diff --git a/PV_ICE/baselines/SupportingMaterial/MaterialComp_CdTeModule.png b/PV_ICE/baselines/SupportingMaterial/MaterialComp_CdTeModule.png
new file mode 100644
index 0000000..69a6e36
Binary files /dev/null and b/PV_ICE/baselines/SupportingMaterial/MaterialComp_CdTeModule.png differ
diff --git a/docs/baseline development documentation/(baseline dev) CdTe Material Composition.html b/docs/baseline development documentation/(baseline dev) CdTe Material Composition.html
index f768d12..d3b56b9 100644
--- a/docs/baseline development documentation/(baseline dev) CdTe Material Composition.html	
+++ b/docs/baseline development documentation/(baseline dev) CdTe Material Composition.html	
@@ -14702,12 +14702,12 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#materials in CdTe</span>
-<span class="n">MATERIALS_CdTe</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'aluminium_frames_cdte'</span><span class="p">,</span><span class="s1">'encapsulant_cdte'</span><span class="p">,</span><span class="s1">'glass_cdte'</span><span class="p">,</span><span class="s1">'copper_cdte'</span><span class="p">,</span><span class="s1">'cadmium'</span><span class="p">,</span><span class="s1">'tellurium'</span><span class="p">]</span>
-<span class="n">tidynameMats_CdTe</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'aluminium_frames'</span><span class="p">,</span><span class="s1">'encapsulant'</span><span class="p">,</span><span class="s1">'glass'</span><span class="p">,</span><span class="s1">'copper'</span><span class="p">,</span><span class="s1">'cadmium'</span><span class="p">,</span><span class="s1">'tellurium'</span><span class="p">]</span>
+<span class="n">MATERIALS_CdTe</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'glass_cdte'</span><span class="p">,</span><span class="s1">'aluminium_frames_cdte'</span><span class="p">,</span><span class="s1">'encapsulant_cdte'</span><span class="p">,</span><span class="s1">'copper_cdte'</span><span class="p">,</span><span class="s1">'cadmium'</span><span class="p">,</span><span class="s1">'tellurium'</span><span class="p">]</span>
+<span class="n">tidynameMats_CdTe</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'glass'</span><span class="p">,</span><span class="s1">'aluminium_frames'</span><span class="p">,</span><span class="s1">'encapsulant'</span><span class="p">,</span><span class="s1">'copper'</span><span class="p">,</span><span class="s1">'cadmium'</span><span class="p">,</span><span class="s1">'tellurium'</span><span class="p">]</span>
 </pre></div>
 
      </div>
@@ -14720,7 +14720,7 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">baselinesFolder</span><span class="p">,</span> <span class="s1">'baseline_material_mass_cadmium.csv'</span><span class="p">),</span> 
@@ -14742,7 +14742,7 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[21]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
 
 
 
@@ -15013,7 +15013,7 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_component_mats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
@@ -15034,7 +15034,7 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_component_mats</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">tidynameMats_CdTe</span>
@@ -15056,7 +15056,7 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[32]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
 
 
 
@@ -15079,9 +15079,9 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
   <thead>
     <tr style="text-align: right;">
       <th></th>
+      <th>glass</th>
       <th>aluminium_frames</th>
       <th>encapsulant</th>
-      <th>glass</th>
       <th>copper</th>
       <th>cadmium</th>
       <th>tellurium</th>
@@ -15099,504 +15099,504 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
   <tbody>
     <tr>
       <th>1995</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>1996</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>1997</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>1998</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>1999</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2000</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2001</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2002</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2003</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2004</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2005</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2006</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2007</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2008</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2009</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2010</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>8.219678</td>
       <td>9.330322</td>
     </tr>
     <tr>
       <th>2011</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2012</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2013</th>
+      <td>16000.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>16000.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2014</th>
+      <td>15947.50000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>15947.50000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2015</th>
+      <td>15895.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>15895.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2016</th>
+      <td>15842.50000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>15842.50000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2017</th>
+      <td>15790.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>15790.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2018</th>
+      <td>15755.00000</td>
       <td>117.532683</td>
       <td>388.1</td>
-      <td>15755.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2019</th>
+      <td>15650.00000</td>
       <td>202.080283</td>
       <td>388.1</td>
-      <td>15650.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2020</th>
+      <td>15475.00000</td>
       <td>405.753968</td>
       <td>388.1</td>
-      <td>15475.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2021</th>
+      <td>14915.00000</td>
       <td>811.507937</td>
       <td>388.1</td>
-      <td>14915.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2022</th>
+      <td>14810.00000</td>
       <td>811.507937</td>
       <td>388.1</td>
-      <td>14810.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2023</th>
+      <td>14635.00000</td>
       <td>811.507937</td>
       <td>388.1</td>
-      <td>14635.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2024</th>
+      <td>14460.00000</td>
       <td>1217.261905</td>
       <td>388.1</td>
-      <td>14460.00000</td>
       <td>56.55</td>
       <td>6.027764</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2025</th>
+      <td>14302.50000</td>
       <td>1623.015873</td>
       <td>388.1</td>
-      <td>14302.50000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.842236</td>
     </tr>
     <tr>
       <th>2026</th>
+      <td>14145.00000</td>
       <td>1623.015873</td>
       <td>388.1</td>
-      <td>14145.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2027</th>
+      <td>14063.33333</td>
       <td>1217.261905</td>
       <td>388.1</td>
-      <td>14063.33333</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2028</th>
+      <td>13981.66667</td>
       <td>811.507937</td>
       <td>388.1</td>
-      <td>13981.66667</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2029</th>
+      <td>13900.00000</td>
       <td>811.507937</td>
       <td>388.1</td>
-      <td>13900.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2030</th>
+      <td>13806.66667</td>
       <td>811.507937</td>
       <td>388.1</td>
-      <td>13806.66667</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2031</th>
+      <td>13713.33333</td>
       <td>405.753968</td>
       <td>388.1</td>
-      <td>13713.33333</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2032</th>
+      <td>13620.00000</td>
       <td>202.080283</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2033</th>
+      <td>13620.00000</td>
       <td>117.532683</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2034</th>
+      <td>13620.00000</td>
       <td>81.150794</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2035</th>
+      <td>13620.00000</td>
       <td>81.150794</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2036</th>
+      <td>13620.00000</td>
       <td>81.150794</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2037</th>
+      <td>13620.00000</td>
       <td>81.150794</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2038</th>
+      <td>13620.00000</td>
       <td>81.150794</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2039</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2040</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2041</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2042</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2043</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2044</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2045</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2046</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2047</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2048</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2049</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
     </tr>
     <tr>
       <th>2050</th>
+      <td>13620.00000</td>
       <td>0.000000</td>
       <td>388.1</td>
-      <td>13620.00000</td>
       <td>56.55</td>
       <td>5.479785</td>
       <td>6.220215</td>
@@ -15617,7 +15617,7 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_component_mats</span><span class="p">[</span><span class="s1">'module_mass_gpm2'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_component_mats</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1">#run only once</span>
@@ -15628,15 +15628,41 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
 </div>
 </div>
 
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_percent_mats</span> <span class="o">=</span> <span class="n">df_component_mats</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="n">df_component_mats</span><span class="o">.</span><span class="n">columns</span> <span class="o">!=</span><span class="s1">'module_mass_gpm2'</span><span class="p">]</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">df_component_mats</span><span class="p">[</span><span class="s1">'module_mass_gpm2'</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
 </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[38]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_component_mats</span><span class="o">/</span><span class="n">df_component_mats</span><span class="p">[</span><span class="s1">'module_mass_gpm2'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">'font.size'</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">stackplot</span><span class="p">(</span><span class="n">df_percent_mats</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df_percent_mats</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">df_percent_mats</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'lower left'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Material Composition of CdTe'</span><span class="p">)</span> 
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Material Composition by weight (%)'</span><span class="p">)</span> 
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">1995</span><span class="p">,</span><span class="mi">2050</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">supportMatfolder</span><span class="p">,</span><span class="s1">'MaterialComp_CdTeModule.png'</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 
      </div>
@@ -15657,54 +15683,35 @@ <h1 id="CdTe-Annual-%25-Material-Composition">CdTe Annual % Material Composition
     <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>
-<span class="ansi-red-intense-fg ansi-bold">---------------------------------------------------------------------------</span>
-<span class="ansi-red-intense-fg ansi-bold">ValueError</span>                                Traceback (most recent call last)
-Cell <span class="ansi-green-intense-fg ansi-bold">In[38], line 1</span>
-<span class="ansi-green-intense-fg ansi-bold">----&gt; 1</span> df_component_mats<span style="color: rgb(98,98,98)">/</span>df_component_mats[<span style="color: rgb(175,0,0)">&#39;</span><span style="color: rgb(175,0,0)">module_mass_gpm2</span><span style="color: rgb(175,0,0)">&#39;</span>]<span style="color: rgb(98,98,98)">.</span>values
-
-File <span class="ansi-green-intense-fg ansi-bold">C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\ops\common.py:81</span>, in <span class="ansi-cyan-fg">_unpack_zerodim_and_defer.&lt;locals&gt;.new_method</span><span class="ansi-blue-intense-fg ansi-bold">(self, other)</span>
-<span class="ansi-green-fg">     77</span>             <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">NotImplemented</span>
-<span class="ansi-green-fg">     79</span> other <span style="color: rgb(98,98,98)">=</span> item_from_zerodim(other)
-<span class="ansi-green-intense-fg ansi-bold">---&gt; 81</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> method(<span style="color: rgb(0,135,0)">self</span>, other)
-
-File <span class="ansi-green-intense-fg ansi-bold">C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\arraylike.py:210</span>, in <span class="ansi-cyan-fg">OpsMixin.__truediv__</span><span class="ansi-blue-intense-fg ansi-bold">(self, other)</span>
-<span class="ansi-green-fg">    208</span> <span style="color: rgb(175,0,255)">@unpack_zerodim_and_defer</span>(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">__truediv__</span><span style="color: rgb(175,0,0)">&#34;</span>)
-<span class="ansi-green-fg">    209</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">__truediv__</span>(<span style="color: rgb(0,135,0)">self</span>, other):
-<span class="ansi-green-intense-fg ansi-bold">--&gt; 210</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_arith_method(other, operator<span style="color: rgb(98,98,98)">.</span>truediv)
-
-File <span class="ansi-green-intense-fg ansi-bold">C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\frame.py:7455</span>, in <span class="ansi-cyan-fg">DataFrame._arith_method</span><span class="ansi-blue-intense-fg ansi-bold">(self, other, op)</span>
-<span class="ansi-green-fg">   7452</span> axis: Literal[<span style="color: rgb(98,98,98)">1</span>] <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(98,98,98)">1</span>  <span style="color: rgb(95,135,135)"># only relevant for Series other case</span>
-<span class="ansi-green-fg">   7453</span> other <span style="color: rgb(98,98,98)">=</span> ops<span style="color: rgb(98,98,98)">.</span>maybe_prepare_scalar_for_op(other, (<span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>shape[axis],))
-<span class="ansi-green-intense-fg ansi-bold">-&gt; 7455</span> <span style="color: rgb(0,135,0)">self</span>, other <span style="color: rgb(98,98,98)">=</span> ops<span style="color: rgb(98,98,98)">.</span>align_method_FRAME(<span style="color: rgb(0,135,0)">self</span>, other, axis, flex<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>, level<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-fg">   7457</span> new_data <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_dispatch_frame_op(other, op, axis<span style="color: rgb(98,98,98)">=</span>axis)
-<span class="ansi-green-fg">   7458</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_construct_result(new_data)
-
-File <span class="ansi-green-intense-fg ansi-bold">C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\ops\__init__.py:260</span>, in <span class="ansi-cyan-fg">align_method_FRAME</span><span class="ansi-blue-intense-fg ansi-bold">(left, right, axis, flex, level)</span>
-<span class="ansi-green-fg">    258</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">isinstance</span>(right, np<span style="color: rgb(98,98,98)">.</span>ndarray):
-<span class="ansi-green-fg">    259</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> right<span style="color: rgb(98,98,98)">.</span>ndim <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(98,98,98)">1</span>:
-<span class="ansi-green-intense-fg ansi-bold">--&gt; 260</span>         right <span style="color: rgb(98,98,98)">=</span> to_series(right)
-<span class="ansi-green-fg">    262</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> right<span style="color: rgb(98,98,98)">.</span>ndim <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(98,98,98)">2</span>:
-<span class="ansi-green-fg">    263</span>         <span style="color: rgb(95,135,135)"># We need to pass dtype=right.dtype to retain object dtype</span>
-<span class="ansi-green-fg">    264</span>         <span style="color: rgb(95,135,135)">#  otherwise we lose consistency with Index and array ops</span>
-<span class="ansi-green-fg">    265</span>         dtype <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-
-File <span class="ansi-green-intense-fg ansi-bold">C:\ProgramData\anaconda3\Lib\site-packages\pandas\core\ops\__init__.py:252</span>, in <span class="ansi-cyan-fg">align_method_FRAME.&lt;locals&gt;.to_series</span><span class="ansi-blue-intense-fg ansi-bold">(right)</span>
-<span class="ansi-green-fg">    250</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-fg">    251</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">len</span>(left<span style="color: rgb(98,98,98)">.</span>columns) <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(0,135,0)">len</span>(right):
-<span class="ansi-green-intense-fg ansi-bold">--&gt; 252</span>         <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">ValueError</span>(
-<span class="ansi-green-fg">    253</span>             msg<span style="color: rgb(98,98,98)">.</span>format(req_len<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">len</span>(left<span style="color: rgb(98,98,98)">.</span>columns), given_len<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">len</span>(right))
-<span class="ansi-green-fg">    254</span>         )
-<span class="ansi-green-fg">    255</span>     right <span style="color: rgb(98,98,98)">=</span> left<span style="color: rgb(98,98,98)">.</span>_constructor_sliced(right, index<span style="color: rgb(98,98,98)">=</span>left<span style="color: rgb(98,98,98)">.</span>columns, dtype<span style="color: rgb(98,98,98)">=</span>dtype)
-<span class="ansi-green-fg">    256</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> right
-
-<span class="ansi-red-intense-fg ansi-bold">ValueError</span>: Unable to coerce to Series, length must be 7: given 56</pre>
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmQAAAG8CAYAAABwlLljAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACcTElEQVR4nOzdd1xV5R/A8c+9l72RjeLeCxcaoiI40tw7zRxpQ7MSs8xyV2pmrlIrc5a5TdPcAxV3bk3LhRtRlCGy7/n9QdyfN4Z4uXgBv+/Xi5dwznOe8z2XK3x5znO+j0pRFAUhhBBCCGEyalMHIIQQQgjxopOETAghhBDCxCQhE0IIIYQwMUnIhBBCCCFMTBIyIYQQQggTk4RMCCGEEMLEJCETQgghhDAxSciEEEIIIUxMEjIhhBBCCBOThEyIAiQ0NBSVSkXp0qWN1ue4ceNQqVT069fPaH2K/KVSqVCpVISHhz/zsYsWLUKlUtG0aVOjx1XQ/PHHHwQGBuLg4KB7zUJDQ00dFpC376F4MUlCJkyuadOmuh9eFStWfGr7wMBAXfvq1asbLY7Q0FDGjRvHunXrjNZnUXHnzh3Gjx9P48aN8fT0xMLCAkdHR2rWrMnbb7/N7t27TR3iC2HdunWMGzeuwCQdprRr1y7atWvH3r17KVasGA0bNiQgIABHR8dn6ic6OpqpU6fSokULihcvjqWlJfb29lSuXJm+ffvy+++/k5aWlud4n/w59ywf8ofUi8PM1AEI8aSLFy8SFhZGo0aNstx/+fJl9u3bly/nDg0NZfz48fTt25eOHTvmyzmexsbGhkqVKlG8eHGTnD8rX3/9NWPGjCExMREAHx8fatWqRXx8PJcvX+bMmTP8+OOPNGjQgNDQUKysrEwcceFXqVIlAMzNzfW2r1u3jsWLFwNkOwLm6OhIpUqVKFmyZL7GaGpz5sxBURQGDx7M7NmzDerjl19+YciQIcTExADg6elJjRo1SE5O5tq1ayxZsoQlS5ZQpUoVdu7ciZeXl8Hx1qhRg9TU1Ezbz5w5Q2xsLO7u7lSoUCHT/tz8kSqKBknIRIFRpUoVzp8/z6JFi7JNyBYuXIiiKLq2RU39+vW5cOGCqcPQCQkJYcaMGajVat5//32GDRtGqVKldPuTkpLYsWMHkydPJiwsjMTEREnIjCAv74FOnTrRqVMnI0ZTMP31118AtGnTxqDjZ8yYQUhICAC9evXis88+o2rVqrr9aWlphIWFMXXqVDZu3Mjdu3fzlJB9++23WW5v2rQpe/bsoXXr1ixatMjg/kXhJ7csRYHRrVs3bGxsWLlyJY8fP860X6vVsmTJEjQaDa+//roJInyxrF69mhkzZgDw888/M3PmTL1kDMDS0pI2bdqwb98+Zs6ciUajMUGk4kWUkJAApI8qP6vDhw8zfPhwACZOnMjSpUv1kjEAjUZDYGAgGzZsYMWKFdja2uY9aCFyIAmZKDDs7e3p2rUrcXFxrFmzJtP+7du3c+PGDVq1apXjX6o3b95k5syZtGrVinLlymFtbY2DgwN169blyy+/5NGjR5mOUalUjB8/HoDFixdnmsfxX5GRkYwcOZKaNWtib2+PjY0N1atXZ8yYMbrbH1mdI2OS74EDB+jUqRMeHh6o1Wpd4pPTpP5//vmHyZMnExQURKlSpbCyssLJyYmAgAC+/fZbUlJSsn1NnpWiKIwZMwaAPn360KtXr6ce8/7772Nvb59p+5YtW2jfvj0eHh5YWFjg6elJp06dsp139t/XYNmyZfj7++Pg4ICrqysdO3bUjY4AHDt2jM6dO+Ph4YG1tTV169Zl7dq1Wfb95AMOiYmJjBkzhkqVKmFtbY27uzs9e/bk77//zvF1WbZsGS1atMDFxQULCwtKlCjBa6+9xokTJ7I9bs+ePXTp0oXixYtjYWGBk5MTFStWpGvXrixcuDBT+/9OCA8PD0elUuluV44fP17v/fnk++Vpk/qjoqL49NNPqV69Ora2ttjZ2VGzZk3GjRuX7Xs3Y/7TokWLePDgAUOHDqV06dJYWlpSvHhx3nzzTSIiIrK9/qd5lvdI6dKl9V6boKAg3euQ2wcZxo4dS1paGk2aNGHkyJFPbd+9e/csbyeuXbuWRo0aYWdnh5OTE02aNGH9+vW5iiG34uLimDhxIn5+fjg6OmJlZUXFihUJCQnJ02suCiBFCBMLDAxUAOXrr79Wdu/erQBKcHBwpnY9evRQAGX16tXKwoULFUCpVq1apnYffvihAijW1tZKmTJlFD8/P6Vs2bKKRqNRAKV69erKw4cP9Y4JCAhQfHx8FEBxd3dXAgIC9D6eFBYWpri4uCiAYm5urlSsWFGpXLmyYmZmpgBKhQoVlBs3bmSKC1AA5auvvlI0Go1ib2+v1KtXTylXrpwyY8YMRVEU3fWXKlUq0/FdunRRAMXOzk4pX7684ufnp5QqVUrXb3BwsJKcnJzpuLFjxyqA0rdv3xy+C/qOHDmi6/fYsWO5Pu6/hg4dquvH3d1d8fPzU1xdXXXbxo4dm+mYJ1+DkSNHKoBSsmRJpVatWoqVlZUCKMWKFVMuXryo/Pbbb4qlpaXi7Oys1K1bV3F2dlYARaVSKStWrMj2tejZs6fy0ksvKYBSvnx5pU6dOoqlpaUCKLa2tsrevXszHZuSkqJ07dpVF3uJEiWUevXqKY6OjgqgaDQa5aeffsp03IIFCxSVSqUAipOTk+Lr66vUrFlTF2vx4sUzHZNxjqtXryqKoih37txRAgICFHd3dwVQfHx89N6fXbt21R2b8X8jMDAwU7/nzp1Tihcvrou3Zs2aSvXq1RW1Wq0ASpkyZZTLly9nOi7j/+jnn3+u+Pj4KGZmZkqNGjWUihUr6o4tW7asEh0dnenYp3nW90jXrl2VgIAA3ferevXqutdhyJAhTz1fZGSk7vuxZs2aZ443w+jRo/XirlevnlKsWDEFUGbMmJHpe5idjNc2q/+f58+fV0qXLq37fpUpU0apUaOG7to9PDyUU6dOGXwNomCRhEyY3JMJmVarVcqWLauoVColPDxc1+bBgweKpaWl4uLioiQlJeWYkO3YsUMJDQ1VUlNT9bZfv35dad++vQIogwcPznRcbhKXGzdu6H7ovv/++3qJ3Z07d5TWrVtn+8sw4we0RqNRRowYoSQmJur2PX78WFGUnBOydevWKYcPH1a0Wq3e9vPnz+uSiylTphh0Xf81bdo0BVAcHR0znS+3Fi1apLveuXPnKmlpaYqiKEpqaqoydepU3S/F3377Te+4jNfAzMxMsbOzU37//XfdvsjISKVOnToKoLRo0UJxdHRUJkyYoKSkpCiKkp409e3bV5fE/Tf2jNfC3NxccXV1VcLCwnT7oqKilDZt2iiA4u3trcTExOgdO27cOAVQbGxslLVr1+q2JyYmKiEhIbqYjx49qtuXmpqqS95nzZqlizPD+fPnlZkzZ2Z67bL7ZZ5xbVklshmyS8gSExOVSpUqKYDSoEED5dq1a7p9Fy9eVGrWrKkASp06dXTfqwwZ/0fNzc2Vli1bKrdv39btO3bsmC5RHDNmTLZxZcXQ94iiKLo/Rnbv3v1M51y7dq3u9Y2KinqmYzNs375d18fUqVN1caekpChjxoxRzM3N85yQxcXFKeXKlVMApUePHsqtW7d0+2JiYnTvhQoVKmT5h5gofCQhEyb3ZEKmKIoyfvx4BVDGjx+va/Pdd9/pkiBFUXJMyHISHx+vmJubK3Z2dpkSttwkLoMGDVIA5dVXX81yf2xsrG4E4uDBg3r7Mn5At2rVKtv+c0rIcnLx4kUFUKpUqZJpnyEJ2bBhwxRAqVWr1jPF8aSMXybvvvtulvt79+6d5TkyXoPsEsw//vhDt/+VV17JtP/+/fu6EYSTJ0/q7ct4LQBl6dKlmY6Ni4vTjVxljFoqiqI8evRIcXBw0Huf/lfjxo0VQOnYsaNu2507d3QjY88iPxKyJUuWKIBiaWmZ5Qju33//rRtFXrdund6+jP+jrq6uyoMHDzId+/XXXyuAUrt27Vxfo6IY/h5RFMMTslmzZhn0PXlScHCwAuiNTD6pRYsWeU7IvvrqKwVQAgICMv2sUpT0pLV27doKoCxbtszQSxEFiMwhEwVOv379UKvVLF68GEVRAHTzbPr375+rPmJjY5k3bx79+/fn5ZdfpnHjxjRq1IiWLVuiVqt59OgRFy9efObYVq9eDcDbb7+d5X57e3tatGgBpNdJysobb7zxzOfNEBkZyaxZs+jduzctWrTQXVdGraILFy7oJjvnRWxsLAB2dnYGHX/hwgUuX74MoJs8/V8fffQRACdPnuT27dtZtnnrrbcybatbt26O+11cXChTpgyALob/8vLyokePHpm229nZMXDgQAA2bdqk275v3z5iY2OxsbFh0KBBWfaZcZ3btm3TlTdwc3PDysqK6Ohofv/99yyPe17++OMPAF599VVKlCiRaX/FihVp3769Xtv/6tWrF87Ozpm2BwQEAHDp0qVcx2Os98izyut7Oz4+nj179gDwwQcfZNlm6NChBvX9pFWrVgEwcODALB+W0Wg0dOjQAcj+Z40oXKTshShwSpYsSXBwMDt27NAVfTx27Bi1a9emVq1aTz1+7969dOvWjcjIyBzbRUVFPVNct2/f5t69ewCMGDEiU42oDNeuXQPSHy7Iyn+f5sqt1atX079//ywfSsigKAoPHjzIcx2zjMn5OZ0rJxkT4+3s7LJddaBq1apoNBrS0tK4cOEC3t7eevtdXV2zLPLp7u6u+7x8+fJZ9u3u7s6FCxeyjb9y5crZPhFarVo1AL2yKhnXU7Zs2WyftqtRowYAjx8/5vr165QtWxaNRsOHH37Il19+SYcOHahatSotWrSgfv36NG3aNNM156eMa8iIMys1atTgt99+y7bsRnY1sTw8PIBne78Y4z1iiLy+ty9duqQrFJvd/+WM91BenDp1CoDp06fz008/Zdnm7t27QPY/a0ThIgmZKJD69+/Pjh07WLRoEU5OTrptTxMbG0vXrl25d+8ezZo145NPPqFmzZo4OzvrEqiSJUty48aNZ34q8eHDh7rPjxw58tT2WZXuAAx6fD48PJzevXuTlJRE9+7def/996lcuTKOjo6YmZmh1Wp1CYYxnrbMGEG5evUqiqJk+aRpTuLi4oD0QpvZMTMzw9XVlbt37+raPym71+nJWJ7WRqvVZrk/I4HIad+TMeXmep588vfJYz///HNKlizJ7NmzOX36tO4JUZVKRbNmzfjmm2+oWbNmtv0ay7NcQ1bfD8j+9Var02+2ZIxoGyuep71HDJHx3o6OjubBgwcUK1bsmY7PiMPMzCzbY3N6f+VGfHy87v/x6dOnn9o+u581onCRhEwUSJ06dcLR0ZFVq1ZhbW2NhYVFrkovbNq0iXv37uHj48PGjRszFSnNGEEyxJO3OB4+fKhLFJ+H5cuXk5SURIMGDVi2bJnuF2CGZx3te5rGjRsDEBMTw4kTJ6hTp84zHZ8xCpHxF3xWUlNTuX//vl775yWnuDL2PRlTxuc5lRm4c+dOpvaQnni99dZbvPXWW9y7d4+wsDB2797NihUr2LFjB8HBwZw+fTrfR8ue5Rqex/fDVO+RjPc2pJdY6dy58zMdnxFHampqtgldTteUGzY2NrqRwZMnT+Lr65un/kThIHPIRIFkbW3Nq6++Snx8PPfv36d9+/a4uLg89birV68C4Ofnl2XF+NOnTxMfH5/lsU8bBSpRooQuCTtw4MBTYzGmjOsKCAjIlIzlRzx169alcuXKAMycOfOZj884Ni4uTncL97/Onz+vu/WT0f55uXDhQrbrE547dw5IXzkiQ0Z8V65cyXY04uzZs0D6L9Psli1yc3OjU6dOzJo1i4sXL1K6dGmioqJYuXJlruJ+1pHKJ2VcQ0acWcnY9+S15xdTvUfc3Nxo2bIlALNmzXrm48uXL68bjX6yHt6TMt5DhlKpVLrbofv3789TX6LwkIRMFFhvv/02zZo1o1mzZrz77ru5Oiajand2E4C//vrrpx6b3aR4jUZDly5dAJg0aZJRFhzOrZyuS1EUpk6datTzqdVqXaHcJUuW8Ouvvz71mG+//VZ3O6dSpUq6+V3Tpk3Lsn1GzLVr136uc6kgfSQoY9L0k+Lj45k/fz4ArVu31m1v1KgRDg4OPH78mB9++CHLPr/55hsAXn75ZczMnn7zwcHBQTfykdsJ6097j+YkY4mh5cuX643mZbh06ZKuqKmhyxE9C1O+R8aNG4dGo2HPnj1MmjTpqe1XrVqlewjI1taWJk2aANkvh2TIHzH/1b17dyB9Dll2f0SKIsaUj3gKoSiZy17kRnZlL06cOKF73HzOnDm67YmJicrIkSMVlUqlWFhYZPm4fEZ9otKlSyuPHj3K8rzXrl3T1ZVq165dpiKaqampyt69e5U33nhDuXnzpt4+cvEYfHZlL3777Tddnav169frtsfGxir9+vXTFUzNqn9Dyl5kGDJkiAIoarVa+eCDD/RqwymKoiQnJytbtmxRmjRpogB6ddkyakyZmZkp8+bN09UES0tLU6ZPn/7UOmQ5lf542muZ8Z5auHCh3vYn65C5u7srBw4c0O178OCBrk6dp6dnpiKnGXXI7Ozs9L4HiYmJykcffZRlHbJz584pb7zxhrJv375Mtb22bdum2NnZKYCycuXKXF1fRn24hg0bZlt7KruyF0lJSbo6ZA0bNlSuX7+u23f58mWlVq1aT61D9t/XM8PVq1d1MT8LQ98jimJ42YsMU6dO1cX82muvKX/99Zfe/rS0NCUsLEzp0KGDAignTpzQ7du6dasC6QWIp0+frleHbPz48UapQxYTE6MrC+Lv75+pAKxWq1X+/PNPZejQocqRI0cMeg1EwSIJmTA5YyZkiqIor7/+uu6Hobe3t1419S+++CLbH+SPHj1SPDw8dDWKGjRooAQGBmb6xXb48GHF29tbd45y5copL730klK9enXF2to62x/EeUnI0tLSlKZNm+r6KF26tFK3bl3FxsZGUavVuhpTxk7IFEVRJk2apKvrBekFV/38/JRq1aoptra2uu0NGzbUK3arKIrywQcf6PZ7eHgofn5+ipubm25bVoVEn0dC9mSl/goVKih169bVJbXW1tZZ/pJPSUnRrZbw5OvwZKX+efPm6R3z5B8INjY2Ss2aNRU/Pz+990/Hjh0zFbDN7vquX7+u2NjYKJBeHb5hw4ZKYGCg0qNHD12bnCr1nz17VndujUaj+Pr6KjVq1Mh1pX5jJ2SKYth7RFHynpApSvprlVFfDlC8vLyUevXqKTVr1tR9XzN+zjxZDFdRFN0qEhnfCz8/P90fa8aq1H/x4kWlcuXKur58fHyUBg0aKL6+voq9vb1ue15eA1FwSEImTM7YCVlqaqry1VdfKZUqVVLMzc0VZ2dnJSgoSPdXdk4/yM+ePat06dJF8fT01C2FlNUvmejoaGXSpEmKv7+/4uTkpJibmyslSpRQXnrpJeWjjz5S9u/fn+tfsk/KKRmJj49XPvnkE6VMmTKKubm54ubmprRp00YJDQ3Nsf+8JmSKoig3b95Uxo4dqwQEBChubm66KvrVqlVTBgwYoOzcuTPbYzdt2qS0bdtWd5y7u7vSoUOHbI95HglZ3759lcePHyujRo1SKlasqFhaWiqurq5K9+7dM42UPEmr1SpLly5VgoODFWdnZ8Xc3Fzx9vZWevbsmeUSU/Hx8cr8+fOVXr16KZUrV1acnZ0VMzMzxdXVVWnevLmyZMmSTKNRT7u+sLAwpXXr1oqrq6sukXrytcopIVOU9MK5I0eOVKpWrapYW1srNjY2SvXq1ZUxY8ZkWlIsQ34mZIry7O8RRTFOQqYo6SOjX331lRIcHKx4enoqFhYWio2NjVKhQgXltddeU9avX59lYVZFUZSVK1cqDRs2VGxsbBQHBwelcePGuqK6xkjIFEVREhISlNmzZytBQUGKi4uLYm5urnh6eip169ZV3n33XWX79u2ZVoAQhZNKUZ7hOWUhhCjExo0bx/jx4+nbty+LFi0ydThCCKEjk/qFEEIIIUysQCZkv/zyC2+//Tb16tXD0tISlUqV41+zsbGxDBs2jFKlSmFpaUmpUqUYNmyYbomMrPz666/Ur18fW1tbnJ2deeWVV/jzzz/z4WqEEEIIIXJWIBOyUaNG8eOPP3Lt2jW96tdZiY+PJzAwkOnTp1OpUiVCQkKoWrUq06dPJzAwMMvHhSdOnMhrr73G3bt3eeedd+jevTv79+8nICCA0NDQfLoqIYQQQoisFciE7KeffiI8PJx79+7xzjvv5Nh2ypQpnDx5ko8//pht27YxefJkNm/ezJgxYzh58iRTpkzRa3/x4kXGjh1LxYoVOX36NN988w0//PADBw4cwMzMjIEDB+oWBhZCCCGEeB4KZELWvHlzSpUq9dR2iqLw008/YWdnx5gxY/T2jRw5EmdnZ+bPn6+3vtrChQtJTU3ls88+01u4uFq1avTp04fLly+za9cu412MEKLAGDduHIqiyIR+IUSBUyATsty6ePEit2/fJiAgINOit1ZWVjRp0oRbt25x6dIl3faMW5IZS2c86eWXXwZgz549+Re0EEIIIcR/FOrFxTOWsqhQoUKW+zO2X7x4Ue9zOzs7PD09c2yfnaSkJJKSknRfa7VaHjx4gIuLS57WmRNCCCHE86MoCnFxcXh7e2e5RvDzVqgTspiYGAC9W49PcnBw0GuX8bm7u3uu2//XpEmTdGv8CSGEEKJwu3HjBiVKlDB1GIU7ITOFkSNHMmzYMN3XMTExlCxZkj+6voOtuaUJIxMFk4I6ORFVQizqhDjUj2JQx8egSk40dWBCCPFCe6RNI/jKFezt7U0dClDIE7KMkbHsRrQy6pA9OYLm6Oj4TO3/y9LSEkvLzImX28md2Gk0uQtcCHmvCCFEgVBQphuZ/qZpHjxtzldWc8wqVKjAo0ePiIiIyFV7IYQQQoj8VugTMm9vb/bv35+pAGxiYiJ79+7F29ub8uXL67YHBgYCsG3btkz9bd26Va+NEEIIIcTzUKhvWapUKgYOHMiECROYMGECX331lW7fpEmTePjwIe+9957ecGT//v2ZOnUqX375JR06dNDdnjx37hxLliyhXLlyBAcHP/drES8mlYUF2FhDfg2ZZ9TgUxRQ/rMN5T9t/tNer80Tx/67T8nUVn8/ipJ+XRkfACpV+v/HJ7flFHd2sjtWq0XRanWfk/G5qZmZodJo0m9Vm5mBRo1Ko0HJzfddq4WUVEhN/1BSUp7++gghCh2VohS8/9k//fQTYWFhAJw5c4bjx48TEBCgG+nq2LEjHTt2BNKXTmrUqBEnT56kRYsW1K1bl1OnTrF582Zq1apFWFhYphplX375JaNGjaJkyZJ07dqV+Ph4li1bRkJCAlu3biUoKCjXscbGxuLo6MhXX3TB2srcOC+AKFISNGnEmaUSq0khTpNMtDqJaHUiD9UJpKoKSMJQxKkVMEODClChQq2oUKNCo1KDAmr+/VpRo0JBgxqNok7f9u92NaBR1GhQoQCpKi1pKKSSRpoaUkgjDS1pKoVUlZZkJZVkVRppKuP/iDVT1FgpZlhhjpXWDEs0WGo1WClmWCoaLBUNFlr1v/9qsFDUaBRV+vVn/AuolH+/VqV/rlbSXx9dO4X/tyX9azNFhXkaWGjVmKWCuRbM0kj/PE1Bk6qg0Sqo0hTUWtCkaVHpvlZQabWo0rSotKBSFODfPxYU0l/Z//xhkP7yZfGHwZMK2K8xXcwZ15JxHXrXW7BifhE9Sk3Fb/ceYmJidFUWTKlAJmT9+vVj8eLF2e4fO3Ys48aN030dExPD+PHjWb16NREREXh6etK1a1fGjh2b7QT9pUuXMmPGDM6dO4eFhQX+/v5MmDABPz+/Z4o1IyGrMrcKGmuZqC2EEEIUBmkJaZwfdF4SsqJCEjIhhBCi8CloCVmhntQvhBBCCFEUSEImhBBCCGFikpAJIYQQQpiYJGRCCCGEECaWpzpkKSkpREZGcv/+fWxsbHBzc8PJyclIoQkhhBBCvBieOSG7fPkyixYtYteuXRw7doyUlBS9/d7e3jRt2pSOHTvSoUMHzMwKde1ZIYQQQoh8l+ts6dChQ4wZM4Zdu3ah1WqxsLCgWrVqeHh4UKxYMRISEnjw4AF///03S5cu5ddff8Xd3Z0hQ4YwbNgwrK2t8/M6hBBCCCEKrVwlZK+++iqrVq3C3d2d9957j+7du1O3bl0sLCyybH/jxg22bdvGL7/8wtixY5k7dy4///zzM1XAF0IIIYR4UeRqUv+xY8eYP38+N2/eZPr06fj7+2ebjAH4+PgwYMAAdu/ezYULF2jevDkHDx40WtBCCCGEEEVJrkbILly4gEZjWBX68uXLs2jRItLS0gw6XgghhBCiqMvVCJmhyZix+xBCCCGEKIqMWofs0aNHPHr0yJhdCiGEEEIUeUZJyKZNm0aJEiVwdHTE0dGRkiVL8u233xqjayGEEEKIIi/PRcJGjhzJ9OnT6d27N3Xr1iUxMZENGzYwdOhQ7t+/z/jx440RpxBCCCFEkaVSFEXJTUNFUVCpVJm2u7q6Mn78eN5991297a1ateLUqVPcuXPHOJEWULGxsTg6OlJlbhU01jJPTgghhCgM0hLSOD/oPDExMTg4OJg6nNzfsnzppZc4c+ZMpu1xcXFUqFAh0/by5csTGxubt+iEEEIIIV4AuU7IrK2tqVevHp999hlJSUm67YGBgQwfPpyDBw+SmJhITEwMv/zyC4sWLSIwMDBfghZCCCGEKEpynZCFhoYye/Zsvv/+e2rUqEFoaCgAc+fORVEUGjVqhK2tLcWKFaNPnz6UK1eOOXPm5FfcQgghhBBFxjNN6h84cCAdOnTgvffeo1mzZvTv35+pU6dy+vRptm3bxt9//w1AlSpVaN68eZZzzoQQQgghhL5cT+r/r82bNzN48GASEhKYOXMmPXr0MHZshYJM6hdCCCEKn0I7qf+/Wrduzblz5+jVqxe9e/embdu23Lhxw5ixCSGEEEK8EJ45IYuMjOTPP/8kMjISGxsbpk2bxuHDh7lz5w7VqlVj1qxZGDjoJoQQQgjxQsp1QhYbG0vnzp3x8vKiQYMGeHl50alTJ2JiYqhTpw5Hjx5lzJgxfPrpp/j7+2dZIkMIIYQQQmSW64Rs2LBhbNy4kfHjx/PHH3/w+eefs3nzZj788MP0jtRqhg8fztmzZ3F2dqZevXp8+umn+Ra4EEIIIURRketJ/Z6enrRr14558+bptr355pts3Lgxy2r8v/76K8OGDSMiIsJ40RZAMqlfCCGEKHwK7aR+lUqVqYyFWq3Odr5Yr169uHDhQt6iE0IIIYR4AeS6DlmrVq1YvHgxZcqUoW7dupw8eZLFixfTs2fPbI9xcnIyRoxCCCGEEEVarhOy6dOnc+/ePT777DPdtldeeYXp06fnS2BCCCGEEC+KXCdkTk5ObNy4kYiICG7cuIGPjw+enp75GZsQQgghxAvhmZZOgvTJ/ZKICSGEEEIYj8GV+oUQQgghhHHkaoSsZs2ajBs3js6dOz/zCW7fvs3EiRPx8fFhxIgRz3y8EEWNpcYSK40VlhobLDVWmKutUBnpbyMFLVolDa2iRUsayr+fpylp/25PI+3fjyc/T9OmoSArbDxPNmY2aFRqEtISSdWmmjocIYSJ5SohK1myJF27dqVMmTK8/vrrdO3alerVq2fbPjo6mh07dvDLL7+wZcsWHB0d+eWXX4wWdEHkFjcMs1QbU4chChpFRUqqhoREDYlJZjxO1hCnVT39OBNQqbSYqRU0GtCotKg1CmYa0KgV1CoFjUaLRqOgVmvRqEGt0qJWK6jVyr9ttKj+PU6tUrBQp/Km5U5skx5inpaGuTYV87QUzLVp6f+mpWKuTUGlKGhVKhSVGoV//1WBFkClRqtSo1K0qBQtagXUus/TP1RaLWoUUswseGxuTYK5VfqHmQUJZuY81piToDEjQa0hUaUiWaUiSQVJKkgBklBIRkuSkv5vgjaVeG0yj9OSiE9LJD71cY4Jk5XGEgdzOxzMbHDQWOKgssBBpcFJUeGk1eKYmoJzShJOyQk4JsTinBCDU/wDzNOSdX2kqTQkWliTYG5NooU1SWZWJJpZkGBuyX1LO25Y23JNo+J62mOuJz0gKulhvr8fhBDPV64Lw+7atYvRo0dz8OBBVCoVdnZ2+Pr64uHhgbOzMwkJCTx48ICLFy9y+fJlABwdHRk0aBCffPIJ9vb2+XohppJRGNZn6ErUlpKQCfGkzh6RfPP4U1Qpj00dSp4kayx5ZGVHvJUdj81tMdOm4pAYg2N8NBZpSc89nnhLe64X8+GagxvXrdKTtfC0x4QnRhKbHPfc4xGiMCpohWFznZBlOHPmDIsWLWLXrl2cOXMGrVart9/FxYXGjRvTsWNHunfvjpWVlVEDLmgkIRMiZ8NLXeLdyHGoFO3TG4s8e2DrwrViJQm3d+GqpSXhqjTCk2N4kBJHsjaFlLQUUhW5RSpEoU/InhQfH8/t27eJiorC2toaNzc3vL29jRlfgScJmRBP90P5w7x8c6apwxD/0qrUJJtZkKKxTP/XzJJkjRnJZpYkacxI1liQZGZGstqcJI2GZLUZyWoNSWo1983MuaRK43JKNDce3yVNSTP15QhhkIKWkD1z2Ysn2draUqFCBSpUqGCseIQQRdDblxqwqUJPqt5YZupQBOnz8KxSErFKScxTP0lmVlx1K8tFRw8uW9twWZXKxaQH3Em4h1ZGRIV4JnlKyIQQIrfaXWrDoTJ3cbu9y9ShCCOxTE2k8p2/qHznL73tqWoz7jl4ctfelUgbB+5a2BBpZsZdlZa72kQiUx4RnRJHUloyKdoUE0UvRMEiCZkQ4rlIU9S0utmXMPdIrO+fNXU4Ih+ZaVPxir6JV/TNp7ZVUJFkbkmSmSXJ5lbp/5pZkKSxIEVjjlalJk2lJk2tJk2lIu3fJ29T1Wq0KlX6E7rP4ZpyS0FNklpDslpNskpNklpNkkpFyr9P9yaq1FxSPNBSMJ+2fpEkWyRxnvOmDkNHEjIhxHMTlWxO5+ihbLAfi1ncLVOHIwoAFcr/b58mxJg6nHylWNgy2WE0e26WNHUoAtAmPQa+M3UYOlKpXwjxXJ1/ZMNgZSSKZdEshSNEVrTWroRYf8kPkoyJbEhCJoR47rbdL8YUh09R1DJIL4q+VAcf+qg+Z91dd1OHIgowSciEECYx90YpVnl9iCJzaUQRllisCu0ejyXsgaOpQxEFnEEJ2YQJE9i7d2+Obfbv38+ECRMMCkoI8WL4+LIv8zzHoJhZmzoUIYwu1qM+QQ9GcP6R1KgUT2dQQjZu3DhCQ0NzbBMWFsb48eMN6V4I8QKZGF6Jj+2+RGvjZupQhDCaCO8WBNwawp1EC1OHIgqJfLtlmZycjFotd0SFEE+3KsKT7mlfkORc0dShCJFnl3y60Di8H3GpMkdS5J7B7xaVKvt5H8nJyezbtw8PDw9DuxdCvGD+jLGnadKnbPKch3PEflOHIwoBRWNJolPBWinmlI0/r14MMnUYohDKdUJWtmxZva+nT5/OwoULM7VLS0vj/v37JCYm8uabb+Y9QiHEC+NOogUvXR/EpnKelLuxxtThiAIqxbEsex3a8OWt2ly5ZWXqcIQwilwnZFqtVjcqplKpUBSFrNYlNzc3p1q1agQHBzN69GjjRSqEeCEkadU0u9iFBRU8Cboxh4JVh12YiqI2J8IrmIVJQcy75YNyV57OFUVLrhOy8PBw3edqtZqQkBDGjBmTHzEJIQRvXAzg09KuvHl/CqrUBFOHI0wk1cGHA45t+eJ2Pf65LE/jiqLLoDlkV69excnJycihCCGEvonhlQj3+oIvoz9CpWhNHY54jlLtizPTejBzbpQiLVIeEBNFn0Hv8lKlSuHoKEXuhBD579c7Xtz2ftnUYYjnKNmpLN2Tx/Lt9TKkKZKMiReDwU9Z3rt3j4ULF3L06FGio6NJS0vL1EalUrFz5848BSiEEONi2vAjW2Q+2QsgwaU6bR+GcPmx3J4ULxaDErLTp08THBzMw4cPs5zYnyGn0hhCCJFb2+8XI6JcC7xubTN1KCIfxbr70fLuu0QkSTFV8eIxaCz4ww8/5MGDB3z22WdcvXqVlJQUtFptpo+sRs2EEMIQn8e2lXUvi7Aor0Aa33lPkjHxwjIoITt48CAdO3ZkwoQJlCpVCo1GY+y4hBBCz6Z7rkR6B5s6DJEPbpZoQ6PrA4lJkcr24sVlUEJmYWFBuXLljB2LEELkaGJ8O1OHIIzsb5/uBF7uSUKa/GEvXmwGJWTBwcH8+eefxo5FCCFytP6uO/e9mpo6DGEkf5Z8g5cvdpQnKYXAwITs66+/5ty5c0ydOtXY8QghRI4mJ3QwdQjCCHb5DKHrP81NHYYQBUaubti/8cYbmbZVq1aNESNG8P333+Pr65tlXTKVSsX8+fPzHuVTaLVa5syZw4IFC7hw4QJmZmbUrl2bDz/8kPbt2+u1HTduHOPHj8+yH0tLSxITE/M9XiGE4VZHePBp6cYUi9hn6lCEgeLc6/HGxYamDkOIAiVXCdmiRYuy3XflyhWuXLmS5b7nkZApikL37t1Zs2YN5cqVY8CAASQlJbF+/Xo6dOjAt99+y5AhQzId17dvX0qXLq23zcxMJpQKURhMSerIZCQhK6wWIqOcQvxXrjKQq1ev5nccBluzZg1r1qwhICCA7du3Y22dXkxw4sSJ1KtXj+HDh9O2bdtMyVe/fv1o2rTp8w9YCJFny+948UnphjhFHDB1KOIZJRWrxPQbZU0dhhAFTq4SslKlSuV3HAZbt24dAJ9++qkuGQNwdXUlJCSEoUOHsnDhwmxvUwohCqdpyZ2ZgCRkhc1qqy4oitSTE+K/Cv2jLXfv3gWgTJkymfZlbNu1a1emffv27WPKlCl88803/PHHHyQlJeVvoEIIo1py25sYjwamDkM8g1T74kwIr2rqMIQokAyaNLVkyZKntlGr1Tg4OFCpUiUqVapkyGlyxc3NDUi/rVqlShW9fRm3Wv/5559Mx40ZM0bvay8vLxYvXkyLFi1yPF9SUpJe8hYbG2tQ3EKIvPs2tTOjOGzqMEQubXfsRtK9Qj8OIES+UCk5LUaZDbVa/UzrVFauXJlvv/2W4GDjV9n++eef6dOnD40bN2bbtm1YWVkBEBUVRb169QgPD8fCwkKXRK1bt47Y2FgCAwPx8PDg5s2bLF++nIkTJ6IoCocOHcLX1zfb82X3lKbP0JWoLW2Mfn1CiJydKTkN+0ipi1jQaa1dqPtoOg+lGr8oILRJj7kxozsxMTE4ODiYOhzDErLFixezdu1aNmzYwMsvv0zDhg3x8PDg7t277N+/n23bttG+fXuaNGnC8ePHWbFiBRqNhn379uHn52fUC0hNTaVly5bs3r2b8uXL06pVK1JSUli3bh0eHh6cPn0aa2trHj9+nGM/8+bN46233qJr166sWrUq23ZZjZD5+PhIQiaEiQzyCWfEvU9NHYZ4igM+b9HrYlNThyGETpFIyNatW0evXr3YsmULTZo0ybQ/NDSUV155heXLl9O+fXv27NlDs2bN6NChA2vWrDFK4E9KSkpi8uTJ/Prrr4SHh+Po6EinTp0YPnw4FStWpGTJkly7di3HPpKTk7G1tcXNzY3bt2/n+tyxsbE4OjpKQiaECZ31+Rq7eydMHYbIhmJuS2Dqt1xPsDJ1KELoFLSEzKCb+RMnTqR79+5ZJmMATZs2pVu3bnzxxRcABAYG0qpVK8LCwgyPNAeWlpaMHTuWv//+m6SkJCIjI/nhhx+4desWAPXq1XtqHxYWFtjb2z91JE0IUfD8pOpi6hBEDs56dpRkTIinMCghO3fuHMWLF8+xTYkSJTh37pzu66pVqxIdHW3I6Qy2dOlSAF599dWntr148SIPHz7MVK9MCFHw/XCrFIq5jFAXRIranM/uBpo6DCEKPIMSMjs7O/bv359jm7CwMOzs7HRfx8fHY29vb8jpniqrJx1Xr17NggUL8PPzo3PnzgDExcVx+vTpTG0fPnzIgAEDAOjZs2e+xCiEyD8JaRpiXLJ/GEeYTrh3G07H2j29oRAvOIMed+nQoQPz58/ngw8+YMyYMbi4uOj2RUVFMX78eMLCwnRJDsDJkycpV65c3iPOQoMGDfDx8aFKlSpYWVlx5MgRQkNDKVu2LKtWrUKj0ehi8/X1pV69etSoUQN3d3du3brF5s2biYqKokWLFoSEhORLjEKI/HXBvDovcdDUYYgnKKgY/yDnUkJCiHQGJWSTJk1i//79fPvtt8ybN4/y5cvj5ubGvXv3uHTpEomJiVSuXJlJkyYBEBERQUJCAv369TNm7Do9evRg7dq1HDp0iJSUFMqUKcOoUaP46KOP9CbqFStWjHfffZdDhw6xYcMGoqOjsbW1pUaNGvTu3ZuBAwfqkjchROGyK6EcL5k6CKEn0juY0CvOpg5DiELBoKcsIf0W5OTJk1m6dCnh4eG67aVLl+a1115jxIgRercsiyp5ylKIgsHFIoU/zQag0qaaOhTxr4+cprEqwtPUYQiRpYL2lKXBFfpsbW35/PPP+fzzz4mLiyM2NhYHB4d8mycmhBA5iUo257F7NWzvnzJ1KAKI8WjAqmuSjAmRW0ZZw8Le3p7ixYtLMiaEMKlLNjVNHYL41zxtB1OHIEShIouKCSGKjL2JFU0dggASXary3Y3Spg5DiEIlV7csy5Yti0qlYseOHZQpU4ayZcvmqnOVSsXly5fzFKAQQuTWqnvFGYIKFQZNjRVGstxCCvUK8axyNUKm1WrRarV6XyuK8tSPJ48RQoj8dj3BimRnGSUzJcXClknXKpk6DCEKnVyNkD35FGVWXwshREFxza4mFR/+beowXlgPXeqQFCuzYYR4VvK/RghRpBxKldEZUzpjVt3UIQhRKOU5IXv06BHHjx9n3759xohHCCHyZG1USVOH8ELb+ih/VmQRoqgzOCELDw+nQ4cOODs74+fnR1BQkG7f/v37qVq1KqGhocaIUQghcu1krB2p9iVMHcYLSTGzZn2k1B4TwhAGJWTXr1/npZdeYtOmTXTo0AF/f3+eLPjfoEED7t+/z7Jly4wWqBBC5NZtx9qmDuGFFOviS3yazIQRwhAG/c8ZO3YsDx8+ZM+ePaxevZoWLfQXjzUzM6Nx48bs37/fKEEKIcSz+FOpbOoQXkh/WUhhXiEMZVBCtnXrVjp16kTDhg2zbVOyZElu3bplcGBCCGGojdGlTR3CC2n7Y5k/JoShDErIHjx4QOnSpZ/aLikpyZDuhRAiT3ZFOaO1djF1GC8URWPBqrtepg5DiELLoITMw8ODS5cu5djm7NmzlCwpTzsJIUzjnrPMI3ueHrnUJC41V6UthRBZMCgha9GiBRs2bODs2bNZ7t+3bx87d+7klVdeyVNwQghhqFPqqqYO4YXyt2UNU4cgRKFmUEI2atQorK2tadSoERMnTtSNlm3evJnRo0fTqlUrXF1d+eijj4warBBC5NaWuNytuSuMY2diBVOHIEShZtD4cunSpdm6dSuvvvoqo0aNQqVSoSgKbdu2RVEUSpYsyerVq/HykvkEQgjT2HDPjW/sbFElx5s6lCJPUZux+m5xU4chRKFm8A3/Bg0acPHiRTZs2MDhw4d58OABDg4ONGjQgA4dOmBhYWHMOIUQ4pmkaFVEF6uFc4SU38lvj12qc++GuanDEKJQy9MMTDMzMzp16kSnTp2MFY8QQhjNX+bVCUASsvx20UrqjwmRVwbNIVu1ahX37t0zdixCCGFUOx/LPLLnYU9SRVOHIEShZ1BC1qNHDzw9PalRowbvvfcea9euJSoqytixCSFEnqyJ9ELRyPSJ/KSo1CyPlLVDhcgrgxKy2bNn07VrV+7du8fs2bPp1q0bHh4e1KpVi6FDh7J+/Xqio6ONHKoQQjybmBQz4otVN3UYRVpisSrcSZSkV4i8MighGzRoECtWrCAiIoKzZ88yc+ZMOnTowO3bt5k1axadO3fG1dWVunXrGjteIYR4JhetpT5Wfrpi42vqEIQoEgxKyJ5UtWpVhgwZwpo1awgPD2fq1Km4urqi1Wo5efKkEUIUQgjD7ZX6WPlqX7LMHxPCGPL0lGVKSgoHDx5k9+7d7N69m8OHD5OcnIy5uTmNGzcmKCjIWHEKIYRBVkUW531UqFBMHUqRo6Bi5T1ZIk8IYzAoIZs4cSK7d+/mwIEDJCQkYGZmRr169Rg2bBjBwcEEBARgZWVl7FiFEOKZ3Uy0JMm7ElYPLpg6lCIn2bkiV+7Iz3ohjMGghCyjOn+zZs0ICQmhSZMm2NraGjs2IYQwimu2vlSShMzowu1k/pgQxmLQHLIqVaqgKAo7duzg9ddfp2/fvsyePZu//vrL2PEJIUSeHUiVeU75YX9qJVOHIESRYdAI2blz57h37x6hoaG6+WNr165FpVLh5uZG06ZNCQoKIigoiIoV5QehEMK01t4vSX9TB1EErbpXytQhCFFkGDyp383NjW7dutGtWzcA7t69q0vO1q1bx6pVq1CpVKSmphotWCGEMMSZOFtS3EthHnvN1KEUGSmOZTl/18bUYQhRZOS57AXA/fv32bdvH/v27WPv3r3cu3cPRVFQFHmqSQhRMNxyqGXqEIqU6w61TR2CEEWKQSNk0dHR7Nmzh927d7Nr1y7OnTsHgKIoupGzjFuWQghREPyprUxp1ps6jCLjUJrMHxPCmAxKyFxdXXUjYC4uLnTq1EmXgFWtWtXYMQohRJ79GlWOl4q/gl1aNFbJDzBPeoA64QEqrUyrMMSaqNKmDkGIIsWghKxt27YEBwcTFBREjRqyLIkQouA7HmNHo5jeettUKgUfqyTKWCdQyiqBEhaPKG32gFKqCNxTbmH/+DqauFuoFK2Joi6YUu1LcPyenanDEKJIMSghW7dunZHDEEKI509RVFxPsOJ6ghXgnGUbW42Weo4x1LF7SGWLSMqqblPhxurnG2gBc8uxDtwzdRRCFC15WjpJCCGKuvg0NXseOLPngTNQFoBL7ocxi71h2sBM6Ii2sqlDEKLIMcpTlkII8SK54VjX1CGY1LqHZUwdghBFjiRkQgjxjA6kvbgPL6XZerL/oaOpwxCiyJGETAghntGK+6VNHYLJ3HGqY+oQhCiSZA6ZEEI8o9OxdqR4lME85qqpQ8kVRW3ONe9XsNXGYp0Sg0VKNGZJMagSo1Epac/U1zFe3NFBIfKTJGRCCGGAaw51KF9IErJHrr40vdQj03aVSqG4ZTIlrRPxtkykhEU8XmaPcNPE4UIsTkoMdmnR2CQ/wCLpAeqEKNZHl37+FyDEC8CghGzmzJn06dMHZ+esHxMXQoiiLiy1KuVZY+owcuWcZdbLHCmKipuJltxMtARyOS8s3nhxCSH+z6A5ZCEhIXh7e/Paa68RGhpq5JCEEKLgW36vlKlDyLU/4iuaOgQhxFMYlJB99913VKlShWXLltGsWTMqVKjAV199RUREhLHjE0KIAunCIxuSncqbOoynUsxtWX3X09RhCCGewqCEbPDgwRw/fpwTJ07wzjvvEBUVxciRIylVqhSdO3dm8+bNKIpi7FiFEKJAuWpf8J84fOBaj4Q0janDEEI8RZ7KXvj6+jJ79mzu3LnDkiVL8Pf3Z/369bRt25ZSpUoxbtw4rl+/bqxYhRCiQNmXUsXUITzVcU1NU4cghMgFo9Qhs7S0pHfv3oSGhrJv3z68vLy4efMmEyZMoGzZsrRr144jR44Y41RCCFFgLIssiYLK1GHk6LeYCqYOQQiRC0YrDLtlyxa6du1KcHAwt2/fxsfHh5EjR9KyZUs2bdpEw4YNWbJkibFOJ4QQJnf5sTXJxQruhHmttSub77uYOgwhRC7kqQ7Z9evXWbBgAQsXLuTmzZuo1Wpat27N22+/zSuvvIJKlf6X47lz52jXrh0TJkygT58+RglcCCEKgsu2daj64G9Th5GliGL1UB4W7BE8IUQ6g0bI1qxZQ6tWrShbtiwTJkxAURTGjBlDeHg4v//+O23atNElYwDVqlXj9ddf59q1a0YLXAghCoI9yZVNHUK2DlHD1CEIIXLJoBGybt26odFo9EbD1Oqcc7vKlSvTqFEjg4IUQoiCalmkD++o1KgUralDyWRFVDlThyCEyCWDErIxY8YwcOBASpQoketjevbsSc+ePQ05nRBCFFjXE6xILF4V66izpg5FT6qDD4cjHUwdhhAilwy6ZTlu3LhnSsaEEKIou2Rby9QhZHLNwc/UIQghnkGeJvXHx8ezfv16Tp48SUxMDI6OjtSqVYsOHTpga2trrBiFEKJA25VYucDN1tqbVs3UIQghnoHBCdmyZcsYMmQI0dHRelX5VSoVTk5OzJ49m1dffdUoQQohREG27G4J3ldrUClppg4FAAUVv0aWNnUYQohnYFBCtmHDBnr37o2VlRWDBw+mcePGeHh4cPfuXfbu3cvChQvp3bs39vb2tGnTxtgxCyFEgRKRZEFCierY3D9l6lAASCpWiYu3rU0dhhDiGRiUkH3xxRfY29tz5MgRKlbUL4rYvXt3hgwZQoMGDfj8888lIRNCvBD+tqlFbQpGQnbRtq6pQxBCPCODJvWfOXOGV199NVMylqFy5cq8+uqrnD59Ok/BCSFEYbEzoZKpQ9DZmVTw19gUQugzKCFzcHDAyckpxzZOTk44Ojoa0r0QQhQ6K+4WR1Hn6Tkpo1DUZvx618fUYQghnpFBCVm7du3YuHEjaWlZT2BNTU3ljz/+oH379nkKLre0Wi3fffcdderUwcbGBgcHBwIDA/n999+zbB8bG8uwYcMoVaoUlpaWlCpVimHDhhEbG/tc4hVCFD33ks2Jd6lp6jCId/UlMsnc1GEIIZ6RQQnZ119/jZWVFa1bt+bw4cN6+w4dOkTr1q2xtrbmq6++MkqQOVEUhe7du/Pee+8RGxvLgAEDePXVV7lw4QIdOnTgu+++02sfHx9PYGAg06dPp1KlSoSEhFC1alWmT59OYGAg8fHx+R6zEKJoumBdy9QhcM6yjqlDEEIYIFfj62XLls20LTk5mRMnTrBz507Mzc1xcXEhKiqKlJQUALy8vKhbty6XL182bsT/sWbNGtasWUNAQADbt2/H2jr9yaKJEydSr149hg8fTtu2bSldujQAU6ZM4eTJk3z88cd6CePYsWOZMGECU6ZMYfz48fkasxCiaNr+uCL1TBzDpvis5/YKIQq2XI2QabVaFEXR+zA3N6dkyZKULFkSLy8vLCws8PLy0m0zNzdHq83/td3WrVsHwKeffqpLxgBcXV0JCQkhKSmJhQsXAumjaT/99BN2dnaMGTNGr5+RI0fi7OzM/Pnz9eqqCSFEbi2/642isTDZ+RVzW1bf9TLZ+YUQhsvVCFl4eHg+h2G4u3fvAlCmTJlM+zK27dq1i/Hjx3Px4kVu377Nyy+/nGklASsrK5o0acL69eu5dOkSFSpUyP/ghRBFSkyKGXFevjhEHjXJ+R+41iX+qkEzUYQQJlbo/+e6ubkBcPXq1Uz7Mrb9888/AFy8eBEg22QrY3tGu6wkJSURGxur9yGEEBnOW9Yy2blPmvma7NxCiLwp9AlZ69atAZg8eTKJiYm67VFRUcyYMQOA6OhoAGJiYgCyLcfh4OCg1y4rkyZNwtHRUffh4yOPlwsh/m/r4/ImO/e6GNOdWwiRN4U+IevZsydBQUHs27ePGjVq8N577/HOO+9QrVo1XYKl0WiMdr6RI0cSExOj+7hx44bR+hZCFH4r7nqjmFk99/NqrYux8Z7rcz+vEMI4Cn1CZmZmxubNmxk3bhxqtZoff/yRtWvX0qFDB1avXg38/7ZmxshYdiNgGbcfcypoa2lpiYODg96HEEJkiE/VEOtS67mfN6JYfRRF9dzPK4QwjkKfkEF6kjR27Fj+/vtvkpKSiIyM5IcffuDWrVsA1KuX/iD60+aIPW2OmRBC5MZZi+c/l+uIqvpzP6cQwniKREKWnaVLlwLw6quvAumJlre3N/v3789UADYxMZG9e/fi7e1N+fIyD0MIYbhNj57/H3Uro8o993MKIYynSCRkWT3puHr1ahYsWICfnx+dO3cGQKVSMXDgQB49esSECRP02k+aNImHDx8ycOBAVCoZ9hdCGG5tpAeKue3TGxpJqn0JDjyUtYOFKMxMvxKuETRo0AAfHx+qVKmClZUVR44cITQ0lLJly7Jq1Sq9Sf0ff/wxv//+O1OmTOHEiRPUrVuXU6dOsXnzZmrVqsXHH39swisRQhQFCWkaHrjWw+XOnudyvuuOfnDvuZxKCJFPDE7IkpOTWbduHUePHiU6OjrLhcZVKhXz58/PU4C50aNHD9auXcuhQ4dISUmhTJkyjBo1io8++ijTpHtbW1tCQ0MZP348q1evJjQ0FE9PT0JCQhg7dmymgrFCCGGI45qatOD5JGT70qo9l/MIIfKPSjFgnaBr167RokULLl++nOMyQyqVKstErSiJjY1Nr0c2dCVqSxtThyOEKCBecbvPnLj3n8u5Wmrm80+89dMbCiF0tEmPuTGjOzExMQWiYoJBI2QhISFcunSJ119/nTfeeIMSJUpgZlYk7n4KIYRRbL7vgtbJFXXC/Xw9T2KxKvxzW5IxIQo7g7KoXbt20axZMxYvXmzseIQQokhQFBV3itWj+K0t+Xqe83b187V/IcTzYdBTllqtltq1axs7FiGEKFIOUyPfz/FHfNV8P4cQIv8ZlJD5+/tz/vx5Y8cihBBFyvKosvnav2Jhy7KI4vl6DiHE82FQQjZ58mR2796tW5pICCFEZkeiHUl1KJlv/d93bUB8WpEoJynEC8+gOWQbNmwgKCiIHj16EBgYSO3atbNc/1GlUjF69Og8BymEEIVVuKMf5WOv50vfh1S18qVfIcTzZ1DZC7U6d3+RSdkLIcSLbkyZ87xx5/N86bun9VwOSoV+IQxSJMpe7N6929hxCCFEkbT0bmn6o0LFM//tm6MUx9IcvCvJmBBFhUEJWWBgoLHjEEKIIunyY2uSvCtj9cC4D0JddmgAd43apRDChGQ2qBBC5LOLdnWN3ufWpPwvqSGEeH7yVF7/wIEDLFq0iJMnT+ruwdauXZs+ffrQqFEjY8UohBCF2vbEykatSKZoLFgS4WPEHoUQpmZwQjZ8+HCmT5+uW8tSrVaj1Wo5duwY8+fP54MPPmDatGlGC1QIIQqrpRE+hJiZo9KmGKW/GLe6RIWbG6UvIUTBYNAtyyVLljBt2jQqVarEsmXLuHPnDqmpqURERLB8+XIqV67MzJkzWbJkibHjFUKIQicq2ZxHrr5G6++4eR2j9SWEKBgMSsjmzp2Lj48Phw8fpkePHnh4eADg7u5O9+7dOXjwICVKlGDOnDlGDVYIIQqrs5bGW25uVXQlo/UlhCgYDErIzp49S5cuXbC3t89yv4ODA507d+bcuXN5Ck4IIYqKTfEVjdJPmq0nm++5GqUvIUTBYfBTlk+rJ6tSqQztWgghipzVdz1RLGzz3M9155eMEI0QoqAxKCGrXr06a9as4dGjR1nuj4uLY82aNVSrVi1PwQkhRFGRkKbhgUu9PPezK03KXQhRFBmUkL3zzjvcvHkTf39/1qxZw/379wG4f/8+q1evpmHDhty8eZNBgwYZNVghhCjMjmnyNrFfUalZHFHGSNEIIQoSg8pe9O3bl5MnTzJz5ky6d+8O/L/sBaTfznzvvffo27ev8SIVQohC7reY8rTMw/GPXWty/YaV0eIRQhQcBtchmz59Ol26dGHhwoWcPHmS2NhYXWHYvn370rhxY2PGKYQQhd6W+y5onVxRJ9w36PgzVn5GjkgIUVDkqVJ/o0aNpCK/EELkkqKouFPMj+K3Nht0/LpHlY0ckRCioJC1LIUQ4jk6ZOAiSlorZ9bc9TRyNEKIgiJXI2TXr18HoHjx4mg0Gt3XuVGyZEnDIiviHCzV2JmrkOogQhR+igKPUhRik7RPbbvsfjm6GHCOCJcGpETLDwwhiqpcJWSlS5dGpVJx/vx5KlasqPv6aVQqFampqXkOsiip6W5Bq/K2lC1mgUYtA5RCFBVpWi1XHiSz5VI8pyOTs233Z4w9Ke6lMI+99kz9hynGW3pJCFHw5Coh69OnDyqVCkdHR72vxbOp6W7BID9nPFycUFvaoNLkaQqfEKIAUdJScS/2mDLO0cw9+jDHpOyaYz3KP2NC9vO98nkNUQhRgOUqI1i0aFGOX4vcaVXeFg8XJzT2LpLQClHEqMwsUFlY4wG8XD45x4RsT2o1yrMm130nFqvEmdt5r/IvhCi45J7Zc+JgqaZsMYv0kTFJxoQoklQqFWpLG8oVs8DBMvsfr0vvlkYh9z8H/ratb4zwhBAFmEEJmUaj4fPPP8+xzVdffYVGozEoqKLIzlyFRq2W25RCFHEqjRkatRpb8+wTriuPrUh0qZLrPv9IkGXohCjqDErIFEV56uLiQp8MignxYlE/5f/8RZu6uepHMbdlaURxI0QkhCjI8u2W5b1797C2ts6v7oUQolDbnpi7Iq9Rbn7Ep8rdBiGKulzfP1uyZIne1ydPnsy0DSAtLY2bN2+ycOFCqlevnvcIhRCiCPolwoeXSr9N+bTLuMSdxyzuVpbtjqhrPd/AhBAmkeuErF+/frrJ6CqVivXr17N+/fpM7TJuZVpbWzNu3DjjRCmEEEXMwxQzXrsYCAQCUNYmkZddImhgdZNK2su4xl3APPYaS6MqmjZQIcRzkeuEbOHChUB6wvXGG2/QsWNHOnTokKmdRqOhWLFi+Pv74+zsbLxIxQtlQLe2/HloP6duPDR1KEI8F1ceWzH3cWnmUhpIXyPYyyqZOw8tTBqXEOL5yHVC1rdvX93ne/bsoVOnTrRv3z5fgnpRtf9uv6lDyNbvQwJMHYIQL5w7iZKMCfGiMKgGQ8ZomRBCCCGEyDspDCuEEEIIYWK5GiFTq9Wo1Wr++usvKlasiFqtlsXFxTNLTU1l8fezWLvsZyLv3sHD05tOr/bm5XadadOoNu279uTz6XOyPT4uNoZVvywkbNd2rl+9wsOHUTg7u9CgcSDvDB2BT+kyeu2TEhNZvvgnNq5Zzu2b10lL0+Li6kb1WnUYOGQYFaqkF9vUarWsW/ELq5cu5kb4FZKTkijm6kqlqjV4/c3B1H1JbtcKIYTIX7lKyJo0aYJKpcLGxkbvayGexdjhQ9i4ZgU+pcrQo89AkpOT+GX+95w6djRXx1+99A9zvpmEn39jglu1wdrGhquXLrJ53Wr27dzG8s2heJcoqWs/KmQw2zb+RsUq1ejQ/TXMLSyIuHWTowfDaBjYTJeQzZw8nkVzZ+FTqgytO3bF1s6OyIg7HD9ykCMH9klCJoQQIt/lKiELDQ3N8WshnuZw2B42rllBtZq1WbD6D6z+LRr81vvD6dE6MFd9lClfkZ1/XsDxP0/vHjmwj7d7dmTerG8YO2UmkD6atv2PdVSrWZuff9+ut4xXWloa8Y8e6b7+bdnPuHt6s2p7GNbWNrrtiqIQGx1t6CULIYQQuSZzyMRzsXHtSgDe+mC4LhkDcHX3oNcbb+eqD3sHx0zJGED9ho0pV7Eyh8JCddtUKhWKomBuaZFpTVWNRoODo6PeNnMLc8z+s86oSqXK8nxCCCGEsRl1peurV6+yY8cOrK2t6dSpE7a2tsbsXhRi/5w/C4BvvQaZ9tWqWz/X/Rw9GMbSn+Zy5uQxoh9E6c1RNLf4f4kAO3sHApo2Y3/oTnq0DqTFK+2pU9+fGrXr6bUDaNm2I6t+WUjn5g15uV0n6r0UQM26ftjYyPtXCCHE82FQQvbVV1/x008/ceTIEV3x19DQUNq2bUtCQgIAX3zxBQcPHpTisAKA+Lg41Go1Ts7FMu1zcXPPVR/bNq7j48FvYGNrR8PAYLxL+GBlbYNKpeL3Vb9y++YNvfZTf1jM/G+nsfn3NXw75QsAbO3s6djjNd4bMVp3e3LEhK8oUao061ctY96sqcybNRVLSytatuvIh6O/wLmYSx6vXgghhMiZQQnZ+vXrKV68uF6y9dFHH6HVahk/fjwRERHMmTOHmTNnyvJJAgBbe3u0Wi3RDx9kSnCi7kXmqo+507/C0tKKZZt2U6pMOb19W35fm6m9jY0t740YzXsjRnPz+jWOHtjHql8WsnT+9yQmJjBm8gwAzM3N6ffO+/R7530iI+5w7NB+1q38lQ2rl3M/MpLvl64x7KKFEEKIXDJoDtmVK1eoVq2a7usbN25w7Ngx3n33XUaNGsV3331Hs2bNWLNGfpGJdBWrpC80f+rPw5n2nTx2JFd93Lx2lTIVKmZKxiIj7nDz2tUcjy1RshSdXu3NglUbsbG1Y8/2LVm2c/f0onXHrsz9ZTUly5TjcFgoif+O+gohhBD5xaCELDo6GicnJ93XYWFhqFQq2rVrp9tWp04drl+/nucARdHQplM3AH6cOZWkxETd9vuRd/l1wQ+56sOruA83wq/ojaglJSby5acfZqp39yDqPmdOHMvUR2xMNMnJSVhaWQGQnJTE4f17URRFr13C43gexz/CzNw800MBQgghhLEZdMvSw8ODa9eu6b7evn07lpaWNGjw/wnbiYmJUqtM6LzUuCmvdOrGpt9W0bVFAE1bvkJychLbNq6jRq267NmxBZU6578PevZ/k8mjR9CjVSDN27QnLTWVQ/tCURSFSlWr8/dfZ3VtIyPu0Lt9c8pVrEyVGr64e3oR8/Ahu7dtIjUlhX7vvA+kv0/ferUDJUqWpkbtungWL8Hj+Hj27dzK/ci79B/8QaaHAIQQQghjMygh8/PzY/369fzxxx9YWVmxcuVKmjZtiqWlpa7NlStX8Pb2NlqgL4KivoD359PmUKZcRdat/IVli37Ew9Ob3gPeoX5AIHt2bMHO3j7H41/t+yZmZuYsW/gja39dgr2jI42DW/LeiNF8PKi/XlvvEiUZNOwTjuzfy+F9e4iOfoCTswtVqtek98DBNAwMBsDaxoahn47jcNhejh85yIOo+zg4OlGmXAU+GDmOVu0759vrIYQQQmRQKf+9V5MLx48fJyAggOTk5PROVCp2795N48aNAYiLi8PLy4tu3boV+YXIY2NjcXR0xGfoStSWNtm2K26vYVyQO+7eJVCZyYjLk9YuW8L4jz/gsy+n0r3PAFOHI0SeKKnJRN6+ybjdkdyKSzN1OEKIbGiTHnNjRndiYmJwcHAwdTiGjZDVqVOHQ4cO8fPPPwPQtWtXXnrpJd3+U6dO0aJFC3r16mWcKEWRcD/yLi5u7nq3su/euc28WVPRaDQ0af6yCaMTQgghTMfgwrC+vr74+vpmua9Ro0Y0atTI4KBE0bRgzgz27dxG7fr+FHN1JeLWTfbu3Eb8ozgGDfsET+8Spg5RCCGEMAmjVOqPj48nNjYWBwcHqc4vshUQ2IzL//zNvl3biI2JxtLSigpVqtLj9QG88u9TmEIIIcSLyOCELCUlha+//ppFixZx+fJl3fayZcvSv39/hg8fjoU8nSaeEBDUnICg5qYOQwghhChwDErIEhISaNGiBQcPHkSj0VCxYkU8PT25e/culy9fZvTo0WzcuJGdO3di/cRC0kIIIYQQIjODCsNOmTKFAwcO0LNnT65cucL58+fZvXs3f/31F1evXuW1117j0KFDTJkyxdjxCiGEEEIUOQYlZMuXL6devXr88ssvlCihPxHb29ubJUuWUK9ePZYvX26UIIUQQgghijKDErLw8HCaN895LlCzZs0IDw83pHshhBBCiBeKQQmZjY0N9+7dy7HNvXv3sLHJvlCqEEIIIYRIZ1BC9tJLL7F8+XLOnTuX5f6//vqLFStW4O/vn6fghBBCCCFeBAY9ZfnZZ5+xfft2/Pz8GDBgAIGBgXh4eHD37l1CQ0NZuHAhKSkpjBw50tjxCiGEEEIUOQaNkDVs2JBly5ZhaWnJ7Nmz6dGjB02bNqVHjx7MmTMHCwsLfv31VwICns9i2YqisHbtWoKCgvDy8sLGxoZKlSrx9ttvc+XKFb2248aNQ6VSZflhZWX1XOIVQgghhHiSwYVhu3Tpwssvv8y6des4ceKErlJ/7dq16dChA/b29saMM0fDhw9n2rRpeHl50bFjRxwcHDh16hTz5s1j2bJlHDhwgOrVq+sd07dvX0qXLq23zczMKAsXCCGEEEI8kzxlIHZ2dvTu3ZvevXsbK55nFhERwYwZMyhdujSnTp3SW7F9xowZhISEMG3aNBYsWKB3XL9+/WjatOlzjjZnNX8qZeoQsnV64LXndq6jB8MY2L0d74SMYNCwT57beXMyOmQwv69exqYDpyjuU9Lgflr71wRg88HTxgrtuYmMuMP0iWM5eiCM+5ERKIrCqRsPTR2WEEIUCYV+Lcvw8HC0Wi0BAQF6yRhAmzZtCAkJITIy8rnGJERRNHrYuxwOC6V1x674lCqDSqUydUhCCFFkGJyQJSUlMXXqVBYvXqy3lmW5cuXo168fH374IZaWlkYJMicVKlTAwsKC/fv3ExcXp3erdNOmTQAEBwdnOm7fvn0cOXIEjUZD5cqVad68ea7iTUpKIikpSfd1bGysEa5CFHTvfzKGN94dirunV576+XHZeiNF9HylJCdzZP8e/JsEM2nWj6YORwghihyDErLo6GiCg4M5deoUZmZmVKpUSfeUZcZalmvWrGHnzp04OTkZOWR9Li4ufPnll3z00UdUqVKF9u3bY29vz5kzZ9ixYwdvvfUW7733XqbjxowZo/e1l5cXixcvpkWLFjmeb9KkSYwfP96o1yAKPjcPT9w8PPPcj0/pMkaI5vm7f+8uWq0WVzd3U4cihBBFkkFPWX766aecPHmSfv36ER4ezl9//aVbyzI8PJy+ffty4sQJPvvsM2PHm6Xhw4ezdOlSYmJimDt3LlOmTGHz5s34+fnRu3dvzM3NdW1r1arF4sWLCQ8PJyEhgYsXL/L5558THR1N+/btOXXqVI7nGjlyJDExMbqPGzdu5PflFQkpycn8uvBH3nmtCy3rV6NeOQ+a1qpAyJuvc/5s7uZT+fo4M6Bb2yz3tfavqZuflWF0yGB8fZy5eS2cxd9/S7sm9ahf3otOwS+xef2a9LhSUpg9dSKtG/riV96Tri0C2B+6M1P/GX3dunFdt+3owTB8fZyZO20y58+c4p3XuuBf2YeAqiUZOrC3XtvcxJlV+7nTJuPr48zRg2FZnvfkn4cZ0L0d/pV9aOpbni8//ZDEhAQA9u/eQd9OL9OgYnGCaldk+sSxpKWlZffyZmtAt7a0eik95t9XL8PXx1l3/ifjv3ktnJ/nzaFTM3/qlfNgdMhgIH3u2ZxvJtG7fQua1qpAvXIetPavyZeffkjU/cwFpo35fQOIfxTHnG8m0amZP/XLe9GoWikG9e7K8SMHM7W9dzeCr8Z+QrvGdalf3ovG1cvQpXlDvvz0Qx7FyWi4ECL/GDRC9ttvv9GwYUPmz5+faZ+XlxcLFizgwoULrF27ltmzZ+c5yKf54osvmDBhAuPGjaNPnz44Oztz8uRJhg0bRlBQECtXrqRz584AdOzYUe/Y8uXLM2rUKDw8PHjrrbf44osvWLVqVbbnsrS0fC63YouamOiHfD1uJHXq+9MouAUOjk7cun6N0O2b2b97BwtW/0H1WnXy5dxTJ3zGmZPHCGzeCrVazZYNaxn53ps4ODqxYvFPXPrnPI2DWpCUlMTmdav5YEAv1u0+QomSuXvI4tzpEyz6/lv8/APo+lo/Lpw7ze6tf3Dpwl+s2XEAy3wqp3LmxJ8snDOThoHBdH2tH0cP7mPlzwt49CiOoJavMDpkMIEtWlG9Vj327drGormzsLOz5833hz/Tedp360WlajVYOv97KlWtTtDLbQCo599Ir93kMR9z+vifNG7WkibNWuLimj6aduzwAZb8OJsGAU2oUbsuZmbmXDh3mpU/L+DAnl0s3xyKvYNjpvMa4/sW8/Ah/bu+wuV/LlCnvj8NmwTzKC6W0G2beLNHe76eu4jgVunXk5DwmL6dW3H7xnX8mwQT3KoNKSkp3LwWzu+rltFv0AfY2TtkilMIIYzBoIQsNjaWwMDAHNs0bdr0qaNNxrBr1y5Gjx5NSEgIn376qW57QEAAGzdupGzZsoSEhOgSsuz07duXwYMHs3///vwO+YXk4OjElkNn8PDy1tt+6e/zvN6hJd9O+Zwffv0tX859+eLfrNoWRjEXVwDad+/F6+1bMGLIAMpXrMLq7fuxsUl/GKVhYDAfD36DpfPnMmL85Fz1v2/nNr6aPZ9W7f//Hvts6DtsXLOCXVv/oHWHLsa/KGB/6E5m/LSUoJdfAdJHjXq1CWLzutUc3LOL+as26pLcwR9+QtvGdfnlp7n0Hzz0mUq8dOjei1s3rv+bkNXI9snXf86fY8WWPXgV99HbXj+gCbuOX8DG1k5v+4bVyxkVMojli+ZlmSQa4/s2eczHXP7nAuOnfkfHHq/ptkfdG02vtsFM+GQoAU2bYWllxeGwPdy6fo3eAwfz0dgv9WKJfxSHhYX8ISaEyD8G3bKsVq3aU2/V3bhxg2rVqhkU1LP4448/AAgKCsq0z83NjRo1anD9+nXu37+fYz8WFhbY29vz+PHjfInzRWdhaZkpGQMoX6kKfv6NOHb4ACkpKfly7jff+1D3Sx2gZu16lChZmriYGN77eJTulzpA81faY2Zuzj9/nc11/3UbNNRLxgA69kgvBXPu1Ik8Rp+9ev6NdMkYgLm5Oc1f6YCiKDRp3kpvxNHWzp4mzV4m+uED7t65nS/x9H3n/UzJGICLq1umZAygbZce2NnbcyhsT5b95fX79vBBFFs3/EaDRoF6yRiAi5s7fd95n4dR9zkUFqq3L6sC0bZ29phbWGR94UIIYQQGjZCNHDmSXr168cYbb2RZy2vXrl2sWrWKFStW5DW+p0pOTgbIdrHzjO1Pu8148eJFHj58iK+vr3EDFDoXzp1h0dyZnDh6iPv3Ikn9TwIW/SDKKBPn/6ty9RqZtrl6eHDzejiVqunv02g0FHNxIzLiTq77r1I983vGwzM9+YyLjXnGaHOvcrWamba5eXgAZLouADf39H2REbfzVEstOzVyuOW8Y/MGVv+yiAtnTxEbE603l+3e3Ygsj8nr9+3cqeOkpaWRlJSom+/2pOtX058OD790kcDmrajboCGu7h7Mnz2dv/86Q+PgltSu/xIVKleTEh9CiHxnUEIWFxdH8+bNadasGS1btqRRo0a4u7sTGRnJvn372L59O23atCEmJoYlS5boHdunTx+jBJ4hICCA7777jmnTptGlSxccHf8/F2Xx4sVcunSJunXrYm9vT1xcHFevXqVmTf1fZA8fPmTAgAEA9OzZ06jxiXQn/zzMm692AMC/cRAtypTDxtYWlUrF7q1/8PdfZ3XJtbHZ2mWe92OmSX/rZzUnyMxMQ2pq7kfr7Bwy96H595ag1oBJ9Lk+bxarYWgyrssui33/xpSampov8RRzzfoJzMU/fMe0L0bj7OKKf5Mg3L28sbKyBmDp/LmkJCdleVxev28x0elFa08ePczJo4ezjTshIX1U3N7BkSXrtjF32iT2bN/Cvl3bAfDw8mbAuyH06Dsw2z6EECKvDErI+vXrh0qlQlEUtm7dytatWzO12bhxo+52IqSvN6lSqYyekHXr1o0ffviB0NBQKlSoQPv27XF2dubUqVNs374dS0tLZsyYAUBUVBS+vr7Uq1ePGjVq4O7uzq1bt9i8eTNRUVG0aNGCkJAQo8Yn0v307TckJyWxaO1mavu9pLfv9PE/gaffIlSpVKSmZZ1MxMXFYl9IJ1yr1OkzB9KySJQeFaI6d1mNIqWmpjJv1te4eXixcutevVuQiqKwaO6sfIvH9t+ktM9bQ/hw9Oe5Oqa4T0m+mD6XtLQ0Ll74i4N7d/Hrgh+ZOOojHBydaN2xa77FK4R4sRmUkC1cuNDYcRhMo9GwZcsWZs6cyYoVK1i2bBnJycl4eHjQq1cvRo4cqVvHslixYrz77rscOnSIDRs2EB0dja2tLTVq1KB3794MHDgQjUZj4isqmm5cC8fRyTlTMpaQ8JjzZ3P38IeDo1OWtxJv3bhOXExMoU3IHBydgPTyECXLlNXbd+Fc4Vti6UnRD6KIi42lfkCgXjIG6fPrEhMT8u3c1X3roFKpOH38yDMfq9FoqFytBpWr1cC3jh/9u7YhdPtmSciEEPnGoISsb9++xo4jTywtLfn444/5+OOPc2zn4ODAd99995yiEk/yKu7DtSuXuPT3ecpXqgJAWloa0z4fzcOonB+4yFC1Zm0O7t3F0YNh+P1bciElOZmpE55Pvbv8Uq1mLQDWr/qVev4Buu3b/1jPn4cK91O/xVzdsLKy5vzZUyQkPMba2gaA2OhoJo8Zka/ndnX3oGXbjmzd8BuLvp9F37ffyzSKd/rEn1SoXBVraxsuXvgLWzs7vEvoz6/LqJVm+e9tViGEyA9GWctSiKfp2f9NDu7dRb8urWnZtiOWllb8eTCMyIg71PNvxJ9PFD7NzusDB3Fw7y6G9O1B6w5dsLK25tC+9BpWbu7GfxjgeQl6uQ3FS5bi91W/cvfOLSpXq8GVS/9wdP8+Gge30M1lKozUajXd+wxgyY/f0b1lY5o0b0X8ozjCdu/Au0QJ3DzythTV03z25TeEX7nE9C/HsnHNCmrW9cPe3oGI27f468wprl+9zM5jF7C2tuFw2B6++XwUteo1oEz5ijg6O3Pz2jX2bN+MlZU1Pfu9ma+xCiFebHlKyLRaLTdv3uTWrVvZlixo0qRJXk7xQjk98JqpQ8g3gc1b8c0Pi/npu2n8sXYV1tbW+DVswvR5v/DDjCm56iMgqDlT5ixg3qypbFy7AkcnZ1q06cD7I0bTpXnDfL6C/GNlbc2Py9YzdfynHDmwj9PH/6RmnXosWP0He3duLdQJGaSvA+ro5MT6VctY+fN8XFzdaNW+M4M//CTfv2+Ozs4s+W0ryxfPY+vvv7Hpt9UoWi0u7u5UrFKdtz4YjlMxFyC9ltntm29x7PABdm7ewOPH8bh7ePFy+870H/Q+ZStUytdYhRAvNpWiKMqzHqQoCpMnT2b69OlERUXl2NaQpVoKk9jYWBwdHfEZuhK1pU227YrbaxgX5I67dwlUZlLPSIiiSklNJvL2TcbtjuRWXNH++SdEYaZNesyNGd2JiYnBIYun5Z83g+uQTZkyBXd3d/r374+Xl9czVf4WQgghhBD/Z1AWtWjRIipVqsTRo0exs8tcgVsIIYQQQuSeQQnZo0eP6N27tyRjQhRSt25c5/dVvz61nb2DI70HDnoOEQkhxIvNoISsVq1a3L6dP+vhCSHy3+2b1/l++ldPbeddwkcSMiGEeA4MSshGjRpFly5dOH78OHXqZL9+nRCiYPLzb8SpGw9NHYYQQoh/GZSQtWrVisWLF9O6dWvat2+Pr69vtk8oGHupJCGEEEKIosaghCwpKYn169dz//595s+fD2Rexy6/1q4UQgghhChqDErIhg0bxtKlS6lZsyZdu3aVshdCCCGEEHlgUBa1atUq6taty8GDByURE0IIIYTII7UhByUmJhIUFCTJmBBCCCGEERiUkNWtW5dLly4ZOxYhhBBCiBeSQQnZxIkT2bJlCxs3bjR2PEIIIYQQLxyD7jlu376dpk2b0qFDB4KCgqhVq1aWZS9UKhWjR4/Oc5BCCCGEEEWZQQnZuHHjdJ/v2rWLXbt2ZdlOEjIhjGfutMl8P/0rflq5AT//RqYORwghhBEZlJDt3r3b2HEI4LWdjU0dQraWNttn6hCEifj6OFPvpQDmr5IpCkIIkV8MSsgCAwONHYcQQgghxAvLoEn9QgghhBDCePKUkB04cIC33nqL+vXrU6lSJfz8/HjrrbcICwszVnyiiDl2aD/v9X+VwJrlqFfOg3aN6/LdlC9ISHisa3P0YBi+Ps7MnTaZ82dO8c5rXfCv7ENA1ZIMHdibWzeuZ9n3zevX+HxkCK0b+lKvnAdNa1VgQLe2rF/5q65NSnIyvy78kXde60LL+tV07ULefJ3zZ09n6nP9yl/x9XFm/cpf2bl5I73aBNOggjfBdSrxxchhxEZHZzrm/JlTfPh2X15uUJ165TwIql2R1zu0ZP7s6XrtfH2cGdCtbZbX0tq/Jq39a+bmJeW35b/wwRu9aO1fE7/ynjSuXoZ3XuvCkQOZbzM/y2ub0Rbgz0P78fVx1n08+ZoKIYTIO4Mruw4fPpzp06ejKAoAarUarVbLsWPHmD9/Ph988AHTpk0zWqCi8Fv58wImfjYcB0cnApu3wtnFhXOnTjDv2284enAfP63YgLmFha79udMnWPT9t/j5B9D1tX5cOHea3Vv/4NKFv1iz4wCWVla6tqeOHeHdPt14FBdHw8BgWrXvTGxMNBfOnmHpgu/p0L0XADHRD/l63Ejq1PenUXALHByduHX9GqHbN7N/9w4WrP6D6rXqZIp9+6b1HNoXSos2HWjQOJBjh/az6peFnD5+lCXrtmFlbQ3AhXNn6NPpZdRqDUEtW+NVwoe4mBgu/3OBtb8uYcC7IUZ/XSeN+oiKVavToFFTnF1ciIy4w+6tm3i7Z0em/fgzQS+/kumY3Ly23iVK8k7ICL6f/hXeJXxo362X7vhK1WoY/TqEEOJFZlBCtmTJEqZNm0blypUZO3YsTZs2xcPDg8jISEJDQxk/fjwzZ86kVq1asri4AODyPxf4aswIKlWrwY+/rsPR2Vm3b/7s6cyaPIFfF/5I37eH6Lbv27mNr2bPp1X7zrptnw19h41rVrBr6x+07tAFgOSkJD4e/Abxjx4xe/FKAoKa65377p1bus8dHJ3YcugMHl7eem0u/X2e1zu05Nspn/PDr79lin/fzm38uGwdDRr9f/7kmA+HsH7lUhb/8B1vD/0IgI1rVpCclMTM+Utp2lI/EYp++CDXr9ezWLvrECVKltLbdu9uBD3bBDPtyzFZJmS5eW2L+5Rk0LBP/k3I0j8XQgiRPwy6ZTl37lx8fHw4fPgwPXr0wMPDAwB3d3e6d+/OwYMHKVGiBHPmzDFqsKLwWr10EampqYwYP1kvGQPoP+gDnF1c2bJ+jd72ug0a6iUMAB179Abg3KkTum2h2zcTcfsWbTp3z5SMAXh4Fdd9bmFpmSkZAyhfqQp+/o04dvgAKSkpmfa/1DhILxkDGPLRZ5iZm7Nh9bJM7S2trDNtc3IulmmbMfw3GQNw8/Ck+SvtuH71MrdvZr7Fm9vXVgghxPNh0AjZ2bNnefPNN7G3t89yv4ODA507d+ann37KU3Ci6Dh9/E8ADoTu5HDYnkz7zczMuXr5ot62KtV9M7Xz8ExPpuJiY3Tbzp48BoB/k6BcxXLh3BkWzZ3JiaOHuH8vktT/JGDRD6Jw8/DU21anvn+mftw9vfApVYarl/4h/lEctnb2tGjTnqXz5zJ0YG9atu2If+Om1K7/El7FfXIVmyFuXgtn/uzpHNm/l8i7d0hOStLbf+9uBN4lSupty+1rK4QQ4vkweA5Zxtyx7KhUKkO7FkVQbPRDAOZ9+02uj7HLYvUHzb8L2mvT0nTb4mJjAXD3zDzy9V8n/zzMm692AMC/cRAtypTDxtYWlUrF7q1/8PdfZ0lOTs50XDFX1yz7c3F14+qlf3gUl56Q+datz7zlvzN/9nS2/L6G31elT36vWqMWIaMmUL+hcWvNXb96hdfaNSP+URx+DRsT2KIVtnb2qNVq/jwYxp+H9md5Pbl9bYUQQjwfBiVk1atXZ82aNXz++efY2dll2h8XF8eaNWuoVq1angMURYPtv6OpB85fx9Yu65FVQ9n/m1xERtx+atufvv2G5KQkFq3dTG2/l/T2pY/inc3yuAf372e5Per+PQDsnhgtrucfQD3/ABITEjhz8hh7tm9h5ZL5vNe3B6u378endBkg/Y+W1LTULPuNi4vF3j5z0vRfv/w0h9iYaCbO+oE2nbrr7ft8ZAh/Htr/1D6EEEKYnkFzyN555x1u3ryJv78/a9as4f6/v6zu37/P6tWradiwITdv3mTQoEFGDVYUXjVq1QX+f+vSmKr/2/fBvU9fQeLGtXAcnZwzJWMJCY85f/ZUtscdP3Iw07bIiDvcuHYVn1Jlskwyrayt8fNvxPAxXzBgyDASExM4FBaq2+/g6ERkxJ1Mx926cZ24mNzdNrxxLRyApi1a623XarWcPHo4V308jVqtJk1GzYQQIl8ZlJD17duXDz74gHPnztG9e3c8PDwwNzfHw8ODHj16cO7cOYYMGULfvn2NHa8opLr3GYCZmRmTx4wg4vbNTPtjY2KyrAOWG01btMbDy5s/1q5kf+jOTPvv3vn/yJlXcR9iY6K59Pd53ba0tDSmfT6ah1FZj4IBHNq3O9Pct+++/pLUlBTade2p23b88AEexcVmOj7qfiQAVk9M9q9asza3b1zn6MH/1+1LSU5m6oTPcrpcPV4lSgBw4ughve0L58zQu8a8cHRy5m4uRh+FEEIYzuA5ZNOnT6dLly4sXLiQkydPEhsbi4ODA7Vr16Zv3740blxw12UUz1+FylX59MupfPnph3QIrE+j4BaUKFWa+Lg4bl6/xrHD+2nfrSejJ01/emf/YWFpyddzFzL49a6826cbAU2bUbFqdeLj4vj7rzMkJCSwcsteAHr2f5ODe3fRr0trWrbtiKWlFX8eDCMy4g71/Bvx58Gsixo3btaSd/t2p0WbDnh6F+fYof2cOnaUSlWr65XqWPLjbA7uC8WvYSNKlCyNpaUl58+e5nDYHkqWLktwqza6tq8PHMTBvbsY0rcHrTt0wcramkP7QrF3cMTN3TNzEFno1vsN1q/8lWFv9uHldp1wdC7GmRNHOX/mNI2btWTfzm3P/Hr+l1/DJmzb+Bsfvt2XytVqoNZoaBLckgpVZEqCEEIYi8EJGUCjRo1o1KiRsWIRRVyXXn2pVLUGP8+bzbHDBwndvhl7ewc8i5eg98BBtH9ipOlZ+datz/JNe5g/ezoH9uzkUNgeHBydKFuhEq/37q9rF9i8Fd/8sJifvpvGH2tXYW1tjV/DJkyf9ws/zJiSbf8tXulApx6v89O337Bz0wZs7e3p2rsf7388RlcUFqDb629g5+DAmRPHOH74IIqi4Fm8BG++P5zeAwfp3doMCGrOlDkLmDdrKhvXrsDRyZkWbTrw/ojRdGneMFfXXaV6Tb5fupbvvv6CnVs2otaoqVW3Pot/20Lo9s1GSchGjJ8EwNEDe9m1ZSNarRZXNw9JyIQQwohUytMelxQ5io2NxdHREZ+hK1Fb2mTbrri9hnFB7rh7l0BlZpFtO1GwrF/5K2M+fJcJ38zWVfsXIidKajKRt28ybnckt+Jk7p0QBZU26TE3ZnQnJiYGhyyePH/ecj2HLD4+ngoVKhAQEJBl4cwMycnJNGrUiMqVK5OQkGCUIIUQQgghirJcJ2QLFy7kypUrTJo0CXNz82zbWVhYMGnSJP755x8WLFhglCCFEEIIIYqyXCdk69evp0qVKjRp0uSpbRs3bkyNGjVYu3ZtnoITQgghhHgR5DohO3XqVK6SsQwBAQGcOXPGoKCEKCg6dO/FqRsPZf6YEEKIfJXrhCw6OhoXF5dcd1ysWDFiclncUgghhBDiRZbrhMzBwYGoqKhcd/zgwYNsFx8XQgghhBD/l+uErGLFiuzduzfXHe/du5dKlSoZFJQQQgghxIsk1wnZK6+8wvnz51m+fPlT265cuZK//vqLNm3aPLWtEEIIIcSLLtcJ2ZAhQ3BycmLgwIEsWrQo23aLFy9mwIABuLi4MHjwYGPEKIQQQghRpOV66SQnJydWrlxJ+/btGTBgAGPHjqVp06aU+Hdx41u3bhEaGsqNGzewsrJi5cqVODk55VfcQgghhBBFxjOtZdmsWTMOHDjA+++/z759+/j5558ztWnSpAmzZs2iZs2aRgtSCCGEEKIoe+bFxX19fdmzZw+XL19m//79REREAODp6UlAQADlypUzepBCCCGEEEXZMydkGcqVKyfJlxBCCCGEEeR6Ur8QQgghhMgfBo+QCeMzb+5v6hCylbLjoKlDEEIIIYosGSETQgghhDAxSciEEEIIIUxMEjLxXB0/fIChA3sTVLsi9cp50LJ+NULefJ3jR/5/SzQh4TFzp02mQ9P6+JX3pHH1Mgzp252Tfx7O1N/caZPx9XHm6MEw1vy6mE7N/PEr78nLDaozc/J4khIT9drfunEdXx9nRocM5uKFvxj8elcCqpakYZWSvNf/VS7/cyHLuOMfxTHnm0l0auZP/fJeNKpWikG9u+rFnWFAt7b4+jiTnJTE7KkTaduoDnXLuDF32uQ8vnpCCCGKKplDJp6b5YvmMXnMCCytrAlu1QYv7xJE3r3DiSOH2LHpd+rU9yc5KYm3Xu3I6eNHqVLDl9cGvMOD+/fZuuE3Du7dzVdzFtC8dbtMfS/58Tv+PLifl9t1okmzloTt2s6C2TO4cPYMc35ehUql0mt/83o4/Tq3onqtunTvM4DrV6+wa8tGThw9xJLftlK2wv/XYY15+JD+XV/h8j8XqFPfn4ZNgnkUF0votk282aM9X89dRHCrzMuEDXvrdf7+6xwNA4NxdHKiRMnSRn9NhRBCFA2SkInn4uL5c0wZNxJXd08W/7aF4j4ldfsUReHe3fR6dgvnzuL08aO80qkbE2f+oEukeg98h9faNWf8R+/j37gptnb2ev0f3reHX//YRflKVQB47+PRvNunOwf27GTj2hW06/KqXvvjRw7y5vvDGfLRZ7ptG1YvZ1TIICaN/ph5y9frtk8e8zGX/7nA+Knf0bHHa7rtUfdG06ttMBM+GUpA02ZYWlnpnSPybgSrt4Xh6Oycl5dOCCHECyBXCdmSJUsMPkGfPn0MPlYUHat+WUhaWhpDPvpMLxkDUKlUuHt6AfD7ql8xMzdn6MixeqNaFatUp323nqz+ZRG7t22ibeceen207dJDl4wBmJmZ8f6I0Rzat5sNq5dnSsgcHJ14492hmfpY9P0sjuzfS8Ttm3h6l+Dhgyi2bviNBo0C9ZIxABc3d/q+8z5fjRnBobBQApu30ts/eNgnkowJIYTIlVwlZP369ct0y+dpFEVBpVJJQiYAOHvyOAD+TYKybfMoLpab18MpW6ESHl7FM+2v91IjVv+yiL/Pnc2UkNWu/1Km9tV8a2NlZc3f585k2le5ek1sbGz1tqlUKmr7vcSlv8/z919n8fQuwblTx0lLSyMpKTHLOWDXr14GIPzSxUwJWfVadbO9ViGEEOJJuUrIFi5cmN9xiCIuLjYGlUqFm4dntm3iH8UBUMzVLcv9ru4eQHri9l/FXLI+ppirK5ERd7Jo75plexc3d71zxEQ/BODk0cOcPJr5oYIMCQmPs+1LCCGEeJpcJWR9+/bN7zhEEWfv4KibK+bh5Z1lm4x5YQ/u38tyf9S9SL12T3oQlfUxD+7fx87eIYv293M8R8YxGefq89YQPhz9eZbHZOdZR5WFEEK8uKTshXguqteqA8DBvbuzbWNn70CJkqW5Hn6Fu3duZ9p/7NB+ACpVq55p34kjhzJtO3fqBImJCVSqViPTvgtnT/P4cXym7RmlNSpVTT9Hdd86qFQqTh8/km3cQgghRF5JQiaei269+6PRaPju6y+5ffO63r4nn7Js160nqSkpzPpqAoqi6NpcvPAX61f+ir2DA8EvZy4xsXHNCi79fV73dWpqKrO+Sh/Ratf11UztY2OiWTB7ht62DauXc/HCX9QPaIKndwkg/TZpy7YdOfnnERZ9P0svpgynT/yZ5S1LIYQQIrcMLntx48YNvvjiC3bs2MHt27dJTk7O1EalUpGampqnAEXRUKFKNT4aN5GvxnxC52YNCXr5FbyL+3D/XiTHDx+gcbOWfDxuEv3feZ99O7excc0Krl78h/qNmvAwKv1Jx9TUFL6YMTfLW5YNGgfyeoeWtGrfGQcnJ8J2befS3+dpGNgs0wMAAHXq+7Ns4Q+cOfEn1WrW5trVy+zashF7BwdGfj5Fr+1nX35D+JVLTP9yLBvXrKBmXT/s7R2IuH2Lv86c4vrVy+w8dgFra5t8e/2EEEIUbQYlZFeuXKFBgwY8fPiQatWqkZSURKlSpbCysuLy5cukpqbi6+uLk5OTkcPNmqIo/Pbbb3z77bdcuHCBmJgYfHx8aNq0KSNGjKBs2bJ67WNjYxk3bhxr1qwhIiICT09PunTpwrhx43BwyDzf6Hkp6gt49+z3FuUrVWXJj9+xf/cOHj+Op5iLKzVq16Nl244AWFpZMW/FehbOmcnWDb/xy09zsbK2pm4DfwYMGUad+lkvwN7nrSEENm/F0gU/cPPaVZyLufDGu0N5+4OPs5zLVaJkaT794mumTxzL8sU/oSgKAUHNGTpynF5RWABHZ2eW/LaV5YvnsfX339j022oUrRYXd3cqVqnOWx8Mx6mYi9FfLyGEEC8OgxKy8ePHExMTw86dOwkMDEStVtO/f3/GjBnD7du3GTRoEOfPn2f79u3GjjdLw4cPZ9q0aXh5edGxY0ccHBw4deoU8+bNY9myZRw4cIDq1dPnBMXHxxMYGMjJkydp0aIFPXv25NSpU0yfPp3du3cTFhaGra3tU84oDOXn3wg//0Y5trGxseXd4Z/y7vBPn6nvrq/1o+tr/XLdvkKVasz5eXWu2lpZW9Pvnffp9877T207f9XGXMcghBBCgIFzyHbs2MErr7xCYGCgblvG3Bpvb29WrlwJwGeffZbl8cYUERHBjBkzKF26NBcuXGDu3Ll89dVXbNmyhWnTphEXF8e0adN07adMmcLJkyf5+OOP2bZtG5MnT2bz5s2MGTOGkydPMmXKlBzOJoQQQghhfAYlZPfv36dy5cq6r83MzHj8+P+Tmi0tLWnRogUbN+b/SEF4eDharZaAgIBMtxvbtEmf/B0ZmV7KQFEUfvrpJ+zs7BgzZoxe25EjR+Ls7Mz8+fOznLgthBBCCJFfDErIXF1diY+P1/s6PDxcr42ZmRnR0dF5iS1XKlSogIWFBfv37ycuLk5v36ZNmwAIDg4G4OLFi9y+fZuAgIBMtyWtrKxo0qQJt27d4tKlS9meLykpidjYWL0PIYQQQoi8MGgOWYUKFbh8+bLu6/r167N161auXLlC2bJluXfvHqtXr6ZcuXJGCzQ7Li4ufPnll3z00UdUqVKF9u3bY29vz5kzZ9ixYwdvvfUW7733HpCekGXEn911ZbTLrs2kSZMYP358PlyJMMSgYZ8waNgnuW5f3Kckp248zMeIhBBCiGdn0AhZ69at2b17t24EbOjQocTFxVGzZk38/PyoWLEiERERukQovw0fPpylS5cSExPD3LlzmTJlCps3b8bPz4/evXtjbm4OQExMDACOjo5Z9pNxyzOjXVZGjhxJTEyM7uPGjRtGvhohhBBCvGgMSsgGDRpEaGgoGo0GgKZNm7J8+XJKlSrF2bNn8fDwYNasWbz55ptGDTY7X3zxBf369WPkyJHcuHGDR48eERYWRmpqKkFBQaxdu9Zo57K0tMTBwUHvQwghhBAiLwy6Zeng4ECDBg30tnXr1o1u3boZJahnsWvXLkaPHk1ISAiffvr/MgkBAQFs3LiRsmXLEhISQufOnXUjY9mNgGXMB8tuBE0IIYQQIj8U+qWT/vjjDwCCgoIy7XNzc6NGjRpcv36d+/fv680Ry8rT5pgJIYQQQuSHQp+QZSzZdO/evSz3Z2y3tLSkQoUKeHt7s3//fr2nRAESExPZu3cv3t7elC9fPn+DFkIIIYR4Qq4SMrVajZmZGf/884/ua41G89QPMzODl8rMtYCAAACmTZuW6Vbk4sWLuXTpEnXr1sXe3h6VSsXAgQN59OgREyZM0Gs7adIkHj58yMCBA7NcakcIIYQQIr/kKmNq0qQJKpUKGxsbva8Lgm7duvHDDz8QGhpKhQoVaN++Pc7Ozpw6dYrt27djaWnJjBkzdO0//vhjfv/9d6ZMmcKJEyeoW7cup06dYvPmzdSqVYuPP/7YdBcjhBBCiBdSrhKy0NDQHL82JY1Gw5YtW5g5cyYrVqxg2bJlJCcn4+HhQa9evRg5cqRuHUsAW1tbQkNDGT9+PKtXryY0NBRPT09CQkIYO3asrGMphBBCiOdOpRiwTtDevXtxcHCgVq1a+RBS4RIbG4ujoyM+Q1eitrTJtl1xew3jgtxx9y6BysziOUYohHielNRkIm/fZNzuSG7FpZk6HCFENrRJj7kxozsxMTEFooSVQZO8goKCeOedd5g9e7ax43mh7fviuKlDyFbjUXVMHcIzmzttMt9P/4qfVm7Az7+R0fs/ejCMgd3b8U7IiGdaLUAIIYT4L4OesnR3d8fCQkZ5hBBCCCGMwaARspdffpk9e/agKEqBmdwvxPNWvVYd1u0+jFMxF1OHIoQQopAzaIRs4sSJREVF8dZbb/HgwQNjxyREoWBtbUOZ8hVxloRMCCFEHhmUkPXu3RsnJycWLFhA8eLFqVq1KkFBQQQHB+t9NGvWzNjxikLu+OEDDB3Ym6DaFalXzoOW9asR8ubrHD9yEIDIiDvM+WYSvdu3oGmtCtQr50Fr/5p8+emHRN3PuvhvxO2bjHh3AI2rl+GlSiV4o2sbjh3an2XbowfD8PVxZu60yZz88zADurfDv7IPTX3L8+WnH5KYkADA/t076NvpZRpULE5Q7YpMnziWtLS0bPt6kq+PMwO6tc3y/K39a9Lav6bettEhg/H1cebmtXAWf/8t7ZrUo355LzoFv8Tm9WsASElJYfbUibRu6ItfeU+6tghgf+jOp7zaQgghCguDblk+WfYiKSmJCxcucOHChUzt5HameNLyRfOYPGYEllbWBLdqg5d3CSLv3uHEkUPs2PQ7der7c+zwAZb8OJsGAU2oUbsuZmbmXDh3mpU/L+DAnl0s3xyKvcP/1xq9dzeC1zu8TGTEbRoGNqNK9ZpcufQPb7/WGT//xtnGcubEnyycM5OGgcF0fa0fRw/uY+XPC3j0KI6glq8wOmQwgS1aUb1WPfbt2saiubOws7PnzfeH59vrM3XCZ5w5eYzA5q1Qq9Vs2bCWke+9iYOjEysW/8Slf87TOKgFSUlJbF63mg8G9GLd7iOUKFkq32ISQgjxfBiUkGm1WmPHIYq4i+fPMWXcSFzdPVn82xaK+5TU7VMUhXt3IwCoH9CEXccvYGNrp3f8htXLGRUyiOWL5uklRbMmTyAy4jZDPvpMb/vqpYv4/JOQbOPZH7qTGT8tJejlV4D0EahebYLYvG41B/fsYv6qjVSvlf5k6eAPP6Ft47r88tNc+g8emm8rUFy++DertoVRzMUVgPbde/F6+xaMGDKA8hWrsHr7fmxs0uvkNQwM5uPBb7B0/lxGjJ+cU7dCCCEKgUK/lqUoHFb9spC0tDSGfPSZXjIG6SOp7p5eALi4umVKxgDadumBnb09h8L26LalJCezdcNvFHN1o89bQ/Tad+7Zh1Jls1+TtJ5/I10yBmBubk7zVzqgKApNmrfSJWMAtnb2NGn2MtEPH3D3zu1nu/Bn8OZ7H+qSMYCatetRomRp4mJieO/jUbpkDKD5K+0xMzfnn7/O5ls8Qgghnp88J2SPHj3i+PHj7Nu3zxjxiCLq7Mn0Gmv+TYKe2nbH5g2881oXmvqWp05pV3x9nKlVshiP4uJ0I2kA4ZcvkpSUSLWatbC0stLrQ61W41u3frbnqFytZqZtbh4eAFSqViPzPvf0fZER+ZeQVa6e+byu2cSk0Wgo5uJGZMSdfItHCCHE82PwvZfw8HA++OADNm3ahFarRaVSkZqaCsD+/ft58803mTNnDk2bNjVWrKIQi4uNQaVS4ebhmWO7xT98x7QvRuPs4op/kyDcvbyxsrIGYOn8uaQkJ/2/z7hYAJxd3LLsy8Ut6+0Advb2mbZpNOn/Hezsstj3723KjPd4frC1y1wp2iwjJvss9plpSE1Nybd4hBBCPD8GJWTXr1/npZdeIioqig4dOhAREcHBgwd1+xs0aMD9+/dZtmyZJGQCAHsHR91cMQ8v7yzbpKamMm/W17h5eLFy616923eKorBo7iz9Pv9NUh5GZf30ZdS9rLfnN5VKRWpa1olbXFysLm4hhBAig0G3LMeOHcvDhw/Zs2cPq1evpkWLFnr7zczMaNy4Mfv3Z116QLx4MuZkHdy7O9s20Q+iiIuNpWadenrJGMC5UydITEzQ21a6XAUsLa04d/okSYmJevu0Wi2njh0xUvTPxsHRKctbibduXCcuJsYEEQkhhCjoDErItm7dSqdOnWjYsGG2bUqWLMmtW7cMDkwULd1690ej0fDd119y++Z1vX0ZI2fFXN2wsrLm/NlTJCQ81u2PjY5m8pgRmfo0t7CgZbuOPLh/jyU/fqe3b+2yJVy7cil/LuYpqtasze0b1zl6MEy3LSU5makTPjNJPEIIIQo+g25ZPnjwgNKlSz+1XVJS0lPbiBdDhSrV+GjcRL4a8wmdmzUk6OVX8C7uw/17kRw/fIDGzVry8bhJdO8zgCU/fkf3lo1p0rwV8Y/iCNu9A+8SJXDz8MrU7wefjOVw2F6++/pLThw9ROVq6XXIwnZvx79JMAf37nru1/r6wEEc3LuLIX170LpDF6ysrTm0L71+mpt7znPohBBCvJgMSsg8PDy4dCnn0YezZ89SsmTJHNsIfY1H1Xl6o0KsZ7+3KF+pKkt+/I79u3fw+HE8xVxcqVG7Hi3bdgTg/U/G4OjkxPpVy1j583xcXN1o1b4zgz/8hC7NM4/Iunl4smTdFqZ/OZYDe3dx7PBBqtbw5YelazlyYJ9JErKAoOZMmbOAebOmsnHtChydnGnRpgPvjxid5TUIIYQQKkVRlGc9aODAgfz8888cO3aM6tWrM378eCZMmKBbWmbfvn0EBgYydOhQpk2bZvSgC5LY2FgcHR3xGboStaVNtu2K22sYF+SOu3cJVGYWzzFCIcTzpKQmE3n7JuN2R3IrLu3pBwghTEKb9JgbM7oTExODg4PpH7YyaA7ZqFGjsLa2plGjRkycOFE3WrZ582ZGjx5Nq1atcHV15aOPPjJqsEIIIYQQRZFBtyxLly7N1q1befXVVxk1ahQqlQpFUWjbti2KolCyZElWr16Nl1fmOT9CCCGEEEKfwYVhGzRowMWLF9mwYQOHDx/mwYMHODg40KBBAzp06ICFhdyWE0IIIYTIjTytkmxmZkanTp3o1KmTseIRQgghhHjhGDSHLDg4mCVLluTYZtmyZQQHBxsUlBBCCCHEi8SghCw0NJTw8PAc21y/fp09e/YY0r0QQgghxAvFoIQsN+Lj4zE3N8+v7oUQQgghioxczyG7fl1/uZvo6OhM2wDS0tK4efMmq1atylU1fyGEEEKIF12uE7LSpUujUqkAUKlUzJw5k5kzZ2bbXlEUvv7667xHKIQQQghRxOU6IevTp4+u3tiSJUvw9fWlVq1amdppNBqKFStGcHAwrVq1MmasQgghhBBFUq4TskWLFuk+37NnD/379+f999/Pj5iEEEIIIV4oBtUhu3r1qrHjEEIIIYR4YeXbU5ZC5Ke50ybj6+PM0YNhett9fZwZ0K1tvp771o3r+Po4MzpkcL6eRwghxIvD4Er9cXFxfPfdd+zYsYPbt2+TlJSUqY1KpeLy5ct5CvBFsv3D3qYOIVstvvklz30cPRjGwO7teCdkBIOGfWKEqIQQQoiiwaCE7N69ezRs2JDLly/j4OBAbGwsjo6OJCcnk5CQAIC3t7fUIRNFkrunF+t2H8bO3sHUoQghhCgiDLplOW7cOC5fvsySJUt4+PAhACEhIcTHx3P48GHq169P6dKlOXfunFGDFaIgMDc3p0z5irh5eJo6FCGEEEWEQQnZpk2baNasGb1799bVJsvg5+fH5s2bCQ8PZ9y4ccaIURQBc6dNZmD3dgB8P/0rfH2cdR+3bqQXGE5JTmbJj7Pp0TqQBhWL41/Zh36dWxO6bVOezj2gW1t8fZyz3Dc6ZLBeDADrV/6Kr48z61f+yt6dW+nXuTX+lX1o7V8TyH4OWWv/mro2uYnhyXlw61YspUvzhtQv70Xrhr4sXfADkF7Pb+n87+nQtD5+5T1p16QeG9YsN/i1EEIIUTAZdMvyzp07dOvWTfe1RqPR3aoEcHZ2pnXr1qxatYopU6bkPcr/tXfncVHW+x7AP8OwDLuAAkKAuBy5GgplZG7pRZFDq0fUq+EVcc1Xi5GJbUKZt1Va9B5LyjQqWyyvmaaiedyOpl0hQhPBI4hwzGTfZID53j88zm0aRhmcBYbP+/Wa10ue5zfP7zsfx/HLM89CXd6wu0bh/pLz+GbzJgwbPhLD7hqlXefu4Ql1UxMenhmPH48cQtitQzDpPxLQ0tyMg9/vxuNzHsKyFa9ieuJ8i9actf1/cOTAPoyOnoip/5mEhro6s8zzyftr8ePRQxgbE4eokWOwZ8c2vJa6DM7Ozsg/lYes7VsxJnoiokaMxs5vvsZzix9GYFAIbou6yyz1EBGR5XWoIfP09ERzc7P2Zy8vL1y4cEFnjIeHB3799debq45sxh3/asC+2bwJw+4apXdQ/+pXV+DHI4fwcPIyLFi8VLvntb6uFvOmPYBVK55HdOx98PXvbbGaD+3bg3c/+RrDR4816zwnjh3BZzv245aQPgCAWQsewb2jb0f6S8/Du6cvNmcdhrdPTwDA/VNmIOH+8dj47mo2ZERENqRDX1n27dsXRUVF2p8jIyORlZWFiooKAEBjYyO2bduG4OBgkxRJtk2j0eCLzPUI7tNXpxkDAFc3d8xf/BSa1Wrs3bnNonWNm3iP2ZsxAJiRtEDbjAGAf8AtiLxjOGprajDv0Se1zRgAhEfejluC++DML3lmr4uIiCynQ3vIYmJi8Oabb6KhoQEuLi5YsGAB4uPjMXToUAwfPhwnTpxAUVERVq5caep6yQYVnS1ATXUVevn54903X9VbX1l+GQBwrrDAonXdGnGbReYJGxyut6ynrx8AYODgW/XX+fkhL/t/zV4XERFZTocasoULF2LQoEHahuwvf/kLXn/9dbz00kv46quv4OzsjOTkZDz11FOmrpdsUHXV1TN1z545jbNnThsc19jQYKmSAAA+PX0tMo9rG5fPsFde/afp6tb2upaWFrPXRUREltOhhqx3796YNm2azrInn3wSixcvxuXLl+Hr66t39iWRIW5u7gCA8XH3Y9V7G02+fYXd1W/mW1paYG+v+5avra0x/Dwj38MKOzs0q5vbXHe9eYiIiEx66ySlUgk/Pz82Y9Qm5b8ao9bWVp3loQMGws3dHadys3VOFjEVD88eAIBLF8t0lms0GpMei+Xh2QMV5b/p7b1qaKjH+XP/MNk8RERke3gvS7IYjx5Xr8P16z91GyN7e3tMmZmEsgslSH/p+TabsoLTp1B++bcOzTt4SAQA4JsvN+ksz1z33yg9X9yhbbZl0JAItDQ3Y8eWL7XLRATvvPIiGhvqTTYPERHZnnZ/ZTlo0CCjN65QKHi1ftK6enX73tj1zddwdnaGX+8AQKHA1JlJWJT8NH75ORefrn8PB/fuxu3DR8LLxweXLv4ThadPIf9UHjK37oZPz15Gz/vA1Ifw4bvvYG36Kzh98mcEhYTiVG42CvN/wbDhI/Hj0cMmeX3TZ83D1i8+xQtLH8ORg/vg7d0TJ44dQW1NNQYOuhX5p3hmJBERta3dDdnp06ehUCggIuash2yYUqlEesZHePu/0vDt11+gvq4WABB7/2QEBnnir5lfYstnmdj21WfYs2Mr1Go1fHr2Qt8BAxGfMBv9w4z/pQC4esZixmff4M2Vy3HkwD4ctz+AO+4ajcytWch45w0ApmnIBvzbYPw1czNWv7oCe3Z8AxcXV4z69wlIfvZFLF2UZJI5iIjINimknR2WnZ0dHBwcEBcXh9mzZ+Pee++FnR2/8bx2Y/WgxV/AzsnF4LhAdyXSxvnCN+AWKOwdLVghEVmStKhxqewC0vZdQmlt642fQERWoWlqQMlbU1FdXQ0PD/0z2i2t3R1Vbm4uHn74YRw+fBiTJk1CYGAgUlJSkJ+fb876iIiIiGxeuxuyW2+9FW+99RZKS0vx+eefIzIyEunp6Rg0aBBGjBiB999/H3VmutcfERERkS0z+jtHBwcHxMfHY8eOHSguLsaLL76Iy5cvY/78+fD390diYqLefS2JiIiIyLCbOggsICAAzz77LM6cOYOdO3fCy8sLmZmZOHHihKnqIyIiIrJ5HbpS/+9lZ2dj/fr12LRpEyoqKuDv74/AwEBT1EZERETULXSoIauoqMAnn3yC9evXIzc3F/b29oiLi0NSUhLi4uKgVCpNXScRERGRzWp3QyYi2LVrF9avX49t27ahqakJgwcPxuuvv46ZM2eiVy/jL9hJREREREY0ZMHBwSgrK4OnpycSExORlJSEO+64w5y12RReT5eoe9Hw3zwRGaHdDVlpaSkcHBwwdOhQFBcXIzU19YbPUSgU2L59+00VaCvqmgWtGg2ktYUXhiWyYdLaglaNBvXN7MiIqP2MOoasubkZ+/fvb/d4hUJhdEG2qqZJg39UqOHr3QCFozOzIbJBIgJNUwPOVqhR06SxdjlE1IW0uyE7d+6cOevoFnYW1iPUqwp+AOycXKBQ3vRJrkTUSUhrCzRNDfi1vAq7CuutXQ4RdTHt7ghCQkLMWUe3kHtJjbXHKzGxvxr9vB2h5L1AiWxGq0aDsxVq7CqsR+4ltbXLIaIuhrtoLCz3khq5l9TwcLKDq4MCdvzmkqjL0whQ3yz8mpKIOowNmZXUNGlQ02TtKoiIiKgzsInvzDZs2ACFQnHdR3R0tHZ8WlqawXEqlcqKr4SIiIi6I5vYQxYREWHwMhybN2/GyZMnMXHiRL11s2bNQp8+fXSW2dvbRCRERETUhdhE9xEREYGIiAi95Wq1GmvWrIG9vT1mzZqltz4xMRFjx441f4FERERE12ETX1kasmXLFpSXl+Pee++Fn5+ftcshIiIiapNN7CEz5IMPPgAAzJ07t831Bw8exLFjx6BUKhEWFobx48fDycnJkiUSERER2W5DVlxcjL179yIwMBCxsbFtjlm+fLnOz71798bGjRsxYcIEg9ttampCU9P/nx5ZXV0NANA0NZigaiIiIrKEa/9vS2e52bTYqNTUVAEgzz33nN66LVu2yMaNG6WoqEgaGxuloKBAVqxYIc7OzqJSqSQnJ+eG2+WDDz744IMPPrr+4+zZs+ZsR9pNIdJZWkPT0Wg0CA0NRUlJCc6ePYvQ0NB2PS8jIwPz589HfHw8vvzyyzbH/HEPmUajQUVFBXx8fLrM/SlramoQFBSEkpISeHh4WLscm8asLYM5WwZztgzmbBnV1dUIDg5GZWUlevToYe1ybPMry6ysLJw/fx7R0dHtbsaAq5fBWLRoEQ4fPmxwjJOTk95xZp3hL7IjPDw8+I/dQpi1ZTBny2DOlsGcLcOuk9zGsHNUYWI3OpjfEEdHR7i7u6OhgceDERERkeXYXENWXl6OrVu3wtvbG5MmTTLquQUFBaisrNS7WCwRERGROdlcQ5aZmQm1Wo2EhIQ2L2FRW1uL3NxcveWVlZWYM2cOAGD69Olmr9OanJyckJqaykt8WACztgzmbBnM2TKYs2V0tpxt7qD+8PBw5OXlITc3F+Hh4Xrri4qKEBoaimHDhiE8PBy+vr4oLS3Fd999h/LyckyYMAHffvstHB0drVA9ERERdUc21ZAdO3YMd955J6KiovDDDz+0OaampgbPPPMMjh49iuLiYlRVVcHV1RXh4eFISEjA3LlzoVQqLVw5ERERdWc21ZARERERdUU2dwwZERERUVfDhoyIiIjIytiQEREREVkZG7JOrLS0FG+99RZiYmIQHBwMR0dH+Pv7Y/Lkydc9aSE5ORkhISFwcnJCSEgIkpOTUVNTY3CeTz/9FFFRUXB1dYWXlxfi4uLw448/GhxfUFCAqVOnolevXnB2dsaQIUOwZs0aaDSam37N1mDunBsaGrBq1SrMmDEDYWFhsLOzg0KhQFFR0XXrYs7G5ZyTk4Pnn38ew4cPh6+vL5ycnNC3b18sWrQIpaWlBuuytZwB82f9008/Yfbs2RgyZAh8fHygUqnQr18/TJ06lZ8dZviM/r177rkHCoUCKpXK4BjmbFzORUVFUCgUBh+fffZZm3OYPGfr3UaTbiQlJUUASL9+/SQpKUmWLVsmkydPFqVSKXZ2dvL555/rjK+rq5OIiAgBIBMmTJCUlBSJjY0VABIRESF1dXV6c6xcuVIASHBwsCQnJ8v8+fPFw8NDHB0dZd++fXrjT548KZ6enuLg4CAPPfSQLF26VMLDwwWAzJs3z1xRmJW5cz537pz2JrYhISHi7e0tAOTcuXMGa2LOxud85513ikKhkKioKHn00UdlyZIlMnr0aAEgPXv2lF9++UWvJlvMWcT8WWdkZIivr6888MAD8sgjj0hKSopMmTJFVCqVKBQKyczM1KvJFrO2xGf0733wwQdiZ2cnKpVKnJyc2hzDnDv+GT106FBJTU3Ve/z88896NZkjZzZkndhXX30lBw4c0Ft+4MABcXBwEG9vb7ly5Yp2+fLlywWALF26VGf8teXLly/XWX7mzBmxt7eXP/3pT1JVVaVdnpeXJy4uLtKvXz9pbm7Wec6YMWMEgGzfvl27TK1WS3R0tACQ77///qZeszWYO+fa2lrZvXu3lJeXi4jIxIkTb9iQMWfjc169erUUFhbqbf+VV14RABIXF6e3zhZzFjF/1o2NjW3Om5eXJyqVSnx9fUWj0eiss8WszZ3z75WUlIinp6ckJydLSEiIwYaMORuf87WGbNasWe2uyRw5syHromJiYgSAHD9+XERENBqNBAQEiJubm17339jYKF5eXhIYGKjzIfn0008LANm4caPe9hcuXCgAZNeuXdpl+fn5AkDGjRunN/7o0aMCQKZPn26ql9gpmCLnP7pRQ8acTZPzNS0tLeLi4iKurq46y7tjziLmzVpEJDIyUgDo/JLXHbM2dc4xMTHSv39/aWhoMNiQMeeO5WxsQ2aunHkMWRfl4OAAALC3twdw9bvssrIyjBw5Eq6urjpjVSoVxowZg9LSUhQWFmqX/+1vfwMAxMTE6G1/4sSJAID9+/e3a3xUVBR69OihM94WmCJnYzFn0+asUCigVCq1276mO+YMmDfrs2fPIj8/H0FBQfD09NQu745ZmzLndevWISsrC++//z6cnZ0Nzsmcby7nsrIyrF27Fi+//DI2btyICxcutDmnuXJmQ9YFnT9/Hnv27IG/v7/29lAFBQUAgAEDBrT5nGvLr4279mc3Nzf4+/u3e7yhORQKBfr374+ysjI0NDR05GV1OqbK2VjM2bQ5b968GbW1tXofnt0tZ8D0Wefk5CAtLQ3PPvssEhISEBkZCQB49913dcZ1t6xNmXNxcTGWLFmChQsX4u67777uvMz55t7PWVlZWLRoEZ555hkkJiYiNDQUTz75pN5B+ubK2f7GQ6gzaW5uxsyZM9HU1ITXXntNe5un6upqAND5rfT3PDw8dMZd+7Ovr69R49s7h4uLS7tfU2dkypyNxZxNl3NJSQkee+wxODs7Y8WKFTrrulPOgHmyzsnJwQsvvKD92c/PDx999JFe89udsjZlziKCOXPmwMvLC6+++uoN52bOHcvZxcUFqampmDRpEvr27YsrV67g6NGjSElJQXp6OhwdHfHyyy9rx5srZ+4h60I0Gg2SkpJw4MABzJs3DzNnzrR2STaJOVuGuXOuqKhAXFwcLl26hHXr1mHgwIEm3X5XYq6sExMTISJobGxEbm4uYmNj8ec//xlvvPGGSbbf1Zg657Vr12Lv3r1477334O7ubqIquz5T5+zr64u0tDQMHToU7u7u6NWrF+677z58//338PHxQXp6OiorK01UvWFsyLoIEcG8efPw8ccfIyEhQe8rgWuduqE9BteuvfL7jt7T09Po8e2Z49pvB12ROXI2FnO++ZwrKysxfvx4nDx5EmvXrkVCQoLemO6QM2CZ97RKpUJ4eDg2bNiA2NhYpKSkIC8vz+g5unLWps65tLQUKSkpSExMRGxsbLtqYM6m/Yz29/dHXFwc1Go1jh8/bvQcxubMhqwL0Gg0mDNnDtavX4/p06djw4YNsLPT/au70TE1bX3nPWDAANTV1eHixYvtHm9oDhFBYWEhAgIC9A6k7CrMlbOxmPPN5VxRUYHo6GhkZ2djzZo1WLBgQZvbsPWcAeu8p2NiYqDRaHDw4MF2zWELWZsj54KCAtTV1WHDhg16FyotLi5GU1OT9ueqqqobzsGcobO8ve/nnj17AoDO8WBmy9no8zLJolpbW2X27NkCQKZNmyYtLS1tjmvPqb4BAQE6p/ouW7aMl734F3Pm/Efd+bIX5s65vLxce9mF1atXX7cWW85ZxLLv6d9bunSpAJB169Zpl9ly1ubK+fTp0zJnzpw2H25ubqJUKrU/NzQ0iAhzFjH9+3nkyJECQLKzs7XLzJUzG7JOrLW1VRITEwWATJkyRe8irX9k7MXw8vPzTXZh2PHjx3fZiw6aO+c/upkLwzJnwzmXl5drr8799ttvt6smW8xZxPxZHzp0qM1tZmdni4eHh9jb2+u9v20xa0t/dlzTkQvDMmfDOf/www+iVqv1trNq1SoBIIMGDWr3hY5vJmeFiIhx+9TIUtLS0vDCCy/Azc0Njz/+uN51lADgwQcfREREBACgvr4eo0aNQk5ODiZMmIDbb78dP/30E7777jtERETg0KFDertQV65cieeeew7BwcGIj49HfX09Nm3ahMbGRuzatQvjxo3TGX/q1CmMGDECjY2NmDp1KgICArBz507k5uZi7ty5yMjIMFse5mKJnJcsWYLLly8DuHpqdVlZGSZPngw3NzcAwLJlyxAWFqYdz5yNz3ns2LHYv38/wsLCMG3atDZrWLx4MXr06KH92RZzBsyfdUREBH777TeMHDkSwcHBaGlpQX5+Pnbv3g0RQXp6OhYvXqwzny1mbYnPjrb06dMHFy9exJUrV/TWMeeOfXacPn0ad999N4KCgtDY2IgjR44gOzsbXl5e2LNnD2677Tad+cySs9EtHFnMrFmzBMB1Hx9++KHOc6qqquSJJ56QoKAgcXBwkKCgIHniiSd09oD90ccffyzDhg0TZ2dn8fT0lNjYWDl27JjB8fn5+RIfHy8+Pj7i5OQkgwcPlnfeeUdaW1tN9dItyhI5h4SEXHf7bd03lDkbl/ONMoaBvZK2lrOI+bP+6KOP5MEHH5SQkBBxdnYWR0dHCQkJkRkzZsjf//53g3XZWtaW+oz+o+vtIRNhziLG5ZyRkSGxsbFyyy23iEqlEpVKJQMHDpTHH39cSkpKDNZl6py5h4yIiIjIyniWJREREZGVsSEjIiIisjI2ZERERERWxoaMiIiIyMrYkBERERFZGRsyIiIiIitjQ0ZERERkZWzIiIiIiKyMDRkRERGRlbEhIyIiIrIyNmREREREVsaGjIiIiMjK/g8a8RGsGQ4K6gAAAABJRU5ErkJggg=="
+class="
+"
+>
 </div>
+
+</div>
+
 </div>
 
 </div>
 
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span> 
+</pre></div>
+
+     </div>
+</div>
+</div>
 </div>
 
 </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
diff --git a/docs/baseline development documentation/(baseline dev) CdTe Material Composition.ipynb b/docs/baseline development documentation/(baseline dev) CdTe Material Composition.ipynb
index d5b15e7..b9e31fe 100644
--- a/docs/baseline development documentation/(baseline dev) CdTe Material Composition.ipynb	
+++ b/docs/baseline development documentation/(baseline dev) CdTe Material Composition.ipynb	
@@ -76,19 +76,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 4,
    "id": "2da66855-84d0-4233-b3a6-e55e103831db",
    "metadata": {},
    "outputs": [],
    "source": [
     "#materials in CdTe\n",
-    "MATERIALS_CdTe = ['aluminium_frames_cdte','encapsulant_cdte','glass_cdte','copper_cdte','cadmium','tellurium']\n",
-    "tidynameMats_CdTe = ['aluminium_frames','encapsulant','glass','copper','cadmium','tellurium']\n"
+    "MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte','encapsulant_cdte','copper_cdte','cadmium','tellurium']\n",
+    "tidynameMats_CdTe = ['glass','aluminium_frames','encapsulant','copper','cadmium','tellurium']\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 5,
    "id": "e8ff3f77-b409-4942-98ed-ec9350b52311",
    "metadata": {
     "scrolled": true
@@ -412,7 +412,7 @@
        "2050       5.479785"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -424,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "id": "0d8686c4-5be4-4c33-b601-82c4dc5ae2a9",
    "metadata": {},
    "outputs": [],
@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "id": "32155f07-a34f-49ba-92dc-c490f358a58e",
    "metadata": {
     "scrolled": true
@@ -466,9 +466,9 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>glass</th>\n",
        "      <th>aluminium_frames</th>\n",
        "      <th>encapsulant</th>\n",
-       "      <th>glass</th>\n",
        "      <th>copper</th>\n",
        "      <th>cadmium</th>\n",
        "      <th>tellurium</th>\n",
@@ -486,504 +486,504 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1995</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1996</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1997</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1998</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1999</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>8.219678</td>\n",
        "      <td>9.330322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
+       "      <td>16000.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>16000.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014</th>\n",
+       "      <td>15947.50000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>15947.50000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015</th>\n",
+       "      <td>15895.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>15895.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016</th>\n",
+       "      <td>15842.50000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>15842.50000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2017</th>\n",
+       "      <td>15790.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>15790.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018</th>\n",
+       "      <td>15755.00000</td>\n",
        "      <td>117.532683</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>15755.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019</th>\n",
+       "      <td>15650.00000</td>\n",
        "      <td>202.080283</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>15650.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2020</th>\n",
+       "      <td>15475.00000</td>\n",
        "      <td>405.753968</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>15475.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2021</th>\n",
+       "      <td>14915.00000</td>\n",
        "      <td>811.507937</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>14915.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2022</th>\n",
+       "      <td>14810.00000</td>\n",
        "      <td>811.507937</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>14810.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023</th>\n",
+       "      <td>14635.00000</td>\n",
        "      <td>811.507937</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>14635.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2024</th>\n",
+       "      <td>14460.00000</td>\n",
        "      <td>1217.261905</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>14460.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>6.027764</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025</th>\n",
+       "      <td>14302.50000</td>\n",
        "      <td>1623.015873</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>14302.50000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.842236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2026</th>\n",
+       "      <td>14145.00000</td>\n",
        "      <td>1623.015873</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>14145.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2027</th>\n",
+       "      <td>14063.33333</td>\n",
        "      <td>1217.261905</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>14063.33333</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2028</th>\n",
+       "      <td>13981.66667</td>\n",
        "      <td>811.507937</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13981.66667</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2029</th>\n",
+       "      <td>13900.00000</td>\n",
        "      <td>811.507937</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13900.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2030</th>\n",
+       "      <td>13806.66667</td>\n",
        "      <td>811.507937</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13806.66667</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2031</th>\n",
+       "      <td>13713.33333</td>\n",
        "      <td>405.753968</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13713.33333</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2032</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>202.080283</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2033</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>117.532683</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2034</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>81.150794</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2035</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>81.150794</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2036</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>81.150794</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2037</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>81.150794</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2038</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>81.150794</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2039</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2040</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2041</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2042</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2043</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2044</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2045</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2046</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2047</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2048</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2049</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2050</th>\n",
+       "      <td>13620.00000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>388.1</td>\n",
-       "      <td>13620.00000</td>\n",
        "      <td>56.55</td>\n",
        "      <td>5.479785</td>\n",
        "      <td>6.220215</td>\n",
@@ -993,67 +993,67 @@
        "</div>"
       ],
       "text/plain": [
-       "      aluminium_frames  encapsulant        glass  copper   cadmium  tellurium\n",
+       "            glass  aluminium_frames  encapsulant  copper   cadmium  tellurium\n",
        "year                                                                         \n",
-       "1995          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "1996          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "1997          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "1998          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "1999          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2000          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2001          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2002          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2003          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2004          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2005          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2006          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2007          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2008          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2009          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2010          0.000000        388.1  16000.00000   56.55  8.219678   9.330322\n",
-       "2011          0.000000        388.1  16000.00000   56.55  6.027764   6.842236\n",
-       "2012          0.000000        388.1  16000.00000   56.55  6.027764   6.842236\n",
-       "2013          0.000000        388.1  16000.00000   56.55  6.027764   6.842236\n",
-       "2014          0.000000        388.1  15947.50000   56.55  6.027764   6.842236\n",
-       "2015          0.000000        388.1  15895.00000   56.55  6.027764   6.842236\n",
-       "2016          0.000000        388.1  15842.50000   56.55  6.027764   6.842236\n",
-       "2017          0.000000        388.1  15790.00000   56.55  6.027764   6.842236\n",
-       "2018        117.532683        388.1  15755.00000   56.55  6.027764   6.842236\n",
-       "2019        202.080283        388.1  15650.00000   56.55  6.027764   6.842236\n",
-       "2020        405.753968        388.1  15475.00000   56.55  6.027764   6.842236\n",
-       "2021        811.507937        388.1  14915.00000   56.55  6.027764   6.842236\n",
-       "2022        811.507937        388.1  14810.00000   56.55  6.027764   6.842236\n",
-       "2023        811.507937        388.1  14635.00000   56.55  6.027764   6.842236\n",
-       "2024       1217.261905        388.1  14460.00000   56.55  6.027764   6.842236\n",
-       "2025       1623.015873        388.1  14302.50000   56.55  5.479785   6.842236\n",
-       "2026       1623.015873        388.1  14145.00000   56.55  5.479785   6.220215\n",
-       "2027       1217.261905        388.1  14063.33333   56.55  5.479785   6.220215\n",
-       "2028        811.507937        388.1  13981.66667   56.55  5.479785   6.220215\n",
-       "2029        811.507937        388.1  13900.00000   56.55  5.479785   6.220215\n",
-       "2030        811.507937        388.1  13806.66667   56.55  5.479785   6.220215\n",
-       "2031        405.753968        388.1  13713.33333   56.55  5.479785   6.220215\n",
-       "2032        202.080283        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2033        117.532683        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2034         81.150794        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2035         81.150794        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2036         81.150794        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2037         81.150794        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2038         81.150794        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2039          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2040          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2041          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2042          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2043          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2044          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2045          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2046          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2047          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2048          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2049          0.000000        388.1  13620.00000   56.55  5.479785   6.220215\n",
-       "2050          0.000000        388.1  13620.00000   56.55  5.479785   6.220215"
+       "1995  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "1996  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "1997  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "1998  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "1999  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2000  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2001  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2002  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2003  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2004  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2005  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2006  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2007  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2008  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2009  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2010  16000.00000          0.000000        388.1   56.55  8.219678   9.330322\n",
+       "2011  16000.00000          0.000000        388.1   56.55  6.027764   6.842236\n",
+       "2012  16000.00000          0.000000        388.1   56.55  6.027764   6.842236\n",
+       "2013  16000.00000          0.000000        388.1   56.55  6.027764   6.842236\n",
+       "2014  15947.50000          0.000000        388.1   56.55  6.027764   6.842236\n",
+       "2015  15895.00000          0.000000        388.1   56.55  6.027764   6.842236\n",
+       "2016  15842.50000          0.000000        388.1   56.55  6.027764   6.842236\n",
+       "2017  15790.00000          0.000000        388.1   56.55  6.027764   6.842236\n",
+       "2018  15755.00000        117.532683        388.1   56.55  6.027764   6.842236\n",
+       "2019  15650.00000        202.080283        388.1   56.55  6.027764   6.842236\n",
+       "2020  15475.00000        405.753968        388.1   56.55  6.027764   6.842236\n",
+       "2021  14915.00000        811.507937        388.1   56.55  6.027764   6.842236\n",
+       "2022  14810.00000        811.507937        388.1   56.55  6.027764   6.842236\n",
+       "2023  14635.00000        811.507937        388.1   56.55  6.027764   6.842236\n",
+       "2024  14460.00000       1217.261905        388.1   56.55  6.027764   6.842236\n",
+       "2025  14302.50000       1623.015873        388.1   56.55  5.479785   6.842236\n",
+       "2026  14145.00000       1623.015873        388.1   56.55  5.479785   6.220215\n",
+       "2027  14063.33333       1217.261905        388.1   56.55  5.479785   6.220215\n",
+       "2028  13981.66667        811.507937        388.1   56.55  5.479785   6.220215\n",
+       "2029  13900.00000        811.507937        388.1   56.55  5.479785   6.220215\n",
+       "2030  13806.66667        811.507937        388.1   56.55  5.479785   6.220215\n",
+       "2031  13713.33333        405.753968        388.1   56.55  5.479785   6.220215\n",
+       "2032  13620.00000        202.080283        388.1   56.55  5.479785   6.220215\n",
+       "2033  13620.00000        117.532683        388.1   56.55  5.479785   6.220215\n",
+       "2034  13620.00000         81.150794        388.1   56.55  5.479785   6.220215\n",
+       "2035  13620.00000         81.150794        388.1   56.55  5.479785   6.220215\n",
+       "2036  13620.00000         81.150794        388.1   56.55  5.479785   6.220215\n",
+       "2037  13620.00000         81.150794        388.1   56.55  5.479785   6.220215\n",
+       "2038  13620.00000         81.150794        388.1   56.55  5.479785   6.220215\n",
+       "2039  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2040  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2041  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2042  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2043  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2044  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2045  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2046  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2047  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2048  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2049  13620.00000          0.000000        388.1   56.55  5.479785   6.220215\n",
+       "2050  13620.00000          0.000000        388.1   56.55  5.479785   6.220215"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1065,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "id": "55d10940-4540-4fc9-88cb-127e7505200a",
    "metadata": {},
    "outputs": [],
@@ -1075,35 +1075,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 9,
    "id": "df79dd65-b5b3-4371-b025-cba53971b4e2",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_percent_mats = df_component_mats.loc[:,df_component_mats.columns !='module_mass_gpm2'].div(df_component_mats['module_mass_gpm2'], axis=0)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "01fcec89-aad4-4988-b446-e2cfeac3225c",
+   "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "Unable to coerce to Series, length must be 7: given 56",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[38], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df_component_mats\u001b[38;5;241m/\u001b[39mdf_component_mats[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodule_mass_gpm2\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\n",
-      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\ops\\common.py:81\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     77\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[0;32m     79\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[1;32m---> 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m method(\u001b[38;5;28mself\u001b[39m, other)\n",
-      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\arraylike.py:210\u001b[0m, in \u001b[0;36mOpsMixin.__truediv__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    208\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__truediv__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    209\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__truediv__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m--> 210\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_arith_method(other, operator\u001b[38;5;241m.\u001b[39mtruediv)\n",
-      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:7455\u001b[0m, in \u001b[0;36mDataFrame._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   7452\u001b[0m axis: Literal[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m  \u001b[38;5;66;03m# only relevant for Series other case\u001b[39;00m\n\u001b[0;32m   7453\u001b[0m other \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39mmaybe_prepare_scalar_for_op(other, (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape[axis],))\n\u001b[1;32m-> 7455\u001b[0m \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39malign_method_FRAME(\u001b[38;5;28mself\u001b[39m, other, axis, flex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, level\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   7457\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dispatch_frame_op(other, op, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   7458\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(new_data)\n",
-      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\ops\\__init__.py:260\u001b[0m, in \u001b[0;36malign_method_FRAME\u001b[1;34m(left, right, axis, flex, level)\u001b[0m\n\u001b[0;32m    258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(right, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m    259\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m right\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 260\u001b[0m         right \u001b[38;5;241m=\u001b[39m to_series(right)\n\u001b[0;32m    262\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m right\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m    263\u001b[0m         \u001b[38;5;66;03m# We need to pass dtype=right.dtype to retain object dtype\u001b[39;00m\n\u001b[0;32m    264\u001b[0m         \u001b[38;5;66;03m#  otherwise we lose consistency with Index and array ops\u001b[39;00m\n\u001b[0;32m    265\u001b[0m         dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\ops\\__init__.py:252\u001b[0m, in \u001b[0;36malign_method_FRAME.<locals>.to_series\u001b[1;34m(right)\u001b[0m\n\u001b[0;32m    250\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    251\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(left\u001b[38;5;241m.\u001b[39mcolumns) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(right):\n\u001b[1;32m--> 252\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    253\u001b[0m             msg\u001b[38;5;241m.\u001b[39mformat(req_len\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(left\u001b[38;5;241m.\u001b[39mcolumns), given_len\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(right))\n\u001b[0;32m    254\u001b[0m         )\n\u001b[0;32m    255\u001b[0m     right \u001b[38;5;241m=\u001b[39m left\u001b[38;5;241m.\u001b[39m_constructor_sliced(right, index\u001b[38;5;241m=\u001b[39mleft\u001b[38;5;241m.\u001b[39mcolumns, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m right\n",
-      "\u001b[1;31mValueError\u001b[0m: Unable to coerce to Series, length must be 7: given 56"
-     ]
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "df_component_mats/df_component_mats['module_mass_gpm2'].values"
+    "plt.rcParams.update({'font.size': 14})\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.stackplot(df_percent_mats.index, df_percent_mats.T)\n",
+    "ax.legend(df_percent_mats.columns, loc='lower left')\n",
+    "plt.title('Material Composition of CdTe') \n",
+    "plt.ylabel('Material Composition by weight (%)') \n",
+    "plt.xlim(1995,2050)\n",
+    "plt.ylim(75,100)\n",
+    "\n",
+    "fig.savefig(os.path.join(supportMatfolder,'MaterialComp_CdTeModule.png'))\n",
+    "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9dc28362-92ad-4406-b560-3332671dee4e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a24cecbf-9fa4-430e-a87b-aebc629e5b04",
+   "id": "d64251e8-82ae-4da7-aaef-67652094f3a8",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/docs/baseline development documentation/(baseline dev) CdTe Material Composition.py b/docs/baseline development documentation/(baseline dev) CdTe Material Composition.py
index 0424c1b..11438da 100644
--- a/docs/baseline development documentation/(baseline dev) CdTe Material Composition.py	
+++ b/docs/baseline development documentation/(baseline dev) CdTe Material Composition.py	
@@ -36,22 +36,22 @@
 #print("PV_ICE version ", PV_ICE.__version__)
 
 
-# In[31]:
+# In[4]:
 
 
 #materials in CdTe
-MATERIALS_CdTe = ['aluminium_frames_cdte','encapsulant_cdte','glass_cdte','copper_cdte','cadmium','tellurium']
-tidynameMats_CdTe = ['aluminium_frames','encapsulant','glass','copper','cadmium','tellurium']
+MATERIALS_CdTe = ['glass_cdte','aluminium_frames_cdte','encapsulant_cdte','copper_cdte','cadmium','tellurium']
+tidynameMats_CdTe = ['glass','aluminium_frames','encapsulant','copper','cadmium','tellurium']
 
 
-# In[21]:
+# In[5]:
 
 
 pd.read_csv(os.path.join(baselinesFolder, 'baseline_material_mass_cadmium.csv'), 
             index_col = 0, usecols=['year','mat_massperm2'], skiprows=[1])
 
 
-# In[28]:
+# In[6]:
 
 
 df_component_mats = pd.DataFrame()
@@ -62,23 +62,45 @@
     
 
 
-# In[32]:
+# In[7]:
 
 
 df_component_mats.columns = tidynameMats_CdTe
 df_component_mats
 
 
-# In[33]:
+# In[8]:
 
 
 df_component_mats['module_mass_gpm2'] = df_component_mats.sum(axis=1) #run only once
 
 
-# In[38]:
+# In[9]:
+
+
+df_percent_mats = df_component_mats.loc[:,df_component_mats.columns !='module_mass_gpm2'].div(df_component_mats['module_mass_gpm2'], axis=0)*100
+
+
+# In[21]:
+
+
+plt.rcParams.update({'font.size': 14})
+fig, ax = plt.subplots()
+ax.stackplot(df_percent_mats.index, df_percent_mats.T)
+ax.legend(df_percent_mats.columns, loc='lower left')
+plt.title('Material Composition of CdTe') 
+plt.ylabel('Material Composition by weight (%)') 
+plt.xlim(1995,2050)
+plt.ylim(75,100)
+
+fig.savefig(os.path.join(supportMatfolder,'MaterialComp_CdTeModule.png'))
+plt.show()
+
+
+# In[23]:
+
 
 
-df_component_mats/df_component_mats['module_mass_gpm2'].values
 
 
 # In[ ]: