diff --git a/docs-python/dev/scripts/gen_ref_pages/index.html b/docs-python/dev/scripts/gen_ref_pages/index.html
index 3e7ce9f..b954726 100644
--- a/docs-python/dev/scripts/gen_ref_pages/index.html
+++ b/docs-python/dev/scripts/gen_ref_pages/index.html
@@ -1879,10 +1879,6 @@
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+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+<!-- MathJax configuration -->
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@@ -2748,172 +2738,154 @@
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+<!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3087b524">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
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-<div id="cell-id=0f214410" class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=becba0c3">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-1">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt">from pathlib import Path</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-1">from pathlib import Path</div>
 </div>
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-
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div id="cell-id=4684e77d" class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=97d6a597">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-2">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">mkdocs_gen_files</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-2">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">mkdocs_gen_files</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt">import mkdocs_gen_files</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-2">import mkdocs_gen_files</div>
 </div>
 </div>
-
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div id="cell-id=069104d1" class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=58872b40">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-3">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">nav</span> <span class="o">=</span> <span class="n">mkdocs_gen_files</span><span class="o">.</span><span class="n">Nav</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-3">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">nav</span> <span class="o">=</span> <span class="n">mkdocs_gen_files</span><span class="o">.</span><span class="n">Nav</span><span class="p">()</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt">nav = mkdocs_gen_files.Nav()</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-3">nav = mkdocs_gen_files.Nav()</div>
 </div>
 </div>
-
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div id="cell-id=09ba2873" class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=e4231b3d">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-4">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;streampipes&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">rglob</span><span class="p">(</span><span class="s2">&quot;*.py&quot;</span><span class="p">)):</span>
-    <span class="n">module_path</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">relative_to</span><span class="p">(</span><span class="s2">&quot;streampipes&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
-    <span class="n">doc_path</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">relative_to</span><span class="p">(</span><span class="s2">&quot;streampipes&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;.md&quot;</span><span class="p">)</span>
-    <span class="n">full_doc_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;reference&quot;</span><span class="p">,</span> <span class="n">doc_path</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-4">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="s2">"streampipes"</span><span class="p">)</span><span class="o">.</span><span class="n">rglob</span><span class="p">(</span><span class="s2">"*.py"</span><span class="p">)):</span>
+    <span class="n">module_path</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">relative_to</span><span class="p">(</span><span class="s2">"streampipes"</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
+    <span class="n">doc_path</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">relative_to</span><span class="p">(</span><span class="s2">"streampipes"</span><span class="p">)</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">".md"</span><span class="p">)</span>
+    <span class="n">full_doc_path</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">"reference"</span><span class="p">,</span> <span class="n">doc_path</span><span class="p">)</span>
 
     <span class="n">parts</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">module_path</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span>
 
-    <span class="k">if</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;__init__&quot;</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"__init__"</span><span class="p">:</span>
         <span class="c1"># parts = parts[:-1]</span>
         <span class="k">continue</span>
-    <span class="k">elif</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;__version__&quot;</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"__version__"</span><span class="p">:</span>
         <span class="k">continue</span>
-    <span class="k">elif</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
         <span class="k">continue</span>
-    <span class="k">elif</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot; &quot;</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">parts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">" "</span><span class="p">:</span>
         <span class="k">continue</span>
 
     <span class="n">nav</span><span class="p">[</span><span class="n">parts</span><span class="p">]</span> <span class="o">=</span> <span class="n">doc_path</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span>
 
-    <span class="k">with</span> <span class="n">mkdocs_gen_files</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">full_doc_path</span><span class="p">,</span> <span class="s2">&quot;w+&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
-        <span class="n">identifier</span> <span class="o">=</span> <span class="s2">&quot;.&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">parts</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;::: streampipes.</span><span class="si">{</span><span class="n">identifier</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">fd</span><span class="p">)</span>
+    <span class="k">with</span> <span class="n">mkdocs_gen_files</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">full_doc_path</span><span class="p">,</span> <span class="s2">"w+"</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
+        <span class="n">identifier</span> <span class="o">=</span> <span class="s2">"."</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">parts</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"::: streampipes.</span><span class="si">{</span><span class="n">identifier</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="n">fd</span><span class="p">)</span>
 
     <span class="n">mkdocs_gen_files</span><span class="o">.</span><span class="n">set_edit_path</span><span class="p">(</span><span class="n">full_doc_path</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt">for path in sorted(Path("streampipes").rglob("*.py")):
+<div class="clipboard-copy-txt" id="cell-4">for path in sorted(Path("streampipes").rglob("*.py")):
     module_path = path.relative_to("streampipes").with_suffix("")
     doc_path = path.relative_to("streampipes").with_suffix(".md")
     full_doc_path = Path("reference", doc_path)
@@ -2937,24 +2909,20 @@
         print(f"::: streampipes.{identifier}", file=fd)
 
     mkdocs_gen_files.set_edit_path(full_doc_path, path)</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
 </div>
 </div>
-<div id="cell-id=a46101f3" class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c464b019">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>with mkdocs_gen_files.open(&quot;reference/SUMMARY.md&quot;, &quot;w+&quot;) as nav_file:
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>with mkdocs_gen_files.open("reference/SUMMARY.md", "w+") as nav_file:
 nav_file.writelines(nav.build_literate_nav())</p>
-
 </div>
 </div>
 </div>
@@ -2963,7 +2931,6 @@
 
 
 
-
   
 
 
diff --git a/docs-python/dev/sitemap.xml b/docs-python/dev/sitemap.xml
index 1c4c29b..b18b2ca 100644
--- a/docs-python/dev/sitemap.xml
+++ b/docs-python/dev/sitemap.xml
@@ -2,242 +2,242 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>https://streampipes.apache.org/docs/docs/python/latest/latest/</loc>
-         <lastmod>2023-07-15</lastmod>
+         <lastmod>2023-07-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://streampipes.apache.org/docs/docs/python/latest/latest/getting-started/developing/</loc>
-         <lastmod>2023-07-15</lastmod>
+         <lastmod>2023-07-22</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://streampipes.apache.org/docs/docs/python/latest/latest/getting-started/first-steps/</loc>
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-<h1 id="Introduction-to-StreamPipes-Python">Introduction to StreamPipes Python<a class="anchor-link" href="#Introduction-to-StreamPipes-Python">&#182;</a></h1><h3 id="Why-there-is-an-extra-Python-library-for-StreamPipes?">Why there is an extra Python library for StreamPipes?<a class="anchor-link" href="#Why-there-is-an-extra-Python-library-for-StreamPipes?">&#182;</a></h3><p><a href="https://streampipes.apache.org/">Apache StreamPipes</a> aims to enable non-technical users to connect and analyze IoT data streams.
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+<h1 id="Introduction-to-StreamPipes-Python">Introduction to StreamPipes Python<a class="anchor-link" href="#Introduction-to-StreamPipes-Python">¶</a></h1><h3 id="Why-there-is-an-extra-Python-library-for-StreamPipes?">Why there is an extra Python library for StreamPipes?<a class="anchor-link" href="#Why-there-is-an-extra-Python-library-for-StreamPipes?">¶</a></h3><p><a href="https://streampipes.apache.org/">Apache StreamPipes</a> aims to enable non-technical users to connect and analyze IoT data streams.
 To this end, it provides an easy-to-use and convenient user interface that allows one to connect to an IoT data source and create some visual
-graphs within a few minutes. <br>
+graphs within a few minutes. <br/>
 Although this is the main use case of Apache StreamPipes, it can also provide great value for people who are eager to work on data analysis or data science with IoT data, but don't we do get in touch with all the hassle associated with extracting data from devices in a suitable format.
 In this scenario, StreamPipes helps you connect to your data source and extract the data for you.
 You then can make the data available outside StreamPipes by writing it into an external source, such as a database, Kafka, etc.
 While this requires another component, you can also extract your data directly from StreamPipes programmatically using the StreamPipes API.
 For convenience, we also provide you with a StreamPipes client both available for Java and Python.
 Specifically with StreamPipes Python, we want to address the amazing data analytics and data science community in Python and benefit from the great universe of Python libraries out there.</p>
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-
+<br/>
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+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">streampipes</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt">%pip install streampipes
-</div>
-        
-        </div>
-    </div>
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+<div class="clipboard-copy-txt" id="cell-1">%pip install streampipes
 </div>
 </div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>If you want to have the current development state you can also execute:</p>
-
 </div>
 </div>
 </div>
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">apache</span><span class="o">/</span><span class="n">streampipes</span><span class="o">.</span><span class="n">git</span><span class="c1">#subdirectory=streampipes-client-python</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
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+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">apache</span><span class="o">/</span><span class="n">streampipes</span><span class="o">.</span><span class="n">git</span><span class="c1">#subdirectory=streampipes-client-python</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt">
+<div class="clipboard-copy-txt" id="cell-2">
 %pip install git+https://github.com/apache/streampipes.git#subdirectory=streampipes-client-python</div>
-        
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-    </div>
-
 </div>
 </div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>The corresponding documentation can be found <a href="https://streampipes.apache.org/docs/docs/python/dev/">here</a>.
-<br></p>
-
+<br/></p>
 </div>
 </div>
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h3 id="How-to-prepare-the-tutorials">How to prepare the tutorials<a class="anchor-link" href="#How-to-prepare-the-tutorials">&#182;</a></h3><p>In case you want to reproduce the first two tutorials exactly on your end, you need to create a simple pipeline in StreamPipes like demonstrated below.</p>
-<p><img src="https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/tutorial-preparation.gif" alt="tutorial-preparation" /></p>
-<br>
-
-
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="How-to-prepare-the-tutorials">How to prepare the tutorials<a class="anchor-link" href="#How-to-prepare-the-tutorials">¶</a></h3><p>In case you want to reproduce the first two tutorials exactly on your end, you need to create a simple pipeline in StreamPipes like demonstrated below.</p>
+<p><img alt="tutorial-preparation" src="https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/tutorial-preparation.gif"/></p>
+<br/>
 </div>
 </div>
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h3 id="How-to-configure-the-Python-client">How to configure the Python client<a class="anchor-link" href="#How-to-configure-the-Python-client">&#182;</a></h3><p>In order to access the resources available in StreamPipes, one must be able to authenticate against the backend.
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="How-to-configure-the-Python-client">How to configure the Python client<a class="anchor-link" href="#How-to-configure-the-Python-client">¶</a></h3><p>In order to access the resources available in StreamPipes, one must be able to authenticate against the backend.
 For this purpose, the client sofar only supports the authentication via an API token that can be generated via the StreamPipes UI, as you see below.</p>
-<p><img src="https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/how-to-get-api-key.gif" alt="how-to-get-api-key" /></p>
-<br>
-
+<p><img alt="how-to-get-api-key" src="https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/how-to-get-api-key.gif"/></p>
+<br/>
 <p>Having generated the API token, one can directly start initializing a client instance as follows:</p>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            <div class="zeroclipboard-container">
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-3">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.config</span> <span class="kn">import</span> <span class="n">StreamPipesClientConfig</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.credential_provider</span> <span class="kn">import</span> <span class="n">StreamPipesApiKeyCredentials</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt">from streampipes.client import StreamPipesClient
+<div class="clipboard-copy-txt" id="cell-3">from streampipes.client import StreamPipesClient
 from streampipes.client.config import StreamPipesClientConfig
 from streampipes.client.credential_provider import StreamPipesApiKeyCredentials</div>
-        
-        </div>
-    </div>
-
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 </div>
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-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
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-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-4">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-4">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
     <span class="n">credential_provider</span><span class="o">=</span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">(</span>
-        <span class="n">username</span><span class="o">=</span><span class="s2">&quot;test@streampipes.apache.org&quot;</span><span class="p">,</span>
-        <span class="n">api_key</span><span class="o">=</span><span class="s2">&quot;API-KEY&quot;</span><span class="p">,</span>
+        <span class="n">username</span><span class="o">=</span><span class="s2">"test@streampipes.apache.org"</span><span class="p">,</span>
+        <span class="n">api_key</span><span class="o">=</span><span class="s2">"API-KEY"</span><span class="p">,</span>
     <span class="p">),</span>
-    <span class="n">host_address</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span>
+    <span class="n">host_address</span><span class="o">=</span><span class="s2">"localhost"</span><span class="p">,</span>
     <span class="n">https_disabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="n">port</span><span class="o">=</span><span class="mi">80</span>
 <span class="p">)</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt">config = StreamPipesClientConfig(
+<div class="clipboard-copy-txt" id="cell-4">config = StreamPipesClientConfig(
     credential_provider=StreamPipesApiKeyCredentials(
         username="test@streampipes.apache.org",
         api_key="API-KEY",
@@ -3081,206 +3046,174 @@
     https_disabled=True,
     port=80
 )</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Please be aware that connecting to StreamPipes via a <code>https</code> connection is currently not supported by the Python client.</p>
 <p>Providing secrets like the <code>api_key</code> as plaintext in the source code is an anti-pattern.
 This is why the StreamPipes client also supports passing the required secrets as environment variables.
 To do so, you must initialize the credential provider like the following:</p>
-
 </div>
 </div>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-5">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">()</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt">StreamPipesApiKeyCredentials()</div>
-        
-        </div>
-    </div>
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+<div class="clipboard-copy-txt" id="cell-5">StreamPipesApiKeyCredentials()</div>
 </div>
 </div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>To ensure that the above code works, you must set the environment variables as expected.
 This can be done like following:</p>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="o">%</span><span class="n">export</span> <span class="n">SP_USERNAME</span><span class="o">=</span><span class="s2">&quot;&lt;USERNAME&gt;&quot;</span>
-<span class="o">%</span><span class="n">export</span> <span class="n">SP_API_KEY</span><span class="o">=</span><span class="s2">&quot;&lt;API-KEY&gt;&quot;</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-6">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="o">%</span><span class="n">export</span> <span class="n">SP_USERNAME</span><span class="o">=</span><span class="s2">"&lt;USERNAME&gt;"</span>
+<span class="o">%</span><span class="n">export</span> <span class="n">SP_API_KEY</span><span class="o">=</span><span class="s2">"&lt;API-KEY&gt;"</span>
 </pre></div>
-<div id="cell-6" class="clipboard-copy-txt">%export SP_USERNAME="<USERNAME>"
-%export SP_API_KEY="<API-KEY>"</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-6">%export SP_USERNAME="<username>"
+%export SP_API_KEY="<api-key>"</api-key></username></div>
 </div>
 </div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Having the <code>config</code> ready, we can now initialize the actual client.</p>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-7">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
 </pre></div>
-<div id="cell-7" class="clipboard-copy-txt">client = StreamPipesClient(client_config=config)</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-7">client = StreamPipesClient(client_config=config)</div>
 </div>
 </div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>That's already it. You can check if everything works out by using the following command:</p>
-
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 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">client</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-8">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
 </pre></div>
-<div id="cell-8" class="clipboard-copy-txt">client.describe()</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-8">client.describe()</div>
 </div>
 </div>
-
+</div>
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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 <div class="jp-OutputArea-child">
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 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>2023-02-24 17:05:49,398 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.
 2023-02-24 17:05:49,457 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.
@@ -3293,33 +3226,27 @@
 </pre>
 </div>
 </div>
-
-</div>
-
-</div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>This prints you a short textual description of the connected StreamPipes instance to the console.</p>
-<br>
-
+<br/>
 <p>The created <code>client</code> instance serves as the central point of interaction with StreamPipes.
 You can invoke a variety of commands directly on this object.</p>
 <p>Are you curious now how you actually can get data out of StreamPipes and make use of it with Python?
 Then check out the next tutorial on <a href="../2-extracting-data-from-the-streampipes-data-lake">extracting Data from the StreamPipes data lake</a>.</p>
-<br>
-
+<br/>
 <p>Thanks for reading this introductory tutorial.
 We hope you like it and would love to receive some feedback from you.
 Just go to our <a href="https://github.com/apache/streampipes/discussions">GitHub discussion page</a> and let us know your impression.
 We'll read and react to them all, we promise!</p>
-
 </div>
 </div>
 </div>
@@ -3328,7 +3255,6 @@
 
 
 
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diff --git a/docs-python/dev/tutorials/2-extracting-data-from-the-streampipes-data-lake/index.html b/docs-python/dev/tutorials/2-extracting-data-from-the-streampipes-data-lake/index.html
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+++ b/docs-python/dev/tutorials/2-extracting-data-from-the-streampipes-data-lake/index.html
@@ -1902,10 +1902,6 @@
         }, 1000)
       })
 </script>
-
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@@ -1957,10 +1953,6 @@
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-<h1 id="Extracting-Data-from-the-StreamPipes-data-lake">Extracting Data from the StreamPipes data lake<a class="anchor-link" href="#Extracting-Data-from-the-StreamPipes-data-lake">&#182;</a></h1><p>In the first tutorial (<a href="../1-introduction-to-streampipes-python-client">Introduction to the StreamPipes Python client</a>) we took the first steps with the StreamPipes Python client and learned how to set everything up.
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="Extracting-Data-from-the-StreamPipes-data-lake">Extracting Data from the StreamPipes data lake<a class="anchor-link" href="#Extracting-Data-from-the-StreamPipes-data-lake">¶</a></h1><p>In the first tutorial (<a href="../1-introduction-to-streampipes-python-client">Introduction to the StreamPipes Python client</a>) we took the first steps with the StreamPipes Python client and learned how to set everything up.
 Now we are ready to get started and want to retrieve some data out of StreamPipes.
 In this tutorial, we'll focus on the StreamPipes Data Lake, the component where StreamPipes stores data internally.
 To get started, we'll use the <code>client</code> instance created in the first tutorial.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.config</span> <span class="kn">import</span> <span class="n">StreamPipesClientConfig</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.credential_provider</span> <span class="kn">import</span> <span class="n">StreamPipesApiKeyCredentials</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt">from streampipes.client import StreamPipesClient
+<div class="clipboard-copy-txt" id="cell-1">from streampipes.client import StreamPipesClient
 from streampipes.client.config import StreamPipesClientConfig
 from streampipes.client.credential_provider import StreamPipesApiKeyCredentials</div>
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+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
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-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># if you want all necessary dependencies required for this tutorial to be installed,</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-2">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># if you want all necessary dependencies required for this tutorial to be installed,</span>
 <span class="c1"># you can simply execute the following command</span>
 <span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">matplotlib</span> <span class="n">streampipes</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt"># if you want all necessary dependencies required for this tutorial to be installed,
+<div class="clipboard-copy-txt" id="cell-2"># if you want all necessary dependencies required for this tutorial to be installed,
 # you can simply execute the following command
 %pip install matplotlib streampipes</div>
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+</div>
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SP_USERNAME&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;admin@streampipes.apache.org&quot;</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SP_API_KEY&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;XXX&quot;</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-3">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_USERNAME"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"admin@streampipes.apache.org"</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_API_KEY"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"XXX"</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt">import os
+<div class="clipboard-copy-txt" id="cell-3">import os
 os.environ["SP_USERNAME"] = "admin@streampipes.apache.org"
 os.environ["SP_API_KEY"] = "XXX"
 </div>
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-        </div>
-    </div>
-
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 </div>
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+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-4">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
     <span class="n">credential_provider</span><span class="o">=</span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">(),</span>
-    <span class="n">host_address</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span>
+    <span class="n">host_address</span><span class="o">=</span><span class="s2">"localhost"</span><span class="p">,</span>
     <span class="n">https_disabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="n">port</span><span class="o">=</span><span class="mi">80</span>
 <span class="p">)</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt">config = StreamPipesClientConfig(
+<div class="clipboard-copy-txt" id="cell-4">config = StreamPipesClientConfig(
     credential_provider=StreamPipesApiKeyCredentials(),
     host_address="localhost",
     https_disabled=True,
     port=80
 )</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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 </div>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-5">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt">client = StreamPipesClient(client_config=config)</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-5">client = StreamPipesClient(client_config=config)</div>
 </div>
 </div>
-
+</div>
+</div>
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 <div class="jp-OutputArea-child">
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+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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 <pre>2023-02-24 17:34:25,860 - streampipes.client.client - [INFO] - [client.py:128] [_set_up_logging] - Logging successfully initialized with logging level INFO.
 </pre>
 </div>
 </div>
-
-</div>
-
-</div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>As a first step, we want to get an overview about all data available in the data lake.
 The data is stored as so-called <code>measures</code>, which refer to a data stream stored in the data lake.
 For his purpose we use the <code>all()</code> method of the <code>dataLakeMeasure</code> endpoint.</p>
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 </div>
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-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">data_lake_measures</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">data_lake_measures</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
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-<div id="cell-6" class="clipboard-copy-txt">data_lake_measures = client.dataLakeMeasureApi.all()</div>
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 <pre>2023-02-24 17:34:25,929 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.
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 <p>So let's see how many measures are available</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">data_lake_measures</span><span class="p">)</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">data_lake_measures</span><span class="p">)</span>
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 <pre>2</pre>
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 <p>All resources of the StreamPipes Python client support the standard Python expressions. If not, <a href="https://github.com/apache/streampipes/issues/new/choose">please let us know</a>.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">data_lake_measures</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">data_lake_measures</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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-<pre>DataLakeMeasure(element_id=&#39;3cb6b5e6f107452483d1fd2ccf4bf9f9&#39;, measure_name=&#39;test&#39;, timestamp_field=&#39;s0::timestamp&#39;, event_schema=EventSchema(event_properties=[EventProperty(class_name=&#39;org.apache.streampipes.model.schema.EventPropertyPrimitive&#39;, element_id=&#39;sp:eventproperty:EiFnkL&#39;, label=&#39;Density&#39;, description=&#39;Denotes the current density of the fluid&#39;, runtime_name=&#39;density&#39;, required=False, domain_properties=[&#39;http://schema.org/Number&#39;], property_scope=&#39;MEASUREMENT_PROPERTY&#39;, index=5, runtime_id=None, runtime_type=&#39;http://www.w3.org/2001/XMLSchema#float&#39;, measurement_unit=None, value_specification=None), EventProperty(class_name=&#39;org.apache.streampipes.model.schema.EventPropertyPrimitive&#39;, element_id=&#39;sp:eventproperty:ghSkQI&#39;, label=&#39;Mass Flow&#39;, description=&#39;Denotes the current mass flow in the sensor&#39;, runtime_name=&#39;mass_flow&#39;, required=False, domain_properties=[&#39;http://schema.org/Number&#39;], property_scope=&#39;MEASUREMENT_PROPERTY&#39;, index=2, runtime_id=None, runtime_type=&#39;http://www.w3.org/2001/XMLSchema#float&#39;, measurement_unit=None, value_specification=None), EventProperty(class_name=&#39;org.apache.streampipes.model.schema.EventPropertyPrimitive&#39;, element_id=&#39;sp:eventproperty:cQAUry&#39;, label=&#39;Sensor ID&#39;, description=&#39;The ID of the sensor&#39;, runtime_name=&#39;sensorId&#39;, required=False, domain_properties=[&#39;https://streampipes.org/vocabulary/examples/watertank/v1/hasSensorId&#39;], property_scope=&#39;DIMENSION_PROPERTY&#39;, index=1, runtime_id=None, runtime_type=&#39;http://www.w3.org/2001/XMLSchema#string&#39;, measurement_unit=None, value_specification=None), EventProperty(class_name=&#39;org.apache.streampipes.model.schema.EventPropertyPrimitive&#39;, element_id=&#39;sp:eventproperty:pbPMyL&#39;, label=&#39;Sensor Fault Flags&#39;, description=&#39;Any fault flags of the sensors&#39;, runtime_name=&#39;sensor_fault_flags&#39;, required=False, domain_properties=[&#39;http://schema.org/Boolean&#39;], property_scope=&#39;MEASUREMENT_PROPERTY&#39;, index=6, runtime_id=None, runtime_type=&#39;http://www.w3.org/2001/XMLSchema#boolean&#39;, measurement_unit=None, value_specification=None), EventProperty(class_name=&#39;org.apache.streampipes.model.schema.EventPropertyPrimitive&#39;, element_id=&#39;sp:eventproperty:Qmayhw&#39;, label=&#39;Temperature&#39;, description=&#39;Denotes the current temperature in degrees celsius&#39;, runtime_name=&#39;temperature&#39;, required=False, domain_properties=[&#39;http://schema.org/Number&#39;], property_scope=&#39;MEASUREMENT_PROPERTY&#39;, index=4, runtime_id=None, runtime_type=&#39;http://www.w3.org/2001/XMLSchema#float&#39;, measurement_unit=&#39;http://qudt.org/vocab/unit#DegreeCelsius&#39;, value_specification=ValueSpecification(class_name=&#39;org.apache.streampipes.model.schema.QuantitativeValue&#39;, element_id=None, min_value=0, max_value=100, step=0.1)), EventProperty(class_name=&#39;org.apache.streampipes.model.schema.EventPropertyPrimitive&#39;, element_id=&#39;sp:eventproperty:YQYhjd&#39;, label=&#39;Volume Flow&#39;, description=&#39;Denotes the current volume flow&#39;, runtime_name=&#39;volume_flow&#39;, required=False, domain_properties=[&#39;http://schema.org/Number&#39;], property_scope=&#39;MEASUREMENT_PROPERTY&#39;, index=3, runtime_id=None, runtime_type=&#39;http://www.w3.org/2001/XMLSchema#float&#39;, measurement_unit=None, value_specification=None)]), pipeline_id=None, pipeline_name=None, pipeline_is_running=False, schema_version=&#39;1.1&#39;)</pre>
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+<pre>DataLakeMeasure(element_id='3cb6b5e6f107452483d1fd2ccf4bf9f9', measure_name='test', timestamp_field='s0::timestamp', event_schema=EventSchema(event_properties=[EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:EiFnkL', label='Density', description='Denotes the current density of the fluid', runtime_name='density', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=5, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:ghSkQI', label='Mass Flow', description='Denotes the current mass flow in the sensor', runtime_name='mass_flow', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=2, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:cQAUry', label='Sensor ID', description='The ID of the sensor', runtime_name='sensorId', required=False, domain_properties=['https://streampipes.org/vocabulary/examples/watertank/v1/hasSensorId'], property_scope='DIMENSION_PROPERTY', index=1, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#string', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:pbPMyL', label='Sensor Fault Flags', description='Any fault flags of the sensors', runtime_name='sensor_fault_flags', required=False, domain_properties=['http://schema.org/Boolean'], property_scope='MEASUREMENT_PROPERTY', index=6, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#boolean', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:Qmayhw', label='Temperature', description='Denotes the current temperature in degrees celsius', runtime_name='temperature', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=4, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit='http://qudt.org/vocab/unit#DegreeCelsius', value_specification=ValueSpecification(class_name='org.apache.streampipes.model.schema.QuantitativeValue', element_id=None, min_value=0, max_value=100, step=0.1)), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:YQYhjd', label='Volume Flow', description='Denotes the current volume flow', runtime_name='volume_flow', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=3, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit=None, value_specification=None)]), pipeline_id=None, pipeline_name=None, pipeline_is_running=False, schema_version='1.1')</pre>
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 <p>To get a more comprehensive overview, you can take a look at the <a href="https://pandas.pydata.org/"><code>pandas</code></a> representation</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">display</span><span class="p">(</span><span class="n">data_lake_measures</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">())</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">display</span><span class="p">(</span><span class="n">data_lake_measures</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">())</span>
 </pre></div>
-<div id="cell-9" class="clipboard-copy-txt">display(data_lake_measures.to_pandas())</div>
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 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>measure_name</th>
-      <th>timestamp_field</th>
-      <th>pipeline_id</th>
-      <th>pipeline_name</th>
-      <th>pipeline_is_running</th>
-      <th>num_event_properties</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>flow-rate</td>
-      <td>s0::timestamp</td>
-      <td>None</td>
-      <td>None</td>
-      <td>False</td>
-      <td>6</td>
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-      <td>False</td>
-      <td>6</td>
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+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>measure_name</th>
+<th>timestamp_field</th>
+<th>pipeline_id</th>
+<th>pipeline_name</th>
+<th>pipeline_is_running</th>
+<th>num_event_properties</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>flow-rate</td>
+<td>s0::timestamp</td>
+<td>None</td>
+<td>None</td>
+<td>False</td>
+<td>6</td>
+</tr>
+<tr>
+<th>1</th>
+<td>test</td>
+<td>s0::timestamp</td>
+<td>None</td>
+<td>None</td>
+<td>False</td>
+<td>6</td>
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 <p>So far, we have only retrieved metadata about the available data lake measure.
 In the following, we will access the actual data of the measure <code>flow-rate</code>.</p>
 <p>For this purpose, we will use the <code>get()</code> method of the <code>dataLakeMeasure</code> endpoint.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">flow_rate_measure</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;flow-rate&quot;</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">flow_rate_measure</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">"flow-rate"</span><span class="p">)</span>
 </pre></div>
-<div id="cell-10" class="clipboard-copy-txt">flow_rate_measure = client.dataLakeMeasureApi.get(identifier="flow-rate")</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-10">flow_rate_measure = client.dataLakeMeasureApi.get(identifier="flow-rate")</div>
 </div>
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 <pre>2023-02-24 17:34:26,020 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.
 </pre>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>For further processing, the easiest way is to turn the data measure into a <code>pandas DataFrame</code>.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">flow_rate_pd</span> <span class="o">=</span> <span class="n">flow_rate_measure</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<clipboard-copy for="cell-11">
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+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">flow_rate_pd</span> <span class="o">=</span> <span class="n">flow_rate_measure</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
 </pre></div>
-<div id="cell-11" class="clipboard-copy-txt">flow_rate_pd = flow_rate_measure.to_pandas()</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-11">flow_rate_pd = flow_rate_measure.to_pandas()</div>
 </div>
 </div>
-
 </div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Let's see how many data points we got...</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">flow_rate_pd</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">flow_rate_pd</span><span class="p">)</span>
 </pre></div>
-<div id="cell-12" class="clipboard-copy-txt">len(flow_rate_pd)</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>... and get a first overview</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">flow_rate_pd</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">flow_rate_pd</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
 </pre></div>
-<div id="cell-13" class="clipboard-copy-txt">flow_rate_pd.describe()</div>
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+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3553,231 +3396,192 @@
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>density</th>
-      <th>mass_flow</th>
-      <th>temperature</th>
-      <th>volume_flow</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>count</th>
-      <td>1000.000000</td>
-      <td>1000.000000</td>
-      <td>1000.000000</td>
-      <td>1000.000000</td>
-    </tr>
-    <tr>
-      <th>mean</th>
-      <td>45.560337</td>
-      <td>5.457014</td>
-      <td>45.480231</td>
-      <td>5.659558</td>
-    </tr>
-    <tr>
-      <th>std</th>
-      <td>3.201544</td>
-      <td>3.184959</td>
-      <td>3.132878</td>
-      <td>3.122437</td>
-    </tr>
-    <tr>
-      <th>min</th>
-      <td>40.007698</td>
-      <td>0.004867</td>
-      <td>40.000992</td>
-      <td>0.039422</td>
-    </tr>
-    <tr>
-      <th>25%</th>
-      <td>42.819497</td>
-      <td>2.654101</td>
-      <td>42.754623</td>
-      <td>3.021625</td>
-    </tr>
-    <tr>
-      <th>50%</th>
-      <td>45.679264</td>
-      <td>5.382355</td>
-      <td>45.435944</td>
-      <td>5.572553</td>
-    </tr>
-    <tr>
-      <th>75%</th>
-      <td>48.206881</td>
-      <td>8.183144</td>
-      <td>48.248473</td>
-      <td>8.338209</td>
-    </tr>
-    <tr>
-      <th>max</th>
-      <td>50.998310</td>
-      <td>10.986015</td>
-      <td>50.964909</td>
-      <td>10.998676</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>density</th>
+<th>mass_flow</th>
+<th>temperature</th>
+<th>volume_flow</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>count</th>
+<td>1000.000000</td>
+<td>1000.000000</td>
+<td>1000.000000</td>
+<td>1000.000000</td>
+</tr>
+<tr>
+<th>mean</th>
+<td>45.560337</td>
+<td>5.457014</td>
+<td>45.480231</td>
+<td>5.659558</td>
+</tr>
+<tr>
+<th>std</th>
+<td>3.201544</td>
+<td>3.184959</td>
+<td>3.132878</td>
+<td>3.122437</td>
+</tr>
+<tr>
+<th>min</th>
+<td>40.007698</td>
+<td>0.004867</td>
+<td>40.000992</td>
+<td>0.039422</td>
+</tr>
+<tr>
+<th>25%</th>
+<td>42.819497</td>
+<td>2.654101</td>
+<td>42.754623</td>
+<td>3.021625</td>
+</tr>
+<tr>
+<th>50%</th>
+<td>45.679264</td>
+<td>5.382355</td>
+<td>45.435944</td>
+<td>5.572553</td>
+</tr>
+<tr>
+<th>75%</th>
+<td>48.206881</td>
+<td>8.183144</td>
+<td>48.248473</td>
+<td>8.338209</td>
+</tr>
+<tr>
+<th>max</th>
+<td>50.998310</td>
+<td>10.986015</td>
+<td>50.964909</td>
+<td>10.998676</td>
+</tr>
+</tbody>
 </table>
 </div>
 </div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>As a final step, we want to create a plot of both attributes</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">flow_rate_pd</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;mass_flow&quot;</span><span class="p">,</span> <span class="s2">&quot;temperature&quot;</span><span class="p">])</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-14">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">flow_rate_pd</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">"mass_flow"</span><span class="p">,</span> <span class="s2">"temperature"</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 id="cell-14" class="clipboard-copy-txt">import matplotlib.pyplot as plt
+<div class="clipboard-copy-txt" id="cell-14">import matplotlib.pyplot as plt
 flow_rate_pd.plot(y=["mass_flow", "temperature"])
 plt.show()</div>
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3YrJNVXbuafZ00cRhNXUczasEgbd1rLHSZCEj7y8vKwc+dO1K5dG506dUJUVBTmzJnjP79161bs27cPaWlpIWfUEVjTiJ4vwb+v6g88vA+f/rGspp8/JvicWWehJxCo1pAzlf6DdoG/Yyrz72WX6fmTkIyFD3YAKSkCPu0qB7mxw/rJmmdzyow349LDCZ8PnjOhkYr02DZgicHMoZQzu51ws6zRUsrNjiBQlK+uMyWFwR2or0Q9wNlZwqiHyO2HVssrAQC5nvIGD1YoWDFO4LnMO8x5Cfj8Qn1nZbauGgkfbBmZTQJYcpjBN7FO4G/VREGSB2+F3f8BUx+UTZAqM5rJTFjXt0hPm2VTAGc1H+t/kZfAZuk42IosX843CTH/bivZ5MViZHYRYWVZPWKDzSl83Vsn7pEm7YMrgaUcM6oRyqRHL59sX1ASJodTFhHB4gwRPiyZXR5//HFce+21aNCgAQ4dOoQXXngBERERuO2225CUlIR77rkHI0eORNWqVZGYmIhhw4YhLS0N3bp1C1f+rcH6VRhpOCbeDAxj1JZrJgJJqUAtToCr9I/5/h6AgPChU0nYWYNepbcy0JgJH2w+9qXLg69pREidjlC73MzSIMxp6Ee38LVKohzfqTYDKRh1FMc1ESwPrpTt7U0vC77XVypHP2QdzwB7wkdxgbrOlBZxVkxotFJ6/h9WsKNV8vnkoHU8WFPZn8OBzoP51x1eJ6/c4ZWV3sox1ezSIN+sf4XIcnKlTEV8TTI2qCNdHtnCf65Z+9dzDNQbqIVMo5wyYYWPX++R/1eWwWoRGUA9JnNWrgCj48fiJnZWZvn7Rj3hg2mfjmg+OM9hy9tXAsBkQmbU11cgLAkfBw4cwG233Ybjx4+jRo0auPDCC7FkyRLUqFEDAPDee+/B6/Wif//+KCwsRJ8+ffDpp5+GJeO2YD34fcVyQ+MFhlGWOAJyRzP1IfnvJ3cHX6sneADmgZ70Komq0yqrjFpBRXdw43Q+ks9YGlbZplkbc45sKmnTH6jeTP9+Fu1s1Oog7POp99/YPR/49hqg/W369xix81/17+LT8mBrxTlsbFnclpGb5dkw28lIPn74cbP33jk3WO099SGg4UWB36VFnBUTBj4fIjOeIIdVFguDglFHK7IENmODHL0RALrcL/7cYk0b1oNtWzwhQJI0bbfs3VlBRa+ctNpFVnhj25LZPj4igdm0mo9Dq4GUNrKAV1oi+7SkXsBEKuZ8Qz2fDx4i7cLOKiP2e7DvZCb45mbIy3D993JMKXpoz9nRopqFItBqKkPFLM6HiGARrvAJDmPJ7DJp0iQcOnQIhYWFOHDgACZNmoQmTZr4z8fGxuKTTz7BiRMnkJ+fjylTphj6e7iO9sOKNP58Rr1qVZ3FNuRP0zhOoDoVSdLMEk5lBXuWb/4DOLYDYphpPnQ6hpmj5KV0n1rQXGlnZ1aED48HOLEzEI1VgXWes4o2nPkbDYC3m6sjE4qiDFa8mA9ajN579wLg+778ZaR7FgT+Li0KHmB9JRr7eQn/b26eJPNZqyh/Pa5/jid85B2RHbCVNsSGtLdSR9gO3mhwZztg3mz+72dlx1ItbBvXG9i0PlKqmSkrfDihhmd9SIrl8OxfXy5H4Fz0vhyOXdmLRw+ez4ceU+4H/n3N+BojgUHvnF4fY8bEm4G5TGh97b1GaWnP2YlA6neA1qkLbPsMl8+Hx6rwoUlj9QTg6z5qU2EF4Nza2yXIU79MnW8kPccyDrBWNrIC1J3PkU3q1S2AQeepUauvncQPEjZJUBsg+cTNLmxZ7Cvz9reixgvSfFhRsXqcGxwB2eSyeZr6WMlpedWA1kwiQkRMIA0FPYHU6L2/vUbsecWn1TE+AAStdlGtwhAQPoxMFVa+1ZoJ+ud4K5Q+TZMjgq5WZrHMs3hOhHqotJeCdVorLABMVFclO2X5UfnT6HzbIHMJOzgU8/+2C09wOrQaeLelvFoG0AQfMzG7mJFz0Hyna22sGRY9YUDVxxj4fOz+T/1bu5oOknzPlAeA2S8Y1wFtEDE7EUhNzS7MNzbTGq2brA7cx8MJnw/t9b8/LC/nnmsiVLrMOS58lGk+jD4oq6qzukRLa3bRSsZCA5ek34kZRi9kkzARPtZMBA4o2gbm2UZmI1Ya/3NEoBFqBx7TgEwPq3//eKvx9SxFBfI+OryO8NgO46BFdpzDlHczW/kAOGPX3rswuDPWaj5Y4ePoFhgi+XSEO+VbOmSL53XyBWWrT3iOpFpNlxF6mo8DK4CF7wXaFCs4WJk06A2SLEGDjGCbsYNesDdAbR4G9H0anI6Q6fHq9ye843FVNJoPg/7222uNn71tpux4v26SrPk5tNo0uyHhr08Cq6/0zC67F8gBwKbcK8fhMFpRxKtzbJ20MhEsKVL3gU7vDRQiDmyOcAahrUAlp+RjvAiAynpqtjLwZlBGaDspngqdm09NBbSivuV1NGY+Hwvflf+9mK0ef0Sfu+IboF4XoHkfaz4fBSeCZ9AiW58r3+a76+QN5677GOh4p/oas9U6diJzKmWoMruEGOfDCJ6a1OdTp60VTgxx0OxihJFjpDILtyOcSZLGDFoszyYjIuWl3IA80HUapBYutcKHkeCptyRUFCe0HSyifgRZ++TAhTxCWf2QfQCARx3Sv7hAfzNC3rO0kx/JJ7f9Fd/Y27WXXWH11xPi99lpk/58C2g+eGaX3QuCNZ2K5kyS5Dyx7YWXRxFtHI9Dq8UnqOXAOSZ8cOyFO/6Rw4BreacFcO+c0AYR7eAd5DwoYHYBQl86Zab50Hu2FaFn3uvycsP4asHP1n2UzZm2InwoO92u+jZY+DBrdLY838vKUMjs4oDwwROQtJqPeaPF09PVfCjnHdJ8GD3DbwKw8azDa9VmqNxMeTbJomxrzpadVvjgzgA5jt3C35AR+M2cTK0iWk+NljTbqYuSJLd/xdfshi8D5/4YBmAY/z5eeyjMA5YzMV8kn5yGaIAxI3gr2fSwVQ5mmg/WzMZJn/XhUlDax4+3AUc2AkOWAVFx+nlk053/pqzhvGOKHG7BKJKw3vuunSTH0+n/FZBUT//+MHNum10kn/6+B3mZwNzXQxQ+NAO+0V4PquOaBqzbofG0HJxGsmeR+dp8BXYmYdiRap6tzEa0Mxm9Rpt9EPjhBs1BgwFJb0UBoPONTAY3O8KH8tySEB1OReHlUSq1L4xKPhMVvEPCh9GeN1EhaD7ma3wReH5QikDPCoha4cNIO2FnlunR8flwAuF6arTqw0Z9kXzqJf+/Ca5I4j1LKlU7fks+ebWX24Sk+dA7z0Yk5r0757somo5tM+R+k9Vemmk+Vn0L7F8aiGRr+G01z/Z45KXkvz0gh1TgBZlzkXNb8+HzGYfv1sZUsIqZ2UUPbYXfJrA9tBEiOzwC8qqak8xy4nDt0gjIG0kFhWYX7ByChEjNN9oxxzzvIqGqtfA0H+E0u3A1H6X26+TsF4xNh05pPoycea04P2rR+j/wvqGvRJ7ZKVoxIFj44AnVUiiaDzZtF30+WAyXnNqZ8fvCM1gDAc2l29ip32bvY7TybeW3AadgFu27s6ZqXh55kw1Fe2IYQoGT1srxgb+tLqBwmHNb+JB8xh7QWp8PqwSZXUoEAx5pKpQV1WIojVqbt3BtlASo90fxX2vUObCaD22azH0FJ4Af+pnnSxVFVhBFxakKBCZoOrMDb9arNbtYYdkXJhe4EPwpFLOL9r15PjEn9wKrf1AfK9T4JxhqPlifDzuDr9OaD8Hlm0Z1wo6mzG4dE7nPCcHcDnafa7TdhMrswpRz/jFg2iP8e05lqSPARkTJbf2/t4C4qsHX8yY4GeuALy8B+rxukHFOntlAm/mc9uMi57bw8fczQLMrrN1jhSCzSxHwtcnzAPHOwunZg4hgZJVts4CqjYOP8wZ/URWiUcC1g6vE8mXH0c1vdmGEMnY7cf91UhjNLjZnpOUJa6YKxewiEqcnl7ODtrYceQLjqZPBO9HaKWcnYj2o0nPC7GKzrMOhKQDK2m95aD7sCh8lEHI4lXxA1n4gvmqwwMuy+EO1ud8bKZtRFryj83xOv6gEuvtea7pW8iJxvp9Hrfks57gf57bwcWA5UM0kcqeTmg9fMZC5wfw+YedQhxvw5IHi14oKPmt+kP9p4XWqomW9ZqJata8XdMsI7cZXIvjNLozwseiD4OtKCt1zOHWScIW9ZoWEUMwuIpoIngCrFQh4K0jW/yz/U6UlWM5sW3AiyiWLsOYjDD4fdrRTIqp8U9+jMGG33eyaJ0d65qFa6r4VeL8NUL0FcOtE/fROZal/e6OMt5Aw+n56wmlRPrg+H+wEU9QPMEyc28IHYD6zCKmj13x8UU94UV+LklOydiXCoc94ZJMz6djFsKyZzmqGZnmdnW3lRTbQ0sJbasu9rtgZAYH3HG14dSfwDwThEj44m8Et/sh6OiJmQN4sUTuAi7ZD0UGbrX+Oaz6cED58wLwx1upNqP5uZmmXh+aD3Y/HChNv0j/HTnaObJT/P7bVuKy1q8G8EUDlmgbPsNHeT2fpaD5Y4YM0H+7Ba0xmDSykjl7TwISFDwt24/SPgAtHmF93JmCnkQHqbyQ6YNgJ2X46C/jmSvMZg6/EmY6bN4sOp+YjXLCzOsknxz6wM+sS0QLwhA+tH4aocC9azuxySqc1H8LCjEF9O5Ul4O+jTc5nT2MimnY5yB5C22lYRa8uGZWdVvjgmkgE09LjVBa4dULRpra/HTjf5s7lDnGOLbXlCR8mH9bJjl60I7GyyuSfF53v8MqNEOJ++P8O48C84F15iZoZTmkneM6LuRlhHBTCMNNNrKfWLq7+QTy8vBaR9sMzu2mPiTpS+0qtb6LmtOZDdKmt0bez4zhuFpgwFMpL8xEO9PpqI/+5IOHDRNCz8x24mg8EfD46DwYaXRR83kXOMeGDMyBo9/4Quccu2h1W9bC6xPXVmsCv94ZPTeoWhmVt8G5HNwPvtpa3tg9Xhwlo9tAwwCnNB4+ZT4XR5yMMZacNcBeKac+28KF5L9HQ+pJPbIdeVdpO+3w44HBqa7WLFF7hozx8PsKB3tLqry7Tv0f77mZO5HY1H9r7WJ+PqHjraToMCR+G1zu0akEhe7/YdXaiJK6fDBzZbP0+u4RjALS6Zp0l56C8tf2+Jc7myQ6nc4L9UpzEccHGI294tel3h9MFQgqKpkVkIOZpi7QCyd5FYs+zJXxUQM2Hlf1A/OmF2exytmAnFlKQ5sNEU2qn/RTmcu7zBEyg0ZWsp+kwJHyY3lMO2gS7wb2y9ro3owhHB2KUpqjWwaptOxwseFs/cq4ThKPspz/mfJpAsOYjFEQGdp5WQ9ueFr0v9jw7Kz5EV6cIpyfqp2BkdrHRn/gcFBq1nE1mFztxXXhmF6OytqW5Kg1ud5IvUJ+iK1tP02FI+DAi1CBjdrGz4yog7wjrlrAUjo7JqKy1gaMqMma7y4ZKOE1LTpN/1DlThJDwwbnGrjBvx3fHac2HKEb5tPP+YXc4PUuEDyf28gmH2cXHET5Y3ytlL5lyhISPcNwTKqGENbe6865d3NZ8nFE43LF2vEv92ylNgkK4B4IpgvuCmCH03jyn8hBWUVkV9MrL+dvQ4dTGABluh1M3dld2A1sbVJYG/zZ0OLVjNuMIH2w9CCXejkOcJTVAkHNB+LATPMsOJHzo4/Rgrl1xoVX1dn3I+Wc4SR4n6uiZgJ1osk7sh2QLh80uy74sX7NLBXCIFMLOZE/7PcxiqtiJx8EzmynP9Xidiw0VAiR8hOOeUAlFlaeNnhcuwlEuog655xpap0eth32VhqE/o1KN0NM425BMZqQ8nNgPyQ6GZhcb/cmi98NndjmdbR7n5UzRjBTmWr9n5xz1b6cXNgDAyT3y7rcsilYuItrZZ9nkDPnCDnHGCB8hzJ7s7NZqRlJq8DE7ja6iYXUlgyhO+91oO2Kt5sPrgNYiNjH0NELlhgrgLMxiR93thA+AHQzNLjb7k3CsfgKASQLBrUQ0ceFqv1bgbYFgFSdXhCmkfyyvYGNR6mZEjLPPssk5JnxYHBTW/ui8fV2E8po96aH1jF45Hpj7WrlkxVFu/r68cyCINky/ZjAJWfjwADEJIabhABVhMGH591Xr91REs4t2BiyK3n4mblAosEeMyAzeG1XxtSiZG93R+iqTlkjSfLiPHS3G5j9tPkuqeB7ddhvh0c3qpZjTHnUmP+VNzZbA+XeUdy7M0dZbrdnFCX+NimBjr2jChx0qoubjbCUiSuyaiu5LNucldwQ9RTAms0s5YKcS5mXafJZJvH67hDJIhHLv8q+AQpdW0rhFRLQzJgstTq94CPKOD4PZpSIIyqLv0bAcw0LX6QDU6ah/vtxWu1TwATYcCGk+zgKB1in8ZhcSPtzHTgO1W3ntbkltRijrs0MdpIpP2d/8rSISER2ezsnpDayCNB8a4cMJobAifFdRzVz3R8KbDyOi4oH75+qfLy+zS3mYh8sbkUG0optc3IQ0H+WI28JHOGYjoWgvvAJqSiOK8sRssWcKEVHh0XwYbSplB8kHnD8g8Fs70NiJsphQO/D3wneBvQvt5c1JRAeKyHJ0mDPLY7iEALO4DOVl7inPgUykb64IGr2KghK8knw+ygE7woBiV7RqV1/1rbzcyWlC0XyI2EiNKMp3bymvG4RL81HkgAc8iyQB17wX+K0daDrY8FsJZ1wPuwgLH+UYIMlsMLOr+TCbVNRqa3x+4xR7zw2V8lw5IST4kPDhhzQf5Ygd4UPppK12eH89bn7NXTaWslVOsX6PQsiaj/zwLOUtLyKiw6OWLbER9dAIqVSe7SsD1LGt6vNxVaynWRHV0aJ5iipP4UMR2nQGNbs+H2b9S/UW9tJ1Et73Kc9ZtNAgKmj67vpgSFkJO7FJgb+72IwY7Bc+aKmt+9gRPhQ1ajgaWeNe4td2vAtI7Qb0ftH+80KNaleUd3ZpPryRZ4ZDmlJvndRWeCtg0xdRkXsigJhyjEmiDMB6gpJd84eZ5iOxtvF5N+BNXspV8xHiZIolFG2aG5qE+GqBv+3uSOt3OHWw3EKgAvZAYSI3A/j3Fev3KdJiecfC7zgIuGcWULmm/TSc0Hw4Eb6906DQ03ACj+fMEj6cFBjOVM1HXJXy/WaKj5BeXnMP2Us32kT4SKhlL10n4ZV7efrfiAz6ohPOUNqDGwIYK3zY9fvzj2Wk+XAXuxE5K8oH83d6Icx+Q/b5yDu7zC5AeBxOnUZZauuk5uNM9fkoLijfb6bk0ek8mPlyJYRZ8yEykPM0pxVe+BBMK5Tv6UYZsMKH3Ymwj5balg92K5diwy1vzYcy6whFQheZMVZrCtRqxz8352XgzxH2n18RMSsTvbJwE7/mw0nhowI2fVHhozwFJzOzi12SUmVzkt7AEG7Nx9Xvml/DNbu4OJA1vAhofxvzbJHJlKD0Ecr3dFv4CHUSScKHy9hV1Z4uW1pa7poPB4QPkUrrjdQf5HIP2382S3lEY6zdnn/ctDwrQOTIsPh8nKGaD0Cdd9eFKI9zz2XD+3sjgce3AyM386+tZMPcqjfINO2t/l05RZ50mKbH6T/c7Be1fZOQ5uMsET5ikwN/1+2sf52Iab28x7IyziHhw6a0eHy7/H+F0XyEsHRMpAw8ERVzVhwKd04FqjXjnxMRSq+wsceHk5DmQ/+68noPJ57b+jomPY+8iod1JqxUE+j7GXDLD/Y2/tMbnG/5IfiYSN1y0+H00bWc50eqBXCRyZTwIoMQ+lU3fD5Su8jRdTvfDaReAPR+iX+diGBRQRxOzwBvO4cI1UmtvFVVSucQitZAZLWL9ywRPhr0APYukv82eieRepE2VPYZmv+Gc/mzQjg0HxXxG4vk6ao3NQOlzUGjUk3zbd15eBzUfPBgB3ipFDi/bAdYO7ue6tVtXj0SqVtcn48w9YtVGgYf83g0mg8HhQ8phF1lXdF8JAL3zwv8rqej/YiMkX3zjKClti4T6qyxvDUf/oYWgvAhMtB6I4EDy+0/o6LAvqvHq//9ReqFxwMk13cmX2a0uBq47iP1MWXgcXK1y5lqdml3s3qgtKsJrBFi3IywCR/Mu7ECh53vpdfeeWmJ1C1RzUdSqnladpB86nJ3Ms5HKBFiecJHQh376QHAeTeof2vfVU8YFRmnynsiXcY5JHyEqPkobzuZ3+wSwqCh5/egek4FHJTsoBI+IvQ1Rmb1ossDgTTcILk+kNpVfazl1c7nIVzv0+1h+/eKDOheTUh8u0JA4572+gSzpba2UTQqjDClnbV3vsdakrqaD07ehTQfPJ8PZiCr2wm4daIsIDrBVW8CbZm0fKXhM7uEEhafNzZUa2I/PUCu5z2fDvzWCgx67yUiWFB4dZcJ1c4l2lGFS6WlPL9yDf6yu17/M76/Tkeg6eXizznTYb+3x6uvVjUbRJTQ5W4JZR4vVKaEfmOBDncK5MGiBkAvrca9gKpNzMN563HlaKDVdebX8RDRYkREaQbKEMwuF9xr/T5PuIQPDtoB8Zp3gU6Dxe/X+8a8chby+eBcw/Z3VRrKgnKo8YQUuj4A9B8b+C2V2nA49Yl951CED16ff2GIqwI9Xo2WR1Om9dP49wn5fJDw4S5WB1WrDajJpfIsWXSQYvfqEMoPk+61HwafN5Nm298mbnaxgqIZEMWtMNHse3gj9GcKvPdVZls9huvb+OOqAtd+EHI2AajLRDswNL8yoBI3mp1aFY70Bs/LXwYeWaX2rrfKTeOB4eut3ycy+w7SfNgUPtglm1ZwYtWZKDzV+tXviN9vJRKmkCaMU9ZsOeRmyP+HGklZD5/G7BIpsM+VG8IHO+A3uhgYshxoepn99ACO8KHp36Niges/Db5PKF4LCR/uYlXNrPUuN3NIuvM34P/eFOuUrvtY9lq2gqqhcUwIZrY+j0esU7DaqVoVVgZOs3a9XVRmF4++jZSX/+s/Ae7+G7j0OeY6Tf3xeJxbMlyzleYAk65WiNLD6nfTaw/K8VDezRthz0dGRJDwajpljxdodgX/2iY6A0Cra8uEdRuCi/IN3NCE8focK8/t9XTwsTod5f8f28Y8RxJMl1cnmGNKWIJwDW5azUe8wJ5GkiTWRznl8xEVD9Robj8tBY9X3R54Zcrb40hE81HeLgRlnDvCh1VnvZgE9W9Rb3OzQaDfV/Z2IWUrTP1unPMmwoc3Ijyaj5wD6t8d71JvgqQlIYSN8aygNbvozWx4nW5kNFC/q1pYCxqsHdwtM7py4G/Jpx74te+hh2XhQyf/5bkKRjgmA5t3j7x09MFF6nIEgDrn69wfwju6anYJYQUGACTWAzoOVB+7+Tv5f207FHkfnvaQbVfKKgunzC6857PtMK6qyE1i7xaS5oPpe53ypfJ4NMIHp0y54e7J4fTMJ0j4EKycZrO3Wm3sqYrZe2KTgFEH5YFewaxCeUSFD4uNJ1sjfHS5X0Ad6kLgLtXsOASHU/91vHJx6D3YfT18peoZDduZOan5MF39Y+PdQh10bO067ZEF81ptONopnTIJJUqp3+HUja3aQ6xfUbHqMml/G5CssxJFpN3z2pCy/QQQ2HQyXHEkfBrNh+huziJtPBRBj+17nVqRFmR24Wgr7EacJeGjgqPdOVNY+DApUqdmTDGVNQOTSQPzRojZgEUrZp0OcuW/9Fn1ca26MNzoBdthCcXh1H8dx+xS2aGQ1+xGUVKp7LjX41Hgshc0nZlBuTpVr6zO3NgOMFR1rh1TD1vXtG1A9104K0t6jQLu+9f8eeFa7WKrzZjck1AneNWXblIiwgdHOGTNFcr3C5fTuq9EXe7xIpoPwfy4pfmo2VosTW394vXLdjf6I+GjgmNX81GUb3KBSYchKs0D6s7AbObi8fID92gRdVLrNQoYtR9ofInmORGwbJJo0MPa9Sx6e15oyyZoZlOWx1A0Hy2ukp1SzYgxMEMBauFDyeflLwMXjVRfZ6QZsDoY6g30yiAoKgjc9mPg71A7Nak04JMgDFPXWE0gYK75YOn1tLxU9MZvgAG/GjwuzEttrWBUdy99Tl4ZJ6Q5E/T5MBM+bikz6YjWg6qNxa5jn8++s5DZBYLCRyg+H8z7mgmRogK61lTM0ybxfPiEltqSz0fFRit8lAoKH6wa0ip1O8urZkSxJHyUna+hdW7UEBUH1DxP/rvbEPNrPR717NdOpxyKmlZPWGPLhqf5UJ4pKnwEdSplNtnLXwJSOT44LE/tMT7Pdl6Gjs0GAoFTg6FVs4tq9UGomg8fcM/f1u5RaS80y831VOBGA0Sb/kCz3uYBuirCrsBGbV7Zv4W9xuj72NZ8MP1d417y/yLtOTYZuHeO+XXa51dntklw1Ozi8FLbUK/1eNUTJlHNB6/stQ7ZFSS8Ogkfetg1u/Aw0jj0eT3wd1yy2PIxPzqrIngonRDPQ5olqhIw6E/glgnBM28WttMRdYrUw0r0WG30RL0loSrhg6P5UAJ5ObJqwWSgNrMDsx2Sz0C74aTmQy/PVle7iA5uQlnyWe8YVcJPtP459Ymy/+z4XrnocGqGoRmlLH/s9zFyBLeq+eg3FrhnNn+ViMhg3/p62WySWNf8WgVfKdCoZ+B3pepi94m8m+jkUqHNjYG/VfXeTPMhqBUS0nwIplVZ41xM4dUrOGZmF2+k+KDJxuTXksZoF2ISZV8OUSQLwofSGZkJN1FxcqfQ6hrjys0KH2zj03ppi8B7zt06M2BtI9QTptiy8XjUHWfXB4H+X5WdE2wC2sFYFYkyRMdA9p2MNB8qwURTxo75fISwZFfbqd31h7W0bDn9GfnB6C0nFlnZoeegXIGED6M2z9PsGQkfVle7tLtZ3uystFD/2SxVG6s1hEr/et9c8+f6n18q902D/pLrVpTgRE1bTtEJwdfwJpdGwh3b71gSupn6ajR+RESp88QToOI5whdvgqJ9DkU4reCYCR/PHwce2yIHsLnnH+O0zFYBKD4Bra8LXi5ohGQwGAXlQVDzwa68MOqQqjLhg9nG7LXh86FtHPUuMFgmqWmErODChvY28vm46g3GV0ST17tn8Z9rJGDYWaXBwr6DkYbNDZ8P/waGgoKASvOh6dQa9wQeWiwHSjtfYHm5Eu+k1yixZwPGgq7pcmKjempSPqFozFK7AjeO0z+v1brqYaRRU/ovj4DmQzTOh5nPhwJbnzsNAtrdKteDS5jvqvRzCSmyICESrFBpww17yHVLlCAhjfNtee3OaPLFCtpWNLdsGRppIKLizGOPVK5pnL6CVjgih9MKxg1fAgN+CfzWStW8WVlcFTniYOoFxmmzHzupXvD5octkJ7fWfe1rPniVLqVN4G/FkZR1buStFIkSED5u+lYtHLB55q12STIJOKUdtKLi1WWmmK2u/SC4k4yIBno+BTS8SP0+QT4fOgO3Nq+8GCra9ABNhyag+WjdN/C3Nvy4SvgwGPRVedA8U6tatYsyWCkBo0yvZ80enE445Tzg9p84gdQYGl8CPLouoEYXceLlPV9L8WmdewTMLorfkxarEU6veV8d16dyLdmvpU0//Xviq4mlbTQzV4QPtp4aCTVCe7twBkszs8t5/YB+X8j9Kesgyjq2N+4pB2g0w+7Os1rhQ5KAu35XH+v2kPw/u3WF0SAdaVPzoZoUGdShqDhzTaB2gqxNX0H7jcjsUsFo3FO2J1ZpKA9k2grrK5H9Icxod0vwMW8k8NRe4Ildas2CQkIt2cnN47Gm+YCB8NHtYfWApNhW2UZz4fDgJEWEj/P6qn9Ha4QPLVqvbO2sWztoxVdVDwxpQ+Xy6zQo2MM9Ihq45H+yn4qROlGv42KFmfMHGNyvKV9WOBUxu9w0PvD3eX2B4RsCv0XNLkaajxu/kWfURis1RFC+36mTgtcbmF1YjFYTxFcDqjRg0rHg92FU9oU5/OMiqyxu/YF/3KrPR+fBcsRcM9j67oQvg6KNZAe4UH0++o+VJxL9mP1WeA727Pdj/67ORP7MP2L+PAVlBRSvb+WRUCeQx5u+5bybFHCOVWjeR94S4LZJgWNGEaETUuDXnLGLBMwWHLBt2KjuRsaZ+xnyhGeewKLNk5O7Y4dAxchFRSAiWh68hq6UQ4BrHRl9JUD7W+W/jTbduuELBNvjPbIzaSWBGQ1PmtVDVZE1lS6uClDC2GMTy7Z4NnU4FTS7sGg1H9r31wpyVRupf2sHLe3Mz1cilx8AXP+x+pzerEM7UOvOIpi8XmU0+9J0FKzwUaOlwX3KYzRROfW+s5GAYXSuRnN5Rt2st3leAH2zltIxKQGjzGA7MiPhz4pDnxWThtGAqkTc1JI2tOwPA82HnpM4z+dDEUhaXqOfnhWUQdZMY2jk86F8Fyd9Pmq3B0asV+9ay9V8sMIHUyfYvqeKpg8w4s7fZKHg4ifErr9ytJzHZ4/Kgr52kNYb9JPrq4UlPc0ZIGtIBs8AHl2r1mYXFxjnTVRjGhUntvxXu3qR9z20wkcooeQdhIQPBaWRRETKlVW7N4WvGLjiVXkWc8cU/XQ8HtkLXHtMFLs+H7zBNi8j8FtRuZo5nIr6fKjuMdF8aH1eug2RnT4V6mpiO2gdqdjGUq2JHEVVQU81qupgPGJBxoy+U5DmgymnPq/Jvj+imwV6NMIHKyQarXax6tfRbQgwaHrgdzKjXWhzIz+mhjKQisY9ENZ8WFxNcN1H8hLBpr2DVzgBckj12CR551M9CnODj9VuHxgE7ZiqeMLHkGXAJc/Ig54ZIn1B53vk1Wb3mviSiZhKRHw+IMntqE4H8/S0cDUfbMRPjYD08BLg8leMtYxa4pLlmDoiGrH7/g1oZnWFYcEl60aChOQDGqQFC6mFOgKvQqvr5Hgy3YcZt/WoeDGB/b456uB4PLO9NvZUBdm5nIQPBe0MmlUDA/JgHh0v2295jj4sqRcAfT+zlw9e5VGWhWox8vmQfIFdJoFAp2eq+WCEE7YhtrtVnq1c9Dgnz8xA6okInlBq1ZdRsbLT59P7gJFbgtfrB5W9phGqlvnqCR8aYUOvoWs3KdNDO1tiyym+quz7wwZLUwbM7o8EpxVXRT27L2FmWEZmlyaX6J9jGTwT6PqQbI5qeGHguMcLXPa8vKlh7fZAy/8Lvtfyahfm+iSDZZNWhY+OdwEDJgN3/KoWmhRaXQs8uQfocp/6+BWvyf9f+QZ/IGC/4wX3yPX6xm/E88Uzu1RvCvR80jjolSJsi0Tk9Xrl1WZm+yCJqM/ZAZT1Z9Di8QD3aiK8XvO+efo8IZVt79r2WbMV0OMRfj/UrI/8f93O5s/VwyyOEWBs7lDVfxtO5mZBJqNiZWHhileNNZlRsWJtJrqS2rev891A86uAq95i8sS0gw53yMJPBaBiiEAVAa00qA2dbbXztLsUj105culzclCdRhfzr2UrLzu4RkTLtub/OGaEel30n121MVD7/MBvdpZWtbHsOMbNM+MLI2J2UYhNkv9tZ5bVtrkxsKW9grbscw4xz+b40PDuEdJ8GAkfBpoPBdZU1/p64OLH1YJV38+AI5vUsQoAjebDQPi49DngxG65Psx+Tv+6BmnyPy0eD3DRY4HfvMFZEYoaXgTsWSC/x6bfg6/zXx8pb1a25sfgUPssocTJ0eukeYNv96HyHiaVqqnrFY/IGP06rcID/0Ck1GWeachoSeeVY4ALRxoIFA5HOFXIy2Tyo9NW/Okx5VmlkdyH/Dnc+J5e/wNmPKGOLuvV8fkwo98XwLrJxs64Zui14bqdgYMryn6UfcvBM4Bpw4Gr3za/X4uu8MHTfDD1R/Wdjcwu8eJtRrXFQRxw+yS5H5lRZqbq9hCw4x/ZXUDE/8glSPhQ0KpDvV7g9p+BiWUDoeXO0+b+Juw20dGV5c5fF6bysirGUQf1VY6trpUrICtkAPJM/ZHV6mPazez0UPmJcN7brJNUZueJdYEbvw4+r7VRnthtnB6gHsQ9XqDpZfLAr/UnERU+mveRlxef2Cn/5sUYqFQj8PeprGCNzvm3q3/XTwP2pctLrGeVLUM00nzEJgJ3lK3IMhI+9NC+H6+jVGb2N30LbJkmr1bQCh+p3QBIcp2o3hxIaW1ST6Gedff/Wtb2/G4SQVehQXdg32IItynFt6rP68D3N8jB8v7iaOxE8XiYfUsMHE61A+0djPOvx+P8js6sSaXjXfIKnZlPqX3S8iw4dqrSFizrLvfJbYv14dBzODUjrgrQ9X7z64zQ8xe6Zzbwcll7VL5lg+7ySkMW9rtGVQKKdTQZKTqroXiaD7b+sOVapwOwdxE/HVGfD0Djd1WmwfdGyP15aTFQowXwyBpjzVc5QMKHEc37BP62rPmwKXyw9u1TJ4yvZaXvFv8ne12ndg0WPNgZucejXvoXGQeUnNI37SgoDp88FGdWgL/U1izOSbUmcuPQevl7IuTBWLumX2SdevMr5fRKCuX8XfKMrL3Rhhpm82okfETFAcNWAi8lB35rYTuBgmPmeRw0XRYAWMEu1G3UjdC+H0/zoVxTqZq8ugiQv5/SEfZ8SjYhiG7qpdB5MHB8O9D0cqDFlfIxUeHj4idkwa7Z5daeWbMl8Nhm+W9F+Eg10PxpUbQ/598OrC5b/WK02oU9NvBPoL5JmwoVtu7W6yK369QLgOotAseN/Foqp8iaEa0mTk5cPA/VmqiPsQJHqDsdi1K3s7x6T0/YYdtmoon5SeH6j4HZz8v178imMi10TyBrn75/DFegZ5f6M+n3/wp4V8dMJLLUlqXX/4DcQ2qhiF3VpXXyrwCEJHyMGTMGo0aNwqOPPor3338fAHD69Gk89thjmDRpEgoLC9GnTx98+umnSElxWOp3i5gkoDDb2FzBw67ZhW082u3qtbDr9iOiZK9wllrtgIx1sh1Qj4cWAet+Bro9qH8NoB/GHFDv1Kg1u9zxK7DoQ+O0AX7jeGyLrOXQduJ9PwWmPwb0fiH4nkfXybOJtjcBEUzQsag4fjmovpNJh8t2TDyzC4tIrAZvRLBGSTRg2WXPA3NeNt9/h0VE88GbOUbFy20AkP1I7BAZI/vFcPNlUu5Rseb104yhK2QNTlcL6dzyPbBjjizYK8KHolVp3EsWTFhBWPUeFqPehrqrbUxlOQ2tPf+SZ2QBnJ1wKNz7D7DhV6DT4MCxK14F/n5OdvjVe5YZeqtdwkm7m2WtrhGDZwLzXpf9gXRh3jO1KzBiQ/Al7P4yWnhxnDwRAEoCaSqwkzYtkYI+Hwq9nhK/toJgW/hYvnw5vvjiC7Rr1051fMSIEZg+fTomT56MpKQkDB06FP369cOiRTrqpfKg7c1y51G3E/BpV2MHp/vnAqu+Y5bnuUBkrKyWrmfieHXZ88CxbfrCxR1TgN3zg4NasVRroo48qIdRbITaTB2IjFF3pE17A6snmKfPo3JNvnNvnfNlL28eVRoEO6waIbraRSEmUY4f0YLjrAnIAuDyr+VvYwfR2c6FI+XAZVZ2BtUKH+ffDmz5U3MNpwyi4gLCh5MoM0JtzIVwUL2Z7INjhbgqQNuyPTyu+xjYvzQQLK77I3Ld5GoNEHrIfRHY78kLGQ7IwlJfHTt/cn3gwhHqY92HyQKaMgmq1hQ4vkOtBTbNF1OHjGJlOEHdTsDBleaCByD7QQ2cZnwN68weKxhpFpC30Jj3hrzRpBaPF3h8h6wNrd5UfU7Jf1Ql2XS5dmIgHw16ANtmiufhDMNWzcjLy8OAAQMwduxYvPrqq/7j2dnZ+PrrrzFx4kRceqkceGXcuHFo1aoVlixZgm7dTHb/DDf3zgHWTgIufSZgj//fYePwuNWa8CuUGXbNLoA8S9u7SN5h04iEWsbL8SrXCHSedhnwC1BwXI4joUdskly2vlJZ+LhwBPDHMOC8G+TzfV4H8o/KqwsqGiqzi8A3G7ZK7ox5Dp2AbPqysjOxFtEojjx1txlas1bLq+Wlj5ExwIcGyyxbXAWsHBfshB0qw9cDB1c5Fx8jnHS8U/6nEBmtdrJUaHKZbF4yM2MqKI6QPM2EGWx9rdFC/zqrsNrXu/4A1v0UMMGJoF3mHk7umS37WVgRFIxgtQ1Wwh7U6SA7evLweOW+uHKN4HO3/yxr5NreKPej+Udls07N8+RVLHHJ+gsOznBsCR9DhgzB1Vdfjd69e6uEj5UrV6K4uBi9ewcCHbVs2RL169dHeno6V/goLCxEYWHA2z8nRycqoRPU6xysTTDzALeL6P4MPJJTgeRbnctLKIja2dly7XgXUL97wJSSWFuOQFoR4cWQMEKvEwmVhDqyzbbl1c6nfdtPwOKP5Nm7FiXs+V1/6If27/OaPLg5nbekenw19ZnMHb/K2hzRQGmDZwC5h61p6xQS68orqE5ny31GOEiqa7y7NQ/W8dpSxGYbeCOcEzwA2YR8Xj+5XoYygQTkgJPTHwNunah/TaXq6knZgMlle+2UabV4Au5ZgmXhY9KkSVi1ahWWL18edC4jIwPR0dFITk5WHU9JSUFGRkbQ9QAwevRovPSSDc1CRafJZXIMgdrtzK89G9GqFysqlarLGwOyy4XLgwf+A/YvkdfoO02LKwNOnnoYbdQVXSmw9wVhjMcjFvxLITLauuBx52/A0i/koHZGfgPlRWS0vB2CxxN+s4vTeDzATQab/lmh/a2y75mViL12dgU/Q7FUM/bv349HH30Us2fPRmyshZ38DBg1ahRGjgxI1jk5OUhNDZMU7yZer2AMAaLcMdsY0A0q1xCzWxNEqKY9NzBaHXcuEcrux2c5lpZkrFy5EkeOHEHHjh0RGRmJyMhIzJ8/Hx9++CEiIyORkpKCoqIiZGVlqe7LzMxErVp8e3FMTAwSExNV/wiCIAiCOHuxpPm47LLLsH79etWxwYMHo2XLlnjqqaeQmpqKqKgozJkzB/37y86SW7duxb59+5CWpuOgRxAEQRDEOYUl4SMhIQFt2rRRHatUqRKqVavmP37PPfdg5MiRqFq1KhITEzFs2DCkpaWV/0oXgiAIgiAqBI57A7333nvwer3o37+/KsgYQRAEQRAEAHgkyY1oOOLk5OQgKSkJ2dnZ5P9BEARBEGcIVsZvmzHACYIgCIIg7EHCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrkLCB0EQBEEQrmJJ+Pjss8/Qrl07JCYmIjExEWlpaZgxY4b//OnTpzFkyBBUq1YNlStXRv/+/ZGZmel4pgmCIAiCOHOxJHzUq1cPY8aMwcqVK7FixQpceumluP7667Fx40YAwIgRIzBt2jRMnjwZ8+fPx6FDh9CvX7+wZJwgCIIgiDMTjyRJUigJVK1aFW+99RZuvPFG1KhRAxMnTsSNN94IANiyZQtatWqF9PR0dOvWTSi9nJwcJCUlITs7G4mJiaFkjSAIgiAIl7Ayftv2+SgtLcWkSZOQn5+PtLQ0rFy5EsXFxejdu7f/mpYtW6J+/fpIT0/XTaewsBA5OTmqfwRBEARBnL1YFj7Wr1+PypUrIyYmBg8++CB+++03tG7dGhkZGYiOjkZycrLq+pSUFGRkZOimN3r0aCQlJfn/paamWn4JgiAIgiDOHCwLHy1atMCaNWuwdOlSPPTQQxg4cCA2bdpkOwOjRo1Cdna2/9/+/fttp0UQBEEQRMUn0uoN0dHRaNq0KQCgU6dOWL58OT744APccsstKCoqQlZWlkr7kZmZiVq1aummFxMTg5iYGOs5JwiCIAjijCTkOB8+nw+FhYXo1KkToqKiMGfOHP+5rVu3Yt++fUhLSwv1MQRBEARBnCVY0nyMGjUKV111FerXr4/c3FxMnDgR8+bNw6xZs5CUlIR77rkHI0eORNWqVZGYmIhhw4YhLS1NeKULQRAEQRBnP5aEjyNHjuCuu+7C4cOHkZSUhHbt2mHWrFm4/PLLAQDvvfcevF4v+vfvj8LCQvTp0weffvppWDJOEARBhI/S0lIUFxeXdzaICkZ0dDS83tCDo4cc58NpKM4HQRBE+SFJEjIyMpCVlVXeWSEqIF6vF40aNUJ0dHTQOSvjt2WHU4IgCOLsRRE8atasifj4eHg8nvLOElFB8Pl8OHToEA4fPoz69euHVDdI+CAIgiAAyKYWRfCoVq1aeWeHqIDUqFEDhw4dQklJCaKiomynQ7vaEgRBEADg9/GIj48v55wQFRXF3FJaWhpSOiR8EARBECrI1ELo4VTdIOGDIAiCIAhXIeGDIAiCIFxk6tSpaNq0KSIiIjB8+HCMHz8+aF+0sx0SPgiCIAjCRR544AHceOON2L9/P1555ZXyzk65QKtdCIIgCMIl8vLycOTIEfTp0wd16tQp7+yUG6T5IAiCIM54evXqhWHDhmH48OGoUqUKUlJSMHbsWOTn52Pw4MFISEhA06ZNMWPGDADyao177rkHjRo1QlxcHFq0aIEPPvhAlea8efPQpUsXVKpUCcnJyejRowf27t0LAFi7di0uueQSJCQkIDExEZ06dcKKFSsM8zhv3jwkJCQAAC699FJ4PB7MmzePe+1nn32GJk2aIDo6Gi1atMD333/vP/f444/jmmuu8f9+//334fF4MHPmTP+xpk2b4quvvhIvQJch4YMgCILgIkkSCopKyuWfneDb3377LapXr45ly5Zh2LBheOihh3DTTTehe/fuWLVqFa644grceeedKCgogM/nQ7169TB58mRs2rQJzz//PP73v//h559/BgCUlJSgb9++6NmzJ9atW4f09HTcf//9/tUeAwYMQL169bB8+XKsXLkSTz/9tGnci+7du2Pr1q0AgF9//RWHDx9G9+7dg6777bff8Oijj+Kxxx7Dhg0b8MADD2Dw4MGYO3cuAKBnz55YuHChf7nr/PnzUb16db8gc/DgQezcuRO9evWyXIZuQeHVCYIgCADA6dOnsXv3bjRq1AixsbEoKCpB6+dnlUteNr3cB/HR4p4BvXr1QmlpKRYsWABA1mwkJSWhX79++O677wDI0Vtr166N9PR07oanQ4cORUZGBn755RecOHEC1apVw7x589CzZ8+gaxMTE/HRRx9h4MCBlt4rKysLVapUwdy5c/3Cwfjx4zF8+HB/SPsePXrgvPPOw5dffum/7+abb0Z+fj6mT5+OrKwsVKtWDUuXLkWnTp1QvXp1PPHEE5g6dSqWLFmCCRMm4KmnnsKBAwcs5U0EbR1hsTJ+k+aDIAiCOCto166d/++IiAhUq1YNbdu29R9LSUkBIG+SCgCffPIJOnXqhBo1aqBy5cr48ssvsW/fPgBA1apVMWjQIPTp0wfXXnstPvjgAxw+fNif1siRI3Hvvfeid+/eGDNmDHbu3OnYe2zevBk9evRQHevRowc2b94MAEhOTkb79u0xb948rF+/HtHR0bj//vuxevVq5OXlYf78+VyBqSJBDqcEQRAEl7ioCGx6uU+5PdsqWrOHx+NRHVNMJj6fD5MmTcLjjz+Od955B2lpaUhISMBbb72FpUuX+q8fN24cHnnkEcycORM//fQTnn32WcyePRvdunXDiy++iNtvvx3Tp0/HjBkz8MILL2DSpEm44YYbbL6xNXr16oV58+YhJiYGPXv2RNWqVdGqVSssXLgQ8+fPx2OPPeZKPuxCwgdBEATBxePxWDJ9nEksWrQI3bt3x8MPP+w/xtNedOjQAR06dMCoUaOQlpaGiRMn+k02zZs3R/PmzTFixAjcdtttGDdunCPCR6tWrbBo0SKVSWfRokVo3bq1/3fPnj3xzTffIDIyEldeeSUAWSD58ccfsW3btgrt7wGQ2YUgCII4B2nWrBlWrFiBWbNmYdu2bXjuueewfPly//ndu3dj1KhRSE9Px969e/H3339j+/btaNWqFU6dOoWhQ4di3rx52Lt3LxYtWoTly5ejVatWjuTtiSeewPjx4/HZZ59h+/btePfddzFlyhQ8/vjj/msuvvhi5Obm4s8///QLGr169cKECRNQu3ZtNG/e3JG8hIuzU6QlCIIgCAMeeOABrF69Grfccgs8Hg9uu+02PPzww/6luPHx8diyZQu+/fZbHD9+HLVr18aQIUPwwAMPoKSkBMePH8ddd92FzMxMVK9eHf369cNLL73kSN769u2LDz74AG+//TYeffRRNGrUCOPGjVNpM6pUqYK2bdsiMzMTLVu2BCALJD6fr8L7ewC02oUgCIIow2glA0EAtNqFIAiCIIgzFBI+CIIgCMIhrrrqKlSuXJn77/XXXy/v7FUYyOeDIAiCIBziq6++wqlTp7jnqlat6nJuKi4kfBAEQRCEQ9StW7e8s3BGQGYXgiAIgiBchYQPgiAIgiBchYQPgiAIgiBchYQPgiAIgiBchYQPgiAIgiBchYQPgiAIgiBchYQPgiAI4oynV69eGD58eHlnI+y8+OKLOP/888s7GyFDwgdBEARBlDNFRUWuPk+SJJSUlLj6TBYSPgiCIIgzmkGDBmH+/Pn44IMP4PF44PF4sGfPHmzYsMEf7jwlJQV33nknjh075r+vV69eGDZsGIYPH44qVaogJSUFY8eORX5+PgYPHoyEhAQ0bdrUv9MtAMybNw8ejwfTp09Hu3btEBsbi27dumHDhg2qPC1cuBAXXXQR4uLikJqaikceeQT5+fn+8w0bNsQrr7yCu+66C4mJibj//vsBAE899RSaN2+O+Ph4NG7cGM899xyKi4sBAOPHj8dLL72EtWvX+t9z/Pjx2LNnDzweD9asWeNPPysrCx6PB/PmzVPle8aMGejUqRNiYmKwcOFC+Hw+jB49Go0aNUJcXBzat2+PX375xelPFAQJHwRBEAQfSQKK8svnn4UN1z/44AOkpaXhvvvuw+HDh3H48GEkJCTg0ksvRYcOHbBixQrMnDkTmZmZuPnmm1X3fvvtt6hevTqWLVuGYcOG4aGHHsJNN92E7t27Y9WqVbjiiitw5513oqCgQHXfE088gXfeeQfLly9HjRo1cO211/qFhJ07d+LKK69E//79sW7dOvz0009YuHAhhg4dqkrj7bffRvv27bF69Wo899xzAICEhASMHz8emzZtwgcffICxY8fivffeAwDccssteOyxx3Deeef53/OWW26x9EmffvppjBkzBps3b0a7du0wevRofPfdd/j888+xceNGjBgxAnfccQfmz59vKV2reCTJwhd2AStb8hIEQRDOEbRdelE+8Hqd8snM/w4B0ZWEL+/VqxfOP/98vP/++wCAV199FQsWLMCsWbP81xw4cACpqanYunUrmjdvjl69eqG0tBQLFiwAAJSWliIpKQn9+vXDd999BwDIyMhA7dq1kZ6ejm7dumHevHm45JJLMGnSJP/Af+LECdSrVw/jx4/HzTffjHvvvRcRERH44osv/M9euHAhevbsifz8fMTGxqJhw4bo0KEDfvvtN8P3evvttzFp0iSsWLECgOzzMXXqVJWWY8+ePWjUqBFWr17t9wfJyspClSpVMHfuXPTq1cuf76lTp+L6668HABQWFqJq1ar4559/kJaW5k/v3nvvRUFBASZOnBiUn6A6wmBl/Ka9XQiCIIizjrVr12Lu3LmoXLly0LmdO3eiefPmAIB27dr5j0dERKBatWpo27at/1hKSgoA4MiRI6o02MG6atWqaNGiBTZv3ux/9rp16zBhwgT/NZIkwefzYffu3WjVqhUAoHPnzkF5++mnn/Dhhx9i586dyMvLQ0lJiaMTcfaZO3bsQEFBAS6//HLVNUVFRejQoYNjz+RBwgdBEATBJype1kCU17NDIC8vD9deey3eeOONoHO1a9cOPCYqSnXO4/Gojnk8HgCAz+ez9OwHHngAjzzySNC5+vXr+/+uVEmt2UlPT8eAAQPw0ksvoU+fPkhKSsKkSZPwzjvvGD7P65U9KFhDhmIC0sI+My8vDwAwffr0oA3xYmJiDJ8ZKiR8EARBEHw8Hkumj/IkOjoapaWl/t8dO3bEr7/+ioYNGyIy0vmhbsmSJX5B4uTJk9i2bZtfo9GxY0ds2rQJTZs2tZTm4sWL0aBBAzzzzDP+Y3v37lVdo31PAKhRowYA4PDhw36NBWuW0aN169aIiYnBvn370LNnT0t5DRVyOCUIgiDOeBo2bIilS5diz549OHbsGIYMGYITJ07gtttuw/Lly7Fz507MmjULgwcPDhq87fDyyy9jzpw52LBhAwYNGoTq1aujb9++AOQVK4sXL8bQoUOxZs0abN++Hb///nuQw6mWZs2aYd++fZg0aRJ27tyJDz/8MMgnpGHDhti9ezfWrFmDY8eOobCwEHFxcejWrZvfkXT+/Pl49tlnTd8hISEBjz/+OEaMGIFvv/0WO3fuxKpVq/DRRx/h22+/tV02IpDwQRAEQZzxPP7444iIiEDr1q1Ro0YNFBUVYdGiRSgtLcUVV1yBtm3bYvjw4UhOTvabKUJhzJgxePTRR9GpUydkZGRg2rRpiI6OBiD7kcyfPx/btm3DRRddhA4dOuD5559HnTrGzrvXXXcdRowYgaFDh+L888/H4sWL/atgFPr3748rr7wSl1xyCWrUqIEff/wRAPDNN9+gpKQEnTp1wvDhw/Hqq68Kvccrr7yC5557DqNHj0arVq1w5ZVXYvr06WjUqJGNUhGHVrsQBEEQAIxXMhAyyqqRkydPIjk5ubyz4zpOrXYhzQdBEARBEK5CwgdBEARBEK5Cq10IgiAIQpBevXqhgnkrnJGQ5oMgCIIgCFch4YMgCIIgCFch4YMgCIJQQWYFQg+n6gYJHwRBEASAQKhx7Q6uBKFQVFQEQN4HJxTI4ZQgCIIAIA8oycnJ/k3U4uPj/XubEITP58PRo0cRHx8fcsh6Ej4IgiAIP7Vq1QIQvIsrQQDyJnb169cPWSgl4YMgCILw4/F4ULt2bdSsWVN3Z1Ti3CU6OtqR8PQkfBAEQRBBREREhGzXJwg9yOGUIAiCIAhXIeGDIAiCIAhXIeGDIAiCIAhXIeGDIAiCIAhXIeGDIAiCIAhXsSR8jB49GhdccAESEhJQs2ZN9O3bF1u3blVdc/r0aQwZMgTVqlVD5cqV0b9/f2RmZjqaaYIgCIIgzlwsCR/z58/HkCFDsGTJEsyePRvFxcW44oorkJ+f779mxIgRmDZtGiZPnoz58+fj0KFD6Nevn+MZJwiCIAjizMQjhbBLzNGjR1GzZk3Mnz8fF198MbKzs1GjRg1MnDgRN954IwBgy5YtaNWqFdLT09GtWzfTNHNycpCUlITs7GwkJibazRpBEARBEC5iZfwOyecjOzsbAFC1alUAwMqVK1FcXIzevXv7r2nZsiXq16+P9PT0UB5FEARBEMRZgu0Ipz6fD8OHD0ePHj3Qpk0bAEBGRgaio6ORnJysujYlJQUZGRncdAoLC1FYWOj/nZOTYzdLBEEQBEGcAdjWfAwZMgQbNmzApEmTQsrA6NGjkZSU5P+XmpoaUnoEQRAEQVRsbAkfQ4cOxZ9//om5c+eiXr16/uO1atVCUVERsrKyVNdnZmb6d0rUMmrUKGRnZ/v/7d+/306WCIIgCII4Q7AkfEiShKFDh+K3337Dv//+i0aNGqnOd+rUCVFRUZgzZ47/2NatW7Fv3z6kpaVx04yJiUFiYqLqH0EQBEEQZy+WfD6GDBmCiRMn4vfff0dCQoLfjyMpKQlxcXFISkrCPffcg5EjR6Jq1apITEzEsGHDkJaWJrTShSAIgiCIsx9LS209Hg/3+Lhx4zBo0CAAcpCxxx57DD/++CMKCwvRp08ffPrpp7pmFy201JYgCIIgzjysjN8hxfkIByR8EARBEMSZh2txPgiCIAiCIKxCwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5CwocAxaU+PDd1A2ZtzHAkvcKSUvy68gAysk87kp4okiRBkqSg4z+v2I/rPl6IzBxn8rPnWD5u+nwx/t2S6Uh65c0PS/bi3b+3Bh37fP5OzNxwGJsP53Dvyyoo4pZ3Readv7eiz3v/Ifd0se41q/adxPG8QgDAst0nsOkQ//3DhSRJOF1cGvZnnGlIkoTiUl95Z4MghCDhQ4Dv0/fi+yV78cD3K7F013HM3XokpPQ+nbsTj01ei//7cEHQueJSH0p94h3fnmP5KCox73AkScKtXy7BXd8sC+pYn/xlHdYdyMbovzYLP9eIkT+vwfI9J3H3+BW27t+akYsX/9iIo7mFIeVj7pYj+JsjMP637SiGTFjlH0DNeHbqBnz47w5syZAH2eyCYjw7dQPGzNiCB39Yhas+CP6OMzdk4PyXZ+PV6YEytTqgHcw6hbzCEkv3hMpH/+7A1sxcjJmxhXs+fedx9Pt0MXq88S+O5JzGzV+kc+uxU+QXluDeb1dgyqoD/mNP/rIOLZ+bie2ZuZbSEi3/rRm56PL6HPywZK+l9AHg9b82495vl8OnacPFpT7cPnYJXvlzk+U0RRn+0xp0eHm265MaPc5EAY5wDxI+BNjIzOxu+XIJBo9bHtLAqAgvJ/KLVMdLSn246I25uOK9+UINd+7WI+j19jzcNnaJ6bUZOaexdPcJLNh+THdAyy8ynk1mFRRh2I+rMc9E+NK+l1Wu+uA/jF+8B49NXms7jcKSUgwevxz3f78S2aeK/ccA4K5vlmH6+sN4aZo8EEiShK8X7saiHccM08wqkNNZcyDL9Pmv/SWn/fXC3QCAnUfz0PX1ORi3aLdQ/g+cLECPMf/iglf/EbreaSYs3YcjHE3YvG3ytz9d7MMhFwa5rxfuxj+bMzHy50BdmLxSFkS++G9XWV5KsflwjmGbyT5VjIvfmouXpm00feZTv67D0dxCPDt1g+X8fvnfLvyz+QiW7TmhOr75cA4W7zyOrxfuxo4jeZbTNWNrRi5+X3MIeYUlWCtQP8PNtLWH0OnVf7B013HsPZ6PJbuOl1te9h0vCLumTMvWjFzc+fVSrNmf5T/m80kosaCZyj5VjBE/rcHL0zbZEuQquvBnWfj477//cO2116JOnTrweDyYOnWq6rwkSXj++edRu3ZtxMXFoXfv3ti+fbtT+S0XjnJmyCcLggfYIzmnce1HCzFhqfGMSa9O7D95Chk5p7HzaD6KS80rzo9L9wEAVu49aXqtSD00u+bd2dswbe0hDBq33DwxA/7bdhS7j+UHHVcapjJpXBdCJ3q6ONDIC4pKsHzPCbR+fha+/G+n//ie43Ie0ncexyt/bsKAr5YGpcM24JKyb7Itw3zG7dP0MS/+sRFHcgv9Ao8ZS3bJg9cplztNlo065iQFD/O3tqM7XVyKD+dsx4aD2SHlwUiQjYrwYNnuE2j53Exc9cEC/LH2kO61Py/fj/0nTmHcoj2613z5305MXrFfV/P4xOS1uPPrpUFaDR5abSR7S6hlwuNIbkAQjPR6DK6UeWnaRjzw/QrdAWrVvpP4Ln2P4QD2+5qDGD5pNXdgH/bjapzIL8I9365Az7fm4dYvl7hungOAFXtO4OK35uKWL9Jdfe7tY5dgwfZjuO3LwMSw76eL0POteUGmMb06/t+2o/ht9UF8s8i6wDry5zW47J35rgtdVrAsfOTn56N9+/b45JNPuOfffPNNfPjhh/j888+xdOlSVKpUCX369MHp0xVDFWjEsB9Xo+HT0zGxbFBXENVyPPf7Bqw/mI1nfrM2Y8orLMGTv6xF+s7A7KBIQEL2mPcx3GslyGrgaWsPBb2rEccEzRRGrN2fhbu+WYZL3p6Haz5a4NfCvDZ9Ezq8PBv7TxT4ry0VEMD0YAeQCI8HT/6yDqU+Ca//FTAnrDuQjeembsCBrFO66bCDRnGZRCHybYzyI4LPxqzl6V/XofGo6VhsosF5c+YWfDhnOyRJwuxNmXhp2kbs4QiDXJhssXXqcPZp3Pplut/M9euqA3h39jZc89HCoCSyOIK7HbweD25mBhWjulxqUp47juTh9b+24Ilf1kEC/9rJKw9gwfZjKk2o6PPYAafEYl0QgU3TqK5tOpSDbxbuxrhFezBrYyY2H+YL0v0+XYznf9+I31Yf1E3r0UlrMHWNcR/C1uMNh5wXusz4ecV+AMDaA848W9Sn5niZQKFMHiRJwroD2TiYdQo7jwYEiU/m7kDHV2Zj0rLgMixkBNhCAdM6y5RVB7HrWD7mbT1q6T43sSx8XHXVVXj11Vdxww03BJ2TJAnvv/8+nn32WVx//fVo164dvvvuOxw6dChIQ1LRyC8swbSymdP/fluvOpct2FmKaCB4fDhnO35ecUD1XBE/Dg8sSB8Mmw/loMWzMzDsx9WqZ5oJM7FREUHH9h0vsOQDs56Z9W04mIOfl8udw9gFu5FbWIKP/93hP282YBjBqjczck5zNS0A8P2SvViwXX+wZjtyRfMhMvNlZ4ylPsmSoKi9X5RJy/fDJwG3czQ4Ct+l78Gn83bi3dnbMHnFAdz33QqMW7QHN35ufWbI1r+vF+7Gkl0ncP/3K1FS6kMmY5JhhY3f1xzE+S/PxjsaB147aGf4EV4PMnNO48M521WaAMBcq8czMekhIhhqvx87aNkRLE3zxNRJo/T/78MFeJnxOzGrlyN/Xosv5u80vEYxazqJJEkoKFKbh5fsOo5P5u5AqU/C8Emr8f4/2wDIWra/N2YgX2NOnrb2EH5ecQCimGkJtmXmou2Ls2zVXbYfYdvNW7PktJ6esj7oHtFvakRMZMX1rHA0Z7t370ZGRgZ69+7tP5aUlISuXbsiPd1dtZdVtB+XlU49nBbKa7PH8uzN6A6cLAg69tzUDY46G7IV/vFf1sJs/OTNnuI4wsfFb83F4HHLVVoblmW71bZvr6Ystc9hzQxWtQUsrHbiuo8XGV5bYFDOPpXZRU5TZObKXtLn/f8MBz9JkoIGS6uvrh3s9ISX538P+DxMX3/Y/zdPq/XGjC2GJkT2U7Lf6mDWKcRFR/p/L99zEuMX7cbMDYfx6KQ1AGTHVhGMBscIr1fz24O7xy/Hu7O34cHvV6rO6WkzFMzMW1aFQa3ZrYTR4okIr1YpVWk+xO+LFhicRus4HyuImHkAdZ+5/0QBhv24Gut1NBIPT1iF1s/PUk0abv1yCd6atRWvTd+MqWsO4f1/ZHP+M79twP3fr8SIn9bgYNYp/N8HCzB5xX4M+3G1UL4A2RTW7qW/8aqBQ/Cr0zfjdLEvqO7+ue4QPirTJGpJiI1EXmEJnmPaHa9O88qwVDOBEYWduMZEnSPCR0aGrHJNSUlRHU9JSfGf01JYWIicnBzVv/JA+20ve2c+3v17K3qM+RcHDdTyTsDTYExffxhvzzKWsK3MptnOd/8J/vsoyX2zcDfav/R3UMfAEz4UFu7gq/du/iJd1XC0bUz7DuwgEMoMUcRnRiEyQr8g2bwXl/0tki/2mh1H8gxnVe/8vQ1dXpujUl9bfXdt/RW5vXJMpOH5LRm5hiZE9tvlMLPfEp+EHGap7n3frcCL0zbhwR9WcdPJKyyxZdL7RuO86/F4/CaRVfuyVOfMyqOAcbbWCg4ALDufsgPHvK1HMJUxX4Si0dODrS/hSB+Qv9OBkwW4e/xyjPxpjf+410D40Dvz8IRVmLb2EK79ONgsBwAzNsjjxffpwcLvDmZiKEkSfi1bCfX3pkw8/es6bDqcgyd+WWfyNmqmrT2EohIfvlq4W9cpNI+p07+uDGhUhk5cjXdmb8MKjuY7MTYKH/27HT8yZhVemURwypD9pnqrz3iwfShPW11RKHexaPTo0UhKSvL/S01NLZd88CTLD//d4ajgIUkSJi7dpzI9GLHVxLGRJ3wUl/q4HbmVydbLf26S/VB+VTfguGh1RWYHVFba1mqK2EakzbO20bFphmIbtxLvICoi0Ay0s5dSjuaDV1de+H0Dbh+7xH+N9or8Qn3h4+O58kzqhT8CA5zVV9fmiSe8aN9N+z31+G31Ae5sndVisU7ZPp9kGCdES8eXZ6Pzq/+E7AsiOAHnYib0TrDgGwWotRuDxi3HFEb4CIfmg20rPp+E7AKx8reSl7TRc3DhG3Px75YjqvcR1XywsJplI3h9XBTzPG0fsT3T+koiSZKwmlmVojiia2E10byVeDzfwKS4KOw9FqzZ1lJY4lP5uwHqb7N094mg83qcYgTpCKv2XhdxVPioVasWACAzUx1cKjMz039Oy6hRo5Cdne3/t3//fiezJEwJb7pjgPJNNx3KQd9PFpku0wSA9F3Hg/xJjDBTtfE0Jv0+XYzOr/6DHUfUgotIJ6Otp5sP56jMKawUrRVyck8HGmawCYB9RrCdnoX1vwhlAifiM6MQzQgfWo0JW26K6pw3s/w2fS8W7zyOpWVmJu0lWwViUrDf26qa/77v1DFVeJ9beyxeUPgY8dNa/+ySTYL9lKzJcdLy/ZixXjwgn2IiC3U1hNakZ4XTDmjc2G+mlDXvO4ZB9lDVna8W7kL7l//Gd+l7zO+z8K5sG2fhzdp5sG3f6Fuxvhu8q1hNpdYRM8PAd2fVvpMY+M0y7DiSixd+34DL352PI7mn8df6DJV5WG/Sk6fz/gq8elM5JjJIs8oz4wNy/BpWwDEySRvBXhceHZgzOCp8NGrUCLVq1cKcOXP8x3JycrB06VKkpaVx74mJiUFiYqLqX3lg17/g0UmrsWZ/FneZppZ9x3UkV512aCoQce5TtCp/rFEvOxRzkgs+xsYQYc0uBUWlKknfSEPEPlvb6ZgNGG/P2ir0bXw+CXd+vRSPTpLtvFY0H2znoL1PbXbx+Z+lR6Djsl6f2GS1z9CLTqswf5va7MX73tpyNDKjadH67mhhV8t8vXC339vfCqF2lNoyUKVtUv9Zs4vdvoBdUaEM6rzHhuLLpIdqVclBWYhj/Xv0KAlhRZkCqzkEgFGM86Re7CBeq9+emYubPl+M816YFbiOcyEr7BRaWEra79PFmL/tKO75dgW+Td+L7Ufy8MTkdVizX20u0et2zXzwhv24Gm/OVJtHvN7g8vF4wDXtpO86jjYvzPIHibNiSpUkCfd+uxzDJ61WOeqGw7nZKSwLH3l5eVizZg3WrFkDQHYyXbNmDfbt2wePx4Phw4fj1VdfxR9//IH169fjrrvuQp06ddC3b1+Hs+4sdhuhFadQPduo3vAbUidl4thphyhmkC4oKlHFOjl4Ul/4YOu/tgjMZk0fz92BX1aaa8MOZp3Cgu3H8PuaQ8gvLLHk8xGl0nxohA8m84XF9hxOraD42bD3F5X4cMuXS9D3k0XCanKRQU/U7ALw3/lwVmCWaSceyVO/rNNoC9San0HjlhnG5dCifb8VexQtlGSqRWNV1Xb7676fBByblffidf7hGBDsRlV3Ii/aNvwjZ9moFrZ7+meTrCm/65tlWL7npOY64/7BzrL3A0xftZ/j7D97UyaW7wkWts36ekkCPp0XvDJIa5Y6nHUaHV6ZrRvwbsYG2RFc+214TtNKnvYcL8A/m49g6ppDQVpon0/CjPWHhc02bmFZ+FixYgU6dOiADh06AABGjhyJDh064PnnnwcAPPnkkxg2bBjuv/9+XHDBBcjLy8PMmTMRGxvrbM4dxvrgLFeoxNgo7lneTEvPNqrXwHgq0X3HC/xBioyapfZRIq9nprVm09AO8AezTkGSJJT6JOzRaHjYRqPVdIjYJPUcZFlYwS4j57QlzQfbebLv9Puagxj4TSCgmqLiNRIAlJREOvVfVh5QOe4B8Dvgsfc3f3YGlu0+gbUHsg3Vyiy8uqM9ZsVWzxM+Bo8PLdjcTyv2q9Jls7fjSF5QjAJJkvDC7+KOnzd+nu7fVuCd2dsMr1WtsnJgQFa+H/c72JBMS30S5m49gpM6GqVSi2ZjbV52Hc2zZKpkYeuRSNsA1H3evWUmw8OciLnKVWx/ejI/4M9SWGw9z2ZasPf+2YabOEvP7U4oojQrij6Ysw25p0t0BWsle9p6os32a9M3oc0Ls7BoxzFVf6fWfAD/bT+KhyaswkVvzrX3AmHC2N2dQ69evQw/nsfjwcsvv4yXX345pIyFk/UHsvHA9yvweJ8W6NexHgA7zo0Sfly2T9eWX+KTVJoCQNw26k+DM3u/+C25Av33xCWGswKtP0gooXYPZp1CnaRY1YBYWOJTdTSFJT4czSvEnM3BMT/YotVm2chT3n+NQLGxAckys0/b7kjZRqwsC1VQwrMbDU5+o4tAcT9uED5e736jlTksXLOLpj6x8pm2rgalFw5HBah9Ldgn8NrjugPZ+Jaz+sGI7FPFfj8c0Xywnf4faw+ZxrngocgCvO9opyi/S9+Dl6ZtQqPqlTD38V5B50PRfMzamIEHvl+J7k2qYeJ93SzPkFVmEMF2p+0HZm44rHOh/B87KUhnQrVbDb6lZdfRfOw6ujukNLT8vkYdmC1K04HlnBLTlpsJwWMXyPl+/a/NeOvG9v7jeYXsyi1JtVw593QxEnQmzG5T7qtdyoNnp67HoezTqv0irM5GJElt29TCG/z0hA+9bt9IIJq37Yiw5qOk1Ce034pe0LIeY/7FO39v0/gkBDeOnFMlWMhxvFWvdtEKZKbZElpTXMzM/DJyTltSx7KpG2lMlI5OJOlQ91XQ05yIJitx8qj9XuxsuUfT6obpWXXIFoVdBaQ2uwRfa2dPFNFmrffsR35cHRTR9Klf15nuAK2UtVNmFyUAol6wPLvampJSyb+cdfHO49hzLN/yDJkViI2WlPskCaOmrMOEpXuDehq9ZdhKn6TXnrWO1v77DLoMJ8VoXjvXTloiNZ2cWVA2vQnMpkM53HpXXOpT1SnWMdYnAdUrx/h/b7O4GWM4OSeFD21lAKx3rmYVmCd8WF2SZiQQaSvR2P92qVafsI2v76eLcMuX5pvPGfHx3B3qqJ2ShCmr1BL+9+l7MH1d8AyGHQi1JSCyQkGk1Fgt0fG8IlPPdD2MfEUUFa+RFkAZ0EPV3Os9QlRDxxvgtHXcyoAVDidJAMhno1hK7J/Bz7Oz0aDeQP/UL+tUNnf2MrN33ZKRixEac5kWpa3w0rJTlqZBAW2qPrR1gDd5AIyFabYNny7RFz7WH8zGj8v245nfNljWkBTrXL9PR0tj1Gc46XIj8im12sqsU8YTQb2689jktbifI2xty8xTaasKS1j/JYkbpbkicE4KH9UqRQcdc3rvDZ6krjfQ6o2/RgJRRvZp1X2v/bVZFdWR1TAo3u9mmMkB7Csv3XUc/25Rm1j0VOJsWWnLTaTcRQQU7d4ZVgYqNgdGmg/lexgJAEUlUlCadtCrX6L73XBn3Nqom6rwzcYDTNiED4OlhU7AExT3nyjATyv2Y9yiPf5JAnvZ8XzzgGebTTbdU9LjvZKd9zS7Q69amMUK0tYJvbYmmmcjHww27QKTHbQVlDusOpaGsuxaQUR7KaLFitJE4j0t6KfCK3O9PWo+ZKKuss6yPkndp4UrAJ0dzk3hg1FDGc1QjDBTlHA1H4L2egWzgUabGhthLxyxZdiGZtb5srBvYUf44L3LoaxT+GTuDn9gKnV4aWsdFZsnI+FD0YoYdTiFJaX4ZO6OkEPj63V8YxfswtUfLjA1o/kkeXZ/zUcL/HUxSPPB1C/tDElLODZDA9QqYjZ/TvWRvM6W3d5d0bCwQorISinR7Ql431E5Vlzqw4if1gitDjErED1tXJ/3/zO8T1sn9JSzRt/fV7YM/IN/tmPmRvH4LiIobd+qD5cT/Z/ZN/53S6ZQ/6VdaiuKXh/AM22x1/7FxNjxadp1BZI9zk3ho2qlgMNNVlkkQKvCh9n1hRz1o1Vp3KjBS5LxMjS7m84ZwWan2EJ5qUI/+/jnKhks++S9yU2fp+OtWVvx+GQ5CivbiVrVQJcKDjwlpT4Ul/oMd/qcuvqgf7OoUNAr3u+X7MXGQzmmy48lScJPK/Zjw8EcfwA8Y82HZNjZhkvzwQppaw9k4+1ZW5FXWGIrTggPnpqZDb3t8wELtx/DTyusBTc0c8BV6jVX81F27peVB/Db6oMYNWU9luw6ju6j5+D3NQf9AjCrFTLXfOhfsSUjBzd/nq4SurT5VLCj+fD55OWp7/2zzTAMuJ06ZObzoXufA9KHmVbj7vErhDQfVmUP/2oXnbRPciIB65Vt7ukSVayVcLVjO1he7XI2wG5IpVRqqzM77Y6LWqx4YYvG+WClW7PchlvzoWeDNbtP22krfYrRSiDeihglqNmC7fJyTFZosKr5UAsf+veW+iR8s3C3YVp6W5RbxayTiI82brq8jks7y1ULhZJhR1piMJMPBdbn47OyGAn5RfrLEK1i1q59koQ7vjYPEKglt7AEkiTB4/Gg1CcFL233Gfl8yM7qrMbj1jKfrEcnrcF7s7fhmatb477vVmB472YY3ru56YzVqL7cM34FDmad8j9DmxcWXROwgVDukyTuMlnedVaxrfmw/KRgxPZwMk/H6mRI0cbp3Xcivwi1k+I0z+BnZMhEtSNvRQo6dk5qPtgBsNSgkzDCzGbJm0Fb/fDajpO93WwQCGWfCz3YZ1oR1uZvPYpDZcJC0IoLgxmiwr7jBRg9YzNmb8oMOldY4sOl78xTeYFbFSTZ67cfycOqfSe51xX7JPy3XT+KJoCgbb3tYlZXzLb/ZgcLRXjTpsm+95GcQsNnrtx7Etd+tBDL9vDLxi55nD1vrJj0zDATREOxgd///UoUlfjQ+935QQKMUvy8dppXWGxoatlzvADPlG3DoOzcalYfjPovo+jD2vv0JgFG/mcSxCY7toSPsv+txO0BnPH5YF9Z7/ki44bV+DrK5XrlxcY5UdilswpKS0USPs45zcf+EwWq2ZZSeaxrPow7f16l5LXfZ35bj6maUOh6afg0mg+jKmzH7GIlyJiVzkBRc+8Zc3XwO/nM/SgUlfjE2H1Y/2KfoPO7jubjwznb/b+tCpLsQP1c2e6l/z1xCec6n6mvT76JRkzhqwW7jPNk8g5mPiWs5k0J5KZNk/X52HUsH5/P189TqU8S3hDRCjxhzYmBQ8HMf4O3JFmU2ZsysXrfSew+lh+0BFYyEKpPCm76pk7P+Lxddbr2Pr2iNzI1KhogM35hdoIVpizd8vD5KCrxIS46AgVFJbjwjbnca0b/tdk0Ha22V3jFms51Zkt1jSj1AS/+sRENqsVjcI9GttNxgnNK+Nh8OAdXfbBAdcwfidCiqt5skOFvKBV8zGi3zGA1OZs+DKUPj0e8UxBFsil8sGjLYFtmLnYcyRXqPPU2ttKesypI8p69JSN49l1cKpnOlEUf/ep0405Lb1tvBZU/ACdPrGZEmWhp31NbTqwA5xbhFj7M6lWo3v96YeVLDYRqkd17tdk2y6XdGa32/fXK3qi++iQxM4eVLQ8UynO1S/uX/8Y/I3tix5FcXQfvScvNfYWs1jHlar37Qom589+2o/h+ibwqkYQPF5mxPjgGhV/zYbFhFJjMPLmaD4ttz1zzod/A/tmcic/n78T7t3Sw9lAD2OfbXS+ufacJS/dZ3q6cBzsIWJ0l8Roz31bvc8TnYdwiY78ROU/img/epazDs2J2MapP5QVPg6MXa8IOZuUYavA0PfOX8lxePeKpzbUUaRzWtfUuM+c0bhu7BAO6NsA9FzayvRrJici1PkkKi48ZYN/nI9TVZgq9350f0v0+CfjyP2Mtp5ax/+1CpegI3W8TSru1E6gvXJxTPh88STKg+bD2QfV2a+Q9S5IkFJVYH7hC8flYsusEjuUVWXKm88BjmC57zq7mI1Rva738sWYGq3nj5WnUb8HRa0t8xstRRXlp2ibTa8yEuzydyKAKbCwBPb+mcC2ftYJTPjJ6mGmQ7OwNwqIXsyGw1Db4nIjmQ+uwrk3n3b+3YdfRfLzyp1yX7AoR2jphRzshaz7CI30o6Vak4FhWWLb7hOU+43h+EZ77faOuaT+UshDdG8oNzinNB6+zVQ5Z7YiNTACAurMYNG45Vu07iWevbmXpGdoORys8hWO2YbikTmV2sd4Adh/LD3m2LS8xDj7OzoyszpK2c2YDWRy7vBzG2FLSlkmIkZuk2YzcLDgXOyNX6rZ2iZ7dzcicJJ/jcOokZvU01L1B9MwuSplzzS4CNvsg4UNjeNGaIZzy+dhgw68nfeexkPwQRKhIwbHcIuc0v0xD6UMPZwecj30+SWhvrXBxbmk+OB2R3dUuu48Zq6/Y9OZvO4rc0yX4h7PpmhW0lS4c1cYsmJDCJhsrEi55e57tDbAURDohqwPK3uNiG2mVlEqOLzXVopSxqeZDtW128Hl2Rl5S6sP6A9m4e7w6NHNFmE2GOvibYSbE8eLxWEHP7HIk5zRmbjjM9VUQje7Jov3GWgHcrhZL257GL95jOY2/1mdg0Y7gGCJO4NHxVzoX0NuALpQ+lO0Xist58nFOaT6Mtre22ni3ZZoIH9zIhpYeEXw/U1f0NAChYmSycKL5hzrbFpH6wzWglfiMA3E5gagmjh3U+GaXwAC3cMcxf6Ax9bPKv0MPdfA349vFxrvghlpX9Mwuk5bvx6Tl+3FTp3ohpa+g/VJah0rbDqcVfFDXc5Y+F9DTkDmlBSoplRBTjhLAuSV8cM0uiubDWie057jxumr+DDm0SqN2OHW+MU5ffxgxUfrKMDP7uQihzpBW7j2J7k2Md2C1649iRonPfKltqCjf2Owd2Bk9b+BhzQF6Absqgs+HVROZVf7ZHBwbhsUsXooZemYXhVkOhRvX9icRGuHD7uBcEQRQI5TVeuei8JGjI3w44SQMhK+fFOWcMrvwOlu7mg/zdffW7+FxNDewyZV22+9wOHlpd6plGbvAfJWGGemcEM9WuH3sUszcELxqiWX+NuNAYHYpKTWOAuoEhSU+fDhnu6lJhD3Pq7oig6rbHXrLWglBx8JtdjEjdM2HcTk7VcLaaqfZq0y4XkaXxfquEi9vMVERTG8ikPARwKmysOO35yTnlPDBkxgX7TiGUVPWcx0MQ8FoYykr3Pj5Yv/fbPZLfeFb3lbR+U5n99xwIzuchr/Bvjt7G2ZtMp4xs8Iyr46ZxREB3B94BvdoGHSs3IWPEFe7mGpuDIrYiklGmwwbvyf7VLHwt1Rua1qzMoCKr/nwb/xZwfMZDnJ1VoI59c1CXWYeKueU2YWn3Xj7720AzEPeihLp9aDEF3BMZKVLO1WGdYZkB5lwrq2v6JTXcjFZ4AsU+kvXnYeP5+5Qaaecwqx/KVH5fNh7htsdeqR2uo7w+3yYEerzzVTXRgNFnMFmilq0Aibb9Nu/9DeuPK+WUDqKsKd8i4quUVCyV9Hz6Sar9p3E3w6Y84pLyrdMzynhw40tw6MivCjxlfo7djPbvBX2nggIIvK7nJvSR4bAJlbhoLhUgtcT+IYNq1cKsr27mReFM8XZMDIiuKzC7fNhRrg1H0YlHBdlQfjQ/NY6nFpdfRYVKQsfFcHvxwi7cZjOZv5an4G/1jsgfJSz5uOcMru4UYGVDlZ5lkrzEcLjl+0+gZs+T/f/PkNMtWHBzlJFJyjxqc0uPp8U9iBZerB12a4DmtsDTxRnb/HyNrucDlHzYZZ/ozZvTfOh/h20i67FziVK2Wywgg/qpPkIH+Xt70OaD4dRHLqUvqDYZEmkKBOWqv0cSn0+w50xCecpKZVQ7A18w1KfpGuXDXtefA6YXVye+fCEj3CYrKwQqubDzGxjtCrNiuYjKMaPRvOh55yoh3+SVMF9KSTSfIQNWu3iIm442CgdLG/PGLuSZu7pYvyu2fl2w0Hnth0/mxl6SVPH0irxSar4GuXZcTtjdnEqN2LwzC7lTahLbU3NLmHTfGiED5OIy1oUn4/FO8MTHMwpyOwSPkj4cBE3ylrpYH2ShAXbj+KhCSv95+x+7BkO2PfOVR67ojkua1nTkbRKSn04xZh8wh3t1IhSn4S/1h9G19f/wfI9J2ym4bLmg+Nw6jQXNKxi6fp3Zm8L6XlmZhej87EWNB8sPp8UZHaxihK6e9nuEygw2aG7PPlk7k7M23qkwmtozkTK29/nnBI+RDQfUSHOzhTNh0+ScOfXy7B6X5b/nB3ho129JMPAX4QxHo8HVStFO5JWsU9SddTl2XaLS314eMIqZOYU4tFJa2yl4Xbn44bmIynOmW8tSig+K5YcTtkdpR3Yk4NdRbf5cG5IaVmla6Oqlq6///uVpPkIA6T5cBERz3qtOtMqUX6HU87zbe0YKaFyecbAPQtwqtsqKvEFxVpxCqvVzgnBwe0Onefz4fwz3DXthLJU1+5qF6vL7PueXyfo2D5m5ZxZlFanibGo8Skq8ZHwYUCETUG0vB1OzynhQ2SWEmq8D8WWyrPD25E0S31AdOQ59Zn8pDWuVt5ZMKRR9UqOpVUp2pqA6URn7L7wEX7BwA0BhyWUpcIRNstD9LuNH3wB/h5xMd6/tQM6N1Cbo27vWt/Ws50g2uI3ql81vtxNBBWZuslxtu4jzYeLiMxS7EqRCsr6eaeED59POmel/tSq9hqV00y6v1vQTPPzOzqiTd0kx55hxfnQKc5GzYfbTq2hmF3s+gyV+CTuDt1a4qMj0TxFDmnfqnai6twTV7RA7aRYW88PFatmZI+n4i8JLk/s1nkKr+4iIsvqQhU+ojVxPliKBTqq+lXjVb+3Zubiud83hJSnM5VIhwarUHzVKkVHoFvjari4WQ3/saS4KFzZprYDOVM/x23cj/PhgubDBadWllCW6tqdePp8kpADJtt8tANUbFQEerdKsZeBEImx0a4rkubj2atb6Z4b3ruZizmRsVvnyzu8+rklfAgM/qzwcaON7bADZpfgcyI+HzzhZ/+JU5bzcTYQ5VDI+1BQNBKsFiY+DIJCnEWzixO4rfkI1Z9KhKjIM8fno1MD8ZU5rKxRIqgNZWOBaLVOHk/oEy272DEjV6Q9aBpU0ze3WvHjcQr7mg8SPlxDpKNgO0grGz8p+M0unM6hSOD55+p+LTzctt/zUISPeEY4CIeJJBwCjRluCx9uDHY1E9w1Jdj1+bixUz1Lq7BYTYdPEhM+2ND/2rL3eIz9265u66xmj8WO8FGRTM9G5VYe5lO7fopkdnERq5oPO8vZlNk6Ty1aJCBpltdeIRURp8wuepxXJ9H0mpSywYyd0cRGkvDBw8yswgr23RpbW24pSoTXgwn3dg1L2jzs+nxUrxxj6Xp2MiOq+WDLW6tF9Ho8hg6vlWLCVx+tasAkqWIJH0aTonD0DWbYFerLe1+lc0v4ELDPVqscmI3Y+aZskDEtIpKmG7PDBtXizS+yQKyNOCSPX9EcPZvXQGODFSNO+QjohbiunaTv0Fq/ajxqJMTgrZvaA1DPaMIRdyWmHDotp22+D/ZsYnieHXPCVc+9Hg96NK0elrR52PVFsDpbZZ8j6oTOugJEaPwCvB6PYR7iw2gGFBE+BnVvqPpdkYQPo7p7JsVkCjW6b6icOSUVIpIkCZldzqsdWMGg3T9BBEUq5q2htjpbEeX/2optp60w6f5ulp9hxDP/p++ApUdq1Xh8e3cX9Gmjn3crA1R0pBcP9TIe/LQYdb7/PXkJlj/T27+cltVMsEsFH7lUDt8+6qqWlp6t5WxY7WJmlmfrdrj8P8yqzAMXNw7Lc61iVauqp/loaDCRYMtY6xfg9RhrWcMZW8hIoTkwrQHuv7ixKvqrBKlCOZwafbqYMIRF0ApiWuyMUwAJH65R4pOEIlLe1b0BAKB17UTTDvLeCxsFHVOED7shr91w1nfSKernB9IMNQh6KOVk1FhFfT4ivB5seflKyw7CVuIssGXGBkkaeUUL7HjtKjxgMus3T9/9pui08GHmFOg18EFwCrM2m1rVWa2fXaxqPlgzbkmpzz8YG2nM2DLWPs/j8QRpQ1gqhVH4MBK8Xrq+Df73f61Uof8lqWIttWXLNSFWXU7h0GCaCTR2W9LpEDdVDJVzRvgQtc2eVycJ/z7WE5Me6GY6i+LNVhVTwYLtxyznEQA8NqqS1XvsSso8IrweIW/ra9rVRvvUZP9vpTM02t9C1OxSWhZuWreR6vRbVgYAVg2tDZLkhG+K3X0+QsHp/twsPa8XaFJD1iRdGCbTiFnVthrgKlxYFb7YmX9hic8vjBip+dlH8J5n1G4rC/h8XNTM3jeM8Hiw4tneeOm681TH/32sp/9v1j2u1FfBNB9MWWonSHp90OWtg5c1i8ZZCVeQSbcj22qpGC3RBQotFHTjGpWRGBtlOoviDRgis/W7ezTC1W1ro0mNSujXsa5wvvQw2rabB9sPGflciBDp9YhrKJjnKquCtIMBu4Y+0qIayOqsw4pzL2t2CYdqtTyW6DmNWdAsr8eDWcMvxoaX+iApLioseTBrs24vxdXDquaDnfnvO1HgDzJmVBdVDqecNmokAJlpPvp1rIsLGtpzGo7welC9cgya1qysOta4RuA3q/koLvU5utQ2VK2bypylSUsvdDzPxDH2rs64vHUKfnu4u+HzzARmu3NJMru4hB2vdLOPyuvoRAZirwf4ZEBH/DOyZ5DXe6hKies5+ziw3NalvirfsVERePyK5rafF+E1dlxTkKBu9EpgHO3MrS0TNdSqw6nIDIGNrWClE2K1XOGYiZSH5sNpzMw4Xo8HkRFeVI6JdGy/neBnGJ+vCMu3AesDIGt2eXjCKszcKO90bSRwm5m5jNqtmfAR4fHY3llX0WKoHJA1HR/7vkUlPkc1H6FqvyIMhDo9YfAqTlDCxjUqYexdndGhvnG8l3BpPsjs4hJ2hA+zWRSv8Yl0KoraTra7amyx4tnjojdB6N0qBX8OuxCv9m0TJOCE0rAjIzxiZgdJOxOT/2Y7z2va1fZrRADrjU5EI8E6b1kZAMKt+TgbhA9Tswtb3GGSPswcOSuK8GFF8+HzSbrtWlTzwXueUf03czj1ejy2zbdZBcVB+dMmxZpdikslR30+Qh3MWYWstvx532Nwj4a45YLU4HQEyy8c/Q1Amg/XiIrw4OLmNcwvZDCrHLzGKzKgGUn8oS5x02uiXg/Qpm4SIryeoEYfSqQ72ewiovmQ1A5wZYMAu0z3o9s6qGYlVgdkbSPtwlEL213uqfL5cKgzGHpJU//f5eFwqmX6Ixfi5s7WA+sp+CQJVeL1zSmsYGDVVCiK2YB4Jvp8GIVSF13ayZsgsAKJtg2baT68XvsrlrIKiuQ0DDQzrLBRXOpzdFl4qEv42Xy/ekMbVX/AE34va5nC/d6i5RdtYk624ycIkPDhGvWqxOO7u7vg0pY1he8x6x94HZ1IhTKKPHhNe+uRBdl2qWd3N5plhBLpLsLrFfbNYN812r/aJdCwPB6PyhfAqsSv/R7f3dPF8Hors89Exqvdqdkz22npLbW1u2OlHTzw4JmrW9tWp0uShCkP99A9z9bBcEXLNsu72xvP6WFlIm9kzjIyu7B+EnzNR6D+ac2/vKB3n9ze0f+314LZRSuQnshXhI/AMW2/qVrd4/DmmqG2X7Yfa56SgFXPXR44x+n/9SYrouUXLs0HOZy6jBWp12wWxUtKpELpqUPfurGdrYbBNlS9JurRaeiS5JLmQ2N2UQaB7k2qITE2El0ayVqK1KrxePSyZnj26lYhL1tTNCe62iArwgcjFJU4tCcCu0xPT8szc/hFjjxLBI9H3jTv9Rva2rq/VJLQqHolPHdNayTERgathmCL20nZ45IWAY2mqcNpOWo+2AFdO5gaRds1Fj7030clfHDaKNv31EgICB8piTHcmTrrICoLH2Lt596LGuP3IQGhVDG7sP2r9nHad3bSP8FJH4oITbC2yrHBGiO9/lG0/FISTVbFkMPpmYGVzsdsbLJrdlEtgYtgG6C4Ao3dPVHEHsrWc+0zBnStL/jUYOSltuZlKkkah9OyeyrFRGLFs5dj0n2BwGcjLm+Oey9qHPaBwormg+3kQ9lGnYWdbeoJHwmxzqwKGZjWwPQapY7Y7ZyVanjPhY2w9vkr0IZxHgbUna2TqxdqM9qhiuxwuvLZwAxZ+/6JBt/5700ZuueMhY/A32Y+H7WYAe7nB9JMr/d6xJ3jI70etE9N9i+1v7pd7eD0NM/TCh+LdtoLXcDDSdOb1+tBbFQExvRri5evP48bNl9X8yHY/zSqUQmv9m2DlrUSQsqrllPkcOouViqemWTKSu6PXd4cUx7uLiTNenQ0H16vWAyOhJhIDO8dWKGisgnr9OmsWKM1wTStmYB1L16B+y5qZPpsAOjLrKiJFF7tIqmey36H6EgvtyE6NUNhTTl65QDIAt3Nneth8oNpQWmw38Up4YMN5c8TPtpqBu9QuM5kFRQQKBu7GifW5Of1Bqvl9cwu/z1xia3nBdIN/M1rPzxzX3nAmta0woeR5XLET2t1zxm1EdafilcurDaEjTmh9QvzX68RFkRn7sp1393dBWPv6uwPw6+ahBmYXQBnzXRORtdV3uHWLvVxV1pD7jWh1rkorwd3dGuA3q2CY4UAsK1GtBJ+Ihycc8KHFZuvFYfTy89LQcf6VYQco/TCTIvqPbQNk50l6DnyqTQfnMckxkbZmhVGCMb5kDUfgd8i38GpvV0evawZujWuirfL9mlR0HbIdZLi8OaN7U3jF4SyjToLO0vSxvl45NKmGHtXZ0eeA4h1uMolvNk0qzbXQztb1T5Tz+xS38JeQ09f1RItUtQzQLOw7f06BGLpVJQ4H1rLnd0BUc9BvWWtBNSrEihXXuoqzQcTpTjS6+W2T7Xmw9jn43vG30p5taS4KFzeOsUvMKknQerEwhnRNDbErQzY7ldE0x3qJEp5ht7GpHa0iIO6N8QDPct3q4FzTviw4hls1h9Uq8RuQidfnF9oPjCx9ZWdTYj2P9rKxv7Wq4ceg4auIOqBX6pS53qFQ8LzzC5GOKX5qFIpGpPuT8ONneppfF/U14l2SiIbFIrAbqmuFbRGXtECtXQiICbYCH1tZXUFbwWFSLht7XihrWeqAdbmVDa1Sjxu15gJ1cJH8D1mwbZYXrn+PMPzojxtstdPkObDpvChDe+twJpl9dJn+546yYG65vWaR0SNiuBrKxVY/xa9uqdeeaY+F66Ipp/f0VHlPB4qIt8tVFOf4tCvtwstT/gwWyo97NKmuOUC++Z2JzjnhA8rUqKZTY51wFKuzD1dYpquqtExFVO0A9IqV+Ki+BVNZMt4FqMB6rHLGTMPk4GICDH1qwT1eCOi1Qi3ilybb9Eoo1a3Q9cjmTEHWdneugojtIgitArLq292ERFeggdU9XmV7GGaGh/e0vBWtQOaEJ62xWvB7OKUT0jNBHUdubOb2udGO7O3G3VTz1ckWBAMvoZd7cI6NZb6JO6qDe2eJnpd6aDuDTXOpPx3Y49rtWZs+TgpLFzZpraj+wqJpGVF2+7xAFe3ra3S1il+gR3qJ3PvsSOnubF7uhnnoPAhfq3Z96nPbFKlqMTyC82Fj1zmmkiveSPVUlw2+L9+Q1t0bVQVr/Zt4z/HDgDThl7IpG2erlHHexljb2Q7ikhB268kqfMWbs2H0W6fCtoyMRM+vhkkh0N+4soWtvN1TbvAUmrWUbdRDfEw93aED7PO5vLWKf5Q+zyzC3t3alX+8l/tYMRqGX+8r5tqQApFrc6qn5c9cxmS4gLloa2KlaIjVd/ZrN455Q/AlneV+Ci8rNGoaF/f7ligp/kI+hZc4SPwd/XK0bikRQ1c0LAKUhJiucvn2WOVoiOQe7o46JpG1SvhxevOM9VGyccDJ47lFanO3V22aWfvVim4oUPoW1CwWN22QQtr2hbZoiHKwvNapCTgkwEd0ZxxLlXGiGvb1cG7N7cPuofXksxyZXVX5XBwzgkfrFPcn8MuNLjSvCOKjPDilevPw8C0BmhdW9Yy5BeZCx9Z+YFGG+RBbnp3oGO5vWt9/PRAGmolxWLls72x9oUrVIIBW8FE0uWt7Q+kFfib7TgjvB4kx0UJaFkklZ1bzOfDvHp+NqAj5jAbUikbOD3Rh6/2Zp+qNQvERRs/79KWKRh7V2dTzUdMpBczHuUvka1fNR5/DrsQ8x7vBQBYMuoyzB5xMWomiG0yBQTHTdDyzk3BHZSR8DHy8uYYe1dnf3mYCX2/D+G3m2Cfj8Df2jgm159fF5WiI3BVm1qGz9LiAVBcEnhOzYRYQ4fT+JgIVT0y07g55YvI5qNOclxQvrR+W+x5K7FdeEs7gWDfL565mdV8xERGYNzgLvj5gTTZmZRTBdg6FBcdgexTwcKHolkycwLWXqOlR9PqWPa/y/DlnZ1U5tDbugRHCrWK1X11tDSpURm1EmPRslaC0CBuZRKllDFbPZT8er0e9OtYL0gTxIvt1CNMGzc6yTknfLCz7zZ1kwxXeGjbTL0qwZ3CnWkN8dL1bfwNLE9A85F1KiDls5Kzx+Ox3flVqxyDpLioIFtpj6bVAAB3aNS+PAyFD51Za4THA6/Xg2lDL8Tnd3QyTJ9tJCKzAZFGe1Xb2mjCbEj14a0d8OewC/3L+YzQlrVTIc4lqE1yWtrUTULDMi1DraRYNEuxtoSuaryx5oMn2Hk9HlzasiYivB50Zva3AYJNPmZBjfQEx+AVHIHrtDPEKpWiseaFK/DpgI7gceV5tbjf3+NBkFO30Sw7PjpS9T5my8L1TAmtals0YZqYHTppvgF7vSRJwgOWnm0/SLHE03wwz1T8fJR+jK/5CFwfGxWBnFP6fZ3RqjL/cSa97k2qBZ2vmRgrL2VlzICDe+j319o8sjSqXsnvcP7ElS1C0qpGRXix8KlL8Ncj5jF4Hrm0qZC/lIJSP1XaFW30V+bbpo+6lOtKMKZ/Wzx2eXPVKqY3+rdF1UrRaFs3yZbfmNOcg8KH+LXa2cJXA81XH4hI1ScLAjOGyAj9jtMO2tnnt4O7YOn/LkNnnRUcbL9gFNpdPZNhjjNSudG7S5J6ticyY9DTfFzbXl422q1x8DvFRUcExZfQw67Ph9W0eUJrKFQ1MbvwOvsIrwdfD+yMjS/1waGsU6pz2o6YtzMnW6v0VM3KEkoFdT0Jvj4qwqs7K/78zk7owRmQAE+Q1z+bNu+bsu9j1j4LdVYUTB92ISY/mIaJ93U1vF+BrbrrD2b7//7viUvw1V2dcbE2AJtGs7jgyUuEnqNvdtFqPozR+vmYxTCKjeJrPpRB08iZVIH9Vs9f21o3b6zWLNZkGbieUDH38V64sZO8dUCTGpWx4cU+humYEWngcMvmYeQV1ky0URzNh7aNsMJ37aS4IB9AAEiOj8awy5rh5wfS0D41GUMvkR1MVzzTG1OH9LC9L4+TlL/44zJaKdHI/1R7bctaifB4jO957Ya2uOK9/wzzkHNKz+wSeoUo0YRKj4zwGkbIY9+lUox+w2Yra7OUBHg8HqQkqs0PRsoMCcBxjV3XDL0Z+Oh+bXFR0+p+E4sVjFa76IU4t4ykTvuDW89H/8/SlVOWWfDkJVi88xie+nU9AKBBdWP/EO7A4ZE3AouNikCxRkAd1KOh6reZUyab/mUta+Lj2zuiVJKCZuFmu6qaEc+ZnXk8arMLoJ1lq6+vFBOhqkdm+dBz/PV6PZa2kNfr3OtXi+cuLVY5X0qSsBZO9DpefthBTNvWzHbBjYuKQPWEYCFY6U9UMX102jErIBkJ1LHM/WZ72Tx/TWs8PWW94TWAc8v4eURHeC05kLOImKNjIiNUEV+NFlGkVo1XLZOvCL4eCuec5kP7nYwGA95HNVss0zwlwVSl9fL1AQdRbWjeUOWPgd1l84o2tLUIRpoPtVoYGHtXZ7zaVx2G20h4kiQpaMZthp7mo3JMJG6+INWW4yVLkM+HY2YXSWhpsx6VNEJQatV4tK4d0OZ0a2Q8CPK+AysYnioKLAcf1L1h0IoJ7cBjtGrl/9rWRlx0BFf9bxRESoTKnPpYr0pc0GoXdQwbjc9HdKRqgPZ6PLi6bW20q8fXjtkdNLQY7d/EQx2ATRLWgur1R+x2AABf88FqSbXCB+/5Wp+PkZe3QN/z62DCvV3RqEwgVnx42PpWrRLfR4pdGVjFwJTImspiIyP8z2JNV42qV8Lipy/FrV3Elo/anfkbmaYVQjHpKOYuI4fsrwd2Rt3kOHxxp2zmDtc+SeHmnNN8aM0SxpoPe884bRKEqgszeLBOX8nxUTicbW2A1nJlm9qY+3gvW6r+SoZmF3XnaHaNFgnA8Xxrmo9wLAdjOzltfsO1rb3Vt6jGcWgtKg3UKdbHRWH84AswaNxyPPN/rZDDWYXAliXrl6Q1lQDyEuBmNStj+5E8AHJnyg5ObLlVT9B3vlX5Ytj4lqwm6sf7uuFQ1imcVycJxaV79Z9T9vdlLWtizpYjuO+ixthzPN9/PtLrwScDOkKSJDQa9VfQM50SPtjB9xdOxFwtLWolAGvlv32SuBa0tiYWzJh+bbH5cA56aXbw5kXLZf3DtIMxb3Bmj6VWiUfVStF4/9YOAOR3XLLrhF8byWqj2Ei+LE3K/KISYiMNncvZrMREeTHxvq6Ysuogbr0gFZ1e/QeA/N3quLAJY5s65ibdULQqfodTg2s6N6yKRU9f6v/t5FYFbnLOCR9WPlRtsw19dLCySyzb5pLjog2DoEVHiqnzGpmo5VnYhm1kdmCva6Gzx4Cx5gN4qFcTfDZvp3AYd5aE2EihGCpmdGlUFQ/1aoJmNStj59E81Tmndo/UahI8Hg9iIr0oLPGhp2ZQ4MHrrFmBw+v14M9hF+Kajxb6j/VqURPbXr0K0ZFe/LR8X9D9epoHXiAzr9eDmcMvRpP/yYNzdIQXdZLjcHePRqhctnrkzRvbYWtGbpDvgh52NB+sGTCN8f/QJsVzOP3izk44kluIOslxyMg5HThfdoHezNep6LWsWlyvvQDArw91x9wtR3DvRY3w1qytAOQ+SlT4iIzwYky/tn5Tg97Mv0qlaDx+RXO8/fc2/zE7O8VOebg78k6XBNWbapVjdJ28a+isDqscE4m1z19hakphiYn0onZSHIZc0lR13M6WB1Me7o6T+UX4bN5OrNh7UnXupk71sOlwDjYeyvEfq5MUi3dvCV5JpkVE86GnQVEEFyvyBAkfZwjJmmWKeuHIAbmj2vHaVRj581pL3u79OtTFlNUHERvlNd2NkT2vzZuWi5tVxz+bjwjnQwS23sYadAJREV78OexCrNp3En3P56+7Zye34wZfgMHjlgeeA+DxK1rgyvNqWQ5+BgCXtKiJP9YesnyfFo/Hg6eulJfhjl+023/8rRvbheyE9d3dXfDa9M1488Z2quNNalRC+qjLsP9EgX9zLSOqccxJyfHRWPz0pX7TEFtXHrhYDpOsdHrXta+LVXuz0KlBFTz56zr5IouvptoPpczJj3UKvLmz+ZJHdk8dWz4fOpq4Ry5rhoU7jmFAV9nEyEs6skxgAozrNUtCbGSQcP/Y5c1xvU59j47w6oa8ZrVLRua8Tg2qBK188fkkIfOrskJEdPC+/+Im2H4kz78Ms7mFVVZKGXesX8X4wjJYZ1Qj82iSSZ8HqLUoem30lECIAy3Ku1zWKgXXf7IIa/dn+c9d3a42Rvdri02Hc3Ddx4sAAFOH9EBNgQmp3ve+vWt9TFy6D5e2rIkPb+vAvUYxuxiNS1rOUNkjfMLHJ598grfeegsZGRlo3749PvroI3Tp0sX8xjDzRJ+W2HeiALdxZgj/jOwZdCwywqtbUfR4uW8bXNS8Oi5oWBUXvjFXdU67pCyPmc3HRkXodjrREV68eWN7vDFjC26+IPS17jz0Gs3AtAZISYxBraRY45UkTN5TNWYfSZIQ4fUIDb4sPZvXwIaD2Rjdry0uaFgFLWpZF1z0uKFjPZwsKMYNHer6l76GwsXNa+BiRrOx/sUrUFTi8+9Ma7ZKRaGJzjJdVq3Mxhp5oo/aoz4uOgJv3NgO2QXFfuEjlA7KrkaIXUJsxezyZn9ZeNNbRlo7KQ4LngyondmATLxdgEU2yru7RyMM7N4AXy/crTretl6S7t4znRpUQfqu49xzbLBBq0KtdgdoHj/d383fFkW3jIiO9OKDWwN9WdOalTHp/m5B0VgV3uzfzl9/rDrD12Xaf6hRY0UenV8Umsbq1wfTMHrGFv/3rxwTicgIr8pMKypAv3vz+Rj4zTKMvKK56vhL152Hfh3qon1qsm6ZRNrQfJTnTs2hEBbh46effsLIkSPx+eefo2vXrnj//ffRp08fbN26FTVr1gzHI4WpkRCDSfcHbLDsRzaKzWCFyjGRuKGDvKwrISYSuYUl+GZQZ2w+nItbNIJDrkBckBeubY2bO6eiUkwk3tDMqkOFXYNeJT4ajatXQtapYpwo889456b26F+2RM2MbGYJMbupVSiMH3wBSnwSoiK8uFNn10i7JMVFYcTlzc0vtAlvIDTi/VvOx/T1h/HIpc1Mr42NisDCpy5BhNejH7uC6St5nZmobdquDZttT3p+QgoT7u2Kt2Ztxeh+bf1axmaC7bF65RhMebg7lu0+ERTDBJDbvBHdGlf1a3Uub52C79L3Gl6vEBXpxZJRl6FUklBYXIoX/tiIBdvlrd9FHBO19OtYF1NWHcTDlzQ1Hewb1ajkb7uhKOy6NeYtZ5a5+YJURHg9GPXbenx5p3EMHy11k+Pw60PdhQVuI2rZNH9bITLCq/Kf4ZWtqPDRpm4SVjzbO0jojIrw6oY88OfDv9RWXPp495b2uP+7lahbJQ7Ldp8Qvq+8CYvw8e677+K+++7D4MGDAQCff/45pk+fjm+++QZPP/10OB5pm74d6mL84j1onuKM4KFl3hO9sO9EATrUr4JLWwYvDe3dqiZe+XOToeDTrl6ypUA1VtD6Eswe2RM+SUKzZ2YAAA6cFHeAZRubU86bHo8nrMviKhJ9O9RFXwuhpM0EvMTYSJxXJxHFpT7V7LZ/x3r4ddUBDO8tJnjZ9d6vHBOJh3s1wZHcQtOonT2aVg+Kyti9aXW8eG1roSBsHetX0TUJtK+XhId7NUFqVX551U0OHL+waXX8MbSHX9VupLWQJEnl+/D+Ledj9IwtuPWCVLStl4T0ncdVmjAz3uzfDvdd1BgtNT4iLVISsDUzV3VMJIiXE/TvVA/Xn1/HNDgbD605yS69WtTAw72acJ1mb+xUD7+sPIAGFnZG1qMlo1Wt7Bc+7K1as2vCVfp5K4rK8+okYdHTl+KXlQfObeGjqKgIK1euxKhRo/zHvF4vevfujfT0dKcfFzLnpyZjwZOXoGai2GZh91zYCF8v3I17LxRzmqxWOYa7ekGhQbVKWPa/y/y2T9YO+93dXbD3eL5jjZiH1v8iwutBBNOxVdXxVOfRu1VN3JXWgDubOlPtkmcyHo8ceVaC2uwxpn9bDO7R0L8lgBnnWzSVsTx5pfHurmYMMoloKYLH4+Hm49u7u+Dn5fvxzNWtVNe2q5fs/200KdCac6pVjvFH0QTgXwkiSmSEV+VbpjhZt66TGCR8sMHFwr1JmB3Bw0n0vh8gmzKa1KiM/2urH6afjXNhRJu66rIHNP5EYezDXry2NX5accCvibXTX55p1hfHhY9jx46htLQUKSnqWX5KSgq2bNkSdH1hYSEKCwv9v3NycoKuCTd6MyIe//u/VujfsV7Q7CQUWCemVrUTMX7wBaidFFfmJS8+c7LC8N7NsGpfFm7qzDep/PJgGuZtPYqbBE0ugNxJsTFMpj9yIa7+UF6R0a+js5tDEWLwfC2iIrxCUWD/euQi/L72YNDKgrOFns1r6K4+WvDkJcgqKOZqbEb3a4uvFuzCCwZROZ1g2f96o1SSsG5/Fn5bfRB1k+Mw4d6u8GmCkNlx4D5bqBQTiYd6qZeLs47AFzevIexnlhwfjVf7tsGpolIkl/l6sDFKjIIwhsqgHo1UgvZFzarj47k7LO1D07SGc2OSG3gkK8YlAQ4dOoS6deti8eLFSEsL+FY8+eSTmD9/PpYuXaq6/sUXX8RLL70UlE52djYSE8/dRnW2kF9Ygm2ZuTg/NblChPQliDORDQezUb9afNAyboV1B7JQtVK0Y75WZzJ5hSXYezwfK/eexHXt6/gFCbtknyqGxxO8hD7crNx7Eg2qxZtuZMkybe0h1KsShw6Cq5KcJicnB0lJSULjt+PCR1FREeLj4/HLL7+gb9++/uMDBw5EVlYWfv/9d9X1PM1HamoqCR8EQRAEcQZhRfhw3EoUHR2NTp06Yc6cOf5jPp8Pc+bMUWlCFGJiYpCYmKj6RxAEQRDE2UtYllCMHDkSAwcOROfOndGlSxe8//77yM/P969+IQiCIAji3CUswsctt9yCo0eP4vnnn0dGRgbOP/98zJw5M8gJlSAIgiCIcw/HfT5CxYrNiCAIgiCIikG5+nwQBEEQBEEYQcIHQRAEQRCuQsIHQRAEQRCuQsIHQRAEQRCuQsIHQRAEQRCuQsIHQRAEQRCuQsIHQRAEQRCuQsIHQRAEQRCuQsIHQRAEQRCuEpbw6qGgBFzNyckp55wQBEEQBCGKMm6LBE6vcMJHbm4uACA1NbWcc0IQBEEQhFVyc3ORlJRkeE2F29vF5/Ph0KFDSEhIgMfjcTTtnJwcpKamYv/+/bRvTBihcnYHKmf3oLJ2BypndwhXOUuShNzcXNSpUwder7FXR4XTfHi9XtSrVy+sz0hMTKSK7QJUzu5A5eweVNbuQOXsDuEoZzONhwI5nBIEQRAE4SokfBAEQRAE4SrnlPARExODF154ATExMeWdlbMaKmd3oHJ2Dyprd6BydoeKUM4VzuGUIAiCIIizm3NK80EQBEEQRPlDwgdBEARBEK5CwgdBEARBEK5CwgdBEARBEK5yzggfn3zyCRo2bIjY2Fh07doVy5YtK+8snVGMHj0aF1xwARISElCzZk307dsXW7duVV1z+vRpDBkyBNWqVUPlypXRv39/ZGZmqq7Zt28frr76asTHx6NmzZp44oknUFJS4uarnFGMGTMGHo8Hw4cP9x+jcnaOgwcP4o477kC1atUQFxeHtm3bYsWKFf7zkiTh+eefR+3atREXF4fevXtj+/btqjROnDiBAQMGIDExEcnJybjnnnuQl5fn9qtUWEpLS/Hcc8+hUaNGiIuLQ5MmTfDKK6+o9v+gcrbOf//9h2uvvRZ16tSBx+PB1KlTVeedKtN169bhoosuQmxsLFJTU/Hmm2868wLSOcCkSZOk6Oho6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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<p>For data lake measurements, the <code>get()</code> method is even more powerful than simply returning all the data for a given data lake measurement. We will look at a selection of these below. The full list of supported parameters can be found in the <a href="../../reference/endpoint/api/data_lake_measure/#streampipes.endpoint.api.data_lake_measure.MeasurementGetQueryConfig">docs</a>. <br>
-Let's start by referring to the graph we created above, where we use only two columns of our data lake measurement. If we already know this, we can directly restrict the queried data to a subset of columns by using the <code>columns</code> parameter. <br>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>For data lake measurements, the <code>get()</code> method is even more powerful than simply returning all the data for a given data lake measurement. We will look at a selection of these below. The full list of supported parameters can be found in the <a href="../../reference/endpoint/api/data_lake_measure/#streampipes.endpoint.api.data_lake_measure.MeasurementGetQueryConfig">docs</a>. <br/>
+Let's start by referring to the graph we created above, where we use only two columns of our data lake measurement. If we already know this, we can directly restrict the queried data to a subset of columns by using the <code>columns</code> parameter. <br/>
 <code>columns</code> takes a list of column names as a comma-separated string:</p>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-15">
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-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">flow_rate_pd</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;flow-rate&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;mass_flow,temperature&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-15">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">flow_rate_pd</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">"flow-rate"</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s2">"mass_flow,temperature"</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
 <span class="n">flow_rate_pd</span>
 </pre></div>
-<div id="cell-15" class="clipboard-copy-txt">flow_rate_pd = client.dataLakeMeasureApi.get(identifier="flow-rate", columns="mass_flow,temperature").to_pandas()
+<div class="clipboard-copy-txt" id="cell-15">flow_rate_pd = client.dataLakeMeasureApi.get(identifier="flow-rate", columns="mass_flow,temperature").to_pandas()
 flow_rate_pd</div>
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 <pre>2023-02-24 17:34:26,492 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.
 </pre>
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+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3791,216 +3595,189 @@
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>timestamp</th>
-      <th>mass_flow</th>
-      <th>temperature</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>2023-02-24T16:19:41.472Z</td>
-      <td>3.309556</td>
-      <td>44.448483</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>2023-02-24T16:19:41.482Z</td>
-      <td>5.608580</td>
-      <td>40.322033</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>2023-02-24T16:19:41.493Z</td>
-      <td>7.692881</td>
-      <td>49.239639</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>2023-02-24T16:19:41.503Z</td>
-      <td>3.632898</td>
-      <td>49.933754</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>2023-02-24T16:19:41.513Z</td>
-      <td>0.711260</td>
-      <td>50.106617</td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <th>995</th>
-      <td>2023-02-24T16:19:52.927Z</td>
-      <td>1.740114</td>
-      <td>46.558231</td>
-    </tr>
-    <tr>
-      <th>996</th>
-      <td>2023-02-24T16:19:52.94Z</td>
-      <td>7.211723</td>
-      <td>48.048622</td>
-    </tr>
-    <tr>
-      <th>997</th>
-      <td>2023-02-24T16:19:52.952Z</td>
-      <td>7.770180</td>
-      <td>48.188026</td>
-    </tr>
-    <tr>
-      <th>998</th>
-      <td>2023-02-24T16:19:52.965Z</td>
-      <td>4.458602</td>
-      <td>48.280899</td>
-    </tr>
-    <tr>
-      <th>999</th>
-      <td>2023-02-24T16:19:52.977Z</td>
-      <td>2.592060</td>
-      <td>47.505951</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>timestamp</th>
+<th>mass_flow</th>
+<th>temperature</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>2023-02-24T16:19:41.472Z</td>
+<td>3.309556</td>
+<td>44.448483</td>
+</tr>
+<tr>
+<th>1</th>
+<td>2023-02-24T16:19:41.482Z</td>
+<td>5.608580</td>
+<td>40.322033</td>
+</tr>
+<tr>
+<th>2</th>
+<td>2023-02-24T16:19:41.493Z</td>
+<td>7.692881</td>
+<td>49.239639</td>
+</tr>
+<tr>
+<th>3</th>
+<td>2023-02-24T16:19:41.503Z</td>
+<td>3.632898</td>
+<td>49.933754</td>
+</tr>
+<tr>
+<th>4</th>
+<td>2023-02-24T16:19:41.513Z</td>
+<td>0.711260</td>
+<td>50.106617</td>
+</tr>
+<tr>
+<th>...</th>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+</tr>
+<tr>
+<th>995</th>
+<td>2023-02-24T16:19:52.927Z</td>
+<td>1.740114</td>
+<td>46.558231</td>
+</tr>
+<tr>
+<th>996</th>
+<td>2023-02-24T16:19:52.94Z</td>
+<td>7.211723</td>
+<td>48.048622</td>
+</tr>
+<tr>
+<th>997</th>
+<td>2023-02-24T16:19:52.952Z</td>
+<td>7.770180</td>
+<td>48.188026</td>
+</tr>
+<tr>
+<th>998</th>
+<td>2023-02-24T16:19:52.965Z</td>
+<td>4.458602</td>
+<td>48.280899</td>
+</tr>
+<tr>
+<th>999</th>
+<td>2023-02-24T16:19:52.977Z</td>
+<td>2.592060</td>
+<td>47.505951</td>
+</tr>
+</tbody>
 </table>
 <p>1000 rows × 3 columns</p>
 </div>
 </div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>By default, the client returns only the first one thousand records of a Data Lake measurement. This can be changed by passing a concrete value for the <code>limit</code> parameter:</p>
-
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-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">flow_rate_pd</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;flow-rate&quot;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-16">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">flow_rate_pd</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="o">=</span><span class="s2">"flow-rate"</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
 <span class="nb">len</span><span class="p">(</span><span class="n">flow_rate_pd</span><span class="p">)</span>
 </pre></div>
-<div id="cell-16" class="clipboard-copy-txt">flow_rate_pd = client.dataLakeMeasureApi.get(identifier="flow-rate", limit=10000).to_pandas()
+<div class="clipboard-copy-txt" id="cell-16">flow_rate_pd = client.dataLakeMeasureApi.get(identifier="flow-rate", limit=10000).to_pandas()
 len(flow_rate_pd)</div>
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 <pre>2023-02-24 17:34:26,736 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.
 </pre>
 </div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>If you want your data to be selected by time of occurrence rather than quantity, you can specify your time window by passing the <code>start_date</code> and <code>end_date</code> parameters:</p>
-
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
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-            <div class="zeroclipboard-container">
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-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-17">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
 <span class="n">flow_rate_pd</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
-    <span class="n">identifier</span><span class="o">=</span><span class="s2">&quot;flow-rate&quot;</span><span class="p">,</span>
+    <span class="n">identifier</span><span class="o">=</span><span class="s2">"flow-rate"</span><span class="p">,</span>
     <span class="n">start_date</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="o">=</span><span class="mi">2023</span><span class="p">,</span> <span class="n">month</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">day</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">hour</span><span class="o">=</span><span class="mi">17</span><span class="p">,</span> <span class="n">minute</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">second</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
     <span class="n">end_date</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="n">year</span><span class="o">=</span><span class="mi">2023</span><span class="p">,</span> <span class="n">month</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">day</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">hour</span><span class="o">=</span><span class="mi">17</span><span class="p">,</span> <span class="n">minute</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">second</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
     <span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
-<span class="n">flow_rate_pd</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;mass_flow&quot;</span><span class="p">,</span> <span class="s2">&quot;temperature&quot;</span><span class="p">])</span>
+<span class="n">flow_rate_pd</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">"mass_flow"</span><span class="p">,</span> <span class="s2">"temperature"</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 id="cell-17" class="clipboard-copy-txt">from datetime import datetime
+<div class="clipboard-copy-txt" id="cell-17">from datetime import datetime
 flow_rate_pd = client.dataLakeMeasureApi.get(
     identifier="flow-rate",
     start_date=datetime(year=2023, month=2, day=24, hour=17, minute=21, second=0),
@@ -4008,102 +3785,78 @@
     ).to_pandas()
 flow_rate_pd.plot(y=["mass_flow", "temperature"])
 plt.show()</div>
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+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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 <pre>2023-02-24 17:34:26,899 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.
 </pre>
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 <p>... from this point on we leave all future processing of the data up to your creativity.
 Keep in mind: the general syntax used in this tutorial (<code>all()</code>, <code>to_pandas()</code>, <code>get()</code>) applies to all endpoints and associated resources of the StreamPipes Python client.</p>
 <p>If you get further and create exiting stuff with data extracted from StreamPipes please <a href="https://github.com/apache/streampipes/discussions/categories/show-and-tell">let us know</a>.
 We are thrilled to see what you as a community will build with the provided client.
 Furthermore, don't hesitate to discuss <a href="https://github.com/apache/streampipes/discussions/812">feature requests</a> to extend the current functionality with us.</p>
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 <p>For now, that's all about the StreamPipes client. Read the next tutorial (<a href="../3-getting-live-data-from-the-streampipes-data-stream">Getting live data from the StreamPipes data stream</a>) if you are interested in making use of the powerful <a href="https://streampipes.apache.org/docs/docs/extend-sdk-functions.html">StreamPipes functions</a> to interact with StreamPipes event-based.</p>
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 <p>How do you like this tutorial?
 We hope you like it and would love to receive some feedback from you.
 Just go to our <a href="https://github.com/apache/streampipes/discussions">GitHub discussion page</a> and let us know your impression.
 We'll read and react to them all, we promise!</p>
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-<h1 id="Getting-live-data-from-the-StreamPipes-data-stream">Getting live data from the StreamPipes data stream<a class="anchor-link" href="#Getting-live-data-from-the-StreamPipes-data-stream">&#182;</a></h1><p>In the last tutorial (<a href="../2-extracting-data-from-the-streampipes-data-lake">Extracting Data from the StreamPipes data lake</a>) we learned how to extract the stored data from a StreamPipes data lake. This tutorial is about the StreamPipes data stream and shows how to get the live data from StreamPipes into Python. Therefore, we first create the <code>client</code> instance as before.</p>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="Getting-live-data-from-the-StreamPipes-data-stream">Getting live data from the StreamPipes data stream<a class="anchor-link" href="#Getting-live-data-from-the-StreamPipes-data-stream">¶</a></h1><p>In the last tutorial (<a href="../2-extracting-data-from-the-streampipes-data-lake">Extracting Data from the StreamPipes data lake</a>) we learned how to extract the stored data from a StreamPipes data lake. This tutorial is about the StreamPipes data stream and shows how to get the live data from StreamPipes into Python. Therefore, we first create the <code>client</code> instance as before.</p>
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-<p><strong>Note</strong><br />
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p><strong>Note</strong><br/>
 As of now we mainly developed the support for StreamPipes functions using NATS as messaging protocol. Consequently, this setup is tested most and should work flawlessly. Visit our <a href="../../getting-started/first-steps">first-steps</a> page to see how to start StreamPipes accordingly.
 Anyhow, you can also use the other <a href="../../reference/functions/broker/broker">brokers</a> that are currently supported in StreamPipes Python.
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.config</span> <span class="kn">import</span> <span class="n">StreamPipesClientConfig</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.credential_provider</span> <span class="kn">import</span> <span class="n">StreamPipesApiKeyCredentials</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt">from streampipes.client import StreamPipesClient
+<div class="clipboard-copy-txt" id="cell-1">from streampipes.client import StreamPipesClient
 from streampipes.client.config import StreamPipesClientConfig
 from streampipes.client.credential_provider import StreamPipesApiKeyCredentials</div>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># You can install all required libraries for this tutorial with the following command</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># You can install all required libraries for this tutorial with the following command</span>
 <span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">matplotlib</span> <span class="n">ipython</span> <span class="n">streampipes</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt"># You can install all required libraries for this tutorial with the following command
+<div class="clipboard-copy-txt" id="cell-2"># You can install all required libraries for this tutorial with the following command
 %pip install matplotlib ipython streampipes</div>
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
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+<div>
+<span class="notice" hidden="">Copied!</span>
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+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SP_USERNAME&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;admin@streampipes.apache.org&quot;</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SP_API_KEY&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;XXX&quot;</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_USERNAME"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"admin@streampipes.apache.org"</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_API_KEY"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"XXX"</span>
 
 <span class="c1"># Use this if you work locally:</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;BROKER-HOST&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;localhost&quot;</span>  
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;KAFKA-PORT&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;9094&quot;</span> <span class="c1"># When using Kafka as message broker</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"BROKER-HOST"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"localhost"</span>  
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"KAFKA-PORT"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"9094"</span> <span class="c1"># When using Kafka as message broker</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt">import os
+<div class="clipboard-copy-txt" id="cell-3">import os
 
 os.environ["SP_USERNAME"] = "admin@streampipes.apache.org"
 os.environ["SP_API_KEY"] = "XXX"
@@ -2906,147 +2882,116 @@
 # Use this if you work locally:
 os.environ["BROKER-HOST"] = "localhost"  
 os.environ["KAFKA-PORT"] = "9094" # When using Kafka as message broker</div>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">client_config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client_config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
     <span class="n">credential_provider</span><span class="o">=</span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">(),</span>
-    <span class="n">host_address</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span>
+    <span class="n">host_address</span><span class="o">=</span><span class="s2">"localhost"</span><span class="p">,</span>
     <span class="n">port</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span>
     <span class="n">https_disabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">client_config</span><span class="p">)</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt">client_config = StreamPipesClientConfig(
+<div class="clipboard-copy-txt" id="cell-4">client_config = StreamPipesClientConfig(
     credential_provider=StreamPipesApiKeyCredentials(),
     host_address="localhost",
     port=80,
     https_disabled=True,
 )
 client = StreamPipesClient(client_config=client_config)</div>
-        
-        </div>
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-
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 </div>
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+</div>
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 <pre>2022-12-14 10:43:37,664 - streampipes.client.client - [INFO] - [client.py:127] [_set_up_logging] - Logging successfully initialized with logging level INFO.
 </pre>
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+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Now we can have a look at the available data streams. We can choose one or more stream to receive the data from and copy their <code>element_id</code>.</p>
-
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 </div>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">client</span><span class="o">.</span><span class="n">dataStreamApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client</span><span class="o">.</span><span class="n">dataStreamApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt">client.dataStreamApi.all().to_pandas()</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-5">client.dataStreamApi.all().to_pandas()</div>
 </div>
 </div>
-
+</div>
+</div>
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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-
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+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>2022-12-14 10:43:39,944 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.
 </pre>
 </div>
 </div>
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
-    
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
-
-
-
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3060,101 +3005,97 @@
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>element_id</th>
-      <th>name</th>
-      <th>description</th>
-      <th>icon_url</th>
-      <th>app_id</th>
-      <th>includes_assets</th>
-      <th>includes_locales</th>
-      <th>internally_managed</th>
-      <th>measurement_object</th>
-      <th>index</th>
-      <th>...</th>
-      <th>uri</th>
-      <th>dom</th>
-      <th>num_transport_protocols</th>
-      <th>num_measurement_capability</th>
-      <th>num_application_links</th>
-      <th>num_included_assets</th>
-      <th>num_connected_to</th>
-      <th>num_category</th>
-      <th>num_event_properties</th>
-      <th>num_included_locales</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>urn:streampipes.apache.org:eventstream:HHoidJ</td>
-      <td>Test2</td>
-      <td></td>
-      <td>None</td>
-      <td>None</td>
-      <td>False</td>
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-      <th>1</th>
-      <td>urn:streampipes.apache.org:eventstream:uPDKLI</td>
-      <td>Test</td>
-      <td></td>
-      <td>None</td>
-      <td>None</td>
-      <td>False</td>
-      <td>False</td>
-      <td>True</td>
-      <td>None</td>
-      <td>0</td>
-      <td>...</td>
-      <td>urn:streampipes.apache.org:eventstream:uPDKLI</td>
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-    </tr>
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+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>element_id</th>
+<th>name</th>
+<th>description</th>
+<th>icon_url</th>
+<th>app_id</th>
+<th>includes_assets</th>
+<th>includes_locales</th>
+<th>internally_managed</th>
+<th>measurement_object</th>
+<th>index</th>
+<th>...</th>
+<th>uri</th>
+<th>dom</th>
+<th>num_transport_protocols</th>
+<th>num_measurement_capability</th>
+<th>num_application_links</th>
+<th>num_included_assets</th>
+<th>num_connected_to</th>
+<th>num_category</th>
+<th>num_event_properties</th>
+<th>num_included_locales</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>urn:streampipes.apache.org:eventstream:HHoidJ</td>
+<td>Test2</td>
+<td></td>
+<td>None</td>
+<td>None</td>
+<td>False</td>
+<td>False</td>
+<td>True</td>
+<td>None</td>
+<td>0</td>
+<td>...</td>
+<td>urn:streampipes.apache.org:eventstream:HHoidJ</td>
+<td>None</td>
+<td>1</td>
+<td>0</td>
+<td>0</td>
+<td>0</td>
+<td>0</td>
+<td>0</td>
+<td>7</td>
+<td>0</td>
+</tr>
+<tr>
+<th>1</th>
+<td>urn:streampipes.apache.org:eventstream:uPDKLI</td>
+<td>Test</td>
+<td></td>
+<td>None</td>
+<td>None</td>
+<td>False</td>
+<td>False</td>
+<td>True</td>
+<td>None</td>
+<td>0</td>
+<td>...</td>
+<td>urn:streampipes.apache.org:eventstream:uPDKLI</td>
+<td>None</td>
+<td>1</td>
+<td>0</td>
+<td>0</td>
+<td>0</td>
+<td>0</td>
+<td>0</td>
+<td>7</td>
+<td>0</td>
+</tr>
+</tbody>
 </table>
 <p>2 rows × 21 columns</p>
 </div>
 </div>
-
-</div>
-
-</div>
-
-</div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Next we can create a StreamPipesFunction. For this we need to implement the 4 following methods:</p>
 <ul>
 <li>In <code>requiredStreamIds</code> you need to insert the <code>element_id</code> of the required streams.</li>
@@ -3163,32 +3104,29 @@
 <li><code>onServiceStopped</code> is called when the function gets stopped.</li>
 </ul>
 <p>For this tutorial we just create a function that saves every new event in a <code>pandas DataFrame</code> and plots the first column of the <code>DataFrame</code> when the function gets stopped.</p>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
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-                <div>
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-6">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -3205,12 +3143,12 @@
         <span class="bp">self</span><span class="o">.</span><span class="n">df</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="k">def</span> <span class="nf">requiredStreamIds</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
-        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;urn:streampipes.apache.org:eventstream:uPDKLI&quot;</span><span class="p">]</span>
+        <span class="k">return</span> <span class="p">[</span><span class="s2">"urn:streampipes.apache.org:eventstream:uPDKLI"</span><span class="p">]</span>
 
     <span class="k">def</span> <span class="nf">onServiceStarted</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="n">FunctionContext</span><span class="p">):</span>
         <span class="c1"># Get the name of the timestamp field</span>
         <span class="k">for</span> <span class="n">event_property</span> <span class="ow">in</span> <span class="n">context</span><span class="o">.</span><span class="n">schema</span><span class="p">[</span><span class="n">context</span><span class="o">.</span><span class="n">streams</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">event_schema</span><span class="o">.</span><span class="n">event_properties</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">event_property</span><span class="o">.</span><span class="n">property_scope</span> <span class="o">==</span> <span class="s2">&quot;HEADER_PROPERTY&quot;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">event_property</span><span class="o">.</span><span class="n">property_scope</span> <span class="o">==</span> <span class="s2">"HEADER_PROPERTY"</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">timestamp</span> <span class="o">=</span> <span class="n">event_property</span><span class="o">.</span><span class="n">runtime_name</span>
 
     <span class="k">def</span> <span class="nf">onEvent</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">streamId</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
@@ -3229,7 +3167,7 @@
         <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</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 id="cell-6" class="clipboard-copy-txt">from typing import List, Dict, Any
+<div class="clipboard-copy-txt" id="cell-6">from typing import List, Dict, Any
 import pandas as pd
 from datetime import datetime
 import matplotlib.pyplot as plt
@@ -3240,12 +3178,12 @@
 
 
 class ExampleFunction(StreamPipesFunction):
-    def __init__(self) -> None:
+    def __init__(self) -&gt; None:
         super().__init__()
         # Create the Dataframe to save the live data
         self.df = pd.DataFrame()
 
-    def requiredStreamIds(self) -> List[str]:
+    def requiredStreamIds(self) -&gt; List[str]:
         return ["urn:streampipes.apache.org:eventstream:uPDKLI"]
 
     def onServiceStarted(self, context: FunctionContext):
@@ -3269,48 +3207,42 @@
         plt.ylabel(self.df.columns[0])
         plt.plot(self.df.iloc[:, 0])
         plt.show()</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>Now we can start the function. We have to register an instance of the <code>StreamPipesFunction</code> and them we can start the functions by initializing the <code>FunctionHandler</code>. (it's also possible to register multiple functions with <code>.register(...).register(...)</code>)</p>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-7">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">example_function</span> <span class="o">=</span> <span class="n">ExampleFunction</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-7">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">example_function</span> <span class="o">=</span> <span class="n">ExampleFunction</span><span class="p">()</span>
 
 <span class="n">registration</span> <span class="o">=</span> <span class="n">Registration</span><span class="p">()</span>
 <span class="n">registration</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">example_function</span><span class="p">)</span>
@@ -3318,77 +3250,63 @@
 <span class="n">function_handler</span> <span class="o">=</span> <span class="n">FunctionHandler</span><span class="p">(</span><span class="n">registration</span><span class="p">,</span> <span class="n">client</span><span class="p">)</span>
 <span class="n">function_handler</span><span class="o">.</span><span class="n">initializeFunctions</span><span class="p">()</span>
 </pre></div>
-<div id="cell-7" class="clipboard-copy-txt">example_function = ExampleFunction()
+<div class="clipboard-copy-txt" id="cell-7">example_function = ExampleFunction()
 
 registration = Registration()
 registration.register(example_function)
 
 function_handler = FunctionHandler(registration, client)
 function_handler.initializeFunctions()</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
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+</div>
+</div>
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+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>2022-12-14 10:43:42,810 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:153] [_make_request] - Successfully retrieved all resources.
 2022-12-14 10:43:42,812 - streampipes.functions.function_handler - [INFO] - [function_handler.py:82] [initializeFunctions] - Using NatsBroker for &lt;__main__.ExampleFunction object at 0x000001F2EF298D90&gt;
 </pre>
 </div>
 </div>
-
-</div>
-
-</div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>The <code>DataFrame</code> of the <code>ExampleFunction</code> gets updated whenever a new event arrives. Let's test this by executing the cell below.</p>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-8">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">asyncio</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-8">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">asyncio</span>
 <span class="kn">from</span> <span class="nn">asyncio.exceptions</span> <span class="kn">import</span> <span class="n">CancelledError</span>
 <span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">clear_output</span>
 
@@ -3400,7 +3318,7 @@
     <span class="k">except</span> <span class="n">CancelledError</span><span class="p">:</span>
         <span class="k">break</span>
 </pre></div>
-<div id="cell-8" class="clipboard-copy-txt">import asyncio
+<div class="clipboard-copy-txt" id="cell-8">import asyncio
 from asyncio.exceptions import CancelledError
 from IPython.display import clear_output
 
@@ -3411,28 +3329,19 @@
         await asyncio.sleep(1)
     except CancelledError:
         break</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
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+</div>
+</div>
 <div class="jp-Cell-outputWrapper">
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-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html">
 <div>
-<style scoped>
+<style scoped="">
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3446,278 +3355,246 @@
     }
 </style>
 <table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>mass_flow</th>
-      <th>density</th>
-      <th>volume_flow</th>
-      <th>sensor_fault_flags</th>
-      <th>temperature</th>
-      <th>sensorId</th>
-    </tr>
-    <tr>
-      <th>timestamp</th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-      <th></th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>2022-12-14 10:43:43.357</th>
-      <td>10.955496</td>
-      <td>47.546290</td>
-      <td>1.001985</td>
-      <td>False</td>
-      <td>44.993413</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:43:44.371</th>
-      <td>6.499040</td>
-      <td>44.392069</td>
-      <td>2.034402</td>
-      <td>False</td>
-      <td>41.232352</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:43:45.382</th>
-      <td>10.168300</td>
-      <td>41.192146</td>
-      <td>9.724287</td>
-      <td>False</td>
-      <td>46.812779</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:43:46.395</th>
-      <td>10.849059</td>
-      <td>50.086308</td>
-      <td>5.832691</td>
-      <td>False</td>
-      <td>45.860412</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:43:47.410</th>
-      <td>3.081855</td>
-      <td>47.254246</td>
-      <td>8.860531</td>
-      <td>False</td>
-      <td>50.505801</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>...</th>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-      <td>...</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:44:43.920</th>
-      <td>1.803572</td>
-      <td>41.978894</td>
-      <td>10.294002</td>
-      <td>False</td>
-      <td>47.820239</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:44:44.932</th>
-      <td>1.967062</td>
-      <td>42.212883</td>
-      <td>3.237440</td>
-      <td>False</td>
-      <td>49.047258</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:44:45.934</th>
-      <td>4.457819</td>
-      <td>47.561256</td>
-      <td>0.315024</td>
-      <td>False</td>
-      <td>40.223413</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:44:46.949</th>
-      <td>8.745343</td>
-      <td>46.346891</td>
-      <td>7.439090</td>
-      <td>False</td>
-      <td>41.982529</td>
-      <td>flowrate02</td>
-    </tr>
-    <tr>
-      <th>2022-12-14 10:44:47.950</th>
-      <td>5.828744</td>
-      <td>47.679720</td>
-      <td>6.307405</td>
-      <td>False</td>
-      <td>42.100354</td>
-      <td>flowrate02</td>
-    </tr>
-  </tbody>
+<thead>
+<tr style="text-align: right;">
+<th></th>
+<th>mass_flow</th>
+<th>density</th>
+<th>volume_flow</th>
+<th>sensor_fault_flags</th>
+<th>temperature</th>
+<th>sensorId</th>
+</tr>
+<tr>
+<th>timestamp</th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+<th></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>2022-12-14 10:43:43.357</th>
+<td>10.955496</td>
+<td>47.546290</td>
+<td>1.001985</td>
+<td>False</td>
+<td>44.993413</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:43:44.371</th>
+<td>6.499040</td>
+<td>44.392069</td>
+<td>2.034402</td>
+<td>False</td>
+<td>41.232352</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:43:45.382</th>
+<td>10.168300</td>
+<td>41.192146</td>
+<td>9.724287</td>
+<td>False</td>
+<td>46.812779</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:43:46.395</th>
+<td>10.849059</td>
+<td>50.086308</td>
+<td>5.832691</td>
+<td>False</td>
+<td>45.860412</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:43:47.410</th>
+<td>3.081855</td>
+<td>47.254246</td>
+<td>8.860531</td>
+<td>False</td>
+<td>50.505801</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>...</th>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+<td>...</td>
+</tr>
+<tr>
+<th>2022-12-14 10:44:43.920</th>
+<td>1.803572</td>
+<td>41.978894</td>
+<td>10.294002</td>
+<td>False</td>
+<td>47.820239</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:44:44.932</th>
+<td>1.967062</td>
+<td>42.212883</td>
+<td>3.237440</td>
+<td>False</td>
+<td>49.047258</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:44:45.934</th>
+<td>4.457819</td>
+<td>47.561256</td>
+<td>0.315024</td>
+<td>False</td>
+<td>40.223413</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:44:46.949</th>
+<td>8.745343</td>
+<td>46.346891</td>
+<td>7.439090</td>
+<td>False</td>
+<td>41.982529</td>
+<td>flowrate02</td>
+</tr>
+<tr>
+<th>2022-12-14 10:44:47.950</th>
+<td>5.828744</td>
+<td>47.679720</td>
+<td>6.307405</td>
+<td>False</td>
+<td>42.100354</td>
+<td>flowrate02</td>
+</tr>
+</tbody>
 </table>
 <p>65 rows × 6 columns</p>
 </div>
 </div>
-
-</div>
-
-</div>
-
-</div>
-
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>The <code>while</code> loop just displays the the <code>DataFrame</code> every second until the cell is stopped. We could achieve the same result manually by executing <code>example_function.df</code> repeatedly.</p>
-
 </div>
 </div>
 </div>
 </div>
-<div  class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <p>You can stop the functions whenever you want by executing the command below.</p>
-
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-9">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">function_handler</span><span class="o">.</span><span class="n">disconnect</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-9">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">function_handler</span><span class="o">.</span><span class="n">disconnect</span><span class="p">()</span>
 </pre></div>
-<div id="cell-9" class="clipboard-copy-txt">function_handler.disconnect()</div>
-        
-        </div>
-    </div>
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+<div class="clipboard-copy-txt" id="cell-9">function_handler.disconnect()</div>
 </div>
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 <pre>2022-12-14 10:44:53,309 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:67] [disconnect] - Stopped connection to stream: urn:streampipes.apache.org:eventstream:uPDKLI
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 <p>That's enough for this tutorial. Now you can try to write your own <code>StreamPipesFunction</code>. All you need to do is creating a new class, implementing the 4 required methods and registering the function.</p>
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 <p>Want to see more exciting use cases you can achieve with StreamPipes functions in Python? Then don't hesitate and jump to our <a href="../4-using-online-machine-learning-on-a-streampipes-data-stream">next tutorial</a> on applying online machine learning algorithms to StreamPipes data streams with <a href="https://riverml.xyz">River</a>.</p>
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 <p>How do you like this tutorial?
 We hope you like it and would love to receive some feedback from you.
 Just go to our <a href="https://github.com/apache/streampipes/discussions">GitHub discussion page</a> and let us know your impression.
 We'll read and react to them all, we promise!</p>
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-<h1 id="Using-Online-Machine-Learning-on-a-StreamPipes-data-stream">Using Online Machine Learning on a StreamPipes data stream<a class="anchor-link" href="#Using-Online-Machine-Learning-on-a-StreamPipes-data-stream">&#182;</a></h1><p>The last tutorial (<a href="../3-getting-live-data-from-the-streampipes-data-stream">Getting live data from the StreamPipes data stream</a>) showed how we can connect to a data stream, and it would be possible to use Online Machine Learning with this approach and train a model with the incoming events at the <code>onEvent</code> method. However, the StreamPipes client also provides an easier way to do this with the use of the <a href="https://riverml.xyz">River library</a> for Online Machine Learning. We will have a look at this now.</p>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h1 id="Using-Online-Machine-Learning-on-a-StreamPipes-data-stream">Using Online Machine Learning on a StreamPipes data stream<a class="anchor-link" href="#Using-Online-Machine-Learning-on-a-StreamPipes-data-stream">¶</a></h1><p>The last tutorial (<a href="../3-getting-live-data-from-the-streampipes-data-stream">Getting live data from the StreamPipes data stream</a>) showed how we can connect to a data stream, and it would be possible to use Online Machine Learning with this approach and train a model with the incoming events at the <code>onEvent</code> method. However, the StreamPipes client also provides an easier way to do this with the use of the <a href="https://riverml.xyz">River library</a> for Online Machine Learning. We will have a look at this now.</p>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.config</span> <span class="kn">import</span> <span class="n">StreamPipesClientConfig</span>
 <span class="kn">from</span> <span class="nn">streampipes.client.credential_provider</span> <span class="kn">import</span> <span class="n">StreamPipesApiKeyCredentials</span>
 </pre></div>
-<div id="cell-1" class="clipboard-copy-txt">from streampipes.client import StreamPipesClient
+<div class="clipboard-copy-txt" id="cell-1">from streampipes.client import StreamPipesClient
 from streampipes.client.config import StreamPipesClientConfig
 from streampipes.client.credential_provider import StreamPipesApiKeyCredentials</div>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># you can install all required dependecies for this tutorial by executing the following command</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-2">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># you can install all required dependecies for this tutorial by executing the following command</span>
 <span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">river</span> <span class="n">streampipes</span>
 </pre></div>
-<div id="cell-2" class="clipboard-copy-txt"># you can install all required dependecies for this tutorial by executing the following command
+<div class="clipboard-copy-txt" id="cell-2"># you can install all required dependecies for this tutorial by executing the following command
 %pip install river streampipes</div>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-3">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SP_USERNAME&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;admin@streampipes.apache.org&quot;</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SP_API_KEY&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;XXX&quot;</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_USERNAME"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"admin@streampipes.apache.org"</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"SP_API_KEY"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"XXX"</span>
 
 <span class="c1"># Use this if you work locally:</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;BROKER-HOST&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;localhost&quot;</span>  
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;KAFKA-PORT&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;9094&quot;</span> <span class="c1"># When using Kafka as message broker</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"BROKER-HOST"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"localhost"</span>  
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"KAFKA-PORT"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"9094"</span> <span class="c1"># When using Kafka as message broker</span>
 </pre></div>
-<div id="cell-3" class="clipboard-copy-txt">import os
+<div class="clipboard-copy-txt" id="cell-3">import os
 
 os.environ["SP_USERNAME"] = "admin@streampipes.apache.org"
 os.environ["SP_API_KEY"] = "XXX"
@@ -2979,135 +2956,105 @@
 # Use this if you work locally:
 os.environ["BROKER-HOST"] = "localhost"  
 os.environ["KAFKA-PORT"] = "9094" # When using Kafka as message broker</div>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">client_config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-4">
+<div>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client_config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
     <span class="n">credential_provider</span><span class="o">=</span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">(),</span>
-    <span class="n">host_address</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span>
+    <span class="n">host_address</span><span class="o">=</span><span class="s2">"localhost"</span><span class="p">,</span>
     <span class="n">port</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span>
     <span class="n">https_disabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">client_config</span><span class="p">)</span>
 </pre></div>
-<div id="cell-4" class="clipboard-copy-txt">client_config = StreamPipesClientConfig(
+<div class="clipboard-copy-txt" id="cell-4">client_config = StreamPipesClientConfig(
     credential_provider=StreamPipesApiKeyCredentials(),
     host_address="localhost",
     port=80,
     https_disabled=True,
 )
 client = StreamPipesClient(client_config=client_config)</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
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+</div>
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 <pre>2023-01-27 16:04:24,784 - streampipes.client.client - [INFO] - [client.py:128] [_set_up_logging] - Logging successfully initialized with logging level INFO.
 </pre>
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">client</span><span class="o">.</span><span class="n">dataStreamApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-5">
+<div>
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+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">client</span><span class="o">.</span><span class="n">dataStreamApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt">client.dataStreamApi.all().to_pandas()</div>
-        
-        </div>
-    </div>
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+<div class="clipboard-copy-txt" id="cell-5">client.dataStreamApi.all().to_pandas()</div>
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 <pre>2023-01-27 16:04:28,212 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:163] [_make_request] - Successfully retrieved all resources.
 </pre>
 </div>
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-    
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@@ -3121,102 +3068,98 @@
     }
 </style>
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-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>element_id</th>
-      <th>name</th>
-      <th>description</th>
-      <th>icon_url</th>
-      <th>app_id</th>
-      <th>includes_assets</th>
-      <th>includes_locales</th>
-      <th>internally_managed</th>
-      <th>measurement_object</th>
-      <th>index</th>
-      <th>...</th>
-      <th>dom</th>
-      <th>rev</th>
-      <th>num_transport_protocols</th>
-      <th>num_measurement_capability</th>
-      <th>num_application_links</th>
-      <th>num_included_assets</th>
-      <th>num_connected_to</th>
-      <th>num_category</th>
-      <th>num_event_properties</th>
-      <th>num_included_locales</th>
-    </tr>
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-      <th>0</th>
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+<th>name</th>
+<th>description</th>
+<th>icon_url</th>
+<th>app_id</th>
+<th>includes_assets</th>
+<th>includes_locales</th>
+<th>internally_managed</th>
+<th>measurement_object</th>
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+<th>...</th>
+<th>dom</th>
+<th>rev</th>
+<th>num_transport_protocols</th>
+<th>num_measurement_capability</th>
+<th>num_application_links</th>
+<th>num_included_assets</th>
+<th>num_connected_to</th>
+<th>num_category</th>
+<th>num_event_properties</th>
+<th>num_included_locales</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<th>0</th>
+<td>sp:spdatastream:xboBFK</td>
+<td>Test</td>
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+<td>0</td>
+<td>7</td>
+<td>0</td>
+</tr>
+<tr>
+<th>1</th>
+<td>urn:streampipes.apache.org:eventstream:Wgyrse</td>
+<td>Test File</td>
+<td></td>
+<td>None</td>
+<td>None</td>
+<td>False</td>
+<td>False</td>
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+<td>None</td>
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-<h2 id="How-to-use-Online-Machine-Learning-with-StreamPipes">How to use Online Machine Learning with StreamPipes<a class="anchor-link" href="#How-to-use-Online-Machine-Learning-with-StreamPipes">&#182;</a></h2><p>After we configured the client as usual, we can start with the new part.<br />
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="How-to-use-Online-Machine-Learning-with-StreamPipes">How to use Online Machine Learning with StreamPipes<a class="anchor-link" href="#How-to-use-Online-Machine-Learning-with-StreamPipes">¶</a></h2><p>After we configured the client as usual, we can start with the new part.<br/>
 The approach is straight forward and you can start with the ML part in just 3 steps:</p>
 <ol>
 <li>Create a pipeline with River and insert the preprocessing steps and model of your choice.</li>
@@ -3226,58 +3169,55 @@
 <p>A StreamPipesFunction is then started, which trains the model for each new event. It also creates an output data stream which will send the prediction of the model back to StreamPipes. This output stream can be seen when creating a new pipeline and can be used like every other data source. So you can use it in a pipeline and save the predictions in a Data Lake.
 You can also stop and start the training with the method <code>set_learning</code>. To stop the whole function use the <code>stop</code> methode and if you want to delete the output stream entirely, you can go to the <code>Pipeline Element Installer</code> in StreamPipes and uninstall it.</p>
 <p>Now let's take a look at some examples. If you want to execute the examples below you have to create an adapter for the <code>Machine Data Simulator</code>, select the <code>flowrate</code> sensor and insert the stream id of this stream.</p>
-
 </div>
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-<h2 id="KMeans">KMeans<a class="anchor-link" href="#KMeans">&#182;</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="KMeans">KMeans<a class="anchor-link" href="#KMeans">¶</a></h2>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-6">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">from</span> <span class="nn">river</span> <span class="kn">import</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">compose</span><span class="p">,</span> <span class="n">preprocessing</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-6">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">river</span> <span class="kn">import</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">compose</span><span class="p">,</span> <span class="n">preprocessing</span>
 <span class="kn">from</span> <span class="nn">streampipes.function_zoo.river_function</span> <span class="kn">import</span> <span class="n">OnlineML</span>
 <span class="kn">from</span> <span class="nn">streampipes.functions.utils.data_stream_generator</span> <span class="kn">import</span> <span class="n">RuntimeType</span>
 
 <span class="n">k_means</span> <span class="o">=</span> <span class="n">compose</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
-    <span class="p">(</span><span class="s2">&quot;drop_features&quot;</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">&quot;sensorId&quot;</span><span class="p">,</span> <span class="s2">&quot;timestamp&quot;</span><span class="p">)),</span>
-    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">()),</span>
-    <span class="p">(</span><span class="s2">&quot;k_means&quot;</span><span class="p">,</span> <span class="n">cluster</span><span class="o">.</span><span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s2">"drop_features"</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">"sensorId"</span><span class="p">,</span> <span class="s2">"timestamp"</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s2">"scale"</span><span class="p">,</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">StandardScaler</span><span class="p">()),</span>
+    <span class="p">(</span><span class="s2">"k_means"</span><span class="p">,</span> <span class="n">cluster</span><span class="o">.</span><span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">)),</span>
 <span class="p">)</span>
 
 <span class="n">clustering</span> <span class="o">=</span> <span class="n">OnlineML</span><span class="p">(</span>
-    <span class="n">client</span><span class="o">=</span><span class="n">client</span><span class="p">,</span> <span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;sp:spdatastream:xboBFK&quot;</span><span class="p">],</span> <span class="n">model</span><span class="o">=</span><span class="n">k_means</span><span class="p">,</span> <span class="n">prediction_type</span><span class="o">=</span><span class="n">RuntimeType</span><span class="o">.</span><span class="n">INTEGER</span><span class="o">.</span><span class="n">value</span>
+    <span class="n">client</span><span class="o">=</span><span class="n">client</span><span class="p">,</span> <span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">"sp:spdatastream:xboBFK"</span><span class="p">],</span> <span class="n">model</span><span class="o">=</span><span class="n">k_means</span><span class="p">,</span> <span class="n">prediction_type</span><span class="o">=</span><span class="n">RuntimeType</span><span class="o">.</span><span class="n">INTEGER</span><span class="o">.</span><span class="n">value</span>
 <span class="p">)</span>
 <span class="n">clustering</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
 </pre></div>
-<div id="cell-6" class="clipboard-copy-txt">from river import cluster, compose, preprocessing
+<div class="clipboard-copy-txt" id="cell-6">from river import cluster, compose, preprocessing
 from streampipes.function_zoo.river_function import OnlineML
 from streampipes.functions.utils.data_stream_generator import RuntimeType
 
@@ -3291,27 +3231,19 @@
     client=client, stream_ids=["sp:spdatastream:xboBFK"], model=k_means, prediction_type=RuntimeType.INTEGER.value
 )
 clustering.start()</div>
-        
-        </div>
-    </div>
-
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 </div>
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+</div>
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 <pre>2023-01-27 16:04:35,599 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:163] [_make_request] - Successfully retrieved all resources.
-2023-01-27 16:04:35,599 - streampipes.functions.function_handler - [INFO] - [function_handler.py:64] [initializeFunctions] - Create output data stream &#34;sp:spdatastream:cwKPoo&#34; for the function &#34;65cf8b86-bcdf-433e-a1c7-3e920eab55d0&#34;
+2023-01-27 16:04:35,599 - streampipes.functions.function_handler - [INFO] - [function_handler.py:64] [initializeFunctions] - Create output data stream "sp:spdatastream:cwKPoo" for the function "65cf8b86-bcdf-433e-a1c7-3e920eab55d0"
 2023-01-27 16:04:37,766 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:163] [_make_request] - Successfully retrieved all resources.
 2023-01-27 16:04:37,767 - streampipes.functions.function_handler - [INFO] - [function_handler.py:78] [initializeFunctions] - Using NatsBroker for RiverFunction
 2023-01-27 16:04:37,791 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:48] [_makeConnection] - Connected to NATS at localhost:4222
@@ -3320,167 +3252,144 @@
 </pre>
 </div>
 </div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
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-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-7">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">clustering</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-7">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">clustering</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
-<div id="cell-7" class="clipboard-copy-txt">clustering.set_learning(False)</div>
-        
-        </div>
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+<div class="clipboard-copy-txt" id="cell-7">clustering.set_learning(False)</div>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">clustering</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">clustering</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
 </pre></div>
-<div id="cell-8" class="clipboard-copy-txt">clustering.stop()</div>
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-        </div>
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+<div class="clipboard-copy-txt" id="cell-8">clustering.stop()</div>
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 <pre>2023-01-27 16:04:57,303 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:82] [disconnect] - Stopped connection to stream: sp:spdatastream:xboBFK
 2023-01-27 16:04:57,304 - streampipes.functions.broker.nats_broker - [INFO] - [nats_broker.py:82] [disconnect] - Stopped connection to stream: sp:spdatastream:cwKPoo
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="HoeffdingTreeRegressor">HoeffdingTreeRegressor<a class="anchor-link" href="#HoeffdingTreeRegressor">&#182;</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="HoeffdingTreeRegressor">HoeffdingTreeRegressor<a class="anchor-link" href="#HoeffdingTreeRegressor">¶</a></h2>
 </div>
 </div>
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-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
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-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
 <span class="kn">from</span> <span class="nn">river</span> <span class="kn">import</span> <span class="n">compose</span><span class="p">,</span> <span class="n">tree</span>
 <span class="kn">from</span> <span class="nn">streampipes.function_zoo.river_function</span> <span class="kn">import</span> <span class="n">OnlineML</span>
 <span class="kn">from</span> <span class="nn">streampipes.functions.utils.data_stream_generator</span> <span class="kn">import</span> <span class="n">RuntimeType</span>
 
 <span class="n">hoeffding_tree</span> <span class="o">=</span> <span class="n">compose</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
-    <span class="p">(</span><span class="s2">&quot;drop_features&quot;</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">&quot;sensorId&quot;</span><span class="p">,</span> <span class="s2">&quot;timestamp&quot;</span><span class="p">)),</span>
-    <span class="p">(</span><span class="s2">&quot;hoeffding_tree&quot;</span><span class="p">,</span> <span class="n">tree</span><span class="o">.</span><span class="n">HoeffdingTreeRegressor</span><span class="p">(</span><span class="n">grace_period</span><span class="o">=</span><span class="mi">5</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s2">"drop_features"</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">"sensorId"</span><span class="p">,</span> <span class="s2">"timestamp"</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s2">"hoeffding_tree"</span><span class="p">,</span> <span class="n">tree</span><span class="o">.</span><span class="n">HoeffdingTreeRegressor</span><span class="p">(</span><span class="n">grace_period</span><span class="o">=</span><span class="mi">5</span><span class="p">)),</span>
 <span class="p">)</span>
 
 
 <span class="k">def</span> <span class="nf">draw_tree</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">,</span> <span class="n">streamId</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Draw the tree and save the image.&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sd">"""Draw the tree and save the image."""</span>
     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning</span><span class="p">:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">n_nodes</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">(</span><span class="s2">&quot;hoeffding_tree&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;png&quot;</span><span class="p">,</span> <span class="n">cleanup</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">(</span><span class="s2">"hoeffding_tree"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"png"</span><span class="p">,</span> <span class="n">cleanup</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 
 <span class="k">def</span> <span class="nf">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Save the trained model.&quot;&quot;&quot;</span>
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;hoeffding_tree.pkl&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">"""Save the trained model."""</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"hoeffding_tree.pkl"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
         <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
 
 
 <span class="n">regressor</span> <span class="o">=</span> <span class="n">OnlineML</span><span class="p">(</span>
     <span class="n">client</span><span class="o">=</span><span class="n">client</span><span class="p">,</span>
-    <span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;sp:spdatastream:xboBFK&quot;</span><span class="p">],</span>
+    <span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">"sp:spdatastream:xboBFK"</span><span class="p">],</span>
     <span class="n">model</span><span class="o">=</span><span class="n">hoeffding_tree</span><span class="p">,</span>
     <span class="n">prediction_type</span><span class="o">=</span><span class="n">RuntimeType</span><span class="o">.</span><span class="n">FLOAT</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
     <span class="n">supervised</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">target_label</span><span class="o">=</span><span class="s2">&quot;temperature&quot;</span><span class="p">,</span>
+    <span class="n">target_label</span><span class="o">=</span><span class="s2">"temperature"</span><span class="p">,</span>
     <span class="n">on_event</span><span class="o">=</span><span class="n">draw_tree</span><span class="p">,</span>
     <span class="n">on_stop</span><span class="o">=</span><span class="n">save_model</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="n">regressor</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
 </pre></div>
-<div id="cell-9" class="clipboard-copy-txt">import pickle
+<div class="clipboard-copy-txt" id="cell-9">import pickle
 from river import compose, tree
 from streampipes.function_zoo.river_function import OnlineML
 from streampipes.functions.utils.data_stream_generator import RuntimeType
@@ -3515,147 +3424,132 @@
     on_stop=save_model,
 )
 regressor.start()</div>
-        
-        </div>
-    </div>
-
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-
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-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
-            <div class="zeroclipboard-container">
-                <clipboard-copy for="cell-10">
-                <div>
-                    <span class="notice" hidden>Copied!</span>
-                    <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
-                        <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">regressor</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-10">
+<div>
+<span class="notice" hidden="">Copied!</span>
+<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
+<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">regressor</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
-<div id="cell-10" class="clipboard-copy-txt">regressor.set_learning(False)</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-10">regressor.set_learning(False)</div>
 </div>
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-
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-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">regressor</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">regressor</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
 </pre></div>
-<div id="cell-11" class="clipboard-copy-txt">regressor.stop()</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-11">regressor.stop()</div>
 </div>
 </div>
-
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-<h2 id="DecisionTreeClassifier">DecisionTreeClassifier<a class="anchor-link" href="#DecisionTreeClassifier">&#182;</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h2 id="DecisionTreeClassifier">DecisionTreeClassifier<a class="anchor-link" href="#DecisionTreeClassifier">¶</a></h2>
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-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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+<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>
 <span class="kn">from</span> <span class="nn">river</span> <span class="kn">import</span> <span class="n">compose</span><span class="p">,</span> <span class="n">tree</span>
 <span class="kn">from</span> <span class="nn">streampipes.function_zoo.river_function</span> <span class="kn">import</span> <span class="n">OnlineML</span>
 <span class="kn">from</span> <span class="nn">streampipes.functions.utils.data_stream_generator</span> <span class="kn">import</span> <span class="n">RuntimeType</span>
 
 <span class="n">decision_tree</span> <span class="o">=</span> <span class="n">compose</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">(</span>
-    <span class="p">(</span><span class="s2">&quot;drop_features&quot;</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">&quot;sensorId&quot;</span><span class="p">,</span> <span class="s2">&quot;timestamp&quot;</span><span class="p">)),</span>
-    <span class="p">(</span><span class="s2">&quot;decision_tree&quot;</span><span class="p">,</span> <span class="n">tree</span><span class="o">.</span><span class="n">ExtremelyFastDecisionTreeClassifier</span><span class="p">(</span><span class="n">grace_period</span><span class="o">=</span><span class="mi">5</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s2">"drop_features"</span><span class="p">,</span> <span class="n">compose</span><span class="o">.</span><span class="n">Discard</span><span class="p">(</span><span class="s2">"sensorId"</span><span class="p">,</span> <span class="s2">"timestamp"</span><span class="p">)),</span>
+    <span class="p">(</span><span class="s2">"decision_tree"</span><span class="p">,</span> <span class="n">tree</span><span class="o">.</span><span class="n">ExtremelyFastDecisionTreeClassifier</span><span class="p">(</span><span class="n">grace_period</span><span class="o">=</span><span class="mi">5</span><span class="p">)),</span>
 <span class="p">)</span>
 
 
 <span class="k">def</span> <span class="nf">draw_tree</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">,</span> <span class="n">streamId</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Draw the tree and save the image.&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sd">"""Draw the tree and save the image."""</span>
     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning</span><span class="p">:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">n_nodes</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">(</span><span class="s2">&quot;decicion_tree&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;png&quot;</span><span class="p">,</span> <span class="n">cleanup</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">(</span><span class="s2">"decicion_tree"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"png"</span><span class="p">,</span> <span class="n">cleanup</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 
 <span class="k">def</span> <span class="nf">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Save the trained model.&quot;&quot;&quot;</span>
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;decision_tree.pkl&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">"""Save the trained model."""</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"decision_tree.pkl"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
         <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
 
 
 <span class="n">classifier</span> <span class="o">=</span> <span class="n">OnlineML</span><span class="p">(</span>
     <span class="n">client</span><span class="o">=</span><span class="n">client</span><span class="p">,</span>
-    <span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;sp:spdatastream:xboBFK&quot;</span><span class="p">],</span>
+    <span class="n">stream_ids</span><span class="o">=</span><span class="p">[</span><span class="s2">"sp:spdatastream:xboBFK"</span><span class="p">],</span>
     <span class="n">model</span><span class="o">=</span><span class="n">decision_tree</span><span class="p">,</span>
     <span class="n">prediction_type</span><span class="o">=</span><span class="n">RuntimeType</span><span class="o">.</span><span class="n">BOOLEAN</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
     <span class="n">supervised</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">target_label</span><span class="o">=</span><span class="s2">&quot;sensor_fault_flags&quot;</span><span class="p">,</span>
+    <span class="n">target_label</span><span class="o">=</span><span class="s2">"sensor_fault_flags"</span><span class="p">,</span>
     <span class="n">on_event</span><span class="o">=</span><span class="n">draw_tree</span><span class="p">,</span>
     <span class="n">on_stop</span><span class="o">=</span><span class="n">save_model</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="n">classifier</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
 </pre></div>
-<div id="cell-12" class="clipboard-copy-txt">import pickle
+<div class="clipboard-copy-txt" id="cell-12">import pickle
 from river import compose, tree
 from streampipes.function_zoo.river_function import OnlineML
 from streampipes.functions.utils.data_stream_generator import RuntimeType
@@ -3690,101 +3584,86 @@
     on_stop=save_model,
 )
 classifier.start()</div>
-        
-        </div>
-    </div>
-
 </div>
 </div>
-
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-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">classifier</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
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+<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
+</svg>
+</div>
+</clipboard-copy>
+</div>
+<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">classifier</span><span class="o">.</span><span class="n">set_learning</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
-<div id="cell-13" class="clipboard-copy-txt">classifier.set_learning(False)</div>
-        
-        </div>
-    </div>
-
+<div class="clipboard-copy-txt" id="cell-13">classifier.set_learning(False)</div>
 </div>
 </div>
-
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
-<div  class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
 <div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-        
-        
-        <div class="CodeMirror cm-s-jupyter">
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-                        <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
-                    </svg>
-                    </div>
-                </clipboard-copy>
-            </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="n">classifier</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="CodeMirror cm-s-jupyter">
+<div class="zeroclipboard-container">
+<clipboard-copy for="cell-14">
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+<span class="notice" hidden="">Copied!</span>
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