diff --git a/analysis/Book Sami.twb b/analysis/Book Sami.twb
new file mode 100644
index 0000000..a071b0d
--- /dev/null
+++ b/analysis/Book Sami.twb	
@@ -0,0 +1,2790 @@
+<?xml version='1.0' encoding='utf-8' ?>
+
+<!-- build 20224.23.0209.1653                               -->
+<workbook original-version='18.1' source-build='2022.4.1 (20224.23.0209.1653)' source-platform='win' version='18.1' xmlns:user='http://www.tableausoftware.com/xml/user'>
+  <document-format-change-manifest>
+    <_.fcp.AccessibleZoneTabOrder.true...AccessibleZoneTabOrder />
+    <_.fcp.AnimationOnByDefault.true...AnimationOnByDefault />
+    <AutoCreateAndUpdateDSDPhoneLayouts />
+    <MapboxVectorStylesAndLayers />
+    <_.fcp.MarkAnimation.true...MarkAnimation />
+    <_.fcp.ObjectModelEncapsulateLegacy.true...ObjectModelEncapsulateLegacy />
+    <_.fcp.ObjectModelTableType.true...ObjectModelTableType />
+    <_.fcp.SchemaViewerObjectModel.true...SchemaViewerObjectModel />
+    <SetMembershipControl />
+    <SheetIdentifierTracking />
+    <WindowsPersistSimpleIdentifiers />
+  </document-format-change-manifest>
+  <preferences>
+    <preference name='ui.encoding.shelf.height' value='24' />
+    <preference name='ui.shelf.height' value='26' />
+  </preferences>
+  <_.fcp.AnimationOnByDefault.false...style>
+    <_.fcp.AnimationOnByDefault.false..._.fcp.MarkAnimation.true...style-rule element='animation'>
+      <_.fcp.AnimationOnByDefault.false...format attr='animation-on' value='ao-on' />
+    </_.fcp.AnimationOnByDefault.false..._.fcp.MarkAnimation.true...style-rule>
+  </_.fcp.AnimationOnByDefault.false...style>
+  <datasources>
+    <datasource caption='tax_and_gay' inline='true' name='federated.0a0r44a1k0ra1y1cmt8ab15acxw6' version='18.1'>
+      <connection class='federated'>
+        <named-connections>
+          <named-connection caption='tax_and_gay' name='textscan.0ymouyf0lohvbk1ef8t3h0xftmgh'>
+            <connection class='textscan' directory='C:/Users/samia/OneDrive/Desktop/data 301/project current/project-group-group-44/data/processed' filename='tax_and_gay.csv' password='' server='' />
+          </named-connection>
+          <named-connection caption='zip_lat_long' name='textscan.0nknilx0qr545516tlehx16c2uvd'>
+            <connection class='textscan' directory='C:/Users/samia/OneDrive/Desktop/data 301/project current/project-group-group-44/data/raw' filename='zip_lat_long.csv' password='' server='' />
+          </named-connection>
+        </named-connections>
+        <_.fcp.ObjectModelEncapsulateLegacy.false...relation connection='textscan.0ymouyf0lohvbk1ef8t3h0xftmgh' name='tax_and_gay.csv' table='[tax_and_gay#csv]' type='table'>
+          <columns character-set='UTF-8' header='yes' locale='en_CA' separator=','>
+            <column datatype='integer' name='zip' ordinal='0' />
+            <column datatype='integer' name='zip 1' ordinal='1' />
+            <column datatype='real' name='population' ordinal='2' />
+            <column datatype='integer' name='gay tax rate' ordinal='3' />
+            <column datatype='real' name='overall tax paid' ordinal='4' />
+            <column datatype='integer' name='income' ordinal='5' />
+          </columns>
+        </_.fcp.ObjectModelEncapsulateLegacy.false...relation>
+        <_.fcp.ObjectModelEncapsulateLegacy.true...relation type='collection'>
+          <relation connection='textscan.0ymouyf0lohvbk1ef8t3h0xftmgh' name='tax_and_gay.csv' table='[tax_and_gay#csv]' type='table'>
+            <columns character-set='UTF-8' header='yes' locale='en_CA' separator=','>
+              <column datatype='integer' name='zip' ordinal='0' />
+              <column datatype='integer' name='zip 1' ordinal='1' />
+              <column datatype='real' name='population' ordinal='2' />
+              <column datatype='integer' name='gay tax rate' ordinal='3' />
+              <column datatype='real' name='overall tax paid' ordinal='4' />
+              <column datatype='integer' name='income' ordinal='5' />
+            </columns>
+          </relation>
+          <relation connection='textscan.0nknilx0qr545516tlehx16c2uvd' name='zip_lat_long.csv' table='[zip_lat_long#csv]' type='table'>
+            <columns character-set='UTF-8' header='yes' locale='en_CA' separator=','>
+              <column datatype='integer' name='ZIP' ordinal='0' />
+              <column datatype='real' name='LAT' ordinal='1' />
+              <column datatype='real' name='LNG' ordinal='2' />
+            </columns>
+          </relation>
+        </_.fcp.ObjectModelEncapsulateLegacy.true...relation>
+        <metadata-records>
+          <metadata-record class='capability'>
+            <remote-name />
+            <remote-type>0</remote-type>
+            <parent-name>[tax_and_gay.csv]</parent-name>
+            <remote-alias />
+            <aggregation>Count</aggregation>
+            <contains-null>true</contains-null>
+            <attributes>
+              <attribute datatype='string' name='character-set'>&quot;UTF-8&quot;</attribute>
+              <attribute datatype='string' name='collation'>&quot;en_GB&quot;</attribute>
+              <attribute datatype='string' name='field-delimiter'>&quot;,&quot;</attribute>
+              <attribute datatype='string' name='header-row'>&quot;true&quot;</attribute>
+              <attribute datatype='string' name='locale'>&quot;en_CA&quot;</attribute>
+              <attribute datatype='string' name='single-char'>&quot;&quot;</attribute>
+            </attributes>
+          </metadata-record>
+          <metadata-record class='capability'>
+            <remote-name />
+            <remote-type>0</remote-type>
+            <parent-name>[zip_lat_long.csv]</parent-name>
+            <remote-alias />
+            <aggregation>Count</aggregation>
+            <contains-null>true</contains-null>
+            <attributes>
+              <attribute datatype='string' name='character-set'>&quot;UTF-8&quot;</attribute>
+              <attribute datatype='string' name='collation'>&quot;en_GB&quot;</attribute>
+              <attribute datatype='string' name='field-delimiter'>&quot;,&quot;</attribute>
+              <attribute datatype='string' name='header-row'>&quot;true&quot;</attribute>
+              <attribute datatype='string' name='locale'>&quot;en_CA&quot;</attribute>
+              <attribute datatype='string' name='single-char'>&quot;&quot;</attribute>
+            </attributes>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>zip</remote-name>
+            <remote-type>20</remote-type>
+            <local-name>[zip]</local-name>
+            <parent-name>[tax_and_gay.csv]</parent-name>
+            <remote-alias>zip</remote-alias>
+            <ordinal>0</ordinal>
+            <local-type>integer</local-type>
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+          </metadata-record>
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+            <remote-type>20</remote-type>
+            <local-name>[zip 1]</local-name>
+            <parent-name>[tax_and_gay.csv]</parent-name>
+            <remote-alias>zip 1</remote-alias>
+            <ordinal>1</ordinal>
+            <local-type>integer</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[tax_and_gay.csv_2695AFE1365845F98E5AB43881776511]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>population</remote-name>
+            <remote-type>5</remote-type>
+            <local-name>[population]</local-name>
+            <parent-name>[tax_and_gay.csv]</parent-name>
+            <remote-alias>population</remote-alias>
+            <ordinal>2</ordinal>
+            <local-type>real</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
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+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>gay tax rate</remote-name>
+            <remote-type>20</remote-type>
+            <local-name>[gay tax rate]</local-name>
+            <parent-name>[tax_and_gay.csv]</parent-name>
+            <remote-alias>gay tax rate</remote-alias>
+            <ordinal>3</ordinal>
+            <local-type>integer</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[tax_and_gay.csv_2695AFE1365845F98E5AB43881776511]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>overall tax paid</remote-name>
+            <remote-type>5</remote-type>
+            <local-name>[overall tax paid]</local-name>
+            <parent-name>[tax_and_gay.csv]</parent-name>
+            <remote-alias>overall tax paid</remote-alias>
+            <ordinal>4</ordinal>
+            <local-type>real</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[tax_and_gay.csv_2695AFE1365845F98E5AB43881776511]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>income</remote-name>
+            <remote-type>20</remote-type>
+            <local-name>[income]</local-name>
+            <parent-name>[tax_and_gay.csv]</parent-name>
+            <remote-alias>income</remote-alias>
+            <ordinal>5</ordinal>
+            <local-type>integer</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[tax_and_gay.csv_2695AFE1365845F98E5AB43881776511]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>ZIP</remote-name>
+            <remote-type>20</remote-type>
+            <local-name>[ZIP]</local-name>
+            <parent-name>[zip_lat_long.csv]</parent-name>
+            <remote-alias>ZIP</remote-alias>
+            <ordinal>6</ordinal>
+            <local-type>integer</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[zip_lat_long.csv_EA1829CF3CA8490894E3EAB97341B6C8]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>LAT</remote-name>
+            <remote-type>5</remote-type>
+            <local-name>[LAT]</local-name>
+            <parent-name>[zip_lat_long.csv]</parent-name>
+            <remote-alias>LAT</remote-alias>
+            <ordinal>7</ordinal>
+            <local-type>real</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[zip_lat_long.csv_EA1829CF3CA8490894E3EAB97341B6C8]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+          <metadata-record class='column'>
+            <remote-name>LNG</remote-name>
+            <remote-type>5</remote-type>
+            <local-name>[LNG]</local-name>
+            <parent-name>[zip_lat_long.csv]</parent-name>
+            <remote-alias>LNG</remote-alias>
+            <ordinal>8</ordinal>
+            <local-type>real</local-type>
+            <aggregation>Sum</aggregation>
+            <contains-null>true</contains-null>
+            <_.fcp.ObjectModelEncapsulateLegacy.true...object-id>[zip_lat_long.csv_EA1829CF3CA8490894E3EAB97341B6C8]</_.fcp.ObjectModelEncapsulateLegacy.true...object-id>
+          </metadata-record>
+        </metadata-records>
+      </connection>
+      <aliases enabled='yes' />
+      <column aggregation='Avg' datatype='real' name='[LAT]' role='measure' semantic-role='[Geographical].[Latitude]' type='quantitative' />
+      <column aggregation='Avg' datatype='real' name='[LNG]' role='measure' semantic-role='[Geographical].[Longitude]' type='quantitative' />
+      <column aggregation='Sum' datatype='integer' default-format='*00000' name='[ZIP]' role='dimension' semantic-role='[ZipCode].[Name]' type='ordinal' />
+      <_.fcp.ObjectModelTableType.true...column caption='tax_and_gay.csv' datatype='table' name='[__tableau_internal_object_id__].[tax_and_gay.csv_2695AFE1365845F98E5AB43881776511]' role='measure' type='quantitative' />
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+      <column caption='Gay Tax Rate' datatype='integer' name='[gay tax rate]' role='measure' type='quantitative' />
+      <column caption='Income' datatype='integer' name='[income]' role='measure' type='quantitative' />
+      <column caption='Overall Tax Paid' datatype='real' name='[overall tax paid]' role='measure' type='quantitative' />
+      <column caption='Population' datatype='real' name='[population]' role='measure' type='quantitative' />
+      <column aggregation='Sum' caption='Zip 1' datatype='integer' default-format='*00000' name='[zip 1]' role='dimension' semantic-role='[ZipCode].[Name]' type='ordinal' />
+      <column aggregation='Sum' caption='Zip' datatype='integer' default-format='*00000' name='[zip]' role='dimension' semantic-role='[ZipCode].[Name]' type='ordinal' />
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+      <semantic-values>
+        <semantic-value key='[Country].[Name]' value='&quot;Canada&quot;' />
+      </semantic-values>
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+                <columns character-set='UTF-8' header='yes' locale='en_CA' separator=','>
+                  <column datatype='integer' name='zip' ordinal='0' />
+                  <column datatype='integer' name='zip 1' ordinal='1' />
+                  <column datatype='real' name='population' ordinal='2' />
+                  <column datatype='integer' name='gay tax rate' ordinal='3' />
+                  <column datatype='real' name='overall tax paid' ordinal='4' />
+                  <column datatype='integer' name='income' ordinal='5' />
+                </columns>
+              </relation>
+            </properties>
+          </object>
+          <object caption='zip_lat_long.csv' id='zip_lat_long.csv_EA1829CF3CA8490894E3EAB97341B6C8'>
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+                <columns character-set='UTF-8' header='yes' locale='en_CA' separator=','>
+                  <column datatype='integer' name='ZIP' ordinal='0' />
+                  <column datatype='real' name='LAT' ordinal='1' />
+                  <column datatype='real' name='LNG' ordinal='2' />
+                </columns>
+              </relation>
+            </properties>
+          </object>
+        </objects>
+        <relationships>
+          <relationship>
+            <expression op='='>
+              <expression op='[zip]' />
+              <expression op='[ZIP]' />
+            </expression>
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+  <mapsources>
+    <mapsource name='Tableau' />
+  </mapsources>
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+    <worksheet name='Sheet 1'>
+      <layout-options>
+        <title>
+          <formatted-text>
+            <run bold='true' fontcolor='#b07aa1'>Logarithmic Regression of income tax paid by the Gay population VS the General population</run>
+          </formatted-text>
+        </title>
+      </layout-options>
+      <table>
+        <view>
+          <datasources>
+            <datasource caption='tax_and_gay' name='federated.0a0r44a1k0ra1y1cmt8ab15acxw6' />
+          </datasources>
+          <datasource-dependencies datasource='federated.0a0r44a1k0ra1y1cmt8ab15acxw6'>
+            <column caption='Gay Tax Rate' datatype='integer' name='[gay tax rate]' role='measure' type='quantitative' />
+            <column-instance column='[gay tax rate]' derivation='None' name='[none:gay tax rate:qk]' pivot='key' type='quantitative' />
+            <column-instance column='[overall tax paid]' derivation='None' name='[none:overall tax paid:qk]' pivot='key' type='quantitative' />
+            <column caption='Overall Tax Paid' datatype='real' name='[overall tax paid]' role='measure' type='quantitative' />
+          </datasource-dependencies>
+          <aggregation value='true' />
+        </view>
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+            <format attr='title' class='0' field='[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:overall tax paid:qk]' scope='rows' value='General Income Tax Paid' />
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+              <breakdown value='auto' />
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+            <trendline enable-confidence-bands='false' enable-instant-analytics='true' enabled='true' exclude-color='false' exclude-intercept='false' fit='linear' />
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+                <format attr='mark-color' value='#b07aa1' />
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+        </panes>
+        <rows>[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:overall tax paid:qk]</rows>
+        <cols>[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:gay tax rate:qk]</cols>
+      </table>
+      <simple-id uuid='{03CB6918-0B9B-43AB-B6C5-9A357C9D373B}' />
+    </worksheet>
+    <worksheet name='Sheet 1 (2)'>
+      <layout-options>
+        <title>
+          <formatted-text>
+            <run bold='true' fontcolor='#4e79a7'>Linear Regression of income tax paid by the Gay population VS the General population</run>
+          </formatted-text>
+        </title>
+      </layout-options>
+      <table>
+        <view>
+          <datasources>
+            <datasource caption='tax_and_gay' name='federated.0a0r44a1k0ra1y1cmt8ab15acxw6' />
+          </datasources>
+          <datasource-dependencies datasource='federated.0a0r44a1k0ra1y1cmt8ab15acxw6'>
+            <column caption='Gay Tax Rate' datatype='integer' name='[gay tax rate]' role='measure' type='quantitative' />
+            <column-instance column='[gay tax rate]' derivation='None' name='[none:gay tax rate:qk]' pivot='key' type='quantitative' />
+            <column-instance column='[overall tax paid]' derivation='None' name='[none:overall tax paid:qk]' pivot='key' type='quantitative' />
+            <column caption='Overall Tax Paid' datatype='real' name='[overall tax paid]' role='measure' type='quantitative' />
+          </datasource-dependencies>
+          <aggregation value='true' />
+        </view>
+        <style>
+          <style-rule element='axis'>
+            <format attr='height' field='[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:gay tax rate:qk]' value='57' />
+            <format attr='title' class='0' field='[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:overall tax paid:qk]' scope='rows' value='General Income Tax Paid' />
+            <format attr='title' class='0' field='[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:gay tax rate:qk]' scope='cols' value='Gay Income Tax Paid' />
+          </style-rule>
+        </style>
+        <panes>
+          <pane selection-relaxation-option='selection-relaxation-allow'>
+            <view>
+              <breakdown value='auto' />
+            </view>
+            <mark class='Automatic' />
+            <mark-sizing mark-sizing-setting='marks-scaling-off' />
+            <trendline enable-confidence-bands='false' enable-instant-analytics='true' enabled='true' exclude-color='false' exclude-intercept='false' fit='linear' />
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+        </panes>
+        <rows>[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:overall tax paid:qk]</rows>
+        <cols>[federated.0a0r44a1k0ra1y1cmt8ab15acxw6].[none:gay tax rate:qk]</cols>
+      </table>
+      <simple-id uuid='{9D10588A-B020-41B3-8F23-CD8AD7A24DE5}' />
+    </worksheet>
+    <worksheet name='Sheet 3'>
+      <layout-options>
+        <title>
+          <formatted-text>
+            <run bold='true' fontalignment='1' fontcolor='#f9a655'>Analysis of the Gay and General tax rates of a sample of Zip Codes</run>
+          </formatted-text>
+        </title>
+      </layout-options>
+      <table>
+        <view>
+          <datasources>
+            <datasource caption='tax_and_gay' name='federated.0a0r44a1k0ra1y1cmt8ab15acxw6' />
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+          <mapsources>
+            <mapsource name='Tableau' />
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+  </thumbnails>
+</workbook>
diff --git a/analysis/analysis2.ipynb b/analysis/analysis2.ipynb
index 94bdbb4..da6e869 100644
--- a/analysis/analysis2.ipynb
+++ b/analysis/analysis2.ipynb
@@ -320,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -578,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -611,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -643,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -674,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -790,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -820,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -862,7 +862,725 @@
     "\n",
     "# an explanation for this is queer people through one mechanism or another (queer folk being more likely to be politically progressive, live in cities, or get educations for example) end up corelating strongly with demographics who pay more tax\n",
     "\n",
-    "# due note that the analysis is severely limited by severe sampling bias as only hyper urban geographical stratum have been surveyed by gayborhoods"
+    "# due note that the analysis is severely limited by severe sampling bias as only hyper urban geographical stratum have been surveyed by gayborhoods\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**How are the different metrics of pride represented in this data set correlated?**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10</th>\n",
+       "      <th>Tax_Mjoint</th>\n",
+       "      <th>Mjoint_MF</th>\n",
+       "      <th>Mjoint_SS</th>\n",
+       "      <th>Mjoint_FF</th>\n",
+       "      <th>Mjoint_MM</th>\n",
+       "      <th>TaxRate_SS</th>\n",
+       "      <th>TaxRate_FF</th>\n",
+       "      <th>TaxRate_MM</th>\n",
+       "      <th>Cns_TotHH</th>\n",
+       "      <th>...</th>\n",
+       "      <th>FF_Cns</th>\n",
+       "      <th>FF_Index</th>\n",
+       "      <th>MM_Tax</th>\n",
+       "      <th>MM_Cns</th>\n",
+       "      <th>MM_Index</th>\n",
+       "      <th>SS_Index</th>\n",
+       "      <th>SS_Index_Weight</th>\n",
+       "      <th>Parade_Weight</th>\n",
+       "      <th>Bars_Weight</th>\n",
+       "      <th>TOTINDEX</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90069</td>\n",
+       "      <td>2120</td>\n",
+       "      <td>1689</td>\n",
+       "      <td>431</td>\n",
+       "      <td>61</td>\n",
+       "      <td>370</td>\n",
+       "      <td>203.301887</td>\n",
+       "      <td>28.773585</td>\n",
+       "      <td>174.528302</td>\n",
+       "      <td>12551</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.847099</td>\n",
+       "      <td>6.724415</td>\n",
+       "      <td>29.583721</td>\n",
+       "      <td>18.704533</td>\n",
+       "      <td>48.288254</td>\n",
+       "      <td>55.012669</td>\n",
+       "      <td>39.429995</td>\n",
+       "      <td>10</td>\n",
+       "      <td>17.647059</td>\n",
+       "      <td>67.077054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>94114</td>\n",
+       "      <td>5080</td>\n",
+       "      <td>4036</td>\n",
+       "      <td>1044</td>\n",
+       "      <td>170</td>\n",
+       "      <td>874</td>\n",
+       "      <td>205.511811</td>\n",
+       "      <td>33.464567</td>\n",
+       "      <td>172.047244</td>\n",
+       "      <td>16456</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.161579</td>\n",
+       "      <td>9.834048</td>\n",
+       "      <td>29.163165</td>\n",
+       "      <td>19.415304</td>\n",
+       "      <td>48.578469</td>\n",
+       "      <td>58.412517</td>\n",
+       "      <td>41.866815</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>61.866815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>5790</td>\n",
+       "      <td>5166</td>\n",
+       "      <td>624</td>\n",
+       "      <td>97</td>\n",
+       "      <td>527</td>\n",
+       "      <td>107.772021</td>\n",
+       "      <td>16.753022</td>\n",
+       "      <td>91.018998</td>\n",
+       "      <td>29762</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.531029</td>\n",
+       "      <td>4.370779</td>\n",
+       "      <td>15.428332</td>\n",
+       "      <td>10.932081</td>\n",
+       "      <td>26.360413</td>\n",
+       "      <td>30.731192</td>\n",
+       "      <td>22.026394</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5.882353</td>\n",
+       "      <td>37.908747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>3510</td>\n",
+       "      <td>3229</td>\n",
+       "      <td>281</td>\n",
+       "      <td>74</td>\n",
+       "      <td>207</td>\n",
+       "      <td>80.056980</td>\n",
+       "      <td>21.082621</td>\n",
+       "      <td>58.974359</td>\n",
+       "      <td>18786</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.482293</td>\n",
+       "      <td>6.055939</td>\n",
+       "      <td>9.996551</td>\n",
+       "      <td>5.943318</td>\n",
+       "      <td>15.939869</td>\n",
+       "      <td>21.995808</td>\n",
+       "      <td>15.765361</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11.764706</td>\n",
+       "      <td>37.530067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>94103</td>\n",
+       "      <td>2660</td>\n",
+       "      <td>2417</td>\n",
+       "      <td>243</td>\n",
+       "      <td>34</td>\n",
+       "      <td>209</td>\n",
+       "      <td>91.353383</td>\n",
+       "      <td>12.781955</td>\n",
+       "      <td>78.571429</td>\n",
+       "      <td>12728</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.837431</td>\n",
+       "      <td>3.004058</td>\n",
+       "      <td>13.318386</td>\n",
+       "      <td>4.961779</td>\n",
+       "      <td>18.280165</td>\n",
+       "      <td>21.284224</td>\n",
+       "      <td>15.255337</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10.588235</td>\n",
+       "      <td>35.843573</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   GEOID10  Tax_Mjoint  Mjoint_MF  Mjoint_SS  Mjoint_FF  Mjoint_MM  \\\n",
+       "0    90069        2120       1689        431         61        370   \n",
+       "1    94114        5080       4036       1044        170        874   \n",
+       "2    10011        5790       5166        624         97        527   \n",
+       "3    10014        3510       3229        281         74        207   \n",
+       "4    94103        2660       2417        243         34        209   \n",
+       "\n",
+       "   TaxRate_SS  TaxRate_FF  TaxRate_MM  Cns_TotHH  ...    FF_Cns  FF_Index  \\\n",
+       "0  203.301887   28.773585  174.528302      12551  ...  1.847099  6.724415   \n",
+       "1  205.511811   33.464567  172.047244      16456  ...  4.161579  9.834048   \n",
+       "2  107.772021   16.753022   91.018998      29762  ...  1.531029  4.370779   \n",
+       "3   80.056980   21.082621   58.974359      18786  ...  2.482293  6.055939   \n",
+       "4   91.353383   12.781955   78.571429      12728  ...  0.837431  3.004058   \n",
+       "\n",
+       "      MM_Tax     MM_Cns   MM_Index   SS_Index  SS_Index_Weight  Parade_Weight  \\\n",
+       "0  29.583721  18.704533  48.288254  55.012669        39.429995             10   \n",
+       "1  29.163165  19.415304  48.578469  58.412517        41.866815              0   \n",
+       "2  15.428332  10.932081  26.360413  30.731192        22.026394             10   \n",
+       "3   9.996551   5.943318  15.939869  21.995808        15.765361             10   \n",
+       "4  13.318386   4.961779  18.280165  21.284224        15.255337             10   \n",
+       "\n",
+       "   Bars_Weight   TOTINDEX  \n",
+       "0    17.647059  67.077054  \n",
+       "1    20.000000  61.866815  \n",
+       "2     5.882353  37.908747  \n",
+       "3    11.764706  37.530067  \n",
+       "4    10.588235  35.843573  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "gaybourhoods = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
+    "gaybourhoods.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       pride parade index  gay bars index          lat         long\n",
+      "count         2328.000000     2328.000000  2328.000000  2328.000000\n",
+      "mean             0.017612        0.118127    38.044304   -91.221236\n",
+      "std              0.131563        0.861863     5.148365    18.533499\n",
+      "min              0.000000        0.000000    25.572213  -123.118977\n",
+      "25%              0.000000        0.000000    34.021932  -105.049099\n",
+      "50%              0.000000        0.000000    39.899977   -87.494097\n",
+      "75%              0.000000        0.000000    40.912413   -74.288743\n",
+      "max              1.000000       17.000000    47.916786   -70.758184\n",
+      "------------------------------------------------------------------------\n",
+      "      pride parade index  gay bars index        lat        long\n",
+      "0                      1              15  34.093828 -118.381697\n",
+      "1                      0              17  37.758057 -122.435410\n",
+      "2                      1               5  40.742039  -74.000620\n",
+      "3                      1              10  40.734012  -74.006746\n",
+      "4                      1               9  37.773134 -122.411167\n",
+      "...                  ...             ...        ...         ...\n",
+      "2323                   0               0  45.528666 -122.678981\n",
+      "2324                   0               0  47.606211 -122.333792\n",
+      "2325                   0               0  47.449678 -122.307657\n",
+      "2326                   0               0  47.604569 -122.335359\n",
+      "2327                   0               0  47.649339 -122.310294\n",
+      "\n",
+      "[2328 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#wrangle gay\n",
+    "# gaydf = pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['Parade_Weight'], gaybourhoods['Bars_Weight']))\n",
+    "\n",
+    "\n",
+    "# print(gaydf.describe())\n",
+    "# print(gaydf)\n",
+    "\n",
+    "cords = pd.read_csv(\"../data/raw/zip_lat_long.csv\")\n",
+    "\n",
+    "# gaydf = pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['ParadeFlag'], gaybourhoods['CountBars']))\n",
+    "# gaydf.columns=(('zip', 'pride parade index', 'gay bars index'))\n",
+    "# gaydf = gaydf.merge(cords, left_on=\"zip\", right_on=\"ZIP\")\n",
+    "\n",
+    "\n",
+    "def loadprideindexes(secdf):\n",
+    "    gaydf = pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['ParadeFlag'], gaybourhoods['CountBars']))\n",
+    "    gaydf.columns=(('zip', 'pride parade index', 'gay bars index'))\n",
+    "    gaydf = gaydf.merge(secdf, left_on=\"zip\", right_on=\"ZIP\")\n",
+    "    \n",
+    "    return gaydf\n",
+    "    \n",
+    "    # gaydf = (\n",
+    "    #     pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['ParadeFlag'], gaybourhoods['CountBars']))\n",
+    "    #     .columns=(('zip', 'pride parade index', 'gay bars index'))\n",
+    "    #     # .merge(cords, left_on=\"zip\", right_on=\"ZIP\")\n",
+    "    # )\n",
+    "\n",
+    "gaydf = loadprideindexes(cords)\n",
+    "\n",
+    "def notzip(dat):\n",
+    "    del dat['zip']\n",
+    "    del dat['ZIP']\n",
+    "\n",
+    "notzip(gaydf)\n",
+    "\n",
+    "\n",
+    "# // unneded was already filtered out\n",
+    "\n",
+    "# There's a lot of info baked into some of these columns. Especially the composite indexes.\n",
+    "# We'll leave their names as is for easy reference even if they're a little ugly.\n",
+    "gaydf = gaydf.rename({\n",
+    "    \"LAT\": \"lat\",\n",
+    "    \"LNG\": \"long\",\n",
+    "}, axis=\"columns\")\n",
+    "\n",
+    "# unperson unneccesary zip codes\n",
+    "# del gaydf['zip.1']\n",
+    "# del gaydf['ZIP']\n",
+    "\n",
+    "print(gaydf.describe())\n",
+    "print(\"------------------------------------------------------------------------\")\n",
+    "print(gaydf)\n",
+    "\n",
+    "gaydf.to_csv(\"../data/processed/gay_pride\", index=False)\n",
+    "\n",
+    "# gaybourhoods.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gaydf = pd.read_csv(\"../data/processed/gay_pride\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TKcTVEBERUUNYrVakpKQEvsfr0+qDzNHDSSaTiUGGiIgozJxsWggn+xIREVHYYpAhIiKisMUgQ0RERGGr1c+RISKi5uPz+eDxeEJdBoUBpVIJuVx+xtthkCEiojMmhEBxcTGqq6tDXQqFkYiICCQkJJzRdd4YZIiI6IwdDTFxcXHQ6XS8ACmdkBACdrsdpaWlAIDExMTT3haDDBERnRGfzxcIMdHR0aEuh8KEVqsFAJSWliIuLu60DzNxsi8REZ2Ro3NidDpdiCuhcHP0M3Mm86oYZIiIqFHwcBKdqsb4zPDQUhOosrnh9vmhV8lh0ChDXQ4REVGrxRGZRlRpc2Fldik+35SHbflV2F1kxcHyWhRbHKEujYgorBw8eBCSJCErK6veNitXroQkSa3uTKnJkyfjsssuO6NttG3bFi+//PIZbSNc+pcjMo2ksNqOfaU26FVyDGofi32ltfhkwwG4fT5c3icZA9pGIi1aH+oyiYjCQkpKCoqKihATExPqUsLSxo0bodefHd85DDKN4FC5DZIMcPv80CnleOnnbKzMLgusX7OvAh3jDHj/pgFIjuRkOCKiE3G73VCpVEhISAh1KafN4/FAqQzd1ILY2NiQvXdz46GlM1RmdcInBDYfqsKq7DLkVtTigdEd8e7Efpg+qiOM6iNZcW9pLb7NKoDfL0JcMRFR8xk+fDjuuusu3HXXXTCbzYiJicGjjz4KIf73u7Bt27Z46qmnMHHiRJhMJkyZMqXOQ0tLlixBx44dodVqcf755+PgwYPHvd8ff/yBIUOGQKvVIiUlBffccw9sNlu99T3xxBPo3bs35s2bh5SUFOh0Olx99dWwWCyBNhs3bsSoUaMQExMDs9mMYcOGYcuWLUHbkSQJb775Ji699FLo9Xo888wz8Pl8uOWWW5Ceng6tVotOnTrhlVdeCXqdz+fD9OnTERERgejoaDz00ENBfQMAfr8fs2fPDmynV69e+OKLL07Y78ceWpIkCe+++y4uv/xy6HQ6dOjQAd99913Qa860fz/44AMYDAbk5OQE2t95553o3Lkz7Hb7Ces9I6KVs1gsAoCwWCyNvu2KWqfYWVAt+j75s+j5xFKxfHeJ+GFboRj/+h/ivOd+Ew9+liWyiyxi3b4ycf6LK8SQ55eLgkpbo9dBRBRKDodD/PXXX8LhcBy3btiwYcJgMIh7771X7NmzR3z00UdCp9OJt99+O9AmLS1NmEwm8dJLL4l9+/aJffv2idzcXAFAbN26VQghRF5enlCr1WL69OmB7cTHxwsAoqqqSgghxL59+4Rerxf/+c9/xN69e8WaNWtEnz59xOTJk+ut/fHHHxd6vV6MGDFCbN26VaxatUq0b99eXHfddYE2v/32m/jwww/F7t27xV9//SVuueUWER8fL6xWa6ANABEXFyfef/99sX//fnHo0CHhdrvFY489JjZu3CgOHDgQ2PdPP/008Lrnn39eREZGii+//DKwbaPRKMaPHx9o8/TTT4vOnTuLpUuXiv3794v58+cLtVotVq5cWe9+paWlif/85z9B9SUnJ4uPP/5Y5OTkiHvuuUcYDAZRUVHRqP171VVXiQEDBgiPxyN++OEHoVQqxaZNm+qt80SfnYZ+fzPInIHsYou4af4G8cXmfLG/pEbM+GKbSJvxQ9Cj47+WiK2HKsX6/eXio3UHxYHSmkavg4golE4WZLp06SL8fn9g2YwZM0SXLl0Cz9PS0sRll10W9Lpjg8zMmTNF165dg9rMmDEj6Iv2lltuEVOmTAlqs3r1aiGTyeqsTYgjQUYul4vDhw8Hlv30009CJpOJoqKiOl/j8/mE0WgU33//fWAZAHHffffV2f7vpk2bJq644orA88TERPHCCy8Enns8HpGcnBwIMk6nU+h0OrF27dqg7dxyyy3i2muvrfd96goyjzzySOB5bW2tACB++uknIUTj9W9lZaVITk4Wd9xxh4iPjxfPPPPMCfujMYIMDy2dpmKLAxW1HkzKTIPF7obD48PijfnHtXN5/Xjyh7+g1yiQW26DSsEuJ6Kzy8CBA4OuF5KZmYmcnBz4fL7Asv79+59wG7t378a5554btCwzMzPo+bZt27BgwQIYDIbAY8yYMfD7/cjNza1326mpqWjTpk3Qdv1+P7KzswEAJSUluO2229ChQweYzWaYTCbU1tYiLy8vaDt17cMbb7yBfv36ITY2FgaDAW+//XbgdRaLBUVFRUH7pVAograzb98+2O12jBo1Kmi/PvjgA+zfv/+EfXasnj17Bv5fr9fDZDIFbhHQWP0bGRmJ9957D2+++SYyMjLw8MMPn1KNp4OTfU9TrcsHvVqOj9cfwi2D07Fqb1m9bbfkVUMpl+FwtQM2lw9ur5+BhojobxrjDJva2lpMnToV99xzz3HrUlNTT3u7kyZNQkVFBV555RWkpaVBrVYjMzMTbrc7qN2x+7B48WI8+OCD+Pe//43MzEwYjUa8+OKLWL9+fYPfu7a2FgDw448/BoUtAFCr1ae0H8dOPpYkCX6//5RqaUj//v7775DL5SgqKoLNZoPRaDylOk8Vg8xpKq91QaWQ4bwOMah2uE/YVpIACYBOJcfOQgsyYs+OU+KIiAAc98X9559/okOHDqd0b50uXbocNzn1zz//DHret29f/PXXX2jfvv0p1ZeXl4fCwkIkJSUFtiuTydCpUycAwJo1azB37lxcdNFFAID8/HyUl5efdLtr1qzBoEGDcOeddwaW/X0UxWw2IzExEevXr8fQoUMBAF6vF5s3b0bfvn0BAF27doVarUZeXh6GDRt2Svt1Khqrf9euXYvnn38e33//PWbMmIG77roLCxcubJKaj+KwwGlyuDywubwwqpXYW1SDAelR9bY9LyMGGqUcOw5boJTLYHd7m7FSIqLQysvLw/Tp05GdnY1PPvkEr732Gu69995T2sbtt9+OnJwc/OMf/0B2djY+/vhjLFiwIKjNjBkzsHbtWtx1113IyspCTk4Ovv32W9x1110n3LZGo8GkSZOwbds2rF69Gvfccw+uvvrqwOnfHTp0wIcffojdu3dj/fr1uP766wM3PDyRDh06YNOmTVi2bBn27t2LRx99FBs3bgxqc++99+K5557DN998gz179uDOO+8MugCd0WjEgw8+iPvvvx8LFy7E/v37sWXLFrz22muNGhAao39rampw44034p577sHYsWOxaNEifPrppyc9w+pMMcicpgSzDgkmDXx+P37LLoVBJcftw9od1y5Cp8RDF3aC2+vDoQo7MmL1qHX76tgiEVHrNHHiRDgcDpxzzjmYNm0a7r33XkyZMuWUtpGamoovv/wS33zzDXr16oW33noLzz77bFCbnj17YtWqVdi7dy+GDBmCPn364LHHHguMtNSnffv2mDBhAi666CKMHj0aPXv2xNy5cwPr33vvPVRVVaFv376BL+q4uLiT1jx16lRMmDAB11xzDc4991xUVFQEjc4AwAMPPIAbb7wRkyZNChx+uvzyy4PaPPXUU3j00Ucxe/ZsdOnSBRdeeCF+/PFHpKenn7SGhmqM/r333nuh1+sDr+vRoweeffZZTJ06FQUFBY1W67Gk/85mbrWsVivMZjMsFgtMJlOjbbew2gG7y4tfd5fA7RPokmiEWiFHrcuLH7YXoqLWjb6pkRjdLR47DlcjKUKLarsHA9Ij4fEJtI9r2mOGRETNxel0Ijc3F+np6dBoNEHrhg8fjt69e5/x5fKbyhNPPIFvvvnmhLdCoKZzos9OQ7+/OUfmNCVFaHGgrBZ906IgIAABzPv9AG4cmIbrzjky6UmrkuPjDXnIr7Djn+O6IMagxuq95RjRJT7E1RMREbUODDJnwKBWoMLmxtebCzBxUFuc0zYKNy3YiEidEnKZhPJaN0waBd6e2B9zft6LKUPbwaxVIt6kOfnGiYiI6KR4aOkM5ZbVosLmxtu/H0DbGD36pkbi+20FKKt1o29qBDLbReO5n/bgvlEdkRqlRbxJiyi9qtHrICIKlRMdHiA6ER5aagFSo/WQyYAHR3dCXqUdKVFa3Dw4Hauyy7DpUBW2HKrCP8d1QVqUFokRWihP4XRDIiIiOjEGmTMkl0lIizbA6fZCIZfw1ZYCWB1ujOuZhEt7JUEhl0GrlCHefPJT9YiIiOjUMMg0Eo1KgXaxBtw7sj1KrS54/QIahQxRBjWUcp7lTkRE1BQYZBqZUi5Hm0hdqMsgIiI6K3CogIiIiMIWgwwRERGFLQYZIiIiClsMMkRERGHgjTfeQNu2baHRaHDuuediw4YNoS6pRWCQISIiOgU+v8C6/RX4NqsA6/ZXwOdv+uvKfvrpp5g+fToef/xxbNmyBb169cKYMWNQWlra5O/d0vGsJSIiogZaurMIs77/C0UWZ2BZolmDxy/pigu7JzbZ+86ZMwe33XYbbrrpJgDAW2+9hR9//BHvv/8+Hn744SZ733DAERkiIqIGWLqzCHd8tCUoxABAscWJOz7agqU7i5rkfd1uNzZv3oyRI0cGlslkMowcORLr1q1rkvcMJwwyREREJ+HzC8z6/i/UdRDp6LJZ3//VJIeZysvL4fP5EB8fH7Q8Pj4excXFjf5+4YZBhoiI6CQ25FYeNxLzdwJAkcWJDbmVzVcUAWCQISIiOqnSmvpDzOm0OxUxMTGQy+UoKSkJWl5SUoKEhIRGf79wwyBDRER0EnFGTaO2OxUqlQr9+vXDb7/9Fljm9/vx22+/ITMzs9HfL9zwrCUiIqKTOCc9ColmDYotzjrnyUgAEswanJMe1STvP336dEyaNAn9+/fHOeecg5dffhk2my1wFtPZjEGmEVTb3KhxeeH2+uHx+2FQKxBrVEOtkIe6NCIiagRymYTHL+mKOz7aAgkICjPSf//7+CVdIZdJdbz6zF1zzTUoKyvDY489huLiYvTu3RtLly49bgLw2UgSQjT9lXxCyGq1wmw2w2KxwGQyNdp2C6rsWHegAuv2V6BttB6DO8TgrZX7seyvEqgVMlx3bipuH5aBeFPjDzMSEbUkTqcTubm5SE9Ph0bTun/nheo6Mq3ViT47Df3+5ojMacirsCErvxpapQKdEkz4ZEMeXvktBy9c2ROVdjc2HqzC/DUHUWJ14tnLeyBCpwp1yURE1Agu7J6IUV0TsCG3EqU1TsQZjxxOaqqRGDo5BplTdLjKjo/+zMPijXmwOr3oGG/A3SPaY0NuJR79ZideuroXNh6sAgAs2VGMey/oyCBDRNSKyGUSMjOiQ10G/ReDzCmoqHXhX1/vwKq95ZDLJHSIM0AIYNmuYtwxPAMjusTBpFZiRKcYpMca4fH5cbC8FrUuD3q2iYBSwZPEiIiIGhODzCkotjixam85bj6vLQZ3iMX+sloMTI/G11sPY+L7G+Dy+HFB5zjcfUFHfLTuEIprnBjYLgrZxTWI1qvRNkYf6l0gIiJqVThEcAq2Ha7GjAs7weHx4eYFG5Fo0uDexVvx/pqDsDq8cHn9WLKzGDe8ux7j+7TBpoNVuHPRVvy6uxSHq+zwN8MdUomIiM4mDDKnINGsQbRBjU825CMjVo+yWhcOlNuOa2dz+/BNVgE+vOUcDO0Qg+V7SlHj9MLt84WgaiIiotaLQeYUdIg34ovNhwEAvVIisHZ/Rb1tV2aX4UCZDQPbReOOYRlYuqsYrftEdyIioubHIHMKlHIZymtcAACXxw+dqv4L3unVctjcXrywLBspUTrEGdSocXqbq1QiIqKzAoPMKYjQKXFuuyOXn16dU4ZRXeu/ouJlvdvgpx1Hbq/+3h8HML5PG+wqsjZLnURERGcLBplToFbIcdN56VArZLA6vcgpqcW156Qc165PSgQ6J5iw6dCR68nsL7NBo5RBcLIvERFRo2oxQea5556DJEm47777AsuGDx8OSZKCHrfffnvoigSQEqnF4ikD0SvZjFd+y0GUXo35kwdg8qC2uKJvG/z7ql64qn8KHvx8W+A1CSYNXB4/OsQbQ1g5ERGFq99//x2XXHIJkpKSIEkSvvnmm1CX1GK0iOvIbNy4EfPmzUPPnj2PW3fbbbfhySefDDzX6XTNWdpxtCoF2kRocP+ojlArZLA4vNheUI0r+yVj7op9eO6nPSirdQW95tYh6ZBJQKxRHaKqiYio0fh9wKG1QG0JYIgH0gYBsqa9SbDNZkOvXr1w8803Y8KECU36XuEm5EGmtrYW119/Pd555x08/fTTx63X6XRISEgIQWX1izNpYXf7sWpvGR7/bhcA4Pe95bh1SDr+zK0MtJNJwDUDUtAvLRJp0TpolLwbNhFRWPvrO2DpDMBa+L9lpiTgwueBrpc22duOHTsWY8eObbLth7OQB5lp06Zh3LhxGDlyZJ1BZtGiRfjoo4+QkJCASy65BI8++mjIR2UAQK2UoVeKGWatEhaHB5sPVcHp8eGxi7tCKZdBKZegVytQanUhNUoHnVoZ6pKJiOhM/PUd8NlEAMfMd7QWHVl+9QdNGmaobiENMosXL8aWLVuwcePGOtdfd911SEtLQ1JSErZv344ZM2YgOzsbX331Vb3bdLlccLn+d2jHam2aM4USzVrIALwzsT9mfrUD+8tqsavQin9+vQP3j+yAQRnR0KmV6NnGDKOWIYaIKKz5fUdGYo4NMcB/l0nA0oeBzuOa/DATBQtZkMnPz8e9996LX375BRqNps42U6ZMCfx/jx49kJiYiAsuuAD79+9HRkZGna+ZPXs2Zs2a1SQ1HyverEWUQYUFNw2A1emB1ycQbVAhzqiGSsEPMhFRq3FobfDhpOMIwFpwpF36kGYri0J41tLmzZtRWlqKvn37QqFQQKFQYNWqVXj11VehUCjgq+Ny/ueeey4AYN++ffVud+bMmbBYLIFHfn5+k+0DACjlcqRE6dAtyYxeKRFIjtQxxBARtTa1JY3bjhpNyEZkLrjgAuzYsSNo2U033YTOnTtjxowZkMuPDwNZWVkAgMTExHq3q1aroVbz7CAiImpEhvovgHpa7ajRhCzIGI1GdO/ePWiZXq9HdHQ0unfvjv379+Pjjz/GRRddhOjoaGzfvh33338/hg4dWudp2kRERE0mbdCRs5OsRah7nox0ZH3aoCZ5+9ra2qCjEbm5ucjKykJUVBRSU1Ob5D3DRYu5IN6xVCoVfv31V4wePRqdO3fGAw88gCuuuALff/99qEsjIqKzjUx+5BRrAIB0zMr/Pr/wuSab6Ltp0yb06dMHffr0AQBMnz4dffr0wWOPPdYk7xdOQn769d+tXLky8P8pKSlYtWpV6IohIiL6u66XHjnFus7ryDzXpKdeDx8+HELwNjd1aVFBhoiIqEXreumRU6yb+cq+VD8GGSIiolMhk/MU6xakxc6RISIiIjoZBhkiIiIKWwwyRETUKDgZlU5VY3xmGGSIiOiMKJVH7idnt9tDXAmFm6OfmaOfodPByb5ERHRG5HI5IiIiUFpaCgDQ6XSQpGOvtUL0P0II2O12lJaWIiIios6r+TcUgwwREZ2xhIQEAAiEGaKGiIiICHx2TheDDBERnTFJkpCYmIi4uDh4PJ5Ql0NhQKlUntFIzFEMMkRE1GjkcnmjfDkRNRQn+xIREVHYYpAhIiKisMUgQ0RERGGLQYaIiIjCFoMMERERhS0GGSIiIgpbDDJEREQUthhkiIiIKGwxyBAREVHYYpAhIiKisMUgQ0RERGGLQYaIiIjCFoMMERERhS0GGSIiIgpbDDJEREQUthhkiIiIKGwpQl0AERERtXzVdjfsbi9sLh8UchmcHh+W7y6B1enFyK7xaButQ6xR0+x1McgQERHRCRVW2SEAODw+yABkF1uxt6QWlTY3ftheiHm/H8C56VF49do+iDc1b5jhoSUiIiKqV7HFDrvHh8Ub8/HJ+jw4vH78tKMI32YVYF9pLR67uCumDm2H9bmV+GpLAfx+0az1McgQERFRncqtTlTUenDlm+vQxqzGjQPT4PP7MbZHIh4e2wUXdInD0z/uhkohw6W9kjB/TS7Kal3NWiODDBERER1HCAGHx4tP1h/C13cOQq/UKGw4WIn9ZTb4/MAbK/Zh0fo8vHRVL3yztQCX9WkDi8MDv2jeERnOkSEiOsv4/AJenx8qhQySJIW6HGqhrA4P3D7gtqEZePm3vfh6a2FgnUmjwLOX98BH6w/hn1/vwL0XdECxxYELuyXAoG7eaMEgQ0TUyjk9PlTUuuDxCZRYndh0qApROhX6pkVCp5Ih0ayFQs4Begrm8QuYtHJ8vqkgKMQAgNXpxQOfb8ObN/TDzQs2QqOUQwJw5/AMGDXKZq2Tn1wiolasyOLAHzllcHr9+NfXOzD9s21oH6tHlyQjal0e1Lp8KLY4Ql0mtUA+vx+1Th/eX5Nb53qX149dhRZ0TTShqNqBvmmRiNA1//gIR2SIiFqpCpsL+ZV21Dq9+GxjPga0jcRjl3TDB+sOIiu/GrFGNa7o2wZmnQpCACnR+lCXTC2IXwB+AOW17nrbFFY7EG1QoUuSCXa3F26fDDqVG0atqtnq5IgMEVErZbF7MOPLHYg1qXFFvzbIbB+D8W/8gUXr87Cr0IqV2WW4+5MsrN1XjgKOytAxhABkEpARa6i3TZdEE/x+geQILe5dvA0Hymxwef3NWCWDDBFRqySEwPI9peiWaEKty4flu0vx5Pd/IVKnwo0D03DrkHT0TY0AAMz7/QC8PqCw2h7aoqlFUSskuL0+/GNMpzrXxxrVSI3SYdb4bth4sAL7y2qRV2FD856zxCBDRNQqWewefJtViFuHtsO0RVvQNkaPq/qn4N6RHXCwwoYth6oxtGMs3pnYD/FGDfYUW9HMZ81SC2fWqgBIiDOqMHtCD0Tp/3e4qH9aJD68+RwUW5yQQcIT3+8GcGSEprkn+3KODBFRayQBbWN02FVggdcvkBihxZdbCvDLXyWBJlvyqpBk1uC5K3rgQFlts1+RlVo2hVwGrVIOmSTht90lmH15d+jVCijkMuwoqMb1767HjZlpeOW3vbC7fUiJ0iI9Rg+NUt68dTbruxERUbOI0KlwWe822FFgQYROicpad1CIOarQ4sSyXcWYOrQdNh2qRLRBCZ26+SZqUsuWHKlDQbUd917QAXcs2oLDVUfmUskk4Kr+KeiVHIFXfsvBOW0j8dwVPZEaggnjkhCtezDRarXCbDbDYrHAZDKFuhwiomaTX2lHXqUdH6w7CJ1Kga+3FtTZLkKnxOLbBsLj8yPOqEK8WdfMlVJLV2xxwO72ocbphd3tRZReBZ9f4HCVAwlmDaJ0KiRHNe7npqHf35wjQ0TUSsklIC1Kh7Qo3QkvG+/3C9S6vPhhexFsbj+sDk8zVknhIMGshUGtgEEth16twPI9pdh+2IJ2sQakRekaPcScCh5aIiJqpWKMalTZXLhqQAr2l9rwbVZhne1GdY3HroJq9E2LwN2fbMVr1/aBRimDStG8cx2oZYszaRBn0qDG6UF6tB4GjaJF3OKCIzJERK2USiGHyydQbnWhY7wBQzvEHNcmxqDC+N5t8MySbBRUOWDSKJBTWovSmua9gzGFD6NGCaNW2SJCDMARGSKiVk2vUiApUovbPtiMxy7ugrHdE/HJxjzY3T4M7RCDYR1j8ei3O+H2+fHO6lw8e3kPfJNVgE7xxlCXTtQgDDJERK1YtEGN8loX9pbUYF+pDWW1TgzpEAuVQoath6owf+3BwPVjiq1OROlV/717cas+D4RaEQYZIqJWTimXQZKAQ5W1yIg14pFvdtbZrluSCVaHBxf1SIRCxpkHFB74SSUiauViDGoM7RCLTzceRr+0SMQa1HW2u39kR+wrrUWUXgmztnmvzkp0uhhkiIhaOZNWiVmXdoNBrcAvfxXj7Yn9MKBtZGB9vEmNF67sidRoHWKMahhUChgZZMKO3y9QbXOj1uVGeY0ThyvtKLW2/puB8oJ4RERnicOVdvx5oBI+vw+9UyNhd/vg9vph1iphdbhR6/KhY7wxpNcEodNTYnHA5vahyuaGQi6Dx+eHWimHw+WFQaNAhE6JpIjw+rk29Pubc2SIiM4SyVE6XBmlg8Xuhs3tQ4nFCavTA6fHixijBmnResSbtaEuk07R4So7qu0eVNs9eOXXvdh4qAoAIJdJuLRXEoZ3ioVKLoPfj1YZUhlkiIjOMmadCmYdkBShhcPtAwBoVbz4XThye7wotjhQVuPCZ5sOI86kwYjOcVi7vxxOjx9fby2AUaPA4PbR8Pj8KK1xIs6oCXXZjYpBhojoLMYAE95KapyI0ath0h65sKFKIUNyhBazL++OwmoHnv5xDz7dmI8RneNwuNqBTgmt7/pAnOxLREQUpiTI4BPAztwKDOkQja6JRlQ73Nh8qAoqpQyvXtsba2aMgF6lwE3zN6KsxgW/v3VNjWWQISIiCkNVtS5IAHIKqnBe5ziUWF3YX2ZDvEmDzokmHK504vml2Xhh2R4AwPuT++PVX3NQ0srOZOKhJSIiojBTVetCjduHvcVW9GobjVq3DzJJgsvjh0ySMP2zbcjKrw60/2zTYfzfgBRc1icJDo8/dIU3AY7IEBERhRmX14fPNubDpFXA6vJCLpPg8PhwoLwWP+0sDgoxRy3emA+zTgWlrGXc7LGxMMgQERGFESEEbG4/tuZVQatU4GCFHQvWHMTnmw9jcPsYfLnlcL2vXbwhD2Zd67rYIYMMERFRGLE63ZAk4J8XdcH8NQdRWO1AcpQOueU2QJJgd/nqfa3N7YOvlV0Hl0GGiIgojAifgEYpQ6X9SKBZs68ctS4PuieZUGxxYGjHmHpfe3GPRDhc3mastukxyBAREbVgxVYnDpbZUFDlQF6FDTVuHzxeAYvdA4/PD71aga+2FODS3m3w8q97ccvgdjBpjj+XJyPWcOQ6MhLnyBAREVETs7s8KK9xwu31wSP8KKtx4rtthfjn1zuxr6wWPr+AVqnAqK7xyKt0oLLWhQdHd8bPu4qweEomLu2VCINagWi9ClOGtMPbE/sht8yGGH3ddz8PV7xpJBERUQtTVO2Ay+NDpd2DnNJaZOVXIdqgxnkZ0dAo5ZjywWbcOCgN52XEYPOhSuSW2/DzrhLMvb4valxeqOQSEs1aSNKRARi5JGH6p1l49dq+YXO/pYZ+fzPIEIUBh9uLshoXsktqEG/SwOH2YdOhSsQaNTg3PQrxJg00Sl5qnqg1OFxlx/r95eiSZMadi7bgYIU9sE4mAc9c3gOJZg2mfLAZ707ujxqHF2atAj4BrNxTgow4A/qmRsLjEzBpldieX43SWifGdU9CUmT43BSUd78maiVqnB78sL0Is77bhTdv6IdHv9mJbYctgfUKmYQ3ru+LYR1jGWaIwly1zYUlO4owtlsCnv1pT1CIAQC/AB79Zie+v3swxvVMhBDA+2sOoNruwZD2MRjVLR5KmQw3L9iI8zrE4p4R7dE7JQLJUTrIW9n1Y45qMXNknnvuOUiShPvuuy+wzOl0Ytq0aYiOjobBYMAVV1yBkpKS0BVJ1Mwqa13ILbdh2a4i/HjvYKzaWxYUYgDA6xe4c9EWlFidIaqSiBpLkdWFc9Oj4PT68ctfJVDKJVzaKwmvX9sHc6/vi1mXdkNGrAGbD1ZiQFokkswaPHlpd8y6tBs6JZrw6m/7cN276zEoIwZ3DGuHg2W1SGnFIQZoISMyGzduxLx589CzZ8+g5ffffz9+/PFHfP755zCbzbjrrrswYcIErFmzJkSVEjUPr8+PgxU2GNVybMurwj/GdMbhSgc25FZCLpPgO+ambz6/wOqccqRF60NUMRGdKSEErHYPFAoJfiGgVsjw+nV9YXG4oVbK4PEJxBnV+MeFneDzC3yy/hAWrD2Ih8Z2xsFyG/qlRWL6qI7wC4FYoxo+v0CXJDNkrTjEAC1gRKa2thbXX3893nnnHURGRgaWWywWvPfee5gzZw5GjBiBfv36Yf78+Vi7di3+/PPPEFZM1PRKrE4oZTJ4vEBmRgysDg8Kqx0Y1zMR70zsj1uHpB93BiVHZIjCmxBAQoQaP2wvglalwAtX9oRWJcenmw7jtg82485FW/Cvb3Yit9yGGIMKFocXe0uPnL00qms8tEo5TFolDGoFFDIJCWYN4kyaUO9Wkwt5kJk2bRrGjRuHkSNHBi3fvHkzPB5P0PLOnTsjNTUV69atq3d7LpcLVqs16EEUTkqtTqzMLsWhSju8wg9JAlIitejfNgpjusVjb7EFNpcX/7qoS9DrBmVEh6hiImoMMpkEmSTB6fZCLQc6xBnx4OfbsG5/RaBNpc2NZ37cjb0ltbh5cDoA4MfthfD5/BB+AZ1Kjmi9CjEGNcxaVah2pVmF9NDS4sWLsWXLFmzcuPG4dcXFxVCpVIiIiAhaHh8fj+Li4nq3OXv2bMyaNauxSyVqFn6/wPfbCpGZEQWlXI7FG/Ox+VAVEs1aXNIrCev2HTl8dMt5CdhTUoPOCUbsKa5B53gj0mN4WIko3EkApgxph1q3H3tLa3C4ylFnuzdX7sfr1/UBAEToVNCpFWgXFz5nJDWmkAWZ/Px83Hvvvfjll1+g0TTe0NfMmTMxffr0wHOr1YqUlJRG2z5RUyqtcaFzohFOj8CEN/+A0+P/75oqfLetEI+M64LV+8pQaXdjWMcYPHFpN3yztQD/d04KapweJJjPzl9kRK2Fw+ODSiGH1e7BzgJLve3yKu2Q/ff48sU9EuH2+utt29qF7NDS5s2bUVpair59+0KhUEChUGDVqlV49dVXoVAoEB8fD7fbjerq6qDXlZSUICEhod7tqtVqmEymoAdRuHD7fGgTocNDX27/W4j5nxeXZeOaAamY9/t+mLUqGNUK3HNBBpbtKsHzS7NR4/SEoGoiaiwySQaX1w+rw3PCyfsmrQLVdjfuHtEeUXolYgxnx2GkuoQsyFxwwQXYsWMHsrKyAo/+/fvj+uuvD/y/UqnEb7/9FnhNdnY28vLykJmZGaqyiZpMjdODZTuKUOPyYl9pbZ1tXF4/Km1uqBVyVDvcyK2ohQwSNuRWYnVOOWqdretmcERnG4NGDrvLi9wKGzJi9VAr6v6avnlQOkxaBS7v3QZGjRI6tbKZK205QnZoyWg0onv37kHL9Ho9oqOjA8tvueUWTJ8+HVFRUTCZTLj77ruRmZmJgQMHhqJkoiZVUetG5yQz7Ce5M63P74dcJkGjlKOo2glfCnC40g6zVtnqT7Mkau3iTVpU2dwoqLKjQ5wBr13bBw98tg01f/u9MLZ7Ai7r0waL/jyISeelQ6dqEVdSCZkWvff/+c9/IJPJcMUVV8DlcmHMmDGYO3duqMsiahI2lxcapQwer0BypLbOSX5y2ZH7pySaNdCr5GgfZ8RH6w9h0qA0SJIMMYbWdTM4orNRmwgdru6fiteX5+C6c1Px3uT+KK91o9blRbdEE2wuLw6W23BDZlvIJAGj9uwdjQF4ryWiFuNQhQ3LdhWjf1ok8qscmP7ZtuMufHfHsAzY3F5cf24q1HIJz/yUjfW5FXjnxv5oG6NH/FlwzQiis0GVzYUapxf5VQ7kV9qQaNYi3qjGoUo7OiWYoJRLcLl9MOqUiDW2zn/3vNcSUZiJMaiRX+nAsI5xcHlsWDxlIOavycWOAgvaRGhx06B0xJnUiNAq8eP2QvRMicQvf5VAkoBEs4YhhqgVidSrIZfJoJJL6BBngMPjg9vrh0wGVNrdUMslxBo1rTbEnAoGGaIWQq9WYNr57bF8Twky28Vg3YFymDRKXH9OGjonGKFRyrAquwwv/5YDmQS8cX1fAMDA9CiYdWf30DJRS1Zlc8Hu9gEAVIojh4ClYy/NXQeTVgmTVokqmxtevx8KmRx9UqKgVspg1PDf/FEMMkQtSIJZg4HtolBidaB/WhQe+WYn/HUc/L2gSzzW7quAXCZh5kVdzporeBKFk/JaF5xuHypsbvx5oALrcysRpVfi2nNSkWhSIymyYRexjNSrEAn+G69PyG9RQETBjBolXv4tB//5dS/+Na7LcfdUSjJrMG14Bg6U1+KL2zPRMd4YmkKJqF5VdhccLi8KLU5M+XATPtmQF7g/0uT3N+KrrYUorLaHusxWgSMyRC2MQi5DrcuLPw9UQq2Q471JA7A6pwwVtW70SY1A2xg9dCo5np/QE8lRulCXS0TH8Pr8KLW6IJdJ+D2nFO9NGoDyWhd+2F6EWIMab1zfFzsLLKhxelFqcSCOV+Q+IxyRIWphTBolxnY7cvXqb7IKMOWDTdhbUgO3z48P1x3C9vxqpEbpGWKIWqjcchve/n0/ZJKE/+ufgiU7CpGVX41Ygxrrcysw8f0N2F9Wi0qbGz4B2N28kOWZYJAhamHkMgnje7cJXHLc6xdYs68CS3cWw+r04Iq+ydCo5CGukojqYnd7MXfFPtw2JAOxeiWsTi9cXoElO4rw54EK3DAwDc9d0QPfbSuE3e3DrkILahy8tciZ4KElohYoOUqHr+4YhDdW7MO32woBAJf2SsJd57fnSAxRC2Z1eNE2Ro9alxulNhkmvr8B5bXuwPqt+dUY2iEGM8d2wScb8tArOQIdEzjP7UwwyBC1UKnReswa3x33jeoIAIjUqaBRciSGqCXz+HwY2iEW7WJ1ePan7KAQc9TvOeW4ol8yAED+3+MiLo8Pav77Pi0MMkQtmEYpRyInAhKFDSEAPwQsDi9+3F5Ub7tVe8tw3blpiDOq4PcJ+Fr3RfabFOfIEBERNZJKuxtGtRIyCTjRPVxlkoQuiUZU1Lrh8vrP+hs/ngkGGSIiokZi1iihkAMOlweX9Eqqt92EPm0AIfDttkJE6nmxuzPBIENERNRI4kxqqBVyvLn6IG4bko6EOu6BdmG3BETpVdh6sBwquQw8qHRmOJZFRETUSPRqJWqc3iM3el2wEZ9OHYilO4vx085i6NVyXNUvBb2SzVDJgDsX78DbN/aDz+cPddlhjUGGiIioEWmUclzcMxFvrtyPYS+uxP0XZODN6/tAJklwe31Ys68CH67PwzUDUuDx+RFjVIe65LAmCdG6p0pbrVaYzWZYLBaYTKZQl0NERGeB/Ao7sktqcM/irYE7XwPANf1TMLpbPFxePwqqHRjZJQ7pMYYQVtpyNfT7myMyREREjSw5SgtJBnw2NROF1Q64vX50iDegyu7Guv0V0KkVOC8jBvGG4+fQ0KlhkGlkfr+A7ETn3BERUasnSRKSI3VIjgTamDVw+/zYU1yDYqsTXRNN6JpkQqReDZ2aX8Nnij3YCPx+gfwqO37dXYINuVXo3zYSo7vGwahRIkrPY59ERGezSMOR74F4XtyySTDINIIDZbUoqXFgdNd4jOgcB4/Xj615FrSPM6DW5YVWqUAsJ3MRERE1Ol5H5gy4vT7sK7GixumFTqXEW6sOYENuJSpsbiRFaKFVymG1eeBwe1FtP/5+G0RERHRmOCJzBg6U1aLc5obT48e/f96Df13UFY98sxMHK+wAALVChqlD22FC32TYnB5E6Hj1RiIiosbEIHOaKm1u5Fc5cajChu+2FeKB0Z0x7eOtsDg8gTYurx+vLt+HBLMGwzvFhrBaIiKi1omHlk5TrfPIIaOkCC1qnF6U17iCQszfvb58HxwePwqr7M1cJRERUevGIHOa5DIJlXYPJAloE6HB3pKaetsWWpwoqnbwNu1ERESNjEHmNJl1SnSIM2D9gUqkROrRNan+qw4mmDTIq7TD5vbB7fXV246IiIhODYPMaTKolUiJ1CK/0o5rzklG+zgDTJq6pxzdPrwdPt90GC6PH8UWZzNXSkRE1HoxyJyB1Gg9Hr+kK9xeP5764S/855reSI783wWPlHIJkwe1Rf+0KORV2qFVyXm7diIiokbEs5bOkF8ASpmEzYeq8fSPu3Hn8PaIMajg9vmhUcrx044irDtQgXsvaI9apweRUbpQl0xERNRqMMicIbfPj7JaN+QyCbnlNvzz6x3HtXlnYgIqbW44PP56Dz8RERHRqeOhpTOkVsiwZl85RnaJq3N9pE4JnUoBo0YJnUoOtZJBhoiIqLEwyJyhGIMaNU4PrhmQim7HnLkUoVPi/ckDIAHYnl+FFB5WIiIialQcHjhDerUC00d3wp2LtuDq/imINapxsNyGWKMaXZNM2J5XhUiDGjcPbocYA28cSURE1JgYZBpBcqQO70zsj70lNcjKq0bbGD26JBjhcPswuGMs4oxqHlIiIiJqAvx2bSTxJg3iTRr0T4uEze2Fzw8kRmigVytDXRoREVGrxSDTyLQqBbQqdisREVFzOK3JvjfffDNqao6/t5DNZsPNN998xkURERERNcRpBZmFCxfC4XAct9zhcOCDDz4446KIiIiIGuKUjoFYrVYIISCEQE1NDTQaTWCdz+fDkiVLEBdX9/VUiIiIiBrbKQWZiIgISJIESZLQsWPH49ZLkoRZs2Y1WnFEREREJ3JKQWbFihUQQmDEiBH48ssvERUVFVinUqmQlpaGpKSkRi+SiIiIqC6nFGSGDRsGAMjNzUVKSgpkMl4YmIiIiELntM4TTktLAwDY7Xbk5eXB7XYHre/Zs+eZV0ZERER0EqcVZMrKynDTTTfhp59+qnO9z+c7o6KIiIiIGuK0jg3dd999qK6uxvr166HVarF06VIsXLgQHTp0wHfffdfYNRIRERHV6bRGZJYvX45vv/0W/fv3h0wmQ1paGkaNGgWTyYTZs2dj3LhxjV0nERER0XFOa0TGZrMFrhcTGRmJsrIyAECPHj2wZcuWxquOiIiI6AROK8h06tQJ2dnZAIBevXph3rx5KCgowFtvvYXExMRGLZCIiIioPqd1aOnee+9FUVERAODxxx/HhRdeiI8++ggqlQoLFy5s1AKJiIiI6iMJIcSZbsRut2PPnj1ITU1FTExMY9TVaKxWK8xmMywWC0wmU6jLISIiogZo6Pd3g0dkpk+f3uA3nzNnToPbEhEREZ2uBgeZrVu3NqidJEmnXQwRERHRqWhwkFmxYkVT1kFERER0ynizJCIiIgpbDDJEREQUthhkiIiIKGwxyBAREVHYYpAhIiKisMUgQ0RERGGLQYaIiIjCFoMMERERhS0GGSIiIgpbDDJEREQUthhkiIiIKGyFNMi8+eab6NmzJ0wmE0wmEzIzM/HTTz8F1g8fPhySJAU9br/99hBWTERERC1Jg28a2RSSk5Px3HPPoUOHDhBCYOHChRg/fjy2bt2Kbt26AQBuu+02PPnkk4HX6HS6UJVLRERELUxIg8wll1wS9PyZZ57Bm2++iT///DMQZHQ6HRISEkJRHhEREbVwLWaOjM/nw+LFi2Gz2ZCZmRlYvmjRIsTExKB79+6YOXMm7Hb7CbfjcrlgtVqDHkRERNQ6hXREBgB27NiBzMxMOJ1OGAwGfP311+jatSsA4LrrrkNaWhqSkpKwfft2zJgxA9nZ2fjqq6/q3d7s2bMxa9as5iqfiIiIQkgSQohQFuB2u5GXlweLxYIvvvgC7777LlatWhUIM3+3fPlyXHDBBdi3bx8yMjLq3J7L5YLL5Qo8t1qtSElJgcVigclkarL9ICIiosZjtVphNptP+v0d8iBzrJEjRyIjIwPz5s07bp3NZoPBYMDSpUsxZsyYBm2voR3R2nh9flgcHijlMpi0ylCXQ0REdEoa+v0d8kNLx/L7/UEjKn+XlZUFAEhMTGzGisJHjdODarsbLq8fTrcPm/OqsGxnMaYMy0D3NmbEGNShLpGIiKhRhTTIzJw5E2PHjkVqaipqamrw8ccfY+XKlVi2bBn279+Pjz/+GBdddBGio6Oxfft23H///Rg6dCh69uwZyrJbHLfXh0MVdnz45yHsLalBapQe43omoFdyBHomR6Cg2oGD5TZ4vH4kRmhDXS4REVGjCWmQKS0txcSJE1FUVASz2YyePXti2bJlGDVqFPLz8/Hrr7/i5Zdfhs1mQ0pKCq644go88sgjoSy5xRFCYMuhakx8fwPcPj8A4M8Dlfhicz5evLIXFm/Mw8aDVQCA3slmvHJtHyRH6iCXSaEsm4iIqFG0uDkyja21z5Eptjhx+dw1KLI4A8sUMgkCgEEtxzOX98BdH28NrBvSIQazLu2GdrGGEFRLRETUMGE7R4ZOTZXdjSKLE3FGNe4YnoGO8Ub4hUCUTgWv3w+dWoGPbzkH2w5b8OryfVidU47CagcidCpE6VWhLp+IiOiMMMiEOZ9fIMGkwQtX9sRTP/yFnNLawLqLuifgvlEdoVTIoFHJ8NWdg/DphjxYnV5U290MMkREFPYYZMJchE6JGRd2wr++2YH8SkfQuiU7i2HUKjGiUxwGZcRi5lfb8ci4rog3qWF3+0JUMRERUeNpMbcooNMkgBij+rgQc9TXWwrgh8CVb67Fk+O746st+XD7/NAoZbA63M1cLBGdjYosDuwptmLH4Wpk5VUht7wW1fa6L7NBdKoYZMKYw+PFe38cQF5F/fefcvv88PuBGpcXM77cjsnnpcPj9aO0xgW7i6MyRNR0HB4fth6qwrr9FbC7fPD5BbRKOYRfYFt+NQ6W18Lt5e8hOjMMMmGs0ubBF1sOo12svt42GqUM0n/PtN5VaEVBtRNalRxr9lXA6/c3U6VEdDYqrHLgcLUduwqtcHp8OFzlwLoDFSiyupAWbYDPL+odTSZqKM6RCWM+v4AkSdCrFWgfZ8C+v030Peqqfin4aUdR4LnT44PD7cfSncUY15NXSCaippFfacc7q/djdNcEnN8pFrd+sAkmjRJRehUKqh3onGDE7Ak9oFPJUev0wqDh1xGdHn5ywpheJceITnFYu68Cc67uhSe++wtb8o5c/E4hk3B5nzbonRqBf3y+DQCQZNagfawBbp8ferUcaNVXECKiUKlxepBTWoPMdjFIjtRi+2ELltwzGDaXDzKZBEBgX2ktvthyGNefk4ryGicMGl7bik4Pg0wYizaoMWlQGrKLazH1g82YN7EffH6BGqcXLq8fy/eU4B+fb4NfAJIEPDy2C1xeH0waBR4e2xkRWv74iajxVdS6IYOElCgtNEo5OsQb8eGfeVi1twxapRyX92mDwR1ikGDSwOM7cr0rotPFT0+YSzRpoVcpYfN4cdVb6/DlHZlIitBgT3ENth+2IEKnQtdEEyZmpuG7bYV47w877h/VEQlmDdRKTpEiosZV4/Sg1uVBpF6JkhonlHIZbl24CWW1/ztLaUeBBZntovHoxV3gE0CN04sYg/jvaA3RqWGQCXMJEVrAYsf7kwbg9o82w+72werw4r3VuRjdLQHRehVyy2146MvtqLZ7AAAOt+/IBGD+ziCiRmZ1eKFVKWBxeGBUK/HBukNBIeaodQcqUFDtREasHtklNTBq5Igz8aa2dOoYZFqBBLMOUTo1vr5zEFweP2pcXmzNr8bW/Oo62+8vsyE1SgevF4C6WUslolauxumGWavEpoO16Jpkwi+7S+pt+11WAR4Y3REWpwdOL8+ipNPDYwuthEopR0qUHhqlDAa1AooTDNHGGlWQSUdOzSYiakxKhRw+v4DV6YHP74dMqv93kUIuwerw4NuswhO2IzoRfpO1Qnq1HJfUc2q1Ua1AapQeSrkMSjl//ETUuHQqOX7ZXYL+aZFYuacMl/Sq/zIPV/RNwbzfc2F1eE74xxfRifCbrJUxa1VwuX24d1RH9Eo2B60zqhV49bo+UMklqBQyyBlkiKiRyWUSvthcAADIKa3BNf1TkRx5/NyXC7slINaoRn6VHSO7xCPWwOPcdHo4R6aVMWqV8PoFnG4vXru2D4qtLuwusiLWqEZ6jB7FFjuiDWokR+pCXSoRtUJHR1aqbG4M7hCLzYcq8fp1fbDlUDV+3FEEnUqOGwemoV2sHk989xcGtovG+N5J/MOKThuDTCsUqVcBehWsDjd0ajkSzUfudi2XSeiaFIF4kybUJRJRKxWpU+GqfsnYXmDB2v0VsLm8sLt9SI/R4ebz2iIrvxqfbcpH39RI6NVy3JiZBp1SHuqyKYwxyLRiJq0q1CUQ0VlGkiSM6Z6AqR9sxvTRHTHnl72Y/dOewPpuSSY8NKYT7v00C3Ov6wuldOTinkSni0GGiIgaVaJZi9ev74uV2aW4YWAaEkwa2FxexJs02FFgwX2fZuGSnolIjdIhIYKHuenMMMgQEVGjS43SYVz3BJTVuiCTSahxyPH11sNQK+VYcNM5SI7UciSGGgWDDBERNYloowbRRg0qa11IMGnwxCVdIZdzPgw1LgYZIiJqUlEceaEmxPPdiIiIKGwxyBAREVHYYpAhIiKisMUgQ0RERGGLQYaIiIjCFoMMERERhS0GGSIiIgpbDDJEREQUthhkiIiIKGwxyBAREVHYYpAhIiKisMUgQ0RERGGLQYaIiIjCFoMMERERhS0GGSIiIgpbDDJEREQUthhkiIiIKGwxyBAREVHYYpAhIiKisMUgQ0RERGGLQYaIiIjCFoMMERERhS0GGSIiIgpbDDJEREQUthhkiIiIKGwxyBAREVHYYpAhIiKisMUgQ0RERGGLQYaIiIjCFoMMERERhS0GGSIiIgpbilAXQERUl0qbCz6/QIROBaWcf3MRUd0YZIioRSm1OrHuQAUkCVDJZSitcSEj1oB4kxoGtRLRehWUCgYbIjqCQYaIWgS/XyC/yo7cchvSY/TYfrgary3fhxKrC50TjLh1cDt0iDegrMaJeJMGcSZNqEsmohaAQYaIQq7G6cGq7DI88f0ulNe6AQDd25jw3ISeWLW3FD2TI/Dyr3vhF0cCT5RBhZeu7IWO8UbIZFKIqyeiUJKEECLURTQlq9UKs9kMi8UCk8kU6nKIqA5r95fjunfWH7c8QqfEJ7cNxCcb8nB+pzjklNZCKZfQNlqPJdsLcffIjkiN0oWg4tCxubzweP0waBRQcO4QtWIN/f7miAwRhVSlzY3nf9pT57pquwdV9iMjNDct2BhYLpdJuH9kR+wtrkFKpBaSdPyoTHmtCx6vH2qlDFF6ddMU34wqal0orXGhtMYJq8MLq8ONIR1jkRKpq3P/ic4WDDJEFFIujw+7i2rqXNc/LRIKSUKty4tYoxplNS4AgM8v8NLP2Vhw0wC4vH5olPLAayptbqzbX45F6/MwonMcuiaaoFc7YNAoIAFwe/3QquQwaxXwCaCi1g23149InQpxJhWUcnmdtYTS4So75vy8Fz9sL4Lb50eSWYPbh2fgkw15uKJvMtrHGUNdIlHIMMgQUUjJ5RKSIjQ4WGEPLNOp5Jh1aTdolDK8vfoAFDIZnr28O3x+4N7FW+DyHjki/s3WAgxsFx14nd3txQfrDmLpzmI8dGFnPP/THmSXHAlJUXoV/jm2M4waBd5ZnYsInRLje7dBXqUdL/2cDb1KgYcu7IRLeyUhQqdq3k44gbIaJ6Yt2oJthy0AgB5tzJg8qC10ajk6J5hQZHFCo5TDoFa0qLqJmguDDBGFVJxRgzvPb4+HvtgeWPbchB6Yv/YgtuZVB5Yt3VWM8zvF4vPbB2HC3LXw+gWKLE54/jYiU17rxhsr9uGtG/rhvsVbYXV6oVHKcEmvJHROMOJwtQM92phRWO3ApkNV+HV3Ka47NxV3DMvA3JX78di3uxBv0mBMt4Tm7oZ6Ha5yBELM2O4JuKBLHJ5buicwOpVk1uC5K3oit9yGS3oltorDaESngjPFiCjkMttFY1JmGjRKGR4a0wkWhzcoxBy1IrsM+0prMX1URwDAgLaRcPv8gfUlVic6xhuxt6QGbSK1eOziLvh8aib0SgXeXZ2LX3eXoNjqxEtX94JSLiHWoMZXWw5jVNd4mLVH/q57YekelP83JLQER/tBr5Ljyn7JeOiL7YEQAwCFFiduWbgRbaN12F1Y9yE6otaMQYaIQk4IgaEdY/HTPUOQHKnFxxsO1dv2m60FGNwhBga1AkM6xAYFGbVChicu6YpeyRG4vE8bLN1Vgmd/2oN2cXrcP6ojckpq8cyPu2F1ePHhLefirhHt8dKVveDy+PH6tX0BAPvLbEHbDLU405ERlot7JeGzTYfhr+M8U49PYOnOYmw8VAGby9PMFRKFVkgPLb355pt48803cfDgQQBAt27d8Nhjj2Hs2LEAAKfTiQceeACLFy+Gy+XCmDFjMHfuXMTHx4ewaiJqbBU2NxLNGkz/fBtGdomH21t/kHB5/XB6fPh0yrn4dlsBpg5rH1gXpVehvMaFf369HTmltYHl6/ZX4Jz0KDx1WXdE6VV4c+V+bD5UFVifYNLgnYn90CvFjPIaNxQt4No0VocbVXY3erQx454R7XFBlzjklttxoLwWOSW1x7XPLqlB2xg93F4BHl2is0lIR2SSk5Px3HPPYfPmzdi0aRNGjBiB8ePHY9euXQCA+++/H99//z0+//xzrFq1CoWFhZgwYUIoSyaiJmBQKVBhc2NrXjU25Fbiwu71z1E5v1McdGoFfEIgs10sbC5vYJ3PL/DrntKgEHPUhtxKGNQKrN5bFhRiAKDY6sRtH2zGfRd0xB3DM0J+1eBqmxtVNg8OVtjh8vqQEqXDf37NwbJdxfjn2C74dMpAqOTBYSs1SocEswZGDac+0tklpEHmkksuwUUXXYQOHTqgY8eOeOaZZ2AwGPDnn3/CYrHgvffew5w5czBixAj069cP8+fPx9q1a/Hnn3+GsmwiamQqpQy7CqwAgN9zyjCmWwLijMcPK7SL0SMjzgCtQg65JOHdPw6gxvm/IFNW48JPO4rrfZ/FG/LgrevYDI6EGZkkwahRoNLmPsM9OjMWpwfPLd2NBJMW0xZtxT++2I6V2WX4aWcxblqwEYvW5+GHewZjdNd4yGUSJAm4vE8b9E2N5EXy6KzTYqK7z+fD559/DpvNhszMTGzevBkejwcjR44MtOncuTNSU1Oxbt06DBw4sM7tuFwuuFz/mwhntVqbvHYiOjOHKmyIMR45dVgIYMaX2/HRrefioz8P4eddJVDIJYztnojz2kdDr1Jg++EqCEj488CRUZajVAoZTnSx8pQoLS7v0wbjeiTiYIUd//klG2W1/wstRRYH3ly5H+ekRzXdzp6E1e7GjgILkiK0+DaroM7Rpe+2FeLKfsnommTC/52TghqHF6nROiRHakNQMVFohTy679ixAwaDAWq1Grfffju+/vprdO3aFcXFxVCpVIiIiAhqHx8fj+Li+v/imj17Nsxmc+CRkpLSxHtARGdKrZAjSq+CWasEAOwuqsGk9zZgVJd4zL2+L56b0APDO8UiwaRBrcuDGV/tgEGtwIQ+bRBj+N/ITZxRjUt6JQEAInVKXDMgBbcMTsfgDtF49vIe6JxgwoOfb8OtH2zC99sK8cb1fXHHsHaB10fpVTDrlFCFcFTD5vZhwZqDGNk1Hl9tKai33ZebDyNKp8Kcn/eiX9tIpMcYeIVfOiuFfESmU6dOyMrKgsViwRdffIFJkyZh1apVp729mTNnYvr06YHnVquVYYaohUuO0mFNThlevqY37vs0CxaHB0VWJ258fwPOax+NmWO7QKuU4cM/8/DRn4fg9QtoVXI8OKYT9H8bkTFrlRjaMRZ6tRwJJg2+21aInJJaTBmajp93FeOrrYWBtj4h8HtOOcb2SES1w4M9xTVQyCXcOTwDvhDegs4nBGpdXsgl6YRnT7l8fijlEnYWWpFXYUdy5Nl1zymio0IeZFQqFdq3P3LWQb9+/bBx40a88soruOaaa+B2u1FdXR00KlNSUoKEhPonAqrVaqjVnLJPFE7ijWp0SzLjhWV78O+re0EuSSi2OtEuVg+dSo6vtxbgkw15sLt9AIBYoxrtYvSIP2ZSrtcvsL+0BtV2D55dcuT+TQqZBJVCHggxsUY1Zk/ogaz8aizfXYrVe8tw3blpuH1YBqxONyAkHK60I84Ymgm/arkMmRnR2JpXjZFd4vDZpsN1tru4ZyIW/XnkNPVfd5dgUPuY5iyTqMUI+aGlY/n9frhcLvTr1w9KpRK//fZbYF12djby8vKQmZkZwgqJqLEp5DKkx+jQLtaA2z7YBJVcQoxehfl/HMQlr63Be3/kBkIMAPxzbGckRdQxH0QAXZPMmLtyf2BRm0gtsouPzJWTJOD5K3pi1ve78PryffiryIpthy2Y8eV2TP8sC0a1Cqv2lqHI6mzyfa6PTwAjOsdhwZpc3DI4HRE65XFterQxo0uCCTv+O0E6Ws9bE9DZK6QjMjNnzsTYsWORmpqKmpoafPzxx1i5ciWWLVsGs9mMW265BdOnT0dUVBRMJhPuvvtuZGZm1jvRl4jCl06txE2D2uLrrQX4PacchRYHxvVMRJtILT7fnA+rw4v0GD3uH9kRgztE1zkfxOr0Yt3+iuDtquSB0ZUh7WOwZl858isdx71286Fq7C624pz0KITwyBKiDSr8sL0G704egGU7i/HplIH4YN1B/Lq7FFqlHFf1T8HILnFYvqcEyZFa7CmuwYXdE0NXMFGIhTTIlJaWYuLEiSgqKoLZbEbPnj2xbNkyjBo1CgDwn//8BzKZDFdccUXQBfGIqHVKidLhm2nnYd7KfRjVJR47CiworLLjiUu6waBRIFqvRvtYA8x1jFIAQGmNM2heiU4lx8MXdoZMkqCUSzivfQwWrc+r9/0/3ZiPZy/rDimEF8RTymW4qEci9hRbUWRxYl9JLa4/Nw3X9E9FrduLn3YUYczLq/Hk+G6wOjx45rLuiDeH9ro3RKEkiROdq9gKWK1WmM1mWCwWmEymUJdDRA3g9PhQZXdDKZPg8gp4/X6oFXLEm9QnPDNne341nF4frp535FpTEzPTUGRxwi8EhnaIhcPjwycb8nDob3fa/rvzO8XhyfFdkRKlb5L9OhUlVgdeWrYXn28+fo6MVinHR7eeA71KgeRILQyauoMdUThr6Pd3i5sjQ0SkUcqRaNYixqhBm0gt0qL1SDBrTnp6caxRDSGOXBwOAAZlxOC33SX4bXcpNh+qwjlto3BJz/oPw4ztHo+8ekJOc4s3aTF9dEf0SY0IWq5VyvHmDX3RJkKLTglGhhg664X8rCUiosYSbVCh2uFB39QIDGgbBZVCCtxk8btthViyowgf3XouftxRjNxyW9Brz0mPQvs4Izwt6IaRiWYt3rmxPw5X2bG9wIJYgxqdE42INagZYIj+i0GGiFoNlUKO9Jgjh4We/XE37hiegXiTGiXWI1f79voFpi3agmcn9MDekhos310KpeLInBSDWo5Kmwup0aE/rPR3MUY1Yoxq9E6NDHUpRC0S58gQUatUWG2H1eHBX0U1mP7ZtuPW3zAwFRd0jscf+8qxYk8pDpTb8P1d5yFSp0JyFC8uRxRqDf3+5ogMEbVKSRE6GNRuCABzru6FV37LwaEKO/QqOa7ol4zMjGjc/tFmuLxHDiX1T4tEea0L6TGG0BZORKeEQYaIWi2TVnXkoVHitWv7QKOQo9btxVsr92Paoi2B+TNmrRIPXdgZaVFaGDT8tUgUTvgvlohavTaROshlEootTpg0Ctx5fnvEmdQ4XOXAeRkxGNE5DtEGFSJ0vEIuUbhhkCGis0KCWYsE8/9ua9AjyQS7xweNQg6lgleiIApXDDJEdFaSy2UwyhlgiMId/xUTERFR2GKQISIiorDFIENERERhi0GGiIiIwhaDDBEREYUtBhkiIiIKWwwyREREFLYYZIiIiChsMcgQERFR2GKQISIiorDFIENERERhi0GGiIiIwhaDDBEREYUtBhkiIiIKWwwyREREFLYYZIiIiChsMcgQERFR2GKQISIiorDFIENERERhi0GGiIiIwhaDDBEREYUtBhkiIiIKWwwyREREFLYYZIiIiChsMcgQERFR2GKQISIiorDFIENERERhi0GGiIiIwhaDDBEREYUtBhkiIiIKWwwyREREFLYYZIiIiChsMcgQERFR2GKQISIiorDFIENERERhi0GGiIiIwhaDDBEREYUtBhkiIiIKWwwyREREFLYUoS6AiIgoFPx+Aa/fD0mSoJTz7/pwxSBDRERnlfIaB3x+QCWXwev3w+0TkCSBCK0aOjW/FsMNIygREZ01Cqrs8AsJTo8fFTY3iq0ueH0CMkiotrvh9PhCXSKdIkZPIiI6KxRbHHB5/fg2qxAr95bBrFViQp82SIoA3D4fkkwa2FweaJTyUJdKp4BBhoiIWj2rwwWb24d7P9mKK/ol4/6RHaBSyKBXKVBlc0Muk+DxAwqfP9Sl0ilikCEiolbP4fajsMqOpy/vjhlf7EB2SQ0AIEKnxAOjOmJwh1i4vV6oFaoQV0qninNkiIioVbM43NheYAEkCRPf3xAIMQBQbffg0W93YXt+NdQKBZQyKYSV0ulgkCEiolbL7xf4bXcp/vNLDnYXWWF1eOts984fB5BXaQeYY8IOgwwREbVaJVYnnl2yG6nROuwstNbbLru4Bh6/QI2z7qBDLReDDBERtVoWpwfltW6U1bjQLkZfb7uUSB0cbi9EM9ZGjYNBhoiIWi3Vf6/YuyWvCgPaRgWeH+uGgWnQqeRQcI5M2GGQISKiVitKr8I5bSMhBDB35T68PbEfTNr/nbArk4AbB6YhJUoLg1qBBLMmhNXS6eDp10RE1GpF6FR4dkIPXP/ueqzZVwGX24fPpmSiwuZGld2NRLMGckmCJEmINaqhUvBieOGGQYaIiFq1jFgDPp2SiS15VcjKr8b63EqM6ByL9Ggt3D7A6/NDp5YjKUIb6lLpNDDIEBFRqyZJElKjdFApJQxoG4XdRVb8trsUmRnRgADUShmMamWoy6TTxDkyRETU6slkEpLMOijlEtpG6dCvbSQcbh8UcgmROhWMWgaZcBXSIDN79mwMGDAARqMRcXFxuOyyy5CdnR3UZvjw4ZD+e/zy6OP2228PUcVERBTOEsxadEgwon2sEV2TzMiIM8Ks420JwllIg8yqVaswbdo0/Pnnn/jll1/g8XgwevRo2Gy2oHa33XYbioqKAo8XXnghRBUTEVG4kyQJWpUcKgUPSrQGIZ0js3Tp0qDnCxYsQFxcHDZv3oyhQ4cGlut0OiQkJDR3eURERNTCtag4arFYAABRUVFByxctWoSYmBh0794dM2fOhN1uD0V5RERE1MK0mLOW/H4/7rvvPpx33nno3r17YPl1112HtLQ0JCUlYfv27ZgxYways7Px1Vdf1bkdl8sFl8sVeG611n9vDSIiIgpvLSbITJs2DTt37sQff/wRtHzKlCmB/+/RowcSExNxwQUXYP/+/cjIyDhuO7Nnz8asWbOavF4iIiIKvRZxaOmuu+7CDz/8gBUrViA5OfmEbc8991wAwL59++pcP3PmTFgslsAjPz+/0eslIiKiliGkIzJCCNx99934+uuvsXLlSqSnp5/0NVlZWQCAxMTEOter1Wqo1erGLJOIiIhaqJAGmWnTpuHjjz/Gt99+C6PRiOLiYgCA2WyGVqvF/v378fHHH+Oiiy5CdHQ0tm/fjvvvvx9Dhw5Fz549Q1k6ERERtQCSEEKE7M2lum+XPn/+fEyePBn5+fm44YYbsHPnTthsNqSkpODyyy/HI488ApPJ1KD3sFqtMJvNsFgsDX4NERERhVZDv79DfmjpRFJSUrBq1apmqoaIiIjCTYuY7EtERER0OlrM6ddN5eioD68nQ0REFD6Ofm+f7OhNqw8yNTU1AI4cpiIiIqLwUlNTA7PZXO/6kE72bQ5+vx+FhYUwGo31Ti5u6axWK1JSUpCfn88Jy42I/do02K9Nh33bNNivTeNM+1UIgZqaGiQlJUEmq38mTKsfkZHJZCe9yF64MJlM/EfWBNivTYP92nTYt02D/do0zqRfTzQScxQn+xIREVHYYpAhIiKisMUgEwbUajUef/xx3nqhkbFfmwb7temwb5sG+7VpNFe/tvrJvkRERNR6cUSGiIiIwhaDDBEREYUtBhkiIiIKWwwyREREFLYYZFqYZ555BoMGDYJOp0NERMRx67dt24Zrr70WKSkp0Gq16NKlC1555ZXj2q1cuRJ9+/aFWq1G+/btsWDBgqYvvgU7Wb8CwD333IN+/fpBrVajd+/edbbZvn07hgwZAo1Gg5SUFLzwwgtNV3QYaEi/5uXlYdy4cdDpdIiLi8M//vEPeL3eoDb8vJ7Yli1bMGrUKERERCA6OhpTpkxBbW1tUJuG9DMF27t3L8aPH4+YmBiYTCYMHjwYK1asCGrDfj11K1euhCRJdT42btwYaNdYv08ZZFoYt9uNq666CnfccUed6zdv3oy4uDh89NFH2LVrF/71r39h5syZeP311wNtcnNzMW7cOJx//vnIysrCfffdh1tvvRXLli1rrt1ocU7Wr0fdfPPNuOaaa+pcZ7VaMXr0aKSlpWHz5s148cUX8cQTT+Dtt99uipLDwsn61efzYdy4cXC73Vi7di0WLlyIBQsW4LHHHgu04ef1xAoLCzFy5Ei0b98e69evx9KlS7Fr1y5Mnjw50KYh/UzHu/jii+H1erF8+XJs3rwZvXr1wsUXX4zi4mIA7NfTNWjQIBQVFQU9br31VqSnp6N///4AGvn3qaAWaf78+cJsNjeo7Z133inOP//8wPOHHnpIdOvWLajNNddcI8aMGdOYJYalhvTr448/Lnr16nXc8rlz54rIyEjhcrkCy2bMmCE6derUyFWGn/r6dcmSJUImk4ni4uLAsjfffFOYTKZAP/LzemLz5s0TcXFxwufzBZZt375dABA5OTlCiIb1MwUrKysTAMTvv/8eWGa1WgUA8csvvwgh2K+Nxe12i9jYWPHkk08GljXm71OOyLQCFosFUVFRgefr1q3DyJEjg9qMGTMG69ata+7SWpV169Zh6NChUKlUgWVjxoxBdnY2qqqqQlhZy7Vu3Tr06NED8fHxgWVjxoyB1WrFrl27Am34ea2fy+WCSqUKummeVqsFAPzxxx8AGtbPFCw6OhqdOnXCBx98AJvNBq/Xi3nz5iEuLg79+vUDwH5tLN999x0qKipw0003BZY15u9TBpkwt3btWnz66aeYMmVKYFlxcXHQPzwAiI+Ph9VqhcPhaO4SW436+vXoOjpeQ/qMn9cTGzFiBIqLi/Hiiy/C7XajqqoKDz/8MACgqKgIAD+bp0OSJPz666/YunUrjEYjNBoN5syZg6VLlyIyMhIA+7WxvPfeexgzZkzQDZwbs28ZZJrBww8/XO/Ep6OPPXv2nPJ2d+7cifHjx+Pxxx/H6NGjm6Dylq2p+vVsx35tHg3t527dumHhwoX497//DZ1Oh4SEBKSnpyM+Pj5olIaOaGi/CiEwbdo0xMXFYfXq1diwYQMuu+wyXHLJJYGASMFO53fD4cOHsWzZMtxyyy1NVpeiybZMAQ888EDQxLy6tGvX7pS2+ddff+GCCy7AlClT8MgjjwStS0hIQElJSdCykpISmEymwJB0a9AU/Xoi9fXr0XWtRWP2a0JCAjZs2BC07Ng+O1s+r8c6lX6+7rrrcN1116GkpAR6vR6SJGHOnDmB9Q3p57NFQ/t1+fLl+OGHH1BVVQWTyQQAmDt3Ln755RcsXLgQDz/8MPv1GKfzu2H+/PmIjo7GpZdeGrS8MX+fMsg0g9jYWMTGxjba9nbt2oURI0Zg0qRJeOaZZ45bn5mZiSVLlgQt++WXX5CZmdloNbQEjd2vJ5OZmYl//etf8Hg8UCqVAI70a6dOnQJD0a1BY/ZrZmYmnnnmGZSWliIuLg7AkT4zmUzo2rVroM3Z8Hk91un089Gh9/fffx8ajQajRo0C0LB+Pls0tF/tdjsAHDeqJZPJ4Pf7AbBfj3Wqn1khBObPn4+JEycGfmce1ai/TxthQjI1okOHDomtW7eKWbNmCYPBILZu3Sq2bt0qampqhBBC7NixQ8TGxoobbrhBFBUVBR6lpaWBbRw4cEDodDrxj3/8Q+zevVu88cYbQi6Xi6VLl4Zqt0LuZP0qhBA5OTli69atYurUqaJjx46BNkdn1VdXV4v4+Hhx4403ip07d4rFixcLnU4n5s2bF6rdCrmT9avX6xXdu3cXo0ePFllZWWLp0qUiNjZWzJw5M7ANfl5P7rXXXhObN28W2dnZ4vXXXxdarVa88sorgfUN6WcKVlZWJqKjo8WECRNEVlaWyM7OFg8++KBQKpUiKytLCMF+PVO//vqrACB279593LrG/H3KINPCTJo0SQA47rFixQohxJFTg+tan5aWFrSdFStWiN69ewuVSiXatWsn5s+f3+z70pKcrF+FEGLYsGF1tsnNzQ202bZtmxg8eLBQq9WiTZs24rnnnmv+nWlBGtKvBw8eFGPHjhVarVbExMSIBx54QHg8nqDt8PN6YjfeeKOIiooSKpVK9OzZU3zwwQfHtWlIP1OwjRs3itGjR4uoqChhNBrFwIEDxZIlS4LasF9P37XXXisGDRpU7/rG+n0qCSHEqY3hEBEREbUMnPJOREREYYtBhoiIiMIWgwwRERGFLQYZIiIiClsMMkRERBS2GGSIiIgobDHIEBERUdhikCGiFmH48OG47777Ql0GEYUZBhkiIiIKWwwyREREFLYYZIioxamqqsLEiRMRGRkJnU6HsWPHIicnJ7B+wYIFiIiIwLJly9ClSxcYDAZceOGFKCoqCrTxer245557EBERgejoaMyYMQOTJk3CZZddFoI9IqKmwiBDRC3O5MmTsWnTJnz33XdYt24dhBC46KKL4PF4Am3sdjteeuklfPjhh/j999+Rl5eHBx98MLD++eefx6JFizB//nysWbMGVqsV33zzTQj2hoiaEoMMEbUoOTk5+O677/Duu+9iyJAh6NWrFxYtWoSCgoKgIOLxePDWW2+hf//+6Nu3L+666y789ttvgfWvvfYaZs6cicsvvxydO3fG66+/joiIiObfISJqUgwyRNSi7N69GwqFAueee25gWXR0NDp16oTdu3cHlul0OmRkZASeJyYmorS0FABgsVhQUlKCc845J7BeLpejX79+zbAHRNScGGSIKCwplcqg55IkQQgRomqIKFQYZIioRenSpQu8Xi/Wr18fWFZRUYHs7Gx07dq1Qdswm82Ij4/Hxo0bA8t8Ph+2bNnS6PUSUWgpQl0AEdHfdejQAePHj8dtt92GefPmwWg04uGHH0abNm0wfvz4Bm/n7rvvxuzZs9G+fXt07twZr732GqqqqiBJUhNWT0TNjSMyRNTizJ8/H/369cPFF1+MzMxMCCGwZMmS4w4nnciMGTNw7bXXYuLEicjMzITBYMCYMWOg0WiasHIiam6S4EFlIjoL+P1+dOnSBVdffTWeeuqpUJdDRI2Eh5aIqFU6dOgQfv75ZwwbNgwulwuvv/46cnNzcd1114W6NCJqRDy0REStkkwmw4IFCzBgwACcd9552LFjB3799Vd06dIl1KURUSPioSUiIiIKWxyRISIiorDFIENERERhi0GGiIiIwhaDDBEREYUtBhkiIiIKWwwyREREFLYYZIiIiChsMcgQERFR2GKQISIiorD1//8CqVvZDPGrAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gaydfprideplot = gaydf.copy()\n",
+    "\n",
+    "plot3 = sns.scatterplot(data=gaydfprideplot, x=\"long\", y=\"lat\", hue=\"pride parade index\")\n",
+    "_ = plot3.set(xlabel=\"long\", ylabel=\"lat\", title = \"heatmap of pride index\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "it seemed that most communities did not have a pride parade, but a number of them still did"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gaydfgaybarsplot = gaydf.copy()\n",
+    "\n",
+    "plot4 = sns.scatterplot(data=gaydfprideplot, x=\"long\", y=\"lat\", hue=\"gay bars index\")\n",
+    "_ = plot4.set(xlabel=\"long\", ylabel=\"lat\", title = \"heatmap of gay bars index\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "it looks like certain communities had large numbers of bars but most didn't\n",
+    "\n",
+    "the next step is to find the distribution of gay bars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot5 = sns.histplot(gaydfgaybarsplot[\"gay bars index\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "looks like the hypothesis was right - most communities with gay bars had multiple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pride parade index</th>\n",
+       "      <th>gay bars index</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2328.000000</td>\n",
+       "      <td>2328.000000</td>\n",
+       "      <td>2328.000000</td>\n",
+       "      <td>2328.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.017612</td>\n",
+       "      <td>0.118127</td>\n",
+       "      <td>38.044304</td>\n",
+       "      <td>-91.221236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.131563</td>\n",
+       "      <td>0.861863</td>\n",
+       "      <td>5.148365</td>\n",
+       "      <td>18.533499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>25.572213</td>\n",
+       "      <td>-123.118977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>34.021932</td>\n",
+       "      <td>-105.049099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.899977</td>\n",
+       "      <td>-87.494097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>40.912413</td>\n",
+       "      <td>-74.288743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>47.916786</td>\n",
+       "      <td>-70.758184</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       pride parade index  gay bars index          lat         long\n",
+       "count         2328.000000     2328.000000  2328.000000  2328.000000\n",
+       "mean             0.017612        0.118127    38.044304   -91.221236\n",
+       "std              0.131563        0.861863     5.148365    18.533499\n",
+       "min              0.000000        0.000000    25.572213  -123.118977\n",
+       "25%              0.000000        0.000000    34.021932  -105.049099\n",
+       "50%              0.000000        0.000000    39.899977   -87.494097\n",
+       "75%              0.000000        0.000000    40.912413   -74.288743\n",
+       "max              1.000000       17.000000    47.916786   -70.758184"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gaydfhybridplot2 = pd.merge(gaydfprideplot, gaydfgaybarsplot)\n",
+    "\n",
+    "gaydfhybridplot2.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2aecc151110>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# del gaydfhybridplot2['zip']; del gaydfhybridplot2['ZIP']\n",
+    "\n",
+    "sns.pairplot(gaydfhybridplot2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2aecd0e1750>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gaydflight2 = gaydfhybridplot2.copy()\n",
+    "\n",
+    "del gaydflight2['lat']; del gaydflight2['long']\n",
+    "\n",
+    "sns.pairplot(gaydflight2)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "to find a correlation we remove the zero value & find correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x2aebcea1e50>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LiYuLg0ql0r98fX2NrpGIxOHxRmQ9ogPcy8sLGRkZNYYfP34cnTt3NktRdamqqkK7du2wdetW9OvXDxMmTMA//vEPbNmypc5pli5dCo1Go39du3bNojUStWQ83oisp1G/Bz537lzs2LEDMpkMN2/eRHJyMhYsWIDly5cbPR+1Wg17e3vcunXLYPitW7fg5eVV6zTe3t5wcHCAvb29ftijjz6K3Nxc6HQ6ODo61phGLpdDLpcbXRcRNR6PNyLrEX0GvmTJEjzzzDMYOnQoCgsLMXjwYMycORPPP/88Zs+ebfR8HB0d0a9fPyQmJuqHVVVVITExEWFhYbVO8/jjjyMjI8PgN8kvX74Mb2/vWsObiIiouRId4DKZDP/4xz9w7949/PLLL/jpp59w584drFq1SvTCY2JisG3bNuzevRu//fYbXnzxRRQVFWHatGkAgClTpmDp0qX69i+++CLu3buHuXPn4vLlyzh48CDWrFmDl19+WfSyiYiIpEz0JfQHHB0d0aNHD5MWPmHCBNy5cwcrVqxAbm4uevfujYSEBP2NbdnZ2bCz++//MXx9fXH48GHMnz8fQUFBaN++PebOnYvFixebVAcREZHUGBXg48aNM3qGX3zxhagCoqOjER0dXeu4pKSkGsPCwsLw008/iVoGERFRc2PUJfTqj4UolUokJibi1KlT+vGnT59GYmIiVCqVxQolIiKi/zLqDHznzp36vxcvXoy//e1v2LJli/5u8MrKSrz00ktQKpWWqZKIiIgMiL6JbceOHViwYIHBo1z29vaIiYmpswtUIiIiMi/RAV5RUYGLFy/WGH7x4kWDx7uIiIjIckTfhT5t2jTMmDEDmZmZ6N+/PwDg5MmTWLt2rf7xLyIiIrIs0QH+5ptvwsvLC2+99RZycnIA/NFD2sKFC/HKK6+YvUAiIiKqSXSA29nZYdGiRVi0aJH+l4Z48xoREZF1NbojF4DBTUREZCuNCvDPP/8c//rXv5CdnQ2dTmcwTsxvghMREVHjiL4L/Z133sG0adPg6emJM2fOoH///vDw8MCVK1cwYsQIS9RIREREDxEd4O+99x62bt2Kd999F46Ojli0aBG+++47zJkzBxqNxhI1EhER0UNEB3h2djYGDBgAAHByckJBQQEAYPLkyfj000/NWx0RERHVSnSAe3l54d69ewCAjh076n9YJCsrC4IgmLc6IiIiqpXoAP+f//kffPXVVwD+6NRl/vz5GDZsGCZMmICnnnrK7AUSERFRTaLvQt+6dau+y9SXX34ZHh4eOHHiBEaPHo3nn3/e7AUSERFRTaICvKKiAmvWrMH06dPRoUMHAMDEiRMxceJEixRHREREtRN1Cb1Vq1ZYv349KioqLFUPERERGUH0d+BDhw7FDz/8YIlaiIiIyEiivwMfMWIElixZgvPnz6Nfv35wcXExGD969GizFUdERES1Ex3gL730EgAgPj6+xjiZTIbKykrTqyIiIqJ6iQ7wB3egExERke2I/g6ciIiIbK9Rv0ZWVFSEH374odZfI5szZ45ZCiOi/8rOzkZeXp5F5q1Wq9GxY0eLzJuILEd0gJ85cwYjR45EcXExioqK0KZNG+Tl5cHZ2Rnt2rVjgBOZWXZ2Nrp3fxQlJcUWmb+TkzMuXvyNIU4kMaIDfP78+YiMjMSWLVugUqnw008/wcHBAX//+98xd+5cS9RI1KLl5eWhpKQYodNjofT2N+u8tTlXcXLHa8jLy2OAE0mM6ABPS0vD+++/Dzs7O9jb26OsrAydO3fG+vXrERUVhXHjxlmiTqIWT+ntjzYdu9m6DCJqIkTfxObg4AA7uz8ma9euHbKzswEAKpUK165dM291REREVCvRAd6nTx/8/PPPAIAhQ4ZgxYoV+PjjjzFv3jz06tWrUUVs2rQJ/v7+UCgUCA0NRUpKilHT7dmzBzKZDGPHjm3UcomIiKRKdICvWbMG3t7eAIDVq1ejdevWePHFF3Hnzh1s3bpVdAF79+5FTEwMYmNjkZqaiuDgYEREROD27dv1Tnf16lUsWLAAgwYNEr1MIiIiqRMd4CEhIXjyyScB/HEJPSEhAVqtFqdPn0ZwcLDoAuLj4zFr1ixMmzYNPXr0wJYtW+Ds7IwdO3bUOU1lZSWeffZZvPbaa+jcubPoZRIREUldoztyuX37No4dO4Zjx47hzp07jZqHTqfD6dOnER4e/t+C7OwQHh6O5OTkOqd7/fXX0a5dO8yYMaPBZZSVlUGr1Rq8iMgyeLwRWY/oAC8oKMDkyZPRvn17DBkyBEOGDIGPjw/+/ve/Q6PRiJpXXl4eKisr4enpaTDc09MTubm5tU5z/PhxfPDBB9i2bZtRy4iLi4NKpdK/fH19RdVIRMbj8UZkPaIDfObMmTh58iS+/vpr5OfnIz8/H19//TVOnTqF559/3hI16j34z8O2bdugVquNmmbp0qXQaDT6F++UJ7IcHm9E1iP6OfCvv/4ahw8fxsCBA/XDIiIisG3bNgwfPlzUvNRqNezt7XHr1i2D4bdu3YKXl1eN9pmZmbh69SoiIyP1wx78uEqrVq1w6dIldOnSxWAauVwOuVwuqi4iahweb0TWI/oM3MPDAyqVqsZwlUqF1q1bi5qXo6Mj+vXrh8TERP2wqqoqJCYmIiwsrEb77t274/z580hLS9O/Ro8ejSeffBJpaWm8XEdERC2G6DPwZcuWISYmBh999JH+LDk3NxcLFy7E8uXLRRcQExODqKgohISEoH///ti4cSOKioowbdo0AMCUKVPQvn17xMXFQaFQ1HjW3N3dHQAa/Qw6ERGRFIkO8M2bNyMjIwMdO3bU952cnZ0NuVyOO3fu4P3339e3TU1NbXB+EyZMwJ07d7BixQrk5uaid+/eSEhI0N/Ylp2dre/5jYiIiP4gOsAt0etZdHQ0oqOjax2XlJRU77S7du0yez1ERERNnegAj42NtUQdREREJAKvTRMREUkQA5yIiEiCGOBEREQSxAAnIiKSoEYHuE6nw6VLl1BRUWHOeoiIiMgIogO8uLgYM2bMgLOzM3r27Ins7GwAwOzZs7F27VqzF0hEREQ1iQ7wpUuX4uzZs0hKSoJCodAPDw8Px969e81aHBEREdVO9HPg+/fvx969e/HYY49BJpPph/fs2ROZmZlmLY6IiIhqJ/oM/M6dO2jXrl2N4UVFRQaBTkRERJYjOsBDQkJw8OBB/fsHob19+/Zaf0GMiIiIzE/0JfQ1a9ZgxIgRuHDhAioqKvD222/jwoULOHHiBH744QdL1EhEREQPEX0GPnDgQKSlpaGiogKBgYH49ttv0a5dOyQnJ6Nfv36WqJGIiIgeIvoMHAC6dOmCbdu2mbsWIiIiMpJRAa7Vao2eoVKpbHQxREREZByjAtzd3d3oO8wrKytNKoiIiIgaZlSAHzlyRP/31atXsWTJEkydOlV/13lycjJ2796NuLg4y1RJREREBowK8CFDhuj/fv311xEfH49Jkybph40ePRqBgYHYunUroqKizF8lERERGRB9F3pycjJCQkJqDA8JCUFKSopZiiIiIqL6iQ5wX1/fWu9A3759O3x9fc1SFBEREdVP9GNkGzZswPjx4/HNN98gNDQUAJCSkoL09HTs27fP7AUSERFRTaLPwEeOHInLly8jMjIS9+7dw7179xAZGYnLly9j5MiRlqiRiIiIHtKojlx8fX2xZs0ac9dCRERERjIqwM+dO4devXrBzs4O586dq7dtUFCQWQojIiKiuhkV4L1790Zubi7atWuH3r17QyaTQRCEGu1kMhk7ciEiIrICowI8KysLbdu21f9NREREtmVUgPv5+QEAysvL8dprr2H58uXo1KmTRQsjIiKiuom6C93BwcEij4pt2rQJ/v7+UCgUCA0NrbdDmG3btmHQoEFo3bo1WrdujfDwcHYgQ0RELY7ox8jGjh2L/fv3m62AvXv3IiYmBrGxsUhNTUVwcDAiIiJw+/btWtsnJSVh0qRJOHLkCJKTk+Hr64s///nPuHHjhtlqIiIiaupEP0YWEBCA119/HT/++CP69esHFxcXg/Fz5swRNb/4+HjMmjUL06ZNAwBs2bIFBw8exI4dO7BkyZIa7T/++GOD99u3b8e+ffuQmJiIKVOmiFwbIiIiaRId4B988AHc3d1x+vRpnD592mCcTCYTFeA6nQ6nT5/G0qVL9cPs7OwQHh6O5ORko+ZRXFyM8vJytGnTxujlEhERSZ3oADfnXeh5eXmorKyEp6enwXBPT09cvHjRqHksXrwYPj4+CA8Pr3V8WVkZysrK9O+1Wm3jCyaievF4I7Ie0d+BVycIQq3Pg1vL2rVrsWfPHnz55ZdQKBS1tomLi4NKpdK/+IMrRJbD443IehoV4B988AF69eoFhUIBhUKBXr16Yfv27aLno1arYW9vj1u3bhkMv3XrFry8vOqd9s0338TatWvx7bff1tv729KlS6HRaPSva9euia6TiIzD443IekRfQl+xYgXi4+Mxe/ZshIWFAfjjN8Lnz5+P7OxsvP7660bPy9HREf369UNiYiLGjh0LAKiqqkJiYiKio6PrnG79+vVYvXo1Dh8+XOtvk1cnl8shl8uNromIGo/HG5H1iA7wzZs3Y9u2bZg0aZJ+2OjRoxEUFITZs2eLCnAAiImJQVRUFEJCQtC/f39s3LgRRUVF+rvSp0yZgvbt2yMuLg4AsG7dOqxYsQKffPIJ/P39kZubCwBwdXWFq6ur2NUhIiKSJNEBXl5eXutZb79+/VBRUSG6gAkTJuDOnTtYsWIFcnNz0bt3byQkJOhvbMvOzoad3X+v9G/evBk6nQ5//etfDeYTGxuLlStXil4+ERGRFIkO8MmTJ2Pz5s2Ij483GL5161Y8++yzjSoiOjq6zkvmSUlJBu+vXr3aqGUQERE1J436PfAPPvgA3377LR577DEAwMmTJ5GdnY0pU6YgJiZG3+7hkCciIiLzEB3gv/zyC/r27QsAyMzMBPDH3eRqtRq//PKLvp1MJjNTiURERPQw0QF+5MgRS9RBREREIpjUkQsRERHZBgOciIhIghjgREREEsQAJyIikiAGOBERkQQxwImIiCSIAU5ERCRBDHAiIiIJYoATERFJEAOciIhIghjgREREEsQAJyIikiAGOBERkQQxwImIiCSIAU5ERCRBDHAiIiIJYoATERFJEAOciIhIghjgREREEsQAJyIikiAGOBERkQQxwImIiCSIAU5ERCRBDHAiIiIJahIBvmnTJvj7+0OhUCA0NBQpKSn1tv/ss8/QvXt3KBQKBAYG4tChQ1aqlIiIqGloZesC9u7di5iYGGzZsgWhoaHYuHEjIiIicOnSJbRr165G+xMnTmDSpEmIi4vDX/7yF3zyyScYO3YsUlNT0atXLxusARE11vX7xSgorYC2pBwqJwe4KlqhQ2tns7W/mV8CTUm5vr3SyQE+7k4mt7U0TbEOeYU6aEvLoXRygNrFESpnxzrbN6XaxRC7nlJmiXW1eYDHx8dj1qxZmDZtGgBgy5YtOHjwIHbs2IElS5bUaP/2229j+PDhWLhwIQBg1apV+O677/D//t//w5YtW6xaOxE13u93i/Dql+fxY8Zd/bCBXT2w+qlA+Hm4WLW92Hlb0s38Eizedw7H0vP0wwYHqLF2fFCtodyUahdD7HpKmaXW1aaX0HU6HU6fPo3w8HD9MDs7O4SHhyM5ObnWaZKTkw3aA0BERESd7Ymo6bl+v7hG6ADA8Yy7+MeX53H9frFJ7W/ml9Tb/mZ+SaPaWpqmWFfjgx4AjqbnYcm+c9AU6wyGN6XaxRC7nlJmyXW1aYDn5eWhsrISnp6eBsM9PT2Rm5tb6zS5ubmi2peVlUGr1Rq8iMgyjD3eCkoraoTOA8cz7qKgtMKk9pqS8nrba0rKG9XW0vIKdTU+6B84mp6HvELDD/umVLsYYtdTyiy5rk3iJjZLiouLg0ql0r98fX1tXRJRs2Xs8aZtIFgKSg3HW7K92HlbkraBZZm6XZoKsespZZZcV5sGuFqthr29PW7dumUw/NatW/Dy8qp1Gi8vL1Htly5dCo1Go39du3bNPMUTUQ3GHm9KJ4d65+OmMBxvyfZi521JygaWZep2aSrErqeUWXJdbRrgjo6O6NevHxITE/XDqqqqkJiYiLCwsFqnCQsLM2gPAN99912d7eVyOZRKpcGLiCzD2OPNTdEKA7t61DpuYFcPuClamdRe5eRQb3tVteAT09bS1K6OGBygrnXc4AA11K6Gdy03pdrFELueUmbJdbX5JfSYmBhs27YNu3fvxm+//YYXX3wRRUVF+rvSp0yZgqVLl+rbz507FwkJCXjrrbdw8eJFrFy5EqdOnUJ0dLStVoGIROrQ2hmrnwqsET4P7p5++NEwse193J3qbV/9zl8xbS1N5eyIteODanzgDw5QY934oBqPHTWl2sUQu55SZsl1tfljZBMmTMCdO3ewYsUK5Obmonfv3khISNDfqJadnQ07u//+P2PAgAH45JNPsGzZMrz66qsICAjA/v37+Qw4kcT4ebhg7fggFJRWoKC0HG4KB7jV81x3Y9qv/2swNCXl+vaqOp6PFtPW0nzcnfDupD7IK9Tpa1G71v3McFOqXQyx6yllllpXmwc4AERHR9d5Bp2UlFRj2NNPP42nn37awlURkaXV1wmLOdr7uDsZHWRi2lqaylnch3tTql0MsespZZZYV5tfQiciIiLxGOBEREQS1CQuoVuTIAgA0GCHLoWFhQAAzfUrqKqoNGsNBbm/AwBSU1P1yzEnOzs7VFVVmX2+1pi/VOdtyflfvnwZgGX3xcLCQqM6OXJzc4NMJjN6/sYeb0RkyJhjTSY8OMJaiOvXr7MzF6JG0mg0oh7F5PFG1DjGHGstLsCrqqpw8+bNBv93o9Vq4evri2vXrvHZcSvhNrc+sdtc7Bm4scebVLWUfZbraX3GHDMt7hK6nZ0dOnToYHR7dv5ifdzm1mepbS72eJOqlrLPcj2bFt7ERkREJEEMcCIiIgligNdBLpcjNjYWcrnc1qW0GNzm1sdtbpqWsv24nk1Ti7uJjYiIqDngGTgREZEEMcCJiIgkiAFOREQkQQxwIiIiCWpxAS4IArRaLXjvHpHl8XgjspwWF+AFBQVQqVQoKCiwdSlEzR6PNyLLaXEBTkRE1BwwwImIiCSIAU5ERCRBDHAiIiIJYoATERFJUIv7PXBjaIp1yCvUQVtaDqWTA9QujlA5O9q6LCIiIj0G+ENu5pdg8b5zOJaepx82OECNteOD4OPuZMPKiIiI/ouX0KvRFOtqhDcAHE3Pw5J956Ap1tmoMiIiIkMM8GryCnU1wvuBo+l5yCtkgBMRUdPAAK9GW1pe7/iCBsYTERFZCwO8GqXCod7xbg2MJyIishYGeDVqV0cMDlDXOm5wgBpqV96JTkRETQMDvBqVsyPWjg+qEeKDA9RYNz6Ij5IREVGTwcfIHuLj7oR3J/VBXqEOBaXlcFM4QO3K58CJiKhpYYDXQuXMwCYioqaNl9CJiIgkiAFOREQkQQxwIiIiCWKAExERSRADnIiISIIY4ERERBLEACciIpIgBjgREZEEMcCJiIgkiAFOREQkQQxwIiIiCWKAExERSRADnIiISIKaVIAfPXoUkZGR8PHxgUwmw/79+w3GT506FTKZzOA1fPhw2xRLRERkQ03q50SLiooQHByM6dOnY9y4cbW2GT58OHbu3Kl/L5fLrVUeUbOiKdYhr1AHbWk5lE4OULvwZ3SJpKRJBfiIESMwYsSIetvI5XJ4eXlZqSKi5ulmfgkW7zuHY+l5+mGDA9RYOz4IPu5ONqyMiIzVpC6hGyMpKQnt2rVDt27d8OKLL+Lu3bu2LolIUjTFuhrhDQBH0/OwZN85aIp1NqqMiMRoUmfgDRk+fDjGjRuHTp06ITMzE6+++ipGjBiB5ORk2Nvb1zpNWVkZysrK9O+1Wq21yiVqkvIKdTXC+4Gj6XnIK9Q1+lI6jzci65FUgE+cOFH/d2BgIIKCgtClSxckJSVh6NChtU4TFxeH1157zVolEjV52tLyescXNDC+PjzeiKxHcpfQq+vcuTPUajUyMjLqbLN06VJoNBr969q1a1askKjpUSoc6h3v1sD4+vB4I7IeSZ2BP+z69eu4e/cuvL2962wjl8t5pzpRNWpXRwwOUONoLZfRBweooXZt/J3oPN6IrKdJnYEXFhYiLS0NaWlpAICsrCykpaUhOzsbhYWFWLhwIX766SdcvXoViYmJGDNmDLp27YqIiAjbFk4kISpnR6wdH4TBAWqD4YMD1Fg3PoiPkhFJhEwQBMHWRTyQlJSEJ598ssbwqKgobN68GWPHjsWZM2eQn58PHx8f/PnPf8aqVavg6elp9DK0Wi1UKhU0Gg2USqU5yyeSlAfPgReUlsNN4QC1q/mfA+fxRmQ5TSrArYEfKETWw+ONyHKa1CV0IiIiMg4DnIiISIIY4ERERBLEACciIpIgBjgREZEEMcCJiIgkiAFOREQkQQxwIiIiCWKAExERSRADnIiISIIY4ERERBLEACciIpIgBjgREZEEMcCJiIgkiAFOREQkQSYHeGlpaZ3jcnJyTJ09ERER1cLkAO/bty/S0tJqDN+3bx+CgoJMnT0RERHVwuQAf+KJJ/DYY49h3bp1AICioiJMnToVkydPxquvvmpygURERFRTK1Nn8N5772HUqFGYOXMmvv76a+Tk5MDV1RUpKSno1auXOWokIiKih5gc4AAwYsQIjBs3Dps3b0arVq1w4MABhjcREZEFmXwJPTMzE2FhYfj6669x+PBhLFq0CKNHj8aiRYtQXl5ujhqJiIjoISYHeO/evdGpUyecPXsWw4YNwxtvvIEjR47giy++QP/+/c1RIxERET3E5AB/7733sGfPHri7u+uHDRgwAGfOnEHfvn1NnT0RERHVQiYIgmCOGel0OmRlZaFLly5o1cosX61bhFarhUqlgkajgVKptHU5RM0ajzciyzH5DLykpAQzZsyAs7MzevbsiezsbADA7Nmz9Y+WERERkXmZHOBLlizB2bNnkZSUBIVCoR8eHh6OPXv2mDp7IiIiqoXJ17r379+PvXv34rHHHoNMJtMP79mzJzIzM02dvU1oinXIK9RBW1oOpZMD1C6OUDk72rosIiIiPZMD/M6dO2jXrl2N4UVFRQaBLhU380uweN85HEvP0w8bHKDG2vFB8HF3smFlRM1Xr959jfrtBG9vb/ySlmqFioiaPpMDPCQkBAcPHsTs2bMBQB/a27dvR1hYmKmztypNsa5GeAPA0fQ8LNl3Du9O6sMzcSILyMnJwbBVXzTY7rvl46xQDZE0mBzga9aswYgRI3DhwgVUVFTg7bffxoULF3DixAn88MMP5qjRavIKdTXC+4Gj6XnIK9QxwImIqEkw+Sa2gQMHIi0tDRUVFQgMDMS3336Ldu3aITk5Gf369TNHjVajLa2/57iCBsYTERFZi1ke2O7SpQu2bdtmjlnZlFLhUO94twbGExERWUujAlyr1RrdVkqdN6hdHTE4QI2jtVxGHxyghtqVl8+JiKhpaFSAu7u7G32HeWVlZWMWYRMqZ0esHR+EJfvOGYT44AA11o0P4vffRETUZDQqwI8cOaL/++rVq1iyZAmmTp2qv+s8OTkZu3fvRlxcnHmqtCIfdye8O6kP8gp1KCgth5vCAWpXPgdORERNS6MCfMiQIfq/X3/9dcTHx2PSpEn6YaNHj0ZgYCC2bt2KqKgo06u0MpUzA5uIiJo2k+9CT05ORkhISI3hISEhSElJMXX2REREVAuTA9zX17fWO9C3b98OX19fU2dPREREtTD5MbINGzZg/Pjx+OabbxAaGgoASElJQXp6Ovbt22dygURERFSTyWfgI0eORHp6OiIjI3Hv3j3cu3cPkZGRuHz5MkaOHGmOGomIiOghZunIpUOHDlizZo05ZkVERERGMEuA5+fnIyUlBbdv30ZVVZXBuClTpphjEURERFSNyQF+4MABPPvssygsLIRSqTTo4EUmkzHAiYiILMDk78BfeeUVTJ8+HYWFhcjPz8f9+/f1r3v37pmjRiIiInqIyQF+48YNzJkzB87Ozuaoh4iIiIxgcoBHRETg1KlT5qiFiIiIjGTyd+CjRo3CwoULceHCBQQGBsLBwfAnN0ePHm3qIoiIiOghJgf4rFmzAPzRJ/rDZDKZpH6NjIiISCpMDvCHHxsjIiIiyzP5O3BzOnr0KCIjI+Hj4wOZTIb9+/cbjBcEAStWrIC3tzecnJwQHh6O9PR02xRLRERkQ406A3/nnXfw3HPPQaFQ4J133qm37Zw5c4yeb1FREYKDgzF9+nSMGzeuxvj169fjnXfewe7du9GpUycsX74cERERuHDhAhQKhej1IGrJNMU65BXqoC0th9LJAWoX/owukZTIBEEQxE7UqVMnnDp1Ch4eHujUqVPdM5fJcOXKlcYVJpPhyy+/xNixYwH8cfbt4+ODV155BQsWLAAAaDQaeHp6YteuXZg4caJR89VqtVCpVNBoNFAqlY2qjUjqbuaXYPG+cziWnqcfNjhAjbXjg+Dj7mS25Rh7vHl4emPYqi8anN93y8fh7q0cs9VHJGWNOgPPysqq9W9LysrKQm5uLsLDw/XDVCoVQkNDkZycbHSAE7V0mmJdjfAGgKPpeViy7xzendSHZ+JEEmCWvtCtITc3FwDg6elpMNzT01M/rjZlZWUoKyvTv9dqtZYpkEgi8gp1NcL7gaPpecgr1DU6wHm8EVlPk7qJzRLi4uKgUqn0L19fX1uXRGRT2tLyescXNDC+PjzeiKxHMgHu5eUFALh165bB8Fu3bunH1Wbp0qXQaDT617Vr1yxaJ1FTp1Q41DverYHx9eHxRmQ9kgnwTp06wcvLC4mJifphWq0WJ0+eRFhYWJ3TyeVyKJVKgxdRS6Z2dcTgAHWt4wYHqKF2bfz33zzeiKynSQV4YWEh0tLSkJaWBuCPG9fS0tKQnZ0NmUyGefPm4Y033sBXX32F8+fPY8qUKfDx8dHfqU5EDVM5O2Lt+KAaIT44QI1144N4AxuRRJh8E1tCQgJcXV0xcOBAAMCmTZuwbds29OjRA5s2bULr1q2NntepU6fw5JNP6t/HxMQAAKKiorBr1y4sWrQIRUVFeO6555Cfn4+BAwciISGBz4ATieTj7oR3J/VBXqEOBaXlcFM4QO3K58CJpKRRz4FXFxgYiHXr1mHkyJE4f/48/vSnPyEmJgZHjhxB9+7dsXPnTnPVahZ8DpzIevgcOJHlmHwGnpWVhR49egAA9u3bh7/85S9Ys2YNUlNTMXLkSJMLJCIioppMDnBHR0cUFxcDAL7//ntMmTIFANCmTRs+A0rUhN24XwxtaQW0JeVQOTnATdEK7Vs727osIjKSyQH++OOPIyYmBo8//jhSUlKwd+9eAMDly5fRoUMHkwskIvP7/W4RXv3yPH7MuKsfNrCrB1Y/FQg/DxcbVkZExjL5LvRNmzbBwcEBn3/+OTZv3oz27dsDAL755hsMHz7c5AKJyLxu3C+uEd4AcDzjLv7x5XncuF9so8qISAyTzsArKiqQlJSEbdu21ehMZcOGDSYVRkSWoS2tqBHeDxzPuAttaQXaW7kmIhLPpDPwVq1a4YUXXjDo+5iImjZtieW6UiUi6zH5Enr//v1x5swZc9RCRFagdLJcV6pEZD0m38T20ksv4ZVXXsH169fRr18/uLgY3gATFBRk6iKIyIyUilYY2NUDx2u5jD6wqweUCsn8SCFRi2bykfrgd7jnzJmjHyaTySAIAmQyGSorK01dBBGZUfvWzlj9VCD+8eV5gxB/cBc6HyUjkgazdORCRNLi5+GCdeODoC2t0HelquRz4ESSYnKA+/n5maMOIrKy9q2debc5kYSZ7cuuCxcuIDs7GzqdzmD46NGjzbUIIiIi+j8mB/iVK1fw1FNP4fz58/rvvoE/vgcHwO/AiYiILMDkx8jmzp2LTp064fbt23B2dsavv/6Ko0ePIiQkBElJSWYo0fpu5pfgtxwtTl65i4s5WtzML7F1SURmpynWIfN2Ic5k30fmnUJoinUNT0RETYbJZ+DJycn4z3/+A7VaDTs7O9jZ2WHgwIGIi4vDnDlzJPeMOPuIppbgZn4JFu87h2PpefphgwPUWDs+CD7uTjasjIiMZfIZeGVlJdzc3AAAarUaN2/eBPDHzW2XLl0ydfZWdTO/pN4+onkmTs2BplhXI7wB4Gh6HpbsO8czcSKJMPkMvFevXjh79iw6deqE0NBQrF+/Ho6Ojti6dSs6d+5sjhqtRlNSXm8f0ZqScp6dkOTlFepqhPcDR9PzkFeog8rZ0cpVEZFYJgf4smXLUFRUBAB4/fXX8Ze//AWDBg2Ch4eH/qdFpYJ9RFNLoG1gP+Z+TiQNJgd4RESE/u+uXbvi4sWLuHfvHlq3bq2/E10q2Ec0tQTKBvZj7udE0mDyd+DVXbt2DdeuXUObNm0kF94AoHJywMCuHrWOG9jVA6oGAp5ICtSujhgcoK513OAANdSuvHxOJAUmB3hFRQWWL18OlUoFf39/+Pv7Q6VSYdmyZSgvl9alOB93J6x+KrBGiD+4C53ff1NzoHJ2xNrxQTVCfHCAGuvGB/H7byKJMPkS+uzZs/HFF19g/fr1CAsLA/DHo2UrV67E3bt3sXnzZpOLtCY/Dxes/2swNCXl+j6iVU4ODG9qVnzcnfDupD7IK9Tp93O1qyPDm0hCTA7wTz75BHv27MGIESP0w4KCguDr64tJkyZJLsCBPz7cGNjU3KmcGdhEUmbyJXS5XA5/f/8awzt16gRHR344EBERWYLJAR4dHY1Vq1ahrKxMP6ysrAyrV69GdHS0qbMnIiKiWjTqEvq4ceMM3n///ffo0KEDgoODAQBnz56FTqfD0KFDTa/QBjTFOuQV6qAtLYfSyQFqF15qJCKipqVRAa5SqQzejx8/3uC9r69v4yuyMfYRTUREUtCoAN+5c6e562gSGuoj+t1JfXgmTkRETYJZO3KROmP6iCYiImoKGODVsI9oIiKSCgZ4NewjmoiIpIIBXg37iCYiIqkwOcCvXLlijjqaBPYRTUREUmFyV6pdu3bFkCFDMGPGDPz1r3+FQqEwR102wz6iiYhICkw+A09NTUVQUBBiYmLg5eWF559/HikpKeaozWZUzo7o0s4VvTu2Rpd2rgxvIiJqckwO8N69e+Ptt9/GzZs3sWPHDuTk5GDgwIHo1asX4uPjcefOHXPUSURERNWY7Sa2Vq1aYdy4cfjss8+wbt06ZGRkYMGCBfD19cWUKVOQk5NjrkURERG1eGYL8FOnTuGll16Ct7c34uPjsWDBAmRmZuK7777DzZs3MWbMGHMtioiIqMUz+Sa2+Ph47Ny5E5cuXcLIkSPx4YcfYuTIkbCz++P/Bp06dcKuXbtq/clRIiIiahyTA3zz5s2YPn06pk6dCm9v71rbtGvXDh988IGpiyIiIqL/Y3KAp6enN9jG0dERUVFRpi6KiIiI/o/JAf5AcXExsrOzodMZ/uBHUFCQuRZBRERE/8fkAL9z5w6mTp2KhISEWsdXVlaauggiIiJ6iMl3oc+bNw8ajQYnT56Ek5MTEhISsHv3bgQEBOCrr74yR41ERET0EJPPwP/zn//g3//+N0JCQmBnZwc/Pz8MGzYMSqUScXFxGDVqlDnqJCIiompMPgMvKipCu3btAACtW7fW97wWGBiI1NRUU2dPREREtTA5wLt164ZLly4BAIKDg/H+++/jxo0b2LJlS52PlREREZFpTL6EPnfuXH03qbGxsRg+fDg+/vhjODo6YteuXabOnoiIiGphcoD//e9/1//dr18//P7777h48SI6duwItVpdz5RERETUWGbrC/0BZ2dn9O3b12LhvXLlSshkMoNX9+7dLbIsoubs+v1i/Jajxckrd3ExR4vr94ttXRIRiWDyGXhMTEytw2UyGRQKBbp27YoxY8agTZs2pi5Kr2fPnvj+++/171u1Mlt/NEQtwu93i/Dql+fxY8Zd/bCBXT2w+qlA+Hm42LAyIjKWycl35swZpKamorKyEt26dQMAXL58Gfb29ujevTvee+89vPLKKzh+/Dh69OhhcsHAH4Ht5eVllnkRtTTX7xfXCG8AOJ5xF//48jzWjg9Ch9bONqquflqtFh6eDd8c6+3tjV/S+BQMNW8mB/iDs+udO3dCqVQCADQaDWbOnImBAwdi1qxZeOaZZzB//nwcPnzY5IKBP/pf9/HxgUKhQFhYGOLi4tCxY8da25aVlaGsrEz/XqvVmqUGIqkqKK2oEd4PHM+4i4LSikbP29LHW1VVFYat+qLBdt8tH2fW5RI1RSZ/B/7Pf/4Tq1at0oc3AKhUKqxcuRLr16+Hs7MzVqxYgdOnT5u6KABAaGgodu3ahYSEBGzevBlZWVkYNGgQCgoKam0fFxcHlUqlf/n6+pqlDiKp0paU1zu+oLT+8fXh8UZkPSYHuEajwe3bt2sMv3Pnjv5/3+7u7jV+5KSxRowYgaeffhpBQUGIiIjAoUOHkJ+fj3/961+1tl+6dCk0Go3+de3aNbPUQSRVSieHese7KeofXx8eb0TWY5ZL6NOnT8dbb72FP/3pTwCAn3/+GQsWLMDYsWMBACkpKXjkkUdMXVSt3N3d8cgjjyAjI6PW8XK5HHK53CLLJpIiN0UrDOzqgeO1XEYf2NUDborGfyzweCOyHpPPwN9//30MHToUEydOhJ+fH/z8/DBx4kQMHToUW7ZsAQB0794d27dvN7nY2hQWFiIzM5O9vhEZqUNrZ6x+KhADu3oYDH9wF3pTvYGNiAyZfAbu6uqKbdu2YcOGDbhy5QoAoHPnznB1ddW36d27t6mL0VuwYAEiIyPh5+eHmzdvIjY2Fvb29pg0aZLZlkHU3Pl5uGDt+CAUlFagoLQcbgoHuClaMbyJJMRsD1C7uroiKCjIXLOr0/Xr1zFp0iTcvXsXbdu2xcCBA/HTTz+hbdu2Fl82UXPCsCaSNsn1gLJnzx5bl0BERGRzZu9KlYiIiCxPcmfg1nAzvwSaknJoS8qhcnKA0skBPu5Oti6LyKw0xTrkFeqgLS2H0skBahdHqJwdbV0WERmJAf4Q9hFNLcHN/BIs3ncOx9Lz9MMGB6ixdnwQ/7NKJBG8hF7NzfySevuIvplfYqPKiMxHU6yrEd4AcDQ9D0v2nYOm2DydLhGRZTHAq9GUlNfbR7SmgS4oiaQgr1BXI7wfOJqeh7xCBjiRFDDAq7FkH9FETYW2gf2Y+zmRNDDAq7FkH9FETYWygf2Y+zmRNDDAq1E5OdToXvKBgV09oGog4ImkQO3qiMEB6lrHDQ5QQ+3KO9GJpIABXo2Pu1O9fUTz7lxqDlTOjlg7PqhGiA8OUGPd+CA+SkYkEXyM7CF+Hi5Y/9dgaErK9X1Eq/gcODUzPu5OeHdSH+QV6vT7udqVz4ETSQkDvBY+7k4MbGr2VM4MbCIp4yV0IiIiCWKAExERSRAvodeCfaFTS3DjfjG0pRX6/dxN0Qrt+ROjRJLBAH8I+0KnloD7OZH08RJ6NewLnVqCG/eL693Pb9wvtlFlRCQGA7wa9oVOLYG2tKLe/VxbWmHlioioMRjg1bAvdGoJuJ8TNQ8M8GrYFzq1BNzPiZoHBng17AudWgKlolW9+7lSwXtbiaSAAV4N+0KnlqB9a+d693M+SkYkDfyv9kPYFzq1BH4eLlg3Pgja0gr9fq7kc+BEksIArwX7QqeWoH1rZ7S3dRFE1Gi8hE5ERCRBDHAiIiIJ4iX0Wly/X4yCan1EuypaoQO/G6Rmhvs5kbQxwB/CPqKpJeB+TiR9vIRezfUG+oi+zj6iqRngfk7UPDDAqylooI/oAvYRTc0A93Oi5oGX0KthH9HUErSE/Vyr1cLD07vBdt7e3vglLdUKFRGZHwO8GvYRTS1BS9jPq6qqMGzVFw22+275OCtUQ2QZvIRejVsDfUS7sY9oaga4nxM1Dwzwajo00Ec0H7Gh5oD7OVHzwP9qP8TPwwVrxwehoFof0W58PpaaGe7nRNLHAK8FP8SoJeB+TiRtDHAionr06t0XOTk5DbbjHe1kbQxwIqJ65OTk8I52apIY4LVgH9HUEnA/J5I2BvhD2Ec0tQTcz/9gTIcvWq3WStU0jrGX+IuLS+Ds7FRvG34NIC0M8Goa6iN67fggnqGQ5HE//y9jOnz5bPb/WKmaxjH2Ev9ns/8HY976pt42/BpAWvgceDXsI5paAu7nRM0DA7yaltBHNBH3c6LmgQFeTUvoI5qI+zlR88AAr4Z9RFNLwP2cqHlggFfDPqKpJeB+TtQ88L/aD2Ef0dQScD8nkj4GeC34IUYtAfdzImnjJXQiIiIJ4hk4EVETZGwPa+bsKc6YnukA8/fYZosfjDH3Mm2xDgxwIqImSEwPa+ZiTM90gPl7bLPFD8aYe5m2WIcWF+CCIABo+v0bEzVFbm5ukMlkRrc39ngTBAGVFZVGzdMW7YxpIwiCWT9XbLVNmvK6mnO55l6muednzLEmEx4cYS3E9evX4evra+syiCRJo9FAqVQa3Z7HG1HjGHOstbgAr6qqws2bNxv8341Wq4Wvry+uXbsm6gOLGo/b3PrEbnOxZ+DGHm9S1VL2Wa6n9RlzzLS4S+h2dnbo0KGD0e2VSqXN/yFbGm5z67PUNhd7vElVS9lnuZ5NCx8jIyIikiAGOBERkQQxwOsgl8sRGxsLuVxu61JaDG5z6+M2N01L2X5cz6apxd3ERkRE1BzwDJyIiEiCGOBEREQSxAAnIiKSIAY4ERGRBLXoAN+0aRP8/f2hUCgQGhqKlJSUett/9tln6N69OxQKBQIDA3Ho0CErVdp8iNnmu3btgkwmM3gpFAorVit9R48eRWRkJHx8fCCTybB///4Gp0lKSkLfvn0hl8vRtWtX7Nq1y+J1Ss3KlStr7Jvdu3e3dVkma2h/EQQBK1asgLe3N5ycnBAeHo709HTbFGuChtZz6tSpNf59hw8fbpti69FiA3zv3r2IiYlBbGwsUlNTERwcjIiICNy+fbvW9idOnMCkSZMwY8YMnDlzBmPHjsXYsWPxyy+/WLly6RK7zYE/ekTKycnRv37//XcrVix9RUVFCA4OxqZNm4xqn5WVhVGjRuHJJ59EWloa5s2bh5kzZ+Lw4cMWrlR6evbsabBvHj9+3NYlmayh/WX9+vV45513sGXLFpw8eRIuLi6IiIhAaWmplSs1jTHHxfDhww3+fT/99FMrVmgkoYXq37+/8PLLL+vfV1ZWCj4+PkJcXFyt7f/2t78Jo0aNMhgWGhoqPP/88xatszkRu8137twpqFQqK1XX/AEQvvzyy3rbLFq0SOjZs6fBsAkTJggREREWrEx6YmNjheDgYFuXYVEP7y9VVVWCl5eX8M9//lM/LD8/X5DL5cKnn35qgwrNo7bjIioqShgzZoxN6hGjRZ6B63Q6nD59GuHh4fphdnZ2CA8PR3Jycq3TJCcnG7QHgIiIiDrbk6HGbHMAKCwshJ+fH3x9fTFmzBj8+uuv1ii3xeJ+brz09HT4+Pigc+fOePbZZ5GdnW3rkiwqKysLubm5BvuHSqVCaGhos9w/kpKS0K5dO3Tr1g0vvvgi7t69a+uSamiRAZ6Xl4fKykp4enoaDPf09ERubm6t0+Tm5opqT4Yas827deuGHTt24N///jf+93//F1VVVRgwYACuX79ujZJbpLr2c61Wi5KSEhtV1fSEhoZi165dSEhIwObNm5GVlYVBgwahoKDA1qVZzIPjtCV8Dg4fPhwffvghEhMTsW7dOvzwww8YMWIEKiuN+911a2lxv0ZG0hEWFoawsDD9+wEDBuDRRx/F+++/j1WrVtmwMmrpRowYof87KCgIoaGh8PPzw7/+9S/MmDHDhpWROUycOFH/d2BgIIKCgtClSxckJSVh6NChNqzMUIs8A1er1bC3t8etW7cMht+6dQteXl61TuPl5SWqPRlqzDZ/mIODA/r06YOMjAxLlEioez9XKpVwcnKyUVVNn7u7Ox555JFmvW8+OE5b4udg586doVarm9y/b4sMcEdHR/Tr1w+JiYn6YVVVVUhMTDQ446suLCzMoD0AfPfdd3W2J0ON2eYPq6ysxPnz5+Ht7W2pMls87ueNU1hYiMzMzGa9b3bq1AleXl4G+4dWq8XJkyeb/f5x/fp13L17t+n9+9r6Ljpb2bNnjyCXy4Vdu3YJFy5cEJ577jnB3d1dyM3NFQRBECZPniwsWbJE3/7HH38UWrVqJbz55pvCb7/9JsTGxgoODg7C+fPnbbUKkiN2m7/22mvC4cOHhczMTOH06dPCxIkTBYVCIfz666+2WgXJKSgoEM6cOSOcOXNGACDEx8cLZ86cEX7//XdBEARhyZIlwuTJk/Xtr1y5Ijg7OwsLFy4UfvvtN2HTpk2Cvb29kJCQYKtVaJJeeeUVISkpScjKyhJ+/PFHITw8XFCr1cLt27dtXZpJGtpf1q5dK7i7uwv//ve/hXPnzgljxowROnXqJJSUlNi4cnHqW8+CggJhwYIFQnJyspCVlSV8//33Qt++fYWAgAChtLTU1qUbaLEBLgiC8O677wodO3YUHB0dhf79+ws//fSTftyQIUOEqKgog/b/+te/hEceeURwdHQUevbsKRw8eNDKFUufmG0+b948fVtPT09h5MiRQmpqqg2qlq4jR44IAGq8HmznqKgoYciQITWm6d27t+Do6Ch07txZ2Llzp9XrbuomTJggeHt7C46OjkL79u2FCRMmCBkZGbYuy2QN7S9VVVXC8uXLBU9PT0EulwtDhw4VLl26ZNuiG6G+9SwuLhb+/Oc/C23bthUcHBwEPz8/YdasWfoTjaaEPydKREQkQS3yO3AiIiKpY4ATERFJEAOciIhIghjgREREEsQAJyIikiAGOBERkQQxwImIiCSIAd4MXL16FTKZDGlpaXW2SUpKgkwmQ35+vtXqsoapU6di7NixJs3D398fGzduNGkezXX7UtNhjn3dXHjMNA38NbJmwNfXFzk5OVCr1bYuRZJ+/vlnuLi42LoMIsngMdM0MMAlTqfTwdHRUdK/BlReXg4HBwebLb9t27Y2WzaRrTXm+OMx0zTwEnoT8sQTTyA6OhrR0dFQqVRQq9VYvnw5qvd26+/vj1WrVmHKlClQKpV47rnnar2EfujQITzyyCNwcnLCk08+iatXr9ZY3vHjxzFo0CA4OTnB19cXc+bMQVFRUZ31rVy5Er1798b7778PX19fODs7429/+xs0Go2+zc8//4xhw4ZBrVZDpVJhyJAhSE1NNZiPTCbD5s2bMXr0aLi4uGD16tWorKzEjBkz0KlTJzg5OaFbt254++23DaarrKxETEwM3N3d4eHhgUWLFuHhnoCrqqoQFxenn09wcDA+//zzerf7w5cDZTIZtm/fjqeeegrOzs4ICAjAV199ZTCNqdv3ww8/hKurK9LT0/XtX3rpJXTv3h3FxcX11ku2VVBQgGeffRYuLi7w9vbGhg0b8MQTT2DevHn6Nh999BFCQkLg5uYGLy8vPPPMM7h9+zYAQBAEdO3aFW+++abBfNPS0iCTyRr8ycrXXnsNbdu2hVKpxAsvvACdTqcfl5CQgIEDB+qPkb/85S/IzMzUj3/wWbF3714MGTIECoUCH3/8MX7//XdERkaidevWcHFxQc+ePXHo0KE6a+Ax00TYtCd2MjBkyBDB1dVVmDt3rnDx4kXhf//3fwVnZ2dh69at+jZ+fn6CUqkU3nzzTSEjI0PIyMgQsrKyBADCmTNnBEEQhOzsbEEulwsxMTH6+Xh6egoAhPv37wuCIAgZGRmCi4uLsGHDBuHy5cvCjz/+KPTp00eYOnVqnfXFxsYKLi4uwv/8z/8IZ86cEX744Qeha9euwjPPPKNvk5iYKHz00UfCb7/9Jly4cEGYMWOG4OnpKWi1Wn0bAEK7du2EHTt2CJmZmcLvv/8u6HQ6YcWKFcLPP/8sXLlyRb/ue/fu1U+3bt06oXXr1sK+ffv083ZzcxPGjBmjb/PGG28I3bt3FxISEoTMzExh586dglwuF5KSkupcLz8/P2HDhg0G9XXo0EH45JNPhPT0dGHOnDmCq6urcPfuXbNu36efflr405/+JJSXlwtff/214ODgIJw6darOOqlpmDlzpuDn5yd8//33wvnz54WnnnpKcHNzE+bOnatv88EHHwiHDh0SMjMzheTkZCEsLEwYMWKEfvzq1auFHj16GMx3zpw5wuDBg+tcblRUlODq6ipMmDBB+OWXX4Svv/5aaNu2rfDqq6/q23z++efCvn37hPT0dOHMmTNCZGSkEBgYKFRWVgqCIOg/K/z9/YV9+/YJV65cEW7evCmMGjVKGDZsmHDu3DkhMzNTOHDggPDDDz/UWQuPmaaBAd6EDBkyRHj00UeFqqoq/bDFixcLjz76qP69n5+fMHbsWIPpHg7wpUuX1vhwWLx4scHBMmPGDOG5554zaHPs2DHBzs6uzp8GjI2NFezt7YXr16/rh33zzTeCnZ2dkJOTU+s0lZWVgpubm3DgwAH9MADCvHnz6tgK//Xyyy8L48eP17/39vYW1q9fr39fXl4udOjQQR/gpaWlgrOzs3DixAmD+cyYMUOYNGlSncup7cNo2bJl+veFhYUCAOGbb74RBMF82/fevXtChw4dhBdffFHw9PQUVq9e3cAWIVvTarWCg4OD8Nlnn+mH5efnC87OzgYB/rCff/5ZACAUFBQIgiAIN27cEOzt7YWTJ08KgiAIOp1OUKvVwq5du+qcR1RUlNCmTRuhqKhIP2zz5s2Cq6urPqAfdufOHQGA/mePH3xWbNy40aBdYGCgsHLlyvpXvhoeM00DL6E3MY899hhkMpn+fVhYGNLT01FZWakfFhISUu88fvvtN4SGhhoMCwsLM3h/9uxZ7Nq1C66urvpXREQEqqqqkJWVVee8O3bsiPbt2xvMt6qqCpcuXQIA3Lp1C7NmzUJAQABUKhWUSiUKCwuRnZ1tMJ/a1mHTpk3o168f2rZtC1dXV2zdulU/nUajQU5OjsF6tWrVymA+GRkZKC4uxrBhwwzW68MPPzS4jGiMoKAg/d8uLi5QKpX6S6Dm2r6tW7fGBx98gM2bN6NLly5YsmSJqBrJ+q5cuYLy8nL0799fP0ylUqFbt24G7U6fPo3IyEh07NgRbm5uGDJkCADo92cfHx+MGjUKO3bsAAAcOHAAZWVlePrpp+tdfnBwMJydnfXvw8LCUFhYiGvXrgEA0tPTMWnSJHTu3BlKpRL+/v4Gy33g4eNvzpw5eOONN/D4448jNjYW586dM3aT6PGYsT7exCZB5rj7s7CwEM8//zzmzJlTY1zHjh0bPd+oqCjcvXsXb7/9Nvz8/CCXyxEWFmbwPR1Qcx327NmDBQsW4K233kJYWBjc3Nzwz3/+EydPnjR62YWFhQCAgwcPGvwnAwDkcrmo9Xj4ph6ZTIaqqipRtRizfY8ePQp7e3vk5OSgqKgIbm5uouqkpqeoqAgRERGIiIjAxx9/jLZt2yI7OxsREREGx8HMmTMxefJkbNiwATt37sSECRMMwrkxIiMj4efnh23btsHHxwdVVVXo1atXg8ffzJkzERERgYMHD+Lbb79FXFwc3nrrLcyePdvoZfOYsT6egTcxDwfWTz/9hICAANjb2xs9j0cffRQpKSk15lNd3759ceHCBXTt2rXGy9HRsc55Z2dn4+bNmwbztbOz05+B/Pjjj5gzZw5GjhyJnj17Qi6XIy8vr8Gaf/zxRwwYMAAvvfQS+vTpg65duxqcNatUKnh7extsn4qKCpw+fVr/vkePHpDL5cjOzq6xTr6+vg3WYCxzbd8TJ05g3bp1OHDgAFxdXREdHW22GskyOnfuDAcHB/z888/6YRqNBpcvX9a/v3jxIu7evYu1a9di0KBB6N69u/5MtLqRI0fCxcUFmzdvRkJCAqZPn97g8s+ePYuSkhL9+59++gmurq7w9fXF3bt3cenSJSxbtgxDhw7Fo48+ivv37xu9br6+vnjhhRfwxRdf4JVXXsG2bduMnrYhPGYsgwHexGRnZyMmJgaXLl3Cp59+infffRdz584VNY8XXngB6enpWLhwIS5duoRPPvkEu3btMmizePFinDhxAtHR0UhLS0N6ejr+/e9/N3hAKBQKREVF4ezZszh27BjmzJmDv/3tb/rH2AICAvDRRx/ht99+w8mTJ/Hss8/CycmpwZoDAgJw6tQpHD58GJcvX8by5csNPiQBYO7cuVi7di3279+Pixcv4qWXXjLoBMLNzQ0LFizA/PnzsXv3bmRmZiI1NRXvvvsudu/ebdzGM4I5tm9BQQEmT56MOXPmYMSIEfj444+xd+/eBu+YJ9tyc3NDVFQUFi5ciCNHjuDXX3/FjBkzYGdnp//qq2PHjnB0dMS7776LK1eu4KuvvsKqVatqzMve3h5Tp07F0qVLERAQUOOScm10Oh1mzJiBCxcu4NChQ4iNjUV0dDTs7OzQunVreHh4YOvWrcjIyMB//vMfxMTEGLVe8+bNw+HDh5GVlYXU1FQcOXIEjz76qLiNUw8eMxZi6y/h6b+GDBkivPTSS8ILL7wgKJVKoXXr1sKrr75qcFPbwzePCELNm9gEQRAOHDggdO3aVZDL5cKgQYOEHTt2GNwwIgiCkJKSIgwbNkxwdXUVXFxchKCgoHpvComNjRWCg4OF9957T/Dx8REUCoXw17/+Vbh3756+TWpqqhASEiIoFAohICBA+Oyzz2q94eXLL780mHdpaakwdepUQaVSCe7u7sKLL74oLFmyRAgODta3KS8vF+bOnSsolUrB3d1diImJEaZMmWJwF3pVVZWwceNGoVu3boKDg4PQtm1bISIiQvQdtQ/Xp1KphJ07d5pt+06bNk0IDAwUSktL9e3feustoU2bNgY3CVLTo9VqhWeeeUZwdnYWvLy8hPj4eKF///7CkiVL9G0++eQTwd/fX5DL5UJYWJjw1Vdf1ThGBUEQMjMzBQAGN2fWJSoqShgzZoywYsUKwcPDQ3B1dRVmzZplsA999913wqOPPirI5XIhKChISEpKMtifa/usEARBiI6OFrp06SLI5XKhbdu2wuTJk4W8vLw6a+Ex0zTIBOGhB2nJZp544gn07t3b5C4KLWXlypXYv39/vV22ErU0RUVFaN++Pd566y3MmDFD1LTHjh3D0KFDce3aNXh6elqoQmqueBMbEZEIZ86cwcWLF9G/f39oNBq8/vrrAIAxY8YYPY+ysjLcuXMHK1euxNNPP83wpkbhd+BERCK9+eabCA4ORnh4OIqKinDs2DFRv0Xw6aefws/PD/n5+Vi/fr0FK6XmjJfQiYiIJIhn4ERERBLEACciIpIgBjgREZEEMcCJiIgkiAFOREQkQQxwIiIiCWKAExERSRADnIiISIIY4ERERBL0/wH+NSJm+WafRQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 500x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gaydflight3 = gaydflight2[gaydflight2['gay bars index'] != 0]\n",
+    "# gaydflight3 = gaydflight3[gaydflight3['pride parade index'] != 0]\n",
+    "\n",
+    "sns.pairplot(gaydflight3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 613.986x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gaydflightparade = gaydflight3[gaydflight3['pride parade index'] != 0]\n",
+    "\n",
+    "gaydflightnoparade = gaydflight3[gaydflight3['pride parade index'] != 1]\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=gaydflight3, kind=\"bar\",\n",
+    "    x='pride parade index', y='gay bars index', hue='gay bars index'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MNR08NzdXe/futS9nZmYqLS1NISEhioiI0JAhQzR+/HjVrl1bkZGRGjlypMLCwnTvvfde03EBAACsyunydvjwYQ0aNOiai5skbd26VW3atLEvX7xWLT4+XnPnztUzzzyjvLw8Pfroozp9+rRatmyp5cuXy8/P75qPDQAAYEVOl7f27dtr69atqlGjxjUfvHXr1jIM47LP22w2Pf/883r++eev+VgAAADXA6fLW6dOnZSUlKRdu3apUaNGKl26tMPznTt3dlk4AAAAOHK6vD3yyCOSdMnZMJvNpoKCgmtPBQAAgEtyurz9+aNBAAAA4D5Of7cpAAAAzHNVM2+vvvqqHn30Ufn5+enVV1+94raDBg1ySTAAAAAUdVXlberUqerRo4f8/Pw0derUy25ns9kobwAAACXoqspbZmbmJX8GAACAe3HNGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAALobwBAABYiNPlbfny5Vq/fr19efr06WrcuLH++c9/6tSpUy4NBwAAAEdOl7ekpCTl5ORIknbu3KmnnnpKHTt2VGZmphITE10eEAAA/M+BAwdks9mUlpZ22W3WrFkjm82m06dPuy2XO/Tu3Vv33nvvNe2jevXqmjZt2jXtw+zX1+nvNs3MzFT9+vUlSR9++KHuueceTZgwQampqerYsaPLAwIAgP8JDw/X0aNHVbFiRbOjWNKWLVsUEBBgdoxr4vTMm4+Pj86ePStJ+vLLL3X33XdLkkJCQuwzcgAAwPXOnTsnLy8vhYaGytvb6fkXj3D+/HlTj3/DDTeoTJkypma4Vk6Xt9tvv12JiYkaN26cNm/erE6dOkmS9uzZo6pVq7o8IAAA16PWrVsrISFBCQkJCg4OVsWKFTVy5EgZhmHfpnr16ho3bpx69eqloKAgPfroo5c8bfr555/rpptukr+/v9q0aaMDBw4UOd769et1xx13yN/fX+Hh4Ro0aJDy8vIum2/MmDFq3Lix3nzzTYWHh6tMmTJ68MEHlZ2dbd9my5Ytuuuuu1SxYkUFBwerVatWSk1NddiPzWZTSkqKOnfurICAAL3wwgsqKChQv379FBkZKX9/f9WpU0evvPKKw7iCggIlJiaqXLlyqlChgp555hmH10aSCgsLlZycbN9PdHS0Pvjggyu+7n8+bWqz2TRz5kx17dpVZcqUUe3atbVs2TKHMdf6+s6fP19ly5ZVRkaGffsnnnhCdevWtU+IOcPp8jZ9+nSVLl1aH3zwgVJSUnTjjTdKkr744gt16NDB6QAA4C6Hnm/k9AMoSfPmzZO3t7c2b96sV155RVOmTNHMmTMdtnnppZcUHR2t7du3a+TIkUX2kZWVpW7duikuLk5paWnq37+/hg8f7rDNvn371KFDB91333367rvvtHjxYq1fv14JCQlXzLd371699957+uSTT7R8+XJt375dTzzxhP35M2fOKD4+XuvXr9fGjRtVu3ZtdezYUWfOnHHYz5gxY9S1a1ft3LlTffv2VWFhoapWrar3339fu3bt0qhRo/Tss8/qvffes495+eWXNXfuXM2ePVvr16/XL7/8oiVLljjsNzk5WfPnz9eMGTP0ww8/aOjQofrXv/6ltWvXXvmF/5OxY8fqwQcf1HfffaeOHTuqR48e+uWXX1z2+vbq1cu+3wsXLuizzz7TzJkztWDBgmLNAtqMP9fYK7hw4YIWLlyou+++W6GhoU4fzAw5OTkKDg5Wdna2goKCzI4DwETFKWMRo3Z6RA7pylmaJs0v1j63Te5VrHGu9nd8TVq3bq0TJ07ohx9+kM1mkyQNHz5cy5Yt065duyT9Pkt08803O5SWAwcOKDIyUtu3b1fjxo317LPP6uOPP9YPP/xg32b48OGaOHGiTp06pXLlyql///7y8vLSm2++ad9m/fr1atWqlfLy8uTn51ck35gxYzR+/HgdPHjQPlGzfPlyderUSYcPH75kDygsLFS5cuW0cOFC3XPPPZJ+n9kaMmSIpk6desXXIyEhQceOHbPPnIWFhWno0KFKSkqS9HsHiYyMVNOmTbV06VLl5+crJCREX375pVq0aGHfT//+/XX27FktXLjwksepXr26hgwZoiFDhtjzPffccxo3bpwkKS8vT2XLlrVPSrnq9T116pSioqIUFxenjz76SIMGDdKzzz57xdfkcpw6Ye7t7a3HHntMu3fvLtbBAADA/9x666324iZJLVq00Msvv6yCggJ5eXlJkmJiYq64j927d6t58+YO6/5YZiRpx44d+u6777RgwQL7OsMwVFhYqMzMTNWrV++S+46IiLAXt4v7LSwsVHp6ukJDQ3X8+HE999xzWrNmjU6cOKGCggKdPXtWhw4dctjPpX6H6dOna/bs2Tp06JB+/fVXnTt3To0bN5YkZWdn6+jRow6/l7e3t2JiYuynTvfu3auzZ8/qrrvuctjvuXPndPPNN1/u5bqkqKgo+88BAQEKCgrSiRMnJLnu9S1fvrxmzZql9u3b67bbbisye+cMp692bNasmbZv365q1aoV+6AAAODquOLOyNzcXA0YMECDBg0q8lxERESx9xsfH6+ff/5Zr7zyiqpVqyZfX1+1aNFC586dc9juz7/DokWL9PTTT+vll19WixYtFBgYqMmTJ2vTpk1Xfezc3FxJ0meffeZQMCXJ19fXqd+jdOnSDss2m02FhYVOZbma13fdunXy8vLS0aNHlZeXp8DAQKdyXuR0eXviiSf01FNP6aefflLTpk2L/IH8sb0CAIDL+3NZuXjd2MVZt6tRr169IhfYb9y40WG5SZMm2rVrl2rVquVUvkOHDunIkSMKCwuz77dUqVKqU6eOJOmbb77RG2+8Yf+osKysLP3f//3fX+73m2++0W233eZw/dy+ffvsPwcHB6tKlSratGmT7rzzTkm/nzbdtm2bmjRpIkmqX7++fH19dejQIbVq1cqp38sZrnp9v/32W02cOFGffPKJhg0bpoSEBM2bN69YmZwubw899JAkObRLm80mwzBks9lUUFBQrCAAAPzdHDp0SImJiRowYIBSU1P12muv6eWXX3ZqH4899phefvllJSUlqX///tq2bZvmzp3rsM2wYcN06623KiEhQf3791dAQIB27dqllStX6vXXX7/svv38/BQfH6+XXnpJOTk5GjRokB588EH79W61a9fWO++8o5iYGOXk5CgpKUn+/v5/mbl27dqaP3++VqxYocjISL3zzjvasmWLIiMj7dsMHjxYL774omrXrq26detqypQpDh+KGxgYqKefflpDhw5VYWGhWrZsqezsbH3zzTcKCgpSfHy8U6/j5bji9T1z5ox69uypQYMGKTY2VlWrVtUtt9yiuLg43X///U5ncvpu08zMzCKP/fv32/8LAACuTq9evfTrr7+qWbNmGjhwoAYPHqxHH33UqX1EREToww8/1NKlSxUdHa0ZM2ZowoQJDttERUVp7dq12rNnj+644w7dfPPNGjVqlH1G7XJq1aqlbt26qWPHjrr77rsVFRWlN954w/78rFmzdOrUKTVp0sReTipVqvSXmQcMGKBu3bqpe/fuat68uX7++WeHWThJeuqpp9SzZ0/Fx8fbT6127drVYZtx48Zp5MiRSk5OVr169dShQwd99tlnDiXwWrni9R08eLACAgLs4xo1aqQJEyZowIABOnz4sNOZnLrb1Iq42xTARdxtWhR3mxblzrtNGzdufM1f1VRSxowZo6VLl17xa7hgjmJ/PPOuXbt06NChIhcldu7c+ZpDAQAA4NKcLm/79++3f9DexWvdJNlvdeaaNwAAgJLj9DVvgwcPVmRkpE6cOKEyZcrohx9+0Lp16xQTE6M1a9aUQEQAAK4/a9as8dhTptLvp005ZeqZnJ5527Bhg7766itVrFhRpUqVUqlSpdSyZUslJydr0KBB2r59e0nkBAAAgIox81ZQUGD/ULmKFSvqyJEjkqRq1aopPT3dtekAAADgwOmZt4YNG2rHjh2KjIxU8+bNNWnSJPn4+Oitt95SjRo1SiIjAAAA/svp8vbcc88pLy9PkvT888/rnnvu0R133KEKFSpo8eLFLg8IAACA/3G6vLVv397+c61atfTjjz/ql19+Ufny5R2+XBcAAACuV+zPeZN+/w4zSQoPD3dJGAAAAFyZ0+XtwoULGjt2rF599VXl5uZKksqWLasnn3xSo0ePVunSpV0eEgAAuF9xv6WiuDzlGz88ndN3mz755JN66623NGnSJG3fvl3bt2/XpEmTNGvWLIcvqwcAAHCH6dOnq3r16vLz81Pz5s21efNmsyOVKKdn3hYuXKhFixYpNjbWvi4qKkrh4eF6+OGHlZKS4tKAAAAAl7N48WIlJiZqxowZat68uaZNm6b27dsrPT1dlSpVMjteiXB65s3X11fVq1cvsj4yMlI+Pj6uyGRXUFCgkSNHKjIyUv7+/qpZs6bGjRtn/0ouAADw9zZlyhQ98sgj6tOnj+rXr68ZM2aoTJkymj17ttnRSozT5S0hIUHjxo1Tfn6+fV1+fr5eeOEFJSQkuDTcxIkTlZKSotdff127d+/WxIkTNWnSJL322msuPQ4AALCec+fOadu2bWrXrp19XalSpdSuXTtt2LDBxGQl66pOm3br1s1h+csvv1TVqlUVHR0tSdqxY4fOnTuntm3bujTct99+qy5duqhTp06SpOrVq+vdd9+94rns/Px8h2KZk5Pj0kwAAMAz/N///Z8KCgpUuXJlh/WVK1fWjz/+aFKqkndV5S04ONhh+b777nNYLqmPCrntttv01ltvac+ePbrpppu0Y8cOrV+/XlOmTLnsmOTkZI0dO7ZE8gB/Z8W962xJ4ORijYsYtbNY4wDgendV5W3OnDklneOShg8frpycHNWtW1deXl4qKCjQCy+8oB49elx2zIgRI5SYmGhfzsnJ4XPoAAC4DlWsWFFeXl46fvy4w/rjx48rNDTUpFQlz+lr3tzpvffe04IFC7Rw4UKlpqZq3rx5eumllzRv3rzLjvH19VVQUJDDAwAAXH98fHzUtGlTrVq1yr6usLBQq1atUosWLUxMVrKu6RsWSlpSUpKGDx+uhx56SJLUqFEjHTx4UMnJyYqPjzc5HQAAMFtiYqLi4+MVExOjZs2aadq0acrLy1OfPn3MjlZiPLq8nT17VqVKOU4Oenl5qbCw0KREAAD8fVjhGw+6d++ukydPatSoUTp27JgaN26s5cuXF7mJ4Xri0eUtLi5OL7zwgiIiItSgQQNt375dU6ZMUd++fc2OBgAAPERCQoLLP67Mkzld3vbv368aNWqURJYiXnvtNY0cOVJPPPGETpw4obCwMA0YMECjRo1yy/EBAAA8jdPlrVatWmrVqpX69eun+++/X35+fiWRS5IUGBioadOmadq0aSV2DAAAACtx+m7T1NRURUVFKTExUaGhoRowYMB1/wWwAAAAnsLp8ta4cWO98sorOnLkiGbPnq2jR4+qZcuWatiwoaZMmaKTJ0+WRE4AAADoGj7nzdvbW926ddP777+viRMnau/evXr66acVHh6uXr166ejRo67MCQAAAF1Dedu6daueeOIJValSRVOmTNHTTz+tffv2aeXKlTpy5Ii6dOniypwAAABQMW5YmDJliubMmaP09HR17NhR8+fPV8eOHe2fxxYZGam5c+eqevXqrs4KAADwt+d0eUtJSVHfvn3Vu3dvValS5ZLbVKpUSbNmzbrmcAAAAHDkdHnLyMj4y218fHz4+ioAAIASUOxvWDh79qwOHTqkc+fOOayPioq65lAAAMB8h55v5NbjRYza6dbjWZXTNyycPHlSnTp1UmBgoBo0aKCbb77Z4QEAAOAO69atU1xcnMLCwmSz2bR06VKzI7mF0+VtyJAhys7O1qZNm+Tv76/ly5dr3rx5ql27tpYtW1YSGQEAAIrIy8tTdHS0pk+fbnYUt3L6tOlXX32ljz/+WDExMSpVqpSqVaumu+66S0FBQUpOTlanTp1KIicAAICD2NhYxcbGmh3D7ZyeecvLy1OlSpUkSeXLl7d/o0KjRo2Umprq2nQAAABw4HR5q1OnjtLT0yVJ0dHRevPNN3X48GHNmDHjsh8dAgAAANdw+rTp4MGD7V99NXr0aHXo0EELFiyQj4+P5s6d6+p8AAAA+AOny9u//vUv+89NmzbVwYMH9eOPPyoiIkIVK1Z0aTgAAAA4KvbnvF1UpkwZNWnSxBVZAAAA8BecLm+JiYmXXG+z2eTn56datWqpS5cuCgkJueZwAKSmSfOLNW7b5F4uTgIAniU3N1d79+61L2dmZiotLU0hISGKiIgwMVnJcrq8bd++XampqSooKFCdOnUkSXv27JGXl5fq1q2rN954Q0899ZTWr1+v+vXruzwwAABwD0//xoOtW7eqTZs29uWLE0zx8fHX9XX4Tpe3i7Nqc+bMUVBQkCQpOztb/fv3V8uWLfXII4/on//8p4YOHaoVK1a4PDAAAIAktW7dWoZhmB3D7Zz+qJDJkydr3Lhx9uImScHBwRozZowmTZqkMmXKaNSoUdq2bZtLgwIAAKAY5S07O1snTpwosv7kyZPKycmRJJUrV67IF9YDAADg2jld3rp06aK+fftqyZIl+umnn/TTTz9pyZIl6tevn+69915J0ubNm3XTTTe5OisAAMDfntPXvL355psaOnSoHnroIV24cOH3nXh7Kz4+XlOnTpUk1a1bVzNnznRtUgAAADhf3sqWLau3335bU6dO1f79+yVJNWrUUNmyZe3bNG7c2GUBAQAA8D/F/pDesmXLKioqypVZAAAA8BecvuYNAAAA5qG8AQAAWAjlDQAAwEIobwAAABZCeQMAALAQyhsAAICFUN4AAAAshPIGAABgIZQ3AAAAC6G8AQAAWAjlDQAAwEIobwAAABbi8eXt8OHD+te//qUKFSrI399fjRo10tatW82OBQAAYApvswNcyalTp3T77berTZs2+uKLL3TDDTcoIyND5cuXNzsaAACAKTy6vE2cOFHh4eGaM2eOfV1kZOQVx+Tn5ys/P9++nJOTU2L5AAAA3M2jT5suW7ZMMTExeuCBB1SpUiXdfPPNevvtt684Jjk5WcHBwfZHeHi4m9ICAACUPI8ub/v371dKSopq166tFStW6PHHH9egQYM0b968y44ZMWKEsrOz7Y+srCw3JgYAAChZHn3atLCwUDExMZowYYIk6eabb9b333+vGTNmKD4+/pJjfH195evr686YAAAAbuPRM29VqlRR/fr1HdbVq1dPhw4dMikRAACAuTy6vN1+++1KT093WLdnzx5Vq1bNpEQAAADm8ujyNnToUG3cuFETJkzQ3r17tXDhQr311lsaOHCg2dEAAABM4dHl7ZZbbtGSJUv07rvvqmHDhho3bpymTZumHj16mB0NAADAFB59w4Ik3XPPPbrnnnvMjgEAAOARPHrmDQAAAI4obwAAABZCeQMAALAQyhsAAICFUN4AAAAshPIGAABgIZQ3AAAAC6G8AQAAWAjlDQAAwEIobwAAABZCeQMAALAQyhsAAICFUN4AAAAshPIGAABgIZQ3AAAAC6G8AQAAWIi32QGA68mh5xsVa1zEqJ0uTgIAuF4x8wYAAGAhlDcAAAALobwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAALobwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAALobwBAABYCOUNAADAQixV3l588UXZbDYNGTLE7CgAAACmsEx527Jli958801FRUWZHQUAAMA0lihvubm56tGjh95++22VL1/+itvm5+crJyfH4QEAAHC98DY7wNUYOHCgOnXqpHbt2mn8+PFX3DY5OVljx451UzK4WtOk+cUatyRwcrHGRYzaWaxxAACYxeNn3hYtWqTU1FQlJydf1fYjRoxQdna2/ZGVlVXCCQEAANzHo2fesrKyNHjwYK1cuVJ+fn5XNcbX11e+vr4lnAwAAMAcHl3etm3bphMnTqhJkyb2dQUFBVq3bp1ef/115efny8vLy8SEAAAA7uXR5a1t27baudPxmqQ+ffqobt26GjZsGMUNAAD87Xh0eQsMDFTDhg0d1gUEBKhChQpF1gMAAPwdePwNCwAAAPgfj555u5Q1a9aYHQEAAMA0zLwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAALobwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAAL8TY7AMzXNGl+scYtCZxcrHERo3YWaxwAAGDmDQAAwFIobwAAABZCeQMAALAQyhsAAICFUN4AAAAshPIGAABgIZQ3AAAAC6G8AQAAWAjlDQAAwEIobwAAABZCeQMAALAQyhsAAICFUN4AAAAshPIGAABgIZQ3AAAAC6G8AQAAWAjlDQAAwEIobwAAABZCeQMAALAQjy9vycnJuuWWWxQYGKhKlSrp3nvvVXp6utmxAAAATOHx5W3t2rUaOHCgNm7cqJUrV+r8+fO6++67lZeXZ3Y0AAAAt/M2O8BfWb58ucPy3LlzValSJW3btk133nmnSakAAADM4fHl7c+ys7MlSSEhIZd8Pj8/X/n5+fblnJwct+QCAABwB0uVt8LCQg0ZMkS33367GjZseMltkpOTNXbsWDcnK56mSfOLNW5J4ORijYsYtbNY4wAAgOfw+Gve/mjgwIH6/vvvtWjRostuM2LECGVnZ9sfWVlZbkwIAABQsiwz85aQkKBPP/1U69atU9WqVS+7na+vr3x9fd2YDAAAwH08vrwZhqEnn3xSS5Ys0Zo1axQZGWl2JAAAANN4fHkbOHCgFi5cqI8//liBgYE6duyYJCk4OFj+/v4mpwMAAHAvj7/mLSUlRdnZ2WrdurWqVKlifyxevNjsaAAAAG7n8TNvhmGYHQEAAMBjePzMGwAAAP6H8gYAAGAhlDcAAAALobwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAALobwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACzE2+wA7tY0aX6xxi0JnFyscRGjdhZrHAAAwKUw8wYAAGAhlDcAAAALobwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAALobwBAABYCOUNAADAQihvAAAAFkJ5AwAAsBDKGwAAgIVQ3gAAACyE8gYAAGAhlDcAAAALobwBAABYiCXK2/Tp01W9enX5+fmpefPm2rx5s9mRAAAATOHx5W3x4sVKTEzU6NGjlZqaqujoaLVv314nTpwwOxoAAIDbeXx5mzJlih555BH16dNH9evX14wZM1SmTBnNnj3b7GgAAABu5212gCs5d+6ctm3bphEjRtjXlSpVSu3atdOGDRsuOSY/P1/5+fn25ezsbElSTk6OJKkg/9diZTlTuqBY4y4e91I8JYun5PC0LMXK8ZvnvCau/t2s/mcjFe/Px1NySNb4e1Jc1+NrEhgYKJvN5pJ9AX9kMwzDMDvE5Rw5ckQ33nijvv32W7Vo0cK+/plnntHatWu1adOmImPGjBmjsWPHujMmAABFZGdnKygoyOwYuA559MxbcYwYMUKJiYn25cLCQv3yyy+qUKFCsf8PKCcnR+Hh4crKyjL9H6KnZPGUHJ6UxVNyeFIWT8nhSVk8JYcnZfGUHK7OEhgY6KJUgCOPLm8VK1aUl5eXjh8/7rD++PHjCg0NveQYX19f+fr6OqwrV66cS/IEBQWZ/sZykadk8ZQckudk8ZQckudk8ZQckudk8ZQckudk8ZQckmdlAf7Mo29Y8PHxUdOmTbVq1Sr7usLCQq1atcrhNCoAAMDfhUfPvElSYmKi4uPjFRMTo2bNmmnatGnKy8tTnz59zI4GAADgdh5f3rp3766TJ09q1KhROnbsmBo3bqzly5ercuXKbsvg6+ur0aNHFzkdawZPyeIpOTwpi6fk8KQsnpLDk7J4Sg5PyuIpOTwtC3A5Hn23KQAAABx59DVvAAAAcER5AwAAsBDKGwAAgIVQ3gAAACyE8nYF69atU1xcnMLCwmSz2bR06VJTciQnJ+uWW25RYGCgKlWqpHvvvVfp6emmZElJSVFUVJT9AyxbtGihL774wpQsf/Tiiy/KZrNpyJAhbj/2mDFjZLPZHB5169Z1e47q1asXyWGz2TRw4EC3ZykoKNDIkSMVGRkpf39/1axZU+PGjZMZ90edOXNGQ4YMUbVq1eTv76/bbrtNW7ZsKfHj/tX7h2EYGjVqlKpUqSJ/f3+1a9dOGRkZbs1x/vx5DRs2TI0aNVJAQIDCwsLUq1cvHTlyxOU5/iqLJPXu3bvI398OHTq4Pcel/h3ZbDZNnjzZ5VmA4qC8XUFeXp6io6M1ffp0U3OsXbtWAwcO1MaNG7Vy5UqdP39ed999t/Ly8tyepWrVqnrxxRe1bds2bd26Vf/4xz/UpUsX/fDDD27PctGWLVv05ptvKioqyrQMDRo00NGjR+2P9evXuz3Dli1bHDKsXLlSkvTAAw+4PcvEiROVkpKi119/Xbt379bEiRM1adIkvfbaa27P0r9/f61cuVLvvPOOdu7cqbvvvlvt2rXT4cOHS/S4f/X+MWnSJL366quaMWOGNm3apICAALVv316//fab23KcPXtWqampGjlypFJTU/XRRx8pPT1dnTt3dmmGq8lyUYcOHRz+Hr/77rtuz/HH4x89elSzZ8+WzWbTfffd5/IsQLEYuCqSjCVLlpgdwzAMwzhx4oQhyVi7dq3ZUQzDMIzy5csbM2fONOXYZ86cMWrXrm2sXLnSaNWqlTF48GC3Zxg9erQRHR3t9uP+lcGDBxs1a9Y0CgsL3X7sTp06GX379nVY161bN6NHjx5uzXH27FnDy8vL+PTTTx3WN2nSxPj3v//tthx/fv8oLCw0QkNDjcmTJ9vXnT592vD19TXeffddt+W4lM2bNxuSjIMHD5ZYjstliY+PN7p06VKix72aHH/WpUsX4x//+Id7AgFXgZk3C8rOzpYkhYSEmJqjoKBAixYtUl5enmlfVzZw4EB16tRJ7dq1M+X4F2VkZCgsLEw1atRQjx49dOjQIVPznDt3Tv/v//0/9e3bVzabze3Hv+2227Rq1Srt2bNHkrRjxw6tX79esbGxbs1x4cIFFRQUyM/Pz2G9v7+/KbOjF2VmZurYsWMOf2+Dg4PVvHlzbdiwwbRc0u/vLzabzWXfCe2sNWvWqFKlSqpTp44ef/xx/fzzz6bkuOj48eP67LPP1K9fP1NzAH/k8d+wAEeFhYUaMmSIbr/9djVs2NCUDDt37lSLFi3022+/qWzZslqyZInq16/v9hyLFi1SamqqW65fupLmzZtr7ty5qlOnjo4ePaqxY8fqjjvu0Pfff6/AwEBTMi1dulSnT59W7969TTn+8OHDlZOTo7p168rLy0sFBQV64YUX1KNHD7fmCAwMVIsWLTRu3DjVq1dPlStX1rvvvqsNGzaoVq1abs3yR8eOHZOkIt8UU7lyZftzZvjtt980bNgwPfzww6Z8KXuHDh3UrVs3RUZGat++fXr22WcVGxurDRs2yMvLy+15JGnevHkKDAxUt27dTDk+cCmUN4sZOHCgvv/+e1NnDerUqaO0tDRlZ2frgw8+UHx8vNauXevWApeVlaXBgwdr5cqVRWZV3O2Ps0lRUVFq3ry5qlWrpvfee8+0/1ufNWuWYmNjFRYWZsrx33vvPS1YsEALFy5UgwYNlJaWpiFDhigsLEzx8fFuzfLOO++ob9++uvHGG+Xl5aUmTZro4Ycf1rZt29yaw9OdP39eDz74oAzDUEpKiikZHnroIfvPjRo1UlRUlGrWrKk1a9aobdu2pmSaPXu2evToYfr7DPBHnDa1kISEBH366adavXq1qlataloOHx8f1apVS02bNlVycrKio6P1yiuvuDXDtm3bdOLECTVp0kTe3t7y9vbW2rVr9eqrr8rb21sFBQVuzfNH5cqV00033aS9e/eacvyDBw/qyy+/VP/+/U05viQlJSVp+PDheuihh9SoUSP17NlTQ4cOVXJystuz1KxZU2vXrlVubq6ysrK0efNmnT9/XjVq1HB7lotCQ0Ml/X5K7o+OHz9uf86dLha3gwcPauXKlabMul1KjRo1VLFiRdP+LX399ddKT0839d8ScCmUNwswDEMJCQlasmSJvvrqK0VGRpodyUFhYaHy8/Pdesy2bdtq586dSktLsz9iYmLUo0cPpaWlmXaKRZJyc3O1b98+ValSxZTjz5kzR5UqVVKnTp1MOb70+12MpUo5vr14eXmpsLDQpERSQECAqlSpolOnTmnFihXq0qWLaVkiIyMVGhqqVatW2dfl5ORo06ZNbr9+9GJxy8jI0JdffqkKFSq49fhX8tNPP+nnn3827d/SrFmz1LRpU0VHR5tyfOByOG16Bbm5uQ7/x5eZmam0tDSFhIQoIiLCbTkGDhyohQsX6uOPP1ZgYKD9mpjg4GD5+/u7LYckjRgxQrGxsYqIiNCZM2e0cOFCrVmzRitWrHBrjsDAwCLX/AUEBKhChQpuvxbw6aefVlxcnKpVq6YjR45o9OjR8vLy0sMPP+zWHNLvRXrOnDmKj4+Xt7d5/7zj4uL0wgsvKCIiQg0aNND27ds1ZcoU9e3b1+1ZVqxYIcMwVKdOHe3du1dJSUmqW7eu+vTpU6LH/av3jyFDhmj8+PGqXbu2IiMjNXLkSIWFhenee+91W44qVaro/vvvV2pqqj799FMVFBTY319CQkLk4+PjtiwhISEaO3as7rvvPoWGhmrfvn165plnVKtWLbVv395tOS6+t+fk5Oj999/Xyy+/7NJjAy5h8t2uHm316tWGpCKP+Ph4t+a4VAZJxpw5c9yawzAMo2/fvka1atUMHx8f44YbbjDatm1r/Oc//3F7jksx66NCunfvblSpUsXw8fExbrzxRqN79+7G3r173Z7DMAxjxYoVhiQjPT3dlONflJOTYwwePNiIiIgw/Pz8jBo1ahj//ve/jfz8fLdnWbx4sVGjRg3Dx8fHCA0NNQYOHGicPn26xI/7V+8fhYWFxsiRI43KlSsbvr6+Rtu2bUvkz+1KOTIzMy/7/rJ69Wq3Zjl79qxx9913GzfccINRunRpo1q1asYjjzxiHDt2zK05LnrzzTcNf39/t/xdAZxlMwwTPvIcAAAAxcI1bwAAABZCeQMAALAQyhsAAICFUN4AAAAshPIGAABgIZQ3AAAAC6G8AQAAWAjlDQAAwEIob8B1onfv3i7/aqXiql69uqZNm3ZN+1izZo1sNptOnz7tkkwAcL3gu00BuNyWLVsUEBBgdgwAuC5R3gBc0fnz51W6dGmnxtxwww0llAYAwGlTwMXOnDmjHj16KCAgQFWqVNHUqVPVunVrDRkyxL7NO++8o5iYGAUGBio0NFT//Oc/deLECUmSYRiqVauWXnrpJYf9pqWlyWazae/evVc8/tixY3XDDTcoKChIjz32mM6dO2d/bvny5WrZsqXKlSunChUq6J577tG+ffvszx84cEA2m02LFy9Wq1at5OfnpwULFujgwYOKi4tT+fLlFRAQoAYNGujzzz+/bIY/nza12WyaOXOmunbtqjJlyqh27dpatmyZw5jPP/9cN910k/z9/dWmTRsdOHCgyH7Xr1+vO+64Q/7+/goPD9egQYOUl5cnSZo/f77Kli2rjIwM+/ZPPPGE6tatq7Nnz17xNQMAK6G8AS6WmJiob775RsuWLdPKlSv19ddfKzU11WGb8+fPa9y4cdqxY4eWLl2qAwcOqHfv3pJ+Lzp9+/bVnDlzHMbMmTNHd955p2rVqnXZY69atUq7d+/WmjVr9O677+qjjz7S2LFj7c/n5eUpMTFRW7du1apVq1SqVCl17dpVhYWFDvsZPny4Bg8erN27d6t9+/YaOHCg8vPztW7dOu3cuVMTJ05U2bJlnXpdxo4dqwcffFDfffedOnbsqB49euiXX36RJGVlZalbt26Ki4tTWlqa+vfvr+HDhzuM37dvnzp06KD77rtP3333nRYvXqz169crISFBktSrVy/7fi9cuKDPPvtMM2fO1IIFC1SmTBmnsgKARzMAuExOTo5RunRp4/3337evO336tFGmTBlj8ODBlx23ZcsWQ5Jx5swZwzAM4/Dhw4aXl5exadMmwzAM49y5c0bFihWNuXPnXnYf8fHxRkhIiJGXl2dfl5KSYpQtW9YoKCi45JiTJ08akoydO3cahmEYmZmZhiRj2rRpDts1atTIGDNmzJV/+T+oVq2aMXXqVPuyJOO5556zL+fm5hqSjC+++MIwDMMYMWKEUb9+fYd9DBs2zJBknDp1yjAMw+jXr5/x6KOPOmzz9ddfG6VKlTJ+/fVXwzAM45dffjGqVq1qPP7440blypWNF1544aozA4BVMPMGuND+/ft1/vx5NWvWzL4uODhYderUcdhu27ZtiouLU0REhAIDA9WqVStJ0qFDhyRJYWFh6tSpk2bPni1J+uSTT5Sfn68HHnjgisePjo52mGVq0aKFcnNzlZWVJUnKyMjQww8/rBo1aigoKEjVq1d3OO5FMTExDsuDBg3S+PHjdfvtt2v06NH67rvvrvYlsYuKirL/HBAQoKCgIPup4t27d6t58+YO27do0cJheceOHZo7d67Kli1rf7Rv316FhYXKzMyUJJUvX16zZs1SSkqKatasWWT2DgCuB5Q3wM3y8vLUvn17BQUFacGCBdqyZYuWLFkiSQ7Xp/Xv31+LFi3Sr7/+qjlz5qh79+7XfPovLi5Ov/zyi95++21t2rRJmzZtKnJcSUXuFO3fv7/279+vnj17aufOnYqJidFrr73m1LH/fNODzWYrcrr2SnJzczVgwAClpaXZHzt27FBGRoZq1qxp327dunXy8vLS0aNH7dfDAcD1hPIGuFCNGjVUunRpbdmyxb4uOztbe/bssS//+OOP+vnnn/Xiiy/qjjvuUN26de0zUH/UsWNHBQQEKCUlRcuXL1ffvn3/8vg7duzQr7/+al/euHGjypYtq/DwcP38889KT0/Xc889p7Zt26pevXo6derUVf9u4eHheuyxx/TRRx/pqaee0ttvv33VY/9KvXr1tHnzZod1GzdudFhu0qSJdu3apVq1ahV5+Pj4SJK+/fZbTZw4UZ988onKli1rvx4OAK4nlDfAhQIDAxUfH6+kpCStXr1aP/zwg/r166dSpUrJZrNJkiIiIuTj46PXXntN+/fv17JlyzRu3Lgi+/Ly8lLv3r01YsQI1a5du8hpxEs5d+6c+vXrp127dunzzz/X6NGjlZCQoFKlSql8+fKqUKGC3nrrLe3du1dfffWVEhMTr+r3GjJkiFasWKHMzEylpqZq9erVqlevnnMvzhU89thjysjIUFJSktLT07Vw4ULNnTvXYZthw4bp22+/VUJCgtLS0pSRkaGPP/7YXtDOnDmjnj17atCgQYqNjdWCBQu0ePFiffDBBy7LCQCegPIGuNiUKVPUokUL3XPPPWrXrp1uv/121atXT35+fpJ+/wy0uXPn6v3331f9+vX14osvFvlYkIv69eunc+fOqU+fPld17LZt26p27dq688471b17d3Xu3FljxoyRJJUqVUqLFi3Stm3b1LBhQw0dOlSTJ0++qv0WFBRo4MCBqlevnjp06KCbbrpJb7zxxlWNvRoRERH68MMPtXTpUkVHR2vGjBmaMGGCwzZRUVFau3at9uzZozvuuEM333yzRo0apbCwMEnS4MGDFRAQYB/XqFEjTZgwQQMGDNDhw4ddlhUAzGYzDMMwOwRwPcvLy9ONN96ol19+Wf369XNq7Ndff622bdsqKytLlStXLqGEAAAr4RsWABfbvn27fvzxRzVr1kzZ2dl6/vnnJUldunS56n3k5+fr5MmTGjNmjB544AGKGwDAjtOmQAl46aWXFB0drXbt2ikvL09ff/21KlaseNXj3333XVWrVk2nT5/WpEmTSjApAMBqOG0KAABgIcy8AQAAWAjlDQAAwEIobwAAABZCeQMAALAQyhsAAICFUN4AAAAshPIGAABgIZQ3AAAAC/n/0CV1Hs9xeIQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 642.986x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gaydflightparade = gaydflight3[gaydflight3['pride parade index'] != 0]\n",
+    "\n",
+    "gaydflightnoparade = gaydflight3[gaydflight3['pride parade index'] != 1]\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=gaydflight3, kind=\"bar\",\n",
+    "    x='gay bars index', y='gay bars index', hue='pride parade index'\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the above shows that my hypothesis 'more gay bars <==> more pride parades' should be rejected\n",
+    "\n",
+    "against my expectation, more gay bars are located in regions pride parades don't pass through\n",
+    "\n",
+    "the obvious explanation I can see is each community has a constant amount of queer activity such that more queer people exist such that the communities with more of A have less B\n",
+    "\n",
+    "alternatively this correlation should be discarded due to the fact that it exists mostly in zip areas with more then 10 bars\n",
+    "\n",
+    "a final explanation would be to cite the sampling bias, which while isn't as extreme as the tax DF from the other research question, is still big enough of a sampling bias to disregard the results"
    ]
   }
  ],
diff --git a/data/processed/gay_pride b/data/processed/gay_pride
new file mode 100644
index 0000000..ee2afdd
--- /dev/null
+++ b/data/processed/gay_pride
@@ -0,0 +1,2329 @@
+pride parade index,gay bars index,lat,long
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diff --git a/feedback list.txt b/feedback list.txt
new file mode 100644
index 0000000..9c217e2
--- /dev/null
+++ b/feedback list.txt	
@@ -0,0 +1,30 @@
+Student hours 1:     	question: how do I plot a map & make adequate visualization for the geography of my data set
+                        answer: use tableau’s map function
+                        consultant: Dr. Moosvi
+
+Student hours 2:     	question: how would I convert zip codes into rough coordinates? I assume there is a formula to calculate this
+                        Answer: use a CSV of width 3, LAT, LONG, ZIP
+                        consultant: Dr. Moosvi
+
+Student hours 3:	question: I have received some unusual results, is this indicative of a flaw in my data analysis or could I blame it on sample bias
+                        Answer: since we are not collecting our own data a sample bias is a reasonable explanation for error
+                        Consultant: Dr. Moosvi
+
+
+Notes from [Saira] on Feedback on the analysis:
+
+			Analysis 1:
+
+				Good job.
+				Add a conclusion to your notebook showing how did you answer your research question.
+
+			Analysis 2:
+
+				I don't see much work done.
+				Improve your analysis.
+				Add details to each graph
+				Add conclusion
+				fix errors in your note book.
+
+
+all 4 feedback sessions were attended, qualifying Nat & Sami for an A+
\ No newline at end of file
diff --git a/images/Dashboard 1 Sami.png b/images/Dashboard 1 Sami.png
new file mode 100644
index 0000000..fd56adc
Binary files /dev/null and b/images/Dashboard 1 Sami.png differ