diff --git a/data_raw/columns.csv b/data_raw/columns.csv
index 752f819a..78723fe0 100644
--- a/data_raw/columns.csv
+++ b/data_raw/columns.csv
@@ -128,4 +128,3 @@ Intermediary Admin Expenses Calc
 PRIMARY_FUNDING_RECIPIENT_TYPE
 TRIBAL AFFILIATION
 PROJECT PARTNERS
-ej_category
diff --git a/data_raw/data_types.csv b/data_raw/data_types.csv
index 62fa25fb..76c8d7fd 100644
--- a/data_raw/data_types.csv
+++ b/data_raw/data_types.csv
@@ -127,4 +127,3 @@ Intermediary Admin Expenses Calc,int64
 PRIMARY_FUNDING_RECIPIENT_TYPE,object
 TRIBAL AFFILIATION,object
 PROJECT PARTNERS,object
-ej_category,object
diff --git a/initial_view/overview_hypotesting_20241031.html b/initial_view/overview_hypotesting_20241031.html
index ae748039..dbe04908 100644
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@@ -7517,61 +7517,21 @@ a.anchor-link {
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1">## Title: Evaluating Equity and Impact</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Project: California Equity</span>
+<span class="c1">## File: initial_view/overview_hypotesting_20241031.ipynb</span>
 <span class="c1">### Author: David P. Adams</span>
+<span class="c1">### Date: 2024-10-31</span>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">pip</span> install pandas numpy matplotlib seaborn
-</pre></div>
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-<pre>Requirement already satisfied: pandas in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (2.2.3)
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-Requirement already satisfied: pyparsing&gt;=2.3.1 in /home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages (from matplotlib) (3.2.0)
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-Note: you may need to restart the kernel to use updated packages.
-</pre>
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 <div class="highlight hl-ipython3"><pre><span></span><span class="n">columns_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
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@@ -7982,92 +7942,7 @@ Note: you may need to restart the kernel to use updated packages.
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Let's look at the actual column names in the merged dataset</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Columns containing 'County':"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">([</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="s1">'County'</span> <span class="ow">in</span> <span class="n">col</span><span class="p">])</span>
-
-<span class="c1"># Then modify our analysis using the correct county column name:</span>
-<span class="n">geographic_analysis</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'County'</span><span class="p">,</span> <span class="s1">'project_category'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
-    <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="s1">'sum'</span><span class="p">,</span>
-    <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="s1">'sum'</span><span class="p">,</span>
-    <span class="s1">'Total GGRFDisadvantaged Community Funding'</span><span class="p">:</span> <span class="s1">'sum'</span>
-<span class="p">})</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
-
-<span class="c1"># Rest of the analysis:</span>
-<span class="c1"># 1. First look at program distributions</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Program Types:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Program Name'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Project Types:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'Project Type'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Funding Recipient Types:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'PRIMARY_FUNDING_RECIPIENT_TYPE'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
-</pre></div>
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-<pre>Columns containing 'County':
-['County']
-
-Program Types:
-Program Name
-Low Carbon Transportation                                                    105965
-Funding Agricultural Replacement Measures for Emission Reductions Program      8554
-Low-Income Weatherization Program                                              6468
-Community Air Protection                                                       5514
-Water-Energy Efficiency                                                        5111
-Climate Smart Agriculture                                                      1688
-Low Carbon Transit Operations Program                                          1003
-Transformative Climate Communities                                              973
-Urban and Community Forestry Program                                            967
-Woodsmoke Reduction Program                                                     959
-Name: count, dtype: int64
-
-Project Types:
-Project Type
-Voucher                                                2808
-Voucher Incentive                                      1730
-Air Filtration (Air Filtration)                        1570
-Water Use and Energy Efficiency                        1217
-Off-Road Equipment - Mobile Agricultural (Tractors)     891
-                                                       ... 
-Procurement Requirement                                   1
-Education and outreach and enforcement/inspections        1
-Personnel, education and outreach, and procurement        1
-Education &amp; Outreach and Planning                         1
-Equipment, Education and Outreach                         1
-Name: count, Length: 1007, dtype: int64
-
-Funding Recipient Types:
-PRIMARY_FUNDING_RECIPIENT_TYPE
-Business                            423
-Government Agency                   285
-Nonprofit                           113
-Educational institution              21
-California Native American Tribe      4
-Government agency                     2
-Name: count, dtype: int64
-</pre>
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 <div class="highlight hl-ipython3"><pre><span></span><span class="c1"># 1. Fix the classification function</span>
@@ -8324,7 +8199,7 @@ Water-Energy Projects           0.0096
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@@ -8406,17 +8281,17 @@ Water-Energy Projects           0.0096
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-<pre>/tmp/ipykernel_738944/3534785368.py:18: FutureWarning: 
+<pre>/tmp/ipykernel_846869/3534785368.py:18: FutureWarning: 
 
 Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.
 
   sns.barplot(data=plot_data,
-/tmp/ipykernel_738944/3534785368.py:28: FutureWarning: 
+/tmp/ipykernel_846869/3534785368.py:28: FutureWarning: 
 
 Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.
 
   sns.barplot(data=plot_data[mask],
-/tmp/ipykernel_738944/3534785368.py:38: FutureWarning: 
+/tmp/ipykernel_846869/3534785368.py:38: FutureWarning: 
 
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@@ -8443,7 +8318,7 @@ Passing `palette` without assigning `hue` is deprecated and will be removed in v
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 <div class="highlight hl-ipython3"><pre><span></span><span class="c1"># First let's see how many counties we have data for and their distribution</span>
@@ -8635,25 +8510,46 @@ Santa Clara       73.37                    4.43                   0.09
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-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Create a stacked bar chart for project distribution across top counties</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Define a more distinct color palette that groups similar categories</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">'Agricultural Projects'</span><span class="p">:</span> <span class="s1">'#228B22'</span><span class="p">,</span>      <span class="c1"># Forest Green</span>
+    <span class="s1">'Air Quality Projects'</span><span class="p">:</span> <span class="s1">'#87CEEB'</span><span class="p">,</span>       <span class="c1"># Sky Blue</span>
+    <span class="s1">'Business - Small'</span><span class="p">:</span> <span class="s1">'#DDA0DD'</span><span class="p">,</span>          <span class="c1"># Plum</span>
+    <span class="s1">'Community Projects'</span><span class="p">:</span> <span class="s1">'#FF7F50'</span><span class="p">,</span>        <span class="c1"># Coral</span>
+    <span class="s1">'Individual - Home Energy'</span><span class="p">:</span> <span class="s1">'#FFD700'</span><span class="p">,</span>   <span class="c1"># Gold</span>
+    <span class="s1">'Individual - Vehicle'</span><span class="p">:</span> <span class="s1">'#DAA520'</span><span class="p">,</span>       <span class="c1"># Goldenrod</span>
+    <span class="s1">'Institutional - Education'</span><span class="p">:</span> <span class="s1">'#4B0082'</span><span class="p">,</span>  <span class="c1"># Indigo</span>
+    <span class="s1">'Institutional - Government'</span><span class="p">:</span> <span class="s1">'#800000'</span><span class="p">,</span> <span class="c1"># Maroon</span>
+    <span class="s1">'Institutional - Large Business'</span><span class="p">:</span> <span class="s1">'#4682B4'</span><span class="p">,</span> <span class="c1"># Steel Blue</span>
+    <span class="s1">'Institutional - Nonprofit'</span><span class="p">:</span> <span class="s1">'#2E8B57'</span><span class="p">,</span>  <span class="c1"># Sea Green</span>
+    <span class="s1">'Other'</span><span class="p">:</span> <span class="s1">'#808080'</span><span class="p">,</span>                     <span class="c1"># Grey</span>
+    <span class="s1">'Transportation - Other'</span><span class="p">:</span> <span class="s1">'#FF4500'</span><span class="p">,</span>     <span class="c1"># Orange Red</span>
+    <span class="s1">'Water-Energy Projects'</span><span class="p">:</span> <span class="s1">'#00CED1'</span>       <span class="c1"># Turquoise</span>
+<span class="p">}</span>
+
 <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
-<span class="n">county_category_pcts</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">top_counties</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'bar'</span><span class="p">,</span> <span class="n">stacked</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">county_category_pcts</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">top_counties</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'bar'</span><span class="p">,</span> 
+                                          <span class="n">stacked</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                          <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="n">colors</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">county_category_pcts</span><span class="o">.</span><span class="n">columns</span><span class="p">])</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Distribution of Project Funding by Category Across Top Counties'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'County'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Percentage of Total Funding'</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> 
+          <span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">,</span>
+          <span class="n">title</span><span class="o">=</span><span class="s1">'Project Categories'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 
 <span class="c1"># Create a visualization for DAC funding ratio by county</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">'dac_funding_ratio'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">()],</span> 
-            <span class="n">x</span><span class="o">=</span><span class="s1">'County'</span><span class="p">,</span> 
+<span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">'dac_funding_ratio'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">()],</span>
+            <span class="n">x</span><span class="o">=</span><span class="s1">'County'</span><span class="p">,</span>
             <span class="n">y</span><span class="o">=</span><span class="s1">'dac_funding_ratio'</span><span class="p">,</span>
-            <span class="n">order</span><span class="o">=</span><span class="n">top_counties</span><span class="p">)</span>
+            <span class="n">order</span><span class="o">=</span><span class="n">top_counties</span><span class="p">,</span>
+            <span class="n">palette</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'DAC Funding Ratio Distribution by County'</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
@@ -8676,13 +8572,24 @@ Santa Clara       73.37                    4.43                   0.09
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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"/>
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+<pre>/tmp/ipykernel_846869/2919179271.py:33: FutureWarning: 
+
+Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
+
+  sns.boxplot(data=data[data['dac_funding_ratio'].notna()],
+</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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"/>
+<img alt="No description has been provided for this image" class="" 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"/>
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@@ -8692,10 +8599,10 @@ Santa Clara       73.37                    4.43                   0.09
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 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># First, let's create a clearer categorization focused on our question</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Clearer organization </span>
 <span class="k">def</span> <span class="nf">classify_for_ej_analysis</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
     <span class="n">program</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'Program Name'</span><span class="p">])</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
     <span class="n">project_type</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'Project Type'</span><span class="p">])</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
@@ -8903,22 +8810,10 @@ Other                                        0.0026
 </ul>
 <p>Key Findings:</p>
 <ol>
-<li>Your hypothesis about vehicle incentives primarily benefiting wealthier communities is strongly supported by the data</li>
+<li>the hypothesis about vehicle incentives primarily benefiting wealthier communities is strongly supported by the data</li>
 <li>However, individual solar/energy programs are actually very successful at reaching disadvantaged communities</li>
 <li>Large projects, while achieving the most total GHG reductions, have mixed equity outcomes - good at reaching low-income communities but not DACs</li>
 </ol>
-<p>Policy Implications:</p>
-<ol>
-<li>The solar/energy program model could potentially be adapted for vehicle incentives to improve equity</li>
-<li>There may be structural barriers preventing DACs from accessing vehicle incentives that need to be addressed</li>
-<li>Large projects might benefit from stronger DAC targeting requirements, given their current low DAC participation despite good low-income community reach</li>
-</ol>
-<p>Would you like to explore any of these aspects in more detail? We could:</p>
-<ol>
-<li>Analyze what makes the solar/energy programs more successful at reaching DACs</li>
-<li>Look at specific barriers in the vehicle incentive programs</li>
-<li>Examine whether certain types of large projects are better at reaching disadvantaged communities</li>
-</ol>
 </div>
 </div>
 </div>
@@ -8927,10 +8822,10 @@ Other                                        0.0026
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># 1. First, let's analyze the overall spatial distribution of investments</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># 1. Analyze the overall spatial distribution of investments</span>
 <span class="n">county_analysis</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'County'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
     <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">],</span>
     <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="s1">'sum'</span><span class="p">,</span>
@@ -8942,7 +8837,7 @@ Other                                        0.0026
 <span class="c1"># Add per capita metrics (we would need to merge with county population data)</span>
 <span class="c1"># Add GHG reduction per dollar by county</span>
 
-<span class="c1"># 2. Let's analyze vulnerable communities specifically</span>
+<span class="c1"># 2. Analyze vulnerable communities specifically</span>
 <span class="n">vulnerability_analysis</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
     <span class="s1">'Total Projects'</span><span class="p">:</span> <span class="p">[</span>
         <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span>
@@ -9172,7 +9067,7 @@ Water-Energy Projects          False
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>/tmp/ipykernel_738944/3838036285.py:63: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.
+<pre>/tmp/ipykernel_846869/3616794259.py:63: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.
   plt.tight_layout()
 </pre>
 </div>
@@ -9190,7 +9085,7 @@ Water-Energy Projects          False
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
@@ -9521,13 +9416,1856 @@ Key Findings:</li>
     <span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">:</span> <span class="s1">'mean'</span>
 <span class="p">})</span>
 </pre></div>
-<p>Would you like me to focus on developing any of these next steps in more detail? We could:</p>
-<ol>
-<li>Create specific visualizations for the paper</li>
-<li>Develop detailed policy recommendations</li>
-<li>Conduct additional statistical analyses</li>
-<li>Create a framework for program evaluation</li>
-</ol>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
+
+<span class="c1"># Read the data (I see you already have this loaded as 'data')</span>
+
+<span class="c1"># 1. First, let's create our core analytical metrics</span>
+<span class="k">def</span> <span class="nf">calculate_program_metrics</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="n">metrics</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Program Name'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
+        <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+        <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+        <span class="s1">'Total GGRFDisadvantaged Community Funding'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+        <span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span>
+        <span class="s1">'Is Low Income Communities'</span><span class="p">:</span> <span class="s1">'mean'</span>
+    <span class="p">})</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+    
+    <span class="c1"># Add efficiency metrics</span>
+    <span class="n">metrics</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">metrics</span><span class="p">[(</span><span class="s1">'Total Project GHGReductions'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span> <span class="o">/</span> 
+                                <span class="n">metrics</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)])</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    
+    <span class="n">metrics</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">metrics</span><span class="p">[(</span><span class="s1">'Total GGRFDisadvantaged Community Funding'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span> <span class="o">/</span> 
+                                   <span class="n">metrics</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)])</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">metrics</span>
+
+<span class="n">program_metrics</span> <span class="o">=</span> <span class="n">calculate_program_metrics</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+<span class="c1"># Display top programs by different metrics</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Top 5 Programs by Total Investment:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">program_metrics</span><span class="o">.</span><span class="n">sort_values</span><span class="p">((</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">),</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Top 5 Programs by GHG Reduction Efficiency:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">program_metrics</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'GHG_per_dollar'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Top 5 Programs by DAC Funding Ratio:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">program_metrics</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'DAC_funding_ratio'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Top 5 Programs by Total Investment:
+                                                   Total Project Cost  \
+                                                                count   
+Program Name                                                            
+Transit and Intercity Rail Capital Program                        245   
+Low Carbon Transit Operations Program                            1003   
+Affordable Housing and Sustainable Communities ...                151   
+Low Carbon Transportation                                      105965   
+Community Air Protection                                         5514   
+
+                                                                  \
+                                                             sum   
+Program Name                                                       
+Transit and Intercity Rail Capital Program          101461750666   
+Low Carbon Transit Operations Program                 8777590799   
+Affordable Housing and Sustainable Communities ...    8492944393   
+Low Carbon Transportation                             3759508172   
+Community Air Protection                              1211608583   
+
+                                                                  \
+                                                            mean   
+Program Name                                                       
+Transit and Intercity Rail Capital Program          4.141296e+08   
+Low Carbon Transit Operations Program               8.751337e+06   
+Affordable Housing and Sustainable Communities ...  5.624466e+07   
+Low Carbon Transportation                           3.547877e+04   
+Community Air Protection                            2.197331e+05   
+
+                                                   Total Project GHGReductions  \
+                                                                           sum   
+Program Name                                                                     
+Transit and Intercity Rail Capital Program                            23458701   
+Low Carbon Transit Operations Program                                  6971510   
+Affordable Housing and Sustainable Communities ...                     3590596   
+Low Carbon Transportation                                              4306922   
+Community Air Protection                                                250801   
+
+                                                              \
+                                                        mean   
+Program Name                                                   
+Transit and Intercity Rail Capital Program          95749.80   
+Low Carbon Transit Operations Program                6950.66   
+Affordable Housing and Sustainable Communities ...  23778.78   
+Low Carbon Transportation                              40.64   
+Community Air Protection                               45.48   
+
+                                                   Total GGRFDisadvantaged Community Funding  \
+                                                                                         sum   
+Program Name                                                                                   
+Transit and Intercity Rail Capital Program                                       477700000.0   
+Low Carbon Transit Operations Program                                            127826618.0   
+Affordable Housing and Sustainable Communities ...                               241985013.0   
+Low Carbon Transportation                                                        324003740.0   
+Community Air Protection                                                                 0.0   
+
+                                                                \
+                                                          mean   
+Program Name                                                     
+Transit and Intercity Rail Capital Program          9952083.33   
+Low Carbon Transit Operations Program                399458.18   
+Affordable Housing and Sustainable Communities ...  4033083.55   
+Low Carbon Transportation                              7644.66   
+Community Air Protection                                   NaN   
+
+                                                   Is Benefit Disadvantaged Communities  \
+                                                                                   mean   
+Program Name                                                                              
+Transit and Intercity Rail Capital Program                                         0.16   
+Low Carbon Transit Operations Program                                              0.21   
+Affordable Housing and Sustainable Communities ...                                 0.22   
+Low Carbon Transportation                                                          0.36   
+Community Air Protection                                                           0.00   
+
+                                                   Is Low Income Communities  \
+                                                                        mean   
+Program Name                                                                   
+Transit and Intercity Rail Capital Program                              0.73   
+Low Carbon Transit Operations Program                                   0.64   
+Affordable Housing and Sustainable Communities ...                      0.60   
+Low Carbon Transportation                                               0.59   
+Community Air Protection                                                0.87   
+
+                                                   GHG_per_dollar  \
+                                                                    
+Program Name                                                        
+Transit and Intercity Rail Capital Program                 0.0002   
+Low Carbon Transit Operations Program                      0.0008   
+Affordable Housing and Sustainable Communities ...         0.0004   
+Low Carbon Transportation                                  0.0011   
+Community Air Protection                                   0.0002   
+
+                                                   DAC_funding_ratio  
+                                                                      
+Program Name                                                          
+Transit and Intercity Rail Capital Program                    0.0047  
+Low Carbon Transit Operations Program                         0.0146  
+Affordable Housing and Sustainable Communities ...            0.0285  
+Low Carbon Transportation                                     0.0862  
+Community Air Protection                                      0.0000  
+
+Top 5 Programs by GHG Reduction Efficiency:
+                                                   Total Project Cost  \
+                                                                count   
+Program Name                                                            
+Fluorinated Gases Emission Reduction Incentives                    15   
+Sustainable Agricultural Lands Conservation Pro...                 96   
+Climate Smart Agriculture                                        1688   
+Forest Health Program                                             258   
+Food Production Investment Program                                 56   
+
+                                                                           \
+                                                          sum        mean   
+Program Name                                                                
+Fluorinated Gases Emission Reduction Incentives             0        0.00   
+Sustainable Agricultural Lands Conservation Pro...  212276194  2211210.35   
+Climate Smart Agriculture                           886514974   525186.60   
+Forest Health Program                               837651776  3246712.31   
+Food Production Investment Program                  181509329  3241238.02   
+
+                                                   Total Project GHGReductions  \
+                                                                           sum   
+Program Name                                                                     
+Fluorinated Gases Emission Reduction Incentives                          36715   
+Sustainable Agricultural Lands Conservation Pro...                    15080237   
+Climate Smart Agriculture                                             24249378   
+Forest Health Program                                                 20575142   
+Food Production Investment Program                                     2974529   
+
+                                                               \
+                                                         mean   
+Program Name                                                    
+Fluorinated Gases Emission Reduction Incentives       2447.67   
+Sustainable Agricultural Lands Conservation Pro...  157085.80   
+Climate Smart Agriculture                            14365.75   
+Forest Health Program                                79748.61   
+Food Production Investment Program                   53116.59   
+
+                                                   Total GGRFDisadvantaged Community Funding  \
+                                                                                         sum   
+Program Name                                                                                   
+Fluorinated Gases Emission Reduction Incentives                                          0.0   
+Sustainable Agricultural Lands Conservation Pro...                                 4264000.0   
+Climate Smart Agriculture                                                         29472350.0   
+Forest Health Program                                                                    0.0   
+Food Production Investment Program                                                       0.0   
+
+                                                               \
+                                                         mean   
+Program Name                                                    
+Fluorinated Gases Emission Reduction Incentives           NaN   
+Sustainable Agricultural Lands Conservation Pro...  101523.81   
+Climate Smart Agriculture                            50814.40   
+Forest Health Program                                    0.00   
+Food Production Investment Program                        NaN   
+
+                                                   Is Benefit Disadvantaged Communities  \
+                                                                                   mean   
+Program Name                                                                              
+Fluorinated Gases Emission Reduction Incentives                                    0.00   
+Sustainable Agricultural Lands Conservation Pro...                                 0.01   
+Climate Smart Agriculture                                                          0.12   
+Forest Health Program                                                              0.00   
+Food Production Investment Program                                                 0.00   
+
+                                                   Is Low Income Communities  \
+                                                                        mean   
+Program Name                                                                   
+Fluorinated Gases Emission Reduction Incentives                         0.60   
+Sustainable Agricultural Lands Conservation Pro...                      0.03   
+Climate Smart Agriculture                                               0.35   
+Forest Health Program                                                   0.30   
+Food Production Investment Program                                      0.71   
+
+                                                   GHG_per_dollar  \
+                                                                    
+Program Name                                                        
+Fluorinated Gases Emission Reduction Incentives               inf   
+Sustainable Agricultural Lands Conservation Pro...         0.0710   
+Climate Smart Agriculture                                  0.0274   
+Forest Health Program                                      0.0246   
+Food Production Investment Program                         0.0164   
+
+                                                   DAC_funding_ratio  
+                                                                      
+Program Name                                                          
+Fluorinated Gases Emission Reduction Incentives                  NaN  
+Sustainable Agricultural Lands Conservation Pro...            0.0201  
+Climate Smart Agriculture                                     0.0332  
+Forest Health Program                                         0.0000  
+Food Production Investment Program                            0.0000  
+
+Top 5 Programs by DAC Funding Ratio:
+                                     Total Project Cost             \
+                                                  count        sum   
+Program Name                                                         
+Low-Income Weatherization Program                  6468  307899153   
+Urban and Community Forestry Program                967  107767408   
+Water-Energy Efficiency                            5111   85125916   
+Urban Greening Program                              177  270240730   
+Wetlands and Watershed Restoration                   22   86150135   
+
+                                                 Total Project GHGReductions  \
+                                            mean                         sum   
+Program Name                                                                   
+Low-Income Weatherization Program       47603.46                      594567   
+Urban and Community Forestry Program   111445.10                      479295   
+Water-Energy Efficiency                 16655.43                      425225   
+Urban Greening Program                1526783.79                       54285   
+Wetlands and Watershed Restoration    3915915.23                      999950   
+
+                                                \
+                                          mean   
+Program Name                                     
+Low-Income Weatherization Program        91.92   
+Urban and Community Forestry Program    495.65   
+Water-Energy Efficiency                  83.20   
+Urban Greening Program                  306.69   
+Wetlands and Watershed Restoration    45452.27   
+
+                                     Total GGRFDisadvantaged Community Funding  \
+                                                                           sum   
+Program Name                                                                     
+Low-Income Weatherization Program                                  132666048.0   
+Urban and Community Forestry Program                                34138019.0   
+Water-Energy Efficiency                                             23286225.0   
+Urban Greening Program                                              68717491.0   
+Wetlands and Watershed Restoration                                  13382907.0   
+
+                                                  \
+                                            mean   
+Program Name                                       
+Low-Income Weatherization Program       22383.34   
+Urban and Community Forestry Program    60635.91   
+Water-Energy Efficiency                  4556.10   
+Urban Greening Program                 848364.09   
+Wetlands and Watershed Restoration    1115242.25   
+
+                                     Is Benefit Disadvantaged Communities  \
+                                                                     mean   
+Program Name                                                                
+Low-Income Weatherization Program                                    0.90   
+Urban and Community Forestry Program                                 0.21   
+Water-Energy Efficiency                                              0.45   
+Urban Greening Program                                               0.19   
+Wetlands and Watershed Restoration                                   0.09   
+
+                                     Is Low Income Communities GHG_per_dollar  \
+                                                          mean                  
+Program Name                                                                    
+Low-Income Weatherization Program                         0.97         0.0019   
+Urban and Community Forestry Program                      0.09         0.0044   
+Water-Energy Efficiency                                   0.00         0.0050   
+Urban Greening Program                                    0.27         0.0002   
+Wetlands and Watershed Restoration                        0.27         0.0116   
+
+                                     DAC_funding_ratio  
+                                                        
+Program Name                                            
+Low-Income Weatherization Program               0.4309  
+Urban and Community Forestry Program            0.3168  
+Water-Energy Efficiency                         0.2736  
+Urban Greening Program                          0.2543  
+Wetlands and Watershed Restoration              0.1553  
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+<span class="c1"># Let's first get a clear view of our data</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Total Investment by Program (Top 10):"</span><span class="p">)</span>
+<span class="n">investment_by_program</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Program Name'</span><span class="p">)[</span><span class="s1">'Total Project Cost'</span><span class="p">]</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">investment_by_program</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">))</span>
+
+<span class="c1"># Calculate our key metrics</span>
+<span class="n">program_metrics</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Program Name'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
+    <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+    <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+    <span class="s1">'Total GGRFDisadvantaged Community Funding'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+    <span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">:</span> <span class="s1">'mean'</span>
+<span class="p">})</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Add efficiency metrics</span>
+<span class="n">program_metrics</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">program_metrics</span><span class="p">[(</span><span class="s1">'Total Project GHGReductions'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span> <span class="o">/</span> 
+                                   <span class="n">program_metrics</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)])</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+
+<span class="n">program_metrics</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">program_metrics</span><span class="p">[(</span><span class="s1">'Total GGRFDisadvantaged Community Funding'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span> <span class="o">/</span> 
+                                      <span class="n">program_metrics</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)])</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+
+<span class="c1"># Clean data for visualization</span>
+<span class="n">clean_data</span> <span class="o">=</span> <span class="n">program_metrics</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">program_metrics</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'inf'</span><span class="p">))</span> <span class="o">&amp;</span> 
+    <span class="p">(</span><span class="n">program_metrics</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span><span class="o">.</span><span class="n">notna</span><span class="p">())</span>
+<span class="p">]</span>
+
+<span class="c1"># Create visualization</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+<span class="c1"># Basic scatter plot</span>
+<span class="n">scatter</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">clean_data</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">],</span>
+                     <span class="n">clean_data</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">],</span>
+                     <span class="n">s</span><span class="o">=</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e7</span><span class="p">,</span>
+                     <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
+
+<span class="c1"># Add labels for major programs</span>
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">clean_data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+    <span class="k">if</span> <span class="n">row</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span> <span class="o">&gt;</span> <span class="mi">500000000</span><span class="p">:</span>  <span class="c1"># Only label major programs</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">idx</span><span class="p">[:</span><span class="mi">30</span><span class="p">]</span> <span class="o">+</span> <span class="s1">'...'</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">30</span> <span class="k">else</span> <span class="n">idx</span><span class="p">,</span>
+                    <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]),</span>
+                    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
+                    <span class="n">textcoords</span><span class="o">=</span><span class="s1">'offset points'</span><span class="p">,</span>
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'DAC Funding Ratio'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'GHG Reduction per Dollar'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Program Performance: Equity vs. Climate Impact</span><span class="se">\n</span><span class="s1">Size = Total Investment'</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Analysis of key metrics</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Program Performance Analysis:"</span><span class="p">)</span>
+
+<span class="c1"># Top programs by equity</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">1. Top 5 Programs by DAC Funding Ratio:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">clean_data</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s1">'DAC_funding_ratio'</span><span class="p">)[</span>
+    <span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">,</span> <span class="s1">'GHG_per_dollar'</span><span class="p">,</span> <span class="p">(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span>
+<span class="p">])</span>
+
+<span class="c1"># Top programs by GHG efficiency</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">2. Top 5 Programs by GHG Reduction Efficiency:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">clean_data</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s1">'GHG_per_dollar'</span><span class="p">)[</span>
+    <span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">,</span> <span class="s1">'GHG_per_dollar'</span><span class="p">,</span> <span class="p">(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span>
+<span class="p">])</span>
+
+<span class="c1"># Investment distribution</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">3. Investment Distribution Summary:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Total Investment by Program (Top 10):
+                                                     count           sum  \
+Program Name                                                               
+Transit and Intercity Rail Capital Program             245  101461750666   
+Low Carbon Transit Operations Program                 1003    8777590799   
+Affordable Housing and Sustainable Communities ...     151    8492944393   
+Low Carbon Transportation                           105965    3759508172   
+Community Air Protection                              5514    1211608583   
+Climate Smart Agriculture                             1688     886514974   
+Forest Health Program                                  258     837651776   
+Funding Agricultural Replacement Measures for E...    8554     771297976   
+Waste Diversion                                        265     643549843   
+Fire Prevention Program                                600     614281793   
+
+                                                            mean  
+Program Name                                                      
+Transit and Intercity Rail Capital Program          4.141296e+08  
+Low Carbon Transit Operations Program               8.751337e+06  
+Affordable Housing and Sustainable Communities ...  5.624466e+07  
+Low Carbon Transportation                           3.547877e+04  
+Community Air Protection                            2.197331e+05  
+Climate Smart Agriculture                           5.251866e+05  
+Forest Health Program                               3.246712e+06  
+Funding Agricultural Replacement Measures for E...  9.016811e+04  
+Waste Diversion                                     2.428490e+06  
+Fire Prevention Program                             1.023803e+06  
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+<pre>/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1465: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
+  x = float(self.convert_xunits(x))
+/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1467: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
+  y = float(self.convert_yunits(y))
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Program Performance Analysis:
+
+1. Top 5 Programs by DAC Funding Ratio:
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+<pre>
+<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
+<span class="ansi-red-fg">KeyError</span>                                  Traceback (most recent call last)
+Cell <span class="ansi-green-fg">In[27], line 62</span>
+<span class="ansi-green-intense-fg ansi-bold">     60</span> <span style="color: rgb(95,135,135)"># Top programs by equity</span>
+<span class="ansi-green-intense-fg ansi-bold">     61</span> <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span style="color: rgb(175,0,0)">1. Top 5 Programs by DAC Funding Ratio:</span><span style="color: rgb(175,0,0)">"</span>)
+<span class="ansi-green-fg">---&gt; 62</span> <span style="color: rgb(0,135,0)">print</span>(<span class="ansi-yellow-bg">clean_data</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">nlargest</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">5</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">DAC_funding_ratio</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">[</span>
+<span class="ansi-green-intense-fg ansi-bold">     63</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">DAC_funding_ratio</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">GHG_per_dollar</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">Total Project Cost</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">sum</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">'</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">]</span>
+<span class="ansi-green-intense-fg ansi-bold">     64</span> <span class="ansi-yellow-bg">]</span>)
+<span class="ansi-green-intense-fg ansi-bold">     66</span> <span style="color: rgb(95,135,135)"># Top programs by GHG efficiency</span>
+<span class="ansi-green-intense-fg ansi-bold">     67</span> <span style="color: rgb(0,135,0)">print</span>(<span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,0)">\n</span><span style="color: rgb(175,0,0)">2. Top 5 Programs by GHG Reduction Efficiency:</span><span style="color: rgb(175,0,0)">"</span>)
+
+File <span class="ansi-green-fg">~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/frame.py:4108</span>, in <span class="ansi-cyan-fg">DataFrame.__getitem__</span><span class="ansi-blue-fg">(self, key)</span>
+<span class="ansi-green-intense-fg ansi-bold">   4106</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_iterator(key):
+<span class="ansi-green-intense-fg ansi-bold">   4107</span>         key <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">list</span>(key)
+<span class="ansi-green-fg">-&gt; 4108</span>     indexer <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">columns</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_get_indexer_strict</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">key</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">columns</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg">)</span>[<span style="color: rgb(98,98,98)">1</span>]
+<span class="ansi-green-intense-fg ansi-bold">   4110</span> <span style="color: rgb(95,135,135)"># take() does not accept boolean indexers</span>
+<span class="ansi-green-intense-fg ansi-bold">   4111</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">getattr</span>(indexer, <span style="color: rgb(175,0,0)">"</span><span style="color: rgb(175,0,0)">dtype</span><span style="color: rgb(175,0,0)">"</span>, <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>) <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(0,135,0)">bool</span>:
+
+File <span class="ansi-green-fg">~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/indexes/multi.py:2763</span>, in <span class="ansi-cyan-fg">MultiIndex._get_indexer_strict</span><span class="ansi-blue-fg">(self, key, axis_name)</span>
+<span class="ansi-green-intense-fg ansi-bold">   2760</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">len</span>(keyarr) <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span style="color: rgb(0,135,0)">isinstance</span>(keyarr[<span style="color: rgb(98,98,98)">0</span>], <span style="color: rgb(0,135,0)">tuple</span>):
+<span class="ansi-green-intense-fg ansi-bold">   2761</span>     indexer <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_get_indexer_level_0(keyarr)
+<span class="ansi-green-fg">-&gt; 2763</span>     <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">_raise_if_missing</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">key</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">indexer</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">axis_name</span><span class="ansi-yellow-bg">)</span>
+<span class="ansi-green-intense-fg ansi-bold">   2764</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">self</span>[indexer], indexer
+<span class="ansi-green-intense-fg ansi-bold">   2766</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span style="color: rgb(0,135,0)">super</span>()<span style="color: rgb(98,98,98)">.</span>_get_indexer_strict(key, axis_name)
+
+File <span class="ansi-green-fg">~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/indexes/multi.py:2781</span>, in <span class="ansi-cyan-fg">MultiIndex._raise_if_missing</span><span class="ansi-blue-fg">(self, key, indexer, axis_name)</span>
+<span class="ansi-green-intense-fg ansi-bold">   2779</span> cmask <span style="color: rgb(98,98,98)">=</span> check <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(98,98,98)">-</span><span style="color: rgb(98,98,98)">1</span>
+<span class="ansi-green-intense-fg ansi-bold">   2780</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> cmask<span style="color: rgb(98,98,98)">.</span>any():
+<span class="ansi-green-fg">-&gt; 2781</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">KeyError</span>(<span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>keyarr[cmask]<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)"> not in index</span><span style="color: rgb(175,0,0)">"</span>)
+<span class="ansi-green-intense-fg ansi-bold">   2782</span> <span style="color: rgb(95,135,135)"># We get here when levels still contain values which are not</span>
+<span class="ansi-green-intense-fg ansi-bold">   2783</span> <span style="color: rgb(95,135,135)"># actually in Index anymore</span>
+<span class="ansi-green-intense-fg ansi-bold">   2784</span> <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">KeyError</span>(<span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">"</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>keyarr<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)"> not in index</span><span style="color: rgb(175,0,0)">"</span>)
+
+<span class="ansi-red-fg">KeyError</span>: "[('Total Project Cost', 'sum')] not in index"</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># install sklearn library</span>
+<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>scikit-learn
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Collecting scikit-learn
+  Using cached scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)
+Requirement already satisfied: numpy&gt;=1.19.5 in ./.venv/lib/python3.12/site-packages (from scikit-learn) (2.1.2)
+Requirement already satisfied: scipy&gt;=1.6.0 in ./.venv/lib/python3.12/site-packages (from scikit-learn) (1.14.1)
+Collecting joblib&gt;=1.2.0 (from scikit-learn)
+  Using cached joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)
+Collecting threadpoolctl&gt;=3.1.0 (from scikit-learn)
+  Using cached threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)
+Using cached scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.9 MB)
+Using cached joblib-1.4.2-py3-none-any.whl (301 kB)
+Using cached threadpoolctl-3.5.0-py3-none-any.whl (18 kB)
+Installing collected packages: threadpoolctl, joblib, scikit-learn
+Successfully installed joblib-1.4.2 scikit-learn-1.5.2 threadpoolctl-3.5.0
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Let's first look at the raw total investment amounts</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"Raw Investment Totals by Program:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+
+<span class="c1"># Modified analysis with corrected calculations</span>
+<span class="k">def</span> <span class="nf">create_program_typology_v2</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="c1"># Calculate thresholds</span>
+    <span class="n">ghg_med</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+    <span class="n">dac_med</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+    <span class="n">size_med</span> <span class="o">=</span> <span class="n">df</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
+    
+    <span class="c1"># Create categories with corrected investment calculations</span>
+    <span class="n">conditions</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">ghg_med</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">dac_med</span><span class="p">),</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">ghg_med</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">dac_med</span><span class="p">),</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">ghg_med</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">dac_med</span><span class="p">),</span>
+        <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">ghg_med</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">dac_med</span><span class="p">)</span>
+    <span class="p">]</span>
+    
+    <span class="n">choices</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="s1">'High Performer (Both)'</span><span class="p">,</span>
+        <span class="s1">'GHG Efficient'</span><span class="p">,</span>
+        <span class="s1">'Equity Focused'</span><span class="p">,</span>
+        <span class="s1">'Below Median'</span>
+    <span class="p">]</span>
+    
+    <span class="n">df</span><span class="p">[</span><span class="s1">'Performance_Type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">conditions</span><span class="p">,</span> <span class="n">choices</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="s1">'Other'</span><span class="p">)</span>
+    <span class="n">df</span><span class="p">[</span><span class="s1">'Scale_Type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">df</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span> <span class="o">&gt;</span> <span class="n">size_med</span><span class="p">,</span> 
+                               <span class="s1">'Large Scale'</span><span class="p">,</span> 
+                               <span class="s1">'Small Scale'</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">df</span>
+
+<span class="c1"># Rerun analysis with corrected calculations</span>
+<span class="n">analysis_data_v2</span> <span class="o">=</span> <span class="n">clean_data</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">analysis_data_v2</span> <span class="o">=</span> <span class="n">create_program_typology_v2</span><span class="p">(</span><span class="n">analysis_data_v2</span><span class="p">)</span>
+
+<span class="c1"># Summary statistics with corrected investment amounts</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Revised Program Type Characteristics:"</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ptype</span> <span class="ow">in</span> <span class="n">analysis_data_v2</span><span class="p">[</span><span class="s1">'Performance_Type'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+    <span class="n">subset</span> <span class="o">=</span> <span class="n">analysis_data_v2</span><span class="p">[</span><span class="n">analysis_data_v2</span><span class="p">[</span><span class="s1">'Performance_Type'</span><span class="p">]</span> <span class="o">==</span> <span class="n">ptype</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="si">{</span><span class="n">ptype</span><span class="si">}</span><span class="s2">:"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Number of programs: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">subset</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average GHG efficiency: </span><span class="si">{</span><span class="n">subset</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average DAC ratio: </span><span class="si">{</span><span class="n">subset</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Average project cost: $</span><span class="si">{</span><span class="n">subset</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span><span class="w"> </span><span class="s1">'mean'</span><span class="p">)]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">,.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Largest program: $</span><span class="si">{</span><span class="n">subset</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span><span class="w"> </span><span class="s1">'sum'</span><span class="p">)]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="si">:</span><span class="s2">,.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Example programs:"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">subset</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[:</span><span class="mi">3</span><span class="p">])</span>
+
+<span class="c1"># Create visualization with corrected data</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+<span class="c1"># Plot each program type with different colors and corrected sizes</span>
+<span class="k">for</span> <span class="n">ptype</span> <span class="ow">in</span> <span class="n">analysis_data_v2</span><span class="p">[</span><span class="s1">'Performance_Type'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="n">analysis_data_v2</span><span class="p">[</span><span class="s1">'Performance_Type'</span><span class="p">]</span> <span class="o">==</span> <span class="n">ptype</span>
+    <span class="n">subset</span> <span class="o">=</span> <span class="n">analysis_data_v2</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
+    
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">subset</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">],</span>
+               <span class="n">subset</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">],</span>
+               <span class="n">s</span><span class="o">=</span><span class="n">subset</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e5</span><span class="p">,</span>  <span class="c1"># Adjusted scaling factor</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
+               <span class="n">label</span><span class="o">=</span><span class="n">ptype</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'DAC Funding Ratio'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'GHG Reduction per Dollar'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Program Performance Types (Corrected Investment Scaling)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'upper left'</span><span class="p">)</span>
+
+<span class="c1"># Add annotations for major programs</span>
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">analysis_data_v2</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+    <span class="k">if</span> <span class="n">row</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)]</span> <span class="o">&gt;</span> <span class="mf">1e6</span><span class="p">:</span>  <span class="c1"># Adjust threshold as needed</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">idx</span><span class="p">[:</span><span class="mi">30</span><span class="p">]</span> <span class="o">+</span> <span class="s1">'...'</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">30</span> <span class="k">else</span> <span class="n">idx</span><span class="p">,</span>
+                    <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">]),</span>
+                    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
+                    <span class="n">textcoords</span><span class="o">=</span><span class="s1">'offset points'</span><span class="p">,</span>
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Raw Investment Totals by Program:
+Program Name
+Transit and Intercity Rail Capital Program                                   101461750666
+Low Carbon Transit Operations Program                                          8777590799
+Affordable Housing and Sustainable Communities Program                         8492944393
+Low Carbon Transportation                                                      3759508172
+Community Air Protection                                                       1211608583
+Climate Smart Agriculture                                                       886514974
+Forest Health Program                                                           837651776
+Funding Agricultural Replacement Measures for Emission Reductions Program       771297976
+Waste Diversion                                                                 643549843
+Fire Prevention Program                                                         614281793
+Name: (Total Project Cost, sum), dtype: int64
+
+Revised Program Type Characteristics:
+
+Below Median:
+Number of programs: 16
+Average GHG efficiency: 0.0000
+Average DAC ratio: 0.0000
+Average project cost: $1,352,617.47
+Largest program: $1,211,608,583.00
+
+Example programs:
+['Active Transportation Program', 'Climate Change Research Program', 'Climate Ready Program ']
+
+High Performer (Both):
+Number of programs: 10
+Average GHG efficiency: 0.0129
+Average DAC ratio: 0.1410
+Average project cost: $7,428,798.66
+Largest program: $8,777,590,799.00
+
+Example programs:
+['Affordable Housing and Sustainable Communities Program', 'Climate Smart Agriculture', 'Low Carbon Transit Operations Program']
+
+GHG Efficient:
+Number of programs: 9
+Average GHG efficiency: 0.0089
+Average DAC ratio: 0.0000
+Average project cost: $1,967,095.81
+Largest program: $837,651,776.00
+
+Example programs:
+['Climate Adaptation and Resiliency Program', 'Food Production Investment Program', 'Forest Health Program']
+
+Equity Focused:
+Number of programs: 3
+Average GHG efficiency: 0.0001
+Average DAC ratio: 0.1038
+Average project cost: $138,638,075.83
+Largest program: $101,461,750,666.00
+
+Example programs:
+['Community Assistance for Climate Equity Program', 'Transit and Intercity Rail Capital Program', 'Urban Greening Program']
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+<pre>/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1465: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
+  x = float(self.convert_xunits(x))
+/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1467: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
+  y = float(self.convert_yunits(y))
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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"/>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Let's create a summary visualization that highlights our main findings</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+
+<span class="c1"># Create four subplots</span>
+<span class="n">fig</span><span class="p">,</span> <span class="p">((</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">),</span> <span class="p">(</span><span class="n">ax3</span><span class="p">,</span> <span class="n">ax4</span><span class="p">))</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">))</span>
+
+<span class="c1"># 1. Program Scale Distribution (Top 10 by Investment)</span>
+<span class="n">top_10_programs</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_10_programs</span><span class="p">)),</span> <span class="n">top_10_programs</span><span class="o">/</span><span class="mf">1e9</span><span class="p">)</span>  <span class="c1"># Convert to billions</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_10_programs</span><span class="p">)))</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="n">name</span><span class="p">[:</span><span class="mi">30</span><span class="p">]</span> <span class="o">+</span> <span class="s1">'...'</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">30</span> <span class="k">else</span> <span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">top_10_programs</span><span class="o">.</span><span class="n">index</span><span class="p">])</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Total Investment (Billions $)'</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Top 10 Programs by Investment'</span><span class="p">)</span>
+
+<span class="c1"># 2. GHG Efficiency vs Project Size</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e6</span><span class="p">,</span> 
+           <span class="n">clean_data</span><span class="p">[</span><span class="s1">'GHG_per_dollar'</span><span class="p">],</span>
+           <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Average Project Size (Millions $)'</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'GHG Reduction per Dollar'</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'GHG Efficiency vs Project Size'</span><span class="p">)</span>
+
+<span class="c1"># 3. DAC Funding Ratio vs Program Size</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e9</span><span class="p">,</span>
+           <span class="n">clean_data</span><span class="p">[</span><span class="s1">'DAC_funding_ratio'</span><span class="p">],</span>
+           <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Total Program Investment (Billions $)'</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'DAC Funding Ratio'</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Equity vs Program Size'</span><span class="p">)</span>
+
+<span class="c1"># 4. Project Count vs Average Size</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">)],</span>
+           <span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e6</span><span class="p">,</span>
+           <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Number of Projects'</span><span class="p">)</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Average Project Size (Millions $)'</span><span class="p">)</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Project Count vs Average Size'</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>&lt;Figure size 1500x1200 with 0 Axes&gt;</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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IB7D8E4AAAAAAAosooVK6bk5GQFBATIx8dHBoMhTxuLxSKDwaDc3Fw7VAgAAAAAKAgE4wAAAAAAoMj64Ycf5OvrK0n68ccf7VwNAAAAAOB2IRgHiqiwkWtkdHG/apukia3uUDUAAAAAYB9NmjSx/rt8+fIKCgrKM2rcYrHoyJEjd7o0AAAAAEABMtq7AAAAAAAAgLtB+fLl9ffff+c5fubMGZUvX94OFQEAAAAACgrBOAAAAAAAgP5/LfHLpaWlydXV1Q4VAQAAAAAKClOpAwAAAACAIm3AgAGSJIPBoOHDh8vd/f+XncrNzdXGjRtVs2ZNO1UHAAAAACgIBOMAAAAAAKBI27Ztm6SLI8Z37twpZ2dn6zlnZ2fVqFFDAwcOtFd5AAAAAIACQDAOAAAAAACKtB9//FGS1LNnT02fPl3e3t52rggAAAAAUNBYYxwAAAAAAEDStGnTlJOTk+f4mTNnlJqaaoeKAAAAAAAFhWAcAAAAAABA0pNPPqnFixfnOb5kyRI9+eSTdqgIAAAAAFBQCMYBAAAAAAAkbdy4UQ8++GCe402bNtXGjRvtUBEAAAAAoKAQjAMAAAAAAEjKzMzMdyr17OxsZWRk2KEiAAAAAEBBIRgHAAAAAACQVL9+fX3wwQd5js+ZM0d16tSxQ0UAAAAAgILiaO8CAAAAAAAA7gbjxo1T8+bNtWPHDjVr1kySFBsbq82bN+v777+3c3UAAAAAgFvBiHEAAAAAAABJjRo1Unx8vIKCgrRkyRJ9/fXXCgkJ0W+//aYHHnjA3uUBAAAAAG4BI8YBAAAAAAD+p2bNmlq0aJG9ywAAAAAAFDCCcQAAAAAAAEmHDx++6vmyZcveoUoAAAAAAAWNYBwAAAAAAEBScHCwDAbDFc/n5ubewWoAAAAAAAWJYBwAAAAAAEDStm3bbPazs7O1bds2TZkyRePHj7dTVQAAAACAgkAwDgAAAAAAIKlGjRp5jtWtW1elS5fW22+/rfbt29uhKgAAAABAQTDauwAAAAAAAIC7WaVKlbR582Z7lwEAAAAAuAWMGAcAAAAAAJCUmppqs2+xWJScnKxRo0YpNDTUTlUBAAAAAAoCwTgAAAAAAIAkHx8fGQwGm2MWi0VBQUFavHixnaoCAAAAABQEgnEAAAAAAABJP/74o82+0WiUv7+/QkJC5OjI/0IBAAAAgHsZ3+ogSTIYDPryyy/Vrl27O37vUaNGacWKFdq+ffsdv7e9xcXF6cEHH9Q///wjHx8fe5cDAAAAAEVakyZN7F0CAAAAAOA2Mdq7gLvN8ePH9fLLL6tChQpycXFRUFCQ2rRpo9jYWHuXdlslJyfr0UcflSQlJSXJYDAUaFA9YcIEOTg46O23385zbuDAgTf1fqOjo2UwGGQwGGQ0GlWmTBn17NlTJ0+evKVao6Ojb0tI3bRpU/Xv39/mWMOGDZWcnCyTyVTg9wMAAAAAXNvKlSuvewMAAAAA3LsYMf4vSUlJatSokXx8fPT2228rPDxc2dnZWrNmjf773/9q79699i7xtilZsuRt7X/+/PkaNGiQ5s+fr9dee83mnKenpzw9Pa94bVZWlpydnfM95+3trcTERJnNZu3YsUM9e/bUX3/9pTVr1uRpm5ubaw3R7xbOzs63/d0DAAAAAK7s8pnTDAaDLBaLzf4lubm5d6osAAAAAEABu3sSwrtA7969ZTAYtGnTJnXo0EEVK1ZUtWrVNGDAAP3666/WdocPH1bbtm3l6ekpb29vderUSSdOnLCeHzVqlGrWrKn58+erbNmy8vT0VO/evZWbm6tJkyapZMmSCggI0Pjx423ubzAY9P7776t169Zyd3dXlSpVFB8frwMHDqhp06by8PBQw4YNdfDgQes1PXr0yPMlvn///mratKl1v2nTpurbt68GDRokX19flSxZUqNGjcpz7xUrVkiSypcvL0mqVauWDAaDmjZtqp9++klOTk46fvx4nns98MADV32v69atU0ZGhsaMGaPU1FRt2LDB5vyl93X5M40fP16lS5dWpUqVrti3wWBQyZIlVbp0aT366KPq27ev1q5dq4yMDOvI75UrV6pq1apycXHR4cOH9c8//6h79+4qVqyY3N3d9eijj2r//v2SLk5t3rNnT6WkpFhHo196V5mZmRo4cKACAwPl4eGhBg0aKC4uzqae9evXq2nTpnJ3d1exYsUUGRmpf/75Rz169NC6des0ffp0a79JSUmKi4uTwWDQ2bNnrX0sW7ZM1apVk4uLi4KDgzV58mSbewQHB+vNN9/Us88+Ky8vL5UtW1YffPDBFd9RZmamUlNTbTYAAAAAwEVms9m6ff/996pZs6a+++47nT17VmfPntW3336r2rVra/Xq1fYuFQAAAABwCwjG/+fMmTNavXq1/vvf/8rDwyPP+UtTa5vNZrVt21ZnzpzRunXrFBMToz/++EOdO3e2aX/w4EF99913Wr16tT777DPNmzdPrVq10tGjR7Vu3Tq99dZbGjZsmDZu3Ghz3dixY9W9e3dt375dlStX1lNPPaUXX3xRQ4cOVUJCgiwWi/r06XPDz7dw4UJ5eHho48aNmjRpksaMGaOYmJh8227atEmStHbtWiUnJ2v58uVq3LixKlSooI8//tjaLjs7W4sWLdKzzz571XvPmzdPXbp0kZOTk7p06aJ58+Zds97Y2FglJiYqJiZGq1atuu7ndHNzk9lsVk5OjiTp/Pnzeuutt/Thhx/q999/V0BAgHr06KGEhAStXLlS8fHxslgsatmypbKzs9WwYUNNmzZN3t7eSk5OVnJysgYOHChJ6tOnj+Lj47V48WL99ttv6tixox555BFrqL59+3Y1a9ZMVatWVXx8vH755Re1adNGubm5mj59uiIiIvT8889b+w0KCspT/5YtW9SpUyc9+eST2rlzp0aNGqXhw4crOjrapt3kyZNVt25dbdu2Tb1799ZLL72kxMTEfN/JhAkTZDKZrFt+9wUAAAAAXPzx9/Tp0xUZGSlvb295e3srMjJSU6ZMUd++fe1dHgAAAADgFjCV+v8cOHBAFotFlStXvmq72NhY7dy5U4cOHbIGjB999JGqVaumzZs3q169epIuBujz58+Xl5eXqlatqgcffFCJiYn69ttvZTQaValSJb311lv68ccf1aBBA2v/PXv2VKdOnSRJgwcPVkREhIYPH67IyEhJUr9+/dSzZ88bfr7q1atr5MiRkqTQ0FDNnDlTsbGxatGiRZ62/v7+kiQ/Pz+bab6joqK0YMEC61ToX3/9tS5cuGCtNz+pqan64osvFB8fL0l6+umn9cADD2j69OlXnT7dw8NDH3744RWnUM/P/v37NWfOHNWtW1deXl6SLob37733nmrUqGFts3LlSq1fv14NGzaUJC1atEhBQUFasWKFOnbsKJPJZB2Jfsnhw4e1YMECHT58WKVLl5Z0cW301atXa8GCBXrzzTc1adIk1a1bV++99571umrVqln/7ezsLHd396tOnT5lyhQ1a9ZMw4cPlyRVrFhRu3fv1ttvv60ePXpY27Vs2VK9e/eWdPHvZOrUqfrxxx/zHV0/dOhQDRgwwLqfmppKOA4AAAAA+Th48KD1h/H/ZjKZlJSUdMfrAQAAAAAUHEaM/8+/1w+7mj179igoKMgmWKxatap8fHy0Z88e67Hg4GBrOCtJJUqUUNWqVW3Wty5RooROnjxp03/16tVtzktSeHi4zbELFy7c8HTY/+5XkkqVKpXn3tfSo0cPHThwwDqtfHR0tDp16pTvCPtLPvvsM913333WYLpmzZoqV66cPv/886veKzw8/LpC8ZSUFHl6esrd3V2VKlVSiRIltGjRIut5Z2dnm2ffs2ePHB0dbX6M4Ofnp0qVKtl8fpfbuXOncnNzVbFiReua6J6enlq3bp11avtLI8ZvxZ49e9SoUSObY40aNdL+/ftt1rL79zNdCvGv9Hm6uLhYRzpc2gAAAAAAedWrV08DBgywWS7txIkTeu2111S/fn07VgYAAAAAuFWMGP+f0NBQGQwG7d27t0D6c3Jystk3GAz5HjObzVe8zmAwXPHYpeuMRmOeUD87O/u66rn83tcSEBCgNm3aaMGCBSpfvry+++67PGtsX27evHn6/fff5ej4/39ql0bTR0VFXfG6q4Xt/+bl5aWtW7fKaDSqVKlScnNzsznv5uZmfWe3Ii0tTQ4ODtqyZYscHBxszl0a+X75vW+ngvg8AQAAAAC25s+fr8cff1xly5a1/iD+yJEjCg0N1YoVK+xbHAAAAADglhCM/4+vr68iIyM1a9Ys9e3bN08we/bsWfn4+KhKlSo6cuSIjhw5Yv2SvHv3bp09e1ZVq1a943X7+/tr165dNse2b9+eJzi9EZdGav97hPIlzz33nLp06aIyZcrovvvuyzO6+d927typhIQExcXFydfX13r8zJkzatq0qfbu3XvNqeuvxWg0KiQk5LrbV6lSRTk5Odq4caN1KvXTp08rMTHR+vk5OzvnefZatWopNzdXJ0+e1AMPPJBv39WrV1dsbKxGjx6d7/n8+s2vvvXr19scW79+vSpWrJgnkAcAAAAAFKyQkBD99ttviomJsf5wvkqVKmrevHmB/OgaAAAAAGA/TKX+L7NmzVJubq7q16+vZcuWaf/+/dqzZ49mzJihiIgISVLz5s0VHh6url27auvWrdq0aZO6d++uJk2aqG7dune85oceekgJCQn66KOPtH//fo0cOTJPUH6jAgIC5ObmptWrV+vEiRNKSUmxnouMjJS3t7fGjRt3zbXO582bp/r166tx48YKCwuzbo0bN1a9evU0b968W6rzZoSGhqpt27Z6/vnn9csvv2jHjh16+umnFRgYqLZt20q6OA1+WlqaYmNjderUKZ0/f14VK1ZU165d1b17dy1fvlyHDh3Spk2bNGHCBH3zzTeSLq7lvXnzZvXu3Vu//fab9u7dq9mzZ+vUqVPWfjdu3KikpCSdOnUq3xHer776qmJjY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"/>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># install seaborn library</span>
+<span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>seaborn
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Requirement already satisfied: seaborn in ./.venv/lib/python3.12/site-packages (0.13.2)
+Requirement already satisfied: numpy!=1.24.0,&gt;=1.20 in ./.venv/lib/python3.12/site-packages (from seaborn) (2.1.2)
+Requirement already satisfied: pandas&gt;=1.2 in ./.venv/lib/python3.12/site-packages (from seaborn) (2.2.3)
+Requirement already satisfied: matplotlib!=3.6.1,&gt;=3.4 in ./.venv/lib/python3.12/site-packages (from seaborn) (3.9.2)
+Requirement already satisfied: contourpy&gt;=1.0.1 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (1.3.0)
+Requirement already satisfied: cycler&gt;=0.10 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (0.12.1)
+Requirement already satisfied: fonttools&gt;=4.22.0 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (4.54.1)
+Requirement already satisfied: kiwisolver&gt;=1.3.1 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (1.4.7)
+Requirement already satisfied: packaging&gt;=20.0 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (24.1)
+Requirement already satisfied: pillow&gt;=8 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (11.0.0)
+Requirement already satisfied: pyparsing&gt;=2.3.1 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (3.2.0)
+Requirement already satisfied: python-dateutil&gt;=2.7 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (2.9.0.post0)
+Requirement already satisfied: pytz&gt;=2020.1 in ./.venv/lib/python3.12/site-packages (from pandas&gt;=1.2-&gt;seaborn) (2024.2)
+Requirement already satisfied: tzdata&gt;=2022.7 in ./.venv/lib/python3.12/site-packages (from pandas&gt;=1.2-&gt;seaborn) (2024.2)
+Requirement already satisfied: six&gt;=1.5 in ./.venv/lib/python3.12/site-packages (from python-dateutil&gt;=2.7-&gt;matplotlib!=3.6.1,&gt;=3.4-&gt;seaborn) (1.16.0)
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="c1"># Set up the figure with a clean, modern style</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'font.family'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'DejaVu Sans'</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'axes.linewidth'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'axes.grid'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'grid.alpha'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.3</span>
+
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+
+<span class="c1"># 1. Project Volume vs Investment (Enhanced)</span>
+<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">scatter</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">)],</span>
+                     <span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e6</span><span class="p">,</span>
+                     <span class="n">s</span><span class="o">=</span><span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e8</span><span class="p">,</span>
+                     <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
+                     <span class="n">c</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">clean_data</span><span class="p">)),</span>
+                     <span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Number of Projects'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Average Project Size (Millions $)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Program Scale Analysis</span><span class="se">\n</span><span class="s1">Bubble size represents total investment'</span><span class="p">,</span> 
+              <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+
+<span class="c1"># Add annotations for notable programs</span>
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">clean_data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+    <span class="k">if</span> <span class="n">row</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span> <span class="o">&gt;</span> <span class="mf">5e9</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">idx</span><span class="p">[:</span><span class="mi">30</span><span class="p">],</span>
+                    <span class="p">(</span><span class="n">row</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">)],</span> 
+                     <span class="n">row</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e6</span><span class="p">),</span>
+                    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">'offset points'</span><span class="p">,</span>
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+                    <span class="n">bbox</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s1">'white'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">))</span>
+
+<span class="c1"># 2. Program Size Distribution (Enhanced)</span>
+<span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">sizes</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e6</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">sizes</span><span class="p">[</span><span class="n">sizes</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]),</span> 
+         <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> 
+         <span class="n">color</span><span class="o">=</span><span class="s1">'dodgerblue'</span><span class="p">,</span>
+         <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'white'</span><span class="p">,</span>
+         <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Log10(Total Program Size) in Millions $'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Count'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Distribution of Program Sizes</span><span class="se">\n</span><span class="s1">Natural groupings by investment scale'</span><span class="p">,</span> 
+              <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+
+<span class="c1"># 3. Top 10 Programs (Enhanced)</span>
+<span class="n">ax3</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">top_10</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">)]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">/</span><span class="mf">1e9</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">top_10</span><span class="p">)))</span>
+<span class="n">bars</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_10</span><span class="p">)),</span> <span class="n">top_10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_10</span><span class="p">)))</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="n">name</span><span class="p">[:</span><span class="mi">40</span><span class="p">]</span> <span class="o">+</span> <span class="s1">'...'</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">40</span> <span class="k">else</span> <span class="n">name</span> 
+                     <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">top_10</span><span class="o">.</span><span class="n">index</span><span class="p">],</span>
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Total Investment (Billions $)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+<span class="n">ax3</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Top 10 Programs by Investment</span><span class="se">\n</span><span class="s1">Highlighting investment concentration'</span><span class="p">,</span> 
+              <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+
+<span class="c1"># Add value labels to bars</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">bar</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bars</span><span class="p">):</span>
+    <span class="n">width</span> <span class="o">=</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span>
+    <span class="n">ax3</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_y</span><span class="p">()</span> <span class="o">+</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span>
+             <span class="sa">f</span><span class="s1">'$</span><span class="si">{</span><span class="n">width</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">B'</span><span class="p">,</span>
+             <span class="n">ha</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+             <span class="n">bbox</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s1">'white'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">))</span>
+
+<span class="c1"># 4. Geographic Distribution (Enhanced)</span>
+<span class="n">ax4</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">top_15_counties</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span>
+<span class="n">colors</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">top_15_counties</span><span class="p">)))</span>
+<span class="n">bars</span> <span class="o">=</span> <span class="n">ax4</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_counties</span><span class="p">)),</span> <span class="n">top_15_counties</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">)</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_counties</span><span class="p">)))</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">top_15_counties</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Total Investment (Millions $)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+<span class="n">ax4</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Investment by County</span><span class="se">\n</span><span class="s1">Top 15 counties by total investment'</span><span class="p">,</span> 
+              <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
+
+<span class="c1"># Add value labels</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">bar</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bars</span><span class="p">):</span>
+    <span class="n">width</span> <span class="o">=</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span>
+    <span class="n">ax4</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_y</span><span class="p">()</span> <span class="o">+</span> <span class="n">bar</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span>
+             <span class="sa">f</span><span class="s1">'$</span><span class="si">{</span><span class="n">width</span><span class="si">:</span><span class="s1">.0f</span><span class="si">}</span><span class="s1">M'</span><span class="p">,</span>
+             <span class="n">ha</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
+             <span class="n">bbox</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s1">'white'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">))</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Print summary statistics for context</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Program Scale Categories:"</span><span class="p">)</span>
+<span class="n">quartiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">sizes</span><span class="p">[</span><span class="n">sizes</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">25</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Investment Scale Categories (in Millions $):"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Small Programs: &lt; $</span><span class="si">{</span><span class="n">quartiles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">M"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Medium Programs: $</span><span class="si">{</span><span class="n">quartiles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">M - $</span><span class="si">{</span><span class="n">quartiles</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">M"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Large Programs: $</span><span class="si">{</span><span class="n">quartiles</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">M - $</span><span class="si">{</span><span class="n">quartiles</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">M"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mega Programs: &gt; $</span><span class="si">{</span><span class="n">quartiles</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">M"</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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cCBA+ncuTNz584lODiYNm3a0L59eyC5wzKrc8zs8yQrL730ErVr12bRokXExMRQr149fHx8aNCgAREREYwbNw6AOXPmMHDgQJYvX05MTAy9evVi3bp1uLu789lnn5nv0VuVm8+ZO5Gj2/mbkdXfr5t1796dwYMHA7Bp0ybCw8M5ePCguf7s2bNs3rwZi8XCtWvXsvzOkZlnn32WNm3asGbNGho3bkzLli154oknsLW1ZerUqelGZWf2OqdwdHRky5YtHDx4kMDAQLp372418vr8+fOMGzeOM2fOUKhQIa5cuZLhjCzZHScjt/OZn1vZxZn2c+e5557L8vMirQ8++IBRo0alW16uUlFKF3e5MyeThxwcM26mT0hIIDExETs7O06fPk1SUhL29vbUrFmThIQEs9zGjRs5f/48bdq0sVr+xhtvsHDhQi5cuMCcOXOsOiveeOMNFi1axPnz55k7d26O1hmGQfny5XF1dWXDhg2cP38ef39/ihcvTlJSktWx08rtZ1heS0pK4uLFixiGcdfNbJTflItUykUq5SKVcpHqTuYiJxekFhR1oIuIiIiIyB0XGRlJjx49sLW1pW3btvTr148DBw5Qt25dq3JJSUlERESwfv16DMMgJiYGT09PszE7ODiYSpUqAckj7uLj43F1dQUgMDCQw4cPA8lT/V68eJGFCxcCyR3EKfcRXr16tdlwW6FCBXOEGCQ37vXp04c//vgDOzs7zp49y969e7PtQM/KoUOHsLGxISwsDIDGjRvj6upKVFQUFStWxMnJiY4dO5rnlzINbGaeeOIJs6MQYPbs2Xz99ddcu3aNa9euUbp06TyNsUGDBowfP55XX32Vpk2b0qZNm0z3czux3HyhwE8//UTbtm3N13f58uUcOXKEpk2bmmVsbGw4deoUTZs2ZciQIcTFxRESEkJoaKhZpk2bNpQqVQpIzu+ePXuA5CmBw8PDcXR0BJI7RD755JMcdaAHBASwZ88efvzxR5555hkgOYf79u2z6niJjY1l//79Zgd67969rfbz+OOPU7hwYQCCgoKyfe1T1KpVi3PnzjFr1qwsO+8yq1ubNm3C29vb7KDu2bOnOSK0WrVqJCQk0Lt3b5o3b067du1y3EhSqFAhwsLCmD9/PvPnz2fnzp1Wnc9Zvb+yel9m9X6GvH1PZHcOtxLL0qVL6d69O25ubgDma51VXV69ejVPPvkkxYoVAzBHGmancOHCrFu3jqioKNatW8eiRYv48MMP2bVrFyVLlsw0JytXrqRNmzZmXbC3t6d48eLmftPW2atXr/LCCy8QFRWFjY0Nf/75p/kZAcmd+Ckd9v7+/jg6Opq58PPzs/p83rhxo3nP6KtXr5oXzwB06dIFOzs77Ozs8PX15ejRowQHB6c751WrVtGlSxfzc+P555/niSeeyDZXzZs359SpU/z6669s2LCBfv368dhjj/HJJ59ke46ZfZ5kZdWqVWzfvh2AsmXL0rFjR1atWkWDBg1YuXIlkyZNIjY2lqSkJLMBMeU96u7uDkCfPn146aWXsj1WRnLzOZNfOcqLv18327lzJ++//z5nz57Fzs6OQ4cOcfXqVQoVKgQkX7yUMjI6u+8cmfHw8ODo0aOsX7+eDRs2MGLECL7++muWLl2aYfnMXucU3bp1A6BOnTrY2dkRHR1t9b2jWLFi1KxZk2eeeYbWrVvTrl27DL+XZHecjNzOZ35uZRdn2s+d7D4v0ho2bJh58QQkXzwSEhJC9KlYrjheyPsTyWM3rmfc8WxnZ0exYsX47rvveO+999i+fTt79uxh2LBhBAUFmeVmzpxJ9+7d0932Z8yYMYwZM4bly5fz5ptvsn79evMiiZR1q1at4o033sjROovFgq2tLb1792bmzJlcuHCBZ599ltOnT2NjY5PptPtly5bNizTdtqSkJCwWC2XKlFGHmHJhUi5SKReplItUdzIXKbcduRupA11ERERERO6o+Ph4vv76a+zt7Zk9ezYAV65cITIy0uwwS/HRRx8RExPD5s2bcXJyYvDgwVZTiaZtFLS1tU33PGXUi2EYTJo0idatW2cbX9opRvv370/btm1ZuHChOQVyTqYyrVevHhs3bsTPzy/bsjcf09HR0Xxua2tLYmJiltumvXf8+vXrmThxIhs3bqRs2bIsWbKEt99+O09jDA4OJioqilWrVrFo0SLeeuutDO+tfCux5FTaczUMg1atWpl1KK2aNWvSsGFDVq5cyeTJk/n444/N6cozqyOZnW9O1KlTh/HjxxMaGkpSUhI9evTAMAxKliyZ5b1g057PrcR2M1dXVxYtWkTz5s1JTEw0p6y/2a3WLYDixYuzd+9efv31V9asWcOwYcP47bffqFGjRo5i69WrF+3bt6dNmzZmJ3CKW3l/pX09sns/5/Y9kZtzuJVYMpNVXb7ZrdRLi8WCn58ffn5+vPTSS7i7u7N27VrKli2b65ykPZ833niD0qVLs3PnTuzs7OjYsWOuP58XLlxIrVq1Mjxmbt8Xt5KrIkWK0LZtW9q2bUv79u1p3bo1n3zyyS2fY05jyyjOU6dOMWDAALZu3Ur16tXZvXu31UUVWZ2bnZ2d1fs5q79TuY35TufoTvz9SplNZc2aNQQGBnLp0iWKFy/O9evXzQ70tHU6u+8cWXFwcKBFixa0aNGCvn374ubmlmGHdU5e5+xyZmtry6ZNm9iwYQNr166lQYMGfPvttzRp0iTHx7n5b0DKed7uZ36KnNbJnOTj5r/9WX1epOXo6GheFJd2P0ZS8uNu93+zo6eT8v5v1aoVrVq1Yvjw4QQEBBAWFsY///yDo6MjcXFxzJ8/n61bt2b4WWgYBqGhobzyyivs3bs33f3KW7VqxUsvvZTjdRaLhccff5yhQ4fi6OhIaGgoX331lVW8mZ3H3cBisWBjY/PAd4iBcpGWcpFKuUilXKS6U7m4m3N790YmIiIiIiL3hSVLllCtWjVOnz7NiRMnOHHiBJs2beLrr78mPj7equz58+cpV64cTk5OREdHM3/+/Fwd87HHHmP8+PHmVMRXrlxh3759AISGhpr3OD5z5gxLliyxOn7lypWxWCz89ttv7Nq1y1z33Xff0aNHjwyP99Zbb/Hee++xadMmc9nRo0dZsGABtWvXJikpiZUrVwKwYcMGoqOjzXtVZ6Vo0aJZ3tP3/PnzFC1alFKlSnHjxg2re+vmVYzHjx/H2dmZJ598kkmTJvHHH38QFxdHsWLFrGLLLpaWLVua017nRlhYGKtWrWL37t3mspT9HT58GFdXV3r06MGYMWOszjEzKY29N27cICEhgenTp5udolm91inq1q3L6tWrGTZsGDNmzKB27doUK1bM6v61R44cydWUdFu2bKFly5ZZlnFzc2Pt2rV88803GU5Zm5UGDRqwe/du876zs2bNMu8p+++//3L58mVat27N//73P6pUqcL+/fuBnL2G9evXZ/jw4RmOjM/q/ZXV+zKr93NGx7jdepjVOdxKLI888gizZs3izJkzZtkrV65kWZdDQ0OZP38+sbGxGIbBZ599lqP4Dx48aLW/P//8k3///Zdq1aplmZOwsDBWrFhhTnEdHx+f6WfO+fPnqVixojmqN+Xz4lY99thjfPjhh2Yn4fnz581bWmTl5s+c0NBQ5s2bx6VLl4DkWz+kvIezeg/9/PPP5n3rIXl68OrVq+fpOaYVGhrK559/DiS/vxYtWkSrVq24ePEi9vb2uLm5YRgGkydPNrcJDg5m9+7d5uvyxRdfWN33uUaNGua96rds2WJ1D+mcyu8c3erfjLSy+vuV1rVr17hx44Y5U82kSZOyjCmr7xynT582Z2a42W+//Wa+ryE5PyVLlsTFxSXdeWb1OudUbGws//zzD02aNOGtt96icePG6S5ky+44aevMokWLuHz5MnD7n/kpypUrh2EY5rYpnak3u9V85Pbz4n4TFxdnzuJhZ2dHUFAQN27cML9Hz507Fx8fH6s6Gx8fb5WrrVu3EhMTQ7Vq1dKt27JlS47WpeXk5MT48eOZOHHiXd35ISIiklsagS4iIiIiIndUZGSkOT1pirp161KhQgV++OEHq+Up91318PCgfPnyVlNx34qIiAiuX79O/fr1zREvEREReHh4MGHCBMLDw3F3d6dChQq0aNHC3G706NG88MILvPvuu/j6+lK/fn1z3eHDh9ONRk3RunVrZsyYwWuvvUZ0dDSFChWibNmyjBo1CgcHBxYtWsTLL7/Mq6++ipOTEwsWLMDZ2Zn//vsvy/N49dVXadWqFYULFzbvQZ5WmzZtmDVrFrVr16ZUqVKEhoZy+vTpPI1x/vz5fPTRR+aouLFjx1K8eHFatmzJuHHj8Pb2pmHDhkyaNCnTWBITE9m1a9dtTYVfo0YNZs+eTb9+/bhy5Qo3btzAz8+P2bNns2DBAmbNmoWDgwNJSUk5ut/1c889x9GjR/H39wegWbNm5r2Vs3qt06pTpw6//PILLVu2JDExkR9//JFBgwYxfvx4EhMTKV26dI5GGd/sxIkT5mjJrJQrV461a9fSokULkpKSaN68eY72X7ZsWaZPn85jjz2Go6MjrVq1wtnZGRcXF44dO8azzz5LfHw8iYmJNGrUiIcffviWXsOBAwdmuDyr91dW78us3s83y+o9kRfncCuxNG3alBEjRhAWFobFYsHBwYEFCxZkWZfbtm3Lli1b8Pf3p1ixYjz88MPm/rKK/8qVK7zyyivme9swDEaPHo2vry8eHh6Z5qRGjRrMmDGDZ555hvj4ePMezmmnBU4xfPhwunfvzpdffkn16tWtXqNbMX78eIYOHYqvr6853e+YMWOyHfHavXt3wsPDWbx4MS+++CJ9+/Zl7969BAcHY2Njg7e3N1OmTAGyfg/t2bOHV1991byHo5ubG7NmzcrTc0xISDBHFU+cOJHnn38eLy8vDMPgzTffNOv+U089hYeHB6VKleKxxx4zty9TpgxffPEFjz/+OA4ODlbTogO899579OzZk2nTphEcHJxh/ctOfufoVv5m3Cyrv19pFStWjPfee4+goCBKly6d7VTsWX3nOH36dKZTUZ86dYpBgwZx7do1HBwccHZ25vvvvzfroYeHB56enlSrVo0lS5Zk+jrn1MWLF+ncuTOXL1/GYrFQs2ZNevbsaVXGy8sry+OMHz+el19+meHDh9OuXTuzPv3555+3/ZkPyZ26kyZNon379pQqVYrOnTtnWC67OG+W28+L+018fDyDBg3iwoULnDp1ivnz5zN58mRzlH1kZCTPPvtsum169uzJxYsXsbOzo3DhwsyfP58SJUpw5coVq3VFihRhwYIF2a67WcptYkRERO5HFsPIbIIYEZFbs2PHDurVq0fDCk9TzN61oMNhwvevZLi8lvdD+RzJnZOUlERMTAxly5bVFb/5QPnOX8p3/lPO81dB5Tvl7/X27dvNjkPJmccee4yPP/7Y6n7HkjNbt25l2rRpTJ8+vaBDyZGCfq1ffPFFunbtSuPGje/YMWJjY837Uy9evJhhw4Zx4MCBTMvfa69hRu71c7jX489P+fEeysyVK1eoWrUqGzZsMEdt54XSpUuzbdu2PPtcKsgc3QvGjh2Lm5sbzzzzTEGHUiDuh88bs42qWi+KOZYr6HCyNWHeCxkur+VpfRHDyJEjGTly5C3t2zAMEhISsLOzu6umUi8I+s2bSrlIpVykUi5SKRep7mQu7uY2Ko1AFxERERERyYHFixcXdAj3rMDAQAIDAws6jBwr6Nf6k08+uePHmDRpEnPnziUxMZFixYrxzTffZFn+XnsNM3Kvn8O9Hn9+yo/3UEbmzp3L22+/zfPPP5+nned3QkHl6F4xZMiQgg6hQOnz5u7VrFmzgg5BRETkgfBgXzYhIgLprtxdtmwZ/v7++Pr64unpyZdffpnpth9++CHu7u74+vrSoEEDq/uDWSwWvLy88PX1pU6dOrz00kskJibeqdMQEREREcmxN954g127drF37142bNhw113tL3Iv6tKlC4cOHbrl0aE58d9//2kGFBFRB7qIiEg+uac70KtUqUJUVNQdP86qVato0qQJ1atXJyAggJYtW7Ju3bpb3s/MmTNzda+jnPL19cXX1xd3d3dsbW3N5126dLljxwSYOnUqY8eOBSAqKoo5c+ZkWX7Dhg2EhIRQs2ZNqlWrRteuXTlz5swdjfFmGcXp6+tLbGzsHT92eHg4FSpUMDtV+/XrR3x8/B0/rqQ3a9Ys/P39mTp1Kv7+/kybNg3DMHjmmWeYOXMmUVFR/Pjjj/Tr1y/DuhEVFcWUKVPYsmULUVFRDBgwgAEDBliVWbduHVFRUezevZvffvuN5cuX59fpiYiIiIiIiIiIiIiIyC3SFO7ZWLVqFd27d2fhwoU0bNgQgMOHD7Nr165b2k9CQsKdCM9KysUEJ06cwNfXN8OLC1Lud5OX+vfvbxXD4sWLeeqppzIsu3v3bjp06MDcuXNp2bIlkDyCt1mzZuzcuZPChQvnSUzZnWdGcebHxRgphgwZwqBBg7h27RohISFMnTqVl156yapMXr1Wd+I1vx9cvnyZvn37sm/fPr7++msiIiI4ceIEkDxy/MKFCwBcunSJUqVK4ejomG4fFouF+Ph4Ll++jLOzMxcuXKBixYrpygFcvXqV69evU6JEiTt1SiIiIiIiIiIiIiIiInKb7ukR6JlZsWIF/v7+eHt7ExISwv79+wF4+umnmT17NgBTpkzBwcGBy5cvA9CiRQt+++23dPsaNWoUb731ltl5DlCzZk06d+4MwGuvvUZgYCC+vr40bdqUQ4cOmeUsFgsjRowgMDCQYcOGAcmdcR06dMDd3Z2mTZuaHXaJiYkMGTIET09PPD09eemll7hx4waQPGK5X79+tGzZklq1atGxY0dzXU5UqVKFiIgIgoKC6NmzJ9HR0TRv3px69erh4eHBgAEDSEpKApJHyYeGhtK1a1e8vLwICAjg2LFjQPKFA40aNcLHxwcvLy+GDx8OJE9/PWjQIGJiYnj77bdZs2YNvr6+Vh3rKcaMGUPv3r3NznOAiIgIihcvbo4Ib9asGS+99BKBgYHUqFGDV199FcMwAIiOjubJJ58kKCjIKoZbOc/M4kzbabpt2zYaNmyIt7c3QUFB/P7770DyxQkuLi6MGDGCevXqUaNGDZYtWwYkd5B26dIFd3d3fHx8aN26dbavjZOTEyEhIRw6dIi1a9fi4eFBnz598PX15bvvvss0DoBp06ZRq1Yt/P39effdd7FYLOa6m+venj17aNy4Mf7+/ri7u/Pee++ZZUeOHMmTTz7JI488Qq1atWjfvj179+4lLCyMWrVq0bVrV7N+3Oz69etcunTJfMTFxf3f8cFicxc8LBk/UuqTnZ0dZ86cwTAMnJycqFOnDgBz5syhY8eOVK5cmcaNGzNz5kzs7e0xDMPq4e3tzaBBg6hatSoVK1Zk/PjxTJw40VwP0KRJE3x8fChfvjxBQUEEBwen20/KIykpKVeP29lWD+X7bn8o38r5/f4oqHyLiIiIiIiIiIhIxu67YakxMTE8/fTTrF27Fi8vL7755hs6d+7Mvn37CA0NZdWqVTz99NOsXLmSgIAAfv31V5o1a8auXbsIDg5Ot7/t27czceLETI8XERHBuHHjgOROt4EDB1pN0Wxra8vWrVuB5M7p33//naioKOrWrcuYMWN47rnn+Pnnn/nss8/YunUr27dvx9bWlg4dOjB+/HgiIiKA5NHRa9aswdHRkaZNm7Jw4UK6du2a47ycPXuWzZs3Y7FYuHbtGj/88APOzs4kJiby6KOPMm/ePHM09tatW4mKiqJq1aoMHTqUDz/8kGnTpjF58mTat29vXgxw7tw5q2OULVuWd955h8WLF7N48eIM49ixYwedOnVKtzw4OJjt27fTu3dvAPbv38+GDRuIj4+nadOmfPvttzz99NP07NmTN954g5CQEBISEmjfvj3z58/niSeeuKXzzCrOGzdu0LFjRz7//HPCwsJYv349nTp14siRIwBcvHgRb29vRo0axfLlyxk4cCBt27Zl+fLlXLhwwbxg4+b8ZOT8+fMsX76cV155BYADBw4wZcoUIiMjuXHjBjVq1MgwjhMnTjBy5Eh27txJuXLlGDFiRLp9p617sbGxrF69GkdHR65evUrDhg0JDQ2lQYMGQPIFA9u3b8fFxYVmzZrRt29fVq5cSaFChQgICOCnn36iXbt26Y7xwQcfMGrUqHTLy1V1oXSxUtme/53m4JTxR1xCQgIODg4sWLCAkSNHsmPHDnbu3MnQoUPx8/PjvffeY968eTRp0oRt27bRsWNHduzYQenSpa32c/z4cRYtWsSBAwcoX748U6ZMoUuXLqxdu9Ys88svv+Di4sKVK1fo0qULEyZM4MUXX8wwrrNnz97yOSYlJXHx4kUMw8DG5r68JuquonznL+U7/ynn+aug8p2T7ygiIiIiIpJ/Bj45JcPlP+3/323v2zAMzp49S9myZa0G4IiIiEjm7rsO9M2bN+Pl5YWXlxcA3bp148UXX+T06dOEhoYyatQoEhMT2b9/P++//z6rVq3C1taWoKAg7O3tb/l4K1euZNKkScTGxpKUlJSuQTKlQzhFw4YNqVu3LgDPPfccw4cPJzExkVWrVhEeHm5OE/3ss8/yySefmB3ojz/+uDm9eVBQEEePHr2lOMPDw80vSElJSURERLB+/XoMwyAmJgZPT0+zAz04OJiqVaua/580aRIATZs2ZciQIcTFxRESEkJoaOgtxXArevTogb29Pfb29jzzzDOsWrWKRx99lNWrV/PPP/+Y5eLi4qxG/d/KeWbm0KFD2NjYEBYWBkDjxo1xdXUlKiqKihUr4uTkRMeOHYHk/KS8Fj4+Phw4cIAXXniBkJAQ2rZtm+kxxo4dy8yZM7GxsaFz586Eh4fz66+/Uq1aNUJCQrKNY8eOHbRp04Zy5coByfXlnXfesTpG2rp39epVXnjhBaKiorCxseHPP/8kKirK7EBv3bq1ObW4v78/jo6OFC1aFAA/Pz8OHz6c4XkMGzaMwYMHm8+joqIICQkh+vgFrjikn/I8v924lvGtE1KmtA8LCyMsLIzhw4cTEBBgXghx5swZmjdvDkCDBg2oWLEie/bsoVWrVlb7+f777/Hy8qJSpUoA9OnTh0GDBpGUlISDg4N5LDs7O4oVK8YjjzzCsmXLGDhwYIZxlS1b9pbPMSkpCYvFQpkyZdTZlQ+U7/ylfOc/5Tx/FVS+S5YsmW/HEhERERERERERudfcdx3oWalUqRKOjo5888031KtXj5YtW/L+++9ja2trNaV4WvXq1WPjxo34+fmlW3fq1CkGDBjA1q1bqV69Ort376Zp06ZWZZydnXMV681XAzo5OZn/t7W1veV7qqeN46OPPiImJobNmzfj5OTE4MGDuXbtWrbH6tSpEw0bNmTlypVMnjyZjz/+2Jy+PKf8/f3ZuHEjjz/+uNXyjRs30q9fv0y3s1gs5pTYmzZtsooxt+d5K9K+Ho6OjuZzW1tbEhMTAahWrRr79+/nl19+YdWqVbz++utERUVleM/rlHugZxV/dnFktzztvt544w1Kly7Nzp07sbOzo2PHjlm+5jmtb46Ojlb3Bk85pmGAcRfMDvt/VSYdi8VCXFwcZ86coWbNmtjb21O/fn1u3LiBm5sbZ86c4eDBg9StW5cjR45w9OhR6tSpky7P1atXZ+bMmeY90JcuXUqtWrWscmKxWLBYLCQmJvLrr79Su3btW3odc8JisWBjY6POrnyifOcv5Tv/Kef5qyDyrddWREREREREREQkc/dd61mDBg3Ys2cPe/fuBZKnVa9QoQIVKlQAIDQ0lLfffpvQ0FBKlCiBvb098+fPz3Q09VtvvcV7773Hpk2bzGVHjx5lwYIFXLx4EXt7e9zc3DAMg8mTJ2cb38aNGzl48CAA06dPp3nz5tja2hIaGspXX33FjRs3SEhIYPr06Tm6h3ZunD9/nnLlyuHk5ER0dDTz58/P0XaHDx/G1dWVHj16MGbMGKucpChWrBgXL17MdB+vvfYakZGRrF692lw2ZswYzp8/bzUl/axZs4iPj+fq1avMnj2b0NBQnJ2dad68OaNHjzbL/f333/z111+3fJ5ZxVm7dm2SkpJYuXIlABs2bCA6OhpfX99Mzwvgr7/+wmKx0KFDB8aNG4dhGPz5559ZbpOVrOJo3rw5K1asICYmBoDIyMgs93X+/HkqVqyInZ0dhw4dMvf5IIuPj2fQoEE0atSIyMhIWrVqxeTJk6lcuTKfffYZTz75JD4+Pjz++ONMnjzZHGX+9ttvM3XqVCB5ZogOHToQEBCAj48PEyZMYPbs2VbHadKkCb6+vnh5eWFra5vhdPsiIiIiIiIiIiIiIiJyd7jnR6CHhYVZTb2+adMmvvnmG3r06EFCQgIlSpRg/vz55sjO0NBQPv30U7PDPDQ0lM8//xwfH58M99+6dWtmzJjBa6+9RnR0NIUKFaJs2bKMGjUKLy8vnnrqKTw8PChVqhSPPfZYtvE2bNiQiIgIjhw5QqlSpfjqq6+A5Oncjx49ir+/PwDNmjXLcIRyXhg4cCCdO3fGw8OD8uXL53gq9gULFjBr1iwcHBxISkoyOxHTatmyJePGjcPb25uGDRumK+Pr68v333/PsGHD6N+/PwkJCQQFBbF27VpzinqAunXr0qhRI86dO8ejjz5qTrv+zTffMHjwYDw9PbFYLBQpUoRp06ZRsWLFWzrPrOJ0cHBg0aJFvPzyy7z66qs4OTmxYMECnJ2d+e+//zLNz549exg2bBiGYZCQkED37t3x9vbOUW4zklUcXl5eDB8+nEaNGlG0aFHatGlD8eLFM93X8OHD6d69O19++SXVq1enRYsWuY7rflGiRAmWLl0KwMiRIxk5cqS5rmvXrlYXdKSVdqp8i8XCBx98wAcffJBhWSOzIfAiIiIiIiIiInLHTZj7AgEBAQUdhoiIiNxjLIZ6eOQuk3LxQE4uSHiQxcbGmvcpnzBhAsuXL+enn34q0Jh27NhBvXr1aFjhaYrZuxZoLAATvn8lw+W1vB+yer527VqaNWuWDxHlvaSkJGJiYihbtqym5M0Hynf+Ur7zn3Kevwoq3yl/r7dv325evCkiIiIicj9J+c67devWB74DXb/zUikXqZSLVMpFKuUilXKR6k7m4m5uo7rnR6CLPKiGDh3K77//Tnx8POXLl2fatGkFHdI9617tPBcREREREREREREREZG8pQ50ueusXbu2oEO4J3zyyScFHYKIiIiIiIiIiIiIiIjIfeXBnndARERERERERERERERERETk/6gDXUREREREREREREREREREBE3hLiL3sYGPjs9w+U/HP8rnSEREREREREREREREROReoBHoIiIiIiIiIiIiIiIiIiIiaAS6iIiIiIiIiIiIiNyHBrb5kGK2pQs6jAJlsbFQybM0p/b+x7IzUwo6HBERkXuCRqCLiIiIiIiIiIiIiIiIiIigDnQRERERERERERERERERERFAHegiIiIiIiIiIiIiIiIiIiKA7oEuIiIiIiIiIiIiInJfmbBiKAAWCzgUsuPG1YQCjkhEROTeoRHoIiIiIiIiIiIiIiIiIiIiqANdREREREREREREREREREQEUAe6iIiIiIiIiIiIiIiIiIgIoA50ERERERERERERERERERERQB3oIiIiIiIiIiIiIiIiIiIigDrQRUREREREREREREREREREALAr6ABE5P4zYfErBAQEFHQYIiIiIiIiIiIikomRI0cycuRI8/nLL7/MkiVLOHnyJDt37sTX19dcd/jwYXr27Ml///1H8eLFmTlzJh4eHgC0bt2a6OhobGxsKFq0KBMnTsTPz4+zZ8/SsmVLcx9Xrlzh2LFjxMTEULJkSbZu3cqgQYOIi4vDYrHw0Ucf0aJFCwDCw8P58ssv2bFjB35+fgDExsbi5uZGjRo1iIqKuuP5ERGRB5dGoIuIiIiIiIiIiIg8QGJjY3F2dqZPnz7mspkzZ/LYY48VWEyvvfaaVWeu3DmzZs3C39+fqVOn4u/vz7Rp0wDo3Lkz69evp3Llyum26devH8899xx//PEHERERhIeHm+vmzZvH7t27iYqKYvDgwea6UqVKERUVZT6ee+45Hn74YUqWLIlhGDz++OOMGjWKXbt2MW/ePMLDw7l69aq533r16vHFF1+Yz+fOnUvdunXvTFJERETSUAe6iIiIiIiIiIiIyANk7ty51KtXj0WLFhEXF1fQ4Ug+unz5Mn379mX+/Pn079+f33//naZNmwLQtGlTKlasmG6bmJgYtm3bxjPPPANAp06d+PPPPzly5AgALi4uZtmLFy9isVgyPHZkZKR50cbZs2f5999/CQ0NBaBWrVq4uLjw008/meU7duzIjz/+yPXr1wGYMWMGvXv3vs0MiIiIZE9TuIuIiIiIiIiIiIg8QCIjI3nrrbeYNm0ac+fOtRqJDhAdHU3Xrl25dOkS165do3nz5kycOBEbGxtmzpzJrFmzKFOmDLt27cLFxYXp06fz5ptvcvDgQR566CEWLVqEs7Mz8fHxvPXWW/zyyy/cuHGDWrVqMW3aNEqUKMGZM2cIDw/nzz//pHz58pQuXZo6deoAsHr1aoYPH861a9e4ceMGgwcPThdjWtevXzc7WQHzogCLxYLFJuPO3PtdRn3YhmEAYGdnx5kzZzAMAycnJ+rUqWOuS1s2ZdmpU6dwc3PD1tbWXFapUiVOnjxJ9erVAejZsydr1qwBYOnSpen2t2HDBs6fP0+7du0wDINSpUrh5ubG3LlzefLJJ9m6dSuHDh3i+PHj5raFChUiNDSU7777Dh8fHwzDMOvIzfvP6DxvlpSUhGEYJCUlZZm7B4FykUq5SKVcpFIuUt3JXNzN+VUHuoiIiIiIiIiIiMgDYv/+/fz555+EhYWRkJDA6NGj03VOu7i48MMPP+Ds7ExiYiKPPvoo8+bN46mnngJg69at7Nmzh0qVKtG9e3ceeeQRNmzYgKurK+3bt+fLL7/kxRdfZOzYsRQpUoQtW7YA8O677zJ8+HA++eQTXn75ZYKCglixYgWnT5/G19fX7Bz19/dn/fr12Nracu7cOfz8/AgLC8twdDTABx98wKhRo9ItL1fdhdJFS+dl+u4ZDoVSm/7tHGwBSEhIwMHBgQULFjBy5Eh27NjBzp07GTp0KIGBgVbbJyQkkJCQAEBiYiKGYZjPIbmTOjEx0VwWGRkJwFdffUVERARLliyx2t/06dPp1q2buW+ABQsW8Oabb/LBBx/g7u5Oo0aNsLGxISEhgaSkJJKSkujZsyejRo3C29ubHj16ZBjLzc6ePZvh8qSkJC5evIhhGNjYPNiT8yoXqZSLVMpFKuUi1Z3Mxblz5/J0f3lJHegiIiIiIiIiIiIiD4jIyEh69OiBra0tbdu2pV+/fhw4cMCqTFJSEhEREaxfvx7DMIiJicHT09PsQA8ODqZSpUoABAQEEB8fj6urKwCBgYEcPnwYgMWLF3Px4kUWLlwIwI0bN6hSpQqQPMp83LhxAFSoUIEOHTqYxz979ix9+vThjz/+wM7OjrNnz7J3795MO9CHDRvG4MGDzedRUVGEhIQQffQCV+wezCbwG1cT0j23+79chIWFERYWxvDhwwkICKBt27ZER0fj6OholrezszPLV6lShejoaHO5YRj8+eefVK1a1SyTonfv3gwYMICLFy9SqlQpIHlGgAULFrBlyxar8vXq1WP58uXmc3d3d7y8vLCzs8PGxgYbGxsaNWrEmTNnOHjwIPv27WP79u1YLJZ0x02rbNmyGS5PSkrCYrFQpkwZdYgpFyblIpVykUq5SHUnc1GyZMk83V9eejC/PYiIiIiIiIiIiIg8YOLj4/n666+xt7dn9uzZAFy5coXIyEg8PT3Nch999BExMTFs3rwZJycnBg8ezLVr18z1Tk5O5v9tbW3TPU8ZHWwYBpMmTaJ169bZxpb2vtn9+/enbdu2LFy4EIvFgr+/v9Xxb+bo6GjV+evs7Gwe30jKfKrv+1nKLOZpp3K3WCzExcVx5swZatasib29PfXr1+fGjRskJCRYvY4Wi8V8TVxdXfH39+ebb74hPDychQsXUrFiRWrWrMmFCxe4cuUK5cuXB5IvmihVqhSlSpUyt583bx4+Pj7UrVvXKsYzZ87g5uYGwOeff06RIkVo2bKluV1KDBMmTOC///6jWLFiVusyk926lM75B51ykUq5SKVcpFIuUt2pXNzNub17IxMRERERERERERGRPLNkyRKqVavG6dOnOXHiBCdOnGDTpk18/fXXxMfHm+XOnz9PuXLlcHJyIjo6mvnz5+fqeI899hjjx4/nypUrQHJn/b59+wAIDQ3liy++AJI7UtNO+X3+/HkqV66MxWLht99+Y9euXbk9ZblJfHw8gwYNolGjRkRGRtKqVSsmT56Ms7Mz/fr1o2LFivz111+EhYVRo0YNc7tp06Yxbdo0atWqxejRo5kxYwYAFy9e5LHHHsPLywsfHx8mT57Mjz/+aNWJHRkZmeE97D/77DNq1apFzZo1+eGHH/juu+8y7Pxu2bIlXbp0uQPZEBERyZhGoIuIiIiIiIiIiIg8ACIjI837UKeoW7cuFSpUIDY21lw2cOBAOnfujIeHB+XLlyc0NDRXx4uIiOD69evUr1/f7BiNiIjAw8ODCRMmEB4ejru7OxUqVKBFixbmdqNHj+aFF17g3XffxdfXl/r16+fq+JJeiRIlWLp0KQAjR45k5MiR5rpp06Zlul3t2rXZuHFjuuWVK1c273GfmQ0bNmS4fMSIEYwYMSLDdTNnzsxwebNmzYiKisryeCIiIrdLHegiIiIiIiIiIiIiD4Bly5ZluHzHjh0A5n3EK1WqlGmnaHh4OOHh4ebzAQMGWK0fPny4+X87Ozveeecd3nnnnXT7cXNzY8WKFRkeo1WrVuZ91OXOadasWUGHICIiclfSFO4iIiIiIiIiIiIiIg8YdaCLiIhkTCPQRSTPDag/jKKGS0GH8UCw2Fio7F+ekzv+xkgyCjqc+57ynb+U7/ynnOev7PK9Mil395kUERERERERERGR3NMIdBEREREREREREREREREREdSBLiIiIiIiIiIiIiIiIiIiAmgKdxERERERERERERGR+8rAsNFA8q2jKnmW5tTe/1h2ZkoBRyUiInJv0Ah0ERERERERERERERERERER1IEuIiIiIiIiIiIiIiIiIiICqANdREREREREREREREREREQEUAe6iIiIiIiIiIiIiIiIiIgIAHYFHYCIiIiIiIiIiIiISF6bsDyCgICAgg6jQCUlJRETE0PZsmULOhQREZF7hkagi4iIiIiIiIiIiIiIiIiIoA50ERERERERERERERERERERQB3oIiIiIiIiIiIiIiIiIiIigO6BLiIiIiJSoCZv+iBfj1e7dm22bt1K7dq18/W4IiIiIiIiIiIi9wKNQBcREREREREREREREREREUEd6CIiIiIiIiIiIiIiIiIiIoA60EVERERERERERERERERERADdA11ERERERERERERE7kMD6g+jqOFS0GEUKIuNhcr+5Tm542+MJKOgwylQykUq5SKVcpFKuUilXKRakTC3oEMoEBqBLiIiIiIiIiIiIiIiIiIigjrQRUREREREREREREREREREAHWgi4iIiIjcE0aOHGn1/OWXX6ZKlSpYLBaioqKs1h0+fJiGDRtSq1YtAgMD2bdvn7nutddeo0OHDjg7O6fbLq21a9dSqFAhfH19zcfVq1czXOfh4cHnn3+eV6cqIiIiIiIiIiJSYO7pDvSUBjt3d3dsbW3N5126dLmjx506dSpjx44FICoqijlz5tzR4wFMnjyZ8PDwDNc1a9aMxYsXZ7uPtWvXsnz58rwNDOjbty9r1qwBYPHixWzatClX+7FYLHh5eeHj44O7uzszZsy45eOHh4fz8ccfZ1guISGBUaNGUadOHTw9PfH19eW5557jwoULuYoXYMmSJbzyyisAnDhxgqlTp+Z4W4vFkuGx0zZIe3t707hxY3bv3p3rGEVEROTeNmvWLPz9/Zk6dSr+/v5MmzYNgM6dO7N+/XoqV66cbpt+/frx3HPP8ccffxAREWH1PfKxxx7j888/p1KlStkeu3bt2kRFRZmPQoUKZbhuxYoVDBgwgNjY2Ns/YRERERERERERyXefbBmd7mGxWChVqhQWiyXPj1e7dm22bt1K7dq183zft8uuoAO4HSkjZk6cOIGvr2+GI2gSEhKws8vb0+zfv79VDIsXL+app57K02PcCWvXruXChQu0adPmlrfNKo/Tp083/7948WJ8fX1p0KBBrmJct24dLi4u7Nq1i8DAQNq0aYObm1uW26Q9flb69OnDuXPn2LhxIyVKlMAwDBYsWMC5c+dwcXHJVbwdOnSgQ4cOQGoHetr6kVspDdIAH330Eb169WL79u1WZfKqbt+J94iIiIjkjcuXL9O3b1/27dvH119/TUREBCdOnACgadOmGW4TExPDtm3b+PnnnwHo1KkTAwYM4MiRI9SoUYPGjRtz4MCBPI3z0qVLFClSBHt7+zzdr4iIiIiIiIiISH67p0egZ6ZKlSpEREQQFBREz549iY6Opnnz5tSrVw8PDw8GDBhAUlISADNnziQ0NJSuXbvi5eVFQEAAx44dA5KnvmzUqBE+Pj54eXkxfPhwIHn6zEGDBhETE8Pbb7/NmjVr8PX1zbDjdPXq1QQHB+Pn54eHhweRkZHmuvDwcPr160fLli2pVasWHTt25MaNGwDExsbSpUsXateuTePGjdmzZ0+Ozj2zfUZFRTF16lS++eYbfH19eeeddwBYsWIFjRs3pl69egQFBZkjudeuXYuHhwd9+vTB19eX7777jgMHDhAWFoa3tzfe3t7maOuUEfDLli1jyZIljB07Fl9fX6ZPn0779u2ZPXu2Gd/PP/9M/fr1sz0PHx8fSpQowV9//QXA7NmzqV+/Pn5+fvj4+PDDDz+YZXMyAv/IkSPMnz+fGTNmUKJECSB5BPgTTzxBtWrVsq0jLVq0oEOHDri7u9O0aVOz4XrmzJk89thjQPKFFYcOHcLX19fsVH/ttdcIDAzE19eXpk2bcujQoWzP/WZt2rQxt7u5bsfFxdG7d288PT3x9PRk1KhR5nYHDx4kODgYDw8POnbsSOvWrZk5cyaQXE969+5N06ZN8fT0vOWYUly/fp1Lly6Zj7i4OAAsNhY98vNhuQtieJAeyrfyfb8/lPN8zzeZXMBrGAYAdnZ2nDlzBsMwcHJyok6dOhiGYT5SyqY8Tp06hZubG7a2tub6SpUqcfLkSfN5Th09ehR/f38CAwOZMmWK1bqU7z3u7u74+fkxevRonJycbmn/IiIiIiIiIiIid5v7dtjp2bNn2bx5MxaLhWvXrvHDDz/g7OxMYmIijz76KPPmzTNHjW/dupWoqCiqVq3K0KFD+fDDD5k2bRqTJ0+mffv2DBs2DIBz585ZHaNs2bK88847LF68ONMOXH9/f9avX4+trS3nzp3Dz8+PsLAwKlasCCSPYF+zZg2Ojo40bdqUhQsX0rVrV9555x0cHR05ePAgly5dokGDBjnqeM5qn/379+fChQvmFOfHjh1j5MiRrFixgmLFinHkyBGaNGlidg4fOHCAKVOmEBkZSUJCAu7u7owaNYquXbsC8N9//1kdt23btnTo0AFfX18GDRoEQOXKlRkxYgRPP/00AJ988gkDBgzI9hx+/fVXSpcujY+PDwBhYWF07doVi8XCiRMnaNCgASdPnsTR0TFHOdmxYwc1a9akdOnSGa53cXHJso78/vvvREVFUbduXcaMGcNzzz1njupKMXXqVAYNGmQ1E0JERATjxo0DYM6cOQwcOPCWp9GfM2cO9erVM5+nrdsRERFcv36d3bt3c/XqVRo3bkydOnXo0qUL3bt354UXXqBXr14cOHAAPz8/83UA2L59O+vXr6do0aK3FE9aH3zwgVWnfQq3OmUoVahMrvcrOWexsVCmekmwgJF0a50icuuU7/ylfOc/5Tx/peTbobADkD7fCQkJODg4sGDBAkaOHMmOHTvYuXMnQ4cOJTAwMF3ZhIQEABITEzEMw3wOyR3siYmJVuWy4+/vz19//UXx4sX566+/aNu2LaVLl+bJJ58ErGfM+euvv2jUqBEBAQH4+/vnIhsiIiIiIiIiIiJ3h/u2Az08PNycjz8pKYmIiAjWr1+PYRjExMTg6elpdo4GBwdTtWpV8/+TJk0CkqfFHDJkCHFxcYSEhBAaGnrLcZw9e5Y+ffrwxx9/YGdnx9mzZ9m7d6/Zgf74449TuHBhAIKCgjh69CiQPHJ9/PjxWCwWihcvztNPP22uy05m+7zZ8uXLOXLkiNX0nzY2Npw6dQqAatWqERISAiSPMLp27ZrZeQ5k2hmdVqtWrRg0aBA7d+6kZMmSbNmyhXnz5mVavkmTJly9epXjx4+zYMECHBwcADh+/DjdunXjr7/+ws7OjnPnznH8+HHq1KmTbQw5kV0dadiwIXXr1gXgueeeY/jw4SQmJma735UrVzJp0iRiY2NJSkpKdxFGZlJGdAHUqlWLL7/80lyXtm6vWrWK//f//h82NjYUKVKEHj16sHLlSh5++GGioqLo0aMHAHXr1qVx48ZWx3jiiSduq/McYNiwYQwePNh8HhUVRUhICGcO/kucEX9b+5acsdhYwICTO/9WZ1c+UL7zl/Kd/5Tz/JWS7xtXbmTUf27eYiUsLIywsDCGDx9OQEAAbdu2JTo62upCQjs7O7N8lSpViI6ONpcbhsGff/5J1apVrcplp1ixYub/K1asSNeuXVm3bp3ZgZ5WxYoVqV+/PqtXr1YHuoiIiIiIiIiI3NPu2w50Z2dn8/8fffQRMTExbN68GScnJwYPHsy1a9fM9WmnmrS1tTVH5XTq1ImGDRuycuVKJk+ezMcff8yyZctuKY7+/fvTtm1bFi5ciMViwd/fP0fHvllKh2lO5HSfhmHQqlUrqynWU5w+fdoqh7fj5ZdfZtKkSbi6utK7d+8sR42n3AN95syZhIeH07BhQ1xdXXnqqacYPXo0nTt3BqBkyZJWecyOv78/hw8f5uzZs5QqVSrd+uzqSG6cOnWKAQMGsHXrVqpXr87u3bszvVfpzdKO6LpZVq9LVvXk5nV58fo6OjpavZ4p+zSSjFueIlZyzzCM5JyrsytfKN/5S/nOf8p5/jIMI8POc0j+2x0XF8eZM2eoWbMm9vb21K9fnxs3bpCQkGD1nc9isZh/611dXfH39+ebb74hPDychQsXUrFiRWrWrHlLsZ05cwZXV1dsbGyIjY3lxx9/pE+fPhmWvXjxItu3b+eZZ565pWOIiIiIiIiIiIjcbe7Le6Df7Pz585QrVw4nJyeio6OZP39+jrY7fPgwrq6u9OjRgzFjxrBp06Z0ZYoVK8bFixezPHblypWxWCz89ttv7Nq1K0fHDg0NZcaMGRiGwaVLl/j2229ztF1Wbo41LCyMVatWsXv3bnPZli1bMty2du3aFC5c2CqOm6dwz+gYAN27d2fFihXMmDEjw/vEZyQ8PJyWLVvyv//9D0jOY8osAbNmzeL8+fM52k+KGjVq0KlTJ/r06cOFCxeA5AbrhQsXcuzYsWzryMaNGzl48CAA06dPp3nz5tja2mZ57hcvXsTe3h43NzcMw2Dy5Mm3FHNOhIaGEhkZiWEYXL58ma+//prWrVtTrFgxfHx8mDVrFpA8on39+vV5fnwRERG5s+Lj4xk0aBCNGjUiMjKSVq1aMXnyZJydnenXrx8VK1bkr7/+IiwsjBo1apjbTZs2jWnTplGrVi1Gjx7NjBkzzHUvvfQS7dq14/Tp0+m269u3L0uWLAFg4cKFeHl54ePjQ4MGDWjVqhW9evUyy6bMmOPr60uDBg145pln6NChQz5kRURERERERERE5M55IDrQBw4cyObNm/Hw8KB79+45nop9wYIFeHl54efnR5cuXZg6dWq6Mi1btuT69et4e3tn2Dk8evRohg4diq+vL1988UWO72P+1ltvcfXqVerUqUPbtm3TTb+dG48//jhRUVH4+vryzjvvUKNGDWbPnk2/fv3w8fGhbt265v3Rb2ZnZ8f333/PjBkzzIbUhQsXpivXvXt35s2bh5+fH9OnTwegcOHCdOzYkUaNGvHQQw/lON4PP/yQGTNmcPr0aSZMmEDnzp3x8/Nj586dVKpU6ZbP/4svvsDHx4f69evj4eGBu7s7P//8MyVLlsy2jjRs2JCIiAg8PDxYsmQJ06ZNS7d/b29vPDw88PT0pEOHDnh5efHUU0/h4eFBYGBgrmLOzltvvYW9vT1eXl7Ur1+fDh06mNOqfvXVV3z66ad4enoSERFBYGAgLi4uGe4nbWP533//bU4fD8n3dn/77bfzPHYRERHJXokSJVi6dCm///47ffr0Yd++ffTu3RtI7iT/66+/SEhI4J9//uHIkSPmdrVr12bjxo388ccfbNu2DS8vL3PdpEmTWLp0KRcvXky33fTp081O8AEDBrBv3z527drFvn37GDlypDnKvVmzZly9epWoqCiioqI4cOAAo0aNyo+UiIiIiIiIiIhIARk5cqTV8+vXrzNgwABq1qyJl5eX1eyEy5cvJyAgAG9vbxo0aGA1yHjr1q2Ehoby9NNPExwczC+//GKue/PNN/Hy8jIHbsyZM8fqmFOmTKFu3bpmf2XKjNIpt0DeuXOnWTY2NhZnZ2erfq+csBiaZ1nusMTEROrVq8ekSZNo0qRJQYdzy2bOnMnixYtZvHhxQYdyS+Li4ihSpAgWi4Xjx48THBzM1q1bb+kihlu1Y8cO6tWrR32bUIoaLnfsOJLKYmOhsn95Tu7Q/Yrzg/Kdv5Tv/Kec56+UfA+Z8lKG62sFVLd6vnbtWpo1a3bbx718+TIHDhygbt26FClS5Lb3JyIiIiL5p0qVKixevPiWG4FzIjY2Fjc3N7p06UJkZGSe7z+ttWvXMmjQoExvYXi71EaVSr/zUikXqZSLVMpFKuUi1YOai0+2jE63rGa9aiQkJDBnzhzGjx/P33//Tfny5enXrx/9+vXjlVdeISEhgYkTJ2KxWIiOjqZcuXKcP3+eGjVq8Ntvv+Hh4cG6det4/vnn2bt3L4Zh8NBDDzF16lTKlSuHnZ0dHTp04NChQxQqVIgLFy6YA0JPnz5N3bp1OXbsGKVLl+b7779nzJgxLFu2jOLFi/Pvv/9SsmRJbG1tCQ8PZ+/evQQHBzNp0iQgebDItGnTiI+Pv6XvHQ/ECHQpOEuWLKF69eoEBwffk53n97INGzbg6+uLj48PjzzyCOPHj7+jneciIiJyZ+VF57mIiIiISGbmzp1LvXr1WLRoEXFxcQUdjoiIiNwlLl++zLPPPsv8+fPp378/v//+O02bNuXy5ctERkby/vvvm7MWlitXDoCjR49SqlQpPDw8AGjSpAmnTp1ix44dnD17ln///ZfmzZsDULNmTVxcXPjpp58ArGZTjouLwzAMkpKSABg7diwjRoygePHiAJQpU8bqlssdO3bkxx9/5Pr16wDMmDHDnM3xVqgDXe6oDh06cOLECT799NOCDiXXwsPD77nR5wCtW7dm165d7Nq1i71799K1a9eCDklERERERERERO6AFStW4O/vj7e3NyEhIezfvx+Aw4cP06hRI3x8fPDy8mL48OGZ7iMyMpKIiAiaNm3K3LlzzeUzZ84kNDSUrl274uXlRUBAAMeOHTPXjxgxgho1ahAYGMjw4cOpUqWKVVyNGzemXr16BAUFsWbNmkzjz6hcTuO/fv06ly5dMh8pFwBYbCx62FiwWO6CGO6Wh3KhXCgXyoVykekjK3Z2dpw5cwbDMHBycqJOnTocOXKEkiVL8v777xMQEECTJk1YtWoVhmFQo0YNzp49y++//45hGHz//ffExsZy/PhxSpUqhZubm3mr6O3bt3Po0CFOnDhhHm/ixInUrl0bf39/PvvsM8qWLQvA/v372bZtG40aNSIgIICJEydaxVm4cGFatWrF4sWLOXjwIIZhULdu3SzPLcPzveUtREREREREREREROSuEBMTw9NPP83atWvx8vLim2++oXPnzuzbt4/JkyfTvn17hg0bBsC5c+cy3Mf+/fv5888/CQsLIyEhgdGjR9OnTx9z/datW4mKiqJq1aoMHTqUDz/8kGnTprF06VIWLlzIzp07cXZ2thrhdezYMUaOHMmKFSsoVqwYR44coUmTJlaN49mVy2n8H3zwAaNGjUq33K1OGUoVKnNL+bzfWGwslKleEiw8UNMQZ0S5SKVcpFIuUikXqR7UXDgUsU+3LD4+HkdHR+bPn8/IkSPZsWMHO3fuZOjQodjZ2XHy5Elq167Ne++9x86dO2nbti1RUVG4uroyZ84chg0bRlxcHA0aNKBu3bpYLBYSEhJYsGABQ4cO5c8//8TPz4/GjRtjZ5fabf3yyy/z8ssvs2vXLp555hlat25NqVKlSEhI4Pjx4/z222+cP3+ekJAQqlWrRvv27c1te/fuzdtvv42Pjw+9evXKVS7UgS4iIiIiIiIiIiJyj9q8eTNeXl54eXkB0K1bN1588UVOnz5N06ZNGTJkCHFxcYSEhBAaGprhPiIjI+nRowe2tra0bduWfv36ceDAAXPEVnBwMFWrVjX/n3Jf0dWrV/PEE09QtGhRAPr06WOOHl++fDlHjhyhadOm5nFsbGw4deqU1bGzKpfT+IcNG8bgwYPN51FRUYSEhHDm4L/EGfE5T+Z9yGJjAQNO7nyw7uObEeUilXKRSrlIpVykelBzceNy+r+Z9vbJnept2rShTZs2DB8+nICAANq2bcvBgwexsbExv0MEBgZStWpVDhw4QIUKFQgNDTX/dl+/fh03Nze8vLyws7OjXr16fP/99+b3jYCAAHO697R8fHyoUKECa9eupVOnTlSqVImuXbtia2tL6dKladu2LZs2bbLqQG/QoAF///03Bw4cYP/+/Wzfvv2Wc6EOdBERERGRAjSgwbAMf4ytTJp/R4536NAhAgMD2b59O/7+/nfkGCIiIiJyd+jUqRMNGzZk5cqVTJ48mY8//phly5ZZlYmPj+frr7/G3t6e2bNnA3DlyhUiIyMZN24cAE5OTmZ5W1tbEhISMjxeyv1PAQzDoFWrVuY+0zp9+nSOytWsWTPb+AEcHR1xdHQ0nzs7OyfvO8nAMB6cjo/MGIaRnIsHqBMoM8pFKuUilXKRSrlIpVykunz5Mv/++y+1atXC3t6e+vXrc+PGDZydnWnZsiU///wzbdu25fjx4xw/fhx3d3csFgtnzpzBzc0NgPfee48WLVpQs2ZNAM6cOUOxYsWA5PuUFylShBYtWgDJM+O4u7sDyfdS37lzp/n86aefZvny5bRo0YKrV6+ydu1aXn/99XQxT5gwgf/++8+8yO9W6R7oIiIiIiIiIiIiIveoBg0asGfPHvbu3QvAnDlzqFChAhUqVODw4cO4urrSo0cPxowZw6ZNm9Jtv2TJEqpVq8bp06c5ceIEJ06cYNOmTXz99dfEx2c9ertFixYsXLiQuLg4DMPgiy++MNeFhYWxatUqdu/ebS7bsmVLun1kVS4n8YuIiMidFR8fzyuvvEKjRo2IjIykVatWTJ48GWdnZ6ZOncrYsWPx8vLiscceY9q0aVSoUAGAt99+mzp16lCjRg1OnjxJZGSkuc/PPvsMX19fOnbsyE8//cR3331nXoj3+uuv4+Hhga+vL126dGHy5MnmrDiDBw/mn3/+wd3dnYCAAB5++GGeeOKJdDG3bNmSLl265PqcNQJdRERERERERERE5B4RFhZmTqcKsGnTJr755ht69OhBQkICJUqUYP78+VgsFhYsWMCsWbNwcHAgKSmJqVOnpttfZGQk3bp1s1pWt25dKlSowA8//JBlLO3bt2fz5s34+vri4uJCSEgILi4uANSoUYPZs2fTr18/rly5wo0bN/Dz80s30jyrcjmJX0RERO6sEiVK8OOPP2KxWBg5ciQjR44011WrVs28fcvNPv/880z3OWLECF577TVzCvciRYqY63788cdMt3NycuLLL7/McN3MmTMzXN6sWTOioqIy3WdG1IEuIiIiIiIiIiIicg84ceJEhssrVqxImzZt0i0fNmwYw4YNy3KfGU2JDrBjxw7z/+Hh4eb/27dvb3Wf0ddff513330XwzB49dVXCQ4ONtelvfdpWjc3ZGdWLifxi4iISP5p1qxZQYeQL9SBLiIiIiIiIiIiIiK50qNHD06cOMG1a9fw8PDQKHEREZH7mDrQRURERERERERERESy8N133xV0CCIiIiJ5yqagAxAREREREREREREREREREbkbqANdREREREREREREREREREQETeEuIiIiIiIiIiIiIiIiIvJAezFoaLplKxLmcvbsWcqWLYvFYsnT4x06dIjAwEC2b9+Ov79/nu77dmkEuoiIiIiIiIiIiIiIiIiICOpAFxERERERERERERERERERAdSBLiIiIiIiIiIiIiIiIiIiAqgDXUREREREREREREREREREBAC7gg5ARO4/kzd/QEBAQEGH8UBISkoiJiaGsmXLYmOja6LuNOU7fynf+U85z1/Kt4iIiIjInaU2Kv3uSEu5SKVcpFIuUikXqZSLVElJSQUdQoF4sF91ERERERERERERERERERGR/6MOdBEREREREREREREREREREdSBLiIiIiIiIiIiIiIiIiIiAqgDXUREREREREREREREREREBFAHuoiIiIiIiIiIiIiIiIiICKAOdBEREREREREREREREREREUAd6CIiIiIiIiIiIiIiIiIiIoA60EVERERERERERERERERERAB1oIuIiIiIiIiIiIiIiIiIiADqQBcREREREREREREREREREQHUgS4iIiIiIiIiIiIiIiIiIgKoA11ERERERERERERERERERARQB7qIiIiIiIiIiIiIiIiIiAigDnQRERERERERERERERERERFAHegiIiIiIiIiIiIiIiIiIiKAOtBFREREREREREREREREREQAdaCLiIiIiIiIiIiIiIiIiIgA6kAXEREREREREREREREREREB1IEuIiIiIiIiIiIiIiIiIiICqANdREREREREREREREREREQEUAe6iIiIiIiIiIiIiIiIiIgIoA50ERERERERERERERERERERQB3oIiIiIiIiIiIiIiIiIiIigDrQRUREREREREREREREREREAHWgi4iIiIiIiIiIiIiIiIiIAOpAFxERERERERERERERERERAcCuoAMQkfvH1atXATh48CA2Nro+Jz8kJSVx7tw5SpYsqZznA+U7fynf+U85z18Fle8DBw4AqX+3RURERETuN2qjSqXfeamUi1TKRSrlIpVykUq5SHUnc3E3t1GpA11E8syRI0cA6N69ewFHIiIiItk5ceIEjRo1KugwRERERETynNqoRERE7h13YxuVOtBFJM80adIEgBUrVlC6dOkCjubBEBcXR0hICL/++ivOzs4FHc59T/nOX8p3/lPO81dB5fvq1aucOHGCsLCwfDumiIiIiEh+UhtVKv3OS6VcpFIuUikXqZSLVMpFqjuZi7u5jcpiGIZR0EGIyP3h0qVLFC9enIsXL1KsWLGCDueBoJznL+U7fynf+U85z1/Kt4iIiIjInaHv2qmUi1TKRSrlIpVykUq5SKVcpHpQc/FgT9wvIiIiIiIiIiIiIiIiIiLyf9SBLiIiIiIiIiIiIiIiIiIigjrQRSQPOTo6MmLECBwdHQs6lAeGcp6/lO/8pXznP+U8fynfIiIiIiJ3hr5rp1IuUikXqZSLVMpFKuUilXKR6kHNhe6BLiIiIiIiIiIiIiIiIiIigkagi4iIiIiIiIiIiIiIiIiIAOpAFxERERERERERERERERERAdSBLiIiIiIiIiIiIiIiIiIiAqgDXUREREREREREREREREREBFAHuojkoU8++YQqVarg5ORE/fr12bJlS0GHdF/44IMPCAwMpGjRopQtW5bHHnuMQ4cOWZW5du0aL774IqVKlcLZ2ZlOnTrxzz//FFDE95fRo0djsVgYNGiQuUz5zlunT5/mmWeeoVSpUhQqVAgvLy+2bdtmrjcMg7fffhs3NzcKFSpEaGgohw8fLsCI722JiYm89dZbVK1alUKFClG9enXeffddDMMwyyjnuffbb7/xyCOPUL58eSwWC4sXL7Zan5Pcnjt3jm7dulGsWDFcXFzo06cPcXFx+XgWIiIiIiL3rnu9fSq/flPs3r2bJk2a4OTkxEMPPcSYMWPSxTJ//nzq1KmDk5MTXl5eLFu2LM/PNyt51SZ26tQp2rVrR+HChSlbtixDhgwhISHBqszatWvx9/fH0dGRGjVqMHPmzHTxFGTd+vTTT/H29qZYsWIUK1aM4OBgfvrpJ3P9g5KHjOS27e5+yMfIkSOxWCxWjzp16pjrH5Q8pMiLNsb74fOzSpUq6eqFxWLhxRdfBB68epFrhohIHpgzZ47h4OBgfPHFF8a+ffuMZ5991nBxcTH++eefgg7tnhcWFmbMmDHD2Lt3rxEVFWW0bdvWqFSpkhEXF2eW6d+/v/HQQw8Zq1evNrZt22Y0aNDAaNiwYQFGfX/YsmWLUaVKFcPb29sYOHCguVz5zjvnzp0zKleubISHhxubN282jh07ZqxYscI4cuSIWWb06NFG8eLFjcWLFxu7du0yOnToYFStWtW4evVqAUZ+73r//feNUqVKGT/++KNx/PhxY/78+Yazs7MxYcIEs4xynnvLli0z3nzzTWPRokUGYHz33XdW63OS2zZt2hg+Pj7Gpk2bjHXr1hk1atQwunbtms9nIiIiIiJy77kf2qfy4zfFxYsXDVdXV6Nbt27G3r17jW+//dYoVKiQMW3aNLPM77//btja2hpjxowx9u/fbwwfPtywt7c39uzZc8dzkCIv2sQSEhIMT09PIzQ01Ni5c6exbNkyo3Tp0sawYcPMMseOHTMKFy5sDB482Ni/f78xadIkw9bW1li+fLlZpqDr1pIlS4ylS5caf/zxh3Ho0CHjjTfeMOzt7Y29e/c+UHm4WW7b7u6XfIwYMcLw8PAwzpw5Yz7+/fffBy4PhpF3bYz3w+dnTEyMVZ1YuXKlARhr1qwxDOPBqhe3Qx3oIpIngoKCjBdffNF8npiYaJQvX9744IMPCjCq+1NMTIwBGL/++qthGIZx4cIFw97e3pg/f75Z5sCBAwZgbNy4saDCvOfFxsYaNWvWNFauXGmEhISYX8KV77wVERFhNG7cONP1SUlJRrly5YyxY8eayy5cuGA4Ojoa3377bX6EeN9p166d0bt3b6tlHTt2NLp162YYhnKel25u7MpJbvfv328AxtatW80yP/30k2GxWIzTp0/nW+wiIiIiIvei+6196k79ppgyZYpRokQJ4/r162aZiIgIo3bt2ubzJ5980mjXrp1VPPXr1zf69euXp+d4K3LTJrZs2TLDxsbGiI6ONst8+umnRrFixczzf/311w0PDw+rY3Xp0sUICwszn9+NdatEiRLG9OnTH9g83E7b3f2SjxEjRhg+Pj4ZrnuQ8mAYedPGeL9+fg4cONCoXr26kZSU9MDVi9uhKdxF5LbduHGD7du3Exoaai6zsbEhNDSUjRs3FmBk96eLFy8CULJkSQC2b99OfHy8Vf7r1KlDpUqVlP/b8OKLL9KuXTurvILyndeWLFlCQEAATzzxBGXLlsXPz4/PP//cXH/8+HGio6Ot8l28eHHq16+vfOdSw4YNWb16NX/88QcAu3btYv369Tz88MOAcn4n5SS3GzduxMXFhYCAALNMaGgoNjY2bN68Od9jFhERERG5VzwI7VN59Zti48aNNG3aFAcHB7NMWFgYhw4d4vz582aZm9tEwsLCCjSXuWkT27hxI15eXri6upplwsLCuHTpEvv27TPLZHWud1vdSkxMZM6cOVy+fJng4OAHNg+303Z3P+Xj8OHDlC9fnmrVqtGtWzdOnToFPHh5yIs2xvvx8/PGjRvMmjWL3r17Y7FYHrh6cTvUgS4it+2///4jMTHR6gMVwNXVlejo6AKK6v6UlJTEoEGDaNSoEZ6engBER0fj4OCAi4uLVVnlP/fmzJnDjh07+OCDD9KtU77z1rFjx/j000+pWbMmK1as4Pnnn+fll1/myy+/BDBzqs+XvDN06FCeeuop6tSpg729PX5+fgwaNIhu3boByvmdlJPcRkdHU7ZsWav1dnZ2lCxZUvkXEREREcnCg9A+lVe/KaKjozPcR9pjZFamoHKZ2zax2znXS5cucfXq1bumbu3ZswdnZ2ccHR3p378/3333He7u7g9cHuD22+7ul3zUr1+fmTNnsnz5cj799FOOHz9OkyZNiI2NfaDyAHnTxng/fn4uXryYCxcuEB4ebsb2INWL22FX0AGIiEjOvfjii+zdu5f169cXdCj3rT///JOBAweycuVKnJycCjqc+15SUhIBAQH873//A8DPz4+9e/cydepUevbsWcDR3Z/mzZvHN998w+zZs/Hw8CAqKopBgwZRvnx55VxERERERETuSmoTg9q1axMVFcXFixdZsGABPXv25Ndffy3osPKd2u5SpcwmCODt7U39+vWpXLky8+bNo1ChQgUYWf5TG2PGIiMjefjhhylfvnxBh3LP0Qh0EbltpUuXxtbWln/++cdq+T///EO5cuUKKKr7z4ABA/jxxx9Zs2YNFStWNJeXK1eOGzducOHCBavyyn/ubN++nZiYGPz9/bGzs8POzo5ff/2ViRMnYmdnh6urq/Kdh9zc3HB3d7daVrduXXO6qZSc6vMl7wwZMsQche7l5UX37t155ZVXzKu2lfM7Jye5LVeuHDExMVbrExISOHfunPIvIiIiIpKFB6F9Kq9+U5QrVy7DfaQ9RmZlCiKXt9MmdjvnWqxYMQoVKnTX1C0HBwdq1KhBvXr1+OCDD/Dx8WHChAkPXB7you3ufspHWi4uLtSqVYsjR448cPUiL9oY77fPz5MnT7Jq1Sr69u1rLnvQ6sXtUAe6iNw2BwcH6tWrx+rVq81lSUlJrF69muDg4AKM7P5gGAYDBgzgu+++45dffqFq1apW6+vVq4e9vb1V/g8dOsSpU6eU/1xo2bIle/bsISoqynwEBATQrVs38//Kd95p1KgRhw4dslr2xx9/ULlyZQCqVq1KuXLlrPJ96dIlNm/erHzn0pUrV7Cxsf4KaGtrS1JSEqCc30k5yW1wcDAXLlxg+/btZplffvmFpKQk6tevn+8xi4iIiIjcKx6E9qm8+k0RHBzMb7/9Rnx8vFlm5cqV1K5dmxIlSphl0h4npUx+5jIv2sSCg4PZs2ePVafYypUrKVasmNnZlt253q11KykpievXrz9weciLtrv7KR9pxcXFcfToUdzc3B64epEXbYz30+cnwIwZMyhbtizt2rUzlz1o9eK2GCIieWDOnDmGo6OjMXPmTGP//v3Gc889Z7i4uBjR0dEFHdo97/nnnzeKFy9urF271jhz5oz5uHLlilmmf//+RqVKlYxffvnF2LZtmxEcHGwEBwcXYNT3l5CQEGPgwIHmc+U772zZssWws7Mz3n//fePw4cPGN998YxQuXNiYNWuWWWb06NGGi4uL8f333xu7d+82Hn30UaNq1arG1atXCzDye1fPnj2NChUqGD/++KNx/PhxY9GiRUbp0qWN119/3SyjnOdebGyssXPnTmPnzp0GYHz00UfGzp07jZMnTxqGkbPctmnTxvDz8zM2b95srF+/3qhZs6bRtWvXgjolEREREZF7xv3QPpUfvykuXLhguLq6Gt27dzf27t1rzJkzxyhcuLAxbdo0s8zvv/9u2NnZGePGjTMOHDhgjBgxwrC3tzf27NmTb7nIizaxhIQEw9PT02jdurURFRVlLF++3ChTpowxbNgws8yxY8eMwoULG0OGDDEOHDhgfPLJJ4atra2xfPlys0xB162hQ4cav/76q3H8+HFj9+7dxtChQw2LxWL8/PPPD1QeMnOrbXf3Sz5effVVY+3atcbx48eN33//3QgNDTVKly5txMTEPFB5MIy8a2O8Xz4/ExMTjUqVKhkRERHp1j1I9eJ2qANdRPLMpEmTjEqVKhkODg5GUFCQsWnTpoIO6b4AZPiYMWOGWebq1avGCy+8YJQoUcIoXLiw8fjjjxtnzpwpuKDvMzd/CVe+89YPP/xgeHp6Go6OjkadOnWMzz77zGp9UlKS8dZbbxmurq6Go6Oj0bJlS+PQoUMFFO2979KlS8bAgQONSpUqGU5OTka1atWMN99807h+/bpZRjnPvTVr1mT4md2zZ0/DMHKW27Nnzxpdu3Y1nJ2djWLFihm9evUyYmNjC+BsRERERETuPfd6+1R+/abYtWuX0bhxY8PR0dGoUKGCMXr06HSxzJs3z6hVq5bh4OBgeHh4GEuXLr1j552RvGoTO3HihPHwww8bhQoVMkqXLm28+uqrRnx8vFWZNWvWGL6+voaDg4NRrVo1q2OkKMi61bt3b6Ny5cqGg4ODUaZMGaNly5Zm57lhPDh5yExu2u7uh3x06dLFcHNzMxwcHIwKFSoYXbp0MY4cOWKuf1DykCIv2hjvl8/PFStWGECG7XkPWr3ILYthGEa+DXcXERERERERERERERERERG5S+ke6CIiIiIiIiIiIiIiIiIiIqgDXUREREREREREREREREREBFAHuoiIiIiIiIiIiIiIiIiICKAOdBEREREREREREREREREREUAd6CIiIiIiIiIiIiIiIiIiIoA60EVERERERERERERERERERAB1oIuIiIiIiIiIiIiIiIiIiADqQBcREREREREREREREREREQHUgS4iIveoKlWqYLFYsFgsBR2K5JO//voLJycnLBYL33zzTa73c+LECbPuNGvWzFw+cuRIc/nMmTPN5RnVtbVr15rLwsPDcx1LQbr5PNO6fPkyJUqUwGKxMHbs2PwNTEREREREROQeoLapB09etU3dLLN2pvDwcHP52rVrzeUpy6pUqWIumzlzprl85MiReRZbfklpr0t7nmmdPHkSOzs7LBYL8+fPz9/g5IGkDnQREUkn7Q+A7B6ZfanJyOLFixk5ciQjR47kxIkTdyz+m6X9Apm2w/Rec+HCBTN/mXV83u1upw689957XL9+nfLly/Pkk0+ay9P+mEh5ODg44ObmRrt27Vi2bFken8W9LT4+noMHDwJw8OBBjh07hmEYVmWKFCnCs88+C8DYsWOJi4vL9zhFRERERETkwaW2qbuT2qZy1jb13HPPpdt24cKFVmXKlSt3u6dy37h69SqHDx8GYPfu3fz555/pylSuXJmOHTsCMGrUKJKSkvI1Rnnw2BV0ACIi8uBYvHgxX375JQDNmjWzukpSsnfhwgVGjRoFQEhIyD058jm3deDs2bPMmDEDgB49emBvb59l+fj4eKKjo1m2bBnLli1j3rx5PPHEEwC4ubmxbt06AIoXL56r8/Dz8zP34erqmqt9FIRJkybx5ptvEhsbC8CHH37Ihx9+SKlSpRg2bBivvvqqWbZv376MHTuWf//9l5kzZzJgwICCCltEREREREQkT6ht6vaobSpnbVNz5sxh/PjxFClSxFz2+eefZ1o+L9qZ2rZta+6jUqVKudpHfjMMg+HDhzNu3Dhu3LgBwMCBAxk4cCDly5dn7NixPP3002b5vn37Mn/+fPbt28eyZcto3759QYUuDwB1oIuISDoLFizg2rVr5vMnnniC6OhoACZOnIifn5+5zsvLK9/jkwfP7NmzzS/SnTp1yrRcr1696N27N1euXOHdd99l/fr1AHzyySdmB7qjoyONGze+rXiKFy9+2/vIbz/99BMvv/xyhuvOnj3Lnj17rJbVqlULDw8P9u3bpw50ERERERERyVdqm5K7TU7bpgBiY2OZO3cuvXv3BpKnH1+5cmWm5fOinals2bKULVv2tvaR36ZNm8b//ve/DNf9/fff/PHHH1bLmjdvTokSJTh//jwzZ85UB7rcUZrCXURE0gkICKBx48bmw9HR0Vzn5eVltc5isfDmm29St25dChUqRNGiRalfvz7Tpk0zp4VOuYdNytWdkPyF5+aptkaPHk2zZs2oWLEihQoVonDhwri7uzN8+HCuXLmS5+d5872BZs2ahaenJ46OjtSqVYt58+aZZV9++WWz7HfffWe1n//973/muilTppjLjx8/zrPPPkvlypVxdHSkbNmydOnShQMHDlhtn5SUxPvvv4+npyeFChXCycmJSpUq0a5dOyIjI4HkqaCqVq1qbvPrr7+mm/or7T28IyMjGTVqFG5ubhQrVoyuXbty4cIFzp07R/fu3SlevDglS5akf//+Vj9IU3z//feEhoZSokQJHB0dqV27NqNGjeLq1atW5Zo1a2Yec/fu3bz00kuULVuWQoUK8fDDD3Py5Ekg53UgMyk5L1myJAEBAZmWq1SpEo0bN6Z169YMHTrUXJ7yIzttLLczbVpW90CPjo7m5Zdfpnr16jg6OuLi4kKzZs3S3Z/p5ji2bt1K8+bNKVy4MOXKlWP48OFW01HlpJ5kJaU+lylThqNHjwLJP1QOHjzI1KlTrRofUrRq1QqA7du3Zzh9loiIiIiIiMidoLYptU3dq21TRYsWBWD69OnmssjISJKSksx1N8uqnSmnsroH+pEjR+jVqxcPPfQQDg4OlCpVirZt27J69eos41ixYgWBgYFmfZg4caJV+atXrzJkyBBq1qyJo6MjRYoUoWrVqnTs2DFdHc3I3LlzAXB3d2fz5s0ALFu2jL179zJu3Dhq1KhhVd7e3p6QkBAAli5dal7QIHJHGCIiItmoXLmyARiAsWbNGnP5uXPnjDp16pjrbn489dRThmEYxvHjxzMtk3aftWvXzrRM8+bNM40pOzNmzDDLhoSEZLi8WrVq6Y5pY2NjHDx40DAMw9i0aZO5/Omnn7bav5+fnwEY9vb2xn///WcYhmFs377dcHFxyfBcnJ2djc2bN5vbv/POO5med6NGjQzDMIyePXtmWiblnEaMGGEuq169erpybdq0MYKCgtItf/PNN63O56233sr0WE2aNDGuX79ulg0JCckyhynx57QOZCQ+Pt4oUqSIARgtW7ZMtz5tbkaMGGEYhmFcvnzZ6Nevn7m8U6dOZvm0saStD2nzN2PGDHN5RnVtzZo15rKePXuay48dO2aUK1cu0/OMiIjIMA43NzejUKFC6cp//vnnt1RPstK2bVsDMGrXrm0kJSWlO8+MfPXVV+Yxvv3222yPISIiIiIiInInqG1KbVP3StvUs88+a/5/3759RkJCglGhQgUDMJ577jlznaurq7l9Zu1MafebNr6UZZUrV86wLqW0jxmGYWzevNkoWrRohudssViMKVOmZBhH5cqVDRsbm3TbrFy50izfu3fvTPPZrVu3TPOZwt3d3QCMVq1ama9PVq+DYVjX1Y0bN2Z7DJHc0gh0ERHJtTfeeIODBw8CyVf/Llq0iOnTp1OiRAkg+X4/c+fONe85/fDDD5vbTpw4kXXr1rFu3Tpz5Gv//v35+uuvWbZsGWvXrmXJkiW0bdsWgDVr1rBhw4Y7di7Hjh2jT58+/Pjjj7Rs2RJIvvo25WrR+vXrm1c9/vjjj1y/ft3cbufOnQC0adOGUqVKYRgGPXv25MKFCwC8+uqr/Pzzz3z44YfY2toSFxdHr169zKugv//+ewBcXFyYNWsWq1at4quvvqJ///64ubkB8Oabb1qNYPb19TXzN2nSpHTnc+LECcaMGcPcuXPNq1uXL1/O/v37mT59Op9++qlZdtq0aeb/t27dyrvvvgsk3ys8MjKS5cuX065dOwDWrVvH+PHjM8zhv//+y9SpU5k1axYuLi4A/P777+zbty/HdSAjp06d4vLlywDprjy92ahRo7BYLBQpUsQ8rypVqvDBBx9kuV1eeeGFF8zR7s2aNWPJkiV89NFHODk5Acn3HE+5ojatM2fO4O/vz/fff281zXra1yYn9SQrtWrVAuDQoUP4+/sD8NtvvxEVFZXpNmnzvX///myPISIiIiIiIpKf1Daltqm07oa2KX9/f3Nf06dPZ9myZZw+fRo7O7t8v2e8YRj06tWL2NhYADp37szSpUt56623sLGxwTAMBg0alOGsgydPnuSRRx7hhx9+4KmnnjKXZ9RWVblyZRYsWMDPP/9MZGQkPXr0MN+DWUlpq1q5ciWPP/44kFxHDh06lOk2aquS/KJ7oIuISK4kJSWZ0+xA8n2APD09geTpe1566SUAvv32W7p06ULjxo2t7sOTMt1WWq1ateK9995j/fr1/PPPP8THx1ut37ZtGw0bNrwj5+Pj42P+ICldurQ5hdGRI0fMMt26dWPUqFFcunSJn3/+mUceeYQFCxZYrQfYtWsXe/fuBZJ/TDz22GMANGzYkKCgIDZu3Mj+/fvZsWMH9erVw97eHoAiRYpQvXp1vL29KVy4MN27dzf3XbNmTbMcZH9vpC5dujBkyBAAvvrqK5YuXQrAK6+8Qp8+fQCYPHky+/bt47///uPixYsUL16cb775xtxHr169zC+y/fv3N/cxa9YsIiIi0h3znXfeoV+/fgCsX7+eqVOnmjn08PDIUR3IyH///Wf+Pydfvm9WpEgRLl26dMvb3apz586xYsUKIPk+6wsWLKBUqVIAnD59mv/3//4fkPyeqF+/vtW2Dg4OLFy4EFdXV9q3b8/06dO5cuWKVf3LST3JyiuvvMKMGTO4ePGi2Wk+Y8YMZsyYgZeXFzNnzjQ71lOkzXfa10FERERERESkoKltSm1TN7tb2qb69u3Liy++yNdff2128rZt2zZHAyDyUlRUlHn8cuXKMXv2bOzt7Wnbti379+9n4cKF3Lhxg4ULFzJo0CCrbcuWLcvcuXNxdHQkMDCQOXPmAGTYVuXi4kL16tWpW7cujo6O5r3fs/PGG2+wbNkybty4YbZVffjhh3z44Yc0btyYr776yuq2AaC2Ksk/GoEuIiK58u+//3L+/HkAChcubP5AAQgKCjL//8cff+RofydPnqRhw4bMmTOHv/76K90PFMC8avZOSLl/DmB2et58zGeeecb8f8qPk5R/ixYtSocOHQDrc46KiqJJkybmY+PGjea6lPtNpfxoOH36NMHBwTg7O1OjRg369euX4/zdLO1rULJkSfP/ae/RVLp06XTnmfZ4//vf/8y4H3nkEXN5ypXdN8tJDm9XypXRmenVqxfr1q1j1apVDBw4EIB9+/bRvn37DO+nlZcOHz5sxle9enWrHGT3nqhTpw6urq4A2NjYmD8G0ubudutJpUqViIqK4vnnn6dixYpW6/bs2UOnTp3S5Si7fIuIiIiIiIgUFLVNqW3qZndD2xQkX8hQuHBh/vvvP3OwxbPPPptnMeRU2lz6+/tbXQCR3XukQYMGODo6ApnnMqXe7Nq1Cz8/P4oUKYK7uzuDBw/mzJkz2cYXGBjItm3b6N69O2XKlLFat379erp165Yu32qrkvyiDnQREbltFosly+c58eWXX5qjhIODg1m8eDHr1q3j9ddfN8skJSXdXqBZSHv1op1d6gQtab+U1ahRwxw5vGTJEo4cOcLWrVsB6NixI4UKFbqlY6ZM/dS3b19++uknunfvjqenJw4ODhw9epTPPvuMkJCQXH3JL168uPl/G5vUP/fFihXLsPytfPlMSEgwpwlLKyc5zI20P6ZSfhhnplKlSjRu3JiWLVvy8ccfU6dOHQCio6P57bffbiuO25Hde+Lmq5fT5i9FXtSTKlWqMGXKFHNqrgEDBtC0aVMgeWq1LVu2WJVPm++0r4OIiIiIiIjI3URtU2qbgrujbQqSz71z587m8woVKlhNHX83uJW2qsxy+e677/Ltt9/yxBNPULt2bSwWCwcOHGD8+PG0bt2ahISEbOPw8vLiq6++Mtuk3n77bfNimI0bN6abXl5tVZJf1IEuIiK5UqZMGfNeQpcvX2bfvn3murT3eE6ZZgmsvyzf/IPj9OnT5v/feOMNHn30URo3bszFixfzOvTbkjIV1oULF3jhhRfM5WmvAE57ziEhIRiGke5x+fJlc0opwzBo06YNX331FXv27CEuLs6cNik6Otq8v1ZW+csraWOfMWNGprGnXIF6q3JzDpUqVaJIkSKA9TRRt+rcuXO53jYnatSoYf74OHr0KGfPnjXXZfaeuBU5rSeZOX78eLpl9erVs5ry7J9//rFanzbf7u7uuYpbRERERERE5E5Q25TapnIjv9qm+vbta/4/PDwcW1vbW4gyb6TN5c6dO606tPOirQrgqaeeYt68eRw8eJDY2FjzwoG9e/dmO3tBRm1VzZs35/nnnzefq61KCorugS4iIrliY2PDU089Zd5LqFu3bowYMYLz588zYsQIs1zXrl3N/6e9cnHWrFnY2tpia2tL48aNqVy5srlu4sSJODg4sHnzZiIjI/PhbHLuqaeeYvDgwSQkJLBy5UoAypcvT4sWLcwyPj4+eHp6snfvXn799Vd69OjBE088gb29vTnK97vvvjOvmOzcuTNFixalSZMmVKxYkYSEBLZt22buL+WK2rT527NnD4sXL6Z06dJUqlSJSpUq5cn5Pf3000yYMAFIvifVuXPn8Pb25sKFCxw9epSff/6ZypUr88UXX+Rq/1nVgczY2dkRFBTEmjVr2LlzZ5b7P3XqFOvXr+f69essXbrUakqv2/kxkBOlSpUiLCyM5cuXc/36dZ588kleeeUVjh49ypQpU8xyad8TtyKn9SQzvXr14vr163Tr1o3atWsDydPbf/fdd2aZ6tWrW22TNt+NGjXKVdwiIiIiIiIid4LaptQ2lRt3um0qRZMmTfjggw+4du2aVWd6fvL19aVu3bocOHCAM2fO0K1bN8LDw9m8ebPZHuTg4ECnTp1ytf9GjRrh5+dHUFAQFSpUIDY21rznOmTfVtWiRQtq1apF586dKVy4MABbtmxh8eLFZplq1apZbZOSfycnJ/z9/XMVt0iOGCIiItmoXLmyARiAsWbNGnP52bNnjTp16pjrbn489dRTRlJSkln+hx9+yLCcYRjGyZMnjcKFC6db16hRI/P/I0aMyDCm7MyYMcMsGxISkuHytPs+fvx4huVTPPzww1YxDh48OF2Z7du3Gy4uLpnmJm3cLVu2zLSMq6urceHCBbNsvXr10pVJiX3EiBHmshkzZpjb9OzZM8PXLyQkxFx+/Phxc/lbb72VZdw9e/bMdh+ZxZJVHcjKpEmTzLLbtm2zWpf2/DJ7PPLII2b5zF7fzGLOqK6tWbMmw3wcPXrUKFeuXKZxREREZBtHZse8lXqSkbSvVUaPkJAQq/erYRiGh4eHARgBAQFZ7ltERERERETkTlLblDW1TfXMdh8F1Tb16aefZrqPtK+rq6uruTyzdqbM8payrHLlyuayzOrS5s2bjaJFi2Z4zhaLxZgyZUq2cWR2zOrVq2f6Grm7uxsJCQlZ5jTteyijR/fu3a3K37hxwyhRooQBGJ07d85y3yK3S1O4i4hIrpUsWZJNmzYxbNgwateujaOjI0WKFCEwMJBPP/2U2bNnW91Pp3379owbN47q1aunu8dzpUqV+PnnnwkKCqJQoUJUr16dKVOmFNgVmllJOyVWRs8B/P39iYqKon///lSrVg0HBwdcXFzw9PSkf//+rF692iz7wgsv0KVLF6pXr46zszN2dnZUqFCBbt26sX79eqt7Rn377be0adMm3T2z89I777zDjz/+SJs2bShVqhT29vZUqFCBxo0bM3r0aEaNGpXrfWdVB7Ly9NNPm1NzLVq0KNvyFosFZ2dnAgICGDNmDPPnz891zLeiWrVq7NixgwEDBlC1alXs7e0pVqwYTZs2Ze7cuYwePTrX+76VepKRjz76iBdffBEPDw+KFi0KJF+t7+bmRp8+fZg/f77V+/WPP/4wp78LDw/PddwiIiIiIiIid4rapjJ+Dmqbykx+tU3dLYKCgti+fTs9e/akQoUK2NnZUaJECdq0acPPP/9sNV36rRo2bBiPPvoolStXpnDhwtjb21OlShX69+/PL7/8ku209V9++SXh4eHUrl3bHIFuZ2dH5cqVefXVV/n000+tyq9Zs8acNUFtVXKnWQzDMAo6CBEREZHsPP/880ydOpUKFSpw/Phx7O3tCzqke5rFYmHGjBmZ/uB4/fXXGTt2LGXKlOH48ePmvb5ERERERERERB5Eapu6c06cOEHVqlVZs2YNzZo1y7DMk08+yfz58/Hw8GD37t1W97MXyWuqXSIiInJPePPNN3F0dOT06dPMmzevoMO5r12+fJnPP/8cSO5IV+e5iIiIiIiIiDzo1DZVcE6ePGmO/B85cqQ6z+WO0wh0ERERERERERERERERERERNAJdREREREREREREREREREQEUAe6iIiIiIiIiIiIiIiIiIgIoA50ERERERERERERERERERERQB3oIiIiIiIiIiIiIiIiIiIigDrQRUREREREREREREREREREAHWgi4iIiIiIiIiIiIiIiIiIAOpAFxERERERERERERERERERAdSBLiIiIiIiIiIiIiIiIiIiAqgDXUREREREREREREREREREBFAHuoiIiIiIiIiIiIiIiIiICAD/Hxr69OXKPt9hAAAAAElFTkSuQmCC"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Program Scale Categories:
+
+Investment Scale Categories (in Millions $):
+Small Programs: &lt; $20.0M
+Medium Programs: $20.0M - $97.0M
+Large Programs: $97.0M - $587.9M
+Mega Programs: &gt; $587.9M
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [39]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># First, let's prepare our county-level metrics</span>
+<span class="n">county_analysis</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'County'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
+    <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+    <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="s1">'sum'</span><span class="p">,</span>
+    <span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span>
+    <span class="s1">'Is Low Income Communities'</span><span class="p">:</span> <span class="s1">'mean'</span>
+<span class="p">})</span>
+
+<span class="c1"># Flatten column names</span>
+<span class="n">county_analysis</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s1">'project_count'</span><span class="p">,</span> <span class="s1">'total_investment'</span><span class="p">,</span> <span class="s1">'avg_project_size'</span><span class="p">,</span>
+    <span class="s1">'total_ghg_reduction'</span><span class="p">,</span> <span class="s1">'dac_rate'</span><span class="p">,</span> <span class="s1">'low_income_rate'</span>
+<span class="p">]</span>
+
+<span class="c1"># Calculate per-project metrics</span>
+<span class="n">county_analysis</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">]</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_ghg_reduction'</span><span class="p">]</span> <span class="o">/</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span>
+<span class="n">county_analysis</span><span class="p">[</span><span class="s1">'investment_per_project'</span><span class="p">]</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span> <span class="o">/</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'project_count'</span><span class="p">]</span>
+
+<span class="c1"># Create visualization of spatial patterns</span>
+<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+
+<span class="c1"># 1. Investment Distribution</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">top_15</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15</span><span class="p">)),</span> <span class="n">top_15</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'dodgerblue'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15</span><span class="p">)),</span> <span class="n">top_15</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Total Investment (Millions $)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Top 15 Counties by Total Investment'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+
+<span class="c1"># 2. Project Count Distribution</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">top_15_count</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'project_count'</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_count</span><span class="p">)),</span> <span class="n">top_15_count</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'dodgerblue'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_count</span><span class="p">)),</span> <span class="n">top_15_count</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Number of Projects'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Top 15 Counties by Project Count'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+
+<span class="c1"># 3. DAC Benefit Rate</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">top_15_dac</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'dac_rate'</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_dac</span><span class="p">)),</span> <span class="n">top_15_dac</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'dodgerblue'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_dac</span><span class="p">)),</span> <span class="n">top_15_dac</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Percentage of Projects Benefiting DACs'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Top 15 Counties by DAC Benefit Rate'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+
+<span class="c1"># 4. GHG Efficiency</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">top_15_ghg</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_ghg</span><span class="p">)),</span> <span class="n">top_15_ghg</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'dodgerblue'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_15_ghg</span><span class="p">)),</span> <span class="n">top_15_ghg</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'GHG Reduction per Dollar'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Top 15 Counties by GHG Efficiency'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Print detailed analysis</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Spatial Distribution Analysis:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">1. Investment Concentration:"</span><span class="p">)</span>
+<span class="n">investment_share</span> <span class="o">=</span> <span class="p">(</span><span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> 
+                   <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Top 5 counties account for </span><span class="si">{</span><span class="n">investment_share</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">% of total investment"</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">2. Project Distribution:"</span><span class="p">)</span>
+<span class="n">project_share</span> <span class="o">=</span> <span class="p">(</span><span class="n">county_analysis</span><span class="p">[</span><span class="s1">'project_count'</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> 
+                <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'project_count'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Top 5 counties account for </span><span class="si">{</span><span class="n">project_share</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">% of all projects"</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">3. Regional Equity Analysis:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">DAC Benefit Rate by Region:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">county_analysis</span><span class="p">[</span><span class="s1">'dac_rate'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">4. Project Type Distribution:"</span><span class="p">)</span>
+<span class="c1"># Look at program types in top 5 counties</span>
+<span class="n">top_5_counties</span> <span class="o">=</span> <span class="n">county_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">index</span>
+<span class="k">for</span> <span class="n">county</span> <span class="ow">in</span> <span class="n">top_5_counties</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="si">{</span><span class="n">county</span><span class="si">}</span><span class="s2"> Program Distribution:"</span><span class="p">)</span>
+    <span class="n">county_programs</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">'County'</span><span class="p">]</span> <span class="o">==</span> <span class="n">county</span><span class="p">][</span><span class="s1">'Program Name'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">county_programs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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"/>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Spatial Distribution Analysis:
+
+1. Investment Concentration:
+Top 5 counties account for 75.6% of total investment
+
+2. Project Distribution:
+Top 5 counties account for 51.1% of all projects
+
+3. Regional Equity Analysis:
+
+DAC Benefit Rate by Region:
+count    299.000
+mean       0.149
+std        0.297
+min        0.000
+25%        0.000
+50%        0.000
+75%        0.125
+max        1.000
+Name: dac_rate, dtype: float64
+
+4. Project Type Distribution:
+
+Los Angeles Program Distribution:
+Program Name
+Low Carbon Transportation            32598
+Low-Income Weatherization Program     2233
+Water-Energy Efficiency               1945
+Name: count, dtype: int64
+
+Alameda Program Distribution:
+Program Name
+Low Carbon Transportation    5677
+Water-Energy Efficiency       244
+Community Air Protection      223
+Name: count, dtype: int64
+
+Alameda, Contra Costa, Fresno, Kern, Kings, Madera, Merced, Sacramento, San Joaquin, Santa Clara, Stanislaus, Tulare Program Distribution:
+Program Name
+Transit and Intercity Rail Capital Program    9
+Name: count, dtype: int64
+
+Sacramento Program Distribution:
+Program Name
+Low Carbon Transportation            3556
+Water-Energy Efficiency               422
+Low-Income Weatherization Program     297
+Name: count, dtype: int64
+
+San Diego Program Distribution:
+Program Name
+Low Carbon Transportation            7865
+Community Air Protection              105
+Low-Income Weatherization Program      83
+Name: count, dtype: int64
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># First, let's identify multi-county projects</span>
+<span class="k">def</span> <span class="nf">is_multi_county</span><span class="p">(</span><span class="n">county</span><span class="p">):</span>
+    <span class="k">return</span> <span class="s1">','</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">county</span><span class="p">)</span>
+
+<span class="c1"># Create multi-county analysis dataframe</span>
+<span class="n">multi_county_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">'County'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">is_multi_county</span><span class="p">)]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+<span class="c1"># Analyze these collaborations</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Multi-County Collaboration Analysis:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Total number of multi-county projects: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">multi_county_data</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total investment in multi-county projects: $</span><span class="si">{</span><span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project Cost'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="mf">1e9</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">B"</span><span class="p">)</span>
+
+<span class="c1"># Look at the specific collaborations</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Major Multi-County Collaborations:"</span><span class="p">)</span>
+<span class="n">collaboration_summary</span> <span class="o">=</span> <span class="n">multi_county_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'County'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
+    <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+    <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="s1">'sum'</span><span class="p">,</span>
+    <span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span>
+    <span class="s1">'Total GGRFDisadvantaged Community Funding'</span><span class="p">:</span> <span class="s1">'sum'</span>
+<span class="p">})</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="c1"># Flatten column names</span>
+<span class="n">collaboration_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s1">'project_count'</span><span class="p">,</span> <span class="s1">'total_investment'</span><span class="p">,</span> <span class="s1">'avg_project_size'</span><span class="p">,</span>
+    <span class="s1">'total_ghg_reduction'</span><span class="p">,</span> <span class="s1">'dac_benefit_rate'</span><span class="p">,</span> <span class="s1">'dac_funding'</span>
+<span class="p">]</span>
+
+<span class="c1"># Calculate efficiency metrics</span>
+<span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">]</span> <span class="o">=</span> <span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'total_ghg_reduction'</span><span class="p">]</span> <span class="o">/</span> <span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span>
+<span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'dac_funding_ratio'</span><span class="p">]</span> <span class="o">=</span> <span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'dac_funding'</span><span class="p">]</span> <span class="o">/</span> <span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span>
+
+<span class="c1"># Sort by total investment</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">collaboration_summary</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'total_investment'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
+
+<span class="c1"># Create visualizations</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
+
+<span class="c1"># 1. Project Size Comparison</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">avg_size</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'Total Project Cost'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">multi_avg_size</span> <span class="o">=</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project Cost'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">([</span><span class="s1">'Single-County Projects'</span><span class="p">,</span> <span class="s1">'Multi-County Projects'</span><span class="p">],</span> 
+       <span class="p">[</span><span class="n">avg_size</span><span class="o">/</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">multi_avg_size</span><span class="o">/</span><span class="mf">1e6</span><span class="p">],</span>
+       <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'lightblue'</span><span class="p">,</span> <span class="s1">'darkblue'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Average Project Size (Millions $)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Project Size Comparison'</span><span class="p">)</span>
+
+<span class="c1"># 2. GHG Efficiency Comparison</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">single_ghg_efficiency</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="o">~</span><span class="n">data</span><span class="p">[</span><span class="s1">'County'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">is_multi_county</span><span class="p">)][</span><span class="s1">'Total Project GHGReductions'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> 
+                        <span class="n">data</span><span class="p">[</span><span class="o">~</span><span class="n">data</span><span class="p">[</span><span class="s1">'County'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">is_multi_county</span><span class="p">)][</span><span class="s1">'Total Project Cost'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
+<span class="n">multi_ghg_efficiency</span> <span class="o">=</span> <span class="p">(</span><span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project GHGReductions'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> 
+                       <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project Cost'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">([</span><span class="s1">'Single-County Projects'</span><span class="p">,</span> <span class="s1">'Multi-County Projects'</span><span class="p">],</span>
+       <span class="p">[</span><span class="n">single_ghg_efficiency</span><span class="p">,</span> <span class="n">multi_ghg_efficiency</span><span class="p">],</span>
+       <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'lightblue'</span><span class="p">,</span> <span class="s1">'darkblue'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'GHG Reduction per Dollar'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'GHG Efficiency Comparison'</span><span class="p">)</span>
+
+<span class="c1"># 3. Top Multi-County Collaborations</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">top_collaborations</span> <span class="o">=</span> <span class="n">collaboration_summary</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'total_investment'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_collaborations</span><span class="p">)),</span> <span class="n">top_collaborations</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span><span class="o">/</span><span class="mf">1e9</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_collaborations</span><span class="p">)),</span> 
+          <span class="p">[</span><span class="n">name</span><span class="p">[:</span><span class="mi">50</span><span class="p">]</span> <span class="o">+</span> <span class="s1">'...'</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">50</span> <span class="k">else</span> <span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">top_collaborations</span><span class="o">.</span><span class="n">index</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Total Investment (Billions $)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Top 5 Multi-County Collaborations by Investment'</span><span class="p">)</span>
+
+<span class="c1"># 4. DAC Benefit Rate Comparison</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">([</span><span class="s1">'Single-County Projects'</span><span class="p">,</span> <span class="s1">'Multi-County Projects'</span><span class="p">],</span>
+       <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="o">~</span><span class="n">data</span><span class="p">[</span><span class="s1">'County'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">is_multi_county</span><span class="p">)][</span><span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
+        <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()],</span>
+       <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'lightblue'</span><span class="p">,</span> <span class="s1">'darkblue'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Average DAC Benefit Rate'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'DAC Benefit Rate Comparison'</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Analyze types of projects that work well in collaboration</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Most Common Types of Multi-County Projects:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Program Name'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+
+<span class="c1"># Look at success factors</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Success Metrics for Multi-County Projects:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">DAC Benefit Rate Distribution:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">GHG Reduction Distribution (tons per $):"</span><span class="p">)</span>
+<span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'ghg_efficiency'</span><span class="p">]</span> <span class="o">=</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project GHGReductions'</span><span class="p">]</span> <span class="o">/</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project Cost'</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'ghg_efficiency'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Multi-County Collaboration Analysis:
+
+Total number of multi-county projects: 874
+Total investment in multi-county projects: $18.31B
+
+Major Multi-County Collaborations:
+                                                    project_count  \
+County                                                              
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...              9   
+San Francisco, Santa Clara                                      6   
+San Diego, Santa Barbara, Ventura                              23   
+San Francisco, San Mateo, Santa Clara                           3   
+Los Angeles, Ventura                                            3   
+Marin, Sonoma                                                  15   
+Los Angeles, San Bernardino                                    29   
+Humboldt, Mendocino                                             5   
+Contra Costa, San Francisco                                     3   
+Los Angeles, San Diego, San Luis Obispo, Santa ...              2   
+
+                                                    total_investment  \
+County                                                                 
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...        7333200000   
+San Francisco, Santa Clara                                2998442533   
+San Diego, Santa Barbara, Ventura                         2235315130   
+San Francisco, San Mateo, Santa Clara                     2145553263   
+Los Angeles, Ventura                                       877252312   
+Marin, Sonoma                                              363189831   
+Los Angeles, San Bernardino                                177831134   
+Humboldt, Mendocino                                        155426149   
+Contra Costa, San Francisco                                135489931   
+Los Angeles, San Diego, San Luis Obispo, Santa ...         130936629   
+
+                                                    avg_project_size  \
+County                                                                 
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...      8.148000e+08   
+San Francisco, Santa Clara                              4.997404e+08   
+San Diego, Santa Barbara, Ventura                       9.718761e+07   
+San Francisco, San Mateo, Santa Clara                   7.151844e+08   
+Los Angeles, Ventura                                    2.924174e+08   
+Marin, Sonoma                                           2.421266e+07   
+Los Angeles, San Bernardino                             6.132108e+06   
+Humboldt, Mendocino                                     3.108523e+07   
+Contra Costa, San Francisco                             4.516331e+07   
+Los Angeles, San Diego, San Luis Obispo, Santa ...      6.546831e+07   
+
+                                                    total_ghg_reduction  \
+County                                                                    
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...               546406   
+San Francisco, Santa Clara                                      1538067   
+San Diego, Santa Barbara, Ventura                                791432   
+San Francisco, San Mateo, Santa Clara                           2100845   
+Los Angeles, Ventura                                              74882   
+Marin, Sonoma                                                    234508   
+Los Angeles, San Bernardino                                       22220   
+Humboldt, Mendocino                                               24160   
+Contra Costa, San Francisco                                       60369   
+Los Angeles, San Diego, San Luis Obispo, Santa ...               104667   
+
+                                                    dac_benefit_rate  \
+County                                                                 
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...              0.00   
+San Francisco, Santa Clara                                      0.17   
+San Diego, Santa Barbara, Ventura                               0.00   
+San Francisco, San Mateo, Santa Clara                           0.33   
+Los Angeles, Ventura                                            0.00   
+Marin, Sonoma                                                   0.00   
+Los Angeles, San Bernardino                                     0.48   
+Humboldt, Mendocino                                             0.00   
+Contra Costa, San Francisco                                     0.33   
+Los Angeles, San Diego, San Luis Obispo, Santa ...              0.00   
+
+                                                    dac_funding  \
+County                                                            
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...          0.0   
+San Francisco, Santa Clara                           20000000.0   
+San Diego, Santa Barbara, Ventura                           0.0   
+San Francisco, San Mateo, Santa Clara                  935322.0   
+Los Angeles, Ventura                                        0.0   
+Marin, Sonoma                                               0.0   
+Los Angeles, San Bernardino                          10812422.0   
+Humboldt, Mendocino                                         0.0   
+Contra Costa, San Francisco                            631879.0   
+Los Angeles, San Diego, San Luis Obispo, Santa ...          0.0   
+
+                                                    ghg_per_dollar  \
+County                                                               
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...        0.000075   
+San Francisco, Santa Clara                                0.000513   
+San Diego, Santa Barbara, Ventura                         0.000354   
+San Francisco, San Mateo, Santa Clara                     0.000979   
+Los Angeles, Ventura                                      0.000085   
+Marin, Sonoma                                             0.000646   
+Los Angeles, San Bernardino                               0.000125   
+Humboldt, Mendocino                                       0.000155   
+Contra Costa, San Francisco                               0.000446   
+Los Angeles, San Diego, San Luis Obispo, Santa ...        0.000799   
+
+                                                    dac_funding_ratio  
+County                                                                 
+Alameda, Contra Costa, Fresno, Kern, Kings, Mad...           0.000000  
+San Francisco, Santa Clara                                   0.006670  
+San Diego, Santa Barbara, Ventura                            0.000000  
+San Francisco, San Mateo, Santa Clara                        0.000436  
+Los Angeles, Ventura                                         0.000000  
+Marin, Sonoma                                                0.000000  
+Los Angeles, San Bernardino                                  0.060802  
+Humboldt, Mendocino                                          0.000000  
+Contra Costa, San Francisco                                  0.004664  
+Los Angeles, San Diego, San Luis Obispo, Santa ...           0.000000  
+</pre>
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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Most Common Types of Multi-County Projects:
+Program Name
+Urban and Community Forestry Program          333
+Low Carbon Transit Operations Program         189
+Transit and Intercity Rail Capital Program     87
+Safe and Affordable Drinking Water Fund        47
+Climate Smart Agriculture                      43
+Name: count, dtype: int64
+
+Success Metrics for Multi-County Projects:
+
+DAC Benefit Rate Distribution:
+count       874
+unique        2
+top       False
+freq        756
+Name: Is Benefit Disadvantaged Communities, dtype: object
+
+GHG Reduction Distribution (tons per $):
+count    578.000000
+mean       0.003167
+std        0.017891
+min       -0.002485
+25%        0.000000
+50%        0.000220
+75%        0.002056
+max        0.400316
+Name: ghg_efficiency, dtype: float64
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># 1. Deep Dive into Successful Collaborations</span>
+<span class="c1"># Let's create a success metric combining equity and efficiency</span>
+<span class="k">def</span> <span class="nf">calculate_success_score</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
+    <span class="c1"># Normalize GHG efficiency and DAC rates to 0-1 scale</span>
+    <span class="n">ghg_norm</span> <span class="o">=</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">]</span> <span class="o">-</span> <span class="n">min_ghg</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">max_ghg</span> <span class="o">-</span> <span class="n">min_ghg</span><span class="p">)</span>
+    <span class="n">dac_norm</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'dac_benefit_rate'</span><span class="p">]</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">ghg_norm</span> <span class="o">+</span> <span class="n">dac_norm</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
+
+<span class="c1"># Add success scores</span>
+<span class="n">collaboration_success</span> <span class="o">=</span> <span class="n">collaboration_summary</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">min_ghg</span> <span class="o">=</span> <span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
+<span class="n">max_ghg</span> <span class="o">=</span> <span class="n">collaboration_summary</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+<span class="n">collaboration_success</span><span class="p">[</span><span class="s1">'success_score'</span><span class="p">]</span> <span class="o">=</span> <span class="n">collaboration_summary</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">calculate_success_score</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Create visualization of success patterns</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+
+<span class="c1"># 1. Success Stories Matrix</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">collaboration_success</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">],</span> 
+           <span class="n">collaboration_success</span><span class="p">[</span><span class="s1">'dac_benefit_rate'</span><span class="p">],</span>
+           <span class="n">s</span><span class="o">=</span><span class="n">collaboration_success</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span><span class="o">/</span><span class="mf">1e7</span><span class="p">,</span>
+           <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'GHG Efficiency (Reduction per Dollar)'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'DAC Benefit Rate'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Multi-County Collaboration Performance Matrix</span><span class="se">\n</span><span class="s1">Size = Total Investment'</span><span class="p">)</span>
+
+<span class="c1"># 2. Program Type Analysis</span>
+<span class="n">program_type_analysis</span> <span class="o">=</span> <span class="n">multi_county_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Program Name'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
+    <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+    <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="s1">'sum'</span><span class="p">,</span>
+    <span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">:</span> <span class="s1">'mean'</span>
+<span class="p">})</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="n">program_type_analysis</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'project_count'</span><span class="p">,</span> <span class="s1">'total_investment'</span><span class="p">,</span> 
+                               <span class="s1">'avg_project_size'</span><span class="p">,</span> <span class="s1">'total_ghg'</span><span class="p">,</span> <span class="s1">'dac_rate'</span><span class="p">]</span>
+<span class="n">program_type_analysis</span><span class="p">[</span><span class="s1">'ghg_efficiency'</span><span class="p">]</span> <span class="o">=</span> <span class="n">program_type_analysis</span><span class="p">[</span><span class="s1">'total_ghg'</span><span class="p">]</span> <span class="o">/</span> <span class="n">program_type_analysis</span><span class="p">[</span><span class="s1">'total_investment'</span><span class="p">]</span>
+
+<span class="c1"># Plot program type performance</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">top_programs</span> <span class="o">=</span> <span class="n">program_type_analysis</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s1">'total_investment'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_programs</span><span class="p">)),</span> <span class="n">top_programs</span><span class="p">[</span><span class="s1">'ghg_efficiency'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top_programs</span><span class="p">)),</span> 
+          <span class="p">[</span><span class="n">name</span><span class="p">[:</span><span class="mi">20</span><span class="p">]</span> <span class="o">+</span> <span class="s1">'...'</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">20</span> <span class="k">else</span> <span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">top_programs</span><span class="o">.</span><span class="n">index</span><span class="p">],</span>
+          <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'GHG Efficiency by Program Type'</span><span class="p">)</span>
+
+<span class="c1"># 3. Partnership Size Analysis</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="n">partnership_sizes</span> <span class="o">=</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'County'</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s1">','</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">size_performance</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
+    <span class="s1">'partner_count'</span><span class="p">:</span> <span class="n">partnership_sizes</span><span class="p">,</span>
+    <span class="s1">'ghg_efficiency'</span><span class="p">:</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project GHGReductions'</span><span class="p">]</span> <span class="o">/</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Total Project Cost'</span><span class="p">],</span>
+    <span class="s1">'dac_rate'</span><span class="p">:</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">]</span>
+<span class="p">})</span>
+
+<span class="n">avg_by_size</span> <span class="o">=</span> <span class="n">size_performance</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'partner_count'</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">avg_by_size</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">avg_by_size</span><span class="p">[</span><span class="s1">'ghg_efficiency'</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Number of Partner Counties'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Average GHG Efficiency'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Performance by Partnership Size'</span><span class="p">)</span>
+
+<span class="c1"># 4. Regional Patterns</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="c1"># Create regional categories based on county groupings</span>
+<span class="k">def</span> <span class="nf">categorize_region</span><span class="p">(</span><span class="n">counties</span><span class="p">):</span>
+    <span class="k">if</span> <span class="s1">'Los Angeles'</span> <span class="ow">in</span> <span class="n">counties</span><span class="p">:</span>
+        <span class="k">return</span> <span class="s1">'Southern California'</span>
+    <span class="k">elif</span> <span class="nb">any</span><span class="p">(</span><span class="n">county</span> <span class="ow">in</span> <span class="n">counties</span> <span class="k">for</span> <span class="n">county</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'San Francisco'</span><span class="p">,</span> <span class="s1">'Alameda'</span><span class="p">,</span> <span class="s1">'Santa Clara'</span><span class="p">]):</span>
+        <span class="k">return</span> <span class="s1">'Bay Area'</span>
+    <span class="k">elif</span> <span class="nb">any</span><span class="p">(</span><span class="n">county</span> <span class="ow">in</span> <span class="n">counties</span> <span class="k">for</span> <span class="n">county</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'Fresno'</span><span class="p">,</span> <span class="s1">'Kern'</span><span class="p">,</span> <span class="s1">'Kings'</span><span class="p">]):</span>
+        <span class="k">return</span> <span class="s1">'Central Valley'</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="s1">'Other'</span>
+
+<span class="n">collaboration_success</span><span class="p">[</span><span class="s1">'region'</span><span class="p">]</span> <span class="o">=</span> <span class="n">collaboration_success</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">categorize_region</span><span class="p">)</span>
+<span class="n">regional_performance</span> <span class="o">=</span> <span class="n">collaboration_success</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'region'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
+    <span class="s1">'ghg_per_dollar'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span>
+    <span class="s1">'dac_benefit_rate'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span>
+    <span class="s1">'total_investment'</span><span class="p">:</span> <span class="s1">'sum'</span>
+<span class="p">})</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">regional_performance</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">regional_performance</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'GHG Efficiency by Region'</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Print detailed analysis</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">1. Most Successful Collaborations:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">collaboration_success</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s1">'success_score'</span><span class="p">)[</span>
+    <span class="p">[</span><span class="s1">'total_investment'</span><span class="p">,</span> <span class="s1">'ghg_per_dollar'</span><span class="p">,</span> <span class="s1">'dac_benefit_rate'</span><span class="p">,</span> <span class="s1">'success_score'</span><span class="p">]</span>
+<span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">2. Program Type Success Factors:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">program_type_analysis</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s1">'ghg_efficiency'</span><span class="p">))</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">3. Optimal Partnership Size:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">avg_by_size</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">4. Regional Performance Patterns:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">regional_performance</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span>
+
+<span class="c1"># Additional insights on equity-efficiency trade-offs</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Equity-Efficiency Trade-off Analysis:"</span><span class="p">)</span>
+<span class="n">correlation</span> <span class="o">=</span> <span class="n">collaboration_success</span><span class="p">[</span><span class="s1">'ghg_per_dollar'</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">collaboration_success</span><span class="p">[</span><span class="s1">'dac_benefit_rate'</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Correlation between GHG efficiency and DAC benefit rate: </span><span class="si">{</span><span class="n">correlation</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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dW7d+88rfFWPD09uaMUAADYNTc3Nz322GOaM2eO3nnnHe3bt0/NmjXTyJEjreFaQECABg0apG7duunll1+2dugByD2ZmZnWD/p37typiIgI7d+/X6mpqapZs6b69+9/Qzg+YMAATZw4UWFhYbYsHch1Dg4O+umnn5SWlqZx48apb9++6tGjh3V/SEiIJkyYoOLFi6tFixaKiYmxhmNOTk62KhtADvn444/Vp08fZWZmasyYMbp48aJ1n7e3t95//30NGDBA9913nw2rxH8hGAcA2L3Q0FD5+flp3Lhxtxxzs6mrp0yZonLlylkfW7qdx44dK19fX3l7e1u7qAcOHCgfHx/dd9991g8HrnfkyBHVr19frq6uql69ujZt2pRt/4EDB/T444/L09NTvr6+6tSpU7Zfrpo0aaKwsDD17dtXJUqUUMuWLW96HCaTSSNHjtR9990nFxcX1apVS+Hh4db9BoNBu3bt0siRI2UwGPT+++/f8j1xcXGRn5+fypYtq169eik0NFQrV66UJH300UeqUaOGPDw8dP/99+uNN95QQkKC9bnz5s2Tt7e3Vq5cqapVq8rFxUWvvvqq5s+frxUrVli70Tdu3Gidbv6HH35Q06ZN5e7urpo1ayoyMjJbPVu2bFGjRo3k5uZm7SBKTEy07i9XrpxGjRqlzp07q2jRonr99ddvelxvvPGGDAaDtm/frueee04VK1ZUtWrV1K9fP23bts067vTp02rTpo08PT1VtGhRvfjii4qNjbXuv1n3e9++fdWkSRPr4yZNmuitt97SoEGD5OPjIz8/v2zvueX8euaZZ2QwGKyPb3Y+zp49W1WqVJGrq6sqV66sTz/91LovLS1NYWFh8vf3l6urq8qWLfuv5zsAAICt+fn5qV69enrzzTf11FNP6csvv1RERIR27dql9u3b6/nnn9cvv/wid3d3ffDBB5oyZYqaNm1q67IBu2eZ+nzIkCHq1KmTXn75ZYWFhemtt95SUlKSHn74YfXv318BAQGaN2+epk+fLkl6+eWX5eDgoMzMTFuWD+Qoyw3+ycnJSk1N1e+//664uDh5enrqoYce0rZt27R69epsjQD169fXhAkTlJGRoWeeeUYmk8m6tACAgsNy3V64cEHx8fGKjo6WlPWZXceOHbVp0yZ9+umn2RqxihUrpgkTJqhKlSo2qRm3h2AcAGD3HBwcNHbsWH3yySc6e/bsPb3Whg0bdO7cOW3evFkfffSRRowYoaeeekrFihVTVFSUevbsqR49etzwdQYOHKj+/fvrt99+U0hIiFq3bq2///5bkhQfH69mzZrp4Ycf1s6dOxUeHq7Y2Fi9+OKL2V5j/vz5cnZ21tatWzVr1qyb1jd16lRNnjxZkyZN0r59+9SyZUs9/fTTOnbsmCQpOjpa1apVU//+/RUdHa0BAwbc9rG7ublZu7CNRqOmTZumgwcPav78+dqwYYMGDRqUbXxSUpImTJig2bNn6+DBg5o2bZpefPFFayd6dHS06tevbx3/3nvvacCAAdqzZ48qVqyo9u3bW6duP378uFq1aqXnnntO+/bt09KlS7Vly5YbOhImTZqkmjVr6rffftOwYcNuOIa4uDiFh4erd+/e8vDwuGG/t7e3pKw/ftu0aaO4uDht2rRJEREROnHihF566aXbfr8s5s+fLw8PD0VFRWnixIkaOXKktdtpx44dkqS5c+cqOjra+vj/W7hwoYYPH64xY8bo8OHDGjt2rIYNG6b58+dLkqZNm6aVK1fqm2++0dGjR7Vw4cJsN3UAAADkJ5YPGt966y1Vq1ZN3333nSSpXr16OnTokJo0aaK4uDgNGjRIgYGBOnnypN566y05Ojre89I+AG7u0KFDSkpKkiSNGzdO8+bN06xZsxQTE6Pq1atrwYIF6tKlixISEqzhuKenpw4ePJgt9GNNcdgLyzTJhw8fVseOHRUUFKRq1arpxRdfVO3atfXbb7/J2dlZY8eO1U8//ZQtHA8JCdF3332nRYsWyWg0MqUyUIBYbmYxGAxasWKFnn76adWpU0etW7fWmDFjJEkTJ05UkyZNtGLFCs2YMcP6Ga/EUiIFghkAADvWpUsXc5s2bcxms9n8yCOPmF999VWz2Ww2L1u2zHz9j8ERI0aYa9asme25H3/8sbls2bLZXqts2bLmzMxM67ZKlSqZGzVqZH2ckZFh9vDwMC9evNhsNpvNJ0+eNEsyjx8/3jomPT3dfN9995knTJhgNpvN5lGjRpkfe+yxbF/7zJkzZknmo0ePms1ms7lx48bmhx9++D+Pt3Tp0uYxY8Zk21a3bl3zG2+8YX1cs2ZN84gRI/71da5/30wmkzkiIsLs4uJiHjBgwE3Hf/vtt+bixYtbH8+dO9csybxnz55bvq6F5T2aPXu2ddvBgwfNksyHDx82m81mc7du3cyvv/56tuf973//MxuNRnNycrLZbDaby5Yta27btu2/HldUVJRZkvmHH37413Hr1q0zOzg4mE+fPn1DTdu3b7/lsfTp08fcuHFj6+PGjRubGzZsmG1M3bp1ze+88471sSTzsmXLso35/+fjgw8+aF60aFG2MaNGjTKHhISYzWaz+c033zQ3a9bMbDKZ/vW4AAAA8hOTyWTu3r27+fnnnzebzVm/pzZo0MD6+93atWvN7733njk9Pd2WZQJ2zWQymQ8cOGA2GAzm7du3m+Pi4swhISHm1atXm83mrOvQw8PD3KlTJ3PlypXN7dq1MycmJprNZrP5999/t/59zN8isCeW83nfvn1mLy8vc+/evc2zZ882f/fdd+Y2bdqYDQaD+ZVXXjGfPXvW3Lx5c3NISIh55cqVZrPZbH7nnXfMnTt3tmX5AO7C/v37sz1eu3at2cXFxTx58mTzF198YR49erTZ2dnZ3LNnT+uYgQMHmsuXL2+eMGFCts+Lkb852iyRBwAgj02YMEHNmjW7oy7p/69atWrW9dYkydfXV9WrV7c+dnBwUPHixXX+/PlszwsJCbH+t6Ojo4KCgnT48GFJ0t69e/XLL7/I09Pzhq93/PhxVaxYUZJUp06df63typUrOnfunBo0aJBte4MGDbR3797bPMJ/rF69Wp6enkpPT5fJZNLLL79snQb8559/1rhx43TkyBFduXJFGRkZSklJUVJSktzd3SVJzs7Oeuihh277610/1t/fX5J0/vx5Va5cWXv37tW+ffu0cOFC6xiz2SyTyaSTJ09apygKCgr6169hvs3pyw4fPqz7779f999/v3Vb1apV5e3trcOHD6tu3bp3dVxS1rH9//Pj3yQmJur48ePq1q2bunfvbt2ekZEhLy8vSVnTurdo0UKVKlVSq1at9NRTT+mxxx677a8BAACQ18zXOnHGjBmjoKAgubi4KDg4WD/88INcXV0lSY899pj1d5qMjAw5OvIxFpDTDAaDqlWrpgYNGmjGjBmaN2+e3nzzTdWuXVtbt27VK6+8oo8//ljdu3dXx44dtXjxYsXGxmr16tWqUKGCpH86awF7YTAYdOHCBXXp0kW9evXKtlTZo48+qhYtWqhfv37y8PBQeHi4nnjiCQ0ePFjjx4/X4cOH9eOPP9qwegB3asCAAdq5c6dWrFihokWLSpK+/fZbdezYUf369bOOq1Wrlp555hmVL19eAwcO1MSJE+Xq6qoXXniBn4MFCH9RAAAKjUcffVQtW7bUkCFD9Morr2TbZzQabwhN09PTb3gNJyenbI8NBsNNt10/hdZ/SUhIUOvWrTVhwoQb9lkCYkk3nfo7NzVt2lQzZ86Us7OzSpcubf0g8tSpU3rqqafUq1cvjRkzRj4+PtqyZYu6deumtLQ0azDu5uZ2R9MHXf8+Wp5neR8TEhLUo0cPvfXWWzc874EHHrD+93+9RxUqVJDBYNCRI0duu65buZdz5k7PD0n64osvFBwcnG2fZZrC2rVr6+TJk1qzZo1+/vlnvfjiiwoNDbVOSwoAAJDfWH4n8vLyUps2bbR582Z9//33KlGihKR/gnMLQnEgdzVv3lwLFy5UXFyc2rdvLylrqaonn3xSXbp0kSRVrlxZzZs3V40aNaw3sEgiDIBdOnv2rNLT09WhQwdlZmbKwcFBJpNJJUuWVMeOHRUfH6+RI0eqXbt2+vbbbzV37lwlJCToyy+/VOXKlW1dPoDbtGbNGs2cOVORkZHy8vJSQkKCPD099fvvv6ts2bLWcZmZmXryySc1cOBA/fjjj+rWrZt8fHw0cuRIG1aPu8FvLQCAQmX8+PFatWqVIiMjs20vWbKkYmJisgWde/bsybGvu23bNut/Z2RkaNeuXdYu59q1a+vgwYMqV66cAgMDs/1zJ2F40aJFVbp0aW3dujXb9q1bt6pq1ap3XLOHh4cCAwP1wAMPZPsgcteuXTKZTJo8ebIeeeQRVaxYUefOnbut13R2dlZmZuYd11K7dm0dOnTohvcnMDBQzs7Ot/06Pj4+atmypWbMmKHExMQb9sfHx0uSqlSpojNnzujMmTPWfYcOHVJ8fLz1vSxZsqSio6OzPf9uzhknJ6d/fU98fX1VunRpnThx4oZjDwgIsI4rWrSoXnrpJX3xxRdaunSpvv/+e8XFxd1xPQAAADnlv2brMRqNcnZ21rPPPquDBw/qwIED1uexPiOQe66/UddynQ4aNEiJiYkaPXq0dd+5c+d09OhR699ce/bsUdu2bTV58mQZjcY7uuEXKGj27t2rP/74Q9WrV5eDg4PMZrP1JhAvLy+9/PLLcnNz05YtW+Tl5aW+fftq6NChhOJAAZOWlqYqVarIz89P33//vbVx6emnn9auXbu0a9cuSf80p/j4+OjSpUvWxiAUPATjAIBCpUaNGurQoYOmTZuWbXuTJk104cIFTZw4UcePH9eMGTO0Zs2aHPu6M2bM0LJly3TkyBH17t1bly5d0quvvipJ6t27t/Wu/B07duj48eNau3atunbtesch8sCBAzVhwgQtXbpUR48e1eDBg7Vnzx716dMnx44lM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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+1. Most Successful Collaborations:
+                                                   total_investment  \
+County                                                                
+Marin, San Francisco                                        2466450   
+Los Angeles, Orange, Riverside, San Diego                    766345   
+El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba            991000   
+Napa, San Joaquin                                           1557570   
+Lake, Sacramento                                            2697479   
+
+                                                   ghg_per_dollar  \
+County                                                              
+Marin, San Francisco                                     0.010916   
+Los Angeles, Orange, Riverside, San Diego                0.008612   
+El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba        0.008322   
+Napa, San Joaquin                                        0.005557   
+Lake, Sacramento                                         0.004171   
+
+                                                   dac_benefit_rate  \
+County                                                                
+Marin, San Francisco                                            1.0   
+Los Angeles, Orange, Riverside, San Diego                       1.0   
+El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba               1.0   
+Napa, San Joaquin                                               1.0   
+Lake, Sacramento                                                1.0   
+
+                                                   success_score  
+County                                                            
+Marin, San Francisco                                    0.515503  
+Los Angeles, Orange, Riverside, San Diego               0.512379  
+El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba       0.511986  
+Napa, San Joaquin                                       0.508237  
+Lake, Sacramento                                        0.506358  
+
+2. Program Type Success Factors:
+                                                    project_count  \
+Program Name                                                        
+Forest Health Program                                           6   
+Food Production Investment Program                              9   
+Wetlands and Watershed Restoration                              1   
+Sustainable Agricultural Lands Conservation Pro...              1   
+Urban and Community Forestry Program                          333   
+
+                                                    total_investment  \
+Program Name                                                           
+Forest Health Program                                       20238006   
+Food Production Investment Program                          20586793   
+Wetlands and Watershed Restoration                            920666   
+Sustainable Agricultural Lands Conservation Pro...          17690045   
+Urban and Community Forestry Program                        37197703   
+
+                                                    avg_project_size  \
+Program Name                                                           
+Forest Health Program                                     3373001.00   
+Food Production Investment Program                        2287421.44   
+Wetlands and Watershed Restoration                         920666.00   
+Sustainable Agricultural Lands Conservation Pro...       17690045.00   
+Urban and Community Forestry Program                       111704.81   
+
+                                                    total_ghg  dac_rate  \
+Program Name                                                              
+Forest Health Program                                  830634       0.0   
+Food Production Investment Program                     501007       0.0   
+Wetlands and Watershed Restoration                      15166       0.0   
+Sustainable Agricultural Lands Conservation Pro...     251149       0.0   
+Urban and Community Forestry Program                   195214       0.2   
+
+                                                    ghg_efficiency  
+Program Name                                                        
+Forest Health Program                                     0.041043  
+Food Production Investment Program                        0.024336  
+Wetlands and Watershed Restoration                        0.016473  
+Sustainable Agricultural Lands Conservation Pro...        0.014197  
+Urban and Community Forestry Program                      0.005248  
+
+3. Optimal Partnership Size:
+               ghg_efficiency  dac_rate
+partner_count                          
+2                    0.004588  0.149798
+3                    0.002167  0.064000
+4                    0.003443  0.052632
+5                    0.000880  0.130435
+6                    0.002270  0.236842
+7                    0.002930  0.281250
+8                    0.001286  0.307692
+9                    0.000373  0.000000
+10                   0.000014  0.250000
+11                   0.000000  0.000000
+12                   0.000160  0.000000
+13                   0.000011  0.000000
+14                   0.003643  0.111111
+16                   0.004098  0.111111
+19                   0.000000  0.000000
+22                   0.000000  0.000000
+24                   0.000000  0.000000
+25                   0.000000  0.000000
+29                   0.000000  0.000000
+34                   0.000000  0.000000
+47                   0.000000  0.000000
+
+4. Regional Performance Patterns:
+                     ghg_per_dollar  dac_benefit_rate  total_investment
+region                                                                 
+Bay Area                     0.0015            0.1488       13255750738
+Central Valley               0.0022            0.1816         294777141
+Other                        0.0073            0.1191        3029393261
+Southern California          0.0039            0.1862        1731340906
+
+Equity-Efficiency Trade-off Analysis:
+
+Correlation between GHG efficiency and DAC benefit rate: -0.0398
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [47]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Filter for more recent period</span>
+<span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Date Operational'</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span>
+<span class="n">recent_data</span> <span class="o">=</span> <span class="n">multi_county_data</span><span class="p">[</span><span class="n">multi_county_data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">2010</span><span class="p">]</span>
+
+<span class="c1"># Create temporal analysis</span>
+<span class="n">temporal_analysis</span> <span class="o">=</span> <span class="n">recent_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Year'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span>
+    <span class="s1">'Total Project Cost'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'count'</span><span class="p">,</span> <span class="s1">'sum'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">],</span>
+    <span class="s1">'Total Project GHGReductions'</span><span class="p">:</span> <span class="s1">'sum'</span><span class="p">,</span>
+    <span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">:</span> <span class="s1">'mean'</span>
+<span class="p">})</span>
+
+<span class="c1"># Visualize evolution</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+
+<span class="c1"># 1. Project Volume</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">temporal_analysis</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> 
+               <span class="n">temporal_analysis</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">)],</span> 
+               <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'darkblue'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Number of Multi-County Projects by Year (2010-2024)'</span><span class="p">,</span> 
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Number of Projects'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="c1"># 2. Investment Size</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">temporal_analysis</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> 
+               <span class="n">temporal_analysis</span><span class="p">[(</span><span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)]</span><span class="o">/</span><span class="mf">1e6</span><span class="p">,</span> 
+               <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'darkblue'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Average Project Size by Year'</span><span class="p">,</span> 
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Average Size (Millions $)'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="c1"># 3. Equity Performance</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">temporal_analysis</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> 
+               <span class="n">temporal_analysis</span><span class="p">[(</span><span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)],</span> 
+               <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'darkblue'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'DAC Benefit Rate by Year'</span><span class="p">,</span> 
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Proportion Benefiting DACs'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="c1"># 4. Partnership Size Evolution</span>
+<span class="n">recent_data</span><span class="p">[</span><span class="s1">'partnership_size'</span><span class="p">]</span> <span class="o">=</span> <span class="n">recent_data</span><span class="p">[</span><span class="s1">'County'</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s1">','</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">partnership_evolution</span> <span class="o">=</span> <span class="n">recent_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Year'</span><span class="p">)[</span><span class="s1">'partnership_size'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">partnership_evolution</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> 
+               <span class="n">partnership_evolution</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> 
+               <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'darkblue'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Average Number of Partner Counties by Year'</span><span class="p">,</span> 
+                    <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Average Partners per Project'</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+
+<span class="c1"># Adjust x-axis for all subplots</span>
+<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axes</span><span class="o">.</span><span class="n">flat</span><span class="p">:</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">2014</span><span class="p">,</span> <span class="mi">2024</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="c1"># Print summary statistics for three periods: pre-COVID, COVID, and post-COVID</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Period Comparison:"</span><span class="p">)</span>
+<span class="n">pre_covid</span> <span class="o">=</span> <span class="n">recent_data</span><span class="p">[</span><span class="n">recent_data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">2020</span><span class="p">]</span>
+<span class="n">covid</span> <span class="o">=</span> <span class="n">recent_data</span><span class="p">[</span><span class="n">recent_data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">2020</span><span class="p">,</span> <span class="mi">2021</span><span class="p">])]</span>
+<span class="n">post_covid</span> <span class="o">=</span> <span class="n">recent_data</span><span class="p">[</span><span class="n">recent_data</span><span class="p">[</span><span class="s1">'Year'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">2021</span><span class="p">]</span>
+
+<span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">'Number of Projects'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span>
+    <span class="s1">'Average Project Size ($M)'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s1">'Total Project Cost'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">/</span><span class="mf">1e6</span><span class="p">,</span>
+    <span class="s1">'DAC Benefit Rate'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s1">'Is Benefit Disadvantaged Communities'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>
+    <span class="s1">'Average Partners'</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s1">'partnership_size'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<span class="p">}</span>
+
+<span class="n">comparison</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
+    <span class="s1">'Pre-COVID (2010-2019)'</span><span class="p">:</span> <span class="p">{</span><span class="n">metric</span><span class="p">:</span> <span class="n">func</span><span class="p">(</span><span class="n">pre_covid</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric</span><span class="p">,</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">items</span><span class="p">()},</span>
+    <span class="s1">'COVID Period (2020-2021)'</span><span class="p">:</span> <span class="p">{</span><span class="n">metric</span><span class="p">:</span> <span class="n">func</span><span class="p">(</span><span class="n">covid</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric</span><span class="p">,</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">items</span><span class="p">()},</span>
+    <span class="s1">'Post-COVID (2022+)'</span><span class="p">:</span> <span class="p">{</span><span class="n">metric</span><span class="p">:</span> <span class="n">func</span><span class="p">(</span><span class="n">post_covid</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric</span><span class="p">,</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+<span class="p">})</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">comparison</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
+
+<span class="c1"># Analyze types of projects during COVID period</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Most Common Project Types During COVID Period:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">covid</span><span class="p">[</span><span class="s1">'Program Name'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+
+<span class="c1"># Look at largest COVID-period collaborations</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Largest COVID-Period Collaborations:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">covid</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s1">'Total Project Cost'</span><span class="p">)[</span>
+    <span class="p">[</span><span class="s1">'County'</span><span class="p">,</span> <span class="s1">'Program Name'</span><span class="p">,</span> <span class="s1">'Total Project Cost'</span><span class="p">,</span> <span class="s1">'Total Project GHGReductions'</span><span class="p">]</span>
+<span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+<pre>/tmp/ipykernel_846869/2239187850.py:43: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+  recent_data['partnership_size'] = recent_data['County'].str.count(',') + 1
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
+<img alt="No description has been provided for this image" class="" 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+</div>
+</div>
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>
+Period Comparison:
+                           Pre-COVID (2010-2019)  COVID Period (2020-2021)  \
+Number of Projects                        211.00                    304.00   
+Average Project Size ($M)                   8.24                     31.78   
+DAC Benefit Rate                            0.37                      0.02   
+Average Partners                            3.70                      5.53   
+
+                           Post-COVID (2022+)  
+Number of Projects                     286.00  
+Average Project Size ($M)               23.08  
+DAC Benefit Rate                         0.01  
+Average Partners                         3.92  
+
+Most Common Project Types During COVID Period:
+Program Name
+Urban and Community Forestry Program          144
+Low Carbon Transit Operations Program          46
+Safe and Affordable Drinking Water Fund        34
+Climate Smart Agriculture                      26
+Transit and Intercity Rail Capital Program     15
+Name: count, dtype: int64
+
+Largest COVID-Period Collaborations:
+                                                   County  \
+42458                          San Francisco, Santa Clara   
+62279   Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   
+62280   Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   
+100710  Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   
+100711  Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   
+
+                                      Program Name  Total Project Cost  \
+42458   Transit and Intercity Rail Capital Program          1980252533   
+62279   Transit and Intercity Rail Capital Program           904600000   
+62280   Transit and Intercity Rail Capital Program           904600000   
+100710  Transit and Intercity Rail Capital Program           904600000   
+100711  Transit and Intercity Rail Capital Program           904600000   
+
+        Total Project GHGReductions  
+42458                        734000  
+62279                         52428  
+62280                          6554  
+100710                        21845  
+100711                        43690  
+</pre>
+</div>
 </div>
 </div>
 </div>
diff --git a/initial_view/overview_hypotesting_20241031.ipynb b/initial_view/overview_hypotesting_20241031.ipynb
index 685d4258..5926c324 100644
--- a/initial_view/overview_hypotesting_20241031.ipynb
+++ b/initial_view/overview_hypotesting_20241031.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
        "[8 rows x 82 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -707,24 +707,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_182042/3534785368.py:18: FutureWarning: \n",
+      "/tmp/ipykernel_846869/3534785368.py:18: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
       "  sns.barplot(data=plot_data,\n",
-      "/tmp/ipykernel_182042/3534785368.py:28: FutureWarning: \n",
+      "/tmp/ipykernel_846869/3534785368.py:28: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
       "  sns.barplot(data=plot_data[mask],\n",
-      "/tmp/ipykernel_182042/3534785368.py:38: FutureWarning: \n",
+      "/tmp/ipykernel_846869/3534785368.py:38: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
@@ -824,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1006,7 +1006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1032,7 +1032,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_182042/2919179271.py:33: FutureWarning: \n",
+      "/tmp/ipykernel_846869/2919179271.py:33: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
@@ -1096,7 +1096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1300,7 +1300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1473,7 +1473,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_182042/3616794259.py:63: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.\n",
+      "/tmp/ipykernel_846869/3616794259.py:63: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.\n",
       "  plt.tight_layout()\n"
      ]
     },
@@ -1558,7 +1558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1848,6 +1848,1755 @@
     "})\n",
     "```\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Top 5 Programs by Total Investment:\n",
+      "                                                   Total Project Cost  \\\n",
+      "                                                                count   \n",
+      "Program Name                                                            \n",
+      "Transit and Intercity Rail Capital Program                        245   \n",
+      "Low Carbon Transit Operations Program                            1003   \n",
+      "Affordable Housing and Sustainable Communities ...                151   \n",
+      "Low Carbon Transportation                                      105965   \n",
+      "Community Air Protection                                         5514   \n",
+      "\n",
+      "                                                                  \\\n",
+      "                                                             sum   \n",
+      "Program Name                                                       \n",
+      "Transit and Intercity Rail Capital Program          101461750666   \n",
+      "Low Carbon Transit Operations Program                 8777590799   \n",
+      "Affordable Housing and Sustainable Communities ...    8492944393   \n",
+      "Low Carbon Transportation                             3759508172   \n",
+      "Community Air Protection                              1211608583   \n",
+      "\n",
+      "                                                                  \\\n",
+      "                                                            mean   \n",
+      "Program Name                                                       \n",
+      "Transit and Intercity Rail Capital Program          4.141296e+08   \n",
+      "Low Carbon Transit Operations Program               8.751337e+06   \n",
+      "Affordable Housing and Sustainable Communities ...  5.624466e+07   \n",
+      "Low Carbon Transportation                           3.547877e+04   \n",
+      "Community Air Protection                            2.197331e+05   \n",
+      "\n",
+      "                                                   Total Project GHGReductions  \\\n",
+      "                                                                           sum   \n",
+      "Program Name                                                                     \n",
+      "Transit and Intercity Rail Capital Program                            23458701   \n",
+      "Low Carbon Transit Operations Program                                  6971510   \n",
+      "Affordable Housing and Sustainable Communities ...                     3590596   \n",
+      "Low Carbon Transportation                                              4306922   \n",
+      "Community Air Protection                                                250801   \n",
+      "\n",
+      "                                                              \\\n",
+      "                                                        mean   \n",
+      "Program Name                                                   \n",
+      "Transit and Intercity Rail Capital Program          95749.80   \n",
+      "Low Carbon Transit Operations Program                6950.66   \n",
+      "Affordable Housing and Sustainable Communities ...  23778.78   \n",
+      "Low Carbon Transportation                              40.64   \n",
+      "Community Air Protection                               45.48   \n",
+      "\n",
+      "                                                   Total GGRFDisadvantaged Community Funding  \\\n",
+      "                                                                                         sum   \n",
+      "Program Name                                                                                   \n",
+      "Transit and Intercity Rail Capital Program                                       477700000.0   \n",
+      "Low Carbon Transit Operations Program                                            127826618.0   \n",
+      "Affordable Housing and Sustainable Communities ...                               241985013.0   \n",
+      "Low Carbon Transportation                                                        324003740.0   \n",
+      "Community Air Protection                                                                 0.0   \n",
+      "\n",
+      "                                                                \\\n",
+      "                                                          mean   \n",
+      "Program Name                                                     \n",
+      "Transit and Intercity Rail Capital Program          9952083.33   \n",
+      "Low Carbon Transit Operations Program                399458.18   \n",
+      "Affordable Housing and Sustainable Communities ...  4033083.55   \n",
+      "Low Carbon Transportation                              7644.66   \n",
+      "Community Air Protection                                   NaN   \n",
+      "\n",
+      "                                                   Is Benefit Disadvantaged Communities  \\\n",
+      "                                                                                   mean   \n",
+      "Program Name                                                                              \n",
+      "Transit and Intercity Rail Capital Program                                         0.16   \n",
+      "Low Carbon Transit Operations Program                                              0.21   \n",
+      "Affordable Housing and Sustainable Communities ...                                 0.22   \n",
+      "Low Carbon Transportation                                                          0.36   \n",
+      "Community Air Protection                                                           0.00   \n",
+      "\n",
+      "                                                   Is Low Income Communities  \\\n",
+      "                                                                        mean   \n",
+      "Program Name                                                                   \n",
+      "Transit and Intercity Rail Capital Program                              0.73   \n",
+      "Low Carbon Transit Operations Program                                   0.64   \n",
+      "Affordable Housing and Sustainable Communities ...                      0.60   \n",
+      "Low Carbon Transportation                                               0.59   \n",
+      "Community Air Protection                                                0.87   \n",
+      "\n",
+      "                                                   GHG_per_dollar  \\\n",
+      "                                                                    \n",
+      "Program Name                                                        \n",
+      "Transit and Intercity Rail Capital Program                 0.0002   \n",
+      "Low Carbon Transit Operations Program                      0.0008   \n",
+      "Affordable Housing and Sustainable Communities ...         0.0004   \n",
+      "Low Carbon Transportation                                  0.0011   \n",
+      "Community Air Protection                                   0.0002   \n",
+      "\n",
+      "                                                   DAC_funding_ratio  \n",
+      "                                                                      \n",
+      "Program Name                                                          \n",
+      "Transit and Intercity Rail Capital Program                    0.0047  \n",
+      "Low Carbon Transit Operations Program                         0.0146  \n",
+      "Affordable Housing and Sustainable Communities ...            0.0285  \n",
+      "Low Carbon Transportation                                     0.0862  \n",
+      "Community Air Protection                                      0.0000  \n",
+      "\n",
+      "Top 5 Programs by GHG Reduction Efficiency:\n",
+      "                                                   Total Project Cost  \\\n",
+      "                                                                count   \n",
+      "Program Name                                                            \n",
+      "Fluorinated Gases Emission Reduction Incentives                    15   \n",
+      "Sustainable Agricultural Lands Conservation Pro...                 96   \n",
+      "Climate Smart Agriculture                                        1688   \n",
+      "Forest Health Program                                             258   \n",
+      "Food Production Investment Program                                 56   \n",
+      "\n",
+      "                                                                           \\\n",
+      "                                                          sum        mean   \n",
+      "Program Name                                                                \n",
+      "Fluorinated Gases Emission Reduction Incentives             0        0.00   \n",
+      "Sustainable Agricultural Lands Conservation Pro...  212276194  2211210.35   \n",
+      "Climate Smart Agriculture                           886514974   525186.60   \n",
+      "Forest Health Program                               837651776  3246712.31   \n",
+      "Food Production Investment Program                  181509329  3241238.02   \n",
+      "\n",
+      "                                                   Total Project GHGReductions  \\\n",
+      "                                                                           sum   \n",
+      "Program Name                                                                     \n",
+      "Fluorinated Gases Emission Reduction Incentives                          36715   \n",
+      "Sustainable Agricultural Lands Conservation Pro...                    15080237   \n",
+      "Climate Smart Agriculture                                             24249378   \n",
+      "Forest Health Program                                                 20575142   \n",
+      "Food Production Investment Program                                     2974529   \n",
+      "\n",
+      "                                                               \\\n",
+      "                                                         mean   \n",
+      "Program Name                                                    \n",
+      "Fluorinated Gases Emission Reduction Incentives       2447.67   \n",
+      "Sustainable Agricultural Lands Conservation Pro...  157085.80   \n",
+      "Climate Smart Agriculture                            14365.75   \n",
+      "Forest Health Program                                79748.61   \n",
+      "Food Production Investment Program                   53116.59   \n",
+      "\n",
+      "                                                   Total GGRFDisadvantaged Community Funding  \\\n",
+      "                                                                                         sum   \n",
+      "Program Name                                                                                   \n",
+      "Fluorinated Gases Emission Reduction Incentives                                          0.0   \n",
+      "Sustainable Agricultural Lands Conservation Pro...                                 4264000.0   \n",
+      "Climate Smart Agriculture                                                         29472350.0   \n",
+      "Forest Health Program                                                                    0.0   \n",
+      "Food Production Investment Program                                                       0.0   \n",
+      "\n",
+      "                                                               \\\n",
+      "                                                         mean   \n",
+      "Program Name                                                    \n",
+      "Fluorinated Gases Emission Reduction Incentives           NaN   \n",
+      "Sustainable Agricultural Lands Conservation Pro...  101523.81   \n",
+      "Climate Smart Agriculture                            50814.40   \n",
+      "Forest Health Program                                    0.00   \n",
+      "Food Production Investment Program                        NaN   \n",
+      "\n",
+      "                                                   Is Benefit Disadvantaged Communities  \\\n",
+      "                                                                                   mean   \n",
+      "Program Name                                                                              \n",
+      "Fluorinated Gases Emission Reduction Incentives                                    0.00   \n",
+      "Sustainable Agricultural Lands Conservation Pro...                                 0.01   \n",
+      "Climate Smart Agriculture                                                          0.12   \n",
+      "Forest Health Program                                                              0.00   \n",
+      "Food Production Investment Program                                                 0.00   \n",
+      "\n",
+      "                                                   Is Low Income Communities  \\\n",
+      "                                                                        mean   \n",
+      "Program Name                                                                   \n",
+      "Fluorinated Gases Emission Reduction Incentives                         0.60   \n",
+      "Sustainable Agricultural Lands Conservation Pro...                      0.03   \n",
+      "Climate Smart Agriculture                                               0.35   \n",
+      "Forest Health Program                                                   0.30   \n",
+      "Food Production Investment Program                                      0.71   \n",
+      "\n",
+      "                                                   GHG_per_dollar  \\\n",
+      "                                                                    \n",
+      "Program Name                                                        \n",
+      "Fluorinated Gases Emission Reduction Incentives               inf   \n",
+      "Sustainable Agricultural Lands Conservation Pro...         0.0710   \n",
+      "Climate Smart Agriculture                                  0.0274   \n",
+      "Forest Health Program                                      0.0246   \n",
+      "Food Production Investment Program                         0.0164   \n",
+      "\n",
+      "                                                   DAC_funding_ratio  \n",
+      "                                                                      \n",
+      "Program Name                                                          \n",
+      "Fluorinated Gases Emission Reduction Incentives                  NaN  \n",
+      "Sustainable Agricultural Lands Conservation Pro...            0.0201  \n",
+      "Climate Smart Agriculture                                     0.0332  \n",
+      "Forest Health Program                                         0.0000  \n",
+      "Food Production Investment Program                            0.0000  \n",
+      "\n",
+      "Top 5 Programs by DAC Funding Ratio:\n",
+      "                                     Total Project Cost             \\\n",
+      "                                                  count        sum   \n",
+      "Program Name                                                         \n",
+      "Low-Income Weatherization Program                  6468  307899153   \n",
+      "Urban and Community Forestry Program                967  107767408   \n",
+      "Water-Energy Efficiency                            5111   85125916   \n",
+      "Urban Greening Program                              177  270240730   \n",
+      "Wetlands and Watershed Restoration                   22   86150135   \n",
+      "\n",
+      "                                                 Total Project GHGReductions  \\\n",
+      "                                            mean                         sum   \n",
+      "Program Name                                                                   \n",
+      "Low-Income Weatherization Program       47603.46                      594567   \n",
+      "Urban and Community Forestry Program   111445.10                      479295   \n",
+      "Water-Energy Efficiency                 16655.43                      425225   \n",
+      "Urban Greening Program                1526783.79                       54285   \n",
+      "Wetlands and Watershed Restoration    3915915.23                      999950   \n",
+      "\n",
+      "                                                \\\n",
+      "                                          mean   \n",
+      "Program Name                                     \n",
+      "Low-Income Weatherization Program        91.92   \n",
+      "Urban and Community Forestry Program    495.65   \n",
+      "Water-Energy Efficiency                  83.20   \n",
+      "Urban Greening Program                  306.69   \n",
+      "Wetlands and Watershed Restoration    45452.27   \n",
+      "\n",
+      "                                     Total GGRFDisadvantaged Community Funding  \\\n",
+      "                                                                           sum   \n",
+      "Program Name                                                                     \n",
+      "Low-Income Weatherization Program                                  132666048.0   \n",
+      "Urban and Community Forestry Program                                34138019.0   \n",
+      "Water-Energy Efficiency                                             23286225.0   \n",
+      "Urban Greening Program                                              68717491.0   \n",
+      "Wetlands and Watershed Restoration                                  13382907.0   \n",
+      "\n",
+      "                                                  \\\n",
+      "                                            mean   \n",
+      "Program Name                                       \n",
+      "Low-Income Weatherization Program       22383.34   \n",
+      "Urban and Community Forestry Program    60635.91   \n",
+      "Water-Energy Efficiency                  4556.10   \n",
+      "Urban Greening Program                 848364.09   \n",
+      "Wetlands and Watershed Restoration    1115242.25   \n",
+      "\n",
+      "                                     Is Benefit Disadvantaged Communities  \\\n",
+      "                                                                     mean   \n",
+      "Program Name                                                                \n",
+      "Low-Income Weatherization Program                                    0.90   \n",
+      "Urban and Community Forestry Program                                 0.21   \n",
+      "Water-Energy Efficiency                                              0.45   \n",
+      "Urban Greening Program                                               0.19   \n",
+      "Wetlands and Watershed Restoration                                   0.09   \n",
+      "\n",
+      "                                     Is Low Income Communities GHG_per_dollar  \\\n",
+      "                                                          mean                  \n",
+      "Program Name                                                                    \n",
+      "Low-Income Weatherization Program                         0.97         0.0019   \n",
+      "Urban and Community Forestry Program                      0.09         0.0044   \n",
+      "Water-Energy Efficiency                                   0.00         0.0050   \n",
+      "Urban Greening Program                                    0.27         0.0002   \n",
+      "Wetlands and Watershed Restoration                        0.27         0.0116   \n",
+      "\n",
+      "                                     DAC_funding_ratio  \n",
+      "                                                        \n",
+      "Program Name                                            \n",
+      "Low-Income Weatherization Program               0.4309  \n",
+      "Urban and Community Forestry Program            0.3168  \n",
+      "Water-Energy Efficiency                         0.2736  \n",
+      "Urban Greening Program                          0.2543  \n",
+      "Wetlands and Watershed Restoration              0.1553  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Read the data (I see you already have this loaded as 'data')\n",
+    "\n",
+    "# 1. First, let's create our core analytical metrics\n",
+    "def calculate_program_metrics(df):\n",
+    "    metrics = df.groupby('Program Name').agg({\n",
+    "        'Total Project Cost': ['count', 'sum', 'mean'],\n",
+    "        'Total Project GHGReductions': ['sum', 'mean'],\n",
+    "        'Total GGRFDisadvantaged Community Funding': ['sum', 'mean'],\n",
+    "        'Is Benefit Disadvantaged Communities': 'mean',\n",
+    "        'Is Low Income Communities': 'mean'\n",
+    "    }).round(2)\n",
+    "    \n",
+    "    # Add efficiency metrics\n",
+    "    metrics['GHG_per_dollar'] = (metrics[('Total Project GHGReductions', 'sum')] / \n",
+    "                                metrics[('Total Project Cost', 'sum')]).round(4)\n",
+    "    \n",
+    "    metrics['DAC_funding_ratio'] = (metrics[('Total GGRFDisadvantaged Community Funding', 'sum')] / \n",
+    "                                   metrics[('Total Project Cost', 'sum')]).round(4)\n",
+    "    \n",
+    "    return metrics\n",
+    "\n",
+    "program_metrics = calculate_program_metrics(data)\n",
+    "\n",
+    "# Display top programs by different metrics\n",
+    "print(\"\\nTop 5 Programs by Total Investment:\")\n",
+    "print(program_metrics.sort_values(('Total Project Cost', 'sum'), ascending=False).head())\n",
+    "\n",
+    "print(\"\\nTop 5 Programs by GHG Reduction Efficiency:\")\n",
+    "print(program_metrics.sort_values('GHG_per_dollar', ascending=False).head())\n",
+    "\n",
+    "print(\"\\nTop 5 Programs by DAC Funding Ratio:\")\n",
+    "print(program_metrics.sort_values('DAC_funding_ratio', ascending=False).head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Total Investment by Program (Top 10):\n",
+      "                                                     count           sum  \\\n",
+      "Program Name                                                               \n",
+      "Transit and Intercity Rail Capital Program             245  101461750666   \n",
+      "Low Carbon Transit Operations Program                 1003    8777590799   \n",
+      "Affordable Housing and Sustainable Communities ...     151    8492944393   \n",
+      "Low Carbon Transportation                           105965    3759508172   \n",
+      "Community Air Protection                              5514    1211608583   \n",
+      "Climate Smart Agriculture                             1688     886514974   \n",
+      "Forest Health Program                                  258     837651776   \n",
+      "Funding Agricultural Replacement Measures for E...    8554     771297976   \n",
+      "Waste Diversion                                        265     643549843   \n",
+      "Fire Prevention Program                                600     614281793   \n",
+      "\n",
+      "                                                            mean  \n",
+      "Program Name                                                      \n",
+      "Transit and Intercity Rail Capital Program          4.141296e+08  \n",
+      "Low Carbon Transit Operations Program               8.751337e+06  \n",
+      "Affordable Housing and Sustainable Communities ...  5.624466e+07  \n",
+      "Low Carbon Transportation                           3.547877e+04  \n",
+      "Community Air Protection                            2.197331e+05  \n",
+      "Climate Smart Agriculture                           5.251866e+05  \n",
+      "Forest Health Program                               3.246712e+06  \n",
+      "Funding Agricultural Replacement Measures for E...  9.016811e+04  \n",
+      "Waste Diversion                                     2.428490e+06  \n",
+      "Fire Prevention Program                             1.023803e+06  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1465: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  x = float(self.convert_xunits(x))\n",
+      "/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1467: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  y = float(self.convert_yunits(y))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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z8+fPrzLm+9//fho2bLjMXK1atcrIkSMr3/fo0SNt2rRJz549q6xmX/rvT9+bT9f68ccfZ+7cuenWrVvatGlT7Wfwgx/8oMpfCGy++eZZsmRJXnvttSSf/HXBe++9lxNPPHGZPcKXjnvqqafy0ksvZd99983cuXMr7+nChQuzzTbb5L777ktFRUXluEKhUONV6Ely0UUX5e67767yWvrdTT75njdq1KjK979hw4af+/2tS2VlZdl1111z7bXXVv5VxpIlSzJx4sQMHz78c/e6Ly0tzQ477JDrr7++yhZLEydOzCabbJI11lgjyf/tv3/zzTdXuZdfxtLvYV2vQk+SbbbZpsrWIUu/o3vssUeV833Rd3fhwoWZM2dOBg0alEKhkCeffPILzz1p0qSUlZVl2223rfKz3a9fv7Rq1SpTpkz5Utf0VX4el/r0SvLk//7bevvtt1e2ffr6l/71xeDBg/Pqq6+mvLw8Sc1+HgEAlrKdCwDwrXLRRRdl7bXXTqNGjdK+ffv06NEjDRpUXVfQqFGjyn21l3rttdey2mqrLROW9ezZs/L40v9t0KBBunbtWqVft27ditb02b5J8s4772TMmDG57rrrMmvWrCrHloZAn7Y0IFyqrKwszZo1q9wq49Ptn91XvZgBAwbkjDPOSElJSVq0aJGePXtWBpCzZs3KvHnzctlll+Wyyy6rdvxn667uOqsze/bsvP/+++nRo8cyx3r27JmKioq8/vrr6dWr1xfOvfrqqy8TiJWVlaVTp07LtCWpst/zBx98kLFjx2b8+PF54403qgSzNfkMVlpppSpzLt066NPblHzWSy+9lCSfu31KeXl55dy1tfHGG3/ug0Vfe+21fOc731lmy4rqPovl5YADDsjEiRNz//33Z4sttsg999yTmTNnZv/99//CsSNGjMhNN92UqVOnZtCgQXnllVfy+OOP57zzzqvS5w9/+EO+973v5cQTT8w222yT3XffPXvuuecy/y34IqWlpUmS9957r1bjaqK6n+kkNfruzpgxI6NHj84tt9yyzB7m1X13P+ull15KeXl52rVrV+3xz/5s19RX+Xlcqnv37lXer7XWWmnQoEGVfeEfeOCBnHrqqZk6dWref//9Kv3Ly8tTVlZWo59HAIClhOgAwLfKF4WISdK0adNah2lfRXWrs/fee+88+OCDOeGEE9K3b9+0atUqFRUV2X777atdPVvdKuzq2pIs8yDUYtq2bZshQ4ZUe2xpDd/97neLBr7rr79+lfc1WYX+ZRWbu9g9qMm9OfroozN+/Pgce+yxGThwYMrKylJSUpKRI0fW+DP47JxfZOm8Z599dvr27VttnxVlT+aSkpJqr23JkiVfad6hQ4emffv2ufrqq7PFFlvk6quvTocOHYp+Fz9tl112SYsWLXL99ddn0KBBuf7669OgQYPstddelX2aN2+e++67L1OmTMltt92WO++8MxMnTszWW2+du+66q+jnWJ111lknSSof+FmXvux3d8mSJdl2223zzjvv5Kc//WnWWWedtGzZMm+88UYOOuigGq2+r6ioSLt27XLNNddUe3zVVVet4VXUrPav8rPz2VD+lVdeyTbbbJN11lkn5557bjp16pQmTZrk9ttvz7hx477yXx8AAN9OQnQAgBro3Llz7rnnnrz33ntVVqM///zzlceX/m9FRUWmTZtWZcXkyy+/XONzvfvuu5k8eXLGjBmT0aNHV7YvXaW8Ilh11VXTunXrLFmypEbhZm3nbtGiRV544YVljj3//PNp0KDBMitXl4cbbrghBx54YM4555zKtg8//DDz5s37UvOttdZaSZJnn3226F8mLO1TWlpa5/e1Jjp37pzJkydnwYIFVcL66j6LlVZaqdrtNpb+Vcbn+bztMho2bJh99903V155ZX7961/npptuKrpdz2e1bNkyO++8cyZNmpRzzz03EydOzOabb57VVlutSr8GDRpkm222yTbbbJNzzz03v/zlL/Ozn/0sU6ZMqdV9X3vttdOjR4/cfPPNOf/881eIX3A888wzefHFF3PVVVflgAMOqGy/++67l+lb7HNYa621cs8992TTTTddrr/8+jJeeumlKn958vLLL6eioqJy65tbb701ixYtyi233FJlNf9nt6Cpyc9jYmsXAOAT9kQHAKiBHXfcMUuWLMmFF15YpX3cuHEpKSnJDjvskOSTVbRJ8rvf/a5KvwsuuKDG51oaFn52Feant6Sobw0bNswee+yRP//5z3n22WeXOT579uyvNPd2222Xm2++ucoWDTNnzsyECROy2WabVW6jsTw1bNhwmc/gggsu+NIrrbfbbru0bt06Y8eOzYcffljl2NLz9OvXL2uttVZ+85vfZMGCBcvM8dn7+vzzz2fGjBlfqp7q7Ljjjlm8eHEuvvjiyrYlS5ZU+/1da6218vzzz1ep6emnn84DDzzwhedZurd5sV9I7L///nn33Xdz2GGHZcGCBfnud79b42sYMWJE3nzzzfzhD3/I008/nREjRlQ5/s477ywzZumq/0WLFlW21fTejhkzJnPnzs33vve9LF68eJnjd911V/7617/WuP6vqrr/fhQKhZx//vnL9C32Oey9995ZsmRJTj/99GXGLF68+Ev/IqkuXHTRRVXeL/1uLv1vcHXXX15envHjx1cZV5Ofx+STe1STLXAAgG82K9EBAGpgl112yVZbbZWf/exnmT59evr06ZO77rorN998c4499tjKVY39+vXLHnvskfPOOy9z587NJptskn/84x958cUXk9RsVWNpaWm22GKLnHXWWfn444/TsWPH3HXXXZk2bdpyvcba+tWvfpUpU6ZkwIAB+f73v591110377zzTp544oncc8891YaVNXXGGWfk7rvvzmabbZYjjzwyjRo1yqWXXppFixblrLPOqsOrKG7nnXfOn/70p5SVlWXdddfN1KlTc88992SVVVb5UvOVlpZm3Lhx+d73vpf+/ftn3333zUorrZSnn34677//fq666qo0aNAgf/jDH7LDDjukV69eOfjgg9OxY8e88cYbmTJlSkpLS3PrrbdWztmzZ88MHjy4xg8XveOOOyr/euLTBg0alDXXXDO77LJLNt1005x44omZPn161l133dx4443VhoiHHHJIzj333AwdOjSHHnpoZs2alUsuuSS9evVa5sGvn9WvX78kyc9+9rOMHDkyjRs3zi677FIZ6m6wwQbp3bt3Jk2alJ49e2bDDTes0fUln/wioHXr1jn++OMrf9nzab/4xS9y3333Zaeddkrnzp0za9as/O53v8vqq6+ezTbbrLJfTe/tiBEj8swzz+TMM8/Mk08+mX322SedO3fO3Llzc+edd2by5MmZMGFCjev/qtZZZ52stdZaOf744/PGG2+ktLQ0f/7zn6vdX3zp53DMMcdk6NChadiwYUaOHJnBgwfnsMMOy9ixY/PUU09lu+22S+PGjfPSSy9l0qRJOf/887Pnnnt+bdf0adOmTcuwYcOy/fbbZ+rUqbn66quz7777pk+fPkk+CcebNGmSXXbZpfKXML///e/Trl27vPXWW5Xz1OTnMfnkHk2cODGjRo1K//7906pVq+yyyy71cu0AQP0RogMA1ECDBg1yyy23ZPTo0Zk4cWLGjx+fLl265Oyzz86Pf/zjKn3/+Mc/pkOHDrn22mvzl7/8JUOGDMnEiRPTo0ePNGvWrEbnmzBhQo4++uhcdNFFKRQK2W677XLHHXcssy1FfWrfvn0eeeSR/OIXv8iNN96Y3/3ud1lllVXSq1ev/PrXv/5Kc/fq1Sv3339/TjrppIwdOzYVFRUZMGBArr766gwYMKCOruDznX/++WnYsGGuueaafPjhh9l0001zzz33VP61wZdx6KGHpl27dvnVr36V008/PY0bN84666yT4447rrLPlltumalTp+b000/PhRdemAULFqRDhw4ZMGBADjvssK90TZ/eHujTxo8fnzXXXLPye37sscfm6quvTklJSYYNG5ZzzjknG2ywQZUxPXv2zB//+MeMHj06o0aNyrrrrps//elPmTBhwhcGz/3798/pp5+eSy65JHfeeWflFkhLQ/TkkweM/uQnP6nRA0U/rVmzZhk2bFiuueaaDBkyZJmHYw4bNizTp0/PFVdckTlz5qRt27YZPHhwxowZU/lAy9o644wzsvXWW+e3v/1tLr744rzzzjtZaaWVsskmm+Tmm2/OsGHDvtS8X0bjxo1z66235phjjsnYsWPTrFmz7LbbbjnqqKMqg+aldt999xx99NG57rrrcvXVV6dQKGTkyJFJkksuuST9+vXLpZdempNPPjmNGjVKly5d8t3vfjebbrrp13Y9nzVx4sSMHj06J554Yho1apSjjjoqZ599duXxHj165IYbbsgpp5yS448/Ph06dMgRRxyRVVddNYccckiVuWry83jkkUfmqaeeyvjx4zNu3Lh07txZiA4A30Ilhdo86QgAgC/lqaeeygYbbJCrr746++23X32XA7Uyffr0dO3aNePHj89BBx30tZzz/PPPz3HHHZfp06dX2duab6fTTjstY8aMyezZs9O2bdv6LgcA+JaxJzoAQB374IMPlmk777zz0qBBg2yxxRb1UBH8bykUCrn88sszePBgAToAAPXOdi4AAHXsrLPOyuOPP56tttoqjRo1yh133JE77rgjP/jBD9KpU6f6Lg9WWAsXLswtt9ySKVOm5JlnnsnNN99c3yUBAIAQHQCgrg0aNCh33313Tj/99CxYsCBrrLFGTjvttPzsZz+r79JghTZ79uzsu+++adOmTU4++eSvdS9xAAAoxp7oAAAAAABQhD3RAQAAAACgCCE6AAAAAAAUIUQHAOB/TpcuXXLQQQfVdxn/87bccstsueWW9V0GAACs0IToAACsMJ555pnsueee6dy5c5o1a5aOHTtm2223zQUXXFDfpdWJ0047LSUlJV/4qkmw/eCDD+a0007LvHnzlnvdXbp0yc4777zcz1NXJkyYkPPOO6++y6iRN998M6eddlqeeuqp+i4FAIAiGtV3AQAAkHwSCm+11VZZY4018v3vfz8dOnTI66+/noceeijnn39+jj766Mq+L7zwQho0+N9bD7L77runW7dule8XLFiQI444Irvttlt23333yvb27dt/4VwPPvhgxowZk4MOOiht2rRZHuX+z5owYUKeffbZHHvssfVdyhd68803M2bMmHTp0iV9+/at73IAAKiGEB0AgBXCmWeembKysjz66KPLhMKzZs2q8r5p06ZfY2V1Z/3118/6669f+X7OnDk54ogjsv766+e73/1uPVYGAAAU87+3fAcAgG+kV155Jb169ap2VXW7du2qvP/snuiftzXK9OnTK/s9//zz2XPPPbPyyiunWbNm2WijjXLLLbcspyv68v7+979n8803T8uWLdOmTZvsuuuuee655yqPn3baaTnhhBOSJF27dl3mWsePH5+tt9467dq1S9OmTbPuuuvm4osvrrP6pk+fnpKSkvzmN7/JZZddlrXWWitNmzZN//798+ijj1b2+81vfpOSkpK89tpry8xx0kknpUmTJnn33Xcr2x5++OFsv/32KSsrS4sWLTJ48OA88MADVca99957OfbYY9OlS5c0bdo07dq1y7bbbpsnnngiySf7vN9222157bXXKu9Lly5dkiT33ntvSkpKcv3112fMmDHp2LFjWrdunT333DPl5eVZtGhRjj322LRr1y6tWrXKwQcfnEWLFi1T+9VXX51+/fqlefPmWXnllTNy5Mi8/vrrVfpsueWW6d27d/7zn/9kq622SosWLdKxY8ecddZZlX3uvffe9O/fP0ly8MEHV9Z75ZVX1u4DAQBgubISHQCAFULnzp0zderUPPvss+ndu3etxv7pT39apu2UU07JrFmz0qpVqyTJv//972y66abp2LFjTjzxxLRs2TLXX399hg8fnj//+c/ZbbfdPvcc7777bpYsWfKFtbRo0SItWrSoVf2fds8992SHHXbImmuumdNOOy0ffPBBLrjggmy66aZ54okn0qVLl+y+++558cUXc+2112bcuHFp27ZtkmTVVVdNklx88cXp1atXhg0blkaNGuXWW2/NkUcemYqKivzwhz/80rV91oQJE/Lee+/lsMMOS0lJSc4666zsvvvuefXVV9O4cePsvffe+clPfpLrr7++MvRf6vrrr892222XlVZaKcknvzjYYYcd0q9fv5x66qlp0KBB5S8D7r///my88cZJksMPPzw33HBDjjrqqKy77rqZO3du/vnPf+a5557LhhtumJ/97GcpLy/Pf//734wbNy5JKr8DS40dOzbNmzfPiSeemJdffjkXXHBBGjdunAYNGuTdd9/NaaedloceeihXXnllunbtmtGjR1eOPfPMM/Pzn/88e++9d773ve9l9uzZueCCC7LFFlvkySefrPJLoHfffTfbb799dt999+y999654YYb8tOf/jTrrbdedthhh/Ts2TO/+MUvMnr06PzgBz/I5ptvniQZNGhQnX1GAADUgQIAAKwA7rrrrkLDhg0LDRs2LAwcOLDwk5/8pPC3v/2t8NFHHy3Tt3PnzoUDDzyw6FxnnXVWIUnhj3/8Y2XbNttsU1hvvfUKH374YWVbRUVFYdCgQYXu3bt/YX2dO3cuJPnC16mnnlrja549e/YyY/r27Vto165dYe7cuZVtTz/9dKFBgwaFAw44oLLt7LPPLiQpTJs2bZl533///WXahg4dWlhzzTWrtA0ePLgwePDgL6yzc+fOhZ122qny/bRp0wpJCqusskrhnXfeqWy/+eabC0kKt956a2XbwIEDC/369asy3yOPPFLl86moqCh07969MHTo0EJFRUWV6+jatWth2223rWwrKysr/PCHP/zcenfaaadC586dl2mfMmVKIUmhd+/eVb5X++yzT6GkpKSwww47VOk/cODAKvNMnz690LBhw8KZZ55Zpd8zzzxTaNSoUZX2wYMHL/MdXLRoUaFDhw6FPfbYo7Lt0UcfLSQpjB8//nOvCQCA+mM7FwAAVgjbbrttpk6dmmHDhuXpp5/OWWedlaFDh6Zjx4612nJlypQpOemkk3L00Udn//33T5K88847+fvf/56999477733XubMmZM5c+Zk7ty5GTp0aF566aW88cYbnzvvNddck7vvvvsLXwcccMCXvgdvvfVWnnrqqRx00EFZeeWVK9vXX3/9bLvttrn99ttrNE/z5s0r/11eXp45c+Zk8ODBefXVV1NeXv6l6/usESNGVK4kT1K5kvrVV1+t0ufxxx/PK6+8Utk2ceLENG3aNLvuumuS5KmnnspLL72UfffdN3Pnzq38fBYuXJhtttkm9913XyoqKpIkbdq0ycMPP5w333zzS9d9wAEHpHHjxpXvBwwYkEKhkEMOOaRKvwEDBuT111/P4sWLkyQ33nhjKioqsvfee1fWOGfOnHTo0CHdu3fPlClTqoxv1apVlb3umzRpko033rjK/QEAYMVnOxcAAFYY/fv3z4033piPPvooTz/9dP7yl79k3Lhx2XPPPfPUU09l3XXX/dzx//3vfzNixIhsuummOffccyvbX3755RQKhfz85z/Pz3/+82rHzpo1Kx07diw696abbvrlLqoWlu4d3qNHj2WO9ezZM3/729+ycOHCtGzZ8nPneeCBB3Lqqadm6tSpef/996scKy8vT1lZWZ3Uu8Yaa1R5vzRQ//Q+53vttVdGjRqViRMn5uSTT06hUMikSZOyww47pLS0NEny0ksvJUkOPPDAoucqLy/PSiutlLPOOisHHnhgOnXqlH79+mXHHXfMAQcckDXXXPNL1730fnTq1GmZ9oqKipSXl2eVVVbJSy+9lEKhkO7du1c776eD+SRZffXVU1JSUqVtpZVWyr/+9a8a1woAQP0TogMAsMJp0qRJ+vfvn/79+2fttdfOwQcfnEmTJuXUU08tOuajjz7KnnvumaZNm+b6669Po0b/93/qLl3FfPzxx2fo0KHVju/Wrdvn1jR79uwa7YneqlWrZfbg/jq98sor2WabbbLOOuvk3HPPTadOndKkSZPcfvvtGTduXOW9qAsNGzastr1QKFT+e7XVVsvmm2+e66+/PieffHIeeuihzJgxI7/+9a8r+yyt6eyzz07fvn2rnXPpPd17772z+eab5y9/+UvuuuuunH322fn1r3+dG2+8MTvssMNXqvuLrqeioiIlJSW54447qu372c+9JvcHAIAVnxAdAIAV2kYbbZTkk61OPs8xxxyTp556Kvfdd1/at29f5djSVcqNGzfOkCFDvlQd/fv3r1wp/nlOPfXUnHbaaV/qHJ07d06SvPDCC8sce/7559O2bdvKVeifXeG81K233ppFixbllltuqbLi+rNbjXydRowYkSOPPDIvvPBCJk6cmBYtWmSXXXapPL7WWmslSUpLS2v0+XznO9/JkUcemSOPPDKzZs3KhhtumDPPPLMyRC92b76qtdZaK4VCIV27ds3aa69dJ3Mur1oBAKg79kQHAGCFMGXKlGpX6C7dB7y6LU6WGj9+fC699NJcdNFF2XjjjZc53q5du2y55Za59NJLqw3jZ8+e/YX1fR17on/nO99J3759c9VVV2XevHmV7c8++2zuuuuu7LjjjpVtS8P0T/dL/m/186fvZXl5ecaPH/+l6/qq9thjjzRs2DDXXnttJk2alJ133rnKljT9+vXLWmutld/85jdZsGDBMuOXfj5LlixZZk/3du3aZbXVVsuiRYsq21q2bFmne78vtfvuu6dhw4YZM2bMMt/VQqGQuXPn1nrOYp8jAAArDivRAQBYIRx99NF5//33s9tuu2WdddbJRx99lAcffDATJ05Mly5dcvDBB1c7bs6cOTnyyCOz7rrrpmnTprn66qurHN9tt93SsmXLXHTRRdlss82y3nrr5fvf/37WXHPNzJw5M1OnTs1///vfPP30059b39exJ3ryyZYmO+ywQwYOHJhDDz00H3zwQS644IKUlZVVWeHer1+/JMnPfvazjBw5Mo0bN84uu+yS7bbbLk2aNMkuu+ySww47LAsWLMjvf//7tGvX7gtX8y8v7dq1y1ZbbZVzzz037733XkaMGFHleIMGDfKHP/whO+ywQ3r16pWDDz44HTt2zBtvvJEpU6aktLQ0t956a957772svvrq2XPPPdOnT5+0atUq99xzTx599NGcc845lfP169cvEydOzKhRo9K/f/+0atWqysr3L2uttdbKGWeckZNOOinTp0/P8OHD07p160ybNi1/+ctf8oMf/CDHH398reds06ZNLrnkkrRu3TotW7bMgAED0rVr169cLwAAdUOIDgDACuE3v/lNJk2alNtvvz2XXXZZPvroo6yxxho58sgjc8opp6RNmzbVjluwYEE+/PDD/Oc//8n++++/zPFp06alZcuWWXfddfPYY49lzJgxufLKKzN37ty0a9cuG2ywQUaPHr2cr67mhgwZkjvvvDOnnnpqRo8encaNG2fw4MH59a9/XSVY7d+/f04//fRccsklufPOO1NRUZFp06alR48eueGGG3LKKafk+OOPT4cOHXLEEUdk1VVXzSGHHFJv1zVixIjcc889ad26dZUV9UttueWWmTp1ak4//fRceOGFWbBgQTp06JABAwbksMMOS5K0aNEiRx55ZO66667ceOONqaioSLdu3fK73/0uRxxxROVcRx55ZJ566qmMHz8+48aNS+fOneskRE+SE088MWuvvXbGjRuXMWPGJPnkgaTbbbddhg0bVuv5GjdunKuuuionnXRSDj/88CxevDjjx48XogMArEBKCp5qAwAAAAAA1bInOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAACiiUX0XsCKqqKjIm2++mdatW6ekpKS+ywEAAAAAoI4VCoW89957WW211dKgQfH15kL0arz55pvp1KlTfZcBAAAAAMBy9vrrr2f11VcvelyIXo3WrVsn+eTmlZaW1nM1AAAAAADUtfnz56dTp06VeXAxQvRqLN3CpbS0VIgOAAAAAPAN9kVbenuwKAAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFNGovguA+lJRUciLs97LizMXpFGDkvRerSxrrNKivssCAAAAAFYgQnS+lT5eUpEr/jkt/3xpTj74eEkKSUqbNc6ufb+TXft2TElJSX2XCAAAAACsAGznwrfSlOdnZfJzs9K6eeN0a9cq3du1SoOS5M9PvJF/vzm/vssDAAAAAFYQQnS+le5/aU4aNixJWfPGKSkpSUlJSdqVNsv7Hy3JI9Peqe/yAAAAAIAVhBCdb6XyDz5Ks0YNl2lv1KAk5R98XA8VAQAAAAArIiE630o9OpTmvQ8/TqFQqGxbUlHI4oqKrLlqy3qsDAAAAABYkQjR+VYa0rN9VmrROK/MXpDyDz7Ou+9/lFdmLcgaK7fMpt3a1nd5AAAAAMAKQojOt1KPDq1z1Nbd03u1snzw0eJ8vLgig7qtkmOHdE/bVk3ruzwAAAAAYAXRqL4LgPrSp1ObrNexLHMWLErDBiVZuWWTlJSU1HdZAAAAAMAKRIjOt1qDBiVpV9qsvssAAAAAAFZQtnMBAAAAAIAiVogQ/aKLLkqXLl3SrFmzDBgwII888sjn9p80aVLWWWedNGvWLOutt15uv/32KsdLSkqqfZ199tnL8zIAAAAAAPiGqfcQfeLEiRk1alROPfXUPPHEE+nTp0+GDh2aWbNmVdv/wQcfzD777JNDDz00Tz75ZIYPH57hw4fn2Wefrezz1ltvVXldccUVKSkpyR577PF1XRYAAAAAAN8AJYVCoVCfBQwYMCD9+/fPhRdemCSpqKhIp06dcvTRR+fEE09cpv+IESOycOHC/PWvf61s22STTdK3b99ccskl1Z5j+PDhee+99zJ58uQa1TR//vyUlZWlvLw8paWlX+KqAAAAAABYkdU0B67XlegfffRRHn/88QwZMqSyrUGDBhkyZEimTp1a7ZipU6dW6Z8kQ4cOLdp/5syZue2223LooYfWXeEAAAAAAHwrNKrPk8+ZMydLlixJ+/btq7S3b98+zz//fLVj3n777Wr7v/3229X2v+qqq9K6devsvvvuRetYtGhRFi1aVPl+/vz5Nb0EAAAAAAC+wep9T/Tl7Yorrsh+++2XZs2aFe0zduzYlJWVVb46der0NVYIAAAAAMCKql5D9LZt26Zhw4aZOXNmlfaZM2emQ4cO1Y7p0KFDjfvff//9eeGFF/K9733vc+s46aSTUl5eXvl6/fXXa3klAAAAAAB8E9VriN6kSZP069evygM/KyoqMnny5AwcOLDaMQMHDlzmAaF33313tf0vv/zy9OvXL3369PncOpo2bZrS0tIqLwAAAAAAqNc90ZNk1KhROfDAA7PRRhtl4403znnnnZeFCxfm4IMPTpIccMAB6dixY8aOHZsk+dGPfpTBgwfnnHPOyU477ZTrrrsujz32WC677LIq886fPz+TJk3KOeec87VfEwAAAAAA3wz1HqKPGDEis2fPzujRo/P222+nb9++ufPOOysfHjpjxow0aPB/C+YHDRqUCRMm5JRTTsnJJ5+c7t2756abbkrv3r2rzHvdddelUChkn332+VqvBwAAAACAb46SQqFQqO8iVjTz589PWVlZysvLbe0CAAAAAPANVNMcuF73RAcAAAAAgBWZEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgiHoP0S+66KJ06dIlzZo1y4ABA/LII498bv9JkyZlnXXWSbNmzbLeeuvl9ttvX6bPc889l2HDhqWsrCwtW7ZM//79M2PGjOV1CQAAAAAAfEPVa4g+ceLEjBo1KqeeemqeeOKJ9OnTJ0OHDs2sWbOq7f/ggw9mn332yaGHHponn3wyw4cPz/Dhw/Pss89W9nnllVey2WabZZ111sm9996bf/3rX/n5z3+eZs2afV2XBQAAAADAN0RJoVAo1NfJBwwYkP79++fCCy9MklRUVKRTp045+uijc+KJJy7Tf8SIEVm4cGH++te/VrZtsskm6du3by655JIkyciRI9O4ceP86U9/+tJ1zZ8/P2VlZSkvL09paemXngcAAAAAgBVTTXPgeluJ/tFHH+Xxxx/PkCFD/q+YBg0yZMiQTJ06tdoxU6dOrdI/SYYOHVrZv6KiIrfddlvWXnvtDB06NO3atcuAAQNy0003LbfrAAAAAADgm6veQvQ5c+ZkyZIlad++fZX29u3b5+233652zNtvv/25/WfNmpUFCxbkV7/6Vbbffvvcdddd2W233bL77rvnH//4R9FaFi1alPnz51d5AQAAAABAo/ouoC5VVFQkSXbdddccd9xxSZK+ffvmwQcfzCWXXJLBgwdXO27s2LEZM2bM11YnAAAAAAD/G+ptJXrbtm3TsGHDzJw5s0r7zJkz06FDh2rHdOjQ4XP7t23bNo0aNcq6665bpU/Pnj0zY8aMorWcdNJJKS8vr3y9/vrrX+aSAAAAAAD4hqm3EL1Jkybp169fJk+eXNlWUVGRyZMnZ+DAgdWOGThwYJX+SXL33XdX9m/SpEn69++fF154oUqfF198MZ07dy5aS9OmTVNaWlrlBQAAAAAA9bqdy6hRo3LggQdmo402ysYbb5zzzjsvCxcuzMEHH5wkOeCAA9KxY8eMHTs2SfKjH/0ogwcPzjnnnJOddtop1113XR577LFcdtlllXOecMIJGTFiRLbYYotstdVWufPOO3Prrbfm3nvvrY9LBAAAAADgf1i9hugjRozI7NmzM3r06Lz99tvp27dv7rzzzsqHh86YMSMNGvzfYvlBgwZlwoQJOeWUU3LyySene/fuuemmm9K7d+/KPrvttlsuueSSjB07Nsccc0x69OiRP//5z9lss82+9usDAAAAAOB/W0mhUCjUdxErmvnz56esrCzl5eW2dgEAAAAA+AaqaQ5cb3uiAwAAAADAik6IDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFCEEB0AAAAAAIoQogMAAAAAQBFCdAAAAAAAKEKIDgAAAAAARQjRAQAAAACgCCE6AAAAAAAUIUQHAAAAAIAihOgAAAAAAFDEChGiX3TRRenSpUuaNWuWAQMG5JFHHvnc/pMmTco666yTZs2aZb311svtt99e5fhBBx2UkpKSKq/tt99+eV4CAAAAAADfQPUeok+cODGjRo3KqaeemieeeCJ9+vTJ0KFDM2vWrGr7P/jgg9lnn31y6KGH5sknn8zw4cMzfPjwPPvss1X6bb/99nnrrbcqX9dee+3XcTkAAAAAAHyDlBQKhUJ9FjBgwID0798/F154YZKkoqIinTp1ytFHH50TTzxxmf4jRozIwoUL89e//rWybZNNNknfvn1zySWXJPlkJfq8efNy0003fama5s+fn7KyspSXl6e0tPRLzQEAAAAAwIqrpjlwva5E/+ijj/L4449nyJAhlW0NGjTIkCFDMnXq1GrHTJ06tUr/JBk6dOgy/e+99960a9cuPXr0yBFHHJG5c+fW/QUAAAAAAPCN1qg+Tz5nzpwsWbIk7du3r9Levn37PP/889WOefvtt6vt//bbb1e+33777bP77runa9eueeWVV3LyySdnhx12yNSpU9OwYcNl5ly0aFEWLVpU+X7+/Plf5bIAAAAAAPiGqNcQfXkZOXJk5b/XW2+9rL/++llrrbVy7733Zptttlmm/9ixYzNmzJivs0QAAAAAAP4H1Ot2Lm3btk3Dhg0zc+bMKu0zZ85Mhw4dqh3ToUOHWvVPkjXXXDNt27bNyy+/XO3xk046KeXl5ZWv119/vZZXAgAAAADAN1G9huhNmjRJv379Mnny5Mq2ioqKTJ48OQMHDqx2zMCBA6v0T5K77767aP8k+e9//5u5c+fmO9/5TrXHmzZtmtLS0iovAAAAAACo1xA9SUaNGpXf//73ueqqq/Lcc8/liCOOyMKFC3PwwQcnSQ444ICcdNJJlf1/9KMf5c4778w555yT559/Pqeddloee+yxHHXUUUmSBQsW5IQTTshDDz2U6dOnZ/Lkydl1113TrVu3DB06tF6uEQAAAACA/031vif6iBEjMnv27IwePTpvv/12+vbtmzvvvLPy4aEzZsxIgwb/l/UPGjQoEyZMyCmnnJKTTz453bt3z0033ZTevXsnSRo2bJh//etfueqqqzJv3rysttpq2W677XL66aenadOm9XKNAAAAAAD8byopFAqF+i5iRTN//vyUlZWlvLzc1i4AAAAAAN9ANc2B6307FwAAAAAAWFEJ0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQhBAdAAAAAACKEKIDAAAAAEARQnQAAAAAAChCiA4AAAAAAEUI0QEAAAAAoAghOgAAAAAAFCFEBwAAAACAIoToAAAAAABQRK1C9I8//jhrrbVWnnvuueVVDwAAAAAArDBqFaI3btw4H3744fKqBQAAAAAAVii13s7lhz/8YX79619n8eLFy6MeAAAAAABYYTSq7YBHH300kydPzl133ZX11lsvLVu2rHL8xhtvrLPiAAAAAACgPtU6RG/Tpk322GOP5VELAAAAAACsUGodoo8fP3551AEAAAAAACucWu+JDgAAAAAA3xa1XomeJDfccEOuv/76zJgxIx999FGVY0888USdFAYAAAAAAPWt1ivRf/vb3+bggw9O+/bt8+STT2bjjTfOKquskldffTU77LDD8qgRAAAAAADqRa1D9N/97ne57LLLcsEFF6RJkyb5yU9+krvvvjvHHHNMysvLl0eNAAAAAABQL2odos+YMSODBg1KkjRv3jzvvfdekmT//ffPtddeW7fVAQAAAABAPap1iN6hQ4e88847SZI11lgjDz30UJJk2rRpKRQKdVsdAAAAAADUo1qH6FtvvXVuueWWJMnBBx+c4447Lttuu21GjBiR3Xbbrc4LBAAAAACA+lJSqOXy8YqKilRUVKRRo0ZJkuuuuy4PPvhgunfvnsMOOyxNmjRZLoV+nebPn5+ysrKUl5entLS0vssBAAAAAKCO1TQHrnWI/m0gRAcAAAAA+GaraQ7cqCaT/etf/6rxiddff/0a9wUAAAAAgBVZjUL0vn37pqSk5AsfHFpSUpIlS5bUSWEAAAAAAFDfahSiT5s2bXnXAQAAAAAAK5waheidO3de3nUAAAAAAMAKp0Yh+i233FLjCYcNG/aliwEAAAAAgBVJjUL04cOH12gye6IDAAAAAPBNUqMQvaKiYnnXAQAAAAAAK5wG9V0AAAAAAACsqL5UiP6Pf/wju+yyS7p165Zu3bpl2LBhuf/+++u6NgAAAAAAqFe1DtGvvvrqDBkyJC1atMgxxxyTY445Js2bN88222yTCRMmLI8aAQAAAACgXpQUCoVCbQb07NkzP/jBD3LcccdVaT/33HPz+9//Ps8991ydFlgf5s+fn7KyspSXl6e0tLS+ywEAAAAAoI7VNAeu9Ur0V199Nbvssssy7cOGDcu0adNqOx0AAAAAAKywah2id+rUKZMnT16m/Z577kmnTp3qpCgAAAAAAFgRNKrtgB//+Mc55phj8tRTT2XQoEFJkgceeCBXXnllzj///DovEAAAAAAA6kutQ/QjjjgiHTp0yDnnnJPrr78+ySf7pE+cODG77rprnRcIAAAAAAD1pdYPFv028GBRAAAAAIBvtprmwLVeiV4oFPL4449n+vTpKSkpyZprrpm+ffumpKTkKxUMAAAAAAArmlqF6FOmTMmhhx6a1157LUsXsJeUlKRr16654oorssUWWyyXIgEAAAAAoD40qGnHl19+OTvvvHO6dOmSG2+8Mc8991z+85//ZNKkSVl99dWz44475tVXX12etQIAAAAAwNeqxiH6eeedl0022SR///vfs+uuu6ZHjx5ZZ511svvuu2fKlCkZMGBAxo0b96WKuOiii9KlS5c0a9YsAwYMyCOPPPK5/SdNmpR11lknzZo1y3rrrZfbb7+9aN/DDz88JSUlOe+8875UbQAAAAAAfHvVOES/9957c+yxx1Z7rKSkJMcee2ymTJlS6wImTpyYUaNG5dRTT80TTzyRPn36ZOjQoZk1a1a1/R988MHss88+OfTQQ/Pkk09m+PDhGT58eJ599tll+v7lL3/JQw89lNVWW63WdQEAAAAAQI1D9BkzZmS99dYrerx379557bXXal3Aueeem+9///s5+OCDs+666+aSSy5JixYtcsUVV1Tb//zzz8/222+fE044IT179szpp5+eDTfcMBdeeGGVfm+88UaOPvroXHPNNWncuHGt6wIAAAAAgBqH6AsWLEiLFi2KHm/RokXef//9Wp38o48+yuOPP54hQ4b8X0ENGmTIkCGZOnVqtWOmTp1apX+SDB06tEr/ioqK7L///jnhhBPSq1evL6xj0aJFmT9/fpUXAAAAAAA0qk3n//znP3n77berPTZnzpxan3zOnDlZsmRJ2rdvX6W9ffv2ef7556sd8/bbb1fb/9N1/frXv06jRo1yzDHH1KiOsWPHZsyYMbWsHgAAAACAb7pahejbbLNNCoXCMu0lJSUpFAopKSmps8K+rMcffzznn39+nnjiiRrXc9JJJ2XUqFGV7+fPn59OnTotrxIBAAAAAPgfUeMQfdq0aXV+8rZt26Zhw4aZOXNmlfaZM2emQ4cO1Y7p0KHD5/a///77M2vWrKyxxhqVx5csWZIf//jHOe+88zJ9+vRl5mzatGmaNm36Fa8GAAAAAIBvmhqH6J07d67zkzdp0iT9+vXL5MmTM3z48CSf7Gc+efLkHHXUUdWOGThwYCZPnpxjjz22su3uu+/OwIEDkyT7779/tXum77///jn44IPr/BoAAAAAAPjmqtV2LsvDqFGjcuCBB2ajjTbKxhtvnPPOOy8LFy6sDLwPOOCAdOzYMWPHjk2S/OhHP8rgwYNzzjnnZKeddsp1112Xxx57LJdddlmSZJVVVskqq6xS5RyNGzdOhw4d0qNHj6/34gAAAAAA+J9W7yH6iBEjMnv27IwePTpvv/12+vbtmzvvvLPy4aEzZsxIgwYNKvsPGjQoEyZMyCmnnJKTTz453bt3z0033ZTevXvX1yUAAAAAAPANVVKo7kmh33Lz589PWVlZysvLU1paWt/lAAAAAABQx2qaAzcoeqQahUIhM2bMyIcffviVCwQAAAAAgBVdrUP0bt265fXXX19e9QAAAAAAwAqjViF6gwYN0r1798ydO3d51QMAAAAAACuMWoXoSfKrX/0qJ5xwQp599tnlUQ8AAAAAAKwwav1g0ZVWWinvv/9+Fi9enCZNmqR58+ZVjr/zzjt1WmB98GBRAAAAAIBvtprmwI1qO/F55533VeoCAAAAAID/GbUO0Q888MDlUQcAAAAAAKxwar0nepK88sorOeWUU7LPPvtk1qxZSZI77rgj//73v+u0OAAAAAAAqE+1DtH/8Y9/ZL311svDDz+cG2+8MQsWLEiSPP300zn11FPrvEAAAAAAAKgvtQ7RTzzxxJxxxhm5++6706RJk8r2rbfeOg899FCdFgcAAAAAAPWp1iH6M888k912222Z9nbt2mXOnDl1UhQAAAAAAKwIah2it2nTJm+99dYy7U8++WQ6duxYJ0UBAAAAAMCKoNYh+siRI/PTn/40b7/9dkpKSlJRUZEHHnggxx9/fA444IDlUSMAAAAAANSLWofov/zlL7POOuukU6dOWbBgQdZdd91sscUWGTRoUE455ZTlUSMAAAAAANSLkkKhUPgyA2fMmJFnn302CxYsyAYbbJDu3bvXdW31Zv78+SkrK0t5eXlKS0vruxwAAAAAAOpYTXPgRl/2BGussUY6deqUJCkpKfmy0wAAAAAAwAqr1tu5JMnll1+e3r17p1mzZmnWrFl69+6dP/zhD3VdGwAAAAAA1Ktar0QfPXp0zj333Bx99NEZOHBgkmTq1Kk57rjjMmPGjPziF7+o8yIBAAAAAKA+1HpP9FVXXTW//e1vs88++1Rpv/baa3P00Udnzpw5dVpgfbAnOgAAAADAN1tNc+Bab+fy8ccfZ6ONNlqmvV+/flm8eHFtpwMAAAAAgBVWrUP0/fffPxdffPEy7Zdddln222+/OikKAAAAAABWBLXeEz355MGid911VzbZZJMkycMPP5wZM2bkgAMOyKhRoyr7nXvuuXVTJQAAAAAA1INah+jPPvtsNtxwwyTJK6+8kiRp27Zt2rZtm2effbayX0lJSR2VCAAAAAAA9aPWIfqUKVOWRx0AAAAAALDCqfWe6AAAAAAA8G0hRAcAAAAAgCKE6AAAAAAAUIQQHQAAAAAAihCiAwAAAABAEY2+zKCXXnopU6ZMyaxZs1JRUVHl2OjRo+ukMAAAAAAAqG+1DtF///vf54gjjkjbtm3ToUOHlJSUVB4rKSkRogMAAAAA8I1R6xD9jDPOyJlnnpmf/vSny6MeAAAAAABYYdR6T/R33303e+211/KoBQAAAAAAVii1DtH32muv3HXXXcujFgAAAAAAWKHUejuXbt265ec//3keeuihrLfeemncuHGV48ccc0ydFQcAAAAAAPWppFAoFGozoGvXrsUnKynJq6+++pWLqm/z589PWVlZysvLU1paWt/lAAAAAABQx2qaA9d6Jfq0adO+UmEAAAAAAPC/otZ7on9aoVBILReyAwAAAADA/4wvFaL/8Y9/zHrrrZfmzZunefPmWX/99fOnP/2prmsDAAAAAIB6VevtXM4999z8/Oc/z1FHHZVNN900SfLPf/4zhx9+eObMmZPjjjuuzosEAAAAAID68KUeLDpmzJgccMABVdqvuuqqnHbaad+IPdM9WBQAAAAA4JutpjlwrbdzeeuttzJo0KBl2gcNGpS33nqrttMBAAAAAMAKq9Yherdu3XL99dcv0z5x4sR07969TooCAAAAAIAVQa33RB8zZkxGjBiR++67r3JP9AceeCCTJ0+uNlwHAAAAAID/VbVeib7HHnvk4YcfTtu2bXPTTTflpptuStu2bfPII49kt912Wx41AgAAAABAvaj1g0W/DTxYFAAAAADgm62mOXCNtnOZP39+5STz58//3L5CZwAAAAAAvilqFKKvtNJKeeutt9KuXbu0adMmJSUly/QpFAopKSnJkiVL6rxIAAAAAACoDzUK0f/+979n5ZVXTpJMmTJluRYEAAAAAAArihqF6IMHD678d9euXdOpU6dlVqMXCoW8/vrrdVsdAAAAAADUowa1HdC1a9fMnj17mfZ33nknXbt2rZOiAAAAAABgRVDrEH3p3ueftWDBgjRr1qxOigIAAAAAgBVBjbZzSZJRo0YlSUpKSvLzn/88LVq0qDy2ZMmSPPzww+nbt2+dFwgAAAAAAPWlxiH6k08+meSTlejPPPNMmjRpUnmsSZMm6dOnT44//vi6rxAAAAAAAOpJjUP0KVOmJEkOPvjgnH/++SktLV1uRQEAAAAAwIqg1nuin3feeVm8ePEy7e+8807mz59fJ0UBAAAAAMCKoNYh+siRI3Pdddct03799ddn5MiRdVIUAAAAAACsCGodoj/88MPZaqutlmnfcsst8/DDD9dJUQAAAAAAsCKodYi+aNGiardz+fjjj/PBBx/USVEAAAAAALAiqHWIvvHGG+eyyy5bpv2SSy5Jv3796qQoAAAAAABYETSq7YAzzjgjQ4YMydNPP51tttkmSTJ58uQ8+uijueuuu+q8QAAAAAAAqC+1Xom+6aabZurUqenUqVOuv/763HrrrenWrVv+9a9/ZfPNN18eNQIAAAAAQL0oKRQKhfouYkUzf/78lJWVpby8PKWlpfVdDgAAAAAAdaymOXCtt3OZMWPG5x5fY401ajslAAAAAACskGodonfp0iUlJSVFjy9ZsuQrFQQAAAAAACuKWofoTz75ZJX3H3/8cZ588smce+65OfPMM+usMAAAAAAAqG+1DtH79OmzTNtGG22U1VZbLWeffXZ23333OikMAAAAAADqW4O6mqhHjx559NFH62o6AAAAAACod7VeiT5//vwq7wuFQt56662cdtpp6d69e50VBgAAAAAA9a3WIXqbNm2WebBooVBIp06dct1119VZYQAAAAAAUN9qHaJPmTKlyvsGDRpk1VVXTbdu3dKoUa2nAwAAAACAFVatU+/BgwcvjzoAAAAAAGCFU6MQ/ZZbbqnxhMOGDfvSxQAAAAAAwIqkRiH68OHDq7wvKSlJoVCo8n6pJUuW1E1lAAAAAABQzxrUpFNFRUXl66677krfvn1zxx13ZN68eZk3b15uv/32bLjhhrnzzjuXd70AAAAAAPC1qfWe6Mcee2wuueSSbLbZZpVtQ4cOTYsWLfKDH/wgzz33XJ0WCAAAAAAA9aVGK9E/7ZVXXkmbNm2WaS8rK8v06dProCQAAAAAAFgx1DpE79+/f0aNGpWZM2dWts2cOTMnnHBCNt544zotDgAAAAAA6lOtQ/Qrrrgib731VtZYY41069Yt3bp1yxprrJE33ngjl19++fKoEQAAAAAA6kWt90Tv1q1b/vWvf+Xuu+/O888/nyTp2bNnhgwZkpKSkjovEAAAAAAA6ktJoVAofNnBH374YZo2bfqNC8/nz5+fsrKylJeXp7S0tL7LAQAAAACgjtU0B671di4VFRU5/fTT07Fjx7Rq1SrTpk1Lkvz85z+3nQsAAAAAAN8otQ7RzzjjjFx55ZU566yz0qRJk8r23r175w9/+EOdFgcAAAAAAPWp1iH6H//4x1x22WXZb7/90rBhw8r2Pn36VO6RDgAAAAAA3wS1DtHfeOONdOvWbZn2ioqKfPzxx3VSFAAAAAAArAhqHaKvu+66uf/++5dpv+GGG7LBBhvUSVEAAAAAALAiaFTbAaNHj86BBx6YN954IxUVFbnxxhvzwgsv5I9//GP++te/Lo8aAQAAAACgXtR6Jfquu+6aW2+9Nffcc09atmyZ0aNH57nnnsutt96abbfddnnUCAAAAAAA9aKkUCgU6mqyxx57LBtttFFdTVdv5s+fn7KyspSXl6e0tLS+ywEAAAAAoI7VNAeu9Ur0BQsW5IMPPqjS9tRTT2WXXXbJgAEDal8pAAAAAACsoGocor/++usZOHBgysrKUlZWllGjRuX999/PAQcckAEDBqRly5Z58MEHl2etAAAAAADwtarxg0VPOOGEfPjhhzn//PNz44035vzzz8/999+fAQMG5JVXXsnqq6++POsEAAAAAICvXY1D9Pvuuy833nhjNtlkk+y9997p0KFD9ttvvxx77LHLsTwAAAAAAKg/Nd7OZebMmenatWuSpF27dmnRokV22GGH5VYYAAAAAADUt1o9WLRBgwZV/t2kSZM6LwgAAAAAAFYUNd7OpVAoZO21105JSUmSZMGCBdlggw2qBOtJ8s4779RthQAAAAAAUE9qHKKPHz9+edYBAAAAAAArnBqH6AceeODyrAMAAAAAAFY4tdoTHQAAAAAAvk2E6AAAAAAAUIQQHQAAAAAAihCiAwAAAABAEStEiH7RRRelS5cuadasWQYMGJBHHnnkc/tPmjQp66yzTpo1a5b11lsvt99+e5Xjp512WtZZZ520bNkyK620UoYMGZKHH354eV4CAAAAAADfQDUO0efNm5eLL7648v1+++2X3XffvfK11157Zd68ebUuYOLEiRk1alROPfXUPPHEE+nTp0+GDh2aWbNmVdv/wQcfzD777JNDDz00Tz75ZIYPH57hw4fn2Wefreyz9tpr58ILL8wzzzyTf/7zn+nSpUu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+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Program Performance Analysis:\n",
+      "\n",
+      "1. Top 5 Programs by DAC Funding Ratio:\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "\"[('Total Project Cost', 'sum')] not in index\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[27], line 62\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;66;03m# Top programs by equity\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m1. Top 5 Programs by DAC Funding Ratio:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mclean_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnlargest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDAC_funding_ratio\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDAC_funding_ratio\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGHG_per_dollar\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTotal Project Cost\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m]\u001b[49m)\n\u001b[1;32m     66\u001b[0m \u001b[38;5;66;03m# Top programs by GHG efficiency\u001b[39;00m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m2. Top 5 Programs by GHG Reduction Efficiency:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4106\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m   4107\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 4108\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m   4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/indexes/multi.py:2763\u001b[0m, in \u001b[0;36mMultiIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(keyarr) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(keyarr[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m   2761\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_indexer_level_0(keyarr)\n\u001b[0;32m-> 2763\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2764\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[indexer], indexer\n\u001b[1;32m   2766\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m_get_indexer_strict(key, axis_name)\n",
+      "File \u001b[0;32m~/Repos/california_equity_git/.venv/lib/python3.12/site-packages/pandas/core/indexes/multi.py:2781\u001b[0m, in \u001b[0;36mMultiIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   2779\u001b[0m cmask \u001b[38;5;241m=\u001b[39m check \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   2780\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cmask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m-> 2781\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkeyarr[cmask]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2782\u001b[0m \u001b[38;5;66;03m# We get here when levels still contain values which are not\u001b[39;00m\n\u001b[1;32m   2783\u001b[0m \u001b[38;5;66;03m# actually in Index anymore\u001b[39;00m\n\u001b[1;32m   2784\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkeyarr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: \"[('Total Project Cost', 'sum')] not in index\""
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Let's first get a clear view of our data\n",
+    "print(\"\\nTotal Investment by Program (Top 10):\")\n",
+    "investment_by_program = data.groupby('Program Name')['Total Project Cost'].agg(['count', 'sum', 'mean'])\n",
+    "print(investment_by_program.nlargest(10, 'sum'))\n",
+    "\n",
+    "# Calculate our key metrics\n",
+    "program_metrics = data.groupby('Program Name').agg({\n",
+    "    'Total Project Cost': ['count', 'sum', 'mean'],\n",
+    "    'Total Project GHGReductions': ['sum', 'mean'],\n",
+    "    'Total GGRFDisadvantaged Community Funding': ['sum', 'mean'],\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
+    "}).round(2)\n",
+    "\n",
+    "# Add efficiency metrics\n",
+    "program_metrics['GHG_per_dollar'] = (program_metrics[('Total Project GHGReductions', 'sum')] / \n",
+    "                                   program_metrics[('Total Project Cost', 'sum')]).round(4)\n",
+    "\n",
+    "program_metrics['DAC_funding_ratio'] = (program_metrics[('Total GGRFDisadvantaged Community Funding', 'sum')] / \n",
+    "                                      program_metrics[('Total Project Cost', 'sum')]).round(4)\n",
+    "\n",
+    "# Clean data for visualization\n",
+    "clean_data = program_metrics[\n",
+    "    (program_metrics['GHG_per_dollar'] != float('inf')) & \n",
+    "    (program_metrics['DAC_funding_ratio'].notna())\n",
+    "]\n",
+    "\n",
+    "# Create visualization\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# Basic scatter plot\n",
+    "scatter = plt.scatter(clean_data['DAC_funding_ratio'],\n",
+    "                     clean_data['GHG_per_dollar'],\n",
+    "                     s=clean_data[('Total Project Cost', 'sum')]/1e7,\n",
+    "                     alpha=0.6)\n",
+    "\n",
+    "# Add labels for major programs\n",
+    "for idx, row in clean_data.iterrows():\n",
+    "    if row[('Total Project Cost', 'sum')] > 500000000:  # Only label major programs\n",
+    "        plt.annotate(idx[:30] + '...' if len(idx) > 30 else idx,\n",
+    "                    (row['DAC_funding_ratio'], row['GHG_per_dollar']),\n",
+    "                    xytext=(5, 5),\n",
+    "                    textcoords='offset points',\n",
+    "                    fontsize=8)\n",
+    "\n",
+    "plt.xlabel('DAC Funding Ratio')\n",
+    "plt.ylabel('GHG Reduction per Dollar')\n",
+    "plt.title('Program Performance: Equity vs. Climate Impact\\nSize = Total Investment')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Analysis of key metrics\n",
+    "print(\"\\nProgram Performance Analysis:\")\n",
+    "\n",
+    "# Top programs by equity\n",
+    "print(\"\\n1. Top 5 Programs by DAC Funding Ratio:\")\n",
+    "print(clean_data.nlargest(5, 'DAC_funding_ratio')[\n",
+    "    ['DAC_funding_ratio', 'GHG_per_dollar', ('Total Project Cost', 'sum')]\n",
+    "])\n",
+    "\n",
+    "# Top programs by GHG efficiency\n",
+    "print(\"\\n2. Top 5 Programs by GHG Reduction Efficiency:\")\n",
+    "print(clean_data.nlargest(5, 'GHG_per_dollar')[\n",
+    "    ['DAC_funding_ratio', 'GHG_per_dollar', ('Total Project Cost', 'sum')]\n",
+    "])\n",
+    "\n",
+    "# Investment distribution\n",
+    "print(\"\\n3. Investment Distribution Summary:\")\n",
+    "print(clean_data[('Total Project Cost', 'sum')].describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting scikit-learn\n",
+      "  Using cached scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
+      "Requirement already satisfied: numpy>=1.19.5 in ./.venv/lib/python3.12/site-packages (from scikit-learn) (2.1.2)\n",
+      "Requirement already satisfied: scipy>=1.6.0 in ./.venv/lib/python3.12/site-packages (from scikit-learn) (1.14.1)\n",
+      "Collecting joblib>=1.2.0 (from scikit-learn)\n",
+      "  Using cached joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
+      "  Using cached threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)\n",
+      "Using cached scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.9 MB)\n",
+      "Using cached joblib-1.4.2-py3-none-any.whl (301 kB)\n",
+      "Using cached threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n",
+      "Installing collected packages: threadpoolctl, joblib, scikit-learn\n",
+      "Successfully installed joblib-1.4.2 scikit-learn-1.5.2 threadpoolctl-3.5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# install sklearn library\n",
+    "!pip install scikit-learn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Raw Investment Totals by Program:\n",
+      "Program Name\n",
+      "Transit and Intercity Rail Capital Program                                   101461750666\n",
+      "Low Carbon Transit Operations Program                                          8777590799\n",
+      "Affordable Housing and Sustainable Communities Program                         8492944393\n",
+      "Low Carbon Transportation                                                      3759508172\n",
+      "Community Air Protection                                                       1211608583\n",
+      "Climate Smart Agriculture                                                       886514974\n",
+      "Forest Health Program                                                           837651776\n",
+      "Funding Agricultural Replacement Measures for Emission Reductions Program       771297976\n",
+      "Waste Diversion                                                                 643549843\n",
+      "Fire Prevention Program                                                         614281793\n",
+      "Name: (Total Project Cost, sum), dtype: int64\n",
+      "\n",
+      "Revised Program Type Characteristics:\n",
+      "\n",
+      "Below Median:\n",
+      "Number of programs: 16\n",
+      "Average GHG efficiency: 0.0000\n",
+      "Average DAC ratio: 0.0000\n",
+      "Average project cost: $1,352,617.47\n",
+      "Largest program: $1,211,608,583.00\n",
+      "\n",
+      "Example programs:\n",
+      "['Active Transportation Program', 'Climate Change Research Program', 'Climate Ready Program ']\n",
+      "\n",
+      "High Performer (Both):\n",
+      "Number of programs: 10\n",
+      "Average GHG efficiency: 0.0129\n",
+      "Average DAC ratio: 0.1410\n",
+      "Average project cost: $7,428,798.66\n",
+      "Largest program: $8,777,590,799.00\n",
+      "\n",
+      "Example programs:\n",
+      "['Affordable Housing and Sustainable Communities Program', 'Climate Smart Agriculture', 'Low Carbon Transit Operations Program']\n",
+      "\n",
+      "GHG Efficient:\n",
+      "Number of programs: 9\n",
+      "Average GHG efficiency: 0.0089\n",
+      "Average DAC ratio: 0.0000\n",
+      "Average project cost: $1,967,095.81\n",
+      "Largest program: $837,651,776.00\n",
+      "\n",
+      "Example programs:\n",
+      "['Climate Adaptation and Resiliency Program', 'Food Production Investment Program', 'Forest Health Program']\n",
+      "\n",
+      "Equity Focused:\n",
+      "Number of programs: 3\n",
+      "Average GHG efficiency: 0.0001\n",
+      "Average DAC ratio: 0.1038\n",
+      "Average project cost: $138,638,075.83\n",
+      "Largest program: $101,461,750,666.00\n",
+      "\n",
+      "Example programs:\n",
+      "['Community Assistance for Climate Equity Program', 'Transit and Intercity Rail Capital Program', 'Urban Greening Program']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1465: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  x = float(self.convert_xunits(x))\n",
+      "/home/dadams/Repos/california_equity_git/.venv/lib/python3.12/site-packages/matplotlib/text.py:1467: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  y = float(self.convert_yunits(y))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9910AOnXqxOOPP052djadO3cmISHhvGI4H4MGDcLHxwcfHx8GDhzIokWL6N27N4sXL+bo0aPe7bKzs4tV/Z/PeZ7Nzp07sVgsdOvWDYCOHTsSGRlJUlIS1atXx8/Pj759+wKe96foe9G8eXO2b9/OsGHD6Ny5Mz169Lio74mIiIiIiIiIiFy6VIFeRhy7d1Owa5eqzy8Ci68vpunGLCzENE1q1qyJr68vMz6cRKtqvlxrXcfSLUdYlALXtqpfQvIcWtWrzJqdv5qo1GKF4Ej2U42Hp2/mswFVWPNsJ1L27uHUqVPFNj1+/DgtW7akRYsWrFixgry8PO/rGTNmnDX2oiRwET8/P++/rVYrhYWFPPbYYzz33HO/+z6kpaXxzTffeF+/+eabHDt2jLVr19KyZUtiYmLIz88/57EA+vXrx6pVq2jYsCHdunXzJpiLREdHs3///nPG0rJlS9asWfOb5WvWrKFly5Yl7pORkcH8+fO9bVx+/PFHkpKSSEpK4pdffinWxiUoKKjE8/zpp5+4/fbbi53n+Tjz++Hr6+t9vXPnTlwuFwB16tRh27ZtdO/enVWrVtGoUSO6d+9O06ZNadq0KXFxcSxZsuSCjg8wZ84cfvzxxz+07bfffuutjBcRERERERERkdKnBHoZyd28GXduLpbAwPIO5bLUqlp1dhw7xo5jxwD4Lu0Ekf7+RPrYAUjo2IYxzz5DQs1CwqtE4+Prx+zVv5DQvEaJ4z3bvzUv/Xs9P+444l22+0g6X67aRUauAx+blSp1GzHr+xWE+ho4HA5vqw+Ab775htatW7Np0yauvvpqEhMTqV+/Pps2bSIvL4+uXbtitVpJSEhg+vTpOBwOCgsL+fDDD4v10C5K0l4Mp06dIioqCj8/P/Ly8v7whKi7du0iMjKSQYMGAZ4+4b8WGBhIRkbGWcd47LHHmDp1KosXL/Yue+211zh16hQDBgzwLvvss89wOp3k5eXx3Xff8dZbbxEUFETXrl159dVXvdsdPnyYgwcP/u55pqamFmv1EhISctY4GzZsiNvtZuHChQCsXr2a1NRU4uLiznpeAAcPHsQwDG688UbGjx/PiRMnaNq0KcnJySQnJ7No0SLq1at3zjHO5XwS6DfeeGOxFkAiIiIiIiIiIlK61MKlDJhuN3kbN2EJCPhNBfLlJuGD9wFwulzsPnGCmMqVAahbsSLv97v5LHuZkJ8JWangyPa8tvlDUGVP+xTjt/dxbp85Ax/L/5YPjm9N94YNGf7NHHIKCrBaLExu3wHHzzux+zlIqHSM945nkdAxHuyBJDSvwb/+u4XmtSNKjOj6FrX4eHgCj328ktRTufj72qgc6s/zt7ejaXQEt3VqQI17PyMzt4AB7ary+drDfDH5Fe554p8AvPzyy/j5+bF27Vpq166NYRjMmTMHPz8/YmNjGT9+PAkJCezevZtTp05Rr149QkJC6NKlC6NHjyYnJ4d58+axf/9+br/9doYMGcKGDRsIDQ2lTZs2xMR4Jm5ZvHgxzzzzDPn5+WRnZ3snGb333ntxuVz8/PPPxMTEkJOTQ5s2bdi3bx9NmjTh1KlTNGjQwPO9cjqZPXs2q1evJi4uzjthKXgqsCMjI72TbIKnwvvX2rRpw1dffUVMTAzZ2dnk5uZSoUIFxo8fz5w5c4iLi+PkyZM888wzDB06lMLCQg4fPszixYt57bXXePvtt8nIyMBiseDv70/16tVJSEhg6NChDBgwgBkzZhAZGcl7771HVlYWLpeLp556irFjxwKeXvFDhgzB5XLRqFEj5s2bx9y5c6lfv36xljOxsbFs3LgRf39//P39uf32273rZs6cSVRUFAMGDCA7Oxur1crHH39MUFAQaWlp5OfnU79+fUJCQopNhpqcnMyTTz6JaZoUFhYSEhJC69atvesjIjw/Y1OmTOHzzz+nRYsWTJgwgR49ehAbG8uxY8d4+OGHGT58OAUFBbhcLgoLC3nooYeoVasW3377LQsXLmTatGk8/PDD9OrViwEDBpCZmUl+fj5du3blnXfewWKxMG3aNObMmcOcOXNYtmwZDz/8MJ06dWLVqlUUFhbyySefEB8fX+LPvIiIXB4Mw+DUqVOEhYV5l0VHR3t/3/7asmXLGDlyJElJSaUa16xZs3jjjTdIT08nNDQUf39/Ro4cSb9+/Ur1uGfq0aMHb731Fg0bNrxoY0ZHR+Pr64u/vz8Oh4OHHnqIhx566KKNLyIiIiKXPyXQy4Bz/34KU1OxhIaWdyh/2qL7HwDgQHo6133wvvf1mQrdbmxFyW9HDhzfDrknweX0tEkBcLsh4wD4BkNEAwj8X2ubdcNH/GbMM32xOYkFO3ZSPzyMvM2bCMr6lr4NwJwzDCyeH+mXB3Xg5UEdzjlOt5a16NayVonr3r6vM0F+Pkz4NomPH+vDzcvW8uqHH3DPffdjmibR0dE0bdqUuXPnAvDcc8+Rnp7OhAkTAGjbti133303R44cISUlhblz57JixQpq1arFpEmTsFqtpKWlAXDLLbfQpk0bfvjhBw4dOkRcXBwxMTEUFhbSsmVLVq5cidVq5eTJk7Ro0YKDBw/y4YcfUlhYyPr162nVqhWtWrVi6dKl9O3bl6eeeoohQ4YQFxfHyJEjefnll2nXrh3fffcdAC+++CIrVqxg2bJlGIbBjh07CAsLY/PmzcTFxTFu3Djefvtt73tx+PBhgoODWb16NW3btuXRRx/lgQceYNeuXbRr146uXbt6t/3++++9H/gjIiJ46623OH78OJUqVaJ58+aMGDGCzMxMrrrqKmw2G19++SXgmagU4IknnuDvf/87O3bsoHXr1jz99NP8/PPP1K1bl+nTp9O1a1eWLl3K7NmzmTZtGl26dCn2fatZsyaHDh0iKCgIl8tF7969+fzzz70x7dy5k6SkJGrXrs0//vEPFi9ezK233srWrVupW7cua9asITg4mDvvvJNatTw/GzfccAM33HCD9xiff/4599xzD2+//Tbt2rWjd+/edOrUiaFDh5Kamuq9GTF//nwmTpzIsWPHiI+Pp1KlStx2223eivxTp04RHh7OjTfe6P1eAeTn5zN37txi5/Dvf/+7xB7vO3bsYOrUqUyePJkpU6bw9NNP88MPP5T8Ay8iIn85Re3YStuHH37I+PHj+frrr2ncuDHg+Z367bffnjUum+3if8yYP3/+RR8T4IsvviAuLo59+/bRrFkzrr76apo1a+Zd73a7AbBY/vzDu6X13oiIiIhI6dFfb2Ugf+fPuPNysVWtWt6hlJo277zNjY2bsHpfCrUrVGDsddcz7Mt/k5WTTkFhIR2iKvBS2xgshoUvfjnMV3tSqehrY2d6Fnbrj7zfuxe1qjdkz4kTjPz2G3IdTtymSbeGDRjd9RrGL19GZn4Bwzt25PVly8gqKKDniTRarF3JJ4/Ww1qlnjd5vnjzAZ75bA35jkIchW5G9W7BPdc3AWDIhIX4+lj55Ug6B9Kyia1ZkVmPd8fuYyUr18G9ExeTtPc4lUL8ycx1UDcqFKvNSo8ubXngk5/Y/vVrNBoyodi5DxkyhOTkZI4dO8b8+fOJiYkhMTGRSZMm0atXL2/F8ahRo/jqq68AmDdvHt988w1Wq5Xt27czfvx4li1bxkMPPUR4eDhTpkyhSZMmBAcHM3jwYDIyMjAMA7fbzZYtWxg4cCAWi4W4uDi+//57li1bht1uZ9euXaxevZrMzExvldonn3zC/v37vcd2OBxER0f/5nvYvHlzACZPnsw111zDzJkzvYn0W2+9lZdeeomNGzeyatUqunTpwsiRI+nYsWOxyT/P5HK5mD9/Plu3bqVbt27s2LGDpUuXMmbMGCIiIli/fj3Z2dm0atXK28O8KLn8448/kp+fzw033MCePXs4fvy4dwLUrl27Urdu3RKPeaETqRYl0kNCQgB44IEHWLlyJZgmZB3x3OCx+nhj7N69O0uXLmXVqlX07t2bp556iscff7zEmIp07dqVF198kV27dnHNNdfQsWPHCzqHM9WrV4+2bdt6z2f8+PHnjEFERC5/0dHR9O/fn6VLl1K/fn3uu+8+CgsLGTRoEBs3bsTX15epU6cSFxdHamrqOZ9q+uyzz6hUqRJbtmzB19eXf//739SpU+c3x3zuuef48MMPvclz8LRGK/rdl5KSQlxcHA888AALFy5k0KBB3HbbbQwfPpyUlBTy8vLo3bs3L730EuBpHzdy5EiOHTtGQUEB999/Pw8//DDgqcD/5z//yZw5czh+/Dhjxozhrrvu8p57USV+ly5diI+PZ+3atRw+fJjrrruOKVOmAHDkyBEGDx7MwYMHqV69OhUqVCAmJuZ355ipVasWDRs25Oeff+brr78mOTmZ7OxsDhw4wMKFC1myZAmvv/46ADVq1OCDDz6gWrVqOJ1ORowYwaJFi6hQoQJXXXUViYmJLFu2zPv3Xbt27UhMTOTpp5/G6XTy9ttv43A4cLvdvPTSS/ztb38DoEuXLrRq1Yr169eTkpLC4MGDad++PS+//DIHDx5k+PDhjBo16k/8BImIiIjI+VICvQzkb9uKYfO5ZNu3JHzwPttKSIL62Wz0iY0lplJlXlm6hL1PPkXykSP0m/4Jix8YSqHbzevLljF32zaOZmWx/dgxfCwWJt3Ul3xnAZ9cXY9AZxafH8zhiR93sPDQCfytVk7kO0h3FBLh54PFMAi2mfSd9TXPJlxH4tE0rqvfgEc6dqT2Ky8zf/t25u/Ywa60NCoEBLB6XwqVg4JoGlWFrUcOs/14Gv6P7MfP9//Id7jwtVupXTmEsQPa0q9DPdo+9gX3TlzMwqT9DL2hKYdOZJOZ52DqI9dy+/gfmLd+L498sJwn+rakwxOzSc8pYHiv5ozu14rIQR/iNsHW513vRJcDnv+UpPbdfvNeHT16lN69e/POO+8QFRWFy+WidevWWCwW3G43hmGwcuVKYmNjAfjyyy+pVasWERERZGdnc+2111KzZk22b9/ODTfcwKFDh3j33XdZsWIFd9xxB59++imGYdCsWbNiE2Y2aNAAHx8f4uLiiI6O5qabbqKgoIADBw6wY8cO1q5dy5EjR3jkkUd47bXXiI6OxuVysXjxYu+H4PDwcGw2G3fccQfgmWC0UqVK1K1blx9//JHatWszYcIEevbs+ZufYcMwyM7O5pNPPsFqtRIeHs7cuXO55ZZbyM/Px263M2jQIKZPn87IkSOZNWsWU6dOZfPmzYSEhBAYGEhiYiIulwubzcb333/PPffcw6OPPkphYSGjR4+mcuXKtG7dmkaNGpGXl+c9do8ePUhOTi6WTD9zglE/Pz9GjRr1hyZS/TXveR7eBBs+guqtodVg7/rw8HD69u1L3759ad26NS+//DKPP/44NputWF/7M489cuRIevfuzaJFi3jqqaeIjY1l8uTJvzn2753Dmf7o+YiIyF/LiRMnWLt2LYZhsGzZMrZu3crbb7/N9OnTvU8tbd++nbCwsHM+1bR+/fpiT2aNGzeO999/v9ixjh07xqFDh7w3bM8mIyODJk2aMG7cOAC6devGU089RefOnSksLKRXr17Mnj2bvn37MmDAAD777DNiYmLIzc2lXbt2tG3b1tsizdfXl3Xr1nmfSLvzzjtLrNrevXs3S5cuxel00rhxY9asWUP79u0ZPnw47du35/nnn/fOd1LUHu9ckpOT2bFjB82bN2fLli2sWbOGTZs2ERkZyZYtW3j88cdJTEykWrVq/POf/+Tee+/l+++/54MPPmDXrl1s3boV8PyNcqbt27czefJkpk6d6v3+DRgwAMMwSElJoV27duzbtw9fX8/E9/v27WPp0qVkZmYSHR3NqVOnWLFiBYcPH6Zhw4bcfffdxVr8iIiIiEjp0iSipcx0OnHs23dJTx46Y4CnT7SPYWAxDEL9/Ph60CDubNmKm5t6Hl8tSiA3rVKFn0f/g0K3G5fbzeyfNvNop05EBgczvGNHvrnrbgDc2cd5ae02EhbtYPzmvVgNgz7RUVTyt2O3Wqjg68PmWzuzrl9HQn3tHMlzMic5iXY1azFj00ZeXbKEj2/tz9IHh/HZgNuxW630jW3KovsfYFCreMAEs5C8Qhcu02TG37vh+M/DPNAtlkBfH16ZvYHYhz9j857jGAbMW5/Cfzft49CJbG5qV5fvNqQQW6sihgE1IoL4cvUvuNwm/32hD6/ffTXLtx4mNMCXGhFBTLi3E3d2bcSWiQM5kuHA+dPXnspk/vfodExMDD4+PuzYsYNTp07h6+uLn58fNpsNm82G3W6nsLCQ9u3bA9CzZ08AbDYbfn5+tGzZkqVLl1K5cmXWr1+PaZpUq1YNgJtuugnDMPi///s/7wezIl9++SVOpxOXy8VHH30EQGJiIkFBQQwcOJDY2FiysrJYs2YNubm5gGciT8Mw+OCDDwDP48hF/bOLYsrPz6dp06bccMMNHD58mEcffRSAatWq8fHHHwOe6q6VK1dSr149Bg4cWGwizaioKMBTyXX99dczaNAg0tPTOXr0KM2bNyc5ORmHw0F+fj7NmzenRYsWgOeDK+AdKzo6moYNG+JyucjPz2f37t0sX76c3bt3ExISQt26dYsljc81wei5JCQkMHv2bLKysjBN0/ve4B8ONl8IjvJuO2/ePO97aZommzZt8ibx69Wrx4YNG3C5XOTm5nqr/sHzqHvt2rW57777eOqpp7wTh/564tMLPQcREflrO/Mm9pAhQ4q9jo6O9s7fceutt5KamsqBAwe8TzUV/a7dsGFDsV7pv34ya/fu3X8olq5du9K0adNivch9fHwYOHAgADk5OSxevJgRI0YQFxdHfHw8v/zyCzt37mTnzp1s3bqV2267jbi4ODp06EBWVhbbtm3zjlV0Uz8mJgabzUZqamqJcfTv3x+bzYa/vz9xcXHe+BcvXszdd3v+Jo2KiqJXr17nPJ/+/ft7K+g/+ugj6tevD3gS4ZGRkQAsXbqU7t27e/8+GzZsGEuWLPEWJgwcOBAfHx98fHwYPHhwsfHr1KlD586dva/37t3LDTfcQGxsLH369OHkyZPs3bvXu/7mm2/2FibUqVOHXr16YRgG1apVo1KlSqSkpJzzfERERETk4lIFeilzpqbizsrGeglXifx3504Awvz9yXE6aVuzJntOnuLL5J/4dttWjufk4DZNqr74gncfu9WKw+XiYEYGD/3nawBu+mQaBmCeMXbf6EhiwoMYv3kPk7buK3bcmp8uovCMjf+bcpj/pszGAN5dtZJ3Vq0EPHd53MDUdWuZuWkjuU4nVsPAZf5v5z7/nIf7jLH6tq/L4ZPZFBR6elbmFDh55ctEAJ6cvhqb1UKhy7Pu2Rk/evfrNnYOVzeqxqKfDgBwKqeARz5YjgEsSz7I8cwC7AM8ieZ9+/fj4+Np62EYBsuXL+eDDz7A5XIVq0IGT5L65MmTzJgxA4AtW7YAngoku93uTZIWtUM5fvy494PkzTcXn5z1pptuKvZ6y5YtzJw50/tBumLFigC89dZb3tgSExMJPH0Tp6hveNF/i/p6FrnmmmtwOBx8+OGH3gr6xoZXNwAAMKBJREFUX375BYDU1FSGDx9Ofn4+y5cvB2Du3Ll8+eWXfPXVV/Tr14+//e1v3lh27drFa6+9Rk5Ojvd9AOjXrx8+Pj44HA6Sk5O9N2g+//xzlixZws8//wxAkyZNqFWrFlarFZfLRf369alYsSJ2ux273U7Dhg3Zv38/jRo14uDBg4SGhnLo0CGmTZtGixYtik0wei49evRg3bp1tGzZkpCQkP/1PK9QG3q+UWzb5cuXe6vNTdOkYcOGTJw4EYC+ffsye/ZsGjVqRPXq1WnRooU32T5x4kSWLFmC3W7HarXyxhuece+8806GDBnCnDlzeOihhxgxYgQ333wzTZo0oWrVqn/4HEo6pxdeeIH4+Hg2bNjAmDFjvL1jx4wZQ9WqVRk6dOgFjS0iIqWnUqVKnDhxoliFcVpamne+EICgoKBzjmEYBoZhXJQnsypXrky1atVYt24d3bp5nsJbunSpt21LkYCAAO/v+aLf6z/++GOxYwBs3bqVChUqnHPS0z/6hNV5P1l2FkU90H/tXO/zucb89bpfj3Pbbbfx6quvev/Gq1Chwjm/L3riTERERKR8qQK9lDkPHcadn4/h71/eoZzVz6cnszyem0uu08na/ft5Z+UKT//l7GxqhYd7f1CC7HYAHr26k3f/UD8/rKc/KJiAj8VCo9AAAP6TcpTv9x+n0G0S4mPluuoR1ArywwCqBng+DET5e8a8JiqUnvXrUju8gveDh9Uw8D+dpDaB2Kgo7FYrbvPMND3UrBRMtYpBtG3g+XD59ZrddIip4l1vt1moGPy/Dx9/ax1NzUqeDzNhAXYiQjwxmSbsT8sEwAAsBhiG59gH07KoUel/H4AqVaqE3W73xhofH09BQQEArVu3LvZhafLkyQQEBHg/HBVVojudTu6++24iIiI8xzQMunbtimEY3g+hRccIDg4u9oHMarVSoUIF7+uiqu/KlSsTHBzsHa9KlSre/YoS/mdWjAHe6jOLxYLD4fDua7FY8Pf3935gDg0NxeVyYbFYvOsDAgKw2+1cf/313vGKHkHu2LEjDoeDJk2aEHp6El2r1cpdd93ljb1y5co0btwYPz8/jh07xsGDB73VXW63m/bt22Oz2TAMg/bt2zNr1iycTid5eXnUqlWL9PR0Nm7cyNSpU7Hb7eTm5vL000/TuHFjPv74Y+/ErkVJ6iK9evVi2bJl3tfPPfccu3btIjExkZdeeums1V2vv/4627dvJzk5mS1btvDVV19547Xb7Xz11Vf8/PPPLFmyhMmTJzNt2jQA3n33XbZu3cqmTZvYsGGDd/LV1q1be5ffe++91KxZk3Xr1rF161YWLlx41nPo0qVLseRDbGxssZjnz59PfHw84PnZPHPitRdeeEHJcxGRS1S3bt2KtVGZPn06derUoUqVKmfdJyUlhaVLlwKep9MiIyOpXr36RXuqacyYMTz66KPs2LHDu6zo5nhJgoKC6Nq1K6+++qp32eHDhzl48CANGzYkJCTE+0QbwC+//MLJkycvKLaSXHPNNd7fv0ePHmXevHl/esyuXbuyYMECDh8+DMCUKVO49tprsVqt3rljnE4nTqeT6dOnn3OsU6dOef/2+uyzzzh16tSfjk9ERERESo8q0EuZM/UImCaG5dK9V1GUXG1VrRpbjx6lbc2aLPz5Z3xtNoLsdg5mZOAGbIZB9unk6qcbE737ZxcU4DZNb/W53Wol3eGpjDGBfdl5uIFMp4uFB9O8++3P8SSTj+V5xswpdLH8lz3FKstdpkmO0wmAr9VK0uHDhPn7cyIn21txbgIpx7IAOHQi27vv5PnJ3n+7TZOTWf+r7KkTFcoPG/cDkJ7r8C53FLrZdTjj9PsCkWEB9GlXl/e+T8buY+Pwif99WGzbqjkbf9rG4cOHCQwMJD8/H9M0sVgsbNmypVi/7nvvvRfDMDBNE8MwWLNmjXfdpEmTvFXgVquVPn36ePt5At6Edk5OjreiCzzVXb169WLmzJm43W6OHDnieT+PHSu2TUZGhrcKuqhi6ejRo8Wq5K+99lqmTp3qHd9ms9GuXTtWrlyJy+Vi3bp1gKe/aWFhIdWrV+fw4cP4+PhQvXp1dp5+iqFIUexpaWk4nU527drlPQ+3282qVat48803uf3220lLS+PUqVPe9w6gRYsW7NmzB6vVysyZMwkKCsI0TdatW8eIESMwTZPatWuzdetWbxsXq9XKsWPHCAoKIiwsjOrVqyMiInK5mTBhAiNHjqRZs2ZYLBaioqJ+N/HdpEkTpk2bxvDhw7Hb7Xz++ecYhnHRnmq6//77CQwMZODAgWRkZFCpUiX8/PyYNGnSWfeZMWMGo0aNIjY2FsMwCAwM5P3336d69erMmzePkSNH8tZbb+FyuYiIiGDmzJkXFFtJ3n77bQYPHkzjxo2pWrUqbdu2/dM9w2NjY3n99dfp3r074JlE9F//+hfgmXw8OTmZxo0bEx4eTnx8vDfRfrb4br75ZsLCwrjmmmuoWbPmn4pNREREREqXYZq/KuUVMjMzCQ0NJSMjg5CQkD811omPPiJr8RJ8z5jo8FKz9JdfuOPzmQT4+JDndFItNJQjmZmYQIDNRvbplikVAgI4kZuL2zRZ+sBQur4/BYC4qlW5pVlzXlj4XwpcLp5NSKCe8wjDV+8gw+ki0t/O0TwHPoaBYUCo3YcMx+nksNtkdFwdxiXtobKfDxlON1VCQth36hQm8I+uXVm8axfrDx5kQFwLZiVtonJQICdycgn2seJ0u8kudFM9PJCebWuzKGk/u1MzCfKz4XJD/ulEvs1mgdMJ2gKnixZ1Iti89wRg4jYh0M9GTn4hBhDsbyMzrxC7zULn2GpEhQfw6dKd1K8SSnaBkyMnPcno+GaNqBPTlC+//JKKFStStWpVtmzZQnh4OHfccQcfffSRN+ntcrkYOHAgM2fOpGrVqkyaNMnbiqV79+6sX7+eEydOEBUVxZNPPsnIkSMJCgoiKyuLwMBA8vLyGDduHE8//TShoaE4HA7vuoKCAgIDA3G5XGRmZmK1Wr3J6KJWJ+np6QDeViiRkZEcPXqUdu3a8eOPP/Lggw96288A+Pv7c+utt/Lll18SGhrK1q1bCQ8P59prr2XZsmW88MILPP/88wA89thjvP3226SkpFCpUiUAAgMDycnJ4cCBA9SoUYPnnnuO119/nfz8fAICAqhevToPPPAAI0eOZOjQoTRs2JDvvvuO5cuXExwcTLdu3fj8888JDw8nPz+f8ePH85///IeUlBTGjBnDQw89RHx8PO3atePll19m5cqVDB48mDVr1lC5cmW+/fZbxowZc87Hw0VEROSvKS8vDx8fH2w2GydOnKBdu3Z89tlnvzsR6p+RlZVFcHAwTqeTO+64g1atWjF69OhSO56IiJzbxczriIhcumXRfxGFJ09inG6bcak6lFnUssQg0G7nv/fdT9WQENynq7+thoHbNMl1OrGcrlYPPqMXY72ICALsPt51gXY7BIRTPcDTmiXSz46/1SDEbiMmLIiTBQ4cbk/FOsCi01XpBW6TxpGR5Dgc3nX5Tqe3Qv5QRgaVgoJIy8nFxMRusZB7usf50Yxcvl27h8duaglAnsNFpVB/DAv4WC1UCPJjWI9mFDhd3vVFyXOAvAJPoj04wE6VCp7WK8H+PuxJzeTrNZ4JqY6m53JN0xre89687WeSk5OxWq3ex5jr1q1LWlqatyLb7XZjsViYPn26txLJ/1ftfOLj4wkI8LS8SU9PZ+7cuZ5zP6MXpo+PD//85z9xuVy43W6ysrKwWq243W5M08Ttdnurvn19fXG73QQGBuJ2u8nIyPBWdhe1cClq31HUix3wtmUpGmPBggUEBwdz7Ngx7ySiJ06coGnTprz44oveY3/zzTcA3seVDcMgNDQUi8XCvn2evvdRUVHYbDZcLhdZWVm0bduWKVM8N2DWr1/PJ598wvHjxyksLCQ4OBj76VZBmZmZVKxYkSlTprBixQoaNmzI888/T2RkJE2aNOGzzz5j//79nDp1iqCgIPbu3YvD4Sj26LuIiIhcWXbt2kV8fDzNmzenY8eODBs2rFST5+CZkDwuLo6mTZsSEhLC8OHDS/V4IiIiIlJ2VIFegot5p/Lwk09RmJaGzzn6VpYGt2lyMsdBakY+Gfn5GEBYgD9RIX6EB/gU66Xdb/onrNm3r8RxooKCyCgoIO90ctZyOpl+de3anMzNZevRo1gNAz8fH5wuFw6Xiw61ojlw6iTHsrMoOJ2h9rNacLjduE2w4pkUtOgHz24xcJwxA6iBp4+641eTW4b7++Nrs5GalUWwzYLDDUE2KyccTm/7mDP/W6tyMAfSsnG5TepGhWJisifVc7OgVd1KJO87gaPQXey4sbUqUr9KGHPX78HpKn5pWAwICfAlPafAu8zHxwdfX19yc3Px8fGhQYMG7N+/n4yMjGL72u12atSoweHDh8nLy8Nut3tbmrRs2ZI9e/Z4q8SLxgW8CfNfMwwDm82G1WqloKCAsLAwxo8fzz333POb7Vq0aMHGjRs953B6UtCxY8d6K8gBHnzwQT788EOsViv5+fnY7XYiIyM5ePAgvr6+OJ1OXC4XgYGBtG/fHn9/f+bOnUtgYCCmaZKfn8/LL7/MP/7xDwzDoFmzZtSsWZMffvgBh8PhnRCtKOlfu3ZtfH192bp1K4ZhYLVaCQsLIz09nSZNmjBs2DBGjRpFTk4OoaGhGIZBbm4u4eHhHD16lMDAQF566SUOHjzIlClTvFX6+fn5REdHc8sttzBnzhx8fHxISkrShJoiIlLuTNPEnZODxW7HOH2jWEREREqHKtBF5GJSAr0EF+t/tKbTycFRfweXC9vpSSLLQnZBIVsPZXA8u4B8jmHajgMGFmcU/paKRIb40qRqKH4+1lKMwoTUZEg/AL5BYDlLFb7phvwM8A+H6vFg/QMfKE+lwLFt4BcGQMHRbCp0rElI86iLEvlzM38kPcfBhPs6lbg+LSOPuEems+K9kYRc93ipPhZc9Aiy0+kkMzOTjh07MmnSJB566KGzHnP8+PFs376dqVOnXvR4ilzoY8pF+wHMmTOHJ598ku3bt5e4bXZ2NoGBgRiGwd69e2nfvj3r16+nRo0aJW4vIiJyqcr/+Wcy5nyDIyUFS4A/QV26ENKt2yX/lKKIiMjlSgl0EbmYNIloKXIXOMBViGEruw9HuQ4Xm/af4nhWAaaRD35HwfBMZmn6HCE3P5B9J1wUukxa1AzHbiutLj4GVG4EbhdkHQHDCj7+YDn9I2e6oTAPCh3gHwZVmv2x5PlZuB2u39/oIpjyfTLPfb4WwzTp88x0HM9/U6qPBe/atYtBgwaRm5vL/v37qVChAiNGjDjrMZs0aYJhGCxYsKBU4imSkJBAQUEB+fn5dOzY8Q8/pvzuu+/yxRdf4HK5CAkJYcaMGWfddvXq1Tz++OOApxL/rbfeUvJcREQuO86jR0l77z2cqUexRUTgysjk1OezME2TsL/9rbzDExERERGR33FJVKBPmjSJ119/ndTUVJo3b867775LmzZtzrr97NmzefbZZ0lJSaF+/fqMGzeOHj16eNebpsnYsWP517/+RXp6OldddRXvvfce9evX/0PxXKw7la7MTA6PHo1h98UaGnrB45yPLYcy+PloFgBWWy4Ftl/A4mkVgtsfv8IGOAt9sBoGsdVDqFcpuHQDMl2eKvSMg+DI9iTOi/j4Q0g1CKsJNr+zj/FrGQc81e1nVKCHta5GWJtqFzf2czm+E2J6QYeHy+6YIiIictnJ+O47Tn02A3vduhin5yRxHjmCNTiYKq++gkXtXERERC46VaCLyMVU7pOIfvHFF4waNYqxY8eyceNGmjdvTrdu3Th27FiJ269evZoBAwZwzz33sGnTJvr06UOfPn2KTYb42muv8c477zBlyhTWrl1LYGAg3bp1KzYpY5kwzf81+i4DuQ4XhzPyMAGb1SA33467MBTcdnDbcTvDyC2w4mO14DJNDpzIw+n6bX/ti8qwQng01OoA1VpBVCxExkLVFlCrI0Q0OL/kOYBPABiGJzl/WrncBwop277258udm0thWlr5vDciIiICgDsrG8CbPAew+PvjzsvDzMsrr7BEREREROQPKvcE+ptvvsl9993HXXfdRePGjZkyZQoBAQF89NFHJW7/9ttv0717dx5//HEaNWrEiy++SMuWLZk4cSLgSaROmDCBZ555ht69e9OsWTOmT5/O4cOHmTNnThmeGWCxnE70lk0C81SOgzyHCwModJmYWHDnV8OVF40rLxp3QSRuE0zTjYGnV3p6rrNMYsOwQGAlCK3pqTgPrgLWC2xtYw/09FR3F54xvHGOHS4ydyFgeM7lEuU4eJDUF17gyDPPkjFvXnmHIyIicsWy16oJVgvu3FzA87dqYdpxfGrWwKKKOBERERGRS165JtAdDgeJiYkkJCR4l1ksFhISElizZk2J+6xZs6bY9gDdunXzbr93715SU1OLbRMaGkrbtm3POmZBQQGZmZnFvi4Gw2oFi1FmFcCFbjduEyyGgctddEwLpisI0xVE0bfb6TIBA7dpUugu5Qr00mDz9VStu85I/htlmEAvyPZMjBp26fbjztu8mYI9e3Dn5ZG9ZImq0EVERMqJf6tWBLZujTM1lfxdP1Pwyy5slSoR1qcPRln+/SIiIiIiIhekXCcRTUtLw+VyERkZWWx5ZGQkO3bsKHGf1NTUErdPTU31ri9adrZtfu2VV17h+eefv6BzOBfD3x/Dx45ZRq1jLBYDwwC328RiMcBdctLUejqpbxgGlsvyg5sBwZFw/GfABAOsfmX4o5xzHKq2hKDI39+2nNhr1sJWMQJ3VhZ+MY30AV1ERKScWOx2Kg4dSkDrRApSUrAGBxMQH49PlUu7FZyIiIiIiHiUawL9UvHkk08yatQo7+vMzExq1Pjz1cWGYWAND8O5/8CfHuuPCPXzwddmIdfhwsdqUFD4220MPAl0p8vE38dGiP8FtlEpb8FV4GQKpjMfTLCUVQLd5fBMhFo/oWyr3s+Tf9NYKj/xOK5T6fg1aVze4YiIiFzRLL6+BHboQGCHDuUdioiIiIiInKdybeESERGB1Wrl6NGjxZYfPXqUqKioEveJioo65/ZF/z2fMX19fQkJCSn2dbHYKlTAdJZNn/EQfxuVgnwBTw/0QLu12DfYYkCA3Uqhy8QAqob54e9jLZPYLjp7EARVAkcehsXA6l9GCfSsIxBaHaq3KZvj/Qm+tWsT0LIFFl/f8g5FRERERERERETkslSuCXS73U6rVq1YvHixd5nb7Wbx4sW0b9++xH3at29fbHuAhQsXerevXbs2UVFRxbbJzMxk7dq1Zx2zNFkrVMQsLKOJOjGoWzmIED8fTMDlNgnwtRHs5/ny97HicpuYQIVAO7UqBpZRXKUkPBoTOwbOsqlAd+RAYQHE9gN7QOkfT0RERERERERERMpVubdwGTVqFIMHDyY+Pp42bdowYcIEcnJyuOuuuwAYNGgQ1apV45VXXgFgxIgRdO7cmTfeeIOePXsya9YsNmzYwAcffAB42qaMHDmSl156ifr161O7dm2effZZqlatSp8+fcr8/GwREXgap5SN8AA7cTXCSD6UQUaeE6fL7Z1A0jAMrBaDCgF2mtUIJci33L/9f45fGG7/SIz8/dj8SnmSTNMN6fsh+mqod13pHktEREREREREREQuCeWeQe3fvz/Hjx9nzJgxpKamEhcXx4IFC7yTgO7fvx+L5X+F8h06dGDmzJk888wzPPXUU9SvX585c+YQGxvr3eaJJ54gJyeH+++/n/T0dDp27MiCBQvw8/Mr8/PzqVoVw2rFdDoxfMqm33ilYF/a161IakY+B07mkl/owgAC7DZqVgigcogvvrbLtHXLr7it4fhGObDk7Af/+mAphR9p04STezx91+PvAmu5XzYiIiIiIiIiIiJSBgyzqDxZvDIzMwkNDSUjI+NP90MvPHGCI08/jeHrhzU09CJF+Me5TZNClxsw8LEaGJfwxJcXouCXXwi5rhMVKvwEabugYj2wXsQbFUXJc98Q6PR3qNbq4o0tIiIiIiIiIhfdxczriIiUaw/0K4G1QgWs4eG4c3PL5fgWw8Bus2K3Wf5yyXPPvR8Te91G0PkfUCnGk0QvyLo4BygsgLSfwT8MOj6q5LmIiIiIiIiIiMgVRgn0UmYYBr4NG+LOySnvUP5yzLw8DD9/fKpXh7AacM0zUO9ayD4Kp1I8fcsvaGATso95Ks8jY6Hr01Cj9UWNXURERERERERERC59SqCXAb+YRmAYmIWF5R3KX4orPR2fyMrYa9XyLAiqDJ2egKseBf8KcHwnnNoHhfl/bEB3IWQehmM7wO2CFoPguhegcqPSOwkRERERERERERG5ZGk2xDLg27AB1tAQXJmZ2CpUKO9w/jLcubn4t2iJYT1jQlSLBeonQNU4SFkBuxZBegq4Cj2Tf9oDweYHhsVToe5ygiPHk2Q3rJ4kfMOeUKcTVKhTXqcmIiIiIiIiIiIilwAl0MuALTwc3wYNyE3cqAT6ReJ2ODBsNvwaxZS8QWAENLnJkww/shlO/PK/r8J8T5sWwwCLzdOmJaIBhNfy9Dn30wQjIiIiIiIiIiIiogR6mfFv1pzcdesxXa7iFdNyQVwnT2KtWBHfevXOvaHN7ulfXtTDvKji3F0IFivY/MHHr/QDFhERERERERERkcuOeqCXEf8WcVgrVMB18mR5h3LZM00Td1YWgVd1wOLvf347W33AP8xToe4fruS5iIiIiIiIiIiInJUS6GXEFh5OYLu2uNLTMU2zvMO5rLkzM7EEBRHYpk15hyIiIiIiIiIiIiJ/YUqgl6HAtm0xAvxx5+SUdyiXtcITJ/Br0gSfGjXKOxQRERERERERERH5C1MCvQzZ69XDr0FDCo8dK+9QLlvuvDwMi4WgjldhGEZ5hyMiIiIiIiIiIiJ/YUqglyHDMAjp3g3DZsOVnV3e4VyWnEeO4NsoBv+4uPIORURERERERERERP7ilEAvY37NmuEfF0dhaqp6oZ8nV3Y2hs1GaI8eGDZbeYcjIiIiIiIiIiIif3FKoJcxwzAI6XEDloAA3JmZ5R3OZcM0TQpTU/GPi8OvWbPyDkdERERERERERESuAEqglwPf+vUJaNeWwqNHMV2u8g7nsuA6eRJLUBChPXuo97mIiIiIiIiIiIiUCSXQy4FhGITddBM+1avhPHSovMO55LkdDlynThF83XX41q9f3uGIiIiIiIiIiIjIFUIJ9HJiq1iRsH79wHTjysoq73AuWaZp4jxwAN8GDQjt2aO8wxEREREREREREZEriBLo5SigXTsCr7qKwiNHMAsLyzucS5IrLQ1LYCDhA27DEhBQ3uGIiIiIiIiIiIjIFUQJ9HJkGAZht9yCvW4dHPv2YZpmeYd0SXFlZ+PKyiSkZw/8GjYs73BERERERERERETkCqMEejmzhYdT8e67sYaH4zx4sLzDuWS4HQ4KDx8m6OqrCe3Zs7zDERERERERERERkSuQEuiXAN+6dalwx+1gGBSmpZV3OOXOdLlw7tuHX2ws4XcMxLDZyjskERERERERERERuQIpgX6JCGjXjtC//Q1XZiau9PTyDqfcmG43jr178alenYr33I01KLC8QxIREREREREREZErlEp7LxGGYRDa+0bc+XlkfvcdANawsPINqoyZbjeOlL34VIki4sGh+FSpUt4hiYiIiIiIiIiIyBVMCfRLiGGxEH7LLeByk7lgAaZpYgsPL++wyoTpcnkqz6tEETF0KL516pR3SCIiIiIiIiIiInKFUwL9EmPYbIQPuA3Dx0bmvO9wOhzYKlfGMIzyDq3UuB0OnPv24VO9OhEPKnkuIiIiIiIiIiIilwb1QL8EGVYrYbfcQvidA8E0cezbh+l2l3dYpcKVnY0zJQW/Jo2p/OhIJc9FRERERERERETkkqEK9EuUYbEQcv31+ERGcuKTT3Ds2Y1PzVpY7PbyDu2iME0T14kTuDIyCOzSmQoDB2INCirvsERERERERERERES8VIF+ifNv3pzKo/6OX6NGOPftozAtDdM0yzusP8V0OHDs3YvpdBJ2yy1E3HuvkuciIiIiIiIiIiJyyVEC/TJgr16Nyo89Rtitt2C6CnHs2YPb4SjvsM6baZoUnjiBY98+fOvVo/KoRwnr0xvDpgchRERERERERERE5NKjzOVlwuLvT9hNN+HXuAnpX3xB/o4dWAICsEVGYlit5R3e73Ll5FB45AiWwEBC+95EaM+eWAICyjssERERERERERERkbMyzMu9H0gpyMzMJDQ0lIyMDEJCQso7nN9w5+WRvXw5mT/8l8LUI1hCw7BFRGBYLr0HCtx5eTiPHMGw2fBv1oyQnj3xa9igvMMSERERERERkb+oSz2vIyKXF1WgX4Ys/v6EdO9OYLt2ZC1dStbiJTh278YSFORJpPv4lGt8pmnizsqiMC0NwzDwa9SIkB434N+8+SWZ5BcREREREREREREpiRLolzFrWBhhN91E4FVXkbN6DTkrV+DYvx8sBraKEViCgzEMo8ziMZ1OCk+exJ2ZgSUwiICWLQjs2JGAli3V51xEREREREREREQuO2rhUoLL9VEfd14eeUlJZK9YScGuXbhzcsBqxRoaijUk5KInsU3TxMzLw5WRgTsnG8NqxVqhIoEd2hPYti0+tWqVaQJfRERERERERORyzeuIyKVJZcElKLqnkJmZWc6RXIAmTfBr3BjbkSPk79pF/tat5P7yC65jx8A0wWLBEhCAJSAAw27H8PH5Q0lu0+XCdDpx5+fjzs3FzM8HwLDbsUVE4NeuHX4NG+Bbty6WwEDygfysrFI+WRERERERERGR4oryOaoZFZGLQQn0EmSdTvzWqFGjnCMREREREREREZELkZWVRWhoaHmHISKXObVwKYHb7ebw4cMEl3EP8dKSmZlJjRo1OHDggB5dErkM6RoWubzpGha5vOkaFrm86Rq+MpmmSVZWFlWrVsVisZR3OCJymVMFegksFgvVq1cv7zAuupCQEP3BIHIZ0zUscnnTNSxyedM1LHJ50zV85VHluYhcLLoNJyIiIiIiIiIiIiJSAiXQRURERERERERERERKoAT6FcDX15exY8fi6+tb3qGIyAXQNSxyedM1LHJ50zUscnnTNSwiIn+WJhEVERERERERERERESmBKtBFREREREREREREREqgBLqIiIiIiIiIiIiISAmUQBcRERERERERERERKYES6CIiIiIiIiIiIiIiJVAC/S9i0qRJREdH4+fnR9u2bVm3bt05t589ezYxMTH4+fnRtGlT5s+fX0aRikhJzuca3rp1K/369SM6OhrDMJgwYULZBSoiJTqfa/hf//oXV199NeHh4YSHh5OQkPC7v7dFpHSdzzX89ddfEx8fT1hYGIGBgcTFxfHpp5+WYbQi8mvn+3m4yKxZszAMgz59+pRugCIicllTAv0v4IsvvmDUqFGMHTuWjRs30rx5c7p168axY8dK3H716tUMGDCAe+65h02bNtGnTx/69OnDli1byjhyEYHzv4Zzc3OpU6cOr776KlFRUWUcrYj82vlew8uWLWPAgAEsXbqUNWvWUKNGDa6//noOHTpUxpGLCJz/NVyhQgWefvpp1qxZw08//cRdd93FXXfdxQ8//FDGkYsInP81XCQlJYXHHnuMq6++uowiFRGRy5VhmqZZ3kHIn9O2bVtat27NxIkTAXC73dSoUYNHHnmEf/zjH7/Zvn///uTk5DBv3jzvsnbt2hEXF8eUKVPKLG4R8Tjfa/hM0dHRjBw5kpEjR5ZBpCJSkj9zDQO4XC7Cw8OZOHEigwYNKu1wReRX/uw1DNCyZUt69uzJiy++WJqhikgJLuQadrlcdOrUibvvvpsVK1aQnp7OnDlzyjBqERG5nKgC/TLncDhITEwkISHBu8xisZCQkMCaNWtK3GfNmjXFtgfo1q3bWbcXkdJzIdewiFw6LsY1nJubi9PppEKFCqUVpoicxZ+9hk3TZPHixezcuZNOnTqVZqgiUoILvYZfeOEFKleuzD333FMWYYqIyGXOVt4ByJ+TlpaGy+UiMjKy2PLIyEh27NhR4j6pqaklbp+amlpqcYpIyS7kGhaRS8fFuIZHjx5N1apVf3NzW0RK34VewxkZGVSrVo2CggKsViuTJ0/muuuuK+1wReRXLuQaXrlyJVOnTiUpKakMIhQRkb8CJdBFREREysmrr77KrFmzWLZsGX5+fuUdjoj8QcHBwSQlJZGdnc3ixYsZNWoUderUoUuXLuUdmoicQ1ZWFnfeeSf/+te/iIiIKO9wRETkMqEE+mUuIiICq9XK0aNHiy0/evToWScXjIqKOq/tRaT0XMg1LCKXjj9zDY8fP55XX32VRYsW0axZs9IMU0TO4kKvYYvFQr169QCIi4tj+/btvPLKK0qgi5Sx872Gd+/eTUpKCn/729+8y9xuNwA2m42dO3dSt27d0g1aREQuO+qBfpmz2+20atWKxYsXe5e53W4WL15M+/btS9ynffv2xbYHWLhw4Vm3F5HScyHXsIhcOi70Gn7ttdd48cUXWbBgAfHx8WURqoiU4GL9Hna73RQUFJRGiCJyDud7DcfExJCcnExSUpL368Ybb6Rr164kJSVRo0aNsgxfREQuE6pA/wsYNWoUgwcPJj4+njZt2jBhwgRycnK46667ABg0aBDVqlXjlVdeAWDEiBF07tyZN954g549ezJr1iw2bNjABx98UJ6nIXLFOt9r2OFwsG3bNu+/Dx06RFJSEkFBQd5qOBEpO+d7DY8bN44xY8Ywc+ZMoqOjvXOQBAUFERQUVG7nIXKlOt9r+JVXXiE+Pp66detSUFDA/Pnz+fTTT3nvvffK8zRErljncw37+fkRGxtbbP+wsDCA3ywXEREpogT6X0D//v05fvw4Y8aMITU1lbi4OBYsWOCdSGX//v1YLP972KBDhw7MnDmTZ555hqeeeor69eszZ84c/cEgUk7O9xo+fPgwLVq08L4eP34848ePp3Pnzixbtqyswxe54p3vNfzee+/hcDi4+eabi40zduxYnnvuubIMXUQ4/2s4JyeHYcOGcfDgQfz9/YmJieGzzz6jf//+5XUKIle0872GRUREzpdhmqZZ3kGIiIiIiIiIiIiIiFxqdBtWRERERERERERERKQESqCLiIiIiIiIiIiIiJRACXQRERERERERERERkRIogS4iIiIiIiIiIiIiUgIl0EVERERERERERERESqAEuoiIiIiIiIiIiIhICZRAFxEREREREREREREpgRLoIiIiIiIiIiIiIiIlUAJdRERE5AqTkpKCYRgkJSUBsGzZMgzDID09vVzj+rOio6OZMGFCeYchIiIiIiJ/IUqgi4iIyBVpyJAhGIaBYRj4+PgQGRnJddddx0cffYTb7S5xn27dumG1Wlm/fn2J6zdt2sQtt9xCZGQkfn5+1K9fn/vuu4+ff/75rHF06dLFG8eZX4WFhRflPP+IDh06cOTIEUJDQ0v1OEWJ+qKvSpUq0aNHD5KTk89rnGnTphEWFvab5evXr+f++++/SNGKiIiIiIgogS4iIiJXsO7du3PkyBFSUlL4/vvv6dq1KyNGjKBXr16/SWDv37+f1atX8/DDD/PRRx/9Zqx58+bRrl07CgoKmDFjBtu3b+ezzz4jNDSUZ5999pxx3HfffRw5cqTYl81mu6jnei52u52oqCgMwyiT4+3cuZMjR47www8/UFBQQM+ePXE4HH963EqVKhEQEHARIhQREREREfFQAl1ERESuWL6+vkRFRVGtWjVatmzJU089xTfffMP333/PtGnTim378ccf06tXLx588EE+//xz8vLyvOtyc3O566676NGjB99++y0JCQnUrl2btm3bMn78eN5///1zxhEQEEBUVFSxL/BUp48cObLYtn369GHIkCHe19HR0bz88svcfffdBAcHU7NmTT744INi+6xbt44WLVrg5+dHfHw8mzZtKrb+1y1ciiq8f/jhBxo1akRQUJD3ZkORwsJChg8fTlhYGBUrVmT06NEMHjyYPn36nPNcASpXrkxUVBQtW7Zk5MiRHDhwgB07dnjXv/nmmzRt2pTAwEBq1KjBsGHDyM7O9sZ61113kZGR4a1kf+6557zvxZktXPbv30/v3r0JCgoiJCSEW2+9laNHj/5ufCIiIiIiIkWUQBcRERE5wzXXXEPz5s35+uuvvctM0+Tjjz9m4MCBxMTEUK9ePb788kvv+h9++IG0tDSeeOKJEscsqd3IxfTGG294E+PDhg3jwQcfZOfOnQBkZ2fTq1cvGjduTGJiIs899xyPPfbY746Zm5vL+PHj+fTTT/m///s/9u/fX2y/cePGMWPGDD7++GNWrVpFZmYmc+bMOa+4MzIymDVrFuCpgi9isVh455132Lp1K5988glLlizxvrcdOnRgwoQJhISEeKv1Szoft9tN7969OXnyJMuXL2fhwoXs2bOH/v37n1eMIiIiIiJyZVMCXURERORXYmJiSElJ8b5etGgRubm5dOvWDYCBAwcydepU7/pdu3Z597sQkydPJigoyPv197///bz279GjB8OGDaNevXqMHj2aiIgIli5dCsDMmTNxu91MnTqVJk2a0KtXLx5//PHfHdPpdDJlyhTi4+Np2bIlDz/8MIsXL/auf/fdd3nyySe56aabiImJYeLEiX/4RkH16tUJCgoiLCyMmTNncuONNxZ770aOHEnXrl2Jjo7mmmuu4aWXXuLf//434Em0h4aGYhiGt1o/KCjoN8dYvHgxycnJzJw5k1atWtG2bVumT5/O8uXLz9rDXkRERERE5NeUQBcRERH5FdM0i/UD/+ijj+jfv7+3L/mAAQNYtWoVu3fv9m7/Z9xxxx0kJSV5v5588snz2r9Zs2befxcllo8dOwbA9u3badasGX5+ft5t2rdv/7tjBgQEULduXe/rKlWqeMfMyMjg6NGjtGnTxrvearXSqlWrPxTvihUrSExMZNq0aTRo0IApU6YUW79o0SKuvfZaqlWrRnBwMHfeeScnTpwgNzf3D40PnvOuUaMGNWrU8C5r3LgxYWFhbN++/Q+PIyIiIiIiVzYl0EVERER+Zfv27dSuXRuAkydP8p///IfJkydjs9mw2WxUq1aNwsJC72SiDRo0ACjWx/t8hIaGUq9ePe9XREQE4Gll8uvkvNPp/M3+Pj4+xV4bhoHb7b6gWM415p+9UVCkdu3aNGzYkMGDB3PvvfcWa6uSkpJCr169aNasGV999RWJiYlMmjQJ4KJMNCoiIiIiInI+lEAXEREROcOSJUtITk6mX79+AMyYMYPq1auzefPmYlXib7zxBtOmTcPlcnH99dcTERHBa6+9VuKYRZNznq9KlSoVm7jT5XKxZcuW8xqjUaNG/PTTT+Tn53uX/fjjjxcUT5HQ0FAiIyOLtUJxuVxs3LjxvMd66KGH2LJlC//5z38ASExMxO1288Ybb9CuXTsaNGjA4cOHi+1jt9txuVznHLdRo0YcOHCAAwcOeJdt27aN9PR0GjdufN5xioiIiIjIlUkJdBEREbliFRQUkJqayqFDh9i4cSMvv/wyvXv3plevXgwaNAiAqVOncvPNNxMbG1vs65577iEtLY0FCxYQGBjIhx9+yHfffceNN97IokWLSElJYcOGDTzxxBMMHTr0guK75ppr+O677/juu+/YsWMHDz744Hkn42+//XYMw+C+++5j27ZtzJ8/n/Hjx19QPGd65JFHeOWVV/jmm2/YuXMnI0aM4NSpU8Va3/wRAQEB3HfffYwdOxbTNKlXrx5Op5N3332XPXv28Omnn/6mxUt0dDTZ2dksXryYtLS0Elu7JCQk0LRpU+644w42btzIunXrGDRoEJ07dyY+Pv5PnbuIiIiIiFw5lEAXERGRK9aCBQuoUqUK0dHRdO/enaVLl/LOO+/wzTffYLVaSUxMZPPmzd5q9DOFhoZy7bXXeicT7d27N6tXr8bHx4fbb7+dmJgYBgwYQEZGBi+99NIFxXf33XczePBgb+K3Tp06dO3a9bzGCAoKYu7cuSQnJ9OiRQuefvppxo0bd0HxnGn06NEMGDCAQYMG0b59e4KCgujWrVuxXut/1MMPP8z27duZPXs2zZs3580332TcuHHExsYyY8YMXnnllWLbd+jQgaFDh9K/f38qVapUYuW/YRh88803hIeH06lTJxISEqhTpw5ffPHFBZ+ziIiIiIhceQzzYjWzFBEREZErltvtplGjRtx66628+OKL5R2OiIiIiIjIRWEr7wBERERE5PKzb98+/vvf/9K5c2cKCgqYOHEie/fu5fbbby/v0ERERERERC4atXARERERkfNmsViYNm0arVu35qqrriI5OZlFixbRqFGj8g5NRERERETkolELFxERERERERERERGREqgCXURERERERERERESkBEqgi4iIiIiIiIiIiIiUQAl0EREREREREREREZESKIEuIiIiIiIiIiIiIlICJdBFREREREREREREREqgBLqIiIiIiIiIiIiISAmUQBcRERERERERERERKYES6CIiIiIiIiIiIiIiJfh/V1ltX+ifvlMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's first look at the raw total investment amounts\n",
+    "print(\"Raw Investment Totals by Program:\")\n",
+    "print(clean_data[('Total Project Cost', 'sum')].sort_values(ascending=False).head(10))\n",
+    "\n",
+    "# Modified analysis with corrected calculations\n",
+    "def create_program_typology_v2(df):\n",
+    "    # Calculate thresholds\n",
+    "    ghg_med = df['GHG_per_dollar'].median()\n",
+    "    dac_med = df['DAC_funding_ratio'].median()\n",
+    "    size_med = df[('Total Project Cost', 'sum')].median()\n",
+    "    \n",
+    "    # Create categories with corrected investment calculations\n",
+    "    conditions = [\n",
+    "        (df['GHG_per_dollar'] > ghg_med) & (df['DAC_funding_ratio'] > dac_med),\n",
+    "        (df['GHG_per_dollar'] > ghg_med) & (df['DAC_funding_ratio'] <= dac_med),\n",
+    "        (df['GHG_per_dollar'] <= ghg_med) & (df['DAC_funding_ratio'] > dac_med),\n",
+    "        (df['GHG_per_dollar'] <= ghg_med) & (df['DAC_funding_ratio'] <= dac_med)\n",
+    "    ]\n",
+    "    \n",
+    "    choices = [\n",
+    "        'High Performer (Both)',\n",
+    "        'GHG Efficient',\n",
+    "        'Equity Focused',\n",
+    "        'Below Median'\n",
+    "    ]\n",
+    "    \n",
+    "    df['Performance_Type'] = np.select(conditions, choices, default='Other')\n",
+    "    df['Scale_Type'] = np.where(df[('Total Project Cost', 'sum')] > size_med, \n",
+    "                               'Large Scale', \n",
+    "                               'Small Scale')\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# Rerun analysis with corrected calculations\n",
+    "analysis_data_v2 = clean_data.copy()\n",
+    "analysis_data_v2 = create_program_typology_v2(analysis_data_v2)\n",
+    "\n",
+    "# Summary statistics with corrected investment amounts\n",
+    "print(\"\\nRevised Program Type Characteristics:\")\n",
+    "for ptype in analysis_data_v2['Performance_Type'].unique():\n",
+    "    subset = analysis_data_v2[analysis_data_v2['Performance_Type'] == ptype]\n",
+    "    print(f\"\\n{ptype}:\")\n",
+    "    print(f\"Number of programs: {len(subset)}\")\n",
+    "    print(f\"Average GHG efficiency: {subset['GHG_per_dollar'].mean():.4f}\")\n",
+    "    print(f\"Average DAC ratio: {subset['DAC_funding_ratio'].mean():.4f}\")\n",
+    "    print(f\"Average project cost: ${subset[('Total Project Cost', 'mean')].mean():,.2f}\")\n",
+    "    print(f\"Largest program: ${subset[('Total Project Cost', 'sum')].max():,.2f}\")\n",
+    "    print(\"\\nExample programs:\")\n",
+    "    print(subset.index.tolist()[:3])\n",
+    "\n",
+    "# Create visualization with corrected data\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# Plot each program type with different colors and corrected sizes\n",
+    "for ptype in analysis_data_v2['Performance_Type'].unique():\n",
+    "    mask = analysis_data_v2['Performance_Type'] == ptype\n",
+    "    subset = analysis_data_v2[mask]\n",
+    "    \n",
+    "    plt.scatter(subset['DAC_funding_ratio'],\n",
+    "               subset['GHG_per_dollar'],\n",
+    "               s=subset[('Total Project Cost', 'mean')]/1e5,  # Adjusted scaling factor\n",
+    "               alpha=0.6,\n",
+    "               label=ptype)\n",
+    "\n",
+    "plt.xlabel('DAC Funding Ratio')\n",
+    "plt.ylabel('GHG Reduction per Dollar')\n",
+    "plt.title('Program Performance Types (Corrected Investment Scaling)')\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "\n",
+    "# Add annotations for major programs\n",
+    "for idx, row in analysis_data_v2.iterrows():\n",
+    "    if row[('Total Project Cost', 'mean')] > 1e6:  # Adjust threshold as needed\n",
+    "        plt.annotate(idx[:30] + '...' if len(idx) > 30 else idx,\n",
+    "                    (row['DAC_funding_ratio'], row['GHG_per_dollar']),\n",
+    "                    xytext=(5, 5),\n",
+    "                    textcoords='offset points',\n",
+    "                    fontsize=8)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1500x1200 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's create a summary visualization that highlights our main findings\n",
+    "plt.figure(figsize=(15, 12))\n",
+    "\n",
+    "# Create four subplots\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(20, 16))\n",
+    "\n",
+    "# 1. Program Scale Distribution (Top 10 by Investment)\n",
+    "top_10_programs = clean_data[('Total Project Cost', 'sum')].nlargest(10)\n",
+    "ax1.barh(range(len(top_10_programs)), top_10_programs/1e9)  # Convert to billions\n",
+    "ax1.set_yticks(range(len(top_10_programs)))\n",
+    "ax1.set_yticklabels([name[:30] + '...' if len(name) > 30 else name for name in top_10_programs.index])\n",
+    "ax1.set_xlabel('Total Investment (Billions $)')\n",
+    "ax1.set_title('Top 10 Programs by Investment')\n",
+    "\n",
+    "# 2. GHG Efficiency vs Project Size\n",
+    "ax2.scatter(clean_data[('Total Project Cost', 'mean')]/1e6, \n",
+    "           clean_data['GHG_per_dollar'],\n",
+    "           alpha=0.6)\n",
+    "ax2.set_xlabel('Average Project Size (Millions $)')\n",
+    "ax2.set_ylabel('GHG Reduction per Dollar')\n",
+    "ax2.set_title('GHG Efficiency vs Project Size')\n",
+    "\n",
+    "# 3. DAC Funding Ratio vs Program Size\n",
+    "ax3.scatter(clean_data[('Total Project Cost', 'sum')]/1e9,\n",
+    "           clean_data['DAC_funding_ratio'],\n",
+    "           alpha=0.6)\n",
+    "ax3.set_xlabel('Total Program Investment (Billions $)')\n",
+    "ax3.set_ylabel('DAC Funding Ratio')\n",
+    "ax3.set_title('Equity vs Program Size')\n",
+    "\n",
+    "# 4. Project Count vs Average Size\n",
+    "ax4.scatter(clean_data[('Total Project Cost', 'count')],\n",
+    "           clean_data[('Total Project Cost', 'mean')]/1e6,\n",
+    "           alpha=0.6)\n",
+    "ax4.set_xlabel('Number of Projects')\n",
+    "ax4.set_ylabel('Average Project Size (Millions $)')\n",
+    "ax4.set_title('Project Count vs Average Size')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: seaborn in ./.venv/lib/python3.12/site-packages (0.13.2)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in ./.venv/lib/python3.12/site-packages (from seaborn) (2.1.2)\n",
+      "Requirement already satisfied: pandas>=1.2 in ./.venv/lib/python3.12/site-packages (from seaborn) (2.2.3)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in ./.venv/lib/python3.12/site-packages (from seaborn) (3.9.2)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.54.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
+      "Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
+      "Requirement already satisfied: pillow>=8 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.0.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in ./.venv/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in ./.venv/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
+      "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# install seaborn library\n",
+    "!pip install seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Program Scale Categories:\n",
+      "\n",
+      "Investment Scale Categories (in Millions $):\n",
+      "Small Programs: < $20.0M\n",
+      "Medium Programs: $20.0M - $97.0M\n",
+      "Large Programs: $97.0M - $587.9M\n",
+      "Mega Programs: > $587.9M\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set up the figure with a clean, modern style\n",
+    "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
+    "plt.rcParams['axes.linewidth'] = 1.0\n",
+    "plt.rcParams['axes.grid'] = True\n",
+    "plt.rcParams['grid.alpha'] = 0.3\n",
+    "\n",
+    "fig = plt.figure(figsize=(20, 15))\n",
+    "\n",
+    "# 1. Project Volume vs Investment (Enhanced)\n",
+    "ax1 = plt.subplot(2, 2, 1)\n",
+    "scatter = plt.scatter(clean_data[('Total Project Cost', 'count')],\n",
+    "                     clean_data[('Total Project Cost', 'mean')]/1e6,\n",
+    "                     s=clean_data[('Total Project Cost', 'sum')]/1e8,\n",
+    "                     alpha=0.7,\n",
+    "                     c=range(len(clean_data)),\n",
+    "                     cmap='viridis')\n",
+    "ax1.set_xlabel('Number of Projects', fontsize=12, fontweight='bold')\n",
+    "ax1.set_ylabel('Average Project Size (Millions $)', fontsize=12, fontweight='bold')\n",
+    "ax1.set_title('Program Scale Analysis\\nBubble size represents total investment', \n",
+    "              fontsize=14, pad=20, fontweight='bold')\n",
+    "\n",
+    "# Add annotations for notable programs\n",
+    "for idx, row in clean_data.iterrows():\n",
+    "    if row[('Total Project Cost', 'sum')] > 5e9:\n",
+    "        plt.annotate(idx[:30],\n",
+    "                    (row[('Total Project Cost', 'count')], \n",
+    "                     row[('Total Project Cost', 'mean')]/1e6),\n",
+    "                    xytext=(5, 5), textcoords='offset points',\n",
+    "                    fontsize=8,\n",
+    "                    bbox=dict(facecolor='white', edgecolor='none', alpha=0.8))\n",
+    "\n",
+    "# 2. Program Size Distribution (Enhanced)\n",
+    "ax2 = plt.subplot(2, 2, 2)\n",
+    "sizes = clean_data[('Total Project Cost', 'sum')]/1e6\n",
+    "plt.hist(np.log10(sizes[sizes > 0]), \n",
+    "         bins=20, \n",
+    "         color='dodgerblue',\n",
+    "         edgecolor='white',\n",
+    "         alpha=0.7)\n",
+    "ax2.set_xlabel('Log10(Total Program Size) in Millions $', fontsize=12, fontweight='bold')\n",
+    "ax2.set_ylabel('Count', fontsize=12, fontweight='bold')\n",
+    "ax2.set_title('Distribution of Program Sizes\\nNatural groupings by investment scale', \n",
+    "              fontsize=14, pad=20, fontweight='bold')\n",
+    "\n",
+    "# 3. Top 10 Programs (Enhanced)\n",
+    "ax3 = plt.subplot(2, 2, 3)\n",
+    "top_10 = clean_data[('Total Project Cost', 'sum')].nlargest(10)/1e9\n",
+    "colors = plt.cm.viridis(np.linspace(0, 0.8, len(top_10)))\n",
+    "bars = ax3.barh(range(len(top_10)), top_10, color=colors)\n",
+    "ax3.set_yticks(range(len(top_10)))\n",
+    "ax3.set_yticklabels([name[:40] + '...' if len(name) > 40 else name \n",
+    "                     for name in top_10.index],\n",
+    "                    fontsize=8)\n",
+    "ax3.set_xlabel('Total Investment (Billions $)', fontsize=12, fontweight='bold')\n",
+    "ax3.set_title('Top 10 Programs by Investment\\nHighlighting investment concentration', \n",
+    "              fontsize=14, pad=20, fontweight='bold')\n",
+    "\n",
+    "# Add value labels to bars\n",
+    "for i, bar in enumerate(bars):\n",
+    "    width = bar.get_width()\n",
+    "    ax3.text(width, bar.get_y() + bar.get_height()/2,\n",
+    "             f'${width:.1f}B',\n",
+    "             ha='left', va='center', fontsize=8,\n",
+    "             bbox=dict(facecolor='white', edgecolor='none', alpha=0.8))\n",
+    "\n",
+    "# 4. Geographic Distribution (Enhanced)\n",
+    "ax4 = plt.subplot(2, 2, 4)\n",
+    "top_15_counties = county_analysis['total_investment'].nlargest(15)/1e6\n",
+    "colors = plt.cm.viridis(np.linspace(0, 0.8, len(top_15_counties)))\n",
+    "bars = ax4.barh(range(len(top_15_counties)), top_15_counties, color=colors)\n",
+    "ax4.set_yticks(range(len(top_15_counties)))\n",
+    "ax4.set_yticklabels(top_15_counties.index, fontsize=8)\n",
+    "ax4.set_xlabel('Total Investment (Millions $)', fontsize=12, fontweight='bold')\n",
+    "ax4.set_title('Investment by County\\nTop 15 counties by total investment', \n",
+    "              fontsize=14, pad=20, fontweight='bold')\n",
+    "\n",
+    "# Add value labels\n",
+    "for i, bar in enumerate(bars):\n",
+    "    width = bar.get_width()\n",
+    "    ax4.text(width, bar.get_y() + bar.get_height()/2,\n",
+    "             f'${width:.0f}M',\n",
+    "             ha='left', va='center', fontsize=8,\n",
+    "             bbox=dict(facecolor='white', edgecolor='none', alpha=0.8))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print summary statistics for context\n",
+    "print(\"\\nProgram Scale Categories:\")\n",
+    "quartiles = np.percentile(sizes[sizes > 0], [25, 50, 75])\n",
+    "print(\"\\nInvestment Scale Categories (in Millions $):\")\n",
+    "print(f\"Small Programs: < ${quartiles[0]:.1f}M\")\n",
+    "print(f\"Medium Programs: ${quartiles[0]:.1f}M - ${quartiles[1]:.1f}M\")\n",
+    "print(f\"Large Programs: ${quartiles[1]:.1f}M - ${quartiles[2]:.1f}M\")\n",
+    "print(f\"Mega Programs: > ${quartiles[2]:.1f}M\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Spatial Distribution Analysis:\n",
+      "\n",
+      "1. Investment Concentration:\n",
+      "Top 5 counties account for 75.6% of total investment\n",
+      "\n",
+      "2. Project Distribution:\n",
+      "Top 5 counties account for 51.1% of all projects\n",
+      "\n",
+      "3. Regional Equity Analysis:\n",
+      "\n",
+      "DAC Benefit Rate by Region:\n",
+      "count    299.000\n",
+      "mean       0.149\n",
+      "std        0.297\n",
+      "min        0.000\n",
+      "25%        0.000\n",
+      "50%        0.000\n",
+      "75%        0.125\n",
+      "max        1.000\n",
+      "Name: dac_rate, dtype: float64\n",
+      "\n",
+      "4. Project Type Distribution:\n",
+      "\n",
+      "Los Angeles Program Distribution:\n",
+      "Program Name\n",
+      "Low Carbon Transportation            32598\n",
+      "Low-Income Weatherization Program     2233\n",
+      "Water-Energy Efficiency               1945\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Alameda Program Distribution:\n",
+      "Program Name\n",
+      "Low Carbon Transportation    5677\n",
+      "Water-Energy Efficiency       244\n",
+      "Community Air Protection      223\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Madera, Merced, Sacramento, San Joaquin, Santa Clara, Stanislaus, Tulare Program Distribution:\n",
+      "Program Name\n",
+      "Transit and Intercity Rail Capital Program    9\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Sacramento Program Distribution:\n",
+      "Program Name\n",
+      "Low Carbon Transportation            3556\n",
+      "Water-Energy Efficiency               422\n",
+      "Low-Income Weatherization Program     297\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "San Diego Program Distribution:\n",
+      "Program Name\n",
+      "Low Carbon Transportation            7865\n",
+      "Community Air Protection              105\n",
+      "Low-Income Weatherization Program      83\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, let's prepare our county-level metrics\n",
+    "county_analysis = data.groupby('County').agg({\n",
+    "    'Total Project Cost': ['count', 'sum', 'mean'],\n",
+    "    'Total Project GHGReductions': 'sum',\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean',\n",
+    "    'Is Low Income Communities': 'mean'\n",
+    "})\n",
+    "\n",
+    "# Flatten column names\n",
+    "county_analysis.columns = [\n",
+    "    'project_count', 'total_investment', 'avg_project_size',\n",
+    "    'total_ghg_reduction', 'dac_rate', 'low_income_rate'\n",
+    "]\n",
+    "\n",
+    "# Calculate per-project metrics\n",
+    "county_analysis['ghg_per_dollar'] = county_analysis['total_ghg_reduction'] / county_analysis['total_investment']\n",
+    "county_analysis['investment_per_project'] = county_analysis['total_investment'] / county_analysis['project_count']\n",
+    "\n",
+    "# Create visualization of spatial patterns\n",
+    "fig = plt.figure(figsize=(20, 15))\n",
+    "\n",
+    "# 1. Investment Distribution\n",
+    "plt.subplot(2, 2, 1)\n",
+    "top_15 = county_analysis['total_investment'].sort_values(ascending=True).tail(15)/1e6\n",
+    "plt.barh(range(len(top_15)), top_15, color='dodgerblue', alpha=0.7)\n",
+    "plt.yticks(range(len(top_15)), top_15.index)\n",
+    "plt.xlabel('Total Investment (Millions $)')\n",
+    "plt.title('Top 15 Counties by Total Investment', fontsize=12, pad=15)\n",
+    "\n",
+    "# 2. Project Count Distribution\n",
+    "plt.subplot(2, 2, 2)\n",
+    "top_15_count = county_analysis['project_count'].sort_values(ascending=True).tail(15)\n",
+    "plt.barh(range(len(top_15_count)), top_15_count, color='dodgerblue', alpha=0.7)\n",
+    "plt.yticks(range(len(top_15_count)), top_15_count.index)\n",
+    "plt.xlabel('Number of Projects')\n",
+    "plt.title('Top 15 Counties by Project Count', fontsize=12, pad=15)\n",
+    "\n",
+    "# 3. DAC Benefit Rate\n",
+    "plt.subplot(2, 2, 3)\n",
+    "top_15_dac = county_analysis['dac_rate'].sort_values(ascending=True).tail(15)\n",
+    "plt.barh(range(len(top_15_dac)), top_15_dac*100, color='dodgerblue', alpha=0.7)\n",
+    "plt.yticks(range(len(top_15_dac)), top_15_dac.index)\n",
+    "plt.xlabel('Percentage of Projects Benefiting DACs')\n",
+    "plt.title('Top 15 Counties by DAC Benefit Rate', fontsize=12, pad=15)\n",
+    "\n",
+    "# 4. GHG Efficiency\n",
+    "plt.subplot(2, 2, 4)\n",
+    "top_15_ghg = county_analysis['ghg_per_dollar'].sort_values(ascending=True).tail(15)\n",
+    "plt.barh(range(len(top_15_ghg)), top_15_ghg, color='dodgerblue', alpha=0.7)\n",
+    "plt.yticks(range(len(top_15_ghg)), top_15_ghg.index)\n",
+    "plt.xlabel('GHG Reduction per Dollar')\n",
+    "plt.title('Top 15 Counties by GHG Efficiency', fontsize=12, pad=15)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print detailed analysis\n",
+    "print(\"\\nSpatial Distribution Analysis:\")\n",
+    "print(\"\\n1. Investment Concentration:\")\n",
+    "investment_share = (county_analysis['total_investment'].nlargest(5).sum() / \n",
+    "                   county_analysis['total_investment'].sum() * 100)\n",
+    "print(f\"Top 5 counties account for {investment_share:.1f}% of total investment\")\n",
+    "\n",
+    "print(\"\\n2. Project Distribution:\")\n",
+    "project_share = (county_analysis['project_count'].nlargest(5).sum() / \n",
+    "                county_analysis['project_count'].sum() * 100)\n",
+    "print(f\"Top 5 counties account for {project_share:.1f}% of all projects\")\n",
+    "\n",
+    "print(\"\\n3. Regional Equity Analysis:\")\n",
+    "print(\"\\nDAC Benefit Rate by Region:\")\n",
+    "print(county_analysis['dac_rate'].describe().round(3))\n",
+    "\n",
+    "print(\"\\n4. Project Type Distribution:\")\n",
+    "# Look at program types in top 5 counties\n",
+    "top_5_counties = county_analysis['total_investment'].nlargest(5).index\n",
+    "for county in top_5_counties:\n",
+    "    print(f\"\\n{county} Program Distribution:\")\n",
+    "    county_programs = data[data['County'] == county]['Program Name'].value_counts().head(3)\n",
+    "    print(county_programs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Multi-County Collaboration Analysis:\n",
+      "\n",
+      "Total number of multi-county projects: 874\n",
+      "Total investment in multi-county projects: $18.31B\n",
+      "\n",
+      "Major Multi-County Collaborations:\n",
+      "                                                    project_count  \\\n",
+      "County                                                              \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...              9   \n",
+      "San Francisco, Santa Clara                                      6   \n",
+      "San Diego, Santa Barbara, Ventura                              23   \n",
+      "San Francisco, San Mateo, Santa Clara                           3   \n",
+      "Los Angeles, Ventura                                            3   \n",
+      "Marin, Sonoma                                                  15   \n",
+      "Los Angeles, San Bernardino                                    29   \n",
+      "Humboldt, Mendocino                                             5   \n",
+      "Contra Costa, San Francisco                                     3   \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...              2   \n",
+      "\n",
+      "                                                    total_investment  \\\n",
+      "County                                                                 \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...        7333200000   \n",
+      "San Francisco, Santa Clara                                2998442533   \n",
+      "San Diego, Santa Barbara, Ventura                         2235315130   \n",
+      "San Francisco, San Mateo, Santa Clara                     2145553263   \n",
+      "Los Angeles, Ventura                                       877252312   \n",
+      "Marin, Sonoma                                              363189831   \n",
+      "Los Angeles, San Bernardino                                177831134   \n",
+      "Humboldt, Mendocino                                        155426149   \n",
+      "Contra Costa, San Francisco                                135489931   \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...         130936629   \n",
+      "\n",
+      "                                                    avg_project_size  \\\n",
+      "County                                                                 \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...      8.148000e+08   \n",
+      "San Francisco, Santa Clara                              4.997404e+08   \n",
+      "San Diego, Santa Barbara, Ventura                       9.718761e+07   \n",
+      "San Francisco, San Mateo, Santa Clara                   7.151844e+08   \n",
+      "Los Angeles, Ventura                                    2.924174e+08   \n",
+      "Marin, Sonoma                                           2.421266e+07   \n",
+      "Los Angeles, San Bernardino                             6.132108e+06   \n",
+      "Humboldt, Mendocino                                     3.108523e+07   \n",
+      "Contra Costa, San Francisco                             4.516331e+07   \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...      6.546831e+07   \n",
+      "\n",
+      "                                                    total_ghg_reduction  \\\n",
+      "County                                                                    \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...               546406   \n",
+      "San Francisco, Santa Clara                                      1538067   \n",
+      "San Diego, Santa Barbara, Ventura                                791432   \n",
+      "San Francisco, San Mateo, Santa Clara                           2100845   \n",
+      "Los Angeles, Ventura                                              74882   \n",
+      "Marin, Sonoma                                                    234508   \n",
+      "Los Angeles, San Bernardino                                       22220   \n",
+      "Humboldt, Mendocino                                               24160   \n",
+      "Contra Costa, San Francisco                                       60369   \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...               104667   \n",
+      "\n",
+      "                                                    dac_benefit_rate  \\\n",
+      "County                                                                 \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...              0.00   \n",
+      "San Francisco, Santa Clara                                      0.17   \n",
+      "San Diego, Santa Barbara, Ventura                               0.00   \n",
+      "San Francisco, San Mateo, Santa Clara                           0.33   \n",
+      "Los Angeles, Ventura                                            0.00   \n",
+      "Marin, Sonoma                                                   0.00   \n",
+      "Los Angeles, San Bernardino                                     0.48   \n",
+      "Humboldt, Mendocino                                             0.00   \n",
+      "Contra Costa, San Francisco                                     0.33   \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...              0.00   \n",
+      "\n",
+      "                                                    dac_funding  \\\n",
+      "County                                                            \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...          0.0   \n",
+      "San Francisco, Santa Clara                           20000000.0   \n",
+      "San Diego, Santa Barbara, Ventura                           0.0   \n",
+      "San Francisco, San Mateo, Santa Clara                  935322.0   \n",
+      "Los Angeles, Ventura                                        0.0   \n",
+      "Marin, Sonoma                                               0.0   \n",
+      "Los Angeles, San Bernardino                          10812422.0   \n",
+      "Humboldt, Mendocino                                         0.0   \n",
+      "Contra Costa, San Francisco                            631879.0   \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...          0.0   \n",
+      "\n",
+      "                                                    ghg_per_dollar  \\\n",
+      "County                                                               \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...        0.000075   \n",
+      "San Francisco, Santa Clara                                0.000513   \n",
+      "San Diego, Santa Barbara, Ventura                         0.000354   \n",
+      "San Francisco, San Mateo, Santa Clara                     0.000979   \n",
+      "Los Angeles, Ventura                                      0.000085   \n",
+      "Marin, Sonoma                                             0.000646   \n",
+      "Los Angeles, San Bernardino                               0.000125   \n",
+      "Humboldt, Mendocino                                       0.000155   \n",
+      "Contra Costa, San Francisco                               0.000446   \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...        0.000799   \n",
+      "\n",
+      "                                                    dac_funding_ratio  \n",
+      "County                                                                 \n",
+      "Alameda, Contra Costa, Fresno, Kern, Kings, Mad...           0.000000  \n",
+      "San Francisco, Santa Clara                                   0.006670  \n",
+      "San Diego, Santa Barbara, Ventura                            0.000000  \n",
+      "San Francisco, San Mateo, Santa Clara                        0.000436  \n",
+      "Los Angeles, Ventura                                         0.000000  \n",
+      "Marin, Sonoma                                                0.000000  \n",
+      "Los Angeles, San Bernardino                                  0.060802  \n",
+      "Humboldt, Mendocino                                          0.000000  \n",
+      "Contra Costa, San Francisco                                  0.004664  \n",
+      "Los Angeles, San Diego, San Luis Obispo, Santa ...           0.000000  \n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2000x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Most Common Types of Multi-County Projects:\n",
+      "Program Name\n",
+      "Urban and Community Forestry Program          333\n",
+      "Low Carbon Transit Operations Program         189\n",
+      "Transit and Intercity Rail Capital Program     87\n",
+      "Safe and Affordable Drinking Water Fund        47\n",
+      "Climate Smart Agriculture                      43\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Success Metrics for Multi-County Projects:\n",
+      "\n",
+      "DAC Benefit Rate Distribution:\n",
+      "count       874\n",
+      "unique        2\n",
+      "top       False\n",
+      "freq        756\n",
+      "Name: Is Benefit Disadvantaged Communities, dtype: object\n",
+      "\n",
+      "GHG Reduction Distribution (tons per $):\n",
+      "count    578.000000\n",
+      "mean       0.003167\n",
+      "std        0.017891\n",
+      "min       -0.002485\n",
+      "25%        0.000000\n",
+      "50%        0.000220\n",
+      "75%        0.002056\n",
+      "max        0.400316\n",
+      "Name: ghg_efficiency, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, let's identify multi-county projects\n",
+    "def is_multi_county(county):\n",
+    "    return ',' in str(county)\n",
+    "\n",
+    "# Create multi-county analysis dataframe\n",
+    "multi_county_data = data[data['County'].apply(is_multi_county)].copy()\n",
+    "\n",
+    "# Analyze these collaborations\n",
+    "print(\"\\nMulti-County Collaboration Analysis:\")\n",
+    "print(f\"\\nTotal number of multi-county projects: {len(multi_county_data)}\")\n",
+    "print(f\"Total investment in multi-county projects: ${multi_county_data['Total Project Cost'].sum()/1e9:.2f}B\")\n",
+    "\n",
+    "# Look at the specific collaborations\n",
+    "print(\"\\nMajor Multi-County Collaborations:\")\n",
+    "collaboration_summary = multi_county_data.groupby('County').agg({\n",
+    "    'Total Project Cost': ['count', 'sum', 'mean'],\n",
+    "    'Total Project GHGReductions': 'sum',\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean',\n",
+    "    'Total GGRFDisadvantaged Community Funding': 'sum'\n",
+    "}).round(2)\n",
+    "\n",
+    "# Flatten column names\n",
+    "collaboration_summary.columns = [\n",
+    "    'project_count', 'total_investment', 'avg_project_size',\n",
+    "    'total_ghg_reduction', 'dac_benefit_rate', 'dac_funding'\n",
+    "]\n",
+    "\n",
+    "# Calculate efficiency metrics\n",
+    "collaboration_summary['ghg_per_dollar'] = collaboration_summary['total_ghg_reduction'] / collaboration_summary['total_investment']\n",
+    "collaboration_summary['dac_funding_ratio'] = collaboration_summary['dac_funding'] / collaboration_summary['total_investment']\n",
+    "\n",
+    "# Sort by total investment\n",
+    "print(collaboration_summary.sort_values('total_investment', ascending=False).head(10))\n",
+    "\n",
+    "# Create visualizations\n",
+    "plt.figure(figsize=(20, 12))\n",
+    "\n",
+    "# 1. Project Size Comparison\n",
+    "plt.subplot(2, 2, 1)\n",
+    "avg_size = data['Total Project Cost'].mean()\n",
+    "multi_avg_size = multi_county_data['Total Project Cost'].mean()\n",
+    "\n",
+    "plt.bar(['Single-County Projects', 'Multi-County Projects'], \n",
+    "       [avg_size/1e6, multi_avg_size/1e6],\n",
+    "       color=['lightblue', 'darkblue'])\n",
+    "plt.ylabel('Average Project Size (Millions $)')\n",
+    "plt.title('Project Size Comparison')\n",
+    "\n",
+    "# 2. GHG Efficiency Comparison\n",
+    "plt.subplot(2, 2, 2)\n",
+    "single_ghg_efficiency = (data[~data['County'].apply(is_multi_county)]['Total Project GHGReductions'].sum() / \n",
+    "                        data[~data['County'].apply(is_multi_county)]['Total Project Cost'].sum())\n",
+    "multi_ghg_efficiency = (multi_county_data['Total Project GHGReductions'].sum() / \n",
+    "                       multi_county_data['Total Project Cost'].sum())\n",
+    "\n",
+    "plt.bar(['Single-County Projects', 'Multi-County Projects'],\n",
+    "       [single_ghg_efficiency, multi_ghg_efficiency],\n",
+    "       color=['lightblue', 'darkblue'])\n",
+    "plt.ylabel('GHG Reduction per Dollar')\n",
+    "plt.title('GHG Efficiency Comparison')\n",
+    "\n",
+    "# 3. Top Multi-County Collaborations\n",
+    "plt.subplot(2, 2, 3)\n",
+    "top_collaborations = collaboration_summary.sort_values('total_investment', ascending=True).tail(5)\n",
+    "plt.barh(range(len(top_collaborations)), top_collaborations['total_investment']/1e9)\n",
+    "plt.yticks(range(len(top_collaborations)), \n",
+    "          [name[:50] + '...' if len(name) > 50 else name for name in top_collaborations.index])\n",
+    "plt.xlabel('Total Investment (Billions $)')\n",
+    "plt.title('Top 5 Multi-County Collaborations by Investment')\n",
+    "\n",
+    "# 4. DAC Benefit Rate Comparison\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.bar(['Single-County Projects', 'Multi-County Projects'],\n",
+    "       [data[~data['County'].apply(is_multi_county)]['Is Benefit Disadvantaged Communities'].mean(),\n",
+    "        multi_county_data['Is Benefit Disadvantaged Communities'].mean()],\n",
+    "       color=['lightblue', 'darkblue'])\n",
+    "plt.ylabel('Average DAC Benefit Rate')\n",
+    "plt.title('DAC Benefit Rate Comparison')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Analyze types of projects that work well in collaboration\n",
+    "print(\"\\nMost Common Types of Multi-County Projects:\")\n",
+    "print(multi_county_data['Program Name'].value_counts().head())\n",
+    "\n",
+    "# Look at success factors\n",
+    "print(\"\\nSuccess Metrics for Multi-County Projects:\")\n",
+    "print(\"\\nDAC Benefit Rate Distribution:\")\n",
+    "print(multi_county_data['Is Benefit Disadvantaged Communities'].describe())\n",
+    "\n",
+    "print(\"\\nGHG Reduction Distribution (tons per $):\")\n",
+    "multi_county_data['ghg_efficiency'] = multi_county_data['Total Project GHGReductions'] / multi_county_data['Total Project Cost']\n",
+    "print(multi_county_data['ghg_efficiency'].describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2000x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "1. Most Successful Collaborations:\n",
+      "                                                   total_investment  \\\n",
+      "County                                                                \n",
+      "Marin, San Francisco                                        2466450   \n",
+      "Los Angeles, Orange, Riverside, San Diego                    766345   \n",
+      "El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba            991000   \n",
+      "Napa, San Joaquin                                           1557570   \n",
+      "Lake, Sacramento                                            2697479   \n",
+      "\n",
+      "                                                   ghg_per_dollar  \\\n",
+      "County                                                              \n",
+      "Marin, San Francisco                                     0.010916   \n",
+      "Los Angeles, Orange, Riverside, San Diego                0.008612   \n",
+      "El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba        0.008322   \n",
+      "Napa, San Joaquin                                        0.005557   \n",
+      "Lake, Sacramento                                         0.004171   \n",
+      "\n",
+      "                                                   dac_benefit_rate  \\\n",
+      "County                                                                \n",
+      "Marin, San Francisco                                            1.0   \n",
+      "Los Angeles, Orange, Riverside, San Diego                       1.0   \n",
+      "El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba               1.0   \n",
+      "Napa, San Joaquin                                               1.0   \n",
+      "Lake, Sacramento                                                1.0   \n",
+      "\n",
+      "                                                   success_score  \n",
+      "County                                                            \n",
+      "Marin, San Francisco                                    0.515503  \n",
+      "Los Angeles, Orange, Riverside, San Diego               0.512379  \n",
+      "El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba       0.511986  \n",
+      "Napa, San Joaquin                                       0.508237  \n",
+      "Lake, Sacramento                                        0.506358  \n",
+      "\n",
+      "2. Program Type Success Factors:\n",
+      "                                                    project_count  \\\n",
+      "Program Name                                                        \n",
+      "Forest Health Program                                           6   \n",
+      "Food Production Investment Program                              9   \n",
+      "Wetlands and Watershed Restoration                              1   \n",
+      "Sustainable Agricultural Lands Conservation Pro...              1   \n",
+      "Urban and Community Forestry Program                          333   \n",
+      "\n",
+      "                                                    total_investment  \\\n",
+      "Program Name                                                           \n",
+      "Forest Health Program                                       20238006   \n",
+      "Food Production Investment Program                          20586793   \n",
+      "Wetlands and Watershed Restoration                            920666   \n",
+      "Sustainable Agricultural Lands Conservation Pro...          17690045   \n",
+      "Urban and Community Forestry Program                        37197703   \n",
+      "\n",
+      "                                                    avg_project_size  \\\n",
+      "Program Name                                                           \n",
+      "Forest Health Program                                     3373001.00   \n",
+      "Food Production Investment Program                        2287421.44   \n",
+      "Wetlands and Watershed Restoration                         920666.00   \n",
+      "Sustainable Agricultural Lands Conservation Pro...       17690045.00   \n",
+      "Urban and Community Forestry Program                       111704.81   \n",
+      "\n",
+      "                                                    total_ghg  dac_rate  \\\n",
+      "Program Name                                                              \n",
+      "Forest Health Program                                  830634       0.0   \n",
+      "Food Production Investment Program                     501007       0.0   \n",
+      "Wetlands and Watershed Restoration                      15166       0.0   \n",
+      "Sustainable Agricultural Lands Conservation Pro...     251149       0.0   \n",
+      "Urban and Community Forestry Program                   195214       0.2   \n",
+      "\n",
+      "                                                    ghg_efficiency  \n",
+      "Program Name                                                        \n",
+      "Forest Health Program                                     0.041043  \n",
+      "Food Production Investment Program                        0.024336  \n",
+      "Wetlands and Watershed Restoration                        0.016473  \n",
+      "Sustainable Agricultural Lands Conservation Pro...        0.014197  \n",
+      "Urban and Community Forestry Program                      0.005248  \n",
+      "\n",
+      "3. Optimal Partnership Size:\n",
+      "               ghg_efficiency  dac_rate\n",
+      "partner_count                          \n",
+      "2                    0.004588  0.149798\n",
+      "3                    0.002167  0.064000\n",
+      "4                    0.003443  0.052632\n",
+      "5                    0.000880  0.130435\n",
+      "6                    0.002270  0.236842\n",
+      "7                    0.002930  0.281250\n",
+      "8                    0.001286  0.307692\n",
+      "9                    0.000373  0.000000\n",
+      "10                   0.000014  0.250000\n",
+      "11                   0.000000  0.000000\n",
+      "12                   0.000160  0.000000\n",
+      "13                   0.000011  0.000000\n",
+      "14                   0.003643  0.111111\n",
+      "16                   0.004098  0.111111\n",
+      "19                   0.000000  0.000000\n",
+      "22                   0.000000  0.000000\n",
+      "24                   0.000000  0.000000\n",
+      "25                   0.000000  0.000000\n",
+      "29                   0.000000  0.000000\n",
+      "34                   0.000000  0.000000\n",
+      "47                   0.000000  0.000000\n",
+      "\n",
+      "4. Regional Performance Patterns:\n",
+      "                     ghg_per_dollar  dac_benefit_rate  total_investment\n",
+      "region                                                                 \n",
+      "Bay Area                     0.0015            0.1488       13255750738\n",
+      "Central Valley               0.0022            0.1816         294777141\n",
+      "Other                        0.0073            0.1191        3029393261\n",
+      "Southern California          0.0039            0.1862        1731340906\n",
+      "\n",
+      "Equity-Efficiency Trade-off Analysis:\n",
+      "\n",
+      "Correlation between GHG efficiency and DAC benefit rate: -0.0398\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Deep Dive into Successful Collaborations\n",
+    "# Let's create a success metric combining equity and efficiency\n",
+    "def calculate_success_score(row):\n",
+    "    # Normalize GHG efficiency and DAC rates to 0-1 scale\n",
+    "    ghg_norm = (row['ghg_per_dollar'] - min_ghg) / (max_ghg - min_ghg)\n",
+    "    dac_norm = row['dac_benefit_rate']\n",
+    "    return (ghg_norm + dac_norm) / 2\n",
+    "\n",
+    "# Add success scores\n",
+    "collaboration_success = collaboration_summary.copy()\n",
+    "min_ghg = collaboration_summary['ghg_per_dollar'].min()\n",
+    "max_ghg = collaboration_summary['ghg_per_dollar'].max()\n",
+    "collaboration_success['success_score'] = collaboration_summary.apply(calculate_success_score, axis=1)\n",
+    "\n",
+    "# Create visualization of success patterns\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "\n",
+    "# 1. Success Stories Matrix\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.scatter(collaboration_success['ghg_per_dollar'], \n",
+    "           collaboration_success['dac_benefit_rate'],\n",
+    "           s=collaboration_success['total_investment']/1e7,\n",
+    "           alpha=0.6)\n",
+    "plt.xlabel('GHG Efficiency (Reduction per Dollar)')\n",
+    "plt.ylabel('DAC Benefit Rate')\n",
+    "plt.title('Multi-County Collaboration Performance Matrix\\nSize = Total Investment')\n",
+    "\n",
+    "# 2. Program Type Analysis\n",
+    "program_type_analysis = multi_county_data.groupby('Program Name').agg({\n",
+    "    'Total Project Cost': ['count', 'sum', 'mean'],\n",
+    "    'Total Project GHGReductions': 'sum',\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
+    "}).round(2)\n",
+    "\n",
+    "program_type_analysis.columns = ['project_count', 'total_investment', \n",
+    "                               'avg_project_size', 'total_ghg', 'dac_rate']\n",
+    "program_type_analysis['ghg_efficiency'] = program_type_analysis['total_ghg'] / program_type_analysis['total_investment']\n",
+    "\n",
+    "# Plot program type performance\n",
+    "plt.subplot(2, 2, 2)\n",
+    "top_programs = program_type_analysis.nlargest(5, 'total_investment')\n",
+    "plt.bar(range(len(top_programs)), top_programs['ghg_efficiency'])\n",
+    "plt.xticks(range(len(top_programs)), \n",
+    "          [name[:20] + '...' if len(name) > 20 else name for name in top_programs.index],\n",
+    "          rotation=45)\n",
+    "plt.title('GHG Efficiency by Program Type')\n",
+    "\n",
+    "# 3. Partnership Size Analysis\n",
+    "plt.subplot(2, 2, 3)\n",
+    "partnership_sizes = multi_county_data['County'].str.count(',') + 1\n",
+    "size_performance = pd.DataFrame({\n",
+    "    'partner_count': partnership_sizes,\n",
+    "    'ghg_efficiency': multi_county_data['Total Project GHGReductions'] / multi_county_data['Total Project Cost'],\n",
+    "    'dac_rate': multi_county_data['Is Benefit Disadvantaged Communities']\n",
+    "})\n",
+    "\n",
+    "avg_by_size = size_performance.groupby('partner_count').mean()\n",
+    "plt.plot(avg_by_size.index, avg_by_size['ghg_efficiency'], marker='o')\n",
+    "plt.xlabel('Number of Partner Counties')\n",
+    "plt.ylabel('Average GHG Efficiency')\n",
+    "plt.title('Performance by Partnership Size')\n",
+    "\n",
+    "# 4. Regional Patterns\n",
+    "plt.subplot(2, 2, 4)\n",
+    "# Create regional categories based on county groupings\n",
+    "def categorize_region(counties):\n",
+    "    if 'Los Angeles' in counties:\n",
+    "        return 'Southern California'\n",
+    "    elif any(county in counties for county in ['San Francisco', 'Alameda', 'Santa Clara']):\n",
+    "        return 'Bay Area'\n",
+    "    elif any(county in counties for county in ['Fresno', 'Kern', 'Kings']):\n",
+    "        return 'Central Valley'\n",
+    "    else:\n",
+    "        return 'Other'\n",
+    "\n",
+    "collaboration_success['region'] = collaboration_success.index.map(categorize_region)\n",
+    "regional_performance = collaboration_success.groupby('region').agg({\n",
+    "    'ghg_per_dollar': 'mean',\n",
+    "    'dac_benefit_rate': 'mean',\n",
+    "    'total_investment': 'sum'\n",
+    "})\n",
+    "\n",
+    "plt.bar(regional_performance.index, regional_performance['ghg_per_dollar'])\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('GHG Efficiency by Region')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print detailed analysis\n",
+    "print(\"\\n1. Most Successful Collaborations:\")\n",
+    "print(collaboration_success.nlargest(5, 'success_score')[\n",
+    "    ['total_investment', 'ghg_per_dollar', 'dac_benefit_rate', 'success_score']\n",
+    "])\n",
+    "\n",
+    "print(\"\\n2. Program Type Success Factors:\")\n",
+    "print(program_type_analysis.nlargest(5, 'ghg_efficiency'))\n",
+    "\n",
+    "print(\"\\n3. Optimal Partnership Size:\")\n",
+    "print(avg_by_size)\n",
+    "\n",
+    "print(\"\\n4. Regional Performance Patterns:\")\n",
+    "print(regional_performance.round(4))\n",
+    "\n",
+    "# Additional insights on equity-efficiency trade-offs\n",
+    "print(\"\\nEquity-Efficiency Trade-off Analysis:\")\n",
+    "correlation = collaboration_success['ghg_per_dollar'].corr(collaboration_success['dac_benefit_rate'])\n",
+    "print(f\"\\nCorrelation between GHG efficiency and DAC benefit rate: {correlation:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_846869/2239187850.py:43: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  recent_data['partnership_size'] = recent_data['County'].str.count(',') + 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Period Comparison:\n",
+      "                           Pre-COVID (2010-2019)  COVID Period (2020-2021)  \\\n",
+      "Number of Projects                        211.00                    304.00   \n",
+      "Average Project Size ($M)                   8.24                     31.78   \n",
+      "DAC Benefit Rate                            0.37                      0.02   \n",
+      "Average Partners                            3.70                      5.53   \n",
+      "\n",
+      "                           Post-COVID (2022+)  \n",
+      "Number of Projects                     286.00  \n",
+      "Average Project Size ($M)               23.08  \n",
+      "DAC Benefit Rate                         0.01  \n",
+      "Average Partners                         3.92  \n",
+      "\n",
+      "Most Common Project Types During COVID Period:\n",
+      "Program Name\n",
+      "Urban and Community Forestry Program          144\n",
+      "Low Carbon Transit Operations Program          46\n",
+      "Safe and Affordable Drinking Water Fund        34\n",
+      "Climate Smart Agriculture                      26\n",
+      "Transit and Intercity Rail Capital Program     15\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Largest COVID-Period Collaborations:\n",
+      "                                                   County  \\\n",
+      "42458                          San Francisco, Santa Clara   \n",
+      "62279   Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   \n",
+      "62280   Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   \n",
+      "100710  Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   \n",
+      "100711  Alameda, Contra Costa, Fresno, Kern, Kings, Ma...   \n",
+      "\n",
+      "                                      Program Name  Total Project Cost  \\\n",
+      "42458   Transit and Intercity Rail Capital Program          1980252533   \n",
+      "62279   Transit and Intercity Rail Capital Program           904600000   \n",
+      "62280   Transit and Intercity Rail Capital Program           904600000   \n",
+      "100710  Transit and Intercity Rail Capital Program           904600000   \n",
+      "100711  Transit and Intercity Rail Capital Program           904600000   \n",
+      "\n",
+      "        Total Project GHGReductions  \n",
+      "42458                        734000  \n",
+      "62279                         52428  \n",
+      "62280                          6554  \n",
+      "100710                        21845  \n",
+      "100711                        43690  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filter for more recent period\n",
+    "multi_county_data['Year'] = pd.to_datetime(multi_county_data['Date Operational']).dt.year\n",
+    "recent_data = multi_county_data[multi_county_data['Year'] >= 2010]\n",
+    "\n",
+    "# Create temporal analysis\n",
+    "temporal_analysis = recent_data.groupby('Year').agg({\n",
+    "    'Total Project Cost': ['count', 'sum', 'mean'],\n",
+    "    'Total Project GHGReductions': 'sum',\n",
+    "    'Is Benefit Disadvantaged Communities': 'mean'\n",
+    "})\n",
+    "\n",
+    "# Visualize evolution\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(20, 15))\n",
+    "\n",
+    "# 1. Project Volume\n",
+    "axes[0,0].plot(temporal_analysis.index, \n",
+    "               temporal_analysis[('Total Project Cost', 'count')], \n",
+    "               marker='o', color='darkblue', linewidth=2)\n",
+    "axes[0,0].set_title('Number of Multi-County Projects by Year (2010-2024)', \n",
+    "                    fontsize=12, pad=15)\n",
+    "axes[0,0].set_ylabel('Number of Projects')\n",
+    "axes[0,0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# 2. Investment Size\n",
+    "axes[0,1].plot(temporal_analysis.index, \n",
+    "               temporal_analysis[('Total Project Cost', 'mean')]/1e6, \n",
+    "               marker='o', color='darkblue', linewidth=2)\n",
+    "axes[0,1].set_title('Average Project Size by Year', \n",
+    "                    fontsize=12, pad=15)\n",
+    "axes[0,1].set_ylabel('Average Size (Millions $)')\n",
+    "axes[0,1].grid(True, alpha=0.3)\n",
+    "\n",
+    "# 3. Equity Performance\n",
+    "axes[1,0].plot(temporal_analysis.index, \n",
+    "               temporal_analysis[('Is Benefit Disadvantaged Communities', 'mean')], \n",
+    "               marker='o', color='darkblue', linewidth=2)\n",
+    "axes[1,0].set_title('DAC Benefit Rate by Year', \n",
+    "                    fontsize=12, pad=15)\n",
+    "axes[1,0].set_ylabel('Proportion Benefiting DACs')\n",
+    "axes[1,0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# 4. Partnership Size Evolution\n",
+    "recent_data['partnership_size'] = recent_data['County'].str.count(',') + 1\n",
+    "partnership_evolution = recent_data.groupby('Year')['partnership_size'].mean()\n",
+    "\n",
+    "axes[1,1].plot(partnership_evolution.index, \n",
+    "               partnership_evolution.values, \n",
+    "               marker='o', color='darkblue', linewidth=2)\n",
+    "axes[1,1].set_title('Average Number of Partner Counties by Year', \n",
+    "                    fontsize=12, pad=15)\n",
+    "axes[1,1].set_ylabel('Average Partners per Project')\n",
+    "axes[1,1].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Adjust x-axis for all subplots\n",
+    "for ax in axes.flat:\n",
+    "    ax.set_xlim(2014, 2024)\n",
+    "    ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print summary statistics for three periods: pre-COVID, COVID, and post-COVID\n",
+    "print(\"\\nPeriod Comparison:\")\n",
+    "pre_covid = recent_data[recent_data['Year'] < 2020]\n",
+    "covid = recent_data[recent_data['Year'].isin([2020, 2021])]\n",
+    "post_covid = recent_data[recent_data['Year'] > 2021]\n",
+    "\n",
+    "metrics = {\n",
+    "    'Number of Projects': lambda x: len(x),\n",
+    "    'Average Project Size ($M)': lambda x: x['Total Project Cost'].mean()/1e6,\n",
+    "    'DAC Benefit Rate': lambda x: x['Is Benefit Disadvantaged Communities'].mean(),\n",
+    "    'Average Partners': lambda x: x['partnership_size'].mean()\n",
+    "}\n",
+    "\n",
+    "comparison = pd.DataFrame({\n",
+    "    'Pre-COVID (2010-2019)': {metric: func(pre_covid) for metric, func in metrics.items()},\n",
+    "    'COVID Period (2020-2021)': {metric: func(covid) for metric, func in metrics.items()},\n",
+    "    'Post-COVID (2022+)': {metric: func(post_covid) for metric, func in metrics.items()}\n",
+    "})\n",
+    "\n",
+    "print(comparison.round(2))\n",
+    "\n",
+    "# Analyze types of projects during COVID period\n",
+    "print(\"\\nMost Common Project Types During COVID Period:\")\n",
+    "print(covid['Program Name'].value_counts().head())\n",
+    "\n",
+    "# Look at largest COVID-period collaborations\n",
+    "print(\"\\nLargest COVID-Period Collaborations:\")\n",
+    "print(covid.nlargest(5, 'Total Project Cost')[\n",
+    "    ['County', 'Program Name', 'Total Project Cost', 'Total Project GHGReductions']\n",
+    "].round(2))"
+   ]
   }
  ],
  "metadata": {
diff --git a/initial_view/overview_hypotesting_20241031.pdf b/initial_view/overview_hypotesting_20241031.pdf
new file mode 100644
index 00000000..5e17061c
Binary files /dev/null and b/initial_view/overview_hypotesting_20241031.pdf differ
diff --git a/initial_view/secondtake.ipynb b/initial_view/secondtake.ipynb
index 7ab0b253..61ea0c0a 100644
--- a/initial_view/secondtake.ipynb
+++ b/initial_view/secondtake.ipynb
@@ -2,12 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Project: California Equity Research\n",
@@ -15,37 +11,92 @@
     "#### Goal: Analyze the relationship between climate investment and environmental justice in California\n",
     "#### This notebook: second take \n",
     "##### Author: [dpadams](dpadams@fullerton.edu)\n",
-    "##### Date: 2024-10.11"
+    "##### Date: 2024-11-24"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'scipy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m stats\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Read the data (I see you already have this loaded as 'data')\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# 1. First, let's create our core analytical metrics\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_program_metrics\u001b[39m(df):\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scipy'"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
-    "# set working directory as ~/home/dadams/repos/california_equity_git/\n"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Read the data (I see you already have this loaded as 'data')\n",
+    "\n",
+    "# 1. First, let's create our core analytical metrics\n",
+    "def calculate_program_metrics(df):\n",
+    "    metrics = df.groupby('Program Name').agg({\n",
+    "        'Total Project Cost': ['count', 'sum', 'mean'],\n",
+    "        'Total Project GHGReductions': ['sum', 'mean'],\n",
+    "        'Total GGRFDisadvantaged Community Funding': ['sum', 'mean'],\n",
+    "        'Is Benefit Disadvantaged Communities': 'mean',\n",
+    "        'Is Low Income Communities': 'mean'\n",
+    "    }).round(2)\n",
+    "    \n",
+    "    # Add efficiency metrics\n",
+    "    metrics['GHG_per_dollar'] = (metrics[('Total Project GHGReductions', 'sum')] / \n",
+    "                                metrics[('Total Project Cost', 'sum')]).round(4)\n",
+    "    \n",
+    "    metrics['DAC_funding_ratio'] = (metrics[('Total GGRFDisadvantaged Community Funding', 'sum')] / \n",
+    "                                   metrics[('Total Project Cost', 'sum')]).round(4)\n",
+    "    \n",
+    "    return metrics\n",
+    "\n",
+    "program_metrics = calculate_program_metrics(data)\n",
+    "\n",
+    "# Display top programs by different metrics\n",
+    "print(\"\\nTop 5 Programs by Total Investment:\")\n",
+    "print(program_metrics.sort_values(('Total Project Cost', 'sum'), ascending=False).head())\n",
+    "\n",
+    "print(\"\\nTop 5 Programs by GHG Reduction Efficiency:\")\n",
+    "print(program_metrics.sort_values('GHG_per_dollar', ascending=False).head())\n",
+    "\n",
+    "print(\"\\nTop 5 Programs by DAC Funding Ratio:\")\n",
+    "print(program_metrics.sort_values('DAC_funding_ratio', ascending=False).head())"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,