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<h1>Aply modifcation for both A and Y matrix</h1>
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<p>This code is made by a second year Master student. Use with caution!</p>
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<section id="aply-modifcation-for-both-a-and-y-matrix">
<h1>Aply modifcation for both A and Y matrix<a class="headerlink" href="#aply-modifcation-for-both-a-and-y-matrix" title="Link to this heading">#</a></h1>
<p>Created on Wed Jun 26 17:30:27 2024</p>
<p>@author: regin</p>
<dl class="py function">
<dt class="sig sig-object py">
<span class="sig-prename descclassname"><span class="pre">cirk_func.</span></span><span class="sig-name descname"><span class="pre">apply_shocks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sequencesA</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sequencesY</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indicatorimpact</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indicatorintensity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensitivity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indicator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cirk_func.html#apply_shocks"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
<dd><p>Modify the A and Y matrix using an excel file with input parameters</p>
<p>Creates a dataframe (same shape as the original dataframe) containing the modified values for both the
A and Y matrix based on the input parameters.
does also include system to perform different parameters</p>
<dl class="simple">
<dt>Parameters:</dt><dd><dl class="simple">
<dt>File_path (string):</dt><dd><p>Dataframe containing results of Man-Kendall results (trend, p, intercept and slope)from mk function from pymannkendall.</p>
</dd>
<dt>A_matrix (Dataframe_like)</dt><dd><p>Baseline A matrix.</p>
</dd>
<dt>Y_matrix (Dataframe_like)</dt><dd><p>Baseline Y matrix.</p>
</dd>
<dt>sequencesA (Lists)</dt><dd><p>List of lists containing the sequence of interventions implemented for each scenario</p>
</dd>
<dt>sequencesY (Lists)</dt><dd><p>List of lists containing the sequence of interventions implemented for each scenario</p>
</dd>
<dt>indicatorimpact (Dataframe_like)</dt><dd><p>Dataframe containing the environmental impact</p>
</dd>
<dt>indicatorintensity (Dataframe_like)</dt><dd><p>Dataframe environmental intensity</p>
</dd>
<dt>Threshold (float)</dt><dd><p>Float of the passable threshold for visualization</p>
</dd>
<dt>Sensitivity (float)</dt><dd><p>Extra input for changing the input of each intervention (Sensitivity = 1 when no sensitivity is applied)</p>
</dd>
<dt>indicator (string)</dt><dd><p>Enviromental impact of interest.</p>
</dd>
</dl>
</dd>
<dt>Returns: df_difference_output (Dictionary)</dt><dd><p>Difference of gross output</p>
</dd>
<dt>Returns: df_difference_impact (Dictionary)</dt><dd><p>Difference of environmental impact of interest</p>
</dd>
<dt>Raises:</dt><dd><p>IOError: An error occurred about the size of the model.</p>
</dd>
</dl>
</dd></dl>
<section id="code">
<h2>Code<a class="headerlink" href="#code" title="Link to this heading">#</a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span> <span class="c1"># Read the Excel file</span>
<span class="linenos"> 2</span> <span class="n">Full_shocks_A</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">'z'</span><span class="p">)</span>
<span class="linenos"> 3</span> <span class="n">Full_shocks_Y</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">'Y'</span><span class="p">)</span>
<span class="linenos"> 4</span>
<span class="linenos"> 5</span> <span class="c1"># Dictionary to store results for each sequence of interventions</span>
<span class="linenos"> 6</span> <span class="n">df_difference_output</span> <span class="o">=</span> <span class="p">{}</span>
<span class="linenos"> 7</span> <span class="n">df_difference_impact</span> <span class="o">=</span> <span class="p">{}</span>
<span class="linenos"> 8</span>
<span class="linenos"> 9</span> <span class="c1"># Ensure sequenceA and sequenceY are the same length</span>
<span class="linenos"> 10</span> <span class="n">max_length</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sequencesA</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">sequencesY</span><span class="p">))</span>
<span class="linenos"> 11</span> <span class="n">sequencesA</span><span class="o">.</span><span class="n">extend</span><span class="p">([[]]</span> <span class="o">*</span> <span class="p">(</span><span class="n">max_length</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">sequencesA</span><span class="p">)))</span>
<span class="linenos"> 12</span> <span class="n">sequencesY</span><span class="o">.</span><span class="n">extend</span><span class="p">([[]]</span> <span class="o">*</span> <span class="p">(</span><span class="n">max_length</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">sequencesY</span><span class="p">)))</span>
<span class="linenos"> 13</span>
<span class="linenos"> 14</span> <span class="c1"># Process sequences together</span>
<span class="linenos"> 15</span> <span class="k">for</span> <span class="n">seq_index</span><span class="p">,</span> <span class="p">(</span><span class="n">seqA</span><span class="p">,</span> <span class="n">seqY</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">sequencesA</span><span class="p">,</span> <span class="n">sequencesY</span><span class="p">)):</span>
<span class="linenos"> 16</span> <span class="c1"># Create copies of the original DataFrames to modify</span>
<span class="linenos"> 17</span> <span class="n">A_modify</span> <span class="o">=</span> <span class="n">A_matrix</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="linenos"> 18</span> <span class="n">Y_modify</span> <span class="o">=</span> <span class="n">Y_matrix</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="linenos"> 19</span>
<span class="linenos"> 20</span> <span class="c1"># Process sequence A and the interventions for the A matrix</span>
<span class="linenos"> 21</span> <span class="k">for</span> <span class="n">seq</span> <span class="ow">in</span> <span class="n">seqA</span><span class="p">:</span>
<span class="linenos"> 22</span> <span class="k">if</span> <span class="n">seq</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">Full_shocks_A</span><span class="p">):</span>
<span class="linenos"> 23</span> <span class="n">row</span> <span class="o">=</span> <span class="n">Full_shocks_A</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">seq</span><span class="p">]</span>
<span class="linenos"> 24</span> <span class="n">country_row</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'row region'</span><span class="p">]</span>
<span class="linenos"> 25</span> <span class="n">sector_row</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'row sector'</span><span class="p">]</span>
<span class="linenos"> 26</span> <span class="n">country_column</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'column region'</span><span class="p">]</span>
<span class="linenos"> 27</span> <span class="n">sector_column</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'column sector'</span><span class="p">]</span>
<span class="linenos"> 28</span> <span class="n">value</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'value'</span><span class="p">]</span>
<span class="linenos"> 29</span> <span class="n">typechange</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s2">"type"</span><span class="p">]</span>
<span class="linenos"> 30</span> <span class="nb">print</span><span class="p">(</span><span class="n">seq</span><span class="p">)</span>
<span class="linenos"> 31</span> <span class="k">if</span> <span class="n">typechange</span> <span class="o">==</span> <span class="s2">"Percentage"</span><span class="p">:</span>
<span class="linenos"> 32</span> <span class="n">A_modify</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">country_row</span><span class="p">,</span> <span class="n">sector_row</span><span class="p">),</span> <span class="p">(</span><span class="n">country_column</span><span class="p">,</span> <span class="n">sector_column</span><span class="p">)]</span> <span class="o">*=</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">value</span><span class="p">)</span><span class="o">*</span><span class="n">sensitivity</span><span class="p">)</span>
<span class="linenos"> 33</span> <span class="k">else</span><span class="p">:</span>
<span class="linenos"> 34</span> <span class="n">A_modify</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">country_row</span><span class="p">,</span> <span class="n">sector_row</span><span class="p">),</span> <span class="p">(</span><span class="n">country_column</span><span class="p">,</span> <span class="n">sector_column</span><span class="p">)]</span> <span class="o">+=</span> <span class="p">(</span><span class="n">value</span><span class="o">*</span><span class="n">sensitivity</span><span class="p">)</span>
<span class="linenos"> 35</span> <span class="k">else</span><span class="p">:</span>
<span class="linenos"> 36</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Index </span><span class="si">{</span><span class="n">seq</span><span class="si">}</span><span class="s2"> is out of range for the DataFrame."</span><span class="p">)</span>
<span class="linenos"> 37</span>
<span class="linenos"> 38</span> <span class="c1"># Process sequence Y and the interventions for the Y matrix</span>
<span class="linenos"> 39</span> <span class="k">for</span> <span class="n">seq</span> <span class="ow">in</span> <span class="n">seqY</span><span class="p">:</span>
<span class="linenos"> 40</span> <span class="k">if</span> <span class="n">seq</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">Full_shocks_Y</span><span class="p">):</span>
<span class="linenos"> 41</span> <span class="n">row</span> <span class="o">=</span> <span class="n">Full_shocks_Y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">seq</span><span class="p">]</span>
<span class="linenos"> 42</span> <span class="n">country_row</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'row region'</span><span class="p">]</span>
<span class="linenos"> 43</span> <span class="n">sector_row</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'row sector'</span><span class="p">]</span>
<span class="linenos"> 44</span> <span class="n">country_column</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'column region'</span><span class="p">]</span>
<span class="linenos"> 45</span> <span class="n">demand_column</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'demand category'</span><span class="p">]</span>
<span class="linenos"> 46</span> <span class="n">value</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'value'</span><span class="p">]</span>
<span class="linenos"> 47</span> <span class="n">typechange</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s2">"type"</span><span class="p">]</span>
<span class="linenos"> 48</span> <span class="nb">print</span><span class="p">(</span><span class="n">seq</span><span class="p">)</span>
<span class="linenos"> 49</span> <span class="k">if</span> <span class="n">typechange</span> <span class="o">==</span> <span class="s2">"Percentage"</span><span class="p">:</span>
<span class="linenos"> 50</span> <span class="n">Y_modify</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">country_row</span><span class="p">,</span> <span class="n">sector_row</span><span class="p">),</span> <span class="p">(</span><span class="n">country_column</span><span class="p">,</span> <span class="n">demand_column</span><span class="p">)]</span> <span class="o">*=</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">value</span><span class="p">)</span><span class="o">*</span><span class="n">sensitivity</span><span class="p">)</span>
<span class="linenos"> 51</span> <span class="k">else</span><span class="p">:</span>
<span class="linenos"> 52</span> <span class="n">Y_modify</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">country_row</span><span class="p">,</span> <span class="n">sector_row</span><span class="p">),</span> <span class="p">(</span><span class="n">country_column</span><span class="p">,</span> <span class="n">demand_column</span><span class="p">)]</span> <span class="o">+=</span> <span class="p">(</span><span class="n">value</span><span class="o">*</span><span class="n">sensitivity</span><span class="p">)</span>
<span class="linenos"> 53</span> <span class="k">else</span><span class="p">:</span>
<span class="linenos"> 54</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Index </span><span class="si">{</span><span class="n">seq</span><span class="si">}</span><span class="s2"> is out of range for the DataFrame."</span><span class="p">)</span>
<span class="linenos"> 55</span>
<span class="linenos"> 56</span> <span class="c1"># Groupby to check results</span>
<span class="linenos"> 57</span> <span class="n">I</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">A_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="linenos"> 58</span> <span class="n">x_modify</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">I</span> <span class="o">-</span> <span class="n">A_modify</span><span class="p">)</span> <span class="o">@</span> <span class="n">Y_modify</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="linenos"> 59</span> <span class="n">x_baseline</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">I</span> <span class="o">-</span> <span class="n">A_matrix</span><span class="p">)</span> <span class="o">@</span> <span class="n">Y_matrix</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="linenos"> 60</span>
<span class="linenos"> 61</span> <span class="n">Results_output</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="linenos"> 62</span> <span class="n">Results_output</span><span class="p">[</span><span class="s2">"baseline"</span><span class="p">]</span> <span class="o">=</span> <span class="n">x_baseline</span>
<span class="linenos"> 63</span> <span class="n">Results_output</span><span class="p">[</span><span class="s2">"changes"</span><span class="p">]</span> <span class="o">=</span> <span class="n">x_modify</span>
<span class="linenos"> 64</span> <span class="n">Results_output</span><span class="p">[</span><span class="s2">"diff"</span><span class="p">]</span> <span class="o">=</span> <span class="n">Results_output</span><span class="p">[</span><span class="s2">"changes"</span><span class="p">]</span> <span class="o">-</span> <span class="n">Results_output</span><span class="p">[</span><span class="s2">"baseline"</span><span class="p">]</span>
<span class="linenos"> 65</span> <span class="n">Difference_output</span> <span class="o">=</span> <span class="n">Results_output</span><span class="p">[</span><span class="s2">"diff"</span><span class="p">]</span>
<span class="linenos"> 66</span>
<span class="linenos"> 67</span> <span class="c1"># Store the Results_output in the dictionary with the sequence index as the key</span>
<span class="linenos"> 68</span> <span class="n">df_difference_output</span><span class="p">[</span><span class="sa">f</span><span class="s1">'intervention</span><span class="si">{</span><span class="n">seq_index</span><span class="si">}</span><span class="s1">'</span><span class="p">]</span> <span class="o">=</span> <span class="n">Difference_output</span>
<span class="linenos"> 69</span> <span class="n">df_difference_output</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">df_difference_output</span><span class="p">)</span>
<span class="linenos"> 70</span>
<span class="linenos"> 71</span> <span class="n">L_ct</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">I</span> <span class="o">-</span> <span class="n">A_modify</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="linenos"> 72</span> <span class="n">x_ct</span> <span class="o">=</span> <span class="n">L_ct</span> <span class="o">@</span> <span class="n">Y_modify</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="linenos"> 73</span> <span class="n">RE_ct</span> <span class="o">=</span> <span class="n">indicatorintensity</span> <span class="o">*</span> <span class="n">x_ct</span>
<span class="linenos"> 74</span> <span class="n">F_diff_RE</span> <span class="o">=</span> <span class="p">(</span><span class="n">RE_ct</span> <span class="o">-</span> <span class="n">indicatorimpact</span><span class="p">)</span>
<span class="linenos"> 75</span> <span class="n">df_difference_impact</span><span class="p">[</span><span class="sa">f</span><span class="s1">'intervention</span><span class="si">{</span><span class="n">seq_index</span><span class="si">}</span><span class="s1">'</span><span class="p">]</span> <span class="o">=</span> <span class="n">F_diff_RE</span>
<span class="linenos"> 76</span> <span class="n">F_diff_RE</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">F_diff_RE</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">A_matrix</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
<span class="linenos"> 77</span> <span class="n">F_relative_change1</span> <span class="o">=</span> <span class="n">F_diff_RE</span><span class="o">/</span><span class="mi">1000</span>
<span class="linenos"> 78</span>
<span class="linenos"> 79</span>
<span class="linenos"> 80</span> <span class="c1"># Filter the DataFrame to include only values above the threshold</span>
<span class="linenos"> 81</span> <span class="n">filtered_df</span> <span class="o">=</span> <span class="n">F_relative_change1</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">F_relative_change1</span><span class="p">)</span> <span class="o">></span> <span class="n">threshold</span><span class="p">]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
<span class="linenos"> 82</span>
<span class="linenos"> 83</span> <span class="c1"># Calculate the sum of values below the threshold</span>
<span class="linenos"> 84</span> <span class="n">below_threshold_sum</span> <span class="o">=</span> <span class="n">F_relative_change1</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">F_relative_change1</span><span class="p">)</span> <span class="o"><=</span> <span class="n">threshold</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">sum</span><span class="p">()</span>
<span class="linenos"> 85</span>
<span class="linenos"> 86</span> <span class="c1"># Add the below-threshold sum as a new row</span>
<span class="linenos"> 87</span> <span class="n">filtered_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="s1">'Below Threshold'</span><span class="p">,</span> <span class="s1">'Sum of below threshold'</span><span class="p">),</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">below_threshold_sum</span>
<span class="linenos"> 88</span>
<span class="linenos"> 89</span> <span class="c1"># Choose a color palette (using Set1)</span>
<span class="linenos"> 90</span> <span class="n">colors</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">'Set1'</span><span class="p">)</span><span class="o">.</span><span class="n">colors</span>
<span class="linenos"> 91</span> <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">'font.size'</span><span class="p">:</span> <span class="mi">18</span><span class="p">})</span>
<span class="linenos"> 92</span>
<span class="linenos"> 93</span> <span class="c1"># Plot the filtered DataFrame with adjusted size and legend placement</span>
<span class="linenos"> 94</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">filtered_df</span><span class="o">.</span><span class="n">unstack</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="s2">"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">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</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="linenos"> 95</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'center left'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
<span class="linenos"> 96</span> <span class="n">ax</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="linenos"> 97</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Filtered differences for scenario </span><span class="si">{</span><span class="n">seq_index</span><span class="si">}</span><span class="se">\n</span><span class="s1"> in </span><span class="si">{</span><span class="n">indicator</span><span class="si">}</span><span class="s1"> (threshold = </span><span class="si">{</span><span class="n">threshold</span><span class="si">}</span><span class="s1">)'</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="linenos"> 98</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">indicator</span><span class="si">}</span><span class="s2"> in kt"</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="linenos"> 99</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Regions'</span><span class="p">)</span>
<span class="linenos">100</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xticklabels</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">ha</span><span class="o">=</span><span class="s1">'right'</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="linenos">101</span>
<span class="linenos">102</span> <span class="c1"># Show the plot</span>
<span class="linenos">103</span> <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="linenos">104</span>
<span class="linenos">105</span> <span class="k">return</span> <span class="n">df_difference_output</span><span class="p">,</span> <span class="n">df_difference_impact</span>
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