diff --git a/Ressources/Bilan CO2 point operation.pptx b/Ressources/Bilan CO2 point operation.pptx
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diff --git a/Ressources/Thumbs.db b/Ressources/Thumbs.db
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diff --git a/Ressources/~$Bilan CO2 point operation.pptx b/Ressources/~$Bilan CO2 point operation.pptx
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diff --git a/Tableau/Bilan_CO2_EPFL.twb b/Tableau/Bilan_CO2_EPFL.twb
index e3bb97a..c4e7f50 100644
--- a/Tableau/Bilan_CO2_EPFL.twb
+++ b/Tableau/Bilan_CO2_EPFL.twb
@@ -1,8808 +1,8593 @@
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Scope</remote-name>
             <remote-type>20</remote-type>
             <local-name>[Scope]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Scope</remote-alias>
             <ordinal>6</ordinal>
             <local-type>integer</local-type>
             <aggregation>Sum</aggregation>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;I8&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Unit</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Unit]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Unit</remote-alias>
             <ordinal>7</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>CO2</remote-name>
             <remote-type>5</remote-type>
             <local-name>[CO2]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>CO2</remote-alias>
             <ordinal>8</ordinal>
             <local-type>real</local-type>
             <aggregation>Sum</aggregation>
             <precision>15</precision>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;R8&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>Source</remote-name>
+            <remote-type>130</remote-type>
+            <local-name>[Source]</local-name>
+            <parent-name>[Sheet1]</parent-name>
+            <remote-alias>Source</remote-alias>
+            <ordinal>9</ordinal>
+            <local-type>string</local-type>
+            <aggregation>Count</aggregation>
+            <contains-null>true</contains-null>
+            <collation flag='1' name='LFR_RFR_S2' />
+            <attributes>
+              <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
+            </attributes>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
           <metadata-record class='column'>
             <remote-name>Data description</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Data description]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Data description</remote-alias>
-            <ordinal>9</ordinal>
+            <ordinal>10</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Key_ID</remote-name>
             <remote-type>20</remote-type>
             <local-name>[Key_ID]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Key_ID</remote-alias>
-            <ordinal>10</ordinal>
+            <ordinal>11</ordinal>
             <local-type>integer</local-type>
             <aggregation>Sum</aggregation>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;I8&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Factor (kg CO2eq)</remote-name>
             <remote-type>5</remote-type>
             <local-name>[Factor (kg CO2eq)]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Factor (kg CO2eq)</remote-alias>
-            <ordinal>11</ordinal>
+            <ordinal>12</ordinal>
             <local-type>real</local-type>
             <aggregation>Sum</aggregation>
             <precision>15</precision>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;R8&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
-          <metadata-record class='column'>
-            <remote-name>Source</remote-name>
-            <remote-type>130</remote-type>
-            <local-name>[Source]</local-name>
-            <parent-name>[Sheet1]</parent-name>
-            <remote-alias>Source</remote-alias>
-            <ordinal>12</ordinal>
-            <local-type>string</local-type>
-            <aggregation>Count</aggregation>
-            <contains-null>true</contains-null>
-            <collation flag='1' name='LFR_RFR_S2' />
-            <attributes>
-              <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
-            </attributes>
-            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
-          </metadata-record>
           <metadata-record class='column'>
             <remote-name>Dataset name</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Dataset name]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Dataset name</remote-alias>
             <ordinal>13</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Comments</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Comments]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Comments</remote-alias>
             <ordinal>14</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Lien source</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Lien source]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Lien source</remote-alias>
             <ordinal>15</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Objectif</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Objectif]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Objectif</remote-alias>
             <ordinal>16</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
         </metadata-records>
       </connection>
       <aliases enabled='yes' />
       <column caption='CO2 -15%' datatype='real' name='[CO2 (copy)_999517661598375941]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description]==&quot;Objectif EPFL 15% de diminution d&apos;ici 2024&quot; THEN [CO2] ELSE 0 END' />
       </column>
       <column caption='url' datatype='string' name='[CO2 -15% (copy)_1805380557052968960]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='&quot;&lt;a href=&apos;https://www.epfl.ch/about/facts/fr/statistiques-institutionnelles/statistiques-durabilite/glossaire-durabilite/&apos;&gt;Définition Scope&lt;/a&gt;&quot;' />
       </column>
       <column caption='opacity field ' datatype='real' name='[Calculation_1052997914151374848]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description] != &quot;Estimé&quot; THEN [Parameters].[Parameter 1] ELSE 0.3 END' />
       </column>
       <column caption='Concatenated unit' datatype='string' name='[Calculation_1052997914207850501]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='&quot;kg CO2&quot;+&quot;/&quot;+[Unit_CO2]' />
       </column>
       <column caption='Unit formated' datatype='string' name='[Calculation_1052997914208116742]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='IF ISNULL([Calculation_1052997914207850501]) THEN &quot;Facteurs multiples&quot; ELSE [Calculation_1052997914207850501] END' />
       </column>
       <column caption='relative CO2' datatype='integer' name='[Calculation_1052997914215399431]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='INT(ROUND(SUM([CO2]) / TOTAL(SUM([CO2]))*100, 0))'>
           <table-calc ordering-type='Rows' />
         </calculation>
       </column>
       <column caption='CO2 by EPT' datatype='real' name='[Calculation_1265230035614871552]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='SUM([Calculation_316377907629977601])/SUM([federated.09xbt491r8ci33145k2s61psbvj3].[EPT])' />
       </column>
       <column caption='x' datatype='string' name='[Calculation_1370501696121757696]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='&quot;x&quot;' />
       </column>
       <column caption='Total CO2' datatype='real' name='[Calculation_316377907626377216]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='TOTAL(SUM([CO2]))/1000'>
           <table-calc ordering-type='Rows' />
         </calculation>
       </column>
       <column caption='CO2e [t]' datatype='real' name='[Calculation_316377907629977601]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='[CO2]/1000' />
       </column>
       <column caption='CO2 -50% energy' datatype='real' name='[Calculation_999517661596172291]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description]==&quot;Objectifs fédéraux 2030&quot; THEN [CO2] ELSE 0 END' />
       </column>
       <column caption='CO2e' datatype='real' name='[Calculation_999517661596495876]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description]!=&quot;Objectif EPFL 15% de diminution d&apos;ici 2024&quot; AND [Data description]!= &quot;Objectifs fédéraux 2030&quot; THEN [CO2] END' />
       </column>
       <column caption='Category_EN' datatype='string' name='[Category (copy)_690458179210276864]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='[Category]' />
       </column>
       <column datatype='string' name='[Category]' role='dimension' type='nominal' />
       <column datatype='string' name='[Data description]' role='dimension' type='nominal' />
       <column aggregation='Sum' datatype='integer' name='[Scope]' role='dimension' type='ordinal' />
       <column caption='Theme EN' datatype='string' name='[Theme (copy)_720294505510551552]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='[Theme]' />
         <aliases>
           <alias key='&quot;Achats (estimation)&quot;' value='Purchases (estimation)' />
           <alias key='&quot;Alimentation&quot;' value='Food' />
           <alias key='&quot;CAD&quot;' value='District heating' />
           <alias key='&quot;Electricite&quot;' value='Electricity' />
           <alias key='&quot;Gaz&quot;' value='Natural gas' />
           <alias key='&quot;Mazout&quot;' value='Fuel oil' />
           <alias key='&quot;Mobilité pendulaire&quot;' value='Commuting' />
           <alias key='&quot;Numérique (fabrication)&quot;' value='Digital (production)' />
           <alias key='&quot;Voyages professionnels&quot;' value='Business trip' />
         </aliases>
       </column>
       <column datatype='string' name='[Theme]' role='dimension' type='nominal'>
         <aliases>
           <alias key='&quot;Electricite&quot;' value='Electricité' />
         </aliases>
       </column>
-      <column caption='Unit CO2' datatype='string' name='[Unit_CO2]' role='dimension' type='nominal' />
       <column caption='Year EN' datatype='integer' name='[Year (copy)_720294505534763010]' role='dimension' type='ordinal'>
         <calculation class='tableau' formula='[Year]' />
         <aliases>
-          <alias key='2023' value='2023 (estimated)' />
           <alias key='2024' value='2024 (estimated)' />
           <alias key='2025' value='2025 (estimated)' />
           <alias key='2026' value='2026 (estimated)' />
           <alias key='2027' value='2027 (estimated)' />
           <alias key='2028' value='2028 (estimated)' />
           <alias key='2029' value='2029 (estimated)' />
           <alias key='2030' value='2030 (Objective)' />
         </aliases>
       </column>
       <column datatype='integer' name='[Year]' role='dimension' type='ordinal'>
         <aliases>
-          <alias key='2023' value='2023 (estimé)' />
           <alias key='2024' value='2024 (estimé)' />
           <alias key='2025' value='2025 (estimé)' />
           <alias key='2026' value='2026 (estimé)' />
           <alias key='2027' value='2027 (estimé)' />
           <alias key='2028' value='2028 (estimé)' />
           <alias key='2029' value='2029 (estimé)' />
           <alias key='2030' value='2030 (Objectif)' />
         </aliases>
       </column>
       <_.fcp.ObjectModelTableType.true...column caption='Sheet1' datatype='table' name='[__tableau_internal_object_id__].[Sheet1_F706AA2D072D422D81961150A43F73A2]' role='measure' type='quantitative' />
       <column caption='relative CO2 EN' datatype='integer' name='[relative CO2 (copy)_720294505511186433]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='INT(ROUND(SUM([CO2]) / TOTAL(SUM([CO2]))*100, 0))'>
           <table-calc ordering-type='Rows' />
         </calculation>
       </column>
       <column-instance column='[Set 1]' derivation='InOut' name='[io:Set 1:nk]' pivot='key' type='nominal' />
       <column-instance column='[Set 2]' derivation='InOut' name='[io:Set 2:nk]' pivot='key' type='nominal' />
       <column-instance column='[Set 3]' derivation='InOut' name='[io:Set 3:nk]' pivot='key' type='nominal' />
       <column-instance column='[Category]' derivation='None' name='[none:Category:nk]' pivot='key' type='nominal' />
       <column-instance column='[Data description]' derivation='None' name='[none:Data description:nk]' pivot='key' type='nominal' />
       <column-instance column='[Forecast Indicator]' derivation='None' forecast-column-base='[Forecast Indicator]' forecast-column-type='forecast-indicator' name='[none:Forecast Indicator:nk]' pivot='key' type='nominal' />
       <column-instance column='[Scope]' derivation='None' name='[none:Scope:ok]' pivot='key' type='ordinal' />
       <column-instance column='[Theme (copy)_720294505510551552]' derivation='None' name='[none:Theme (copy)_720294505510551552:nk]' pivot='key' type='nominal' />
       <column-instance column='[Theme]' derivation='None' name='[none:Theme:nk]' pivot='key' type='nominal' />
       <group caption='Interpolation Objectif 2030' name='[Set 1]' name-style='unqualified' user:ui-builder='lasso-group'>
         <groupfilter function='crossjoin'>
           <groupfilter function='member' level='[Theme]' member='&quot;Electricité&quot;' />
           <groupfilter function='union'>
             <groupfilter function='member' level='[Year]' member='2022' />
             <groupfilter function='member' level='[Year]' member='2023' />
             <groupfilter function='member' level='[Year]' member='2024' />
             <groupfilter function='member' level='[Year]' member='2025' />
             <groupfilter function='member' level='[Year]' member='2026' />
             <groupfilter function='member' level='[Year]' member='2027' />
             <groupfilter function='member' level='[Year]' member='2028' />
             <groupfilter function='member' level='[Year]' member='2029' />
             <groupfilter function='member' level='[Year]' member='2030' />
           </groupfilter>
         </groupfilter>
       </group>
       <group caption='Objectif -15% EPFL' name='[Set 2]' name-style='unqualified' user:ui-builder='lasso-group'>
         <groupfilter function='crossjoin'>
           <groupfilter function='member' level='[Theme]' member='&quot;Electricité&quot;' />
           <groupfilter function='union'>
             <groupfilter function='member' level='[Year]' member='2022' />
             <groupfilter function='member' level='[Year]' member='2023' />
             <groupfilter function='member' level='[Year]' member='2024' />
           </groupfilter>
         </groupfilter>
       </group>
       <group caption='Voyage_2030' name='[Set 3]' name-style='unqualified' user:ui-builder='lasso-group'>
         <groupfilter function='crossjoin'>
           <groupfilter function='member' level='[Theme]' member='&quot;Voyages professionnels&quot;' />
           <groupfilter function='union'>
             <groupfilter function='member' level='[Year]' member='2023' />
             <groupfilter function='member' level='[Year]' member='2024' />
             <groupfilter function='member' level='[Year]' member='2025' />
             <groupfilter function='member' level='[Year]' member='2026' />
             <groupfilter function='member' level='[Year]' member='2027' />
             <groupfilter function='member' level='[Year]' member='2028' />
             <groupfilter function='member' level='[Year]' member='2029' />
             <groupfilter function='member' level='[Year]' member='2030' />
           </groupfilter>
         </groupfilter>
       </group>
       <group caption='predicted' name='[Set 4]' name-style='unqualified' user:ui-builder='lasso-group'>
         <groupfilter function='union'>
           <groupfilter function='crossjoin'>
             <groupfilter function='member' level='[Theme]' member='&quot;Alimentation&quot;' />
             <groupfilter function='member' level='[Year]' member='2030' />
           </groupfilter>
           <groupfilter function='crossjoin'>
             <groupfilter function='union'>
               <groupfilter function='member' level='[Theme]' member='&quot;CAD&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Electricité&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Gaz&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Mazout&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Mobilité pendulaire&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Numérique&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Voyages professionnels&quot;' />
             </groupfilter>
             <groupfilter function='union'>
               <groupfilter function='member' level='[Year]' member='2023' />
               <groupfilter function='member' level='[Year]' member='2024' />
               <groupfilter function='member' level='[Year]' member='2025' />
               <groupfilter function='member' level='[Year]' member='2026' />
               <groupfilter function='member' level='[Year]' member='2027' />
               <groupfilter function='member' level='[Year]' member='2028' />
               <groupfilter function='member' level='[Year]' member='2029' />
               <groupfilter function='member' level='[Year]' member='2030' />
             </groupfilter>
           </groupfilter>
         </groupfilter>
       </group>
       <drill-paths>
         <drill-path name='Data set'>
           <field>[Set 1]</field>
           <field>[Set 3]</field>
           <field>[Set 2]</field>
         </drill-path>
       </drill-paths>
       <layout _.fcp.SchemaViewerObjectModel.false...dim-percentage='0.5' _.fcp.SchemaViewerObjectModel.false...measure-percentage='0.4' dim-ordering='alphabetic' measure-ordering='alphabetic' show-aliased-fields='true' show-structure='true' />
       <style>
         <style-rule element='mark'>
           <encoding attr='color' field='[io:Set 3:nk]' type='palette'>
             <map to='#a1daf8'>
               <bucket>true</bucket>
             </map>
             <map to='#a5d7d5'>
               <bucket>false</bucket>
             </map>
           </encoding>
           <encoding attr='color' field='[io:Set 2:nk]' type='palette'>
             <map to='#a69dcd'>
               <bucket>false</bucket>
             </map>
             <map to='#d0a9d0'>
               <bucket>true</bucket>
             </map>
           </encoding>
           <encoding attr='color' field='[io:Set 1:nk]' type='palette'>
             <map to='#a69dcd'>
               <bucket>false</bucket>
             </map>
             <map to='#d0a9d0'>
               <bucket>true</bucket>
             </map>
           </encoding>
-          <encoding attr='color' field='[none:Theme (copy)_720294505510551552:nk]' palette='Couleurs officielles EPFL' type='palette'>
-            <map to='#007480'>
-              <bucket>&quot;Numérique (fabrication)&quot;</bucket>
-            </map>
-            <map to='#00a79f'>
-              <bucket>&quot;Alimentation&quot;</bucket>
-            </map>
-            <map to='#6d6b6b'>
-              <bucket>&quot;Mazout&quot;</bucket>
-            </map>
-            <map to='#888686'>
-              <bucket>&quot;Gaz&quot;</bucket>
-            </map>
-            <map to='#b51f1f'>
-              <bucket>&quot;Voyages professionnels&quot;</bucket>
-            </map>
-            <map to='#cac7c7'>
-              <bucket>&quot;Achats (estimation)&quot;</bucket>
-            </map>
-            <map to='#cac7c7'>
-              <bucket>&quot;CAD&quot;</bucket>
-            </map>
-            <map to='#cac7c7'>
-              <bucket>&quot;Electricite&quot;</bucket>
-            </map>
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             <map to='#cac7c7'>
               <bucket>&quot;CAD&quot;</bucket>
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           <encoding attr='color' field='[none:Category:nk]' palette='EPFL_strategie' type='palette'>
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               <bucket>&quot;moto&quot;</bucket>
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             </map>
             <map to='#a4bde4'>
               <bucket>&quot;Véhicules MOBILITY diesel&quot;</bucket>
             </map>
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               <bucket>&quot;Achats (estimation)&quot;</bucket>
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               <bucket>&quot;Véhicules EPFL Essence&quot;</bucket>
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               <bucket>&quot;Menu viande&quot;</bucket>
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             <map to='#a5d7d5'>
               <bucket>&quot;Restauration&quot;</bucket>
             </map>
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               <bucket>&quot;Sandwich veg&quot;</bucket>
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               <bucket>&quot;Sandwich viande&quot;</bucket>
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               <bucket>&quot;Rail international&quot;</bucket>
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               <bucket>&quot;voiture&quot;</bucket>
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               <bucket>&quot;Rail domestique&quot;</bucket>
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               <bucket>&quot;Véhicules privés&quot;</bucket>
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               <bucket>&quot;à pied&quot;</bucket>
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               <bucket>&quot;Avion staff&quot;</bucket>
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               <bucket>&quot;Véhicules de service diesel&quot;</bucket>
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               <bucket>&quot;bicyclette&quot;</bucket>
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               <bucket>%null%</bucket>
             </map>
             <map to='#fff482'>
               <bucket>&quot;moto/scooter&quot;</bucket>
             </map>
           </encoding>
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+            <map to='#f39869'>
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+            </map>
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+            </map>
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         </style-rule>
       </style>
       <semantic-values>
         <semantic-value key='[Country].[Name]' value='&quot;France&quot;' />
       </semantic-values>
       <date-options start-of-week='monday' />
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         <manual-sort column='[none:Forecast Indicator:nk]' direction='ASC'>
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                   <column datatype='integer' name='Year' ordinal='0' />
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                 <alias key='&quot;Electricite&quot;' value='Electricité' />
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 tonnes de CO2-eq émises par l&apos;EPFL pour l&apos;année </run>
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 tons CO2-eq is emitted by EPFL for the year </run>
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+              <bucket>&quot;Avion étudiants&quot;</bucket>
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+              <bucket>&quot;CAD&quot;</bucket>
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-                <groupfilter function='member' level='[none:Data description:nk]' member='&quot;Objectifs 2030 (confédération+EPFL)&quot;' />
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               <groupfilter function='member' level='[none:Year (copy)_720294505534763010:ok]' member='2022' />
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+                  <format attr='border-color' value='#000000' />
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+              <zone h='31693' id='22' type-v2='text' w='10341' x='89191' y='67422'>
+                <formatted-text>
+                  <run>Open source code: </run>
+                  <run auto-url='true' hyperlink='tabdoc:load-url url=&amp;quot;https://c4science.ch/source/bilan_CO2/&amp;quot;'>https://c4science.ch/source/bilan_CO2/</run>
+                  <run>Æ&#10;</run>
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+                <zone h='98230' id='11' name='Category complete' pane-specification-id='0' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]' type-v2='color' w='10339' x='78852' y='885'>
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                     <run>Open source code: </run>
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diff --git a/Tableau/Bilan_CO2_EPFL.twb b/Tableau/Bilan_CO2_EPFL_deprecated.twb
similarity index 56%
copy from Tableau/Bilan_CO2_EPFL.twb
copy to Tableau/Bilan_CO2_EPFL_deprecated.twb
index e3bb97a..be268fc 100644
--- a/Tableau/Bilan_CO2_EPFL.twb
+++ b/Tableau/Bilan_CO2_EPFL_deprecated.twb
@@ -1,8808 +1,8843 @@
 <?xml version='1.0' encoding='utf-8' ?>
 
 <!-- build 20223.23.0623.1714                               -->
 <workbook original-version='18.1' source-build='2022.3.7 (20223.23.0623.1714)' source-platform='win' version='18.1' xml:base='https://tableau.epfl.ch' xmlns:user='http://www.tableausoftware.com/xml/user'>
   <document-format-change-manifest>
     <_.fcp.AccessibleZoneTabOrder.true...AccessibleZoneTabOrder />
     <_.fcp.AnimationOnByDefault.true...AnimationOnByDefault />
     <AutoCreateAndUpdateDSDPhoneLayouts />
+    <IntuitiveSorting />
+    <IntuitiveSorting_SP2 />
     <_.fcp.MarkAnimation.true...MarkAnimation />
     <_.fcp.ObjectModelEncapsulateLegacy.true...ObjectModelEncapsulateLegacy />
     <_.fcp.ObjectModelTableType.true...ObjectModelTableType />
     <_.fcp.SchemaViewerObjectModel.true...SchemaViewerObjectModel />
     <SetMembershipControl />
     <SheetIdentifierTracking />
     <SortTagCleanup />
     <WindowsPersistSimpleIdentifiers />
     <WorksheetBackgroundTransparency />
     <ZoneBackgroundTransparency />
     <ZoneFriendlyName />
   </document-format-change-manifest>
   <repository-location id='BilanCO2EPFL' path='/workbooks' revision='4.3' />
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     <preference name='ui.shelf.height' value='26' />
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-            <remote-alias>Category</remote-alias>
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-            <attributes>
-              <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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-            <remote-type>20</remote-type>
-            <local-name>[Year]</local-name>
-            <parent-name>[Sheet1]</parent-name>
-            <remote-alias>Year</remote-alias>
-            <ordinal>1</ordinal>
-            <local-type>integer</local-type>
-            <aggregation>Sum</aggregation>
-            <contains-null>true</contains-null>
-            <attributes>
-              <attribute datatype='string' name='DebugRemoteType'>&quot;I8&quot;</attribute>
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-          </metadata-record>
-          <metadata-record class='column'>
-            <remote-name>EPT</remote-name>
-            <remote-type>5</remote-type>
-            <local-name>[EPT]</local-name>
-            <parent-name>[Sheet1]</parent-name>
-            <remote-alias>EPT</remote-alias>
-            <ordinal>2</ordinal>
-            <local-type>real</local-type>
-            <aggregation>Sum</aggregation>
-            <precision>15</precision>
-            <contains-null>true</contains-null>
-            <attributes>
-              <attribute datatype='string' name='DebugRemoteType'>&quot;R8&quot;</attribute>
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-          </metadata-record>
-        </metadata-records>
-      </connection>
-      <aliases enabled='yes' />
-      <column caption='CO2/EPT' datatype='real' name='[Calculation_41376881237925888]' role='measure' type='quantitative'>
-        <calculation class='tableau' formula='SUM([federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[Calculation_316377907629977601])/SUM([EPT])' />
-      </column>
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-        <column caption='CO2 by EPT' datatype='real' name='[Calculation_1265230035614871552]' role='measure' type='quantitative'>
-          <calculation class='tableau' formula='SUM([Calculation_316377907629977601])/SUM([federated.09xbt491r8ci33145k2s61psbvj3].[EPT])' />
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-        <column caption='CO2e [t]' datatype='real' name='[Calculation_316377907629977601]' role='measure' type='quantitative'>
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     <datasource caption='L2B_data' inline='true' name='federated.1vvrr5x1t7r13w1ei0rzz04rhce3' version='18.1'>
       <repository-location id='Bilan_data_16873382300950' path='datasources' revision='1.0' />
       <connection class='federated'>
         <named-connections>
           <named-connection caption='L2B_data' name='excel-direct.0vp0ukd0lpl99t16fcifo0k00qk0'>
             <connection class='excel-direct' cleaning='no' compat='no' dataRefreshTime='' filename='M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets transversaux/Bilan_CO2/data/Resultats/L3B_data.xlsx' interpretationMode='0' password='' server='' validate='no' workgroup-auth-mode='as-is' />
           </named-connection>
         </named-connections>
         <_.fcp.ObjectModelEncapsulateLegacy.false...relation connection='excel-direct.0vp0ukd0lpl99t16fcifo0k00qk0' name='Sheet1' table='[Sheet1$]' type='table'>
-          <columns gridOrigin='A1:Q589:no:A1:Q589:0' header='yes' outcome='2'>
+          <columns gridOrigin='A1:Q591:no:A1:Q591:0' header='yes' outcome='2'>
             <column datatype='integer' name='Year' ordinal='0' />
             <column datatype='string' name='Campus' ordinal='1' />
             <column datatype='real' name='Value' ordinal='2' />
             <column datatype='string' name='Category' ordinal='3' />
             <column datatype='string' name='Path' ordinal='4' />
             <column datatype='string' name='Theme' ordinal='5' />
             <column datatype='integer' name='Scope' ordinal='6' />
             <column datatype='string' name='Unit' ordinal='7' />
             <column datatype='real' name='CO2' ordinal='8' />
             <column datatype='string' name='Data description' ordinal='9' />
             <column datatype='integer' name='Key_ID' ordinal='10' />
             <column datatype='real' name='Factor (kg CO2eq)' ordinal='11' />
             <column datatype='string' name='Source' ordinal='12' />
             <column datatype='string' name='Dataset name' ordinal='13' />
             <column datatype='string' name='Comments' ordinal='14' />
             <column datatype='string' name='Lien source' ordinal='15' />
             <column datatype='string' name='Objectif' ordinal='16' />
           </columns>
         </_.fcp.ObjectModelEncapsulateLegacy.false...relation>
         <_.fcp.ObjectModelEncapsulateLegacy.true...relation connection='excel-direct.0vp0ukd0lpl99t16fcifo0k00qk0' name='Sheet1' table='[Sheet1$]' type='table'>
-          <columns gridOrigin='A1:Q589:no:A1:Q589:0' header='yes' outcome='2'>
+          <columns gridOrigin='A1:Q591:no:A1:Q591:0' header='yes' outcome='2'>
             <column datatype='integer' name='Year' ordinal='0' />
             <column datatype='string' name='Campus' ordinal='1' />
             <column datatype='real' name='Value' ordinal='2' />
             <column datatype='string' name='Category' ordinal='3' />
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             <column datatype='real' name='CO2' ordinal='8' />
             <column datatype='string' name='Data description' ordinal='9' />
             <column datatype='integer' name='Key_ID' ordinal='10' />
             <column datatype='real' name='Factor (kg CO2eq)' ordinal='11' />
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             <column datatype='string' name='Dataset name' ordinal='13' />
             <column datatype='string' name='Comments' ordinal='14' />
             <column datatype='string' name='Lien source' ordinal='15' />
             <column datatype='string' name='Objectif' ordinal='16' />
           </columns>
         </_.fcp.ObjectModelEncapsulateLegacy.true...relation>
         <metadata-records>
           <metadata-record class='capability'>
             <remote-name />
             <remote-type>0</remote-type>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias />
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='integer' name='context'>2</attribute>
-              <attribute datatype='string' name='gridOrigin'>&quot;A1:Q589:no:A1:Q589:0&quot;</attribute>
+              <attribute datatype='string' name='gridOrigin'>&quot;A1:Q591:no:A1:Q591:0&quot;</attribute>
               <attribute datatype='boolean' name='header'>true</attribute>
               <attribute datatype='integer' name='outcome'>2</attribute>
             </attributes>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Year</remote-name>
             <remote-type>20</remote-type>
             <local-name>[Year]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Year</remote-alias>
             <ordinal>0</ordinal>
             <local-type>integer</local-type>
             <aggregation>Sum</aggregation>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;I8&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Campus</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Campus]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Campus</remote-alias>
             <ordinal>1</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Value</remote-name>
             <remote-type>5</remote-type>
             <local-name>[Value]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Value</remote-alias>
             <ordinal>2</ordinal>
             <local-type>real</local-type>
             <aggregation>Sum</aggregation>
             <precision>15</precision>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;R8&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
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             <remote-name>Category</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Category]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Category</remote-alias>
             <ordinal>3</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
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           <metadata-record class='column'>
             <remote-name>Path</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Path]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Path</remote-alias>
             <ordinal>4</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
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             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
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             <remote-name>Theme</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Theme]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Theme</remote-alias>
             <ordinal>5</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
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           <metadata-record class='column'>
             <remote-name>Scope</remote-name>
             <remote-type>20</remote-type>
             <local-name>[Scope]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Scope</remote-alias>
             <ordinal>6</ordinal>
             <local-type>integer</local-type>
             <aggregation>Sum</aggregation>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;I8&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Unit</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Unit]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Unit</remote-alias>
             <ordinal>7</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>CO2</remote-name>
             <remote-type>5</remote-type>
             <local-name>[CO2]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>CO2</remote-alias>
             <ordinal>8</ordinal>
             <local-type>real</local-type>
             <aggregation>Sum</aggregation>
             <precision>15</precision>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;R8&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Data description</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Data description]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Data description</remote-alias>
             <ordinal>9</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Key_ID</remote-name>
             <remote-type>20</remote-type>
             <local-name>[Key_ID]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Key_ID</remote-alias>
             <ordinal>10</ordinal>
             <local-type>integer</local-type>
             <aggregation>Sum</aggregation>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;I8&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Factor (kg CO2eq)</remote-name>
             <remote-type>5</remote-type>
             <local-name>[Factor (kg CO2eq)]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Factor (kg CO2eq)</remote-alias>
             <ordinal>11</ordinal>
             <local-type>real</local-type>
             <aggregation>Sum</aggregation>
             <precision>15</precision>
             <contains-null>true</contains-null>
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;R8&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Source</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Source]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Source</remote-alias>
             <ordinal>12</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
             </attributes>
             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Dataset name</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Dataset name]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Dataset name</remote-alias>
             <ordinal>13</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Comments</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Comments]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Comments</remote-alias>
             <ordinal>14</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Lien source</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Lien source]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Lien source</remote-alias>
             <ordinal>15</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
             <attributes>
               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
           <metadata-record class='column'>
             <remote-name>Objectif</remote-name>
             <remote-type>130</remote-type>
             <local-name>[Objectif]</local-name>
             <parent-name>[Sheet1]</parent-name>
             <remote-alias>Objectif</remote-alias>
             <ordinal>16</ordinal>
             <local-type>string</local-type>
             <aggregation>Count</aggregation>
             <contains-null>true</contains-null>
             <collation flag='1' name='LFR_RFR_S2' />
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               <attribute datatype='string' name='DebugRemoteType'>&quot;WSTR&quot;</attribute>
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             <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[Sheet1_F706AA2D072D422D81961150A43F73A2]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
           </metadata-record>
         </metadata-records>
       </connection>
       <aliases enabled='yes' />
       <column caption='CO2 -15%' datatype='real' name='[CO2 (copy)_999517661598375941]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description]==&quot;Objectif EPFL 15% de diminution d&apos;ici 2024&quot; THEN [CO2] ELSE 0 END' />
       </column>
       <column caption='url' datatype='string' name='[CO2 -15% (copy)_1805380557052968960]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='&quot;&lt;a href=&apos;https://www.epfl.ch/about/facts/fr/statistiques-institutionnelles/statistiques-durabilite/glossaire-durabilite/&apos;&gt;Définition Scope&lt;/a&gt;&quot;' />
       </column>
       <column caption='opacity field ' datatype='real' name='[Calculation_1052997914151374848]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description] != &quot;Estimé&quot; THEN [Parameters].[Parameter 1] ELSE 0.3 END' />
       </column>
       <column caption='Concatenated unit' datatype='string' name='[Calculation_1052997914207850501]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='&quot;kg CO2&quot;+&quot;/&quot;+[Unit_CO2]' />
       </column>
       <column caption='Unit formated' datatype='string' name='[Calculation_1052997914208116742]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='IF ISNULL([Calculation_1052997914207850501]) THEN &quot;Facteurs multiples&quot; ELSE [Calculation_1052997914207850501] END' />
       </column>
       <column caption='relative CO2' datatype='integer' name='[Calculation_1052997914215399431]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='INT(ROUND(SUM([CO2]) / TOTAL(SUM([CO2]))*100, 0))'>
           <table-calc ordering-type='Rows' />
         </calculation>
       </column>
       <column caption='CO2 by EPT' datatype='real' name='[Calculation_1265230035614871552]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='SUM([Calculation_316377907629977601])/SUM([federated.09xbt491r8ci33145k2s61psbvj3].[EPT])' />
       </column>
       <column caption='x' datatype='string' name='[Calculation_1370501696121757696]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='&quot;x&quot;' />
       </column>
       <column caption='Total CO2' datatype='real' name='[Calculation_316377907626377216]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='TOTAL(SUM([CO2]))/1000'>
           <table-calc ordering-type='Rows' />
         </calculation>
       </column>
       <column caption='CO2e [t]' datatype='real' name='[Calculation_316377907629977601]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='[CO2]/1000' />
       </column>
       <column caption='CO2 -50% energy' datatype='real' name='[Calculation_999517661596172291]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description]==&quot;Objectifs fédéraux 2030&quot; THEN [CO2] ELSE 0 END' />
       </column>
       <column caption='CO2e' datatype='real' name='[Calculation_999517661596495876]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='IF [Data description]!=&quot;Objectif EPFL 15% de diminution d&apos;ici 2024&quot; AND [Data description]!= &quot;Objectifs fédéraux 2030&quot; THEN [CO2] END' />
       </column>
       <column caption='Category_EN' datatype='string' name='[Category (copy)_690458179210276864]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='[Category]' />
       </column>
       <column datatype='string' name='[Category]' role='dimension' type='nominal' />
       <column datatype='string' name='[Data description]' role='dimension' type='nominal' />
       <column aggregation='Sum' datatype='integer' name='[Scope]' role='dimension' type='ordinal' />
       <column caption='Theme EN' datatype='string' name='[Theme (copy)_720294505510551552]' role='dimension' type='nominal'>
         <calculation class='tableau' formula='[Theme]' />
         <aliases>
           <alias key='&quot;Achats (estimation)&quot;' value='Purchases (estimation)' />
           <alias key='&quot;Alimentation&quot;' value='Food' />
           <alias key='&quot;CAD&quot;' value='District heating' />
           <alias key='&quot;Electricite&quot;' value='Electricity' />
           <alias key='&quot;Gaz&quot;' value='Natural gas' />
           <alias key='&quot;Mazout&quot;' value='Fuel oil' />
           <alias key='&quot;Mobilité pendulaire&quot;' value='Commuting' />
           <alias key='&quot;Numérique (fabrication)&quot;' value='Digital (production)' />
           <alias key='&quot;Voyages professionnels&quot;' value='Business trip' />
         </aliases>
       </column>
       <column datatype='string' name='[Theme]' role='dimension' type='nominal'>
         <aliases>
           <alias key='&quot;Electricite&quot;' value='Electricité' />
         </aliases>
       </column>
       <column caption='Unit CO2' datatype='string' name='[Unit_CO2]' role='dimension' type='nominal' />
       <column caption='Year EN' datatype='integer' name='[Year (copy)_720294505534763010]' role='dimension' type='ordinal'>
         <calculation class='tableau' formula='[Year]' />
         <aliases>
           <alias key='2023' value='2023 (estimated)' />
           <alias key='2024' value='2024 (estimated)' />
           <alias key='2025' value='2025 (estimated)' />
           <alias key='2026' value='2026 (estimated)' />
           <alias key='2027' value='2027 (estimated)' />
           <alias key='2028' value='2028 (estimated)' />
           <alias key='2029' value='2029 (estimated)' />
           <alias key='2030' value='2030 (Objective)' />
         </aliases>
       </column>
       <column datatype='integer' name='[Year]' role='dimension' type='ordinal'>
         <aliases>
           <alias key='2023' value='2023 (estimé)' />
           <alias key='2024' value='2024 (estimé)' />
           <alias key='2025' value='2025 (estimé)' />
           <alias key='2026' value='2026 (estimé)' />
           <alias key='2027' value='2027 (estimé)' />
           <alias key='2028' value='2028 (estimé)' />
           <alias key='2029' value='2029 (estimé)' />
           <alias key='2030' value='2030 (Objectif)' />
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       <column caption='relative CO2 EN' datatype='integer' name='[relative CO2 (copy)_720294505511186433]' role='measure' type='quantitative'>
         <calculation class='tableau' formula='INT(ROUND(SUM([CO2]) / TOTAL(SUM([CO2]))*100, 0))'>
           <table-calc ordering-type='Rows' />
         </calculation>
       </column>
       <column-instance column='[Set 1]' derivation='InOut' name='[io:Set 1:nk]' pivot='key' type='nominal' />
       <column-instance column='[Set 2]' derivation='InOut' name='[io:Set 2:nk]' pivot='key' type='nominal' />
       <column-instance column='[Set 3]' derivation='InOut' name='[io:Set 3:nk]' pivot='key' type='nominal' />
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       <column-instance column='[Data description]' derivation='None' name='[none:Data description:nk]' pivot='key' type='nominal' />
       <column-instance column='[Forecast Indicator]' derivation='None' forecast-column-base='[Forecast Indicator]' forecast-column-type='forecast-indicator' name='[none:Forecast Indicator:nk]' pivot='key' type='nominal' />
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       <column-instance column='[Theme]' derivation='None' name='[none:Theme:nk]' pivot='key' type='nominal' />
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         <groupfilter function='crossjoin'>
           <groupfilter function='member' level='[Theme]' member='&quot;Electricité&quot;' />
           <groupfilter function='union'>
             <groupfilter function='member' level='[Year]' member='2022' />
             <groupfilter function='member' level='[Year]' member='2023' />
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             <groupfilter function='member' level='[Year]' member='2030' />
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       <group caption='Objectif -15% EPFL' name='[Set 2]' name-style='unqualified' user:ui-builder='lasso-group'>
         <groupfilter function='crossjoin'>
           <groupfilter function='member' level='[Theme]' member='&quot;Electricité&quot;' />
           <groupfilter function='union'>
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             <groupfilter function='member' level='[Year]' member='2023' />
             <groupfilter function='member' level='[Year]' member='2024' />
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       </group>
       <group caption='Voyage_2030' name='[Set 3]' name-style='unqualified' user:ui-builder='lasso-group'>
         <groupfilter function='crossjoin'>
           <groupfilter function='member' level='[Theme]' member='&quot;Voyages professionnels&quot;' />
           <groupfilter function='union'>
             <groupfilter function='member' level='[Year]' member='2023' />
             <groupfilter function='member' level='[Year]' member='2024' />
             <groupfilter function='member' level='[Year]' member='2025' />
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             <groupfilter function='member' level='[Year]' member='2029' />
             <groupfilter function='member' level='[Year]' member='2030' />
           </groupfilter>
         </groupfilter>
       </group>
       <group caption='predicted' name='[Set 4]' name-style='unqualified' user:ui-builder='lasso-group'>
         <groupfilter function='union'>
           <groupfilter function='crossjoin'>
             <groupfilter function='member' level='[Theme]' member='&quot;Alimentation&quot;' />
             <groupfilter function='member' level='[Year]' member='2030' />
           </groupfilter>
           <groupfilter function='crossjoin'>
             <groupfilter function='union'>
               <groupfilter function='member' level='[Theme]' member='&quot;CAD&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Electricité&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Gaz&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Mazout&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Mobilité pendulaire&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Numérique&quot;' />
               <groupfilter function='member' level='[Theme]' member='&quot;Voyages professionnels&quot;' />
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             <groupfilter function='union'>
               <groupfilter function='member' level='[Year]' member='2023' />
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       <drill-paths>
         <drill-path name='Data set'>
           <field>[Set 1]</field>
           <field>[Set 3]</field>
           <field>[Set 2]</field>
         </drill-path>
       </drill-paths>
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           </encoding>
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+          <encoding attr='color' field='[none:Theme:nk]' palette='Couleurs officielles EPFL' type='palette'>
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               <bucket>&quot;Alimentation&quot;</bucket>
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               <bucket>&quot;Voyages professionnels&quot;</bucket>
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               <bucket>&quot;Achats (estimation)&quot;</bucket>
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               <bucket>&quot;CAD&quot;</bucket>
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               <bucket>&quot;CAD&quot;</bucket>
             </map>
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-              <bucket>%many-values%</bucket>
+            <map to='#cac7c7'>
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               <bucket>&quot;Mobilité pendulaire&quot;</bucket>
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               <bucket>%many-values%</bucket>
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           <encoding attr='color' field='[none:Category:nk]' palette='EPFL_strategie' type='palette'>
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+                <columns gridOrigin='A1:Q591:no:A1:Q591:0' header='yes' outcome='2'>
                   <column datatype='integer' name='Year' ordinal='0' />
                   <column datatype='string' name='Campus' ordinal='1' />
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         </objects>
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     </datasource>
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+            <column datatype='real' name='EPT' ordinal='2' />
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+            <column datatype='real' name='EPT' ordinal='2' />
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+            <parent-name>[Sheet1]</parent-name>
+            <remote-alias />
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+              <attribute datatype='boolean' name='header'>true</attribute>
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+            <local-name>[Category]</local-name>
+            <parent-name>[Sheet1]</parent-name>
+            <remote-alias>Category</remote-alias>
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+            <parent-name>[Sheet1]</parent-name>
+            <remote-alias>Year</remote-alias>
+            <ordinal>1</ordinal>
+            <local-type>integer</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <attributes>
+              <attribute datatype='string' name='DebugRemoteType'>&quot;I8&quot;</attribute>
+            </attributes>
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+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>EPT</remote-name>
+            <remote-type>5</remote-type>
+            <local-name>[EPT]</local-name>
+            <parent-name>[Sheet1]</parent-name>
+            <remote-alias>EPT</remote-alias>
+            <ordinal>2</ordinal>
+            <local-type>real</local-type>
+            <aggregation>Sum</aggregation>
+            <precision>15</precision>
+            <contains-null>true</contains-null>
+            <attributes>
+              <attribute datatype='string' name='DebugRemoteType'>&quot;R8&quot;</attribute>
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+          </metadata-record>
+        </metadata-records>
+      </connection>
+      <aliases enabled='yes' />
+      <column caption='CO2/EPT' datatype='real' name='[Calculation_41376881237925888]' role='measure' type='quantitative'>
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+      <column datatype='integer' name='[Year]' role='dimension' type='quantitative' />
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+        <semantic-value key='[Country].[Name]' value='&quot;France&quot;' />
+      </semantic-values>
+      <date-options start-of-week='monday' />
+      <datasource-dependencies datasource='federated.1vvrr5x1t7r13w1ei0rzz04rhce3'>
+        <column caption='CO2 by EPT' datatype='real' name='[Calculation_1265230035614871552]' role='measure' type='quantitative'>
+          <calculation class='tableau' formula='SUM([Calculation_316377907629977601])/SUM([federated.09xbt491r8ci33145k2s61psbvj3].[EPT])' />
+        </column>
+        <column caption='CO2e [t]' datatype='real' name='[Calculation_316377907629977601]' role='measure' type='quantitative'>
+          <calculation class='tableau' formula='[CO2]/1000' />
+        </column>
+        <column-instance column='[Calculation_316377907629977601]' derivation='Sum' name='[sum:Calculation_316377907629977601:qk]' pivot='key' type='quantitative' />
+        <column-instance column='[Calculation_1265230035614871552]' derivation='User' name='[usr:Calculation_1265230035614871552:qk]' pivot='key' type='quantitative' />
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+                  <column datatype='string' name='Category' ordinal='0' />
+                  <column datatype='integer' name='Year' ordinal='1' />
+                  <column datatype='real' name='EPT' ordinal='2' />
+                </columns>
+              </relation>
+            </properties>
+          </object>
+        </objects>
+      </_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
+    </datasource>
   </datasources>
   <actions>
     <action caption='Highlight 1 (generated)' name='[Action1_A2DF55A2573D440A953C9709F4C21A82]'>
       <activation auto-clear='true' type='on-select' />
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     <action caption='Highlight 1 (generated) 1 1 1' name='[Action5_04F21E479E3A4E468EE667A00794AA51]'>
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           <formatted-text>
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             <column caption='Total CO2' datatype='real' name='[Calculation_316377907626377216]' role='measure' type='quantitative'>
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               <aliases>
                 <alias key='&quot;Electricite&quot;' value='Electricité' />
               </aliases>
             </column>
             <column datatype='string' name='[Unit]' role='dimension' type='nominal' />
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             <column datatype='real' name='[Value]' role='measure' type='quantitative' />
             <column datatype='integer' name='[Year]' role='dimension' type='ordinal'>
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                 <alias key='2023' value='2023 (estimé)' />
                 <alias key='2024' value='2024 (estimé)' />
                 <alias key='2025' value='2025 (estimé)' />
                 <alias key='2026' value='2026 (estimé)' />
                 <alias key='2027' value='2027 (estimé)' />
                 <alias key='2028' value='2028 (estimé)' />
                 <alias key='2029' value='2029 (estimé)' />
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               <bucket>&quot;Sandwich veg&quot;</bucket>
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               <bucket>&quot;Electricite&quot;</bucket>
               <bucket>&quot;Mobility diesel&quot;</bucket>
               <bucket>&quot;Mobility essence&quot;</bucket>
               <bucket>&quot;Véhicules de service diesel&quot;</bucket>
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-                <groupfilter function='member' level='[none:Data description:nk]' member='&quot;Objectifs 2030 (confédération+EPFL)&quot;' />
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-            <groupfilter function='member' level='[none:Theme:nk]' member='&quot;Voyages professionnels&quot;' user:ui-domain='database' user:ui-enumeration='inclusive' user:ui-marker='enumerate' />
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           <filter class='categorical' column='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]'>
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 tons CO2-eq is emitted by EPFL for the year </run>
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       <size sizing-mode='automatic' />
       <zones>
         <zone h='100000' id='4' type-v2='layout-basic' w='100000' x='0' y='0'>
-          <zone h='98230' id='7' param='horz' type-v2='layout-flow' w='99064' x='468' y='885'>
-            <zone h='98230' id='5' type-v2='layout-basic' w='89702' x='468' y='885'>
-              <zone h='98230' id='3' name='Bilan_energy_EN' w='89702' x='468' y='885'>
+          <zone h='98230' id='7' param='horz' type-v2='layout-flow' w='99064' x='468' y='885'>
+            <zone h='98230' id='5' type-v2='layout-basic' w='89702' x='468' y='885'>
+              <zone h='98230' id='3' name='Bilan_energy_EN' w='89702' x='468' y='885'>
+                <zone-style>
+                  <format attr='border-color' value='#000000' />
+                  <format attr='border-style' value='none' />
+                  <format attr='border-width' value='0' />
+                  <format attr='margin' value='4' />
+                </zone-style>
+              </zone>
+            </zone>
+            <zone fixed-size='160' h='98230' id='6' is-fixed='true' param='vert' type-v2='layout-flow' w='9362' x='90170' y='885'>
+              <zone h='11726' id='9' name='Bilan_energy_EN' pane-specification-id='0' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]' type-v2='color' w='9362' x='90170' y='885'>
+                <zone-style>
+                  <format attr='border-color' value='#000000' />
+                  <format attr='border-style' value='none' />
+                  <format attr='border-width' value='0' />
+                  <format attr='margin' value='4' />
+                </zone-style>
+              </zone>
+            </zone>
+          </zone>
+          <zone-style>
+            <format attr='border-color' value='#000000' />
+            <format attr='border-style' value='none' />
+            <format attr='border-width' value='0' />
+            <format attr='margin' value='8' />
+          </zone-style>
+        </zone>
+      </zones>
+      <devicelayouts>
+        <devicelayout auto-generated='true' name='Phone'>
+          <size maxheight='700' minheight='700' sizing-mode='vscroll' />
+          <zones>
+            <zone h='100000' id='35' type-v2='layout-basic' w='100000' x='0' y='0'>
+              <zone h='84000' id='34' param='vert' type-v2='layout-flow' w='84000' x='8000' y='8000'>
+                <zone fixed-size='280' h='98230' id='3' is-fixed='true' name='Bilan_energy_EN' w='89702' x='468' y='885'>
+                  <zone-style>
+                    <format attr='border-color' value='#000000' />
+                    <format attr='border-style' value='none' />
+                    <format attr='border-width' value='0' />
+                    <format attr='margin' value='4' />
+                    <format attr='padding' value='0' />
+                  </zone-style>
+                </zone>
+                <zone h='11726' id='9' name='Bilan_energy_EN' pane-specification-id='0' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]' type-v2='color' w='9362' x='90170' y='885'>
+                  <zone-style>
+                    <format attr='border-color' value='#000000' />
+                    <format attr='border-style' value='none' />
+                    <format attr='border-width' value='0' />
+                    <format attr='margin' value='4' />
+                    <format attr='padding' value='0' />
+                  </zone-style>
+                </zone>
+              </zone>
+              <zone-style>
+                <format attr='border-color' value='#000000' />
+                <format attr='border-style' value='none' />
+                <format attr='border-width' value='0' />
+                <format attr='margin' value='8' />
+              </zone-style>
+            </zone>
+          </zones>
+        </devicelayout>
+      </devicelayouts>
+      <simple-id uuid='{BCEBD1F6-9A6F-47FB-8746-7741F92248CA}' />
+    </dashboard>
+    <dashboard _.fcp.AccessibleZoneTabOrder.true...enable-sort-zone-taborder='true' name='Dashboard 7'>
+      <style />
+      <size sizing-mode='automatic' />
+      <datasources>
+        <datasource caption='L2B_data' name='federated.1vvrr5x1t7r13w1ei0rzz04rhce3' />
+      </datasources>
+      <datasource-dependencies datasource='federated.1vvrr5x1t7r13w1ei0rzz04rhce3'>
+        <column caption='Year EN' datatype='integer' name='[Year (copy)_720294505534763010]' role='dimension' type='ordinal'>
+          <calculation class='tableau' formula='[Year]' />
+          <aliases>
+            <alias key='2023' value='2023 (estimated)' />
+            <alias key='2024' value='2024 (estimated)' />
+            <alias key='2025' value='2025 (estimated)' />
+            <alias key='2026' value='2026 (estimated)' />
+            <alias key='2027' value='2027 (estimated)' />
+            <alias key='2028' value='2028 (estimated)' />
+            <alias key='2029' value='2029 (estimated)' />
+            <alias key='2030' value='2030 (Objective)' />
+          </aliases>
+        </column>
+        <column-instance column='[Year (copy)_720294505534763010]' derivation='None' name='[none:Year (copy)_720294505534763010:ok]' pivot='key' type='ordinal' />
+      </datasource-dependencies>
+      <zones>
+        <zone h='100000' id='4' type-v2='layout-basic' w='100000' x='0' y='0'>
+          <zone h='98438' id='7' param='horz' type-v2='layout-flow' w='99064' x='468' y='781'>
+            <zone h='98438' id='5' type-v2='layout-basic' w='89702' x='468' y='781'>
+              <zone h='49219' id='3' name='cat_by_scope' w='89702' x='468' y='781'>
+                <zone-style>
+                  <format attr='border-color' value='#000000' />
+                  <format attr='border-style' value='none' />
+                  <format attr='border-width' value='0' />
+                  <format attr='margin' value='4' />
+                </zone-style>
+              </zone>
+              <zone h='49219' id='16' name='Source facteurs' w='89702' x='468' y='50000'>
                 <zone-style>
                   <format attr='border-color' value='#000000' />
                   <format attr='border-style' value='none' />
                   <format attr='border-width' value='0' />
                   <format attr='margin' value='4' />
                 </zone-style>
               </zone>
             </zone>
-            <zone fixed-size='160' h='98230' id='6' is-fixed='true' param='vert' type-v2='layout-flow' w='9362' x='90170' y='885'>
-              <zone h='11726' id='9' name='Bilan_energy_EN' pane-specification-id='0' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]' type-v2='color' w='9362' x='90170' y='885'>
+            <zone fixed-size='160' h='98438' id='6' is-fixed='true' param='vert' type-v2='layout-flow' w='9362' x='90170' y='781'>
+              <zone h='45801' id='8' name='cat_by_scope' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]' type-v2='filter' w='9362' x='90170' y='781'>
                 <zone-style>
                   <format attr='border-color' value='#000000' />
                   <format attr='border-style' value='none' />
                   <format attr='border-width' value='0' />
                   <format attr='margin' value='4' />
                 </zone-style>
               </zone>
             </zone>
           </zone>
           <zone-style>
             <format attr='border-color' value='#000000' />
             <format attr='border-style' value='none' />
             <format attr='border-width' value='0' />
             <format attr='margin' value='8' />
           </zone-style>
         </zone>
       </zones>
       <devicelayouts>
         <devicelayout auto-generated='true' name='Phone'>
           <size maxheight='700' minheight='700' sizing-mode='vscroll' />
           <zones>
-            <zone h='100000' id='31' type-v2='layout-basic' w='100000' x='0' y='0'>
-              <zone h='84000' id='30' param='vert' type-v2='layout-flow' w='84000' x='8000' y='8000'>
-                <zone fixed-size='280' h='98230' id='3' is-fixed='true' name='Bilan_energy_EN' w='89702' x='468' y='885'>
+            <zone h='100000' id='20' type-v2='layout-basic' w='100000' x='0' y='0'>
+              <zone h='98438' id='19' param='vert' type-v2='layout-flow' w='99064' x='468' y='781'>
+                <zone h='45801' id='8' mode='checkdropdown' name='cat_by_scope' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]' type-v2='filter' w='9362' x='90170' y='781'>
                   <zone-style>
                     <format attr='border-color' value='#000000' />
                     <format attr='border-style' value='none' />
                     <format attr='border-width' value='0' />
                     <format attr='margin' value='4' />
                     <format attr='padding' value='0' />
                   </zone-style>
                 </zone>
-                <zone h='11726' id='9' name='Bilan_energy_EN' pane-specification-id='0' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]' type-v2='color' w='9362' x='90170' y='885'>
+                <zone fixed-size='280' h='49219' id='3' is-fixed='true' name='cat_by_scope' w='89702' x='468' y='781'>
+                  <zone-style>
+                    <format attr='border-color' value='#000000' />
+                    <format attr='border-style' value='none' />
+                    <format attr='border-width' value='0' />
+                    <format attr='margin' value='4' />
+                    <format attr='padding' value='0' />
+                  </zone-style>
+                </zone>
+                <zone fixed-size='280' h='49219' id='16' is-fixed='true' name='Source facteurs' w='89702' x='468' y='50000'>
                   <zone-style>
                     <format attr='border-color' value='#000000' />
                     <format attr='border-style' value='none' />
                     <format attr='border-width' value='0' />
                     <format attr='margin' value='4' />
                     <format attr='padding' value='0' />
                   </zone-style>
                 </zone>
               </zone>
               <zone-style>
                 <format attr='border-color' value='#000000' />
                 <format attr='border-style' value='none' />
                 <format attr='border-width' value='0' />
                 <format attr='margin' value='8' />
               </zone-style>
             </zone>
           </zones>
         </devicelayout>
       </devicelayouts>
-      <simple-id uuid='{BCEBD1F6-9A6F-47FB-8746-7741F92248CA}' />
+      <simple-id uuid='{B47CD094-23A0-4CAE-98E9-2388F0A871FF}' />
     </dashboard>
-    <dashboard _.fcp.AccessibleZoneTabOrder.true...enable-sort-zone-taborder='true' name='Dashboard 7'>
+    <dashboard _.fcp.AccessibleZoneTabOrder.true...enable-sort-zone-taborder='true' name='Dashboard 8'>
       <style />
-      <size sizing-mode='automatic' />
+      <size maxheight='800' maxwidth='1000' minheight='800' minwidth='1000' />
       <datasources>
         <datasource caption='L2B_data' name='federated.1vvrr5x1t7r13w1ei0rzz04rhce3' />
       </datasources>
       <datasource-dependencies datasource='federated.1vvrr5x1t7r13w1ei0rzz04rhce3'>
-        <column caption='Year EN' datatype='integer' name='[Year (copy)_720294505534763010]' role='dimension' type='ordinal'>
-          <calculation class='tableau' formula='[Year]' />
+        <column datatype='string' name='[Campus]' role='dimension' type='nominal' />
+        <column datatype='string' name='[Data description]' role='dimension' type='nominal' />
+        <column datatype='string' name='[Theme]' role='dimension' type='nominal'>
           <aliases>
-            <alias key='2023' value='2023 (estimated)' />
-            <alias key='2024' value='2024 (estimated)' />
-            <alias key='2025' value='2025 (estimated)' />
-            <alias key='2026' value='2026 (estimated)' />
-            <alias key='2027' value='2027 (estimated)' />
-            <alias key='2028' value='2028 (estimated)' />
-            <alias key='2029' value='2029 (estimated)' />
-            <alias key='2030' value='2030 (Objective)' />
+            <alias key='&quot;Electricite&quot;' value='Electricité' />
           </aliases>
         </column>
-        <column-instance column='[Year (copy)_720294505534763010]' derivation='None' name='[none:Year (copy)_720294505534763010:ok]' pivot='key' type='ordinal' />
+        <column datatype='integer' name='[Year]' role='dimension' type='ordinal'>
+          <aliases>
+            <alias key='2023' value='2023 (estimé)' />
+            <alias key='2024' value='2024 (estimé)' />
+            <alias key='2025' value='2025 (estimé)' />
+            <alias key='2026' value='2026 (estimé)' />
+            <alias key='2027' value='2027 (estimé)' />
+            <alias key='2028' value='2028 (estimé)' />
+            <alias key='2029' value='2029 (estimé)' />
+            <alias key='2030' value='2030 (Objectif)' />
+          </aliases>
+        </column>
+        <column-instance column='[Campus]' derivation='None' name='[none:Campus:nk]' pivot='key' type='nominal' />
+        <column-instance column='[Data description]' derivation='None' name='[none:Data description:nk]' pivot='key' type='nominal' />
+        <column-instance column='[Theme]' derivation='None' name='[none:Theme:nk]' pivot='key' type='nominal' />
+        <column-instance column='[Year]' derivation='None' name='[none:Year:ok]' pivot='key' type='ordinal' />
       </datasource-dependencies>
       <zones>
         <zone h='100000' id='4' type-v2='layout-basic' w='100000' x='0' y='0'>
-          <zone h='98438' id='7' param='horz' type-v2='layout-flow' w='99064' x='468' y='781'>
-            <zone h='98438' id='5' type-v2='layout-basic' w='89702' x='468' y='781'>
-              <zone h='49219' id='3' name='cat_by_scope' w='89702' x='468' y='781'>
+          <zone h='98000' id='7' param='horz' type-v2='layout-flow' w='98400' x='800' y='1000'>
+            <zone h='98000' id='5' type-v2='layout-basic' w='82400' x='800' y='1000'>
+              <zone h='98000' id='3' name='Theme complete' w='82400' x='800' y='1000'>
                 <zone-style>
                   <format attr='border-color' value='#000000' />
                   <format attr='border-style' value='none' />
                   <format attr='border-width' value='0' />
                   <format attr='margin' value='4' />
                 </zone-style>
               </zone>
-              <zone h='49219' id='9' name='Source facteurs' w='89702' x='468' y='50000'>
+            </zone>
+            <zone fixed-size='160' h='98000' id='6' is-fixed='true' param='vert' type-v2='layout-flow' w='16000' x='83200' y='1000'>
+              <zone h='27750' id='9' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]' type-v2='filter' w='16000' x='83200' y='1000'>
                 <zone-style>
                   <format attr='border-color' value='#000000' />
                   <format attr='border-style' value='none' />
                   <format attr='border-width' value='0' />
                   <format attr='margin' value='4' />
                 </zone-style>
               </zone>
-            </zone>
-            <zone fixed-size='160' h='98438' id='6' is-fixed='true' param='vert' type-v2='layout-flow' w='9362' x='90170' y='781'>
-              <zone h='47656' id='8' name='cat_by_scope' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]' type-v2='filter' w='9362' x='90170' y='781'>
+              <zone h='6500' id='10' mode='checkdropdown' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]' type-v2='filter' w='16000' x='83200' y='28750'>
+                <zone-style>
+                  <format attr='border-color' value='#000000' />
+                  <format attr='border-style' value='none' />
+                  <format attr='border-width' value='0' />
+                  <format attr='margin' value='4' />
+                </zone-style>
+              </zone>
+              <zone h='6500' id='11' mode='checkdropdown' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]' type-v2='filter' w='16000' x='83200' y='35250'>
+                <zone-style>
+                  <format attr='border-color' value='#000000' />
+                  <format attr='border-style' value='none' />
+                  <format attr='border-width' value='0' />
+                  <format attr='margin' value='4' />
+                </zone-style>
+              </zone>
+              <zone h='6500' id='12' mode='checkdropdown' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]' type-v2='filter' w='16000' x='83200' y='41750'>
+                <zone-style>
+                  <format attr='border-color' value='#000000' />
+                  <format attr='border-style' value='none' />
+                  <format attr='border-width' value='0' />
+                  <format attr='margin' value='4' />
+                </zone-style>
+              </zone>
+              <zone h='20750' id='13' name='Theme complete' pane-specification-id='0' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]' type-v2='color' w='16000' x='83200' y='48250'>
                 <zone-style>
                   <format attr='border-color' value='#000000' />
                   <format attr='border-style' value='none' />
                   <format attr='border-width' value='0' />
                   <format attr='margin' value='4' />
                 </zone-style>
               </zone>
             </zone>
           </zone>
           <zone-style>
             <format attr='border-color' value='#000000' />
             <format attr='border-style' value='none' />
             <format attr='border-width' value='0' />
             <format attr='margin' value='8' />
           </zone-style>
         </zone>
       </zones>
       <devicelayouts>
         <devicelayout auto-generated='true' name='Phone'>
           <size maxheight='700' minheight='700' sizing-mode='vscroll' />
           <zones>
             <zone h='100000' id='15' type-v2='layout-basic' w='100000' x='0' y='0'>
-              <zone h='84000' id='14' param='vert' type-v2='layout-flow' w='84000' x='8000' y='8000'>
-                <zone h='47656' id='8' mode='checkdropdown' name='cat_by_scope' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year (copy)_720294505534763010:ok]' type-v2='filter' w='9362' x='90170' y='781'>
+              <zone h='98000' id='14' param='vert' type-v2='layout-flow' w='98400' x='800' y='1000'>
+                <zone h='27750' id='9' mode='checkdropdown' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]' type-v2='filter' w='16000' x='83200' y='1000'>
                   <zone-style>
                     <format attr='border-color' value='#000000' />
                     <format attr='border-style' value='none' />
                     <format attr='border-width' value='0' />
                     <format attr='margin' value='4' />
                     <format attr='padding' value='0' />
                   </zone-style>
                 </zone>
-                <zone fixed-size='280' h='49219' id='3' is-fixed='true' name='cat_by_scope' w='89702' x='468' y='781'>
+                <zone h='6500' id='10' mode='checkdropdown' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]' type-v2='filter' w='16000' x='83200' y='28750'>
+                  <zone-style>
+                    <format attr='border-color' value='#000000' />
+                    <format attr='border-style' value='none' />
+                    <format attr='border-width' value='0' />
+                    <format attr='margin' value='4' />
+                    <format attr='padding' value='0' />
+                  </zone-style>
+                </zone>
+                <zone h='6500' id='11' mode='checkdropdown' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]' type-v2='filter' w='16000' x='83200' y='35250'>
+                  <zone-style>
+                    <format attr='border-color' value='#000000' />
+                    <format attr='border-style' value='none' />
+                    <format attr='border-width' value='0' />
+                    <format attr='margin' value='4' />
+                    <format attr='padding' value='0' />
+                  </zone-style>
+                </zone>
+                <zone h='6500' id='12' mode='checkdropdown' name='Theme complete' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]' type-v2='filter' w='16000' x='83200' y='41750'>
+                  <zone-style>
+                    <format attr='border-color' value='#000000' />
+                    <format attr='border-style' value='none' />
+                    <format attr='border-width' value='0' />
+                    <format attr='margin' value='4' />
+                    <format attr='padding' value='0' />
+                  </zone-style>
+                </zone>
+                <zone fixed-size='280' h='98000' id='3' is-fixed='true' name='Theme complete' w='82400' x='800' y='1000'>
                   <zone-style>
                     <format attr='border-color' value='#000000' />
                     <format attr='border-style' value='none' />
                     <format attr='border-width' value='0' />
                     <format attr='margin' value='4' />
                     <format attr='padding' value='0' />
                   </zone-style>
                 </zone>
-                <zone fixed-size='280' h='49219' id='9' is-fixed='true' name='Source facteurs' w='89702' x='468' y='50000'>
+                <zone h='20750' id='13' name='Theme complete' pane-specification-id='0' param='[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]' type-v2='color' w='16000' x='83200' y='48250'>
                   <zone-style>
                     <format attr='border-color' value='#000000' />
                     <format attr='border-style' value='none' />
                     <format attr='border-width' value='0' />
                     <format attr='margin' value='4' />
                     <format attr='padding' value='0' />
                   </zone-style>
                 </zone>
               </zone>
               <zone-style>
                 <format attr='border-color' value='#000000' />
                 <format attr='border-style' value='none' />
                 <format attr='border-width' value='0' />
                 <format attr='margin' value='8' />
               </zone-style>
             </zone>
           </zones>
         </devicelayout>
       </devicelayouts>
-      <simple-id uuid='{B47CD094-23A0-4CAE-98E9-2388F0A871FF}' />
+      <simple-id uuid='{2C7D312E-5BF8-4A09-B306-E9CB17E6A842}' />
     </dashboard>
     <dashboard _.fcp.AccessibleZoneTabOrder.true...enable-sort-zone-taborder='true' name='Electricité objectifs'>
       <style />
       <size sizing-mode='automatic' />
       <zones>
         <zone h='100000' id='4' type-v2='layout-basic' w='100000' x='0' y='0'>
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diff --git a/Tableau/Bilan_CO2_EPFL_sandbox.twb b/Tableau/Bilan_CO2_EPFL_sandbox.twb
deleted file mode 100644
index 7e41473..0000000
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+++ /dev/null
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-      <column caption='Concatenated unit' datatype='string' name='[Calculation_1052997914207850501]' role='dimension' type='nominal'>
-        <calculation class='tableau' formula='&quot;kg CO2&quot;+&quot;/&quot;+[Unit_CO2]' />
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-      <column caption='CO2e [t]' datatype='real' name='[Calculation_316377907629977601]' role='measure' type='quantitative'>
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-      <column caption='CO2e' datatype='real' name='[Calculation_999517661596495876]' role='measure' type='quantitative'>
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-          <alias key='&quot;Avion collaborateur-trice-s&quot;' value='Voyages estudiantins' />
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-      <column datatype='string' name='[Data description]' role='dimension' type='nominal' />
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-        <calculation class='tableau' formula='[Theme]' />
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-          <alias key='&quot;Achats (estimation)&quot;' value='Purchases (estimation)' />
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diff --git a/Tableau/Bilan.twb b/Tableau/Bilan_CO2_deprecated.twb
similarity index 100%
rename from Tableau/Bilan.twb
rename to Tableau/Bilan_CO2_deprecated.twb
diff --git a/Tableau/~Bilan_CO2_EPFL__7728.twbr b/Tableau/~Bilan_CO2_EPFL__7728.twbr
new file mode 100644
index 0000000..e69de29
diff --git a/data/Brute/42.05.01-reporting-pendulaire-2023.xlsx b/data/Brute/42.05.01-reporting-pendulaire-2023.xlsx
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index 0000000..f877c40
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diff --git a/data/Brute/Copie de 42.06.01-Reporting-voyages-2023.xlsx b/data/Brute/Copie de 42.06.01-Reporting-voyages-2023.xlsx
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index 0000000..ab680ea
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diff --git a/data/Resultats/L1_data.xlsx b/data/Resultats/L1_data.xlsx
index 9f32b94..ba187ec 100644
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diff --git a/data/Resultats/L2_data.xlsx b/data/Resultats/L2_data.xlsx
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diff --git a/data/Resultats/L3A_data.xlsx b/data/Resultats/L3A_data.xlsx
index 7c11f3f..6d721b5 100644
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diff --git a/data/Resultats/L3B_data.xlsx b/data/Resultats/L3B_data.xlsx
index 653fc72..104b882 100644
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diff --git a/data/facteur emission.xlsx b/data/facteur emission.xlsx
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diff --git a/data/inputs.xlsx b/data/inputs.xlsx
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diff --git a/data/~$emission_factor.xlsx b/data/~$inputs.xlsx
similarity index 100%
rename from data/~$emission_factor.xlsx
rename to data/~$inputs.xlsx
diff --git a/data/~$join_factor.xlsx b/data/~$join_factor.xlsx
deleted file mode 100644
index 2c6bc29..0000000
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diff --git a/scripts/footprint.py b/scripts/Carbon_footprint.py
similarity index 50%
copy from scripts/footprint.py
copy to scripts/Carbon_footprint.py
index 1e5d456..285c177 100644
--- a/scripts/footprint.py
+++ b/scripts/Carbon_footprint.py
@@ -1,565 +1,283 @@
 import pandas as pd
 import numpy as np
 from functions import *
 
-class footprint():
-    def __init__(self, CO2_col, EPT_infile, input_file):
+class Carbon_footprint():
+    # Constants for column names
+    YEAR = "Year"
+    CATEGORY = "Category"
+    VALUE = "Value"
+    
+    CO2_col = 'Factor [kg CO2eq]'
+    def __init__(self, EPT_infile, input_file, nomenclature):
         self.general_attributes = {
             "author": "Hugo Cruz",
             "author_mail": "hugo.cruz@epfl.ch",
         }
-        self.CO2_col = CO2_col
+        self.nomenclature = nomenclature
         self.bilan = pd.DataFrame()
         self.data = pd.DataFrame()
         self.category_name = {}
         self.Theme_name = {}
         self.ignore_column = []
-        #open EPT file, this EPT is not up to date, the evolution is not yet taken into account
-        EPT = pd.read_excel(EPT_infile)
-        self.EPT_year = EPT[EPT["Category"]== 'Population GRI'].groupby("Year").sum()["EPT"]
-
-        #Nomenclature utilisée, à gauche est le nom tel qu'il est dans le fichier d'entrée, à droite est le nom tel qu'il est écrit dans le fichier facteur emission
-        self.nomenclature = {"Alimentation": "Alimentation",
-                             "Véhicule de location": "Véhicules de location (pkm)",
-                             "Mobility essence": "Véhicules MOBILITY essence",
-                                "Mobility diesel": "Véhicules MOBILITY diesel",
-                                "Mobility electrique": "Véhicules MOBILITY electrique",
-                                "voiture": "Voiture thermique (pkm)",
-                                "moto/scooter":"Moto/scooter",
-                              "Achats":"Achats (estimation)",
-                              "Véhicules privés": "Véhicules privés",
-                              "Véhicules de service essence": "Véhicules EPFL Essence",
-                              "Véhicules de service diesel": "Véhicules EPFL Diesel",
-                              "Total Domestique": "Rail domestique",
-                              "Total International": "Rail international",
-                              "Electricite": "Electricite",
-                              }
+
+        #Translate this into english : Nomenclature utilisée, à gauche est le nom tel qu'il est dans le fichier d'entrée, à droite est le nom tel qu'il est écrit dans le fichier facteur emission
+
         self.input_file = input_file
 
+    def import_database(self, path):
+        self.data = pd.read_excel(path)
+        
     def read_emission_factor(self, emission_factor_path):
         factor = pd.read_excel(self.input_file, sheet_name = "Facteur emission",index_col=0)
         factor.fillna(method='ffill', inplace=True)
         factor.reset_index(inplace=True)
 
-
         if "google" in emission_factor_path:
             factor = pd.read_csv(emission_factor_path, encoding= 'cp1252', delimiter=';', decimal=",")
         else:
             factor = pd.read_excel(emission_factor_path, index_col=0)
         CO2_col = self.CO2_col
         #Standardize unit: MWh, m3, km
         factor_df = factor.copy()
         
         mask_kWh = factor_df["Unit"]=="kWh"
         factor_df[CO2_col] = factor_df[CO2_col].where(~mask_kWh,factor_df[CO2_col]*1000)
         factor_df["Unit"] = factor_df["Unit"].replace("kWh", "MWh")
 
         mask_MJ = factor_df["Unit"]=="MJ"
         factor_df[CO2_col] = factor_df[CO2_col].where(~mask_MJ,factor_df[CO2_col]*3600)
         factor_df["Unit"] = factor_df["Unit"].replace("MJ", "MWh")
 
         mask_m = factor_df["Unit"]=="m"
         factor_df[CO2_col] = factor_df[CO2_col].where(~mask_m,factor_df[CO2_col]/1000)
         factor_df["Unit"] = factor_df["Unit"].replace("m", "km")
         self.emission_factor = factor_df
 
-    def run_energy_emission(self, elec_path):
-        df = pd.read_excel(elec_path)
-        df = df.rename(columns={"year": "Year", "campus": "Campus", "category": "Category", "value": "Value"})
-        df["Theme"] = df["Category"] #We understand that the theme is Energy, however it's more relevant to separate when we visualize it
-        df["Scope"] = 2
-        df["Unit"] = df["unit"]
-        df["Path"] = elec_path
-        df.drop("unit", axis=1, inplace=True)
-
-        mask_mazout =  df["Category"]=="Mazout"
-        mask_gaz = df["Category"]=="Gaz"
-        df.loc[df["Category"]=="Electricite","Category"] = self.nomenclature["Electricite"]
-
-        df.loc[mask_gaz|mask_mazout, "Scope"] = 1
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def run_dechets_emission(self, dechets_path):
-        #Dechets is not yet taken into account
-        df = pd.read_excel(dechets_path)
-        df = df.rename(columns={"waste_type":"Category", "kg":"Value", "year": "Year"})
-        df["Scope"] = 3
-        df["Unit"] = "kg"
-        df["Theme"] = "Dechets"
-        df["ID_category"] = df["Category"]
-        #Strategy ojective 2025
-        mask = (df["disposal_method"]=="Recycling")&(df["Year"]==2022)
-        recycled = df[(df["disposal_method"]=="Recycling")&(df["Year"]==2022)]["Value"].sum()
-        incineration = df[(df["disposal_method"]=="incineration")&(df["Year"]==2022)]["Value"].sum()
-        total = recycled+incineration
-        recycled_2025 = total*0.8
-        incineration_2025 = total*0.2
-        df["Path"] = dechets_path
-        df.drop("disposal_method", axis=1, inplace=True)
-        print("In 2025, the amount of recycled waste should be {} kg based on 80% recycled in 2022".format(recycled_2025))
-        
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def run_plane_emission(self,path):
-        staff_emission = pd.read_excel(path, sheet_name="Avions (staff)", skiprows=1)
-        staff_CO2 = staff_emission.set_index("Data*").loc["CO2 footprint (tons) / Year        Données: Atmosfair"]
-        staff_CO2.index = staff_CO2.index.astype(int)
-        staff_CO2 = staff_CO2.astype(float)
-        Students_emission = pd.read_excel(path, sheet_name="Avions (étudiant-e-s)")
-        Students_group = Students_emission.groupby("Year").sum()
-        Students_CO2 = Students_group["CO2 RFI2,7"]
-        
-        CO2_staff = staff_CO2*1000 #Convert t to kg
-        CO2_students = Students_CO2*1000 #Convert t to kg
-        df_stud = pd.DataFrame(CO2_students).reset_index()
-        df_staff = pd.DataFrame(CO2_staff).reset_index()
-        self.category_name["Avion etudiant-e-s"] = "Avion etudiant-e-s"
-        self.category_name["Avion collaborateur-trice-s"] = "Avion collaborateur-trice-s"
-        df_staff["Category"] =  self.category_name["Avion collaborateur-trice-s"]
-        df_stud["Category"] = self.category_name["Avion etudiant-e-s"]
-        df_staff.columns = ["Year", "CO2", "Category"]
-        df_stud.columns = ["Year", "CO2", "Category"]
-
-        df = pd.concat([df_stud, df_staff], ignore_index=True)
-        df["Campus"] = "EPFL"
-        df["Theme"] = "Voyages professionnels"
-        df["Unit"] = "kg CO2e"
-        df["Scope"] = 3
-        df["Path"] = path
-        self.ignore_column = np.append(self.ignore_column, df["Category"].unique())
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def run_train_emission(self, input_file):
-        #Open and format the data
-        df_train = pd.read_excel(input_file,skiprows=1, sheet_name="Train")
-        df_train = df_train.set_index("Km").stack().reset_index()
-        df_train.columns=["Category", "Year", "Value"]
-        #Select only interesting data 
-        df = df_train[(df_train["Category"]=="Total Domestique")|(df_train["Category"]=="Total International")]
-        #Change df["Category"] == "Total Domestique" to self.nomenclature["Total Domestique"]
-        df.loc[df["Category"]=="Total Domestique", "Category"] = self.nomenclature["Total Domestique"]
-        df.loc[df["Category"]=="Total International", "Category"] = self.nomenclature["Total International"]
-
-        df["Year"] = df["Year"].astype(int)
-        df["Value"] = df["Value"].astype(float)
-        df["Unit"] = "km"
-        df["Theme"] = "Voyages professionnels"
-        df["Scope"] = 3
-        df["Path"] = input_file
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def run_cars_emission(self, input_file):
-        df_cars = pd.read_excel(input_file, skiprows=1, sheet_name="Voiture")
-
-        V_service = df_cars.iloc[0:5]
-        V_service.reset_index(drop=True, inplace=True)
-        V_service.iloc[3,0] = self.nomenclature["Véhicule de location"]
-        V_service.iloc[4,0] = self.nomenclature["Véhicules privés"]
-
-        Mobility = df_cars.iloc[9:12]
-        Mobility.reset_index(drop=True, inplace=True)
-        Mobility.iloc[0,0] = self.nomenclature["Mobility essence"]
-        Mobility.iloc[1,0] = self.nomenclature["Mobility diesel"]
-        Mobility.iloc[2,0] = self.nomenclature["Mobility electrique"]
-        Mobility.columns = df_cars.iloc[8]
-        col_int = list(Mobility.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
-
-
-        #Set Mobility.columns[1:] to col_int
-        Mobility.columns = ["Litres / kW pour électriques"] + col_int
-        db_Mobility = Mobility.set_index("Litres / kW pour électriques").stack().reset_index()
-        db_Mobility.columns=["Category", "Year", "Value"]
-        db_Mobility["Value"] = db_Mobility["Value"].astype(str).str.replace("-", "NaN").str.split(".").str[0].astype(float)
-        db_Mobility["Year"] = db_Mobility["Year"].astype(int)
-        db_Mobility["Unit"] = "L"
-
-        db_service = V_service.set_index("Kilomètres parcourus").stack().reset_index()
-        db_service.columns=["Category", "Year", "Value"]
-        db_service["Year"] = db_service["Year"].astype(int)
-        db_service["Value"] = db_service["Value"].astype(float)
-        db_service["Unit"] = "km"
-
-        V_service_L = df_cars.iloc[15:17]
-        V_service_L.reset_index(drop=True, inplace=True)
-        V_service_L.iloc[0,0] = self.nomenclature["Véhicules de service essence"]
-        V_service_L.iloc[1,0] = self.nomenclature["Véhicules de service diesel"]
-
-        V_service_L.columns = df_cars.iloc[14]
-        col_int = list(V_service_L.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
-        #Set Mobility.columns[1:] to col_int
-        V_service_L.columns = ["Litres"] + col_int
-        db_service_L = V_service_L.set_index("Litres").stack().reset_index()
-        db_service_L.columns=["Category", "Year", "Value"]
-        db_service_L["Year"] = db_service_L["Year"].astype(int)
-        db_service_L["Value"] = db_service_L["Value"].astype(float)
-        db_service_L["Unit"] = "L"
-        #Merge db_service db_Mobility and db_service_L into new dataframe
-        #Avoid a double count by selecting only the volume of L used in service vehicles:
-        db_service = db_service[~db_service["Category"].isin(["Véhicules de service essence", "Véhicules de service diesel", "Mobility carsharing"])]
-        db_Mobility = db_Mobility[~db_Mobility["Category"].isin(["Mobility électriques"])] #L'Electricite est achetée par l'EPFL et redistribuée, elle est donc deja comptabilisée 
-        df = pd.concat([db_service, db_Mobility, db_service_L], ignore_index=True)
-
-        df["Theme"] = "Voyages professionnels"
-        df["Scope"] = 3
-        df.loc[df["Category"]==self.nomenclature["Véhicules de service essence"], "Scope"] = 1
-        df.loc[df["Category"]==self.nomenclature["Véhicules de service diesel"], "Scope"] = 1
-        df.loc[df["Category"]==self.nomenclature["Mobility diesel"], "Scope"] = 1
-        df.loc[df["Category"]==self.nomenclature["Mobility essence"], "Scope"] = 1
-        df["Path"] = input_file
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def run_mobilite_EPFL(self, pendulair_final):
-        df = pd.read_excel(pendulair_final, skiprows=2, index_col="Unnamed: 0")
-        #Select columns to keep in dataframe
-        col_dist=[]
-        for col in df.columns:
-            if "km" in col:
-                col_dist.append(col)
-
-        df_dist = df.iloc[:7][col_dist].T.replace("-", np.nan)
-
-        #if an index contain "jour" drop it
-        for idx in df_dist.index:
-            if "jour" in idx:
-                df_dist = df_dist.drop(idx)
-            if "Hiver" in idx:
-                #Replace "hiver" in index by ""
-                df_dist.loc[idx] = df_dist.loc[idx.replace("Hiver", "Ete")]+df_dist.loc[idx]
-                #Rename idx by idx.replace("Hiver", "")
-                df_dist = df_dist.rename(index={idx:idx.replace("Hiver", "")})
-                #Drop idx.replace("Hiver", "Ete")
-                df_dist = df_dist.drop(idx.replace("Hiver", "Ete"))
-        
-        df_dist.index = df_dist.index.str.extract("(\d+)").squeeze()
-        #if index==np.nan drop it
-        df_dist = df_dist.loc[~df_dist.index.isna()]
-        df_dist = df_dist.reset_index().rename(columns={0:"Year"})
-        #Columns of df_dist are transport mode, convert df_dist to database
-        db = df_dist.set_index("Year").stack().reset_index()
-        db.rename(columns={"level_1":"Category", 0:"Value"}, inplace=True)
-        #Convert index to int
-        
-        #Change db["Category"] == "voiture" to self.nomenclature["voiture"]
-        db.loc[db["Category"]=="voiture", "Category"] = self.nomenclature["voiture"]
-        db.loc[db["Category"]=="moto/scooter", "Category"] = self.nomenclature["moto/scooter"]
-
-        db["Year"] = db["Year"].astype(int)
-        db["Theme"] = "Mobilité pendulaire"
-        db["Scope"] = 3
-        db["Unit"] = "km"
-        #Filter Category=="autres"
-        db = db[db["Category"]!="autres"]
-        #Remove from db db["Year"] below 2018. Before 2018, the data collection has changed and is not comparable (3 order of magnitude lower)
-        self.raw_pendular = db.copy()
-        db.loc[db["Year"]<2018, "Value"] = np.nan
-        db["Path"] = pendulair_final
-        self.data = pd.concat([self.data, db], ignore_index=True)
-
-    def run_numeric(self, numeric_infile):
-        df_manual_input = pd.read_excel(numeric_infile)
-        df_numeric = df_manual_input[~df_manual_input["Numérique"].isna()]
-        df = pd.DataFrame()
-        df["CO2"] = df_numeric["Numérique"] * 1000 #Convert to kg
-        df["Year"] = df_numeric["Year"]
-        df["Campus"] = "EPFL"
-        self.numeric_name = "Numérique (fabrication)"
-        df["Category"] = self.numeric_name
-        df["Theme"] = "Numérique (fabrication)"
-        df["Scope"] = 3
-        df["Path"] = numeric_infile
-        self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def run_alimentation(self, alimentation_infile):
-        df = pd.read_excel(alimentation_infile) #Read file of computed CO2 by quantis
-        df["CO2"] = df["CO2"]*1000 #Convert to kg
-        df["Campus"] = "EPFL"
-        df["Theme"] = "Alimentation"
-        df["Scope"] = 3
-        df["Path"] = alimentation_infile
-
-        #Filter all values before 2020 because the methodology has changed and extrapolation of 2019 seems incorrect:
-        df = df[df["Year"]>=2020]
-        #append self.ignore_column with df["Category"].unique()
-        self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
-
-        self.data = pd.concat([self.data, df], ignore_index=True)
-        
-    def run_achats(self, achat_infile):
-        df_manual_input = pd.read_excel(achat_infile)
-        df_achats = df_manual_input[~df_manual_input["Achats"].isna()]
-        df = pd.DataFrame()
-        df["CO2"] = df_achats["Achats"]*1000 #Convert to kg
-        df["Year"] = df_achats["Year"]
-        df["Campus"] = "EPFL"
-        self.achats_name = "Achats (estimation)"
-        df["Category"] = self.achats_name
-        df["Theme"] = "Achats (estimation)"
-        df["Scope"] = 3
-        df["Path"]= achat_infile
-        self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def add_manual_input(self):
-
-        self.Scope1_categories = ["Mazout", "Gaz"]
-        self.Scope2_categories = ["Electricite", "CAD"]
-        manual_input_path = self.input_file
-        inputs = pd.read_excel(manual_input_path, sheet_name="Manual input")
-        inputs.columns = ["Year", "Theme", "CO2"]
-        inputs["CO2"] = inputs["CO2"]*1000 #Convert to kg
-        inputs.loc[inputs["Theme"].isin(self.Scope1_categories), "Scope"] = 1
-        inputs.loc[inputs["Theme"].isin(self.Scope2_categories), "Scope"] = 2
-        inputs.loc[~inputs["Theme"].isin(self.Scope1_categories+self.Scope2_categories), "Scope"] = 3
-        inputs["Category"] = inputs["Theme"]
-        inputs["Path"] = manual_input_path
-        self.data = pd.concat([self.data, inputs], ignore_index=True)
-
-    def export_database(self, path, Level="L1"):
-        if Level=="L1":
-            self.data.to_excel(path, index=False)
-        if Level=="L2":
-            self.data_L2.to_excel(path, index=False)
-        if Level=="L3A":
-            self.data_L3A.to_excel(path, index=False)
-        if Level=="L3B":
-            self.data_L3B.to_excel(path, index=False)
-
-    def import_database(self, path):
-        self.data = pd.read_excel(path)
-        
     def calculate_emission(self):
         
         manual_input_path = self.input_file
         inputs = pd.read_excel(manual_input_path, sheet_name="Facteurs emissions")
         inputs[["Theme", "Scope", "Data name mapping"]] = inputs[["Theme", "Scope", "Data name mapping"]].fillna(method='ffill')
         
         raw_data = self.data.copy()
         raw_data["Data description"] = "Valeur calculée"
         NaN_values = raw_data[(raw_data["Value"].isna())&(~raw_data["CO2"].isna())]
         raw_data = raw_data[~raw_data["Value"].isna()]
         facteurs_emissions = inputs.copy()
         raw_data["Year"] = self.data["Year"].astype(float).astype(str)
         raw_data["Value"] = self.data["Value"].astype(float)
         facteurs_emissions["Year"] = facteurs_emissions["Year"].astype(str)
 
         facteurs_emissions.rename(columns={'Data name mapping': 'Category'}, inplace=True)
 
         # Attempt to join on Category, Year, and Campus
         facteurs_emissions_sub = facteurs_emissions[~facteurs_emissions["Campus"].isna()&(~(facteurs_emissions["Year"].isna()=="nan"))]
         joined_data = pd.merge(raw_data, facteurs_emissions_sub, on=['Category', 'Year', 'Campus'], how='left', suffixes=('', '_drop'))
 
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
         # Now, replace the index of unmatched with the original indices
         #Filter with not Nan Year so it's won't merge based on common NaN Year
         facteurs_emissions_sub = facteurs_emissions[(facteurs_emissions["Campus"].isna())&(~(facteurs_emissions["Year"]=="nan"))]
         # Join on Category and Year if previous join had no match
         if not unmatched.empty:
             unmatched_index=unmatched.index
             joined_data_year = pd.merge(unmatched, facteurs_emissions_sub, on=['Category', 'Year'], how='left', suffixes=('', '_drop'))
             joined_data_year.index = unmatched_index
             joined_data_year_not_nan = joined_data_year[~joined_data_year['Factor [kg CO2eq]'].isna()]
             #Subsitute the original columns with the "_drop" columns
             joined_data.update(joined_data_year_not_nan)
 
         # Check for unmatched entries again
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
         #Filter with not Nan campus so it's won't merge based on common NaN campus
         facteurs_emissions_sub = facteurs_emissions[(~facteurs_emissions["Campus"].isna())&((facteurs_emissions["Year"]=="nan"))]
         # Join on Category and Campus if previous join had no match
         if not unmatched.empty:
             unmatched_index=unmatched.index
             joined_data_campus = pd.merge(unmatched, facteurs_emissions_sub, on=['Category', 'Campus'], how='left', suffixes=('', '_drop'))
             joined_data_campus.index = unmatched_index
             joined_data_campus_not_nan = joined_data_campus[~joined_data_campus['Factor [kg CO2eq]'].isna()]
             joined_data.update(joined_data_campus_not_nan)
 
         # Check for unmatched entries again
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
         facteurs_emissions_sub = facteurs_emissions[(facteurs_emissions["Campus"].isna())&((facteurs_emissions["Year"]=="nan"))]
         # Finally, join just on Category if none of the above conditions are met
         if not unmatched.empty:
             unmatched_index=unmatched.index
             joined_data_category = pd.merge(unmatched, facteurs_emissions_sub, on='Category', how='left', suffixes=('', '_drop'))
             joined_data_category.index = unmatched_index
             joined_data_category_not_nan = joined_data_category[~joined_data_category['Factor [kg CO2eq]'].isna()]
             joined_data.update(joined_data_category_not_nan)
 
         # Drop duplicate columns resulted from the merges
         joined_data = joined_data[[col for col in joined_data.columns if not col.endswith('_drop')]]
         joined_data.drop_duplicates(subset = ['Category', 'Year', 'Campus'],inplace=True, keep="first")
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()]
         print(unmatched["Category"].unique())
         joined_data["CO2"] = joined_data["Factor [kg CO2eq]"]*joined_data["Value"]
 
         joined_data["Year"] = joined_data["Year"].astype(float).astype(int)
         #Add NaN_values to joined_data
         joined_data = pd.concat([joined_data, NaN_values], ignore_index=True)
         self.data_L2 = joined_data.copy()
 
-    def extrapolate(self):
+    def extrapolate(self, EPT_year, raw_pendular):
         #Si une valeur est manquante pour une année, l'algorithme cherche une valeur dans le future et l'adapte selon la croissance
         #Si c'est une valeur dans le future n'est pas disponible, alors elle va chercher dans le passé et adpate selon croissance
         data = self.data_L2.copy()
         years = data["Year"].unique()
         years.sort()
         years = years[:-1]
 
-        EPT_year = self.EPT_year 
 
         ## Student plane interpolation
-        Category = self.category_name["Avion etudiant-e-s"]
-        Stud_years = [2016,2017, 2018]
+        Category = self.nomenclature["Avion etudiants"]
+        Stud_years = [2016, 2017, 2018]
         data = extrapolate_student_plane(data, EPT_year, Stud_years, Category)
 
         for year in years:
             for Theme in data["Theme"].unique():
                 mask = (data["Year"]==year)&(data["Theme"]==Theme)
                 #For each Theme where there is no data in this year, take the data from the nearest year
                 if data.loc[mask, "CO2"].isna().all():
                     #Find the nearest year where data["Theme"] is not empty
                     nearest_year = year
                     while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
                         nearest_year += 1
                         if nearest_year>2100:
                             nearest_year = year
                             while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
                                 nearest_year -= 1
                                 
 
                     nearest_mask = (data["Year"]==nearest_year)&(data["Theme"]==Theme)
                     data_masked = data.loc[nearest_mask]
 
                     for category in data_masked["Category"].unique():
                         serie = pd.Series()
                         serie["Year"] = year
                         serie["Theme"] = Theme
                         serie["Category"] = category
-                        if Theme == "Electricite":
-                            print(1)
+                    
                         if Theme == "Mobilité pendulaire":
                             #Produit en croix pour estimer la valeur de la mobilité pendulaire en fonction de l'évolution de la population
                             data_pendular = data.loc[(data["Year"]==nearest_year)&(data["Theme"]=="Mobilité pendulaire")]
+                            self.raw_pendular = raw_pendular
                             pendular_year = self.raw_pendular.loc[(self.raw_pendular["Year"] == year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
                             pendular_year["Part_modale"] = pendular_year["Value"]/pendular_year["Value"].sum()
                             pendular_nearest = self.raw_pendular.loc[(self.raw_pendular["Year"] == nearest_year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
                             pendular_nearest["Part_modale"] = pendular_nearest["Value"]/pendular_nearest["Value"].sum()
 
                             mask = data_pendular["Category"]==category
                             pendular_EPT_a = pendular_year[pendular_year["Category"]==category]["Part_modale"].values*EPT_year[year]
                             pendular_CO2_n = data_pendular[mask]["CO2"].values
                             pendular_EPT_n = pendular_nearest[pendular_nearest["Category"]==category]["Part_modale"].values*EPT_year[nearest_year]
                             #Produit en croix:
                             serie["CO2"] = (pendular_EPT_a*pendular_CO2_n/pendular_EPT_n)[0]   
                         else:
                             ratio_evolution = ((EPT_year.loc[year]-EPT_year.loc[nearest_year])/(EPT_year.loc[nearest_year]))
                             serie["CO2"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "CO2"].sum()* (1+ratio_evolution)
                         serie["Value"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "Value"].sum()*(1+ratio_evolution)
                         serie["Data description"] = "Estimé"
                         serie["Scope"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "Scope"].median()
                         serie["Campus"] = "EPFL"
                         serie["Category"] = Theme
                         data = data.append(serie, ignore_index=True)
 
         self.data_L3A = data
 
     def objectives(self, year_reference=2022):
         data_L3A = self.data_L3A.copy()
 
         #Objectif 2024
         self.df_objectifs_2024 = objectives_2024(data_L3A, year_reference)
         self.year_reference = year_reference
         #Objectif 2030
         df = pd.DataFrame()
         df["Theme"] = data_L3A["Theme"].unique()
         df["Year"] = 2030
         df["Campus"] = "EPFL"
         df["Scope"] = np.nan
         #Definition des objectifs 2030:
         mask = (data_L3A["Theme"] == "Electricite")|(data_L3A["Theme"] == "Mazout")|(data_L3A["Theme"]=="CAD")|(data_L3A["Theme"] == "Gaz")
         Energy_2006 = data_L3A.loc[(data_L3A["Year"]==2006)&(mask)].sum()
         Objective_2030_energy = Energy_2006["CO2"]*0.5
         gaz_2030 = data_L3A.loc[(data_L3A["Year"]==2006)&(data_L3A["Theme"] == "Gaz")]["CO2"]*0.5 #Assume que le gaz en 2030 est la moitié de 2006
         elec_2030 = Objective_2030_energy-gaz_2030
         df.loc[df["Theme"]=="Electricite", "CO2"] = elec_2030.squeeze()
         df.loc[df["Theme"]=="Electricite", "Objectif"] = "-50% energie par rapport à 2006"
         df.loc[df["Theme"]=="Gaz", "CO2"] = gaz_2030.squeeze()
         df.loc[df["Theme"]=="Gaz", "Objectif"] = "-50% energie par rapport à 2006"        
         df.loc[df["Theme"]=="Mazout", "CO2"] = 0 
         df.loc[df["Theme"]=="Mazout", "Objectif"] = "Remplacement par Electricite"
         df.loc[df["Theme"]=="CAD", "CO2"] = 0
         df.loc[df["Theme"]=="CAD", "Objectif"] = "Remplacement par Electricite"
 
         mask_voyages_prof = (data_L3A["Theme"]=="Voyages professionnels")
         trip_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(mask_voyages_prof)]["CO2"].sum()
         Objective_2030_trip = trip_2019*0.7
         df.loc[df["Theme"]=="Voyages professionnels", "CO2"] = Objective_2030_trip
         df.loc[df["Theme"]=="Voyages professionnels", "Objectif"] = "-30% par rapport à 2019"
 
         Pendulaire_2019 =  data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Mobilité pendulaire")]["CO2"].sum()
         Objective_2030_pendulaire = Pendulaire_2019*0.7
         df.loc[df["Theme"]=="Mobilité pendulaire", "CO2"] = Objective_2030_pendulaire
         df.loc[df["Theme"]=="Mobilité pendulaire", "Objectif"] = "-30% par rapport à 2019"
 
         Alimentation_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Alimentation")]["CO2"].sum()
         Objective_2030_alimentation = Alimentation_2019*0.6
         df.loc[df["Theme"]=="Alimentation", "CO2"] = Objective_2030_alimentation
         df.loc[df["Theme"]=="Alimentation", "Objectif"] = "-40% par rapport à 2019"
 
         df.loc[df["Theme"]=="Numérique (fabrication)", "CO2"] = 4829e3
         df.loc[df["Theme"]=="Numérique (fabrication)", "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
 
         
         df.loc[df["Theme"]==self.nomenclature["Achats"], "CO2"] = 42000e3
         df.loc[df["Theme"]==self.nomenclature["Achats"], "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
 
         df.loc[df["Theme"]=="Dechets", "CO2"] = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Dechets")]["CO2"].sum()
 
         df["Data description"] = "Objectifs 2030 (confédération+EPFL)"
         df["Category"] = df["Theme"]
 
         for Theme in df["Theme"].unique():
             df.loc[df["Theme"]==Theme, "Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
 
         self.df_2030 = df
 
     def interpolation_objectifs(self):
 
         data_L3A = self.data_L3A.copy()
         df_2030 = self.df_2030
 
         years = np.arange(data_L3A["Year"].max(),2030)
         for Theme in df_2030["Theme"].unique():
             for year in years:
                 df_temp = pd.Series()
                 df_temp["Theme"] = Theme
                 df_temp["Year"] = year
                 df_temp["Campus"] = "EPFL"
                 df_temp["Category"] = df_temp["Theme"]
                 df_temp["Data description"] = "Objectifs 2030 (confédération+EPFL)"
                 df_temp["Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
                 mask = (data_L3A["Year"]==data_L3A["Year"].max())&(data_L3A["Theme"]==Theme)
 
                 if mask.sum():
                     df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[mask, "CO2"].sum(), df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
                     
                     if year != data_L3A["Year"].max():
                         df_2030 = df_2030.append(df_temp, ignore_index=True)
                 else:
                     #If this data is not available for a specific theme, we take the value of the last year
                     df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[(data_L3A["Year"]==data_L3A["Year"].max()-1)&(data_L3A["Theme"]==Theme), "CO2"].iloc[0], df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
                     df_2030 = df_2030.append(df_temp, ignore_index=True)
                 
         data_L3B =  pd.concat([data_L3A, df_2030, self.df_objectifs_2024],  ignore_index=True)
         Elect_emission_factor = data_L3B[(data_L3B["Theme"]=="Electricite")&(data_L3B["Year"]==self.year_reference)][self.CO2_col].mean()
         nan_mask = (data_L3B["Value"].isna())&(data_L3B["Category"] == "Electricite")
 
         data_L3B.loc[nan_mask, "Value"] = data_L3B[nan_mask]["CO2"].iloc[0]/Elect_emission_factor
         self.data_L3B = data_L3B.copy()
-
-    def simulation():
-        #Ajoute les reformes et leurs impacts sur le bilan CO2 et les predictions: Exemple: politique voyage
-        
-        #Dans le cas de la simple croissance, des simulations peuvent être faites.
-
-        print(1)
\ No newline at end of file
diff --git a/scripts/Data_importer.py b/scripts/Data_importer.py
new file mode 100644
index 0000000..1f9cc7a
--- /dev/null
+++ b/scripts/Data_importer.py
@@ -0,0 +1,260 @@
+import pandas as pd
+import numpy as np
+from functions import *
+
+class Data_importer():
+    # Constants for column names
+    YEAR = "Year"
+    CATEGORY = "Category"
+    VALUE = "Value"
+    def __init__(self, input_file, nomenclature):
+        self.category_name = {}
+        self.Theme_name = {}
+        self.ignore_column = []
+        self.general_attributes = {
+            "author": "Hugo Cruz",
+            "author_mail": "hugo.cruz@epfl.ch",
+        }
+        self.data = pd.DataFrame()
+        #Nomenclature utilisée, à gauche est le nom tel qu'il est dans le fichier d'entrée, à droite est le nom tel qu'il est écrit dans le fichier facteur emission
+        self.nomenclature = nomenclature
+        self.input_file = input_file
+
+
+    def run_population(self, EPT_infile):
+        #open EPT file, this EPT is not up to date, the evolution is not yet taken into account
+        EPT = pd.read_excel(EPT_infile)
+        self.EPT_year = EPT[EPT["Category"]== 'Population GRI'].groupby("Year").sum()["EPT"]
+
+    def run_energy_emission(self, elec_path):
+        df = pd.read_excel(elec_path)
+        df = df.rename(columns={"year": self.YEAR, "campus": "Campus", "category": "Category", "value": "Value"})
+        df["Theme"] = df["Category"] #We understand that the theme is Energy, however it's more relevant to separate when we visualize it
+        df["Scope"] = 2
+        df["Unit"] = df["unit"]
+        df["Path"] = elec_path
+        df.drop("unit", axis=1, inplace=True)
+
+        mask_mazout =  df["Category"]=="Mazout"
+        mask_gaz = df["Category"]=="Gaz"
+        df.loc[df["Category"]=="Electricite","Category"] = self.nomenclature["Electricite"]
+
+        df.loc[mask_gaz|mask_mazout, "Scope"] = 1
+        self.data = pd.concat([self.data, df], ignore_index=True)
+
+    def run_dechets_emission(self, dechets_path):
+        #Dechets is not yet taken into account
+        df = pd.read_excel(dechets_path)
+        df = df.rename(columns={"waste_type":"Category", "kg":"Value", "year": YEAR})
+        df["Scope"] = 3
+        df["Unit"] = "kg"
+        df["Theme"] = "Dechets"
+        df["ID_category"] = df["Category"]
+        #Strategy ojective 2025
+        mask = (df["disposal_method"]=="Recycling")&(df["Year"]==2022)
+        recycled = df[(df["disposal_method"]=="Recycling")&(df["Year"]==2022)]["Value"].sum()
+        incineration = df[(df["disposal_method"]=="incineration")&(df["Year"]==2022)]["Value"].sum()
+        total = recycled+incineration
+        recycled_2025 = total*0.8
+        incineration_2025 = total*0.2
+        df["Path"] = dechets_path
+        df.drop("disposal_method", axis=1, inplace=True)
+        print("In 2025, the amount of recycled waste should be {} kg based on 80% recycled in 2022".format(recycled_2025))
+        
+        self.data = pd.concat([self.data, df], ignore_index=True)
+
+    def run_plane_emission(self,path):
+        staff_emission = pd.read_excel(path, sheet_name="Avions (staff)", skiprows=1)
+        staff_CO2 = staff_emission.set_index("Data*").loc["CO2 footprint (tons) / Year        Données: Atmosfair"]
+        staff_CO2.index = staff_CO2.index.astype(int)
+        staff_CO2 = staff_CO2.astype(float)
+        Students_emission = pd.read_excel(path, sheet_name="Avions (étudiant-e-s)")
+        Students_group = Students_emission.groupby("Year").sum()
+        Students_CO2 = Students_group["CO2 RFI2,7"]
+        
+        CO2_staff = staff_CO2*1000 #Convert t to kg
+        CO2_students = Students_CO2*1000 #Convert t to kg
+        df_stud = pd.DataFrame(CO2_students).reset_index()
+        df_staff = pd.DataFrame(CO2_staff).reset_index()
+        df_staff["Category"] =  self.nomenclature["Avion staff"]
+        df_stud["Category"] = self.nomenclature["Avion etudiants"]
+        df_staff.columns = ["Year", "CO2", "Category"]
+        df_stud.columns = ["Year", "CO2", "Category"]
+
+        df = pd.concat([df_stud, df_staff], ignore_index=True)
+        df["Campus"] = "EPFL"
+        df["Theme"] = "Voyages professionnels"
+        df["Unit"] = "kg CO2e"
+        df["Scope"] = 3
+        df["Path"] = path
+        df["Source"] = "Atmosfair"
+        self.ignore_column = np.append(self.ignore_column, df["Category"].unique())
+        self.data = pd.concat([self.data, df], ignore_index=True)
+
+    def run_train_emission(self, input_file):
+        #Open and format the data
+        df_train = pd.read_excel(input_file,skiprows=1, sheet_name="Train")
+        df_train = df_train.set_index("Km").stack().reset_index()
+        df_train.columns=["Category", "Year", "Value"]
+        #Select only interesting data 
+        df = df_train[(df_train["Category"]=="Total Domestique")|(df_train["Category"]=="Total International")]
+        #Change df["Category"] == "Total Domestique" to self.nomenclature["Total Domestique"]
+        df.loc[df["Category"]=="Total Domestique", "Category"] = self.nomenclature["Total Domestique"]
+        df.loc[df["Category"]=="Total International", "Category"] = self.nomenclature["Total International"]
+
+        df["Year"] = df["Year"].astype(int)
+        df["Value"] = df["Value"].astype(float)
+        df["Unit"] = "km"
+        df["Theme"] = "Voyages professionnels"
+        df["Scope"] = 3
+        df["Path"] = input_file
+        self.data = pd.concat([self.data, df], ignore_index=True)
+
+    def run_cars_emission(self, input_file):
+        df_cars = pd.read_excel(input_file, skiprows=1, sheet_name="Voiture")
+
+        V_service = df_cars.iloc[0:5]
+        V_service.reset_index(drop=True, inplace=True)
+        V_service.iloc[3,0] = self.nomenclature["Véhicule de location"]
+        V_service.iloc[4,0] = self.nomenclature["Véhicules privés"]
+
+        Mobility = df_cars.iloc[9:12]
+        Mobility.reset_index(drop=True, inplace=True)
+        Mobility.iloc[0,0] = self.nomenclature["Mobility essence"]
+        Mobility.iloc[1,0] = self.nomenclature["Mobility diesel"]
+        Mobility.iloc[2,0] = self.nomenclature["Mobility electrique"]
+        Mobility.columns = df_cars.iloc[8]
+        col_int = list(Mobility.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
+
+
+        #Set Mobility.columns[1:] to col_int
+        Mobility.columns = ["Litres / kW pour électriques"] + col_int
+        db_Mobility = Mobility.set_index("Litres / kW pour électriques").stack().reset_index()
+        db_Mobility.columns=["Category", "Year", "Value"]
+        db_Mobility["Value"] = db_Mobility["Value"].astype(str).str.replace("-", "NaN").str.split(".").str[0].astype(float)
+        db_Mobility["Year"] = db_Mobility["Year"].astype(int)
+        db_Mobility["Unit"] = "L"
+
+        db_service = V_service.set_index("Kilomètres parcourus").stack().reset_index()
+        db_service.columns=["Category", "Year", "Value"]
+        db_service["Year"] = db_service["Year"].astype(int)
+        db_service["Value"] = db_service["Value"].astype(float)
+        db_service["Unit"] = "km"
+
+        V_service_L = df_cars.iloc[15:17]
+        V_service_L.reset_index(drop=True, inplace=True)
+        V_service_L.iloc[0,0] = self.nomenclature["Véhicules de service essence"]
+        V_service_L.iloc[1,0] = self.nomenclature["Véhicules de service diesel"]
+
+        V_service_L.columns = df_cars.iloc[14]
+        col_int = list(V_service_L.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
+        #Set Mobility.columns[1:] to col_int
+        V_service_L.columns = ["Litres"] + col_int
+        db_service_L = V_service_L.set_index("Litres").stack().reset_index()
+        db_service_L.columns=["Category", "Year", "Value"]
+        db_service_L["Year"] = db_service_L["Year"].astype(int)
+        db_service_L["Value"] = db_service_L["Value"].astype(float)
+        db_service_L["Unit"] = "L"
+        #Merge db_service db_Mobility and db_service_L into new dataframe
+        #Avoid a double count by selecting only the volume of L used in service vehicles:
+        db_service = db_service[~db_service["Category"].isin(["Véhicules de service essence", "Véhicules de service diesel", "Mobility carsharing"])]
+        db_Mobility = db_Mobility[~db_Mobility["Category"].isin(["Mobility électriques"])] #L'Electricite est achetée par l'EPFL et redistribuée, elle est donc deja comptabilisée 
+        df = pd.concat([db_service, db_Mobility, db_service_L], ignore_index=True)
+
+        df["Theme"] = "Voyages professionnels"
+        df["Scope"] = 3
+        df.loc[df["Category"]==self.nomenclature["Véhicules de service essence"], "Scope"] = 1
+        df.loc[df["Category"]==self.nomenclature["Véhicules de service diesel"], "Scope"] = 1
+        df.loc[df["Category"]==self.nomenclature["Mobility diesel"], "Scope"] = 1
+        df.loc[df["Category"]==self.nomenclature["Mobility essence"], "Scope"] = 1
+        df["Path"] = input_file
+        self.data = pd.concat([self.data, df], ignore_index=True)
+
+    def run_mobilite_EPFL(self, pendulair_final):
+        df = pd.read_excel(pendulair_final, skiprows=2, index_col="Unnamed: 0")
+        
+        #Select columns to keep in dataframe
+        col_dist=[]
+        for col in df.columns:
+            if "km" in col:
+                col_dist.append(col)
+
+        df_dist = df.iloc[:7][col_dist].T.replace("-", np.nan)
+
+        #if an index contain "jour" drop it
+        for idx in df_dist.index:
+            if "jour" in idx:
+                df_dist = df_dist.drop(idx)
+            if "Hiver" in idx:
+                #Replace "hiver" in index by ""
+                df_dist.loc[idx] = df_dist.loc[idx.replace("Hiver", "Ete")]+df_dist.loc[idx]
+                #Rename idx by idx.replace("Hiver", "")
+                df_dist = df_dist.rename(index={idx:idx.replace("Hiver", "")})
+                #Drop idx.replace("Hiver", "Ete")
+                df_dist = df_dist.drop(idx.replace("Hiver", "Ete"))
+        
+        df_dist.index = df_dist.index.str.extract("(\d+)").squeeze()
+        #if index==np.nan drop it
+        df_dist = df_dist.loc[~df_dist.index.isna()]
+        df_dist = df_dist.reset_index().rename(columns={0:"Year"})
+        #Columns of df_dist are transport mode, convert df_dist to database
+        db = df_dist.set_index("Year").stack().reset_index()
+        db.rename(columns={"level_1":"Category", 0:"Value"}, inplace=True)
+        #Convert index to int
+        
+        #Change db["Category"] == "voiture" to self.nomenclature["voiture"]
+        db.loc[db["Category"]=="voiture", "Category"] = self.nomenclature["voiture"]
+        db.loc[db["Category"]=="moto/scooter", "Category"] = self.nomenclature["moto/scooter"]
+
+        db["Year"] = db["Year"].astype(int)
+        db["Theme"] = "Mobilité pendulaire"
+        db["Scope"] = 3
+        db["Unit"] = "km"
+        #Filter Category=="autres"
+        db = db[db["Category"]!="autres"]
+        #Remove from db db["Year"] below 2018. Before 2018, the data collection has changed and is not comparable (3 order of magnitude lower)
+        self.raw_pendular = db.copy()
+        db.loc[db["Year"]<2018, "Value"] = np.nan
+        db["Path"] = pendulair_final
+        self.data = pd.concat([self.data, db], ignore_index=True)
+
+    def run_alimentation(self, alimentation_infile, sheet_name="Alimentation"):
+        df_alimentation = pd.read_excel(alimentation_infile, sheet_name=sheet_name, header=5) #Read file of computed CO2 by quantis
+        df_melted = df_alimentation.melt(id_vars=['Unnamed: 0'], var_name='Year', value_name='Value')
+        # Pivot the DataFrame to get the desired format
+        df_pivot = df_melted.pivot(index='Year', columns='Unnamed: 0', values='Value').reset_index()
+
+        # Rename the columns
+        df_pivot.columns = ['Year', 'repas', 'marchandise_CO2','marchandise_repas']
+        df_pivot["CO2"] = df_pivot["marchandise_CO2"]*df_pivot["marchandise_repas"]*df_pivot["repas"]
+
+        df = df_pivot[["Year", "CO2"]]
+        df["Campus"] = "Ecublens"
+        df["Theme"] = "Alimentation"
+        df["Scope"] = 3
+        df["Path"] = alimentation_infile
+        #df["Value"] = df_pivot["repas"] #You should not put this, because if there is a value, an emission factor will be applied which should not be the case here.
+        df["Unit"] = "Couverts"
+        df["Source"] = "Beelong"
+        df["Category"] = "Restauration EPFL"
+
+        #Filter all values before 2020 because the methodology has changed and extrapolation of 2019 seems incorrect:
+        #df = df[df["Year"]>=2020]
+        #append self.ignore_column with df["Category"].unique()
+        #self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
+
+        self.data = pd.concat([self.data, df], ignore_index=True)
+        
+    def add_manual_input(self):
+        self.Scope1_categories = ["Mazout", "Gaz"]
+        self.Scope2_categories = ["Electricite", "CAD"]
+        manual_input_path = self.input_file
+        inputs = pd.read_excel(manual_input_path, sheet_name="Manual input")
+        inputs.columns = ["Year", "Theme", "CO2"]
+        inputs["CO2"] = inputs["CO2"]*1000 #Convert to kg
+        inputs.loc[inputs["Theme"].isin(self.Scope1_categories), "Scope"] = 1
+        inputs.loc[inputs["Theme"].isin(self.Scope2_categories), "Scope"] = 2
+        inputs.loc[~inputs["Theme"].isin(self.Scope1_categories+self.Scope2_categories), "Scope"] = 3
+        inputs["Category"] = inputs["Theme"]
+        inputs["Path"] = manual_input_path
+        self.data = pd.concat([self.data, inputs], ignore_index=True)
diff --git a/scripts/__pycache__/Carbon_footprint.cpython-310.pyc b/scripts/__pycache__/Carbon_footprint.cpython-310.pyc
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index f6dee60..3aa7bff 100644
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diff --git a/scripts/__pycache__/functions.cpython-310.pyc b/scripts/__pycache__/functions.cpython-310.pyc
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diff --git a/scripts/footprint.py b/scripts/footprint.py
index 1e5d456..1e65412 100644
--- a/scripts/footprint.py
+++ b/scripts/footprint.py
@@ -1,565 +1,522 @@
 import pandas as pd
 import numpy as np
 from functions import *
 
 class footprint():
+    # Constants for column names
+    YEAR = "Year"
+    CATEGORY = "Category"
+    VALUE = "Value"
+    CO2 = 'Factor [kg CO2eq]'
     def __init__(self, CO2_col, EPT_infile, input_file):
         self.general_attributes = {
             "author": "Hugo Cruz",
             "author_mail": "hugo.cruz@epfl.ch",
         }
         self.CO2_col = CO2_col
         self.bilan = pd.DataFrame()
         self.data = pd.DataFrame()
         self.category_name = {}
         self.Theme_name = {}
         self.ignore_column = []
-        #open EPT file, this EPT is not up to date, the evolution is not yet taken into account
-        EPT = pd.read_excel(EPT_infile)
-        self.EPT_year = EPT[EPT["Category"]== 'Population GRI'].groupby("Year").sum()["EPT"]
-
-        #Nomenclature utilisée, à gauche est le nom tel qu'il est dans le fichier d'entrée, à droite est le nom tel qu'il est écrit dans le fichier facteur emission
-        self.nomenclature = {"Alimentation": "Alimentation",
-                             "Véhicule de location": "Véhicules de location (pkm)",
-                             "Mobility essence": "Véhicules MOBILITY essence",
-                                "Mobility diesel": "Véhicules MOBILITY diesel",
-                                "Mobility electrique": "Véhicules MOBILITY electrique",
-                                "voiture": "Voiture thermique (pkm)",
-                                "moto/scooter":"Moto/scooter",
-                              "Achats":"Achats (estimation)",
-                              "Véhicules privés": "Véhicules privés",
-                              "Véhicules de service essence": "Véhicules EPFL Essence",
-                              "Véhicules de service diesel": "Véhicules EPFL Diesel",
-                              "Total Domestique": "Rail domestique",
-                              "Total International": "Rail international",
-                              "Electricite": "Electricite",
-                              }
-        self.input_file = input_file
-
-    def read_emission_factor(self, emission_factor_path):
-        factor = pd.read_excel(self.input_file, sheet_name = "Facteur emission",index_col=0)
-        factor.fillna(method='ffill', inplace=True)
-        factor.reset_index(inplace=True)
-
 
-        if "google" in emission_factor_path:
-            factor = pd.read_csv(emission_factor_path, encoding= 'cp1252', delimiter=';', decimal=",")
-        else:
-            factor = pd.read_excel(emission_factor_path, index_col=0)
-        CO2_col = self.CO2_col
-        #Standardize unit: MWh, m3, km
-        factor_df = factor.copy()
-        
-        mask_kWh = factor_df["Unit"]=="kWh"
-        factor_df[CO2_col] = factor_df[CO2_col].where(~mask_kWh,factor_df[CO2_col]*1000)
-        factor_df["Unit"] = factor_df["Unit"].replace("kWh", "MWh")
+        #Translate this into english : Nomenclature utilisée, à gauche est le nom tel qu'il est dans le fichier d'entrée, à droite est le nom tel qu'il est écrit dans le fichier facteur emission
 
-        mask_MJ = factor_df["Unit"]=="MJ"
-        factor_df[CO2_col] = factor_df[CO2_col].where(~mask_MJ,factor_df[CO2_col]*3600)
-        factor_df["Unit"] = factor_df["Unit"].replace("MJ", "MWh")
+        self.input_file = input_file
 
-        mask_m = factor_df["Unit"]=="m"
-        factor_df[CO2_col] = factor_df[CO2_col].where(~mask_m,factor_df[CO2_col]/1000)
-        factor_df["Unit"] = factor_df["Unit"].replace("m", "km")
-        self.emission_factor = factor_df
+    def run_population(self, EPT_infile):
+        #open EPT file, this EPT is not up to date, the evolution is not yet taken into account
+        EPT = pd.read_excel(EPT_infile)
+        self.EPT_year = EPT[EPT["Category"]== 'Population GRI'].groupby("Year").sum()["EPT"]
 
     def run_energy_emission(self, elec_path):
         df = pd.read_excel(elec_path)
         df = df.rename(columns={"year": "Year", "campus": "Campus", "category": "Category", "value": "Value"})
         df["Theme"] = df["Category"] #We understand that the theme is Energy, however it's more relevant to separate when we visualize it
         df["Scope"] = 2
         df["Unit"] = df["unit"]
         df["Path"] = elec_path
         df.drop("unit", axis=1, inplace=True)
 
         mask_mazout =  df["Category"]=="Mazout"
         mask_gaz = df["Category"]=="Gaz"
         df.loc[df["Category"]=="Electricite","Category"] = self.nomenclature["Electricite"]
 
         df.loc[mask_gaz|mask_mazout, "Scope"] = 1
         self.data = pd.concat([self.data, df], ignore_index=True)
 
     def run_dechets_emission(self, dechets_path):
         #Dechets is not yet taken into account
         df = pd.read_excel(dechets_path)
         df = df.rename(columns={"waste_type":"Category", "kg":"Value", "year": "Year"})
         df["Scope"] = 3
         df["Unit"] = "kg"
         df["Theme"] = "Dechets"
         df["ID_category"] = df["Category"]
         #Strategy ojective 2025
         mask = (df["disposal_method"]=="Recycling")&(df["Year"]==2022)
         recycled = df[(df["disposal_method"]=="Recycling")&(df["Year"]==2022)]["Value"].sum()
         incineration = df[(df["disposal_method"]=="incineration")&(df["Year"]==2022)]["Value"].sum()
         total = recycled+incineration
         recycled_2025 = total*0.8
         incineration_2025 = total*0.2
         df["Path"] = dechets_path
         df.drop("disposal_method", axis=1, inplace=True)
         print("In 2025, the amount of recycled waste should be {} kg based on 80% recycled in 2022".format(recycled_2025))
         
         self.data = pd.concat([self.data, df], ignore_index=True)
 
     def run_plane_emission(self,path):
         staff_emission = pd.read_excel(path, sheet_name="Avions (staff)", skiprows=1)
         staff_CO2 = staff_emission.set_index("Data*").loc["CO2 footprint (tons) / Year        Données: Atmosfair"]
         staff_CO2.index = staff_CO2.index.astype(int)
         staff_CO2 = staff_CO2.astype(float)
         Students_emission = pd.read_excel(path, sheet_name="Avions (étudiant-e-s)")
         Students_group = Students_emission.groupby("Year").sum()
         Students_CO2 = Students_group["CO2 RFI2,7"]
         
         CO2_staff = staff_CO2*1000 #Convert t to kg
         CO2_students = Students_CO2*1000 #Convert t to kg
         df_stud = pd.DataFrame(CO2_students).reset_index()
         df_staff = pd.DataFrame(CO2_staff).reset_index()
         self.category_name["Avion etudiant-e-s"] = "Avion etudiant-e-s"
         self.category_name["Avion collaborateur-trice-s"] = "Avion collaborateur-trice-s"
         df_staff["Category"] =  self.category_name["Avion collaborateur-trice-s"]
         df_stud["Category"] = self.category_name["Avion etudiant-e-s"]
         df_staff.columns = ["Year", "CO2", "Category"]
         df_stud.columns = ["Year", "CO2", "Category"]
 
         df = pd.concat([df_stud, df_staff], ignore_index=True)
         df["Campus"] = "EPFL"
         df["Theme"] = "Voyages professionnels"
         df["Unit"] = "kg CO2e"
         df["Scope"] = 3
         df["Path"] = path
         self.ignore_column = np.append(self.ignore_column, df["Category"].unique())
         self.data = pd.concat([self.data, df], ignore_index=True)
 
     def run_train_emission(self, input_file):
         #Open and format the data
         df_train = pd.read_excel(input_file,skiprows=1, sheet_name="Train")
         df_train = df_train.set_index("Km").stack().reset_index()
         df_train.columns=["Category", "Year", "Value"]
         #Select only interesting data 
         df = df_train[(df_train["Category"]=="Total Domestique")|(df_train["Category"]=="Total International")]
         #Change df["Category"] == "Total Domestique" to self.nomenclature["Total Domestique"]
         df.loc[df["Category"]=="Total Domestique", "Category"] = self.nomenclature["Total Domestique"]
         df.loc[df["Category"]=="Total International", "Category"] = self.nomenclature["Total International"]
 
         df["Year"] = df["Year"].astype(int)
         df["Value"] = df["Value"].astype(float)
         df["Unit"] = "km"
         df["Theme"] = "Voyages professionnels"
         df["Scope"] = 3
         df["Path"] = input_file
         self.data = pd.concat([self.data, df], ignore_index=True)
 
     def run_cars_emission(self, input_file):
         df_cars = pd.read_excel(input_file, skiprows=1, sheet_name="Voiture")
 
         V_service = df_cars.iloc[0:5]
         V_service.reset_index(drop=True, inplace=True)
         V_service.iloc[3,0] = self.nomenclature["Véhicule de location"]
         V_service.iloc[4,0] = self.nomenclature["Véhicules privés"]
 
         Mobility = df_cars.iloc[9:12]
         Mobility.reset_index(drop=True, inplace=True)
         Mobility.iloc[0,0] = self.nomenclature["Mobility essence"]
         Mobility.iloc[1,0] = self.nomenclature["Mobility diesel"]
         Mobility.iloc[2,0] = self.nomenclature["Mobility electrique"]
         Mobility.columns = df_cars.iloc[8]
         col_int = list(Mobility.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
 
 
         #Set Mobility.columns[1:] to col_int
         Mobility.columns = ["Litres / kW pour électriques"] + col_int
         db_Mobility = Mobility.set_index("Litres / kW pour électriques").stack().reset_index()
         db_Mobility.columns=["Category", "Year", "Value"]
         db_Mobility["Value"] = db_Mobility["Value"].astype(str).str.replace("-", "NaN").str.split(".").str[0].astype(float)
         db_Mobility["Year"] = db_Mobility["Year"].astype(int)
         db_Mobility["Unit"] = "L"
 
         db_service = V_service.set_index("Kilomètres parcourus").stack().reset_index()
         db_service.columns=["Category", "Year", "Value"]
         db_service["Year"] = db_service["Year"].astype(int)
         db_service["Value"] = db_service["Value"].astype(float)
         db_service["Unit"] = "km"
 
         V_service_L = df_cars.iloc[15:17]
         V_service_L.reset_index(drop=True, inplace=True)
         V_service_L.iloc[0,0] = self.nomenclature["Véhicules de service essence"]
         V_service_L.iloc[1,0] = self.nomenclature["Véhicules de service diesel"]
 
         V_service_L.columns = df_cars.iloc[14]
         col_int = list(V_service_L.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
         #Set Mobility.columns[1:] to col_int
         V_service_L.columns = ["Litres"] + col_int
         db_service_L = V_service_L.set_index("Litres").stack().reset_index()
         db_service_L.columns=["Category", "Year", "Value"]
         db_service_L["Year"] = db_service_L["Year"].astype(int)
         db_service_L["Value"] = db_service_L["Value"].astype(float)
         db_service_L["Unit"] = "L"
         #Merge db_service db_Mobility and db_service_L into new dataframe
         #Avoid a double count by selecting only the volume of L used in service vehicles:
         db_service = db_service[~db_service["Category"].isin(["Véhicules de service essence", "Véhicules de service diesel", "Mobility carsharing"])]
         db_Mobility = db_Mobility[~db_Mobility["Category"].isin(["Mobility électriques"])] #L'Electricite est achetée par l'EPFL et redistribuée, elle est donc deja comptabilisée 
         df = pd.concat([db_service, db_Mobility, db_service_L], ignore_index=True)
 
         df["Theme"] = "Voyages professionnels"
         df["Scope"] = 3
         df.loc[df["Category"]==self.nomenclature["Véhicules de service essence"], "Scope"] = 1
         df.loc[df["Category"]==self.nomenclature["Véhicules de service diesel"], "Scope"] = 1
         df.loc[df["Category"]==self.nomenclature["Mobility diesel"], "Scope"] = 1
         df.loc[df["Category"]==self.nomenclature["Mobility essence"], "Scope"] = 1
         df["Path"] = input_file
         self.data = pd.concat([self.data, df], ignore_index=True)
 
     def run_mobilite_EPFL(self, pendulair_final):
         df = pd.read_excel(pendulair_final, skiprows=2, index_col="Unnamed: 0")
         #Select columns to keep in dataframe
         col_dist=[]
         for col in df.columns:
             if "km" in col:
                 col_dist.append(col)
 
         df_dist = df.iloc[:7][col_dist].T.replace("-", np.nan)
 
         #if an index contain "jour" drop it
         for idx in df_dist.index:
             if "jour" in idx:
                 df_dist = df_dist.drop(idx)
             if "Hiver" in idx:
                 #Replace "hiver" in index by ""
                 df_dist.loc[idx] = df_dist.loc[idx.replace("Hiver", "Ete")]+df_dist.loc[idx]
                 #Rename idx by idx.replace("Hiver", "")
                 df_dist = df_dist.rename(index={idx:idx.replace("Hiver", "")})
                 #Drop idx.replace("Hiver", "Ete")
                 df_dist = df_dist.drop(idx.replace("Hiver", "Ete"))
         
         df_dist.index = df_dist.index.str.extract("(\d+)").squeeze()
         #if index==np.nan drop it
         df_dist = df_dist.loc[~df_dist.index.isna()]
         df_dist = df_dist.reset_index().rename(columns={0:"Year"})
         #Columns of df_dist are transport mode, convert df_dist to database
         db = df_dist.set_index("Year").stack().reset_index()
         db.rename(columns={"level_1":"Category", 0:"Value"}, inplace=True)
         #Convert index to int
         
         #Change db["Category"] == "voiture" to self.nomenclature["voiture"]
         db.loc[db["Category"]=="voiture", "Category"] = self.nomenclature["voiture"]
         db.loc[db["Category"]=="moto/scooter", "Category"] = self.nomenclature["moto/scooter"]
 
         db["Year"] = db["Year"].astype(int)
         db["Theme"] = "Mobilité pendulaire"
         db["Scope"] = 3
         db["Unit"] = "km"
         #Filter Category=="autres"
         db = db[db["Category"]!="autres"]
         #Remove from db db["Year"] below 2018. Before 2018, the data collection has changed and is not comparable (3 order of magnitude lower)
         self.raw_pendular = db.copy()
         db.loc[db["Year"]<2018, "Value"] = np.nan
         db["Path"] = pendulair_final
         self.data = pd.concat([self.data, db], ignore_index=True)
 
-    def run_numeric(self, numeric_infile):
-        df_manual_input = pd.read_excel(numeric_infile)
-        df_numeric = df_manual_input[~df_manual_input["Numérique"].isna()]
-        df = pd.DataFrame()
-        df["CO2"] = df_numeric["Numérique"] * 1000 #Convert to kg
-        df["Year"] = df_numeric["Year"]
-        df["Campus"] = "EPFL"
-        self.numeric_name = "Numérique (fabrication)"
-        df["Category"] = self.numeric_name
-        df["Theme"] = "Numérique (fabrication)"
-        df["Scope"] = 3
-        df["Path"] = numeric_infile
-        self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
     def run_alimentation(self, alimentation_infile):
         df = pd.read_excel(alimentation_infile) #Read file of computed CO2 by quantis
         df["CO2"] = df["CO2"]*1000 #Convert to kg
         df["Campus"] = "EPFL"
         df["Theme"] = "Alimentation"
         df["Scope"] = 3
         df["Path"] = alimentation_infile
 
         #Filter all values before 2020 because the methodology has changed and extrapolation of 2019 seems incorrect:
         df = df[df["Year"]>=2020]
         #append self.ignore_column with df["Category"].unique()
         self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
 
         self.data = pd.concat([self.data, df], ignore_index=True)
         
-    def run_achats(self, achat_infile):
-        df_manual_input = pd.read_excel(achat_infile)
-        df_achats = df_manual_input[~df_manual_input["Achats"].isna()]
-        df = pd.DataFrame()
-        df["CO2"] = df_achats["Achats"]*1000 #Convert to kg
-        df["Year"] = df_achats["Year"]
-        df["Campus"] = "EPFL"
-        self.achats_name = "Achats (estimation)"
-        df["Category"] = self.achats_name
-        df["Theme"] = "Achats (estimation)"
-        df["Scope"] = 3
-        df["Path"]= achat_infile
-        self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
-        self.data = pd.concat([self.data, df], ignore_index=True)
-
-    def add_manual_input(self):
+    def add_manual_input(self, input_file):
 
+        inputs = pd.read_excel(input_file, sheet_name="Manual input")
         self.Scope1_categories = ["Mazout", "Gaz"]
         self.Scope2_categories = ["Electricite", "CAD"]
-        manual_input_path = self.input_file
-        inputs = pd.read_excel(manual_input_path, sheet_name="Manual input")
         inputs.columns = ["Year", "Theme", "CO2"]
         inputs["CO2"] = inputs["CO2"]*1000 #Convert to kg
         inputs.loc[inputs["Theme"].isin(self.Scope1_categories), "Scope"] = 1
         inputs.loc[inputs["Theme"].isin(self.Scope2_categories), "Scope"] = 2
         inputs.loc[~inputs["Theme"].isin(self.Scope1_categories+self.Scope2_categories), "Scope"] = 3
         inputs["Category"] = inputs["Theme"]
-        inputs["Path"] = manual_input_path
+        inputs["Path"] = input_file
         self.data = pd.concat([self.data, inputs], ignore_index=True)
 
     def export_database(self, path, Level="L1"):
         if Level=="L1":
             self.data.to_excel(path, index=False)
         if Level=="L2":
             self.data_L2.to_excel(path, index=False)
         if Level=="L3A":
             self.data_L3A.to_excel(path, index=False)
         if Level=="L3B":
             self.data_L3B.to_excel(path, index=False)
 
     def import_database(self, path):
         self.data = pd.read_excel(path)
         
+    def format_unit(self, emission_factor_df):
+        factor = emission_factor_df
+        factor.fillna(method='ffill', inplace=True)
+        CO2_col = self.CO2_col
+        #Standardize unit: MWh, m3, km
+        factor_df = factor.copy()
+        
+        mask_kWh = factor_df["Unit"]=="kWh"
+        factor_df[CO2_col] = factor_df[CO2_col].where(~mask_kWh,factor_df[CO2_col]*1000)
+        factor_df["Unit"] = factor_df["Unit"].replace("kWh", "MWh")
+
+        mask_MJ = factor_df["Unit"]=="MJ"
+        factor_df[CO2_col] = factor_df[CO2_col].where(~mask_MJ,factor_df[CO2_col]*3600)
+        factor_df["Unit"] = factor_df["Unit"].replace("MJ", "MWh")
+
+        mask_m = factor_df["Unit"]=="m"
+        factor_df[CO2_col] = factor_df[CO2_col].where(~mask_m,factor_df[CO2_col]/1000)
+        factor_df["Unit"] = factor_df["Unit"].replace("m", "km")
+        self.emission_factor = factor_df
+        return factor_df
+
     def calculate_emission(self):
         
         manual_input_path = self.input_file
         inputs = pd.read_excel(manual_input_path, sheet_name="Facteurs emissions")
+        inputs = self.format_unit(inputs)
         inputs[["Theme", "Scope", "Data name mapping"]] = inputs[["Theme", "Scope", "Data name mapping"]].fillna(method='ffill')
         
         raw_data = self.data.copy()
         raw_data["Data description"] = "Valeur calculée"
         NaN_values = raw_data[(raw_data["Value"].isna())&(~raw_data["CO2"].isna())]
         raw_data = raw_data[~raw_data["Value"].isna()]
         facteurs_emissions = inputs.copy()
         raw_data["Year"] = self.data["Year"].astype(float).astype(str)
         raw_data["Value"] = self.data["Value"].astype(float)
         facteurs_emissions["Year"] = facteurs_emissions["Year"].astype(str)
 
         facteurs_emissions.rename(columns={'Data name mapping': 'Category'}, inplace=True)
-
+        
         # Attempt to join on Category, Year, and Campus
         facteurs_emissions_sub = facteurs_emissions[~facteurs_emissions["Campus"].isna()&(~(facteurs_emissions["Year"].isna()=="nan"))]
         joined_data = pd.merge(raw_data, facteurs_emissions_sub, on=['Category', 'Year', 'Campus'], how='left', suffixes=('', '_drop'))
 
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
         # Now, replace the index of unmatched with the original indices
         #Filter with not Nan Year so it's won't merge based on common NaN Year
         facteurs_emissions_sub = facteurs_emissions[(facteurs_emissions["Campus"].isna())&(~(facteurs_emissions["Year"]=="nan"))]
         # Join on Category and Year if previous join had no match
         if not unmatched.empty:
             unmatched_index=unmatched.index
             joined_data_year = pd.merge(unmatched, facteurs_emissions_sub, on=['Category', 'Year'], how='left', suffixes=('', '_drop'))
             joined_data_year.index = unmatched_index
             joined_data_year_not_nan = joined_data_year[~joined_data_year['Factor [kg CO2eq]'].isna()]
             #Subsitute the original columns with the "_drop" columns
             joined_data.update(joined_data_year_not_nan)
 
         # Check for unmatched entries again
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
         #Filter with not Nan campus so it's won't merge based on common NaN campus
         facteurs_emissions_sub = facteurs_emissions[(~facteurs_emissions["Campus"].isna())&((facteurs_emissions["Year"]=="nan"))]
         # Join on Category and Campus if previous join had no match
         if not unmatched.empty:
             unmatched_index=unmatched.index
             joined_data_campus = pd.merge(unmatched, facteurs_emissions_sub, on=['Category', 'Campus'], how='left', suffixes=('', '_drop'))
             joined_data_campus.index = unmatched_index
             joined_data_campus_not_nan = joined_data_campus[~joined_data_campus['Factor [kg CO2eq]'].isna()]
             joined_data.update(joined_data_campus_not_nan)
 
         # Check for unmatched entries again
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
         facteurs_emissions_sub = facteurs_emissions[(facteurs_emissions["Campus"].isna())&((facteurs_emissions["Year"]=="nan"))]
         # Finally, join just on Category if none of the above conditions are met
         if not unmatched.empty:
             unmatched_index=unmatched.index
             joined_data_category = pd.merge(unmatched, facteurs_emissions_sub, on='Category', how='left', suffixes=('', '_drop'))
             joined_data_category.index = unmatched_index
             joined_data_category_not_nan = joined_data_category[~joined_data_category['Factor [kg CO2eq]'].isna()]
             joined_data.update(joined_data_category_not_nan)
 
         # Drop duplicate columns resulted from the merges
         joined_data = joined_data[[col for col in joined_data.columns if not col.endswith('_drop')]]
         joined_data.drop_duplicates(subset = ['Category', 'Year', 'Campus'],inplace=True, keep="first")
         unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()]
         print(unmatched["Category"].unique())
         joined_data["CO2"] = joined_data["Factor [kg CO2eq]"]*joined_data["Value"]
 
         joined_data["Year"] = joined_data["Year"].astype(float).astype(int)
         #Add NaN_values to joined_data
         joined_data = pd.concat([joined_data, NaN_values], ignore_index=True)
         self.data_L2 = joined_data.copy()
 
     def extrapolate(self):
         #Si une valeur est manquante pour une année, l'algorithme cherche une valeur dans le future et l'adapte selon la croissance
         #Si c'est une valeur dans le future n'est pas disponible, alors elle va chercher dans le passé et adpate selon croissance
         data = self.data_L2.copy()
         years = data["Year"].unique()
         years.sort()
         years = years[:-1]
 
         EPT_year = self.EPT_year 
 
         ## Student plane interpolation
         Category = self.category_name["Avion etudiant-e-s"]
         Stud_years = [2016,2017, 2018]
         data = extrapolate_student_plane(data, EPT_year, Stud_years, Category)
 
         for year in years:
             for Theme in data["Theme"].unique():
                 mask = (data["Year"]==year)&(data["Theme"]==Theme)
                 #For each Theme where there is no data in this year, take the data from the nearest year
                 if data.loc[mask, "CO2"].isna().all():
                     #Find the nearest year where data["Theme"] is not empty
                     nearest_year = year
                     while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
                         nearest_year += 1
                         if nearest_year>2100:
                             nearest_year = year
                             while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
                                 nearest_year -= 1
                                 
 
                     nearest_mask = (data["Year"]==nearest_year)&(data["Theme"]==Theme)
                     data_masked = data.loc[nearest_mask]
 
                     for category in data_masked["Category"].unique():
                         serie = pd.Series()
                         serie["Year"] = year
                         serie["Theme"] = Theme
                         serie["Category"] = category
                         if Theme == "Electricite":
                             print(1)
                         if Theme == "Mobilité pendulaire":
                             #Produit en croix pour estimer la valeur de la mobilité pendulaire en fonction de l'évolution de la population
                             data_pendular = data.loc[(data["Year"]==nearest_year)&(data["Theme"]=="Mobilité pendulaire")]
                             pendular_year = self.raw_pendular.loc[(self.raw_pendular["Year"] == year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
                             pendular_year["Part_modale"] = pendular_year["Value"]/pendular_year["Value"].sum()
                             pendular_nearest = self.raw_pendular.loc[(self.raw_pendular["Year"] == nearest_year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
                             pendular_nearest["Part_modale"] = pendular_nearest["Value"]/pendular_nearest["Value"].sum()
 
                             mask = data_pendular["Category"]==category
                             pendular_EPT_a = pendular_year[pendular_year["Category"]==category]["Part_modale"].values*EPT_year[year]
                             pendular_CO2_n = data_pendular[mask]["CO2"].values
                             pendular_EPT_n = pendular_nearest[pendular_nearest["Category"]==category]["Part_modale"].values*EPT_year[nearest_year]
                             #Produit en croix:
                             serie["CO2"] = (pendular_EPT_a*pendular_CO2_n/pendular_EPT_n)[0]   
                         else:
                             ratio_evolution = ((EPT_year.loc[year]-EPT_year.loc[nearest_year])/(EPT_year.loc[nearest_year]))
                             serie["CO2"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "CO2"].sum()* (1+ratio_evolution)
                         serie["Value"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "Value"].sum()*(1+ratio_evolution)
                         serie["Data description"] = "Estimé"
                         serie["Scope"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "Scope"].median()
                         serie["Campus"] = "EPFL"
                         serie["Category"] = Theme
                         data = data.append(serie, ignore_index=True)
 
         self.data_L3A = data
 
     def objectives(self, year_reference=2022):
         data_L3A = self.data_L3A.copy()
 
         #Objectif 2024
         self.df_objectifs_2024 = objectives_2024(data_L3A, year_reference)
         self.year_reference = year_reference
         #Objectif 2030
         df = pd.DataFrame()
         df["Theme"] = data_L3A["Theme"].unique()
         df["Year"] = 2030
         df["Campus"] = "EPFL"
         df["Scope"] = np.nan
         #Definition des objectifs 2030:
         mask = (data_L3A["Theme"] == "Electricite")|(data_L3A["Theme"] == "Mazout")|(data_L3A["Theme"]=="CAD")|(data_L3A["Theme"] == "Gaz")
         Energy_2006 = data_L3A.loc[(data_L3A["Year"]==2006)&(mask)].sum()
         Objective_2030_energy = Energy_2006["CO2"]*0.5
         gaz_2030 = data_L3A.loc[(data_L3A["Year"]==2006)&(data_L3A["Theme"] == "Gaz")]["CO2"]*0.5 #Assume que le gaz en 2030 est la moitié de 2006
         elec_2030 = Objective_2030_energy-gaz_2030
         df.loc[df["Theme"]=="Electricite", "CO2"] = elec_2030.squeeze()
         df.loc[df["Theme"]=="Electricite", "Objectif"] = "-50% energie par rapport à 2006"
         df.loc[df["Theme"]=="Gaz", "CO2"] = gaz_2030.squeeze()
         df.loc[df["Theme"]=="Gaz", "Objectif"] = "-50% energie par rapport à 2006"        
         df.loc[df["Theme"]=="Mazout", "CO2"] = 0 
         df.loc[df["Theme"]=="Mazout", "Objectif"] = "Remplacement par Electricite"
         df.loc[df["Theme"]=="CAD", "CO2"] = 0
         df.loc[df["Theme"]=="CAD", "Objectif"] = "Remplacement par Electricite"
 
         mask_voyages_prof = (data_L3A["Theme"]=="Voyages professionnels")
         trip_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(mask_voyages_prof)]["CO2"].sum()
         Objective_2030_trip = trip_2019*0.7
         df.loc[df["Theme"]=="Voyages professionnels", "CO2"] = Objective_2030_trip
         df.loc[df["Theme"]=="Voyages professionnels", "Objectif"] = "-30% par rapport à 2019"
 
         Pendulaire_2019 =  data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Mobilité pendulaire")]["CO2"].sum()
         Objective_2030_pendulaire = Pendulaire_2019*0.7
         df.loc[df["Theme"]=="Mobilité pendulaire", "CO2"] = Objective_2030_pendulaire
         df.loc[df["Theme"]=="Mobilité pendulaire", "Objectif"] = "-30% par rapport à 2019"
 
         Alimentation_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Alimentation")]["CO2"].sum()
         Objective_2030_alimentation = Alimentation_2019*0.6
         df.loc[df["Theme"]=="Alimentation", "CO2"] = Objective_2030_alimentation
         df.loc[df["Theme"]=="Alimentation", "Objectif"] = "-40% par rapport à 2019"
 
         df.loc[df["Theme"]=="Numérique (fabrication)", "CO2"] = 4829e3
         df.loc[df["Theme"]=="Numérique (fabrication)", "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
 
         
         df.loc[df["Theme"]==self.nomenclature["Achats"], "CO2"] = 42000e3
         df.loc[df["Theme"]==self.nomenclature["Achats"], "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
 
         df.loc[df["Theme"]=="Dechets", "CO2"] = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Dechets")]["CO2"].sum()
 
         df["Data description"] = "Objectifs 2030 (confédération+EPFL)"
         df["Category"] = df["Theme"]
 
         for Theme in df["Theme"].unique():
             df.loc[df["Theme"]==Theme, "Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
 
         self.df_2030 = df
 
     def interpolation_objectifs(self):
 
         data_L3A = self.data_L3A.copy()
         df_2030 = self.df_2030
 
         years = np.arange(data_L3A["Year"].max(),2030)
         for Theme in df_2030["Theme"].unique():
             for year in years:
                 df_temp = pd.Series()
                 df_temp["Theme"] = Theme
                 df_temp["Year"] = year
                 df_temp["Campus"] = "EPFL"
                 df_temp["Category"] = df_temp["Theme"]
                 df_temp["Data description"] = "Objectifs 2030 (confédération+EPFL)"
                 df_temp["Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
                 mask = (data_L3A["Year"]==data_L3A["Year"].max())&(data_L3A["Theme"]==Theme)
 
                 if mask.sum():
                     df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[mask, "CO2"].sum(), df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
                     
                     if year != data_L3A["Year"].max():
                         df_2030 = df_2030.append(df_temp, ignore_index=True)
                 else:
                     #If this data is not available for a specific theme, we take the value of the last year
                     df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[(data_L3A["Year"]==data_L3A["Year"].max()-1)&(data_L3A["Theme"]==Theme), "CO2"].iloc[0], df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
                     df_2030 = df_2030.append(df_temp, ignore_index=True)
                 
         data_L3B =  pd.concat([data_L3A, df_2030, self.df_objectifs_2024],  ignore_index=True)
         Elect_emission_factor = data_L3B[(data_L3B["Theme"]=="Electricite")&(data_L3B["Year"]==self.year_reference)][self.CO2_col].mean()
         nan_mask = (data_L3B["Value"].isna())&(data_L3B["Category"] == "Electricite")
 
         data_L3B.loc[nan_mask, "Value"] = data_L3B[nan_mask]["CO2"].iloc[0]/Elect_emission_factor
         self.data_L3B = data_L3B.copy()
 
     def simulation():
         #Ajoute les reformes et leurs impacts sur le bilan CO2 et les predictions: Exemple: politique voyage
         
         #Dans le cas de la simple croissance, des simulations peuvent être faites.
 
         print(1)
\ No newline at end of file
diff --git a/scripts/functions.py b/scripts/functions.py
index 99ec479..4b299fd 100644
--- a/scripts/functions.py
+++ b/scripts/functions.py
@@ -1,126 +1,88 @@
 import numpy as np
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import pandas as pd
 import datetime
 import os
 import shutil
 
+
+def export_database(data, path):
+    data.to_excel(path, index=False)
+
 def extrapolate_student_plane(data, EPT_year, Stud_years, Category):
     ## Student plane interpolation
     for year in Stud_years:
         mask = (data["Year"]==year)&(data["Category"]==Category)
         nearest_year = year
         while (data.loc[(data["Year"]==nearest_year)&(data["Category"]==Category), "CO2"].isna().all()):
             nearest_year += 1
         serie = pd.Series()
         serie["Year"] = year
         ratio_evolution = ((EPT_year.loc[year]-EPT_year.loc[nearest_year])/(EPT_year.loc[nearest_year]))
         serie["CO2"] = data.loc[(data["Year"]==nearest_year)&(data["Category"]==Category)&(data["Category"]==Category), "CO2"].sum()* (1+ratio_evolution)
         serie["Value"] = data.loc[(data["Year"]==nearest_year)&(data["Category"]==Category)&(data["Category"]==Category), "Value"].sum()
         serie["Data description"] = "Estimé"
         serie["Scope"] = data.loc[(data["Year"]==nearest_year)&(data["Category"]==Category), "Scope"].median()
         serie["Campus"] = "EPFL"
         serie["Category"] = Category
         serie["Theme"] = "Voyages professionnels"
         data = data.append(serie, ignore_index=True)
     return data
 
 def objectives_2024(data_objectives, year_reference):
     mask = (data_objectives["Category"] == "Electricite")&(data_objectives["Year"]==2019)
     Electricity_2019 = data_objectives.loc[mask].sum()
     Objective_2024_Electricity = Electricity_2019["CO2"]*0.85 #CO2 et kWh varient lineairement, le facteur de reduction est donc le meme
     Objective_2024_Electricity_value = Electricity_2019["Value"]*0.85
     Electricity_ref_year = data_objectives.loc[(data_objectives["Category"] == "Electricite")&(data_objectives["Year"]==year_reference)].sum()
     Obj_2024= np.interp([2023], [year_reference, 2024], [Electricity_ref_year["CO2"], Objective_2024_Electricity]) 
     Obj_2024_value = np.interp([2023], [year_reference, 2024], [Electricity_ref_year["Value"], Objective_2024_Electricity_value])
     
     #Append Objective 2024 to obj_2023
     df_2024 = pd.DataFrame()
     df_2024["Theme"] = ["Electricite","Electricite"]
     df_2024["Year"] = [2023, 2024]
     df_2024["Campus"] = ["EPFL", "EPFL"]
     df_2024["CO2"] =  np.append(Obj_2024, Objective_2024_Electricity)
     df_2024["Data description"] = "Objectif EPFL 15% de diminution d'ici 2024"
     df_2024["Category"] = "Electricite"
     df_2024["Scope"] = "2"
     df_2024["Unit"] = "kg CO2"
     df_2024["Value"] = np.append(Obj_2024_value, Objective_2024_Electricity_value)
     return df_2024
 
 def Check_join_data_category(data, join_factor, ignore_column = ["Plane staff", "Plane students"], ignore_year = 2006):
     data = data[data["Year"]!=ignore_year]
     NaN_factor = join_factor[join_factor["factor name"].isna()]["Category"]
     mask_data_NaN = ~data["Category"].isin(NaN_factor)
     filtered_nan_data = data[mask_data_NaN]
     NaN_categories = data[~mask_data_NaN]["Category"].unique()
     missing_category = filtered_nan_data[~filtered_nan_data["Category"].isin(join_factor[~join_factor["factor name"].isna()]["Category"])]["Category"]
     missing_category = missing_category[~missing_category.isin(ignore_column)]
     #Print if filtered_nan_data["Category"] is not in join_factor["Category"] and reversed
     if len(missing_category.unique())>0:
         print("Missing category in join_factor")
         #Substract NaN_categories from missing_category
         missing_category = missing_category[~missing_category.isin(NaN_categories)]
         print(missing_category.unique())
         ValueError("Check coherency between join_factor and data")
         
     if len(join_factor[~join_factor["factor name"].isna()]["Category"].unique()) != len(join_factor[~join_factor["factor name"].isna()]["Category"]):
         print("Duplicate category in join_factor")
         ValueError("Check coherency between join_factor and data")
 
 def print_missing_factor(df, emission_factor):
     #Remove None
     df = df[~df["factor name"].isna()]
     if not df["factor name"].isin(emission_factor["Data name mapping"]).all():
         print(df["factor name"][~df["factor name"].isin(emission_factor["Data name mapping"])])
         raise ValueError("Some factor name are not found in the emission factor dataframe")
-    
-def join_emission_factor(emission_join, factor_df, Primary_key, CO2_col="GWP [kg CO2eq]"):
-    emission_factor = emission_join[Primary_key]
-    name = factor_df[factor_df["Data name mapping"]==emission_factor][CO2_col].squeeze()
-    if not name:
-        ValueError("Could not find the data name")
-    return name
-
-def sum_variable(df, meta, field):
-    meta_field = list(field.keys())[0]
-    mask = np.where(meta.loc[meta_field]==field[meta_field])[0]+1
-    tot = np.sum(df[mask],1)
-    return tot 
 
 def parse_df_from_ID(df_format, L0_ID_elec):
     df_elec_L0 = df_format[L0_ID_elec]
     df_elec_L0.columns = df_elec_L0.iloc[0]
     df_elec_L0 = df_elec_L0[1:]
     df_elec_L0=df_elec_L0.replace("-", np.nan)
     return df_elec_L0
 
-
-def export_history(year, emission_factor_path, factor_join, df):
-    date = str(datetime.now().year) + "_"+str(datetime.now().month)+"_"+str(datetime.now().day)
-    export_folder = "data/Level1/"+str(year)+"/"+date
-    if date not in os.listdir("data/Level1/"+str(year)):
-        os.makedirs(export_folder)
-    shutil.copy(factor_join, export_folder+"/factor_join.json")
-    
-    #shutil.copy(emission_factor_path, export_folder+"/emission_factor_db.xlsx")
-    df.to_csv(export_folder+f"/bilan_{year}.csv")
-
-def join_factor_variable(df, factor, join_df):
-    df_emission = pd.DataFrame(np.nan, index=df.index, columns=df.columns)
-    for ID in df.columns:
-        if "factor_name" in join_df[str(ID)]:
-            for elem in join_df[str(ID)]["factor_name"]:
-                date_range = list(elem.values())[0]
-                factor_name = list(elem.keys())[0]
-                for date in date_range:
-                    idx_factor=np.where(factor["Data name mapping"] == factor_name)[0]
-                    if not idx_factor:
-                        print(f"Could not find the factor {factor_name}")
-                    emission_factor = factor["GWP [kg CO2eq]"].iloc[idx_factor]
-                    df_emission[ID][int(date)] = df.loc[int(date)]*emission_factor 
-        else:
-            idx_factor=np.where(factor["Data name mapping"] == join_df[str(ID)])[0]
-            emission = factor["GWP [kg CO2eq]"].iloc[idx_factor]
-            df_emission[ID] = emission*df[ID]
-    return df_emission
\ No newline at end of file
diff --git a/scripts/input_path.yaml b/scripts/input_path.yaml
index 4c679eb..f3649b3 100644
--- a/scripts/input_path.yaml
+++ b/scripts/input_path.yaml
@@ -1,24 +1,23 @@
 #L0 Data
-reporting_pendulaire: "M:/42-DUR-MOB-E-E/42.05-Mobilite/42.05.01-Aff-en-cours/05-Reporting-inventaires/Rapport-environnemental-2022/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
-reporting_voyages: "M:/42-DUR-MOB-E-E/42.06-Voyages/42.06.01-Aff-en-cours/05-Reporting/42.06.01-Reporting-voyages-2023.xlsx"
+reporting_pendulaire: "M:/42-DUR-MOB-E-E/42.05-Mobilite/42.05.01-Aff-en-cours/05-Reporting-inventaires/Reporting env. 2023/42.05.01-reporting-pendulaire-2023-VF.xlsx"
+reporting_voyages: "M:/42-DUR-MOB-E-E/42.06-Voyages/42.06.01-Aff-en-cours/05-Reporting/42.06.01-Reporting-voyages-2023-VF.xlsx"
 plane_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Avions/data/Resultats/atmosfair/atmosfair_evolution.xlsx"
 dechets_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Dechets/data/Resultats/waste.xlsx"
 papier_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Achats/papier_epfl/data/Resultats/papier_db.xlsx"
 energy_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/energy_db.xlsx"
 eau_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/eau_db.xlsx"
 alimentation_infile: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Alimentation/data/Resultats/Format_Alimentation_CO2.xlsx"
 #L0 Data Local Files:
-Manual_input: "data/Brute/manual_input.xlsx"
 EPT_infile: "data/Brute/df_EPT.xlsx"
-factor_join: "data/join_factor.xlsx"
-emission_factor: "data/emission_factor.xlsx"
+inputs: "data/inputs.xlsx"
 
-#Backup files:
-reporting_pendulaire_backup: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Backup/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
+#Backup files: 
+reporting_pendulaire_backup: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets transversaux/Bilan_CO2/data/Brute/42.05.01-reporting-pendulaire-2023.xlsx"
 reporting_voyages_backup: "M:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Backup/voyages/42.06.01-Reporting-voyages-2023.xlsx"
 
 #Output
+Output_folder: "data/Resultats/"
 L1_data: "data/Resultats/L1_data.xlsx"
 L2_data: "data/Resultats/L2_data.xlsx"
 L3A_data: "data/Resultats/L3A_data.xlsx"
 L3B_data: "data/Resultats/L3B_data.xlsx"
\ No newline at end of file
diff --git a/scripts/main.py b/scripts/main.py
index ea2279c..0a69549 100644
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -1,36 +1,59 @@
 import yaml
 from footprint import *
+from Carbon_footprint import *
+from Data_importer import *
+from functions import *
+year_reference = 2023
+local_run = True
 
-year_reference = 2022
-local_run = False
-inputs = r"data\facteur emission.xlsx"
+#Nomenclature utilisée, à gauche est le nom tel qu'il est dans le fichier d'entrée, à droite est le nom tel qu'il est écrit dans le fichier facteur emission
+nomenclature = {"Alimentation": "Alimentation",
+                        "Véhicule de location": "Véhicules de location (pkm)",
+                        "Mobility essence": "Véhicules MOBILITY essence",
+                        "Mobility diesel": "Véhicules MOBILITY diesel",
+                        "Mobility electrique": "Véhicules MOBILITY electrique",
+                        "voiture": "Voiture thermique (pkm)",
+                        "moto/scooter":"Moto/scooter",
+                        "Achats":"Achats (estimation)",
+                        "Véhicules privés": "Véhicules privés",
+                        "Véhicules de service essence": "Véhicules EPFL Essence",
+                        "Véhicules de service diesel": "Véhicules EPFL Diesel",
+                        "Total Domestique": "Rail domestique",
+                        "Total International": "Rail international",
+                        "Electricite": "Electricite",
+                        "Avion etudiants": "Avion étudiants",
+                        "Avion staff": "Avion staff",
+                        }
 
 with open("scripts/input_path.yaml", "r", encoding='utf8') as f:
     directories = yaml.load(f, Loader=yaml.FullLoader)
 
-fp = footprint(CO2_col='Factor [kg CO2eq]', EPT_infile=directories["EPT_infile"], input_file=inputs)
+inputs = directories["inputs"]
 
-if not local_run:
-    fp.run_energy_emission(directories["energy_infile"])
-    fp.run_cars_emission(directories["reporting_voyages"])
-    fp.run_train_emission(directories["reporting_voyages"])
-    fp.run_plane_emission(directories["reporting_voyages"])
-    fp.run_mobilite_EPFL(directories["reporting_pendulaire"])
-    #fp.run_dechets_emission(directories["dechets_infile"])
-    fp.run_alimentation(directories["alimentation_infile"])
-    fp.add_manual_input()
-    fp.export_database(directories["L1_data"], Level="L1")
+fp = Carbon_footprint(EPT_infile=directories["EPT_infile"], input_file=inputs, nomenclature=nomenclature)
+if local_run:
+    data_importer = Data_importer(input_file=inputs, nomenclature=nomenclature)
+    data_importer.run_population(EPT_infile=directories["EPT_infile"])
+    data_importer.run_energy_emission(directories["energy_infile"])
+    data_importer.run_cars_emission(directories["reporting_voyages"])
+    data_importer.run_train_emission(directories["reporting_voyages"])
+    data_importer.run_plane_emission(directories["reporting_voyages"])
+    data_importer.run_mobilite_EPFL(directories["reporting_pendulaire"])
+    data_importer.run_alimentation(inputs, sheet_name="Alimentation")
+    data_importer.add_manual_input()
+    L1_path = directories["Output_folder"]+"L1_data.xlsx"
+    export_database(data_importer.data, L1_path)
+    raw_pendular = data_importer.raw_pendular
 else:
-    fp.import_database(directories["L1_data"], Level="L1")
+    
+    data_importer = Data_importer(input_file=inputs, nomenclature=nomenclature)
+    data_importer.run_population(EPT_infile=directories["EPT_infile"])
+fp.import_database(L1_path)
 
-fp.read_emission_factor(directories["emission_factor"])
 fp.calculate_emission()
-#fp.calculate_emission()
-fp.export_database(directories["L2_data"], Level="L2")
-fp.extrapolate()
-fp.export_database(directories["L3A_data"], Level="L3A")
+export_database(fp.data_L2,L1_path.replace("L1", "L2"))
+fp.extrapolate(EPT_year=data_importer.EPT_year, raw_pendular=raw_pendular)
+export_database(fp.data_L3A,L1_path.replace("L1", "L3A"))
 fp.objectives(year_reference=year_reference)
 fp.interpolation_objectifs()
-fp.export_database(directories["L3B_data"], Level="L3B")
-
-#fp.simulations()
\ No newline at end of file
+export_database(fp.data_L3B,L1_path.replace("L1", "L3B"))