From cc4ef428e5c20274776a698acd00927af0d096a8 Mon Sep 17 00:00:00 2001
From: MelissaGraham <melissalynngraham@gmail.com>
Date: Thu, 29 Jan 2026 05:17:09 +0000
Subject: [PATCH 1/2] updated comm nb html

---
 .../notebook/101_lsstcam_visits_database.html | 151 ++++++-
 .../notebook/102_rubin_schedule_viewer.html   | 378 ++++++++++++------
 2 files changed, 393 insertions(+), 136 deletions(-)

diff --git a/docs/tutorials/notebook/101_lsstcam_visits_database.html b/docs/tutorials/notebook/101_lsstcam_visits_database.html
index eafaf9c..6febc99 100644
--- a/docs/tutorials/notebook/101_lsstcam_visits_database.html
+++ b/docs/tutorials/notebook/101_lsstcam_visits_database.html
@@ -9959,12 +9959,12 @@ <h1 id="101.-LSSTCam-visits-database">101. LSSTCam visits database<a class="anch
 rkJggg==
 "/></p>
 </div>
-<p>For the Rubin Science Platform at data.lsst.cloud. <br/>
-Container Size: Large <br/>
-LSST Science Pipelines version: v29.2.0 <br/>
-Last verified to run: 2025-11-12 <br/>
-Repository: <a href="https://github.com/lsst/tutorial-notebooks">github.com/lsst/tutorial-notebooks</a> <br/>
-DOI: <a href="https://doi.org/10.11578/rubin/dc.20250909.20">10.11578/rubin/dc.20250909.20</a> <br/></p>
+<p>For the Rubin Science Platform at data.lsst.cloud.<br/>
+Container Size: Large<br/>
+LSST Science Pipelines version: v29.2.0<br/>
+Last verified to run: 2026-01-28<br/>
+Repository: <a href="https://github.com/lsst/tutorial-notebooks">github.com/lsst/tutorial-notebooks</a><br/>
+DOI: <a href="https://doi.org/10.11578/rubin/dc.20250909.20">10.11578/rubin/dc.20250909.20</a></p>
 </div>
 </div>
 </div>
@@ -11571,11 +11571,11 @@ <h3 id="4.2.-Summary-statistics">4.2. Summary statistics<a class="anchor-link" h
 </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>Recreate the table above, but for the SV DDF, and include all field (do not restrict to fields with &gt;50 visits).</p>
+<p>Recreate the two tables above, but for the SV DDF, and include all fields (do not restrict to fields with &gt;50 visits).</p>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b2a9711a-d24a-427a-8661-6accb6fe0bb6">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b862d38b-706d-48a6-a243-4c239f96ec13">
 <div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
@@ -11584,6 +11584,49 @@ <h3 id="4.2.-Summary-statistics">4.2. Summary statistics<a class="anchor-link" h
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="s2">"observation_reason.str.contains('ddf')"</span>
+<span class="n">df_ddf</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'observation_reason'</span><span class="p">,</span> <span class="s1">'band'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">'seq_num'</span><span class="p">:</span> <span class="s1">'count'</span><span class="p">})</span>
+<span class="n">df_ddf</span><span class="o">.</span><span class="n">rename</span><span class="p">({</span><span class="s1">'seq_num'</span><span class="p">:</span> <span class="s1">'count'</span><span class="p">},</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df_ddf</span> <span class="o">=</span> <span class="n">df_ddf</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="s1">'band'</span><span class="p">)</span><span class="o">.</span><span class="n">pivot</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"band"</span><span class="p">])</span><span class="o">.</span><span class="n">droplevel</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">df_ddf</span> <span class="o">=</span> <span class="n">df_ddf</span><span class="p">[[</span><span class="s1">'u'</span><span class="p">,</span> <span class="s1">'g'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">,</span> <span class="s1">'i'</span><span class="p">,</span> <span class="s1">'z'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">]]</span>
+<span class="n">df_ddf</span><span class="p">[</span><span class="s1">'all'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_ddf</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">df_ddf</span> <span class="o">=</span> <span class="n">df_ddf</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">"all &gt; 0"</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'all'</span><span class="p">)</span>
+<span class="n">table</span> <span class="o">=</span> <span class="n">tabulate</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df_ddf</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">0</span><span class="p">)),</span> <span class="n">headers</span><span class="o">=</span><span class="s1">'keys'</span><span class="p">)</span>
+<span class="n">table</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'nan'</span><span class="p">,</span> <span class="s1">'  0'</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">table</span><span class="p">)</span>
+<span class="k">del</span> <span class="n">query</span><span class="p">,</span> <span class="n">df_ddf</span><span class="p">,</span> <span class="n">table</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>observation_reason      u    g    r    i    z    y    all
+--------------------  ---  ---  ---  ---  ---  ---  -----
+ddf_xmm_lss            30    0    0    0    0    0     30
+ddf_edfs_a              0   20   21   28   18    0     87
+ddf_edfs_b              0   24   21   29   18    0     92
+ddf_ecdfs               0   36   39   41   44    0    160
+ddf_elaiss1            39  101  103  168  112   16    539
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=b2a9711a-d24a-427a-8661-6accb6fe0bb6">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [35]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">query</span> <span class="o">=</span> <span class="s2">"observation_reason.str.contains('ddf')"</span>
 <span class="n">df_ddf</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'observation_reason'</span><span class="p">])</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">'seq_num'</span><span class="p">:</span> <span class="s1">'count'</span><span class="p">,</span>
                                                               <span class="s1">'seeingFwhmEff'</span><span class="p">:</span> <span class="s1">'median'</span><span class="p">,</span>
                                                               <span class="s1">'airmass'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span>
@@ -11642,7 +11685,7 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 <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 [35]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'RUBIN_SIM_DATA_DIR'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'/rubin/rubin_sim_data'</span>
@@ -11667,7 +11710,7 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [37]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">opsim_fname</span> <span class="o">=</span> <span class="n">db_filename</span>
@@ -11708,7 +11751,7 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 <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 [37]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">metric</span> <span class="o">=</span> <span class="n">maf</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">CountMetric</span><span class="p">(</span><span class="n">col</span><span class="o">=</span><span class="s1">'observationStartMJD'</span><span class="p">,</span>
@@ -11750,7 +11793,7 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [39]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">bundle</span> <span class="o">=</span> <span class="n">maf</span><span class="o">.</span><span class="n">MetricBundle</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="n">slicer</span><span class="p">,</span> <span class="n">constraint</span><span class="p">,</span> <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">)</span>
@@ -11775,7 +11818,7 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [39]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">group</span> <span class="o">=</span> <span class="n">maf</span><span class="o">.</span><span class="n">MetricBundleGroup</span><span class="p">({</span><span class="s1">'nvisits'</span><span class="p">:</span> <span class="n">bundle</span><span class="p">},</span>
@@ -11801,7 +11844,7 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">group</span><span class="o">.</span><span class="n">run_all</span><span class="p">()</span>
@@ -11826,7 +11869,7 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [42]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">plot_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'color_min'</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s1">'color_max'</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
@@ -11871,6 +11914,84 @@ <h2 id="5.-MAF-sky-map">5. MAF sky map<a class="anchor-link" href="#5.-MAF-sky-m
 </div>
 </div>
 </div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f40d0511-12bc-451f-832d-ff5eabffee03">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h4 id="5.1.-Sky-map-for-DDFs-only">5.1. Sky map for DDFs only<a class="anchor-link" href="#5.1.-Sky-map-for-DDFs-only">¶</a></h4><p>Create a sky map for only the DDFs, which will be where alert production will start in early 2026.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=e95d55ce-5a17-403a-a7ea-2bf48caa653d">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [43]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">constraint</span> <span class="o">=</span> <span class="s2">"observation_reason like </span><span class="se">\'</span><span class="si">%d</span><span class="s2">df%</span><span class="se">\'</span><span class="s2">"</span>
+<span class="n">bundle</span> <span class="o">=</span> <span class="n">maf</span><span class="o">.</span><span class="n">MetricBundle</span><span class="p">(</span><span class="n">metric</span><span class="p">,</span> <span class="n">slicer</span><span class="p">,</span> <span class="n">constraint</span><span class="p">,</span> <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">)</span>
+<span class="n">group</span> <span class="o">=</span> <span class="n">maf</span><span class="o">.</span><span class="n">MetricBundleGroup</span><span class="p">({</span><span class="s1">'nvisits'</span><span class="p">:</span> <span class="n">bundle</span><span class="p">},</span>
+                              <span class="n">opsim_fname</span><span class="p">,</span> <span class="n">out_dir</span><span class="o">=</span><span class="n">output_path</span><span class="p">)</span>
+<span class="n">group</span><span class="o">.</span><span class="n">run_all</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=113eccfe-9598-4ce9-8708-b50a1cbbc952">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">ph</span> <span class="o">=</span> <span class="n">maf</span><span class="o">.</span><span class="n">PlotHandler</span><span class="p">(</span><span class="n">savefig</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fig_format</span><span class="o">=</span><span class="s1">'png'</span><span class="p">,</span> <span class="n">thumbnail</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">270</span><span class="p">)</span>
+<span class="n">ph</span><span class="o">.</span><span class="n">set_metric_bundles</span><span class="p">([</span><span class="n">bundle</span><span class="p">])</span>
+<span class="n">ph</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">plot_func</span><span class="o">=</span><span class="n">maf</span><span class="o">.</span><span class="n">plots</span><span class="o">.</span><span class="n">HealpixSkyMap</span><span class="p">(),</span>
+        <span class="n">plot_dicts</span><span class="o">=</span><span class="p">{</span><span class="s1">'color_min'</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span> <span class="s1">'color_max'</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span> <span class="s1">'figsize'</span><span class="p">:</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+                    <span class="s1">'labelsize'</span><span class="p">:</span> <span class="s1">'x-large'</span><span class="p">,</span> <span class="s1">'fontsize'</span><span class="p">:</span> <span class="s1">'x-large'</span><span class="p">,</span> <span class="s1">'extend'</span><span class="p">:</span> <span class="s1">'max'</span><span class="p">,</span>
+                    <span class="s1">'title'</span><span class="p">:</span> <span class="s1">'Deep Drilling Field LSSTCam Visits as of 2025-09-30'</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span><span class="mf">0.40</span><span class="p">,</span> <span class="mf">0.62</span><span class="p">,</span> <span class="s1">'XMM LSS'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">'large'</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'white'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span><span class="mf">0.36</span><span class="p">,</span> <span class="mf">0.50</span><span class="p">,</span> <span class="s1">'ECDFS'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">'large'</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'white'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span><span class="mf">0.36</span><span class="p">,</span> <span class="mf">0.33</span><span class="p">,</span> <span class="s1">'EDFS'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">'large'</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'white'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span><span class="mf">0.47</span><span class="p">,</span> <span class="mf">0.43</span><span class="p">,</span> <span class="s1">'ELAIS-S1'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">'large'</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'white'</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
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+</div>
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"/>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2a9aaa25-6d90-41c7-acaf-485ea7dead82">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<blockquote>
+<p><strong>Figure 7:</strong> The locations of the four Deep Drilling Fields (DDFs), with their names labeled. See Figure 1 for the field central coordinates.</p>
+</blockquote>
+</div>
+</div>
+</div>
+</div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=135c0da0-4d13-4230-9abe-2add68bb1266">
 <div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
diff --git a/docs/tutorials/notebook/102_rubin_schedule_viewer.html b/docs/tutorials/notebook/102_rubin_schedule_viewer.html
index 50c5580..231cfdc 100644
--- a/docs/tutorials/notebook/102_rubin_schedule_viewer.html
+++ b/docs/tutorials/notebook/102_rubin_schedule_viewer.html
@@ -9959,12 +9959,12 @@ <h1 id="102.-Rubin-Schedule-Viewer">102. Rubin Schedule Viewer<a class="anchor-l
 rkJggg==
 "/></p>
 </div>
-<p>For the Rubin Science Platform at data.lsst.cloud. <br/>
-Container Size: Large <br/>
-LSST Science Pipelines version: v29.2.0 <br/>
-Last verified to run: 2025-12-04 <br/>
-Repository: <a href="https://github.com/lsst/tutorial-notebooks">github.com/lsst/tutorial-notebooks</a><br>
-DOI: <a href="https://doi.org/10.11578/rubin/dc.20250909.20">10.11578/rubin/dc.20250909.20</a></br></p>
+<p>For the Rubin Science Platform at data.lsst.cloud.<br/>
+Container Size: Large<br/>
+LSST Science Pipelines version: v29.2.0<br/>
+Last verified to run: 2025-01-28<br/>
+Repository: <a href="https://github.com/lsst/tutorial-notebooks">github.com/lsst/tutorial-notebooks</a><br/>
+DOI: <a href="https://doi.org/10.11578/rubin/dc.20250909.20">10.11578/rubin/dc.20250909.20</a></p>
 </div>
 </div>
 </div>
@@ -10025,28 +10025,35 @@ <h3 id="1.1.-ObsLocTAP---Observation-Locator-Table-Access-Protocol">1.1. ObsLocT
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
 <h3 id="1.2.-Key-columns">1.2. Key columns<a class="anchor-link" href="#1.2.-Key-columns">¶</a></h3><p>The ObsLocTAP data model (table schema) for the scheduled observations is defined in the <code>ivoa.obsplan</code> table -- <a href="https://www.ivoa.net/documents/ObsLocTAP/20210609/PR-ObsLocTAP-1.0-20210609.pdf">Observation Locator
 Table Access Protocol Version 1.0</a></p>
-<p>The database contains 28 columns in total, but these are the key columns used in this tutorial.</p>
+<p>The database contains 28 columns in total, and these are the key columns.</p>
 <ul>
-<li><code>t_planning</code> : Time (MJD) when this observation has been added or modified into the planning log.</li>
-<li><code>target_name</code> : The name of the astronomical object observed, if any.</li>
-<li><code>obs_id</code> : The observation ID from the Camera.</li>
-<li><code>s_ra</code> : The Right Ascension of the visit or planned observation (degrees).</li>
-<li><code>s_dec</code> : The Declination of the visit or planned observation (degrees).</li>
-<li><code>s_fov</code> : The diameter (bounds) of the covered region.</li>
-<li><code>s_region</code> : Sky region covered by the data product (expressed in ICRS frame).</li>
-<li><code>s_resolution</code> : The spatial resolution of the data expressed as FWHM.</li>
 <li><code>t_min</code> : The start time (MJD).</li>
 <li><code>t_max</code> : The stop time (MJD).</li>
-<li><code>t_exptime</code> : The total exposure time = <code>t_max - t_min</code>.</li>
-<li><code>t_resolution</code> : Temporal resolution.</li>
-<li><code>facility_name</code> : Name of the facility used for this observation e.g Rubin Simonyi, AuxTel.</li>
-<li><code>instrument_name</code> : Name of the instrument used for this observation e.g LSSTCam, LSSTComCam.</li>
-<li><code>t_plan_exptime</code> : The planned or scheduled exposure time.</li>
+<li><code>s_ra</code> : The Right Ascension of the visit or planned observation (deg).</li>
+<li><code>s_dec</code> : The Declination of the visit or planned observation (deg).</li>
+<li><code>em_min</code> : The filter minimum wavelength (m).</li>
+<li><code>em_max</code> : The filter maximum wavelength (m).</li>
+<li><code>target_name</code> : The name of the astronomical object observed, if any.</li>
+<li><code>tracking_type</code> : One of the following values: Sidereal, Solar-system-object-tracking, Fixed-az-el-transit.</li>
 <li><code>priority</code> : The Priority level { 0, 1, 2}. 0 = in the target queue as the next visit
 (meaning its execution is highly likely), 1 = in the 1 hour look-ahead summit queue (meaning likely),
 2 = 24 hour pre-night simulation, meaning it is not in the summit queue and has a high degree of uncertainty.</li>
+<li><code>t_planning</code> : Time (MJD) when this observation has been added or modified into the planning log.</li>
 <li><code>execution_status</code> : Execution status of the prediction: One of the following values: Planned, Scheduled, Unscheduled, Performed, Aborted.</li>
-<li><code>tracking_type</code> : One of the following values: Sidereal, Solar-system-object-tracking, Fixed-az-el-transit.</li>
+</ul>
+<p>Key columns that remained yet unpopulated when this tutorial was last updated.</p>
+<ul>
+<li><code>s_region</code> : Sky region covered by the data product (expressed in ICRS frame).</li>
+<li><code>obs_id</code> : The observation ID from the Camera (currently the same as <code>target_name</code>).</li>
+</ul>
+<p>These are also columns of interest, but are generally the same for most observations taken with LSSTCam.</p>
+<ul>
+<li><code>t_plan_exptime</code> : The planned or scheduled exposure time (s).</li>
+<li><code>t_exptime</code> : The total exposure time = <code>t_max - t_min</code> (s).</li>
+<li><code>s_fov</code> : The diameter (bounds) of the covered region (deg).</li>
+<li><code>s_resolution</code> : The spatial resolution of the data expressed as FWHM (arcsec/pixel).</li>
+<li><code>facility_name</code> : Name of the facility used for this observation e.g Rubin Simonyi, AuxTel.</li>
+<li><code>instrument_name</code> : Name of the instrument used for this observation e.g LSSTCam, LSSTComCam.</li>
 </ul>
 <p>See  <a href="https://dmtn-263.lsst.io/">DMTN-263</a> for a description of all fields in the schema.</p>
 </div>
@@ -10129,9 +10136,9 @@ <h3 id="1.3.-Import-packages">1.3. Import packages<a class="anchor-link" href="#
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@@ -10803,6 +10810,58 @@ <h3 id="1.3.-Import-packages">1.3. Import packages<a class="anchor-link" href="#
 </div>
 </div>
 </div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=cd2a8cab-f364-420d-80ec-3c9f99e47cf7">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<h3 id="1.4.-Define-functions">1.4. Define functions<a class="anchor-link" href="#1.4.-Define-functions">¶</a></h3><p>Define a function to convert the <code>em_min</code> and <code>em_max</code> columns into the bandpass (filter) name.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=27684781-bdf6-4fb4-a57b-e8b510253375">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">em_min_max_to_band</span><span class="p">(</span><span class="n">em_min</span><span class="p">,</span> <span class="n">em_max</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">"""</span>
+<span class="sd">    Convert the wavelength minimum and maximum to band name.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    em_min: float</span>
+<span class="sd">        Wavelength minimum, in m.</span>
+<span class="sd">    em_max: float</span>
+<span class="sd">        Wavelength maximum, in m.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    band: string</span>
+<span class="sd">        Band (filter) name.</span>
+<span class="sd">    """</span>
+
+    <span class="n">band</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">band_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'u'</span><span class="p">:</span> <span class="p">(</span><span class="mf">2.95e-7</span><span class="p">,</span> <span class="mf">4.00e-7</span><span class="p">),</span> <span class="s1">'g'</span><span class="p">:</span> <span class="p">(</span><span class="mf">4.00e-7</span><span class="p">,</span> <span class="mf">5.55e-7</span><span class="p">),</span>
+                 <span class="s1">'r'</span><span class="p">:</span> <span class="p">(</span><span class="mf">5.50e-7</span><span class="p">,</span> <span class="mf">6.92e-7</span><span class="p">),</span> <span class="s1">'i'</span><span class="p">:</span> <span class="p">(</span><span class="mf">6.90e-7</span><span class="p">,</span> <span class="mf">8.20e-7</span><span class="p">),</span>
+                 <span class="s1">'z'</span><span class="p">:</span> <span class="p">(</span><span class="mf">8.15e-7</span><span class="p">,</span> <span class="mf">9.25e-7</span><span class="p">),</span> <span class="s1">'y'</span><span class="p">:</span> <span class="p">(</span><span class="mf">9.20e-7</span><span class="p">,</span> <span class="mf">11.1e-7</span><span class="p">)}</span>
+    <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">band_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">em_min</span> <span class="o">&gt;</span> <span class="n">band_dict</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="n">em_max</span> <span class="o">&lt;</span> <span class="n">band_dict</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="n">band</span> <span class="o">=</span> <span class="n">b</span>
+    <span class="k">if</span> <span class="n">band</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">em_min</span><span class="p">,</span> <span class="n">em_max</span><span class="p">,</span> <span class="s1">' no band found'</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">band</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d7cbe189-5a7c-4373-8fcb-076d661b0a6f">
 <div class="jp-Cell-inputWrapper" tabindex="0">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
@@ -10819,7 +10878,7 @@ <h2 id="2.-Rubin-Schedule-Viewer">2. Rubin Schedule Viewer<a class="anchor-link"
 <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 [3]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">obsloctap_url</span> <span class="o">=</span> <span class="s2">"https://usdf-rsp.slac.stanford.edu/obsloctap"</span>
@@ -10850,6 +10909,9 @@ <h3 id="2.1.-Graphical-User-Interface">2.1. Graphical User Interface<a class="an
 <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>Programmatically check the health of the static UI by executing the following cell, and assert that the static user interface service must be alive.</p>
+<blockquote>
+<p><strong>Warning:</strong> If <code>RemoteDisconnect</code> errors are returned, it means the service is temporarily unstable or unavailable, and to try again later.</p>
+</blockquote>
 </div>
 </div>
 </div>
@@ -10858,10 +10920,10 @@ <h3 id="2.1.-Graphical-User-Interface">2.1. Graphical User Interface<a class="an
 <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 [4]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">rubin_static_schedule_url</span>  <span class="o">=</span> <span class="n">obsloctap_url</span> <span class="o">+</span> <span class="s2">"/static/viewer.html"</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">rubin_static_schedule_url</span> <span class="o">=</span> <span class="n">obsloctap_url</span> <span class="o">+</span> <span class="s2">"/static/viewer.html"</span>
 <span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">rubin_static_schedule_url</span><span class="p">)</span>
 <span class="k">assert</span> <span class="n">response</span><span class="o">.</span><span class="n">status_code</span> <span class="o">==</span> <span class="mi">200</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"request failed with status </span><span class="si">{</span><span class="n">response</span><span class="o">.</span><span class="n">status_code</span><span class="si">}</span><span class="s2">"</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Rubin Schedule Viewer at </span><span class="si">{</span><span class="n">response</span><span class="o">.</span><span class="n">url</span><span class="si">}</span><span class="s2"> is alive."</span><span class="p">)</span>
@@ -10911,7 +10973,7 @@ <h3 id="2.2.-Programmatic-access">2.2. Programmatic access<a class="anchor-link"
 <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 [5]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">obsloctap_url</span><span class="p">)</span>
@@ -10951,7 +11013,7 @@ <h3 id="2.3.-Query-the-service-header-information">2.3. Query the service header
 <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 [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">headers</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
@@ -10992,7 +11054,7 @@ <h3 id="2.3.-Query-the-service-header-information">2.3. Query the service header
 <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 [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span>
@@ -11034,7 +11096,7 @@ <h2 id="3.-Retrieve-the-future-schedule">3. Retrieve the future schedule<a class
 <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 [8]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">schedule_url</span> <span class="o">=</span> <span class="n">obsloctap_url</span> <span class="o">+</span> <span class="s2">"/schedule"</span>
@@ -11059,7 +11121,7 @@ <h2 id="3.-Retrieve-the-future-schedule">3. Retrieve the future schedule<a class
 <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 [9]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">schedule_url</span><span class="p">)</span>
@@ -11101,7 +11163,7 @@ <h3 id="3.1.-Retrieve-the-next-24-hours-of-schedule">3.1. Retrieve the next 24 h
 <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 [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">forward_24h_schedule</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
@@ -11118,7 +11180,7 @@ <h3 id="3.1.-Retrieve-the-next-24-hours-of-schedule">3.1. Retrieve the next 24 h
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 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>1828
+<pre>1545
 </pre>
 </div>
 </div>
@@ -11140,7 +11202,7 @@ <h3 id="3.1.-Retrieve-the-next-24-hours-of-schedule">3.1. Retrieve the next 24 h
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">forward_24h_schedule</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
@@ -11154,7 +11216,7 @@ <h3 id="3.1.-Retrieve-the-next-24-hours-of-schedule">3.1. Retrieve the next 24 h
 </div>
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[11]:</div>
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
 <style scoped="">
@@ -11200,21 +11262,21 @@ <h3 id="3.1.-Retrieve-the-next-24-hours-of-schedule">3.1. Retrieve the next 24 h
 <tbody>
 <tr>
 <th>0</th>
-<td>61061.376824</td>
-<td>bulgy</td>
-<td>bulgy</td>
+<td>61070.197551</td>
+<td>dusty_plane</td>
+<td>dusty_plane</td>
 <td></td>
-<td>257.367864</td>
-<td>-35.545737</td>
+<td>117.564849</td>
+<td>-44.751188</td>
 <td>3.0</td>
 <td></td>
 <td>0.2</td>
-<td>61061.369880</td>
+<td>61070.190607</td>
 <td>...</td>
 <td>0</td>
 <td>Rubin:Simonyi</td>
 <td>LSSTCam</td>
-<td>15.0</td>
+<td>30.0</td>
 <td>Fixed</td>
 <td>2</td>
 <td>Scheduled</td>
@@ -11224,93 +11286,93 @@ <h3 id="3.1.-Retrieve-the-next-24-hours-of-schedule">3.1. Retrieve the next 24 h
 </tr>
 <tr>
 <th>1</th>
-<td>61061.376774</td>
-<td>bulgy</td>
-<td>bulgy</td>
+<td>61070.197269</td>
+<td>lowdust</td>
+<td>lowdust</td>
 <td></td>
-<td>256.627221</td>
-<td>-43.181502</td>
+<td>131.981199</td>
+<td>-14.429881</td>
 <td>3.0</td>
 <td></td>
 <td>0.2</td>
-<td>61061.369830</td>
+<td>61070.190324</td>
 <td>...</td>
 <td>0</td>
 <td>Rubin:Simonyi</td>
 <td>LSSTCam</td>
-<td>15.0</td>
+<td>30.0</td>
 <td>Fixed</td>
 <td>2</td>
-<td>Not Observed</td>
+<td>Scheduled</td>
 <td>Sidereal</td>
 <td>0.0</td>
 <td>1</td>
 </tr>
 <tr>
 <th>2</th>
-<td>61061.376642</td>
-<td>bulgy</td>
-<td>bulgy</td>
+<td>61070.196802</td>
+<td>lowdust</td>
+<td>lowdust</td>
 <td></td>
-<td>262.368045</td>
-<td>-39.274767</td>
+<td>133.842926</td>
+<td>-11.950495</td>
 <td>3.0</td>
 <td></td>
 <td>0.2</td>
-<td>61061.369697</td>
+<td>61070.189857</td>
 <td>...</td>
 <td>0</td>
 <td>Rubin:Simonyi</td>
 <td>LSSTCam</td>
-<td>15.0</td>
+<td>30.0</td>
 <td>Fixed</td>
 <td>2</td>
-<td>Not Observed</td>
+<td>Scheduled</td>
 <td>Sidereal</td>
 <td>0.0</td>
 <td>1</td>
 </tr>
 <tr>
 <th>3</th>
-<td>61061.376642</td>
-<td>bulgy</td>
-<td>bulgy</td>
+<td>61070.196752</td>
+<td>lowdust, dusty_plane</td>
+<td>lowdust, dusty_plane</td>
 <td></td>
-<td>262.368045</td>
-<td>-39.274767</td>
+<td>109.353194</td>
+<td>-36.823477</td>
 <td>3.0</td>
 <td></td>
 <td>0.2</td>
-<td>61061.369697</td>
+<td>61070.189807</td>
 <td>...</td>
 <td>0</td>
 <td>Rubin:Simonyi</td>
 <td>LSSTCam</td>
-<td>15.0</td>
+<td>30.0</td>
 <td>Fixed</td>
 <td>2</td>
-<td>Not Observed</td>
+<td>Scheduled</td>
 <td>Sidereal</td>
 <td>0.0</td>
 <td>1</td>
 </tr>
 <tr>
 <th>4</th>
-<td>61061.376544</td>
-<td>bulgy</td>
-<td>bulgy</td>
+<td>61070.196337</td>
+<td>lowdust</td>
+<td>lowdust</td>
 <td></td>
-<td>256.391046</td>
-<td>-32.622280</td>
+<td>132.802581</td>
+<td>-8.968057</td>
 <td>3.0</td>
 <td></td>
 <td>0.2</td>
-<td>61061.369600</td>
+<td>61070.189393</td>
 <td>...</td>
 <td>0</td>
 <td>Rubin:Simonyi</td>
 <td>LSSTCam</td>
-<td>15.0</td>
+<td>30.0</td>
 <td>Fixed</td>
 <td>2</td>
 <td>Scheduled</td>
@@ -11334,6 +11396,80 @@ <h3 id="3.1.-Retrieve-the-next-24-hours-of-schedule">3.1. Retrieve the next 24 h
 <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>Note that the values in the "Execution Status" field  are all "Scheduled".</p>
+<h4 id="3.1.1.-Planned-DDF-observations">3.1.1. Planned DDF observations<a class="anchor-link" href="#3.1.1.-Planned-DDF-observations">¶</a></h4><p>When alerts begin streaming in early 2026, template images will only exist for the DDFs, and thus alerts will only stream in the DDFs.</p>
+<p>Print the number of planned observations per DDF in the next 24h, and in which filters the observations are planned.</p>
+<p>First, add a column for the band (filter), converted from the <code>em_min</code> and <code>em_max</code> columns.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=6769ff38-6b03-4649-9f39-01c475a9e478">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">em_min</span> <span class="o">=</span> <span class="n">forward_24h_schedule</span><span class="p">[</span><span class="s1">'em_min'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+<span class="n">em_max</span> <span class="o">=</span> <span class="n">forward_24h_schedule</span><span class="p">[</span><span class="s1">'em_max'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+<span class="n">temp</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">forward_24h_schedule</span><span class="p">)):</span>
+    <span class="n">temp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">em_min_max_to_band</span><span class="p">(</span><span class="n">em_min</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">em_max</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+<span class="n">forward_24h_schedule</span><span class="p">[</span><span class="s1">'band'</span><span class="p">]</span> <span class="o">=</span> <span class="n">temp</span>
+<span class="k">del</span> <span class="n">temp</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4148b3ff-9173-484a-991b-495ee6d25b95">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<p>Define the names of the DDF fields, in the format of the <code>target_name</code> column.
+For each field, sum the number of planned observations and print the unique filters for those observations.</p>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=fec6e949-a299-4eae-b4b4-f15da82c9e16">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="n">ddf_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'ddf_cosmos'</span><span class="p">,</span> <span class="s1">'ddf_ecdfs'</span><span class="p">,</span> <span class="s1">'ddf_elaiss1'</span><span class="p">,</span>
+             <span class="s1">'ddf_edfs_a'</span><span class="p">,</span> <span class="s1">'ddf_edfs_b'</span><span class="p">,</span> <span class="s1">'ddf_xmm_lss'</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">ddf_name</span> <span class="ow">in</span> <span class="n">ddf_names</span><span class="p">:</span>
+    <span class="n">temp</span> <span class="o">=</span> <span class="n">forward_24h_schedule</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">"target_name.str.contains(@ddf_name)"</span><span class="p">)</span>
+    <span class="n">band_list</span> <span class="o">=</span> <span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">temp</span><span class="p">[</span><span class="s1">'band'</span><span class="p">]))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">%-12s</span><span class="s1"> </span><span class="si">%3i</span><span class="s1">  bands: </span><span class="si">%-15s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">ddf_name</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">temp</span><span class="p">),</span> <span class="n">band_list</span><span class="p">))</span>
+    <span class="k">del</span> <span class="n">temp</span><span class="p">,</span> <span class="n">band_list</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>ddf_cosmos    29  bands: giry
+ddf_ecdfs      8  bands: rz
+ddf_elaiss1    8  bands: rz
+ddf_edfs_a     5  bands: ryz
+ddf_edfs_b     4  bands: rz
+ddf_xmm_lss    8  bands: rz
+</pre>
+</div>
 </div>
 </div>
 </div>
@@ -11353,7 +11489,7 @@ <h3 id="3.2.-Retrieve-the-next-2-hours-of-schedule">3.2. Retrieve the next 2 hou
 <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 [12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"time"</span><span class="p">:</span> <span class="s2">"2"</span><span class="p">}</span>
@@ -11383,7 +11519,7 @@ <h3 id="3.2.-Retrieve-the-next-2-hours-of-schedule">3.2. Retrieve the next 2 hou
 <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 [13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">next_visits</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
@@ -11402,7 +11538,7 @@ <h3 id="3.2.-Retrieve-the-next-2-hours-of-schedule">3.2. Retrieve the next 2 hou
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>There are 0 visits scheduled in the next 2 hours.
+<pre>There are 455 visits scheduled in the next 2 hours.
 </pre>
 </div>
 </div>
@@ -11424,7 +11560,7 @@ <h3 id="3.2.-Retrieve-the-next-2-hours-of-schedule">3.2. Retrieve the next 2 hou
 <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 [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="c1"># next_visits</span>
@@ -11451,7 +11587,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <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 [15]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"time"</span><span class="p">:</span> <span class="s2">"24"</span><span class="p">,</span> <span class="s2">"start"</span><span class="p">:</span> <span class="s2">"2025-10-01"</span><span class="p">}</span>
@@ -11465,7 +11601,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <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 [16]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">schedule_url</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
@@ -11494,7 +11630,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <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 [17]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">next_visits_from_date</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
@@ -11537,7 +11673,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">past_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">Time</span><span class="p">(</span><span class="s2">"2025-10-29 22:00:00"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">"iso"</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s2">"utc"</span><span class="p">))</span><span class="o">.</span><span class="n">mjd</span>
@@ -11565,7 +11701,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <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 [19]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"time"</span><span class="p">:</span> <span class="s2">"24"</span><span class="p">,</span> <span class="s2">"start"</span><span class="p">:</span> <span class="n">past_time</span><span class="p">}</span>
@@ -11579,7 +11715,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <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 [20]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">response</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">schedule_url</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
@@ -11608,7 +11744,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <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 [21]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">next_visits_from_date_iso</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
@@ -11650,7 +11786,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [25]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">next_visits_from_date_iso</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
@@ -11664,7 +11800,7 @@ <h2 id="4.-Retrieve-a-past-planned-schedule">4. Retrieve a past planned schedule
 </div>
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[25]:</div>
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <div>
 <style scoped="">
@@ -11852,7 +11988,7 @@ <h3 id="4.1.-Execution-status">4.1. Execution status<a class="anchor-link" href=
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">next_visits_from_date_iso</span><span class="p">[</span><span class="s2">"execution_status"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
@@ -11866,7 +12002,7 @@ <h3 id="4.1.-Execution-status">4.1. Execution status<a class="anchor-link" href=
 </div>
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[26]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
 <pre>['Performed', 'Not Observed']</pre>
 </div>
@@ -11912,7 +12048,7 @@ <h3 id="5.1.-Inter-visit-time-gaps">5.1. Inter-visit time gaps<a class="anchor-l
 <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 [24]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">t_planning</span> <span class="o">=</span> <span class="n">next_visits_from_date_iso</span><span class="p">[</span><span class="s2">"t_planning"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
@@ -11957,12 +12093,12 @@ <h3 id="5.1.-Inter-visit-time-gaps">5.1. Inter-visit time gaps<a class="anchor-l
 <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 [25]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Min gap: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">obs_visit_gap</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Max gap: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">obs_visit_gap</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean gap: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">obs_visit_gap</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Min gap: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">obs_visit_gap</span><span class="p">)</span><span class="si">:</span><span class="s2"> .2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Max gap: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">obs_visit_gap</span><span class="p">)</span><span class="si">:</span><span class="s2"> .2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean gap: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">obs_visit_gap</span><span class="p">)</span><span class="si">:</span><span class="s2"> .2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -11975,9 +12111,9 @@ <h3 id="5.1.-Inter-visit-time-gaps">5.1. Inter-visit time gaps<a class="anchor-l
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Min gap: 34.07
-Max gap: 326.45
-Mean gap: 42.20
+<pre>Min gap:  34.07
+Max gap:  326.45
+Mean gap:  42.20
 </pre>
 </div>
 </div>
@@ -11999,7 +12135,7 @@ <h3 id="5.1.-Inter-visit-time-gaps">5.1. Inter-visit time gaps<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">hover</span> <span class="o">=</span> <span class="n">HoverTool</span><span class="p">(</span>
@@ -12026,7 +12162,7 @@ <h3 id="5.1.-Inter-visit-time-gaps">5.1. Inter-visit time gaps<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">histogram</span> <span class="o">=</span> <span class="n">hv</span><span class="o">.</span><span class="n">Histogram</span><span class="p">(</span>
@@ -12058,14 +12194,14 @@ <h3 id="5.1.-Inter-visit-time-gaps">5.1. Inter-visit time gaps<a class="anchor-l
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 </div>
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[27]:</div>
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[30]:</div>
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
-<div id="bc5fdfef-cdd0-4d47-90e0-2ef3a6e54da9">
-<div data-root-id="bc5fdfef-cdd0-4d47-90e0-2ef3a6e54da9" id="eda86b1d-6764-42ae-9207-980fba164681" style="display: contents;"></div>
+<div id="a8e91413-9ac3-488e-9bd9-e99ec72d8e75">
+<div data-root-id="a8e91413-9ac3-488e-9bd9-e99ec72d8e75" id="aa4865aa-496c-48e8-bb52-3f79b2e02014" style="display: contents;"></div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div>
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 <div class="highlight hl-ipython3"><pre><span></span><span class="n">df_plot</span> <span class="o">=</span> <span class="n">next_visits_from_date_iso</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
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 <div class="highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
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 <div class="highlight hl-ipython3"><pre><span></span><span class="n">scatter_plots</span> <span class="o">=</span> <span class="p">[]</span>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [34]:</div>
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 <div class="cm-editor cm-s-jupyter">
 <div class="highlight hl-ipython3"><pre><span></span><span class="n">plot</span> <span class="o">=</span> <span class="n">plot</span><span class="o">.</span><span class="n">opts</span><span class="p">(</span>
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From e133ff3a2dab176bc73a48c3d4cccc4c4ee43a78 Mon Sep 17 00:00:00 2001
From: MelissaGraham <melissalynngraham@gmail.com>
Date: Thu, 29 Jan 2026 05:42:21 +0000
Subject: [PATCH 2/2] add template area info

---
 docs/observations/index.rst | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/docs/observations/index.rst b/docs/observations/index.rst
index 245778d..9d3a3f5 100644
--- a/docs/observations/index.rst
+++ b/docs/observations/index.rst
@@ -24,6 +24,14 @@ The projected Rubin schedule can be obtained via ObsLocTAP `programatically <htt
 A :ref:`tutorial notebook <tutorials-notebook>` for the Rubin Schedule Viewer is available.
 
 
+Incremental templates
+---------------------
+
+During early Alert Production, templates (and thus alerts) only exist in the `Deep Drilling Fields <https://survey-strategy.lsst.io/baseline/ddf.html>`_.
+
+Additional template images will be generated incrementally during the early science phase, and information about template regions will be added here.
+
+
 Alert stream status
 ===================