    "                    input_references_list=[np.zeros_like(test_set)],\n",
    "                    batch_size=1000,\n",
    "                    progress_update=None))\n",
    "        method_to_task_to_scores[method_name][task_idx] = scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = method_to_task_to_scores['revealcancel']\n",
    "scores1 = method_to_task_to_scores['rescale_conv_revealcancel_fc']\n",
    "scores2 = method_to_task_to_scores['rescale_all_layers']\n",
    "scores3 = method_to_task_to_scores['guided_backprop_times_inp']\n",
    "scores4 = method_to_task_to_scores['grad_times_inp']\n",
    "scores5 = method_to_task_to_scores['integrated_grads_5']\n",
    "scores6 = method_to_task_to_scores['integrated_grads_10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance_score = np.reshape(scores[0], (scores[0].shape[0], scores[0].shape[1]))\n",
    "importance_score1 = np.reshape(scores1[0], (scores1[0].shape[0], scores1[0].shape[1]))\n",
    "importance_score2 = np.reshape(scores2[0], (scores2[0].shape[0], scores2[0].shape[1]))\n",
    "importance_score3 = np.reshape(scores3[0], (scores3[0].shape[0], scores3[0].shape[1]))\n",
    "importance_score4 = np.reshape(scores4[0], (scores4[0].shape[0], scores4[0].shape[1]))\n",
    "importance_score5 = np.reshape(scores5[0], (scores5[0].shape[0], scores5[0].shape[1]))\n",
    "importance_score6 = np.reshape(scores6[0], (scores6[0].shape[0], scores6[0].shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ave_revealcancel = np.mean(importance_score2, axis = 0)\n",
    "ave_revealcancel = np.round(ave_revealcancel, 3)\n",
    "ave_revealcancel = pd.Series(ave_revealcancel, index=['Admit Type', 'Ethnicity', 'Gender', 'Age', 'Admission Weight',\n",
    "       'Heart Rate', 'Respiratory Rate', 'SpO2',\n",
    "       'Inspired O2 Fraction', 'PEEP set', 'Mean Airway Pressure',\n",
    "       'Tidal Volume (observed)', 'PH (Arterial)',\n",
    "       'Respiratory Rate(Spont)', 'Richmond-RAS Scale',\n",
    "       'Peak Insp. Pressure', 'O2 Flow', 'Plateau Pressure',\n",
    "       'Arterial O2 pressure', 'Arterial CO2 Pressure',\n",
    "       'Blood Pressure (systolic)', 'Blood Pressure (diastolic)',\n",
    "       'Blood Pressure (mean)', 'Spontaneous breathing trials', 'Ventilator Mode'])\n",
    "ave_revealcancel = ave_revealcancel.sort_values(ascending=True)\n",
    "ave_revealcancel.plot(kind='barh', figsize=(5,10))\n",
    "#plt.savefig('feature_importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 6))\n",
    "for i in range(len(importance_score2)):\n",
    "    idx_importance = importance_score2[i]\n",
    "    plt.plot(idx_importance)\n",
    "    plt.grid(True)\n",
    "    plt.title('rescale_all_layers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.555477  , -0.        , -0.12399741, -0.08712155,  0.65607524,\n",
       "        0.19493589, -0.26156178,  0.30150905,  0.9874981 ,  1.4664364 ,\n",
       "        1.0772973 ,  0.73139524, -0.20668273, -0.        , -2.0716505 ,\n",
       "        1.1696159 , -0.2752966 , -1.0004053 ,  0.04960745, -0.94403994,\n",
       "        0.1562203 , -0.66200495,  0.24426539, -0.63540006,  3.132909  ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance_score2[124]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19584, 25)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance_score2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18, 6))\n",
    "plt.plot(importance_score2[124], 'go-')\n",
    "#plt.plot([0, 25],[0,0], '--',linewidth=1)\n",
    "plt.grid(True)\n",
    "plt.xticks(np.arange(25),\n",
    "          ['Admit Type', 'Ethnicity', 'Gender', 'Age', 'Admission Weight',\n",
    "       'Heart Rate', 'Respiratory Rate', 'SpO2',\n",
    "       'Inspired O2 Fraction', 'PEEP set', 'Mean Airway Pressure',\n",
    "       'Tidal Volume (observed)', 'PH (Arterial)',\n",
    "       'Respiratory Rate(Spont)', 'Richmond-RAS Scale',\n",
    "       'Peak Insp. Pressure', 'O2 Flow', 'Plateau Pressure',\n",
    "       'Arterial O2 pressure', 'Arterial CO2 Pressure',\n",
    "       'Blood Pressure (systolic)', 'Blood Pressure (diastolic)',\n",
    "       'Blood Pressure (mean)', 'Spontaneous breathing trials', 'Ventilator Mode'], rotation=45,horizontalalignment='right', multialignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vented_1    1.0\n",
       "Name: 124, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testy.iloc[124]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19584, 25, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = keras_model.predict(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.925094], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test[124]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set_ori=read_csv('data/test_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Admittype                              1.00\n",
       "Ethnicity                              0.00\n",
       "Gender                                 1.00\n",
       "Age                                   78.20\n",
       "Admission Weight (Kg)                 86.50\n",
       "Heart Rate                           119.00\n",
       "Respiratory Rate                      24.00\n",
       "O2 saturation pulseoxymetry           98.00\n",
       "Inspired O2 Fraction                 100.00\n",
       "PEEP set                              10.00\n",
       "Mean Airway Pressure                  14.00\n",
       "Tidal Volume (observed)              541.00\n",
       "PH (Arterial)                          7.46\n",
       "Respiratory Rate_spontaneous           0.00\n",
       "Richmond-RAS Scale                    -1.00\n",
       "Peak Insp. Pressure                   21.00\n",
       "O2 Flow                                5.00\n",
       "Plateau Pressure                      19.00\n",
       "Arterial O2 pressure                 124.00\n",
       "Arterial CO2 Pressure                 33.00\n",
       "Arterial Blood Pressure systolic     101.00\n",
       "Arterial Blood Pressure diastolic     65.00\n",
       "Arterial Blood Pressure mean          76.00\n",
       "SBT_value                              0.00\n",
       "ventilator_category                   18.00\n",
       "Name: 124, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set_ori.iloc[124]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "background = np.zeros((1,25,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12549205]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras_model.predict(background)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#shap explanation##########"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "keras is no longer supported, please use tf.keras instead.\n"
     ]
    }
   ],
   "source": [
    "# we use the first 100 training examples as our background dataset to integrate over\n",
    "explainer = shap.DeepExplainer(keras_model,  background)\n",
    "shap_values = explainer.shap_values(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance_shap = np.reshape(shap_values[0], (shap_values[0].shape[0], shap_values[0].shape[1]))\n",
    "ave_shap= np.mean(importance_shap, axis = 0)\n",
    "ave_shap = np.round(ave_shap, 4)\n",
    "ave_shap = pd.Series(ave_shap, index=column)\n",
    "ave_shap = ave_shap.sort_values(ascending=True)\n",
    "ave_shap.plot(kind='barh', figsize=(5,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OOB19WvH/CRERERUVZ+6cgYRkoSEHLDQQEZESiamJmBg4ET3W9oCLgwtCvUMxusloCCFy9f3tqrTDyVEn4dvdF2G3w9Dwp4aYEDAB9x7fM3ByIiIiKuo0URoA4NKJHLDQQERERncm+gyaLWuGhScWYvLrk3Hc63ieOjZbmFlgbNOx+Gf8PxjVeBQWhyxGzYU1sSR4CdLS0wyQnIiIiChjtGUxy2Ko4lhFdRSTxEIDEREZjZQSi4MXo8myJoiKj0KgZyDmd50PGwubfB23tF1p+Pbwxd+j/oaHswfGBoxFI79GOHj1oJ6SExEREf1LG61FPad6MBN8S50dPitERGQUd+LvoPf63hgXMA7t3NpBM1qDrtW76vUc9Z3rI2hYEH575zc8SHyAtmvaYuDvA3Et9ppez0NERERFl5QyY+KEE5dN5ISFBiIiMrg/L/+J+j/Vx+7Lu/FD1x+wc/BOONs7G+RcQgj0r9sf58edx5dtv8SOCztQe1FtfHngS47DJCIiony7/eg2Yh7HwMOZjSBzwkIDEREZTFJqEqbumYrOv3ZGKdtSCP4gGBNfn5jrho/5YWtpiy/afIHz48+jZ62emHlwJmr71sZvZ37jOEwiIiLKM220FgAbQb4MCw1ERGQQ5++eR4sVLTD36FyMbTIWIR+EKHlBrlyiMjb034ADww/A0cYRA34fgPY/t3/aLZqIiIhIF5m/Q3C0Zc5YaCAiIr2SUmJZ6DI0WtoI12KvYdugbfDt4QtbS1uludq4tUGodyiW9FgCTZQGry19DeN2jkNMQozSXERERFSwaKO1cLF3QWm70qqjmCwWGoiISG9iEmLQb2M/eP/hjVaVW0EzRoNetXqpjvWUuZk5RjcZjYsTLmJsk7FYGroUNRfVhO8JX6Smp6qOR0RERAWAJkrDZROvwEIDERHpxf7w/WjwUwP88c8f+L7T99g9ZDfKO5RXHStbpWxLYWH3hQgbHYaG5RpifOB4NFraCPvD96uORmSy/LX+cFvghvYH28NtgRv8tf6qIxERGV1qeirO3TnHZROvwEIDERHlS0paCj7d+yk6/NwB9lb2OOZ1DFNaTikQc6Xdndyxd+hebBqwCQ+TH6L9z+3xzm/vIOJBhOpoRCbFX+sP7x3eiIiNgIRERGwEvHd4s9hAREXOxZiLSEpL4sSJV9DLb4FCiK5CiAtCiEtCiE+yefwjIcRZIYRGCLFPCOGqj/MSEZFal+5dQquVrfDt4W/h1cgLod6haOTSSHUsnQgh8Hadt3F27Fl81e4rBFwMQG3f2vjv/v8iISVBdTwikzBj34wX/j0kpCRgxr4ZihIREamR2QiSSydeLt+FBiGEOQBfAN0A1AXwrhCi7nO7/Q2giZSyPoDfAczO73mJiEgdKSVWh61Gw58a4tK9S9g0YBP8evqhmFUx1dHyzNbSFv9p/R+cH3cefWr3way/ZqH2otrYcHoDx2FSkXct9ppO24mICitttBbmwhx1ytRRHcWk6eOKhmYALkkpr0gpkwGsB9A76w5Syv1Syswy+DEAFfVwXiIiUuBB4gMM2jQI7217D00rNIVmjAZv13lbdSy9qVSiEtb1W4e/RvyF0nalMWjTILRd0xanbp9SHY1ImcolKuu0nYiosNJGa1GrTC1YW1irjmLS9FFoqADgepb7N55sy8lIAIF6OC8RERnZoYhDaPBTA2w+txnfdPgGe4fuRcXihbN2/Kbrmwj5IARL31qKs3fOopFfI4z5YwzuJtxlUzwqcnw6+MDCzOKZbXaWdvDp4KMoERGRGpooDRtB5oLI7+WgQoh3AHSRUno9uT8UQDMp5YRs9h0CYDyANlLKpGwe9wbgDQDOzs6N169fn69sKjx69Aj29vaqY7yAuXTDXLphLt0UxFxpMg1rrq6B/zV/lLMph//U+Q/qFDfeJYOqn7OHKQ+xJmINttzcAkthiTSkIVX+Ow7T2swaU2tORUfnjsoyZqX6+coJc+nG1HINPzEckYmRT//uv13+bUyo8cKve8qY2vOVibl0w1y6YS7d5DdXQmoCehzugZFuIzHEdYjJ5FKlXbt2oVLKJtk+KKXM1w1ACwC7s9z/FMCn2ezXEcA5AE65OW7jxo1lQbR//37VEbLFXLphLt0wl24KWq4r967IFstbSMyEHL5luIxLjDNuMGk6z9npqNPS5msbiZl44eY631V1vKdM5fl6HnPpxpRypaWnSYf/Ocgxf4yRe4P2yqo/VJXNljWT6enpqqM9ZUrPV1bMpRvm0g1z6Sa/uY5cOyIxE3L7+e36CfSEqT5frwIgRObwfl4fSyeCAdQQQlQRQlgBGARge9YdhBCvAVgKoJeUMloP5yQiIiPw1/ijwU8NcPbOWazrtw6r+6yGg7WD6ljK1HOqh6TUFy7IA8CmeFS4/RPzDx4mP0TT8k1hLswxtcVUnLh5An9F/KU6GhGR0WROnOBoy1fLd6FBSpmKjOUQu5FxxcJGKeUZIcQsIUSvJ7vNAWAP4DchRJgQYnsOhyMiIhMQlxSHoVuGYsiWIajvXB+nRp/CIPdBqmOZhJya31mZW+HI9SNGTkNkHME3gwEATSs0BQCMaDgCZe3KYvYRDhIjoqJDG62Fg5UDXEu4qo5i8vRxRQOklAFSyppSympSSp8n276QUm5/8nVHKaWzlLLhk1uvlx+RiIiM5fnGhjMPzETDnxpinXYdvmz7JQ6MOADXknxBzeTTwQd2lnbPbLM0s4S1uTVarWyFHmt74OStk4rSERlGcGQw7CztULtMbQAZ42Anvj4RARcDoI3SKk5HRGQcmigNPJw9IIRQHcXk6aXQQEREBZO/1h/eO7wRERsBCYmI2Ah8efBLPEx6iL/e+wtftPnihU7zRZ2nhyf8evrBtYQrBARcS7hiVZ9ViJwSiW87fIuj14+isV9j9N/YH2fvnFUdl0gvgiOD0cil0TM/D8Y2HYtilsUw58gchcmIiIxDSglttJYTJ3KJhQYioiJsxr4ZSEhJeGG7raUtWlZqqSBRweDp4Ymrk68iqE0Qrk6+Ck8PTxSzKoaP3/gY4ZPC8d82/8Wey3vgvtgdQ7cMxeV7l1VHJsqzlLQUhN0OQ9PyTZ/ZXsq2FD5o9AHWnV7HHiVEVOjdfHgTDxIfsNCQSyw0EBEVYTm9ObgRd8PISQqPEjYlMLPtTFyZdAVTW07FprObUNu3NkbtGIXrsddVxyPS2Zk7Z5CYmvhCoQEAPmzxIaSUmH90voJkRETGk9kIsr5zfcVJCgYWGoiIirAKxStkuz2nhoeUe2XsymB2p9m4PPEyRjcejVVhq1B9YXVM3jUZUY+iVMcjyrXMRpBNyr84Kr1yicp41+NdLDu5DPce3zN2NCIio8nsR+Pu5K44ScHAQgMRUREVmxgLSzPLF7bbWdrBp4OPgkSFk4uDCxZ2X4iLEy5iaP2hWHRiEar+WBWf7v2Ub8yoQAiJDEFJm5KoXqp6to9PazkN8SnxWBK8xMjJiIiMRxutRaXileBo66g6SoHAQgMRURH0MOkhuq/tjutx1/FR84+eaWzo19MPnh6eqiMWOq4lXbG813KcG3cOfWr3wXeHv0OVH6pg1sFZiEuKUx2PKEfBkcFoUr5Jjl3W6zvXR7fq3fDjiR/xOOWxkdMRERlH5sQJyh0WGoiIipiElAT0XNcTx28cx/p+6zG3y9wXGhuS4dQoXQP+b/vj1OhT6FClA/574L+o+kNVzDk8J9vGnEQqJaYmQhutzbY/Q1bTW01HdHw01pxaY6RkRETGk5KWgvN3z7MRpA5YaCAiKkISUxPRe31vHLp2CL/0/QX96vZTHanI8nD2wOaBmxH8QcanxdP3Tke1H6th0YlFSEpNUh2PCAAQdjsMqemp2fZnyKqNaxs0Ld8U3x/5HmnpaUZKR0RkHBdiLiAlPYWNIHXAQgMRURGRlJqEtze8jX1X9mFlr5V41+Nd1ZEIGQ32dg3Zhb9G/IWapWtiQuAE1FxUEyv/XonU9FTV8aiIC4kMAYBXXtEghMDHrT7G5fuXseX8FmNEIyIymsyJE7yiIfdYaCAiKgKS05Ix4PcBCLwUiKVvLcXwhsNVR6LnvOn6Jg4MP4DdQ3bDuZgzRm4fibq+dbFOuw7pMl11PCqigiOD4VzMGRWLV3zlvn1q90H1UtXx3eHvIKU0QjoiIuPQRmlhYWaBWmVqqY5SYLDQQERUyKWmp2LwpsHYfmE7FnVbhA8af6A6EuVACIHO1TrjuNdxbB24FTYWNhi8eTAa/NQAW89v5Zs3Mrrgm8FoWqFpjo0gszI3M8fUFlMREhmCA1cPGD4cEZGRaKI1qFOmDqzMrVRHKTBYaCAiKsTS0tMwbMswbDq3CfM6z8O4ZuNUR6JcEEKgd+3eCBsdhnX91iE5LRl9N/RFs+XNsPvSbhYcyCgeJj3E+bvn0cTl5f0ZshrecDicijlh9pHZBkxGRGRc2igtJ07oiIUGIqJCKl2mY+T2kVh3eh2+6fANPmzxoepIpCMzYYZB7oNwZuwZrOy1Enfi76Crf1e0Wd0GhyIOqY5HhdzJWychIdG0wsv7M2RlY2GDSa9Pwq5Lu3Dq9ikDpiMiMo4HiQ9wPe466juxEaQuWGggIiqE0mU6Rv8xGmtOrcHMNjPxyRufqI5E+WBhZoH3XnsPF8ZfwKJui3Dx3kW0Xt0aXX7tguCbwarjUSEVHJnxd+tVjSCfN6bJGNhb2WPOkTmGiEVEZFTaKC0A8IoGHbHQQERUyEgpMTFwIpadXIZP3/gUX7T5QnUk0hNrC2uMazYOlydexpxOcxAaGYpmy5uhz/o+0EZp4a/1h9sCN7Q/2B5uC9zgr/VXHZkKsODIYLiWcEXZYmV1+j5HW0d4N/LG+tPrEfEgwkDpiIiMQxv9pNDAiRM6YaGBiKgQkVJi6p6p8A32xZQWU+DT3idXTdyoYLGztMPUllMRPikcs9rOwv6r+1H/p/oYvmU4ImIjICERERsB7x3eLDZQngXfDEaT8rnvz5DV5OaTIYTAvKPz9JyKiMi4tFFalLQpmavpO/QvFhqIiAoJKSVmBM3AvGPzMKHZBMzpNIdFhkLOwdoBn7f5HOGTwlHcujjSZNozjyekJGDGvhmK0lFBFpMQg/AH4Tovm8hUqUQlDPYYjOV/L0dMQoye0xERGY8mWgMPJw/+TqUjFhqIiAqJWQdn4Zv/+wbejbzxQ9cf+IJYhJSyLYWHSQ+zfexa7DUjp6HCICQyBAB0agT5vGktpyEhJQGLgxfrKxYRkVFJKXE6+jSXTeQBCw1EBQDXXdOrfHPoG8w8OBMjGo7AkreWsMhQBFUuUVmn7UQvk9kIspFLozwfw93JHT1q9MCPJ35EQkqCvqIRERnNtdhriEuKQ31nTpzQFQsNRCbOX+sP7x3eXHdNOZp3dB4+C/os4zLlnsthJvijvSjy6eADO0u7Z7ZZmlnCp4OPokRUkAVHBqNm6ZooaVMyX8eZ3mo67ibcxeqw1foJRkRkRJooDQBOnMgL/jZKZOJm7JvxwidBXHdNmRadWIQpe6agf93+WNNnDczNzFVHIkU8PTzh19MPriVcISBgZ2GH1PRU1CpdS3U0KoBCIkPy3J8hqzcrv4nmFZtj7tG5SE1P1UMyIiLjyZw44e7krjhJwcNCA5GJy2l9Ndddk1+oHyYETkDvWr2x9u21sDCzUB2JFPP08MTVyVcR1CYINz66gQrFK2DwpsGIT45XHY0KkMiHkYh8GKmXQoMQAtNbTseV+1ew6ewmPaQjIjIeTZQGbiXdUNy6uOooBQ4LDUQmrrxD+Wy321na4UHiAyOnIVOxOmw1Rv8xGt1rdMeG/htgaW6pOhKZGEdbR/zS9xdcuncJH+7+UHUcKkCCb2b0Z8jraMvn9arVCzVL18TsI7MhpdTLMYmIjEEbrWUjyDxioYHIhN1NuIu09LQXtluaWSIhJQH1l9THviv7FCQjldZq1+L9be+jQ9UO2DRgE6wtrFVHIhPV1q0tPm71MZadXIYt57aojkMFRHBkMMyFOV5zeU0vxzM3M8e0ltNw8tZJBIUH6eWYRESGlpSahAt3L7ARZB6x0KAnnApA+hafHI+31r6FB0kP8Hnrz5+uu3Yt4YpVfVbhmNcx2EsR6UsAACAASURBVFraouMvHTF512Q8TnmsOjIZwe9nf8ewLcPQ2rU1tg3aBhsLG9WRyMR92e5LNHZpDK8dXoh8GKk6DhUAIZEhqOdU74XmovkxpP4QlLMvh+8Of6e3YxIRGdK5u+eQJtN4RUMesdCgB5wKQPqWmp6Kgb8PRHBkMNb1W4dZ7WY9XXd9dfJVeHp4olmFZvh71N+Y0GwCfjj+Axr7NUZoZKjq6GRA285vw7ub3kXzis3xx+A/9PomgAovK3Mr+L/tj8TURAzbMgzpMl11JDJhUkoERwbrpT9DVjYWNpj0+iT8eeVP/H3rb70em4jIELRRGY0gOXEib1ho0ANOBSB9klJi1I5R2HlxJ3y7+6JP7T457mtnaYcfu/2IPUP2IC4pDs1XNMesg7PY2bsQCrgYgHd+eweNXBohwDMA9lb2qiNRAVKrTC380PUH7Avfh/lH56uOQyYs/EE47j2+p7f+DFmNbjIaDlYOmHNkjt6PTUSkb9poLazMrVCzdE3VUQokFhr0gFMBSJ++2P8FVoatxBetv8DoJqNz9T2dqnWCdowWA+oNwH8P/BetVrbChbsXDJyUjOXPy3/i7Q1vw93JHbs8d7HzMeXJyNdGom/tvvh036cIux2mOg6ZqMxGkPq+ogEAStqUxKjGo7DxzEaE3w/X+/GJiPRJE6VB3bJ1OdUrj1ho0IPKJSpnu72UbSkjJ6GCbnHwYnx96Gt4veaFmW1n6vS9jraO8H/bHxv6b8Cle5fw2tLX4HvClx2+C7iDVw+i9/reqFm6Jv4c+iccbR1VR6ICSgiBZT2XoWyxshi8afALV+IRARn9GazMrQx2qfCk5pNgJsww7+g8gxyfiEhfOHEif1ho0AOfDj4vrJU2E2aIeRyDT/Z+wvWwlCubz23G+IDx6FmzJ5a8tQRCiDwdZ0C9AdCO0aKNWxuMDxyPLr92wc24m3pOS8Zw+Nph9FjbA24l3bB32F6UtiutOhIVcKXtSmNNnzU4d/ccpu6ZqjoOmaDgyGA0LNcQVuZWBjl+xeIV4VnfEyv+XoG7CXcNcg4iovyKSYhB5MNITpzIBxYa9MDTwxN+Pf2enQrQexVGNR6F7w5/h74b+uJh0kPVMcmE/RXxFwZvGozmFZtjff/1+b5Eq7xDeQQMDsCSHktw+PphuC9xxzrtOj2lJWM4cfMEuvl3Q3mH8tg3bB+cijmpjkSFRMeqHTG1xVQsCVmCHRd2qI5DJiQtPQ2ht0LRxEX//RmymtZyGh6nPobvCV+DnoeIKK+00U8aQfKKhjxjoUFPPD08n5kKMKzBMCzpsQQ/dv0Rf/zzB1qtbIWIBxGqY5IJ0kZp0WtdL1RxrIId7+7Q2yQBIQRGNxmNsFFhqF2mNgZvHoxBvw/Cvcf39HJ8MpyTt06iy69dULZYWQQND4KLg4vqSFTIfN3+azQs1xDvb38ftx/dVh2HTMSFmAt4lPwITSvovz9DVnXL1kXPmj2x8MRCxCfHG/RcRER5kTlxglc05B0LDQYkhMCE1ycg0DMQ12Kvoemypjh87bDqWGRCrsVeQzf/bihmVQy7PHcZ5NL4GqVr4NB7h+DT3gebzm2C+2J37Lq0S+/nIf3QRGnQ6ZdOKG5dHEHDglCxeEXVkagQsrawxtq31+JR8iOM2DqCS/wIQEZ/BsAwjSCfN73VdMQ8jsGqsFUGPxcRka40URqUti2NcvblVEcpsFhoMILO1TrjuNdxlLQpiXZr2mF12GrVkcgE3Ht8D11/7YpHyY+wy3MXXEu6GuxcFmYW+OzNz3DC6wQcbR3Rzb8bxu4cy0+STMzZO2fR8eeOsLWwRdCwIIP+nSCqU7YO5nWeh92Xd2Ph8YWq45AJCL4ZjGKWxVC7TG2Dn+uNym+gZaWWmHt0LkcyE5HJ0UZr4eHskeeeacRCg9HUKlMLx7yOobVra7y37T1M/3M60tLTVMciRR6nPEbPdT1x+f5lbBu0zWDdvZ/3mstrCPUOxUfNP8JPIT+h4dKGOHbjmFHOTS/3T8w/6PBzB5ibmWPfsH2oVqqa6khUBIxuMho9a/bE9L3ToYnSqI5DigVHBqORSyOYm5kb5XzTW07H1QdX8duZ34xyPiKi3EiX6TgdfRr1nbhsIj9YaDCiUralEOgZiDFNxmDOkTnos6EPm0QWQanpqRi0aRCOXj8K/7f90catjVHPb2Nhg7ld5iJoeBBS0lLQamUr/CfoP0hOSzZqDvrXlftX0H5Ne6Slp2HfsH2oVaaW6khURAghsKLXCjjaOMJzsycepzxWHYkUSUlLQdjtMKMsm8jUs1ZP1C5TG7OPzOYoZiIyGeH3wxGfEm+0DwILKxYajMzS3BKLeyyGb3dfBF4MRMuVLRF+P1x1LDISKSXG7RyH7Re248duP6J/3f7KsrR1awvNGA2GNRgGn0M+aLGiBc7eOassT1EV8SAC7de0x+PUx9g7bC/qlq2rOhIVMWWLlcXqPqtxOvo0Ptn7ieo4pMjp6NNISksyeCPIrMyEGaa1nIaw22HYe2Wv0c5LRPQynDihHyw0KDK26VjsGrILN+JuoNnyZjgUcUh1JDKCWQdnwe+kHz574zOMbzZedRwUty6OVb1XYfOAzbgWew2NljbC/KPz2RjOSG7E3UD7n9vjQeID/Dn0T3Y2JmW6Vu+KSa9Pwo8nfkTgxUDVcUiB4MhgAMZpBJmVp4cnXOxd8N3h74x6XiKinGijtBAQqOdUT3WUAo2FBoU6Vu2I417HUcq2FDr83AEr/16pOhIZkF+oH2YenIkRDUfg6/Zfq47zjL51+uL0mNPoXK0zPtrzETr83IHjWA3AX+sPtwVuaH+wPSrOq4imfk1xJ/4Odg/ZjUYujVTHoyLu247fwsPJAyO2jUB0fLTqOGRkwTeD4WjjiKqOVY16XmsLa0xuPhn7wvchNDLUqOcmIsqOJlqDqo5VYW9lrzpKgcZCg2I1S9fEsZHH0MatDUZuH4kpu6ewSWQhtO38NozZOQbda3SH31t+JtnB1tneGdsGbcOKXisQEhmC+j/Vx5qwNVw3qyf+Wn947/BGRGwEJCRuPryJ2/G3Mbn5ZLxe8XXV8YhgY2GDtf3WIjYxFu9ve5//9ouYkFshaFK+iZLXp1GNR6G4dXHMOTLH6OcmInqeNkrLq0z1gIUGE+Bo64hAz0CMbzoe847NQ6/1vRCXFKc6FunJ4WuHMWjTIDQp3wQb+2+Epbml6kg5EkLg/dfeh2a0Bg2cG2DEthHot7Ef7sTfUR2twJuxbwYSUhJe2P7zqZ8VpCHKnruTO+Z0moOdF3diScgS1XHISB6nPIY2Smv0ZROZStiUwOjGo/Hb2d9w+d5lJRmIiICMn4cX711kfwY9YKHBRFiYWWBh94VY0mMJdl/ajRYrWuDK/SuqY1E+nb1zFj3X9UTlEpWxc/BOFLMqpjpSrlRxrIL9w/c/fcPhvsQdOy7sUB2rQLsWe02n7USqjG82Ht2qd8OUPVNwJvqM6jhkBGG3w5Am04zaCPJ5k5pPgoWZBeYdnacsAxHR2TtnkS7TOXFCD1hoMDGjm4zGnqF7cOvhLTRb1gwHrx5UHYny6EbcDXT9tSusLayxy3MXytiVUR1JJ+Zm5pjacipCPgiBi70Leq3vBa/tXhzJmkdl7cpmu71yicpGTkL0ckIIrOq9Cg5WDhi8eTCSUpNURyIDy2wE2aR8E2UZyjuUx9D6Q7EybCWvoiMqwLL2o3Jb4AZ/rb/qSDrJnDjBpRP5x0KDCWpfpT2Oex1HGbsy6PhLRyw/uVx1JNLR/cf30c2/Gx4kPkCgZyCqOFZRHSnPPJw9cNzrOD5p9QlWha1Cg58acEqKjnxP+CI6IRoCz659trO0g08HH0WpiHLmbO+MVb1XQROlwWf7PlMdhwwsJDIE5ezLoYJDBaU5pracisTURCw8sVBpDiLKm+f7UUXERsB7h3eBKjZoojSwtbBFNcdqqqMUeCw0mKgapWvgmNcxtK/SHh/s+AAf7voQqempqmNRLjxOeYze63vjwt0L2DpoKxqWa6g6Ur5ZW1jjm47f4K8Rf0EIgTar2+DjPz/GmrA1BbpqbWip6amYEDAB4wPHo2fNnljeazlcS7hCQMC1hCv8evrB08NTdUyibPWo2QPjmo7DvGPz8OflP1XHIQMKjgxG0/JNlTcqrl2mNnrX6o1FJxbhUfIjpVmISHfZ9aNKSEnAjH0zFCXSnTZai7pl68LczFx1lAKPhQYTVtKmJHYO3omJzSZiwfEF6LmuJ2ITY1XHopdIS0+D52ZPHLp2CL/0/QXtq7RXHUmvWlVuhVOjT+GDRh9g9pHZeG/bewW6am1IsYmxeGvtW1gUvAhTWkzBloFb8P5r7+Pq5KsIahOEq5OvsshAJm9OpzmoU6YOhm8djrsJd1XHIQOIS4rDhbsXlDWCfN7HrT7G/cT7HPlNVAAVhn5UnDihPyw0mDgLMwv80O0HLH1rKfZe2YvmK5rj0r1LqmNRNqSUGB8wHlvOb8GCLgsw0H2g6kgGYW9lj6U9l6KsXVlIPDv+rqBVrQ0l/H44Wq5siX3h+7Cs5zJ83/l7VsapQLK1tMW6fusQ8zgGXtu9OPKyEAqNDIWEVNqfIasWlVrgjcpvYO7RuUhJS1Edh4h0kFM/soLSjyo6PhpR8VGcOKEnLDQUEN6NvfHn0D8RHR+N15e/jv3h+1VHouf4HPLBT6E/4eNWH2NS80mq4xhcTp9uFqSqtSH837X/Q7PlzXDr4S3sGbIHXo28VEciypcG5Rrg2w7fYtuFbVh2cpnqOKRnIZEhAKB04sTzprecjmux17DxzEbVUYgKfHNDY7kZdxMJKQkv9KMCgL61+ypIpDttFBtB6hMLDQVIW7e2OO51HM7FnNH5187wC/VTHYmeWH5yOT7f/zmGNRiGbzp8ozqOUeRUnS5pU7LIfur5y6lf0OHnDnC0ccQxr2NoV6Wd6khEejGp+SR0qtoJk3dNxvm751XHIT0KjgyGW0k3k5qM1KNmD9QtWxezj8wusq8nZBoKQ3NDY8hcOgwAszvNftqPqnLxyqhSsgpWha3ClftXFKd8tcyJExxtqR8sNBQw1UtVx9GRR9GpaieM+mMUJgZOZJNIxXZc2IFRf4xCl2pdsLzncuXNtIzFp4MP7CztntlmJsxwP/E+uvzaBTfibihKZnzpMh3/CfoPhm0dhlaVWuGY1zHULF1TdSwivTETZljdZzXsLO3gudkTyWnJqiORnmQ2gjQlZsIM01pOgyZKgz2X96iOQ0VYYWhuaAxf/fUVDkYcxOIeizG15dSn/agiPozAvmH7IITAO7+9g8TURNVRX0oTpYFTMSc4FXNSHaVQYKGhACphUwI73t2BD5t/iIUnFqLH2h54kPhAdawi6ej1oxj4+0A0cmmE3wf8DktzS9WRjMbTwxN+Pf2emaLwc5+fsaTHEhy+fhjui93xy6lfCv2nUQkpCRj4+0D4HPKB12te2DVkF0rZllIdi0jvyjuUx4peK3Dy1kl8HvS56jikB3fi7+Dqg6sm058hq8Eeg1HeoTy+O/yd6ihUhBWG5oaGduDqAXz111cY1mAYhjUY9sLjVRyrYE2fNTh56yQ+2v2RgoS5p41mI0h9YqGhgDI3M8e8LvOwvOdyBIUHofny5rgYc1F1rCLl/N3zeGvdW6hQvAJ2Dt4Jeyt71ZGMztPD89kpCvU9MbrJaGhGa+Du5I5hW4eh/2/9cSf+juqoBnHr4S20Wd0Gm85uwvedvodfTz9YmVupjkVkML1r98aoxqMw58gcBIUHqY5D+RR6KxQATO6KBgCwMrfCh80/xP6r+xF8M1h1HCqiyjuUz3Z7QWluaGh34u/Ac7MnqpeqDt/uvjnu16tWL0xtMRVLQpZgnXadERPmXlp6Gk5Hn2YjSD1ioaGAG9loJPYO3Yu7CXfx+vLX+YufkUQ+jESXX7vAwswCu4fs5iVWz6lWqhoOjjiI2R1n449//kC9xfWw9fxW1bH06u9bf6PZ8mY4d+cctg7aiiktpxSZZTNUtM3tPBc1S9fEsC3DcO/xPdVxKB+CbwZDQKBx+caqo2TLu7E3SliXwJwjc1RHoSKqgkOFbLd3qNLByElMT7pMx4htIxCTEIMN/Te88gO3/3X4H1pVaoUPdnxgkr1+Lt+/jMTURBYa9IiFhkKgjVsbnPjgBFwcXND5l85YErxEdaRC7UHiA3T9tSvuPb6HQM9AVHWsqjqSSTI3M8e0VtMQ6h2KisUrou+Gvhi+dXihWOaz7fw2vLHqDQgIHH7/MHrV6qU6EpHRFLMqhrX91iI6PhreO7wL/fKowiw4Mhi1ytRCceviqqNkq7h1cYxpMgabzm3iaG894hSF3NlzeQ9ORJ5Avzr9/m1uWKIy6jvVx8+an3Eo4pDqiEotOLYAARcDMLfzXDQs1/CV+1uaW2J9//WwtbRF/439X+h9oRonTugfCw2FRFXHqjg68ii6VO+CsQFjMT5gPJtEGkBiaiL6rO+D83fPY/OAzWjk0kh1JJPn7uSOY17H8Hnrz+Gv8YfHEg/svbJXdaw8kVJizuE56LuhL+qVrYfjXsfRoFwD1bGIjK6RSyN83f5rbDq3CavCVqmOQ3kgpURwZLBJ9mfIauLrE2FhZoG5R+aqjlIocIpC7iSmJmJcwDjUKFUDv77967/NDSdH4OB7B1HNsRr6beyHiAcRqqMqEXwzGJ/s/QR9a/fF2KZjc/19FYtXhP/b/jh75yzGBYwzYELdaaI0MBNmqFu2ruoohYZeCg1CiK5CiAtCiEtCiE+yedxaCLHhyePHhRBu+jgvPau4dXFsH7QdU1pMgW+wL7r5d4NfqB+r1nqSlp6GoVuG4mDEQazusxqdqnVSHanAsDK3wqx2s3Bk5BHYW9mj0y+dMD5gPOKT41VHy7XktGR4bffC9L3T8U69d3BwxEG4OLiojkWkzNSWU9G+SntMDJxYYHsEFeVPdiMfRuL2o9sm2Z8hKxcHFwxvMByrwlYh6lGU6jgFHqco5M6cw3Nw6d4lLOq+CDYWNs88VtKmJLYN2oaktCT02dCnQP0uow+xibEY+PtAuDi4YEWvFTovG+1crTM+b/05Voetxsq/Vxoope600VrUKFUDtpa2qqMUGvkuNAghzAH4AugGoC6Ad4UQz5eCRgK4L6WsDmA+ALYQNhBzM3N83/l7rOi1AkFXgjD6j9GsWuuBlBKTd03G72d/x9zOczHYY7DqSAVSswrNcNL7JCa/Phm+wb5ouLQhjl4/qjrWK8UkxKDzL52xMmwlPm/9Odb1W8cXIiryzIQZ1vRZAytzK3hu9kRKWorqSDop6p/sBkdmNFg09UIDAExpMQXJaclYeGKh6igFHqcovFr4/XD87//+h/51+6Nztc7Z7lOrTC2s67cOp26fwnvb3isyS8iklPD+wxvXYq9hXb91cLR1zNNxvmjzBdpXaY9xAeOgidLoOWXeaKO18HBmfwZ90scVDc0AXJJSXpFSJgNYD6D3c/v0BrDmyde/A+gg2DXNoN5/7X2ULVYWEs/+4GPVOm++O/wdFgUvwpQWU/BRC9MezWPqbC1tMb/rfOwfvh8paSl4Y9Ub+HTvp0hKTVIdLVsX7l5A8xXNcfTGUfza91fMajcLZoKrzoiAjMtgl/VchuDIYMw8MFN1HJ0U9U92g28Gw1yY52pttWq1ytRCn9p94Bvsi0fJj1THKdDK2ZfLdjunKGSQUmJC4ASYC3PM7zL/pft2r9Ed33b8Fr+d/Q3/O/Q/IyVUa/nJ5dh4ZiO+avcVWlZqmefjmJuZY+3ba+Fo44j+G/sjLilOjyl1F58cj8v3LrMRpJ6J/FbghBD9AXSVUno9uT8UwOtSyvFZ9jn9ZJ8bT+5ffrLP3eeO5Q3AGwCcnZ0br1+/Pl/ZVHj06BHs7U1jzGH7g+1fKDRkGlxpMKrbV0d1++oob1se5sLcyOkymNLzlVXWXLtu78J3F75DB6cO+Kz2Z0rfZBaE50sX8anxWHx5MQJuB6Bqsar4tPanqG5fXXmuTKH3QzHz7ExYCAt8Ve8ruJdwN4lchmKquQDTzcZcGWZfmI1dt3dhfoP5aFAy574lpvB8xaXE4di9Y/jm/DfZPi4gENTGNCY4GfL5mqaZhvvJ97G8yXKdv1fFn+PZuLMY9/c4jKs2Dv0r9jeZXLlhKrnSZBqGHB+C20m3n9kuIPBJrU/QuVz2n94bm8rn6//u/h8+P/M5RlcdjYGVBr4yl5QS/zv/P+yN3ouv632NVmVaGTNujrkMITw+HKNPjoZHcQ/Mrj/7lb8P5ybXqQen8NGpj9C6bGt8UecLo0zvyi7XubhzGPv3WHxV7yu8UeYNg2fIba6CoF27dqFSyuyb/Ugp83UD8A6A5VnuDwWw8Ll9zgComOX+ZQClX3bcxo0by4Jo//79qiM85TrfVWImXrhZzrKU5l+aP71v52Mnmy9vLkftGCWXBC+RR64dkQ+THholoyk9X1ll5gr4J0Caf2kuO/7cUSalJqkNJU3/+cqrHRd2yHLfl5OWsyylz18+MiUtRXmupSFLpcUsC1nPt54Mvx+ulzyZCuufoyGZajbmyvAw6aGs/mN1WWleJXn/8f0c91P1fIXfD5cLji6Q7Va3e/r6l/V1MOvNdb6rkozZMdTzlZ6eLh2/dZRe27zy9P2q/hxbr2otK82rJJNTk7N9nP8eX27ukbkSMyHH7xwvXee7SjFTyNLflZaYCTnvyDzV8Z5S9XzFJ8dL1/musp5vvWz/juWUKyE5QTbxayLt/2cvT0edNnDKFxnj+YpPjpd1fetKpzlO8tbDW7n6ntzm+ubQNxIzIRcdX5SPhLmXXa7locslZkJeirlklAzZMZWfE7oCECJzeD+vj49mbwColOV+RQCROe0jhLAAUAIAh28bmE8HH9hZ2j2zzc7SDqv6rMKjzx4h1DsUK3uthNdrXrA2t8b60+sxZucYtFzZEsW/KY6aC2tiwG8D4POXD3b+sxM34m4U+jVoWRuDucx1Qe/1vdGgXANsHrAZVuZWquMVWm/VfAunx5xG3zp9MSNoBt5c9Sb+iflHSZa09DR8tPsjjPpjFDpV7YQjI4/AraSbkixEBYW9lT3Wvr0Wtx7dwug/Rit/rZBS4uStk/jv/v+i4U8NUeWHKpi8ezKi46PxcauPccLrBFb3WZ3ta6RPBx9FqY3nyv0ruJ94H00rmH5/hqymt5yO63HXsf50wbviVbXw++H4fP/n6FGjB37s9uPTKQp3pt1Bz5o98VnQZ7hw94LqmEr5/OWDiNgILO6xGJbmlrn+PltLW2wZuAXFLIuh9/reuPe48L3FmRQ4CefunMOvfX/NcflNXk1vNR3da3THh7s/RPDNYL0eO7c0URoUsyyGKo5VlJy/sLLQwzGCAdQQQlQBcBPAIADPd8rbDmA4gKMA+gMIkqp/CykCPD08AWSsQ70Wew2VS1SGTwefp9sbuTR6ZjyjlBLXYq8h7HYYTkWdQtjtMITeCsVvZ397uk9p29JoUK4BGjo3RMNyDdGgXAPUKVNHpx/IpiqzMVjmmt3bj25DQGDkayPhYO2gOF3hV9quNDb035AxKmnnWDT8qSFmd5qNsU3HGm25ysOkh3h307vYeXEnJjabiLld5sLCTB8/JokKv6YVmuLLtl9iRtAMdK/RHcMaDDPq+VPSUnAw4iC2nt+K7Re243rcdZgJM7Sq1ApzO89F71q9Ua1UtWfyCiHw8Z8f4+bDmyhpXRKLeix6+hpZmBWkRpBZda/RHe5O7ph9ZDaG1B9ilMusCwMpJcYGZLyWLu6x+JnnTQiBpW8tRb3F9TBi2wj833v/B3MzNctpVTp/9zzmHJmDofWHorVra52/v2LxitgycAvarmmLgb8PRKBnYKH5/WH96fVY/vdyfPrGpwaZuGYmzPBzn5/RyK8RBvw+ACe9T+a5yWReaaO1cHdyZw8uPcv3vwApZaoQYjyA3QDMAayUUp4RQsxCxqUU2wGsAPCLEOISMq5kGJTf81LueHp4wtPDEwcOHEDbtm1fuq8QAq4lXeFa0hW9a//bzzMuKQ6aKE1GAeL2KYRFhWFxyGIkpiYCyBhdWLds3YzCg3ODp/819g8JXUkpkZiaiNikWMQlxWHqnqkvNAaTkJh9eLZOM4Ipfwa5D0Jr19bw2u6FCYETsPX8VqzsvdLgjaoiHkSg57qeOHvnLHy7+/LPnCgPPm71MXZf3o1xAePQqlKrZ97YG0JcUhwCLwZi24VtCLgYgNikWNha2KJL9S6Y1W4WetTogbLFyub4/ZmvkXXm1YG9g32RKDIAGY0grc2t4e6kn74zxiKEwLSW0zB863AEXgpE9xrdVUcqENadXoddl3bhh64/ZPta6uLggkXdF8FzsyfmHZ2Haa2mKUipjpQS4wPGw87SDnM6zcnzcVpUaoGfevyE97e/j2l7pmF+15c3kywILt+7DO8d3mhRsQW+bPulwc5T2q40NvbfiDdXvYnhW4dj26BtRiskSimhidKgb+2+RjlfUaKXUpuUMgBAwHPbvsjydSIyejlQAVTcujjeqPwG3qj8b3OU1PRU/BPzT0bh4ckVEIEXA7E6bPXTfSqXqPxM4aFhuYao4lgFZsIM/lr/f6+0CHv2SotXkVIiISUBcUlxT2+ZxYKcbjk9npqe+srzceST8ZV3KI+dg3di+cnl+GjPR/BY4oEfu/6IYQ2GGeSF59iNY+i9vjeSUpMQ6BlokIo9UVFgbmaOX/r+gvpL6mPIliE49N4hvX+qdzPuJrZf2I5tF7YhKDwIKekpKGtXFv3q9EPv2r3RsWrHF5ZEvErrMq3hF+6HiAcRcC3pqte8pijkVggalmtYIK9GHOQ+CDOCZmD24dksNORCTEIMJu+ajGYVmmFc03E57veu+7v4/ezvGcsravZA3bLPT6ovvDac2YB94fuwqNsiONs7/j8ZogAAIABJREFU5+tY7732Hk5FncKC4wvQoFwDjGg4Qj8hFUhOS8agTYNgbmaOdf3WGfznxesVX8ecTnMwefdkzD06F1NbTjXo+TLdfnQbMY9jONrSAArHNT1kdBZmFqhbti7qlq2Ldz3efbr99qPbzxQfwm6HYefFnUiX6QAy1vG62Lsg/EH40zf5EbEReH/b+wi8GIiapWvmqlCQebyXsbGwQQnrEihuXfzprUrJKk+/fv6xD3d/iDsJd144Dkc+qSGEwAeNP0DHqh0xYtsIjNg2AlvOb8HSt5bm+xeBrNafXo8RW0egQvEKODD8AOqUraO3YxMVRZVLVMbSt5Zi0KZB+PqvrzGz7cx8HU9KiTN3zmDb+W3YemErQiJDAAA1StXApNcnoXft/2fvvsOjqNo+jn9PEkLvIFVApAVFUIpgQ0QRRFqCoGLBhor1URQVBRVQH8WGCor4KCqvKNKkWBALdhMRAaVZACVK7z3Jef84s7DEoCzZndnA73NdubI7O5m5MzM7c+aeU7rQqnqrfFX3PqOiSzRMWjSJW1vemq944112TjbfZX7HFU2uCDqUQ5KcmMxtLW/jtg9u45s/vuHk6icHHVJcu/2D29mwcwMfdvrwH78jxhhGdhzpmlBM7s2XV3152FT9/yebd23mtvdv46QqJ3Fds+uissxh7Ybx45ofuXbatdQvX59WR7eKynL9dveHd5ORmcHEHhN9S8DefPLNfLbiM+768C5aVm+530POWJm/ej4AJ1Q6IebrOtIc/mcQ8VXlEpWpXKcy59Y5d++0HXt2sGD1gr2Jh9FzRv+tJsHu7N2MnT8WcJ1xhScAShUuRd3idf827UAJg1KFS1GycMnIO2807NdHQyiWI6FjsHh2TNlj+Pjyj3nq66e4Z9Y9HD/yeJ7v+DxpDdPytVxrLQ9++iD3f3o/p9c4nYk9J1KhWIUoRS1yZOt5fE9m/DyDwbMHc07tczi1RmRDvmXlZPHl718yedFkpiyewq8bfgXg5Gon89BZD9G1QVcaVGgQtRpO1YpW44RKJzBh4YTDPtGwaO0itu3ZRrOqeY9GVhBcfdLVPDj7QR798lEm9JgQdDhx68NfP2TMD2O4+7S7D+omqlKJSozoOIKeb/fksS8e4+7T7/YhymDd/8n9/LX1Lyb1nBS1vimSEpJ4s/ubtHixBalvpZJxTQbVSlWLyrL9Mn3JdJ74+gluaH4D3VL8a1JgjOGlzi8x96+59Hy7J3OvnfuPzd+iYd6qeQA0Oko1GqJNiQaJuaKFitK8WvO9vVuPSB+R53wGw+77dgeWQf+3zjMlOAkmgdta3Ub7Ou25bNJldB/fnUtOuITh7YcfUl8gO7N2cuWUK3ljwRtc3vhyXjj/BQonFY5B5CJHrmc6PMPnKz7nkkmXMPfauZQuUvof59+2exsf/PIBUxZPYdqSaazbsY7kxGTaHtOW/qf2p1O9TlQpWSVm8aalpO296Yh2r+rxJFQjpKCNOBGuZOGS9G3Wl4c/f5gl65ZQr3y9oEOKO9v3bOfaaddSp1wd7jvjvoP+ux7H9eDtn95m0CeDOL/e+Yd1dfJ5q+Yx/JvhXHPSNVGvGVOuaDmmXDiFli+1pOubXZndezZFCxWN6jpi5Y/Nf3D55MtpXKkxw9oN8339pYuU5u0eb9NydEsumXQJMy6eEdMOSuevnk/VklUpX6x8zNZxpFLXmuK7AzVFqFG6RuDV9Ho16rV3yKdlty5TkiHONKzYkK+u+or7W9/PuAXjaDSyER/88kFEy1i1dRVtxrThjQVv8HDbh3m5y8tKMojEQKnCpXi92+v8vul3Ov5fx71DB9d6qtbeGmyrt63mpTkv0fmNzlR4rAKpb6UyZfEUOtTtwPgLxrP2jrXM6DWDPk37xDTJAJCakorFMnnR5JiuJ2jpmemUSC5B/fL1gw4lX24++WaSE5N5/MvHgw4lLj346YP8uuFXRp0/KuIb3OfOe44yRcrQe0pv9mTviVGEwcqxOfSd3peyRcvyUNuHYrKO4446jrGpY/ku8zuumXpN4MP+HozsnGx6TezFzqydvNn9TYokFQkkjiaVm/BMh2f44JcPGPpZbGsWz181X7UZYkSJBvHd0LZDj9ixyyX/CiUWYtCZg/j6qq8pXaQ0575+Ln2n92Xr7q3/+rfzV82nxegW/PDXD0zoMYG7TrtLw6OJxFCro1vRpUEXvvj9C5ZvWo7FsnzTcq6YfAX1nqlH5WGVuXrq1cxbNY8+J/Vh1mWzWN1vNa91e43uDbv7OrTwcRWPo175ekxcONG3dQYhPTOdplWaFvghDCuVqETvJr0Z88MY/tr6V9DhxJW5f81l2JfDuLLJlbQ5pk3Ef1+xeEVGdhzJnD/n8Mjnj8QgwuC9+sOrfPH7F/z37P/G9El25/qdGdxmMGPnj2XYl/7XDojU4NmDmb18NiM6jqB+hWCTkVefdDWXnHAJ939yP7N+nRWTdWTlZPHTmp+UaIgRJRrEd70a9WJUp1HULF0Tg6Fm6ZqM6jRKtQckIk2rNuW7Pt9xe6vbeT7jeZo834QvVnxxwPmnL5nOKf87haycLD674jNSU1J9jFbkyBWqqh9uT84elm1cxqDWg/j+2u/57ZbfeLrD05x1zFmBjYRgjCG1QSofL/uY9TvWBxJDrO3O3s3cv+YW6P4Zwt3e6nZ2Z+9m+DfDgw4lbmTnZHPN1GsoX6w8j7U79KEa0xqmcdHxF/Hg7AeZ+9fcKEYYvPU71nPHzDtoVb2VL6NC3HP6PfQ4rgf9P+zPu0vfjfn6DtUnyz5h8OzBXNb4Mi5rfFnQ4eztoLRBhQZcPPFiMrdkRn0dS9ctZVf2LnUEGSNKNEgg1ERBoqFIUhGGtRvGJ70/IcfmcPrLp9N/Zn9emfvK3mraNZ+qySUTL6HzuM7UK1+Pb6/+lqZVmwYdusgR4/dNv+c5PSsni0FnDqJJ5SZxU7MorWEaWTlZTF08NehQYmLB6gXszt5N86oFt3+GcHXL1yU1JZUR6SPYsmtL0OHEheHfDCcjM4On2z9NuaLl8rWsZzo8Q/mi5ek9uTe7s3dHKcLgDZg1gPU71jOy40gSTOxvhYwx/K/z/2hcuTEXTbiIxWsXx3ydkVqzbQ29JvaiTrk6PHfec0GHs1eJ5BK83eNttu7eykUTLjqoYekjERpx4nDuiyRISjSISIF3Rs0z+OG6H7jmpGt49MtHuXLKlXuraa/YtIKx88dyUuWTmN17doHr+VmkoPunfnniTdMqTalRugYTFh6eIxmkr0wHCnZHkLn1P7U/m3ZtouZTNf/WB8iRZtnGZdz78b2cV/c8eh7XM9/LK1+sPC+c/wI/rPqBobMPj+at6SvTeeG7F7ipxU00rtzYt/UWTy7OlAunkJyYTOdxndm4c6Nv6/43OTaH3lN6s277Ot7s/iYlkksEHdJ+GlZsyAvnv8Ds5bO576OD79j0YMxbNY9Ek0hKBQ1tHgtKNIjIYaFk4ZK80OkFjip2FJa/d7i0evtqiicXDyAykSNbQeqXJ9R84oNfPjgsn5CnZ6ZTrmg5jilzTNChRM2S9UtIMAls2Llhbx8gfab2OeKSDdZa+k7vi8FVN49WLaEuDbpw6QmXMvSzocz5c05UlhmU7Jxsrp9+PZVLVObBNg/6vv4apWswoccEft3wKxdNuIjsnGzfY8jLk189yYylM3i83eM0qdwk6HDydMkJl3DNSdfwyBePMH3J9Kgtd/7q+dSvUF+dgseIEg0iclhZs31NntMPVH1bRGKroPXLk5qSyq7sXcxYOiPoUKIuPTOdZlWbxU1TlWgYMGsAOTZnv2nb92xnwKwBAUUUjHELxvHuz+8y9KyhUa8t9HT7p6lUohKXT76cXVm7orpsP436bhTf/fkdj7d7nFKFSwUSw+k1T+fZDs/y3s/vcfesuwOJIVz6ynTumnUX3Rp0o2/zvkGH84+GdxhOk8pNuHTSpSzfuDwqy5y3ap46gowhJRpE5LBSkKppixwpClK/PKccfQqVildi4qLDa/SJ7Xu28+PqHw+b/hlCVmxaEdH0w9G67eu45b1baF61OTe2uDHqyy9btCwvdnqRBasX8OCn/tcEiIbV21Zzz0f30KZWGy48/sJAY7m22bVc3+x6HvvyMV6f93pgcWzauYmeb/ekasmqvNT5pbhPQBZJKsL4C8aTlZNFj7d75LvfkC27trBs4zJ1BBlDSjSIyGGlIFXTFpH4k5iQSNcGXZm+ZDo79uwIOpyomfvXXLJt9mGXaFByGfrN7Mf6Het5sdOLMRu29Ly653Flkyt55ItH+HbltzFZRyzdOfNOtu3exnPnPRcXN9RPt3+a1jVbc/U7V+/tO8VP1lr6TOvDik0reCPtDcoWLet7DIeiTrk6vNzlZb5d+S13zrwzX8tasHoBgGo0xJASDSJyWClo1bRFJP6kpaSxbc82Zv46M+hQouZw7AgS8k4uA9x88s0BROO/j377iFfmvsIdp9wR884Nnzj3CaqWrMrlky9nZ9bOmK4rmj5b/hljfhjD7a1uJ6VifHT6VyixEOMvGE/lEpXp+mZX/tzyp6/rHz1nNG/9+BaD2wzmlKNP8XXd+ZXWMI1bTr6Fp795mrd/evuQlzNv1TxAI07EkhINInLYKUjVtEUk/pxZ60zKFil7WI0+kZ6ZTpUSVahasmrQoURV7uRylRJVKF6oOM9++6zvN29+27FnB32m9qFOuToMbD0w5usrXaQ0L3V+iUVrFzHw49ivLxr2ZO+h74y+1Chdg3vPuDfocPZTsXhFplw4hY07N5L2Vppv/V8sWL2Am9+7mbNrn03/0/r7ss5oe/ScR2lRrQVXTrmSn9f/fEjLmL96PiWTS1KzdM0oRychSjSIiIiIhCmUWIjO9TvzzuJ32JO9J+hwoiIjM+Owq80QEp5czrw9k48v/5jV21Zz7uvnsmHHhqDDi5kHP32QXzb8wgvnv0DRQkV9WWe7Y9vR56Q+DPtyGF/+/qUv68yPZ759hgWrF/B0+6fjcuSpxpUb82rXV/nqj6+4fvr1WPv3UbOiafue7fR8uyelCpfitW6vkWAK5q1gcmIyb3V/i6SEJC4Yf8EhNXObv3o+jSo1ioumNIergnl0iYiIiMRQakoqG3du5ONlHwcdSr5t2rmJxesWH3b9MxxI82rNmXzhZBavW8z5b5zP9j3bgw4p6n746wce+/IxrmhyBWcdc5av6x7Wbhg1Steg9+Tecb1tV25eyaBPBnFe3fPoUr9L0OEcUFrDNAaeMZCX577M8G+Gx3Rdt7x7CwvXLOT1bq9TuUTlmK4r1mqWqclr3V5j7l9zueW9WyL6W2utRpzwgRINIiIiIrm0O7YdxQsVZ+LCgj/6xHd/fgdwxCQaAM6ufTZjU8fy1e9f0f2t7odNzRSA7Jxsrpl6DeWKluOxcx7zff0lC5fkpc4vsXT9Uu79KL6aI4S77YPbyMrJ4pkOz8T9U+tBZw6iW4Nu3P7B7Xz464cxWce4BeMY/f1o7jrtLs459pyYrMNvHet15K5T7+LFOS/y2g+vHfTfrdyyko07N2rEiRhTokFEREQklyJJRehYryOTFk0iOyc76HDyJdQRZNOqTQOOxF/dG3bn+fOf592f36X3lN7k2JygQ4qKZ799lvTMdJ5u/zTli5UPJIa2tdvSt1lfnvr6KT5b/lkgMfyTmb/M5K0f3+Lu0+6mdtnaQYfzrxJMAq92e5WUiin0GN/jkPsdOJBf1v9Cn6l9aFW9FQ+c+UBUlx20wWcN5oyaZ3Dd9Ov4ac1PB/U381fNBzTiRKwp0SAiIiKSh7SUNFZvW80Xv38RdCj5kvFnBseUOYYKxSoEHYrv+jTtw0NnPcT/zf8/bn3v1pi3gY+15RuXM+CjAXSo04ELj78w0Fj+e85/qVWmFldMuYJtu7cFGku4XVm7uGHGDRxb9ljuPDV/QyD6qURyCaZcOAVjDF3GdWHzrs1RWe7u7N1cOOFCEhMSeSPtDQolForKcuNFUkISb6S9QYnkEnR/qztbd2/917/RiBP+UKJBREREJA8d6nSgcGLhAt98In1l+mHbEeTBuOu0u7it5W088+0zDJk9JOhwDpm1lr4z+gIwsuPIwJsDlEguwctdXuaXDb9w96y7A40l3LAvh7F0/VKePe9ZiiQVCTqciNQuW5vxF4xn8drFXDLxkqjUwrnrw7vIyMzgf53/R80yh+cIC1VLVuX/Uv+PRWsXcd206/41oTh/9XyOLnU0ZYqU8SnCI5MSDSIiIiJ5KFm4JOfWOZeJCycW2Cfha7atYfmm5UdU/wy5GWN4rN1jXNb4MgZ+MpAR6SOCDumQvPnjm8xYOoMhZw2JmxvG1rVac3OLm3nm22f4ZNknQYfDbxt+Y8hnQ0hLSaN9nfZBh3NIzjrmLJ5q/xRTl0zN9zCi05ZM48mvn+SG5jfQLaVblCKMT21rt+X+M+9n7PyxjJ4z+h/nnbdqnmoz+ECJBhEREZEDSG2Qyu+bfycjMyPoUA5Jeqbrn6FZ1WYBRxKsBJPA6E6j6VSvEzfOuJFxC8YFHVJE1u9Yzy3v3UKzqs24qcVNQYezn4faPkSdcnW4YsoVB1VtPZZuee8WEk0iT577ZKBx5NcNzW/g6hOvZuhnQ3nrx7cOaRl/bP6D3pN707hSY4a1GxblCOPTvWfcS7tj23HTuzfx/Z/f5zlPVk4Wi9Yu4oSj1BFkrCnRICIiInIAnep3IikhiQkLJwQdyiHJyMzAYGha5cjqCDIvhRIL8Wb3NzmtxmlcNuky3v/5/aBDOmh3fHAH67avY3Sn0SQmJAYdzn6KJxfn5S4vs3zjcu6cGVyfCFMXT2XqkqkMaj2Io0sfHVgc0WCM4bmOz3Hq0afSe3LvA940H0hWTha9JvZiZ9ZO3uz+ZoFrQnKoEkwCr3d7nQrFKnDB+AvYtHPT3+b5fcfv7MnZoxoNPlCiQUREROQAyhUtR5tabZiwcEKBbD6RnplOgwoNKFm4ZNChxIWihYoy9aKpNKzYkNS3Uvn6j6+DDulfffzbx/xv7v/od0o/GlduHHQ4eTqtxmn8p+V/GJkxMmbDM/6T7Xu2c/N7N9OwYkNubXmr7+uPheTEZCb0mED5YuXpMq4Lq7etPui/HTJ7CLOXz2ZExxHUr1A/hlHGn4rFKzKu+ziWbVzGVe9c9bfz9i9bfwE04oQflGgQERER+QdpKWn8vP5nFqxeEHQoEbHWHvEdQealdJHSvH/J+1QpUYXzxp7Hj6t/DDqkA9qxZwd9pvXh2LLHMqj1oKDD+UdDzhpCvfL1uOqdq6I2YsLBeuizh1i2cRkjzhtxWI2qUKlEJSb3nMya7WtIeyuN3dm7//VvPln2CYNnD+ayxpdxWePLfIgy/pxW4zQeOfsRJiycwPBvhu/32W/bfqNQQqEjLgETBCUaRERERP5BlwZdMJgCN/rEH5v/YNW2VTSrcmT3z5CXSiUqMfPSmRRJKkK719uxbOOyoEPK0+DZg/l5/c+8cP4LFC1UNOhw/lHRQkUZ03UMf2z+g34f9PNtvUvWLeGxLx/jkhMuoXWt1r6t1y9Nqzblf53/x+crPuemGTf9Y82qNdvWcPGEi6lTrg7Pnfecj1HGn9tb3U6nep3oN7PffjWXft32Kw0qNCA5MTnA6I4MSjSIiIiI/IPKJSpzWo3TClw/DaEOLFWjIW/HlD2G9y95n+17tnPOa+dEVDXdD/NWzeOxLx+jd5PetK3dNuhwDkrL6i3p16ofL8550Zc+MKy13DDjBoomFeWxcx6L+fqCclGji7jr1LsYNWcUz2c8n+c8OTaHyydfzvod63mz+5uUSC7hc5TxxRjDmK5jqF6qOj3G92Dd9nWASzSofwZ/KNEgIiIi8i9SU1KZv3o+S9ctDTqUg5aemU5SQhJNKjcJOpS41ahSI6ZfPJ2Vm1fS/vX2vlf5P5DsnGyumXoNZYuUZdg5BWvEgAfaPEBKhRSueucqNu7cGNN1jf9pPB/++iFDzhpC5RKVY7quoA05awgd63bk5vdu5tNln/7t8ye/epJ3f36Xx9s9ru+8p2zRsoy/YDyrtq3issmXsWHHBlbvWq0RJ3yiRIOIiIjIv0hNSQUoUM0n0jPTaXRUoyOmx/lDdcrRpzChxwTmr55P5zc6szNrZ9Ah8Vz6c3y78lueav8U5YuVDzqciBRJKsKYrmP4a+tf3Pb+bTFbz5ZdW/jP+//hxMoncn2z62O2nniRmJDI2NSx1ClXh+7ju+/X3Cd9ZTp3zbqLbg260bd53+CCjEPNqjbjiXZPMGPpDKo9UQ2Ax796nLHzxwYc2eFPiQYRERGRf1GjdA2aVW3GxEUFI9FgrSUjM4NmVdU/w8HoULcDY7qO4dPln3Lh2xeSlZMVWCwrNq3gnln30L5Oey46/qLA4siP5tWa0//U/rw892WmL5kek3Xc/8n9/LnlT0Z2HBl3Q37GSukipXnnwnfIysnijJfPoMaTNTjr07No9VIrShUuxUudX8IYE3SYcadMkTIkmkR2ZO0AYM32NfSZ2kfJhhhTokFERETkIKSlpPHtym/5fdPvQYfyr37Z8Asbd26keVX1z3CwLm50McPbD2fK4in0mdonkOFMrbX0nd4Xi2Vkx5EF+qZxYOuBHH/U8Vwz9Ro27NgQ1WXPXzWfp795mqtPupqTq58c1WXHu7rl63Jt02v5ffPv/L75dyyWbJvNtt3bmPHzjKDDi0sDPhpAts3eb9r2PdsZMGtAQBEdGZRoEBERETkIoeYTkxZNCjiSf5e+Mh1QR5CRuunkmxjUehAvz32Z/h/29339438az/Sl0xnSZgi1ytTyff3RVDipMGO6jmH1ttXc8t4tUVuutZa+M/pSpkgZHm77cNSWW5CMWzDub9N2Ze/SjfMBrNi0IqLpEh1KNIiIiIgchHrl63H8UccXiNEn0jPTKZJUhOMqHhd0KAXOoNaDuKH5DTz25WM8+sWjvq13w44N3PzuzTSr2oybT77Zt/XG0klVTmLA6QN4bd5rTFk0JSrLfPWHV/l8xef89+z/Frj+K6JFN86RqVG6RkTTJTqUaBARERE5SGkpaXy2/DNWbV0VdCj/KCMzgyaVm1AosVDQoRQ4xhiGdxjOhcdfSP8P+zN6zmhf1nvHzDtYu30tL3Z68bDqc2DAGQNoXKkx1067du8Qg4dqw44N3DHzDlpVb8UVJ14RpQgLHt04R2Zo26EUK1Rsv2nFChVjaNuhAUV0ZFCiQUREROQgpaakYrFMWRydp7OxkJ2TzZw/56h/hnxIMAmM6TqGc489l2unXRvz0UY+WfYJL33/Ere3uv2wG5owOTGZMV3HsG7HOm5898Z8Levej+5l3Y51jOg4ggRz5N7G6MY5Mr0a9WJUp1HULF0Tg6Fm6ZqM6jSKXo16BR3aYe3I/YaKiIiIRKjRUY2oU65OXA9zuXDtQrbt2aZEQz4lJyYzoccEWlRrwUUTLuLj3z6OyXp27NlBn6l9qF22NoPOHBSTdQStceXGDDxjIOMWjOPtn94+pGVkZGYwMmMkNza/8bBLxkRKN86R69WoF8tuXcZHrT9i2a3LtK18oESDiIiIyEEyxpDaIJVZv82Kek/60aKOIKOneHJxpl88nbrl6tJ5XGcyMjOivo6hnw1l6fqlvHD+C397Sn04ueu0u2hapSnXT7+e1dtWR/S32TnZ9J3el0olKvFgmwdjFGHBohtniXdKNIiIiIhEIK1hGlk5WUxbMi3oUPKUkZlByeSS1CtfL+hQDgvlipbj/Uvep3zR8nQY24HFaxdHbdnzV83nv1/8l8sbX87Ztc+O2nLjUaHEQrzS9RU279rshvCMYPjQF+e8SHpmOo+3e5zSRUrHMEoRiRYlGkREREQi0KxqM6qXqh63o0+kZ6bTtGrTI7oNe7RVK1WNmZfOxGA457Vz+GPzH/leZnZONtdMvYYyRcowrN2wKEQZ/44/6ngeOPMBJiycwFs/vnVQf7Nm2xrumXUPbWq14aLjL4pxhCISLboCiYiIiEQgwSSQ2iCV9395n627twYdzn52Z+/mh1U/qH+GGKhbvi7vX/I+G3dupN1r7fI9gsKI9BF8s/Ibnjr3KSoUqxClKONfv1P60aJaC/rO6MtfW//61/n7f9ifLbu38Nx5z2GM8SFCEYkGJRpEREREIpSaksrOrJ28u/TdoEPZz7xV89idvVuJhhg5scqJvHPRO/y64VfO+7/zDjnR9Pum37nno3s499hzubjRxVGOMr4lJSTxSpdX2LZ7G9dNu+4fm1B8seILXp77Mre3up2Uiik+Riki+aVEg4iIiEiETqtxGhWLVYy75hOhzgqbVW0WcCSHrzNrncmb3d8kIzOD1DdT2ZW1K6K/t9bSd0ZfcmwOz5///BH5lD6lYgpDzhrClMVT+L/5/5fnPFk5WfSd0ZejSx3NfWfc53OEIpJfSjSIiIiIRCgxIZGuDboyfel0dmbtDDqcvdJXplO+aHlqlakVdCiHtS4NujC602hm/jqTSyddSnZO9kH/7ds/vc20JdMY3GbwEb2f/tPyP7Sq3oqb3r2JzC2Zf/v82W+fZd6qeTzd/mmKJxcPIEIRyQ8lGkREREQOQVpKGlt3b2XmLzODDmWv9Mx0mldrfkQ+JffbFSdewWPnPMb4n8Zzw4wbDmoUhQ07NnDTuzfRtEpTbj75Zh+ijF+JCYm80vUVdmTt4Npp1+63/TK3ZDLw44F0qNOBrg26BhiliBwqJRpEREREDkGbY9pQunBpJi6aGHQoAGzbvY0f1/yo/hl81O+UfvQ/tT8vfPcCAz8e+K/z3znzTtZuX8uLnV4kKSHJhwjjW73y9Xi47cNMWzKNV394de/02z+4nd3Zu3mmwzNKmokUUDrDiYiIiByC5MRkOtfvzJRFU9hz/h4KJRYKNJ65f80lx+aofwafPdz2YdZuX8uQz4YNmMnqAAAgAElEQVRQvlh5bm15a57zfbrsU0Z/P5o7TrmDE6uc6HOU8evmk29m4sKJXD/tegZ8NICVW1YCkNoglWPLHRtwdCJyqFSjQUREROQQpaaksmHnBj5d/mnQoZCemQ6gGg0+M8bw/PnPk5qSyn/e/w+v/fDa3+bZmbWTPtP6ULtsbe4/837/g4xjCSaBrg26siN7x94kA8B7P7/H2PljA4xMRPJDiQYRERGRQ3TusedSrFAxJvwU/OgT6ZnpVCtZjSolqwQdyhEnKSGJsaljaVOrDVdMuYLpS6bv9/nQ2UNZsm4Jz3d8nmKFigUUZfwa/s3wv03bnrWdAbMGBBCNiESDEg0iIiIih6hooaKcV/c8Ji2aFNHIA7GQvjJdzSYCVCSpCJMvnEyTyk3oPr47Az8aSK2natHm0zYM+WwIpx19Gucce07QYcalFZtWRDRdROKfEg0iIiIi+ZCWksaqbav46o+vAoth486NLF2/VM0mAlaqcCne7fUuZQqXYfBng1m+afnez7778zs1BTiAGqVrRDRdROKfEg0iIiIi+XBe3fNITkxm4sLgRp/4LvM7AJpXU6IhaBWLVyQxIfFv03dk7VBTgAMY2nbo35qUFCtUjKFthwYUkYjklxINIiIiIvlQqnAp2h3bjokLJ2KtDSSGUEeQajoRHzK3ZOY5XU0B8tarUS9GdRpFzdI1MRhqlq7JqE6j6NWoV9ChicghUqJBREREJJ9SG6SyfNNy5vw5J5D1p2emU7tsbcoVLRfI+mV/agoQuV6NerHs1mV81Pojlt26TEkGkQJOiQYRERGRfOpcvzOJJpEJC4MZfSIjM0P9M8QRNQUQkSNdvhINxphyxpiZxpil3u+yeczTxBjzlTHmR2PMPGNMz/ysU0RERCTelC9WnjNrncmEhRN8bz6xettqVmxaoURDHFFTABE50uW3RsNdwCxrbV1glvc+t+3AZdba44D2wFPGmDL5XK+IiIhIXElLSWPJuiX8tOYnX9ebvtL1z6COIOOLmgKIyJEsv4mGLsAY7/UYoGvuGay1S6y1S73XmcBqoGI+1ysiIiISV7o26IrB+D76RHpmOgbDiZVP9HW9IiIiB5LfREMla+2fAN7vo/5pZmNMCyAZ+CWf6xURERGJK1VKVuGUo0/xvZ+GjMwMUiqmULJwSV/XKyIiciDm39oRGmM+BCrn8dEAYIy1tkzYvBustX/rp8H7rArwCXC5tfbrA8zTB+gDUKlSpabjxo07mP8hrmzdupUSJUoEHcbfKK7IKK7IKK7IKK7IxWtsiisyR0Jcb/3+FiN/HcnrLV6nWtFqMY/LWkvaV2m0KNeCuxrk1YI1+o6E/RhNiisyiisyiisyiiu62rRp8521Nu9xla21h/wDLAaqeK+rAIsPMF8pYA5wwcEuu2nTprYg+vjjj4MOIU+KKzKKKzKKKzKKK3LxGpviisyRENdvG36z3I999PNH872sg4lr+cbllvuxz37zbL7Xd7COhP0YTYorMoorMoorMooruoAMe4D7+fw2nXgHuNx7fTkwJfcMxphkYBLwqrV2fD7XJyIiIhK3apWpRdMqTZm4yJ9+GjIyMwBoVjXvB0oiIiJByG+i4RHgHGPMUuAc7z3GmGbGmNHePD2AM4Dexpi53k+TfK5XREREJC6lpqTy9R9f88fmP2K+rvSV6SQlJNG4cuOYr0tERORg5SvRYK1dZ61ta62t6/1e703PsNZe7b1+3VpbyFrbJOxnbjSCFxEREYk3aSlpAExaOCnm60rPTOeESidQJKlIzNclIiJysPJbo0FEREREwtSvUJ+GFRvGvPlEjs0hIzOD5lWbx3Q9IiIikVKiQURERCTK0lLSmL18Nmu2rYnZOn5Z/wubdm1S/wwiIhJ3lGgQERERibLUlFRybA5TFv+tn+yoSc9MB1CNBhERiTtKNIiIiIhEWeNKjaldtjYTFk6I2TrSV6ZTNKkoxx11XMzWISIiciiUaBARERGJMmMMqQ1SmfXrLDbu3BiTdaRnpnNilRNJSkiKyfJFREQOlRINIiIiIjGQ1jCNPTl7mLZkWtSXnZWTxfd/fU+zKuqfQURE4o8SDSIiIiIx0KJaC6qWrMrEhdEffWLhmoVs37Od5tXUP4OIiMQfJRpEREREYiDBJJDaIJX3fn6Pbbu3RXXZ6ghSRETimRINIiIiIjGSmpLKjqwdvPfze1FdbvrKdEoVLkXd8nWjulwREZFoUKJBREREJEZOr3k6FYpViProExl/ZtC0SlMSjIpyIiISf3R1EhEREYmRpIQkutTvwrQl09iVtSsqy9yVtYsf/vpBzSZERCRuKdEgIiIiEkNpKWls2b2FD3/9MCrLm7dqHnty9qgjSBERiVtKNIiIiIjE0FnHnEWpwqWiNvqEOoIUEZF4p0SDiIiISAwVTipMp3qdmLJ4Clk5WfleXkZmBhWKVaBG6RpRiE5ERCT6lGgQERERibHUlFTW7VjH7OWz872s9Mx0mldtjjEmCpGJiIhEnxINIiIiIjHWvk57iiYVZcJP+Rt9Ytvubfy05ic1mxARkbimRIOIiIhIjBUrVIwOdTswadEkcmzOIS9nzp9zyLE56ghSRETimhINIiIiIj5IS0njz61/8vUfXx/yMjIyMwBoVrVZtMISERGJOiUaRERERHzQsW5HCiUUytfoE+mZ6VQvVZ3KJSpHMTIREZHoUqJBRERExAeli5TmnGPPYcLCCVhrD2kZoY4gRURE4pkSDSIiIiI+SW2QyrKNy/j+r+8j/tsNOzbw8/qflWgQEZG4p0SDiIiIiE+6NOhCgkk4pOYT3/35HaD+GUREJP4p0SAiIiLikwrFKtC6ZmsmLIx8mMv0lemAEg0iIhL/lGgQERER8VFaShqL1i5i4ZqFEf1demY6dcrVoWzRsjGKTEREJDqUaBARERHxUbeUbgAR12pQR5AiIlJQKNEgIiIi4qOqJavSqnqriPpp+GvrX/yx+Q81mxARkQJBiQYRERERn6WlpPH9X9/z64ZfD2r+jMwMANVoEBGRAkGJBhERERGfpaakAjBp4aSDmj99ZToJJoGTqpwUy7BERESiQokGEREREZ8dU/YYTqx84kH305DxZwYNKzakeHLxGEcmIiKSf0o0iIiIiAQgNSWVr/74iswtmf84n7WW9JXp6p9BREQKDCUaRERERAKQlpIG/HvziRWbVrBm+xr1zyAiIgWGEg0iIiIiAUipmEKDCg2YuOifR59Iz0wH1BGkiIgUHEo0iIiIiAQkLSWNT5d9ytrtaw84T0ZmBoUSCnFCpRN8jExEROTQKdEgIiIiEpDUlFSybTbvLH7ngPOkZ6ZzQqUTKJxU2MfIREREDp0SDSIiIiIBObHyidQqU+uAo0/k2BwyMjPUbEJERAoUJRpEREREAmKMIbVBKh/++iGbdm762+dL1y1l867NNK+mRIOIiBQcSjSIiIiIBCitYRq7s3czfen0v32WkZkBoKEtRUSkQFGiQURERCRALau3pEqJKkxc+PfRJ9Iz0ymaVJSGFRsGEJmIiMihUaJBREREJEAJJoFuDbrx7s/vsn3P9v0+S89M56QqJ5GUkBRQdCIiIpFTokFEREQkYKkpqWzfs533f35/77Rsm833f36vjiBFRKTAUaJBREREJGCta7WmXNFy+40+sWzbMnZk7VD/DCIiUuAo0SAiIiISsKSEJLrU78LUJVPZlbULgEVbFgFoxAkRESlwlGgQERERiQNpKWls3rWZj377CIDFWxZTunBp6pSrE3BkIiIikVGiQURERCQOnF37bEoml9zbfGLRlkU0q9qMBKPimoiIFCy6comIiIjEgcJJhTm/3vlMWTyFbbu38du239Q/g4iIFEhKNIiIiIjEidSUVNZuX8tz6c+RZbM04oSIiBRISjSIiIiIxIkOdTqQZJK4+8O7AbjlvVsYO39swFGJiIhERokGERERkTgxefFkLJYccgBYuWUlfab2UbJBREQKFCUaREREROLEgFkDyLbZ+03bvmc7A2YNCCgiERGRyCnRICIiIhInVmxaEdF0ERGReKREg4iIiEicqFG6RkTTRURE4pESDSIiIiJxYmjboRQrVGy/acUKFWNo26EBRSQiIhI5JRpERERE4kSvRr0Y1WkUNUvXxGCoWbomozqNolejXkGHJiIictCSgg5ARERERPbp1agXvRr14pNPPuHMM88MOhwREZGIqUaDiIiIiIiIiERNvhINxphyxpiZxpil3u+y/zBvKWPMSmPMs/lZp4iIiIiIiIjEr/zWaLgLmGWtrQvM8t4fyGDg03yuT0RERERERETiWH4TDV2AMd7rMUDXvGYyxjQFKgEf5HN9IiIiIiIiIhLHjLX20P/YmI3W2jJh7zdYa8vmmicB+Ai4FGgLNLPW3niA5fUB+gBUqlSp6bhx4w45tqBs3bqVEiVKBB3G3yiuyCiuyCiuyCiuyMVrbIorMoorMoorMoorMoorMoorMoorMvEa179p06bNd9baZnl+aK39xx/gQ2BBHj9dgI255t2Qx9/fCNzpve4NPPtv67TW0rRpU1sQffzxx0GHkCfFFRnFFRnFFRnFFbl4jU1xRUZxRUZxRUZxRUZxRUZxRUZxRSZe4/o3QIY9wP38vw5vaa09+0CfGWNWGWOqWGv/NMZUAVbnMVsr4HRjTF+gBJBsjNlqrf2n/hxEREREREREpAD610TDv3gHuBx4xPs9JfcM1tpeodfGmN64phNKMoiIiIiIiIgchvLbGeQjwDnGmKXAOd57jDHNjDGj8xuciIiIiIiIiBQs+arRYK1dh+vgMff0DODqPKa/ArySn3WKiIiIiIiISPzK16gTsWSMWQMsDzqOQ1ABWBt0EHlQXJFRXJFRXJFRXJGL19gUV2QUV2QUV2QUV2QUV2QUV2QUV2TiNa5/U9NaWzGvD+I20VBQGWMy7IGG+AiQ4oqM4oqM4oqM4opcvMamuCKjuCKjuCKjuCKjuCKjuCKjuCITr3HlR377aBARERERERER2UuJBhERERERERGJGiUaom9U0AEcgOKKjOKKjOKKjOKKXLzGprgio7gio7gio7gio7gio7gio7giE69xHTL10SAiIiIiIiIiUaMaDSIiIiIiIiISNUo0iIiIiIiIiEjUKNEgMWOMMUHHUJAYY/R9lCOOMaZk2OujgoxFDm/xdE2Kp1gKGmNM0aBjkMNTvH8v4y2+eItH4o9ubCQmjDHGeh2AGGNSgo7nn8TDidIYkwxcbIwpaYw50xjzSNAxwf7bxosxbsXJfqxmjGlljDk23hJH8bB9cvOOqYuMMZcaY3oCdxhjigQdV7wzxhQOOoa8GGNKBR1DbsaYUsaYRABrrY2H72Wu62Ph0I1zPMSWl3g6dxhjTgZuj5fzRO5tE0/bKt4caNvEw3FvjKnovUzy3gceU4gx5lRjzBngzmFBxxMSfh6LJ6HjzBhTNuhYQo7k80JS0AEUFKEvlDGmCVAVmA+ssdbuDDi0vYwxdYE91tplQccSVoi6DLjCGNMJ2Bb0SSlsP1YH1gFYa3cEfcK01u42xiQBvwBrgcuDiiVc2H7sAxxrjFkHjLPWrggyrrD9eKw3aYW1dk+Q+9EY0xB4BViNO7Y+B14MIpa8eNvrTOAcYCqwyFq7MeCYdhtj3gW+BbKButbancaYRGttdlBxhR1fLYHawCJggbV2d1AxhRhjrgBqGWNyrLUPBB1PiDGmG9DaGJMBvBHk/guLKQl4FFhrjCkP3GCtzQk4pvAkw21AE+BoY8wj1tr3g4zNiyl07FcBcqy1q7z3gZxb81hvMlAdKArsDPicH74vawGrgD1AVhDxhAvbj62A44C5wDJr7dqgYgrbVjcBlYBquO/k9qBiCjPQGJMFJBljnrTW/hp0QLC3lt/pQD1jzLXAA8BKa+22gOPqClxtjHnKi2dhkPGEhB335wMDvDhXB1m+z3WeuA5oCOwAJllrvw4qLr/ETcYu3nkHbidgDNAWeBNXYI8L3pOkm3EX4LjInhljTgV6Ab2stVuJg+PN248dgAnA3cDrxpgSQRZUwvbVWOAnoAjwm/d54MlA78bmMuBlYACQGmxEe/fjecBk4AZgoTGmfID7sTrwGvCItfZ8YBbQPIhYDsQYcwrwBFAeGAj0MMZUCyiW8PPTFlyiYTVwJUDQN6ne8dUelziqALwPpAZ9XjWumck7uATWicaYF4wx5xljigccV1HgU+BV4FLgXmNMWsAxGWttFnAP8BjuxnSKMaZNkNsrrMB5OdAeuBrIAa4IKqZw3rHfEZeM/K8x5t0gb+bDtldnY0x/a+1nwE7gyfDPA47tdmA4bmi6a+PhSWrYNXI0UAZ3LusSB+ewa4GuwDNAR9z3M/RZYLFZa2/CXR+XAzOMq1kaaJnVS7ivttY+Yq29EvgTuBXoaYwpE2Rs1trJuDLrScAwY0yPIOMJlZW94/4s4BGgn7V2Fe7cH5iw80RfoDvuOtkSuCDIuPwS+I1fQWGMqYR7ytwadxNRDPjK+yzwm3pr7WZcQf0BY0zhoJ48hL0uApwANAB6ejFmB72tjDEnAA/hCsM7gcpAYtjnvsZnPcaYStbaPbgk1pPAp8aY4621WcaYOn7GFM670DYCrgNaAN8Az4Z9FlRc9XBJj/NxNzjW+wl97vdxVhyYZq2d6L2fADQyxjTyOY48GWMa4C68t1hrr8PdqDYFOnpJEj9jCc/uN8Ul1noD3YBexpg7vc/OMMbU9zO2sBhL4G78zsclQf4EPg74qUhPYBBwlLX2D2ttV2AJcC6QZgKqSm6M6Q48BZSw1s4BrsLVyjrTGHN1QDEVxSXUAMpbazdZa3sDH+GOtVO9+Xw7TxhjGhpjrgybVAzoh3tAsBu41BiT6JU1AmOMaQzch/s+fgzUAcL7UfFlm+UqTyTjyhH3G2OGACOAOl6yJlDek9OO1trOuPJEE2BjHNykFgMuBNoBX+NqWUzzyhuFfIwj9/FSHXdu7Qlk4PZpMgSTNArF5z1w+t1aOwz4L67mwNneZ77vSy/JkO09izoRwFrbD/gCd4ydZYxJ8LusE74trLVv4L6LTwPDg/o+GldbbZQxprY36RhgGLDFGNMbmG2MudPsax7jV1x1vHuOkApAF+AUYDvQ37imc+XzXMBhQomGg7cFV619IN5F2Fq71hhzDuB7B2ahL7sxpoYxph2AtXYw8APQKnwen+IJv3moBhS11o7EnaxTjDEXeDHagJMNOcBIoAYuq36ptXaTMeYUY0whvy50xrXl7+G9bo/LoI8DWltrnwH+B7xhjLkG+Mgr/PkRVzljTHPv9Rm4qo2/4C4kF1lr23nJj3twhRhfGGMKhQpHxrVP34CrXXQmLuHQ3lq73hjT3hiT5GeBxTv2F+OemoaSbDtwzSdC34nKAR/35YGKuJtArLWTgOm4KpmdjY9t/sPOE3fgqrUPxxXsDHAjcLkxZhKuoLDDr7hCjDEn4fbbV7gbweFAZ2vtKmNMDxNcnzO/eXGleokjrLWPA/NwtWeOgUCSbKtwx/pNxpga1to/cLV7vgIaGmNa+BwPQDNcO/7rgZmhZK219kngO6CvMaakX9cj4/qIOAmXfAnVXCiBeyJ4InCel2jui+unxNeabGE3W6WBjbhj/jQvnnbW2s3G1VD07WYw7DxxPFAK6AO8jtte7XAPCi41xlT2I56QPI4XC4w3xtyAa/51gxf7sX/7Y594CdydwGLcuXUY0MU7h3UCfDmH5SoX9vDKyyWB53FPdFO9Wkc3GVfTwVeh+IyrwXO/8ZLu1tqXcTVBnjHG1LE+N7fy4sr2yvBvA21C12hrbajm6/lAop9lai/5keO9LuvFs9Va+wHuSf09xphz/Ygll8K45PZQbx/+BlyMS4IUBZ4DTmZf8jnmjEt234k7R4WSDZVxybXW1toO3rF/OdAp6MRkTFlr9ZPHD2C832WBCt7r+3AHyene+9NwbXeb+BhXCaC097ol8CAwEVctrinwAnB7gNutH+4G5kvgLqAmrjr0SNxNfVBxVcP1rVED+ANXPa6o91lrXFOYSj7Gcx4wG+iPq/7fCteU4wXgAm+ei3GFhHN9jOtY3Ml5EvAB7iTdzduf5+L6demOa+/ZwKeYCuESCmfhmm3cBxwPzAGWAknefC2Bz3Dt/P2IqwEu8fiitz+beNMTvN9v4J4GtvC+o7V83I+h81fF0HGNu9C+DtwfNl83oJFfcYWttyUww3s9ElcDJLTdqnnfBV/2Yx5xfYa7menvHeetvM9O8s73p/kcU2LY6xG4vj8eBlLCpj8BvORzXAm5ttvDwONADW9aOW9aP7/3o7f+N3CJqsu89ybss+dx/Uj4GU8Z4CJc9fruuL4GvvD2aVXgGmBB+H71Ob6zcAXyDrgbmTlAEe+zM7zrwdE+xBG+n8rjbviG42rVnYRrxlcflzRdgavhE8T2uhFX/mqBqzHwSdhnt+IS4ckBxNUSl0xr4m2jb3A1LkLXgMXAqT7H1CrsfH8GsDkspotxfZ75fr731n86sDCvbQLchksGFvc5ptD1+yXg8dzTvddjgSd9jCl0fU7ANSV8G3gt1zw9cUmtkuGx+hRfddyDzXHeubY8+8o+tXD3bif4vK1Ke+evIbgH0jVx5f5B3udXeMdePT+3ld8/gQcQzz9AJ1yh83vvhH0xroDyCq56+0LgfJ9jau4dqFfi2uo2wj0JfARXMP4R14Ti9AC2V2fgPe/1S8A73uuyuHb0w4GSPsYTOlk397bVQFzmMw1349wTV6thLi7b7+e2KuVtr6mEFXhxhakXvGOtEGE3GX5tM+/Y3hA6GXrT+3n79D3gE3y+OcUlOWbiCpbtvWlnAuu9Y+sO3JPdzj7Fc5y3vtuA+71t9jZe4cmb51ncTcQcoJOf28tbf1fcU+V0L75TvZ9XcX1J+BlLQq73Lbxz1oPAu0Bhb3prv7dTWEz1cYWCq7z3lXCJmRdxN60/+HV85bX9cIW7QcBNXlz3EZbswyUH2/sUT2Lu/YpL/j3K/smGY3A3Or7sV/YviJ/rnUtfxTXjSwqfD1cgjumNM7kK27iC58W4MkQnXPOJcd4x9g7QMKDj6zhc85eTvffXAn/hEqh9cDeCMT+H5dp/R+Ouk6VxD1Gm4spjbwONvXmKBbS9zsYlR0vimn4Nx91M9MbdPHwPHB9AXMd654FLvPe1cEnIsbiHKQv8vhbhElifAA+ETevhnRde8z7zbVt5x1XzsPeDgHu816HzWiHvd2lcsrS8T7E9xr4HiSWA8cCJ3vvQNbK497sSrkxbwuf9+ZAXZzlvH04K++w4XG1cX5J/eZxfy3nxvYn3YAf3cOoHoKuf28lb96XeeWsV7lrUGJcs/crbt18Gdc73dTsEHUC8/nhfmPe836fjCqBX43oLbYfLqocuyjHP3OGydXW81//DNQHomWuewrinEWOA3rGOzdsWI9n3RLmDdwG5F1coTvamH4srKJcOYD92wN2gvoDL5N+A60X+bFzBbgSu2qpf+zGU/Cjj/e6Ja5rQO2yem3E39VV82ka5T9ZNcZ14vgX8J2x6XVzBr5yP+y+0vUrj+kaZ4l04QtvvNNwN10DgLD/2I65gORG4PGxaVVwycgL7noD/D1d9tY1PcYU/+a7tXcxScJn9x4DBuMLJqd5Fzq+aH+E3Dx2981QNbxt+BpTyPrsel0QN4jxhcE/a3vOO++re9LK4p4Ad2FdjxdcnNd46jwM+Cnt/Ku6G62H23XRdALT0IZa9SQZcAuYeXBXRBO94ewR3E3+sN19nfHyCirte9wgd395x/zbuBqMd8B9cErcfMaw9kOu474xrl9vIe38R7jrdKWweX5+ahq23EO5aOCd0rvKmX+2dN54Dzsn9P0V7W+XaXnfg+t5ZiEvk1sP1pfQQLtn8ufc+IRbx/Mu+PMo7t/+E9+AEl6S83Js+ggCSDF4cTXCJ2w/wbvZw7cJTcJ2Xh44/X85huOtisrddpnjfwdA1vRquPFHB522U5m2nEt77G4Ch3utQWbZp2La6EzjDp9hSvd/lvd+P4h5mlAmb5wHv+1AKuAQvAeFTfPfhyvYtw6Z9CUwOe38HXi2yGMcSOo7aAbfgmnkZXPJvCO7aVM3b16eF/41P26q1d05N9L5/L3rnr0q4a2Wx8P16OP8EHkA8/ngnw9G4Qm/oYD4Rl9VvF1BMN3oxFMZlyYYDK/GSD7nmbY3LEses9oD3RQllL5/GVak/C1dlbxL7qg7diCu8Fw1gm5X3LrqhG71zcZnO/rlPOH6cgMKOpUa4jmA6eO/TcFnPy8LmreHTNgovQPXEFdBD2+s8XOHgau/1QHwq2OXaXqGmS8VwTwFHAFd700oA1Xw+rgp7x1XLXNOreBe4C7337YA0n2I6CtdkKfQkpj6uI7dQYbMcrnB+vffe9wsc3ugg7HvS0BdX6+M5XDOr+cBxAcSVgnviVwTXtn8ELtnnS6LvADEl5npfGJiGV9vCm9YPl6jp7L2vjk+FdlyBbiYuyXARrknJCO86kIKr+XeRN++xsT7ews4VrXAJ5XdwtQZCMQwB/g+X1A01TSuFDzXscIXgUH8fs8LO+z1xSa1Lwv8Hn4+zBrjaHuVwycfBfh1DB9h/Cd4+/BKXAKmNqyEzNOzz5niJwADibOf9PsHbd4PZl/QO/Q+FAoirCftqkNbHlQ+HBrEvw2I6AVc+bOPty9dwI00Ete9qhF17KuLKpWd42+sHXJ8HVXBPnH8Cmob9bUyTgMDteLVzcLVicnDX9Ba4e5H7cAnUCcBzYX8X02ONv1+HOuCa+t5JWG0w4FfgNu91cqy3V9h6z8fVHuqKK9+87p0jinvnjfGE1WTz+XhrjbtGhj9wXYQrawSSiAzq5/DtfOIQeZ12FMP1Tr0duMLrNOp73BO4mt58fo9O8CwusfAqbjz3m3GFuc+MMSWMMQ2MMUVqiH4AACAASURBVP1D/wbuJjsx76Xlj9dRTY619kfczU0DYKC19iNcthNch2W346pd3m6tjXmnbsaY+saYC8M69FkH/I7rkCzRujHKZ+BueHp5f5PgzWtjHFuCtXuH1rwM97RmrDHmfGvtBNwFuXeoozBr7YpYxhMS+r+NG9f6P7iOrd4xxnS31s7AFQwuwD3ZGm997BQpbHtNMsYMBq6w1k7FbbsTjTEjcRcZX3vstdbuwvX2Xwb2G1bpT1yneJd4HVJ+YK2dYDwxjmk1bv/VMcZUta5zyiW4zucqWmvX42rJFPHm3xjLeHIzbmjNq3BV6Jd5Hby9j7t5XozrRK27d07x22ZcW/7nccmOcbhCwaUmgBEAvHNFtnE9it9mjOmFKwxPBmoaNw43uP4/3rbWvgNg3UgUa2MY16Vm3wg4Z+BubB7CNfcag6t19ASu35QHrOuRHGvtL7E+3rxzRUtcwqqbdSMAfAKcaoy50Fp7Ly4pcq61dry3jTdba7fEMi5jzOm4Wjyn4W5uknA9jXey1r6JS3zPCv0PsYwlV1wJxo1KkIp7GFAVl/hrguso07fe2Y0xDXHDExf2ri9JwFbcTc6vuAcZXY0xaV65I926Dkd95ZUV7jHGTLTWzsM9bS4N3GqMKRPaf9Z16um3+UCSMWaCd+4PnevvNsH1aL8e15S3Gy55dDXuafNgvzvw9KQCH3qdO67BPSzoh7sxvd77eQpX5rnbWvtdWPlwW4xjOxfXoWgRa+0ruPLWV7hr+HO4RM3VwFJr7Q2wtxwes2PN7Bv1IsEY08sYcxzu4cWjuCYA7Y0xRwNYa2tba5/wXu+O1fYyxhztlSUwxpTDleMvwJ0zLO74moS7ng8C7rWuw8WYCi/fGWOKesfNj8DPQFtjTGlr7S+4skU2kBnrmOJK0JmOePhh/3amLwBjvddX4Z64jcRl8pYSVrXQp9hCWfLK3u/HcQmPULWuB7y45uP1F4Gr4h7zatG4Kl1TvW32IfBfb3pfXDZ9BP51GGi8bbMH9+TqKVxWsy+uBkNrb74GuOTIPD9iY/8qb9VwT3RDsXTF3eSc673vTljbQR+PsZNwhd2S3j79CtjFvk7UiuBjh1thx3wb77g+0dufPwFDvM9a4W4cOvi9vbz13+bFUylXzJ2Bp3yMo1DY62TcCBybcFX+z8E9DXkW19Hcb8DZAW2vk73vZz/v94+4gl6z8O3nc0zVwl/jngK+hivUtfXO+7UC2l4Gl1Ab6J3vx+FqW1zsnXNnAWPC549xPEexr/p8A+93UVy19tB5f6B3frvCr7hyxdgD9ySwt/e+GC6pOxqvBpQf+y3sdSIuuVANVwvxU2+bDcM9AfS1f6dccYZqHKbgro9P4/rYOAr3kGUwPjwJZF9ttTdwfSUV9vbby7gyV6i2wGC8migBba9Qc6HCuITfOO99M9xDgrsDOodVYd+T8ETcE9zJ3vum3jnN147mcOWYULOpqrhE1khcTZTCuIRu5YD245244Ypre++v965DjXAJhwrAMd5nftRyDW/u+CauyUuoL4ZhuBvVUBmjSNi8fjUZSsDV/HjJ+3kCV7ZoiXsyfythTWljGZcXS0dckiPU7KW6d976HjeiQ21cfwjTfTymws/5t+Bq0r2LK6M+gCvvhGptfkpAZYogfwIPINB/Po+25rib09dwT5CScB37fIQrrISqzflWfdxb3/m4NqbVvPcPexe747z3J+HTDX1YTKE28+W8941wtS32Vq/H5ypLuKrqn+OeRk7BVZcd5e27p3AJiB9xtVKeIMY3Xbgb97uBqqHt4R1bxcPmGYK7MTzTx+10Iu5G6piwaUfhMv6zvfd9cYX2C32MK/ymOQk3fGaKt1/Tcf1qfIxXjTZsXj+bvdRj/1FoluDayzfEVXP8CZ9uIHAFy+twCZnGwEhv+qO4Akpp7zx2K64zyLZ+7cs8tlsx9g1lGWovORLo69c+zBVXKWAZ+5qSGFyTuWneeSwZH/siyb0NcM2pHvBef4YbMi903k0Eaub1dzGKK7y38fa4KuOneNMGsq/K7Iv4eDMYdmyVYd8IQr2872Cow9hiuGu4rx1u4Z4+3h8WYz+8Dodx1aSHhu9Dn2NriHswELpBrY9L2o7GPaSohJcAjHEcHXEJhlDndq/gHgAk4ZqVjMLdlN6BS8z8rZmoT9urKy5RFDrGknA1I0d770/CxxGrwuIq450brgzbl0m4MtAE770fTYNyN0N9GHfjV8t7Xx1X5f99vP6LfN5OueO7F1eeCCVD+uBqP/naLJp9yavwZMNr3nczlGx4FMjC3dyHziV+Jm//633/knBl53Hed7IUrjPu6wPYn8VxDws7ee8b4TUnwTUvuT+g46wrrk+GGrhOrp8EuuCaUNyGqykTyIhCQf8EHkBg/7i7uH6A6xX7NO8gCXX2NRy40Xud6J2IhoVfbHyMsxmup+Dc7cEHe/H7NVxL7pN1eS+u0FOuZO/LtJB9T7iCyPBPBu7zXl+Ba27yo7f/nsX1wnwm7gaxZoxjKYq7ga/GvhuaKYQNQ4d76vwKbuidmMbjra+Dt98+8S5qncM+uwwY7L3uicvC1vdpvzXEFXTfCB3TuERNcdyNTQtv2mu4rLDvJ2xvX/2GS1h9g3ta2RtXbfxzXAEh1JmTX51t1WVfVbzwEQiG4ZokhJ6G+JL08y6qfdj/qXao34jwmmOpuMKobzcP7CuohTrVPRXXZvLKsHkewBWIfTmvHiDOqrjC0/9554Vbvemh0XvCC6Z+JhkexSX/bsMVNpt4P3O9Y3+8X3GFraezd079GHdzXx9XuJvDvhp+fj8Y6Ovtt/DhR+/F9TtwP66WVsyHicwVU+jYT8Y9VX4Zd7McukFtgrvJH4EPvdjjqop/T67kp3d+T8eVu47HNQd7BJ+uQ+HbKnTs4BIiX+KqaIe2V1tcM7lnA9qPDXA1r0KjMV3CvoTNbbhrfMyHXc+1rVJxT5QTcUn4r9hXc+A2XLna14QM+z8gOJl9Izrc5h1n4TUbfKtNmuu82u//2TvvcKuKq43/1qUKAjasUayoxAKooCIiNhB7wV6w947YFXtLjGKvsXdRVKxYEjs27LEnlsQWo4m9sL4/3rU5c48XY764Z19y7zzPfji7XM46M7NnVnnXu1AkvLA/LqQxsmHj3HLF5zbRZzOgPXEk4mWbhNAX0yfPlr0PFeO4MnKsjUDr/lDEL/MA0u//RqagHQqAbZWcjyKp5oX0oIeooS+yVpBrTkflAlTyo6WMPEUNGr5JTNLbY2PbGG26PYsJEgvBiZTMiI4cH5sk59tQi1QajaO+R5MQ1pQoU7qZ9AW6x+ctos+WT84PJyL4mce0WLj7ISfMkmiz3QFFwo9AyntB/FMqGUuyMDbEfLoGQQrbICP1GgRZfR5BHM+gZFLDWJTfRoZMGwSzPyG5vz4y6n8f8z8LaRNCLUxEjqGzkdEyR9zripxHayMo2k3kq5YwBzUYZW9kaA2I8/2iLwuU0QzU0D05ERYdkHPmM+rKNyHUzofxTOmbXMyvl9CG+z5wcr288XktpBxnLZEa371OvH9943wAcsjsg1AEd1Nh1AEpUfsgJ9aNyNlXMKGPBc6vSK5RwOj4PD+Kyp+P0GPTEdHB+rEuWaZFYo1fEhmBx6D9pytSRF9GUOhSHQ00Rpd0QMiORYvz5N6I6MesRGDJWrEOSpGYHqXDnQPclYzpjWRAfsR4/blYrxDCMC19eBkyAgtDK5uCXrdObUcNrbMecmZtivSw9REUev6cYxmyrIsMmGXifChC3e5JzQDLnS6xOwrozJ9cOwQ5wI9Fxul8OWVK5FgLBZZuBh6Odb4bitS/lK5dmeWyGMdzUPrNeUSVL+Rwe4rGJXnLXsfaJHL1oYb46Eakk8f51WSoKtGEfEug/XmxWGe3RvZHn1jnBxG2SAZZZor5Ppkame9QFHTqlTx3U7HeU0HgtbkclQuQ/QfL0PwLMKHuehtkWJyPDIp/EdHJ5H7pTO3IGF6GGjx75Vh0ulNTGFYk8vozyJNuvLsh5e1hxF+xWLzsf0URrrfIZAT+hLyzIiPmK2Dn5Hqn5HOpXvVknGagxji7HjLgN0LOh52R0vnrGM9JlOygQXn6H1DLsZ4RQdDWpFb3fgByGGUxtpBR8BBwWXLtdCKKG+ebI4/6k+Sr4rAIUkI2RVDiSbGxLEDNqXUiiYKc60jm16rIOTN9vIufElUJkjHOoggjB+R71BAd88Rc+9HGj6Kq3Svot2ViHBeO81mRkbwQUvLGISLB7GOZnA9H0fgZUbRyDIoM3lb3juR0Zu0fa/7eyb0F4/p16ZqfWa6VSXJxUfTtXmrop9Id3ggh9jzhuIpr1xOouuTaIJIc69wHUoKfJviB4lpH5Nh9HiF7cqV8zRnjtBNyOjwCHFD3zA3A4/E5KyIlvnM3ZOgtmFxbEe2X16LqJdkNVOTwnoIEQwZWN7RfnRDrxLqZZeobc6sIDqyEUMINyKjfj/ypvcUaMRMyTgvH8rbIEVicH07e0rspYqAPcG0hb/TZhdT2pz0rmF8NiAPoDGQkH46QM2+FbLcBY+r7ucwxjM/zo2DwFcm1mRDK/AEiVS7XvIrPI1DQ4nPC1kBOoyOQ3jgc2UvZ+M2a61G5AFl/rCbrCyhidDXytBYki1NKEyHl88hY0LPDXeL7JyEURfvYdPdD0O1lkVKwQmaZ1kVQ3g7USgzujuDtvZCBWonHuglZ+wGPJRtfQ/pvJhnWjgXwRqQAtI+F5zwSYjJk/PyJTNFdhPB4FnmHj0EK0zOI8OdBMtWLTuTphpS604HN49pxSKG7B0XhForrWRADKL3mBRqXEpwVRdkOSq7tBpyas7+S714Fbf4Dk/m9AqqUcxyK0vfLJEtRcu5S5IAs3rvLqIPvlj12/0bO5ag5+0ahCOW9IXsDtQhqroh8mgLRO/l8ErUUpjmQQprWLc8S2UrOZ0P5pmeTROMREikbGSu1Pbpj/DszcnRsnDwzhkzRNuToe5a63G5k1B9PIBOR0vlHKiiXmvTZaciBXHDx/L7ot5hfWVKYkrVqXmS0vAYcmNxvn3zO1l80NiBmQvvhoki/GYGMrL4oeLAY1fFrLIccosMQYuAupD8UpLpFYCOLARjnsyHCuzOQMVg4RgvESk69q1OyPsyDDPixRD5/XD+ZMPAzj10a6V4RIXneJuFDQQSL29X9XU5OhjOQ3TEHcrSNiOtd0Z65Xw65kBO0QI8uGGvWaIR2XTN5bqZ4P0vnlInvS8ndu6OA4WrARwh9Pj0qjX0tcjhnR202x6MtLaRF+ZGeCP55g5nNiYy+PczsNK+VBfveVTLmqCh1uRBayEuXz2P2uvtXZrY3Wrw/QIRvI6mVBxrl7g+VLVMi26zoZZ7LVdrvVjObjIzpXVGk7aVc8vyM9gyKPg80sxs8SjJ6iaUZoyzR1/F5WaQEDEc15ndEObq3I1Kd1czsblcJyw9Rnup7ZcmWNne/MErv3Au84e4LmFl7xIWwOyoHmq25+2dmdj2CNw4ws9VRlGt/FC1cFbjQzJZy93fib7xksQYD97r7RdFXfRHPxlhgbzObD6V6bImcSNlarGNt47sPd/cHzaxNlKJ6yMyWQ4b0ru4+MYM8Q9H8PcDMbkRR5vaxds6AHDZTWoaxS2Uzd3czWwn4AqVzvIHezVMQdHYwMmgmo0orWWRMSli2Q47v+c2sI2JovxtY3czau0qm/q3uN5W5jk0prYkcC2+iNepgZKxuFM885+4vo4hNo/2rrBZjOTRk+Aitp48Ci5lZH2QcrolqqZfW4h1sh/bEU939bjPrguZ7X4Tw+yuwhZltjUhGN4+xzNKS8Zgfzfk/o/V0L2QEfgkMNrObXaW7szR3nxzz589mthOKAP5gZjMCn7r7t1Ea+Hv0vpbe0rkbpYH/amZ/QM7uR4GPkUF4ADK8Xpj6/1aObFEmFYSyexY5sk5Gjra1kePmSXf/Fspbw+r6ak+kj56EHNvzIBLKPczseLSP31zmetVEWwa9d3cjx9pIFHVe2MzedJVPvgXYLJlnpbdYTzc1s3mQg/Rldx9lZqejctgNsV93RGUap7Qy19Um1u2/orTZC5Ez5pIoadzB3U9Of0/J4zozsLiZ7Y9Q3suh9KBvgUFm9p2rfPgnZnZFjnE0s0GoBOnhwDPuPjFKfk5GXHpPo1SXMcAYM5ve3T8vW65polXt6ch5UMt1LXKR5kCLzrFEqkLybG+kRGXJU4/vLIyEpeJ8INpUtkieKVIqckGWij5bBinAxyX31kfK54xVj20Tv6EfGfK1kMd8xhinXnGtIBfdGCkqRZ5/AXesqrRTOq47og3l11WPVcjTHeXEPkNUIkjuzZJZlkEIKj4E5U5eHWvBcYiI8iWkrBfjWgW09wRq0YaCbHFJ5FHPFZEfgjbXVZNrKyMkw4tE1I/M1WfqZFwDVeAYmFwrIl5LhZy5UTxT2MNRBOs3KNJ8OSKGPR4ZNwfllCuVDxlZhyPo89ch37woRe50MpIZUtuv+yOY/UbxXh6Fom9DUOm1C8lYMhI5rPaKvfF8hDz8IPbEdeJd7EUFaUIh3xqxVs1DrSRdActeAkUs580sUzGWKbLhdgQ3zrrONyHb/rHWd0eOpPWpkeluGOPbrgK5hiJn0YDkWqGX9Y+9aWBmmfZDKY8/Is1FCJ6nyUjgWff9tyLH8rA4Xxpx3VyFIvZ/ImN6STLXOyAU4svJvSKo8mdki1yXUa4fobWRXvg9cFhy7WaSNOSM8u2IytVfnlybG3GjjCFBNmSSZwQigL2JWop9T+QAmR45RCZTp7+2Ht4yHA00oXgnL/8cMXF+kyoEsdlkY8hFeW3vhpLyafESAcsjIpuClCgnjGpnFPkbhSIjy6Jo0dHJM51zydOcj5g/tyEo10ookvsgNZbjVWPBnjmTPDNT411YGujaxDM7IARDNrbl+F6byucZUQ7lGBpXLchd+rATijJPQsrlwJCtL8GAjpT0LPwM1AzTeVDEow3iRklJM4tc51xEmauj3O6V4rwHsFN8XjXWsZ2osGY0qqv9NFHqKsZvtRi/JZFzcJ0K5fsNcG7dtZUQCuoTEjLNXHMsPi+BEAztkHN5r7jeHu2XIzLJNFuyfs6LIONHx3mnWL/Oo5byMl39bylZvs2QwvlB/LsaQjWcBhxZ1bwK2ZZBekNBGNglubc6mTgZkJF8MnKgzRDX6p0N8yCjdRQVOG1Dhu3Qfl0EcrpSY4vfPdaRKshr54zv7p+M69qI22lh5CjN5lwLGaZHRvuMse5vhZykC6Agy9jcfUVtj5wOOSCvBu5IxnO2kG1bauTlOfhkirneDgW/1kH79oU05muYhYTAM9d7gFIGxyKH9/B4Fw+K93HDWHMvzjmWiWztYm4dhvSuQtdZHDkmszuyYs+5J+bSWSj49HeC26l4L6vor+Z8FC/n/2Qzsw4oZ+1fAf//2BO4TwH/MbO5EEHMPu7+agVy9kL53je7+wQzWx/lO2/q7reb2QoAXnK6RAqHMrNt0ea7PcpNP4xaSbPDgYnufmwOyGxzbTGnvnX3TwP6eRzwiLtfYWanoDzKTVBO5+EoF/W2DHK1QXm3u6BoZHdkBH7WxLPbAA+6+5tly1X3vSui6NC9dfOuO8rH64e86tngxk3IOJO7f5Kcr4RgosuilKpLUe7nx03/D7+oLMMQp8adKDJyAIqoDkRGaU+ENropgyztkALyvrtvZ2ZzI4fMhe5+QTyzFhrHJ4Hz3P2HsuVqQs6OSEH5CkXk50TG8nXIuTyTu/8p1xpWN89nRgrxxohv59W6Zxd099fLlim+a140ll+b2ZJIwTsJKe0Xu/uF8dypqHTxB3FeWr+Z2aJoTz4XKcGLofVsOWBHd38qnnsYEQk+UoYcU5EtHccZUYDi1USf2BvlFe8L/FDF/hjvX1/EEbQ6MrBeRQ7UIcBb7n5fyTIMQ+/faJQatxRy+n2ZPFP02dwAHulxuZuZHYjSSZ5DKIGhyBF5AUKG3OmC3Vch2wnIEJ2MDMEvkfPhbEQ090rJ72Kj/9vM2qL8844Iyv4k2q/fdPe9zayru/+zDFl+Sj4zWw+tDye6+z/MbAzilFk5Uhb6uPu4jHIVc7sBcWlc7e4XR+rVBJSOswvSI37j7k+mv6dEudoU+7GZHYnW+VfRevEGWjNWQY76ye5+Uvp7ypKrTsb2HmlAkbKwFtJlnwk5z/FM6cYhQ7rmH4jWslNRYYG+wD+Ax3L1zzTXqvZ0lHkgCPTuiKTjXJqIJlPzqrfPKVvx3QimegyKkO5JLSqzPtpY1s4kywrIsVCQCv0WeQ63RlGtAp7dERmxpZZibO5H9MN7CMWwWVzbHCnFRV+djLyeVxNVQshUbzg+X4ZIaobHebOp44s4Bh6jCbgscoxkS1n6GbK2Q06jZ2lMRJQlJQBFsV5Ezo2jUT7slcho7hH3F880v/oAv0Is53ci59pEYI+4n0Zp+pMxTaj47YjEbal4R9dHhs6qcW974KIK5lBaOqwn4kSZETkbrqdWz71t3d+VTfzYFSm7B8b7eEhcPxd4Jz53CBmzlNaM/plIHakjchSNRuiBVVAp6JfIU5JxWRL0y9TeM7SHPkvmEqnJ3O8c/84W43kXisL1RIi6wZnkmQk59NZIrl1KkmaVXM+KYqDxHtk1/l0HoS4eQnvTcJQilDXtJRnHXsiJPAMyso6ghh7bMuO7mPbVtrF+bolQdetSK+88DBmolVRWoemqKtMhPfZFhDBdpSLZbiAQTkiX6IZ0/+tQitoVGWWZQoyOEFinEnYR2it/h5yRMzX1dxnk+1XyOSVLXgHpPq+RCYVYv8bTWLcZFWOXpZzmtH5ULkApP0p5PG2QEn47IhXaMO41+cJMTXEoSb7CEC1Y9BtQntbZiI22YA7eiAxlLGORfgY5FVaOa3shwsC7k+cOJYzq1mMKjOpKlB98Xmxsd6PIbvpch0zy1JfeuQPB4MaSKJhU41SrX7TbxiZXOLaajROkTs52qKLKBMLph4xFy7FmoJSlIciwXxVFj5ZCRsMNZMxrRpG9J4Al43yR2GxvrHtuu1jLsleYQArviwi+/ioJBwmKdj1LplJYyfcWxkMDyue8AHGArIWM5UNjLLNCLmnMffA3xD8yW3L/cgQnv40kxaPscUXor6OS8wWR4bcAcn6fgnKabyL4NcqUKfbHlxHvTtfkerrezkEN/bdYWbL8GznXjDX/ApTf3ECUdY6+e5bgf8ogSydkHLSnpu9cRlIiteoD8XtcgBwK/Ylyt3FvHaQTZedTQqkRzyOizIdJjBm0Fz1N8A9klGlPVDllICrnt1lybx+EBKmEYR/txUcjmP0cCPJ/OXKINMR7m6UCUxOydQhZNkWpVpehakdFKvQCybNlO5ULXg9DdtG4WPevTJ5ZDzncsu2R1PbHhZDNsX29zMn5bOnflC1TfN6EcIDQ2NmwH0pX7V/F3JqWjsoFKOVHSVFZLD6fjAyt0UTOeoVyLUiNMHBdlAP+O2plsA5D5YFWoXGZpzKVqEGILK1/3fXDkCI3FCkNG4W8WSM1ze1A0dxiYZwPGctzx5w7Dhn4bwIb5Ri/qci4FopAzhwb7W7IWFgM5YBvV/amNhW5VkVw/8K5MBK4oeox/Rlyt6OuDG7J31coBAMRJ8q8yDFzJrXI/G8Q6VXfTH0wJDbVApkzM0IMLIByFg+I65sjlu/sxla8h/fFO7oaykMvojULIGTROrnGsQn5xqD0PFAlh0KWXyHjOVvddGqRrTYIzXAAQgrsS+MSlguSlPIrc90geASQAncMioqfHmvZS7H/DEEG4SHRnwUSpJTxRAboS0yFMJTGCunCVEf0OxChInvFPjSJmtG8FjJc18sgx68QzP9H/FYxt7aLz2uTkVC0CVm2QOWnp0fw5+PieudYw16iAqJkVN3ozujDYSgSX6xhC6OypOvWz70S5Ejn9XTI+OwY7+atsW50RPrFiWRAFU1FzgL5sRsqKTgx1obTkKMmK8KCxlH4ImA4AjmtTkTOwFWo498pcyzj/5+x+B4U1Dwzzrsih/x5ybNZnJF18q0d43cn2sN3bapPK5BrNYQKmzW5ljob9qhyHZtWjsoFKOVHafGbB0G52sXi/VuknLeJzXCFCuTaBaVDrImUug1jEbqI8OIhz+x51EGXSpRpH+qiDIj1/M/IAXFkvPz301oTFhR5fADBzKZDnurbked61ejP72Izzk6UGcrIzSgfsLhW1Pt9CkXmelYg10AU5b4YMT/fiEjnHqliY2uOBzKgCidWb1RZYmict0dOh0NDUbkPWCSDTIaUuckE9BQZn/cTTOdx/w7k0H2aCpRO5IzpjJBYByBnR2GErh17QoEgy0UW2Kbu/ADEyXAXMDKu/Qo5bapYKwyVJz4+mXOXImNiMaSsL5o+X6IsHZGztkusYY8i6P+dhIGM9sr7kzl5PHLQlGZMIH6Dw+Jz91jvxyAytwItUFlVlWKeoWovg5Bu8Si1ii89Yn4VZIJljuG68d03x/s3Oh0btDfuiNApb5JEczP0UT2ibv/osx3ifSxQF10Q8mneCsaxR4zlIdF3jyVr2Cqxvk0xGHP0VfTR4igqfyFy+hXzfg/Ee1DJvA8ZjgcuiM99qFX2WhztRfNnlGUKySnS4U+n5kzuljx3OXBWRrkGUXOkLQNcg3TYReNaF5SOef3U5kHJ8s1KIGJijV0r1pDKCMHjO/uigEBhm3VI7lVCWjutHpUL8Iv+mMYLZEdkFN4Yi/ci1KKAbwHLViFbbLRfA7+N8xmRcnAR4cUrFstM8pwBHJtcXwMZNEujiOB2IWMWx8e0cCBj5qzYMGaLPjoh2WiGk+Sm5hjH5HwmBOH9I0lZ1GJeUYH3FUWTxxOswXHtUKSwfw4cUfWYVn0g4+oPxbqEkE6vorzYAuHQO9a08UQqWEb5rkJG3rwI3lgYysU6YpfCzAAAIABJREFU0jMUmGyIJ2pR+RWjT/pF/7wIzBn3+qHoZG4W9FTpXCk+j4w1df/kuQk0jt7kRMwchuD0uyX3ilJw48mINkJ5y5cSEOfYcxqVj0XOyhuolSddhJKr+KCI1ucoAj4RoWLOQAixJ3POqanItwriodoBReYfpsY7sCoKaHTKIMfgWK+WQrwCPZGhfBw1I34P4K8hYzZnJI31wh2Rs28rlBZ0R7KGHYoIm6sYxxVjfV0aReKfoYbKHYAMwawIi5Dprvi8Pyp7WFSy2jzWjh4V9VexJrRDiLoi5bcNQj1lqarSlFyxNoxCqRLfE6Uhka54MY3LNeZY75cCTkvOl0dOkJHUyt12BU6paCznQgGnwoE1I0KiP0ydDluyHE1VJzwf2YsFqqhZpvg296NyAX6xH1LbLBYgvKwo8vF75B1rgzx3m5GJEClk6EytvNrKsdjshKLev47rXVFu1OVkJsFDiso9BAQ7Fu4C8nVozhe9uR80hsUtgkrAfYAiNRcQuevJMznzyDZA0bdiro1AzqtKOTVQxPZaxF0xc929DiiV41Uqgl42hwORFz4KbFN3/TjEAbIQNeOwI5nK+BEM/9SMv3MQsqEod1gY00NDxirScRZHDqsC+dEfwcZHI+ff82Qi1G1CtgZksJ+VjPPtiDdldxQdzEZMiQzPdA3rghTfi2nsBJyJJM2w7HmWfM9msU7MX3fdkHL8DBmIwKgrBYwct5egdI75kuvjqShVIr6/F3IKFUSw5yLU2gwIxfMimWrNI12hIIQtHEE9UFS5QIQMosL0S6R/3YKQiEsgVNgI5BTZJOZXFekSRQWjwXE+GBmrpyC4fZYSlihFtthnCuTV8OT+cchpex5yulXFRbIUIngfFOcHUYs6d4z3daWM8hyafF4z9p4u0X9FZYK9kS2wbPJstv0y5tNvkvPVUOD1oPo5X/Z6T81W655cKwJ3hbNhk+i780n2prJlis+DENp87hizIxFJbMEP0eps+E/7t2oBftEfI8jNawi2d31ca4cgX/eSifcg+Y7pkDE1DkX7XqAWsdwf1V8tlISuZGY4ju/tHAvjySSEOUjpe5iM0LNp4aifNyiF4iaEUrmuIpn2QtGj7VBqRAHX2xoxG2+UWZ76PtoIRY42pjGZWmGonk84SFraEe/9QzQmZDqFGvT5TKSELppjzUpkGIKM9KviOCiuX4zKOBXPjYj1dt5Mci2COAWOQYb8gSiydjA1B0wfhATZm0iRy9l3iawXA6PjcwNCFP0apSucAIxKni2bCGxv5CS6FRlcBVdQl9ibzqAJJ3fmOdeAqnAcVOyFsX+OiPWtdH4NRGB4PzJkulBTijvXPbc1MrZmyNg/c8X8LwIU7yBnx4zJM+ciB9btBKqu5P4q+uecZK5bsrYvgWDaM6NyeT/ibihRth7UUAFLIKfttcn9dRBy7CbkgMhqOBf9FO/m0ygNoOi3pdG+uScwIMM4DkVOvotQiu8cCEVxRt1zy8calhUZmb6HKKByaLx/u8R68SKRElr2WtqEbLvVnXdC5YGLlLRDgB9orF/nct4WyI8eSI9IiRZXi/GuAvkxLNb0yxFSbeHosycQOusVpINcS8Y0JpQ2+DRCwt8Y7+bsyEZ6ngpstP+Fo3IBfoGJUSxAbRDZUMGIPoEoG4OI1C4Blsko18LUCFeGojqrF8d58fLvi+oQ9664D+dC5ZMeiI33eFp4lLl+ftVda6CxB7QHivxmZbOP714GIVI6xhj+AdVCLkpabkYGj3B9fyFj5iCkKE2PnIBXIMdMmq/YE8HmFqp6rCuaX90QkdVvEBx1HHBq3TMXIsh26RDoZOz+WcwbpJCPo8ZxcA2CHW+FnCRZooDIyHoq5tUDKErTBjnazkHomOwVVRL5UpKoDtR4eEaHfF8Q5SOn9nclyjY34ju4DUVJ7yHKRyJn/MVI6ZuxbFkSmYq1YkFUXaUNMrBOR3wWBUy7OzUjouxo2+bAN7H/nRBHGqBYONa0SbnmfXzvIvGdV6HKMyujHOLXSKLOyXhm5fxAyMgJBN8O2iPboZKkYwmiz4zyDEN530/FvF4CpW7cSpJ2hvam9jnlS+b9zNQcCyOQU3l42XO8CXmGIDTHxih1dhIy5nvG/Norpzw/0V9ro32nII2dP9bWE9F+tUtczxJxBkYknw8D3k7OD0QBg8LpsG/FfThdjO8FJJxsZOB5Sr6rsHuWQfrEIBQwGIP2yekR0m8kShPtj5xJpeqvyfzqjvbIIvVsLWQPFZWNjqKiVKFp/ahcgF/kR2hTGYeUpymMvDFpxlYgT89YuAuG5flRpGQSdfnoscGs3gz6cDqUCzgaGc0t0vCbSt8MiI23d3Ktoe4ZS/8tUZZ6tMAMyFG0KfBgXDsYObBKZxmfioyroSjzyNjYXqEG570BRWoKBas9LZz/Iza4bWNTTcvJpkRqOY2aJVAEZvPk2t00jsiMRelfWZyR0UdPE6RWce0xlIbWBpHojkHpAZWR88W+M3PM6xVQxPlIRE68ADL0szn+ErkaUHRmyzjvjxAOV6DUr1mIXOLMcg1DkaLfITRWN+SYPxqlPC5dv9ZmkGkUIs8dHHK9iKJsC6NUsBsyv48LIH1iszhfH6VatY819a36tb7sfagJGVNk5FLJ9Y0RQiSnA2s1RDZZkN2NQ0iPdii4cyqwfs7+aULGNWOPvAuhiTojpNNp6bqbQY5fIafa7sm1/aiVX+yN0iWyc1fQOM1rFYQILgiIu9Q9Owq4P6Ns0yEeiLOTa2OBx+Pz8ihAMImk3HnZa1n9/09ShhvpYKuHnBfE2BdGdZlomYWJIC8ifryLxjwVe6PUiQ2ppW2vgJCSS5YoV1pJojfSIx6ksSPy5HSMW4//39HANN7MbCGkBNyBFJVhZjbI1YYCM5lZ74zy9EJwn6Pc/WIza0CR7ofQi7SxmR1gZqub2f3ANe5+t5lZLhmbau7+lbs/7O6j3f0Cd3+tSnmqbsV4mFl/FE3eDtjbzLYHcPfJMbbEuaf/liVT8f+bWX8z6wt85e7vobzqq+LRj5Ay9WJZstTLVXdpJeAcd/+Nu++I0jdudPdbkTPwVXf/IX7Pt+7+SQ45m1NL+8zdP0JG1bnA62a2flz/2szaxecsYxnf9RwyRM8ys23NbCRKDXomeWYDBJ99KZNYXRDy5V9mtmxcuwf4zt1/QIbpV2iN7ZpJJgDMbNfkdAJCBzyAlOE13f0oxF59KPCOu/8tk1zrmdmaoPUK5QqPMrMZkBH9OFI4twA2dffz4u+y7EVm1hP1yVqov9oDP7j7nSgKOA4Zr9vEvlq2PMV6PhmlON6P3skFUBWO+1A0bsuc7yPi33E030Fz7C9IUb4VRQEvMLMNiz8ocx9qqrn7F2gufQGcamYnmtnRaPz2cfd/5JDDzDqiSOkryBEKMuBnc/fvEGfX28AaxbuRu8W83xzB/rdEPDMnufs5yGm0nJnNnkMWd38XuAxY2cyWjsuLAt3MrI27T0JpQsPNbMYcMgGYWVfgZDObPi71QEbfP8xsG+A+MxtZ9JO7nwx8H/pa2bK1d/ev0FowyMzOCRk2AN43swfc/RF33wGhLHaIv7NYh8uSq03opWZms5vZ7LEOWMj3qbvfjYJSXyJE4sbxd2WuF/MCncysk7t/iFISljWzTUKu01GVu6GICBLgX8jgf7ZEuVY0szvNbHeE4u6C0E+Lm9mK8cwLwLdm1rZEOf7nm2Xej/7rZmbdqZEMLYSUkYvc/RQzWxh5ihdEhs29Fci3AvBHd2+I8wnAM+5+QJwvgIzA9sjTeWNuGVvbz2tmtgqKGP0ewTA3iPMH3f2iCuXaA6VEPIFg7X3jfA2UNrEqcm69l0GWjgjW/JyZLYKQFNsig2F0GC3tEYx8l9igW2wzsw7u/k1ynjqPitJO/YGH3P2KisQsZFsGIRk+dff54lo7YHIY97nlWQwp6F8gZ8IKCDb+17jfFpH1ZXWSmtlEFEG9H0HFj0ERuKuBPd39SjO7FPg2HG+Nxr0kmRqQAfqDu5+dXD8CQf9fRmRq2RSAxGn2nZl1QGO4PkpV2B1F7N8wsyHAffHc3Egp/srdvyxBpr5Ij3jS3d9Irt+BOJT6AQe4+7hQPt9391d/aTl+hpynorm1JyojuQXi3vl73F8b+DycI5U1M5sOoVCGAB8Dd7j7K5llWBTpgfMhlNMWSCfc2t2/D4N5c1RV5YPMss2FEDF/D3k+ieuTUPrcTSgP/M8ZZGkoDF8zOwRYFvgMoZuGububWdvos0b7VgbZuqB0hEPj+7dCY9YFBVS+Q4bpwe7+ajhvbgaGuPs7JcrVEMZ8B+C3cXlr4FZ33yKeuQUhnhao/7uy5Eq/Bzkin0L9tZW739eUHLFftnX3rzPINQPwHkKcTzCzLZDOeqm7Xx/PzJtj3tfJ9RiwJEJmvhX7wRC0fryFdLEN3P2FnHL9zzVvBrCKn3tQI4u6gBoU53IEOS7YShdC5CsXoihvFUzoayDFcwJTKd1HhnrIrcf/e/wKB9yRKLpVlE6aBSnG15IRZkxjsq8BKCWoA4KB/jG5ty7KQ81ZOmxhajnyryOP9GIIVTEinikY4+dpyfMdRYouAc6OedS1iWdmQbDti6gAYt+EPEsgfplmUX2GiP4hOO8UckAqSJegMbT3JuTk2zS5NoxaebiFk+tZ9iSUsvcoCXdMvIsPUctzni65VyZ8th1COq2MHLaHxzrxNMoDL9jul0Xw1YXLkiWRaSiKfF8ba9fKyb21UJStIFRsl3t+NTHHTo++eYYgSEP8UylfUItdX+v6rSCNfYKAs6fjWGU/IXTkwzH/ivdwP2CnzHIYjXll9kIBlQFNPVtBP10KnJecL0aNJHZexFVSkKl3AWYpWZ5UDzuJIHBGqXITCU64uLZPRXPrckQ0b8C7JPs25EnvTb6vPo1jB+RgGxTnmyHn/CYZ+8dS2ZCudRNyzBRk0u2QY3I1kupLrcd/0e9VC/CfTpD4PAblTi4U51eg1IliovQkKUNVkbyroBq66UK+HGI+nrUquVqPnxyzYhFKN5SDEex5rjifBXEMLJFJph7IsVaUvpovFscjUbS5yGlbj4py01Hk4Rsal09aDuVeX4DgZ1nKrDXXAzlkXkRoj4MRJLtJhxDiI8ha5vbfyL40crhtm/l70zW/bfJ5IUTSdyCwfEV90qYJuS5H+afFOjIL4mRIK63kZhvfDEXeFk/uTSBR4DP22RAE/3+bWjnSlYBPEKLhAGTo5ChhOQBF3IvSrXsTvAdxPj9ygqySc9x+aizj85FE1Luq9b45H3VrxoIoMHUesGAzkC0dx53jXTgm9IlXyMDVhbgguiTn9c6GgxG0fVBVcz5ZPzujCiq7pn2IUJzPkpGDKvoj5QfaASGdivM5ENz/yrq/y8LJgByObVA6zpKoyt6ouNeLjPoECWk1teopBZH05oi4syBY3JJMJP11a0N/oE9yfjnwXHxemzqC3dbjvzumxdSJgYj4pReCfx7o7i+a2WXIyznEmwk828yGAWPcfUETl8SNqEzc7RWL1trqWgFljjHbCaUA3IuQMfsjRXigu79dwAkzyTU/yqmbC0Gx30CGwrfu3iee2RJFSTZx5fvnkCuF/M+FHB1zo5JrN7j7BzHnv0Qbz2tlw8WbazOzNsgx+qG7HxvXzgf+5e77Vyrcz2xm1gf40vPDoAcB77n763Wwz8XQnP8aONHd/5lRpjYujpEGlIP+GfCCu99lZtchVM+VSMnq5u5b5ZYr6adFkTL3PYKxPxYpMYNdec055CrW1m6IiOxzFK28z90/jXTDwcjoecjd7ytzrYhx2xitq1e5+3Vx/XqUivm0u79kZpshWPT6wDdVrl11Y3oqMqIP8nwcKdNMS/fnSOdbC5W8PcXFOZBDhgWRMwt3vzSuFbnyxb45AqEI7kd75qMlz/s1UepNJ+Bmdz81uZfOr2OQM3eEZ4DVT0XWgidm7Tged/cL494aKE3owVw6hZkthdIQ9onzzRFp+pYeKapmdibaI0eVLU98X7HeG0JAXo/WtXWQc6ZY124Cbnf3CzLINBNy2t4fly5BaIG+yDH0rJltitJfVnb3B8qWqQkZ90VcTh/Hpb3d/S9mdg0KCDWgNL7WtfUXatOUoyEW74K1/hOk5LVF5eBeN7Or4/MT1UnZuJnZUKRcvQWMdPc7KhaptSWtzmBeDCmaWyHoZQ9kJB9gZiej2urzIAK60vPtEhm7IGdHQZ7WBTlBTkPpQSujTTBLHlliOAxGzoWP3P0OM1sHwUGfAD4FlkJRgGbh+KuymdkciKvi3VAO1kMb7V5xP0sO57TWzOxw5FText2/rbu3OHK4ZXV+xHcbUqZeRnDQuVF5s8PN7HLEk7KLu48rni9TIY6c117AaBe3QRsPHo1wLKwW929192vT31GyXMVaMYu7f2xmnRDabw1k0F9oInzr5hk4ZRK5uoYMg4E/IkV4YwTHngPpFbcDp3szIautMwbHoHLZWQzn5t7CITkE5fR73fz/NZr/17j7+xlkWQQFla4jShanxmedzrEtsAkiOvxjWQEMM1sdcYPtiSLK41BlieuSZ9L5NbMH/0eVLXL7V0ZlPz9w930SnoSse6aZ3Qa87DW+tRMRh8sDwIrISTo67uVygBiqXLeou4+KtXQiWrseRk7mL9x967JlCXnmROm7htC3v3X3J8zsMOTw28XdJ0Vw7EMXOWXZMs3kNS6UDRBv0uCQaTdE8nuwu79jIpp+24P3qbX9Qs2bAazi5x5o4v4BmDPOu6GX/F4SaGhzO5BiVWk5pdajyXGZB23yBQx6MHBZcn9ppCwsFudZag7HfEnhgrMiSPH1KBVhPuQI2QU5ILKXIkVe86fRQn0PcHRcXxNF79+kovKazeUAZkMlpH40b5BickN87o8g7i0eCg0/Kt/aE3FaFGW4snPuJLL0QfDUNiHXVYXM8U5eTq382j7J35Vd8rYBRWjGAAdRy0NP8/tnBQYio3p/YKWM/bYG4hY4hiijF+vuWYjb5TUypaLVydU13rs7kQFRXJ8HcVmUzhPxE7I1OWfSMW3JBzXIeEO8j0Njbzy06Lu6+Z+FYyPm1P3F/h3v3Ysk/B+p/PF5HxSM6lySTB0Rb9n6ybURRAnLqcmVcSynjBc1rpa0f9oBs0cfnYaqiPyI3yjDXOuBkFg7JPc2iLVs//rfU7Y88XldxCXzO2qp4zNFPx0JHJljbBFnWOf43Bul0z4C7JY8c0i8C31z9BXal+eI/lk9rv0K7dU7IGfMdMim/GOV6/3/+tFsS3aYKkj0AR72GovsJ8jgGmRm97ng2echeGOzjZp6VL9oqdDxZtymR0ruDGb2OSIE+7WZre/uN7n7k2b2FVo4X0D1pnO0fwJnmtknrujjWGRsXYHSOg5CbL3nZpKnUYvo/OYoXWJ5BBVf1MxOcXn7x5vZb9393ZY6503l+K5ExG0rB9rqcXe/OR5pg0p19UXwwl08UzpOc27u7ma2EnKkXe1iFO+MlKb9vSLUR0RFD0XIii/MrD1REs7dnwTeMrMPEWrgQXc/Lf6u9Pnviu6NQ+RfBwK7mtk5niAbXGXFPoyITldE1lpaq0M9nYz26G2ADc1sDnc/zMzeRs7dvVwlVcuUp2DPL+Qyd/+nmd2K+EdWM7NN3f0ad38b8Uhka4lcnRFK57v0HlLIK6n20hxbsg7MGfvMH1Algg2QkXOMCzlWzP/vpvqf/bJtMqoecVfMuQ/N7EGEZmskfxGRd/fTzOwSV4nQX7y5yiRfjVKW0rYSQjk0kqsMGabWknm/DnKWdjKz89x9QiLTd8D7wAZhF7RB5V6ztKRPPgTGo7Wii7v/zt3Hps/mQFl4DXWynrvfbEpX2ALoZWbPu/snZrZvuu+UKZcpNbQf0gE/R3vglehdmM/Mhrj7Xe5+vKnSxXTJbylzHLu5+99MqUBjzGxPd78n1tMlgGvd/SszuxkF95oFcu1/sTX8+0fyt5gIO6EI0UlmdqqZdXT3z1D0YRXgCDPbE+W4Hevur1cn8c9rLdHgaq4tFt6XkAPhRhRV+BQxe69pZvuZ8vKWBv4E+TZhV+pPf+BcM/sYOMvdz3X3zxHk8W1U17pTvCvZWkDLBiCjaxbk9NgQlQDdKBx/uOpzt8g5b2a/QqW2TnP37RDK4xM0rzaIx95GkNALUErVHyoRtnm2b5DBcEwoCScDc5pZ99zzvWju/iJKkxhpqqP+Aqq9fbgpNQHEEVRvUJSZljA6FDfCSdUPVeVYDDjAzNp5jUuiUOo/dvc33f1fJclUlLD0kG02lJLQHa0bewEDzOw4d3/U3Y93pV2VNq7hJLrflL7h0R/F93VGiKx7gHVMZfSyt8TYugq428yGmtlsxT1ghirkaq7NzBpMHEBvm9lQV3reo4h8dYCZHRnzPYtjxlTWE5SjP97dv08cx/9AEXnMbCFTGePC2VDwNnxagkxLmNlgq/HcpM7FtxB/Ema2jSn1KntLnJGHoFToycCRxTpStGINc/dX3P2lstav9LuSc4u59BUi4L4eGGhm55vZPKbU1uL3lKYjFnLE58HI8bKLu/8euAURn/YJ51qjfadMueId+xDt2acCT7j7yygw9imwSqxtuPvR7v5wWbIUzcRtdmzYjRchNN25ZrZq9M0kYCsT3806wB6eid+sJbZm6WiIiXAX8DgqgzUfcjgchdIkzkZ5SIugnNTSJ25r+99psWlMNrMFUBRkJHJebYrgXlcDqyNSm8MiYpm1xXeuSOK9D+fIG2gBP8bdv8xpyJtqVR8HPBVydEMkQ39B5Jk3AOfnkqcZtyWBx9z90phrLwKXIUN1uXjmSwRpPcrdx1ckZ7NoifLU24QEeQFBoX+DSFBPQs6slapwXCVG8K2ISK1XnF+K8jvHmtlYFIk+I6NoyyJOAcxsawThHYqcfrMD+4dTZDKU7/SLsTvHzK42syXC0BqPnGo7oLSJCQh5sYKJqJKyZYv37zXgElPu+eRY/wciHWN6FMC4ETkcsjczWwKVKz4WraPDkWOyrZnNAkwws9nqjaCW1FJnVIzhawglc6WZrRb74R0IETgXKjuYQ65FgavM7CKE5htgQqYUrUs81xe9D7Mlv6Ms4sd1UdnWHRB5+iQz65PMn3eB90zEfCNRFYeq2qwoYNEbQdu3dKGxpoxfriBPGOmTw6af3cxmjzGa4hBy8QpsilDUWwCbFA7fMptHi9MH0H7Uy8x2dfcxyAkyBqV4ZGnJO/kacio8ilDBs7uQYRcgwuYBhYMtR3P3N4EjgGXMbJC7Xxnn54Xj7Xqkq3ZD+9Kfc8nWIps3g/yNqR0oKnh4fC7SIx5F0ecseT6tx//mgbyYjwK947wfcmLtFucNQIf4XGVps2VQRGSXCr57VqB/fF4cec1PSu73R/lvJ6BqEytVPa7N4UC58NfG53bF/EFM8X+iVlJvhvi3xa9fiFn8aVSK8VJUPai4Ny/iI3mYCksDI8fQaSgfdu7k+gIk5cMov6TZMtTycc+L9eFRajwW0yFehEuBtTL1zaLI+b8tCgRMAOaIe11jL18bOdpuIhOvDLX86oGohODTKIe5A6ootE7ybFXl/OZF6M0bkmvF+7BgnM9ehWzN5UjHBiHEtgZ6xPlwVPllbWT43QTMlkmunqgS1M7IUB4Vc397okw2qk5wC/AQsHYGmZYBXiJ0m7h2FHKiLR7nSyH0wLNk4p5qQs6VkSNkH2Q43wPME/fWRUZzxwrkakAO5FOA9/hpfo22OWUELiZ0QeT82BClKWwX10ovkZrIUug16d63IEIFnxDnXULGLOVlEUKtffJ5vxjL5eLalijgU3A2VMb51JKOygVoUqiactAPQV6WRFGu7VCk+RhgqarlbD2mzSPm1SSC/AWlAHRGyJnHUGSp2SxAiVKwbcbvbBPK0oJIKW+PovJ3Io95scksj6DRg6rup+ZyIOj6O8AKcd6WGsnVWakC2HpMcWI9giJ9+wLPI1TRBnXPXUIQAVcgY7EndUFG4anI4OlQ91zZRGDnIYP+RsSUTSjkL9c9145MBIvIkfAQjYl0T6cxIebmCKX4JLBh5rHbLb57KIr6TUTOhumLMSt73JqQqVg/u8f6eghydKeEfZcU70Bz2o+qPBCj/WMIZv8G4UhDsO2bkbGajVgUGaNH1l3bELiDMFCRU2Qy4TzNsEasTs3Q65RcPzbW1g4oFed6qnMyzI+4p+ZGusZEFP1ui9ClLwNDK5LtckSYawj5sUVyz9J/M8jSpu68cEBunVy7CBGQrpxcyyXfMFS+8lTgzLjWO9b/cfGO9skkS3vk6F4HOJga2faB8T4uH+fbI+6szrnX/ZZ6VC7ATwqniOo9CMmwc3J9uqplaz2m3QNYAcGL1wMOQB7PCQgSvRQRxW9OByJGzcqKGwt3d2TILI+Ml8uBMxDEsXWRbtxfadRtR0RgNSC51h9FmppthZwK+qwjMvr6hoL5THw+DTFBD4/n+oXyOU8GmZpk9qdWzWH6UERPRFVpepAo9CXKdQVCC1gonFdSc2ZdHwrfj97Jst9TBD/dLZTLzePacQgmfk8ofgvF9ZlyyFQn3yXApsn5VUhZnyWXDE2NB3J8TECO7llCIR4TSvIySEnvV4WMzfFAqJT7Ys3YHSFU7iOcM7FfZY2Ao6DXtvE5Ner3QgisjshZv2Q69iXJUiAwNwUeSa63TT7fDiwdn7tVNI5zR99cUfRZzP974l29FxiWUZ7CidwWOT12QcHNe4FRca8XSeQ+k1xFNbQGlE5VoEtXj7V++zg/F9ipgnFcATmuFkF69IfUjPtZEFpl1cwyLYZS9d4kcTjG2norERAjY9WS1sObJ0dD0VxM2YcieNctMCVPvdlWmGhtza8lOeCDIk/yrwjyPxpB4w5CCt8C7v6Uuz9ekahTbe7+jLu/kuO7EoKqb5Fz4VMET10SbR5dUN+zHw/ZAAAgAElEQVTNlUOe5twib3pwkA4VRHO4+wUoJ3CsmZ1nZqejKP3B7v58lTI3hxa55/MihamTuz+NIiHHxufXUSmsotLLa8AqrrzPMuVq40GgaGYHmNkQM1sexH4e+8/nyBEyGkXy+1Ey35GZzYVQAS+42q2IF2XhkG046qt36v/WQ7Mqq7lImq9HKLEBZnYJinT9Nq6tCjxsZnN71DMvS6apcBi8S5DxRdsdrV1nVMF5EOvEQOSwPcJF0PkxSsn5M7ARsCewo7tPbKm8DCknA4C7P4jegbWRc2FhhIQ6z8xWB75z968zi/k5sH2sG1+aWYe4fiPwF8Tb8rq7l8qBEL//yJhXtyGSzD1jvfo+kesrZFAX723WFmvAOyhgMT8iL+wQ838tRAK/qbvfnkmeKZwMKGd/dRQQuA84z91PjkePQ+lo2VrsQ4bG861CL3VxROwFHGVmDwBd3P38+D1ZyJLjewyVB54H6YfLAbOZ2dhY0y509wlly1TH3fICcmC9Dywe/Da4+0kovXBvE3FraWSire3HbVrYwJ5BCudAy1A6prX9b7WCgdfM1kD5bZ1crOtHAcu6+1XAD8BWyKBu8S36q7+ZrYg800cib/U2yGO8MyLF61adlM2mbYr6Y8VwNqRM4ucj5ekOFEHd3N3H5VIGmmNL+uZ7FwHTdcAIU7nINsBpZrYJ6tPfu/uz8Q7/w93/WrZ8XqvScCdK5RiCFPjZ4/7kIPj8wd2/dpFw3RjOhzLleg85NI42s23ick/0XhbPbIYqYWRvLsbu25DjaEngAne/z90PcPe9gF5eK1NdphxF6bd1wrE8HyLF29HMNgiCuRUR3PiQ3PpEkMw1IKfaGOAlM9vJzJ4AjkaIlWuRM7wwCFuczhPvmMfngTGW3d39fYSyezkefRZFx58v26FWL198PBPx7pxgIl79Jq73RM6tmVJHURkymtnayKk3EXgj1qJbkBNyr/jeb8xsw5Cr9PdwKnLOBhxoZvu4SnNfjQKJS5pKgX4TwY2Pc8mUGPMjgI9dZKJ7Ah8A/cxsfTO7EfhXBA9Kb3WOxWXQGn+KmfUzs7PN7EjkkOwN7OnuW8TflVpKOQnYdUQomQeRc3tjlKrzBuLaWNBUsQ0ovfJSuk6saGbzoADPtghVOjzurQGcg9BHX+VcK1pbDcLXrJuZ9UMT+5GqZWlt00Yzs7mBv4U3fw4EGdzV3R8zsz7AjGjD7YS864e5+y3VSVx9KxbtiOBeiQzklYAL3f1UMzsQRSEuBJ5syYt13QZ3PIL/Xw/8MYl8tzgD4ec0MxuAEAFbIVhxX+AKd3/TzI5GiJn7ImpfhXzrI/KqU8xsIjKaLzCzGbyEMnT/oWzLIJ6B7xEHwpVxvZ2r3nzxXKnzr27+p59nRClpfYBnXKXXsijBiQxbIYLam5FhNRJB2A9H1V56ARt5JoRYKl/y7zJoT3o2/n0GlXHdAvg7qnhkiHy3VCdWc25mdgBCx/wFrbGHIbLTI1B0fh5gszByypZlZnf/e901QxDyrRAp7GlItxiN3s9SdQpTic/rEfrliWR+zYBSc9ZHe/bjKPVk67LRFXXyNVonECJlGeAVdz/HzPZACJ6D3P2xjHJNWR9NFTpORySih7j7V2Y2E5pj/wCIwFSOdbVA1BkK8nREc6k3SiX8CvFrTHD3scnflbq+Jt+zDuLKc2C8u19oZmNQqsKfkcNmpLu/nkGWdG7tgNb3F5Cz+xzUf6cjdORwlIb2WtlytbYft2nC0dDaWtt/2szscOCWYlM1Qdc/R9GQHihadA3K25rJ3V/KtVg352ZmKyBo6u3u/odw2DyMUA03IgX4Bled5BbfzGxlxGw8O/ANYvf+Q0SQWvx8aqqF4/hGFFU2xD7+kLsfHPfbhbMml/LUSHk0s/UQeqcbcLO7j4koziZkQC/8u2ZmiyOlcxd3v7ZQTiuQY0XEW3FvneLeHZHz9UMO3L9llGkjpJSf7+5vm9kWCI69v7s/GQZEO3f/IJdMiWxDkEF6J3KCdAZ+cPePIxJ3MzKaXzFBfr3esG1JzcyWRGWc1zGzYxCp3FrhzOqB1o3xORxGpvKFDwAPu/uBcW2K8wgZf/siFFQbtEfemcHBthBKv9nKlILwTfEuhlztUT99CvzVVYo6a4vARU93vyRk2gihip5x94vNbB/Ur09UINt67n6zmW2LnHwHInTMt/Vjl8t5a0I03IF4No6K9b6TR+qEqZTyQ+5+almyTEW+FZEzdGPkvB3o7n3MbNW41hMYkzpAMsk1HM3xkYgvYh3kjPwtQu33Bv7k7m/llKu1Jc2bAVFE69F6/NIHWmB+haJFbZDxfCKwWtzfCSmjlcvanA7EHvwZsF5ybVXgovicvdxUcz0QqdVEYNE4PwKl5wwmiANbj0b9tSi10lxjEAv6YojnYDKCgeaWqSDcMoIdGyEqxgM3Jc/dAvyu6j5M5Fka+AQ4oEIZtkQVAH5EqogcuqWTp1ELlhSEbrejyNoicd4JGRCTSEqmZuyjQq4+yFAdjRBhJxGVs5Dh9Uqx5tJCK0yQELQh9EJ3FJEcE+NalK1bvYo+QsiAZ0iqTBTzr6kxzyTTsiFT0TdTKqig1ImBTcmYQa6UGHkIIjbdMs7boMDF00Q58ZxyJf0zGFXSKvakvVD6V3+mQgicScbxwOjkfLb4dw6UanhpRXKtFu/eBogbZb643iX+7Vw/9mXOrRjLjtQ4LIr7/VDA50wyE6i3Hk0f0wJHQ2trbT+7hcccl+f5IwQ1uwq4w90Pcvd7zGw5xJKe1fPaHFvRX2a2rJn1c/f9EPPzvmY2azzWGehhItH5thpJm2X7ApEKTQ/g7kcjT/qpwKrWQkncilbMrfi8LDIctjPlmF6HolqO4JYXI6M1a/MaJ8N44EIzuxg5P84FPjGzB83sVuAjd983t3xTa+7+JOL/WCDXd6bjGe0apHDOG/fbJPJ95O7vli2Ph2aJDFPcfRgy6I8PpMeXCClwHMqlz9KKtdMVWe6FCMpOd/fRaG59CWwYUftJKK3v5vhNLS7lyszaAcPNbBcz2xwZop3Q2toHMex/a2Y7orSYmTPL18bd30RG1vBYwyjmX927kQ3F5ko3mAScaWbThzzFe7g6MIjMXGxJZH41Mxvi7nehSg4jzWwbF/rqXvQ+PpBTNo8Wpw8gRGsvM9vVxbdzN3Js9cglU90+OQNyIP/ezHYzswuA50w8HLMAD7r7NvFslnE1s1XMbD/kaDsWkelu4O5vmdlaiFS3K1rTSPq3DFnSNb+Li/x1W+BVM7s8vn8iKmf8VyL1pbVV21pTJ1rb/0xLNrgFUHTkmVjEL0alUtdDxs5pKCf8llZ4O5jZMBQ52t7d/xjXzkSKyvUoj/4ad7+pOimrb8n8KpiMPw5I77vA3bHxroIgfHu7+6s/9f+1lGZmPZHzZSdEbHUOypscDLzk7vuZiDSzMcan6QamvNO+7j7azH6HnGm3A0+i6Na37v5QPNssuTcyppmsiiJGd7tSEUYiUt2Nyv7un5BpZ7S2vws85e7nmtnVqGLOpu7+fWZ5DKVHvOfu24XxcDsw2d1XiGf6oFSc9igvPHe1hGbXIuXmaZTWuICrisNmyCE5Aypbtxawsbu/mEmmYs2fGSEVPjJVy7kVpVGNziHHv5GtN6oG1RnYw92/CDj5QQhFkD3NMXSK3yG0wP1xbWXE/XQLgrrv7O73VSDbxcDEWCcMOY82AO51pXKs7qrskEOWH6W9hRNrCEpZvRKR667o7tsnz2TZh0KXPgalwL1pZreg93MrhKY5BXEyZKkSksi1K9qH/oLKor6B9Ip/uPt28UxWnaK1Tb21Ohpa2/9UCw/r75BX+G13Hx7RkrOA+RBBUjd3/6TVyQChNN2ISKJeNJX/7O7ud5nZUUgZ3tTdJzW1Kba0Zsrf3wXxMYxHiIaVkMHwLiIu28fFyNwim5nNiaJq7yIn1VPAye5+bNxvCwxAyspWQA8Xm3wu+Yoc5ga0VrRBRuEJZtYFMaF3RnnWf6j/u1xyNrdmKp23GYKrfgHMiSCq56K0l6cqkGldFGXbCkHFB6Nc9KPN7H60B2zzU/9HSXLNgNaHp9x9r5hXlwFfu6qDYGJm/6e3YIKyxFg2hFI4G0VOb3D3E+OZxdA6MhMi2y2daK5OtvUQAtKRw/33Ju6iccBdHtwyGeQZgFI/R9fJ1walpe2LjNJXgIWAHdz9uRyyJTI2IG6bW4FDXTxPyyOEwP1o7VgF5cw/nEmmRnpLoAOOAk5z98vi2kVo3h1TOD/K1g+T8WtAzpfngcUR30EHd/9HItvf3X1UWbI0IVsDStW4HiGDdy72aDO7BvGdzQGc6arUUbY8U4hYzWxLVJVqS4SMuSKCBN0Ryu5Vd9+1Vb9vPq3V0dDapvlWt+HuhRjrnzWzCcD77r5lGDcXI/hqdoW4ubbos98hiOqniJH9e+BRdz/OzM5BnuNVi42vpbZwwpyJnFXHAL3dfZCZ/Ropdn2BB6qI0jSnZmYHIcXpVVfVl7NQGdAeXkekaGYL5TS0UuXDzM5G8OxXgF2RsTzOlCL0W/QOXJ5LtubcIrI1Bhkvf4trhyLyu+2QI+noDHLUE7RtCczhqhAyHXI2HIQiux+b2Tzu/nbZctXJWDDHd0NQ7InuvmecnwdM5+7r5pSpOba6d3EOFI382szmQiU+H3D3w0wkdO95hsoSTci4CkrVGIYqXmyM5vppZtYDkfatm2MNi6DAA6gKVOG0rSeyXQIFWb519w+b+G/Kkq3QwTrGGP4WRb5nR7n0M6NKCSfkNACTd7EBpeud4u6Pm9nqaFzPdveLzOxc4GlXSeqszZSa90eUyvskKoN9RzgrL0FlN3eIZ8t2frT1BAFmqsB0AOqrCe7+VVxvQKkLn5UlSyJDD5RKNRqlQxyEHAwLIgfzmi7y6C4o4DOdl5y219r+w+bNgCii9Wg9/tsDKQLjEIxq3bhWQFjHVi1fczmoORcXRbDwOVGE4SwEZ+yMSgGdmvzNGGQkVi5/lf2G2Iv3QSiPRxC8l5beN1PprzmRsTB/nJ+L0iW6xXmH+LcgyyudsCz9DqS4XI/KJoOgs5NQ7im0knmm/farGMu7gZnr7nVAiJ5XgV65xi+5tnrMq97JtXGIEb3KPmsX/3ZDpQXHxPkMqIxen6rHtcK+MRq/iyNjn56IIqcgh/ckZMg/mWuNjbn+22SfHA4sj5jsH0YO09cJsr5iHcsgV0Fa2wOV8Duyrj+zraNNjWf8O6zQtYA1UYWqFeN8I8SV0iG3jNE/tyMnQ3p9AELdPQBcWf97MoxlQ/THKERAfC+wX9zrGf+ulPxdaSSjwKzJ56EoJWJX5CgagtAoQ9P5nmscgV8jzoWCPHfrWBsmJM+MBPbKOa9aj/9gDKsWoPVoPf7bA0WTxyJI+xgUNRqU3H8gVURb+oHymZ9ACI9rgeWSeyugPNk1q5azuRzImDkXMXiPj74rDOh1kQE2U5mKQHM/ECKmY921S4HLCSMBcaO8T8IuX6G8+wN/ADZEERDi8wfA0slz2RX35nDU/+4wFO5AEd20OkChNJ+friMly7YzIlfcMBThbYH7gDVCvsfJUPGiqf4imNfr+qYbMlLPT6+3Hg7ibbk/Pl+GUtEKY2sWVDp4wcwyLYkITgsn5PTA1cDycX4uikBnq6oSnwvn1XzI2TC66vFLZFsjZFq9iXsDUVrAGhnlaUg+90PIgFnj89nI0TxXzLHFm+rvkuRKqxydBCyHSJDfBzZJnruSxk6G0uRCDo+ngTNQOsQjwOGolOXT8S4Mi3V1WEXz62Ckd02PAj43IX1/DhT4eYaSHd2tx///aEtra23TWItcrB5oEVwIRbAucpH7LIy86ZsEDOxed1+pOmmbVzPVa98ZsVEPR6kmrwaPxQLAHsDR7j6+pea4WWOiwIXQhnawq779BsA7QN9ImTgOOMjdP6lO4mpb5CvfCrwScPoP3f2f7r5NQFJPMrMD3X0fM2tPpJhUJOsoFJU5xsy+RdGaz8zsQXe/0czedlV0AMpl0G6uLYFBr4wU8y+A3wNfo4jud2Z2n7t/5oIl90SVOj4uSZ4p8PCAsm+C5tswtP7fhRjPd0VVhnb0TNDZoq+ivwYAK5nZiYj48YdYSz4zszWBe82sl7u/lEO25taCh2FJ5IDsHe/WR8A2ZrYPSmMaAtxuZp3d/RhEIptLvgI2/gZyenQzs6Hu/rmZvQ9sG/DsOVFZ2WxVVUykmHOa2fPufreZrQGMN7Mfop8qawGj7490h5fMbCO0Z54OvIRy6Q/2DLn8IU+RLmHIkfA34AfkKP0jShGdDejv7mOJdSuHvhNytQM2R2vEo2a2N0o1NDObH5Vh/9rdH0j+rjS5XHxFQ5HjfSBwgrtfiwTaCaGx1jFxL/29LDnSZmYzofSfItVyDHJ49HH3B83sWuRwuByRN2/dUtfVaaG1cjS0tmmqxaZ2JNrsz3f3J0xlbRZGHuAvwzgcjsh9RgGfegslcTOVvPo82XznQlGijxAcdCt3f8NUfvB1tKf9vQU7GbojUq3RqFTlcYj1fAt3f97MpkdK1DxAV+BaVz5li+wvmELu+ADqj/cQ+eNL7n5V3D8Fpers4e5/jmu5qiRMyTk1sw4InbI28KK7nx5K3uII1ntn8mxLJ35cDfgNUuQWRu9Af6SIboNIt26KNaU9MH0ZzrY6Y+vXiOn8/XCErooM009Q3vpH9TnGZTYzmw2h57ZzkQtvAczjykNPnSNtXVwlLZ5MF8DMrkd789LhoJkVuAgZ73+K/XwhYIhnyAEPmQrn2mBUbnc7hPZrh1AyvdC8XxE4wt1vyyFXyLY7sAUyts6N7x8TDt5HgTPc/aRc8oRM9Vwpe6F++ztKAeiKnJTrIjvj8xxrfjKODcix8Ii7H2VmiwOd3P3xeG4s8JC7Z3FkhRPyC+BZlH55GLC7u18T9wch3qdXgC/dfe/095QsW7E+zYhQA6+6ygQX+tDpwG7u/mmZciTyFOllT6FynuPi+vHAfF4j022LnJOTvY77qbU1r9bqaGht00yrUzrHIC/12e7+mpldgQiHNnD3ryLK9p27v1WhyJU2M1sEcS+8BfwZERl+gQyIwcAId386IpdnAOt5C2ZAhymkZNMhL/nnCF55AMpBvzacMkXVguk8yJFaakscWDsB3yHlYGbgKmS8X4OiSCcA17n70xXI2AAs4aqc0gmlB20IPOfuZ5nZ4ahSwUW5ZWsurQnD4TjgHXc/N86PQdDxVUzlJB919+cyOox2Qe/hR8hgWCKuD0Yoi1dR5O27smWpk+s6NN83REiLmd39+CaeM2iZCBkAM1sOoV6edfeJZnY+MkT7hGF4Nkpb+gRYGpXTe6cCGXdALPb3x7WbEUpmWxfJ4azu/mGZ895UjeQ5F8FdL4Tq2ARFwbdH+9Ld7n58BA46uPubZcgyFfkKY34N5Hz8AeX0LwZ85O5/CbmuRxWrspKxhozjgSe8VqFjNnf/IPb304GvPFM1GhOqbwmUHvGNu29mZheiYNlaiUOykT6R09mdOBtmRikw1yHegyXRHj7MM5brNpEPD0DojksQR8T9iMvl9+5+ZS5ZWtt/31odDa1tmmumMmujUJThZeBAV2nGyxC8akirAWi9UN70JcBkoA8izxlnKte1Nqou8QbKcT4gZ5SmObeANh6PWI23RTmxIxBx1FgP9vOWjGKob2bWDykng0LRfAvlerZHhtgqOfvKzPYDnkO5+ysjHo3BrnJrXYD1ETJlrLv/JpdczbGZWUdEPvZcOCe/RfP+B1fZMEPjeD6wS+611cxWQMRy27v7P83sYeBf7j407g8EXvG8LPtpetXvEUT7bjTXJyF49vfI2f14LrmaYzOzIci5/Xxces7dTzSzq4BF3L2vqbLEWggxs4O7v1iBnAcjBNsAd380uX43Gsc1cxh/pooNJ7v7B3HeHe3fB7n7yqZKAFejd/GSMmX5CRnXAI5G6L9TgRfcfbu4twlwCOKQuCmTPGkQagYUODkMpdH2QejNHVDAZSV3PyOeLXU8w4HWHTlDu6G0oZtdlS7Go8DPFuFUSlFQWUpr1l0rnA2zIALW7xFXxCPufldZsvwbOXsifqB+iDjzL8Dn7j6yCnla2/+vNVQtQGtrbf9JM7MF0SayL7AM8Cawu5kt6O5bI+j2YhWKWHkzQZlvBT5x9wtRfvVfiH5x95uRkvAoMip2cffbiqhbS2zpb4+o6MnIiXUmmmOXIDjvxmGYtdjoZFPN3SciJf0UM3sDRZe3cPfhwMgK+qojisgMdPcJKE94nJkNcvd/IVLPd1F0EGg8B1pY64G4Bc4BbgP+gWDju5vZiBi7pVBUrnvZ/VT8/6Y2K7AZSt/oDeDuA4AOZvZonD+Y2clggeLpGt+/LVojTkPO70HIUbMP0KLXiEDLXY9IAjdH0dHlzGzGOH/NzB5EEOlRwGq5nAzJPJsRwN1PQGvYOSYuI+L66sAR8blMo7QhvmN/YB4z+4OZtXf3j1BVgn/Fo9+inP4JZcnyM1p/tKbOHPIcCVP6tA0KXNyUY00Np9+U98wF8X8dOWPmQ6jOUQix+XxGJ8O8qCLPHe7+gyu17EFEaIi7r4nQk3fWy5LDyWBmg81seVOJc8LJ0NbdP0br/Q/AXVU5GUKmV4GT3H09hJZcAhgRwYLWNo20VkRDa5ummpnNh4y+zdz9r6ba5OPQoriPuz//U3/fUpqZLYOMqdHufrYJHj4CRZU+A84B/uSZ8u6mhWYibFsVGalHIoVgO2BuZDTMh0iaWkmHmmhm1hcxel/p7mdYBTnpdZHmfVBE6zh3f8CUQ38OsBuKkjzr7lMU5JbsODKzwxDT+BlFtMgEJT8fsaIvh5Bj4zPK1C4ifXMjx/JnwHgPsk4zux05SbNBsxMlfRiaQ+8Al7j7W6Z0kxWQUf2NmXVy9y9zydYcm5ktgRjht/IaZ8vdKDViYpzfgap1rJj7PTSztdG++DeEwBqPCJKHIIK5LCkJdRH5jVGVkgsQAesmqGLCKQg50x3Y2CtIc4w1oQHN/V8BM6KSpG+YiJK7uPulGeVJORluQfrN4ohXo4O7/yOeuwj4eziz/q+9M4+7fazX//va2EpSqEyliQahDCfzUBkajHUaTNtMprArw9FkSkqmRESKNJF5m8vRSU6IJhElU0imIhHt6/fHda+9V8+vOtFe3/XsvT7v18vLftaz1l6fvYb7e9+f4bo6Q9KaZF09kSQZvgUc5Sa22O4z2R1pRfQ959uI5sf27hOdbL/rrbud6d38M8Z8NxYA6HX7FDMHlWgoxi2Kg8QywJVu85otsXAQ2RR815m724R46+5m+1dDC3icIWl54FIy27YAEYFckPiCL0cEfjqbuxvPSHoDESQ7iMxbz0F0Be4mLZgvJJvloV94xxtjNgKfI4J4Gw4hjp5eRH+yYVsy23xgSzZsRA7Ntr3P2PhHiTHv2yLE9vYl5PB8RltbFyeODnM5WjiDnE2f5sigjL6sDtxCNumPkmTfn0iV7ap/+BcNGEWE8ghS0T2ZvF5H2r5cGd9bnHQ1TK314m+uQ5NJBXx14F3u09OQtIjt33Yc13+Qw/w7SALySfKePgHsT1xNVrH9eIcxrU20SDZwNCHOIqOPm5PE9+rAj93G97pEcUT4AllPFyL7igMdUd1VyfXz/W76Fh3Hdh7RAjqTtP1v6og0P58Upu63vV27byeilJCLjKIj83GyBzvJbVSvdav8pf8xHcQ1gSSrppDC3JXte7AAcGP/52o8XRfHUyzF06cSDcW4pC3Uh5HqwulESOe/2sX3HWTG+gngJnLh+5DtK4cV73hF0jJkTv0I2wf0VQBe0FrkRh5FkfpDwK2292+37U9m+leX9GJScfvlMOMcNn2fnSVJe+yNvY2Sps93LkgqJR+2fXuHsfWefwI5BE4l7bMnk7ViK6Z3NvQfsEfSXaK/fZYkF37fNuYbAG8l6uMPk4TkwR6gLkNb60WquDeQLooDSXfMa4C1yXXgYZL0u4vMhT/RcfV7NqJVcRBx43gR0XK5goxMfNKxXnudh6AxMJ5ph5lLiAPUy9ttc9BsQDuKYazg6UbkgPVLIjq3SetMeYntO5VxzM4KF5JeT8Yh9rd9TN/t3ybz/W/tMnGltKc/3qrbLyOdo990EztVxmJOJI5DryduGJ10PPUllSeQosDuJFl0Nul6OlzSq2zfLGnNXtV+0Ou9pAVt39v+3H+deSPpSDm6xddZ8mpMfL1uhaPI/nkB0pUyN9GOOHoYcRWzNpVoKMYtknqzkVuSpMMdZLN5CNnYLUVEYs71EOfIxjuKivWFxB/5iHbbSGeI+w5acxIB0UPJ+M1HbN/Y7nMRyfrfNLxIxwd9r9daZOb1EfKZOst9ThKKq8P87lgxvj33bOQw8xOSgFyM6A9sSSqCO5FK10gnjHq0pMInyGFhY+Ik8TFNHyHaEJjsaLoMMo6ei8tzyEFhHqLxcVrrYNuCiLl9kHje/9XdazL0H1AnkoPf14jL0SOSbiIV1X1td+I1P7OhjFFcQWxuO1WNV3R13mr77HagfylJWB0JPJ+o6t+ltP+/DfjAIJNrLaa/J8h3GBEOXKl/DVVctfbtal1VRGFPIkmYCba3UmxHFwPe3HttWhIe4FldJWX6kgwiCaKzSXL5ZcDubmMJkk4DvtiXZBi0wOJmZK3anVhEekyy4U1kP3sWcJy7d8h5NVmz1iQJ5DWINsp3JW1BEsyTukr8FaNDiUEW4xbbl5CN5aa2NyZiMHuRlr3VgZ/Z3sX2xb1WteL/x/aPiMvEAZIWrSTDtEPzOsBniTXeAaQte31FJGkpIkA3ctXuv0d7vZYj+gbrEoX4uYGN2u29+z3WZZJB0kGSvtR+fB6pzE+2fQKpIN1NLNZOJEmkSjIAis3bpmRc4kFS1XqtpM/YnmJ7T2D1djAb2NravuPoDsYAAB2XSURBVIu975hJG/tDZC4d238gSu2Xkk6CB4aRZGhrwqcVRf0lyOz8/MCySkv5LeTwUEmGf4Dtn5LulFMlbd3x0z9FPt83km6UnxOB5BtIpX4xSSuTxNu5XSYZJG0gaRtlfG9/Upk/o3URAGB78w6TDK8CvkpGDg4DntPWhS2AB4BTWjcKtu9q/3XW+dGSDHOQcdmpzhjV7qQrSpJeodjO/tV9+gOD3vO05NnNLZZX9Z6zt346IyWfIt2RnSYZ2vP/koyVnE/Gb/ZvSYZVSEfnqZVkKAZBJRqKcUlriYO0pk5sVYi9gF3IvNv85MIClAPA/4VjsbaI7TtG/bXqSzIcA5zucB05yCxDNgN7ADu11suRT2JJejZJVq1JKlz3ktb25wDvba3Rw+CbwByKG81UYAVFlwFHrf12UoXD9oUwfX52VJG0IvEo34/M6+5DdElOBv5T0vGQQ0T7/6A0GfoPW7uScYi/ku6K+fvieIQcvrZy30zzoOlLMqxLnI5+REY43tdiOoasFecBx9u+vqvYZlYcIc/liMZSJ7T38SkiavoYGbm5tSWFTiaJtg+2/z5i+7xBrxF9n/s9iCbDwsApJLl3MEl+XKI+94suaB1+hxOh6C86minHkC4jbK9HRhXOljR7x7GtIukN7b3prRc/aXH9kAhA7gzsC9zjuJANfL3v26tCEpBvAb7WEjbTkg3tc3ix427SGZJeqmhVYHtHso5dJmleSfMRm+eP2L6oy7iK0aESDcW4pK/KdRuwItkkfM72l1qnwydbpb7413kERvug1a73zyKHmcm2r5D0bknfJFWI3UlV4iZyQR7ZJFb/56RV+I4ibZ+HKOJttwDHE5eOPwwhvgmk4+lZpNX4YbJpeqekHdvdVgL+pjo5qu8nTKtWHgz8yBH+eh5wgaOn8RfgDKKRMHD6DlvvIe4uBzk2cH8mle9XS/pau++jbgryg0YZ/4Hp+6NViMr+HcSO7nPt9jNJguadts/vIrZZAdvXd9VZ1N+RQtavXYDvS7pU0vMc54szbK9P2sbP7aDFvmeruTCwgu3VSKfAvcTqcA5ir/xFooXTGbafIF1+C/QStiT5Pr+kni3jRuS7sXRXcUn6AulQ+xjwNWcE9Cxgkqbbgl4BrGt7e9u7t8dN6KCTYWp7rjNIV+SqZP9wQH+yoevrjqTZFJ2Nk4lVcS/ZMJmsZZeQNX+XLpJrxehSiYZiXNPaZPcjmetzYdrFY6CtjbMivQvdKB60+toX7QgxXQucLOkcYHkifvcpogFyNHHmeHevRXTU6Nugv13SfpL2bwe9yST592lFNO1mYnvYmXtJazHG9lTb9wDfAPZXxNsuJpWuPSV9HXiyVQhHEkkvkrRC+/NSpBX6Wtu/aXd5DNhU0iFkQzpl0AlcSav3YmosRSwi75I0hyJY9hiZlX+uMubRCcps+hmSjgN2b0mHu8lrcySwvu3fSloPWMv23V0dmounT1vD3gDsBmxt+3+J3eDNwFmKy8Oxkha2/afeYwYRi6QXKPoClvRa4PfA3ZLOJBokb3Na1zch3Yef6fueDpy+qvvlJBm5ueKishnRKnpU0SfB9tvcp80z4LiOJR2sqwHbAXNL2tZxkTDwjb7r9BP9/x53JPTbihezk6Tt721vRs5XX1fEkztjTIHgEZJ8XxnYXtK87Vfnk9dqSdt/bPcduX1h0Q2VaChmBq4ns5SraURV4ot/j7a5W13Svooq/EnAu4lbyd7E2eQBYB7b15AxirM9hFnK8UB7vd5BNpzfAd4l6QJnZv4g0klweGu1feKf/FUzFMX/+yRJR0taTNKzbH+bVJeXb7F/h7Rn72B78/a4kbvWKeKY6wMPtPfplySR9vrWTqvWcrwpWWM39xhP9QExNzlgLdB+vht4iaS5bD/pqKK/C3ip7fVbMmngSFqCCGOeCVwHvJKIiF5HKpVfb0mGFUjC5uEu4iqeOcrI14okabUgpDuGjMD8iFTIj7d994DjmNDiOEnSnsQ95QXkmjM/sJ+jPbAFsDc5QHeCpust9DqM1NaBjxCBxW+2JKDc4ehSi+VlZFzvwtbt9CDwP2QNwfY7SJfRRWP3hgPuSpmt789qxYvfAEsrAraQkZgXEEeOTugrELyFfNb2J65CWwNvIgn4XUnyaLuWeCuKgVKuE8VMgWIPNLvtzmY7i5kfTVeoXoXMef+MtIt/CzjP9kOSNiZ2eh+1fdagW2dnBlqb7LHEjWMxolkhYE7bK7XN1ELu2JGjPe9zSXX5IbLJnEyqgGvYfuffeczIvp+a7pDwUdL5cQ3wJXJIPhT47TBeG0kvBb5PDvK3k8/aN4AbgZeTOesNbd/RUTwT23PfYHuDdjDcC5jX9t6StiFOHAsSl4KP2z6vi9iKfx1FWX8Z4Eo38cTWMr4zUdn/WEuu9e4/n+0HB7lG9P/dipPRqsDytm+StCxxxXk5SWYtSTRAOrFHlfQicu07uvecY+JdgyQcziJdR491EdeYGNck69eJJMnwLeAoN3eJdp/Jtg/vKJ6eS84EknSfCFwEzAXsQNyYbiSaETd3FVdffGsAXyDJ0NlJF8jhpGCwLRG5PtP2uV3GVYwulWgoimKWQ5m//UP781JkHOLDtq9tVaNVgSvJxXdl4DHbU0b5UDoWSS8A5gO+DrwZ+CNptb/K9puHFFMvcTQnsDhpC30dcDFRbP+g+zzoR5Uxh4WFyev0XOA0kmw7nijxf8JN9HHA8axKZrpvBX5g+4+K88AHgK1IEmtb4MXAnMA+jktBZyiCplPIa3KspN2BpW1v26q+s5MK759s31FrxfiitYwfRj5TpwO/Iwmrp8jna1fiVnWY7Su7iqnve7gLsEKL54VEE+KhlghZmHQ23OaOrYEV/YPZgMM93dq5P+61iAD3Jl2sFX1xTRt3VDQ2Pg4sAJxk+7B2n4n9XRZdfSdbbGcBd5Kx3h2IcPmfyWdsceB3tnfrMq72XFsQi+kj28/LEM2PbWzf2df1UOtX0QmdqsYWRVEMmnYIvU7SMY5o1IKkKv8+Mp9+qqSpxKZxduAU20+N8oW3b/OxGjnsPejYxs5NNlJzAv9BNvKXdRzbmsCvbd/ZkgwTHNGynwO7Kq4AiwJP0tTRR532Xq5A3reHySZ9H1I9/TKwI9EdeB4w0MNDe38OI5+jVYA3SjrE9smS/kqs9LaxvVtrdZ/YSxJ2ie1rJL0duFRxpZmHJEEAnnLGqG7su/9IrhXjlfaZv5gc5j9KPnOfIp0CnyHJtb8A+0na1BGPHXhMEAtL0hHzgXbYO5Y45qwDvIFYHk4ZdDz9SJrdceP4Iun2O0HSNrZv6R1E27/hMknXtbGFLuJa0Pa9Y2K4XFLvffyNMjL3uMeMcnT4nVwamKsvkXAD6SL4gO39+l7bgScZ/s7fPxux/jyy/fxTMp7WGzcZWa2uYjiM3NxqURSzNu0Qujmwr6TtbF9KVO1f1aqUPc/ry4BrehuCUb3warpA2TrEcWAqcKGkSaQSOIFUa84Cvus4dXSiUC3pdcTj++2tMt+v8t1TG7/Y9heBV9r+ZBdxjVd674uklckowvva//d0bNXuItW3pYHNBt2iLenNpLq8jqOX8Q1gEVLVxfYpROX+LElvsf3nYSQZejj2i28ibfaXt86FKsjMJDiOVPcDm9remOhr7EE6nrYH7iOz6Z3paygCowcCV7ckw2zESvOOdkA9Erilq3h6tOT62qTb71PkO3lIGz8Zez3syvFlM+DLkl7dX3lv8VwNfIJ0puygDoWa1afJ0LgDeFDSCpKebfsqcu1cscXaaZJB0lsk7SlpK9KBeLWki1q3zHJkvR9JYeti+FSioSiKWY524X8H8BlJ27dkw+eA1SXt1e5ziu2fDTPOYaJ4aNO6BOYlyZj3ML0d9FJH9Xwf4qW+saNI3llSph2Eey4gG/SSDe1304S/2obrd+3PI3tda5vOVYENga1s70w6d/ZoowrHkWTDox29h/cDzyGHdxxdg9cBkyVtLGke218GdiJiakPH9o9J9Xk3SXvafmpUk5AzE33f+08CEyW9nuhs7EY6HOYDfuXBCz8uMOamP5Ik7ZaSVnCzcHVcEyYDG7hD154WYy9RvD5wvu0zbb+JfF9PlPSa/kNyh+v9acQRZHdiN91b06Z1NpCkyHPckVCzpo/rTZC0kaRNyQjhXcTe9o2K68TapGOm/98zaGtNKw44nwJ+S5LIH7X9fpJUO5VcPz/ujkfRiqJHaTQURTHLosxdX0L0GU5UXAt2IVWR20f1AKGoeV8OfMP2vu22vYAXkfb2zW3/WtJ2wM/dsTp1b3PX9/M6pB30+8C5gz4szMxIOpzoHWxp++x221pkxnrbXttxh/EsD1xKDlULE5eLn5KxjuXI/PAJXR0c/lXa6MllJDFyl8vtaKZAETg8jejw7GH7+Hb7XB6wmGHrXvgF6VK4sXVa9cb5diZaNwe5T5BymEjaAXgFcKijFyFiX/wdYDc3y88O4pjmGCHp0yRR+ihZs25ut0/TbOgipjHxiaxhd5DX6zdEwHZj4KVkPPOWdsDvOq6jgENIN8VewHs8XQh1TmBu2w8MuruiKP4R1RJYFMUsizN3vQ4wRdIcto+TdI3t+4cd25B5inS0rSnpcNuTicbBJsDaLcmwNGk73qXLwPoqSALWA66zfUmb0d253aeSDY2+9tkVgam2J7dW3z0l/cD2faSr4KWKBkJndqSQcYT2HbyEaH+8si/29YDrx1uSAcD2DyUt4uYzX8wc2L5P0n6kknsuTDvIduGY8CfgKiJE+Z+K5s23yBjOEZL+QrrsPujYKHdG3zqxLNFmeYi40KxHOv2uI84J/00sPztJMsDfjMOdQcRqVyXJmgMkfcz2zV0fkiUtYfsX7To0CXjI9jbtd4eSrotJxHXiJbZvab/r0oJ9duK89GmirbR5G8/ZkIxAng88CKM7GloMn5FtMS2KYjRoG7oNyAbvJcS7fKRx1MM/B5wNTJV0qCOceT7wCUlfJaKB/2X7io5j+2trg74U+E/gSEmTgevJ4WFVYNM2fzrytMPD20mb7LPabbuTDfv3JR1Mugg+31q2O6/Mt+/gGsB8iip67/bzbf+263ieBo/A37SaFzMH1wM3AKt1efBrleSrgWXJ6N6FRBviAknLkZG0Y4B7u4hnTGw9HZ4zWmznERHd00ll/pj2u68Po+OijR/MDlxg+/e2NyNnlK9LWrLjWFYCLpe0Vjug3w483rplsL03Odiv64hS9pIMGuRnrbcOSVpc0kItQftVYE3g1FYgWI2IZj7oxqDiKYp/hepoKIpilsf2/7bq5NCE5oaNpJcDb/R0//GfEB/wfYF3STrQ9o5ttnl+slH5cVctl+396R06jwEutP1ZSbe22+YilZsvAku4Q0G38UwbgzkQ2Mj2Da1i+ULbu0raH3gv8L72Xv7NSEqX2P6pIj53taLKfvIw4ng69D73tVmfubD9pKTjgdm7SjL0rZN7A6cALwDuAZYiSdP9iPr/fl1fh9oB9blkZHBn2xdJOpu40RwBbA0sRNrsO9GL6F+L2mv3uKTfAEtLurm9Rh8Gvge8nrgMdYLtq9oo4WF9Se65gRUlXW37XjLa8fiYx3Uh/Lgu6fb4g6Svkc/WbqSQsiIZodjTHVm4FsX/RWk0FEUxEvRdqEduVlHSRCKytSgRjrqatMiuBzwf+B/SCvonN8uujuPbimwmT2yH5TcDPwK+TbosfkE6MM4BPuamMTCK7+VY2pjEEWQj/DCwBBmNucr2wZKOA94IrGW7E/X4f4bi6/6Y7V8OO5aimJG0A/1EIj75CtLZsI/tsyW9Cvj9ML+Diq3mD4Bv2f6LpI3JeNzbOhot6cUxwfbU1rl2EHnNLiLJ5B1IJ8iNRJz4ZtuHdxTXJiR5cGUbwZlEkh2TiGvDXmTEcD7gPttb/MO/bDDxLU8SWfsCCxB3rV+RroaJJJmE7V90GVdR/DNqdKIoipFglKuTjt/4hqQFdBViW3k+sBKwnO2fkCrJcyQtMYQQLyabzA0lvdz2d8lm7o+2j3Rs624Ffuo+IcNRfC/72mdfqwgWLkASMH8m7+m7gK+QrhRs7wRcCcwzlIDHYPv6SjIUsyKtU/0JMsa0FnCamyBr0xnoLMnQt04sLOml7eZfAssQAUOIuOHvaXazXdGSDALOJIfjm4hbyASSUF4YeD/wZC/JMOjxJUlfaM+5FnCqIpp7CnA46VB5EtgKOAw4tpdkUEcuR5KeSzpPlrX9q9ax8HXglcCOwETbv6gkQzHeqNGJoiiKEcD2T5pI1OVkc7cF8CFgoVZt+xmwu+1HuoqpV9myfU/bR24OLC7pEJIUeUPbAL4QuMn2V9vjRraToXXlbERasX9GhB6PtL0LgGJvuS+pqvYe84FhxFoUo4jtX0ramwiwDtztYix93XvrEUeCeyQ9SkasdgI+LsnAksABLRHdNUsDc/U66CTdAHwB+IDt/dp41VP9/55BBSLpNGL5u4ak5wGfBRaV9IDtkyVNJcnbfW1PId12nWgy9BVIHpF0NLCYpM8TV5DvtUTHZkT8sSjGHdXRUBRFMSI4XtpvJVWjldrh9H3Ara0a11mSocUzVeEcUl3blVTityAV+FWIavb1tveE0U4yAEhalFSw1gCuABYDbpY0h2Kvtys5PEwZdBWwKIp/yFXEvrUzFFeZ3p9fAfwXMMn2OiRxu3277VAiArmD7bO6WCfaiFc/dwAPSlpB0rNtXwWcQDQG6DDJsAhxW7qu3bQ9Gdn4MHCdpGVsf4XYWa7S/9iONBnWlrStpO1aJ9huxGni8Jao/29gL9u/GVQsRfHvUBoNRVEUI4ak/wCmEE/3o4fw/LvbPqr9+WXE4WJjx8v9FcBxRJn9QNu/6ntcl9Zh4wJJc9t+VNNtPxcBJpOW5w2ALRy18RXJvK5dvulFMXS67GZorfUXAJ+0faHikHAC8KHeGirpPOAG2/t0EVNfbL21awJZs+Yi+jsHk0r8FOCHwDeAKxwHpC7jW55oRPwAmBPY0fZtkj4CbAMs2XVXSotrPTJS8mHgZOBrtj8kaTGibfGg7Z1H8bpYzDxUR0NRFMWI4dgNrg8cLGnRruZMASQdQywqe7HcBvwa2EjS823fChxFKluvHhP3SG2mWofCOZJOBPZVLD3vI2OPmwK7tiTDm4GTgHltPwCjqV9RFOOJLg+nrRvtVLKmr2P7PiIOu6ykedvdTgY6d15qSQYBl5BEww5kTOKbRB9iB5JsuLfrJEOL71qizbAaEYK8rf3qU8RxYs6uY2rv2U7ENWhO4Dbg3ZJOaomjj5PXcOSui8XMRWk0FEVRjCC2f6hYSv6xq+dsegvPtf1uSXuSls+FiKf7OsCzJJ0PvB34UpuHHUmaKOcJpNtjKhFxW8P2OZIuJy4TO0r6NREJ+7Cbn3tRFKOBpBeSg+gfbZ8g6Qngs5K2JiNyBwLLS3qArBOTO4xtCdu/aEmGScBDtrdpvzuUOB1NIo4JL+mtX8Oo0Dv2v2sBl0m607Hf/SrRbhi4iGcbe1m6XZdfS16TrYnY7/5kDOflwC2S/mx710HHVBQzgupoKIqiGF0egcErerfnOIHYaR4D0CpX35P0/abMPoUIhJ0MzG770K5iG28odqTnkdbYE8lrcjsRb6O9XgeQOfC/AO+3ff4ovlZFMaq0jqfLSRv9+ZLma3oCh5E14ylgZ2JtPB+wk+0LOlrvVwIul7RW6666HXi8jXRge2/gxcC6th/vSzIMVGDxn2H7R8DaJFFzJ3C/7S17cQ346ecF1pf0TZJc/kPrSpkIXNtekxcSocpzBhxLUcwwqqOhKIpiROlTtB5om72krwAvAk4E1mmzy9+x/V5J327JhlWBKZIWsn1Pe9xI6gw4HvfvI6/HzraPbRWvrZq+xh+IjsXZth/ue9zIvVZFMYq0jqdjyZjZV4g98QKSHrH9lXYu/jIwuSUrp9HFOmH7Kkl7AYdJmkxGEOYGVpR0te17gUeBx8c8bqhrmO1rJa0DbNvrGuiiw8L23ZLuAfYAvtU3viFgHsVpYgPgvbZ/MKrXxmLmo8Qgi6IoioGhWGfuZ3tLSQsSV4TZgIubYjaSTicuGC/uU9se+Y1UEym7lFQtFyBtzwsCK5NW2p1t3zy8CIui6BpJcwA3Esvf9drPtwHfISNWk2xfL2l7smasTrqj/tpBbJuQ5MGVtu+TNImIGU4C5gD2Ap4kHRb32d5i0DH9Oww6ydB/nWtCyKsDawK32f5Eu30d4AmSh/neoGIpikFQiYaiKIqiMyQtTuwr5wAusn1Fu30X258fanDjEEnLAN8FjrB9QF8i5gW27x92fEVRdE+fc9ABRDj3YaJ5sA+wJ7BEO+gvbPvujmL6AvBa4OfEdndD2483vYjJJNlwCxH5fbHtc9rjRto1oWlDLAnc7tiNrkQ6G35C3uNNiWXxn4YYZlE8IyrRUBRFUQycMZWbxYHNiIjZ5bYv6bvfSG86/x6SlgMuBA7pqbJXx0dRjDZ9HU832V6p7/bTgGNtX9lhLKcR4cQdJT2PaAl8GnjAsdvdEvggsG+/yO+or2MtqXAaEf79AHCc7QPbe7svsCywi+0LhhhmUTxjSgyyKIqiGDj9m8km/HUa8Gzg+WPuV0mGMTSRsvWBA5od6UhvzouimGbLuCbwmjYmgaSVgTcCXboJLQJsAlzXbtoeeA8ZmbhO0jJNpPJYYJX+x47yOiZpKdKtsI/tTwErATtL2s/2tbbfBaxVSYZiZqY6GoqiKIqhIGkB278bdhwzC5LmcYd2pEVRjH9a9fsC4AzgdcCn3bE1cIvhIuAHpFNtR9u3SfoIsA2wpO3HuoxpvNI3/rYdsC3w38Bnbd8vaVHgBtKRsvcw4yyKGUElGoqiKIqhUhX6f40SyiyK4u/RNBu+C2zh2N8OI4Y3EOHaI2wf0G6bHfgmsJ3th4YR13ihb/1+MbHOfFzSW4lm0enA92w/2JINi9v+zlADLooZQNlbFkVRFEOlDs3/Gl3ZkRZFMXNh+5pmDfzosBKRtn/chA0vk3Sn7ZOBrxLthpFOMkDW7ZZY+CRwg6SFgY2BicC7gImSvmP7DuCOSigXswKl0VAURVEURVEUMzdDdyVoejJrA5+VdCep3G8JqegPNbghI2kxIpC5B7AD8MP232WkE2R9+grAlWQoZgVqdKIoiqIoiqIoihlC02zY1vZO7eeRdxOStBDwSdtb941RnAD81PYxkhZt3QxFMctQHQ1FURRFURRFUcwQmmvCSCcZeh0ckp7dbnoEWEbS1n3dCjcCcwNUkqGYFSmNhqIoiqIoiqIoZjijmGSAaZoM7wAmS7qNiHVuAFzdBB/vArYkNqBFMUtSHQ1FURRFURRFURQzCEmLA1sBnwNOIyKQ6wHLkkLva4F9bF86rBiLYtCURkNRFEVRFEVRFMUzpLlIzAncAbwCuBg4x/ae7fcvIqKPk5poZu9x5S5RzLJUR0NRFEVRFEVRFMUzQNJrgEuB5YFn274FmAKs2xIQ2L6POEw8r/+xlWQoZmUq0VAURVEURVEURfE0kfQy4AzgcNunA48B2N6NdDWcKemdklYjGg1PDCnUouicGp0oiqIoiqIoiqJ4mkjaGniD7d0lTQCWBlYG7rR9nqSPAjsDxwPn2f5RjUsUo0K5ThRFURRFURRFUTx9bgW2k7Qu8F7g2cCSwPWS1rO9o6R5gbWAzwwxzqLonBqdKIqiKIqiKIqiePpcA5wOHArMAxwLrE6SCnMB2J4M3AacIWm26mYoRoUanSiKoiiKoiiKoniGSJrP9oN9P68BHAxsYvvOdtuCtu8dVoxF0TXV0VAURVEURVEURfEM6SUZJM0h6e3A0cAhtu+UNFu7TyUZipGiOhqKoiiKoiiKoij+DSTNAbwR2B84yvZ5Qw6pKIZKJRqKoiiKoiiKoij+TVqyYX7b95a7RDHqVKKhKIqiKIqiKIqiKIoZRmk0FEVRFEVRFEVRFEUxw6hEQ1EURVEURVEURVEUM4xKNBRFURRFURRFURRFMcOoRENRFEVRFEVRFEVRFDOMSjQURVEURVEURVEURTHDqERDURRFURRFURRFURQzjEo0FEVRFEVRFEVRFEUxw/h/fqKCE0LbO/4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18, 6))\n",
    "plt.plot(importance_shap[124], 'go-')\n",
    "#plt.plot([0, 25],[0,0], '--',linewidth=1)\n",
    "plt.grid(True)\n",
    "plt.xticks(np.arange(25),\n",
    "          ['Admit Type', 'Ethnicity', 'Gender', 'Age', 'Admission Weight',\n",
    "       'Heart Rate', 'Respiratory Rate', 'SpO2',\n",
    "       'Inspired O2 Fraction', 'PEEP set', 'Mean Airway Pressure',\n",
    "       'Tidal Volume (observed)', 'PH (Arterial)',\n",
    "       'Respiratory Rate(Spont)', 'Richmond-RAS Scale',\n",
    "       'Peak Insp. Pressure', 'O2 Flow', 'Plateau Pressure',\n",
    "       'Arterial O2 pressure', 'Arterial CO2 Pressure',\n",
    "       'Blood Pressure (systolic)', 'Blood Pressure (diastolic)',\n",
    "       'Blood Pressure (mean)', 'Spontaneous breathing trials', 'Ventilator Mode'], rotation=45,horizontalalignment='right', multialignment='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dice_ml3d\n",
    "#from dice_ml3d.utils import helpers # helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_csv('data/orginal_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['timestamp', 'hadm', 'Admittype', 'Admdays', 'Ethnicity', 'Gender',\n",
       "       'Age', 'Admission Weight (Kg)', 'Heart Rate', 'Respiratory Rate',\n",
       "       'O2 saturation pulseoxymetry', 'Inspired O2 Fraction', 'PEEP set',\n",
       "       'Mean Airway Pressure', 'Ventilator Mode', 'Tidal Volume (observed)',\n",
       "       'PH (Arterial)', 'Respiratory Rate_spontaneous', 'Richmond-RAS Scale',\n",
       "       'Peak Insp. Pressure', 'O2 Flow', 'Plateau Pressure',\n",
       "       'Arterial O2 pressure', 'Arterial CO2 Pressure', 'Vented',\n",
       "       'Arterial Blood Pressure systolic', 'Arterial Blood Pressure diastolic',\n",
       "       'Arterial Blood Pressure mean', 'SBT_value', 'Vented_1',\n",
       "       'ventilator_category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['timestamp', 'hadm', 'Admdays', 'Ventilator Mode', 'Vented'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = dice_ml3d.Data(dataframe=df, continuous_features= ['Admittype', 'Ethnicity', 'Gender', 'Age', 'Admission Weight (Kg)',\n",
    "       'Heart Rate', 'Respiratory Rate', 'O2 saturation pulseoxymetry',\n",
    "       'Inspired O2 Fraction', 'PEEP set', 'Mean Airway Pressure',\n",
    "       'Tidal Volume (observed)', 'PH (Arterial)',\n",
    "       'Respiratory Rate_spontaneous', 'Richmond-RAS Scale',\n",
    "       'Peak Insp. Pressure', 'O2 Flow', 'Plateau Pressure',\n",
    "       'Arterial O2 pressure', 'Arterial CO2 Pressure',\n",
    "       'Arterial Blood Pressure systolic', 'Arterial Blood Pressure diastolic',\n",
    "       'Arterial Blood Pressure mean', 'SBT_value', 'ventilator_category'], outcome_name='Vented_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.client.session.InteractiveSession at 0x7ff28d67ee90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.get_default_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = 'TF'+tf.__version__[0] # TF1\n",
    "m = dice_ml3d.Model(model=keras_model, backend=backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = dice_ml3d.Dice(d,m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### query_instance must be in list format and not normalized ####\n",
    "query_instance = test_set_ori.iloc[124]\n",
    "query_instance = query_instance.values\n",
    "query_instance = list(query_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 78.2,\n",
       " 86.5,\n",
       " 119.0,\n",
       " 24.0,\n",
       " 98.0,\n",
       " 100.0,\n",
       " 10.0,\n",
       " 14.0,\n",
       " 541.0,\n",
       " 7.46,\n",
       " 0.0,\n",
       " -1.0,\n",
       " 21.0,\n",
       " 5.0,\n",
       " 19.0,\n",
       " 124.0,\n",
       " 33.0,\n",
       " 101.0,\n",
       " 65.0,\n",
       " 76.0,\n",
       " 0.0,\n",
       " 18.0]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root: MAD for feature Admittype is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature Ethnicity is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature Gender is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature PEEP set is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature SBT_value is 0, so replacing it with 1.0 to avoid error.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diverse Counterfactuals found! total time taken: 00 min 18 sec\n"
     ]
    }
   ],
   "source": [
    "dice_exp1 = exp.generate_counterfactuals(query_instance,total_CFs=11, desired_class=\"opposite\", features_to_vary=['Heart Rate', 'Respiratory Rate', 'O2 saturation pulseoxymetry',\n",
    "       'Inspired O2 Fraction', 'PEEP set', 'Mean Airway Pressure',\n",
    "       'Tidal Volume (observed)', 'PH (Arterial)',\n",
    "       'Respiratory Rate_spontaneous', 'Richmond-RAS Scale',\n",
    "       'Peak Insp. Pressure', 'O2 Flow', 'Plateau Pressure',\n",
    "       'Arterial O2 pressure', 'Arterial CO2 Pressure',\n",
    "       'Arterial Blood Pressure systolic', 'Arterial Blood Pressure diastolic',\n",
    "       'Arterial Blood Pressure mean', 'SBT_value', 'ventilator_category'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query instance (original outcome : 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Admittype</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Admission Weight (Kg)</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Respiratory Rate</th>\n",
       "      <th>O2 saturation pulseoxymetry</th>\n",
       "      <th>Inspired O2 Fraction</th>\n",
       "      <th>PEEP set</th>\n",
       "      <th>...</th>\n",
       "      <th>O2 Flow</th>\n",
       "      <th>Plateau Pressure</th>\n",
       "      <th>Arterial O2 pressure</th>\n",
       "      <th>Arterial CO2 Pressure</th>\n",
       "      <th>Arterial Blood Pressure systolic</th>\n",
       "      <th>Arterial Blood Pressure diastolic</th>\n",
       "      <th>Arterial Blood Pressure mean</th>\n",
       "      <th>SBT_value</th>\n",
       "      <th>ventilator_category</th>\n",
       "      <th>Vented_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>119.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.925094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Admittype  Ethnicity  Gender   Age  Admission Weight (Kg)  Heart Rate  \\\n",
       "0        1.0        0.0     1.0  78.2                   86.5       119.0   \n",
       "\n",
       "   Respiratory Rate  O2 saturation pulseoxymetry  Inspired O2 Fraction  \\\n",
       "0              24.0                         98.0                 100.0   \n",
       "\n",
       "   PEEP set  ...  O2 Flow  Plateau Pressure  Arterial O2 pressure  \\\n",
       "0      10.0  ...      5.0              19.0                 124.0   \n",
       "\n",
       "   Arterial CO2 Pressure  Arterial Blood Pressure systolic  \\\n",
       "0                   33.0                             101.0   \n",
       "\n",
       "   Arterial Blood Pressure diastolic  Arterial Blood Pressure mean  SBT_value  \\\n",
       "0                               65.0                          76.0        0.0   \n",
       "\n",
       "   ventilator_category  Vented_1  \n",
       "0                 18.0  0.925094  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Diverse Counterfactual set (new outcome : 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Admittype</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Admission Weight (Kg)</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Respiratory Rate</th>\n",
       "      <th>O2 saturation pulseoxymetry</th>\n",
       "      <th>Inspired O2 Fraction</th>\n",
       "      <th>PEEP set</th>\n",
       "      <th>...</th>\n",
       "      <th>O2 Flow</th>\n",
       "      <th>Plateau Pressure</th>\n",
       "      <th>Arterial O2 pressure</th>\n",
       "      <th>Arterial CO2 Pressure</th>\n",
       "      <th>Arterial Blood Pressure systolic</th>\n",
       "      <th>Arterial Blood Pressure diastolic</th>\n",
       "      <th>Arterial Blood Pressure mean</th>\n",
       "      <th>SBT_value</th>\n",
       "      <th>ventilator_category</th>\n",
       "      <th>Vented_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>86.5</td>\n",
       "      <td>119.1</td>\n",
       "      <td>23.9</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>152.1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>93.9</td>\n",
       "      <td>65.2</td>\n",
       "      <td>84.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.130</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>118.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>124.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>40.7</td>\n",
       "      <td>75.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.032</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>118.9</td>\n",
       "      <td>24.1</td>\n",
       "      <td>94.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>124.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>64.8</td>\n",
       "      <td>92.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>118.9</td>\n",
       "      <td>24.9</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>111.4</td>\n",
       "      <td>60.9</td>\n",
       "      <td>75.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>129.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>123.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>100.8</td>\n",
       "      <td>87.9</td>\n",
       "      <td>67.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.016</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>138.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>123.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>59.4</td>\n",
       "      <td>75.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.194</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>107.7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>124.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>100.8</td>\n",
       "      <td>65.2</td>\n",
       "      <td>75.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.457</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>118.9</td>\n",
       "      <td>23.9</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>123.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>105.6</td>\n",
       "      <td>67.5</td>\n",
       "      <td>42.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>119.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>...</td>\n",
       "      <td>22.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>124.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>96.3</td>\n",
       "      <td>70.8</td>\n",
       "      <td>80.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>119.1</td>\n",
       "      <td>24.1</td>\n",
       "      <td>96.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>123.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>100.8</td>\n",
       "      <td>65.2</td>\n",
       "      <td>71.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>86.5</td>\n",
       "      <td>119.1</td>\n",
       "      <td>23.9</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>123.6</td>\n",
       "      <td>52.1</td>\n",
       "      <td>101.2</td>\n",
       "      <td>60.1</td>\n",
       "      <td>76.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Admittype  Ethnicity  Gender   Age  Admission Weight (Kg)  Heart Rate  \\\n",
       "0         1.0        0.0     1.0  78.2                   86.5       119.1   \n",
       "1         1.0        0.0     1.0  78.2                   86.5       118.9   \n",
       "2         1.0        0.0     1.0  78.2                   86.5       118.9   \n",
       "3         1.0        0.0     1.0  78.2                   86.5       118.9   \n",
       "4         1.0        0.0     1.0  78.2                   86.5       129.5   \n",
       "5         1.0        0.0     1.0  78.2                   86.5       138.2   \n",
       "6         1.0        0.0     1.0  78.2                   86.5       107.7   \n",
       "7         1.0        0.0     1.0  78.2                   86.5       118.9   \n",
       "8         1.0        0.0     1.0  78.2                   86.5       119.1   \n",
       "9         1.0        0.0     1.0  78.2                   86.5       119.1   \n",
       "10        1.0        0.0     1.0  78.2                   86.5       119.1   \n",
       "\n",
       "    Respiratory Rate  O2 saturation pulseoxymetry  Inspired O2 Fraction  \\\n",
       "0               23.9                         98.0                 100.0   \n",
       "1               29.0                         98.0                 100.0   \n",
       "2               24.1                         94.0                 100.0   \n",
       "3               24.9                         98.0                 100.0   \n",
       "4               36.0                         98.0                 100.0   \n",
       "5               24.1                         98.0                 100.0   \n",
       "6               14.0                         98.0                 100.0   \n",
       "7               23.9                         98.0                 100.0   \n",
       "8               22.8                         98.0                 100.0   \n",
       "9               24.1                         96.0                 100.0   \n",
       "10              23.9                         98.0                 100.0   \n",
       "\n",
       "    PEEP set  ...  O2 Flow  Plateau Pressure  Arterial O2 pressure  \\\n",
       "0       10.2  ...      5.0              19.0                 152.1   \n",
       "1        0.0  ...      9.2              19.0                 124.4   \n",
       "2       10.3  ...      5.0              19.0                 124.4   \n",
       "3       10.0  ...      5.0              19.0                  60.0   \n",
       "4        0.0  ...      6.2              21.4                 123.6   \n",
       "5       10.0  ...      5.0              19.0                 123.6   \n",
       "6        2.7  ...      5.0              19.0                 124.4   \n",
       "7        0.0  ...      5.0              19.0                 123.6   \n",
       "8        4.9  ...     22.6              19.0                 124.4   \n",
       "9        5.1  ...      6.6              19.0                 123.6   \n",
       "10       4.5  ...      2.8              19.0                 123.6   \n",
       "\n",
       "    Arterial CO2 Pressure  Arterial Blood Pressure systolic  \\\n",
       "0                    33.0                              93.9   \n",
       "1                    33.0                              91.5   \n",
       "2                    28.0                             101.2   \n",
       "3                    33.0                             111.4   \n",
       "4                    33.0                             100.8   \n",
       "5                    33.0                             101.2   \n",
       "6                    33.0                             100.8   \n",
       "7                    33.0                             105.6   \n",
       "8                    33.0                              96.3   \n",
       "9                    33.0                             100.8   \n",
       "10                   52.1                             101.2   \n",
       "\n",
       "    Arterial Blood Pressure diastolic  Arterial Blood Pressure mean  \\\n",
       "0                                65.2                          84.1   \n",
       "1                                40.7                          75.7   \n",
       "2                                64.8                          92.3   \n",
       "3                                60.9                          75.7   \n",
       "4                                87.9                          67.8   \n",
       "5                                59.4                          75.7   \n",
       "6                                65.2                          75.7   \n",
       "7                                67.5                          42.4   \n",
       "8                                70.8                          80.2   \n",
       "9                                65.2                          71.7   \n",
       "10                               60.1                          76.3   \n",
       "\n",
       "    SBT_value  ventilator_category  Vented_1  \n",
       "0         1.0                  3.0     0.130  \n",
       "1         1.0                 10.0     0.032  \n",
       "2         1.0                 13.0     0.222  \n",
       "3         0.0                  2.0     0.482  \n",
       "4         1.0                  9.0     0.016  \n",
       "5         1.0                 10.0     0.194  \n",
       "6         0.0                 21.0     0.457  \n",
       "7         1.0                  4.0     0.074  \n",
       "8         0.0                  0.0     0.146  \n",
       "9         0.0                 18.0     0.099  \n",
       "10        0.0                 18.0     0.457  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dice_exp1.visualize_as_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### test_instance which is normalized ######\n",
    "instance = dice_exp1.test_instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "instance = np.reshape(instance, (1, 25, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### check the predcition of 124th patient, should be the same as y_pred_test[124] #########\n",
    "result = keras_model.predict(instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.92509407]], dtype=float32)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_scale = read_csv('data/test_set_scaled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### final_cfs_df are the counterfactural examples that already de-normalized #########\n",
    "counterfacturals = dice_exp1.final_cfs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_counter_instance = [1,0,1,78.2,86.5,110,24,98,40,5,10,541,7.46,24,0,21,5,19,118,33,101,65,76,1,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root: MAD for feature Admittype is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature Ethnicity is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature Gender is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature PEEP set is 0, so replacing it with 1.0 to avoid error.\n",
      "WARNING:root: MAD for feature SBT_value is 0, so replacing it with 1.0 to avoid error.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diverse Counterfactuals found! total time taken: 00 min 07 sec\n"
     ]
    }
   ],
   "source": [
    "dice_exp_counter = exp.generate_counterfactuals(query_counter_instance,total_CFs=1, desired_class=\"opposite\", features_to_vary=['Heart Rate', 'Respiratory Rate', 'O2 saturation pulseoxymetry',\n",
    "       'Inspired O2 Fraction', 'PEEP set', 'Mean Airway Pressure',\n",
    "       'Tidal Volume (observed)', 'PH (Arterial)',\n",
    "       'Respiratory Rate_spontaneous', 'Richmond-RAS Scale',\n",
    "       'Peak Insp. Pressure', 'O2 Flow', 'Plateau Pressure',\n",
    "       'Arterial O2 pressure', 'Arterial CO2 Pressure',\n",
    "       'Arterial Blood Pressure systolic', 'Arterial Blood Pressure diastolic',\n",
    "       'Arterial Blood Pressure mean', 'SBT_value', 'ventilator_category'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_instance = dice_exp_counter.test_instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_instance = np.reshape(query_instance, (1, 25, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16797139]], dtype=float32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###### prediction results for changing Arterial O2 pressure from 118 to 124 in the counterfactual example######\n",
    "keras_model.predict(query_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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