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FNALLPC · Jan 14, 2025 · 2fe4298 · 2fe4298
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diff --git a/_sources/notebooks/1-datasets-uproot.ipynb b/_sources/notebooks/1-datasets-uproot.ipynb
@@ -13,6 +13,22 @@
     "Authors: Javier Duarte, Raghav Kansal\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/_sources/notebooks/2-boosted-decision-tree.ipynb b/_sources/notebooks/2-boosted-decision-tree.ipynb
@@ -13,6 +13,23 @@
     "Authors: Raghav Kansal"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot\n",
+    "!pip install xgboost"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/_sources/notebooks/3.1-dense-keras.ipynb b/_sources/notebooks/3.1-dense-keras.ipynb
@@ -13,6 +13,23 @@
     "Authors: Javier Duarte, Raghav Kansal"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot\n",
+    "!pip install xgboost"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/_sources/notebooks/3.2-dense-pytorch.ipynb b/_sources/notebooks/3.2-dense-pytorch.ipynb
@@ -13,6 +13,23 @@
     "Authors: Javier Duarte, Tyler Mitchell, Raghav Kansal"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run this cell if you are using google colab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install uproot\n",
+    "!pip install xgboost"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/_sources/notebooks/3.3-dense-bayesian-optimization.ipynb b/_sources/notebooks/3.3-dense-bayesian-optimization.ipynb
diff --git a/_sources/notebooks/8-SetTransformer-PointCloud.ipynb b/_sources/notebooks/8-SetTransformer-PointCloud.ipynb
@@ -17,6 +17,7 @@
       },
       "source": [
         "# Jet Tagging with Permutation Invariance\n",
+        "Author: Abhijith Gandrakota, Jennifer Ngadiuba\n",
         "\n",
         "In this notebook we will see an implementation of the Transformer architecture for sets applied to the jet tagging task. For *sets* it is meant here a point cloud, i.e. a set of nodes without edges. We will instead use Multi-Head Attention to learn which nodes (or particles) have strong pair-wise interaction.\n",
         "\n",

diff --git a/notebooks/1-datasets-uproot.html b/notebooks/1-datasets-uproot.html
@@ -426,10 +426,11 @@ <h2> Contents </h2>
             </div>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-datasets-from-root-files-using-uproot">1.1. Load datasets from <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files using <code class="docutils literal notranslate"><span class="pre">uproot</span></code></a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-root-files">1.2. Load <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#convert-tree-to-pandas-dataframes">1.3. Convert tree to <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plotting-in-matplotlib">1.4. Plotting in <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#run-this-cell-if-you-are-using-google-colab">1.1. Run this cell if you are using google colab</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-datasets-from-root-files-using-uproot">1.2. Load datasets from <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files using <code class="docutils literal notranslate"><span class="pre">uproot</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-root-files">1.3. Load <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#convert-tree-to-pandas-dataframes">1.4. Convert tree to <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plotting-in-matplotlib">1.5. Plotting in <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code></a></li>
 </ul>
             </nav>
         </div>
@@ -444,8 +445,18 @@ <h2> Contents </h2>
   <section class="tex2jax_ignore mathjax_ignore" id="loading-datasets">
 <h1><span class="section-number">1. </span>Loading Datasets<a class="headerlink" href="#loading-datasets" title="Link to this heading">#</a></h1>
 <p>Authors: Javier Duarte, Raghav Kansal</p>
+<section id="run-this-cell-if-you-are-using-google-colab">
+<h2><span class="section-number">1.1. </span>Run this cell if you are using google colab<a class="headerlink" href="#run-this-cell-if-you-are-using-google-colab" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>uproot
+</pre></div>
+</div>
+</div>
+</div>
+</section>
 <section id="load-datasets-from-root-files-using-uproot">
-<h2><span class="section-number">1.1. </span>Load datasets from <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files using <code class="docutils literal notranslate"><span class="pre">uproot</span></code><a class="headerlink" href="#load-datasets-from-root-files-using-uproot" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">1.2. </span>Load datasets from <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files using <code class="docutils literal notranslate"><span class="pre">uproot</span></code><a class="headerlink" href="#load-datasets-from-root-files-using-uproot" title="Link to this heading">#</a></h2>
 <p>Here we load the <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> datasets in python using <code class="docutils literal notranslate"><span class="pre">uproot</span></code> (see: <a class="github reference external" href="https://github.com/scikit-hep/uproot">scikit-hep/uproot</a>). For more information about how to use uproot, see the <a class="reference external" href="https://indico.cern.ch/event/1297663/"><code class="docutils literal notranslate"><span class="pre">Uproot</span> <span class="pre">and</span> <span class="pre">Awkward</span> <span class="pre">Array</span> <span class="pre">for</span> <span class="pre">columnar</span> <span class="pre">analysis</span> <span class="pre">HATS&#64;LPC</span> <span class="pre">2023</span></code></a> tutorial.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -467,7 +478,7 @@ <h2><span class="section-number">1.1. </span>Load datasets from <code class="doc
 </div>
 </section>
 <section id="load-root-files">
-<h2><span class="section-number">1.2. </span>Load <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files<a class="headerlink" href="#load-root-files" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">1.3. </span>Load <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files<a class="headerlink" href="#load-root-files" title="Link to this heading">#</a></h2>
 <p>Here we simply open two <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files using <code class="docutils literal notranslate"><span class="pre">uproot</span></code> and display the branch content of one of the trees.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -491,7 +502,7 @@ <h2><span class="section-number">1.2. </span>Load <code class="docutils literal
 </div>
 </section>
 <section id="convert-tree-to-pandas-dataframes">
-<h2><span class="section-number">1.3. </span>Convert tree to <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames<a class="headerlink" href="#convert-tree-to-pandas-dataframes" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">1.4. </span>Convert tree to <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames<a class="headerlink" href="#convert-tree-to-pandas-dataframes" title="Link to this heading">#</a></h2>
 <p>In my opinion, <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames are a more convenient/flexible data container in python: <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html">https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html</a>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -528,7 +539,7 @@ <h2><span class="section-number">1.3. </span>Convert tree to <code class="docuti
 </div>
 </section>
 <section id="plotting-in-matplotlib">
-<h2><span class="section-number">1.4. </span>Plotting in <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code><a class="headerlink" href="#plotting-in-matplotlib" title="Link to this heading">#</a></h2>
+<h2><span class="section-number">1.5. </span>Plotting in <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code><a class="headerlink" href="#plotting-in-matplotlib" title="Link to this heading">#</a></h2>
 <p>Finally, it is always useful to visualize the dataset before using machine learning. Here, we plot some key features in <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> with <code class="docutils literal notranslate"><span class="pre">uproot</span></code></p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -627,10 +638,11 @@ <h2><span class="section-number">1.4. </span>Plotting in <code class="docutils l
   </div>
   <nav class="bd-toc-nav page-toc">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-datasets-from-root-files-using-uproot">1.1. Load datasets from <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files using <code class="docutils literal notranslate"><span class="pre">uproot</span></code></a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-root-files">1.2. Load <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#convert-tree-to-pandas-dataframes">1.3. Convert tree to <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plotting-in-matplotlib">1.4. Plotting in <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#run-this-cell-if-you-are-using-google-colab">1.1. Run this cell if you are using google colab</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-datasets-from-root-files-using-uproot">1.2. Load datasets from <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files using <code class="docutils literal notranslate"><span class="pre">uproot</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-root-files">1.3. Load <code class="docutils literal notranslate"><span class="pre">ROOT</span></code> files</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#convert-tree-to-pandas-dataframes">1.4. Convert tree to <code class="docutils literal notranslate"><span class="pre">pandas</span></code> DataFrames</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plotting-in-matplotlib">1.5. Plotting in <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code></a></li>
 </ul>
   </nav></div>