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Date: Tue, 4 Jun 2024 17:22:58 +0000
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Deployed from commit e59dcca13c0b9e22286a7a382ed15b715ed4bc2c (refs/heads/master)

Deployed from commit bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d (refs/heads/master)
---
 master/.buildinfo                             |     2 +-
 master/01_overview.html                       |    78 +-
 master/02_contributing.html                   |   220 +-
 master/03_authors.html                        |    62 +-
 master/04_features.html                       |    44 +-
 master/05_history.html                        |   117 +-
 master/06_introduction.html                   |    44 +-
 master/07_krotovs_method.html                 |   152 +-
 master/08_qutip_usage.html                    |    46 +-
 master/09_examples.html                       |    48 +-
 master/10_howto.html                          |   166 +-
 master/11_other_methods.html                  |   104 +-
 master/12_related_software.html               |    56 +-
 master/99_bibliography.html                   |   795 +-
 master/API/krotov.convergence.html            |   121 +-
 master/API/krotov.conversions.html            |   135 +-
 master/API/krotov.functionals.html            |   147 +-
 master/API/krotov.html                        |    60 +-
 master/API/krotov.info_hooks.html             |   103 +-
 master/API/krotov.mu.html                     |    79 +-
 master/API/krotov.objectives.html             |   205 +-
 master/API/krotov.optimize.html               |   109 +-
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 master/API/krotov.propagators.html            |   109 +-
 master/API/krotov.result.html                 |   209 +-
 master/API/krotov.second_order.html           |    98 +-
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 master/_modules/index.html                    |    44 +-
 master/_modules/krotov/convergence.html       |    79 +-
 master/_modules/krotov/conversions.html       |    79 +-
 master/_modules/krotov/functionals.html       |   119 +-
 master/_modules/krotov/info_hooks.html        |    59 +-
 master/_modules/krotov/mu.html                |    49 +-
 master/_modules/krotov/objectives.html        |    89 +-
 master/_modules/krotov/optimize.html          |    49 +-
 master/_modules/krotov/parallelization.html   |    76 +-
 master/_modules/krotov/propagators.html       |    71 +-
 master/_modules/krotov/result.html            |    66 +-
 master/_modules/krotov/second_order.html      |    66 +-
 master/_modules/krotov/shapes.html            |    74 +-
 master/_sources/09_examples.rst.txt           |     2 +-
 ...01_example_simple_state_to_state.ipynb.txt |   172 +-
 ..._lambda_system_rwa_complex_pulse.ipynb.txt |   276 +-
 ..._lambda_system_rwa_non_hermitian.ipynb.txt |   262 +-
 ..._example_dissipative_qubit_reset.ipynb.txt |   234 +-
 .../05_example_transmon_xgate.ipynb.txt       |   267 +-
 .../notebooks/06_example_3states.ipynb.txt    |   341 +-
 .../notebooks/07_example_PE.ipynb.txt         |   268 +-
 .../notebooks/08_example_ensemble.ipynb.txt   |   363 +-
 .../notebooks/09_example_numpy.ipynb.txt      |   189 +-
 .../_sphinx_javascript_frameworks_compat.js   |   123 +
 master/_static/basic.css                      |   162 +-
 master/_static/doctools.js                    |   383 +-
 master/_static/documentation_options.js       |    11 +-
 master/_static/graphviz.css                   |     2 +-
 master/_static/jquery-3.5.1.js                | 10872 ----------------
 master/_static/jquery.js                      |     4 +-
 master/_static/language_data.js               |   104 +-
 master/_static/searchtools.js                 |   879 +-
 master/_static/sphinx_highlight.js            |   154 +
 master/_static/underscore-1.12.0.js           |  2027 ---
 master/_static/underscore.js                  |     6 -
 master/genindex.html                          |    50 +-
 master/index.html                             |    61 +-
 .../01_example_simple_state_to_state.html     |    94 +-
 .../01_example_simple_state_to_state.ipynb    |   172 +-
 ...ample_lambda_system_rwa_complex_pulse.html |   138 +-
 ...mple_lambda_system_rwa_complex_pulse.ipynb |   276 +-
 ...ample_lambda_system_rwa_non_hermitian.html |   103 +-
 ...mple_lambda_system_rwa_non_hermitian.ipynb |   262 +-
 .../04_example_dissipative_qubit_reset.html   |   133 +-
 .../04_example_dissipative_qubit_reset.ipynb  |   234 +-
 .../notebooks/05_example_transmon_xgate.html  |   126 +-
 .../notebooks/05_example_transmon_xgate.ipynb |   267 +-
 master/notebooks/06_example_3states.html      |   119 +-
 master/notebooks/06_example_3states.ipynb     |   341 +-
 master/notebooks/07_example_PE.html           |   143 +-
 master/notebooks/07_example_PE.ipynb          |   268 +-
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 master/notebooks/08_example_ensemble.ipynb    |   363 +-
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 master/notebooks/09_example_numpy.ipynb       |   189 +-
 master/objects.inv                            |   Bin 8673 -> 8712 bytes
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 master/search.html                            |    44 +-
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 112 files changed, 6307 insertions(+), 18939 deletions(-)
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diff --git a/master/.buildinfo b/master/.buildinfo
index 9028670a..581ddb0d 100644
--- a/master/.buildinfo
+++ b/master/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 10873f38ea59eb787b42b752cc193d79
+config: 322ec63f26f632f1f6f680d097ff86da
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/master/01_overview.html b/master/01_overview.html
index 6e41afc1..7393eb49 100644
--- a/master/01_overview.html
+++ b/master/01_overview.html
@@ -1,23 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Krotov Python Package &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
+  <title>Krotov Python Package &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/docs-versions-menu.js"></script>
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="_static/copybutton.js?v=f281be69"></script>
+        <script src="_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -38,7 +44,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -48,7 +54,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Krotov Python Package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#purpose">Purpose</a></li>
@@ -131,8 +137,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="krotov-python-package">
-<h1>Krotov Python Package<a class="headerlink" href="#krotov-python-package" title="Permalink to this headline">¶</a></h1>
+  <section id="krotov-python-package">
+<h1>Krotov Python Package<a class="headerlink" href="#krotov-python-package" title="Link to this heading">¶</a></h1>
 <a class="reference external image-reference" href="https://github.com/qucontrol/krotov"><img alt="Source code on Github" src="https://img.shields.io/badge/github-qucontrol/krotov-blue.svg" /></a>
 <a class="reference external image-reference" href="https://qucontrol.github.io/krotov"><img alt="Documentation" src="https://img.shields.io/badge/docs-gh--pages-blue.svg" /></a>
 <a class="reference external image-reference" href="https://pypi.python.org/pypi/krotov"><img alt="Krotov on the Python Package Index" src="https://img.shields.io/pypi/v/krotov.svg" /></a>
@@ -147,8 +153,8 @@ <h1>Krotov Python Package<a class="headerlink" href="#krotov-python-package" tit
 <p>The <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package is built on top of <a class="reference external" href="http://qutip.org">QuTiP</a>.</p>
 <p>Development happens on <a class="reference external" href="https://github.com/qucontrol/krotov">Github</a>. You can read the full documentation <a class="reference external" href="https://qucontrol.github.io/krotov">online</a>.</p>
 <p>If you use the <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package in your research, please <a class="reference internal" href="#citingkrotov"><span class="std std-ref">cite it</span></a>.</p>
-<div class="section" id="purpose">
-<h2>Purpose<a class="headerlink" href="#purpose" title="Permalink to this headline">¶</a></h2>
+<section id="purpose">
+<h2>Purpose<a class="headerlink" href="#purpose" title="Link to this heading">¶</a></h2>
 <p>Optimal control is a cornerstone of quantum technology: relying not
 just on a passive understanding of quantum mechanics, but on the <em>active</em>
 utilization of the quantum properties of matter. Quantum optimal control asks
@@ -179,9 +185,9 @@ <h2>Purpose<a class="headerlink" href="#purpose" title="Permalink to this headli
 <li><p>Enable the more widespread use of Krotov’s method, for example in the design
 of experiments.</p></li>
 </ul>
-</div>
-<div class="section" id="prerequisites">
-<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="prerequisites">
+<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading">¶</a></h2>
 <p>The Krotov package is available for Python versions &gt;= 3.7. Its main dependency is <a class="reference external" href="http://qutip.org">QuTiP</a>
 (apart from the <a class="reference external" href="https://www.scipy.org">core packages of the Python scientific ecosystem</a>).
 Thus, you should consider <a class="reference external" href="http://qutip.org/docs/latest/installation.html">QuTiP’s installation instructions</a>.</p>
@@ -191,9 +197,9 @@ <h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to
 that do not provide compiled whels, and if you do not have the necessary
 compilers installed (Windows, macOS), the <a class="reference external" href="https://conda.io/docs/index.html">conda</a> package manager provides a
 good solution.</p>
-</div>
-<div class="section" id="installation">
-<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
 <p>To install the latest released version of <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> into your current (conda)
 environment, run this command in your terminal:</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>krotov
@@ -206,9 +212,9 @@ <h2>Installation<a class="headerlink" href="#installation" title="Permalink to t
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/qucontrol/krotov.git@master#egg<span class="o">=</span>krotov
 </pre></div>
 </div>
-</div>
-<div class="section" id="usage">
-<h2>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="usage">
+<h2>Usage<a class="headerlink" href="#usage" title="Link to this heading">¶</a></h2>
 <p>To use Krotov’s method for quantum optimal control in a Python script or
 <a class="reference external" href="https://jupyter.org">Jupyter notebook</a>, start with:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">krotov</span>
@@ -223,9 +229,9 @@ <h2>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">
 number of control fields over all the objectives.</p></li>
 </ol>
 <p>See <a class="reference internal" href="08_qutip_usage.html#using-krotov-with-qutip"><span class="std std-ref">Using Krotov with QuTiP</span></a> and <a class="reference internal" href="09_examples.html#krotov-example-notebooks"><span class="std std-ref">Examples</span></a> for details.</p>
-</div>
-<div class="section" id="citing-the-krotov-package">
-<span id="citingkrotov"></span><h2>Citing the Krotov Package<a class="headerlink" href="#citing-the-krotov-package" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="citing-the-krotov-package">
+<span id="citingkrotov"></span><h2>Citing the Krotov Package<a class="headerlink" href="#citing-the-krotov-package" title="Link to this heading">¶</a></h2>
 <div class="admonition attention">
 <p class="admonition-title">Attention</p>
 <p>Please cite the <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package as</p>
@@ -234,12 +240,12 @@ <h2>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">
 </ul>
 </div>
 <p>You can also print this from <code class="docutils literal notranslate"><span class="pre">krotov.__citation__</span></code>:</p>
-<div class="highlight-pycon3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">krotov</span><span class="o">.</span><span class="n">__citation__</span><span class="p">)</span>
+<div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">krotov</span><span class="o">.</span><span class="n">__citation__</span><span class="p">)</span>
 <span class="go">M. H. Goerz et al., Krotov: A Python implementation of Krotov&#39;s method for quantum optimal control, SciPost Phys. 7, 080 (2019)</span>
 </pre></div>
 </div>
 <p>The corresponding BibTeX entry is available in <code class="docutils literal notranslate"><span class="pre">krotov.__bibtex__</span></code>:</p>
-<div class="highlight-pycon3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">krotov</span><span class="o">.</span><span class="n">__bibtex__</span><span class="p">)</span>
+<div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">krotov</span><span class="o">.</span><span class="n">__bibtex__</span><span class="p">)</span>
 <span class="go">@article{GoerzSPP2019,</span>
 <span class="go">    author = {Michael H. Goerz and Daniel Basilewitsch and Fernando Gago-Encinas and Matthias G. Krauss and Karl P. Horn and Daniel M. Reich and Christiane P. Koch},</span>
 <span class="go">    title = {Krotov: A {Python} implementation of {Krotov&#39;s} method for quantum optimal control},</span>
@@ -251,8 +257,8 @@ <h2>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">
 <span class="go">}</span>
 </pre></div>
 </div>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -266,7 +272,7 @@ <h2>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/02_contributing.html b/master/02_contributing.html
index 1805788b..811f0ce3 100644
--- a/master/02_contributing.html
+++ b/master/02_contributing.html
@@ -1,26 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Contributing &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Contributing</a><ul>
@@ -159,18 +165,18 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="contributing">
-<h1>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline">¶</a></h1>
+  <section id="contributing">
+<h1>Contributing<a class="headerlink" href="#contributing" title="Link to this heading">¶</a></h1>
 <p>Contributions are welcome, and they are greatly appreciated! Every little bit
 helps, and credit will always be given.</p>
-<div class="section" id="code-of-conduct">
-<h2>Code of Conduct<a class="headerlink" href="#code-of-conduct" title="Permalink to this headline">¶</a></h2>
+<section id="code-of-conduct">
+<h2>Code of Conduct<a class="headerlink" href="#code-of-conduct" title="Link to this heading">¶</a></h2>
 <p>Everyone interacting in the <code class="docutils literal notranslate"><span class="pre">krotov</span></code> project’s code base,
 issue tracker, and any communication channels is expected to follow the
 <a class="reference external" href="https://www.pypa.io/en/latest/code-of-conduct/">PyPA Code of Conduct</a>.</p>
-</div>
-<div class="section" id="report-bugs">
-<h2>Report Bugs<a class="headerlink" href="#report-bugs" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="report-bugs">
+<h2>Report Bugs<a class="headerlink" href="#report-bugs" title="Link to this heading">¶</a></h2>
 <p>Report bugs at <a class="reference external" href="https://github.com/qucontrol/krotov/issues">https://github.com/qucontrol/krotov/issues</a>.</p>
 <p>If you are reporting a bug, please include:</p>
 <ul class="simple">
@@ -178,9 +184,9 @@ <h2>Report Bugs<a class="headerlink" href="#report-bugs" title="Permalink to thi
 <li><p>Any details about your local setup that might be helpful in troubleshooting.</p></li>
 <li><p>Detailed steps to reproduce the bug.</p></li>
 </ul>
-</div>
-<div class="section" id="submit-feedback">
-<h2>Submit Feedback<a class="headerlink" href="#submit-feedback" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="submit-feedback">
+<h2>Submit Feedback<a class="headerlink" href="#submit-feedback" title="Link to this heading">¶</a></h2>
 <p>The best way to send feedback is to file an issue at <a class="reference external" href="https://github.com/qucontrol/krotov/issues">https://github.com/qucontrol/krotov/issues</a>.</p>
 <p>If you are proposing a feature:</p>
 <ul class="simple">
@@ -189,9 +195,9 @@ <h2>Submit Feedback<a class="headerlink" href="#submit-feedback" title="Permalin
 <li><p>Remember that this is a volunteer-driven project, and that contributions
 are welcome :)</p></li>
 </ul>
-</div>
-<div class="section" id="pull-request-guidelines">
-<h2>Pull Request Guidelines<a class="headerlink" href="#pull-request-guidelines" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="pull-request-guidelines">
+<h2>Pull Request Guidelines<a class="headerlink" href="#pull-request-guidelines" title="Link to this heading">¶</a></h2>
 <p>Before you submit a pull request, check that it meets these guidelines:</p>
 <ol class="arabic simple">
 <li><p>The pull request should include tests.</p></li>
@@ -201,9 +207,9 @@ <h2>Pull Request Guidelines<a class="headerlink" href="#pull-request-guidelines"
 <li><p>Check <a class="reference external" href="https://github.com/qucontrol/krotov/actions">https://github.com/qucontrol/krotov/actions</a>
 and make sure that the tests pass for all supported Python versions.</p></li>
 </ol>
-</div>
-<div class="section" id="get-started">
-<h2>Get Started!<a class="headerlink" href="#get-started" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="get-started">
+<h2>Get Started!<a class="headerlink" href="#get-started" title="Link to this heading">¶</a></h2>
 <p>Ready to contribute? Follow <a class="reference external" href="https://www.asmeurer.com/git-workflow/">Aaron Meurer’s Git Workflow Notes</a> (with <code class="docutils literal notranslate"><span class="pre">qucontrol/krotov</span></code> instead of <code class="docutils literal notranslate"><span class="pre">sympy/sympy</span></code>)</p>
 <p>In short, if you are not a member of the <a class="reference external" href="https://github.com/qucontrol">qucontrol organization</a>,</p>
 <ol class="arabic simple">
@@ -229,17 +235,17 @@ <h2>Get Started!<a class="headerlink" href="#get-started" title="Permalink to th
 for development.</p>
 <p>If you are a member of the <a class="reference external" href="https://github.com/qucontrol">qucontrol organization</a>, there is no need to fork
 <code class="docutils literal notranslate"><span class="pre">krotov</span></code> - you can directly pull and push to <code class="docutils literal notranslate"><span class="pre">git&#64;github.com:qucontrol/krotov.git</span></code>.</p>
-</div>
-<div class="section" id="development-prerequisites">
-<span id="developmentprerequisites"></span><h2>Development Prerequisites<a class="headerlink" href="#development-prerequisites" title="Permalink to this headline">¶</a></h2>
-<p>Contributing to the package’s developments requires that you have Python 3.8
+</section>
+<section id="development-prerequisites">
+<span id="developmentprerequisites"></span><h2>Development Prerequisites<a class="headerlink" href="#development-prerequisites" title="Link to this heading">¶</a></h2>
+<p>Contributing to the package’s developments requires that you have Python 3.12
 and <a class="reference external" href="https://hatch.pypa.io/latest/">hatch</a> installed (tested with Hatch 1.11). It is strongly recommended that
 you also have installations of all other supported Python versions. The
 recommended way to install multiple versions of Python at the same time is
 through <a class="reference external" href="https://github.com/pyenv/pyenv">pyenv</a> (or <a class="reference external" href="https://github.com/pyenv-win/pyenv-win">pyenv-win</a> on Windows).</p>
-</div>
-<div class="section" id="branching-model">
-<span id="branchingmodel"></span><h2>Branching Model<a class="headerlink" href="#branching-model" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="branching-model">
+<span id="branchingmodel"></span><h2>Branching Model<a class="headerlink" href="#branching-model" title="Link to this heading">¶</a></h2>
 <p>For developers with direct access to the repository,
 <code class="docutils literal notranslate"><span class="pre">krotov</span></code> uses a simple branching model where all
 developments happens directly on the <code class="docutils literal notranslate"><span class="pre">master</span></code> branch. Releases are tags on
@@ -291,9 +297,9 @@ <h2>Get Started!<a class="headerlink" href="#get-started" title="Permalink to th
 creating a pull-request for merging the branch into <code class="docutils literal notranslate"><span class="pre">master</span></code> (follow the
 propmts on the Github website).</p>
 <p>Summarize the changes of the branch relative to <code class="docutils literal notranslate"><span class="pre">master</span></code> in the pull request.</p>
-</div>
-<div class="section" id="commit-message-guidelines">
-<h2>Commit Message Guidelines<a class="headerlink" href="#commit-message-guidelines" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="commit-message-guidelines">
+<h2>Commit Message Guidelines<a class="headerlink" href="#commit-message-guidelines" title="Link to this heading">¶</a></h2>
 <p>Write commit messages according to this template:</p>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Short (50 chars or less) summary (&quot;subject line&quot;)
 
@@ -323,9 +329,9 @@ <h2>Commit Message Guidelines<a class="headerlink" href="#commit-message-guideli
 </div>
 <p>See <a class="reference external" href="https://help.github.com/articles/closing-issues-using-keywords/">Closing issues using keywords</a> for details on references to issues that
 Github will understand.</p>
-</div>
-<div class="section" id="testing">
-<h2>Testing<a class="headerlink" href="#testing" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="testing">
+<h2>Testing<a class="headerlink" href="#testing" title="Link to this heading">¶</a></h2>
 <p>The Krotov package includes a full test-suite using <a class="reference external" href="https://docs.pytest.org/en/latest/">pytest</a>. We strive for a <a class="reference external" href="https://codecov.io/gh/qucontrol/krotov">test coverage</a> above 90%.</p>
 <p>From a checkout of the <code class="docutils literal notranslate"><span class="pre">krotov</span></code> repository, you can use</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span><span class="nb">test</span>
@@ -343,13 +349,13 @@ <h2>Testing<a class="headerlink" href="#testing" title="Permalink to this headli
 file (<code class="docutils literal notranslate"><span class="pre">*.rst</span></code>) are picked up (by the <a class="reference external" href="https://docs.pytest.org/en/latest/doctest.html">pytest doctest plugin</a>). Lastly, all
 <a class="reference internal" href="#contributeexamples"><span class="std std-ref">example notebooks</span></a> are validated as a test, through
 the <a class="reference external" href="https://nbval.readthedocs.io/en/latest/">nbval plugin</a>.</p>
-</div>
-<div class="section" id="code-style">
-<h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this headline">¶</a></h2>
-<p>All code must be compatible with <span class="target" id="index-0"></span><a class="pep reference external" href="https://www.python.org/dev/peps/pep-0008"><strong>PEP 8</strong></a>. The line length limit
+</section>
+<section id="code-style">
+<h2>Code Style<a class="headerlink" href="#code-style" title="Link to this heading">¶</a></h2>
+<p>All code must be compatible with <span class="target" id="index-0"></span><a class="pep reference external" href="https://peps.python.org/pep-0008/"><strong>PEP 8</strong></a>. The line length limit
 is 79 characters, although exceptions are permissible if this improves
 readability significantly.</p>
-<p>Beyond <span class="target" id="index-1"></span><a class="pep reference external" href="https://www.python.org/dev/peps/pep-0008"><strong>PEP 8</strong></a>, this project adopts the <a class="reference external" href="https://github.com/ambv/black/#the-black-code-style">Black code style</a>, with
+<p>Beyond <span class="target" id="index-1"></span><a class="pep reference external" href="https://peps.python.org/pep-0008/"><strong>PEP 8</strong></a>, this project adopts the <a class="reference external" href="https://github.com/ambv/black/#the-black-code-style">Black code style</a>, with
 <code class="docutils literal notranslate"><span class="pre">--skip-string-normalization</span> <span class="pre">--line-length</span> <span class="pre">79</span></code>. You can
 run <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">black-check</span></code> or <code class="docutils literal notranslate"><span class="pre">hatch</span> <span class="pre">run</span> <span class="pre">lint:black-check</span></code> to check adherence to the
 code style, and <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">black</span></code> or <code class="docutils literal notranslate"><span class="pre">hatch</span> <span class="pre">run</span> <span class="pre">lint:black</span></code> to apply it.</p>
@@ -372,9 +378,9 @@ <h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this
 <a class="reference external" href="http://flake8.pycqa.org">flake8</a> and <a class="reference external" href="http://pylint.pycqa.org">pylint</a>, but there is no strict requirement for a perfect score
 with either one of these linters. They only serve as a guideline for code that
 might be improved.</p>
-</div>
-<div class="section" id="write-documentation">
-<span id="id1"></span><h2>Write Documentation<a class="headerlink" href="#write-documentation" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="write-documentation">
+<span id="id1"></span><h2>Write Documentation<a class="headerlink" href="#write-documentation" title="Link to this heading">¶</a></h2>
 <p>The <code class="docutils literal notranslate"><span class="pre">krotov</span></code> package could always use more documentation, whether
 as part of the official docs, in docstrings, or even on the web in blog posts,
 articles, and such.</p>
@@ -418,9 +424,9 @@ <h2>Code Style<a class="headerlink" href="#code-style" title="Permalink to this
 </pre></div>
 </div>
 <p>to generate the documentation locally.</p>
-</div>
-<div class="section" id="deploy-the-documentation">
-<h2>Deploy the documentation<a class="headerlink" href="#deploy-the-documentation" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="deploy-the-documentation">
+<h2>Deploy the documentation<a class="headerlink" href="#deploy-the-documentation" title="Link to this heading">¶</a></h2>
 <p>The documentation is automatically deployed to
 <a class="reference external" href="https://qucontrol.github.io/krotov/">https://qucontrol.github.io/krotov/</a> (the <a class="reference external" href="https://pages.github.com">gh-pages</a> associated with the
 <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package’s Github repository) every time commits are pushed to
@@ -434,9 +440,9 @@ <h2>Deploy the documentation<a class="headerlink" href="#deploy-the-documentatio
 branch will be deployed to the online documentation. Do not routinely perform
 manual edits on the <code class="docutils literal notranslate"><span class="pre">gh-pages</span></code> branch! Let Github Actions do its job of
 automatically deploying documentation instead.</p>
-</div>
-<div class="section" id="contribute-examples">
-<span id="contributeexamples"></span><h2>Contribute Examples<a class="headerlink" href="#contribute-examples" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="contribute-examples">
+<span id="contributeexamples"></span><h2>Contribute Examples<a class="headerlink" href="#contribute-examples" title="Link to this heading">¶</a></h2>
 <p>Examples should be contributed in the form of <a class="reference external" href="https://jupyter.readthedocs.io/en/latest/index.html">Jupyter notebooks</a>.</p>
 <p>Example notebooks are automatically rendered as part of the documentation
 (<a class="reference internal" href="09_examples.html#krotov-example-notebooks"><span class="std std-ref">Examples</span></a>), and they are also verified by the automated
@@ -480,8 +486,8 @@ <h2>Deploy the documentation<a class="headerlink" href="#deploy-the-documentatio
 </div></blockquote>
 </li>
 </ul>
-<div class="section" id="math-in-example-notebooks">
-<span id="id2"></span><h3>Math in Example Notebooks<a class="headerlink" href="#math-in-example-notebooks" title="Permalink to this headline">¶</a></h3>
+<section id="math-in-example-notebooks">
+<span id="id2"></span><h3>Math in Example Notebooks<a class="headerlink" href="#math-in-example-notebooks" title="Link to this heading">¶</a></h3>
 <p>You may use the same tex macros described in the <a class="reference internal" href="#write-documentation"><span class="std std-ref">Write Documentation</span></a> section.
 However, for the macros to work when viewing the notebook by itself, they must
 be redefined locally. To this end, add a markdown cell underneath the top cell
@@ -518,12 +524,12 @@ <h2>Deploy the documentation<a class="headerlink" href="#deploy-the-documentatio
 </div>
 <p>Upon executing the cell the definitions will be hidden, but the defined macros
 will be available in any cell in the rest of the notebook.</p>
-</div>
-</div>
-<div class="section" id="versioning">
-<h2>Versioning<a class="headerlink" href="#versioning" title="Permalink to this headline">¶</a></h2>
+</section>
+</section>
+<section id="versioning">
+<h2>Versioning<a class="headerlink" href="#versioning" title="Link to this heading">¶</a></h2>
 <p>Releases should follow <a class="reference external" href="https://semver.org">Semantic Versioning</a>, and version numbers published to
-<a class="reference external" href="http://pypi.org">PyPI</a> must be compatible with <span class="target" id="index-2"></span><a class="pep reference external" href="https://www.python.org/dev/peps/pep-0440"><strong>PEP 440</strong></a>.</p>
+<a class="reference external" href="http://pypi.org">PyPI</a> must be compatible with <span class="target" id="index-2"></span><a class="pep reference external" href="https://peps.python.org/pep-0440/"><strong>PEP 440</strong></a>.</p>
 <p>In short, versions number follow the pattern <cite>major.minor.patch</cite>, e.g.
 <code class="docutils literal notranslate"><span class="pre">0.1.0</span></code> for the first release, and <code class="docutils literal notranslate"><span class="pre">1.0.0</span></code> for the first <em>stable</em> release.
 If necessary, pre-release versions might be published as e.g:</p>
@@ -540,7 +546,7 @@ <h2>Versioning<a class="headerlink" href="#versioning" title="Permalink to this
 they are not supported by the <a class="reference external" href="https://semver.org">Semantic Versioning</a> specification.</p>
 <p>The current version is available through the <code class="docutils literal notranslate"><span class="pre">__version__</span></code> attribute of the
 <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package:</p>
-<div class="highlight-pycon3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">krotov</span>
+<div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">krotov</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">krotov</span><span class="o">.</span><span class="n">__version__</span>   
 </pre></div>
 </div>
@@ -551,13 +557,13 @@ <h2>Versioning<a class="headerlink" href="#versioning" title="Permalink to this
 The version string “1.0.0-dev1+dev” is a valid value after the “1.0.0-dev1”
 pre-release. The “+dev” suffix must never be included in a release to <a class="reference external" href="http://pypi.org">PyPI</a>.</p>
 <p>Note that <a class="reference external" href="https://twine.readthedocs.io/en/latest/">twine</a> applies <a class="reference external" href="https://legacy.python.org/dev/peps/pep-0440/#id29">normalization</a> to the above recommended forms to
-make them strictly compatible with <span class="target" id="index-3"></span><a class="pep reference external" href="https://www.python.org/dev/peps/pep-0440"><strong>PEP 440</strong></a>, before uploading to <a class="reference external" href="http://pypi.org">PyPI</a>. Users
+make them strictly compatible with <span class="target" id="index-3"></span><a class="pep reference external" href="https://peps.python.org/pep-0440/"><strong>PEP 440</strong></a>, before uploading to <a class="reference external" href="http://pypi.org">PyPI</a>. Users
 installing the package through <a class="reference external" href="https://pip.readthedocs.io/en/stable/">pip</a> may use the original version specification
 as well as the normalized one (or any other variation that normalizes to the
 same result).</p>
-</div>
-<div class="section" id="making-a-release">
-<h2>Making a Release<a class="headerlink" href="#making-a-release" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="making-a-release">
+<h2>Making a Release<a class="headerlink" href="#making-a-release" title="Link to this heading">¶</a></h2>
 <p>Relesases can only be made by administrators of the Krotov Github repo who are
 also listed as Maintainers on <a class="reference external" href="https://pypi.org/project/krotov/">https://pypi.org/project/krotov/</a>.</p>
 <p>They must have GPG set up to allow for signed commits, and be able to locally
@@ -572,13 +578,13 @@ <h2>Making a Release<a class="headerlink" href="#making-a-release" title="Permal
 after pushing the tag, an official Github-release must be created manually at
 <a class="reference external" href="https://github.com/qucontrol/krotov/releases">https://github.com/qucontrol/krotov/releases</a>, with the proper release notes and
 the documentation artifacts as binary attachments.</p>
-</div>
-<div class="section" id="developers-how-tos">
-<h2>Developers’ How-Tos<a class="headerlink" href="#developers-how-tos" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="developers-how-tos">
+<h2>Developers’ How-Tos<a class="headerlink" href="#developers-how-tos" title="Link to this heading">¶</a></h2>
 <p>The following assumes your current working directory is a checkout of
 <code class="docutils literal notranslate"><span class="pre">krotov</span></code>, and that have <code class="docutils literal notranslate"><span class="pre">hatch</span></code> installed on your system.</p>
-<div class="section" id="how-to-run-a-jupyter-notebook-server-for-working-on-the-example-notebooks">
-<h3>How to run a jupyter notebook server for working on the example notebooks<a class="headerlink" href="#how-to-run-a-jupyter-notebook-server-for-working-on-the-example-notebooks" title="Permalink to this headline">¶</a></h3>
+<section id="how-to-run-a-jupyter-notebook-server-for-working-on-the-example-notebooks">
+<h3>How to run a jupyter notebook server for working on the example notebooks<a class="headerlink" href="#how-to-run-a-jupyter-notebook-server-for-working-on-the-example-notebooks" title="Link to this heading">¶</a></h3>
 <p>A notebook server that is isolated to the proper testing environment can be started via the Makefile:</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>jupyter-notebook
 </pre></div>
@@ -596,9 +602,9 @@ <h3>How to run a jupyter notebook server for working on the example notebooks<a
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>jupyter-lab
 </pre></div>
 </div>
-</div>
-<div class="section" id="how-to-convert-an-example-notebook-to-a-script-for-easier-debugging">
-<h3>How to convert an example notebook to a script for easier debugging<a class="headerlink" href="#how-to-convert-an-example-notebook-to-a-script-for-easier-debugging" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-convert-an-example-notebook-to-a-script-for-easier-debugging">
+<h3>How to convert an example notebook to a script for easier debugging<a class="headerlink" href="#how-to-convert-an-example-notebook-to-a-script-for-easier-debugging" title="Link to this heading">¶</a></h3>
 <p>Interactive debugging in notebooks is difficult. It becomes much easier if
 you convert the notebook to a script first.  To convert a notebook to an
 (I)Python script and run it with automatic debugging, execute e.g.:</p>
@@ -610,13 +616,13 @@ <h3>How to convert an example notebook to a script for easier debugging<a class=
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.terminal.debugger</span> <span class="kn">import</span> <span class="n">set_trace</span><span class="p">;</span> <span class="n">set_trace</span><span class="p">()</span> <span class="c1"># DEBUG</span>
 </pre></div>
 </div>
-</div>
-<div class="section" id="how-to-make-git-diff-work-for-notebooks">
-<h3>How to make <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">diff</span></code> work for notebooks<a class="headerlink" href="#how-to-make-git-diff-work-for-notebooks" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-make-git-diff-work-for-notebooks">
+<h3>How to make <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">diff</span></code> work for notebooks<a class="headerlink" href="#how-to-make-git-diff-work-for-notebooks" title="Link to this heading">¶</a></h3>
 <p>Install <a class="reference external" href="https://nbdime.readthedocs.io/en/latest/index.html">nbdime</a> and run <code class="docutils literal notranslate"><span class="pre">nbdime</span> <span class="pre">config-git</span> <span class="pre">--enable</span> <span class="pre">--global</span></code> to <a class="reference external" href="https://nbdime.readthedocs.io/en/latest/index.html#git-integration-quickstart">enable the git integration</a>.</p>
-</div>
-<div class="section" id="how-to-commit-failing-tests-or-example-notebooks">
-<h3>How to commit failing tests or example notebooks<a class="headerlink" href="#how-to-commit-failing-tests-or-example-notebooks" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-commit-failing-tests-or-example-notebooks">
+<h3>How to commit failing tests or example notebooks<a class="headerlink" href="#how-to-commit-failing-tests-or-example-notebooks" title="Link to this heading">¶</a></h3>
 <p>The test-suite on the <code class="docutils literal notranslate"><span class="pre">master</span></code> branch should always pass without error. If you
 would like to commit any example notebooks or tests that currently fail, as a
 form of <a class="reference external" href="https://en.wikipedia.org/wiki/Test-driven_development">test-driven development</a>, you have two options:</p>
@@ -642,9 +648,9 @@ <h3>How to commit failing tests or example notebooks<a class="headerlink" href="
 See the <a class="reference external" href="https://nbval.readthedocs.io/en/latest/#Skipping-specific-cells">documentation of the nbval pluging on skipping and exceptions</a> for details.</p>
 </li>
 </ul>
-</div>
-<div class="section" id="how-to-run-a-subset-of-tests">
-<h3>How to run a subset of tests<a class="headerlink" href="#how-to-run-a-subset-of-tests" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-run-a-subset-of-tests">
+<h3>How to run a subset of tests<a class="headerlink" href="#how-to-run-a-subset-of-tests" title="Link to this heading">¶</a></h3>
 <p>To run e.g. only the tests defined in <code class="docutils literal notranslate"><span class="pre">tests/test_krotov.py</span></code>, use any of the following:</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span><span class="nb">test</span><span class="w"> </span><span class="nv">TESTS</span><span class="o">=</span>tests/test_krotov.py
 
@@ -656,24 +662,24 @@ <h3>How to run a subset of tests<a class="headerlink" href="#how-to-run-a-subset
 <p>See the <a class="reference external" href="https://docs.pytest.org/en/latest/usage.html#specifying-tests-selecting-tests">pytest test selection docs</a> for details. The <code class="docutils literal notranslate"><span class="pre">-e</span> <span class="pre">py38</span></code> selects the
 Python version to run the tests under. You may replace this with an equivalent
 name for any supported Python versions.</p>
-</div>
-<div class="section" id="how-to-run-only-as-single-test">
-<h3>How to run only as single test<a class="headerlink" href="#how-to-run-only-as-single-test" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-run-only-as-single-test">
+<h3>How to run only as single test<a class="headerlink" href="#how-to-run-only-as-single-test" title="Link to this heading">¶</a></h3>
 <p>Decorate the test with e.g. <code class="docutils literal notranslate"><span class="pre">&#64;pytest.mark.xxx</span></code>, and then run, e.g:</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>hatch<span class="w"> </span>run<span class="w"> </span>--<span class="w"> </span>pytest<span class="w"> </span>-m<span class="w"> </span>xxx<span class="w"> </span>tests/
 </pre></div>
 </div>
 <p>See the <a class="reference external" href="https://docs.pytest.org/en/latest/example/markers.html">pytest documentation on markers</a> for details.</p>
-</div>
-<div class="section" id="how-to-run-only-the-doctests">
-<h3>How to run only the doctests<a class="headerlink" href="#how-to-run-only-the-doctests" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-run-only-the-doctests">
+<h3>How to run only the doctests<a class="headerlink" href="#how-to-run-only-the-doctests" title="Link to this heading">¶</a></h3>
 <p>Run the following:</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>hatch<span class="w"> </span>run<span class="w"> </span>--<span class="w"> </span>pytest<span class="w"> </span>--doctest-modules<span class="w"> </span>src
 </pre></div>
 </div>
-</div>
-<div class="section" id="how-to-go-into-an-interactive-debugger">
-<h3>How to go into an interactive debugger<a class="headerlink" href="#how-to-go-into-an-interactive-debugger" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-go-into-an-interactive-debugger">
+<h3>How to go into an interactive debugger<a class="headerlink" href="#how-to-go-into-an-interactive-debugger" title="Link to this heading">¶</a></h3>
 <p>Optionally, install the <cite>pdbpp</cite> package into the testing environment, for a
 better experience:</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>hatch<span class="w"> </span>run<span class="w"> </span>--<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>pdbpp
@@ -693,9 +699,9 @@ <h3>How to go into an interactive debugger<a class="headerlink" href="#how-to-go
 </li>
 </ul>
 <p>You may also see the <a class="reference external" href="https://docs.pytest.org/en/latest/usage.html#dropping-to-pdb-python-debugger-on-failures">pytest documentation on automatic debugging</a>.</p>
-</div>
-<div class="section" id="how-to-see-the-debug-logger-output-in-the-example-notebooks">
-<h3>How to see the debug logger output in the example notebooks<a class="headerlink" href="#how-to-see-the-debug-logger-output-in-the-example-notebooks" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-see-the-debug-logger-output-in-the-example-notebooks">
+<h3>How to see the debug logger output in the example notebooks<a class="headerlink" href="#how-to-see-the-debug-logger-output-in-the-example-notebooks" title="Link to this heading">¶</a></h3>
 <p>The <a class="reference internal" href="API/krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> routine generates some logger messages for
 debugging purposes. To see these messages, set the level of “krotov” logger to
 INFO or DEBUG:</p>
@@ -715,13 +721,13 @@ <h3>How to see the debug logger output in the example notebooks<a class="headerl
 </pre></div>
 </div>
 <p>See the <a class="reference external" href="https://docs.python.org/3/howto/logging.html#configuring-logging">Configure Logging</a> section of the Python documentation for more details.</p>
-</div>
-<div class="section" id="how-to-use-quantum-mechanical-tex-macros">
-<h3>How to use quantum mechanical tex macros<a class="headerlink" href="#how-to-use-quantum-mechanical-tex-macros" title="Permalink to this headline">¶</a></h3>
+</section>
+<section id="how-to-use-quantum-mechanical-tex-macros">
+<h3>How to use quantum mechanical tex macros<a class="headerlink" href="#how-to-use-quantum-mechanical-tex-macros" title="Link to this heading">¶</a></h3>
 <p>For docstrings or <code class="docutils literal notranslate"><span class="pre">*.rst</span></code> files, see <a class="reference internal" href="#write-documentation"><span class="std std-ref">Write Documentation</span></a>. For notebooks, see <a class="reference internal" href="#math-in-example-notebooks"><span class="std std-ref">Math in Example Notebooks</span></a>.</p>
-</div>
-</div>
-</div>
+</section>
+</section>
+</section>
 
 
            </div>
@@ -735,7 +741,7 @@ <h3>How to use quantum mechanical tex macros<a class="headerlink" href="#how-to-
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/03_authors.html b/master/03_authors.html
index 89cdf1a9..92cd412e 100644
--- a/master/03_authors.html
+++ b/master/03_authors.html
@@ -1,23 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Credits &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
+  <title>Credits &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/docs-versions-menu.js"></script>
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="_static/copybutton.js?v=f281be69"></script>
+        <script src="_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -38,7 +44,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -48,7 +54,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -129,16 +135,16 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="credits">
-<h1>Credits<a class="headerlink" href="#credits" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="development-lead">
-<h2>Development Lead<a class="headerlink" href="#development-lead" title="Permalink to this headline">¶</a></h2>
+  <section id="credits">
+<h1>Credits<a class="headerlink" href="#credits" title="Link to this heading">¶</a></h1>
+<section id="development-lead">
+<h2>Development Lead<a class="headerlink" href="#development-lead" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://michaelgoerz.net">Michael Goerz</a> &lt;<a class="reference external" href="mailto:mail&#37;&#52;&#48;michaelgoerz&#46;net">mail<span>&#64;</span>michaelgoerz<span>&#46;</span>net</a>&gt;</p></li>
 </ul>
-</div>
-<div class="section" id="development-team">
-<h2>Development Team<a class="headerlink" href="#development-team" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="development-team">
+<h2>Development Team<a class="headerlink" href="#development-team" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Daniel Basilewitsch &lt;<a class="reference external" href="mailto:daniel&#46;basilewitsch&#37;&#52;&#48;uibk&#46;ac&#46;at">daniel<span>&#46;</span>basilewitsch<span>&#64;</span>uibk<span>&#46;</span>ac<span>&#46;</span>at</a>&gt;</p></li>
 <li><p>Fernando Gago Encinas &lt;<a class="reference external" href="mailto:fernando&#46;gago&#37;&#52;&#48;fu-berlin&#46;de">fernando<span>&#46;</span>gago<span>&#64;</span>fu-berlin<span>&#46;</span>de</a>&gt;</p></li>
@@ -147,9 +153,9 @@ <h2>Development Team<a class="headerlink" href="#development-team" title="Permal
 <li><p><a class="reference external" href="https://www.physik.fu-berlin.de/en/einrichtungen/ag/ag-koch/Mitarbeiterinnen/reich/index.html">Daniel Reich</a> &lt;<a class="reference external" href="mailto:danreich&#37;&#52;&#48;zedat&#46;fu-berlin&#46;de">danreich<span>&#64;</span>zedat<span>&#46;</span>fu-berlin<span>&#46;</span>de</a>&gt;</p></li>
 <li><p><a class="reference external" href="https://www.physik.fu-berlin.de/en/einrichtungen/ag/ag-koch/index.html">Christiane Koch</a> &lt;<a class="reference external" href="mailto:christiane&#46;koch&#37;&#52;&#48;fu-berlin&#46;de">christiane<span>&#46;</span>koch<span>&#64;</span>fu-berlin<span>&#46;</span>de</a>&gt;</p></li>
 </ul>
-</div>
-<div class="section" id="acknowledgements">
-<h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="acknowledgements">
+<h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Link to this heading">¶</a></h2>
 <p>We thank
 <a class="reference external" href="https://www.bifi.es/~acastro/">Alberto Castro</a>,
 <a class="reference external" href="https://github.com/daviehh">&#64;daviehh</a>,
@@ -160,8 +166,8 @@ <h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permal
 <a class="reference external" href="https://github.com/uiofgh">&#64;uiofgh</a>, and
 <a class="reference external" href="https://github.com/zachmanson">&#64;zachmanson</a>,
 for reporting bugs and suggesting improvements.</p>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -175,7 +181,7 @@ <h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permal
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/04_features.html b/master/04_features.html
index be9efcfa..070ef5a2 100644
--- a/master/04_features.html
+++ b/master/04_features.html
@@ -1,23 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Features &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
+  <title>Features &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="_static/mycss.css" type="text/css" />
+
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-  <script id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
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-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/docs-versions-menu.js"></script>
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="_static/copybutton.js?v=f281be69"></script>
+        <script src="_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -38,7 +44,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -48,7 +54,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -124,8 +130,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="features">
-<h1>Features<a class="headerlink" href="#features" title="Permalink to this headline">¶</a></h1>
+  <section id="features">
+<h1>Features<a class="headerlink" href="#features" title="Link to this heading">¶</a></h1>
 <ul class="simple">
 <li><p>Simultaneously optimize over an arbitrary list of objectives</p></li>
 <li><p>Optimize over multiple control fields at the same time</p></li>
@@ -144,7 +150,7 @@ <h1>Features<a class="headerlink" href="#features" title="Permalink to this head
 <li><p>non-linear controls</p></li>
 <li><p>state-dependent constraints</p></li>
 </ul>
-</div>
+</section>
 
 
            </div>
@@ -158,7 +164,7 @@ <h1>Features<a class="headerlink" href="#features" title="Permalink to this head
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/05_history.html b/master/05_history.html
index 7fe569c7..07707dc3 100644
--- a/master/05_history.html
+++ b/master/05_history.html
@@ -1,23 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>History &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
+  <title>History &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
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     <script src="_static/js/html5shiv.min.js"></script>
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-        <script src="_static/jquery.js"></script>
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-        <script src="_static/doctools.js"></script>
-        <script src="_static/clipboard.min.js"></script>
-        <script src="_static/copybutton.js"></script>
-        <script src="_static/docs-versions-menu.js"></script>
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="_static/copybutton.js?v=f281be69"></script>
+        <script src="_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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     <link rel="index" title="Index" href="genindex.html" />
@@ -38,7 +44,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -48,13 +54,14 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="03_authors.html">Credits</a></li>
 <li class="toctree-l1"><a class="reference internal" href="04_features.html">Features</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">History</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#dev-unreleased">2.0.0-dev (unreleased)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#id1">1.3.0 (2024-06-03)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#id2">1.2.1 (2021-01-13)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#id3">1.2.0 (2020-08-17)</a></li>
@@ -137,10 +144,16 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="history">
-<h1>History<a class="headerlink" href="#history" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="id1">
-<h2>1.3.0 (2024-06-03)<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+  <section id="history">
+<h1>History<a class="headerlink" href="#history" title="Link to this heading">¶</a></h1>
+<section id="dev-unreleased">
+<h2>2.0.0-dev (unreleased)<a class="headerlink" href="#dev-unreleased" title="Link to this heading">¶</a></h2>
+<ul class="simple">
+<li><p>Changed: minimum supported Python version is Python 3.10 (drop support for Python 3.7-3.9)</p></li>
+</ul>
+</section>
+<section id="id1">
+<h2>1.3.0 (2024-06-03)<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Bugfix: <cite>∫gₐdt</cite> and total functional were reported incorrectly (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/96">#96</a>, thanks to <a class="reference external" href="https://github.com/daviehh">&#64;daviehh</a>)</p></li>
 <li><p>Bugfix: Restrict <code class="docutils literal notranslate"><span class="pre">qutip</span></code> to version 4. Support for <code class="docutils literal notranslate"><span class="pre">qutip</span> <span class="pre">5.0</span></code> will be added in a later release</p></li>
@@ -148,26 +161,26 @@ <h2>1.3.0 (2024-06-03)<a class="headerlink" href="#id1" title="Permalink to this
 <li><p>Changed: The documentation will no longer be provided in PDF format</p></li>
 <li><p>Changed: Development is now organized around <a class="reference external" href="https://hatch.pypa.io/latest/">hatch</a> instead of <a class="reference external" href="https://tox.wiki/en/latest/">tox</a></p></li>
 </ul>
-</div>
-<div class="section" id="id2">
-<h2>1.2.1 (2021-01-13)<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id2">
+<h2>1.2.1 (2021-01-13)<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Bugfix: Crash when initializing discretized numpy-array controls (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/79">#79</a>, thanks to <a class="reference external" href="https://github.com/loganbvh">&#64;loganbvh</a>)</p></li>
 <li><p>Bugfix: Corrected definition of co-states in Dissipative Qubit Reset example (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/80">#80</a>, thanks to <a class="reference external" href="https://www.bifi.es/~acastro/">Alberto Castro</a>)</p></li>
 <li><p>Update: Switched Testing and Documentation deployment from Travis to Github Actions (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/82">#82</a>)</p></li>
 </ul>
-</div>
-<div class="section" id="id3">
-<h2>1.2.0 (2020-08-17)<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id3">
+<h2>1.2.0 (2020-08-17)<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added: <code class="docutils literal notranslate"><span class="pre">via_midpoints</span></code> argument to <code class="docutils literal notranslate"><span class="pre">krotov.conversions.discretize</span></code> function</p></li>
 <li><p>Changed: Controls and update shapes are now discretized in a way that ensures numerical stability (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/74">#74</a>, thanks to <a class="reference external" href="https://github.com/zachmanson">&#64;zachmanson</a>)</p></li>
 <li><p>Changed: Replaced <code class="docutils literal notranslate"><span class="pre">uniseg</span></code> dependency with <code class="docutils literal notranslate"><span class="pre">grapheme</span></code> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/76">#76</a>)</p></li>
 </ul>
 <p>Note: due to the changes in the time discretization of the controls and update shapes, this version will generally not reproduce optimization results from previous versions to machine precision.</p>
-</div>
-<div class="section" id="id4">
-<h2>1.1.0 (2020-03-24)<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id4">
+<h2>1.1.0 (2020-03-24)<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added: Support for Python 3.8</p></li>
 <li><p>Added: Support for QuTiP 4.5.0</p></li>
@@ -178,9 +191,9 @@ <h2>1.1.0 (2020-03-24)<a class="headerlink" href="#id4" title="Permalink to this
 <li><p>Added: <cite>limit_thread_pool</cite> option to <code class="docutils literal notranslate"><span class="pre">krotov.optimize_pulses</span></code></p></li>
 <li><p>Changed: <code class="docutils literal notranslate"><span class="pre">krotov.propagators.expm</span></code> now guarantees single-threaded execution</p></li>
 </ul>
-</div>
-<div class="section" id="id5">
-<h2>1.0.0 (2019-12-16)<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id5">
+<h2>1.0.0 (2019-12-16)<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Update: Citation info now points to <a class="reference external" href="https://scipost.org/SciPostPhys.7.6.080">SciPost paper</a> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/61">#61</a>)</p></li>
 <li><p>Added: parameters <cite>col_formats</cite> and <cite>col_headers</cite> to customize the output of <code class="docutils literal notranslate"><span class="pre">krotov.info_hooks.print_table</span></code> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/65">#65</a>)</p></li>
@@ -189,9 +202,9 @@ <h2>1.0.0 (2019-12-16)<a class="headerlink" href="#id5" title="Permalink to this
 <li><p>Added: “Related Software” in the documentation</p></li>
 <li><p>Update: Documentation is now hosted on <a class="reference external" href="https://qucontrol.github.io/krotov">gh-pages</a> and deployed by <a class="reference external" href="https://drdoctr.github.io">Doctr</a> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/68">#68</a>)</p></li>
 </ul>
-</div>
-<div class="section" id="id6">
-<h2>0.5.0 (2019-12-04)<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id6">
+<h2>0.5.0 (2019-12-04)<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Update: Documentation now contains all information from <a class="reference external" href="https://arxiv.org/abs/1902.11284v5">https://arxiv.org/abs/1902.11284v5</a></p></li>
 <li><p>Added: Allow to pass <cite>args</cite> to time-dependent control functions (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/56">#56</a>, thanks to <a class="reference external" href="https://github.com/timohillmann">&#64;timohillmann</a>)</p></li>
@@ -200,16 +213,16 @@ <h2>0.5.0 (2019-12-04)<a class="headerlink" href="#id6" title="Permalink to this
 <li><p>Added: <code class="docutils literal notranslate"><span class="pre">krotov.result.Result</span></code> is now exposed at the top level of the API, as <code class="docutils literal notranslate"><span class="pre">krotov.Result</span></code> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/59">#59</a>, thanks to <a class="reference external" href="https://github.com/nathanshammah">&#64;nathanshammah</a>)</p></li>
 <li><p>Added: str-representation of <code class="docutils literal notranslate"><span class="pre">krotov.result.Result</span></code> now includes the total running time (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/60">#60</a>, thanks to <a class="reference external" href="https://github.com/nathanshammah">&#64;nathanshammah</a>)</p></li>
 </ul>
-</div>
-<div class="section" id="id7">
-<h2>0.4.1 (2019-10-11)<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id7">
+<h2>0.4.1 (2019-10-11)<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Update: Documentation now contains all information from <a class="reference external" href="https://arxiv.org/abs/1902.11284v4">https://arxiv.org/abs/1902.11284v4</a> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/54">#54</a>)</p></li>
 <li><p>Added: a PDF of the documentation is now available at <a class="reference external" href="https://github.com/qucontrol/krotov/tree/master/docs/pdf">https://github.com/qucontrol/krotov/tree/master/docs/pdf</a> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/42">#52</a>, thanks to <a class="reference external" href="https://github.com/TejasAvinashShetty">&#64;TejasAvinashShetty</a>)</p></li>
 </ul>
-</div>
-<div class="section" id="id8">
-<h2>0.4.0 (2019-10-08)<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id8">
+<h2>0.4.0 (2019-10-08)<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added: Support for Python 3.7</p></li>
 <li><p>Changed: The <code class="docutils literal notranslate"><span class="pre">'shape'</span></code> key in <code class="docutils literal notranslate"><span class="pre">pulse_options</span></code> was renamed to <code class="docutils literal notranslate"><span class="pre">'update_shape'</span></code>, to further avoid confusion between pulse shapes and update shapes.</p></li>
@@ -227,9 +240,9 @@ <h2>0.4.0 (2019-10-08)<a class="headerlink" href="#id8" title="Permalink to this
 <li><p>Bugfix: Correctly calculate ∂H/∂ϵ=0 if the specific ϵ currently being updated does not occur in H (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/48">#48</a>)</p></li>
 <li><p>Added: Method <code class="docutils literal notranslate"><span class="pre">objectives_with_controls</span></code> for <code class="docutils literal notranslate"><span class="pre">Result</span></code> object.</p></li>
 </ul>
-</div>
-<div class="section" id="id9">
-<h2>0.3.0 (2019-03-01)<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id9">
+<h2>0.3.0 (2019-03-01)<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added: Preprint citation information (<code class="docutils literal notranslate"><span class="pre">krotov.__arxiv__</span></code>, <code class="docutils literal notranslate"><span class="pre">krotov.__citation__</span></code>, <code class="docutils literal notranslate"><span class="pre">krotov.__bibtex__</span></code>)</p></li>
 <li><p>Added: Ability to continue from a previous optimization (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/26">#26</a>)</p></li>
@@ -242,9 +255,9 @@ <h2>0.3.0 (2019-03-01)<a class="headerlink" href="#id9" title="Permalink to this
 <li><p>Bugfix: Interface for passing <code class="docutils literal notranslate"><span class="pre">tau_vals</span></code> to <code class="docutils literal notranslate"><span class="pre">chi_constructor</span></code> (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/36">#36</a>)</p></li>
 <li><p>Added: function <code class="docutils literal notranslate"><span class="pre">above_value</span></code> for convergence check (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/35">#35</a>)</p></li>
 </ul>
-</div>
-<div class="section" id="id10">
-<h2>0.2.0 (2019-02-14)<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id10">
+<h2>0.2.0 (2019-02-14)<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added: Implementation of all the standard functionals</p></li>
 <li><p>Added: The <code class="docutils literal notranslate"><span class="pre">info_hook</span></code> receives additional information, including ∫gₐ(t)dt (<a class="reference external" href="https://github.com/qucontrol/krotov/issues/32">#32</a>)</p></li>
@@ -255,15 +268,15 @@ <h2>0.2.0 (2019-02-14)<a class="headerlink" href="#id10" title="Permalink to thi
 <li><p>Added: Support for the second-order pulse update</p></li>
 <li><p>Changed: The options for the controls (λₐ, update-shape) are now passed through a simplified <code class="docutils literal notranslate"><span class="pre">dict</span></code> interface, instead of a custom <code class="docutils literal notranslate"><span class="pre">PulseOptions</span></code> class.</p></li>
 </ul>
-</div>
-<div class="section" id="id11">
-<h2>0.1.0 (2018-12-24)<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="id11">
+<h2>0.1.0 (2018-12-24)<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Initial release with complete implementation of first-order Krotov’s method</p></li>
 <li><p>Support for state-to-state and gate optimization, for both closed and open systems</p></li>
 </ul>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -277,7 +290,7 @@ <h2>0.1.0 (2018-12-24)<a class="headerlink" href="#id11" title="Permalink to thi
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/06_introduction.html b/master/06_introduction.html
index ba099a16..9ff523f0 100644
--- a/master/06_introduction.html
+++ b/master/06_introduction.html
@@ -1,23 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
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@@ -38,7 +44,7 @@
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           </a>
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+                1.3.0+dev
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-              <p class="caption"><span class="caption-text">Contents:</span></p>
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 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -124,8 +130,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="introduction">
-<h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h1>
+  <section id="introduction">
+<h1>Introduction<a class="headerlink" href="#introduction" title="Link to this heading">¶</a></h1>
 <p>Quantum information science has changed our perception of quantum
 physics from passive understanding to a source of technological
 advances <span id="id1">[<a class="reference internal" href="99_bibliography.html#id41" title="A. Acín, I. Bloch, H. Buhrman, T. Calarco, C. Eichler, J. Eisert, D. Esteve, N. Gisin, S. J. Glaser, F. Jelezko, S. Kuhr, M. Lewenstein, M. F. Riedel, P. O. Schmidt, R. Thew, A. Wallraff, I. Walmsley, and F. K. Wilhelm. The quantum technologies roadmap: a European community view. New J. Phys.  20, 080201 (2018). doi:10.1088/1367-2630/aad1ea.">1</a>]</span>. By way of actively exploiting
@@ -221,7 +227,7 @@ <h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to t
 Thus, the <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package can serve as a reference implementation,
 leveraging Python’s reputation as “executable pseudocode”, and as a foundation
 against which to test other implementations.</p>
-</div>
+</section>
 
 
            </div>
@@ -235,7 +241,7 @@ <h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to t
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/07_krotovs_method.html b/master/07_krotovs_method.html
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+++ b/master/07_krotovs_method.html
@@ -1,26 +1,32 @@
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@@ -40,7 +46,7 @@
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-                1.3.0
+                1.3.0+dev
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@@ -50,7 +56,7 @@
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-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -138,10 +144,10 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="krotovs-method">
-<span id="krotovsmethod"></span><h1>Krotov’s Method<a class="headerlink" href="#krotovs-method" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="the-quantum-control-problem">
-<h2>The quantum control problem<a class="headerlink" href="#the-quantum-control-problem" title="Permalink to this headline">¶</a></h2>
+  <section id="krotovs-method">
+<span id="krotovsmethod"></span><h1>Krotov’s Method<a class="headerlink" href="#krotovs-method" title="Link to this heading">¶</a></h1>
+<section id="the-quantum-control-problem">
+<h2>The quantum control problem<a class="headerlink" href="#the-quantum-control-problem" title="Link to this heading">¶</a></h2>
 <p>Quantum optimal control methods formalize the problem of finding
 “control fields” that steer the time evolution of a quantum system in
 some desired way. For closed systems, described by a Hilbert space state
@@ -209,14 +215,14 @@ <h2>The quantum control problem<a class="headerlink" href="#the-quantum-control-
 the functional. The updated controls then become the guess for the next
 iteration of the algorithm, until the value of the functional is
 sufficiently small, or convergence is reached.</p>
-</div>
-<div class="section" id="optimization-functional">
-<h2>Optimization functional<a class="headerlink" href="#optimization-functional" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="optimization-functional">
+<h2>Optimization functional<a class="headerlink" href="#optimization-functional" title="Link to this heading">¶</a></h2>
 <p>Mathematically, Krotov’s method, when applied to quantum
 systems <span id="id7">[<a class="reference internal" href="99_bibliography.html#id34" title="D.J. Tannor, V. Kazakov, and V. Orlov. Control of photochemical branching: novel procedures for finding optimal pulses and global upper bounds. In J. Broeckhove and L. Lathouwers, editors, Time-dependent quantum molecular dynamics, pages 347-360. Plenum (1992).">5</a>, <a class="reference internal" href="99_bibliography.html#id7" title="D. M. Reich, M. Ndong, and C. P. Koch. Monotonically convergent optimization in quantum control using Krotov's method. J. Chem. Phys.  136, 104103 (2012). doi:10.1063/1.3691827.">22</a>]</span>, minimizes a functional
 of the most general form</p>
 <div class="math notranslate nohighlight" id="equation-functional">
-<span class="eqno">(1)<a class="headerlink" href="#equation-functional" title="Permalink to this equation">¶</a></span>\[J[\{\ket{\phi_k^{(i)}(t)}\}, \{\epsilon_l^{(i)}(t)\}]
+<span class="eqno">(1)<a class="headerlink" href="#equation-functional" title="Link to this equation">¶</a></span>\[J[\{\ket{\phi_k^{(i)}(t)}\}, \{\epsilon_l^{(i)}(t)\}]
     = J_T(\{\ket{\phi_k^{(i)}(T)}\})
         + \sum_l \int_0^T g_a(\epsilon_l^{(i)}(t)) \dd t
         + \int_0^T g_b(\{\phi^{(i)}_k(t)\}) \dd t\,,\]</div>
@@ -240,7 +246,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 used expression (and the only one currently supported by the
 <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package) is <span id="id8">[<a class="reference internal" href="99_bibliography.html#id9" title="J. P. Palao and R. Kosloff. Optimal control theory for unitary transformations. Phys. Rev. A  68, 062308 (2003). doi:10.1103/PhysRevA.68.062308.">47</a>]</span></p>
 <div class="math notranslate nohighlight" id="equation-g-a">
-<span class="eqno">(2)<a class="headerlink" href="#equation-g-a" title="Permalink to this equation">¶</a></span>\[\begin{split}\begin{split}
+<span class="eqno">(2)<a class="headerlink" href="#equation-g-a" title="Link to this equation">¶</a></span>\[\begin{split}\begin{split}
     g_a(\epsilon_l^{(i)}(t))
     &amp;= \frac{\lambda_{a,l}}{S_l(t)} \left(
         \epsilon_l^{(i)}(t) - \epsilon_{l, \text{ref}}^{(i)}(t)
@@ -253,7 +259,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 <p>with the inverse “step width” <span class="math notranslate nohighlight">\(\lambda_{a,l} &gt; 0\)</span>, the “update
 shape” function <span class="math notranslate nohighlight">\(S_{l}(t) \in [0, 1]\)</span>, and the <a class="reference internal" href="#iterativecontrolupdate"><span class="std std-ref">Iterative control update</span></a></p>
 <div class="math notranslate nohighlight" id="equation-update">
-<span class="eqno">(3)<a class="headerlink" href="#equation-update" title="Permalink to this equation">¶</a></span>\[\Delta\epsilon_l^{(i)}(t)
+<span class="eqno">(3)<a class="headerlink" href="#equation-update" title="Link to this equation">¶</a></span>\[\Delta\epsilon_l^{(i)}(t)
 \equiv \epsilon_l^{(i)}(t) - \epsilon_l^{(i-1)}(t)\,,\]</div>
 <p>where <span class="math notranslate nohighlight">\(\epsilon_l^{(i-1)}(t)\)</span> is the optimized control of the
 previous iteration – that is, the guess control of the current
@@ -278,7 +284,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 be expressed in terms of the complex overlaps of the target states with the
 final-time states under the given control. Thus,</p>
 <div class="math notranslate nohighlight" id="equation-tauk">
-<span class="eqno">(4)<a class="headerlink" href="#equation-tauk" title="Permalink to this equation">¶</a></span>\[  \tau_k = \Braket{\phi_k^\tgt}{\phi_k(T)}\]</div>
+<span class="eqno">(4)<a class="headerlink" href="#equation-tauk" title="Link to this equation">¶</a></span>\[  \tau_k = \Braket{\phi_k^\tgt}{\phi_k(T)}\]</div>
 <p>in Hilbert space, or</p>
 <div class="math notranslate nohighlight">
 \[\tau_k
@@ -294,32 +300,32 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 <li><p>Optimize for simultaneous state-to-state transitions, with completely
 arbitrary phases <span class="math notranslate nohighlight">\(\varphi_k\)</span>,</p>
 <div class="math notranslate nohighlight" id="equation-jtss">
-<span class="eqno">(5)<a class="headerlink" href="#equation-jtss" title="Permalink to this equation">¶</a></span>\[J_{T,\text{ss}} = 1- \frac{1}{N} \sum_{k=1}^{N} \Abs{\tau_k}^2\,,\]</div>
+<span class="eqno">(5)<a class="headerlink" href="#equation-jtss" title="Link to this equation">¶</a></span>\[J_{T,\text{ss}} = 1- \frac{1}{N} \sum_{k=1}^{N} \Abs{\tau_k}^2\,,\]</div>
 <p>cf. <a class="reference internal" href="API/krotov.functionals.html#krotov.functionals.J_T_ss" title="krotov.functionals.J_T_ss"><code class="xref py py-func docutils literal notranslate"><span class="pre">J_T_ss()</span></code></a>.</p>
 </li>
 <li><p>Optimize for simultaneous state-to-state transitions, with an arbitrary
 <em>global</em> phase, i.e., <span class="math notranslate nohighlight">\(\varphi_k = \varphi_{\text{global}}\)</span> for all
 <span class="math notranslate nohighlight">\(k\)</span> with arbitrary <span class="math notranslate nohighlight">\(\varphi_{\text{global}}\)</span>,</p>
 <div class="math notranslate nohighlight" id="equation-jtsm">
-<span class="eqno">(6)<a class="headerlink" href="#equation-jtsm" title="Permalink to this equation">¶</a></span>\[J_{T,\text{sm}} = 1- \frac{1}{N^2} \Abs{\sum_{k=1}^{N} \tau_k}^2
+<span class="eqno">(6)<a class="headerlink" href="#equation-jtsm" title="Link to this equation">¶</a></span>\[J_{T,\text{sm}} = 1- \frac{1}{N^2} \Abs{\sum_{k=1}^{N} \tau_k}^2
         = 1- \frac{1}{N^2} \sum_{k=1}^{N} \sum_{k'=1}^{N} \tau_{k'}^* \tau_{k}\,,\]</div>
 <p>cf. <a class="reference internal" href="API/krotov.functionals.html#krotov.functionals.J_T_sm" title="krotov.functionals.J_T_sm"><code class="xref py py-func docutils literal notranslate"><span class="pre">J_T_sm()</span></code></a>.</p>
 </li>
 <li><p>Optimize for simultaneous state-to-state transitions, with a global phase of zero, i.e.,
 <span class="math notranslate nohighlight">\(\varphi_k = 0\)</span> for all <span class="math notranslate nohighlight">\(k\)</span>,</p>
 <div class="math notranslate nohighlight" id="equation-jtre">
-<span class="eqno">(7)<a class="headerlink" href="#equation-jtre" title="Permalink to this equation">¶</a></span>\[J_{T,\text{re}} = 1-\frac{1}{N} \Re \left[\, \sum_{k=1}^{N} \tau_k \,\right]\,,\]</div>
+<span class="eqno">(7)<a class="headerlink" href="#equation-jtre" title="Link to this equation">¶</a></span>\[J_{T,\text{re}} = 1-\frac{1}{N} \Re \left[\, \sum_{k=1}^{N} \tau_k \,\right]\,,\]</div>
 <p>cf. <a class="reference internal" href="API/krotov.functionals.html#krotov.functionals.J_T_re" title="krotov.functionals.J_T_re"><code class="xref py py-func docutils literal notranslate"><span class="pre">J_T_re()</span></code></a>.</p>
 </li>
 </ul>
-</div>
-<div class="section" id="iterative-control-update">
-<span id="iterativecontrolupdate"></span><h2>Iterative control update<a class="headerlink" href="#iterative-control-update" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="iterative-control-update">
+<span id="iterativecontrolupdate"></span><h2>Iterative control update<a class="headerlink" href="#iterative-control-update" title="Link to this heading">¶</a></h2>
 <p>Starting from the initial guess control <span class="math notranslate nohighlight">\(\epsilon_l^{(0)}(t)\)</span>, the
 optimized field <span class="math notranslate nohighlight">\(\epsilon_l^{(i)}(t)\)</span> in iteration <span class="math notranslate nohighlight">\(i &gt; 0\)</span>
 is the result of applying a control update,</p>
 <div class="math notranslate nohighlight" id="equation-eps-update">
-<span class="eqno">(8)<a class="headerlink" href="#equation-eps-update" title="Permalink to this equation">¶</a></span>\[\epsilon_l^{(i)}(t)
+<span class="eqno">(8)<a class="headerlink" href="#equation-eps-update" title="Link to this equation">¶</a></span>\[\epsilon_l^{(i)}(t)
 = \epsilon_l^{(i-1)}(t) + \Delta\epsilon_l^{(i)}(t)\,.\]</div>
 <p>Krotov’s method is a clever construction of a particular
 <span class="math notranslate nohighlight">\(\Delta\epsilon_l^{(i)}(t)\)</span> that ensures</p>
@@ -348,7 +354,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 optimization. A typical example for an update shape is</p>
 <blockquote>
 <div><div class="math notranslate nohighlight" id="equation-flattop">
-<span class="eqno">(9)<a class="headerlink" href="#equation-flattop" title="Permalink to this equation">¶</a></span>\[\begin{split}S_l(t) = \begin{cases}
+<span class="eqno">(9)<a class="headerlink" href="#equation-flattop" title="Link to this equation">¶</a></span>\[\begin{split}S_l(t) = \begin{cases}
       B(t; t_0=0, t_1=2 t_{\text{on}})
         &amp; \text{for} \quad 0 &lt; t &lt; t_{\text{on}} \\
       1 &amp; \text{for} \quad t_{\text{on}} \le t \le T - t_{\text{off}} \\
@@ -357,7 +363,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
     \end{cases}\end{split}\]</div>
 <p>cf. <a class="reference internal" href="API/krotov.shapes.html#krotov.shapes.flattop" title="krotov.shapes.flattop"><code class="xref py py-func docutils literal notranslate"><span class="pre">krotov.shapes.flattop()</span></code></a>, with the <a class="reference external" href="https://en.wikipedia.org/wiki/Window_function#Blackman_window">Blackman shape</a></p>
 <div class="math notranslate nohighlight" id="equation-blackman">
-<span class="eqno">(10)<a class="headerlink" href="#equation-blackman" title="Permalink to this equation">¶</a></span>\[B(t; t_0, t_1) =
+<span class="eqno">(10)<a class="headerlink" href="#equation-blackman" title="Link to this equation">¶</a></span>\[B(t; t_0, t_1) =
       \frac{1}{2}\left(
         1 - a - \cos\left(2\pi \frac{t - t_0}{t_1 - t_0}\right)
         + a \cos\left(4\pi \frac{t - t_0}{t_1 - t_0}\right)
@@ -377,9 +383,9 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 “black box” optimization tool, so you may skip ahead to
 <a class="reference internal" href="08_qutip_usage.html#using-krotov-with-qutip"><span class="std std-ref">Using Krotov with QuTiP</span></a> and come back at a later time.</p>
 </div>
-</div>
-<div class="section" id="first-order-update">
-<span id="firstorderupdate"></span><h2>First order update<a class="headerlink" href="#first-order-update" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="first-order-update">
+<span id="firstorderupdate"></span><h2>First order update<a class="headerlink" href="#first-order-update" title="Link to this heading">¶</a></h2>
 <p>Krotov’s method is based on a rigorous examination of the conditions for
 calculating the updated fields <span class="math notranslate nohighlight">\(\{\epsilon_l^{(i)}(t)\}\)</span> such that
 <span class="math notranslate nohighlight">\(J(\{\ket{\phi_k^{(i)}(t)}\}, \{\epsilon_l^{(i)}(t)\}) \leq
@@ -389,7 +395,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 Eq. <a class="reference internal" href="#equation-functional">(1)</a>, with a convex final-time
 functional <span class="math notranslate nohighlight">\(J_T\)</span>, the condition for monotonic convergence is</p>
 <div class="math notranslate nohighlight" id="equation-krotov-first-order-proto-update">
-<span class="eqno">(11)<a class="headerlink" href="#equation-krotov-first-order-proto-update" title="Permalink to this equation">¶</a></span>\[\frac{\partial g_a}{\partial \epsilon_l(t)}\bigg\vert_{(i)}
+<span class="eqno">(11)<a class="headerlink" href="#equation-krotov-first-order-proto-update" title="Link to this equation">¶</a></span>\[\frac{\partial g_a}{\partial \epsilon_l(t)}\bigg\vert_{(i)}
   =  2 \Im \left[\,
       \sum_{k=1}^{N} \Bigg\langle \chi_k^{(i-1)}(t) \Bigg\vert \Bigg(
       \frac{\partial \Op{H}}{\partial \epsilon_l(t)}\bigg\vert_{
@@ -410,7 +416,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 <em>update</em>
 equation <span id="id14">[<a class="reference internal" href="99_bibliography.html#id34" title="D.J. Tannor, V. Kazakov, and V. Orlov. Control of photochemical branching: novel procedures for finding optimal pulses and global upper bounds. In J. Broeckhove and L. Lathouwers, editors, Time-dependent quantum molecular dynamics, pages 347-360. Plenum (1992).">5</a>, <a class="reference internal" href="99_bibliography.html#id10" title="S. E. Sklarz and D. J. Tannor. Loading a Bose-Einstein condensate onto an optical lattice: an application of optimal control theory to the nonlinear Schrödinger equation. Phys. Rev. A  66, 053619 (2002). doi:10.1103/PhysRevA.66.053619.">46</a>, <a class="reference internal" href="99_bibliography.html#id9" title="J. P. Palao and R. Kosloff. Optimal control theory for unitary transformations. Phys. Rev. A  68, 062308 (2003). doi:10.1103/PhysRevA.68.062308.">47</a>]</span>,</p>
 <div class="math notranslate nohighlight" id="equation-krotov-first-order-update">
-<span class="eqno">(12)<a class="headerlink" href="#equation-krotov-first-order-update" title="Permalink to this equation">¶</a></span>\[\Delta\epsilon^{(i)}_l(t)
+<span class="eqno">(12)<a class="headerlink" href="#equation-krotov-first-order-update" title="Link to this equation">¶</a></span>\[\Delta\epsilon^{(i)}_l(t)
   = \frac{S_l(t)}{\lambda_{a,l}} \Im \left[\,
       \sum_{k=1}^{N} \Bigg\langle \chi_k^{(i-1)}(t) \Bigg\vert \Bigg(
       \frac{\partial \Op{H}}{\partial \epsilon_l(t)}
@@ -421,18 +427,18 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 <span class="math notranslate nohighlight">\(\ket{\phi_k^{(i)}}\)</span> under the optimized controls
 <span class="math notranslate nohighlight">\(\{\epsilon_l^{(i)}(t)\}\)</span> of the iteration <span class="math notranslate nohighlight">\((i)\)</span>,</p>
 <div class="math notranslate nohighlight" id="equation-fw-eqm">
-<span class="eqno">(13)<a class="headerlink" href="#equation-fw-eqm" title="Permalink to this equation">¶</a></span>\[\frac{\partial}{\partial t} \Ket{\phi_k^{(i)}(t)}
+<span class="eqno">(13)<a class="headerlink" href="#equation-fw-eqm" title="Link to this equation">¶</a></span>\[\frac{\partial}{\partial t} \Ket{\phi_k^{(i)}(t)}
    = -\frac{\mathrm{i}}{\hbar} \Op{H}^{(i)} \Ket{\phi_k^{(i)}(t)}\,.\]</div>
 <p>The co-states <span class="math notranslate nohighlight">\(\ket{\chi_k^{(i-1)}(t)}\)</span> are propagated backwards
 in time under the guess controls of iteration <span class="math notranslate nohighlight">\((i)\)</span>, i.e., the
 optimized controls from the previous iteration <span class="math notranslate nohighlight">\((i-1)\)</span>, as</p>
 <div class="math notranslate nohighlight" id="equation-bw-eqm">
-<span class="eqno">(14)<a class="headerlink" href="#equation-bw-eqm" title="Permalink to this equation">¶</a></span>\[\frac{\partial}{\partial t} \Ket{\chi_k^{(i-1)}(t)}
+<span class="eqno">(14)<a class="headerlink" href="#equation-bw-eqm" title="Link to this equation">¶</a></span>\[\frac{\partial}{\partial t} \Ket{\chi_k^{(i-1)}(t)}
   = -\frac{\mathrm{i}}{\hbar} \Op{H}^{\dagger\,(i-1)} \Ket{\chi_k^{(i-1)}(t)}
     + \left.\frac{\partial g_b}{\partial \Bra{\phi_k}}\right\vert_{(i-1)}\,,\]</div>
 <p>with the boundary condition</p>
 <div class="math notranslate nohighlight" id="equation-chi-boundary">
-<span class="eqno">(15)<a class="headerlink" href="#equation-chi-boundary" title="Permalink to this equation">¶</a></span>\[\Ket{\chi_k^{(i-1)}(T)}
+<span class="eqno">(15)<a class="headerlink" href="#equation-chi-boundary" title="Link to this equation">¶</a></span>\[\Ket{\chi_k^{(i-1)}(T)}
   = - \left.\frac{\partial J_T}{\partial \Bra{\phi_k(T)}}
     \right\vert_{(i-1)}\,,\]</div>
 <p>where the right-hand-side is evaluated for the set of states
@@ -456,11 +462,11 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 in general.</p>
 <p>More precisely, the right hand side of Eq. <a class="reference internal" href="#equation-chi-boundary">(15)</a> is a <a class="reference external" href="https://en.wikipedia.org/wiki/Wirtinger_derivatives">Wirtinger derivative</a>. That is, if the j’th component of <span class="math notranslate nohighlight">\(\ket{\phi_k(T)}\)</span> is the complex number <span class="math notranslate nohighlight">\(x_j + \ii y_j\)</span> then the j’th component of <span class="math notranslate nohighlight">\(\ket{\chi_k(T)}\)</span> is</p>
 <div class="math notranslate nohighlight" id="equation-wirtinger-deriv">
-<span class="eqno">(16)<a class="headerlink" href="#equation-wirtinger-deriv" title="Permalink to this equation">¶</a></span>\[ \chi_j = - \frac{1}{2} \left(\frac{\partial J_T}{\partial x_j} + \ii \frac{\partial J_T}{\partial y_j} \right)\,.\]</div>
+<span class="eqno">(16)<a class="headerlink" href="#equation-wirtinger-deriv" title="Link to this equation">¶</a></span>\[ \chi_j = - \frac{1}{2} \left(\frac{\partial J_T}{\partial x_j} + \ii \frac{\partial J_T}{\partial y_j} \right)\,.\]</div>
 <p>Generally, though, the state <span class="math notranslate nohighlight">\(\ket{\chi_k(T)}\)</span> can be calculated simply by fully expanding <span class="math notranslate nohighlight">\(J_T\)</span> and treating co-states as independent variables, as in the example for <span class="math notranslate nohighlight">\(J_{T,ss}\)</span> above. Hence the intuitive notation in Eq. <a class="reference internal" href="#equation-chi-boundary">(15)</a>.</p>
-</div>
-<div class="section" id="second-order-update">
-<span id="secondorderupdate"></span><h2>Second order update<a class="headerlink" href="#second-order-update" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="second-order-update">
+<span id="secondorderupdate"></span><h2>Second order update<a class="headerlink" href="#second-order-update" title="Link to this heading">¶</a></h2>
 <p>The update
 Eq. <a class="reference internal" href="#equation-krotov-first-order-update">(12)</a>
 assumes that the equation of motion is linear (<span class="math notranslate nohighlight">\(\Op{H}\)</span> does not
@@ -472,7 +478,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 Eqs. <a class="reference internal" href="#equation-krotov-first-order-proto-update">(11)</a>, <a class="reference internal" href="#equation-krotov-first-order-update">(12)</a> <span id="id15">[<a class="reference internal" href="99_bibliography.html#id7" title="D. M. Reich, M. Ndong, and C. P. Koch. Monotonically convergent optimization in quantum control using Krotov's method. J. Chem. Phys.  136, 104103 (2012). doi:10.1063/1.3691827.">22</a>, <a class="reference internal" href="99_bibliography.html#id6" title="A.I. Konnov and V. F. Krotov. On global methods of successive improvement of controlled processes. Autom. Rem. Contr.  60, 1427 (1999).">43</a>]</span>,</p>
 <p>The full update equation then reads</p>
 <div class="math notranslate nohighlight" id="equation-krotov-second-order-update">
-<span class="eqno">(17)<a class="headerlink" href="#equation-krotov-second-order-update" title="Permalink to this equation">¶</a></span>\[\begin{split}\begin{split}
+<span class="eqno">(17)<a class="headerlink" href="#equation-krotov-second-order-update" title="Link to this equation">¶</a></span>\[\begin{split}\begin{split}
   \Delta\epsilon^{(i)}_l(t)
     &amp;= \frac{S_l(t)}{\lambda_{a,l}} \Im \left[\,
         \sum_{k=1}^{N} \Bigg\langle \chi_k^{(i-1)}(t) \Bigg\vert \Bigg(
@@ -525,13 +531,15 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 <span class="math notranslate nohighlight">\(\ket{\Delta\phi_k(t)}\)</span>), you should always try the optimization with
 the first-order update equation first.</p>
 </div>
-</div>
-<div class="section" id="time-discretization">
-<span id="timediscretization"></span><h2>Time discretization<a class="headerlink" href="#time-discretization" title="Permalink to this headline">¶</a></h2>
-<div class="figure align-default" id="id21">
+</section>
+<section id="time-discretization">
+<span id="timediscretization"></span><h2>Time discretization<a class="headerlink" href="#time-discretization" title="Link to this heading">¶</a></h2>
+<figure class="align-default" id="id21">
 <span id="figkrotovscheme"></span><a class="reference internal image-reference" href="_images/krotovscheme.svg"><img alt="Sequential update scheme in Krotov’s method on a time grid." src="_images/krotovscheme.svg" width="100%" /></a>
-<p class="caption"><span class="caption-number">Fig. 1 </span><span class="caption-text">Sequential update scheme in Krotov’s method on a time grid.</span><a class="headerlink" href="#id21" title="Permalink to this image">¶</a></p>
-</div>
+<figcaption>
+<p><span class="caption-number">Fig. 1 </span><span class="caption-text">Sequential update scheme in Krotov’s method on a time grid.</span><a class="headerlink" href="#id21" title="Link to this image">¶</a></p>
+</figcaption>
+</figure>
 <p>The derivation of Krotov’s method assumes time-continuous control
 fields. Only in this case, monotonic convergence is mathematically
 guaranteed. However, for practical numerical applications, we have to
@@ -565,7 +573,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 <span class="math notranslate nohighlight">\(\{\ket{\phi_k}\} = \{\ket{\phi_k(t=t_0=0)}\}\)</span>, calculate the pulse
 update for the first time step according to</p>
 <div class="math notranslate nohighlight" id="equation-update-discretized0">
-<span class="eqno">(18)<a class="headerlink" href="#equation-update-discretized0" title="Permalink to this equation">¶</a></span>\[\Delta\epsilon^{(i)}_1
+<span class="eqno">(18)<a class="headerlink" href="#equation-update-discretized0" title="Link to this equation">¶</a></span>\[\Delta\epsilon^{(i)}_1
       \equiv \Delta\epsilon^{(i)}(\tilde{t}_0)
       = \frac{S(\tilde{t}_0)}{\lambda_{a}} \Im \left[\,
           \sum_{k=1}^{N} \bigg\langle \chi_k^{(i-1)}(t_0) \bigg\vert
@@ -584,7 +592,7 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 updates then proceed sequentially, using the discretized update
 equation</p>
 <div class="math notranslate nohighlight" id="equation-update-discretized">
-<span class="eqno">(19)<a class="headerlink" href="#equation-update-discretized" title="Permalink to this equation">¶</a></span>\[\Delta\epsilon^{(i)}_{n+1}
+<span class="eqno">(19)<a class="headerlink" href="#equation-update-discretized" title="Link to this equation">¶</a></span>\[\Delta\epsilon^{(i)}_{n+1}
       \equiv \Delta\epsilon^{(i)}(\tilde{t}_n)
       = \frac{S(\tilde{t}_n)}{\lambda_{a}} \Im \left[\,
           \sum_{k=1}^{N} \bigg\langle \chi_k^{(i-1)}(t_n) \bigg\vert
@@ -623,14 +631,14 @@ <h2>Optimization functional<a class="headerlink" href="#optimization-functional"
 details. For a second-order update, the forward propagated states from step 4,
 both for the current iteration and the previous iteration, must be stored in
 memory over the entire time grid.</p>
-</div>
-<div class="section" id="pseudocode">
-<h2>Pseudocode<a class="headerlink" href="#pseudocode" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="pseudocode">
+<h2>Pseudocode<a class="headerlink" href="#pseudocode" title="Link to this heading">¶</a></h2>
 <p>A complete pseudocode for Krotov’s method as described in the previous section
 <a class="reference internal" href="#timediscretization"><span class="std std-ref">Time discretization</span></a> is available in PDF format: <a class="reference external" href="krotov_pseudocode.pdf">krotov_pseudocode.pdf</a>.</p>
-</div>
-<div class="section" id="choice-of-a">
-<span id="choiceoflambdaa"></span><h2>Choice of λₐ<a class="headerlink" href="#choice-of-a" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="choice-of-a">
+<span id="choiceoflambdaa"></span><h2>Choice of λₐ<a class="headerlink" href="#choice-of-a" title="Link to this heading">¶</a></h2>
 <p>The monotonic convergence of Krotov’s method is only guaranteed in the
 continuous limit; a coarse
 time step must be compensated by larger values of the inverse step size
@@ -670,9 +678,9 @@ <h2>Pseudocode<a class="headerlink" href="#pseudocode" title="Permalink to this
 <span class="math notranslate nohighlight">\(\lambda_{a,l}\)</span> requires some trial and error, but once a suitable value
 has been found, it does not have to be adjusted further. In particular, it is
 not necessary to perform a line search over <span class="math notranslate nohighlight">\(\lambda_{a,l}\)</span>.</p>
-</div>
-<div class="section" id="complex-controls-and-the-rwa">
-<h2>Complex controls and the RWA<a class="headerlink" href="#complex-controls-and-the-rwa" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="complex-controls-and-the-rwa">
+<h2>Complex controls and the RWA<a class="headerlink" href="#complex-controls-and-the-rwa" title="Link to this heading">¶</a></h2>
 <p>When using the rotating wave approximation (RWA), it is important to remember
 that the target states are usually defined in the lab frame, not in the
 rotating frame. This is relevant for the construction of
@@ -700,9 +708,9 @@ <h2>Complex controls and the RWA<a class="headerlink" href="#complex-controls-an
 <span class="math notranslate nohighlight">\(\epsilon_{\text{im}}(t) = \Im[\epsilon(t)]\)</span>.</p>
 <p>See the <a class="reference internal" href="notebooks/02_example_lambda_system_rwa_complex_pulse.html"><span class="std std-ref">Optimization of a State-to-State Transfer in a Lambda System in the RWA</span></a> for an
 example.</p>
-</div>
-<div class="section" id="optimization-in-liouville-space">
-<h2>Optimization in Liouville space<a class="headerlink" href="#optimization-in-liouville-space" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="optimization-in-liouville-space">
+<h2>Optimization in Liouville space<a class="headerlink" href="#optimization-in-liouville-space" title="Link to this heading">¶</a></h2>
 <p>The coupled equations <a class="reference internal" href="#equation-krotov-first-order-update">(12)</a>–<a class="reference internal" href="#equation-bw-eqm">(14)</a> can be
 generalized to open system dynamics by replacing Hilbert space states with
 density matrices, <span class="math notranslate nohighlight">\(\Op{H}\)</span> with <span class="math notranslate nohighlight">\(\mathrm{i} \Liouville\)</span>, and brakets (inner products) with Hilbert-Schmidt products,
@@ -717,8 +725,8 @@ <h2>Optimization in Liouville space<a class="headerlink" href="#optimization-in-
 Liouvillian <span id="id19">[<a class="reference internal" href="99_bibliography.html#id19" title="M. H. Goerz, D. M. Reich, and C. P. Koch. Optimal control theory for a unitary operation under dissipative evolution. New J. Phys.  16, 055012 (2014). doi:10.1088/1367-2630/16/5/055012.">27</a>, <a class="reference internal" href="99_bibliography.html#id75" title="A. Bartana, R. Kosloff, and D. J. Tannor. Laser cooling of molecular internal degrees of freedom by a series of shaped pulses. J. Chem. Phys.  99, 196 (1993). doi:10.1063/1.465797.">51</a>, <a class="reference internal" href="99_bibliography.html#id74" title="Y. Ohtsuki, W. Zhu, and H. Rabitz. Monotonically convergent algorithm for quantum optimal control with dissipation. J. Chem. Phys.  110, 9825 (1999). doi:10.1063/1.478036.">52</a>]</span> and a non-Hermitian
 Hamiltonian <span id="id20">[<a class="reference internal" href="99_bibliography.html#id11" title="M. M. Müller, H. Haakh, T. Calarco, C. P. Koch, and C. Henkel. Prospects for fast Rydberg gates on an atom chip. Quantum Inf. Process.  10, 771 (2011). doi:10.1007/s11128-011-0296-0.">25</a>, <a class="reference internal" href="99_bibliography.html#id73" title="M. H. Goerz and K. Jacobs. Efficient optimization of state preparation in quantum networks using quantum trajectories. Quantum Sci. Technol.  3, 045005 (2018). doi:10.1088/2058-9565/aace16.">53</a>]</span>.</p>
 <p>See the <a class="reference internal" href="notebooks/04_example_dissipative_qubit_reset.html"><span class="std std-ref">Optimization of Dissipative Qubit Reset</span></a> for an example.</p>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -732,7 +740,7 @@ <h2>Optimization in Liouville space<a class="headerlink" href="#optimization-in-
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/08_qutip_usage.html b/master/08_qutip_usage.html
index 947500ef..178376cb 100644
--- a/master/08_qutip_usage.html
+++ b/master/08_qutip_usage.html
@@ -1,26 +1,32 @@
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+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -126,8 +132,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="using-krotov-with-qutip">
-<span id="id1"></span><h1>Using Krotov with QuTiP<a class="headerlink" href="#using-krotov-with-qutip" title="Permalink to this headline">¶</a></h1>
+  <section id="using-krotov-with-qutip">
+<span id="id1"></span><h1>Using Krotov with QuTiP<a class="headerlink" href="#using-krotov-with-qutip" title="Link to this heading">¶</a></h1>
 <p>The <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package is designed around <a class="reference external" href="http://qutip.org">QuTiP</a>, a very powerful “Quantum
 Toolbox” in Python. This means that all operators and states are expressed as
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qutip.Qobj</span></code></a> quantum objects. The <a class="reference internal" href="API/krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> interface
@@ -180,7 +186,7 @@
 <p>While the guess controls that are in the <cite>objectives</cite> on input may be
 functions, or an array of control values on the time grid, the output
 <a class="reference internal" href="API/krotov.result.html#krotov.result.Result.optimized_controls" title="krotov.result.Result.optimized_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">optimized_controls</span></code></a> will always be an array of control values.</p>
-</div>
+</section>
 
 
            </div>
@@ -194,7 +200,7 @@
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/09_examples.html b/master/09_examples.html
index 0614437e..d3b8c056 100644
--- a/master/09_examples.html
+++ b/master/09_examples.html
@@ -1,24 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
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+
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-  <title>Examples &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
+  <title>Examples &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=649a27d8" />
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+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
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@@ -38,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -48,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -135,9 +143,9 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="examples">
-<span id="krotov-example-notebooks"></span><h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1>
-<a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs%2Fnotebooks" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a><div class="toctree-wrapper compound">
+  <section id="examples">
+<span id="krotov-example-notebooks"></span><h1>Examples<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h1>
+<a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/master/docs/notebooks" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/master?filepath=docs%2Fnotebooks" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a><div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="notebooks/01_example_simple_state_to_state.html">Optimization of a State-to-State Transfer in a Two-Level-System</a></li>
 <li class="toctree-l1"><a class="reference internal" href="notebooks/02_example_lambda_system_rwa_complex_pulse.html">Optimization of a State-to-State Transfer in a Lambda System in the RWA</a></li>
@@ -150,7 +158,7 @@
 <li class="toctree-l1"><a class="reference internal" href="notebooks/09_example_numpy.html">Optimization with numpy Arrays</a></li>
 </ul>
 </div>
-</div>
+</section>
 
 
            </div>
@@ -164,7 +172,7 @@
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/10_howto.html b/master/10_howto.html
index b4c9b09e..78432548 100644
--- a/master/10_howto.html
+++ b/master/10_howto.html
@@ -1,26 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
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+
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-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="_static/copybutton.css" type="text/css" />
+  <title>How-Tos &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
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+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -148,10 +154,10 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="how-tos">
-<h1>How-Tos<a class="headerlink" href="#how-tos" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="how-to-optimize-towards-a-quantum-gate">
-<span id="howtogateoptimization"></span><h2>How to optimize towards a quantum gate<a class="headerlink" href="#how-to-optimize-towards-a-quantum-gate" title="Permalink to this headline">¶</a></h2>
+  <section id="how-tos">
+<h1>How-Tos<a class="headerlink" href="#how-tos" title="Link to this heading">¶</a></h1>
+<section id="how-to-optimize-towards-a-quantum-gate">
+<span id="howtogateoptimization"></span><h2>How to optimize towards a quantum gate<a class="headerlink" href="#how-to-optimize-towards-a-quantum-gate" title="Link to this heading">¶</a></h2>
 <p>To optimize towards a quantum gate <span class="math notranslate nohighlight">\(\Op{O}\)</span> in a <em>closed</em> quantum system,
 set one <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> for each state in the logical basis, with the basis
 state <span class="math notranslate nohighlight">\(\ket{\phi_k}\)</span> as the <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a> and
@@ -162,9 +168,9 @@ <h1>How-Tos<a class="headerlink" href="#how-tos" title="Permalink to this headli
 advanced gate optimizations, also see <a class="reference internal" href="#howtolioptimization"><span class="std std-ref">How to optimize towards a two-qubit gate up to single-qubit corrections</span></a>,
 <a class="reference internal" href="#howtopeoptimization"><span class="std std-ref">How to optimize towards an arbitrary perfect entangler</span></a>, <a class="reference internal" href="#howtodissipativeoptimization"><span class="std std-ref">How to optimize in a dissipative system</span></a>, and
 <a class="reference internal" href="#howtorobustoptimization"><span class="std std-ref">How to optimize for robust pulses</span></a>.</p>
-</div>
-<div class="section" id="how-to-optimize-complex-valued-control-fields">
-<h2>How to optimize complex-valued control fields<a class="headerlink" href="#how-to-optimize-complex-valued-control-fields" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-optimize-complex-valued-control-fields">
+<h2>How to optimize complex-valued control fields<a class="headerlink" href="#how-to-optimize-complex-valued-control-fields" title="Link to this heading">¶</a></h2>
 <p>This implementation of Krotov’s method requires real-valued control fields. You
 must rewrite your Hamiltonian to contain the real part and the imaginary part
 of the field as two independent controls. This is always possible. For example,
@@ -178,9 +184,9 @@ <h2>How to optimize complex-valued control fields<a class="headerlink" href="#ho
 <span class="math notranslate nohighlight">\(\epsilon_{\text{im}}(t) = \Im[\epsilon(t)]\)</span> are considered as two
 independent (real-valued) controls.</p>
 <p>See the <a class="reference internal" href="notebooks/02_example_lambda_system_rwa_complex_pulse.html"><span class="std std-ref">Optimization of a State-to-State Transfer in a Lambda System in the RWA</span></a> for an example.</p>
-</div>
-<div class="section" id="how-to-use-args-in-time-dependent-control-fields">
-<span id="howtouseargs"></span><h2>How to use <cite>args</cite> in time-dependent control fields<a class="headerlink" href="#how-to-use-args-in-time-dependent-control-fields" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-use-args-in-time-dependent-control-fields">
+<span id="howtouseargs"></span><h2>How to use <cite>args</cite> in time-dependent control fields<a class="headerlink" href="#how-to-use-args-in-time-dependent-control-fields" title="Link to this heading">¶</a></h2>
 <p>QuTiP requires that the functions that are used to express time-dependencies
 have the signature <code class="docutils literal notranslate"><span class="pre">func(t,</span> <span class="pre">args)</span></code> where <cite>t</cite> is a scalar value for the time
 and <cite>args</cite> is a dict containing values for static parameters, see
@@ -261,9 +267,9 @@ <h2>How to optimize complex-valued control fields<a class="headerlink" href="#ho
 In the latter, the values for the parameters in the control fields and the
 Hamiltonian are hardcoded, while in the former, all parameters are centrally
 defined in a dict which is passed to the optimization and propagation routines.</p>
-</div>
-<div class="section" id="how-to-stop-the-optimization-when-the-error-crosses-some-threshold">
-<h2>How to stop the optimization when the error crosses some threshold<a class="headerlink" href="#how-to-stop-the-optimization-when-the-error-crosses-some-threshold" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-stop-the-optimization-when-the-error-crosses-some-threshold">
+<h2>How to stop the optimization when the error crosses some threshold<a class="headerlink" href="#how-to-stop-the-optimization-when-the-error-crosses-some-threshold" title="Link to this heading">¶</a></h2>
 <p>By default, an optimization stops after a predefined number of iterations
 (<cite>iter_stop</cite> parameter in <a class="reference internal" href="API/krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>). However, through the
 interplay of the <cite>info_hook</cite> and the <cite>check_convergence</cite> routine  passed to
@@ -279,15 +285,15 @@ <h2>How to stop the optimization when the error crosses some threshold<a class="
 the optimization when <span class="math notranslate nohighlight">\(J_T\)</span> falls below some given threshold.</p>
 <p>See the thee <a class="reference internal" href="notebooks/02_example_lambda_system_rwa_complex_pulse.html"><span class="std std-ref">Optimization of a State-to-State Transfer in a Lambda System in the RWA</span></a> for
 an example.</p>
-</div>
-<div class="section" id="how-to-exclude-a-control-from-the-optimization">
-<h2>How to exclude a control from the optimization<a class="headerlink" href="#how-to-exclude-a-control-from-the-optimization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-exclude-a-control-from-the-optimization">
+<h2>How to exclude a control from the optimization<a class="headerlink" href="#how-to-exclude-a-control-from-the-optimization" title="Link to this heading">¶</a></h2>
 <p>In order to force the optimization to leave any particular control field
 unchanged, set its update shape to <a class="reference internal" href="API/krotov.shapes.html#krotov.shapes.zero_shape" title="krotov.shapes.zero_shape"><code class="xref py py-func docutils literal notranslate"><span class="pre">krotov.shapes.zero_shape()</span></code></a>
 in the <cite>pulse_options</cite> that you pass to <a class="reference internal" href="API/krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p>
-</div>
-<div class="section" id="how-to-define-a-new-optimization-functional">
-<h2>How to define a new optimization functional<a class="headerlink" href="#how-to-define-a-new-optimization-functional" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-define-a-new-optimization-functional">
+<h2>How to define a new optimization functional<a class="headerlink" href="#how-to-define-a-new-optimization-functional" title="Link to this heading">¶</a></h2>
 <p>In order to define a new optimization functional <span class="math notranslate nohighlight">\(J_T\)</span>:</p>
 <ul>
 <li><p>Decide on what should go in <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective.target" title="krotov.objectives.Objective.target"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Objective.target</span></code></a> to best describe the
@@ -315,9 +321,9 @@ <h2>How to define a new optimization functional<a class="headerlink" href="#how-
 </ul>
 <p>See <a class="reference internal" href="API/krotov.functionals.html#module-krotov.functionals" title="krotov.functionals"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.functionals</span></code></a> for some standard functionals. An example for a
 more advanced functional is the <a class="reference internal" href="notebooks/07_example_PE.html"><span class="std std-ref">Optimization towards a Perfect Entangler</span></a>.</p>
-</div>
-<div class="section" id="how-to-penalize-population-in-a-forbidden-subspace">
-<h2>How to penalize population in a forbidden subspace<a class="headerlink" href="#how-to-penalize-population-in-a-forbidden-subspace" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-penalize-population-in-a-forbidden-subspace">
+<h2>How to penalize population in a forbidden subspace<a class="headerlink" href="#how-to-penalize-population-in-a-forbidden-subspace" title="Link to this heading">¶</a></h2>
 <p>In principle, <a class="reference internal" href="API/krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> has a <cite>state_dependent_constraint</cite>.
 However, this has some caveats. Most notably, it results in an inhomogeneous
 equation of motion, which is currently not implemented.</p>
@@ -326,9 +332,9 @@ <h2>How to penalize population in a forbidden subspace<a class="headerlink" href
 good way to realize this. See the
 <a class="reference internal" href="notebooks/03_example_lambda_system_rwa_non_hermitian.html"><span class="std std-ref">Optimization of a Dissipative State-to-State Transfer in a Lambda System</span></a>
 for an example.</p>
-</div>
-<div class="section" id="how-to-optimize-towards-a-two-qubit-gate-up-to-single-qubit-corrections">
-<span id="howtolioptimization"></span><h2>How to optimize towards a two-qubit gate up to single-qubit corrections<a class="headerlink" href="#how-to-optimize-towards-a-two-qubit-gate-up-to-single-qubit-corrections" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-optimize-towards-a-two-qubit-gate-up-to-single-qubit-corrections">
+<span id="howtolioptimization"></span><h2>How to optimize towards a two-qubit gate up to single-qubit corrections<a class="headerlink" href="#how-to-optimize-towards-a-two-qubit-gate-up-to-single-qubit-corrections" title="Link to this heading">¶</a></h2>
 <p>On many quantum computing platforms, applying arbitrary single-qubit
 gates is easy compared to entangling two-qubit gates. A specific
 entangling gate like CNOT is combined with single-qubit gates to form a
@@ -351,9 +357,9 @@ <h2>How to penalize population in a forbidden subspace<a class="headerlink" href
 pass to <a class="reference internal" href="API/krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p>
 <p>The optimization towards a local equivalence class may require use of the
 second-order update equation, see <a class="reference internal" href="07_krotovs_method.html#secondorderupdate"><span class="std std-ref">Second order update</span></a>.</p>
-</div>
-<div class="section" id="how-to-optimize-towards-an-arbitrary-perfect-entangler">
-<span id="howtopeoptimization"></span><h2>How to optimize towards an arbitrary perfect entangler<a class="headerlink" href="#how-to-optimize-towards-an-arbitrary-perfect-entangler" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-optimize-towards-an-arbitrary-perfect-entangler">
+<span id="howtopeoptimization"></span><h2>How to optimize towards an arbitrary perfect entangler<a class="headerlink" href="#how-to-optimize-towards-an-arbitrary-perfect-entangler" title="Link to this heading">¶</a></h2>
 <p>The relevant property of a gate is often its entangling power, and the
 requirement for a two-qubit gate in a universal set of gates is that it is a
 “perfect entangler”. A perfect entangler can produce a maximally entangled
@@ -375,9 +381,9 @@ <h2>How to penalize population in a forbidden subspace<a class="headerlink" href
 <p>Both the optimization towards a local equivalence class and an arbitrary perfect
 entangler may require use of the second-order update equation, see
 <a class="reference internal" href="07_krotovs_method.html#secondorderupdate"><span class="std std-ref">Second order update</span></a>.</p>
-</div>
-<div class="section" id="how-to-optimize-in-a-dissipative-system">
-<span id="howtodissipativeoptimization"></span><h2>How to optimize in a dissipative system<a class="headerlink" href="#how-to-optimize-in-a-dissipative-system" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-optimize-in-a-dissipative-system">
+<span id="howtodissipativeoptimization"></span><h2>How to optimize in a dissipative system<a class="headerlink" href="#how-to-optimize-in-a-dissipative-system" title="Link to this heading">¶</a></h2>
 <p>To optimize a dissipative system, it is sufficient to set an <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a>
 with a density matrix for the <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a> and
 <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective.target" title="krotov.objectives.Objective.target"><code class="xref py py-attr docutils literal notranslate"><span class="pre">target</span></code></a>, and a Liouvillian in <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective.H" title="krotov.objectives.Objective.H"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Objective.H</span></code></a>.
@@ -411,9 +417,9 @@ <h2>How to penalize population in a forbidden subspace<a class="headerlink" href
 performing the optimization in Liouville space. See the
 <a class="reference internal" href="notebooks/03_example_lambda_system_rwa_non_hermitian.html"><span class="std std-ref">Optimization of a Dissipative State-to-State Transfer in a Lambda System</span></a> for an
 example.</p>
-</div>
-<div class="section" id="how-to-optimize-for-robust-pulses">
-<span id="howtorobustoptimization"></span><h2>How to optimize for robust pulses<a class="headerlink" href="#how-to-optimize-for-robust-pulses" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-optimize-for-robust-pulses">
+<span id="howtorobustoptimization"></span><h2>How to optimize for robust pulses<a class="headerlink" href="#how-to-optimize-for-robust-pulses" title="Link to this heading">¶</a></h2>
 <p>Control fields can be made robust with respect to variations in the
 system by performing an “ensemble
 optimization” <span id="id9">[<a class="reference internal" href="99_bibliography.html#id18" title="M. H. Goerz, E. J. Halperin, J. M. Aytac, C. P. Koch, and K. B. Whaley. Robustness of high-fidelity Rydberg gates with single-site addressability. Phys. Rev. A  90, 032329 (2014). doi:10.1103/PhysRevA.90.032329.">26</a>]</span>. The idea is to sample a
@@ -439,9 +445,9 @@ <h2>How to penalize population in a forbidden subspace<a class="headerlink" href
 <p>A simpler example of an ensemble optimization is
 <a class="reference internal" href="notebooks/08_example_ensemble.html"><span class="std std-ref">Ensemble Optimization for Robust Pulses</span></a>, which considers a state-to-state
 transition in a Lamba-System with a dissipative intermediary state.</p>
-</div>
-<div class="section" id="how-to-apply-spectral-constraints">
-<span id="howtospectralconstraints"></span><h2>How to apply spectral constraints<a class="headerlink" href="#how-to-apply-spectral-constraints" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-apply-spectral-constraints">
+<span id="howtospectralconstraints"></span><h2>How to apply spectral constraints<a class="headerlink" href="#how-to-apply-spectral-constraints" title="Link to this heading">¶</a></h2>
 <p>In principle, Krotov’s method can include spectral constraints while
 maintaining the guarantee for monotonic convergence <span id="id12">[<a class="reference internal" href="99_bibliography.html#id55" title="D. M. Reich, J. P. Palao, and C. P. Koch. Optimal control under spectral constraints: enforcing multi-photon absorption pathways. J. Mod. Opt.  61, 822 (2014). doi:10.1080/09500340.2013.844866.">59</a>]</span> .
 However, the calculation of the pulse update with such spectral constraints
@@ -527,9 +533,9 @@ <h2>How to penalize population in a forbidden subspace<a class="headerlink" href
 problem physically cannot be solved with controls in the desired spectral
 range. Without a good physical intuition, trial and error may be
 required.</p>
-</div>
-<div class="section" id="how-to-limit-the-amplitude-of-the-controls">
-<h2>How to limit the amplitude of the controls<a class="headerlink" href="#how-to-limit-the-amplitude-of-the-controls" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-limit-the-amplitude-of-the-controls">
+<h2>How to limit the amplitude of the controls<a class="headerlink" href="#how-to-limit-the-amplitude-of-the-controls" title="Link to this heading">¶</a></h2>
 <p>Amplitude constraints on the control can be realized indirectly through
 parametrization <span id="id13">[<a class="reference internal" href="99_bibliography.html#id53" title="M. M. Müller, D. M. Reich, M. Murphy, H. Yuan, J. Vala, K. B. Whaley, T. Calarco, and C. P. Koch. Optimizing entangling quantum gates for physical systems. Phys. Rev. A  84, 042315 (2011). doi:10.1103/PhysRevA.84.042315.">60</a>]</span>. For example, consider the physical
 Hamiltonian <span class="math notranslate nohighlight">\(\Op{H} = \Op{H}_0 + \epsilon(t) \Op{H}_1\)</span>.</p>
@@ -582,9 +588,9 @@ <h2>How to limit the amplitude of the controls<a class="headerlink" href="#how-t
 In the meantime, you could modify the control to fit within the desired
 amplitude constaints in the same way as applying spectral constaints, see
 <a class="reference internal" href="#howtospectralconstraints"><span class="std std-ref">How to apply spectral constraints</span></a>.</p>
-</div>
-<div class="section" id="how-to-parallelize-the-optimization">
-<h2>How to parallelize the optimization<a class="headerlink" href="#how-to-parallelize-the-optimization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-parallelize-the-optimization">
+<h2>How to parallelize the optimization<a class="headerlink" href="#how-to-parallelize-the-optimization" title="Link to this heading">¶</a></h2>
 <p>Krotov’s method is inherently parallel across different objectives. See
 <a class="reference internal" href="API/krotov.parallelization.html#module-krotov.parallelization" title="krotov.parallelization"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.parallelization</span></code></a>.</p>
 <p>It is exceedingly important to ensure that you do not use any accidental nested
@@ -592,9 +598,9 @@ <h2>How to parallelize the optimization<a class="headerlink" href="#how-to-paral
 multi-threaded mode that does not combine well with the process-based
 parallelization in <a class="reference internal" href="API/krotov.parallelization.html#module-krotov.parallelization" title="krotov.parallelization"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.parallelization</span></code></a>. See
 <a class="reference internal" href="#howtolimitthreadpool"><span class="std std-ref">How to avoid over-subscribing the CPU when using parallelization</span></a>.</p>
-</div>
-<div class="section" id="how-to-avoid-over-subscribing-the-cpu-when-using-parallelization">
-<span id="howtolimitthreadpool"></span><h2>How to avoid over-subscribing the CPU when using parallelization<a class="headerlink" href="#how-to-avoid-over-subscribing-the-cpu-when-using-parallelization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-avoid-over-subscribing-the-cpu-when-using-parallelization">
+<span id="howtolimitthreadpool"></span><h2>How to avoid over-subscribing the CPU when using parallelization<a class="headerlink" href="#how-to-avoid-over-subscribing-the-cpu-when-using-parallelization" title="Link to this heading">¶</a></h2>
 <p>A common caveat of parallelization is that the number of numerically intensive
 threads or processes should not be larger than the number of CPUs on the
 machine. “Oversubscribing” the CPUs can make a parallelized program run slower
@@ -627,9 +633,9 @@ <h2>How to parallelize the optimization<a class="headerlink" href="#how-to-paral
 calculated single-threadedly.</p>
 <p>Always monitor your processes in a tool like <a class="reference external" href="https://hisham.hm/htop/">htop</a> to watch out for unexpected
 CPU usage.</p>
-</div>
-<div class="section" id="how-to-prevent-losing-an-optimization-result">
-<span id="howtostoreresult"></span><h2>How to prevent losing an optimization result<a class="headerlink" href="#how-to-prevent-losing-an-optimization-result" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-prevent-losing-an-optimization-result">
+<span id="howtostoreresult"></span><h2>How to prevent losing an optimization result<a class="headerlink" href="#how-to-prevent-losing-an-optimization-result" title="Link to this heading">¶</a></h2>
 <p>Optimizations usually take several hundred to several thousand iterations to
 fully converge. Thus, the <a class="reference internal" href="API/krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> routine  may require
 significant runtime (often multiple days for large problems). Once an
@@ -647,9 +653,9 @@ <h2>How to parallelize the optimization<a class="headerlink" href="#how-to-paral
 <cite>continue_from</cite>.  The new optimization will start from the most recent
 optimized controls as a guess, and continue to count iterations from the
 previous result. See <a class="reference internal" href="#howtocontinueoptimization"><span class="std std-ref">How to continue from a previous optimization</span></a> for further details.</p>
-</div>
-<div class="section" id="how-to-continue-from-a-previous-optimization">
-<span id="howtocontinueoptimization"></span><h2>How to continue from a previous optimization<a class="headerlink" href="#how-to-continue-from-a-previous-optimization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-continue-from-a-previous-optimization">
+<span id="howtocontinueoptimization"></span><h2>How to continue from a previous optimization<a class="headerlink" href="#how-to-continue-from-a-previous-optimization" title="Link to this heading">¶</a></h2>
 <p>See <a class="reference internal" href="#howtostoreresult"><span class="std std-ref">How to prevent losing an optimization result</span></a> for how to continue from an optimization that ended
 (crashed) prematurely.  Even when an optimization has completed normally, you
 may still want to continue with further iterations – either because you find
@@ -663,9 +669,9 @@ <h2>How to parallelize the optimization<a class="headerlink" href="#how-to-paral
 (otherwise, you may and up with inconsistencies in your <a class="reference internal" href="API/krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a>). The
 <a class="reference internal" href="API/krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> object returned by the second optimization will include all
 the data from the first optimization.</p>
-</div>
-<div class="section" id="how-to-maximize-numerical-efficiency">
-<h2>How to maximize numerical efficiency<a class="headerlink" href="#how-to-maximize-numerical-efficiency" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-maximize-numerical-efficiency">
+<h2>How to maximize numerical efficiency<a class="headerlink" href="#how-to-maximize-numerical-efficiency" title="Link to this heading">¶</a></h2>
 <p>For systems of non-trivial size, the main numerical effort should be in the
 simulation of the system dynamics. Every iteration of Krotov’s method requires
 a full backward propagation and a full forward propagation of the states associated with each
@@ -676,9 +682,9 @@ <h2>How to maximize numerical efficiency<a class="headerlink" href="#how-to-maxi
 <a class="reference internal" href="API/krotov.propagators.html#krotov.propagators.DensityMatrixODEPropagator" title="krotov.propagators.DensityMatrixODEPropagator"><code class="xref py py-class docutils literal notranslate"><span class="pre">krotov.propagators.DensityMatrixODEPropagator</span></code></a>. Going further, you
 might consider implementing the propagator with the help of lower-level instructions, e.g.,
 by using <a class="reference external" href="https://cython.org">Cython</a>.</p>
-</div>
-<div class="section" id="how-to-deal-with-the-optimization-running-out-of-memory">
-<h2>How to deal with the optimization running out of memory<a class="headerlink" href="#how-to-deal-with-the-optimization-running-out-of-memory" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-deal-with-the-optimization-running-out-of-memory">
+<h2>How to deal with the optimization running out of memory<a class="headerlink" href="#how-to-deal-with-the-optimization-running-out-of-memory" title="Link to this heading">¶</a></h2>
 <p>Krotov’s method requires the storage of at least one set of propagated state
 over the entire time grid, for each objective. For the second-order update
 equation, up to three sets of stored states per objective may be required. In
@@ -690,14 +696,14 @@ <h2>How to deal with the optimization running out of memory<a class="headerlink"
 storage objects, such as <a class="reference external" href="http://docs.dask.org/en/latest/">Dask</a> arrays. This may carry a significant penalty in
 runtime, however, as states will have to be read from disk, or across the
 network.</p>
-</div>
-<div class="section" id="how-to-avoid-the-overhead-of-qutip-objects">
-<h2>How to avoid the overhead of QuTiP objects<a class="headerlink" href="#how-to-avoid-the-overhead-of-qutip-objects" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="how-to-avoid-the-overhead-of-qutip-objects">
+<h2>How to avoid the overhead of QuTiP objects<a class="headerlink" href="#how-to-avoid-the-overhead-of-qutip-objects" title="Link to this heading">¶</a></h2>
 <p>If you know what you are doing, it is possible to set up an <a class="reference internal" href="API/krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a>
 without any <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qutip.Qobj</span></code></a> instances, using arbitrary low-level objects
 instead.  See the <a class="reference internal" href="notebooks/09_example_numpy.html"><span class="std std-ref">Optimization with numpy Arrays</span></a> for an example.</p>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -711,7 +717,7 @@ <h2>How to avoid the overhead of QuTiP objects<a class="headerlink" href="#how-t
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/11_other_methods.html b/master/11_other_methods.html
index 64499f75..3a7d2ed6 100644
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+++ b/master/11_other_methods.html
@@ -1,26 +1,32 @@
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -134,14 +140,14 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="other-optimization-methods">
-<h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-methods" title="Permalink to this headline">¶</a></h1>
+  <section id="other-optimization-methods">
+<h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-methods" title="Link to this heading">¶</a></h1>
 <p>In the following, we compare Krotov’s method to other numerical
 optimization methods that have been used widely in quantum control, with
 an emphasis on methods that have been implemented as open source
 software.</p>
-<div class="section" id="iterative-schemes-from-variational-calculus">
-<span id="iterativeschemes"></span><h2>Iterative schemes from variational calculus<a class="headerlink" href="#iterative-schemes-from-variational-calculus" title="Permalink to this headline">¶</a></h2>
+<section id="iterative-schemes-from-variational-calculus">
+<span id="iterativeschemes"></span><h2>Iterative schemes from variational calculus<a class="headerlink" href="#iterative-schemes-from-variational-calculus" title="Link to this heading">¶</a></h2>
 <p>Gradient-based optimal control methods derive the condition for the
 optimal control field from the application of the variational principle
 to the optimization functional in
@@ -159,7 +165,7 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 this extended functional. Evaluation of the extremum condition results
 in <span id="id2">[<a class="reference internal" href="99_bibliography.html#id33" title="D. J. Tannor and Y. Jin. Design of femtosecond pulse sequences to control photochemical products. In Mode Selective Chemistry, pages 333–345. Springer (1991).">64</a>]</span></p>
 <div class="math notranslate nohighlight" id="equation-variational-update">
-<span class="eqno">(20)<a class="headerlink" href="#equation-variational-update" title="Permalink to this equation">¶</a></span>\[\Delta \epsilon_l(t) \propto \frac{\delta J}{\delta \epsilon_l} \propto
+<span class="eqno">(20)<a class="headerlink" href="#equation-variational-update" title="Link to this equation">¶</a></span>\[\Delta \epsilon_l(t) \propto \frac{\delta J}{\delta \epsilon_l} \propto
     \Im \big\langle
     \chi_k(t)
     \big\vert
@@ -178,7 +184,7 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 <p>The most intuitive time-discretization yields a <em>concurrent</em> update
 scheme <span id="id3">[<a class="reference internal" href="99_bibliography.html#id34" title="D.J. Tannor, V. Kazakov, and V. Orlov. Control of photochemical branching: novel procedures for finding optimal pulses and global upper bounds. In J. Broeckhove and L. Lathouwers, editors, Time-dependent quantum molecular dynamics, pages 347-360. Plenum (1992).">5</a>, <a class="reference internal" href="99_bibliography.html#id4" title="J. Somlói, V. A. Kazakov, and D. J. Tannor. Controlled dissociation of I2 via optical transitions between the X and B electronic states. Chem. Phys.  172, 85 (1993). doi:10.1016/0301-0104(93)80108-L.">44</a>, <a class="reference internal" href="99_bibliography.html#id33" title="D. J. Tannor and Y. Jin. Design of femtosecond pulse sequences to control photochemical products. In Mode Selective Chemistry, pages 333–345. Springer (1991).">64</a>]</span>,</p>
 <div class="math notranslate nohighlight" id="equation-concurrent-update">
-<span class="eqno">(21)<a class="headerlink" href="#equation-concurrent-update" title="Permalink to this equation">¶</a></span>\[\Delta \epsilon_l^{(i)}(t) \propto
+<span class="eqno">(21)<a class="headerlink" href="#equation-concurrent-update" title="Link to this equation">¶</a></span>\[\Delta \epsilon_l^{(i)}(t) \propto
     \Im \big\langle
     \chi_k^{(i-1)}(t)
     \big\vert
@@ -204,7 +210,7 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 propagation, respectively, and use the update
 scheme <span id="id7">[<a class="reference internal" href="99_bibliography.html#id89" title="J. Werschnik and E. K. U. Gross. Quantum optimal control theory. J. Phys. B  40, R175 (2007). doi:10.1088/0953-4075/40/18/r01.">68</a>]</span></p>
 <div class="math notranslate nohighlight" id="equation-zhu-rabitz-update">
-<span class="eqno">(22)<a class="headerlink" href="#equation-zhu-rabitz-update" title="Permalink to this equation">¶</a></span>\[\begin{split}\begin{aligned}
+<span class="eqno">(22)<a class="headerlink" href="#equation-zhu-rabitz-update" title="Link to this equation">¶</a></span>\[\begin{split}\begin{aligned}
     \epsilon_l^{(i)}(t)
       &amp; = (1-\delta) \tilde\epsilon_l^{(i-1)}(t) - \frac{\delta}{\alpha}
       \Im \big\langle
@@ -228,9 +234,9 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 <span class="math notranslate nohighlight">\(\alpha\)</span>. For the control of wavepacket dynamics, an
 implementation of this generalized class of algorithms is available in
 the <a class="reference external" href="https://sourceforge.net/p/wavepacket/wiki/Home/">WavePacket Matlab package</a> <span id="id8">[<a class="reference internal" href="99_bibliography.html#id88" title="B. Schmidt and C. Hartmann. WavePacket: a Matlab package for numerical quantum dynamics. II: open quantum systems, optimal control, and model reduction. Comput. Phys. Commun.  228, 229 (2018). doi:10.1016/j.cpc.2018.02.022.">69</a>]</span>.</p>
-</div>
-<div class="section" id="krotov-s-method">
-<span id="comparisonkrotov"></span><h2>Krotov’s method<a class="headerlink" href="#krotov-s-method" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="krotov-s-method">
+<span id="comparisonkrotov"></span><h2>Krotov’s method<a class="headerlink" href="#krotov-s-method" title="Link to this heading">¶</a></h2>
 <p>The method developed by
 Krotov <span id="id9">[<a class="reference internal" href="99_bibliography.html#id105" title="V. F. Krotov and I. N. Fel'dman. An iterative method for solving optimal-control problems. Engrg. Cybernetics  21, 123 (1983). doi:.">40</a>, <a class="reference internal" href="99_bibliography.html#id106" title="V. F. Krotov. A technique of global bounds in optimal control theory. Control and Cybernetics  17, 115 (1988). doi:.">41</a>, <a class="reference internal" href="99_bibliography.html#id5" title="V. Krotov. Global Methods in Optimal Control Theory. CRC Press (1995).">42</a>, <a class="reference internal" href="99_bibliography.html#id6" title="A.I. Konnov and V. F. Krotov. On global methods of successive improvement of controlled processes. Autom. Rem. Contr.  60, 1427 (1999).">43</a>]</span>
 and later translated to the language of quantum control by Tannor and
@@ -243,7 +249,7 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 can be boiled down to the update in each iteration <span class="math notranslate nohighlight">\((i)\)</span>,
 Eq. <a class="reference internal" href="07_krotovs_method.html#equation-update">(3)</a>, taking the form</p>
 <div class="math notranslate nohighlight" id="equation-sequential-update">
-<span class="eqno">(23)<a class="headerlink" href="#equation-sequential-update" title="Permalink to this equation">¶</a></span>\[\Delta \epsilon_l^{(i)}(t) \propto
+<span class="eqno">(23)<a class="headerlink" href="#equation-sequential-update" title="Link to this equation">¶</a></span>\[\Delta \epsilon_l^{(i)}(t) \propto
     \Im \big\langle
     \chi_k^{(i-1)}(t)
     \big\vert
@@ -299,9 +305,9 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 numerical instability and should be avoided. A mixture of <em>update</em> and
 <em>replacement</em> is possible when a penalty of the pulse fluence is
 necessary <span id="id16">[<a class="reference internal" href="99_bibliography.html#id2" title="M. Goerz. Optimizing Robust Quantum Gates in Open Quantum Systems. PhD thesis, Universität Kassel, (2015). URL: https://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2015052748381.">73</a>]</span>.</p>
-</div>
-<div class="section" id="gradient-ascent-pulse-engineering-grape">
-<span id="grape"></span><h2>GRadient Ascent Pulse Engineering (GRAPE)<a class="headerlink" href="#gradient-ascent-pulse-engineering-grape" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="gradient-ascent-pulse-engineering-grape">
+<span id="grape"></span><h2>GRadient Ascent Pulse Engineering (GRAPE)<a class="headerlink" href="#gradient-ascent-pulse-engineering-grape" title="Link to this heading">¶</a></h2>
 <p>While the monotonically convergent methods based on variational calculus
 must “guess” the appropriate time discretization, and Krotov’s method
 finds the sequential time discretization by a clever construction, the
@@ -314,7 +320,7 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 <span class="math notranslate nohighlight">\(\epsilon^{(i-1)}_n\)</span> in iteration <span class="math notranslate nohighlight">\((i)\)</span> of the optimization
 are obtained as</p>
 <div class="math notranslate nohighlight" id="equation-discrete-time-evolution">
-<span class="eqno">(24)<a class="headerlink" href="#equation-discrete-time-evolution" title="Permalink to this equation">¶</a></span>\[\ket{\phi^{(i-1)}_k(T)} = \Op{U}^{(i-1)}_{N_T} \dots
+<span class="eqno">(24)<a class="headerlink" href="#equation-discrete-time-evolution" title="Link to this equation">¶</a></span>\[\ket{\phi^{(i-1)}_k(T)} = \Op{U}^{(i-1)}_{N_T} \dots
 \Op{U}^{(i-1)}_{n} \dots \Op{U}^{(i-1)}_{1} \big\vert \phi_k \big\rangle\,,\]</div>
 <p>where <span class="math notranslate nohighlight">\(\Op{U}^{(i-1)}_{n}\)</span> is the time evolution operator on the
 time interval <span class="math notranslate nohighlight">\(n\)</span> in Hilbert space,</p>
@@ -338,7 +344,7 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 Eqs. <a class="reference internal" href="07_krotovs_method.html#equation-jtss">(5)</a>, <a class="reference internal" href="07_krotovs_method.html#equation-jtre">(7)</a>. Using Eq. <a class="reference internal" href="#equation-discrete-time-evolution">(24)</a>
 leads to</p>
 <div class="math notranslate nohighlight" id="equation-gradient">
-<span class="eqno">(25)<a class="headerlink" href="#equation-gradient" title="Permalink to this equation">¶</a></span>\[\begin{split}\begin{split}
+<span class="eqno">(25)<a class="headerlink" href="#equation-gradient" title="Link to this equation">¶</a></span>\[\begin{split}\begin{split}
   \frac{\partial \tau_k}{\partial \epsilon_n} &amp;= \frac{\partial}{\partial
   \epsilon_n} \big\langle \phi_k^{\tgt} \big\vert \Op{U}^{(i-1)}_{N_T} \dots
   \Op{U}^{(i-1)}_{n} \dots \Op{U}^{(i-1)}_{1} \big\vert \phi_k \big\rangle \\
@@ -460,9 +466,9 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 field, and thus may avoid the associated numerical error. This allows to
 optimize to extremely high fidelities as required for some error
 correction protocols <span id="id36">[<a class="reference internal" href="99_bibliography.html#id96" title="S. Machnes, E. Assémat, D. Tannor, and F. K. Wilhelm. Tunable, flexible, and efficient optimization of control pulses for practical qubits. Phys. Rev. Lett.  120, 150401 (2018). doi:10.1103/PhysRevLett.120.150401.">94</a>]</span>.</p>
-</div>
-<div class="section" id="grape-in-qutip">
-<span id="grapeinqutip"></span><h2>GRAPE in QuTiP<a class="headerlink" href="#grape-in-qutip" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="grape-in-qutip">
+<span id="grapeinqutip"></span><h2>GRAPE in QuTiP<a class="headerlink" href="#grape-in-qutip" title="Link to this heading">¶</a></h2>
 <p>An implementation of GRAPE is included in QuTiP, see the <a class="reference external" href="http://qutip.org/docs/latest/guide/guide-control.html">section on Quantum
 Optimal Control in the QuTiP docs</a>.  It is used via the
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.control.pulseoptim.optimize_pulse" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.control.pulseoptim.optimize_pulse()</span></code></a> function.
@@ -494,9 +500,9 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 </ul>
 <p>Thus, QuTiP’s GRAPE implementation and the implementation of Krotov’s method in
 this package complement each other, but will not compare directly.</p>
-</div>
-<div class="section" id="gradient-free-optimization">
-<span id="gradientfreeoptimization"></span><h2>Gradient-free optimization<a class="headerlink" href="#gradient-free-optimization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="gradient-free-optimization">
+<span id="gradientfreeoptimization"></span><h2>Gradient-free optimization<a class="headerlink" href="#gradient-free-optimization" title="Link to this heading">¶</a></h2>
 <p>In situations where the problem can be reduced to a relatively small
 number of control parameters (typically less than <span class="math notranslate nohighlight">\(\approx20\)</span>,
 although this number may be pushed to <span class="math notranslate nohighlight">\(\approx50\)</span> by sequential
@@ -564,12 +570,13 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 <a class="reference external" href="https://github.com/facebookresearch/nevergrad">Nevergrad</a> <span id="id43">[<a class="reference internal" href="99_bibliography.html#id117" title="J. Rapin and O. Teytaud. Nevergrad - A gradient-free optimization platform. https://GitHub.com/FacebookResearch/Nevergrad, (2018).">101</a>]</span> that may be worth exploring for
 improvements in numerical efficiency and additional functionality such
 as support for non-linear constraints.</p>
-</div>
-<div class="section" id="choosing-an-optimization-method">
-<span id="id44"></span><h2>Choosing an optimization method<a class="headerlink" href="#choosing-an-optimization-method" title="Permalink to this headline">¶</a></h2>
-<div class="figure align-default" id="id52">
+</section>
+<section id="choosing-an-optimization-method">
+<span id="id44"></span><h2>Choosing an optimization method<a class="headerlink" href="#choosing-an-optimization-method" title="Link to this heading">¶</a></h2>
+<figure class="align-default" id="id52">
 <span id="figoctdecisiontree"></span><a class="reference internal image-reference" href="_images/oct_decision_tree.svg"><img alt="OCT decision tree." src="_images/oct_decision_tree.svg" width="100%" /></a>
-<p class="caption"><span class="caption-number">Fig. 2 </span><span class="caption-text">Decision tree for the choice of a numerical open-loop
+<figcaption>
+<p><span class="caption-number">Fig. 2 </span><span class="caption-text">Decision tree for the choice of a numerical open-loop
 optimization method.
 The choice of control method is most directly associated with the number of
 control parameters (<span class="math notranslate nohighlight">\(n\)</span>).
@@ -583,8 +590,9 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 components. Each method in the diagram is meant to include all its
 variants, a multitude of gradient-free methods and e.g. DCRAB for
 CRAB, GRAPE-LBFGS and sequential/hybrid gradient-descent for GRAPE,
-and K-BFGS for Krotov’s method, see text for detail.</span><a class="headerlink" href="#id52" title="Permalink to this image">¶</a></p>
-</div>
+and K-BFGS for Krotov’s method, see text for detail.</span><a class="headerlink" href="#id52" title="Link to this image">¶</a></p>
+</figcaption>
+</figure>
 <p>In the following, we discuss some of the concerns in the choice of
 optimization methods. The discussion is limited to iterative open-loop
 methods, where the optimization is based on a numerical simulation of
@@ -633,8 +641,8 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 landscape of a particular system <span id="id50">[<a class="reference internal" href="99_bibliography.html#id98" title="M. H. Goerz, F. Motzoi, K. B. Whaley, and C. P. Koch. Charting the circuit QED design landscape using optimal control theory. npj Quantum Information  3, 37 (2017). doi:10.1038/s41534-017-0036-0.">103</a>]</span> or
 determining the quantum speed limit, i.e., the minimum time in which the
 system can reach a given target <span id="id51">[<a class="reference internal" href="99_bibliography.html#id43" title="J. J. W. H. Sørensen, M. K. Pedersen, M. Munch, P. Haikka, J. H. Jensen, T. Planke, M. G. Andreasen, M. Gajdacz, K. Mølmer, A. Lieberoth, J. F. Sherson, and Q. M. players. Exploring the quantum speed limit with computer games. Nature  532, 210 (2016). doi:10.1038/nature17620.">15</a>, <a class="reference internal" href="99_bibliography.html#id99" title="T. Caneva, M. Murphy, T. Calarco, R. Fazio, S. Montangero, V. Giovannetti, and G. E. Santoro. Optimal control at the quantum speed limit. Phys. Rev. Lett.  103, 240501 (2009). doi:10.1103/PhysRevLett.103.240501.">104</a>, <a class="reference internal" href="99_bibliography.html#id100" title="M. H. Goerz, T. Calarco, and C. P. Koch. The quantum speed limit of optimal controlled phasegates for trapped neutral atoms. J. Phys. B  44, 154011 (2011). doi:10.1088/0953-4075/44/15/154011.">105</a>]</span>.</p>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -648,7 +656,7 @@ <h1>Other Optimization Methods<a class="headerlink" href="#other-optimization-me
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/12_related_software.html b/master/12_related_software.html
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+++ b/master/12_related_software.html
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@@ -38,7 +44,7 @@
             Krotov
           </a>
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-                1.3.0
+                1.3.0+dev
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@@ -48,7 +54,7 @@
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-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -128,11 +134,11 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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-  <div class="section" id="related-software">
-<h1>Related Software<a class="headerlink" href="#related-software" title="Permalink to this headline">¶</a></h1>
+  <section id="related-software">
+<h1>Related Software<a class="headerlink" href="#related-software" title="Link to this heading">¶</a></h1>
 <p>Is there any software missing from this list? <a class="reference external" href="https://github.com/qucontrol/krotov/issues/new?assignees=&amp;labels=documentation&amp;template=related-software.md&amp;title=">Open an issue!</a></p>
-<div class="section" id="other-implementations-of-quantum-control">
-<h2>Other implementations of quantum control<a class="headerlink" href="#other-implementations-of-quantum-control" title="Permalink to this headline">¶</a></h2>
+<section id="other-implementations-of-quantum-control">
+<h2>Other implementations of quantum control<a class="headerlink" href="#other-implementations-of-quantum-control" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.qdyn-library.net">QDYN</a> (Fortran) – comprehensive package for quantum dynamics and control. The implementation of the <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a> package was inspired by QDYN.</p></li>
 <li><p><a class="reference external" href="http://qutip.org">QuTiP</a> (Python) – Quantum Toolbox in Python <span id="id1">[<a class="reference internal" href="99_bibliography.html#id25" title="J.R. Johansson, P.D. Nation, and F. Nori. QuTiP: an open-source Python framework for the dynamics of open quantum systems. Comput. Phys. Commun.  183, 1760 (2012). doi:10.1016/j.cpc.2012.02.021.">106</a>, <a class="reference internal" href="99_bibliography.html#id26" title="J.R. Johansson, P.D. Nation, and F. Nori. QuTiP 2: a Python framework for the dynamics of open quantum systems. Comput. Phys. Commun.  184, 1234 (2013). URL: http://qutip.org, doi:10.1016/j.cpc.2012.11.019.">107</a>]</span>. Provides the basic data structures for the <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a>. Contains implementation of <a class="reference internal" href="11_other_methods.html#grapeinqutip"><span class="std std-ref">GRAPE</span></a> and <a class="reference internal" href="11_other_methods.html#gradientfreeoptimization"><span class="std std-ref">CRAB</span></a>.</p></li>
@@ -140,15 +146,15 @@ <h2>Other implementations of quantum control<a class="headerlink" href="#other-i
 <li><p><a class="reference external" href="https://github.com/shaimach/Dynamo">DYNAMO</a> (Matlab) <span id="id3">[<a class="reference internal" href="99_bibliography.html#id50" title="S. Machnes, U. Sander, S. J. Glaser, P. de Fouquières, A. Gruslys, S. Schirmer, and T. Schulte-Herbrüggen. Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework. Phys. Rev. A  84, 022305 (2011). doi:10.1103/PhysRevA.84.022305.">71</a>]</span> – Implementation of <a class="reference internal" href="11_other_methods.html#grape"><span class="std std-ref">GRadient Ascent Pulse Engineering (GRAPE)</span></a>, including concurrent/sequenetial/hybrid schemes</p></li>
 <li><p><a class="reference external" href="https://gitlab.com/quatomic/qengine">QEngine</a> (C++) <span id="id4">[<a class="reference internal" href="99_bibliography.html#id122" title="J. J. Sørensen, J. H. M. Jensen, T. Heinzel, and J. F. Sherson. QEngine: a C++ library for quantum optimal control of ultracold atoms. Comput. Phys. Commun.  243, 135 (2019). doi:10.1016/j.cpc.2019.04.020.">93</a>]</span> – Implementation of <a class="reference internal" href="11_other_methods.html#grape"><span class="std std-ref">“GRadient Optimization Using Parametrization” (GROUP)</span></a> <span id="id5">[<a class="reference internal" href="99_bibliography.html#id108" title="J. J. W. H. Sørensen, M. O. Aranburu, T. Heinzel, and J. F. Sherson. Quantum optimal control in a chopped basis: applications in control of Bose-Einstein condensates. Phys. Rev. A  98, 022119 (2018). doi:10.1103/PhysRevA.98.022119.">92</a>]</span></p></li>
 </ul>
-</div>
-<div class="section" id="accessories">
-<h2>Accessories<a class="headerlink" href="#accessories" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="accessories">
+<h2>Accessories<a class="headerlink" href="#accessories" title="Link to this heading">¶</a></h2>
 <p>The following packages integrate closely with <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a>.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/qucontrol/weylchamber">weylchamber</a> (Python) – Package for analyzing two-qubit gates in the Weyl chamber. Provides <a class="reference internal" href="10_howto.html#howtolioptimization"><span class="std std-ref">Local-Invariants</span></a> and <a class="reference internal" href="10_howto.html#howtopeoptimization"><span class="std std-ref">Perfect Entangler</span></a> functionals for use with <a class="reference internal" href="API/krotov.html#module-krotov" title="krotov"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov</span></code></a>.</p></li>
 </ul>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -162,7 +168,7 @@ <h2>Accessories<a class="headerlink" href="#accessories" title="Permalink to thi
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/99_bibliography.html b/master/99_bibliography.html
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--- a/master/99_bibliography.html
+++ b/master/99_bibliography.html
@@ -1,23 +1,29 @@
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+  <title>References &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=649a27d8" />
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@@ -38,7 +44,7 @@
             Krotov
           </a>
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+                1.3.0+dev
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@@ -48,7 +54,7 @@
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-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -124,334 +130,441 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="references">
-<h1>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h1>
+  <section id="references">
+<h1>References<a class="headerlink" href="#references" title="Link to this heading">¶</a></h1>
 <div class="docutils container" id="id1">
-<dl class="citation">
-<dt class="label" id="id41"><span class="brackets">1</span></dt>
-<dd><p>A. Acín, I. Bloch, H. Buhrman, T. Calarco, C. Eichler, J. Eisert, D. Esteve, N. Gisin, S. J. Glaser, F. Jelezko, S. Kuhr, M. Lewenstein, M. F. Riedel, P. O. Schmidt, R. Thew, A. Wallraff, I. Walmsley, and F. K. Wilhelm. <em>The quantum technologies roadmap: a European community view</em>. New J. Phys.  <strong>20</strong>, 080201 (2018). <a class="reference external" href="https://doi.org/10.1088/1367-2630/aad1ea">doi:10.1088/1367-2630/aad1ea</a>.</p>
-</dd>
-<dt class="label" id="id31"><span class="brackets">2</span></dt>
-<dd><p>M. A. Nielsen and I. L. Chuang. <em>Quantum Computation and Quantum Information</em>. Cambridge University Press (2000).</p>
-</dd>
-<dt class="label" id="id32"><span class="brackets">3</span></dt>
-<dd><p>C. L. Degen, F. Reinhard, and P. Cappellaro. <em>Quantum sensing</em>. Rev. Mod. Phys.  <strong>89</strong>, 035002 (2017). <a class="reference external" href="https://doi.org/10.1103/RevModPhys.89.035002">doi:10.1103/RevModPhys.89.035002</a>.</p>
-</dd>
-<dt class="label" id="id28"><span class="brackets">4</span></dt>
-<dd><p>S. J. Glaser, U. Boscain, T. Calarco, C. P. Koch, W. Köckenberger, R. Kosloff, I. Kuprov, B. Luy, S. Schirmer, T. Schulte-Herbrüggen, D. Sugny, and F. K. Wilhelm. <em>Training Schrödinger's cat: quantum optimal control</em>. Eur. Phys. J. D  <strong>69</strong>, 279 (2015). <a class="reference external" href="https://doi.org/10.1140/epjd/e2015-60464-1">doi:10.1140/epjd/e2015-60464-1</a>.</p>
-</dd>
-<dt class="label" id="id34"><span class="brackets">5</span></dt>
-<dd><p>D.J. Tannor, V. Kazakov, and V. Orlov. <em>Control of photochemical branching: novel procedures for finding optimal pulses and global upper bounds</em>. In J. Broeckhove and L. Lathouwers, editors, <em>Time-dependent quantum molecular dynamics</em>, pages 347–360. Plenum (1992).</p>
-</dd>
-<dt class="label" id="id35"><span class="brackets">6</span></dt>
-<dd><p>P. Gross, D. Neuhauser, and H. Rabitz. <em>Optimal control of curve-crossing systems</em>. J. Chem. Phys.  <strong>96</strong>, 2834 (1992). <a class="reference external" href="https://doi.org/10.1063/1.461980">doi:10.1063/1.461980</a>.</p>
-</dd>
-<dt class="label" id="id36"><span class="brackets">7</span></dt>
-<dd><p>J. B. Murdoch, A. H. Lent, and M. R. Kritzer. <em>Computer-optimized narrowband pulses for multislice imaging</em>. J. Magnet. Res.  <strong>74</strong>, 226 (1987). <a class="reference external" href="https://doi.org/10.1016/0022-2364(87)90336-2">doi:10.1016/0022-2364(87)90336-2</a>.</p>
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-<dd><p>S. J. Glaser and G. P. Drobny. <em>The tailored TOCSY experiment: Chemical shift selective coherence transfer</em>. Chem. Phys. Lett.  <strong>164</strong>, 456 (1989). <a class="reference external" href="https://doi.org/10.1016/0009-2614(89)85238-8">doi:10.1016/0009-2614(89)85238-8</a>.</p>
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-<dd><p>J. Cui, R. van Bijnen, T. Pohl, S. Montangero, and T. Calarco. <em>Optimal control of Rydberg lattice gases</em>. Quantum Sci. Technol.  <strong>2</strong>, 035006 (2017). <a class="reference external" href="https://doi.org/10.1088/2058-9565/aa7daf">doi:10.1088/2058-9565/aa7daf</a>.</p>
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-<dd><p>S. Patsch, D. M. Reich, J.-M. Raimond, M. Brune, S. Gleyzes, and C. P. Koch. <em>Fast and accurate circularization of a Rydberg atom</em>. Phys. Rev. A  <strong>97</strong>, 053418 (2018). <a class="reference external" href="https://doi.org/10.1103/PhysRevA.97.053418">doi:10.1103/PhysRevA.97.053418</a>.</p>
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-<dd><p>N. Ofek, A. Petrenko, R. Heeres, P. Reinhold, Z. Leghtas, B. Vlastakis, Y. Liu, L. Frunzio, S. M. Girvin, L. Jiang, M. Mirrahimi, M. H. Devoret, and R. J. Schoelkopf. <em>Extending the lifetime of a quantum bit with error correction in superconducting circuits</em>. Nature  <strong>536</strong>, 441 (2016). <a class="reference external" href="https://doi.org/10.1038/nature18949">doi:10.1038/nature18949</a>.</p>
-</dd>
-<dt class="label" id="id43"><span class="brackets">15</span></dt>
-<dd><p>J. J. W. H. Sørensen, M. K. Pedersen, M. Munch, P. Haikka, J. H. Jensen, T. Planke, M. G. Andreasen, M. Gajdacz, K. Mølmer, A. Lieberoth, J. F. Sherson, and Q. M. players. <em>Exploring the quantum speed limit with computer games</em>. Nature  <strong>532</strong>, 210 (2016). <a class="reference external" href="https://doi.org/10.1038/nature17620">doi:10.1038/nature17620</a>.</p>
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+<p>I.R. Petersen. <em>Quantum control theory and applications: a survey</em>. IET Control Theory &amp; Applications  <strong>4</strong>, 2651 (2010). <a class="reference external" href="https://doi.org/10.1049/iet-cta.2009.0508">doi:10.1049/iet-cta.2009.0508</a>.</p>
+</div>
+<div class="citation" id="id98" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>103<span class="fn-bracket">]</span></span>
+<p>M. H. Goerz, F. Motzoi, K. B. Whaley, and C. P. Koch. <em>Charting the circuit QED design landscape using optimal control theory</em>. npj Quantum Information  <strong>3</strong>, 37 (2017). <a class="reference external" href="https://doi.org/10.1038/s41534-017-0036-0">doi:10.1038/s41534-017-0036-0</a>.</p>
+</div>
+<div class="citation" id="id99" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>104<span class="fn-bracket">]</span></span>
+<p>T. Caneva, M. Murphy, T. Calarco, R. Fazio, S. Montangero, V. Giovannetti, and G. E. Santoro. <em>Optimal control at the quantum speed limit</em>. Phys. Rev. Lett.  <strong>103</strong>, 240501 (2009). <a class="reference external" href="https://doi.org/10.1103/PhysRevLett.103.240501">doi:10.1103/PhysRevLett.103.240501</a>.</p>
+</div>
+<div class="citation" id="id100" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>105<span class="fn-bracket">]</span></span>
+<p>M. H. Goerz, T. Calarco, and C. P. Koch. <em>The quantum speed limit of optimal controlled phasegates for trapped neutral atoms</em>. J. Phys. B  <strong>44</strong>, 154011 (2011). <a class="reference external" href="https://doi.org/10.1088/0953-4075/44/15/154011">doi:10.1088/0953-4075/44/15/154011</a>.</p>
+</div>
+<div class="citation" id="id25" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>106<span class="fn-bracket">]</span></span>
+<p>J.R. Johansson, P.D. Nation, and F. Nori. <em>QuTiP: an open-source Python framework for the dynamics of open quantum systems</em>. Comput. Phys. Commun.  <strong>183</strong>, 1760 (2012). <a class="reference external" href="https://doi.org/10.1016/j.cpc.2012.02.021">doi:10.1016/j.cpc.2012.02.021</a>.</p>
+</div>
+<div class="citation" id="id26" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>107<span class="fn-bracket">]</span></span>
+<p>J.R. Johansson, P.D. Nation, and F. Nori. <em>QuTiP 2: a Python framework for the dynamics of open quantum systems</em>. Comput. Phys. Commun.  <strong>184</strong>, 1234 (2013). URL: <a class="reference external" href="http://qutip.org">http://qutip.org</a>, <a class="reference external" href="https://doi.org/10.1016/j.cpc.2012.11.019">doi:10.1016/j.cpc.2012.11.019</a>.</p>
+</div>
 </div>
 </div>
+</section>
 
 
            </div>
@@ -465,7 +578,7 @@ <h1>References<a class="headerlink" href="#references" title="Permalink to this
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.convergence.html b/master/API/krotov.convergence.html
index 3ff4a2ec..131882ce 100644
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
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@@ -50,7 +56,7 @@
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -70,7 +76,16 @@
 <li class="toctree-l1 current"><a class="reference internal" href="krotov.html">API of the Krotov package</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.convergence module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.convergence.Or"><code class="docutils literal notranslate"><span class="pre">Or()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.convergence.value_below"><code class="docutils literal notranslate"><span class="pre">value_below()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.convergence.value_above"><code class="docutils literal notranslate"><span class="pre">value_above()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.convergence.delta_below"><code class="docutils literal notranslate"><span class="pre">delta_below()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.convergence.check_monotonic_error"><code class="docutils literal notranslate"><span class="pre">check_monotonic_error()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.convergence.check_monotonic_fidelity"><code class="docutils literal notranslate"><span class="pre">check_monotonic_fidelity()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.convergence.dump_result"><code class="docutils literal notranslate"><span class="pre">dump_result()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.conversions.html">krotov.conversions module</a></li>
@@ -153,8 +168,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.convergence">
-<span id="krotov-convergence-module"></span><h1>krotov.convergence module<a class="headerlink" href="#module-krotov.convergence" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.convergence">
+<span id="krotov-convergence-module"></span><h1>krotov.convergence module<a class="headerlink" href="#module-krotov.convergence" title="Link to this heading">¶</a></h1>
 <p>Routines for <cite>check_convergence</cite> in <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">krotov.optimize.optimize_pulses()</span></code></a></p>
 <p>A <cite>check_convergence</cite> function may be used to determine whether an optimization
 is converged, and thus can be stopped before the maximum number of
@@ -212,14 +227,10 @@
 <a class="reference internal" href="#krotov.convergence.Or" title="krotov.convergence.Or"><code class="xref py py-func docutils literal notranslate"><span class="pre">Or()</span></code></a>. Dumping the current state of the optimization at regular intervals
 protects against losing the results of a long running optimization in the event
 of a crash.</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.convergence.Or" title="krotov.convergence.Or"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Or</span></code></a></p></td>
 <td><p>Chain multiple <cite>check_convergence</cite> functions together in a logical Or.</p></td>
@@ -245,36 +256,36 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.convergence.Or" title="krotov.convergence.Or"><code class="xref py py-func docutils literal notranslate"><span class="pre">Or</span></code></a>, <a class="reference internal" href="#krotov.convergence.check_monotonic_error" title="krotov.convergence.check_monotonic_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_monotonic_error</span></code></a>, <a class="reference internal" href="#krotov.convergence.check_monotonic_fidelity" title="krotov.convergence.check_monotonic_fidelity"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_monotonic_fidelity</span></code></a>, <a class="reference internal" href="#krotov.convergence.delta_below" title="krotov.convergence.delta_below"><code class="xref py py-func docutils literal notranslate"><span class="pre">delta_below</span></code></a>, <a class="reference internal" href="#krotov.convergence.dump_result" title="krotov.convergence.dump_result"><code class="xref py py-func docutils literal notranslate"><span class="pre">dump_result</span></code></a>, <a class="reference internal" href="#krotov.convergence.value_above" title="krotov.convergence.value_above"><code class="xref py py-func docutils literal notranslate"><span class="pre">value_above</span></code></a>, <a class="reference internal" href="#krotov.convergence.value_below" title="krotov.convergence.value_below"><code class="xref py py-func docutils literal notranslate"><span class="pre">value_below</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.convergence.Or">
-<code class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></code><code class="sig-name descname"><span class="pre">Or</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">funcs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#Or"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.Or" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.convergence.Or">
+<span class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></span><span class="sig-name descname"><span class="pre">Or</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">funcs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#Or"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.Or" title="Link to this definition">¶</a></dt>
 <dd><p>Chain multiple <cite>check_convergence</cite> functions together in a logical Or.</p>
 <p>Each parameter must be a function suitable to pass to
 <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> as <cite>check_convergence</cite>. It
 must receive a <a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> object and should return None or a string
 message.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p>A function <code class="docutils literal notranslate"><span class="pre">check_convergence(result)</span></code> that returns the
 result of the first “non-passing” function in <cite>*funcs</cite>. A “non-passing”
 result is one that evaluates to True in a Boolean context (should be a
 string message)</p>
 </dd>
-<dt class="field-even">Return type</dt>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
 <dd class="field-even"><p>callable</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.convergence.value_below">
-<code class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></code><code class="sig-name descname"><span class="pre">value_below</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-</span> <span class="pre">1])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#value_below"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.value_below" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.convergence.value_below">
+<span class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></span><span class="sig-name descname"><span class="pre">value_below</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-1])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#value_below"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.value_below" title="Link to this definition">¶</a></dt>
 <dd><p>Constructor for routine that checks if a value is below <cite>limit</cite></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>limit</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – A float value (or str-representation of a float)
 against which to compare the value extracted from <a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a></p></li>
@@ -285,19 +296,19 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>). This should be some kind of error
 measure, e.g., the value of the functional <span class="math notranslate nohighlight">\(J_T\)</span> that is being
 minimized.</p></li>
-<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – A name identifying the checked value, used for the
+<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><em>None</em>) – A name identifying the checked value, used for the
 message returned by the <cite>check_convergence</cite> routine. Defaults to
 <code class="docutils literal notranslate"><span class="pre">str(spec)</span></code>.</p></li>
 <li><p><strong>**kwargs</strong> – Keyword arguments to pass to <a class="reference external" href="https://glom.readthedocs.io/en/latest/api.html#glom.glom" title="(in glom v23.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">glom()</span></code></a> (see Note)</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>A function <code class="docutils literal notranslate"><span class="pre">check_convergence(result)</span></code> that extracts the
 value specified by <cite>spec</cite> from the <a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> object, and checks
 it against <cite>limit</cite>. If the value is below the <cite>limit</cite>, it returns an
 appropriate message string. Otherwise, it returns None.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>callable</p>
 </dd>
 </dl>
@@ -327,8 +338,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.convergence.value_above">
-<code class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></code><code class="sig-name descname"><span class="pre">value_above</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-</span> <span class="pre">1])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#value_above"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.value_above" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.convergence.value_above">
+<span class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></span><span class="sig-name descname"><span class="pre">value_above</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-1])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#value_above"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.value_above" title="Link to this definition">¶</a></dt>
 <dd><p>Constructor for routine that checks if a value is above <cite>limit</cite></p>
 <p>Like <a class="reference internal" href="#krotov.convergence.value_below" title="krotov.convergence.value_below"><code class="xref py py-func docutils literal notranslate"><span class="pre">value_below()</span></code></a>, but for checking whether an extracted value is
 <em>above</em>, not below a value. By default, it looks at the last value in
@@ -355,12 +366,12 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.convergence.delta_below">
-<code class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></code><code class="sig-name descname"><span class="pre">delta_below</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-</span> <span class="pre">1])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-</span> <span class="pre">2])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">absolute_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#delta_below"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.delta_below" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.convergence.delta_below">
+<span class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></span><span class="sig-name descname"><span class="pre">delta_below</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">limit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-1])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('info_vals',</span> <span class="pre">T[-2])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">absolute_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#delta_below"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.delta_below" title="Link to this definition">¶</a></dt>
 <dd><p>Constructor for a routine that checks if
 <span class="math notranslate nohighlight">\(\Abs{v_1 - v_0} &lt; \varepsilon\)</span></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>limit</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – A float value (or str-representation of a float)
 for <span class="math notranslate nohighlight">\(\varepsilon\)</span></p></li>
@@ -372,7 +383,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 extracting the last-but-one value in <a class="reference internal" href="krotov.result.html#krotov.result.Result.info_vals" title="krotov.result.Result.info_vals"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Result.info_vals</span></code></a>.</p></li>
 <li><p><strong>absolute_value</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If False, check for <span class="math notranslate nohighlight">\(v_1 - v_0 &lt; \varepsilon\)</span>,
 instead of the absolute value.</p></li>
-<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – A name identifying the delta, used for the
+<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><em>None</em>) – A name identifying the delta, used for the
 message returned by the <cite>check_convergence</cite> routine. Defaults to
 <code class="docutils literal notranslate"><span class="pre">&quot;Δ({spec1},{spec0}&quot;</span></code>.</p></li>
 <li><p><strong>**kwargs</strong> – Keyword arguments to pass to <a class="reference external" href="https://glom.readthedocs.io/en/latest/api.html#glom.glom" title="(in glom v23.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">glom()</span></code></a></p></li>
@@ -407,8 +418,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.convergence.check_monotonic_error">
-<code class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></code><code class="sig-name descname"><span class="pre">check_monotonic_error</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#check_monotonic_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.check_monotonic_error" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.convergence.check_monotonic_error">
+<span class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></span><span class="sig-name descname"><span class="pre">check_monotonic_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#check_monotonic_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.check_monotonic_error" title="Link to this definition">¶</a></dt>
 <dd><p>Check for monotonic convergence with respect to the error</p>
 <p>Check that the last value in <a class="reference internal" href="krotov.result.html#krotov.result.Result.info_vals" title="krotov.result.Result.info_vals"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Result.info_vals</span></code></a> is
 smaller than the last-but-one value. If yes, return None. If no, return an
@@ -437,8 +448,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.convergence.check_monotonic_fidelity">
-<code class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></code><code class="sig-name descname"><span class="pre">check_monotonic_fidelity</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#check_monotonic_fidelity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.check_monotonic_fidelity" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.convergence.check_monotonic_fidelity">
+<span class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></span><span class="sig-name descname"><span class="pre">check_monotonic_fidelity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#check_monotonic_fidelity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.check_monotonic_fidelity" title="Link to this definition">¶</a></dt>
 <dd><p>Check for monotonic convergence with respect to the fidelity</p>
 <p>This is like <a class="reference internal" href="#krotov.convergence.check_monotonic_error" title="krotov.convergence.check_monotonic_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">check_monotonic_error()</span></code></a>, but looking for a monotonic
 <em>increase</em> in the values in <a class="reference internal" href="krotov.result.html#krotov.result.Result.info_vals" title="krotov.result.Result.info_vals"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Result.info_vals</span></code></a>. Thus, it is assumed
@@ -458,8 +469,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.convergence.dump_result">
-<code class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></code><code class="sig-name descname"><span class="pre">dump_result</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">every</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#dump_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.dump_result" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.convergence.dump_result">
+<span class="sig-prename descclassname"><span class="pre">krotov.convergence.</span></span><span class="sig-name descname"><span class="pre">dump_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">every</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/convergence.html#dump_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.convergence.dump_result" title="Link to this definition">¶</a></dt>
 <dd><p>Return a function for dumping the result every so many iterations</p>
 <p>For long-running optimizations, it can be useful to dump the current state
 of the optimization every once in a while, so that the result is not lost
@@ -469,7 +480,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <a class="reference internal" href="krotov.result.html#krotov.result.Result.dump" title="krotov.result.Result.dump"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Result.dump()</span></code></a>). Failure to write the dump file stops the
 optimization.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – Name of file to dump to. This may include a field
 <code class="docutils literal notranslate"><span class="pre">{iter}</span></code> which will be formatted with the most recent iteration
@@ -501,8 +512,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </ul>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -516,7 +527,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.conversions.html b/master/API/krotov.conversions.html
index d3d55cc6..b8d2f08d 100644
--- a/master/API/krotov.conversions.html
+++ b/master/API/krotov.conversions.html
@@ -1,23 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.conversions module &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
+  <title>krotov.conversions module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../_static/mycss.css" type="text/css" />
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-        <script src="../_static/docs-versions-menu.js"></script>
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+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../_static/copybutton.js?v=f281be69"></script>
+        <script src="../_static/docs-versions-menu.js?v=3d6a1aea"></script>
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     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
@@ -38,7 +44,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -48,7 +54,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -69,7 +75,16 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.convergence.html">krotov.convergence module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.conversions module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.conversions.control_onto_interval"><code class="docutils literal notranslate"><span class="pre">control_onto_interval()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.conversions.discretize"><code class="docutils literal notranslate"><span class="pre">discretize()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.conversions.extract_controls"><code class="docutils literal notranslate"><span class="pre">extract_controls()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.conversions.extract_controls_mapping"><code class="docutils literal notranslate"><span class="pre">extract_controls_mapping()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.conversions.plug_in_pulse_values"><code class="docutils literal notranslate"><span class="pre">plug_in_pulse_values()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.conversions.pulse_onto_tlist"><code class="docutils literal notranslate"><span class="pre">pulse_onto_tlist()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.conversions.pulse_options_dict_to_list"><code class="docutils literal notranslate"><span class="pre">pulse_options_dict_to_list()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.functionals.html">krotov.functionals module</a></li>
@@ -151,8 +166,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.conversions">
-<span id="krotov-conversions-module"></span><h1>krotov.conversions module<a class="headerlink" href="#module-krotov.conversions" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.conversions">
+<span id="krotov-conversions-module"></span><h1>krotov.conversions module<a class="headerlink" href="#module-krotov.conversions" title="Link to this heading">¶</a></h1>
 <p>Routines for structural conversions.</p>
 <p>Conversion between between data structures used by QuTiP’s
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.mesolve.mesolve" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mesolve()</span></code></a> and data structures used internally in an
@@ -160,14 +175,10 @@
 time discretization of control fields, and in particular converting between a
 discretization defined on the <em>points</em> of the time grid (“controls”) and
 piecewise-constant “pulses” defined on the <em>intervals</em> of the time grid.</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.conversions.control_onto_interval" title="krotov.conversions.control_onto_interval"><code class="xref py py-obj docutils literal notranslate"><span class="pre">control_onto_interval</span></code></a></p></td>
 <td><p>Convert control on time grid to control on time grid intervals.</p></td>
@@ -193,21 +204,21 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.conversions.control_onto_interval" title="krotov.conversions.control_onto_interval"><code class="xref py py-func docutils literal notranslate"><span class="pre">control_onto_interval</span></code></a>, <a class="reference internal" href="#krotov.conversions.discretize" title="krotov.conversions.discretize"><code class="xref py py-func docutils literal notranslate"><span class="pre">discretize</span></code></a>, <a class="reference internal" href="#krotov.conversions.extract_controls" title="krotov.conversions.extract_controls"><code class="xref py py-func docutils literal notranslate"><span class="pre">extract_controls</span></code></a>, <a class="reference internal" href="#krotov.conversions.extract_controls_mapping" title="krotov.conversions.extract_controls_mapping"><code class="xref py py-func docutils literal notranslate"><span class="pre">extract_controls_mapping</span></code></a>, <a class="reference internal" href="#krotov.conversions.plug_in_pulse_values" title="krotov.conversions.plug_in_pulse_values"><code class="xref py py-func docutils literal notranslate"><span class="pre">plug_in_pulse_values</span></code></a>, <a class="reference internal" href="#krotov.conversions.pulse_onto_tlist" title="krotov.conversions.pulse_onto_tlist"><code class="xref py py-func docutils literal notranslate"><span class="pre">pulse_onto_tlist</span></code></a>, <a class="reference internal" href="#krotov.conversions.pulse_options_dict_to_list" title="krotov.conversions.pulse_options_dict_to_list"><code class="xref py py-func docutils literal notranslate"><span class="pre">pulse_options_dict_to_list</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.conversions.control_onto_interval">
-<code class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></code><code class="sig-name descname"><span class="pre">control_onto_interval</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">control</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#control_onto_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.control_onto_interval" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.conversions.control_onto_interval">
+<span class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></span><span class="sig-name descname"><span class="pre">control_onto_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">control</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#control_onto_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.control_onto_interval" title="Link to this definition">¶</a></dt>
 <dd><p>Convert control on time grid to control on time grid intervals.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>control</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – values of controls on time grid</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>pulse defined on the intervals of the time grid</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">numpy.ndarray</a></p>
 </dd>
 </dl>
@@ -225,14 +236,14 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.conversions.discretize">
-<code class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></code><code class="sig-name descname"><span class="pre">discretize</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">control</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">via_midpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#discretize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.discretize" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.conversions.discretize">
+<span class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></span><span class="sig-name descname"><span class="pre">discretize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">control</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(None,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">via_midpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#discretize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.discretize" title="Link to this definition">¶</a></dt>
 <dd><p>Discretize the given <cite>control</cite> onto the <cite>tlist</cite> time grid.</p>
 <p>If <cite>control</cite> is a callable, return array of values for <cite>control</cite> evaluated
 for all points in <cite>tlist</cite>.  If <cite>control</cite> is already discretized, check that
 the discretization matches <cite>tlist</cite> (by size).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>control</strong> (<em>callable</em><em> or </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – control to be discretized. If
 callable, must take time value <cite>t</cite> as its first argument.</p></li>
@@ -241,7 +252,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 arguments to pass to <cite>control</cite>. The default passes a single value
 None, to match the requirements for a callable control function in
 QuTiP.</p></li>
-<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#dict" title="(in Python v3.7)"><em>dict</em></a>) – If <cite>control</cite> is callable, further keyword
+<li><p><strong>kwargs</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#dict" title="(in Python v3.7)"><em>dict</em></a>) – If <cite>control</cite> is callable, further keyword
 arguments to pass to <cite>control</cite>. If None, no keyword arguments will
 be passed.</p></li>
 <li><p><strong>via_midpoints</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, sample <cite>control</cite> at the midpoints of
@@ -263,14 +274,14 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 Krotov’s method, see <a class="reference internal" href="../07_krotovs_method.html#timediscretization"><span class="std std-ref">Time discretization</span></a>.</p>
 </div>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p>Discretized array of real <cite>control</cite> values, same length
 as <cite>tlist</cite></p>
 </dd>
-<dt class="field-even">Return type</dt>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">numpy.ndarray</a></p>
 </dd>
-<dt class="field-odd">Raises</dt>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#TypeError" title="(in Python v3.7)"><strong>TypeError</strong></a> – If <cite>control</cite> is not a function that takes two arguments
     (<cite>t</cite>, <cite>args</cite>), or a numpy array</p></li>
@@ -281,16 +292,16 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.conversions.extract_controls">
-<code class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></code><code class="sig-name descname"><span class="pre">extract_controls</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#extract_controls"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.extract_controls" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.conversions.extract_controls">
+<span class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></span><span class="sig-name descname"><span class="pre">extract_controls</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#extract_controls"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.extract_controls" title="Link to this definition">¶</a></dt>
 <dd><p>Extract a list of (unique) controls from the <cite>objectives</cite>.</p>
 <p>Controls are unique if they are not the same object, cf.
 <a class="reference external" href="https://docs.python.org/3/reference/expressions.html#is">Python’s is keyword</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>objectives</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – List of <a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> instances</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>list of controls in <cite>objectives</cite></p>
 </dd>
 </dl>
@@ -298,8 +309,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.conversions.extract_controls_mapping">
-<code class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></code><code class="sig-name descname"><span class="pre">extract_controls_mapping</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">controls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#extract_controls_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.extract_controls_mapping" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.conversions.extract_controls_mapping">
+<span class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></span><span class="sig-name descname"><span class="pre">extract_controls_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">controls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#extract_controls_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.extract_controls_mapping" title="Link to this definition">¶</a></dt>
 <dd><p>Extract a map of where <cite>controls</cite> are used in <cite>objectives</cite>.</p>
 <p>The result is a nested list where the first index relates to the
 <cite>objectives</cite>, the second index relates to the Hamiltonian (0) or the
@@ -365,11 +376,11 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.conversions.plug_in_pulse_values">
-<code class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></code><code class="sig-name descname"><span class="pre">plug_in_pulse_values</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conjugate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#plug_in_pulse_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.plug_in_pulse_values" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.conversions.plug_in_pulse_values">
+<span class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></span><span class="sig-name descname"><span class="pre">plug_in_pulse_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conjugate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#plug_in_pulse_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.plug_in_pulse_values" title="Link to this definition">¶</a></dt>
 <dd><p>Plug pulse values into H.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>H</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – nested list for a QuTiP-time-dependent operator</p></li>
 <li><p><strong>pulses</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – list of pulses in array format</p></li>
@@ -380,12 +391,12 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <li><p><strong>conjugate</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, use conjugate complex pulse values</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>a list with the same structure as <cite>H</cite> that contains the same
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Qobj</span></code></a> operators as <cite>H</cite>, but where every time dependency
 is replaced by the value of the appropriate pulse at <cite>time_index</cite>.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a></p>
 </dd>
 </dl>
@@ -412,19 +423,19 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.conversions.pulse_onto_tlist">
-<code class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></code><code class="sig-name descname"><span class="pre">pulse_onto_tlist</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pulse</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#pulse_onto_tlist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.pulse_onto_tlist" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.conversions.pulse_onto_tlist">
+<span class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></span><span class="sig-name descname"><span class="pre">pulse_onto_tlist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pulse</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#pulse_onto_tlist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.pulse_onto_tlist" title="Link to this definition">¶</a></dt>
 <dd><p>Convert <cite>pulse</cite> from time-grid intervals to time-grid points.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>pulse</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – values defined on the interval of a time grid</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>values of the control defined directly on the time grid
 points. The size of the returned array is one greater than the size
 of <cite>pulse</cite>.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">numpy.ndarray</a></p>
 </dd>
 </dl>
@@ -435,21 +446,21 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.conversions.pulse_options_dict_to_list">
-<code class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></code><code class="sig-name descname"><span class="pre">pulse_options_dict_to_list</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pulse_options</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">controls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#pulse_options_dict_to_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.pulse_options_dict_to_list" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.conversions.pulse_options_dict_to_list">
+<span class="sig-prename descclassname"><span class="pre">krotov.conversions.</span></span><span class="sig-name descname"><span class="pre">pulse_options_dict_to_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pulse_options</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">controls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/conversions.html#pulse_options_dict_to_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.conversions.pulse_options_dict_to_list" title="Link to this definition">¶</a></dt>
 <dd><p>Convert <cite>pulse_options</cite> into a list.</p>
 <p>Given a dict <cite>pulse_options</cite> that contains an options-dict
 for every control in <cite>controls</cite> (cf. <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>), return a
 list of the options-dicts in the same order as <cite>controls</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ValueError" title="(in Python v3.7)"><strong>ValueError</strong></a> – if <cite>pulse_options</cite> to not contain all of the <cite>controls</cite></p>
 </dd>
 </dl>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -463,7 +474,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.functionals.html b/master/API/krotov.functionals.html
index fc2c8175..44475e7e 100644
--- a/master/API/krotov.functionals.html
+++ b/master/API/krotov.functionals.html
@@ -1,26 +1,32 @@
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+  <title>krotov.functionals module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -72,7 +78,24 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.conversions.html">krotov.conversions module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.functionals module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.f_tau"><code class="docutils literal notranslate"><span class="pre">f_tau()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.F_ss"><code class="docutils literal notranslate"><span class="pre">F_ss()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.J_T_ss"><code class="docutils literal notranslate"><span class="pre">J_T_ss()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.chis_ss"><code class="docutils literal notranslate"><span class="pre">chis_ss()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.F_sm"><code class="docutils literal notranslate"><span class="pre">F_sm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.J_T_sm"><code class="docutils literal notranslate"><span class="pre">J_T_sm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.chis_sm"><code class="docutils literal notranslate"><span class="pre">chis_sm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.F_re"><code class="docutils literal notranslate"><span class="pre">F_re()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.J_T_re"><code class="docutils literal notranslate"><span class="pre">J_T_re()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.chis_re"><code class="docutils literal notranslate"><span class="pre">chis_re()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.J_T_hs"><code class="docutils literal notranslate"><span class="pre">J_T_hs()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.chis_hs"><code class="docutils literal notranslate"><span class="pre">chis_hs()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.F_avg"><code class="docutils literal notranslate"><span class="pre">F_avg()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.gate"><code class="docutils literal notranslate"><span class="pre">gate()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.functionals.mapped_basis"><code class="docutils literal notranslate"><span class="pre">mapped_basis()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.info_hooks.html">krotov.info_hooks module</a></li>
@@ -153,8 +176,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.functionals">
-<span id="krotov-functionals-module"></span><h1>krotov.functionals module<a class="headerlink" href="#module-krotov.functionals" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.functionals">
+<span id="krotov-functionals-module"></span><h1>krotov.functionals module<a class="headerlink" href="#module-krotov.functionals" title="Link to this heading">¶</a></h1>
 <p>Functionals and <cite>chi_constructor</cite> routines.</p>
 <p>Any <cite>chi_constructor</cite> routine passed to <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> must take the
 following keyword-arguments:</p>
@@ -197,14 +220,10 @@
 Thus, they calculate <span class="math notranslate nohighlight">\(J_T\)</span> from the same keyword arguments as the <cite>info_hook</cite>.
 The values for <span class="math notranslate nohighlight">\(J_T\)</span> may be used in a convergence analysis, see
 <a class="reference internal" href="krotov.convergence.html#module-krotov.convergence" title="krotov.convergence"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.convergence</span></code></a>.</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.functionals.F_avg" title="krotov.functionals.F_avg"><code class="xref py py-obj docutils literal notranslate"><span class="pre">F_avg</span></code></a></p></td>
 <td><p>Average gate fidelity</p></td>
@@ -254,12 +273,12 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.functionals.F_avg" title="krotov.functionals.F_avg"><code class="xref py py-func docutils literal notranslate"><span class="pre">F_avg</span></code></a>, <a class="reference internal" href="#krotov.functionals.F_re" title="krotov.functionals.F_re"><code class="xref py py-func docutils literal notranslate"><span class="pre">F_re</span></code></a>, <a class="reference internal" href="#krotov.functionals.F_sm" title="krotov.functionals.F_sm"><code class="xref py py-func docutils literal notranslate"><span class="pre">F_sm</span></code></a>, <a class="reference internal" href="#krotov.functionals.F_ss" title="krotov.functionals.F_ss"><code class="xref py py-func docutils literal notranslate"><span class="pre">F_ss</span></code></a>, <a class="reference internal" href="#krotov.functionals.J_T_hs" title="krotov.functionals.J_T_hs"><code class="xref py py-func docutils literal notranslate"><span class="pre">J_T_hs</span></code></a>, <a class="reference internal" href="#krotov.functionals.J_T_re" title="krotov.functionals.J_T_re"><code class="xref py py-func docutils literal notranslate"><span class="pre">J_T_re</span></code></a>, <a class="reference internal" href="#krotov.functionals.J_T_sm" title="krotov.functionals.J_T_sm"><code class="xref py py-func docutils literal notranslate"><span class="pre">J_T_sm</span></code></a>, <a class="reference internal" href="#krotov.functionals.J_T_ss" title="krotov.functionals.J_T_ss"><code class="xref py py-func docutils literal notranslate"><span class="pre">J_T_ss</span></code></a>, <a class="reference internal" href="#krotov.functionals.chis_hs" title="krotov.functionals.chis_hs"><code class="xref py py-func docutils literal notranslate"><span class="pre">chis_hs</span></code></a>, <a class="reference internal" href="#krotov.functionals.chis_re" title="krotov.functionals.chis_re"><code class="xref py py-func docutils literal notranslate"><span class="pre">chis_re</span></code></a>, <a class="reference internal" href="#krotov.functionals.chis_sm" title="krotov.functionals.chis_sm"><code class="xref py py-func docutils literal notranslate"><span class="pre">chis_sm</span></code></a>, <a class="reference internal" href="#krotov.functionals.chis_ss" title="krotov.functionals.chis_ss"><code class="xref py py-func docutils literal notranslate"><span class="pre">chis_ss</span></code></a>, <a class="reference internal" href="#krotov.functionals.f_tau" title="krotov.functionals.f_tau"><code class="xref py py-func docutils literal notranslate"><span class="pre">f_tau</span></code></a>, <a class="reference internal" href="#krotov.functionals.gate" title="krotov.functionals.gate"><code class="xref py py-func docutils literal notranslate"><span class="pre">gate</span></code></a>, <a class="reference internal" href="#krotov.functionals.mapped_basis" title="krotov.functionals.mapped_basis"><code class="xref py py-func docutils literal notranslate"><span class="pre">mapped_basis</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.functionals.f_tau">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">f_tau</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#f_tau"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.f_tau" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.f_tau">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">f_tau</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#f_tau"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.f_tau" title="Link to this definition">¶</a></dt>
 <dd><p>Average complex overlaps of the target states with the <cite>fw_states_T</cite>.</p>
 <p>That is,</p>
 <div class="math notranslate nohighlight">
@@ -288,8 +307,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.F_ss">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">F_ss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_ss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_ss" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.F_ss">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">F_ss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_ss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_ss" title="Link to this definition">¶</a></dt>
 <dd><p>State-to-state phase-insensitive fidelity</p>
 <div class="math notranslate nohighlight">
 \[F_{\text{ss}} = \frac{1}{N} \sum_{k=1}^{N} w_k \Abs{\tau_k}^2
@@ -300,8 +319,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.J_T_ss">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">J_T_ss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_ss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_ss" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.J_T_ss">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">J_T_ss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_ss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_ss" title="Link to this definition">¶</a></dt>
 <dd><p>State-to-state phase-insensitive functional  <span class="math notranslate nohighlight">\(J_{T,\text{ss}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[J_{T,\text{ss}} = 1 - F_{\text{ss}} \quad\in [0, 1].\]</div>
@@ -309,8 +328,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.chis_ss">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">chis_ss</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_ss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_ss" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.chis_ss">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">chis_ss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_ss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_ss" title="Link to this definition">¶</a></dt>
 <dd><p>States <span class="math notranslate nohighlight">\(\ket{\chi_k}\)</span> for functional <span class="math notranslate nohighlight">\(J_{T,\text{ss}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[\Ket{\chi_k}
@@ -320,8 +339,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.F_sm">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">F_sm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_sm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_sm" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.F_sm">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">F_sm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_sm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_sm" title="Link to this definition">¶</a></dt>
 <dd><p>Square-modulus fidelity</p>
 <div class="math notranslate nohighlight">
 \[F_{\text{sm}} = \Abs{f_{\tau}}^2 \quad\in [0, 1].\]</div>
@@ -329,8 +348,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.J_T_sm">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">J_T_sm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_sm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_sm" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.J_T_sm">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">J_T_sm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_sm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_sm" title="Link to this definition">¶</a></dt>
 <dd><p>Square-modulus functional  <span class="math notranslate nohighlight">\(J_{T,\text{sm}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[J_{T,\text{sm}} = 1 - F_{\text{sm}} \quad\in [0, 1]\]</div>
@@ -339,8 +358,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.chis_sm">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">chis_sm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_sm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_sm" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.chis_sm">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">chis_sm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_sm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_sm" title="Link to this definition">¶</a></dt>
 <dd><p>States <span class="math notranslate nohighlight">\(\ket{\chi_k}\)</span> for functional <span class="math notranslate nohighlight">\(J_{T,\text{sm}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[\Ket{\chi_k}
@@ -351,8 +370,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.F_re">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">F_re</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_re"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_re" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.F_re">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">F_re</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_re"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_re" title="Link to this definition">¶</a></dt>
 <dd><p>Real-part fidelity</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}F_{\text{re}} = \Re[f_{\tau}] \quad\in \begin{cases}
@@ -363,8 +382,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.J_T_re">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">J_T_re</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_re"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_re" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.J_T_re">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">J_T_re</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_re"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_re" title="Link to this definition">¶</a></dt>
 <dd><p>Real-part functional <span class="math notranslate nohighlight">\(J_{T,\text{re}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[\begin{split}J_{T,\text{re}} = 1 - F_{\text{re}} \quad\in \begin{cases}
@@ -383,8 +402,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.chis_re">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">chis_re</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_re"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_re" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.chis_re">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">chis_re</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_re"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_re" title="Link to this definition">¶</a></dt>
 <dd><p>States <span class="math notranslate nohighlight">\(\ket{\chi_k}\)</span> for functional <span class="math notranslate nohighlight">\(J_{T,\text{re}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[\Ket{\chi_k}
@@ -397,8 +416,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.J_T_hs">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">J_T_hs</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_hs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_hs" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.J_T_hs">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">J_T_hs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#J_T_hs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.J_T_hs" title="Link to this definition">¶</a></dt>
 <dd><p>Hilbert-Schmidt distance measure functional <span class="math notranslate nohighlight">\(J_{T,\text{hs}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[\begin{split}J_{T,\text{hs}}
@@ -441,8 +460,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.chis_hs">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">chis_hs</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_hs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_hs" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.chis_hs">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">chis_hs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#chis_hs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.chis_hs" title="Link to this definition">¶</a></dt>
 <dd><p>States <span class="math notranslate nohighlight">\(\Op{\chi}_k\)</span> for functional <span class="math notranslate nohighlight">\(J_{T,\text{hs}}\)</span></p>
 <div class="math notranslate nohighlight">
 \[\Op{\chi}_k
@@ -476,8 +495,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.F_avg">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">F_avg</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapped_basis_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_avg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_avg" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.F_avg">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">F_avg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapped_basis_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#F_avg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.F_avg" title="Link to this definition">¶</a></dt>
 <dd><p>Average gate fidelity</p>
 <div class="math notranslate nohighlight">
 \[F_{\text{avg}}
@@ -515,7 +534,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <p>where <span class="math notranslate nohighlight">\(\Op{U}\)</span> the gate that maps <cite>basis_states</cite> to the result of a forward
 propagation of those basis states, stored in <cite>fw_states_T</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>fw_states_T</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em>]</em>) – The forward propagated states. For
 dissipative dynamics, this must be the forward propagation of the
@@ -526,7 +545,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 states</p></li>
 <li><p><strong>gate</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a>) – The <span class="math notranslate nohighlight">\(N \times N\)</span> quantum gate in the logical
 subspace, e.g. <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.qip.gates.cnot" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.qip.gates.cnot()</span></code></a>.</p></li>
-<li><p><strong>mapped_basis_states</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em>]</em>) – If given, the result of
+<li><p><strong>mapped_basis_states</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em>]</em>) – If given, the result of
 applying gate to <cite>basis_states</cite>. If not given, this will be
 calculated internally via <a class="reference internal" href="#krotov.functionals.mapped_basis" title="krotov.functionals.mapped_basis"><code class="xref py py-func docutils literal notranslate"><span class="pre">mapped_basis()</span></code></a>. It is recommended
 to pass pre-calculated <cite>mapped_basis_states</cite> when evaluating
@@ -541,8 +560,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.gate">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">gate</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#gate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.gate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.gate">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">gate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#gate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.gate" title="Link to this definition">¶</a></dt>
 <dd><p>Gate that maps <cite>basis_states</cite> to <cite>fw_states_T</cite></p>
 <p class="rubric">Example</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">qutip</span> <span class="kn">import</span> <span class="n">ket</span>
@@ -559,8 +578,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.functionals.mapped_basis">
-<code class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></code><code class="sig-name descname"><span class="pre">mapped_basis</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">O</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#mapped_basis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.mapped_basis" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.functionals.mapped_basis">
+<span class="sig-prename descclassname"><span class="pre">krotov.functionals.</span></span><span class="sig-name descname"><span class="pre">mapped_basis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">O</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/functionals.html#mapped_basis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.functionals.mapped_basis" title="Link to this definition">¶</a></dt>
 <dd><p>Apply the gate <cite>O</cite> to <cite>basis_states</cite>.</p>
 <p class="rubric">Example</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">qutip</span> <span class="kn">import</span> <span class="n">ket</span>
@@ -578,8 +597,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </div>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -593,7 +612,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.html b/master/API/krotov.html
index 89a098db..9b196807 100644
--- a/master/API/krotov.html
+++ b/master/API/krotov.html
@@ -1,24 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
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+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov package &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
+  <title>krotov package &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
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+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../_static/mycss.css" type="text/css" />
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+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -38,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -48,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -145,8 +153,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov">
-<span id="krotov-package"></span><h1>krotov package<a class="headerlink" href="#module-krotov" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov">
+<span id="krotov-package"></span><h1>krotov package<a class="headerlink" href="#module-krotov" title="Link to this heading">¶</a></h1>
 <p>The main function exposed here is <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p>
 <p>This acts on a list of <a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> instances, which may either be
 constructed manually, or through the helper functions <a class="reference internal" href="krotov.objectives.html#krotov.objectives.gate_objectives" title="krotov.objectives.gate_objectives"><code class="xref py py-func docutils literal notranslate"><span class="pre">gate_objectives()</span></code></a>
@@ -174,14 +182,10 @@
 <li class="toctree-l1"><a class="reference internal" href="krotov.shapes.html">krotov.shapes module</a></li>
 </ul>
 </div>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code> Classes:</p>
 <table class="docutils align-default">
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a></p></td>
 <td><p>A single objective for optimization with Krotov’s method.</p></td>
@@ -193,10 +197,6 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code> Functions:</p>
 <table class="docutils align-default">
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="krotov.objectives.html#krotov.objectives.ensemble_objectives" title="krotov.objectives.ensemble_objectives"><code class="xref py py-func docutils literal notranslate"><span class="pre">ensemble_objectives</span></code></a></p></td>
 <td><p>Extend <cite>objectives</cite> for an “ensemble optimization”</p></td>
@@ -209,8 +209,8 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tr>
 </tbody>
 </table>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -224,7 +224,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.info_hooks.html b/master/API/krotov.info_hooks.html
index 2a46f515..d4a514ab 100644
--- a/master/API/krotov.info_hooks.html
+++ b/master/API/krotov.info_hooks.html
@@ -1,26 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" />
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+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.info_hooks module &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
+  <title>krotov.info_hooks module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
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+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -73,7 +79,12 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.functionals.html">krotov.functionals module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.info_hooks module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.info_hooks.chain"><code class="docutils literal notranslate"><span class="pre">chain()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.info_hooks.print_debug_information"><code class="docutils literal notranslate"><span class="pre">print_debug_information()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.info_hooks.print_table"><code class="docutils literal notranslate"><span class="pre">print_table()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.mu.html">krotov.mu module</a></li>
@@ -153,17 +164,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.info_hooks">
-<span id="krotov-info-hooks-module"></span><h1>krotov.info_hooks module<a class="headerlink" href="#module-krotov.info_hooks" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.info_hooks">
+<span id="krotov-info-hooks-module"></span><h1>krotov.info_hooks module<a class="headerlink" href="#module-krotov.info_hooks" title="Link to this heading">¶</a></h1>
 <p>Routines that can be passed as <cite>info_hook</cite> to <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a></p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.info_hooks.chain" title="krotov.info_hooks.chain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chain</span></code></a></p></td>
 <td><p>Chain multiple <cite>info_hook</cite> or <cite>modify_params_after_iter</cite> callables together.</p></td>
@@ -177,12 +184,12 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.info_hooks.chain" title="krotov.info_hooks.chain"><code class="xref py py-func docutils literal notranslate"><span class="pre">chain</span></code></a>, <a class="reference internal" href="#krotov.info_hooks.print_debug_information" title="krotov.info_hooks.print_debug_information"><code class="xref py py-func docutils literal notranslate"><span class="pre">print_debug_information</span></code></a>, <a class="reference internal" href="#krotov.info_hooks.print_table" title="krotov.info_hooks.print_table"><code class="xref py py-func docutils literal notranslate"><span class="pre">print_table</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.info_hooks.chain">
-<code class="sig-prename descclassname"><span class="pre">krotov.info_hooks.</span></code><code class="sig-name descname"><span class="pre">chain</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">hooks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/info_hooks.html#chain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.info_hooks.chain" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.info_hooks.chain">
+<span class="sig-prename descclassname"><span class="pre">krotov.info_hooks.</span></span><span class="sig-name descname"><span class="pre">chain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">hooks</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/info_hooks.html#chain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.info_hooks.chain" title="Link to this definition">¶</a></dt>
 <dd><p>Chain multiple <cite>info_hook</cite> or <cite>modify_params_after_iter</cite> callables
 together.</p>
 <p class="rubric">Example</p>
@@ -201,14 +208,14 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.info_hooks.print_debug_information">
-<code class="sig-prename descclassname"><span class="pre">krotov.info_hooks.</span></code><code class="sig-name descname"><span class="pre">print_debug_information</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">*</span></em>, <em class="sig-param"><span class="pre">objectives</span></em>, <em class="sig-param"><span class="pre">adjoint_objectives</span></em>, <em class="sig-param"><span class="pre">backward_states</span></em>, <em class="sig-param"><span class="pre">forward_states</span></em>, <em class="sig-param"><span class="pre">forward_states0</span></em>, <em class="sig-param"><span class="pre">guess_pulses</span></em>, <em class="sig-param"><span class="pre">optimized_pulses</span></em>, <em class="sig-param"><span class="pre">g_a_integrals</span></em>, <em class="sig-param"><span class="pre">lambda_vals</span></em>, <em class="sig-param"><span class="pre">shape_arrays</span></em>, <em class="sig-param"><span class="pre">fw_states_T</span></em>, <em class="sig-param"><span class="pre">tlist</span></em>, <em class="sig-param"><span class="pre">tau_vals</span></em>, <em class="sig-param"><span class="pre">start_time</span></em>, <em class="sig-param"><span class="pre">stop_time</span></em>, <em class="sig-param"><span class="pre">iteration</span></em>, <em class="sig-param"><span class="pre">info_vals</span></em>, <em class="sig-param"><span class="pre">shared_data</span></em>, <em class="sig-param"><span class="pre">propagator</span></em>, <em class="sig-param"><span class="pre">chi_constructor</span></em>, <em class="sig-param"><span class="pre">mu</span></em>, <em class="sig-param"><span class="pre">sigma</span></em>, <em class="sig-param"><span class="pre">iter_start</span></em>, <em class="sig-param"><span class="pre">iter_stop</span></em>, <em class="sig-param"><span class="pre">out=&lt;_io.TextIOWrapper</span> <span class="pre">name='&lt;stdout&gt;'</span> <span class="pre">mode='w'</span> <span class="pre">encoding='utf-8'&gt;</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/info_hooks.html#print_debug_information"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.info_hooks.print_debug_information" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.info_hooks.print_debug_information">
+<span class="sig-prename descclassname"><span class="pre">krotov.info_hooks.</span></span><span class="sig-name descname"><span class="pre">print_debug_information</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adjoint_objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_states0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guess_pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimized_pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g_a_integrals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambda_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fw_states_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iteration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">info_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_constructor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_stop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out=&lt;_io.TextIOWrapper</span> <span class="pre">name='&lt;stdout&gt;'</span> <span class="pre">mode='w'</span> <span class="pre">encoding='utf-8'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/info_hooks.html#print_debug_information"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.info_hooks.print_debug_information" title="Link to this definition">¶</a></dt>
 <dd><p>Print full debug information about the current Krotov iteration.</p>
 <p>This routine is intended to be passed to <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> as
 <cite>info_hook</cite>, and it exemplifies the full signature of a routine suitable
 for this purpose.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Keyword Arguments</dt>
+<dt class="field-odd">Keyword Arguments<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>objectives</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><em>Objective</em></a><em>]</em>) – list of the objectives</p></li>
 <li><p><strong>adjoint_objectives</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><em>Objective</em></a><em>]</em>) – list of the adjoint objectives</p></li>
@@ -257,12 +264,12 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <li><p><strong>shared_data</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#dict" title="(in Python v3.7)"><em>dict</em></a>) – Dict of data shared between any
 <cite>modify_params_after_iter</cite> and any <cite>info_hook</cite> functions chained
 together via <a class="reference internal" href="#krotov.info_hooks.chain" title="krotov.info_hooks.chain"><code class="xref py py-func docutils literal notranslate"><span class="pre">chain()</span></code></a>.</p></li>
-<li><p><strong>propagator</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#callable" title="(in Python v3.7)"><em>callable</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3.7/library/functions.html#callable" title="(in Python v3.7)"><em>callable</em></a><em>]</em>) – The <cite>propagator</cite> function(s)
+<li><p><strong>propagator</strong> (<em>callable</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><em>callable</em><em>]</em>) – The <cite>propagator</cite> function(s)
 used by <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p></li>
-<li><p><strong>chi_constructor</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#callable" title="(in Python v3.7)"><em>callable</em></a>) – The <cite>chi_constructor</cite> function
+<li><p><strong>chi_constructor</strong> (<em>callable</em>) – The <cite>chi_constructor</cite> function
 used by <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p></li>
-<li><p><strong>mu</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#callable" title="(in Python v3.7)"><em>callable</em></a>) – The <cite>mu</cite> function used by <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p></li>
-<li><p><strong>sigma</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference internal" href="krotov.second_order.html#krotov.second_order.Sigma" title="krotov.second_order.Sigma"><em>krotov.second_order.Sigma</em></a>) – The argument passed to
+<li><p><strong>mu</strong> (<em>callable</em>) – The <cite>mu</cite> function used by <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p></li>
+<li><p><strong>sigma</strong> (<em>None</em><em> or </em><a class="reference internal" href="krotov.second_order.html#krotov.second_order.Sigma" title="krotov.second_order.Sigma"><em>krotov.second_order.Sigma</em></a>) – The argument passed to
 <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> as <cite>sigma</cite>.</p></li>
 <li><p><strong>iter_start</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – The formal iteration number at which the optimization
 started</p></li>
@@ -292,8 +299,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.info_hooks.print_table">
-<code class="sig-prename descclassname"><span class="pre">krotov.info_hooks.</span></code><code class="sig-name descname"><span class="pre">print_table</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">*</span></em>, <em class="sig-param"><span class="pre">J_T</span></em>, <em class="sig-param"><span class="pre">show_g_a_int_per_pulse=False</span></em>, <em class="sig-param"><span class="pre">J_T_prev=None</span></em>, <em class="sig-param"><span class="pre">unicode=True</span></em>, <em class="sig-param"><span class="pre">col_formats=('%d'</span></em>, <em class="sig-param"><span class="pre">'%.2e'</span></em>, <em class="sig-param"><span class="pre">'%.2e'</span></em>, <em class="sig-param"><span class="pre">'%.2e'</span></em>, <em class="sig-param"><span class="pre">'%.2e'</span></em>, <em class="sig-param"><span class="pre">'%.2e'</span></em>, <em class="sig-param"><span class="pre">'%.2e'</span></em>, <em class="sig-param"><span class="pre">'%d')</span></em>, <em class="sig-param"><span class="pre">col_headers=None</span></em>, <em class="sig-param"><span class="pre">out=&lt;_io.TextIOWrapper</span> <span class="pre">name='&lt;stdout&gt;'</span> <span class="pre">mode='w'</span> <span class="pre">encoding='utf-8'&gt;</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/info_hooks.html#print_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.info_hooks.print_table" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.info_hooks.print_table">
+<span class="sig-prename descclassname"><span class="pre">krotov.info_hooks.</span></span><span class="sig-name descname"><span class="pre">print_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">J_T</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_g_a_int_per_pulse=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">J_T_prev=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unicode=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">col_formats=('%d'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'%.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'%.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'%.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'%.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'%.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'%.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'%d')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">col_headers=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out=&lt;_io.TextIOWrapper</span> <span class="pre">name='&lt;stdout&gt;'</span> <span class="pre">mode='w'</span> <span class="pre">encoding='utf-8'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/info_hooks.html#print_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.info_hooks.print_table" title="Link to this definition">¶</a></dt>
 <dd><p>Print a tabular overview of the functional values in the iteration.</p>
 <p>An example output is:</p>
 <div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">iter.      J_T    ∫gₐ(t)dt          J       ΔJ_T         ΔJ  secs</span>
@@ -355,7 +362,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 spikes in the optimized pulses, or a dramatic increase in the pulse
 amplitude.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>J_T</strong> (<em>callable</em>) – A function that extracts the value of the final time
 functional from the keyword-arguments passed to the <cite>info_hook</cite>.</p></li>
@@ -364,7 +371,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 \frac{\lambda_{a, i}}{S_i(t)} \Abs{\Delta \epsilon_i(t)}^2 \dd t\)</span>
 for every pulse <span class="math notranslate nohighlight">\(\epsilon_i(t)\)</span>. Otherwise, only print the sum over
 those integrals for all pulses.</p></li>
-<li><p><strong>J_T_prev</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><em>callable</em>) – A function that extracts the value of the
+<li><p><strong>J_T_prev</strong> (<em>None</em><em> or </em><em>callable</em>) – A function that extracts the value of the
 final time functional <em>from the previous iteration</em>. If None, use
 the last values from the <cite>info_vals</cite> passed to the <cite>info_hook</cite>.</p></li>
 <li><p><strong>unicode</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Whether to use unicode symbols for the column headers.
@@ -375,10 +382,10 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 column of values in the table (see items 1-8 above). These must
 each format a single value (an integer for the first and last
 column, and a float for all other columns).</p></li>
-<li><p><strong>col_headers</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#tuple" title="(in Python v3.7)"><em>tuple</em></a>) – A tuple of exactly 8 strings that will be
+<li><p><strong>col_headers</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#tuple" title="(in Python v3.7)"><em>tuple</em></a>) – A tuple of exactly 8 strings that will be
 used for column headers (see items 1-8 above). If None, default
 values depending on <cite>unicode</cite> will be used. The third element
-(“<code class="docutils literal notranslate"><span class="pre">lbl</span></code>”) of the tuple must support <code class="docutils literal notranslate"><span class="pre">lbl.format(l=l)</span></code> for an
+(”<code class="docutils literal notranslate"><span class="pre">lbl</span></code>”) of the tuple must support <code class="docutils literal notranslate"><span class="pre">lbl.format(l=l)</span></code> for an
 integer <code class="docutils literal notranslate"><span class="pre">l</span></code> (the one-based index of the control; since there will
 be one
 column for each control).</p></li>
@@ -389,15 +396,15 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <p>The widths of the columns are automatically determined both from the length
 of the column headers and the length of the formatted values.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ValueError" title="(in Python v3.7)"><strong>ValueError</strong></a> – If <cite>col_formats</cite> and/or <cite>col_headers</cite> are of the wrong
     length, type, or invalid format.</p>
 </dd>
 </dl>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -411,7 +418,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.mu.html b/master/API/krotov.mu.html
index 88c5feb7..becdd145 100644
--- a/master/API/krotov.mu.html
+++ b/master/API/krotov.mu.html
@@ -1,26 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" />
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+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.mu module &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
+  <title>krotov.mu module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
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     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -74,7 +80,10 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.info_hooks.html">krotov.info_hooks module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.mu module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.mu.derivative_wrt_pulse"><code class="docutils literal notranslate"><span class="pre">derivative_wrt_pulse()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.objectives.html">krotov.objectives module</a></li>
@@ -153,8 +162,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.mu">
-<span id="krotov-mu-module"></span><h1>krotov.mu module<a class="headerlink" href="#module-krotov.mu" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.mu">
+<span id="krotov-mu-module"></span><h1>krotov.mu module<a class="headerlink" href="#module-krotov.mu" title="Link to this heading">¶</a></h1>
 <p>Routines for <cite>mu</cite> in <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">krotov.optimize.optimize_pulses()</span></code></a></p>
 <p>The first-order Krotov update equation is usually written as</p>
 <div class="math notranslate nohighlight">
@@ -207,14 +216,10 @@
 circumstances, such as when the derivative still depends on the control values
 or on the states. (Or, if you can provide a more efficient problem-specific
 implementation).</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.mu.derivative_wrt_pulse" title="krotov.mu.derivative_wrt_pulse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">derivative_wrt_pulse</span></code></a></p></td>
 <td><p>Calculate ∂H/∂ϵ for the standard equations of motion.</p></td>
@@ -222,15 +227,15 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.mu.derivative_wrt_pulse" title="krotov.mu.derivative_wrt_pulse"><code class="xref py py-func docutils literal notranslate"><span class="pre">derivative_wrt_pulse</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.mu.derivative_wrt_pulse">
-<code class="sig-prename descclassname"><span class="pre">krotov.mu.</span></code><code class="sig-name descname"><span class="pre">derivative_wrt_pulse</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_objective</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulses_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_pulse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/mu.html#derivative_wrt_pulse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.mu.derivative_wrt_pulse" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.mu.derivative_wrt_pulse">
+<span class="sig-prename descclassname"><span class="pre">krotov.mu.</span></span><span class="sig-name descname"><span class="pre">derivative_wrt_pulse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_objective</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulses_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_pulse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/mu.html#derivative_wrt_pulse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.mu.derivative_wrt_pulse" title="Link to this definition">¶</a></dt>
 <dd><p>Calculate ∂H/∂ϵ for the standard equations of motion.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>objectives</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – List of <a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> instances</p></li>
 <li><p><strong>i_objective</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – The index of the objective in <cite>objectives</cite> whose
@@ -246,14 +251,14 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 considers equations of motion that are linear in the controls.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>The quantum operator or super-operator that
 represents ∂H/∂ϵ. In general, the return type can be any callable <cite>mu</cite>
 so that <code class="docutils literal notranslate"><span class="pre">mu(state)</span></code> calculates the result of applying ∂H/∂ϵ to
 <cite>state</cite>. In most cases, a <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Qobj</span></code></a> will be returned, which
 is just the most convenient example of an appropriate callable.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>callable</p>
 </dd>
 </dl>
@@ -279,8 +284,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </ul>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -294,7 +299,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.objectives.html b/master/API/krotov.objectives.html
index c5eca06d..529c16cb 100644
--- a/master/API/krotov.objectives.html
+++ b/master/API/krotov.objectives.html
@@ -1,26 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -75,7 +81,14 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.mu.html">krotov.mu module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.objectives module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.objectives.Objective"><code class="docutils literal notranslate"><span class="pre">Objective</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.objectives.gate_objectives"><code class="docutils literal notranslate"><span class="pre">gate_objectives()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.objectives.ensemble_objectives"><code class="docutils literal notranslate"><span class="pre">ensemble_objectives()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.objectives.liouvillian"><code class="docutils literal notranslate"><span class="pre">liouvillian()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.objectives.FIX_QUTIP_932"><code class="docutils literal notranslate"><span class="pre">FIX_QUTIP_932</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.optimize.html">krotov.optimize module</a></li>
@@ -153,8 +166,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.objectives">
-<span id="krotov-objectives-module"></span><h1>krotov.objectives module<a class="headerlink" href="#module-krotov.objectives" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.objectives">
+<span id="krotov-objectives-module"></span><h1>krotov.objectives module<a class="headerlink" href="#module-krotov.objectives" title="Link to this heading">¶</a></h1>
 <p>Routines for formulating objectives.</p>
 <p>Objectives, represented as an <a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> instance, describe the
 <em>physical</em> objective of an optimization, e.g. a state-to-state transformation,
@@ -162,29 +175,21 @@
 optimization functional (<a class="reference internal" href="krotov.functionals.html#module-krotov.functionals" title="krotov.functionals"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.functionals</span></code></a>). For the same physical
 objective, there are usually several different functionals whose minimization
 achieve that objective.</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Classes:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Objective</span></code></a></p></td>
-<td><p>A single objective for optimization with Krotov’s method.</p></td>
+<td><p>A single objective for optimization with Krotov's method.</p></td>
 </tr>
 </tbody>
 </table>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.objectives.ensemble_objectives" title="krotov.objectives.ensemble_objectives"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ensemble_objectives</span></code></a></p></td>
-<td><p>Extend <cite>objectives</cite> for an “ensemble optimization”</p></td>
+<td><p>Extend <cite>objectives</cite> for an &quot;ensemble optimization&quot;</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#krotov.objectives.gate_objectives" title="krotov.objectives.gate_objectives"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gate_objectives</span></code></a></p></td>
 <td><p>Construct a list of objectives for optimizing towards a quantum gate</p></td>
@@ -195,21 +200,21 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a>, <a class="reference internal" href="#krotov.objectives.ensemble_objectives" title="krotov.objectives.ensemble_objectives"><code class="xref py py-func docutils literal notranslate"><span class="pre">ensemble_objectives</span></code></a>, <a class="reference internal" href="#krotov.objectives.gate_objectives" title="krotov.objectives.gate_objectives"><code class="xref py py-func docutils literal notranslate"><span class="pre">gate_objectives</span></code></a>, <a class="reference internal" href="#krotov.objectives.liouvillian" title="krotov.objectives.liouvillian"><code class="xref py py-func docutils literal notranslate"><span class="pre">liouvillian</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py class">
-<dt id="krotov.objectives.Objective">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></code><code class="sig-name descname"><span class="pre">Objective</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></span><span class="sig-name descname"><span class="pre">Objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective" title="Link to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/functions.html#object" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>A single objective for optimization with Krotov’s method.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>initial_state</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a>) – value for <a class="reference internal" href="#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a></p></li>
 <li><p><strong>H</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – value for <a class="reference internal" href="#krotov.objectives.Objective.H" title="krotov.objectives.Objective.H"><code class="xref py py-attr docutils literal notranslate"><span class="pre">H</span></code></a></p></li>
-<li><p><strong>target</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – value for <a class="reference internal" href="#krotov.objectives.Objective.target" title="krotov.objectives.Objective.target"><code class="xref py py-attr docutils literal notranslate"><span class="pre">target</span></code></a></p></li>
-<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – value for <a class="reference internal" href="#krotov.objectives.Objective.c_ops" title="krotov.objectives.Objective.c_ops"><code class="xref py py-attr docutils literal notranslate"><span class="pre">c_ops</span></code></a></p></li>
+<li><p><strong>target</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em> or </em><em>None</em>) – value for <a class="reference internal" href="#krotov.objectives.Objective.target" title="krotov.objectives.Objective.target"><code class="xref py py-attr docutils literal notranslate"><span class="pre">target</span></code></a></p></li>
+<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><em>None</em>) – value for <a class="reference internal" href="#krotov.objectives.Objective.c_ops" title="krotov.objectives.Objective.c_ops"><code class="xref py py-attr docutils literal notranslate"><span class="pre">c_ops</span></code></a></p></li>
 </ul>
 </dd>
 </dl>
@@ -225,7 +230,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </pre></div>
 </div>
 <dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ValueError" title="(in Python v3.7)"><strong>ValueError</strong></a> – If any arguments have an invalid type or structure. This
     can be surpressed by setting the <a class="reference internal" href="#krotov.objectives.Objective.type_checking" title="krotov.objectives.Objective.type_checking"><code class="xref py py-attr docutils literal notranslate"><span class="pre">type_checking</span></code></a> class
     attribute to False.</p>
@@ -239,33 +244,33 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 operator instead, see <a class="reference internal" href="#krotov.objectives.liouvillian" title="krotov.objectives.liouvillian"><code class="xref py py-func docutils literal notranslate"><span class="pre">liouvillian()</span></code></a>.</p>
 </div>
 <dl class="py attribute">
-<dt id="krotov.objectives.Objective.H">
-<code class="sig-name descname"><span class="pre">H</span></code><a class="headerlink" href="#krotov.objectives.Objective.H" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.H">
+<span class="sig-name descname"><span class="pre">H</span></span><a class="headerlink" href="#krotov.objectives.Objective.H" title="Link to this definition">¶</a></dt>
 <dd><p>The (time-dependent) Hamiltonian or
 Liouvillian in nested-list format, cf.
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.mesolve.mesolve" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.mesolve.mesolve()</span></code></a>. This includes the control fields.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)">qutip.Qobj</a> or <a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.objectives.Objective.initial_state">
-<code class="sig-name descname"><span class="pre">initial_state</span></code><a class="headerlink" href="#krotov.objectives.Objective.initial_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.initial_state">
+<span class="sig-name descname"><span class="pre">initial_state</span></span><a class="headerlink" href="#krotov.objectives.Objective.initial_state" title="Link to this definition">¶</a></dt>
 <dd><p>The initial state, as a Hilbert space
 state, or a density matrix.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)">qutip.Qobj</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.objectives.Objective.target">
-<code class="sig-name descname"><span class="pre">target</span></code><a class="headerlink" href="#krotov.objectives.Objective.target" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.target">
+<span class="sig-name descname"><span class="pre">target</span></span><a class="headerlink" href="#krotov.objectives.Objective.target" title="Link to this definition">¶</a></dt>
 <dd><p>An object describing the “target” of the optimization, for the
 dynamics starting from <a class="reference internal" href="#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a>. Usually, this will be
 the target state (the state into which <a class="reference internal" href="#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a> should
@@ -275,28 +280,28 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.objectives.Objective.c_ops">
-<code class="sig-name descname"><span class="pre">c_ops</span></code><a class="headerlink" href="#krotov.objectives.Objective.c_ops" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.c_ops">
+<span class="sig-name descname"><span class="pre">c_ops</span></span><a class="headerlink" href="#krotov.objectives.Objective.c_ops" title="Link to this definition">¶</a></dt>
 <dd><p>List of collapse operators, cf.
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.mesolve.mesolve" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mesolve()</span></code></a>, in lieu of <a class="reference internal" href="#krotov.objectives.Objective.H" title="krotov.objectives.Objective.H"><code class="xref py py-attr docutils literal notranslate"><span class="pre">H</span></code></a> being a
 Liouvillian.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a> or <a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)">None</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a> or None</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.objectives.Objective.str_use_unicode">
-<code class="sig-name descname"><span class="pre">str_use_unicode</span></code><em class="property"> <span class="pre">=</span> <span class="pre">True</span></em><a class="headerlink" href="#krotov.objectives.Objective.str_use_unicode" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.str_use_unicode">
+<span class="sig-name descname"><span class="pre">str_use_unicode</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">True</span></em><a class="headerlink" href="#krotov.objectives.Objective.str_use_unicode" title="Link to this definition">¶</a></dt>
 <dd><p>Whether the string representation of an <a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> may use
 unicode symbols, cf. <a class="reference internal" href="#krotov.objectives.Objective.summarize" title="krotov.objectives.Objective.summarize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">summarize()</span></code></a> (class attribute).</p>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.objectives.Objective.type_checking">
-<code class="sig-name descname"><span class="pre">type_checking</span></code><em class="property"> <span class="pre">=</span> <span class="pre">True</span></em><a class="headerlink" href="#krotov.objectives.Objective.type_checking" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.type_checking">
+<span class="sig-name descname"><span class="pre">type_checking</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">True</span></em><a class="headerlink" href="#krotov.objectives.Objective.type_checking" title="Link to this definition">¶</a></dt>
 <dd><p>By default, instantiating <a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> with invalid types raises a
 <a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ValueError" title="(in Python v3.7)"><code class="xref py py-exc docutils literal notranslate"><span class="pre">ValueError</span></code></a>. Setting this to False disables type checks in the
 initializer, allowing certain advanced use cases such as using plain numpy
@@ -304,8 +309,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.objectives.Objective.adjoint">
-<code class="sig-name descname"><span class="pre">adjoint</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.adjoint" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.adjoint">
+<span class="sig-name descname"><span class="pre">adjoint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.adjoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.adjoint" title="Link to this definition">¶</a></dt>
 <dd><p>The <a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a> containing the adjoint of all components.</p>
 <p>This does not affect the controls in <a class="reference internal" href="#krotov.objectives.Objective.H" title="krotov.objectives.Objective.H"><code class="xref py py-attr docutils literal notranslate"><span class="pre">H</span></code></a>: these are
 assumed to be real-valued. Also, <a class="reference internal" href="#krotov.objectives.Objective.target" title="krotov.objectives.Objective.target"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Objective.target</span></code></a> will be left
@@ -314,8 +319,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.objectives.Objective.mesolve">
-<code class="sig-name descname"><span class="pre">mesolve</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.mesolve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.mesolve" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.mesolve">
+<span class="sig-name descname"><span class="pre">mesolve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.mesolve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.mesolve" title="Link to this definition">¶</a></dt>
 <dd><p>Run <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.mesolve.mesolve" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.mesolve.mesolve()</span></code></a> on the system of the objective.</p>
 <p>Solve the dynamics for the <a class="reference internal" href="#krotov.objectives.Objective.H" title="krotov.objectives.Objective.H"><code class="xref py py-attr docutils literal notranslate"><span class="pre">H</span></code></a> and <a class="reference internal" href="#krotov.objectives.Objective.c_ops" title="krotov.objectives.Objective.c_ops"><code class="xref py py-attr docutils literal notranslate"><span class="pre">c_ops</span></code></a> of the
 objective, starting from the objective’s <a class="reference internal" href="#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a>, by
@@ -323,39 +328,39 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 the dynamical generator for the propagation can be overridden by
 passing <cite>rho0</cite> and <cite>H</cite>/<cite>c_ops</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>tlist</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – array of time grid points on which the
 states are defined</p></li>
-<li><p><strong>rho0</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – The initial state for the propagation.
+<li><p><strong>rho0</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em> or </em><em>None</em>) – The initial state for the propagation.
 If None, the <a class="reference internal" href="#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a> attribute is used.</p></li>
-<li><p><strong>H</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – The dynamical generator (Hamiltonian or
+<li><p><strong>H</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em> or </em><em>None</em>) – The dynamical generator (Hamiltonian or
 Liouvillian) for the propagation. If None, the <a class="reference internal" href="#krotov.objectives.Objective.H" title="krotov.objectives.Objective.H"><code class="xref py py-attr docutils literal notranslate"><span class="pre">H</span></code></a>
 attribute is used.</p></li>
-<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – List of collapse (Lindblad) operators. If
+<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><em>None</em>) – List of collapse (Lindblad) operators. If
 None, the <a class="reference internal" href="#krotov.objectives.Objective.c_ops" title="krotov.objectives.Objective.c_ops"><code class="xref py py-attr docutils literal notranslate"><span class="pre">c_ops</span></code></a> attribute is used.</p></li>
-<li><p><strong>e_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – A list of operators whose expectation values
+<li><p><strong>e_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><em>None</em>) – A list of operators whose expectation values
 to calculate, for every point in <cite>tlist</cite>. See
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.mesolve.mesolve" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.mesolve.mesolve()</span></code></a>.</p></li>
-<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#dict" title="(in Python v3.7)"><em>dict</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – dictionary of parameters for time-dependent
+<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#dict" title="(in Python v3.7)"><em>dict</em></a><em> or </em><em>None</em>) – dictionary of parameters for time-dependent
 Hamiltonians and collapse operators</p></li>
 <li><p><strong>**kwargs</strong> – All further arguments will be passed to
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.mesolve.mesolve" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.mesolve.mesolve()</span></code></a>.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>Result of the propagation, see
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.mesolve.mesolve" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.mesolve.mesolve()</span></code></a> for details.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.solver.Result" title="(in QuTiP: Quantum Toolbox in Python v4.5)">qutip.solver.Result</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.objectives.Objective.propagate">
-<code class="sig-name descname"><span class="pre">propagate</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">tlist</span></em>, <em class="sig-param"><span class="pre">*</span></em>, <em class="sig-param"><span class="pre">propagator</span></em>, <em class="sig-param"><span class="pre">rho0=None</span></em>, <em class="sig-param"><span class="pre">H=None</span></em>, <em class="sig-param"><span class="pre">c_ops=None</span></em>, <em class="sig-param"><span class="pre">e_ops=None</span></em>, <em class="sig-param"><span class="pre">args=None</span></em>, <em class="sig-param"><span class="pre">expect=&lt;function</span> <span class="pre">expect&gt;</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.propagate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.propagate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.propagate">
+<span class="sig-name descname"><span class="pre">propagate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho0=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e_ops=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expect=&lt;function</span> <span class="pre">expect&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.propagate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.propagate" title="Link to this definition">¶</a></dt>
 <dd><p>Propagate the system of the objective over the entire time grid.</p>
 <p>Solve the dynamics for the <cite>H</cite> and <cite>c_ops</cite> of the objective. If <cite>rho0</cite>
 is not given, the <cite>initial_state</cite> will be propagated. This is similar
@@ -375,29 +380,29 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 estimate the “time discretization error”. If the error is significant,
 a shorter time step shoud be used.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p>Result of the propagation, using the same
 structure as <a class="reference internal" href="#krotov.objectives.Objective.mesolve" title="krotov.objectives.Objective.mesolve"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mesolve()</span></code></a>.</p>
 </dd>
-<dt class="field-even">Return type</dt>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.solver.Result" title="(in QuTiP: Quantum Toolbox in Python v4.5)">qutip.solver.Result</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.objectives.Objective.reset_symbol_counters">
-<em class="property"><span class="pre">classmethod</span> </em><code class="sig-name descname"><span class="pre">reset_symbol_counters</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.reset_symbol_counters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.reset_symbol_counters" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.reset_symbol_counters">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset_symbol_counters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.reset_symbol_counters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.reset_symbol_counters" title="Link to this definition">¶</a></dt>
 <dd><p>Reset the internal symbol counters used for printing objectives.</p>
 <p>See <a class="reference internal" href="#krotov.objectives.Objective.summarize" title="krotov.objectives.Objective.summarize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">summarize()</span></code></a>.</p>
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.objectives.Objective.summarize">
-<code class="sig-name descname"><span class="pre">summarize</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">use_unicode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reset_symbol_counters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.summarize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.summarize" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.Objective.summarize">
+<span class="sig-name descname"><span class="pre">summarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">use_unicode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reset_symbol_counters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#Objective.summarize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.Objective.summarize" title="Link to this definition">¶</a></dt>
 <dd><p>Return a one-line summary of the objective as a string.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>use_unicode</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If False, only use ascii symbols in the output</p></li>
 <li><p><strong>reset_symbol_counters</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, reset the internal object
@@ -471,11 +476,11 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.objectives.gate_objectives">
-<code class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></code><code class="sig-name descname"><span class="pre">gate_objectives</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_invariants</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">liouville_states_set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#gate_objectives"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.gate_objectives" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.gate_objectives">
+<span class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></span><span class="sig-name descname"><span class="pre">gate_objectives</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">basis_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_invariants</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">liouville_states_set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#gate_objectives"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.gate_objectives" title="Link to this definition">¶</a></dt>
 <dd><p>Construct a list of objectives for optimizing towards a quantum gate</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>basis_states</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a><em>]</em>) – A list of <span class="math notranslate nohighlight">\(n\)</span> canonical basis states</p></li>
 <li><p><strong>gate</strong> – The gate to optimize for, as a <span class="math notranslate nohighlight">\(n \times n\)</span> matrix-like object
@@ -485,18 +490,18 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 perfect entangler.</p></li>
 <li><p><strong>H</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a>) – The Hamiltonian (or Liouvillian) for the time
 evolution, in nested-list format.</p></li>
-<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – A list of collapse (Lindblad) operators, or None
+<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><em>None</em>) – A list of collapse (Lindblad) operators, or None
 for unitary dynamics or if <cite>H</cite> is a Liouvillian (preferred!)</p></li>
 <li><p><strong>local_invariants</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If True, initialize the objectives for an
 optimization towards a two-qubit gate that is “locally equivalent”
 to <cite>gate</cite>. That is, the result of the optimization should implement
 <cite>gate</cite> up to single-qubit operations.</p></li>
-<li><p><strong>liouville_states_set</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – If not None, one of “full”,
+<li><p><strong>liouville_states_set</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – If not None, one of “full”,
 “3states”, “d+1”. This sets the objectives for a gate
 optimization in Liouville space, using the states defined in
 Goerz et al. New J. Phys. 16, 055012 (2014). See Examples for
 details.</p></li>
-<li><p><strong>weights</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – If given as a list, weights for the different
+<li><p><strong>weights</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – If given as a list, weights for the different
 objectives. These will be added as a custom attribute to the
 respective <a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><code class="xref py py-class docutils literal notranslate"><span class="pre">Objective</span></code></a>, and may be used by a particular
 functional (<cite>chi_constructor</cite>). The intended use case is for the
@@ -509,7 +514,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 number of objectives. IF False, the weights will be used unchanged.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p>The objectives that define the optimization towards
 the gate. For a “normal” gate with a basis in Hilbert space, the
 objectives will have the <cite>basis_states</cite> as each
@@ -525,10 +530,10 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 optimization.</p>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective">Objective</a>]</p>
 </dd>
-<dt class="field-even">Raises</dt>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ValueError" title="(in Python v3.7)"><strong>ValueError</strong></a> – If <cite>gate</cite>, <cite>basis_states</cite>, and <cite>local_invariants</cite> are
     incompatible, or <cite>gate</cite> is invalid (not a recognized string)</p>
 </dd>
@@ -698,8 +703,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.objectives.ensemble_objectives">
-<code class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></code><code class="sig-name descname"><span class="pre">ensemble_objectives</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Hs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_original_objectives</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#ensemble_objectives"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.ensemble_objectives" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.ensemble_objectives">
+<span class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></span><span class="sig-name descname"><span class="pre">ensemble_objectives</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Hs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_original_objectives</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#ensemble_objectives"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.ensemble_objectives" title="Link to this definition">¶</a></dt>
 <dd><p>Extend <cite>objectives</cite> for an “ensemble optimization”</p>
 <p>This creates a list of objectives for an optimization for robustness with
 respect to variations in some parameter of the Hamiltonian. The trick is to
@@ -708,7 +713,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 Goerz, Halperin, Aytac, Koch, Whaley. Phys. Rev. A 90, 032329 (2014)
 for details.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>objectives</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective"><em>Objective</em></a><em>]</em>) – The <span class="math notranslate nohighlight">\(n\)</span> original objectives</p></li>
 <li><p><strong>Hs</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – List of <span class="math notranslate nohighlight">\(m\)</span> variations of the original
@@ -719,7 +724,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 straightforward)</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>List of <span class="math notranslate nohighlight">\(n (m+1)\)</span> new objectives that consists of the
 original objectives, plus one copy of the original objectives per
 element of <cite>Hs</cite> where the <cite>H</cite> attribute of each objectives is
@@ -727,15 +732,15 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <code class="docutils literal notranslate"><span class="pre">keep_original_objectives=False</span></code>, list of <span class="math notranslate nohighlight">\(n m\)</span> new objectives
 without the original objectives.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference internal" href="#krotov.objectives.Objective" title="krotov.objectives.Objective">Objective</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.objectives.liouvillian">
-<code class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></code><code class="sig-name descname"><span class="pre">liouvillian</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#liouvillian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.liouvillian" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.liouvillian">
+<span class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></span><span class="sig-name descname"><span class="pre">liouvillian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/objectives.html#liouvillian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.objectives.liouvillian" title="Link to this definition">¶</a></dt>
 <dd><p>Convert Hamiltonian and Lindblad operators into a Liouvillian.</p>
 <p>This is like <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.superoperator.liouvillian" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.superoperator.liouvillian()</span></code></a>, but <cite>H</cite> may be a
 time-dependent Hamiltonian in nested-list format. <cite>H</cite> is assumed to contain
@@ -744,8 +749,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py data">
-<dt id="krotov.objectives.FIX_QUTIP_932">
-<code class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></code><code class="sig-name descname"><span class="pre">FIX_QUTIP_932</span></code><em class="property"> <span class="pre">=</span> <span class="pre">False</span></em><a class="headerlink" href="#krotov.objectives.FIX_QUTIP_932" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.objectives.FIX_QUTIP_932">
+<span class="sig-prename descclassname"><span class="pre">krotov.objectives.</span></span><span class="sig-name descname"><span class="pre">FIX_QUTIP_932</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#krotov.objectives.FIX_QUTIP_932" title="Link to this definition">¶</a></dt>
 <dd><p>Workaround for <a class="reference external" href="https://github.com/qutip/qutip/issues/932">QuTiP issue 932</a>.</p>
 <p>If True, in <a class="reference internal" href="#krotov.objectives.Objective.mesolve" title="krotov.objectives.Objective.mesolve"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Objective.mesolve()</span></code></a>, replace any array controls with an
 equivalent function. This results in a signficant slowdown of the propagation,
@@ -753,8 +758,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 any non-Linux system.</p>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -768,7 +773,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.optimize.html b/master/API/krotov.optimize.html
index 674d4e53..a5350a6e 100644
--- a/master/API/krotov.optimize.html
+++ b/master/API/krotov.optimize.html
@@ -1,26 +1,32 @@
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -76,7 +82,10 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.objectives.html">krotov.objectives module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.optimize module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.optimize.optimize_pulses"><code class="docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.parallelization.html">krotov.parallelization module</a></li>
@@ -153,34 +162,30 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.optimize">
-<span id="krotov-optimize-module"></span><h1>krotov.optimize module<a class="headerlink" href="#module-krotov.optimize" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+  <section id="module-krotov.optimize">
+<span id="krotov-optimize-module"></span><h1>krotov.optimize module<a class="headerlink" href="#module-krotov.optimize" title="Link to this heading">¶</a></h1>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_pulses</span></code></a></p></td>
-<td><p>Use Krotov’s method to optimize towards the given <cite>objectives</cite>.</p></td>
+<td><p>Use Krotov's method to optimize towards the given <cite>objectives</cite>.</p></td>
 </tr>
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.optimize.optimize_pulses">
-<code class="sig-prename descclassname"><span class="pre">krotov.optimize.</span></code><code class="sig-name descname"><span class="pre">optimize_pulses</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulse_options</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_constructor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_stop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_convergence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">info_hook</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modify_params_after_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'array'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallel_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_all_pulses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">continue_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_initial_forward_propagation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_thread_pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/optimize.html#optimize_pulses"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.optimize.optimize_pulses" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.optimize.optimize_pulses">
+<span class="sig-prename descclassname"><span class="pre">krotov.optimize.</span></span><span class="sig-name descname"><span class="pre">optimize_pulses</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulse_options</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tlist</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_constructor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_stop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_convergence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">info_hook</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modify_params_after_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'array'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallel_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_all_pulses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">continue_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_initial_forward_propagation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_thread_pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/optimize.html#optimize_pulses"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.optimize.optimize_pulses" title="Link to this definition">¶</a></dt>
 <dd><p>Use Krotov’s method to optimize towards the given <cite>objectives</cite>.</p>
 <p>Optimize all time-dependent controls found in the Hamiltonians or
 Liouvillians of the given <cite>objectives</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>objectives</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><em>Objective</em></a><em>]</em>) – List of objectives</p></li>
 <li><p><strong>pulse_options</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#dict" title="(in Python v3.7)"><em>dict</em></a>) – <p>Mapping of time-dependent controls found in the
@@ -214,17 +219,17 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <p>For example, for <cite>objectives</cite> that contain a Hamiltonian of the
 form <code class="docutils literal notranslate"><span class="pre">[H0,</span> <span class="pre">[H1,</span> <span class="pre">u],</span> <span class="pre">[H2,</span> <span class="pre">g]]</span></code>, where <code class="docutils literal notranslate"><span class="pre">H0</span></code>, <code class="docutils literal notranslate"><span class="pre">H1</span></code>, and <code class="docutils literal notranslate"><span class="pre">H2</span></code>
 are <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Qobj</span></code></a> instances, <code class="docutils literal notranslate"><span class="pre">u</span></code> is a numpy array</p>
-<div class="highlight-pycon3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">u</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
+<div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">u</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>and <code class="docutils literal notranslate"><span class="pre">g</span></code> is a control function</p>
-<div class="highlight-pycon3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">g</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
+<div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">g</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
 <span class="gp">... </span>    <span class="n">E0</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;E0&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">E0</span>
 </pre></div>
 </div>
 <p>then a possible value for <cite>pulse_options</cite> would look like this:</p>
-<div class="highlight-pycon3 notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">krotov.shapes</span> <span class="kn">import</span> <span class="n">flattop</span>
+<div class="highlight-pycon notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">krotov.shapes</span> <span class="kn">import</span> <span class="n">flattop</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">pulse_options</span> <span class="o">=</span> <span class="p">{</span>
 <span class="gp">... </span>    <span class="nb">id</span><span class="p">(</span><span class="n">u</span><span class="p">):</span> <span class="p">{</span><span class="s1">&#39;lambda_a&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;update_shape&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>
@@ -253,7 +258,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 condition for the backward propagation. This is where the
 final-time functional (indirectly) enters the optimization.
 See <a class="reference internal" href="krotov.functionals.html#module-krotov.functionals" title="krotov.functionals"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.functionals</span></code></a> for details.</p></li>
-<li><p><strong>mu</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><em>callable</em>) – Function that calculates the derivative
+<li><p><strong>mu</strong> (<em>None</em><em> or </em><em>callable</em>) – Function that calculates the derivative
 <span class="math notranslate nohighlight">\(\frac{\partial H}{\partial\epsilon}\)</span> for an equation of motion
 <span class="math notranslate nohighlight">\(\dot{\phi}(t) = -i H[\phi(t)]\)</span> of an abstract operator <span class="math notranslate nohighlight">\(H\)</span> and an
 abstract state <span class="math notranslate nohighlight">\(\phi\)</span>. If None, defaults to
@@ -261,7 +266,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 Schrödinger and master equations. See <a class="reference internal" href="krotov.mu.html#module-krotov.mu" title="krotov.mu"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.mu</span></code></a> for a
 full explanation of the role of <cite>mu</cite> in the optimization, and the
 required function signature.</p></li>
-<li><p><strong>sigma</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference internal" href="krotov.second_order.html#krotov.second_order.Sigma" title="krotov.second_order.Sigma"><em>krotov.second_order.Sigma</em></a>) – Function (instance of a
+<li><p><strong>sigma</strong> (<em>None</em><em> or </em><a class="reference internal" href="krotov.second_order.html#krotov.second_order.Sigma" title="krotov.second_order.Sigma"><em>krotov.second_order.Sigma</em></a>) – Function (instance of a
 <a class="reference internal" href="krotov.second_order.html#krotov.second_order.Sigma" title="krotov.second_order.Sigma"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sigma</span></code></a> subclass) that calculates the
 second-order contribution. If None, the first-order Krotov method
 is used.</p></li>
@@ -269,11 +274,11 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 optimization</p></li>
 <li><p><strong>iter_stop</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – The iteration number after which to end the
 optimization, whether or not convergence has been reached</p></li>
-<li><p><strong>check_convergence</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><em>callable</em>) – Function that determines whether
+<li><p><strong>check_convergence</strong> (<em>None</em><em> or </em><em>callable</em>) – Function that determines whether
 the optimization has converged. If None, the optimization will only
 end when <cite>iter_stop</cite> is reached. See <a class="reference internal" href="krotov.convergence.html#module-krotov.convergence" title="krotov.convergence"><code class="xref py py-mod docutils literal notranslate"><span class="pre">krotov.convergence</span></code></a> for
 details.</p></li>
-<li><p><strong>info_hook</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><em>callable</em>) – Function that is called after each
+<li><p><strong>info_hook</strong> (<em>None</em><em> or </em><em>callable</em>) – Function that is called after each
 iteration of the optimization, for the purpose of analysis. Any
 value returned by <cite>info_hook</cite> (e.g. an evaluated functional
 <span class="math notranslate nohighlight">\(J_T\)</span>) will be stored, for each iteration, in the <cite>info_vals</cite>
@@ -281,7 +286,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 have the same signature as
 <a class="reference internal" href="krotov.info_hooks.html#krotov.info_hooks.print_debug_information" title="krotov.info_hooks.print_debug_information"><code class="xref py py-func docutils literal notranslate"><span class="pre">krotov.info_hooks.print_debug_information()</span></code></a>. It should not
 modify its arguments in any way, except for <cite>shared_data</cite>.</p></li>
-<li><p><strong>modify_params_after_iter</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><em>callable</em>) – Function that is called
+<li><p><strong>modify_params_after_iter</strong> (<em>None</em><em> or </em><em>callable</em>) – Function that is called
 after each iteration, which may modify its arguments for certain
 advanced use cases, such as dynamically adjusting <cite>lambda_vals</cite>, or
 applying spectral filters to the <cite>optimized_pulses</cite>. It has the
@@ -294,7 +299,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 an empty array-like container of length <cite>N</cite>. The default value
 ‘array’ is equivalent to
 <code class="docutils literal notranslate"><span class="pre">functools.partial(numpy.empty,</span> <span class="pre">dtype=object)</span></code>.</p></li>
-<li><p><strong>parallel_map</strong> (<em>callable</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#tuple" title="(in Python v3.7)"><em>tuple</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – Parallel function evaluator.
+<li><p><strong>parallel_map</strong> (<em>callable</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#tuple" title="(in Python v3.7)"><em>tuple</em></a><em> or </em><em>None</em>) – Parallel function evaluator.
 If given as a callable, the argument must have the same
 specification as <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.parallel.serial_map" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.parallel.serial_map()</span></code></a>.
 A value of None is the same as passing
@@ -308,22 +313,22 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 details.</p></li>
 <li><p><strong>store_all_pulses</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Whether or not to store the optimized pulses
 from <em>all</em> iterations in <a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a>.</p></li>
-<li><p><strong>continue_from</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><em>Result</em></a>) – If given, continue an optimization from
+<li><p><strong>continue_from</strong> (<em>None</em><em> or </em><a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><em>Result</em></a>) – If given, continue an optimization from
 a previous <a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a>. The result must have identical
 <cite>objectives</cite>.</p></li>
 <li><p><strong>skip_initial_forward_propagation</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If given as <cite>True</cite> together
 with <cite>continue_from</cite>, skip the initial forward propagation (“zeroth
 iteration”), and take the forward-propagated states from
 <a class="reference internal" href="krotov.result.html#krotov.result.Result.states" title="krotov.result.Result.states"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Result.states</span></code></a> instead.</p></li>
-<li><p><strong>norm</strong> (<em>callable</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – A single-argument function to calculate the
+<li><p><strong>norm</strong> (<em>callable</em><em> or </em><em>None</em>) – A single-argument function to calculate the
 norm of states. If None, delegate to the <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj.norm" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">norm()</span></code></a>
 method of the states.</p></li>
-<li><p><strong>overlap</strong> (<em>callable</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – A two-argument function to calculate the
+<li><p><strong>overlap</strong> (<em>callable</em><em> or </em><em>None</em>) – A two-argument function to calculate the
 complex overlap of two states. If None, delegate to
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj.overlap" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">qutip.Qobj.overlap()</span></code></a> for Hilbert space states and to the
 Hilbert-Schmidt norm <span class="math notranslate nohighlight">\(\tr[\rho_1^\dagger \rho2]\)</span> for density
 matrices or operators.</p></li>
-<li><p><strong>limit_thread_pool</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – If True, try to eliminate
+<li><p><strong>limit_thread_pool</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em> or </em><em>None</em>) – If True, try to eliminate
 multi-threading in low-level numerical routines like <a class="reference external" href="https://numpy.org/doc/stable/reference/index.html#module-numpy" title="(in NumPy v1.26)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code></a>,
 via the use of the <code class="docutils literal notranslate"><span class="pre">threadpoolctl</span></code> package. Single-threaded
 execution is usually faster, but if you know what you are doing and
@@ -333,13 +338,13 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <a class="reference internal" href="krotov.parallelization.html#krotov.parallelization.USE_THREADPOOL_LIMITS" title="krotov.parallelization.USE_THREADPOOL_LIMITS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">krotov.parallelization.USE_THREADPOOL_LIMITS</span></code></a>.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>The result of the optimization.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result">Result</a></p>
 </dd>
-<dt class="field-even">Raises</dt>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ValueError" title="(in Python v3.7)"><strong>ValueError</strong></a> – If any controls are not real-valued, or if any update
     shape is not a real-valued function in the range [0, 1]; if using
     <cite>continue_from</cite> with a <a class="reference internal" href="krotov.result.html#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> with differing
@@ -349,8 +354,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dl>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -364,7 +369,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.parallelization.html b/master/API/krotov.parallelization.html
index 80131a69..9cf02ceb 100644
--- a/master/API/krotov.parallelization.html
+++ b/master/API/krotov.parallelization.html
@@ -1,26 +1,32 @@
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+  <title>krotov.parallelization module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -77,7 +83,16 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.optimize.html">krotov.optimize module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.parallelization module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.parallelization.set_parallelization"><code class="docutils literal notranslate"><span class="pre">set_parallelization()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.parallelization.parallel_map"><code class="docutils literal notranslate"><span class="pre">parallel_map()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.parallelization.Consumer"><code class="docutils literal notranslate"><span class="pre">Consumer</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.parallelization.FwPropStepTask"><code class="docutils literal notranslate"><span class="pre">FwPropStepTask</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.parallelization.parallel_map_fw_prop_step"><code class="docutils literal notranslate"><span class="pre">parallel_map_fw_prop_step()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.parallelization.USE_LOKY"><code class="docutils literal notranslate"><span class="pre">USE_LOKY</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.parallelization.USE_THREADPOOL_LIMITS"><code class="docutils literal notranslate"><span class="pre">USE_THREADPOOL_LIMITS</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.propagators.html">krotov.propagators module</a></li>
@@ -153,8 +168,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.parallelization">
-<span id="krotov-parallelization-module"></span><h1>krotov.parallelization module<a class="headerlink" href="#module-krotov.parallelization" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.parallelization">
+<span id="krotov-parallelization-module"></span><h1>krotov.parallelization module<a class="headerlink" href="#module-krotov.parallelization" title="Link to this heading">¶</a></h1>
 <p>Support routines for running the optimization in parallel across the
 objectives.</p>
 <p>The time-propagation that is the main numerical effort in an optimization with
@@ -236,14 +251,10 @@
 <p>You may attempt to use the various options to <a class="reference internal" href="#krotov.parallelization.set_parallelization" title="krotov.parallelization.set_parallelization"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_parallelization()</span></code></a> in
 order to find a combination of settings that minimizes the runtime in your
 particular environment.</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Classes:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.parallelization.Consumer" title="krotov.parallelization.Consumer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Consumer</span></code></a></p></td>
 <td><p>A process-based task consumer.</p></td>
@@ -254,11 +265,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.parallelization.parallel_map" title="krotov.parallelization.parallel_map"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parallel_map</span></code></a></p></td>
 <td><p>Map function <cite>task</cite> onto <cite>values</cite>, in parallel.</p></td>
@@ -272,24 +279,24 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.parallelization.Consumer" title="krotov.parallelization.Consumer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Consumer</span></code></a>, <a class="reference internal" href="#krotov.parallelization.FwPropStepTask" title="krotov.parallelization.FwPropStepTask"><code class="xref py py-class docutils literal notranslate"><span class="pre">FwPropStepTask</span></code></a>, <a class="reference internal" href="#krotov.parallelization.parallel_map" title="krotov.parallelization.parallel_map"><code class="xref py py-func docutils literal notranslate"><span class="pre">parallel_map</span></code></a>, <a class="reference internal" href="#krotov.parallelization.parallel_map_fw_prop_step" title="krotov.parallelization.parallel_map_fw_prop_step"><code class="xref py py-func docutils literal notranslate"><span class="pre">parallel_map_fw_prop_step</span></code></a>, <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.parallel.serial_map" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">serial_map</span></code></a>, <a class="reference internal" href="#krotov.parallelization.set_parallelization" title="krotov.parallelization.set_parallelization"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_parallelization</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.parallelization.set_parallelization">
-<code class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></code><code class="sig-name descname"><span class="pre">set_parallelization</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">use_loky</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loky_pickler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_threadpool_limits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#set_parallelization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.set_parallelization" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.parallelization.set_parallelization">
+<span class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></span><span class="sig-name descname"><span class="pre">set_parallelization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">use_loky</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loky_pickler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_threadpool_limits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#set_parallelization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.set_parallelization" title="Link to this definition">¶</a></dt>
 <dd><p>Configure multi-process parallelization.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>use_loky</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Value for <a class="reference internal" href="#krotov.parallelization.USE_LOKY" title="krotov.parallelization.USE_LOKY"><code class="xref py py-obj docutils literal notranslate"><span class="pre">USE_LOKY</span></code></a>.</p></li>
-<li><p><strong>start_method</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – One of ‘fork’, ‘spawn’, and ‘forkserver’,
+<li><p><strong>start_method</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – One of ‘fork’, ‘spawn’, and ‘forkserver’,
 see <a class="reference external" href="https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing.set_start_method" title="(in Python v3.7)"><code class="xref py py-func docutils literal notranslate"><span class="pre">multiprocessing.set_start_method()</span></code></a>. If <code class="docutils literal notranslate"><span class="pre">use_loky=True</span></code>,
 also ‘loky’ and ‘loky_int_main’, see <a class="reference external" href="https://loky.readthedocs.io/en/stable/API.html#module-loky" title="(in loky v3.4.1)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">loky</span></code></a>. If None, a
 platform and version-dependent default will be chosen automatically
 (e.g., ‘fork’ on Linux, ‘spawn’ on Windows, ‘loky’ if
 <code class="docutils literal notranslate"><span class="pre">use_loky=True</span></code>)</p></li>
-<li><p><strong>loky_pickler</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – Serialization module to use for
+<li><p><strong>loky_pickler</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – Serialization module to use for
 <a class="reference external" href="https://loky.readthedocs.io/en/stable/API.html#module-loky" title="(in loky v3.4.1)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">loky</span></code></a>. One of ‘cloudpickle’, ‘pickle’. This forces the
 serialization for <em>all</em> objects. The default value None chooses the
 serialization automatically depending of the type of object. Using
@@ -299,7 +306,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <li><p><strong>use_threadpool_limits</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Value for <a class="reference internal" href="#krotov.parallelization.USE_THREADPOOL_LIMITS" title="krotov.parallelization.USE_THREADPOOL_LIMITS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">USE_THREADPOOL_LIMITS</span></code></a>.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Raises</dt>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ImportError" title="(in Python v3.7)"><strong>ImportError</strong></a> – if <code class="docutils literal notranslate"><span class="pre">use_loky=True</span></code> but <a class="reference external" href="https://loky.readthedocs.io/en/stable/API.html#module-loky" title="(in loky v3.4.1)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">loky</span></code></a> is not installed.</p>
 </dd>
 </dl>
@@ -319,8 +326,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.parallelization.parallel_map">
-<code class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></code><code class="sig-name descname"><span class="pre">parallel_map</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_cpus</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress_bar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#parallel_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.parallel_map" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.parallelization.parallel_map">
+<span class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></span><span class="sig-name descname"><span class="pre">parallel_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_cpus</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress_bar</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#parallel_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.parallel_map" title="Link to this definition">¶</a></dt>
 <dd><p>Map function <cite>task</cite> onto <cite>values</cite>, in parallel.</p>
 <p>This function’s interface is identical to
 <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/functions.html#qutip.parallel.parallel_map" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip.parallel.parallel_map()</span></code></a> as of QuTiP 4.5.0, but has the option
@@ -330,13 +337,13 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py class">
-<dt id="krotov.parallelization.Consumer">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></code><code class="sig-name descname"><span class="pre">Consumer</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#Consumer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.Consumer" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing.Process" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">multiprocessing.context.Process</span></code></a></p>
+<dt class="sig sig-object py" id="krotov.parallelization.Consumer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></span><span class="sig-name descname"><span class="pre">Consumer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#Consumer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.Consumer" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing.Process" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Process</span></code></a></p>
 <p>A process-based task consumer.</p>
 <p>This is for internal use in <a class="reference internal" href="#krotov.parallelization.parallel_map_fw_prop_step" title="krotov.parallelization.parallel_map_fw_prop_step"><code class="xref py py-func docutils literal notranslate"><span class="pre">parallel_map_fw_prop_step()</span></code></a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>task_queue</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/multiprocessing.html#multiprocessing.JoinableQueue" title="(in Python v3.7)"><em>multiprocessing.JoinableQueue</em></a>) – A queue from which to read
 tasks.</p></li>
@@ -347,8 +354,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd>
 </dl>
 <dl class="py method">
-<dt id="krotov.parallelization.Consumer.run">
-<code class="sig-name descname"><span class="pre">run</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#Consumer.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.Consumer.run" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.parallelization.Consumer.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#Consumer.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.Consumer.run" title="Link to this definition">¶</a></dt>
 <dd><p>Execute all tasks on the <cite>task_queue</cite>.</p>
 <p>Each task must be a callable that takes <cite>data</cite> as its only argument.
 The return value of the task will be put on the <cite>result_queue</cite>. A None
@@ -359,8 +366,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py class">
-<dt id="krotov.parallelization.FwPropStepTask">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></code><code class="sig-name descname"><span class="pre">FwPropStepTask</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulse_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#FwPropStepTask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.FwPropStepTask" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.parallelization.FwPropStepTask">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></span><span class="sig-name descname"><span class="pre">FwPropStepTask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">i_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pulse_vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#FwPropStepTask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.FwPropStepTask" title="Link to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/functions.html#object" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>A task that performs a single forward-propagation step.</p>
 <p>The task object is a callable, receiving the single tuple of the same
@@ -369,7 +376,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 the task.</p>
 <p>This is for internal use in <a class="reference internal" href="#krotov.parallelization.parallel_map_fw_prop_step" title="krotov.parallelization.parallel_map_fw_prop_step"><code class="xref py py-func docutils literal notranslate"><span class="pre">parallel_map_fw_prop_step()</span></code></a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>i_state</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – The index of the state to propagation. That is, the
 index of the objective from whose <a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective.initial_state" title="krotov.objectives.Objective.initial_state"><code class="xref py py-attr docutils literal notranslate"><span class="pre">initial_state</span></code></a>
@@ -389,11 +396,11 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.parallelization.parallel_map_fw_prop_step">
-<code class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></code><code class="sig-name descname"><span class="pre">parallel_map_fw_prop_step</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shared</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#parallel_map_fw_prop_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.parallel_map_fw_prop_step" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.parallelization.parallel_map_fw_prop_step">
+<span class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></span><span class="sig-name descname"><span class="pre">parallel_map_fw_prop_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shared</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/parallelization.html#parallel_map_fw_prop_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.parallelization.parallel_map_fw_prop_step" title="Link to this definition">¶</a></dt>
 <dd><p><cite>parallel_map</cite> function for the forward-propagation by one time step.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>shared</strong> – A global object to which we can attach attributes for sharing
 data between different calls to <a class="reference internal" href="#krotov.parallelization.parallel_map_fw_prop_step" title="krotov.parallelization.parallel_map_fw_prop_step"><code class="xref py py-func docutils literal notranslate"><span class="pre">parallel_map_fw_prop_step()</span></code></a>,
@@ -424,15 +431,15 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py data">
-<dt id="krotov.parallelization.USE_LOKY">
-<code class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></code><code class="sig-name descname"><span class="pre">USE_LOKY</span></code><em class="property"> <span class="pre">=</span> <span class="pre">False</span></em><a class="headerlink" href="#krotov.parallelization.USE_LOKY" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.parallelization.USE_LOKY">
+<span class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></span><span class="sig-name descname"><span class="pre">USE_LOKY</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#krotov.parallelization.USE_LOKY" title="Link to this definition">¶</a></dt>
 <dd><p>Whether to use <a class="reference external" href="https://loky.readthedocs.io/en/stable/API.html#module-loky" title="(in loky v3.4.1)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">loky</span></code></a> instead of <a class="reference external" href="https://docs.python.org/3.7/library/multiprocessing.html#module-multiprocessing" title="(in Python v3.7)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">multiprocessing</span></code></a>.</p>
 <p>Set by <a class="reference internal" href="#krotov.parallelization.set_parallelization" title="krotov.parallelization.set_parallelization"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_parallelization()</span></code></a>.</p>
 </dd></dl>
 
 <dl class="py data">
-<dt id="krotov.parallelization.USE_THREADPOOL_LIMITS">
-<code class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></code><code class="sig-name descname"><span class="pre">USE_THREADPOOL_LIMITS</span></code><em class="property"> <span class="pre">=</span> <span class="pre">True</span></em><a class="headerlink" href="#krotov.parallelization.USE_THREADPOOL_LIMITS" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.parallelization.USE_THREADPOOL_LIMITS">
+<span class="sig-prename descclassname"><span class="pre">krotov.parallelization.</span></span><span class="sig-name descname"><span class="pre">USE_THREADPOOL_LIMITS</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">True</span></em><a class="headerlink" href="#krotov.parallelization.USE_THREADPOOL_LIMITS" title="Link to this definition">¶</a></dt>
 <dd><p>Whether to limit the number of low-level BLAS/OpenMP threads.</p>
 <p>When using multi-process parallelization, <em>nested parallelization</em> must be
 avoided. That is, low-level numerical routines e.g. in <a class="reference external" href="https://numpy.org/doc/stable/reference/index.html#module-numpy" title="(in NumPy v1.26)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy</span></code></a> should not
@@ -453,8 +460,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </div>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -468,7 +475,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.propagators.html b/master/API/krotov.propagators.html
index 155d5623..c530059b 100644
--- a/master/API/krotov.propagators.html
+++ b/master/API/krotov.propagators.html
@@ -1,26 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" />
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+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.propagators module &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
+  <title>krotov.propagators module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
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+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
     <script src="../_static/js/theme.js"></script>
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -78,7 +84,12 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.parallelization.html">krotov.parallelization module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.propagators module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.propagators.expm"><code class="docutils literal notranslate"><span class="pre">expm()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.propagators.Propagator"><code class="docutils literal notranslate"><span class="pre">Propagator</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.propagators.DensityMatrixODEPropagator"><code class="docutils literal notranslate"><span class="pre">DensityMatrixODEPropagator</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.result.html">krotov.result module</a></li>
@@ -153,8 +164,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.propagators">
-<span id="krotov-propagators-module"></span><h1>krotov.propagators module<a class="headerlink" href="#module-krotov.propagators" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.propagators">
+<span id="krotov-propagators-module"></span><h1>krotov.propagators module<a class="headerlink" href="#module-krotov.propagators" title="Link to this heading">¶</a></h1>
 <p>Routines that can be passed as <cite>propagator</cite> to <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a></p>
 <p>The numerical effort involved in the optimization is almost entirely within the
 simulation of the system dynamics. In every iteration and for every objective,
@@ -215,14 +226,10 @@
 <a class="reference internal" href="#krotov.propagators.DensityMatrixODEPropagator" title="krotov.propagators.DensityMatrixODEPropagator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DensityMatrixODEPropagator</span></code></a> is an example for such a propagator. In
 general, any stateful <cite>propagator</cite> should be an instance of
 <a class="reference internal" href="#krotov.propagators.Propagator" title="krotov.propagators.Propagator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Propagator</span></code></a>.</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Classes:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.propagators.DensityMatrixODEPropagator" title="krotov.propagators.DensityMatrixODEPropagator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DensityMatrixODEPropagator</span></code></a></p></td>
 <td><p>Propagator for density matrix evolution under a Lindbladian</p></td>
@@ -233,11 +240,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.propagators.expm" title="krotov.propagators.expm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expm</span></code></a></p></td>
 <td><p>Propagate using matrix exponentiation.</p></td>
@@ -245,12 +248,12 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.propagators.DensityMatrixODEPropagator" title="krotov.propagators.DensityMatrixODEPropagator"><code class="xref py py-class docutils literal notranslate"><span class="pre">DensityMatrixODEPropagator</span></code></a>, <a class="reference internal" href="#krotov.propagators.Propagator" title="krotov.propagators.Propagator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Propagator</span></code></a>, <a class="reference internal" href="#krotov.propagators.expm" title="krotov.propagators.expm"><code class="xref py py-func docutils literal notranslate"><span class="pre">expm</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.propagators.expm">
-<code class="sig-prename descclassname"><span class="pre">krotov.propagators.</span></code><code class="sig-name descname"><span class="pre">expm</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backwards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initialize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#expm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.expm" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.propagators.expm">
+<span class="sig-prename descclassname"><span class="pre">krotov.propagators.</span></span><span class="sig-name descname"><span class="pre">expm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backwards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initialize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#expm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.expm" title="Link to this definition">¶</a></dt>
 <dd><p>Propagate using matrix exponentiation.</p>
 <p>This supports <cite>H</cite> being a Hamiltonian (for a Hilbert space <cite>state</cite>) or a
 Liouvillian (for <cite>state</cite> being a density matrix) in nested-list format.
@@ -261,16 +264,16 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py class">
-<dt id="krotov.propagators.Propagator">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">krotov.propagators.</span></code><code class="sig-name descname"><span class="pre">Propagator</span></code><a class="reference internal" href="../_modules/krotov/propagators.html#Propagator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.Propagator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/abc.html#abc.ABC" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></a></p>
+<dt class="sig sig-object py" id="krotov.propagators.Propagator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">krotov.propagators.</span></span><span class="sig-name descname"><span class="pre">Propagator</span></span><a class="reference internal" href="../_modules/krotov/propagators.html#Propagator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.Propagator" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/abc.html#abc.ABC" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></a></p>
 <p>Abstract base class for stateful propagators</p>
 <dl class="py method">
-<dt id="krotov.propagators.Propagator.__call__">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backwards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initialize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#Propagator.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.Propagator.__call__" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.propagators.Propagator.__call__">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backwards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initialize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#Propagator.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.Propagator.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Evaluation of a single propagation step</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>H</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – A Hamiltonian or Liouvillian in qutip’s nested-list
 format, with a scalar value in the place of a time-dependency.
@@ -280,7 +283,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 time.</p></li>
 <li><p><strong>state</strong> (<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><em>qutip.Qobj</em></a>) – The state to propagate</p></li>
 <li><p><strong>dt</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – The time step over which to propagate</p></li>
-<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – A list of Lindblad operators. Using explicit
+<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><em>None</em>) – A list of Lindblad operators. Using explicit
 Lindblad operators should be avoided: it is usually more
 efficient to convert them into a Lindbladian, passed as <cite>H</cite></p></li>
 <li><p><strong>backwards</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Whether the propagation is forward in time or
@@ -304,9 +307,9 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py class">
-<dt id="krotov.propagators.DensityMatrixODEPropagator">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">krotov.propagators.</span></code><code class="sig-name descname"><span class="pre">DensityMatrixODEPropagator</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'adams'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsteps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reentrant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#DensityMatrixODEPropagator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.DensityMatrixODEPropagator" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#krotov.propagators.Propagator" title="krotov.propagators.Propagator"><code class="xref py py-class docutils literal notranslate"><span class="pre">krotov.propagators.Propagator</span></code></a></p>
+<dt class="sig sig-object py" id="krotov.propagators.DensityMatrixODEPropagator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">krotov.propagators.</span></span><span class="sig-name descname"><span class="pre">DensityMatrixODEPropagator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'adams'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsteps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reentrant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#DensityMatrixODEPropagator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.DensityMatrixODEPropagator" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#krotov.propagators.Propagator" title="krotov.propagators.Propagator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Propagator</span></code></a></p>
 <p>Propagator for density matrix evolution under a Lindbladian</p>
 <p>See <a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.solver.Options" title="(in QuTiP: Quantum Toolbox in Python v4.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">qutip.solver.Options</span></code></a> for all arguments except <cite>reentrant</cite>.
 Passing True for the <cite>reentrant</cite> re-initializes the propagator in every
@@ -324,11 +327,11 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 overhead.</p>
 </div>
 <dl class="py method">
-<dt id="krotov.propagators.DensityMatrixODEPropagator.__call__">
-<code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backwards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initialize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#DensityMatrixODEPropagator.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.DensityMatrixODEPropagator.__call__" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.propagators.DensityMatrixODEPropagator.__call__">
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backwards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initialize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/propagators.html#DensityMatrixODEPropagator.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.propagators.DensityMatrixODEPropagator.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Evaluation of a single propagation step</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>H</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – A Liouvillian superoperator in qutip’s nested-list
 format, with a scalar value in the place of a time-dependency.
@@ -344,7 +347,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 stored) state that was the result from the previous propagation
 step.</p></li>
 <li><p><strong>dt</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – The time step over which to propagate</p></li>
-<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a>) – An empty list, or None. Since this propagator
+<li><p><strong>c_ops</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em> or </em><em>None</em>) – An empty list, or None. Since this propagator
 assumes a full Liouvillian, it cannot be combined with Lindblad
 operators.</p></li>
 <li><p><strong>backwards</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Whether the propagation is forward in time or
@@ -362,8 +365,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -377,7 +380,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.result.html b/master/API/krotov.result.html
index 76da58bf..4add845e 100644
--- a/master/API/krotov.result.html
+++ b/master/API/krotov.result.html
@@ -1,23 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.result module &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
+  <title>krotov.result module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/docs-versions-menu.js"></script>
+  
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../_static/copybutton.js?v=f281be69"></script>
+        <script src="../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
@@ -38,7 +44,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -48,7 +54,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -77,7 +83,10 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.propagators.html">krotov.propagators module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.result module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.result.Result"><code class="docutils literal notranslate"><span class="pre">Result</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.second_order.html">krotov.second_order module</a></li>
@@ -151,18 +160,14 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.result">
-<span id="krotov-result-module"></span><h1>krotov.result module<a class="headerlink" href="#module-krotov.result" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.result">
+<span id="krotov-result-module"></span><h1>krotov.result module<a class="headerlink" href="#module-krotov.result" title="Link to this heading">¶</a></h1>
 <p>Module defining the <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> object that is returned by
 <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Classes:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Result</span></code></a></p></td>
 <td><p>Result of a Krotov optimization with <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p></td>
@@ -170,126 +175,126 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py class">
-<dt id="krotov.result.Result">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">krotov.result.</span></code><code class="sig-name descname"><span class="pre">Result</span></code><a class="reference internal" href="../_modules/krotov/result.html#Result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">krotov.result.</span></span><span class="sig-name descname"><span class="pre">Result</span></span><a class="reference internal" href="../_modules/krotov/result.html#Result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result" title="Link to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/functions.html#object" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Result of a Krotov optimization with <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p>
 <dl class="py attribute">
-<dt id="krotov.result.Result.objectives">
-<code class="sig-name descname"><span class="pre">objectives</span></code><a class="headerlink" href="#krotov.result.Result.objectives" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.objectives">
+<span class="sig-name descname"><span class="pre">objectives</span></span><a class="headerlink" href="#krotov.result.Result.objectives" title="Link to this definition">¶</a></dt>
 <dd><p>The control objectives</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective">Objective</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.tlist">
-<code class="sig-name descname"><span class="pre">tlist</span></code><a class="headerlink" href="#krotov.result.Result.tlist" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.tlist">
+<span class="sig-name descname"><span class="pre">tlist</span></span><a class="headerlink" href="#krotov.result.Result.tlist" title="Link to this definition">¶</a></dt>
 <dd><p>The time grid values</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">numpy.ndarray</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.iters">
-<code class="sig-name descname"><span class="pre">iters</span></code><a class="headerlink" href="#krotov.result.Result.iters" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.iters">
+<span class="sig-name descname"><span class="pre">iters</span></span><a class="headerlink" href="#krotov.result.Result.iters" title="Link to this definition">¶</a></dt>
 <dd><p>Iteration numbers, starting at 0.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#int" title="(in Python v3.7)">int</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.iter_seconds">
-<code class="sig-name descname"><span class="pre">iter_seconds</span></code><a class="headerlink" href="#krotov.result.Result.iter_seconds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.iter_seconds">
+<span class="sig-name descname"><span class="pre">iter_seconds</span></span><a class="headerlink" href="#krotov.result.Result.iter_seconds" title="Link to this definition">¶</a></dt>
 <dd><p>for each iteration number, the number of
 seconds that were spent in the optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#int" title="(in Python v3.7)">int</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.info_vals">
-<code class="sig-name descname"><span class="pre">info_vals</span></code><a class="headerlink" href="#krotov.result.Result.info_vals" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.info_vals">
+<span class="sig-name descname"><span class="pre">info_vals</span></span><a class="headerlink" href="#krotov.result.Result.info_vals" title="Link to this definition">¶</a></dt>
 <dd><p>For each iteration, the return value of <cite>info_hook</cite>,
 or None</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.tau_vals">
-<code class="sig-name descname"><span class="pre">tau_vals</span></code><a class="headerlink" href="#krotov.result.Result.tau_vals" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.tau_vals">
+<span class="sig-name descname"><span class="pre">tau_vals</span></span><a class="headerlink" href="#krotov.result.Result.tau_vals" title="Link to this definition">¶</a></dt>
 <dd><p>for each iteration, a list of complex
 overlaps between the target state and the forward-propagated state
 for each objective, assuming <a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective.target" title="krotov.objectives.Objective.target"><code class="xref py py-attr docutils literal notranslate"><span class="pre">Objective.target</span></code></a> contains the
 target state. If there is no target state, an empty list.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#complex" title="(in Python v3.7)">complex</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.guess_controls">
-<code class="sig-name descname"><span class="pre">guess_controls</span></code><a class="headerlink" href="#krotov.result.Result.guess_controls" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.guess_controls">
+<span class="sig-name descname"><span class="pre">guess_controls</span></span><a class="headerlink" href="#krotov.result.Result.guess_controls" title="Link to this definition">¶</a></dt>
 <dd><p>List of the guess controls in
 array format</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">numpy.ndarray</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.optimized_controls">
-<code class="sig-name descname"><span class="pre">optimized_controls</span></code><a class="headerlink" href="#krotov.result.Result.optimized_controls" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.optimized_controls">
+<span class="sig-name descname"><span class="pre">optimized_controls</span></span><a class="headerlink" href="#krotov.result.Result.optimized_controls" title="Link to this definition">¶</a></dt>
 <dd><p>List of the optimized control
 fields, in the order corresponding to <a class="reference internal" href="#krotov.result.Result.guess_controls" title="krotov.result.Result.guess_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">guess_controls</span></code></a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">numpy.ndarray</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.controls_mapping">
-<code class="sig-name descname"><span class="pre">controls_mapping</span></code><a class="headerlink" href="#krotov.result.Result.controls_mapping" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.controls_mapping">
+<span class="sig-name descname"><span class="pre">controls_mapping</span></span><a class="headerlink" href="#krotov.result.Result.controls_mapping" title="Link to this definition">¶</a></dt>
 <dd><p>A nested list that indicates where in
 <a class="reference internal" href="#krotov.result.Result.objectives" title="krotov.result.Result.objectives"><code class="xref py py-attr docutils literal notranslate"><span class="pre">objectives</span></code></a> the <a class="reference internal" href="#krotov.result.Result.guess_controls" title="krotov.result.Result.guess_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">guess_controls</span></code></a> and
 <a class="reference internal" href="#krotov.result.Result.optimized_controls" title="krotov.result.Result.optimized_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">optimized_controls</span></code></a> are used (as returned by
 <a class="reference internal" href="krotov.conversions.html#krotov.conversions.extract_controls_mapping" title="krotov.conversions.extract_controls_mapping"><code class="xref py py-func docutils literal notranslate"><span class="pre">extract_controls_mapping()</span></code></a>)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.all_pulses">
-<code class="sig-name descname"><span class="pre">all_pulses</span></code><a class="headerlink" href="#krotov.result.Result.all_pulses" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.all_pulses">
+<span class="sig-name descname"><span class="pre">all_pulses</span></span><a class="headerlink" href="#krotov.result.Result.all_pulses" title="Link to this definition">¶</a></dt>
 <dd><p>If the optimization was performed with
 <code class="docutils literal notranslate"><span class="pre">store_all_pulses=True</span></code>, for each iteration, a list of the
 optimized pulses (in the order corresponding to
@@ -297,110 +302,110 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 These pulses are defined at midpoints of the <cite>tlist</cite> intervals.
 Empty list if <code class="docutils literal notranslate"><span class="pre">store_all_pulses=False</span></code></p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.states">
-<code class="sig-name descname"><span class="pre">states</span></code><a class="headerlink" href="#krotov.result.Result.states" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.states">
+<span class="sig-name descname"><span class="pre">states</span></span><a class="headerlink" href="#krotov.result.Result.states" title="Link to this definition">¶</a></dt>
 <dd><p>for each objective, a list of states
 for each value in <cite>tlist</cite>, obtained from propagation under the
 final optimized control fields.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference external" href="https://qutip.org/docs/4.5/apidoc/classes.html#qutip.Qobj" title="(in QuTiP: Quantum Toolbox in Python v4.5)">qutip.Qobj</a>]]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.start_local_time">
-<code class="sig-name descname"><span class="pre">start_local_time</span></code><a class="headerlink" href="#krotov.result.Result.start_local_time" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.start_local_time">
+<span class="sig-name descname"><span class="pre">start_local_time</span></span><a class="headerlink" href="#krotov.result.Result.start_local_time" title="Link to this definition">¶</a></dt>
 <dd><p>Time stamp of when the
 optimization started</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/time.html#time.struct_time" title="(in Python v3.7)">time.struct_time</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.end_local_time">
-<code class="sig-name descname"><span class="pre">end_local_time</span></code><a class="headerlink" href="#krotov.result.Result.end_local_time" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.end_local_time">
+<span class="sig-name descname"><span class="pre">end_local_time</span></span><a class="headerlink" href="#krotov.result.Result.end_local_time" title="Link to this definition">¶</a></dt>
 <dd><p>Time stamp of when the optimization
 ended</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/time.html#time.struct_time" title="(in Python v3.7)">time.struct_time</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.message">
-<code class="sig-name descname"><span class="pre">message</span></code><a class="headerlink" href="#krotov.result.Result.message" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.message">
+<span class="sig-name descname"><span class="pre">message</span></span><a class="headerlink" href="#krotov.result.Result.message" title="Link to this definition">¶</a></dt>
 <dd><p>Description of why <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a> completed,
 E.g, “Reached 1000 iterations”</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
-<dt id="krotov.result.Result.time_fmt">
-<code class="sig-name descname"><span class="pre">time_fmt</span></code><em class="property"> <span class="pre">=</span> <span class="pre">'%Y-%m-%d</span> <span class="pre">%H:%M:%S'</span></em><a class="headerlink" href="#krotov.result.Result.time_fmt" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.time_fmt">
+<span class="sig-name descname"><span class="pre">time_fmt</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'%Y-%m-%d</span> <span class="pre">%H:%M:%S'</span></em><a class="headerlink" href="#krotov.result.Result.time_fmt" title="Link to this definition">¶</a></dt>
 <dd><p>Format used in <a class="reference internal" href="#krotov.result.Result.start_local_time_str" title="krotov.result.Result.start_local_time_str"><code class="xref py py-attr docutils literal notranslate"><span class="pre">start_local_time_str</span></code></a> and
 <a class="reference internal" href="#krotov.result.Result.end_local_time_str" title="krotov.result.Result.end_local_time_str"><code class="xref py py-attr docutils literal notranslate"><span class="pre">end_local_time_str</span></code></a></p>
 </dd></dl>
 
-<dl class="py method">
-<dt id="krotov.result.Result.start_local_time_str">
-<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">start_local_time_str</span></code><a class="headerlink" href="#krotov.result.Result.start_local_time_str" title="Permalink to this definition">¶</a></dt>
+<dl class="py property">
+<dt class="sig sig-object py" id="krotov.result.Result.start_local_time_str">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">start_local_time_str</span></span><a class="headerlink" href="#krotov.result.Result.start_local_time_str" title="Link to this definition">¶</a></dt>
 <dd><p>The <a class="reference internal" href="#krotov.result.Result.start_local_time" title="krotov.result.Result.start_local_time"><code class="xref py py-attr docutils literal notranslate"><span class="pre">start_local_time</span></code></a> attribute formatted as a string</p>
 </dd></dl>
 
-<dl class="py method">
-<dt id="krotov.result.Result.end_local_time_str">
-<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">end_local_time_str</span></code><a class="headerlink" href="#krotov.result.Result.end_local_time_str" title="Permalink to this definition">¶</a></dt>
+<dl class="py property">
+<dt class="sig sig-object py" id="krotov.result.Result.end_local_time_str">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">end_local_time_str</span></span><a class="headerlink" href="#krotov.result.Result.end_local_time_str" title="Link to this definition">¶</a></dt>
 <dd><p>The <a class="reference internal" href="#krotov.result.Result.end_local_time" title="krotov.result.Result.end_local_time"><code class="xref py py-attr docutils literal notranslate"><span class="pre">end_local_time</span></code></a> attribute formatted as a string</p>
 </dd></dl>
 
-<dl class="py method">
-<dt id="krotov.result.Result.optimized_objectives">
-<em class="property"><span class="pre">property</span> </em><code class="sig-name descname"><span class="pre">optimized_objectives</span></code><a class="headerlink" href="#krotov.result.Result.optimized_objectives" title="Permalink to this definition">¶</a></dt>
+<dl class="py property">
+<dt class="sig sig-object py" id="krotov.result.Result.optimized_objectives">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optimized_objectives</span></span><a class="headerlink" href="#krotov.result.Result.optimized_objectives" title="Link to this definition">¶</a></dt>
 <dd><p>A copy of the <a class="reference internal" href="#krotov.result.Result.objectives" title="krotov.result.Result.objectives"><code class="xref py py-attr docutils literal notranslate"><span class="pre">objectives</span></code></a> with the
 <a class="reference internal" href="#krotov.result.Result.optimized_controls" title="krotov.result.Result.optimized_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">optimized_controls</span></code></a> plugged in.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective">Objective</a>]</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.result.Result.objectives_with_controls">
-<code class="sig-name descname"><span class="pre">objectives_with_controls</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">controls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/result.html#Result.objectives_with_controls"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result.objectives_with_controls" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.objectives_with_controls">
+<span class="sig-name descname"><span class="pre">objectives_with_controls</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">controls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/result.html#Result.objectives_with_controls"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result.objectives_with_controls" title="Link to this definition">¶</a></dt>
 <dd><p>List of objectives with the given <cite>controls</cite> plugged in.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>controls</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a><em>]</em>) – A list of control fields, defined
 on the points of <a class="reference internal" href="#krotov.result.Result.tlist" title="krotov.result.Result.tlist"><code class="xref py py-attr docutils literal notranslate"><span class="pre">tlist</span></code></a>. Must be of the same length as
 <a class="reference internal" href="#krotov.result.Result.guess_controls" title="krotov.result.Result.guess_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">guess_controls</span></code></a> and <a class="reference internal" href="#krotov.result.Result.optimized_controls" title="krotov.result.Result.optimized_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">optimized_controls</span></code></a>.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>A copy of <a class="reference internal" href="#krotov.result.Result.objectives" title="krotov.result.Result.objectives"><code class="xref py py-attr docutils literal notranslate"><span class="pre">objectives</span></code></a>, where all control
 fields are replaced by the elements of the <cite>controls</cite>.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)">list</a>[<a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective">Objective</a>]</p>
 </dd>
-<dt class="field-even">Raises</dt>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3.7/library/exceptions.html#ValueError" title="(in Python v3.7)"><strong>ValueError</strong></a> – If <cite>controls</cite> does not have the same number controls as
     <a class="reference internal" href="#krotov.result.Result.guess_controls" title="krotov.result.Result.guess_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">guess_controls</span></code></a> and <a class="reference internal" href="#krotov.result.Result.optimized_controls" title="krotov.result.Result.optimized_controls"><code class="xref py py-attr docutils literal notranslate"><span class="pre">optimized_controls</span></code></a>, or if
     any <cite>controls</cite> are not defined on the points of the time grid.</p>
@@ -415,15 +420,15 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.result.Result.load">
-<em class="property"><span class="pre">classmethod</span> </em><code class="sig-name descname"><span class="pre">load</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">finalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/result.html#Result.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result.load" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.load">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">finalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/result.html#Result.load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result.load" title="Link to this definition">¶</a></dt>
 <dd><p>Construct <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> object from a <a class="reference internal" href="#krotov.result.Result.dump" title="krotov.result.Result.dump"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dump()</span></code></a> file</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – The file from which to load the <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a>.
 Must be in the format created by <a class="reference internal" href="#krotov.result.Result.dump" title="krotov.result.Result.dump"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dump()</span></code></a>.</p></li>
-<li><p><strong>objectives</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><em>Objective</em></a><em>]</em>) – If given, after loading
+<li><p><strong>objectives</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a><em>[</em><a class="reference internal" href="krotov.objectives.html#krotov.objectives.Objective" title="krotov.objectives.Objective"><em>Objective</em></a><em>]</em>) – If given, after loading
 <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> from the given <cite>filename</cite>, overwrite
 <a class="reference internal" href="#krotov.result.Result.objectives" title="krotov.result.Result.objectives"><code class="xref py py-attr docutils literal notranslate"><span class="pre">objectives</span></code></a> with the given <cite>objectives</cite>. This is
 necessary because <a class="reference internal" href="#krotov.result.Result.dump" title="krotov.result.Result.dump"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dump()</span></code></a> does not preserve time-dependent
@@ -435,25 +440,25 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <a class="reference internal" href="krotov.convergence.html#krotov.convergence.dump_result" title="krotov.convergence.dump_result"><code class="xref py py-func docutils literal notranslate"><span class="pre">dump_result()</span></code></a>.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>The <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> instance loaded from <cite>filename</cite></p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result">Result</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.result.Result.dump">
-<code class="sig-name descname"><span class="pre">dump</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/result.html#Result.dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result.dump" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.result.Result.dump">
+<span class="sig-name descname"><span class="pre">dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/result.html#Result.dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.result.Result.dump" title="Link to this definition">¶</a></dt>
 <dd><p>Dump the <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> to a binary <a class="reference external" href="https://docs.python.org/3.7/library/pickle.html#module-pickle" title="(in Python v3.7)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">pickle</span></code></a> file.</p>
 <p>The original <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a> object can be restored from the resulting
 file using <a class="reference internal" href="#krotov.result.Result.load" title="krotov.result.Result.load"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load()</span></code></a>. However, time-dependent control fields that
 are callables/functions will not be preserved, as they are not
 “pickleable”.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – Name of file to which to dump the <a class="reference internal" href="#krotov.result.Result" title="krotov.result.Result"><code class="xref py py-class docutils literal notranslate"><span class="pre">Result</span></code></a>.</p>
 </dd>
 </dl>
@@ -461,8 +466,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -476,7 +481,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.second_order.html b/master/API/krotov.second_order.html
index 8ee121a9..9a61f728 100644
--- a/master/API/krotov.second_order.html
+++ b/master/API/krotov.second_order.html
@@ -1,26 +1,32 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
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+
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-  <title>krotov.second_order module &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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-      <link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
+  <title>krotov.second_order module &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
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@@ -40,7 +46,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -50,7 +56,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -80,7 +86,11 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.result.html">krotov.result module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.second_order module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.second_order.Sigma"><code class="docutils literal notranslate"><span class="pre">Sigma</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.second_order.numerical_estimate_A"><code class="docutils literal notranslate"><span class="pre">numerical_estimate_A()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.shapes.html">krotov.shapes module</a></li>
@@ -153,17 +163,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.second_order">
-<span id="krotov-second-order-module"></span><h1>krotov.second_order module<a class="headerlink" href="#module-krotov.second_order" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.second_order">
+<span id="krotov-second-order-module"></span><h1>krotov.second_order module<a class="headerlink" href="#module-krotov.second_order" title="Link to this heading">¶</a></h1>
 <p>Support functions for the second-order update equation</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Classes:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.second_order.Sigma" title="krotov.second_order.Sigma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sigma</span></code></a></p></td>
 <td><p>Function σ(t) for the second order update equation.</p></td>
@@ -171,11 +177,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.second_order.numerical_estimate_A" title="krotov.second_order.numerical_estimate_A"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numerical_estimate_A</span></code></a></p></td>
 <td><p>Update the second-order parameter <span class="math notranslate nohighlight">\(A\)</span>.</p></td>
@@ -183,13 +185,13 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.second_order.Sigma" title="krotov.second_order.Sigma"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sigma</span></code></a>, <a class="reference internal" href="#krotov.second_order.numerical_estimate_A" title="krotov.second_order.numerical_estimate_A"><code class="xref py py-func docutils literal notranslate"><span class="pre">numerical_estimate_A</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py class">
-<dt id="krotov.second_order.Sigma">
-<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">krotov.second_order.</span></code><code class="sig-name descname"><span class="pre">Sigma</span></code><a class="reference internal" href="../_modules/krotov/second_order.html#Sigma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.Sigma" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/abc.html#abc.ABC" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></a></p>
+<dt class="sig sig-object py" id="krotov.second_order.Sigma">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">krotov.second_order.</span></span><span class="sig-name descname"><span class="pre">Sigma</span></span><a class="reference internal" href="../_modules/krotov/second_order.html#Sigma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.Sigma" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3.7/library/abc.html#abc.ABC" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ABC</span></code></a></p>
 <p>Function σ(t) for the second order update equation.</p>
 <p>This is an abstract bases class. For any optimization that requires the
 second-order update equation, an appropriate problem-specific subclass of
@@ -202,19 +204,19 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <p>An instantiation of that subclass is then passed as <cite>sigma</cite> to
 <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>.</p>
 <dl class="py method">
-<dt id="krotov.second_order.Sigma.__call__">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">__call__</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/second_order.html#Sigma.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.Sigma.__call__" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.second_order.Sigma.__call__">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/second_order.html#Sigma.__call__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.Sigma.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Evaluate σ(t)</p>
 </dd></dl>
 
 <dl class="py method">
-<dt id="krotov.second_order.Sigma.refresh">
-<em class="property"><span class="pre">abstract</span> </em><code class="sig-name descname"><span class="pre">refresh</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">forward_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_states0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_norms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimized_pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guess_pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/second_order.html#Sigma.refresh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.Sigma.refresh" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.second_order.Sigma.refresh">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">refresh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">forward_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_states0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_norms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimized_pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guess_pulses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">objectives</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/second_order.html#Sigma.refresh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.Sigma.refresh" title="Link to this definition">¶</a></dt>
 <dd><p>Recalculate the parametric dependencies of σ(t)</p>
 <p>This is called at the end of each control iteration, and may be used to
 estimate the internal parameters in σ(t)</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>forward_states</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – For each objective, an array-like container
 (cf. <cite>storage</cite> in <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>) of the initial
@@ -239,8 +241,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.second_order.numerical_estimate_A">
-<code class="sig-prename descclassname"><span class="pre">krotov.second_order.</span></code><code class="sig-name descname"><span class="pre">numerical_estimate_A</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">forward_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_states0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_norms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Delta_J_T</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/second_order.html#numerical_estimate_A"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.numerical_estimate_A" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.second_order.numerical_estimate_A">
+<span class="sig-prename descclassname"><span class="pre">krotov.second_order.</span></span><span class="sig-name descname"><span class="pre">numerical_estimate_A</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">forward_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_states0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chi_norms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Delta_J_T</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/second_order.html#numerical_estimate_A"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.second_order.numerical_estimate_A" title="Link to this definition">¶</a></dt>
 <dd><p>Update the second-order parameter <span class="math notranslate nohighlight">\(A\)</span>.</p>
 <p>Calculate the new value of <span class="math notranslate nohighlight">\(A\)</span> according to the equation</p>
 <div class="math notranslate nohighlight">
@@ -258,7 +260,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 \[\Delta J_T
 = J_T(\{\ket{\phi_k^{(i)}(T)}\} - J_T(\{\ket{\phi_k^{(i-1)}(T)}\}.\]</div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>forward_states</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/stdtypes.html#list" title="(in Python v3.7)"><em>list</em></a>) – For each objective, the result of a
 forward-propagation with the optimized pulses of the current
@@ -276,8 +278,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dl>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -291,7 +293,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/API/krotov.shapes.html b/master/API/krotov.shapes.html
index 0415bc53..a2ffc003 100644
--- a/master/API/krotov.shapes.html
+++ b/master/API/krotov.shapes.html
@@ -1,26 +1,32 @@
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@@ -39,7 +45,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -49,7 +55,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -80,7 +86,15 @@
 <li class="toctree-l2"><a class="reference internal" href="krotov.second_order.html">krotov.second_order module</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">krotov.shapes module</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#summary">Summary</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#reference">Reference</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.shapes.qutip_callback"><code class="docutils literal notranslate"><span class="pre">qutip_callback()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.shapes.zero_shape"><code class="docutils literal notranslate"><span class="pre">zero_shape()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.shapes.one_shape"><code class="docutils literal notranslate"><span class="pre">one_shape()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.shapes.flattop"><code class="docutils literal notranslate"><span class="pre">flattop()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.shapes.box"><code class="docutils literal notranslate"><span class="pre">box()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#krotov.shapes.blackman"><code class="docutils literal notranslate"><span class="pre">blackman()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="krotov.html#summary">Summary</a></li>
@@ -152,19 +166,15 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-krotov.shapes">
-<span id="krotov-shapes-module"></span><h1>krotov.shapes module<a class="headerlink" href="#module-krotov.shapes" title="Permalink to this headline">¶</a></h1>
+  <section id="module-krotov.shapes">
+<span id="krotov-shapes-module"></span><h1>krotov.shapes module<a class="headerlink" href="#module-krotov.shapes" title="Link to this heading">¶</a></h1>
 <p>Functions that may be used for the <cite>update_shape</cite> value in the options-dict
 for each control (<cite>pulse_options</cite> parameter in <a class="reference internal" href="krotov.optimize.html#krotov.optimize.optimize_pulses" title="krotov.optimize.optimize_pulses"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimize_pulses()</span></code></a>), or
 for generating guess pulses</p>
-<div class="section" id="summary">
-<h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>
+<section id="summary">
+<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading">¶</a></h2>
 <p>Functions:</p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
+<table class="autosummary longtable docutils align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="#krotov.shapes.blackman" title="krotov.shapes.blackman"><code class="xref py py-obj docutils literal notranslate"><span class="pre">blackman</span></code></a></p></td>
 <td><p>Blackman window shape</p></td>
@@ -187,12 +197,12 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headli
 </tbody>
 </table>
 <p><code class="docutils literal notranslate"><span class="pre">__all__</span></code>: <a class="reference internal" href="#krotov.shapes.blackman" title="krotov.shapes.blackman"><code class="xref py py-func docutils literal notranslate"><span class="pre">blackman</span></code></a>, <a class="reference internal" href="#krotov.shapes.box" title="krotov.shapes.box"><code class="xref py py-func docutils literal notranslate"><span class="pre">box</span></code></a>, <a class="reference internal" href="#krotov.shapes.flattop" title="krotov.shapes.flattop"><code class="xref py py-func docutils literal notranslate"><span class="pre">flattop</span></code></a>, <a class="reference internal" href="#krotov.shapes.one_shape" title="krotov.shapes.one_shape"><code class="xref py py-func docutils literal notranslate"><span class="pre">one_shape</span></code></a>, <a class="reference internal" href="#krotov.shapes.qutip_callback" title="krotov.shapes.qutip_callback"><code class="xref py py-func docutils literal notranslate"><span class="pre">qutip_callback</span></code></a>, <a class="reference internal" href="#krotov.shapes.zero_shape" title="krotov.shapes.zero_shape"><code class="xref py py-func docutils literal notranslate"><span class="pre">zero_shape</span></code></a></p>
-</div>
-<div class="section" id="reference">
-<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Link to this heading">¶</a></h2>
 <dl class="py function">
-<dt id="krotov.shapes.qutip_callback">
-<code class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></code><code class="sig-name descname"><span class="pre">qutip_callback</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#qutip_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.qutip_callback" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.shapes.qutip_callback">
+<span class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></span><span class="sig-name descname"><span class="pre">qutip_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#qutip_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.qutip_callback" title="Link to this definition">¶</a></dt>
 <dd><p>Convert <cite>func</cite> into the correct form of a QuTiP time-dependent control</p>
 <p>QuTiP requires that “callback” functions that are used to express
 time-dependent controls take a parameter <cite>t</cite> and <cite>args</cite>. This function
@@ -203,20 +213,20 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.shapes.zero_shape">
-<code class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></code><code class="sig-name descname"><span class="pre">zero_shape</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#zero_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.zero_shape" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.shapes.zero_shape">
+<span class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></span><span class="sig-name descname"><span class="pre">zero_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#zero_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.zero_shape" title="Link to this definition">¶</a></dt>
 <dd><p>Shape function 0 for all values of <cite>t</cite></p>
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.shapes.one_shape">
-<code class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></code><code class="sig-name descname"><span class="pre">one_shape</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#one_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.one_shape" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.shapes.one_shape">
+<span class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></span><span class="sig-name descname"><span class="pre">one_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#one_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.one_shape" title="Link to this definition">¶</a></dt>
 <dd><p>Shape function 1 for all values of <cite>t</cite></p>
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.shapes.flattop">
-<code class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></code><code class="sig-name descname"><span class="pre">flattop</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_stop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_rise</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'blackman'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#flattop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.flattop" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.shapes.flattop">
+<span class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></span><span class="sig-name descname"><span class="pre">flattop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_stop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_rise</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'blackman'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#flattop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.flattop" title="Link to this definition">¶</a></dt>
 <dd><p>Flat shape (one) with a switch-on/switch-off from zero</p>
 <p>The flattop function starts at 0, and ramps to to 1 during the <cite>t_rise</cite>
 interval. For <code class="docutils literal notranslate"><span class="pre">func='blackman'</span></code>, the switch-on shape is half of a
@@ -224,7 +234,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 sine-squared curve. The function then remains at value 1, before ramping
 down to 0 again during <cite>t_fall</cite>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>t</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – Time  point</p></li>
 <li><p><strong>t_start</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – Start of flattop window</p></li>
@@ -247,12 +257,12 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.shapes.box">
-<code class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></code><code class="sig-name descname"><span class="pre">box</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_stop</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.box" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.shapes.box">
+<span class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></span><span class="sig-name descname"><span class="pre">box</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_stop</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.box" title="Link to this definition">¶</a></dt>
 <dd><p>Box-shape (Theta-function)</p>
 <p>The shape is 0 before <cite>t_start</cite> and after <cite>t_stop</cite> and 1 elsewhere.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>t</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – Time point or time grid</p></li>
 <li><p><strong>t_start</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – First value of <cite>t</cite> for which the box has value 1</p></li>
@@ -268,8 +278,8 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 </dd></dl>
 
 <dl class="py function">
-<dt id="krotov.shapes.blackman">
-<code class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></code><code class="sig-name descname"><span class="pre">blackman</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_stop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.16</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#blackman"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.blackman" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="krotov.shapes.blackman">
+<span class="sig-prename descclassname"><span class="pre">krotov.shapes.</span></span><span class="sig-name descname"><span class="pre">blackman</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_stop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.16</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/krotov/shapes.html#blackman"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#krotov.shapes.blackman" title="Link to this definition">¶</a></dt>
 <dd><p>Blackman window shape</p>
 <div class="math notranslate nohighlight">
 \[B(t; t_0, t_1) =
@@ -284,7 +294,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 however, it will go exactly to zero at the edges. Thus, Blackman pulses
 are often preferable to Gaussians.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>t</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em> or </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – Time point or time grid</p></li>
 <li><p><strong>t_start</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – Starting point <span class="math notranslate nohighlight">\(t_0\)</span> of Blackman shape</p></li>
@@ -292,20 +302,20 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 <li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – Blackman coefficient.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>If <cite>t</cite> is a float, return the value of the
 Blackman shape at <cite>t</cite>.  If <cite>t</cite> is an array, return an array of same
 size as <cite>t</cite>, containing the values for the Blackman shape (zero before
 <cite>t_start</cite> and after <cite>t_stop</cite>)</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3.7/library/functions.html#float" title="(in Python v3.7)">float</a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">numpy.ndarray</a></p>
 </dd>
 </dl>
 </dd></dl>
 
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -318,7 +328,7 @@ <h2>Reference<a class="headerlink" href="#reference" title="Permalink to this he
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
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diff --git a/master/_modules/index.html b/master/_modules/index.html
index 990b710a..a66fa627 100644
--- a/master/_modules/index.html
+++ b/master/_modules/index.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Overview: module code &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
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-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>Overview: module code &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../_static/mycss.css" type="text/css" />
+
+  
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   <![endif]-->
-  <script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
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-        <script src="../_static/clipboard.min.js"></script>
-        <script src="../_static/copybutton.js"></script>
-        <script src="../_static/docs-versions-menu.js"></script>
+  
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../_static/copybutton.js?v=f281be69"></script>
+        <script src="../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -152,7 +148,7 @@ <h1>All modules for which code is available</h1>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/convergence.html b/master/_modules/krotov/convergence.html
index 551860f5..b0c73edb 100644
--- a/master/_modules/krotov/convergence.html
+++ b/master/_modules/krotov/convergence.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.convergence &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
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+  <title>krotov.convergence &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
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     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
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-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -215,7 +211,9 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
 <span class="p">]</span>
 
 
-<div class="viewcode-block" id="Or"><a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.Or">[docs]</a><span class="k">def</span> <span class="nf">Or</span><span class="p">(</span><span class="o">*</span><span class="n">funcs</span><span class="p">):</span>
+<div class="viewcode-block" id="Or">
+<a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.Or">[docs]</a>
+<span class="k">def</span> <span class="nf">Or</span><span class="p">(</span><span class="o">*</span><span class="n">funcs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Chain multiple `check_convergence` functions together in a logical Or.</span>
 
 <span class="sd">    Each parameter must be a function suitable to pass to</span>
@@ -240,7 +238,10 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">check_convergence</span></div>
 
 
-<div class="viewcode-block" id="value_below"><a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.value_below">[docs]</a><span class="k">def</span> <span class="nf">value_below</span><span class="p">(</span><span class="n">limit</span><span class="p">,</span> <span class="n">spec</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;info_vals&#39;</span><span class="p">,</span> <span class="n">glom</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="value_below">
+<a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.value_below">[docs]</a>
+<span class="k">def</span> <span class="nf">value_below</span><span class="p">(</span><span class="n">limit</span><span class="p">,</span> <span class="n">spec</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;info_vals&#39;</span><span class="p">,</span> <span class="n">glom</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Constructor for routine that checks if a value is below `limit`</span>
 
 <span class="sd">    Args:</span>
@@ -302,7 +303,10 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">check_convergence</span></div>
 
 
-<div class="viewcode-block" id="value_above"><a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.value_above">[docs]</a><span class="k">def</span> <span class="nf">value_above</span><span class="p">(</span><span class="n">limit</span><span class="p">,</span> <span class="n">spec</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;info_vals&#39;</span><span class="p">,</span> <span class="n">glom</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="value_above">
+<a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.value_above">[docs]</a>
+<span class="k">def</span> <span class="nf">value_above</span><span class="p">(</span><span class="n">limit</span><span class="p">,</span> <span class="n">spec</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;info_vals&#39;</span><span class="p">,</span> <span class="n">glom</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Constructor for routine that checks if a value is above `limit`</span>
 
 <span class="sd">    Like :func:`value_below`, but for checking whether an extracted value is</span>
@@ -342,7 +346,10 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">check_convergence</span></div>
 
 
-<div class="viewcode-block" id="delta_below"><a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.delta_below">[docs]</a><span class="k">def</span> <span class="nf">delta_below</span><span class="p">(</span>
+
+<div class="viewcode-block" id="delta_below">
+<a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.delta_below">[docs]</a>
+<span class="k">def</span> <span class="nf">delta_below</span><span class="p">(</span>
     <span class="n">limit</span><span class="p">,</span>
     <span class="n">spec1</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;info_vals&#39;</span><span class="p">,</span> <span class="n">glom</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
     <span class="n">spec0</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;info_vals&#39;</span><span class="p">,</span> <span class="n">glom</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]),</span>
@@ -429,6 +436,7 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">check_convergence</span></div>
 
 
+
 <span class="n">_monotonic_convergence</span> <span class="o">=</span> <span class="n">delta_below</span><span class="p">(</span>
     <span class="n">limit</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
     <span class="n">spec1</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;info_vals&#39;</span><span class="p">,</span> <span class="n">glom</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]),</span>
@@ -447,7 +455,9 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
 <span class="p">)</span>
 
 
-<div class="viewcode-block" id="check_monotonic_error"><a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.check_monotonic_error">[docs]</a><span class="k">def</span> <span class="nf">check_monotonic_error</span><span class="p">(</span><span class="n">result</span><span class="p">):</span>
+<div class="viewcode-block" id="check_monotonic_error">
+<a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.check_monotonic_error">[docs]</a>
+<span class="k">def</span> <span class="nf">check_monotonic_error</span><span class="p">(</span><span class="n">result</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Check for monotonic convergence with respect to the error</span>
 
 <span class="sd">    Check that the last value in :attr:`.Result.info_vals` is</span>
@@ -480,7 +490,10 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">_monotonic_convergence</span><span class="p">(</span><span class="n">result</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="check_monotonic_fidelity"><a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.check_monotonic_fidelity">[docs]</a><span class="k">def</span> <span class="nf">check_monotonic_fidelity</span><span class="p">(</span><span class="n">result</span><span class="p">):</span>
+
+<div class="viewcode-block" id="check_monotonic_fidelity">
+<a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.check_monotonic_fidelity">[docs]</a>
+<span class="k">def</span> <span class="nf">check_monotonic_fidelity</span><span class="p">(</span><span class="n">result</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Check for monotonic convergence with respect to the fidelity</span>
 
 <span class="sd">    This is like :func:`check_monotonic_error`, but looking for a monotonic</span>
@@ -501,7 +514,10 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">_monotonic_fidelity</span><span class="p">(</span><span class="n">result</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="dump_result"><a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.dump_result">[docs]</a><span class="k">def</span> <span class="nf">dump_result</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">every</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+
+<div class="viewcode-block" id="dump_result">
+<a class="viewcode-back" href="../../API/krotov.convergence.html#krotov.convergence.dump_result">[docs]</a>
+<span class="k">def</span> <span class="nf">dump_result</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">every</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Return a function for dumping the result every so many iterations</span>
 
 <span class="sd">    For long-running optimizations, it can be useful to dump the current state</span>
@@ -551,6 +567,7 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="kc">None</span>
 
     <span class="k">return</span> <span class="n">_dump_result</span></div>
+
 </pre></div>
 
            </div>
@@ -561,7 +578,7 @@ <h1>Source code for krotov.convergence</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/conversions.html b/master/_modules/krotov/conversions.html
index db2651ce..81a7f0bf 100644
--- a/master/_modules/krotov/conversions.html
+++ b/master/_modules/krotov/conversions.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.conversions &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
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-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.conversions &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -192,7 +188,9 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="o">-</span><span class="mi">1</span>
 
 
-<div class="viewcode-block" id="discretize"><a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.discretize">[docs]</a><span class="k">def</span> <span class="nf">discretize</span><span class="p">(</span><span class="n">control</span><span class="p">,</span> <span class="n">tlist</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,),</span> <span class="n">kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">via_midpoints</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="discretize">
+<a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.discretize">[docs]</a>
+<span class="k">def</span> <span class="nf">discretize</span><span class="p">(</span><span class="n">control</span><span class="p">,</span> <span class="n">tlist</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,),</span> <span class="n">kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">via_midpoints</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Discretize the given `control` onto the `tlist` time grid.</span>
 
 <span class="sd">    If `control` is a callable, return array of values for `control` evaluated</span>
@@ -271,7 +269,10 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
         <span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="extract_controls"><a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.extract_controls">[docs]</a><span class="k">def</span> <span class="nf">extract_controls</span><span class="p">(</span><span class="n">objectives</span><span class="p">):</span>
+
+<div class="viewcode-block" id="extract_controls">
+<a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.extract_controls">[docs]</a>
+<span class="k">def</span> <span class="nf">extract_controls</span><span class="p">(</span><span class="n">objectives</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Extract a list of (unique) controls from the `objectives`.</span>
 
 <span class="sd">    Controls are unique if they are not the same object, cf.</span>
@@ -298,6 +299,7 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">controls</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_control_indices_in_nested_list</span><span class="p">(</span><span class="n">nested_list</span><span class="p">,</span> <span class="n">control</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Given a nested list (QuTiP Hamiltonian), find the indices that contain</span>
 <span class="sd">    `control` and return them as a list&quot;&quot;&quot;</span>
@@ -310,7 +312,9 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">result</span>
 
 
-<div class="viewcode-block" id="extract_controls_mapping"><a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.extract_controls_mapping">[docs]</a><span class="k">def</span> <span class="nf">extract_controls_mapping</span><span class="p">(</span><span class="n">objectives</span><span class="p">,</span> <span class="n">controls</span><span class="p">):</span>
+<div class="viewcode-block" id="extract_controls_mapping">
+<a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.extract_controls_mapping">[docs]</a>
+<span class="k">def</span> <span class="nf">extract_controls_mapping</span><span class="p">(</span><span class="n">objectives</span><span class="p">,</span> <span class="n">controls</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Extract a map of where `controls` are used in `objectives`.</span>
 
 <span class="sd">    The result is a nested list where the first index relates to the</span>
@@ -388,7 +392,10 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">controls_mapping</span></div>
 
 
-<div class="viewcode-block" id="pulse_options_dict_to_list"><a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.pulse_options_dict_to_list">[docs]</a><span class="k">def</span> <span class="nf">pulse_options_dict_to_list</span><span class="p">(</span><span class="n">pulse_options</span><span class="p">,</span> <span class="n">controls</span><span class="p">):</span>
+
+<div class="viewcode-block" id="pulse_options_dict_to_list">
+<a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.pulse_options_dict_to_list">[docs]</a>
+<span class="k">def</span> <span class="nf">pulse_options_dict_to_list</span><span class="p">(</span><span class="n">pulse_options</span><span class="p">,</span> <span class="n">controls</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert `pulse_options` into a list.</span>
 
 <span class="sd">    Given a dict `pulse_options` that contains an options-dict</span>
@@ -419,7 +426,10 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">pulse_options_list</span></div>
 
 
-<div class="viewcode-block" id="plug_in_pulse_values"><a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.plug_in_pulse_values">[docs]</a><span class="k">def</span> <span class="nf">plug_in_pulse_values</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">pulses</span><span class="p">,</span> <span class="n">mapping</span><span class="p">,</span> <span class="n">time_index</span><span class="p">,</span> <span class="n">conjugate</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+
+<div class="viewcode-block" id="plug_in_pulse_values">
+<a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.plug_in_pulse_values">[docs]</a>
+<span class="k">def</span> <span class="nf">plug_in_pulse_values</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">pulses</span><span class="p">,</span> <span class="n">mapping</span><span class="p">,</span> <span class="n">time_index</span><span class="p">,</span> <span class="n">conjugate</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Plug pulse values into H.</span>
 
 <span class="sd">    Args:</span>
@@ -464,7 +474,10 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">H</span></div>
 
 
-<div class="viewcode-block" id="control_onto_interval"><a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.control_onto_interval">[docs]</a><span class="k">def</span> <span class="nf">control_onto_interval</span><span class="p">(</span><span class="n">control</span><span class="p">):</span>
+
+<div class="viewcode-block" id="control_onto_interval">
+<a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.control_onto_interval">[docs]</a>
+<span class="k">def</span> <span class="nf">control_onto_interval</span><span class="p">(</span><span class="n">control</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert control on time grid to control on time grid intervals.</span>
 
 <span class="sd">    Args:</span>
@@ -499,7 +512,10 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
         <span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="pulse_onto_tlist"><a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.pulse_onto_tlist">[docs]</a><span class="k">def</span> <span class="nf">pulse_onto_tlist</span><span class="p">(</span><span class="n">pulse</span><span class="p">):</span>
+
+<div class="viewcode-block" id="pulse_onto_tlist">
+<a class="viewcode-back" href="../../API/krotov.conversions.html#krotov.conversions.pulse_onto_tlist">[docs]</a>
+<span class="k">def</span> <span class="nf">pulse_onto_tlist</span><span class="p">(</span><span class="n">pulse</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert `pulse` from time-grid intervals to time-grid points.</span>
 
 <span class="sd">    Args:</span>
@@ -522,6 +538,7 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
         <span class="n">control</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">pulse</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">pulse</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
     <span class="n">control</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">pulse</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">control</span></div>
+
 </pre></div>
 
            </div>
@@ -532,7 +549,7 @@ <h1>Source code for krotov.conversions</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/functionals.html b/master/_modules/krotov/functionals.html
index 6b6dc209..088234dd 100644
--- a/master/_modules/krotov/functionals.html
+++ b/master/_modules/krotov/functionals.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.functionals &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.functionals &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -213,7 +209,9 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
 <span class="p">]</span>
 
 
-<div class="viewcode-block" id="f_tau"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.f_tau">[docs]</a><span class="k">def</span> <span class="nf">f_tau</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<div class="viewcode-block" id="f_tau">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.f_tau">[docs]</a>
+<span class="k">def</span> <span class="nf">f_tau</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Average complex overlaps of the target states with the `fw_states_T`.</span>
 
 <span class="sd">    That is,</span>
@@ -270,7 +268,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">objectives</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="F_ss"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_ss">[docs]</a><span class="k">def</span> <span class="nf">F_ss</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="F_ss">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_ss">[docs]</a>
+<span class="k">def</span> <span class="nf">F_ss</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;State-to-state phase-insensitive fidelity</span>
 
 <span class="sd">    .. math::</span>
@@ -296,7 +297,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">real</span></div>
 
 
-<div class="viewcode-block" id="J_T_ss"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_ss">[docs]</a><span class="k">def</span> <span class="nf">J_T_ss</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="J_T_ss">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_ss">[docs]</a>
+<span class="k">def</span> <span class="nf">J_T_ss</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;State-to-state phase-insensitive functional  $J_{T,\text{ss}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -308,7 +312,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">F_ss</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="chis_ss"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_ss">[docs]</a><span class="k">def</span> <span class="nf">chis_ss</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
+
+<div class="viewcode-block" id="chis_ss">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_ss">[docs]</a>
+<span class="k">def</span> <span class="nf">chis_ss</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;States $\ket{\chi_k}$ for functional $J_{T,\text{ss}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -331,7 +338,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span></div>
 
 
-<div class="viewcode-block" id="F_sm"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_sm">[docs]</a><span class="k">def</span> <span class="nf">F_sm</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="F_sm">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_sm">[docs]</a>
+<span class="k">def</span> <span class="nf">F_sm</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Square-modulus fidelity</span>
 
 <span class="sd">    .. math::</span>
@@ -343,7 +353,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">f_tau</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span></div>
 
 
-<div class="viewcode-block" id="J_T_sm"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_sm">[docs]</a><span class="k">def</span> <span class="nf">J_T_sm</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="J_T_sm">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_sm">[docs]</a>
+<span class="k">def</span> <span class="nf">J_T_sm</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Square-modulus functional  $J_{T,\text{sm}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -356,7 +369,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">F_sm</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="chis_sm"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_sm">[docs]</a><span class="k">def</span> <span class="nf">chis_sm</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
+
+<div class="viewcode-block" id="chis_sm">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_sm">[docs]</a>
+<span class="k">def</span> <span class="nf">chis_sm</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;States $\ket{\chi_k}$ for functional $J_{T,\text{sm}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -387,7 +403,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span></div>
 
 
-<div class="viewcode-block" id="F_re"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_re">[docs]</a><span class="k">def</span> <span class="nf">F_re</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="F_re">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_re">[docs]</a>
+<span class="k">def</span> <span class="nf">F_re</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Real-part fidelity</span>
 
 <span class="sd">    .. math::</span>
@@ -402,7 +421,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">f_tau</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">)</span><span class="o">.</span><span class="n">real</span></div>
 
 
-<div class="viewcode-block" id="J_T_re"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_re">[docs]</a><span class="k">def</span> <span class="nf">J_T_re</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="J_T_re">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_re">[docs]</a>
+<span class="k">def</span> <span class="nf">J_T_re</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Real-part functional $J_{T,\text{re}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -424,7 +446,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">F_re</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="chis_re"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_re">[docs]</a><span class="k">def</span> <span class="nf">chis_re</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
+
+<div class="viewcode-block" id="chis_re">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_re">[docs]</a>
+<span class="k">def</span> <span class="nf">chis_re</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;States $\ket{\chi_k}$ for functional $J_{T,\text{re}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -451,7 +476,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span></div>
 
 
-<div class="viewcode-block" id="J_T_hs"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_hs">[docs]</a><span class="k">def</span> <span class="nf">J_T_hs</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="J_T_hs">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.J_T_hs">[docs]</a>
+<span class="k">def</span> <span class="nf">J_T_hs</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Hilbert-Schmidt distance measure functional $J_{T,\text{hs}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -520,7 +548,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">objectives</span><span class="p">))</span></div>
 
 
-<div class="viewcode-block" id="chis_hs"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_hs">[docs]</a><span class="k">def</span> <span class="nf">chis_hs</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
+
+<div class="viewcode-block" id="chis_hs">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.chis_hs">[docs]</a>
+<span class="k">def</span> <span class="nf">chis_hs</span><span class="p">(</span><span class="n">fw_states_T</span><span class="p">,</span> <span class="n">objectives</span><span class="p">,</span> <span class="n">tau_vals</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;States $\Op{\chi}_k$ for functional $J_{T,\text{hs}}$</span>
 
 <span class="sd">    .. math::</span>
@@ -571,7 +602,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span></div>
 
 
-<div class="viewcode-block" id="F_avg"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_avg">[docs]</a><span class="k">def</span> <span class="nf">F_avg</span><span class="p">(</span>
+
+<div class="viewcode-block" id="F_avg">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.F_avg">[docs]</a>
+<span class="k">def</span> <span class="nf">F_avg</span><span class="p">(</span>
     <span class="n">fw_states_T</span><span class="p">,</span> <span class="n">basis_states</span><span class="p">,</span> <span class="n">gate</span><span class="p">,</span> <span class="n">mapped_basis_states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prec</span><span class="o">=</span><span class="mf">1e-5</span>
 <span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Average gate fidelity</span>
@@ -676,6 +710,7 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid type of state: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">fw_states_T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">type</span><span class="p">)</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_F_avg_rho</span><span class="p">(</span>
     <span class="n">fw_states_T</span><span class="p">,</span> <span class="n">basis_states</span><span class="p">,</span> <span class="n">gate</span><span class="p">,</span> <span class="n">mapped_basis_states</span><span class="p">,</span> <span class="n">imag_limit</span><span class="o">=</span><span class="mf">1e-10</span>
 <span class="p">):</span>
@@ -704,7 +739,9 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">real</span> <span class="o">/</span> <span class="p">(</span><span class="n">N</span> <span class="o">*</span> <span class="p">(</span><span class="n">N</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
 
 
-<div class="viewcode-block" id="gate"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.gate">[docs]</a><span class="k">def</span> <span class="nf">gate</span><span class="p">(</span><span class="n">basis_states</span><span class="p">,</span> <span class="n">fw_states_T</span><span class="p">):</span>
+<div class="viewcode-block" id="gate">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.gate">[docs]</a>
+<span class="k">def</span> <span class="nf">gate</span><span class="p">(</span><span class="n">basis_states</span><span class="p">,</span> <span class="n">fw_states_T</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Gate that maps `basis_states` to `fw_states_T`</span>
 
 <span class="sd">    Example:</span>
@@ -728,7 +765,10 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">qutip</span><span class="o">.</span><span class="n">Qobj</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="n">dims</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="mapped_basis"><a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.mapped_basis">[docs]</a><span class="k">def</span> <span class="nf">mapped_basis</span><span class="p">(</span><span class="n">O</span><span class="p">,</span> <span class="n">basis_states</span><span class="p">):</span>
+
+<div class="viewcode-block" id="mapped_basis">
+<a class="viewcode-back" href="../../API/krotov.functionals.html#krotov.functionals.mapped_basis">[docs]</a>
+<span class="k">def</span> <span class="nf">mapped_basis</span><span class="p">(</span><span class="n">O</span><span class="p">,</span> <span class="n">basis_states</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Apply the gate `O` to `basis_states`.</span>
 
 <span class="sd">    Example:</span>
@@ -753,6 +793,7 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
             <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">O</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
         <span class="p">]</span>
     <span class="p">)</span></div>
+
 </pre></div>
 
            </div>
@@ -763,7 +804,7 @@ <h1>Source code for krotov.functionals</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/info_hooks.html b/master/_modules/krotov/info_hooks.html
index 51561c52..f5465dd3 100644
--- a/master/_modules/krotov/info_hooks.html
+++ b/master/_modules/krotov/info_hooks.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.info_hooks &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.info_hooks &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -155,7 +151,9 @@ <h1>Source code for krotov.info_hooks</h1><div class="highlight"><pre>
     <span class="p">)</span>
 
 
-<div class="viewcode-block" id="chain"><a class="viewcode-back" href="../../API/krotov.info_hooks.html#krotov.info_hooks.chain">[docs]</a><span class="k">def</span> <span class="nf">chain</span><span class="p">(</span><span class="o">*</span><span class="n">hooks</span><span class="p">):</span>
+<div class="viewcode-block" id="chain">
+<a class="viewcode-back" href="../../API/krotov.info_hooks.html#krotov.info_hooks.chain">[docs]</a>
+<span class="k">def</span> <span class="nf">chain</span><span class="p">(</span><span class="o">*</span><span class="n">hooks</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Chain multiple `info_hook` or `modify_params_after_iter` callables</span>
 <span class="sd">    together.</span>
 
@@ -190,7 +188,10 @@ <h1>Source code for krotov.info_hooks</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">info_hook</span></div>
 
 
-<div class="viewcode-block" id="print_debug_information"><a class="viewcode-back" href="../../API/krotov.info_hooks.html#krotov.info_hooks.print_debug_information">[docs]</a><span class="k">def</span> <span class="nf">print_debug_information</span><span class="p">(</span>
+
+<div class="viewcode-block" id="print_debug_information">
+<a class="viewcode-back" href="../../API/krotov.info_hooks.html#krotov.info_hooks.print_debug_information">[docs]</a>
+<span class="k">def</span> <span class="nf">print_debug_information</span><span class="p">(</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">objectives</span><span class="p">,</span>
     <span class="n">adjoint_objectives</span><span class="p">,</span>
@@ -427,6 +428,7 @@ <h1>Source code for krotov.info_hooks</h1><div class="highlight"><pre>
     <span class="n">out</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_grapheme_len</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">fail_with_zero</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the number of graphemes in `text`.</span>
 
@@ -483,7 +485,9 @@ <h1>Source code for krotov.info_hooks</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="s2">&quot;∫gₐ(ϵ&quot;</span> <span class="o">+</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">as_subscript</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">digits</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;)dt&quot;</span>
 
 
-<div class="viewcode-block" id="print_table"><a class="viewcode-back" href="../../API/krotov.info_hooks.html#krotov.info_hooks.print_table">[docs]</a><span class="k">def</span> <span class="nf">print_table</span><span class="p">(</span>
+<div class="viewcode-block" id="print_table">
+<a class="viewcode-back" href="../../API/krotov.info_hooks.html#krotov.info_hooks.print_table">[docs]</a>
+<span class="k">def</span> <span class="nf">print_table</span><span class="p">(</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">J_T</span><span class="p">,</span>
     <span class="n">show_g_a_int_per_pulse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
@@ -755,6 +759,7 @@ <h1>Source code for krotov.info_hooks</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">info_hook</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_pulse_range</span><span class="p">(</span><span class="n">pulse</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the range information about the given pulse value array</span>
 
@@ -783,7 +788,7 @@ <h1>Source code for krotov.info_hooks</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/mu.html b/master/_modules/krotov/mu.html
index fcd82502..d59e6273 100644
--- a/master/_modules/krotov/mu.html
+++ b/master/_modules/krotov/mu.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.mu &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.mu &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -205,7 +201,9 @@ <h1>Source code for krotov.mu</h1><div class="highlight"><pre>
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;derivative_wrt_pulse&#39;</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="derivative_wrt_pulse"><a class="viewcode-back" href="../../API/krotov.mu.html#krotov.mu.derivative_wrt_pulse">[docs]</a><span class="k">def</span> <span class="nf">derivative_wrt_pulse</span><span class="p">(</span>
+<div class="viewcode-block" id="derivative_wrt_pulse">
+<a class="viewcode-back" href="../../API/krotov.mu.html#krotov.mu.derivative_wrt_pulse">[docs]</a>
+<span class="k">def</span> <span class="nf">derivative_wrt_pulse</span><span class="p">(</span>
     <span class="n">objectives</span><span class="p">,</span> <span class="n">i_objective</span><span class="p">,</span> <span class="n">pulses</span><span class="p">,</span> <span class="n">pulses_mapping</span><span class="p">,</span> <span class="n">i_pulse</span><span class="p">,</span> <span class="n">time_index</span>
 <span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Calculate ∂H/∂ϵ for the standard equations of motion.</span>
@@ -272,6 +270,7 @@ <h1>Source code for krotov.mu</h1><div class="highlight"><pre>
                 <span class="s2">&quot;Time-dependent collapse operators not implemented&quot;</span>
             <span class="p">)</span>
     <span class="k">return</span> <span class="n">mu</span></div>
+
 </pre></div>
 
            </div>
@@ -282,7 +281,7 @@ <h1>Source code for krotov.mu</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/objectives.html b/master/_modules/krotov/objectives.html
index da316455..26b7786c 100644
--- a/master/_modules/krotov/objectives.html
+++ b/master/_modules/krotov/objectives.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.objectives &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.objectives &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -227,7 +223,9 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
                         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot calculate adjoint of </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">op</span><span class="p">)</span>
 
 
-<div class="viewcode-block" id="Objective"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective">[docs]</a><span class="k">class</span> <span class="nc">Objective</span><span class="p">:</span>
+<div class="viewcode-block" id="Objective">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective">[docs]</a>
+<span class="k">class</span> <span class="nc">Objective</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;A single objective for optimization with Krotov&#39;s method.</span>
 
 <span class="sd">    Args:</span>
@@ -371,7 +369,9 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="ow">not</span> <span class="n">result</span>
 
-<div class="viewcode-block" id="Objective.adjoint"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.adjoint">[docs]</a>    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<div class="viewcode-block" id="Objective.adjoint">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.adjoint">[docs]</a>
+    <span class="k">def</span> <span class="nf">adjoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;The :class:`Objective` containing the adjoint of all components.</span>
 
 <span class="sd">        This does not affect the controls in :attr:`H`: these are</span>
@@ -391,7 +391,10 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
                 <span class="nb">setattr</span><span class="p">(</span><span class="n">adjoint_objective</span><span class="p">,</span> <span class="n">attr</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr</span><span class="p">))</span>
         <span class="k">return</span> <span class="n">adjoint_objective</span></div>
 
-<div class="viewcode-block" id="Objective.mesolve"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.mesolve">[docs]</a>    <span class="k">def</span> <span class="nf">mesolve</span><span class="p">(</span>
+
+<div class="viewcode-block" id="Objective.mesolve">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.mesolve">[docs]</a>
+    <span class="k">def</span> <span class="nf">mesolve</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">tlist</span><span class="p">,</span>
         <span class="o">*</span><span class="p">,</span>
@@ -469,7 +472,10 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
             <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
         <span class="p">)</span></div>
 
-<div class="viewcode-block" id="Objective.propagate"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.propagate">[docs]</a>    <span class="k">def</span> <span class="nf">propagate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="Objective.propagate">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.propagate">[docs]</a>
+    <span class="k">def</span> <span class="nf">propagate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">tlist</span><span class="p">,</span>
         <span class="o">*</span><span class="p">,</span>
@@ -566,7 +572,10 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
         <span class="n">result</span><span class="o">.</span><span class="n">expect</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">result</span><span class="o">.</span><span class="n">expect</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">result</span></div>
 
-<div class="viewcode-block" id="Objective.reset_symbol_counters"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.reset_symbol_counters">[docs]</a>    <span class="nd">@classmethod</span>
+
+<div class="viewcode-block" id="Objective.reset_symbol_counters">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.reset_symbol_counters">[docs]</a>
+    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">reset_symbol_counters</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Reset the internal symbol counters used for printing objectives.</span>
 
@@ -576,7 +585,10 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
         <span class="bp">cls</span><span class="o">.</span><span class="n">_counter</span><span class="p">[</span><span class="s1">&#39;u</span><span class="si">{count}</span><span class="s1">(t)&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
         <span class="bp">cls</span><span class="o">.</span><span class="n">_count_cache</span> <span class="o">=</span> <span class="p">{}</span></div>
 
-<div class="viewcode-block" id="Objective.summarize"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.summarize">[docs]</a>    <span class="k">def</span> <span class="nf">summarize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_unicode</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">reset_symbol_counters</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+
+<div class="viewcode-block" id="Objective.summarize">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.Objective.summarize">[docs]</a>
+    <span class="k">def</span> <span class="nf">summarize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_unicode</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">reset_symbol_counters</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Return a one-line summary of the objective as a string.</span>
 
 <span class="sd">        Args:</span>
@@ -705,6 +717,7 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
             <span class="n">res</span> <span class="o">+=</span> <span class="s1">&#39;)}&#39;</span>
         <span class="k">return</span> <span class="n">res</span></div>
 
+
     <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">summarize</span><span class="p">(</span><span class="n">use_unicode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">str_use_unicode</span><span class="p">)</span>
 
@@ -712,6 +725,7 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="s2">&quot;</span><span class="si">%s</span><span class="s2">[</span><span class="si">%s</span><span class="s2">]&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_Objective_reduce_init</span><span class="p">(</span><span class="n">initial_state</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">c_ops</span><span class="p">):</span>
     <span class="c1"># args-only version of Objective.__init__, for _Objective_reduce</span>
     <span class="k">return</span> <span class="n">Objective</span><span class="p">(</span>
@@ -835,7 +849,9 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">d</span><span class="p">)</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">([</span><span class="n">psi</span> <span class="o">*</span> <span class="n">psi</span><span class="o">.</span><span class="n">dag</span><span class="p">()</span> <span class="k">for</span> <span class="n">psi</span> <span class="ow">in</span> <span class="n">basis_states</span><span class="p">])</span>
 
 
-<div class="viewcode-block" id="gate_objectives"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.gate_objectives">[docs]</a><span class="k">def</span> <span class="nf">gate_objectives</span><span class="p">(</span>
+<div class="viewcode-block" id="gate_objectives">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.gate_objectives">[docs]</a>
+<span class="k">def</span> <span class="nf">gate_objectives</span><span class="p">(</span>
     <span class="n">basis_states</span><span class="p">,</span>
     <span class="n">gate</span><span class="p">,</span>
     <span class="n">H</span><span class="p">,</span>
@@ -1166,6 +1182,7 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">objectives</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_gate_objectives_li_pe</span><span class="p">(</span><span class="n">basis_states</span><span class="p">,</span> <span class="n">gate</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">c_ops</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Objectives for two-qubit local-invariants or perfect-entangler</span>
 <span class="sd">    optimizaton&quot;&quot;&quot;</span>
@@ -1185,7 +1202,9 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
     <span class="p">]</span>
 
 
-<div class="viewcode-block" id="ensemble_objectives"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.ensemble_objectives">[docs]</a><span class="k">def</span> <span class="nf">ensemble_objectives</span><span class="p">(</span><span class="n">objectives</span><span class="p">,</span> <span class="n">Hs</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">keep_original_objectives</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<div class="viewcode-block" id="ensemble_objectives">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.ensemble_objectives">[docs]</a>
+<span class="k">def</span> <span class="nf">ensemble_objectives</span><span class="p">(</span><span class="n">objectives</span><span class="p">,</span> <span class="n">Hs</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">keep_original_objectives</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Extend `objectives` for an &quot;ensemble optimization&quot;</span>
 
 <span class="sd">    This creates a list of objectives for an optimization for robustness with</span>
@@ -1228,7 +1247,10 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">new_objectives</span></div>
 
 
-<div class="viewcode-block" id="liouvillian"><a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.liouvillian">[docs]</a><span class="k">def</span> <span class="nf">liouvillian</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">c_ops</span><span class="p">):</span>
+
+<div class="viewcode-block" id="liouvillian">
+<a class="viewcode-back" href="../../API/krotov.objectives.html#krotov.objectives.liouvillian">[docs]</a>
+<span class="k">def</span> <span class="nf">liouvillian</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">c_ops</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert Hamiltonian and Lindblad operators into a Liouvillian.</span>
 
 <span class="sd">    This is like :func:`qutip.superoperator.liouvillian`, but `H` may be a</span>
@@ -1255,6 +1277,7 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
         <span class="p">)</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_obj_str_pattern</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">role</span><span class="p">,</span> <span class="n">use_unicode</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;For a given `obj` return a &quot;string pattern&quot; to be used summerizing that</span>
 <span class="sd">    `obj` as the component of an Objective.</span>
@@ -1486,7 +1509,7 @@ <h1>Source code for krotov.objectives</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/optimize.html b/master/_modules/krotov/optimize.html
index 5882e318..e626c3bc 100644
--- a/master/_modules/krotov/optimize.html
+++ b/master/_modules/krotov/optimize.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.optimize &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
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-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.optimize &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -164,7 +160,9 @@ <h1>Source code for krotov.optimize</h1><div class="highlight"><pre>
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;optimize_pulses&#39;</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="optimize_pulses"><a class="viewcode-back" href="../../API/krotov.optimize.html#krotov.optimize.optimize_pulses">[docs]</a><span class="k">def</span> <span class="nf">optimize_pulses</span><span class="p">(</span>
+<div class="viewcode-block" id="optimize_pulses">
+<a class="viewcode-back" href="../../API/krotov.optimize.html#krotov.optimize.optimize_pulses">[docs]</a>
+<span class="k">def</span> <span class="nf">optimize_pulses</span><span class="p">(</span>
     <span class="n">objectives</span><span class="p">,</span>
     <span class="n">pulse_options</span><span class="p">,</span>
     <span class="n">tlist</span><span class="p">,</span>
@@ -724,6 +722,7 @@ <h1>Source code for krotov.optimize</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">result</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_shape_val_to_callable</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">val</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">one_shape</span>
@@ -1053,7 +1052,7 @@ <h1>Source code for krotov.optimize</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/parallelization.html b/master/_modules/krotov/parallelization.html
index 301981a5..2b1e146e 100644
--- a/master/_modules/krotov/parallelization.html
+++ b/master/_modules/krotov/parallelization.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.parallelization &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
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-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.parallelization &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -307,7 +303,9 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
     <span class="k">yield</span> <span class="kc">None</span>
 
 
-<div class="viewcode-block" id="set_parallelization"><a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.set_parallelization">[docs]</a><span class="k">def</span> <span class="nf">set_parallelization</span><span class="p">(</span>
+<div class="viewcode-block" id="set_parallelization">
+<a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.set_parallelization">[docs]</a>
+<span class="k">def</span> <span class="nf">set_parallelization</span><span class="p">(</span>
     <span class="n">use_loky</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
     <span class="n">start_method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
     <span class="n">loky_pickler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@@ -364,7 +362,10 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
         <span class="n">multiprocessing</span><span class="o">.</span><span class="n">set_start_method</span><span class="p">(</span><span class="n">start_method</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="parallel_map"><a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.parallel_map">[docs]</a><span class="k">def</span> <span class="nf">parallel_map</span><span class="p">(</span>
+
+<div class="viewcode-block" id="parallel_map">
+<a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.parallel_map">[docs]</a>
+<span class="k">def</span> <span class="nf">parallel_map</span><span class="p">(</span>
     <span class="n">task</span><span class="p">,</span>
     <span class="n">values</span><span class="p">,</span>
     <span class="n">task_args</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@@ -433,6 +434,7 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_process_threadpool_limits_initializier</span><span class="p">():</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Initializer for settings threadpool limits.</span>
 
@@ -445,7 +447,9 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
     <span class="n">threadpool_limits</span><span class="p">(</span><span class="n">limits</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
 
-<div class="viewcode-block" id="Consumer"><a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.Consumer">[docs]</a><span class="k">class</span> <span class="nc">Consumer</span><span class="p">(</span><span class="n">multiprocessing</span><span class="o">.</span><span class="n">Process</span><span class="p">):</span>
+<div class="viewcode-block" id="Consumer">
+<a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.Consumer">[docs]</a>
+<span class="k">class</span> <span class="nc">Consumer</span><span class="p">(</span><span class="n">multiprocessing</span><span class="o">.</span><span class="n">Process</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;A process-based task consumer.</span>
 
 <span class="sd">    This is for internal use in :func:`parallel_map_fw_prop_step`.</span>
@@ -464,7 +468,9 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">result_queue</span> <span class="o">=</span> <span class="n">result_queue</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
 
-<div class="viewcode-block" id="Consumer.run"><a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.Consumer.run">[docs]</a>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<div class="viewcode-block" id="Consumer.run">
+<a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.Consumer.run">[docs]</a>
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Execute all tasks on the `task_queue`.</span>
 
 <span class="sd">        Each task must be a callable that takes `data` as its only argument.</span>
@@ -485,10 +491,14 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
             <span class="k">with</span> <span class="n">_threadpool_limits</span><span class="p">(</span><span class="n">limits</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
                 <span class="n">answer</span> <span class="o">=</span> <span class="n">next_task</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">task_queue</span><span class="o">.</span><span class="n">task_done</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">result_queue</span><span class="o">.</span><span class="n">put</span><span class="p">(</span><span class="n">answer</span><span class="p">)</span></div></div>
+            <span class="bp">self</span><span class="o">.</span><span class="n">result_queue</span><span class="o">.</span><span class="n">put</span><span class="p">(</span><span class="n">answer</span><span class="p">)</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="FwPropStepTask"><a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.FwPropStepTask">[docs]</a><span class="k">class</span> <span class="nc">FwPropStepTask</span><span class="p">:</span>
+<div class="viewcode-block" id="FwPropStepTask">
+<a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.FwPropStepTask">[docs]</a>
+<span class="k">class</span> <span class="nc">FwPropStepTask</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;A task that performs a single forward-propagation step.</span>
 
 <span class="sd">    The task object is a callable, receiving the single tuple of the same</span>
@@ -564,7 +574,10 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">states</span><span class="p">[</span><span class="n">i_state</span><span class="p">]</span></div>
 
 
-<div class="viewcode-block" id="parallel_map_fw_prop_step"><a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.parallel_map_fw_prop_step">[docs]</a><span class="k">def</span> <span class="nf">parallel_map_fw_prop_step</span><span class="p">(</span><span class="n">shared</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">task_args</span><span class="p">):</span>
+
+<div class="viewcode-block" id="parallel_map_fw_prop_step">
+<a class="viewcode-back" href="../../API/krotov.parallelization.html#krotov.parallelization.parallel_map_fw_prop_step">[docs]</a>
+<span class="k">def</span> <span class="nf">parallel_map_fw_prop_step</span><span class="p">(</span><span class="n">shared</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">task_args</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;`parallel_map` function for the forward-propagation by one time step.</span>
 
 <span class="sd">    Args:</span>
@@ -629,6 +642,7 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">res</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_parallel_map_fw_prop_step_loky</span><span class="p">(</span><span class="n">shared</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">task_args</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Loky-based implementation of :func:`parallel_map_fw_prop_step`.&quot;&quot;&quot;</span>
     <span class="n">tlist</span> <span class="o">=</span> <span class="n">task_args</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
@@ -746,7 +760,7 @@ <h1>Source code for krotov.parallelization</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/propagators.html b/master/_modules/krotov/propagators.html
index b6627920..11d2e61d 100644
--- a/master/_modules/krotov/propagators.html
+++ b/master/_modules/krotov/propagators.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.propagators &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
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-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.propagators &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
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+
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     <script src="../../_static/js/html5shiv.min.js"></script>
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-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
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-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -210,7 +206,9 @@ <h1>Source code for krotov.propagators</h1><div class="highlight"><pre>
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;expm&#39;</span><span class="p">,</span> <span class="s1">&#39;Propagator&#39;</span><span class="p">,</span> <span class="s1">&#39;DensityMatrixODEPropagator&#39;</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="expm"><a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.expm">[docs]</a><span class="k">def</span> <span class="nf">expm</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">c_ops</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">backwards</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">initialize</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="expm">
+<a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.expm">[docs]</a>
+<span class="k">def</span> <span class="nf">expm</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">c_ops</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">backwards</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">initialize</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Propagate using matrix exponentiation.</span>
 
 <span class="sd">    This supports `H` being a Hamiltonian (for a Hilbert space `state`) or a</span>
@@ -256,10 +254,15 @@ <h1>Source code for krotov.propagators</h1><div class="highlight"><pre>
         <span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="Propagator"><a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.Propagator">[docs]</a><span class="k">class</span> <span class="nc">Propagator</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
+
+<div class="viewcode-block" id="Propagator">
+<a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.Propagator">[docs]</a>
+<span class="k">class</span> <span class="nc">Propagator</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Abstract base class for stateful propagators&quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="Propagator.__call__"><a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.Propagator.__call__">[docs]</a>    <span class="nd">@abstractmethod</span>
+<div class="viewcode-block" id="Propagator.__call__">
+<a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.Propagator.__call__">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">c_ops</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">backwards</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">initialize</span><span class="o">=</span><span class="kc">False</span>
     <span class="p">):</span>
@@ -290,10 +293,14 @@ <h1>Source code for krotov.propagators</h1><div class="highlight"><pre>
 <span class="sd">            when `initialize` is False. That is, the state to propagate is the</span>
 <span class="sd">            result of the previous call to the propagator.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">pass</span></div></div>
+        <span class="k">pass</span></div>
+</div>
 
 
-<div class="viewcode-block" id="DensityMatrixODEPropagator"><a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.DensityMatrixODEPropagator">[docs]</a><span class="k">class</span> <span class="nc">DensityMatrixODEPropagator</span><span class="p">(</span><span class="n">Propagator</span><span class="p">):</span>
+
+<div class="viewcode-block" id="DensityMatrixODEPropagator">
+<a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.DensityMatrixODEPropagator">[docs]</a>
+<span class="k">class</span> <span class="nc">DensityMatrixODEPropagator</span><span class="p">(</span><span class="n">Propagator</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Propagator for density matrix evolution under a Lindbladian</span>
 
 <span class="sd">    See :class:`qutip.solver.Options` for all arguments except `reentrant`.</span>
@@ -339,7 +346,9 @@ <h1>Source code for krotov.propagators</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_y</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># current vectorized state</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">reentrant</span> <span class="o">=</span> <span class="n">reentrant</span>
 
-<div class="viewcode-block" id="DensityMatrixODEPropagator.__call__"><a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.DensityMatrixODEPropagator.__call__">[docs]</a>    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
+<div class="viewcode-block" id="DensityMatrixODEPropagator.__call__">
+<a class="viewcode-back" href="../../API/krotov.propagators.html#krotov.propagators.DensityMatrixODEPropagator.__call__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">c_ops</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">backwards</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">initialize</span><span class="o">=</span><span class="kc">False</span>
     <span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Evaluation of a single propagation step</span>
@@ -393,6 +402,7 @@ <h1>Source code for krotov.propagators</h1><div class="highlight"><pre>
             <span class="n">isherm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
         <span class="p">)</span></div>
 
+
     <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">_rhs</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">rho</span><span class="p">,</span> <span class="n">L_list</span><span class="p">):</span>
         <span class="c1"># _rhs being a staticmethod enables the propagator to be pickled (for</span>
@@ -459,6 +469,7 @@ <h1>Source code for krotov.propagators</h1><div class="highlight"><pre>
         <span class="n">r</span><span class="o">.</span><span class="n">set_f_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_L_list</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_r</span> <span class="o">=</span> <span class="n">r</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_t</span> <span class="o">=</span> <span class="mf">0.0</span></div>
+
 </pre></div>
 
            </div>
@@ -469,7 +480,7 @@ <h1>Source code for krotov.propagators</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/result.html b/master/_modules/krotov/result.html
index b8d90db1..40b505bd 100644
--- a/master/_modules/krotov/result.html
+++ b/master/_modules/krotov/result.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.result &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
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-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.result &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -149,7 +145,9 @@ <h1>Source code for krotov.result</h1><div class="highlight"><pre>
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Result&#39;</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="Result"><a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result">[docs]</a><span class="k">class</span> <span class="nc">Result</span><span class="p">:</span>
+<div class="viewcode-block" id="Result">
+<a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result">[docs]</a>
+<span class="k">class</span> <span class="nc">Result</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Result of a Krotov optimization with :func:`.optimize_pulses`.</span>
 
 <span class="sd">    Attributes:</span>
@@ -264,7 +262,9 @@ <h1>Source code for krotov.result</h1><div class="highlight"><pre>
 <span class="sd">        :attr:`optimized_controls` plugged in.&quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">objectives_with_controls</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimized_controls</span><span class="p">)</span>
 
-<div class="viewcode-block" id="Result.objectives_with_controls"><a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result.objectives_with_controls">[docs]</a>    <span class="k">def</span> <span class="nf">objectives_with_controls</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">controls</span><span class="p">):</span>
+<div class="viewcode-block" id="Result.objectives_with_controls">
+<a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result.objectives_with_controls">[docs]</a>
+    <span class="k">def</span> <span class="nf">objectives_with_controls</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">controls</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;List of objectives with the given `controls` plugged in.</span>
 
 <span class="sd">        Args:</span>
@@ -321,7 +321,10 @@ <h1>Source code for krotov.result</h1><div class="highlight"><pre>
             <span class="p">)</span>
         <span class="k">return</span> <span class="n">objectives</span></div>
 
-<div class="viewcode-block" id="Result.load"><a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result.load">[docs]</a>    <span class="nd">@classmethod</span>
+
+<div class="viewcode-block" id="Result.load">
+<a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result.load">[docs]</a>
+    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">objectives</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">finalize</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct :class:`Result` object from a :meth:`dump` file</span>
 
@@ -378,7 +381,10 @@ <h1>Source code for krotov.result</h1><div class="highlight"><pre>
                 <span class="k">break</span>  <span class="c1"># only warn once</span>
         <span class="k">return</span> <span class="n">result</span></div>
 
-<div class="viewcode-block" id="Result.dump"><a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result.dump">[docs]</a>    <span class="k">def</span> <span class="nf">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
+
+<div class="viewcode-block" id="Result.dump">
+<a class="viewcode-back" href="../../API/krotov.result.html#krotov.result.Result.dump">[docs]</a>
+    <span class="k">def</span> <span class="nf">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Dump the :class:`Result` to a binary :mod:`pickle` file.</span>
 
 <span class="sd">        The original :class:`Result` object can be restored from the resulting</span>
@@ -393,7 +399,9 @@ <h1>Source code for krotov.result</h1><div class="highlight"><pre>
             <span class="n">pickler</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">Pickler</span><span class="p">(</span><span class="n">dump_fh</span><span class="p">)</span>
             <span class="n">pickler</span><span class="o">.</span><span class="n">dispatch_table</span> <span class="o">=</span> <span class="n">copyreg</span><span class="o">.</span><span class="n">dispatch_table</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
             <span class="n">pickler</span><span class="o">.</span><span class="n">dispatch_table</span><span class="p">[</span><span class="n">Objective</span><span class="p">]</span> <span class="o">=</span> <span class="n">_Objective_reduce</span>
-            <span class="n">pickler</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div></div>
+            <span class="n">pickler</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
+</div>
+
 
 
 <span class="k">def</span> <span class="nf">_contains_control_placeholders</span><span class="p">(</span><span class="n">lst</span><span class="p">):</span>
@@ -420,7 +428,7 @@ <h1>Source code for krotov.result</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/second_order.html b/master/_modules/krotov/second_order.html
index 38b51135..e2810fcf 100644
--- a/master/_modules/krotov/second_order.html
+++ b/master/_modules/krotov/second_order.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.second_order &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.second_order &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -140,7 +136,9 @@ <h1>Source code for krotov.second_order</h1><div class="highlight"><pre>
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Sigma&#39;</span><span class="p">,</span> <span class="s1">&#39;numerical_estimate_A&#39;</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="Sigma"><a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.Sigma">[docs]</a><span class="k">class</span> <span class="nc">Sigma</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
+<div class="viewcode-block" id="Sigma">
+<a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.Sigma">[docs]</a>
+<span class="k">class</span> <span class="nc">Sigma</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Function σ(t) for the second order update equation.</span>
 
 <span class="sd">    This is an abstract bases class. For any optimization that requires the</span>
@@ -156,12 +154,17 @@ <h1>Source code for krotov.second_order</h1><div class="highlight"><pre>
 <span class="sd">    :func:`.optimize_pulses`.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="Sigma.__call__"><a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.Sigma.__call__">[docs]</a>    <span class="nd">@abstractmethod</span>
+<div class="viewcode-block" id="Sigma.__call__">
+<a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.Sigma.__call__">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>  <span class="c1"># pragma: nocover</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Evaluate σ(t)&quot;&quot;&quot;</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
 
-<div class="viewcode-block" id="Sigma.refresh"><a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.Sigma.refresh">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Sigma.refresh">
+<a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.Sigma.refresh">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">refresh</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">forward_states</span><span class="p">,</span>
@@ -197,7 +200,9 @@ <h1>Source code for krotov.second_order</h1><div class="highlight"><pre>
 <span class="sd">            result (Result): The result object, up-to-date for the current</span>
 <span class="sd">                iteration</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div></div>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
+</div>
+
 
 
 <span class="k">def</span> <span class="nf">_overlap</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">complex</span><span class="p">]:</span>
@@ -217,7 +222,9 @@ <h1>Source code for krotov.second_order</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="kc">None</span>
 
 
-<div class="viewcode-block" id="numerical_estimate_A"><a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.numerical_estimate_A">[docs]</a><span class="k">def</span> <span class="nf">numerical_estimate_A</span><span class="p">(</span>
+<div class="viewcode-block" id="numerical_estimate_A">
+<a class="viewcode-back" href="../../API/krotov.second_order.html#krotov.second_order.numerical_estimate_A">[docs]</a>
+<span class="k">def</span> <span class="nf">numerical_estimate_A</span><span class="p">(</span>
     <span class="n">forward_states</span><span class="p">,</span> <span class="n">forward_states0</span><span class="p">,</span> <span class="n">chi_states</span><span class="p">,</span> <span class="n">chi_norms</span><span class="p">,</span> <span class="n">Delta_J_T</span>
 <span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Update the second-order parameter $A$.</span>
@@ -273,6 +280,7 @@ <h1>Source code for krotov.second_order</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">numer</span> <span class="o">/</span> <span class="n">denom</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">return</span> <span class="mi">0</span></div>
+
 </pre></div>
 
            </div>
@@ -283,7 +291,7 @@ <h1>Source code for krotov.second_order</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_modules/krotov/shapes.html b/master/_modules/krotov/shapes.html
index 5ad2f6d0..c0f637cd 100644
--- a/master/_modules/krotov/shapes.html
+++ b/master/_modules/krotov/shapes.html
@@ -1,32 +1,28 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>krotov.shapes &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/graphviz.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/copybutton.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>krotov.shapes &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../../_static/graphviz.css?v=fd3f3429" />
+      <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
       <link rel="stylesheet" href="../../_static/mycss.css" type="text/css" />
+
+  
   <!--[if lt IE 9]>
     <script src="../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
-  <script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-        <script src="../../_static/jquery.js"></script>
-        <script src="../../_static/underscore.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/clipboard.min.js"></script>
-        <script src="../../_static/copybutton.js"></script>
-        <script src="../../_static/docs-versions-menu.js"></script>
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=21dc01d9"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+        <script src="../../_static/copybutton.js?v=f281be69"></script>
+        <script src="../../_static/docs-versions-menu.js?v=3d6a1aea"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
@@ -45,7 +41,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
@@ -55,7 +51,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../02_contributing.html">Contributing</a></li>
@@ -151,7 +147,9 @@ <h1>Source code for krotov.shapes</h1><div class="highlight"><pre>
 <span class="p">]</span>
 
 
-<div class="viewcode-block" id="qutip_callback"><a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.qutip_callback">[docs]</a><span class="k">def</span> <span class="nf">qutip_callback</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<div class="viewcode-block" id="qutip_callback">
+<a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.qutip_callback">[docs]</a>
+<span class="k">def</span> <span class="nf">qutip_callback</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert `func` into the correct form of a QuTiP time-dependent control</span>
 
 <span class="sd">    QuTiP requires that &quot;callback&quot; functions that are used to express</span>
@@ -172,17 +170,26 @@ <h1>Source code for krotov.shapes</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">callback</span></div>
 
 
-<div class="viewcode-block" id="zero_shape"><a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.zero_shape">[docs]</a><span class="k">def</span> <span class="nf">zero_shape</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
+
+<div class="viewcode-block" id="zero_shape">
+<a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.zero_shape">[docs]</a>
+<span class="k">def</span> <span class="nf">zero_shape</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Shape function 0 for all values of `t`&quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="mi">0</span></div>
 
 
-<div class="viewcode-block" id="one_shape"><a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.one_shape">[docs]</a><span class="k">def</span> <span class="nf">one_shape</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
+
+<div class="viewcode-block" id="one_shape">
+<a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.one_shape">[docs]</a>
+<span class="k">def</span> <span class="nf">one_shape</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Shape function 1 for all values of `t`&quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="mi">1</span></div>
 
 
-<div class="viewcode-block" id="flattop"><a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.flattop">[docs]</a><span class="k">def</span> <span class="nf">flattop</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">,</span> <span class="n">t_rise</span><span class="p">,</span> <span class="n">t_fall</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="s1">&#39;blackman&#39;</span><span class="p">):</span>
+
+<div class="viewcode-block" id="flattop">
+<a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.flattop">[docs]</a>
+<span class="k">def</span> <span class="nf">flattop</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">,</span> <span class="n">t_rise</span><span class="p">,</span> <span class="n">t_fall</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="s1">&#39;blackman&#39;</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Flat shape (one) with a switch-on/switch-off from zero</span>
 
 <span class="sd">    The flattop function starts at 0, and ramps to to 1 during the `t_rise`</span>
@@ -217,6 +224,7 @@ <h1>Source code for krotov.shapes</h1><div class="highlight"><pre>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid func: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">func</span><span class="p">)</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_flattop_sinsq</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">,</span> <span class="n">t_rise</span><span class="p">,</span> <span class="n">t_fall</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">t_start</span> <span class="o">&lt;=</span> <span class="n">t</span> <span class="o">&lt;=</span> <span class="n">t_stop</span><span class="p">:</span>
         <span class="n">f</span> <span class="o">=</span> <span class="mf">1.0</span>
@@ -241,7 +249,9 @@ <h1>Source code for krotov.shapes</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="mf">0.0</span>
 
 
-<div class="viewcode-block" id="box"><a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.box">[docs]</a><span class="k">def</span> <span class="nf">box</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">):</span>
+<div class="viewcode-block" id="box">
+<a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.box">[docs]</a>
+<span class="k">def</span> <span class="nf">box</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Box-shape (Theta-function)</span>
 
 <span class="sd">    The shape is 0 before `t_start` and after `t_stop` and 1 elsewhere.</span>
@@ -262,7 +272,10 @@ <h1>Source code for krotov.shapes</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="mf">1.0</span></div>
 
 
-<div class="viewcode-block" id="blackman"><a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.blackman">[docs]</a><span class="k">def</span> <span class="nf">blackman</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mf">0.16</span><span class="p">):</span>
+
+<div class="viewcode-block" id="blackman">
+<a class="viewcode-back" href="../../API/krotov.shapes.html#krotov.shapes.blackman">[docs]</a>
+<span class="k">def</span> <span class="nf">blackman</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">t_start</span><span class="p">,</span> <span class="n">t_stop</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mf">0.16</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Blackman window shape</span>
 
 <span class="sd">    .. math::</span>
@@ -306,6 +319,7 @@ <h1>Source code for krotov.shapes</h1><div class="highlight"><pre>
             <span class="o">+</span> <span class="n">a</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mf">4.0</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="p">(</span><span class="n">t</span> <span class="o">-</span> <span class="n">t_start</span><span class="p">)</span> <span class="o">/</span> <span class="n">T</span><span class="p">)</span>
         <span class="p">)</span>
     <span class="p">)</span></div>
+
 </pre></div>
 
            </div>
@@ -316,7 +330,7 @@ <h1>Source code for krotov.shapes</h1><div class="highlight"><pre>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/_sources/09_examples.rst.txt b/master/_sources/09_examples.rst.txt
index 56fcc8eb..f23ee7ff 100644
--- a/master/_sources/09_examples.rst.txt
+++ b/master/_sources/09_examples.rst.txt
@@ -6,7 +6,7 @@ Examples
 
 .. raw:: html
 
-    <a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs%2Fnotebooks" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>
+    <a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/master/docs/notebooks" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/master?filepath=docs%2Fnotebooks" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>
 
 .. toctree::
     :maxdepth: 1
diff --git a/master/_sources/notebooks/01_example_simple_state_to_state.ipynb.txt b/master/_sources/notebooks/01_example_simple_state_to_state.ipynb.txt
index b7ed4c81..ada25919 100644
--- a/master/_sources/notebooks/01_example_simple_state_to_state.ipynb.txt
+++ b/master/_sources/notebooks/01_example_simple_state_to_state.ipynb.txt
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.114927Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.113597Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.657268Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.656634Z"
+     "iopub.execute_input": "2024-06-03T14:31:55.328265Z",
+     "iopub.status.busy": "2024-06-03T14:31:55.327934Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.201526Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.201160Z"
     }
    },
    "outputs": [
@@ -33,14 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
+      "numpy     : 1.26.4\n",
+      "krotov    : 1.3.0+dev\n",
+      "scipy     : 1.12.0\n",
       "qutip     : 4.7.6\n",
-      "scipy     : 1.10.1\n",
       "matplotlib: 3.7.5\n",
-      "krotov    : 1.3.0\n",
-      "numpy     : 1.24.4\n",
       "\n"
      ]
     }
@@ -123,10 +123,10 @@
      "start_time": "2019-07-14T20:35:02.380331Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.682891Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.682662Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.686515Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.685952Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.223555Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.223348Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.226065Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.225754Z"
     }
    },
    "outputs": [],
@@ -158,10 +158,10 @@
      "start_time": "2019-07-14T20:35:02.389898Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.688481Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.688335Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.691208Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.690793Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.227590Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.227478Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.229589Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.229260Z"
     }
    },
    "outputs": [],
@@ -188,10 +188,10 @@
      "start_time": "2019-07-14T20:35:02.399999Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.693184Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.693040Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.695372Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.695003Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.231217Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.231070Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.233357Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.232936Z"
     },
     "lines_to_next_cell": 2
    },
@@ -214,10 +214,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.697366Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.697145Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.700210Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.699654Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.235044Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.234897Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.237077Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.236790Z"
     }
    },
    "outputs": [],
@@ -246,10 +246,10 @@
      "n": "11"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.702077Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.701934Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.839686Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.839088Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.238533Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.238417Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.355081Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.347586Z"
     }
    },
    "outputs": [
@@ -295,10 +295,10 @@
      "start_time": "2019-07-14T20:35:02.631007Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.841887Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.841730Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.846200Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.845750Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.369351Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.368050Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.412091Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.403810Z"
     }
    },
    "outputs": [
@@ -344,10 +344,10 @@
      "start_time": "2019-07-14T20:35:02.642363Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.848173Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.848029Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.850815Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.850363Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.431875Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.430962Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.445428Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.437356Z"
     },
     "lines_to_next_cell": 2
    },
@@ -381,10 +381,10 @@
      "start_time": "2019-07-14T20:35:02.651951Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.852963Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.852701Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.855192Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.854803Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.454736Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.453874Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.480447Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.470708Z"
     },
     "lines_to_next_cell": 0
    },
@@ -429,10 +429,10 @@
      "start_time": "2019-07-14T20:35:02.658095Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.857281Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.857137Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.860434Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.859883Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.484702Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.484254Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.487715Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.487348Z"
     }
    },
    "outputs": [],
@@ -455,10 +455,10 @@
      "n": "12"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.862394Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.862192Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.921201Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.920671Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.489575Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.489477Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.524433Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.524060Z"
     }
    },
    "outputs": [],
@@ -489,10 +489,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.923206Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.923060Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.926296Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.925804Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.526936Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.526787Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.529206Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.528817Z"
     }
    },
    "outputs": [],
@@ -521,10 +521,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.928214Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.928070Z",
-     "iopub.status.idle": "2024-06-03T10:28:57.034270Z",
-     "shell.execute_reply": "2024-06-03T10:28:57.033676Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.530896Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.530759Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.595226Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.594947Z"
     }
    },
    "outputs": [
@@ -600,10 +600,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:57.036409Z",
-     "iopub.status.busy": "2024-06-03T10:28:57.036253Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.428987Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.428497Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.596878Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.596763Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.156636Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.156360Z"
     }
    },
    "outputs": [
@@ -772,10 +772,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.431053Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.430862Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.434488Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.434023Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.158163Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.158052Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.160158Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.159889Z"
     }
    },
    "outputs": [
@@ -784,11 +784,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:28:57\n",
+       "- Started at 2024-06-03 10:31:56\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 18\n",
        "- Reason for termination: Reached convergence: J_T < 1e-3\n",
-       "- Ended at 2024-06-03 12:29:28 (0:00:31)"
+       "- Ended at 2024-06-03 10:32:22 (0:00:26)"
       ]
      },
      "execution_count": 15,
@@ -831,10 +831,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.436510Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.436335Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.464276Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.463660Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.161579Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.161476Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.201351Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.200891Z"
     }
    },
    "outputs": [],
@@ -857,16 +857,16 @@
      "n": "19"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.466530Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.466134Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.604413Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.603904Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.203077Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.202958Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.270730Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.270433Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -891,10 +891,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.606505Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.606350Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.612791Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.612413Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.272302Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.272192Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.275871Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.275613Z"
     }
    },
    "outputs": [],
@@ -973,16 +973,16 @@
      "start_time": "2019-07-14T21:07:20.914008Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.614631Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.614482Z",
-     "iopub.status.idle": "2024-06-03T10:29:29.389014Z",
-     "shell.execute_reply": "2024-06-03T10:29:29.388467Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.277161Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.277080Z",
+     "iopub.status.idle": "2024-06-03T14:32:23.061812Z",
+     "shell.execute_reply": "2024-06-03T14:32:23.061489Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x1000 with 2 Axes>"
       ]
@@ -1020,7 +1020,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb.txt b/master/_sources/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb.txt
index 35c2a4e9..c0eabb9c 100644
--- a/master/_sources/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb.txt
+++ b/master/_sources/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb.txt
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.234562Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.234410Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.737411Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.736828Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.143219Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.142398Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.934955Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.934649Z"
     }
    },
    "outputs": [
@@ -24,13 +24,13 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "scipy     : 1.10.1\n",
+      "krotov    : 1.3.0+dev\n",
       "matplotlib: 3.7.5\n",
-      "numpy     : 1.24.4\n",
+      "scipy     : 1.12.0\n",
+      "numpy     : 1.26.4\n",
       "qutip     : 4.7.6\n",
       "\n"
      ]
@@ -267,10 +267,10 @@
      "n": "6"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.763940Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.763603Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.767750Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.767233Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.957846Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.957636Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.960968Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.960638Z"
     }
    },
    "outputs": [],
@@ -309,10 +309,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.769643Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.769495Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.774715Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.774217Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.962402Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.962262Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.965179Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.964936Z"
     }
    },
    "outputs": [],
@@ -346,10 +346,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.776668Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.776335Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.780392Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.779969Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.966449Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.966349Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.968911Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.968658Z"
     }
    },
    "outputs": [],
@@ -376,10 +376,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.782260Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.782114Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.785703Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.785203Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.970425Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.970263Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.972638Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.972392Z"
     }
    },
    "outputs": [],
@@ -409,10 +409,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.787521Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.787374Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.790048Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.789547Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.973845Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.973764Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.975527Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.975301Z"
     }
    },
    "outputs": [],
@@ -426,10 +426,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.791868Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.791714Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.794112Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.793725Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.976763Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.976687Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.978454Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.978125Z"
     }
    },
    "outputs": [],
@@ -458,10 +458,10 @@
      "n": "5"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.795968Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.795822Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.801179Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.800807Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.979849Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.979774Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.983133Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.982878Z"
     }
    },
    "outputs": [
@@ -509,10 +509,10 @@
      "n": "4"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.803094Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.802948Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.805464Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.805083Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.984518Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.984435Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.985912Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.985694Z"
     }
    },
    "outputs": [],
@@ -541,10 +541,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.807345Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.807170Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.810290Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.809793Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.987100Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.987022Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.988980Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.988755Z"
     }
    },
    "outputs": [],
@@ -573,10 +573,10 @@
      "n": "11"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.812103Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.811957Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.045265Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.044695Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.990334Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.990246Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.191599Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.190621Z"
     }
    },
    "outputs": [
@@ -627,10 +627,10 @@
      "n": "12"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.047348Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.047190Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.050451Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.049922Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.197491Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.196869Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.202478Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.201756Z"
     }
    },
    "outputs": [],
@@ -661,10 +661,10 @@
      "n": "3"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.052462Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.052313Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.055616Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.055234Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.206707Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.206571Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.209506Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.208887Z"
     }
    },
    "outputs": [],
@@ -688,10 +688,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.057681Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.057509Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.371749Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.371256Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.214148Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.213771Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.378228Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.377856Z"
     }
    },
    "outputs": [],
@@ -713,10 +713,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.373831Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.373681Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.377295Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.376808Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.380028Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.379894Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.382566Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.382102Z"
     }
    },
    "outputs": [],
@@ -746,10 +746,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.379336Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.378953Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.497059Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.496546Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.384249Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.384131Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.453493Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.453209Z"
     }
    },
    "outputs": [
@@ -799,10 +799,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.499151Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.498995Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.501830Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.501340Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.455244Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.455137Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.457235Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.456924Z"
     }
    },
    "outputs": [],
@@ -819,10 +819,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.503663Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.503443Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.506400Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.505904Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.458641Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.458539Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.460482Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.460170Z"
     }
    },
    "outputs": [],
@@ -856,10 +856,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.508319Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.508173Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.112419Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.111851Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.461832Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.461726Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.129548Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.129125Z"
     }
    },
    "outputs": [
@@ -868,91 +868,91 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T   g_a_int_1   g_a_int_2   g_a_int_3   g_a_int_4    g_a_int          J  Delta J_T    Delta J  secs\n",
-      "0     1.01e+00    0.00e+00    0.00e+00    0.00e+00    0.00e+00   0.00e+00   1.01e+00        n/a        n/a     1\n"
+      "0     1.01e+00    0.00e+00    0.00e+00    0.00e+00    0.00e+00   0.00e+00   1.01e+00        n/a        n/a     0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1     6.72e-01    8.60e-02    2.87e-04    8.17e-02    3.72e-04   1.68e-01   8.40e-01  -3.37e-01  -1.68e-01     2\n"
+      "1     6.72e-01    8.60e-02    2.87e-04    8.17e-02    3.72e-04   1.68e-01   8.40e-01  -3.37e-01  -1.68e-01     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2     4.02e-01    7.20e-02    4.21e-04    6.22e-02    4.20e-04   1.35e-01   5.37e-01  -2.70e-01  -1.35e-01     2\n"
+      "2     4.02e-01    7.20e-02    4.21e-04    6.22e-02    4.20e-04   1.35e-01   5.37e-01  -2.70e-01  -1.35e-01     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3     2.22e-01    4.91e-02    4.64e-04    3.99e-02    3.88e-04   8.98e-02   3.12e-01  -1.80e-01  -8.98e-02     2\n"
+      "3     2.22e-01    4.91e-02    4.64e-04    3.99e-02    3.88e-04   8.98e-02   3.12e-01  -1.80e-01  -8.98e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4     1.17e-01    2.89e-02    3.87e-04    2.29e-02    3.01e-04   5.25e-02   1.69e-01  -1.05e-01  -5.25e-02     2\n"
+      "4     1.17e-01    2.89e-02    3.87e-04    2.29e-02    3.01e-04   5.25e-02   1.69e-01  -1.05e-01  -5.25e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5     6.00e-02    1.56e-02    2.69e-04    1.23e-02    2.10e-04   2.84e-02   8.84e-02  -5.69e-02  -2.84e-02     2\n"
+      "5     6.00e-02    1.56e-02    2.69e-04    1.23e-02    2.10e-04   2.84e-02   8.84e-02  -5.69e-02  -2.84e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6     3.05e-02    8.08e-03    1.71e-04    6.37e-03    1.39e-04   1.48e-02   4.52e-02  -2.95e-02  -1.48e-02     2\n"
+      "6     3.05e-02    8.08e-03    1.71e-04    6.37e-03    1.39e-04   1.48e-02   4.52e-02  -2.95e-02  -1.48e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7     1.54e-02    4.08e-03    1.06e-04    3.24e-03    9.10e-05   7.51e-03   2.30e-02  -1.50e-02  -7.51e-03     2\n"
+      "7     1.54e-02    4.08e-03    1.06e-04    3.24e-03    9.10e-05   7.51e-03   2.30e-02  -1.50e-02  -7.51e-03     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8     7.85e-03    2.04e-03    6.65e-05    1.63e-03    5.99e-05   3.79e-03   1.16e-02  -7.59e-03  -3.79e-03     2\n"
+      "8     7.85e-03    2.04e-03    6.65e-05    1.63e-03    5.99e-05   3.79e-03   1.16e-02  -7.59e-03  -3.79e-03     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9     4.02e-03    1.02e-03    4.31e-05    8.14e-04    4.01e-05   1.91e-03   5.94e-03  -3.83e-03  -1.91e-03     2\n"
+      "9     4.02e-03    1.02e-03    4.31e-05    8.14e-04    4.01e-05   1.91e-03   5.94e-03  -3.83e-03  -1.91e-03     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10    2.09e-03    5.05e-04    2.88e-05    4.07e-04    2.73e-05   9.68e-04   3.05e-03  -1.94e-03  -9.68e-04     2\n"
+      "10    2.09e-03    5.05e-04    2.88e-05    4.07e-04    2.73e-05   9.68e-04   3.05e-03  -1.94e-03  -9.68e-04     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11    1.10e-03    2.52e-04    1.99e-05    2.03e-04    1.88e-05   4.94e-04   1.59e-03  -9.87e-04  -4.94e-04     2\n"
+      "11    1.10e-03    2.52e-04    1.99e-05    2.03e-04    1.88e-05   4.94e-04   1.59e-03  -9.87e-04  -4.94e-04     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12    5.90e-04    1.26e-04    1.40e-05    1.02e-04    1.31e-05   2.54e-04   8.45e-04  -5.09e-04  -2.54e-04     2\n"
+      "12    5.90e-04    1.26e-04    1.40e-05    1.02e-04    1.31e-05   2.54e-04   8.45e-04  -5.09e-04  -2.54e-04     1\n"
      ]
     }
    ],
@@ -987,10 +987,10 @@
      "n": "17"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.114577Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.114397Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.118223Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.117659Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.131611Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.131435Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.134135Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.133861Z"
     }
    },
    "outputs": [
@@ -999,11 +999,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:32:15\n",
+       "- Started at 2024-06-03 10:29:08\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 12\n",
        "- Reason for termination: Reached convergence: J_T < 0.001\n",
-       "- Ended at 2024-06-03 12:32:45 (0:00:30)"
+       "- Ended at 2024-06-03 10:29:29 (0:00:21)"
       ]
      },
      "execution_count": 20,
@@ -1028,10 +1028,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.120239Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.119937Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.122575Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.122206Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.136014Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.135896Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.137898Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.137629Z"
     }
    },
    "outputs": [],
@@ -1057,10 +1057,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.124539Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.124370Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.483149Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.482639Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.139511Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.139395Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.327709Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.327446Z"
     }
    },
    "outputs": [
@@ -1134,10 +1134,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.485258Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.485074Z",
-     "iopub.status.idle": "2024-06-03T10:32:46.190606Z",
-     "shell.execute_reply": "2024-06-03T10:32:46.190081Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.329382Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.329269Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.504967Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.504672Z"
     }
    },
    "outputs": [
@@ -1148,37 +1148,9 @@
       "Physical electric pump pulse in the lab frame:\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
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",
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CQgKOHDmCI0eO4PTp03Z3jShw27bJG1PdukC/ftXfR1Vyliyxrl8UGrxevapixlCU0yo2x45JGxUFJCQEfh01FHXkiFRDyTVCKti89NJLKCgowNChQ9G8efPyjw8++MDurhEFbs0aafv3B6Kjq7/PZZdJu3MncPSoNf2i0FBYKMuygfCo2Kif/8aNAztOQUlOlsn6Xq8+tEWuEFLBRtO0aj8mVFweSxRq1q6VdsCAmu+TlCS7EQPA6tXm94lCh5pfExcnH0ZxasWmYrAJRmSkPvmYw1GuElLBhsiVVMWmtmADAJdcIi2DDVVkxsRhIDQqNsHiBGJXYrAhslNxMbB9u9zu37/2+w4cKO3335vbJwotZgUbt1dsAE4gdikGGyI77dghY/xNm+ovsjVRFZ2NG515fg/ZI9wqNmrysBHBhscquBKDDZGdtm2Ttpbds8t16iTb3J88CRw4YGq3KISwYhM4VmxcicGGyE7+BJvoaP1+GRmmdYlCjAo2tZyZFxCnVmyMDDbcfdiVGGyI7ORPsAGAnj2lZbAhhRWbwHHysCsx2BDZicGGghVuc2xUsGnUKPhrcSjKlRhsiOxy/Lj+gtqli2+P6dFDWgYbUswONqdPA6Wlxl47GGYMRR0/Ln9PcgUGGyK7qGXe6emAr4ezqmCTlaWfD0ThzaxgU/Fn0ilVG00zNtg0aKBvanj4cPDXI0dgsCGyi7/DUID8Fq0Of9282fg+UegxK9hERQH16sltp8yzKS4GSkrkthHBxuPhkm8XYrAhsksgwQbgPBuqzKxgA+gTiJ1SsVF72MTGyqGxRuA8G9dhsCGyS6DBhvNsqCIzg40ajiosNP7agTDqAMyKGGxch8GGyC4//iitrxOHFRWE1OMpfHm9+jBRuAUbo3AoynUYbIjsUFAA5OfL7fbt/Xts587S7twpkykpfJ08qR+vwWATGFZsXIfBhsgOe/dKm5wMJCT499h27YDISHlT42+Z4U0NQ8XF6at7jBQOwYYVG9dhsCGyw5490rZr5/9jY2KAtm3lNoejwpuZ82sA5wYbIzbnU1ixcR0GGyI7qIpNIMEGqDwcReErXIONGUNR2dkc2nUJBhsiO6iKjaq8+EsFG1ZswpvapDFcgo1a7m1GsDl92jnL2ikoDDZEdghmKAoAOnWSlsHGONnZwD33yD5Bd90F5OXZ3aMLM7tio45VcEqwMWMoqk4dfb8e7j7sClF2d4AoLAUbbFixMdb+/cDQoUBmpvx582bgm2+AH36QCd5OZdVQlFMqGapCZWSwAaRqc+KEzLNR/7coZLFiQ2S1U6f0iYrBVmxyc/U3NwpMWRkwbpyEmg4dgFdfBdq0kbDzm9/Y3bvahdscGxVskpKMvS4nELsKgw2R1fbtk7Zhw8BfoOvX15epcgJxcF59FVi9Wr6nX38NTJwIfPKJnJX0ySfAd9/Z3cOaMdgYg8HGVRhsiKwW7MRhhfNsgnf6NPDYY3L78cflpHVA5tlMmCC3n3rKjp75JpyCTWmpHIIJGP/3ZbBxFQYbIqsFO79GUcFm9+7grhPO5syRCaPp6TJxuKKHHpL2s8+AQ4es75svwinYqL+rx6NPajYKg42rMNhQaDp1CvjnP4Fhw4Bf/hJYt87uHvnOqGCjjmL46afgrhOuNA147jm5/fvfy4nRFXXqBAweLPf78EPr++cL9WZv9NCM4sRgk5goO28bicHGVRhsKPScOAFcfjkwdSqwdCnwwQfAwIHA/Pl298w3+/dL26ZNcNdhsAnOqlUyjFe3rj7sVNXYsdK+955l3fKLVRWb0lKgpMSc5/CVWfNrAAYbl2GwodCiacDttwNr18oL3NNPAyNHysqWsWP1HX2dTC0pbtkyuOuoYLNnj34QIvnu3XelHTOm5qGNW26RoY/16525x4nZwabiOWZ2V22sCjbcfTjkMdhQaHnzTeCLL2TYYMkSGUL49FOp4Jw5A/z2t3b3sHaaBmRlye1gg02rVlKSP33amW+6TlZaqg8v/epXNd8vORno3Vtuf/21+f3yh9cr1UvAvGATGQnUqye37Q42Zoa4Zs2kLS3VAxSFLAYbCh0nTwJ/+YvcfvxxoFcvuR0VBbz8srQLFzp7vs2JE/rKjtTU4K4VHS3hBuBwlL8WL5Y3sKZNJRTX5uqrpf3qK/P75Y+TJ/VKnVnBBnDOPBszKzaxsfoxDRyOCnkMNhQ6XnpJtrlv1w743e8qf61DB30+xMyZ1vfNV2oYqnFjID4++Otxnk1g1DDUrbdKIK7N8OHSLl3qrGEKVcGIi5MPszhl92Gzh904z8Y1GGwoNJw9C8yaJbf/8hcgJub8+6jlufPm6YflOY1Rw1BKxXk25JszZ4AFC+R2bcNQSv/+8vOWl+es77PZb/RKOFRsAAYbF2GwodAwb57sJZKcrFdmqurRQ4anzp6VlVJOpCo2aWnGXE8tGWfFxncrV8pwYEqKhJYLiYsD+vWT26tWmds3f4RbsGHFhnzEYEOh4ZlnpL3vvtrL7nfcIa0aanAasyo2DDa+W7RI2quvlhVPvhg8WNpvvzWnT4EIt2DDig35iMGGnG/TJjnLJzoauP/+2u97003Srl4N5Oeb3zd/GV2x4ZJv/y1cKO011/j+mEsukXbtWuP7EygGG2Mx2LgGgw0539y50o4apS/LrEnLlnLOj9cLfPml6V3zm9EVm1atZPLrmTN8QfbFgQNyaGhkpOxa7au+faX98UegqMiUrvkt3IINh6LIRww25GxlZfqw0u23+/aYkSOl/e9/zelTMFSwMapiExXFJd/+UNWagQOBBg18f1xKinx4vUBGhhk981+4BRtWbMhHDDbkbMuWAUeOyIuZ2k/kQtTy3GXLnDU8U1amH6ZoVMUG4DwbfwQyDKX06SPthg3G9ScYVgUbtSuzncFG06yr2Bw+7KzXDfIbgw052zvvSPuLX1S/xLs6/fvL+T/HjgFbt5rXN3/l5gLnzskwSPPmxl2XS759U1IiYRcILNio4ahwCzZOqNgUFcn/HcC8ik3TpjKZvKzMmfPzyGcMNuRcZ84An3wit2+7zffHxcQAQ4bI7aVLje9XoNTE4ZQUY08n5pJv33z7rSzzbtZM5mH5q3t3abdtM7RbAVNDM+EQbNTfNTbWmI0tqxMVJeEG4HBUiGOwIedaskR+U2vRQuZE+GPoUGm//97wbgXM6InDCoeifKOGofxZ5l3RRRdJ++OP8lu93ayu2Ni583DFv2sg/3a+4jwbV2CwIeeaN0/a0aOBCD9/VC++WNoffjC0S0ExeuKwooLN3r2cG1AbtX9NIMNQANCmjeyhdOYMsG+fcf0KVDgNRZk9cVhhsHEFBhtyprIy4LPP5PaNN/r/+D59ZLgnO1ufsGs3NRRldMUmPV1f8u2Uv6vTZGYCO3ZIQL7qqsCuERkJdOokt7dvN65vgQqnYGPV35XBxhUYbMiZvvsOOHpUXsjUfBl/1K0LdO0qt9esMbZvgTKrYhMVBbRuLbc5gbh6ahjq4ouDe3NUP1N2BxuvV06KB8Ij2LBiQ35gsCFnUsNQ118vOw4HQg1HOSXYmFWxAbgy6kKCWeZdkZpnY3ewOXlSH3a0KticOQOUlpr7XDVRFRsGG/IBgw05j6YB8+fL7UCGoZQBA6R1yjwbsyo2gL4yisHmfKWl+uo4o4KN3Suj1Bt9XJx5q4SUhAT99smT5j5XTaxaAcZg4woMNuQ8mzfL1vfx8cCIEYFfR1VsNmzQ98CwS0mJ7GMDmBNsuDKqZqtWyeq65GQ5/T0YKtjs2mXvz5RVc04AqZiq8GTXcJT6+/qzW3QgGGxcgcGGnEcNQ40YAdSpE/h1OnaUXVNPnbL/N2w1qTc+HmjUyPjrs2JTs4rLvP1dXVdVq1byM1laau/32spgA9i/+7BV84lUsFGbaVJIYrAh51HDUKNHB3ediAigXz+5vW5dcNcKVsVhKDP24VDBhku+zxfsMu+KIiKALl3ktp3zbKwONnZPILYq2DRpIqvfNE2vsFLIYbAhZ9m3D9iyRV5c1GGWwVA7zG7ZEvy1gmHmxGFAP+X79GmW0SvKypJqXTDLvKtSwWbHDmOuFwi7go1dm/SpYGP2UFREhH7cCf8fhSwGG3IWNQx12WXGrIBQ2+DbHWzMnDgMVD7lm8NROlWt6d/fuCHAihsi2sWqybSKUyo2ZgcbgPNsXIDBhpyl4m7DRqgYbDTNmGsGwuyKDcAzo6pj1DLvipwwn8mq5c8Kg42znDghqz03bpQqLVXCYEPOkZurn+1kVLDp3FmqGSdO6FUTO5hdsQG4l01VpaVy3hhgbLBxwvc5nObYaBqDjbJ3L3DTTVJ9HDhQdlhv3Bi4+25n9tcmAQWbJUuWYNCgQRg4cCAee+wxnD171uh+UThasEBexPr2NS4AxMRIuAHsHY5SFRszg40TKglO8v33su9KkybyBmCUtm2lzc21b1+XcAo2FTcGDOdgs2QJ0Lu3VLW9XnktadxYVn2+8QbQoQMwa5a9lWmHCCjYTJo0CX/6058wa9YsHDhwAH//+9+N7heFIzUMFcymfNXp0UNaO4ONWSd7V+TEoSivF3j1VdlTqHlz+S3zpZcAK34Z+vxzaY1Y5l1RgwbyhgLYN88mnIKNqtZERAD16pn/fE4MNmvWADfcIN//QYNkQnxmJpCXB6xcKZuRFhcDv/sdMGFC2A9PBfS/vU6dOhg1ahT69u2LV199FUtUuZcoUAUF+u6wRgcbNc9m82Zjr+urggL9DcGqoSgn/NZWXCyh4t575YX5yBGZF/Cb3wCXXmr+G4cKNjfcYPy17a6OWXV2kuKEYNOggTlbJVTltGBz/Dhwyy0SVq69Fli2TN8o0uOR/0vffw88/bSsJv3Pf+R+RUX29ttGAQWb/Px8fPTRR9iyZQvOnTuHUrvODyH3+PJL+S2+Y0d96Mgodq+MUtWapCQ5nNMs6enywnb6NHD4sHnP44uyMpkLsHix/J1nzpS9hP7v/+QNas0aYOhQCTtm2LVLKlfR0cDw4cZf3+5gE04b9Fk5vwZwXrD5wx9kg88OHYD335fh9aoiIoDf/15WASYkAMuXy7wyOw8utVFAweahhx7CokWLMHHiRCQnJ2PHjh34xS9+gcceewzz1eZqRP4waxgK0Ieidu+2p0RrxcRhQF7w0tPltt3DUf/4B/D117JL75IlwEMPydypBx8E1q+Xfv70EzBmjDnDUp99Ju3ll+vVBiPZPewXjkNRVgebo0flKBQ7rVsHzJ4tt996q/K5XdUZNkx+mUhMlKNEhg/Xv39hJKBg8/vf/x5vvPEG1qxZg4KCAvz444+4/fbbERUVhY8//tjoPpLbnTmjL8s1I9g0bSpzIrxe4Mcfjb/+hVix1Ftxwoqd3buBxx+X2y+/rJ/ZpbRtK6Gnfn158Z02zfg+mDkMBdhbsfF6rduJVwmnYJOUpFdFzKoo+urRR6W9/XaZn+aLAQNkuCopSSqjI0bYt7GiTfwKNsOGDcNC9QZUQXp6OkaOHIlp06Zh7ty5hnWOwsTixTIe3KKF/FZvNI9H3y3WjmBjVcUGsL+SAEjpvLRUSuG33Vb9fTp0kN9AAeDf/9Y30jPC0aPAd9/J7euvN+66FdkZIAsK9DlU4bDzsNXBxuNxxnDUxo3AF1/IMNP/+3/+PbZ3bwk3jRoBa9dK5SaMwo1fwWb9+vVo9b/dTQ8ePFj++ddffx233367oR2jMPLee9Lecouxq1cqUvN23B5s7K7YrF0r1ZKICJlPU9tkzxtvlFUcAHDffcZNdvz4Y6lq9OqlD80ZTQXInByZJG0lNQxVpw4QG2vNc9pZsbHqZO+KnBBsVNXzl7+UXwT81aOHDAMnJcn/yzCq3Pj1LlJaWoqE/43xdevWDfv27QMAXHLJJViqVrQQ+aO4WPavAYCxY817HjuDjZVDUXZPav3Xv6S9/XaZCH4hTzwh4ePgQeBvfzOmD++/L62ZP09JSXq1xOol31bPrwHCaygKsD/YHDigzzv8618Dv07PnhJuGjaUYanhw6Wi6XJ+BZv27dtj7dq1KCgoQHFxMQr+l/4SEhJwXC0/JPLH55/LBlNt2sh5PmYJl4pNxWBj9ZLvQ4f0k9n/8AffHlOvnszDAYDnnpPyezCys2VfDwC49dbgrnUhaqO+/fvNfZ6q7Aw2p04B585Z97yAvcEmO9u656xIDdNeeaU+jB6oXr1kKw1VuRk0yPqfWYv5FWx++9vfYuLEibjiiivQvXt3vPHGGwCAb7/9Fk2bNjWlg2SwEyekVP+3vwHjxkmZ8667gGeflTcVq98M331X2rFjzd2jQgWbn36yZnM4xeu1ZnM+pXVrGQYqLrZ+4uOrr8oy7yFDgK5dfX/c1VfLv7/XK3vceL2B9+GDD+RnePBg87/f6tDRAwfMfZ6q7Ag2FVfjWL3bsh3BJjVV2kOHrHtOxevVV0L9+tfGXLNXL5mo37KlTO7v1w/46itjru1AfgWbu+++G6+99hrGjBmDJUuWYNeuXWjTpg0mTpyIX/ziF2b18TwvvPACWrVqhbi4OAwYMABr16617LlD1rffAtddJ9vLjxkj47fvvitvBG++Kctw+/SR/wBz5gT35uKrY8f0SaO/+pW5z5WaKtWBc+esHTrIz5eJtBUnJJqp4pJvK4ejSkuB116T25Mm+f/4mTPlzXPNGvl5DJSar/XLXwZ+DV/ZFWysPtkbkLk8aj6P1cNRVq8AA/Tqqh3nyy1bJsPXiYnGrhLt3Fk28uvTR157r7lGtmFw4UZ+fs/UvOmmmzB16lQ0atQICxcuxD//+U+8/vrrePLJJ83o33k++OADTJkyBdOnT8fGjRvRo0cPjBgxAnl5eZY8v180TX5ocnKAnTtl59t9+6z9QVq+XPbyGDJENsE7d05+wO++G3jqKanUTJ8uq0diY6WPEyYAl1xi/k69//mPVE969Qq+3HohHg/QqZPctnI4Ss2vSUmRzeKsYMfKqM8+kwpRs2aBvRinpADqaJapU+WF118bNsgeOdHREt7NpoJNhYUUlrD6ZG/Frnk2dlRs7Aw2qlrzq18B8fHGXrtFC6nc3HOPvD89/bTsYvzqq7LthktEBfXgqCiMseIFpIKnn34aEydOxJ133gkAePnll/Hf//4Xb775JqZOnXre/UtKSlBSYZOlQrP+U958s1QCTp2Sj6IieQGoaWinRQtJzlddBYwaZez8C02T1P/oo/p8g+ho4M47ZXdK9QZf1fHj8gP+xBPym/PFFwMvviiPM5qmAa+8Irfvucf461enc2d549uxw5z9cqpj5fwapX17WUJvZcVGVUrGjw88wP32t/KivnWr7G3z6qv+PX7WLGnHjAGSkwPrgz9UZSwchqIAqSDk54dXsDl0SKrXZq3WrOr0aX0xxfjx5jxHXJy89o4aJUO/Bw/KsSd//SswerRMMO7VS4at1H4+mibB59gxeZ+orq36uWXLZA8xO2ghpKSkRIuMjNTmzZtX6fN33HGHdsMNN1T7mOnTp2sAzvsoKCgwtnOdO2ua/POf/xEZqWkNG2pas2aaVrfu+V/3eDTtqqs07b33NO3MmcD74PVq2ldfadqgQfq1Y2I07Te/0bSDB32/Tna2pl17rX6NKVM0raws8H5VZ8UKuXbduppm9L9FTf7xD3nOceOseT5N07RnnpHnHDPGuud8+ml5zltuseb5Cgo0LTZWnjMjI7hrffut/n/ihx98f1xent6H1auD64Ovtm6V52vY0JrnUyZOlOd99FFrn7d3b3neL7+09nmbNJHn3brVuuc8e1bTIiLkeXNyrHveBQvkOdPS5PXcbMXF8hqVmlr9e1edOvr/K38/TPj3Kigo8On9O6iKjdWOHj2KsrKy8yYqN23aFDt37qz2MdOmTcOUKVPK/1xYWIg0M357fvFFmWdQp46UDxMSpHRbv778ueLE2BMngO3bZd7Ll19Ku3ixfDRpAkycKAna18mPZ8/KapSnn5ZDBgEZVrrnHuBPf9InwvkqJUVWKz3xBPDww3LdY8eA118Hogz6kVHVmrFjzdnyvjp2rIyycqm3YvVQ1IIFsvV8p076uVyBGjxYflOdM0d+m1y7Vs6/upDXXpM+9O0rO69aQVVsfv5Z9gdR5ymZzY45NoA9m/Rpmj0Vm6goOY0+O1uqrs2bW/O8aon36NHWHPhZpw7wwAPyf23lSnn+NWukalpSIqMPFUVFyaZ/SUnnt1U/p4Zq7RBocjp16pR26NCh8z6/bdu2QC95QdnZ2RoA7fvvv6/0+T/+8Y9a//79fbqGr4nPUnv3atrf/qZpLVroaTciQtNGjdK0zz+XVF2V1yuJ+JFHKqftuDhNe/BBqboYYc4cqTipSse5c8FfMzNT06Kj5Zrr1gV/PV/t3Kn/FmLFb0OaJpUaQH4rssru3fKc8fHGV9qqc9118nyPPGLM9XJzNS0x0ffvW2Gh/lv9nDnG9MFXjRrJ827ebN1zXnGFPOc771j3nJqmaTfcIM/7yivWPWdxsf7advKkdc+raZp28cXyvB9/bM3znT2r/zwtW2bNc9akrEzTjh/XtH37pNp/5Ij8P7PqdbMGvr5/BzRw+PHHH6N9+/a47rrr0L17d6xZs6b8a2buQNy4cWNERkYiNze30udzc3PRrFkz057XdG3ayHyYAweATz4BrrhCxnUXLABGjpT0O2CAVDfGjZPlsc2aAd26AY88IuPAyclSXdm/X3Z8NWoFzh13yPLwqCjgnXdkh9hgl4Q/9ZRUmYYONecIhZq0aSO//Z86Zd3GW3ZUbFq3lnkup0/rz2+W48f1ZaNG7RuTnAzMmCG3//xnICOj9vv/+98y96NdO3M35auOHSuj7JpjY8fkYVWtiYyUU+KtZPUE4m+/lcp4o0bApZda85w1iYiQn6/WreW1q2lTGYWwoopkgICCzeOPP44NGzYgIyMDs2fPxl133YV3/7cfiWbiPigxMTHo06dPpV2OvV4vli5dioG+HhDmZFFRwE03yWZK27fLZMq0NJm0tXat7Kj67rvyRpKXJ8NN118PvP22vIH9/e8SeIw2ejQwd678sL/+uiwND/Tf+cgRfVmwv+efBCs6Wv6jAtYN09gxeTgqSj9aoYYhWsN89ZWstOvateZJ6YG47z752S4pkcBU094pu3bpIeiJJ6xbeaYw2Jir4jCU1W+q6pcRq4KN2tzyhhuMG/IPUwF9986ePVs+z6VPnz5YuXIlbrzxRuzZswcek3/4pkyZgvHjx6Nv377o378/nnnmGRQXF5evknKNLl1kJ9Znn5U3px075MUzIkL2Y+nWTc4CMXo5YE1uvVUC1oQJ0q86dYB//MP/F5uZM+U6F18slSmrdeggq4V275aKkZlKS4HDh+W2lRUbQELGjh3ys3P11eY9jzoU97rrjL2uxyO7r/bsKf9WN98sFcyKP+9FRbICqrRU/o4Wr9AEoM+zsXLJd7gGG6tZXbFR/5dGjrTm+VwsoGCTnJyMLVu2oPv/JgomJSVh8eLFGD9+PLZs2WJoB6u69dZbkZ+fj4cffhhHjhxBz549sWjRIvfufOzxyKRXNfHVTuPHyzDOb34DPPmklIcfe8z3cLN7t4QiQIbN7ChrqkrG7t3mP1dOjlS2YmNlUriVVPXEzIqN16tvsHjNNcZfv1Ej4NNPZR+mxYslCM+eLX+3Awdk2GnrVimTv/66PT9PVldsysr0ybsMNuayMtjs3y9V5MhIe37hc5mAgs3bb7+NqCqlspiYGLz33nuYPHmyIR2rzeTJky15HqrG/ffLb8gPPiilf49H5gdd6E3F65XhhbNn5bdrM6sItVGn5FoxFKVeEFNTrX/TVUHYzGCzYYPMbalfXzZ0NEO/frJycNQoWfHXubN8P3Ny5GeqYUPZHLBFC3Oe/0KsDjbqjR5gsDGblcHm66+lHTjQutV1LhbQHJvU1NQaJ+tWDTzkQg88IBOUATma4Y9/lN8ka/PPfwLffCNDWLNm2TcJTQUbKyo2dkwcVqyo2KjS+VVXmTu3ZcgQOcfshhvk50ZtmnbFFcDq1eYennohVgcbNQxVr57184nUG66VwUb9fe0MNocPm3/wp5qAP2KEuc8TJgzfTtHqnYjJJg8+KPvbALIq5cYb9Rehqt5+W3a1BIBnntH3WbGDGorau/fCYSxYdkwcVjp2lDY3t+Z/l2B9+aW0ZgxDVdW6tcyxOXxYKjeHDskke/X3tIuaY3P8uDWHQ9o1vwawZx8bOys2yckSHr1ec1dRnj0rP8sAg41BAiqv1HTgpaZpOK42jyL3+/3vZX7DXXfJhn4dOsicm9tvl6WZR4/KBGNV3Zk8WTYftFNamsx5KSmRCZ9t2pj3XHZWbBISZHgmO1tWDl18sbHXP3pUVuoB1g4rNm1q3zbt1alfXyoZBQUSZM0+88yuzfmA8BuKioiQYc/9++Xf1qz/x2vWyPe0USOgd29zniPMBBRslixZgrfffhv16tWr9HlN07BSnU1E4eFXv5IqyJ13yhL1+++XZepNm+qTZwEJQTNn2ttXQF6s2rWTvu7ebW6wsbNiA8hwVHa2DEcZHWy++kr+bXv0sG9+i1OkpVkXbJxQsQmXYANImNm/X35JGTTInOdYskTaYcN822WbLiigoaihQ4ciISEBl112WaWPoUOHlq+UojDSrx+waZMsTW/dWsajs7Plja9PH+CLL2TYyqqD5C7EqgnEKtjYUbEBzD3NXM2vsWIYyunUv6/ZmyEC9p3sDdgbbOwIcoA+h2r/fvOeY/lyabkayjABVWw+/fTTGr+2ePHigDtDISw6Gvjd76Rak5kpQxUpKdadseIPqyYQqzc6uyo2qnqwfbux1y0r0yc7MthYu3rGzoqNmjxcVCQ/A1ZUF+yu2KgNPc0KNmfO6Of7mb2vVhjx61foLKs2KqLQ5fHIhMo+fZwZagB9ArGZFZuiIv1NyK5g062btFu3Gnvd9esluNavL8tTw124BJuKh9VaMVEacH+wWbNG5vs1b66/LlHQ/KrYpKenIykpCT169EDPnj3LP0pLS/Hcc89hzpw5ZvWTyDhWVGzUm1xionWnl1elgk1mprxBGPXmoIahhg+3fsmxE1kZbOycPBwbq0+8Lyy0Jmy4PdioYaihQ0PmHKZQ4Few2b9/PzZt2oSMjAxs2rQJH374IXL+twyuvl0v3kT+Ur8ZHTwoL9KxscY/h90ThwF5M0hLk75s2wYMHmzMdTm/pjI7KjZ2zLEBJKTn51u35NspwSYzU+YOGr1Pmwo2l11m7HXDnF9DUenp6Rg9ejQeeeQRLFiwAFlZWVi1ahXatm2Ll156yaw+EhlLnVTr9QL79pnzHHYu9a5ITeY36qiT/Hxg3Tq5bdfu0U5TMdiYeAgwAHuHogBrJxBrmv3BJiUFiImRUHPokLHXPnNGNpgEOL/GYEEvUxk4cCCeffZZzHTCUl4iX3g85g9HOaFiAxg/z6biMu+UFGOuGepSU6U9fVofKjKL3cHGyt2HT53Sd/y1K9hEROibMBo9HKXm1zRrpr8ekSH8CjalpaXVfr59+/bYbvTKCyIzmT2B2O6l3orRFRs1DHXttcZczw3i4vRDTs0ejrJzjg1g7e7DqloTFSVHsdjFrHk2nF9jGr8GDOvVq4cuXbqgV69e6NmzJ3r16oWUlBTMmjULw4YNM6uPRMYzu2Jj91JvRQWbrVul0hLMC2hZmbmneYeytDQZpsvKAnr2NO95VLCxc44NYE3FpuIwlJ1v/GoTTzODDRnKr4rNsmXLMHHiRERHR+Odd97B1VdfjQ4dOmDWrFkoKyvDww8/jI8++gg7zTx4j8gIVlVs7A42HTrIyqWTJ2WydDDWrZM31sRELvOuSlXmzKzYlJQAxcVyu1Ej856nNlYORdk9v0Yxo2LD+TWm8qtiM3jwYAyusLLC6/Vi165dyMjIQEZGBtauXYvXXnsNeXl5KDP7gEGiYJhZsdE050wejo6Wjfo2b5bhKLWTaiAqLvM2enVIqFMB1szdh48dkzYiQg8YVrNjKMqNwWbtWgmqTZtyfo0Jgnp1ioiIQOfOndG5c2eMHTu2/PO5ublBd4zIVKpik5Mjm+lVOfcsKHl58huZx6NPLLVTjx4SbDZuBG64IfDrWHmad6ixYsl3xWEou44nsWsoyk5mBBt1puJll3F+jQlM+d/R1Emn7xJVp2FDoHFjub1nj7HXVkM+aqmo3fr3l3bNmsCvkZcnOw4DXOZdHSuCjarY2DUMBVg7FKVWgDkl2Bw+LCvfjLBihbRDhhhzParEIacSEtnArOEoFWzUMlG7DRgg7Zo1ge+zos6G6tnTuUdl2Clcgk04DkUlJel/byOqNmfPAt9/L7cZbEzBYEPhy6wJxGqehVOCTffusrvyzz8H/nf94gtpucy7eirYZGfLxo9mcFKwCaehqIr7Xu3aFfz1Nm6UPXqSkoCLLgr+enQeBhsKX0a+WFXktIpNTIwcSgoENhxVWqpPHB450rh+uUlKisx7OXsWMGuOoROCjR2rouzas6eiTp2kNWLFr5pfc+ml9s2Vcjl+Vyl8deworduHooDKw1H+WrlSlosnJ+vzdaiyqCh9iM6s4SgVbOzawwYIz6EowNhgw/k1pmOwofBVsWJj5Bk/bgs2n38u7fXX8zfM2pg9z8YJFZtwHIoCjAs2ZWXAqlVym8HGNHyVovDVrp2Mn584ARw9atx1VbCxew+bilSw2bxZlqL7StP0YMNhqNqZvUmfE4JNOG7QB1QONsH8ErR1q1S7EhLM3aE6zDHYUPiKj9ffjIwajios1F+QnVSxSU+XzcDOntWXbftixw5ZCRIbC1x1lXn9c4NwqticOiU/S2ZyUrBp106qlYWFwJEjgV9Hza8ZNIibXJqIwYbCm9ETiFW1JinJ2E3/guXx6KXvZct8f9yHH0o7bBhQt67x/XKTcAo2gMy7MpOTgk1srH5mVDDDUZxfYwkGGwpvRk8gduL8GuXKK6X1NdhoGvDee3L7l780p09uYvaxCmrnYTuDTXS0VDoBc4ejNM1ZwQbQXysCDTaapldsGGxMxWBD4c2sio0Tg80VV0i7erV+mGJtNm6UfW/i4oBRo8ztmxuoYHPokPHX1jRnBBvAmpVRxcUy0RZwTrBR82wCfa3YuVPm8sXFAf36GdcvOg+DDYU3o3cfdnKwaddODsEsLQWWLr3w/d99V9qRI2WyI9VOBZvDh4Fz54y9dmGhfk2nBBszKzaqWlOxQmS3YFdGqWrNwIHOOGrFxRhsKLyp8vKePfpviMFwcrDxePRDMBcsqP2+Xi/wwQdyu8IBt1SL5GR5I/Z65XBVI6n5NXXqyG/8drJiZVTFYSinHBKpgs2PPwb2eM6vsQyDDYW3tDSZGFhaqoeSYKizZFq1Cv5aZlDB5vPPa68qfP21HA+QmMjTvH0VEQG0aCG3jZ5A7ISJw4oVQ1FOm18DAF26SJuZ6f/fXdOAb76R25ddZmy/6DwMNhTeIiP1M6OMGI7au1fatm2Dv5YZhgyRN8f8fGDx4prv9+KL0k6YYH+FIJSYNc/GicHGqoqNUyQl6f++W7b499jt22WZeHw8cMklxveNKmGwITJqAvGJE/oET7U01Gmio4Ff/Upuz55d/X127tQPvbz/fmv65RZmLfl2UrCxYijq55+ldVKwAfRN9TIy/Huc+iViyBCpEJOpGGyIjJpAvG+ftMnJztrDpqoJE6SdP7/6N+B//ENK56NG6XOQyDepqdK6OdiE61AUAPToIe3mzf49bskSabnJpSUYbIjUm3ewFRunD0MpvXsDQ4fKzrH/+lflr61bB8ydK7f/3/+zvGshLxwqNuE6FAXoFZtNm3x/TGmpPnF42DDDu0TnY7AhMqpiEyrBBtBDy0svSZgBZCfZX/9aqjW33w707Wtf/0KV2XNs7DzZW7FyVVTDhuY9RyD69JF2yxbg9GnfHvPDD7IvT3Iy0K2beX2jcgw2RKpik5Xl28Z1NQmlYHPllbKMu6wMuO464OmnpUy+bRvQrBnw1FN29zA0mVWxccrmfEB4D0Wlp0tAOXfO96rN119Le+WVsnKOTMfvMlGjRvpvwnv2BH6dUAo2APDCC0CvXrJC6qGHgDVr5E1r/nw5MJP8p4JNbq4MQRhFnT7vpGATjkNRHg9w8cVye80a3x7z2WfSXn21OX2i8zDYEAF61SbQzbcAffJwqASbhg2B5cuBhx+WOTf33iuTIgcMsLtnoatxY1n1ommyD5BR8vOlTU427pqBsnqDPqdR/z9Wr77wfffuBbZulW0lrr/e3H5ROZ6bTgQAF10kL1Tbtwf2+NJSffghVIINIL99//3vdvfCPTweWRm1d6/Ms2nd2pjrqmDTpIkx1wtGOA9FAcCgQdJ++60E2Np2Rp43T9qhQ50xPypMsGJDBEiwAQIPNgcOyFb6des647dqso/R82w0DcjLk9tO+NkK56EoQIai4uJkw70LVXjnz5d29Gize0UVMNgQAcEHGzW/pk0b55xtQ/YwOtgUFMjSfMAZFZtwH4qKjQUGD5bbtR0mm5sLfP+93GawsRSDDRGgB5s9e4AzZ/x/fKhNHCbzGB1s1DBUQoIzjrdQFZszZ4ydIK1omrODDSArnABg0aKa7/PJJ/J36dtX37iRLMFgQwQAzZvLi6jXG9hGfT/9JC2DDak3MaP2snHSMBQgAUsxo2pTVCT/DwHnBpuRI6VdsqTm78Gbb0o7bpw1faJyDDZEgAwfBTMcpcba1QnAFL7Mqtg4YRgKAKKiZC4ZYE6wUedExcQ4o0JVnS5dgE6dpGKlzlWrKCMD2LBBzmZjsLEcgw2R0rWrtIEEG/UYBhsyOtg4rWIDmLsyquKuw06dr+bxALfcIrerO0z23/+W9sYbnRNIwwiDDZESaMXmxAkgJ0dud+5saJcoBKlgk58f2HytqlSwcdIbpJkro5x6sndVd90lOwkvWVL5NeOnn4D33pPbf/qTPX0Lcww2REqgwUYNQ6Wm6itGKHw1bAjEx8ttI+bZOGlzPsXMlVEq2DjtnKiqWrUCRo2S23/+s0wU9nqByZPlqJJrrtHPliJLMdgQKSrY7N0LnDrl++N27JCWw1AEyDCFkYdhhutQlNMrNgDwxBMyj+a//wV+9ztgwgQ5GyomBnj2Wbt7F7YYbIiU5GT50DTZBt1XDDZUlZHzbJw2eRhgxUbp3BmYMUNuP/888Pbbcvu114D27e3rV5hjsCFSPB69dLxhg++PY7ChqowMNk6u2JgRbCpOHg4FDz0EzJ0LXHopcPnlskrqjjvs7lVY41lRRBX16QMsXAisX+/7Y7giiqoyI9g4qWJj5lBUqEwermjcOC7rdhBWbIgq6ttXWl8rNoWF+psXV0SRYtQmfV4vcPSo3HZSxYZDUeRgDDZEFamhqO3bgdOnL3z/nTulbdaMp/eSzqiKzc8/ywobAGjcOLhrGcmKoahQqtiQozDYEFXUooX8ZlxWBmzefOH7b9kiLYehqCKjgo2aONyggay0cQorhqJYsaEAMdgQVeTx+Dccpe7D/SqoIhVsjh/3b+uAqpw4cRjgUBQ5GoMNUVUqpPgygZjBhqqTmKgfFhnMPBsnThwGOBRFjsZgQ1SVr8GmtFQfilJVHiJFTSAOZjjKibsOAxyKIkdjsCGqasAAabdv119kq7N9O1BSIr9ZtmljSdcohKjhqMzMwK/h1IqNWUNRJSX6pH1WbChADDZEVTVrBnToIDsQr1pV8/3U1/r3d+4pxGSf9HRpgwk2R45I27Rp8P0xkllDUWoYyuPhuWsUMAYboupcdpm0K1bUfJ9vv5V2yBDz+0Ohp1UraQ8cCPwa6tT4Fi2C7Y2xVLApLZUqi1FUhTQxUU7OJgoAf3KIqqOCzTffVP91TQNWrpTbl15qTZ8otBgZbFJSgu2NsdTEaMDYeTacOEwGYLAhqs6VV0q7cSNw+PD5X9+1C8jNlb1F+ve3tm8UGtwcbCIi9HBj5HAUJw6TAUIm2Bw4cAB33XUXWrdujfj4eLRt2xbTp09HaWmp3V0jN2rWTF/ptHDh+V//4gtpL7sMiIuzrl8UOtQcm6ws4Nw5/x9fVqbPsXFasAHMmWfDig0ZIGSCzc6dO+H1evHKK69g+/bt+L//+z+8/PLL+Mtf/mJ318itrrtO2gULzv/a559LO3Kkdf2h0NK8ORAdLQElO9v/x+flyVlRERHOW+4N6JN7jRyKYsWGDBAywebqq6/G7NmzMXz4cLRp0wY33HAD/vCHP+DTTz+1u2vkVrfcIu2XX+r7iQDyW/R338nt66+3vl8UGiIi9KrNwYP+P14NQzVrBkRGGtcvo5hRsWGwIQOETLCpTkFBAZIucPBgSUkJCgsLK30Q+aRrVxmOOncOmDtX//wbb8hv4QMHAq1b29c/cr5g5tmoKo8Th6EADkWRY4VssNmzZw9mzZqFe++9t9b7zZgxA4mJieUfaWrTLCJf3H23tE89JWf+FBcDL78sn7v/fvv6RaFBVWwCCTZOnTisqKEoFUaMwIoNGcD2YDN16lR4PJ5aP3bu3FnpMdnZ2bj66qsxZswYTJw4sdbrT5s2DQUFBeUfWcGetkvhZcIE+a378GEJOffcI2f/pKXpQ1VENQmmYuPUPWwUVVXhcm9ymCi7O/DQQw9hwoQJtd6nTYXt6nNycnD55ZfjkksuwauvvnrB68fGxiI2NjbYblK4io0Fnn0WGD0aeO89/fMvvgjEx9vWLQoRRgQbp1ZsVPhgxYYcxvZg06RJEzTx8RyU7OxsXH755ejTpw9mz56NCO5MSVa44QbgnXeA6dNlp9V//IOThsk34RBsajtPzV8MNmQA24ONr7KzszF06FCkp6dj5syZyK+wSqVZs2Y29ozCwtix8kHkDxVssrJkwrk/q5ucHmxU+DCyYsOhKDJAyASbxYsXY8+ePdizZw9SU1MrfU3TNJt6RURUi+bNgagoWVmXk6Of+O0LpwcbDkWRQ4XMWM6ECROgaVq1H0REjhQZqa+M2rvX98eVlup7J4VLsPF69YnIrNhQEEIm2BARhaR27aT96SffH6OOUoiOBho1Mr5PRjA62BQWyuGyACs2FBQGGyIiM7VvL60/wabiMJTHY3yfjGB0sFHDUPHxshqRKEAMNkREZlLBZs8e3x+jdh1u3tz4/hilYrDxeoO/HicOk0EYbIiIzBRIxSYzU1o1P8eJ1HCR1wsUFQV/PU4cJoMw2BARmalixcbXyoba98bJwSYuTh8yMmI4ihUbMgiDDRGRmVq1kiXfZ87oQ0wXooKN2gfHqYycZ8OKDRmEwYaIyExRUfop8L4ORx08KC2DDZHfGGyIiMzm7zybUBiKAowNNhyKIoMw2BARmc2fYHPihL5RXagEGyPOi2LFhgzCYENEZDZ/go0ahmrcGKhb17w+GYEVG3IgBhsiIrP5E2zU0Qtt2pjXH6MYeRAmKzZkEAYbIiKzqWCzd6+c8l0bFX7UY5zMjMnDrNhQkBhsiIjM1rKlHBVQWnrhwzB375Y23ILN8ePSJiUFfy0Kaww2RERmi4wELrpIbm/bVvt9w7Vio4KNUw/9pJDBYENEZIWuXaW9ULBRFZsOHcztjxGMWhXl9TLYkGEYbIiIrOBLsCksBHJz5XY4VWwKCvTjJjgURUFisCEisoIKNlu31nyfnTulTU4GEhPN71OwjFoVpao1devq508RBYjBhojICt27S7t7N1BcXP19VOhR93U6oyo2x45Jy2EoMgCDDRGRFZo3B1JSZMglI6P6+2zZIm23bpZ1Kygq2BQW+n5yeXW4IooMxGBDRGSVvn2lXb+++q+HWsVGDZdpmoSbQLFiQwZisCEiskqfPtJWF2w0Ta/YhEqwiY2V/XmA4FZGMdiQgRhsiIisoio2P/xw/tcyM+UNPioK6NzZ2n4Fw4gl3xyKIgMx2BARWeWSS4CICGDPHiA7u/LXvvtO2l699CpIKFAro1ixIYdgsCEiskqDBhJcAGDFispfW7VK2kGDLO1S0FQYUVWXQDDYkIEYbIiIrDR0qLTLllX+vKrYhGqwUeEkEByKIgMx2BARWemqq6T9/HP9pO9Dh/SJw4MH29OvQKkwEkywYcWGDMRgQ0RkpSuukDCQl6cPR336qbSDBgHNmtnXt0AYWbFhsCEDMNgQEVkpOhq46Sa5/cYbssz7nXfkz7fcYl+/AmXkHBsORZEBGGyIiKx2773Svvce8JvfAGvXAnFxwC9+YW+/AhFsxebcOTkEs+K1iILAYENEZLW+fSXEaBrw8svyuYcekiMXQk2wc2wqLhNXe+IQBSHK7g4QEYWll14CSkqAL74A7rgD+Otf7e5RYIKt2KjHNWggmxMSBYk/RUREdkhKAubPB0pLgZgYu3sTuGDn2HBFFBmMQ1FERHYK5VAD6ENRx48HdsI397AhgzHYEBFR4FSlxevVJwH7gxUbMhiDDRERBS42FqhbV24HMs+Ge9iQwRhsiIgoOMHMs+EeNmQwBhsiIgpOMEu+ORRFBmOwISKi4ASz5Ds/X9rkZOP6Q2GNwYaIiIITzFBUXp60TZoY1x8Kaww2REQUnGCGolSwYcWGDMJgQ0REwQlmKIrBhgzGYENERMEJNNiUlgInTshtBhsyCIMNEREFJ9A5NmricFQUD8AkwzDYEBFRcAKdY1Nx4nAE347IGPxJIiKi4AQ6FMX5NWQCBhsiIgoOgw05CIMNEREFRwWbkydlQrCvGGzIBAw2REQUnIYN9TkyR4/6/jgGGzIBgw0REQUnIkLfOViFFV8w2JAJGGyIiCh4Kpww2JDNGGyIiCh4DDbkEAw2REQUPBVO1KZ7vmCwIRMw2BARUfD8rdhoGoMNmYLBhoiIgudvsCkqAs6ckdtq4jGRARhsiIgoeP4GG3W/unXlg8ggDDZERBS8QIMNh6HIYAw2REQUvECDDYehyGAMNkREFDwVbI4ckYnBF8KKDZmEwYaIiILXvLm0Z84ABQUXvn9OTuXHERmEwYaIiIIXHw8kJsrtw4cvfH8VbFJSzOsThSUGGyIiMoYKKQw2ZCMGGyIiMoYaVvIn2LRoYV5/KCwx2BARkTECCTas2JDBQjLYlJSUoGfPnvB4PMjIyLC7O0REBOjBRoWWmpw9C+Tmym0GGzJYSAabP/3pT0jhfwYiImfxtWKTmytLwiMjuY8NGS7kgs3ChQvx9ddfY+bMmXZ3hYiIKvI12FRc6h0Rcm9D5HBRdnfAH7m5uZg4cSLmz5+POnXq+PSYkpISlJSUlP+5sLDQrO4REYU3VUm/0FBUdnbl+xMZKGSisqZpmDBhAu677z707dvX58fNmDEDiYmJ5R9paWkm9pKIKIy1bCltVlbtuw9nZla+P5GBbA82U6dOhcfjqfVj586dmDVrFk6ePIlp06b5df1p06ahoKCg/CMrK8ukvwkRUZhr0QLweGT34aNHa77fwYPSMtiQCWwfinrooYcwYcKEWu/Tpk0bLFu2DKtXr0ZsbGylr/Xt2xfjxo3DnDlzqn1sbGzseY8hIiITxMQAzZrJHJvMzJonBquKTXq6dX2jsGF7sGnSpAma+DAr/rnnnsPjjz9e/uecnByMGDECH3zwAQYMGGBmF4mIyFctW+rBpk+f6u+jKjYMNmQC24ONr1pWKVnWq1cPANC2bVukpqba0SUiIqoqLQ1Ys0avylSHc2zIRLbPsSEiIhepOIG4OqdPA3l5cpsVGzJByFRsqmrVqhW02mbdExGR9VSwqaliowJP3bpAw4bW9InCCis2RERkHFWF2bev+q/v3Stt69aygorIYAw2RERknHbtpN2zp/q9bH76Sdr27a3rE4UVBhsiIjJO27bSFhRUv5fN7t3SduhgXZ8orDDYEBGRceLjZWUUoFdnKmLFhkzGYENERMaqOBxVlQo2rNiQSRhsiIjIWKoaU7ViU1Kib87HYEMmYbAhIiJjqdCyc2flz+/aBXi9QP36QHKy9f2isMBgQ0RExureXdqMjMqf37RJ2p49udSbTMNgQ0RExurVS9o9e4DCQv3zKtiorxOZgMGGiIiM1bgxoM7w27xZ/zyDDVmAwYaIiIzXs6e0Ksx4vfrQFIMNmYjBhoiIjNevn7Tffivtpk0yLJWQAHTubF+/yPUYbIiIyHgjRki7eDFw7hywaJH8edgwIDravn6R6zHYEBGR8fr2BRo1kqMVVq8GvvxSPn/11fb2i1yPwYaIiIwXGQlce63cHjIE+P57ICICuOYae/tFrsdgQ0RE5pg6VQKOctdd+jlSRCZhsCEiInN06QI8+ijQoIGsknr0Ubt7RGHAo2maZncnrFRYWIjExEQUFBSgfv36dneHiIiIfODr+zcrNkREROQaDDZERETkGgw2RERE5BoMNkREROQaDDZERETkGgw2RERE5BoMNkREROQaDDZERETkGgw2RERE5BoMNkREROQaDDZERETkGgw2RERE5BoMNkREROQaDDZERETkGlF2d8BqmqYBkOPPiYiIKDSo9231Pl6TsAs2J0+eBACkpaXZ3BMiIiLy18mTJ5GYmFjj1z3ahaKPy3i9XuTk5CAhIQEej8ew6xYWFiItLQ1ZWVmoX7++Ydelyvh9tg6/19bg99ka/D5bw8zvs6ZpOHnyJFJSUhARUfNMmrCr2ERERCA1NdW069evX5//aSzA77N1+L22Br/P1uD32RpmfZ9rq9QonDxMRERErsFgQ0RERK7BYGOQ2NhYTJ8+HbGxsXZ3xdX4fbYOv9fW4PfZGvw+W8MJ3+ewmzxMRERE7sWKDREREbkGgw0RERG5BoMNERERuQaDDREREbkGg41BXnjhBbRq1QpxcXEYMGAA1q5da3eXXGflypUYOXIkUlJS4PF4MH/+fLu75DozZsxAv379kJCQgOTkZIwePRq7du2yu1uu89JLL6F79+7lm5gNHDgQCxcutLtbrvfkk0/C4/HgwQcftLsrrvPII4/A4/FU+ujUqZMtfWGwMcAHH3yAKVOmYPr06di4cSN69OiBESNGIC8vz+6uuUpxcTF69OiBF154we6uuNaKFSswadIk/PDDD1i8eDHOnj2L4cOHo7i42O6uuUpqaiqefPJJbNiwAevXr8cVV1yBUaNGYfv27XZ3zbXWrVuHV155Bd27d7e7K6510UUX4fDhw+Ufq1atsqUfXO5tgAEDBqBfv354/vnnAch5VGlpafjtb3+LqVOn2tw7d/J4PJg3bx5Gjx5td1dcLT8/H8nJyVixYgWGDBlid3dcLSkpCU899RTuuusuu7viOkVFRejduzdefPFFPP744+jZsyeeeeYZu7vlKo888gjmz5+PjIwMu7vCik2wSktLsWHDBgwbNqz8cxERERg2bBhWr15tY8+IgldQUABA3nTJHGVlZXj//fdRXFyMgQMH2t0dV5o0aRKuu+66Sq/TZLyffvoJKSkpaNOmDcaNG4fMzExb+hF2h2Aa7ejRoygrK0PTpk0rfb5p06bYuXOnTb0iCp7X68WDDz6IQYMGoWvXrnZ3x3W2bt2KgQMH4syZM6hXrx7mzZuHLl262N0t13n//fexceNGrFu3zu6uuNqAAQPw1ltvoWPHjjh8+DD+/ve/49JLL8W2bduQkJBgaV8YbIioWpMmTcK2bdtsGyd3u44dOyIjIwMFBQX4+OOPMX78eKxYsYLhxkBZWVl44IEHsHjxYsTFxdndHVe75pprym93794dAwYMQHp6Oj788EPLh1cZbILUuHFjREZGIjc3t9Lnc3Nz0axZM5t6RRScyZMn44svvsDKlSuRmppqd3dcKSYmBu3atQMA9OnTB+vWrcOzzz6LV155xeaeuceGDRuQl5eH3r17l3+urKwMK1euxPPPP4+SkhJERkba2EP3atCgATp06IA9e/ZY/tycYxOkmJgY9OnTB0uXLi3/nNfrxdKlSzleTiFH0zRMnjwZ8+bNw7Jly9C6dWu7uxQ2vF4vSkpK7O6Gq1x55ZXYunUrMjIyyj/69u2LcePGISMjg6HGREVFRdi7dy+aN29u+XOzYmOAKVOmYPz48ejbty/69++PZ555BsXFxbjzzjvt7pqrFBUVVUr/+/fvR0ZGBpKSktCyZUsbe+YekyZNwrvvvosFCxYgISEBR44cAQAkJiYiPj7e5t65x7Rp03DNNdegZcuWOHnyJN59910sX74cX331ld1dc5WEhITz5ofVrVsXjRo14rwxg/3hD3/AyJEjkZ6ejpycHEyfPh2RkZEYO3as5X1hsDHArbfeivz8fDz88MM4cuQIevbsiUWLFp03oZiCs379elx++eXlf54yZQoAYPz48Xjrrbds6pW7vPTSSwCAoUOHVvr87NmzMWHCBOs75FJ5eXm44447cPjwYSQmJqJ79+746quvcNVVV9ndNaKAHDp0CGPHjsWxY8fQpEkTDB48GD/88AOaNGlieV+4jw0RERG5BufYEBERkWsw2BAREZFrMNgQERGRazDYEBERkWsw2BAREZFrMNgQERGRazDYEBERkWsw2BAREZFrMNgQkaMcOHAAHo8HrVq1srsrRBSCGGyIyHKtWrWCx+PBgQMH7O4KEbkMz4oiIkdp0aIFfvzxR0RHR9vdFSIKQQw2ROQo0dHR6NSpk93dIKIQxaEoIrLMW2+9BY/Hg4MHDwIAWrduDY/HU/6xfPnyWufYqPsBwNy5c9G/f3/Uq1cPTZo0wdixY5GZmQkA0DQNzz//PHr27Im6deuicePGmDBhAvLy8mrs2+7du3Hvvfeibdu2iIuLQ2JiIoYMGYK5c+ca/40gItPwdG8issyqVavw+uuv4+OPP0ZxcTFuvvlm1KtXr/zrU6dORVxcHFq3bo309PTz5uCoUDN16lTMnDkTQ4YMQVJSEtauXYvMzEykpaVh8+bNuO+++/DZZ59h6NChiI+Px3fffYe8vDx0794d69atQ0xMTKXrfvTRR7jjjjtw5swZdOrUCZ07d0ZBQQHWrFmD4uJi3HnnnXjzzTdN//4QkQE0IiKLpaenawC0/fv3n/e1/fv3awC09PT0874GQAOgNWrUSMvIyCj//KlTp7TBgwdrALRu3bppbdu21Q4cOFD+9fz8fK1du3YaAG3u3LmVrrllyxYtNjZWi4uL0z755JNKXztw4IDWrVs3DYA2Z86c4P7SRGQJDkURUch59NFH0aNHj/I/x8fHY8qUKQCArVu34rnnnkN6enr51xs3boz7778fALB06dJK13riiSdQUlKCxx9/HDfddFOlr6Wnp+ONN94AADz33HOm/F2IyFgMNkQUcq699trzPte+fXsAQFRUFIYPH17j13Nycso/5/V6sXDhQgDArbfeWu1z9e3bF/Xq1cOmTZtw5syZoPtOROZisCGikNOyZcvzPqfm6jRv3hxRUecv+ExISACASuHk2LFjKCwsBACkpaVVmsisPiIiIlBUVASv14tjx46Z8dchIgNxuTcRhZyIiJp/J6vta1V5vd7y2+PHj7/g/WNjY32+NhHZg8GGiMJW48aNER8fj9OnT2PmzJlo3Lix3V0ioiBxKIqILKeWW587d87WfkRGRuKqq64CAHz44Ye29oWIjMFgQ0SWS01NBQBs377d5p4A06dPR0xMDP74xz9izpw5lYanlG3btuHTTz+1oXdE5C8GGyKy3M033wwAuO2223DzzTfj7rvvxt13341du3ZZ3pfevXuX7y48YcIEpKenY8SIEbjttttw7bXXIi0tDd26dWNFhyhEcI4NEVnu/vvvx8mTJzF37lx8+eWX5SuVbrvttmqPUjDbmDFj0K9fPzz33HNYvHgxvvvuO5SVlaFp06Zo164dJk+ejFtuucXyfhGR/3ikAhEREbkGh6KIiIjINRhsiIiIyDUYbIiIiMg1GGyIiIjINRhsiIiIyDUYbIiIiMg1GGyIiIjINRhsiIiIyDUYbIiIiMg1GGyIiIjINRhsiIiIyDUYbIiIiMg1/j+8VTEEmSL17gAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1195,7 +1167,7 @@
     },
     {
      "data": {
-      "image/png": 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x6rofZXC5XHC5XMVvZ2dn+70uClBmGF8DeILN6dPSwhQWZmw9ZF9z5kiocTiA2bOBiRPlHAAeeAB48EFg3jxg7FiZadijh5HVEgEwWVdUSW63G5MnT0bPnj3Rtm3bcj8vNTUVMTExxY/EQH9SI/8xw4woAKhXzxNmTp40thayrz17gCeflPO//x2YNMkTagBp9XznHeD224GLF4HRo2UvNiKDmTbYTJw4ETt27MCiRYsq/Lxp06bB6XQWP46qT25kP2ZYwwYoPQZIHexMpCe3Gxg/HnC5gCFDZNBwWYKCgHffBZo1kxbRF17Qt06iMpgy2EyaNAmff/45Vq9ejQR108ByhIeHIzo6utSDbMosLTYAx9mQsd58E1i/HqhVC3jjjdItNZeLjgZee03OZ88Gdu/Wp0aicpgq2CiKgkmTJmHp0qVYtWoVmjZtanRJZCZmGWMDMNiQcfLzPS0vL75YtRbOIUOAm24CLl0CHnvMv/URVcJUwWbixImYP38+Fi5ciKioKJw6dQqnTp1Cfn6+0aWRGZilKwpgsCHjvPWWjO1KTAQefrjqX/fyyzLu5quvgA0b/FcfUSVMFWzmzp0Lp9OJPn36ID4+vvjx4YcfGl0aBbpLlzwDcdliQ1S2/HxZeA8A/vzn6s3Ia9FCZkcBMtiYyCCmmu6tKIrRJZBZnTghAyJDQ4H69Y2upnIMNmSE99/3tNbcd1/1v/6JJ2Sm1CefyFib1q21r5GoEqZqsSHymjq+JiFBZnIEOgYb0puieAYBT5ni3fpJrVsDt9wi5y+9pF1tRNVggr/wRBow0/gaoHSwYUsl6WH9ellJODISGDfO++v86U9yXLgQOH9ek9KIqoPBhuzBTFO9AaBRIzm6XMC5c8bWQvbw+utyHDMGqF3b++t07w60awcUFEi4IdIZgw3Zg9mCTXg4EBcn5+yOIn/LzAQ+/ljOJ0zw7VoOhyzuB8h4GyKdMdiQPZhpDRsVx9mQXhYtkm0RunQBOnb0/XpjxsgYnW3bgK1bfb8eUTUw2JA9mG2MDcBgQ/qZP1+O6nRtX9WtC9x2m5yz1YZ0xmBD9mC2riiA+0WRPvbsAbZsAYKDgTvv1O666gDk//1P1pEi0gmDDVlffj5w5oycmynYqAOIucM3+dOCBXIcPFjbNZ7695ed6rOygNWrtbsuUSUYbMj61NaaWrV8m+2hN7XF5sQJY+sg61IUT7C5+25trx0SAtxxh5wvWqTttYkqwGBD1leyG6qiXYoDjdpiwzE25C/btgEHDgA1agA336z99e+6S45LlgCFhdpfn6gMDDZkfWYcXwN4gg1bbMhfliyR49ChQM2a2l//+uvl9/jXX4EVK7S/PlEZGGzI+swebE6flqm4RFpTg406g0lrwcGe7qilS/1zD6LLMNiQ9alr2JhpqjcgAy9DQ2UcxKlTRldDVrNrlzxCQ4Fhw/x3H3XvqM8/B4qK/Hcfot8w2JD1mbXFJigIiI+Xc3ZHkdbUFpQBA4CYGP/dp1cvuX5WFrBpk//uQ/QbBhuyPrMGG4DjbMh/1GDjr24oVWgocOONcv7pp/69FxEYbMjqFMWc2ymoODOK/OHUKVmUDwBuusn/9xs+XI4MNqQDBhuyNqcTyM2VczMGG65lQ/7w1Vdy7NwZaNjQ//cbMkTWtfn5Z2DfPv/fj2yNwYasTe2GqltX1uowG3ZFkT988YUc1S4if6tTB+jdW84/+0yfe5JtMdiQtZl5fA3AYEPau3gR+PprOdcr2ACeBQDZHUV+xmBD1mbGXb1L4g7fpLUNG4DsbCAuDujSRb/7quNsvv0WOHdOv/uS7TDYkLWZeeAwwBYb0t7y5XIcMkSWFNBLs2bA1VfLWjbffKPffcl2GGzI2qzSFfXrr8CFC4aWQhah9/iakgYPliO3VyA/YrAhazN7sImO9gx6ZqsN+erwYWDnTmmpGTRI//uXDDaKov/9yRYYbMjazD7GxuHglG/SzpdfyrFHDyA2Vv/79+4NhIfLmLFdu/S/P9kCgw1Zl9tt/hYbgONsSDvqbKghQ4y5f2SkZ9o3u6PITxhsyLqysoDCQmn1UMOBGTHYkBaKioDVq+V8wADj6uA4G/IzBhuyLrW1Jj5e9qsxK075Ji1s2yaD0KOjgU6djKtDDTZr1wL5+cbVQZbFYEPWZfbxNSq22JAWVq6UY58+sr2BUa65RsJ6QQGwfr1xdZBleRVsDh8+DKfTWfz2ypUr8cc//hGzZs2Cy+XSrDgin5h9DRsVgw1pQQ02/fsbW4fD4ZmRxe4o8gOvgs3IkSORl5cHAEhPT8edd96J5ORk7Ny5Ew899JCmBRJ5zQoDhwHu8E2+c7k8rSNGBxuA42zIr7xqjywoKECj3/7Y/ve//8WDDz6Ixx9/HIqiICUlRdMCibymttiYvSuq5HRvRZFXvETV8f33Mp6lYUNZ/ddoarjasQPIzAQaNDC2HrIUr1ps3G433G43AOCbb77BgN9G2Dv4B5cCiVXG2MTHyzE/HyjRBUxUZWo3VL9+gRGM69UDOnSQ81WrDC2FrMerYDNq1CgMHDgQo0aNQlBQEPr27QsAOHDgAKKiojQtkMhrVhljExkJ1Kkj5xxnQ94IlPE1Jam1qLURacSrYPPMM89gxowZuOuuu7BhwwYE/baR2qVLlzBnzhxNCyTySmEhcPKknJu9xQbglG/yXnY2sGmTnDPYkA14Peeve/fuV7zvqquu8qkYIs2o41HCw4G4OKOr8V2jRjIegS02VF3r1snifM2bA8nJRlfj0auXTDs/dAg4cEB2/ybSQLVabNLT03Hs2LEr3p+WloYFCxZoVhSRz0p2QwXCmAJfcco3eSsQu6EAoFYtoFs3Oec4G9JQtYLN73//e/zyyy8AgJycnOL3h4WF4YUXXtC2snKsW7cOw4cPR6NGjeBwOLBs2TJd7ksmY5UZUSpO+SZvBWqwAdgdRX5RrWCzf/9+NPutubBx48Y4cOAAAKBp06Y4oj6R+FleXh7at2+P1157TZf7kUlZLdhwh2/yxunTwPbtcv7bJI+AogabVauk65hIA9UaYxMUFASXy4VLly4hPz8f58+fBwCcOXNGt9lQQ4cOxdChQ6v8+S6Xq9RqyNnZ2f4oiwKNVRbnU7EriryhdvG0bx+YY82uuw6oUUMC2I4dQLt2RldEFlCtFpsOHTpgxowZeO655xAfH4/U1FRs374df/nLX8ocTBwIUlNTERMTU/xItMoTHVXMai027IoibwRyNxQAhIXJIGKA3VGkmWoFm3/84x/47rvvMH/+fCxduhROpxPt27fHtm3bMHPmTH/V6JNp06bB6XQWP46qr+TJ2qwabE6eBH5bHJOoUoEebIDS3VFEGqhWV1SXLl2wf//+4rfT0tJw9uxZ1K1bV/PCtBIeHo7w8HCjyyC9Wa0rqmFDmd1VVARkZXEJeqrcwYPyCAkBevc2upry9esnx7VrgUuXjN15nCzBqwX6SgrkUEM25XR6th6wSrAJCfGEGY6zoapQW2u6dZOp1YGqQwdZWTs7G9iyxehqyAJ8DjZEAUdtrYmNDew/6NXFcTZUHWbohgKA4GDPjC2OsyENeB1szp8/j3PnzgEAsrKysGTJEuzcuVOzwsqTm5uLjIwMZGRkAAAOHjyIjIwM3aabkwlYZY+oy3HKN1WV222eYANwPRvSlFfB5u2330anTp3QuXNnzJ07FyNGjMDKlStx11134e2339a6xlK2bNmCa6+9Ftdeey0AYMqUKbj22mvx7LPP+vW+ZCJW2dX7cpzyTVW1Y4eMxapRQ6ZUBzo12GzYILvYE/nAq1Fas2fPxs6dO5Gfn4+kpCQcPHgQcXFxcDqduOGGGzB+/Hit6yzWp08fKFzIiSpitRlRKnZFUVWpLR+9esmU6kB31VXSInn8uIQbM7QyUcDyqsUmJCQEkZGRiI2NRYsWLRD328JPMTExcFhhXx4yN6t2RbHFhqrKTN1QgMz4U2dHsTuKfORVsAkODkZBQQEAYO3atcXvz83N1aYqIl9YtSuKY2yoKi5elKnTgHmCDcBxNqQZr4LNN998U7w2TExMTPH7L1y4gDfffFObyoi8ZfWuKAYbqsjmzUBurswK7NDB6GqqTg02W7YAv/5qaClkbl4Fm/K6nOrXr48uXbr4XBSR19xu4NgxObdqV9Tp00BhobG1UOBSWzz69gWCTLSiR0KCjLVxuz0tTkRe8Pq3Pj8/H8fLGMSox5RvonJlZkpTfFCQJwhYRb16QGionJ86ZWwtFLjMNr6mJG6vQBrwKtgsXrwYLVu2xLBhw5CSkoIffvih+GP33HOPZsURVZvaDdW4sfWWZnc42B1FFbtwAfj+ezk3c7DhOBvygVfBZsaMGUhPT0dGRgbee+893H///Vi4cCEAcCo2GcuqM6JUnPJNFVm/XropExKAli2Nrqb6+vSRAL9zJ1slyWtevaS9ePEiGvy2b02nTp2wbt06jBgxAvv27eN0bzKWVQcOq9hiQxUp2Q1lxr/FdesC114LbN0q38uYMUZXRCbkVYtN/fr18dNPPxW/HRsbi7S0NOzatavU+4l0Z9Wp3ipO+aaKfPONHM3YDaUaOFCOX39tbB1kWl4Fm//+97/FLTaA7BX18ccf44MPPii1rg2R7uzSFcVgQ5c7cwbYtk3OBwwwthZfqMEmLQ3g0AbyQrW6ol544YUy379//34sWbIEo0ePRs+ePTUpjMgrdumK4hgbutyqVRIE2rYF4uONrsZ7PXsCkZHAyZMy1qZtW6MrIpOpVrBZunRpqbeLiopw9OhRZGdnY8aMGZoWRuQVq3dFscWGypOWJke1xcOsIiKA3r2BFSvke2KwoWqqVrDZpjZzlnDp0iVMnjyZ69eQ8fLzZfE6wLpdURxjQ2VRFE+wMXM3lGrgQAk2X38NPPaY0dWQyfi80EdISAgmT56MlJQULeoh8p664nCNGrKcvBWpLTZOJ5CXB9SsaWw9FBj27wcOH5YFHG+4wehqfDdokBzXrgVcLuC3LXyIqkKT9bYPHz6Mpk2banEpIu8dOiTHJk3MOdW1KqKiPGGGrTakUltrevSwRtht2xZo2FBaYTdsMLoaMplqtdjMnj37ivdlZmbi3XffxfDhw0t9/I9//KPv1RFVhxpskpMNLcOvHA7pjtq7V4KNGRdhI+1ZqRsKkN/zAQOA+fOlO6pvX6MrIhOpVrB5+eWXy3x/REQE0tLSkPbbfy6Hw8FgQ/o7fFiOTZoYWobfNWokwYYzowgAioo8eyuZfeBwSYMGSbBJSwNSU42uhkykWsHm4MGD/qqDyHclu6KsjDOjqKQtW2TMVe3aQOfORlejHbX1aetWWaOnXj1j6yHTMNGe9kSVUFtsrNwVBTDYUGlqN1S/fkBwsLG1aCk+XsbaKAo3xaRqYbAh67BLiw2nfFNJ6jYKVhlfU5I6O4rbK1A1MNiQNRQWesacWD3YcPVhUuXmemYNWWl8jUr9nlas4PYKVGUMNmQNx47JH76ICKB+faOr8S92RZFq7Vrg4kUJ882bG12N9m64QbZXOH4c4AbLVEUMNmQNJad6W3UNG1XJrii+irW35cvlOGSINX/vIyM9O5V//rmxtZBpMNiQNdhlfA3g2eCwoAA4f97YWsg4igJ88YWcDxtmbC3+dNNNclS/V6JKMNiQNdhlRhQg3W3qlhHsjrKvXbvk9z483NoL2N14oxw3bpRp30SVYLAha7BTiw3AcTbk6Ybq08ca2yiUJzERaN9eWqi+/NLoasgEGGzIGuwWbDjlm+zQDaVSv0d2R1EVMNiQNdipKwrglG+7czqB9evlXO2qsTJ1nM1XX8ksMKIKMNiQ+V26JNO9Afu02LAryt6++UZ+71u1suY078t17SpbKjid3O2bKsVgQ+Z3/LhsBBgWBjRsaHQ1+mCwsTe1S8YOrTWAbBUxZIicf/qpsbVQwGOwIfNTx9ckJQFBNvmVVsfYsCvKftxuzyBauwQbALj1Vjl+/DHXb6IK2eRZgCzNbgOHAbbY2NnGjcCpU0B0NNCrl9HV6GfoUKBGDRlPl55udDUUwBhsyPzUgcN2DDanTkk3HNnHkiVyHD5c1rCxixo1PLOjFi82thYKaAw2ZH4lt1OwiwYNpNutqAjIyjK6GtKLokhXDADcdpuxtRjhjjvkuHgxu6OoXAw2ZH527IoKCZFwA3CcjZ1s2ya/75GRnsG0dnLjjbLy9v79wI8/Gl0NBShTBpvXXnsNTZo0QUREBLp164ZNmzYZXZLvnE5g3Tpg9mzgoYekP7ltWxkk2qCBTHVs3lx2u/3jH4FFi+RryJ4tNgDH2ZTnwgXg3XeBESOA1q2BZs1kLMpzzwG7dxtdnW/Ubih1vInd1Kol3zvA7igqn2IyixYtUsLCwpR3331X2blzp/LAAw8otWvXVjIzM6v09U6nUwGgOJ1OP1dajqIiRdm3T1GWLlWU559XlBEjFKVpU0WRhtXqPcLDFeXOOxVl2zZjvpdAUFioKMHB8vM4ftzoavQ1fLh83//+t9GVBI6lSxWlUaOK/9+MGCH/B82odWv5HhYsMLoS4yxYID+Dq65SFLfb6GpIR1V9/nYoirk6Krt164YuXbpgzpw5AAC3243ExEQ88sgjmDp1aqVfn52djZiYGDidTkRHR2tXWFERkJ8P5OXJK8a8PCAzEzh6VB6HDgE7dgA7d8rHypKUBHToALRrBzRtKnuk1KsHhIbKeIrz5+U6mzcDX39d+tXnnXdKa4/aPWEX+/YBLVtK8/SFC4DDYXRF+pkwAfj3v4FnngFeeMHoaozldgPPP+/5OSQlAQ88APToId02u3ZJa8eXX8rnhoXJ/5cHHzTP78yuXcDVV8vfg6wsICbG6IqMkZ0NxMUBhYXSHZWSYnRFVJLbDeTkyP+7sDBNL13V5+8QTe/qZ4WFhUhPT8e0adOK3xcUFIQBAwbg+++/L/NrXC4XXC5X8dvZ2dn+KW7gQGD16qp9bng40KaNBJhrr5Uw0769Z8fmilx/PXD33fLac9s2YNYs6Zb66CNg5UrgzTftNajwwAE5NmtmnicorbArSigK8Ic/AG+8IW8/9hjw179K2FV17w78/vfywmLyZFm59+GHZRXbt97S/A+wX6iDhgcOtG+oAWSa+403AsuWAfPnA3//u9EVaefSJXmxduSIvCA+f15eNBcVSddjbKy82G3WTB5G/t6eOQOsWCHPQzt2AHv3AufOSfBUFGDNGhk6YQBTBZszZ86gqKgIDS5rlWjQoAF2l9N3npqaiueff97/xZXs765RQ3bbrVtXXjkmJspRDTMtWsjgT184HEDHjsDChcCf/gTcdx+QkQHcfjswYwbwf/9njyf6/fvlaIdl5S/HYCNeeklCjcMBvPOO/F8ozzXXSGvnrFnA1KnAf/4jrR+LFwf2mBVFkRcwgL1euJRn7FhPsPnrX33/e2oURQG2b5fQun498MMP5bfoXy4oSCZMtGkjW0506wZ06VK1F8je1rpzJ/D558BnnwHff1/xzDQDx4Ca9Leh6qZNm4YpU6YUv52dnY3ExETtb/TBB7Lsd0SE/qvfdugAbNokAeeVV4Cnn5aZMq+9Zv1wY+dgwx2+ZWuBp56S81deqTjUqBwO4MknpQtjxAjpnrrpJmD58tKtPIHkp5/kSSUsTF682N2wYfLC8eRJaX0z2wyxX3+VbuT//Ee6GEuqWVOGIiQkSOtMSIg8p1y4IC0ip09Lq05urrRYHzhQetfzli2lhbJnT+mKvfpq75+TXC5g7VpPmFEnaqjat5eB+e3ayX3i4oDataVF0cD/S6YKNvXq1UNwcDAyMzNLvT8zMxMNy9kjKDw8HOF6LGIVFeX/e1QkNBR4+WX5pZ40CZg7V16BvvSStcNNya4ou7H7Dt/nz8s4GkDGG/3xj9X7+sGDgbQ06dZYvVq6eD/8UF6gBJr58+U4fLg8cdhdWBgwejQwZ46EA7MEm8xM4B//kFCTkyPvCw+X38EhQySItGlT+e+gosjinHv2SIvPDz/IY98+4Jdf5PGf/8jn1q4tQadHDwk7XbtKeCpLfr4ErW+/lf8ba9dKgFKFhwP9+snv4bBh0hMRiHQZyqyhrl27KpMmTSp+u6ioSGncuLGSmppapa83fFaUHt5+2zMD5KWXjK7Gv1JS5Pv84gujK9FfVpbn37mgwOhq9DdunGd2zIUL3l9n1SpFCQuTa/3hD4E30+bSJc9Mr2XLjK4mcGzaJD+TiAhFOXfO6GoqVlioKLNmKUpUlOf/bNu2ivLOO4ry66/a3efMGUVZvlxRnn5aUfr1U5SaNa+cFRgcrCgtWijKDTcoyrBhMruyd29Fad5cURyOKz+/QQNFuf9++d3LzdWuVi9U9fnbdMFm0aJFSnh4uDJv3jzl559/Vh588EGldu3ayqlTp6r09bYINoqiKC+/LL+UQUGKkpZmdDX+4XZ7/uPu3m10Nfpzuz1PyIcOGV2Nvtaske/b4VCU777z/XoffeT5o17FF0m6+eYbqSs2VlFcLqOrCRxut6K0ayc/m5dfNrqa8m3YoCht2niCQufO8kJMjwB98aKibNmiKLNnK8qoUYqSmFj5MiKxsYoyaJCi/P3vipKeLkuUBAjLBhtFUZR//etfSlJSkhIWFqZ07dpV2bhxY5W/1jbBxu1WlN//3vOLeviw0RVpLzPT8+RmxxYLRVGUJk3kZ6DFk7tZuN2K0q2bfN8PP6zddWfP9vw+LV+u3XV9NXq09t+rVcydKz+bli0D6glYURRppXnmGXlxCShKXJy0phtd59GjirJ2raJ88IGivPuuorz5pqJ8+KG0XJ48GXgtliVYdh0bX/ltHZtAVFAA9O4t69706yd9pnoPbPan77+XfuPERJkeaUc9e8qU5f/9z7OPjtUtWSIDaGvUkMHj5Yyv88pDD8mSCbVry/+bFi20u7Y3zpyRQeKFhbKjdceOxtYTaHJz5eeTnS2z3QYONLoiceQIMHKkTOoAgDFjgH/9C6hTx9i6TK6qz98WepajK0REAAsWyBPAqlXA668bXZG27DwjSmW3Kd9FRcCf/yznU6ZoG2oAWbSve3eZtXLrrZ4BnkZ5/30JNZ06MdSUpVYt4N575fy3RVsNt3q1/Htt2iQB+YMPZPA3Q41uGGysrmVLzwJWf/rTldP1zMzOM6JU6pRvu8yM+uQTWXG7Th3giSe0v354uKwpEh8v06vHjzduF2lFkdYjQFqSqGx/+IMcP/vsyqnTeps9W1qNzpyRxVe3bQPuusvYmmyIwcYOJkyQFSDz8+VVrlWwxcZ+LTb/+IccJ0zw3+q78fESbkJDZUXvV17xz30qs2qVrOZaqxafHCvSurWsR6QowMyZxtTgdsuK148+Kq2K99wDfPedLKBHumOwsYOgIGmmDQ4Gli6VZbCtgMHGXsHm++9lPFFYmKzV5E/du8u6UIAs5rdunX/vV5ZZs+Q4dqzx62QFOnWbnQUL9G+VLiiQ4KkG4L/9TboQIyP1rYOKMdjYRdu2ngXMHn1U9iQxO3ZF2SvY/POfchwzRlpV/O0Pf5BF+4qKZJNZPX/G27cDX30lL0qs1MrqL126SBdQURGQmqrffc+dAwYNksH7oaESrP70J2svimoCDDZ2Mn26LNG9Zw8wb57R1fjmwgVZTh2wd4uNXcbYnDolrY2ANPnrweGQPahSUmTF2JEjZSCvHtQut9tus/fvd3U884wc335bxkf52+HDsinxt9/KxpwrVgC/+53/70uVYrCxk5gYz4yS556TMTdmpbbW1K7tv03fzEBtscnJMX4Gjz/95z/yarx7d9mXRi81ash4m5gY6QZ78kn/3/PwYdncFvDPAGmr6tVLxtq43f7/d8rIkN/FXbvkxcX69UDfvv69J1UZg43dPPywrPuibpJpVuyGElFRMrgU8LRgWY2iyKtwALj/fv3v36IF8N//yvns2Z7Q4S/PPQdcvChrT3Xr5t97Wc3f/iZdQl9+KQ9/WLFC1gc7eVK6+Ddu1DdsU6UYbOwmIkL+cAIyDfzCBUPL8RoHDntYfTPMb7+VTf1q1QJGjTKmhuHDgaeflvMHHpAxMP7w88+ezQv/+lf/3MPKWrYEHnlEzh98UNYj0tKbb8rmjzk50kKzfr3swk0BhcHGju65R6YhZmUB775rdDXeYbDxsPo4m/fek+OoUZ7WKSM895wMFL1wQca+OJ3a3+PPf5aulBEj2FrjrRdekFa2Y8eqv+N7edxu4KmnZD2hoiKZqfbVV/5bcoB8wmBjR6GhMnIfkFYbvQZEamnvXjledZWxdQQCKwebggLZQgHwrDBrlOBg6YZKTgb27ZN63G7trv/FF8CyZXKfGTO0u67d1KwprV5BQdKF+M47vl3P6ZSB4+pCp889J5MvwsJ8rZT8hMHGru67D2jQADh61P9jBvxhzx45MtjImClA/i2t5quvZB+ghATZF8todesCixfLk9onn3i6p3yVm+tZQfexx4Crr9bmunbVvTvw7LNy/tBDso+UNzIygM6dJVyHhsr6NNOnczp3gGOwsauICM/6GK+8Ytyy8d7Iz/dseslgY+1gs2iRHEeNCpwNXDt3lmnggKyZoi6k54v/+z/5nU5O9oyBI988+6yseVRUJJumLl9e9a+9dEmm3F93nbTOJSXJWK+xY/1XL2kmQP5SkCHGj5fVMX/8Uf7TmsW+fXKsU0fW5bE7qwabvDzZ/wcIvC0Fxo3zLN//5JOeWVve+Ogj2fkZAP79b+lKId85HNINNXCgtIgNHy6zpi5eLP9rFAVIS5PxTU88AbhcMlh42zaOeTIRBhs7i42VgcSATGM1i5Lja9gkbN1g89lnMlC3eXPZLTnQPPWUPACZgePN8gk7dgC//73nekOGaFcfyaamn38uywS43cDUqTJFe+5cWS+oqEiCzp49Ei6vu04GiG/dKmtkvf028Omn9l4ry4RCjC6ADPbIIzKFcelSaQpPSjK6ospxfE1parA5c0a66ayyR4260vDIkYEbYFNTpWVpzhzZv+rIEeDFF4GQKvxp3bED6N9fvr5vXw4Y9pewMOCtt4AePWRPqb17PeOZABmsXVTkeTs8XNb7mjZNxiGS6bDFxu7atpWFwNxu4PXXja6majgjqrQ6dWSFXECmuFqBy+VZYG3ECGNrqYjDIa2df/mLvP33vwM33OBZjqA869ZJmDl9GujQQfYaqkoYIu84HNIy9ssv8m/Uo4cnLBcVyf+fHj1k49NDh2TcIUONaTHYkGeth7feMsc2C2qwadXK2DoChcNhve6o1atlEbT4eBmsG8gcDpkdtWiR7Bm0YQPQpo20CmRklB6Yv2ePdFv16SMtbJ06AStXymwr8r/oaBkT9d13spRAZqYsk5CTI++bPBlo2NDoKslHDDYE3HSTLNh37pwMZAx0bLG5ktWCzSefyPHmmwNnNlRlRo2SgfiDBsm4jblzgWuvlVf+HTrIlPXWreUFhKLIAOQ1azh+wyhhYUD9+rJyt1l+x6hK+K9J0sc8fryc+7qYlb+dPSsPQFYXJWGlYON2y4BNALj1VkNLqbYmTWQvoTVrgFtukS6OrCwJPMePS3fTsGHSFfXee8aupExkUezUJTFunKz78O230iISqK0hamtNQgKnxZZkpWCTng6cOCEbfJp1x+QbbpBHQQHw00/SGlq7NnDNNfJ9EZHfsMWGROPGwNChch7IrTacEVU2dTabFYLNV1/JceBAmaFiZhERQNeuMo37uusYaoh0wGBDHvffL8f33694ESsj/fyzHLnkfGlWarFRgw3XdCEiLzDYkMdNN8lgusxM2ZAvEDHYlM0qweb8eWDjRjkfPNjYWojIlBhsyCM01LODcqB2R+3cKcdrrjG2jkCjBhunUzaNNKuVK2XwcJs25lgskogCDoMNlaZ2Ry1fDpw6ZWwtl8vLk8WzALbYXK5WLRmcCpi71WbFCjmytYaIvMRgQ6W1aiWbvbndnp2VA8Xu3XKMi+Pml2Uxe3eUonB8DRH5jMGGrnT33XKcP9/YOi7H8TUVM3uw2bVLtoSIiAB69za6GiIyKQYbutKoUbKQWHq6PNkECgabipk92KjdUDfcYJ2NPIlIdww2dKW4OE9XQCC12qgDhxlsymb2YKN2Q3F8DRH5gMGGyqZ2Ry1YIONtAoHaYsMZUWUzc7ApKJBtBgAGGyLyCYMNle3mm2WV1MOHgfXrja5Gdh0/cEDO2WJTNjMHm40bJdw0bChTvYmIvMRgQ2WLjATuuEPOA6E7atcumTUTGyuLCNKVSgYbRTG2lupavVqOffsCDoextRCRqTHYUPnU7qiPPpJX00b68Uc5tm/PJ77yJCTIMT9fNl00k5LBhojIBww2VL4+feTJ0uk0fouFjAw5tm9vaBkBLSICaNBAzo8cMbaW6rhwwbONAoMNEfmIwYbKFxQE/O53cm50d5TaYtOhg6FlBLwmTeR48KChZVTLhg2y6WpCAtC8udHVEJHJmSrYvPjii+jRowdq1KiB2ury8eRfY8bIcfly2aDQCIrCFpuqUoONuvWEGXB8DRFpyFTBprCwECNHjsSECROMLsU+UlKAtm2BwkJg8WJjajhyRLrDQkM5I6oyZg42ffoYWgYRWYOpgs3zzz+Pxx57DO3atTO6FHtRW20WLjTm/mo3VJs2QFiYMTWYhdmCTW4usHmznHN8DRFpwFTBxhsulwvZ2dmlHlRNo0fLce1a2ctHb2o3FMfXVK5pUzmaJdisXw9cugQkJ3tqJyLygeWDTWpqKmJiYoofiepaH1R1yclAr14y1uWDD/S/f8mp3lSxki02ZljLhtO8iUhjhgebqVOnwuFwVPjYvXu319efNm0anE5n8eOoGVdlDQRqd9SCBfrfmy02VZeUJMecHOMGe1eHuo0Cx9cQkUZCjC7g8ccfx7hx4yr8nGbNmnl9/fDwcISHh3v99fSbO+4AHnlEWk927tRvv6bz5z1bKbDFpnKRkbItwalT0moTG2t0ReXLz5cd5AFpESQi0oDhwSYuLg5xcXFGl0GVqVtXdvz+7DNptfnrX/W576ZNcmzRQmqgyjVp4gk2HTsaXU35Nm+W9Wvi4zm+hog0Y3hXVHUcOXIEGRkZOHLkCIqKipCRkYGMjAzk5uYaXZo9lJwdpdeO32qw6dZNn/tZgVlmRqmbq15/PdevISLNGN5iUx3PPvss3n///eK3r732WgDA6tWr0Yd99P43fDhQq5bs+L1hgzwh+dsPP8ixa1f/38sqzBZsevY0tg4ishRTtdjMmzcPiqJc8WCo0UmNGsBtt8m5HoOIFcUTbNhiU3VmCDZut4RjQJ+ATES2YapgQwFA7Y766CNZjdifDh0CzpyRRfk4I6rqzBBsdu6U1aRr1uSgcCLSFIMNVU+/frKD9LlzwNdf+/deamtNhw4AZ7ZVnRnWslG7obp3B0JM1SNORAGOwYaqJyQEuOsuOfd3dxTH13jHDGvZlBw4TESkIQYbqj61O+qTT+TJ0182bpQjx9dUj7qWDRC43VEcOExEfsJgQ9XXuTPQsqUssLZsmX/ukZsLbNki53xVX31qd9TBg4aWUaajR2XH9uBghlYi0hyDDVWfw+H/LRbUzRGbNPE8SVPVBfIA4u++k2OHDkBUlKGlEJH1MNiQd9Rgk5bmnx2/uTmibwI52HB8DRH5EYMNeadFC+CGG2Q9kvfe0/76a9bIkWsUeYfBhohsisGGvDd+vBzfeUfbLRaysz2bI7LFxjuBOsbG6QR++knOOXCYiPyAwYa8d/vtQO3assXCN99od91vvwWKioDmzYHERO2uayfNm8vxwAH99vWqio0bZW2dZs1k80siIo0x2JD3IiOBu++W87ff1u66q1bJkd1Q3ktOlllH+fnAyZNGV+PBbigi8jMGG/KN2h21bBmQleX79RQF+PRTOR80yPfr2VVoqIQbANi/39haSmKwISI/Y7Ah37RvL+vaXLwIlNh53Wu7dgH79sn+UEOH+n49O2vRQo779hlbh+riRc9q0gw2ROQnDDbkuwcflOPrr8vYGF988okc+/fnGie+UsfZBEqw2bZNusZiY4FWrYyuhogsisGGfDdmjDxZHTzoCSbeUr/+llt8r8vu1BabQOmKKrmNQhD/9BCRf/CvC/muRg3g4Yfl/OWXvb/OyZOerorhw32vy+4CrSuK42uISAcMNqSNiRNlwOr69cDmzd5dY+lSOXbtCjRqpF1tdlUy2CiKsbUoCoMNEemCwYa00agRcNddcj5jhnfXUAcfjx6tTU1217SpHLOzgbNnja3ll19k1lx4ONCpk7G1EJGlMdiQdv78Zxk78emnnp25q+rnn4FNm4CQEOB3v/NPfXYTGQkkJMi50d1R6saXXbpIuCEi8hMGG9JOq1aeBfumT6/e1772mhxvugmoX1/buuwsUMbZsBuKiHTCYEPaeuYZWfF2+XLPDt2V+fVXTzfUI4/4rTRbUqd8Gz0zisGGiHTCYEPaatECeOghOX/4YcDlqvxrXn4ZyMsD2rXjppdaU1tsfvnFuBpOnwb27pXzHj2Mq4OIbIHBhrT34otAw4byZDZzZsWfm5npmSL+7LOAw+H/+uxEXQhvzx7jatiwQY5t2wJ16hhXBxHZAoMNaa92beDVV+X8xRc9a9OU5fHHgZwcoGNH4LbbdCnPVlq3luPu3cZN+S65MB8RkZ8x2JB/jBwJ3HGH7A80YgRw4MCVnzNvHrBggbTS/PvfXI3WH5o3lzFPubnAiRPG1MDxNUSkIz6TkH84HMA770j3w8mT8qSmDiZWFOCNNzw7g0+fLtOASXthYUCzZnK+e7f+98/LA9LT5ZzBhoh0wGBD/hMdDaSlecJNv34y5iM5WQYWFxUBY8fKTCryH7U7yohxNj/8AFy6JOvpJCfrf38ish0GG/Kvhg1l8OiECbL43t69wNGjQM2awN/+Brz3Hrug/K3kOBu9ffutHHv14sBwItJFiNEFkA1ERQGvvy5bLWzeLAHnuusk3JD/GRls1q2TY+/e+t+biGyJwYb0ExsLDB5sdBX2Y1SwuXgR2LhRznv10vfeRGRb7AMgsjp1LZujR2Uwr162bgUuXJBA26aNfvclIltjsCGyurp1gXr15FxdAVgPajdUr14cR0VEuuFfGyI7ULujdu3S754lBw4TEemEwYbIDtSuoJ9/1ud+brdnYT4GGyLSEYMNkR20ayfH7dv1ud/PPwPnzwM1agDXXqvPPYmIwGBDZA96Bxt1fE2PHkBoqD73JCKCiYLNoUOHcP/996Np06aIjIxE8+bNMX36dBQWFhpdGlHgU4PNwYOy6ai/cXwNERnENOvY7N69G263G2+88QZatGiBHTt24IEHHkBeXh5mzZpldHlEga1uXaBRI9kIc+dOWSDRXxSFwYaIDGOaYDNkyBAMGTKk+O1mzZphz549mDt3boXBxuVyweVyFb+dnZ3t1zqJAla7dhJsfvrJv8Hm4EHg+HHpgurWzX/3ISIqg2m6osridDoRGxtb4eekpqYiJiam+JGYmKhTdUQBRq9xNmprTefOMniYiEhHpg02+/btw7/+9S889NBDFX7etGnT4HQ6ix9Hjx7VqUKiAKNXsFmzRo7shiIiAxgebKZOnQqHw1HhY/dle9wcP34cQ4YMwciRI/HAAw9UeP3w8HBER0eXehDZUkqKHLdvl3Ew/qAowMqVct6/v3/uQURUAYei+OsvXNVkZWXh7NmzFX5Os2bNEBYWBgA4ceIE+vTpg+uuuw7z5s1DUDWXas/OzkZMTAycTidDDtmLyyU7qhcVyRiYRo20v8fevbI3VViYZx0bIiINVPX52/DBw3FxcYiLi6vS5x4/fhx9+/ZFp06d8N5771U71BDZWni4hI6ffwa2bfNPsFFba3r0YKghIkOYJhkcP34cffr0QVJSEmbNmoWsrCycOnUKp06dMro0IvPo1EmOW7b45/rffCPHAQP8c30iokoY3mJTVWlpadi3bx/27duHhISEUh8zuDeNyDy6dAH++19g82btr11UBKxeLecMNkRkENO02IwbNw6KopT5IKIq6tJFjps3az+AeNs2GVcTHe1pGSIi0plpgg0RaaBDByAkBDh9GtB66QN1fE3fvnIPIiIDMNgQ2UlEhGc9G627o9TxNZzmTUQGYrAhspuS3VFaKSgA1q+Xc46vISIDMdgQ2U3nznLUMtisXy/hJj4eaN1au+sSEVUTgw2R3agtNlu2AG63Ntf84gs5Dh0KOBzaXJOIyAsMNkR2c801sgJxdjawc6fv11MU4LPP5Pymm3y/HhGRDxhsiOwmNBTo2VPO1Q0rfbF3L7B/v2yjwPE1RGQwBhsiO+rTR45aBJvPP/dcMyrK9+sREfmAwYbIjtRgs3at7+Ns1GDDbigiCgAMNkR21LmzbFJ59qxv42zOnQO+/VbOhw3TpjYiIh8w2BDZUWgocP31cu5Ld9SSJbJHVPv2QLNmmpRGROQLBhsiu+rbV46+BJsPP5TjqFE+l0NEpAUGGyK7UoPNypVAYWH1v/70aWDVKjlnsCGiAMFgQ2RXXboADRsCTiewenX1v/7jj2XgcefO7IYiooDBYENkV0FBwC23yPmSJdX/+kWL5MjWGiIKIAw2RHZ2221y/OQTGQRcVbt3A+vWSThisCGiAMJgQ2RnffoAtWsDmZnA999X/eveeEOOw4YBiYn+qIyIyCsMNkR2FhbmWVhv8eKqfc2FC8C8eXI+YYJfyiIi8haDDZHd3XmnHBcsAFyuyj//ww+BX38FmjYFBg/2a2lERNXFYENkd0OHAo0bA2fOAEuXVvy5bjfwj3/I+UMPyRgbIqIAwr9KRHYXEgKMHy/ns2YBilL+5378sWzBEBMjwYaIKMAw2BARMHGi7B2Vng58+WXZn1NQADz1lJxPniyDjomIAgyDDREBcXGegcBTppQ91uaFF4CDB6Xb6okn9K2PiKiKGGyISDz9NNCgAbBnD/Dkk6U/9vnnwMyZcv7qq0CtWvrXR0RUBQw2RCRq1wbefFPO//Uv4A9/ALZvB155BbjjDhl788ADwO23G1klEVGFHIpS0UhB68nOzkZMTAycTieio6ONLoco8Pzzn8Djj1/5/hEjZKp3aKj+NRGR7VX1+ZstNkRU2pQpwBdfAN27S5fT1VcDc+bIAn4MNUQU4EKMLoCIAtCNN8qDiMhk2GJDRERElsFgQ0RERJbBYENERESWwWBDRERElsFgQ0RERJbBYENERESWwWBDRERElmGqYHPzzTcjKSkJERERiI+Pxz333IMTJ04YXRYREREFCFMFm759++Kjjz7Cnj178PHHH2P//v244447jC6LiIiIAoSp94r69NNPceutt8LlciG0iku9c68oIiIi86nq87dpt1Q4d+4cFixYgB49elQYalwuF1wuV/Hb2dnZepRHREREBjBVVxQAPPXUU6hZsybq1q2LI0eO4JNPPqnw81NTUxETE1P8SExM1KlSIiIi0pvhwWbq1KlwOBwVPnbv3l38+U8++SS2bduGr7/+GsHBwRg7diwq6k2bNm0anE5n8ePo0aN6fFtERERkAMPH2GRlZeHs2bMVfk6zZs0QFhZ2xfuPHTuGxMREbNiwAd27d6/S/TjGhoiIyHxMM8YmLi4OcXFxXn2t2+0GgFJjaCqj5jiOtSEiIjIP9Xm7svYYw4NNVf3www/YvHkzrr/+etSpUwf79+/HM888g+bNm1e5tQYAcnJyAIBjbYiIiEwoJycHMTEx5X7c8K6oqtq+fTseffRR/Pjjj8jLy0N8fDyGDBmCp59+Go0bN67yddxuN06cOIGoqCg4HA7N6svOzkZiYiKOHj3KLi4/489aH/w564M/Z33w56wPf/6cFUVBTk4OGjVqhKCg8ocIm6bFpl27dli1apXP1wkKCkJCQoIGFZUtOjqa/2l0wp+1Pvhz1gd/zvrgz1kf/vo5V9RSozJ8VhQRERGRVhhsiIiIyDIYbDQSHh6O6dOnIzw83OhSLI8/a33w56wP/pz1wZ+zPgLh52yawcNERERElWGLDREREVkGgw0RERFZBoMNERERWQaDDREREVkGg41GXnvtNTRp0gQRERHo1q0bNm3aZHRJlrNu3ToMHz4cjRo1gsPhwLJly4wuyXJSU1PRpUsXREVFoX79+rj11luxZ88eo8uypLlz5yIlJaV4IbPu3bvjyy+/NLosS5s5cyYcDgcmT55sdCmW89xzz8HhcJR6tG7d2pBaGGw08OGHH2LKlCmYPn06tm7divbt22Pw4ME4ffq00aVZSl5eHtq3b4/XXnvN6FIsa+3atZg4cSI2btyItLQ0XLx4EYMGDUJeXp7RpVlOQkICZs6cifT0dGzZsgX9+vXDLbfcgp07dxpdmiVt3rwZb7zxBlJSUowuxbKuueYanDx5svixfv16Q+rgdG8NdOvWDV26dMGcOXMAyH5UiYmJeOSRRzB16lSDq7Mmh8OBpUuX4tZbbzW6FEvLyspC/fr1sXbtWvTu3dvociwvNjYWL730Eu6//36jS7GU3NxcdOzYEa+//jpmzJiBDh064JVXXjG6LEt57rnnsGzZMmRkZBhdCltsfFVYWIj09HQMGDCg+H1BQUEYMGAAvv/+ewMrI/Kd0+kEIE+45D9FRUVYtGgR8vLy0L17d6PLsZyJEydi2LBhpf5Ok/Z++eUXNGrUCM2aNcOYMWNw5MgRQ+owzSaYgerMmTMoKipCgwYNSr2/QYMG2L17t0FVEfnO7XZj8uTJ6NmzJ9q2bWt0OZa0fft2dO/eHQUFBahVqxaWLl2Kq6++2uiyLGXRokXYunUrNm/ebHQpltatWzfMmzcPrVq1wsmTJ/H888+jV69e2LFjB6KionSthcGGiMo0ceJE7Nixw7B+cjto1aoVMjIy4HQ6sXjxYtx7771Yu3Ytw41Gjh49ikcffRRpaWmIiIgwuhxLGzp0aPF5SkoKunXrhuTkZHz00Ue6d60y2PioXr16CA4ORmZmZqn3Z2ZmomHDhgZVReSbSZMm4fPPP8e6deuQkJBgdDmWFRYWhhYtWgAAOnXqhM2bN+PVV1/FG2+8YXBl1pCeno7Tp0+jY8eOxe8rKirCunXrMGfOHLhcLgQHBxtYoXXVrl0bV111Ffbt26f7vTnGxkdhYWHo1KkTVq5cWfw+t9uNlStXsq+cTEdRFEyaNAlLly7FqlWr0LRpU6NLshW32w2Xy2V0GZbRv39/bN++HRkZGcWPzp07Y8yYMcjIyGCo8aPc3Fzs378f8fHxut+bLTYamDJlCu6991507twZXbt2xSuvvIK8vDzcd999RpdmKbm5uaXS/8GDB5GRkYHY2FgkJSUZWJl1TJw4EQsXLsQnn3yCqKgonDp1CgAQExODyMhIg6uzlmnTpmHo0KFISkpCTk4OFi5ciDVr1mDFihVGl2YZUVFRV4wPq1mzJurWrctxYxp74oknMHz4cCQnJ+PEiROYPn06goODMXr0aN1rYbDRwKhRo5CVlYVnn30Wp06dQocOHfDVV19dMaCYfLNlyxb07du3+O0pU6YAAO69917MmzfPoKqsZe7cuQCAPn36lHr/e++9h3HjxulfkIWdPn0aY8eOxcmTJxETE4OUlBSsWLECAwcONLo0omo7duwYRo8ejbNnzyIuLg7XX389Nm7ciLi4ON1r4To2REREZBkcY0NERESWwWBDRERElsFgQ0RERJbBYENERESWwWBDRERElsFgQ0RERJbBYENERESWwWBDRERElsFgQ0QB5dChQ3A4HGjSpInRpRCRCTHYEJHumjRpAofDgUOHDhldChFZDPeKIqKA0rhxY+zatQuhoaFGl0JEJsRgQ0QBJTQ0FK1btza6DCIyKXZFEZFu5s2bB4fDgcOHDwMAmjZtCofDUfxYs2ZNhWNs1M8DgPnz56Nr166oVasW4uLiMHr0aBw5cgQAoCgK5syZgw4dOqBmzZqoV68exo0bh9OnT5db2969e/HQQw+hefPmiIiIQExMDHr37o358+dr/4MgIr/h7t5EpJv169fj7bffxuLFi5GXl4fbb78dtWrVKv741KlTERERgaZNmyI5OfmKMThqqJk6dSpmzZqF3r17IzY2Fps2bcKRI0eQmJiIH3/8EQ8//DA+/fRT9OnTB5GRkfjuu+9w+vRppKSkYPPmzQgLCyt13f/9738YO3YsCgoK0Lp1a7Rp0wZOpxM//PAD8vLycN999+Hdd9/1+8+HiDSgEBHpLDk5WQGgHDx48IqPHTx4UAGgJCcnX/ExAAoApW7dukpGRkbx+y9cuKBcf/31CgClXbt2SvPmzZVDhw4VfzwrK0tp0aKFAkCZP39+qWv+9NNPSnh4uBIREaF8/PHHpT526NAhpV27dgoA5f333/ftmyYiXbAriohM54UXXkD79u2L346MjMSUKVMAANu3b8fs2bORnJxc/PF69ephwoQJAICVK1eWutaLL74Il8uFGTNm4Lbbbiv1seTkZLzzzjsAgNmzZ/vleyEibTHYEJHp3HjjjVe8r2XLlgCAkJAQDBo0qNyPnzhxovh9brcbX375JQBg1KhRZd6rc+fOqFWrFrZt24aCggKfayci/2KwISLTSUpKuuJ96lid+Ph4hIRcOeEzKioKAEqFk7NnzyI7OxsAkJiYWGogs/oICgpCbm4u3G43zp49649vh4g0xOneRGQ6QUHlvyar6GOXc7vdxef33ntvpZ8fHh5e5WsTkTEYbIjIturVq4fIyEjk5+dj1qxZqFevntElEZGP2BVFRLpTp1tfunTJ0DqCg4MxcOBAAMBHH31kaC1EpA0GGyLSXUJCAgBg586dBlcCTJ8+HWFhYXjyySfx/vvvl+qeUu3YsQNLliwxoDoiqi4GGyLS3e233w4AuPvuu3H77bdj/PjxGD9+PPbs2aN7LR07dixeXXjcuHFITk7G4MGDcffdd+PGG29EYmIi2rVrxxYdIpPgGBsi0t2ECROQk5OD+fPnY/ny5cUzle6+++4yt1Lwt5EjR6JLly6YPXs20tLS8N1336GoqAgNGjRAixYtMGnSJNxxxx2610VE1cctFYiIiMgy2BVFRERElsFgQ0RERJbBYENERESWwWBDRERElsFgQ0RERJbBYENERESWwWBDRERElsFgQ0RERJbBYENERESWwWBDRERElsFgQ0RERJbBYENERESW8f9SNW7wvXVEPwAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1252,10 +1224,10 @@
      "n": "19"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:46.192581Z",
-     "iopub.status.busy": "2024-06-03T10:32:46.192427Z",
-     "iopub.status.idle": "2024-06-03T10:32:46.227403Z",
-     "shell.execute_reply": "2024-06-03T10:32:46.227008Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.506506Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.506353Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.575616Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.575302Z"
     }
    },
    "outputs": [],
@@ -1278,16 +1250,16 @@
      "n": "20"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:46.229183Z",
-     "iopub.status.busy": "2024-06-03T10:32:46.229034Z",
-     "iopub.status.idle": "2024-06-03T10:32:46.344415Z",
-     "shell.execute_reply": "2024-06-03T10:32:46.343885Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.577145Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.577053Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.647757Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.647509Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1321,7 +1293,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb.txt b/master/_sources/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb.txt
index c36b9556..bf7397d2 100644
--- a/master/_sources/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb.txt
+++ b/master/_sources/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb.txt
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.058730Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.057958Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.630385Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.629760Z"
+     "iopub.execute_input": "2024-06-03T14:27:51.989457Z",
+     "iopub.status.busy": "2024-06-03T14:27:51.989274Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.490207Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.489897Z"
     }
    },
    "outputs": [
@@ -33,14 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "qutip     : 4.7.6\n",
+      "numpy     : 1.26.4\n",
       "matplotlib: 3.7.5\n",
-      "numpy     : 1.24.4\n",
-      "scipy     : 1.10.1\n",
+      "qutip     : 4.7.6\n",
+      "scipy     : 1.12.0\n",
+      "krotov    : 1.3.0+dev\n",
       "\n"
      ]
     }
@@ -217,10 +217,10 @@
      "start_time": "2019-11-10T17:36:51.300148Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.656109Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.655815Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.660910Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.660332Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.511590Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.511407Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.514485Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.514201Z"
     }
    },
    "outputs": [],
@@ -273,10 +273,10 @@
      "start_time": "2019-11-10T17:36:51.319722Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.663022Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.662601Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.667769Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.667344Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.515988Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.515900Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.518841Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.518614Z"
     }
    },
    "outputs": [],
@@ -322,10 +322,10 @@
      "start_time": "2019-11-10T17:36:51.337196Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.669836Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.669665Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.673029Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.672559Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.520217Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.520134Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.522134Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.521849Z"
     }
    },
    "outputs": [],
@@ -366,10 +366,10 @@
      "start_time": "2019-11-10T17:36:51.346901Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.674987Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.674817Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.679145Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.678761Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.523434Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.523352Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.525901Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.525628Z"
     },
     "lines_to_next_cell": 2
    },
@@ -399,10 +399,10 @@
      "n": "3"
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     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.681058Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.680916Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.684359Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.683870Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.527187Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.527108Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.529146Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.528908Z"
     },
     "lines_to_next_cell": 2
    },
@@ -445,10 +445,10 @@
      "start_time": "2019-11-10T17:36:51.366724Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.686271Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.686056Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.690338Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.689864Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.530531Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.530448Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.533144Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.532910Z"
     }
    },
    "outputs": [],
@@ -479,10 +479,10 @@
      "start_time": "2019-11-10T17:36:51.381216Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.692216Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.692073Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.697846Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.697458Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.534402Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.534325Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.537683Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.537387Z"
     },
     "lines_to_next_cell": 2
    },
@@ -521,10 +521,10 @@
      "start_time": "2019-11-10T17:36:51.393811Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.700054Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.699578Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.702321Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.701934Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.538935Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.538849Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.540403Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.540172Z"
     }
    },
    "outputs": [],
@@ -549,10 +549,10 @@
      "start_time": "2019-11-10T17:36:51.401596Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.704273Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.704047Z",
-     "iopub.status.idle": "2024-06-03T10:30:24.970693Z",
-     "shell.execute_reply": "2024-06-03T10:30:24.970052Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.541671Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.541593Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.211487Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.211213Z"
     }
    },
    "outputs": [],
@@ -581,10 +581,10 @@
      "start_time": "2019-11-10T17:36:54.723039Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:24.973053Z",
-     "iopub.status.busy": "2024-06-03T10:30:24.972852Z",
-     "iopub.status.idle": "2024-06-03T10:30:24.977850Z",
-     "shell.execute_reply": "2024-06-03T10:30:24.977365Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.213177Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.213083Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.216103Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.215851Z"
     }
    },
    "outputs": [],
@@ -628,10 +628,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:24.979660Z",
-     "iopub.status.busy": "2024-06-03T10:30:24.979518Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.221995Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.221476Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.217420Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.217337Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.347849Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.347559Z"
     },
     "lines_to_end_of_cell_marker": 2
    },
@@ -684,10 +684,10 @@
      "start_time": "2019-11-10T17:36:55.327833Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.224163Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.223974Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.226801Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.226397Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.349456Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.349350Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.351109Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.350866Z"
     }
    },
    "outputs": [],
@@ -710,10 +710,10 @@
      "start_time": "2019-11-10T17:36:55.336694Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.228750Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.228576Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.231894Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.231464Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.352356Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.352263Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.354219Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.354014Z"
     }
    },
    "outputs": [],
@@ -758,10 +758,10 @@
      "start_time": "2019-11-10T17:36:55.351667Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.233940Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.233725Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.236392Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.235916Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.355606Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.355510Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.357240Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.357006Z"
     }
    },
    "outputs": [],
@@ -781,10 +781,10 @@
      "start_time": "2019-11-10T17:36:55.360831Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.238191Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.238048Z",
-     "iopub.status.idle": "2024-06-03T10:32:00.125630Z",
-     "shell.execute_reply": "2024-06-03T10:32:00.125113Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.358559Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.358481Z",
+     "iopub.status.idle": "2024-06-03T14:29:00.830132Z",
+     "shell.execute_reply": "2024-06-03T14:29:00.829877Z"
     }
    },
    "outputs": [
@@ -1107,10 +1107,10 @@
      "n": "17"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:00.128080Z",
-     "iopub.status.busy": "2024-06-03T10:32:00.127688Z",
-     "iopub.status.idle": "2024-06-03T10:32:00.456986Z",
-     "shell.execute_reply": "2024-06-03T10:32:00.456418Z"
+     "iopub.execute_input": "2024-06-03T14:29:00.831745Z",
+     "iopub.status.busy": "2024-06-03T14:29:00.831640Z",
+     "iopub.status.idle": "2024-06-03T14:29:01.012675Z",
+     "shell.execute_reply": "2024-06-03T14:29:01.012396Z"
     }
    },
    "outputs": [
@@ -1192,10 +1192,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:00.459065Z",
-     "iopub.status.busy": "2024-06-03T10:32:00.458854Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.622010Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.621465Z"
+     "iopub.execute_input": "2024-06-03T14:29:01.014157Z",
+     "iopub.status.busy": "2024-06-03T14:29:01.014047Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.589048Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.588654Z"
     }
    },
    "outputs": [],
@@ -1220,10 +1220,10 @@
      "n": "19"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.624294Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.624102Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.836207Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.835626Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.590858Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.590739Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.743541Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.743222Z"
     }
    },
    "outputs": [
@@ -1275,10 +1275,10 @@
      "start_time": "2019-11-10T17:39:10.406834Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.838287Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.838127Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.845799Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.845259Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.745086Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.744975Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.750856Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.750594Z"
     }
    },
    "outputs": [],
@@ -1312,10 +1312,10 @@
      "n": "21"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.847734Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.847585Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.850705Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.850230Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.752257Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.752150Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.754116Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.753861Z"
     }
    },
    "outputs": [
@@ -1345,10 +1345,10 @@
      "n": "22"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.852487Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.852343Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.855401Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.854825Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.755654Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.755549Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.757552Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.757286Z"
     }
    },
    "outputs": [],
@@ -1382,42 +1382,14 @@
      "n": "23"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.857327Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.857183Z",
-     "iopub.status.idle": "2024-06-03T10:32:03.522361Z",
-     "shell.execute_reply": "2024-06-03T10:32:03.521853Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.758894Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.758806Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.903500Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.903193Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -1456,10 +1428,10 @@
      "n": "25"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:03.524726Z",
-     "iopub.status.busy": "2024-06-03T10:32:03.524287Z",
-     "iopub.status.idle": "2024-06-03T10:32:03.880818Z",
-     "shell.execute_reply": "2024-06-03T10:32:03.880259Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.905100Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.904982Z",
+     "iopub.status.idle": "2024-06-03T14:29:03.086960Z",
+     "shell.execute_reply": "2024-06-03T14:29:03.086642Z"
     }
    },
    "outputs": [
@@ -1516,10 +1488,10 @@
      "start_time": "2019-11-10T17:39:11.633126Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:03.882753Z",
-     "iopub.status.busy": "2024-06-03T10:32:03.882595Z",
-     "iopub.status.idle": "2024-06-03T10:32:06.052386Z",
-     "shell.execute_reply": "2024-06-03T10:32:06.050825Z"
+     "iopub.execute_input": "2024-06-03T14:29:03.088715Z",
+     "iopub.status.busy": "2024-06-03T14:29:03.088575Z",
+     "iopub.status.idle": "2024-06-03T14:29:04.638557Z",
+     "shell.execute_reply": "2024-06-03T14:29:04.638201Z"
     },
     "lines_to_next_cell": 2
    },
@@ -1545,10 +1517,10 @@
      "n": "26"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:06.058093Z",
-     "iopub.status.busy": "2024-06-03T10:32:06.057700Z",
-     "iopub.status.idle": "2024-06-03T10:32:06.280643Z",
-     "shell.execute_reply": "2024-06-03T10:32:06.280081Z"
+     "iopub.execute_input": "2024-06-03T14:29:04.640300Z",
+     "iopub.status.busy": "2024-06-03T14:29:04.640188Z",
+     "iopub.status.idle": "2024-06-03T14:29:04.776702Z",
+     "shell.execute_reply": "2024-06-03T14:29:04.776442Z"
     }
    },
    "outputs": [
@@ -1599,10 +1571,10 @@
      "start_time": "2019-11-10T17:39:14.994569Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:06.282827Z",
-     "iopub.status.busy": "2024-06-03T10:32:06.282663Z",
-     "iopub.status.idle": "2024-06-03T10:32:06.507575Z",
-     "shell.execute_reply": "2024-06-03T10:32:06.507040Z"
+     "iopub.execute_input": "2024-06-03T14:29:04.778194Z",
+     "iopub.status.busy": "2024-06-03T14:29:04.778081Z",
+     "iopub.status.idle": "2024-06-03T14:29:04.910860Z",
+     "shell.execute_reply": "2024-06-03T14:29:04.910609Z"
     }
    },
    "outputs": [
@@ -1615,7 +1587,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1632,7 +1604,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1693,7 +1665,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/04_example_dissipative_qubit_reset.ipynb.txt b/master/_sources/notebooks/04_example_dissipative_qubit_reset.ipynb.txt
index 3ca71d22..fa7bfd24 100644
--- a/master/_sources/notebooks/04_example_dissipative_qubit_reset.ipynb.txt
+++ b/master/_sources/notebooks/04_example_dissipative_qubit_reset.ipynb.txt
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:18.998818Z",
-     "iopub.status.busy": "2024-06-03T10:39:18.998624Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.507620Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.507068Z"
+     "iopub.execute_input": "2024-06-03T14:27:45.304871Z",
+     "iopub.status.busy": "2024-06-03T14:27:45.304735Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.012221Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.011869Z"
     }
    },
    "outputs": [
@@ -33,14 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
+      "krotov    : 1.3.0+dev\n",
+      "numpy     : 1.26.4\n",
       "qutip     : 4.7.6\n",
+      "scipy     : 1.12.0\n",
       "matplotlib: 3.7.5\n",
-      "krotov    : 1.3.0\n",
-      "scipy     : 1.10.1\n",
-      "numpy     : 1.24.4\n",
       "\n"
      ]
     }
@@ -156,10 +156,10 @@
      "n": "2"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.534004Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.533759Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.536805Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.536261Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.034965Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.034733Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.036866Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.036616Z"
     }
    },
    "outputs": [],
@@ -205,10 +205,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.538744Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.538596Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.547969Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.547428Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.038150Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.038050Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.043402Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.043167Z"
     }
    },
    "outputs": [],
@@ -305,10 +305,10 @@
      "n": "6"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.550005Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.549856Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.553297Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.552921Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.045003Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.044914Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.047008Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.046802Z"
     }
    },
    "outputs": [],
@@ -346,10 +346,10 @@
      "n": "7"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.555244Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.555097Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.558549Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.558060Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.048337Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.048246Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.050395Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.050181Z"
     }
    },
    "outputs": [],
@@ -387,10 +387,10 @@
      "n": "8"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.560542Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.560364Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.563350Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.562884Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.051684Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.051603Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.053489Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.053291Z"
     }
    },
    "outputs": [],
@@ -423,10 +423,10 @@
      "n": "9"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.565291Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.565141Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.570491Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.570126Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.054823Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.054739Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.058150Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.057884Z"
     }
    },
    "outputs": [
@@ -469,10 +469,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.572561Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.572257Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.574843Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.574470Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.059506Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.059411Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.061166Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.060923Z"
     }
    },
    "outputs": [],
@@ -507,10 +507,10 @@
      "n": "11"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.576798Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.576646Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.579503Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.579013Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.062393Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.062320Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.064439Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.063910Z"
     }
    },
    "outputs": [],
@@ -538,10 +538,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.581579Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.581264Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.583854Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.583352Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.066273Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.066158Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.067792Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.067560Z"
     }
    },
    "outputs": [],
@@ -561,10 +561,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.585737Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.585587Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.587899Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.587515Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.069225Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.069145Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.070811Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.070612Z"
     }
    },
    "outputs": [],
@@ -586,10 +586,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.589724Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.589575Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.592757Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.592265Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.072118Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.072001Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.074152Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.073905Z"
     }
    },
    "outputs": [],
@@ -626,10 +626,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.594718Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.594540Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.735869Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.735373Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.075405Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.075326Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.169070Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.168707Z"
     }
    },
    "outputs": [
@@ -661,10 +661,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.737943Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.737731Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.744435Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.743960Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.174207Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.173948Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.182369Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.181589Z"
     }
    },
    "outputs": [],
@@ -693,10 +693,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.746275Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.746126Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.790154Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.789778Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.188678Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.188498Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.233985Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.226614Z"
     }
    },
    "outputs": [],
@@ -720,10 +720,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.792085Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.791911Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.908482Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.907976Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.236709Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.236564Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.355393Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.354576Z"
     }
    },
    "outputs": [
@@ -807,10 +807,10 @@
      "start_time": "2019-02-12T04:47:27.118668Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.911006Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.910814Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.913931Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.913550Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.357352Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.357219Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.359780Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.359366Z"
     }
    },
    "outputs": [],
@@ -859,10 +859,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.915863Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.915672Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.918699Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.918211Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.361247Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.361152Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.363293Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.363069Z"
     }
    },
    "outputs": [],
@@ -888,10 +888,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.920612Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.920464Z",
-     "iopub.status.idle": "2024-06-03T10:39:27.823290Z",
-     "shell.execute_reply": "2024-06-03T10:39:27.822652Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.364685Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.364602Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.835176Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.834803Z"
     }
    },
    "outputs": [
@@ -909,7 +909,7 @@
       "    S(t) (ranges): [0.000000, 1.000000]\n",
       "    iter_start: 0\n",
       "    iter_stop: 5\n",
-      "    duration: 0.2 secs (started at 2024-06-03 12:39:19)\n",
+      "    duration: 0.1 secs (started at 2024-06-03 10:27:46)\n",
       "    optimized pulses (ranges): [0.00, 2.00]\n",
       "    ∫gₐ(t)dt: 0.00e+00\n",
       "    λₐ: 1.00e-01\n",
@@ -930,11 +930,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 1\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:20)\n",
+      "    duration: 0.6 secs (started at 2024-06-03 10:27:46)\n",
       "    optimized pulses (ranges): [0.00, 2.06]\n",
       "    ∫gₐ(t)dt: 8.64e-03\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.98e-01:0.00π)\n"
      ]
@@ -951,11 +951,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 2\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:21)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:47)\n",
       "    optimized pulses (ranges): [0.00, 2.36]\n",
-      "    ∫gₐ(t)dt: 4.73e-02\n",
+      "    ∫gₐ(t)dt: 4.72e-02\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.92e-01:0.00π)\n"
      ]
@@ -972,11 +972,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 3\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:23)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:47)\n",
       "    optimized pulses (ranges): [0.00, 2.44]\n",
-      "    ∫gₐ(t)dt: 6.87e-03\n",
+      "    ∫gₐ(t)dt: 6.88e-03\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.76e-01:0.00π)\n"
      ]
@@ -985,19 +985,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    qubit error: 4.8e-02\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    qubit error: 4.8e-02\n",
       "Iteration 4\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:24)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:48)\n",
       "    optimized pulses (ranges): [0.00, 2.42]\n",
-      "    ∫gₐ(t)dt: 7.32e-04\n",
+      "    ∫gₐ(t)dt: 7.33e-04\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.82e-01:0.00π)\n"
      ]
@@ -1014,11 +1008,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 5\n",
-      "    duration: 1.6 secs (started at 2024-06-03 12:39:26)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:49)\n",
       "    optimized pulses (ranges): [0.00, 2.43]\n",
-      "    ∫gₐ(t)dt: 1.25e-04\n",
+      "    ∫gₐ(t)dt: 1.23e-04\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.80e-01:0.00π)\n"
      ]
@@ -1062,10 +1056,10 @@
      "n": "20"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:27.825609Z",
-     "iopub.status.busy": "2024-06-03T10:39:27.825411Z",
-     "iopub.status.idle": "2024-06-03T10:39:27.829561Z",
-     "shell.execute_reply": "2024-06-03T10:39:27.828992Z"
+     "iopub.execute_input": "2024-06-03T14:27:49.837292Z",
+     "iopub.status.busy": "2024-06-03T14:27:49.837153Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.839825Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.839511Z"
     }
    },
    "outputs": [
@@ -1074,11 +1068,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:39:19\n",
+       "- Started at 2024-06-03 10:27:46\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 5\n",
        "- Reason for termination: Reached 5 iterations\n",
-       "- Ended at 2024-06-03 12:39:27 (0:00:08)"
+       "- Ended at 2024-06-03 10:27:49 (0:00:03)"
       ]
      },
      "execution_count": 20,
@@ -1115,16 +1109,16 @@
      "n": "21"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:27.831673Z",
-     "iopub.status.busy": "2024-06-03T10:39:27.831506Z",
-     "iopub.status.idle": "2024-06-03T10:39:27.931477Z",
-     "shell.execute_reply": "2024-06-03T10:39:27.930868Z"
+     "iopub.execute_input": "2024-06-03T14:27:49.841504Z",
+     "iopub.status.busy": "2024-06-03T14:27:49.841391Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.897493Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.897183Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1156,16 +1150,16 @@
      "n": "22"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:27.933785Z",
-     "iopub.status.busy": "2024-06-03T10:39:27.933596Z",
-     "iopub.status.idle": "2024-06-03T10:39:28.092799Z",
-     "shell.execute_reply": "2024-06-03T10:39:28.092180Z"
+     "iopub.execute_input": "2024-06-03T14:27:49.899157Z",
+     "iopub.status.busy": "2024-06-03T14:27:49.898997Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.997285Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.996999Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1203,7 +1197,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/05_example_transmon_xgate.ipynb.txt b/master/_sources/notebooks/05_example_transmon_xgate.ipynb.txt
index e28b0836..94110ab7 100644
--- a/master/_sources/notebooks/05_example_transmon_xgate.ipynb.txt
+++ b/master/_sources/notebooks/05_example_transmon_xgate.ipynb.txt
@@ -16,10 +16,10 @@
      "start_time": "2020-03-21T21:17:42.571725Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.338767Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.338588Z",
-     "iopub.status.idle": "2024-06-03T10:29:32.853063Z",
-     "shell.execute_reply": "2024-06-03T10:29:32.852499Z"
+     "iopub.execute_input": "2024-06-03T14:31:16.323104Z",
+     "iopub.status.busy": "2024-06-03T14:31:16.322439Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.135543Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.135244Z"
     }
    },
    "outputs": [
@@ -28,16 +28,15 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "qutip     : 4.7.6\n",
-      "numpy     : 1.24.4\n",
-      "sys       : 3.8.16 (default, May 26 2024, 19:41:26) \n",
-      "[GCC 12.2.0]\n",
+      "sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
+      "krotov    : 1.3.0+dev\n",
       "matplotlib: 3.7.5\n",
-      "scipy     : 1.10.1\n",
+      "scipy     : 1.12.0\n",
+      "qutip     : 4.7.6\n",
+      "numpy     : 1.26.4\n",
       "\n"
      ]
     }
@@ -163,10 +162,10 @@
      "start_time": "2020-03-21T21:17:56.572502Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.878973Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.878699Z",
-     "iopub.status.idle": "2024-06-03T10:29:32.882224Z",
-     "shell.execute_reply": "2024-06-03T10:29:32.881711Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.157920Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.157739Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.159978Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.159714Z"
     },
     "lines_to_next_cell": 2
    },
@@ -188,10 +187,10 @@
      "start_time": "2020-03-21T21:17:57.275685Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.884079Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.883914Z",
-     "iopub.status.idle": "2024-06-03T10:29:32.886974Z",
-     "shell.execute_reply": "2024-06-03T10:29:32.886611Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.161331Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.161219Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.163292Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.163075Z"
     }
    },
    "outputs": [],
@@ -217,10 +216,10 @@
      "start_time": "2020-03-21T21:17:57.957925Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.888796Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.888650Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.028652Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.028190Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.164724Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.164612Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.266454Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.265154Z"
     }
    },
    "outputs": [
@@ -255,10 +254,10 @@
      "start_time": "2020-03-21T21:17:59.814723Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.030668Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.030486Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.034761Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.034268Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.271474Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.270543Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.282541Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.276920Z"
     }
    },
    "outputs": [],
@@ -295,10 +294,10 @@
      "start_time": "2020-03-21T21:18:00.539883Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.036682Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.036370Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.039637Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.039270Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.286426Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.286021Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.294392Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.291689Z"
     }
    },
    "outputs": [],
@@ -326,10 +325,10 @@
      "start_time": "2020-03-21T21:18:01.468153Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.041664Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.041267Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.046599Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.046227Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.298554Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.297671Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.315070Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.312721Z"
     }
    },
    "outputs": [
@@ -374,10 +373,10 @@
      "start_time": "2020-03-21T21:18:02.813232Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.048475Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.048301Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.051477Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.051094Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.328660Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.328300Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.332172Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.331055Z"
     }
    },
    "outputs": [],
@@ -423,10 +422,10 @@
      "start_time": "2020-03-21T21:18:04.764412Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.053365Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.053191Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.059132Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.058762Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.336580Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.336262Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.343732Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.342734Z"
     },
     "lines_to_next_cell": 2,
     "scrolled": true
@@ -468,10 +467,10 @@
      "start_time": "2020-03-21T21:18:05.911538Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.061042Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.060869Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.351168Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.350676Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.347951Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.347584Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.512515Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.512227Z"
     }
    },
    "outputs": [],
@@ -490,10 +489,10 @@
      "start_time": "2020-03-21T21:18:07.421285Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.353166Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.352929Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.356387Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.355886Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.514026Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.513936Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.516242Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.516027Z"
     }
    },
    "outputs": [],
@@ -518,16 +517,16 @@
      "start_time": "2020-03-21T21:18:08.195251Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.358203Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.358053Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.574590Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.574061Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.517518Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.517440Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.649044Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.648758Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -574,10 +573,10 @@
      "start_time": "2020-03-21T21:18:11.065096Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.576774Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.576582Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.579917Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.579394Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.650603Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.650498Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.652441Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.652219Z"
     }
    },
    "outputs": [],
@@ -600,10 +599,10 @@
      "start_time": "2020-03-21T21:18:15.565391Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.581739Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.581593Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.159622Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.159083Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.653824Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.653686Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.351456Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.351150Z"
     }
    },
    "outputs": [
@@ -612,42 +611,42 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T    g_a_int          J  Delta J_T    Delta J  secs\n",
-      "0     1.00e+00   0.00e+00   1.00e+00        n/a        n/a     3\n"
+      "0     1.00e+00   0.00e+00   1.00e+00        n/a        n/a     2\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1     2.80e-01   3.61e-01   6.41e-01  -7.20e-01  -3.59e-01     7\n"
+      "1     2.80e-01   3.61e-01   6.41e-01  -7.20e-01  -3.59e-01     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2     2.12e-01   3.42e-02   2.46e-01  -6.81e-02  -3.39e-02     7\n"
+      "2     2.12e-01   3.42e-02   2.46e-01  -6.81e-02  -3.39e-02     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3     1.35e-01   3.88e-02   1.74e-01  -7.72e-02  -3.83e-02     7\n"
+      "3     1.35e-01   3.88e-02   1.74e-01  -7.72e-02  -3.83e-02     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4     9.79e-02   1.87e-02   1.17e-01  -3.71e-02  -1.84e-02     7\n"
+      "4     9.79e-02   1.87e-02   1.17e-01  -3.71e-02  -1.84e-02     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5     7.13e-02   1.34e-02   8.47e-02  -2.65e-02  -1.32e-02     7\n"
+      "5     7.13e-02   1.34e-02   8.47e-02  -2.65e-02  -1.32e-02     5\n"
      ]
     }
    ],
@@ -688,10 +687,10 @@
      "start_time": "2020-03-21T21:19:54.217987Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.161727Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.161569Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.166455Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.166082Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.353103Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.352994Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.356622Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.356341Z"
     },
     "lines_to_next_cell": 2
    },
@@ -732,10 +731,10 @@
      "start_time": "2020-03-21T21:19:55.178133Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.168260Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.168112Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.171865Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.171357Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.358438Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.358312Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.360725Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.360475Z"
     }
    },
    "outputs": [
@@ -769,10 +768,10 @@
      "start_time": "2020-03-21T21:19:57.099117Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.173733Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.173502Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.176385Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.175901Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.362151Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.362041Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.363960Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.363690Z"
     }
    },
    "outputs": [],
@@ -794,41 +793,13 @@
      "start_time": "2020-03-21T21:19:58.026972Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.178193Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.178047Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.900021Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.899389Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.365326Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.365199Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.560241Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.559934Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -867,10 +838,10 @@
      "start_time": "2020-03-21T21:20:00.433074Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.902243Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.902057Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.000492Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.999982Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.562113Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.561995Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.620363Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.620067Z"
     }
    },
    "outputs": [
@@ -905,16 +876,16 @@
      "start_time": "2020-03-21T21:20:02.755327Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.002529Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.002374Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.107341Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.106830Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.622021Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.621890Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.688084Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.687782Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Th+MNDQ347rvvJCnKXZjXAeIsMCIiIq/icBfY4cOHzZ8fO3YMxcXF5q/1ej22b9+OuLg4aatzsUA1W4CIiIi8kcMBKC0tDQqFAgqFAhMnTuxwe48ePfDKK69IWpyrmQdBcwwQERGRV3E4AOXn50MQBPTv3x/79+9Hr169zLep1WpERUVBpVLJUqSrXJ0Gzy4wIiIib+JwAOrXrx8AwGAwyFaMu+FCiERERN7JoQD02WefYfr06fD398dnn33W6X1NK0N7A64DRERE5J0cCkAzZ85EcXExoqKiMHPmTJv3UygU0Ou9p7uIK0ETERF5J4cCUNtuL1/qAmvbAiQIAhQKhYsrIiIiIimIXgfIlwSqjWOADALQ2OI7wY+IiMjbid4KAwCysrKQlZWF0tLSDi1CmzZtkqQwdxCkvvrtqW1sQYC/d81yIyIi8lWiA9Dq1auxZs0ajBkzBrGxsV7dLaRUKhCoVqG+SY+6xhZEBmtcXRIRERFJQHQA2rBhA95++23cd999ctTjdoI0fqhv0nMgNBERkRcRPQaoqakJ1157rRy1uKWrA6G9Z3YbERGRrxMdgH7/+9/j3XfflaMWt8TFEImIiLyP6C6whoYGvPHGG/jmm2+QkpICf39/i9vXrVsnWXHuwDQQml1gRERE3kN0ADp8+DDS0tIAAHl5eRa3eeOAaFMXWD03RCUiIvIaogPQrl275KjDbQVyQ1QiIiKvw4UQ7QjmGCAiIiKvI7oF6Oabb+60q2vnzp1OFeRuTGOAGICIiIi8h+gAZBr/Y9Lc3Izc3Fzk5eVh7ty5UtXlNrghKhERkfcRHYBeeOEFq8efeuop1NbWOl2Qu2m7ISoRERF5B8nGAN17771etQ+YSRAHQRMREXkdyQJQdnY2AgICpHo6t2FaCJHT4ImIiLyH6C6wO++80+JrQRBQVFSEAwcOYOXKlZIV5i44CJqIiMj7iA5AWq3W4mulUokhQ4ZgzZo1mDJlimSFuQsOgiYiIvI+ogPQP//5TznqcFvcDJWIiMj7cCFEO7gZKhERkfdhALLD3ALU1AJBEFxcDREREUmBAcgO0xgggwBcaWY3GBERkTdweQBav349EhISEBAQgPT0dOzfv7/T+1dVVWHhwoWIjY2FRqPB4MGD8eWXX8pWXw9/FUw7f3AcEBERkXdwaQDasmULlixZglWrViEnJwepqamYOnUqSktLrd6/qakJkydPxvnz5/Hhhx/i5MmT2LhxI+Li4mSrUalUINCf44CIiIi8iehZYLZ8+umnqK6uxpw5cxx+zLp16zB//nzMmzcPALBhwwZs27YNmzZtwrJlyzrcf9OmTaisrMQPP/wAf39/AEBCQoIk9XcmSOOHuiY9p8ITERF5CclagJYuXWoOMo5oamrCwYMHkZGRcbUYpRIZGRnIzs62+pjPPvsM48ePx8KFCxEdHY3k5GQ888wz0Ottd001NjZCp9NZfIjF/cCIiIi8i2QB6MSJE50GkfbKy8uh1+sRHR1tcTw6OhrFxcVWH3Pu3Dl8+OGH0Ov1+PLLL7Fy5Uo8//zz+Otf/2rzdTIzM6HVas0f8fHxDtdoEtRmJhgRERF5PpcPghbDYDAgKioKb7zxBkaPHo1Zs2bhiSeewIYNG2w+Zvny5aiurjZ/FBYWin5d01pA3BCViIjIO3RpDNDly5fx1ltv4fjx4wCAYcOG4f7770dERITDzxEZGQmVSoWSkhKL4yUlJYiJibH6mNjYWPj7+0OlUpmPDRs2DMXFxWhqaoJare7wGI1GA41G43Bd1rALjIiIyLuIbgH69ttvkZiYiJdffhmXL1/G5cuX8corryAxMRHffvutw8+jVqsxevRoZGVlmY8ZDAZkZWVh/PjxVh9z3XXX4cyZMzAYDOZjp06dQmxsrNXwI5UgBiAiIiKvIjoALVy4EL/73e+Qn5+PrVu3YuvWrTh37hzuuusuLFy4UNRzLVmyBBs3bsS//vUvHD9+HAsWLEBdXZ15MPWcOXOwfPly8/0XLFiAyspKLF68GKdOncK2bdvwzDPPiH5dsQLV3A+MiIjIm4juAjtz5gw+/PBDi24olUqFJUuW4N///reo55o1axbKysrw5JNPori4GGlpadi+fbt5YHRBQQGUyqsZLT4+Hl9//TUee+wxpKSkIC4uDosXL8bSpUvFnoYowab9wDgImoiIyCuIDkCjRo3C8ePHMWTIEIvjx48fR2pqqugCFi1ahEWLFlm9bffu3R2OjR8/Hvv27RP9Os4wdYFxHSAiIiLv4FAAOnz4sPnzRx55BIsXL8aZM2cwbtw4AMC+ffuwfv16rF27Vp4qXYyDoImIiLyLQwEoLS0NCoXCYjf0//3f/+1wv9mzZ2PWrFnSVecmOAiaiIjIuzgUgPLz8+Wuw62xC4yIiMi7OBSA+vXrBwBobm7GH/7wB6xcuRKJiYmyFuZOTIOg65s4C4yIiMgbiJoG7+/vj48++kiuWtyWaRo8W4CIiIi8g+h1gGbOnIlPPvlEhlLcFwdBExEReRfR0+AHDRqENWvW4Pvvv8fo0aMRFBRkcfsjjzwiWXHu4uogaHaBEREReQPRAeitt95CWFgYDh48iIMHD1rcplAovDQAXV0IURAEKBQKF1dEREREzhAdgHxxRpipC0wQjAOhTS1CRERE5JlEjwHyRT38VVC2NvpwHBAREZHn61JTxoULF/DZZ5+hoKAATU1NFretW7dOksLciUKhQJDaDzWNLajjVHgiIiKPJzoAZWVl4bbbbkP//v1x4sQJJCcn4/z58xAEAaNGjZKjRrcQqFEZAxBbgIiIiDye6C6w5cuX4/HHH8eRI0cQEBCAjz76CIWFhbjxxhvx29/+Vo4a3QJXgyYiIvIeogPQ8ePHMWfOHACAn58frly5guDgYKxZswbPPvus5AW6C64FRERE5D1EB6CgoCDzuJ/Y2FicPXvWfFt5ebl0lbmZIK4GTURE5DVEjwEaN24c9u7di2HDhuGWW27Bn/70Jxw5cgRbt27FuHHj5KjRLXAxRCIiIu8hOgCtW7cOtbW1AIDVq1ejtrYWW7ZswaBBg7xyBpiJaUNUdoERERF5PtEBqH///ubPg4KCsGHDBkkLclfmFqAmBiAiIiJP59AYIEEQ5K7D7QVxEDQREZHXcCgADR8+HJs3b+6w6GF7p0+fxoIFC7B27VpJinMnVwdBcwwQERGRp3OoC+yVV17B0qVL8fDDD2Py5MkYM2YMevfujYCAAFy+fBnHjh3D3r17cfToUSxatAgLFiyQu+5uF8QxQERERF7DoQA0adIkHDhwAHv37sWWLVvwzjvv4JdffsGVK1cQGRmJkSNHYs6cObjnnnsQHh4ud80uwXWAiIiIvIeoQdATJkzAhAkT5KrFrXElaCIiIu/B3eAdFMxZYERERF6DAchBphageg6CJiIi8ngMQA4KVBsHQbMLjIiIyPMxADmIg6CJiIi8BwOQg66uBK2HwcCFIYmIiDxZlwLQ2bNnsWLFCtx9990oLS0FAHz11Vc4evSopMW5E1MLEADUN3McEBERkScTHYD27NmDESNG4Mcff8TWrVvNG6P+/PPPWLVqleQFuosAfyWUCuPn7AYjIiLybKID0LJly/DXv/4VO3bsgFqtNh+fOHEi9u3bJ2lx7kShUHAtICIiIi8hOgAdOXIEd9xxR4fjUVFRKC8vl6QodxXMqfBEREReQXQACgsLQ1FRUYfjhw4dQlxcnCRFuStOhSciIvIOogPQXXfdhaVLl6K4uBgKhQIGgwHff/89Hn/8ccyZM0eOGt0Gp8ITERF5B9EB6JlnnsHQoUMRHx+P2tpaJCUl4YYbbsC1116LFStWyFGj2wjidhhEREReQdRmqACgVquxceNGrFy5Enl5eaitrcXIkSMxaNAgOepzKxwETURE5B1EByCTvn37om/fvlLW4vbYBUZEROQdHApAS5YscfgJ161b1+Vi3F2QxjQImrPAiIiIPJlDAejQoUMWX+fk5KClpQVDhgwBAJw6dQoqlQqjR4+WvkI3cnVHeLYAEREReTKHAtCuXbvMn69btw4hISH417/+hfDwcADA5cuXMW/ePFx//fXyVOkmgtQcBE1EROQNRM8Ce/7555GZmWkOPwAQHh6Ov/71r3j++eclLc7d9GhdB+hkcQ2yz1ZAz01RiYiIPJLoAKTT6VBWVtbheFlZGWpqaiQpyh1tzyvCqzvPAAByCqpw98Z9mPDsTmzP67goJBEREbk30QHojjvuwLx587B161ZcuHABFy5cwEcffYQHHngAd955pxw1utz2vCIs+E8Oqq80Wxwvrm7Agv/kMAQRERF5GNHT4Dds2IDHH38cs2fPRnOzMRD4+fnhgQcewN///nfJC3Q1vUHA6s+PwVpnlwBAAWD158cwOSkGKtN28UREROTWFIIgdGkgS11dHc6ePQsAGDBgAIKCgiQtTC46nQ5arRbV1dUIDQ21e//ssxW4e6P9Xe7fmz8O4wf0lKJEIiIiakfs+7c9XV4IMSgoCCkpKU4X4O5KaxokvR8RERG5nugAdPPNN0OhsN3Vs3PnTqcKcjdRIQGS3o+IiIhcT3QASktLs/i6ubkZubm5yMvLw9y5c6Wqy22MTYxArDYAxdUNVscBKQDEaAMwNjGiu0sjIiKiLhIdgF544QWrx5966inU1tY6XZC7USkVWDUjCQv+kwMFYBGCTO1gq2YkcQA0ERGRBxE9Dd6We++9F5s2bZLq6dzKtORYvH7vKMRoLbu5YrQBeP3eUZiWHOuiyoiIiKgrujwIur3s7GwEBHjvOJhpybGYnBSDmev34shFHRbcOACPTx3Clh8iIiIPJDoAtV/sUBAEFBUV4cCBA1i5cqVkhbkjlVKBAb2CceSiDmGB/gw/REREHkp0AAoNDbWYBaZUKjFkyBCsWbMGU6ZMkbQ4dxQZrAEAVNQ1ubgSIiIi6irRAejtt9+WoQzPERliDEDlNY0uroSIiIi6SvQg6P79+6OioqLD8aqqKvTv379LRaxfvx4JCQkICAhAeno69u/f79DjNm/eDIVCgZkzZ3bpdbvC1AJUVssARERE5KlEB6Dz589Dr9d3ON7Y2IiLFy+KLmDLli1YsmQJVq1ahZycHKSmpmLq1KkoLS21W8fjjz+O66+/XvRrOiMyWA0AKK9lFxgREZGncrgL7LPPPjN//vXXX0Or1Zq/1uv1yMrKQkJCgugC1q1bh/nz52PevHkAjJutbtu2DZs2bcKyZcusPkav1+Oee+7B6tWr8d1336Gqqkr063aVqQWonC1AREREHsvhAGTqZlIoFB1WfPb390dCQgKef/55US/e1NSEgwcPYvny5eZjSqUSGRkZyM7Otvm4NWvWICoqCg888AC+++67Tl+jsbERjY1Xw4pOpxNVY3umAFRZ1wSDQYCSM8GIiIg8jsMByGAwAAASExPx008/ITIy0ukXLy8vh16vR3R0tMXx6OhonDhxwupj9u7di7feegu5ubkOvUZmZiZWr17tbKlmPVu7wPQGAZfrm9CzNRARERGR5xA9Big/P1+S8NMVNTU1uO+++7Bx40aHa1i+fDmqq6vNH4WFhU7V4K9SIizQHwCnwhMREXkqh1qAXn75ZTz44IMICAjAyy+/3Ol9H3nkEYdfPDIyEiqVCiUlJRbHS0pKEBMT0+H+Z8+exfnz5zFjxgzzMVPLlJ+fH06ePIkBAwZYPEaj0UCjkbaVJjJYg6r6ZpTXNGJwdIikz01ERETycygAvfDCC7jnnnsQEBBgczNUwDg+SEwAUqvVGD16NLKyssxjjAwGA7KysrBo0aIO9x86dCiOHDlicWzFihWoqanBSy+9hPj4eIdf2xmRwWqcKeVUeCIiIk/lUADKz8+3+rkUlixZgrlz52LMmDEYO3YsXnzxRdTV1Zlnhc2ZMwdxcXHIzMxEQEAAkpOTLR4fFhYGAB2Oy+nqTDB2gREREXkiyTZD7apZs2ahrKwMTz75JIqLi5GWlobt27ebB0YXFBRAqZRs03pJcCo8ERGRZ1MIgiDYu9OSJUscfsJ169Y5VZDcdDodtFotqqurERoa2qXneHXnafzff0/ht6P74O+/TZW4QiIiImpPivfvthxqATp06JBDT9Z2k1Rvxg1RiYiIPJtDAWjXrl1y1+FR2AVGRETk2ZwaXFNYWOj0ujqeiDvCExEReTbRAailpQUrV66EVqtFQkICEhISoNVqsWLFCjQ3N8tRo9tpuyGqA0OoiIiIyM2IngX2xz/+EVu3bsVzzz2H8ePHAwCys7Px1FNPoaKiAq+//rrkRbobUxdYk94AXUMLtD38XVwRERERiSE6AL377rvYvHkzpk+fbj6WkpKC+Ph43H333T4RgAL8VQjW+KG2sQXltY0MQERERB5GdBeYRqNBQkJCh+OJiYlQq9VS1OQRTN1gFVwMkYiIyOOIDkCLFi3C008/jcbGqwOAGxsb8be//c3q9hXeijPBiIiIPJfoLrBDhw4hKysLffr0QWqqcRHAn3/+GU1NTZg0aRLuvPNO8323bt0qXaVuhgGIiIjIc4kOQGFhYfj1r39tcay7NiF1J5EhrTPBOBWeiIjI44gOQP/85z/lqMPjmFqAyjgGiIiIyOO41y6jHqQnu8CIiIg8lugWoIqKCjz55JPYtWsXSktLYTAYLG6vrKyUrDh31su8GCIDEBERkacRHYDuu+8+nDlzBg888ACio6N9ZgPU9swborILjIiIyOOIDkDfffcd9u7da54B5qs4C4yIiMhziR4DNHToUFy5ckWOWjyKaUPU+iY96ptaXFwNERERiSE6AL322mt44oknsGfPHlRUVECn01l8+IogtQoB/sZvX3kNu8GIiIg8SZfWAdLpdJg4caLFcUEQoFAooNfrJSvOnSkUCvQM0uBi1RWU1Taib89AV5dEREREDhIdgO655x74+/vj3Xff9elB0ICxG+xi1RWOAyIiIvIwogNQXl4eDh06hCFDhshRj0fpxQ1RiYiIPJLoMUBjxoxBYWGhHLV4HM4EIyIi8kyiW4D++Mc/YvHixfjzn/+MESNGwN/f3+L2lJQUyYpzdwxAREREnkl0AJo1axYA4P777zcfUygUPjcIGgAiuRo0ERGRRxIdgPLz8+WowyOZ1gLiNHgiIiLPIjoA9evXT446PFLPIHaBEREReSLRAcjk2LFjKCgoQFOTZevHbbfd5nRRnqJXCLvAiIiIPJHoAHTu3DnccccdOHLkiHnsDwDzekC+NQbI2AKka2hBY4seGj+ViysiIiIiR4ieBr948WIkJiaitLQUgYGBOHr0KL799luMGTMGu3fvlqFE96Xt4Q9/lTH4cS0gIiIizyE6AGVnZ2PNmjWIjIyEUqmEUqnEhAkTkJmZiUceeUSOGt2WaTsMgN1gREREnkR0ANLr9QgJCQEAREZG4tKlSwCMg6NPnjwpbXUeIJLjgIiIiDyO6DFAycnJ+Pnnn5GYmIj09HQ899xzUKvVeOONN9C/f385anRr5hYgToUnIiLyGKID0IoVK1BXVwcAWLNmDX71q1/h+uuvR8+ePbFlyxbJC3R35tWg69gCRERE5ClEB6CpU6eaPx84cCBOnDiByspKhIeH++TO8OYuMLYAEREReYwurwPUVkREhBRP45F6cT8wIiIijyN6EDRZ4oaoREREnocByEkMQERERJ6HAchJPc07wnMMEBERkadgAHKSqQXocn0TWvQGF1dDREREjmAAclJEkBpKBSAIQGU9W4GIiIg8AQOQk1RKBSKCOBWeiIjIkzAASYADoYmIiDwLA5AEGICIiIg8CwOQBK7OBGMAIiIi8gQMQBK42gLEMUBERESegAFIAuwCIyIi8iwMQBKI5GKIREREHoUBSAKRIa0tQDVsASIiIvIEDEAS4I7wREREnoUBSAKmWWAVdU0wGAQXV0NERET2MABJoGeQsQVIbxBQdaXZxdUQERGRPQxAElD7KaHt4Q8AqGA3GBERkdtjAJKIaSZYGQMQERGR22MAkggXQyQiIvIcDEAS4VR4IiIiz+EWAWj9+vVISEhAQEAA0tPTsX//fpv33bhxI66//nqEh4cjPDwcGRkZnd6/u3AqPBERkedweQDasmULlixZglWrViEnJwepqamYOnUqSktLrd5/9+7duPvuu7Fr1y5kZ2cjPj4eU6ZMwcWLF7u5cks9g7ghKhERkadweQBat24d5s+fj3nz5iEpKQkbNmxAYGAgNm3aZPX+77zzDh5++GGkpaVh6NChePPNN2EwGJCVldXNlVsyd4FxDBAREZHbc2kAampqwsGDB5GRkWE+plQqkZGRgezsbIeeo76+Hs3NzYiIiLB6e2NjI3Q6ncWHHEyDoDkNnoiIyP25NACVl5dDr9cjOjra4nh0dDSKi4sdeo6lS5eid+/eFiGqrczMTGi1WvNHfHy803Vbww1RiYiIPIfLu8CcsXbtWmzevBkff/wxAgICrN5n+fLlqK6uNn8UFhbKUoupBaisthGCwO0wiIiI3JmfK188MjISKpUKJSUlFsdLSkoQExPT6WP/7//+D2vXrsU333yDlJQUm/fTaDTQaDSS1NsZUwBqajGgprEFoQH+sr8mERERdY1LW4DUajVGjx5tMYDZNKB5/PjxNh/33HPP4emnn8b27dsxZsyY7ijVrh5qFYLUKgBcC4iIiMjdubwLbMmSJdi4cSP+9a9/4fjx41iwYAHq6uowb948AMCcOXOwfPly8/2fffZZrFy5Eps2bUJCQgKKi4tRXFyM2tpaV52CmWlX+E9yLyH7bAX03BmeiIjILbm0CwwAZs2ahbKyMjz55JMoLi5GWloatm/fbh4YXVBQAKXyak57/fXX0dTUhN/85jcWz7Nq1So89dRT3Vm6he15RSiqbgAAvJx1Gi9nnUasNgCrZiRhWnKsy+oiIiKijhSCj43Y1el00Gq1qK6uRmhoqCTPuT2vCAv+k4P230hF67+v3ztK9hCkNwjYn1+J0poGRIUEYGxiBFRKhf0HEhEReQCp379d3gLk6fQGAas/P9Yh/ACAAGMIWv35MUxOipEtkGzPK8Lqz4+ZW6AAsPWJiIioEy4fA+Tp9udXWgSP9gQARdUN2J9fKcvrm1qf2tdQXN2ABf/Jwfa8Illel4iIyJMxADmptMZ2+OnK/cSw1/oEGFufOBibiIjIEgOQk6JCrC/A2NX7ieHq1iciIiJPxQDkpLGJEYjVBsDW6B4FjONxxiZa36vMGa5sfSIiIvJkDEBOUikVWDUjCQA6hCDT16tmJMkyANqVrU9ERESejAFIAtOSY/H6vaMQo7UMGjHaAFmnwLuy9YmMY7Cyz1bg09yLXPiSiMjDcBq8RKYlx2JyUgwyvzyON/fmI7l3KD5dNEHWtXhMrU8P/Senw21ytz75Oi49IB+uaUVE3YEBSEIqpQIzR8bhzb35KKisR3f8zp6WHIvbUmPx2c+W091j+GYsG1sLX5qWHuiOhS+9lbPBkuGJiBzFACSxwdEhUKuU0DW0oKCyHv16Bsn+mqWtm69GhWhQWtOI6wb0xL8fSOcvfhm4w8KX3srZYMlWOSISg2OAJKb2U2JYbAgA4PCFatlfr76pBTm/VAEAfn99IgCguqGZb74y4dID8nB2TSsuCEpEYjEAyWBEHy0A4MhF+QPQT+cvo0lvQFxYD0wcatxA9lxZHXxsi7duw6UH5OFMsOSCoETUFQxAMkiJCwMAHL5QJftrfX+mHABw3cCe6NczEH5KBeqb9CjW8Q1YDlx6QB7OBEu2yhFRVzAAySAl3tgClHdRB4PMf3VeDUCR8Fcp0bdnIADgbGmdrK/rq7j0gDycCZZslSOirmAAksHAXsEI8FeitrEF+RXyBZHKuiYcvaQDAFw7IBIA0D8yGABwrrxWttf1ZW0XvrSFSw+IZwqWtnQWLNkqR0RdwQAkAz+VEsN7t44DknEg9A9nja0/Q2NC0CtEAwAYEGWcdXa2lAFILtOSY/HaPaOgaJdxNH5KToHvos6Cpb01rdgqR0RdwQAkkxFxxgAk50ywtt1fJgN6mVqA2AUmp2GxoRAEwE+pwONThgAADIKA6wf1cnFlnmvq8BhEtQb5tuytqM5WOSLqCgYgmaT0MQWgKtleY29rAJpgEYDYAtQdDhVeBmCc8bfw5gHo1zMQzXoBe06Vubgyz3W6tBalNY3wVyrw5pwxUKuMgeWtudfYbVUzbUejVln+SgsP9GerHBFZxQAkE1MAOnpJhxa9QfLnL6ioR2HlFfgpFRZN+6YxQJeqG1Df1CL565KRae2lUX3DoVAoMHV4DADg66PFLqzKs32dZ/zeXT+4FzKSojEmwfhzbQqb9kwaFm1efd30h8Cdo+IYfojIKgYgmSRGBiNIrcKVZj3OlknfHfV96/ifUX3DEaS5uqB3eJAaEUFqAMb1gEgepjflUX3DAQBThxvXYNp5ohRNLdIHXl+wvTU8TmsNk2P6Gb+3B39xLADlXaxGQ4sBYYH+WHDTQABAbqH8a3ERkWdiAJKJSqnA8Dj5usH2Whn/Y2LuBivz/G4wd9xxvb6pBceLagAAI/uGGf+ND0dksAY1DS3Yd65Cktdxx3OXS2FlPY5e0kGpACYNiwIAjG5tAXI0AJnW+RnTLwKjW8PTkYvVDKREZBX3ApNRSpwW+/MrceRiNX47Jl6y5zUYBPzQZgHE9vpHBuOn85c9vgXIXfd2OnyhGnqDgJjQAPQO6wEAUCoVmJwUjff2F+Dro8W4YbBzg6Hd9dzlYuo6HJsYgZ7BxoHQI/uGQaEAfqmoR1lNo3mmoy0/nTcGoPTECCT0DERYoD+q6ptxvEiH1PgwWesnIs/DFiAZjegjz0ywY0U6XK5vRpBaZfUXu3kqvAe3ALnz3k6HCqoAXG39MTF1g+04VuLUApjufO5y+bpd9xcAhAb4Y0i0cV89e61ABoOAn84b73NNYgQUCgVGtv7fOFTgWAsSEfkWBiAZpfYJA2AMLM0SDoQ2TX8f178n/FUdL6F5KryHtgC5+95OOQWW439Mrh0QiRCNH0prGnGosKpLz+3u5y6HsppGHGgNOFPaBCAAGGUeB9T5NhanSmtQfaUZgWoVhvcOBQCMbL0+Xb0WROTdGIBk1K9nIEIC/NDUYsCpkhrJnvf7s8YxJtbG/wBA/15XV4OWeysOObjz3k6CIJhbgEb1C7O4Te2nxM1DjeNX/tvF2WDufO5y2XGsBIIApPbRmrsUTUwDoQ/YaQEyfT9G9ws3/1FgaqHLYQsQEVnBACQjhUJhng4v1YrQjS167M83BqAJg6wHoPjwHvBXKdDQbMCl6iuSvG53cue9nS5cvoLy2kb4qxTm1b7bmtLaDfb10WIIgvjw6c7nLhfT7K+pyTEdbhvTzzgQOu9iNRqa9Taf48fWAHRNwtUlIVLjjWOICiuvoKymUcqSicgLMADJbIRpZ/iL0gSgnF+q0NBsQK8QDQZFBVu9j59KiX49jeOAPLEbzJ33djK1JiT11iLAX9Xh9puGREHtp8T5inqc7sJilO587nKovtKM7NYlHaYO7xiA4iN6IDJYg2a9gCM2/g8JgoCfWgNQ2zWxQgP8zf9HctkNRkTtMADJTOoVoc3bXwzoCUX7zaja6M6p8FJP13ZmY0y5mQdA25hVFKzxM6/MbVrYT4yxiRGICXXPc3eUmJ+HXSdK0awXMCgq2Dx2rS2FQnG1G+y89a6sXyrqjStIqxRIa3ddRsa3jgNiNxgRtcNp8DIz7Ql2srgGDc16q60GjtAbBOzPr8TnP18CAIwf0HH6e1vGcUAlsrcAyTFdW6VUYNn0oVi8OdfmfVy1t5N5AHS/cJv3mTo8GjtPlOLrY8X446RBop5fpVRg4tBeeHd/oc37uPO+VmJ/Hra3hsRpVrq/TMYkhGP70WKbM8H2t05/T+0T1uH/18i+YdhyoNAcXImITNgCJLM+4T0QHuiPZr2Ak8VdGwi9Pa8IE57dibs37sMvlfUAgOf/e6rT6dCmv6blbAFydLp2V1qILlw2jl1StXufVyqA9bNds7dTQ7Mexy7pAACj2k2BbyujdUuGvIs6XLhcL+o1ahtb8N9jJQCA0ICOf58smz7UbdcBEjt9/0qT3rx3mrXuLxNT2MwpuGx1XJVpAPQ1VlrFTDPBfr5QJcuWNETkuRiAZKZQKDCidTp8V8YB2XpTKatp7HRNGFMXmFwtQI5O1/7y8NXwtnhzLu7euA8Tnt3ZaXirqm/Chj1nAQDP/SYF780fh//7bQoC/JQwCEBsmGvGvxy5WI0Wg4BeIRrEtZut1FbPYI15H6sdrWHGUW/sOYvy2iYkRgZh/xMZeG/+OLx0Vxqua23x+yqv2C1n9nVl+v63p8twpVmPuLAe5qnr1iT31kLtp0RlXRPyyzv+PJsWQLTWLTgoKhghGj/UN+lxqsRz18UiIukxAHWDlDjTTLAqUY9zZk0Y01T4Yl0Dahul3xTV0enaD78rfkG/13afRU1DC4bGhOCOkX0wfkBP/GZ0PCa3thJ81YWxNVI4ZF7/J6zT8VcAMCXJOBvs/QOFDrd8FVc34I3vzgEAlk4bggB/FcYP6Inb0+KwblYagtQq5BZW4cODFyQ4G2mJmb5vahF881vjuU5Oiu70+6n2UyK1dSxd++nwJboG/FJRD6UC5u0v2lIqFebFQh3dVJWIfAMDUDfo6orQzqwJo+3hj8jWLQXOydAN5sw07M7C26WqK3j7h/MAgKXTh0LZZqzLLcmmAFTUpSnm1ojpnjPtAD+yr+3xPyamsSjHi2ocbvlat+MkGpoNGNMvvEOXUHRoAB7NGAwAWLv9BKrrm+3W0J0c/XnYcazY3CL4U2uY+fznS3ZXtx7dOh3+YLuB0Kaf/WGxoQgN8Lf6WNN6QBwHRERtMQB1A9OK0KdLa3GlyfZaJu05uyZMfxm7wZydhm0rvL34zSk0tRiQnhiBm9rtp3XTkCj08FehsPIKjraOxXFG27FV9kKKIAg2V4C29rwrP8nrcLyzlq/jRTp80Nqy85dbh1ltEfmf6xIwKCoYlXVNeO7rE261UaqjPw+bvj/fIdRX1jXZ3eLDvDN8gfUA1NmsuKsBiC1ARHQVA1A3iA7VoFeIBnqDgGNF1t+4rbVEOLsmjJwDoU1T1Z2di1RcfcV83h8eKMQHB4whYOn0oR1CQA+1CjcPNYaiL484tx+W2AG7l6obUFrTCD+lwjyzzxox3ZZtr/nSjw5DEIBbU2JtBix/lRKrbx8OAHjnxwJR46rk5szPgyNbfJgGQp8prUVVfZP5uHn8T4LtAJTWOhX+bFmd27WcEZHrcBp8N1AoFEiJ0yLrRCmOXKjqMFbB2tThmNAAjG631UKH5wUQ08maMHIOhFYpFVg1IwkP/SfHal2Otkc8ve04KuuaLI6lxWtthoBpybH48kgxvsorxp+nDrE7FscaeyFFAeOb8eSkGPN085zW7pphsaHooba9lIGj3Zav7jyDzT8VdLjvuMTOlzfQXbH+Bm4Kbq/f65oZcs7+PLRtEbS2xENEkBr9ewXhXFkdDv5yGZOGRaOqvgknWmdWWpsB1vaxCT0Dcb6iHocKL+OmIVEizoyIvBVbgLrJ8DjjLJdtR4osuixstkToGrDtyNXBvu3f5k1fd7YmjNxT4aclx2JwdMfF62K0AXht9kiHWgTahx8AyC2sttmaMXGocaXl/PI6nOzi/mpdGVtl3v+rk+nvgOPdli98c8pqDU9+mmfz3E3BzVbNgGs3Sp2SFIPoEE2H4zHaADxwXYJDz9HZ98/cDdYaRk27v/fvFWQe72aLKVBzHBARmbAFqBtszyvC/8v+BYDxl/bdG/chVhuAlbcOw9Pbjnf613FYD388c8cIPL2tXQuRA4sNmneFL6+D3iBIvnheqa7BvN3Dy3elQYCxO25sYgRUSgWUSgUW/CdHVIsQYL0FxiRY44cbB/fCjmMl+PJIMYbG2J4+bbPuLoytMo3/sTcAWootKmydu5jgZm+hTDnsOV2GkppGBKtVeHn2KNQ0NJt/HvbnV+Kt78/bfY7Ovn+j+4Xj/QMXzDPBTN1f6Q6sij2ybxi2HrrIneGJyIwBSGamFp72AaC4ugEPv3vI7uOrrjQjPEiNvUsnYn9+JUprGixCRmfiwntA7adEU4sBl6quID4i0Ikz6WjbkSIIgrFV5La0uA63T0uOxev3jurQvRcR5I/KOttjMey9kd8yIgY7jpXgqyNFWDJ5sOi6xY6tamjW4+gl4ww+ewOgTWNhiqsbRIU+k87O3d03St20Nx8AcNfYvpg41LKbyd73xV53LnB1JtjPhVVo1husboBqiym45hZchsEgWMwuFMO0IruY/4dE5J4YgGTkyIBYR5TWNEClVIj+q16lVCCxZxBOltTgTFmt5AHoi8PGrpoZqb1t3mdaciwmJ8VYvGkU6xrw2JZcu89v64184tBo+KsUOF1ai9MlNRgUHSKq7rGJEQhUq1BvY0Ze+zfjo5d0aNYLiAxWIz7C9gKIwNWxMNZavsS0hFk7d3feKPV0SQ2+O10OpQKYe21Ch9vtfV8A+1t89I8MQligP6rqm/HT+UocbV1Y1JF90YbEhCDAXwldQwvOlddhoI2NhDsjx7YvROQ6HAMkI3tdFo5y5g1NrqnwFy7X4+Avl6FQALeO6PyXvym83Z4Wh/EDena62Wdbts5b28PfvOFoVxZF/H/Z522GH8D45vzErcPMb8am6dNp8eEODbo2tXzFtNvQNUYbgMcyHNsbzNq525tp5cqNUv/ZunbT5KRom0G7s++LI4O3lUoFRre25Lz5XT5aDALiwnqgT7j9YO+vUiIlLgzA1e5MMcTOGiQi98cWIBk52xXhSLeAPXINhN7W2vozLrEnohwMNCZSdIdMHxGLXSfL8FVeMR7pZMPR9l0WNVeaseYL40DimWm98aONkLrrRBmmDY/BT+cvm1u60vranv7enrWWL9P5bP6psEvn3lkrClq/dsVGqVX1TdiaY1y+4P7rEju9r63vi6M1j04IR9aJUuw8UQoASOgZ6PD4tpF9w7D/fCUOFVThd2PiHXo9oGuzBonI/TEAyUhMy01XuwXsGRBlagGSNgB9fti4K/2vUsU3/UvRHTIlKRp/USpwvEiH/PI6JEYGdbiPtS4Lk7uuiUfmnSNgEGDxZlzb0IyH3snBRzkXsP1oEeoar7YUbdp7HgN7BTvc3WGr29KZc7c1rspE4297ir5c3ttfiIZmA5JiQx0K613pzjVp0VvGkO/PVmDCszsd6oYaaZ4JJq4FyN0HnxNR17ALTEaOdlm8Nrvr3QL29I80tQBJ1wV2rqwWeRd1UCkVmN7F+pztDgkLVJvfbL6y0v1gq8vCZMKgSCgUig7dc5OHx2D22L4AYBF+AOCyAysWO8LZc5+WHIu9SyeaN0p9b/44/E/ruJvlHx1BtY21gsRwdIuQZr0B/84+DwC4f0Jil9ZlctT2vCK8sONUh+OOdkOZVoQ+VVIjan88dx98TkRdwxYgGTna0jEtORZTk7veLdAZ0xigsppG6Bqabe6XJIapS2jCwEhEBKm7/DzOdodMT47Fd6fL8cGBC4gL62HRzWSrywIwfu//tu04pifHdngtvUHAN8et7+AuZXeHs+fevhUlLT4Me06VIb+8Dn/94hj+/tvULtcmZrDv10eLUVTdgMhgNWZ0oTXQUVJ0Q0WHBqC3NgCXqhuwYfcZXDewV4fvubVZXuGBjv2fcXbwOWeYEXUvBiCZ2eqyaL+OjzPdAp0JCfBHVIgGpTWNOFdWh7TWnbG7ShAEfPazsfurs9lfjnLmvP1UxjeH/PI6LN6cC8D4Rn3XNfFd7rLozu4OKa95D7UKz/0mBb/7RzY+OHgB05JjEKj2s/lmauvNtrNlG0wrTbcNbq/uPA0AuCe9HzR+8nW/SXFdtucVobJ1G41Xd53Fq7vOWgQ7a8GvV4gGPfztN5RHhWgwul84ss9WdCnAcIYZUfdjAOoGzv6176wBvYJRWtOIs6W1TgegkyU1OFNaC7VKiSnDo6UpsAu25xVh6YeHOxwvqm7AC9+cdug5rHVZeHJ3xzUJEZh3bSI2fZ+P+f8+gLa9Vvbe6O0tzGlqZVm29Qie+uwYinWW5987TN6p985eF3vB7sEbEvHGt/kdbi+raQQA83patpYy0DU0Y3xmFirarGzuaIBxJHQyBBFJj2OAukn7sSbd2bRtngpf7vxA6M9bW39uGtJLku60ruisO0QMa10W7rzWjiNS440z1doP2TG9mWZ+eczmdO6H3z1kt5Wlqr65Q/gBgGUfHZF1Krgz18Ve95kAYON3HcNPW2E9/K2O1YsK0SAiUI2GZoNF+AEcG5skZvNcexwdt0VERmwB8gHmqfClzg2EFgQBn/9sf/FDuTm7vlJnU82lmKLvKnqDgLVfnbB6m+lcbL3RS/FWKedUcGeuiyM/L/ayQmlNo9UV2Uf3C8f1z+20+pj2Y5NMtbRtBZaqy5VdaETiMQD5gAGtq97mXarCp7kXO3TBOTr48vCFahRU1qOHvwqThrluR20x3U9ip5pLMUXfVaR4o+8quaeCO3NdpOqutLYie/bZCpToGm0+xvR9eXXnGWz+qaBDQJno4P+jzs6hO7rQ7P2O4ABu8kQMQD6gsLIeAHDhcoPFYOFVM5IAwO5fjqZfbm98exYAMGlYFALVrvvRcbQ75LGMwR3edBzZRNbRgevuxh3GJclZQ1evi1Tdldaex9HzfeGbjtP3i6ob8M6+Aodf21rIAGzPeGzbAjVxaDQO/nK5SwHGXuuSu7c+ORPOGOy8m0IQBJ/qKNbpdNBqtaiurkZoqPidxD2Nrb8OO9uXyvTf+/V7RwHoGJDCA/2ReecIl/1y0xsETHh2p93ukL1LJwLo2O3grb/8ss9W4O6N+yR5LjH7lrX13vxxsi8GKPa62Pt5AQClAhAE6+fc9uep/etI+T23Ra1S4q8zk/HCN6c6hIy7rol3aNB/RJAalTYGaHcWYADY/P0BwObg8ba/Q6Ylx7oshNgLZ84EP0dqc+Z2T/v90x2kfv9mAPJipl/8XRkvowCgDfRHdX2z3V9urmAKdoD17hBfnDkj1Rv9yluT8PS2dq0soRo0tBis/jy0fay1kOAO7P28mN7Ibd1u6+fJke+5o8SGzq6GVNNjgc4DjACYN5+1Ramw3a3a2c9Td4SQzv74M537Zz8XdSn42frjUEzLmL3gKXf4cobc4czW8zMAOcmXApCcf526wxueuze9u4JUb/TWfgHtOFbs0aFTrq6czr7njv5yvf+6BHyVV9zhte+fkIi1Xx6HXqbf0p0FGDnJHUJMSzqI/ePPkeDnyB+H9lrG7AVPW68LSBO+gK63PsndMtbZ89fV1uA344d4VwBav349/v73v6O4uBipqal45ZVXMHbsWJv3/+CDD7By5UqcP38egwYNwrPPPotbbrnFodfypQD0ae5F85gfuXRHl0dn2EzckZxjNjw9dMr1V7Ot74ujXVTvzR9nnhXWfpaY3F1s7kaKEOLqNzV7NXQ1eEoRvuwFKHu3ydky1tnzCwAMjfUofPF33hOAtmzZgjlz5mDDhg1IT0/Hiy++iA8++AAnT55EVFTHGRI//PADbrjhBmRmZuJXv/oV3n33XTz77LPIyclBcnKy3dfzpQDUHeMTXrorDbenxcn6GiSenM3jDJ3W2Rqk7Oh4NWvfQzF/xLjDGz+5nr1uya4Ex+5oGXOky9XrAlB6ejquueYavPrqqwAAg8GA+Ph4/PGPf8SyZcs63H/WrFmoq6vDF198YT42btw4pKWlYcOGDXZfz5cCkJTjE2xxdQsQkbtzZryao3/EWJvxGBHkj8o65zfG7UxnY8qI2nM2pEsdgFy6EnRTUxMOHjyIjIwM8zGlUomMjAxkZ2dbfUx2drbF/QFg6tSpNu/f2NgInU5n8eErTGunAFd/2ZoobHze9uuwQH+7O9m744KARO7ENH2//SrSMdoAu+OmTAtA2vt/uGjiQOxdOhHvzR+Hl+5Kw3vzx2Hf8oxOHwsYA0xnz236HWDtd4QCwPzrE81ft7+dqD13C8ouDUDl5eXQ6/WIjrbcUyo6OhrFxcVWH1NcXCzq/pmZmdBqteaP+Ph4aYr3EJ398t1w7yhs6OS2tXeOAGD7l5u7LghI5G6mJcd2CCh7l060O27KkT9iTP8P22+3o/ZTdvpYRwLM2jtHdBrelt+SZPP212aPtBvAuoPY17cX/IDO/zgEOg+WjtxO3cPrF0Jcvnw5lixZYv5ap9P5ZAjqbDPWzm7zxAUBidxR+1WkHeXMwpyOPHZk33C7z93Z74jOfr8olQqrq3fb03Y8CWB9dl2YjfEmpvt0NgX/ttTYTmdDmv74s/V9AdDpquTzrzeOdenK7YKVzx09b4DdkmK4NABFRkZCpVKhpKTE4nhJSQliYmKsPiYmJkbU/TUaDTQajTQFe7DOfvl2dpurd7InIuf+H9p7rCPPbS+82brdVgDrjhBiCnBTk62fm7PBz9lg2dntzpy3vXBlL0DZ4mwoBeyvQ2bv+aXmFoOgx44di1deeQWAcRB03759sWjRIpuDoOvr6/H555+bj1177bVISUnhIGgiIjfU2WKFzqwp4+yyDM7OaHTVStDOLrJob90qW+HKkSn0zq5D1tnze906QFu2bMHcuXPxj3/8A2PHjsWLL76I999/HydOnEB0dDTmzJmDuLg4ZGZmAjBOg7/xxhuxdu1a3Hrrrdi8eTOeeeYZToMnIvJAcocQbyXXYoOAc6tQS7EOmU+tBP3qq6+aF0JMS0vDyy+/jPT0dADATTfdhISEBLz99tvm+3/wwQdYsWKFeSHE5557jgshEhEROUjOfcjkWofMKwNQd2IAIiIi8jxSv3+7dBo8ERERkSswABEREZHPYQAiIiIin8MARERERD6HAYiIiIh8DgMQERER+RwGICIiIvI5DEBERETkcxiAiIiIyOe4dDd4VzAtfK3T6VxcCRERETnK9L4t1QYWPheAKioqAADx8fEuroSIiIjEqqiogFardfp5fC4ARUREAAAKCgok+QZ6Cp1Oh/j4eBQWFvrUHmg8b563L+B587x9QXV1Nfr27Wt+H3eWzwUgpdI47Emr1frUD45JaGgoz9uH8Lx9C8/bt/jqeZvex51+HkmehYiIiMiDMAARERGRz/G5AKTRaLBq1SpoNBpXl9KteN48b1/A8+Z5+wKetzTnrRCkmk9GRERE5CF8rgWIiIiIiAGIiIiIfA4DEBEREfkcBiAiIiLyOT4XgNavX4+EhAQEBAQgPT0d+/fvd3VJsnrqqaegUCgsPoYOHerqsiT37bffYsaMGejduzcUCgU++eQTi9sFQcCTTz6J2NhY9OjRAxkZGTh9+rRripWQvfP+n//5nw7Xf9q0aa4pVkKZmZm45pprEBISgqioKMycORMnT560uE9DQwMWLlyInj17Ijg4GL/+9a9RUlLiooql4ch533TTTR2u+UMPPeSiiqXx+uuvIyUlxbzw3/jx4/HVV1+Zb/fGaw3YP29vvNbtrV27FgqFAo8++qj5mFTX26cC0JYtW7BkyRKsWrUKOTk5SE1NxdSpU1FaWurq0mQ1fPhwFBUVmT/27t3r6pIkV1dXh9TUVKxfv97q7c899xxefvllbNiwAT/++COCgoIwdepUNDQ0dHOl0rJ33gAwbdo0i+v/3nvvdWOF8tizZw8WLlyIffv2YceOHWhubsaUKVNQV1dnvs9jjz2Gzz//HB988AH27NmDS5cu4c4773Rh1c5z5LwBYP78+RbX/LnnnnNRxdLo06cP1q5di4MHD+LAgQOYOHEibr/9dhw9ehSAd15rwP55A953rdv66aef8I9//AMpKSkWxyW73oIPGTt2rLBw4ULz13q9Xujdu7eQmZnpwqrktWrVKiE1NdXVZXQrAMLHH39s/tpgMAgxMTHC3//+d/OxqqoqQaPRCO+9954LKpRH+/MWBEGYO3eucPvtt7uknu5UWloqABD27NkjCILx+vr7+wsffPCB+T7Hjx8XAAjZ2dmuKlNy7c9bEAThxhtvFBYvXuy6orpJeHi48Oabb/rMtTYxnbcgePe1rqmpEQYNGiTs2LHD4jylvN4+0wLU1NSEgwcPIiMjw3xMqVQiIyMD2dnZLqxMfqdPn0bv3r3Rv39/3HPPPSgoKHB1Sd0qPz8fxcXFFtdeq9UiPT3d6689AOzevRtRUVEYMmQIFixYgIqKCleXJLnq6moAVzc7PnjwIJqbmy2u+dChQ9G3b1+vuubtz9vknXfeQWRkJJKTk7F8+XLU19e7ojxZ6PV6bN68GXV1dRg/frzPXOv2523irdd64cKFuPXWWy2uKyDt/22f2Qy1vLwcer0e0dHRFsejo6Nx4sQJF1Ulv/T0dLz99tsYMmQIioqKsHr1alx//fXIy8tDSEiIq8vrFsXFxQBg9dqbbvNW06ZNw5133onExEScPXsWf/nLXzB9+nRkZ2dDpVK5ujxJGAwGPProo7juuuuQnJwMwHjN1Wo1wsLCLO7rTdfc2nkDwOzZs9GvXz/07t0bhw8fxtKlS3Hy5Els3brVhdU678iRIxg/fjwaGhoQHByMjz/+GElJScjNzfXqa23rvAHvvdabN29GTk4Ofvrppw63Sfl/22cCkK+aPn26+fOUlBSkp6ejX79+eP/99/HAAw+4sDLqDnfddZf58xEjRiAlJQUDBgzA7t27MWnSJBdWJp2FCxciLy/PK8e2dcbWeT/44IPmz0eMGIHY2FhMmjQJZ8+exYABA7q7TMkMGTIEubm5qK6uxocffoi5c+diz549ri5LdrbOOykpySuvdWFhIRYvXowdO3YgICBA1tfymS6wyMhIqFSqDiPFS0pKEBMT46Kqul9YWBgGDx6MM2fOuLqUbmO6vr5+7QGgf//+iIyM9Jrrv2jRInzxxRfYtWsX+vTpYz4eExODpqYmVFVVWdzfW665rfO2Jj09HQA8/pqr1WoMHDgQo0ePRmZmJlJTU/HSSy95/bW2dd7WeMO1PnjwIEpLSzFq1Cj4+fnBz88Pe/bswcsvvww/Pz9ER0dLdr19JgCp1WqMHj0aWVlZ5mMGgwFZWVkW/anerra2FmfPnkVsbKyrS+k2iYmJiImJsbj2Op0OP/74o09dewC4cOECKioqPP76C4KARYsW4eOPP8bOnTuRmJhocfvo0aPh7+9vcc1PnjyJgoICj77m9s7bmtzcXADw+GvensFgQGNjo9dea1tM522NN1zrSZMm4ciRI8jNzTV/jBkzBvfcc4/5c8mut3Rjtt3f5s2bBY1GI7z99tvCsWPHhAcffFAICwsTiouLXV2abP70pz8Ju3fvFvLz84Xvv/9eyMjIECIjI4XS0lJXlyapmpoa4dChQ8KhQ4cEAMK6deuEQ4cOCb/88osgCIKwdu1aISwsTPj000+Fw4cPC7fffruQmJgoXLlyxcWVO6ez866pqREef/xxITs7W8jPzxe++eYbYdSoUcKgQYOEhoYGV5fulAULFgharVbYvXu3UFRUZP6or6833+ehhx4S+vbtK+zcuVM4cOCAMH78eGH8+PEurNp59s77zJkzwpo1a4QDBw4I+fn5wqeffir0799fuOGGG1xcuXOWLVsm7NmzR8jPzxcOHz4sLFu2TFAoFMJ///tfQRC881oLQufn7a3X2pr2s92kut4+FYAEQRBeeeUVoW/fvoJarRbGjh0r7Nu3z9UlyWrWrFlCbGysoFarhbi4OGHWrFnCmTNnXF2W5Hbt2iUA6PAxd+5cQRCMU+FXrlwpREdHCxqNRpg0aZJw8uRJ1xYtgc7Ou76+XpgyZYrQq1cvwd/fX+jXr58wf/58rwj81s4ZgPDPf/7TfJ8rV64IDz/8sBAeHi4EBgYKd9xxh1BUVOS6oiVg77wLCgqEG264QYiIiBA0Go0wcOBA4c9//rNQXV3t2sKddP/99wv9+vUT1Gq10KtXL2HSpEnm8CMI3nmtBaHz8/bWa21N+wAk1fVWCIIgdLGlioiIiMgj+cwYICIiIiITBiAiIiLyOQxARERE5HMYgIiIiMjnMAARERGRz2EAIiIiIp/DAEREREQ+hwGIiIiIfA4DEBEREfkcBiAiIiLyOQxARERE5HMYgIjIo3388cfQaDQoKyvrcNuf//xnjBw50gVVEZG742aoROTRZs6cCQD45JNPOtyWk5OD0aNH4+eff0ZKSkr3FkZEbo0tQETksQwGA3bu3InJkydbvX3UqFHQarXYsWNHN1dGRO6OAYiIPFZpaSlqamowcOBA87H77rsPH374ofnrAQMG4PTp064oj4jcGAMQEXm8lpYW8+e5ubkoKCgwf61UKqFQKFxRFhG5MQYgIvJYUVFRCAwMxKlTpwAAjY2NOH36NI4dOwbA2EWWn5+PhIQEF1ZJRO7Iz9UFEBF1lVKpxO23345XX30VN954Iz766CNMnDgRH3/8MR544AHs2rULlZWVmDFjhqtLJSI3wwBERB7tpZdewpw5c3Dttddi8ODB+PDDD/HFF19g+vTpCAgIwOuvv46kpCRXl0lEbobT4ImIiMjncAwQERER+RwGICIiIvI5DEBERETkcxiAiIiIyOcwABEREZHPYQAiIiIin8MARERERD6HAYiIiIh8DgMQERER+RwGICIiIvI5DEBERETkc/4/VxEKXsQTQUwAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -974,10 +945,10 @@
      "start_time": "2020-03-21T21:20:04.164574Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.109294Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.109140Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.275426Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.274900Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.689566Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.689446Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.957380Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.956982Z"
     }
    },
    "outputs": [],
@@ -997,16 +968,16 @@
      "start_time": "2020-03-21T21:20:05.686139Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.277553Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.277392Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.395549Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.395000Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.958925Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.958776Z",
+     "iopub.status.idle": "2024-06-03T14:31:50.033475Z",
+     "shell.execute_reply": "2024-06-03T14:31:50.033065Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1028,17 +999,17 @@
      "start_time": "2020-03-21T21:20:06.250554Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.397416Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.397256Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.511438Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.510886Z"
+     "iopub.execute_input": "2024-06-03T14:31:50.035372Z",
+     "iopub.status.busy": "2024-06-03T14:31:50.035191Z",
+     "iopub.status.idle": "2024-06-03T14:31:50.106452Z",
+     "shell.execute_reply": "2024-06-03T14:31:50.106153Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1074,10 +1045,10 @@
      "start_time": "2020-03-21T21:20:07.755573Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.513438Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.513277Z",
-     "iopub.status.idle": "2024-06-03T10:30:19.132873Z",
-     "shell.execute_reply": "2024-06-03T10:30:19.132312Z"
+     "iopub.execute_input": "2024-06-03T14:31:50.108228Z",
+     "iopub.status.busy": "2024-06-03T14:31:50.108088Z",
+     "iopub.status.idle": "2024-06-03T14:31:53.038747Z",
+     "shell.execute_reply": "2024-06-03T14:31:53.038395Z"
     }
    },
    "outputs": [],
@@ -1108,10 +1079,10 @@
      "start_time": "2020-03-21T21:20:15.951751Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:19.135386Z",
-     "iopub.status.busy": "2024-06-03T10:30:19.135210Z",
-     "iopub.status.idle": "2024-06-03T10:30:19.138920Z",
-     "shell.execute_reply": "2024-06-03T10:30:19.138289Z"
+     "iopub.execute_input": "2024-06-03T14:31:53.040706Z",
+     "iopub.status.busy": "2024-06-03T14:31:53.040458Z",
+     "iopub.status.idle": "2024-06-03T14:31:53.042707Z",
+     "shell.execute_reply": "2024-06-03T14:31:53.042470Z"
     }
    },
    "outputs": [
@@ -1119,7 +1090,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time discretization error = 1.9e-05\n"
+      "Time discretization error = 2.9e-05\n"
      ]
     }
    ],
@@ -1153,7 +1124,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/06_example_3states.ipynb.txt b/master/_sources/notebooks/06_example_3states.ipynb.txt
index b168459b..d75f4faf 100644
--- a/master/_sources/notebooks/06_example_3states.ipynb.txt
+++ b/master/_sources/notebooks/06_example_3states.ipynb.txt
@@ -16,10 +16,10 @@
      "start_time": "2020-03-21T21:31:16.947553Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.206961Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.206139Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.725494Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.724916Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.376969Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.376836Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.861352Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.860942Z"
     }
    },
    "outputs": [
@@ -28,16 +28,15 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "numpy     : 1.24.4\n",
       "qutip     : 4.7.6\n",
-      "scipy     : 1.10.1\n",
+      "krotov    : 1.3.0+dev\n",
+      "sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
       "matplotlib: 3.7.5\n",
-      "krotov    : 1.3.0\n",
-      "sys       : 3.8.16 (default, May 26 2024, 19:41:26) \n",
-      "[GCC 12.2.0]\n",
+      "numpy     : 1.26.4\n",
+      "scipy     : 1.12.0\n",
       "\n"
      ]
     }
@@ -124,10 +123,10 @@
      "start_time": "2020-03-21T21:31:20.724436Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.752291Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.752017Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.758455Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.757921Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.882411Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.882238Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.886521Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.886280Z"
     },
     "lines_to_next_cell": 2
    },
@@ -179,10 +178,10 @@
      "start_time": "2020-03-21T21:31:22.096718Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.760380Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.760230Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.762898Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.762342Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.887847Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.887740Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.889367Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.889143Z"
     }
    },
    "outputs": [],
@@ -216,10 +215,10 @@
      "start_time": "2020-03-21T21:31:24.057786Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.764918Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.764733Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.768205Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.767589Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.890703Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.890597Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.892721Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.892434Z"
     }
    },
    "outputs": [],
@@ -246,10 +245,10 @@
      "start_time": "2020-03-21T21:31:24.964035Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.770016Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.769863Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.772690Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.772126Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.894184Z",
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+     "shell.execute_reply": "2024-06-03T14:24:00.895476Z"
     }
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    "outputs": [],
@@ -273,10 +272,10 @@
      "start_time": "2020-03-21T21:31:26.257115Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.774616Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.774466Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.776836Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.776461Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.897109Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.897026Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.898648Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.898398Z"
     }
    },
    "outputs": [],
@@ -300,10 +299,10 @@
      "start_time": "2020-03-21T21:31:27.288042Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.778668Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.778519Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.781301Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.780802Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.900017Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.899936Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.901808Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.901551Z"
     }
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    "outputs": [],
@@ -329,10 +328,10 @@
      "start_time": "2020-03-21T21:31:28.060909Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.783146Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.782993Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.785342Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.784841Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.903239Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.903160Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.905094Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.904643Z"
     }
    },
    "outputs": [],
@@ -357,10 +356,10 @@
      "start_time": "2020-03-21T21:31:29.232057Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.787239Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.787014Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.795619Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.795235Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.906732Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.906637Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.912774Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.912364Z"
     }
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    "outputs": [],
@@ -386,10 +385,10 @@
      "start_time": "2020-03-21T21:31:30.666954Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.797523Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.797371Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.800583Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.800165Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.914704Z",
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+     "shell.execute_reply": "2024-06-03T14:24:00.916732Z"
     }
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    "outputs": [],
@@ -415,10 +414,10 @@
      "start_time": "2020-03-21T21:31:32.090987Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.802338Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.802185Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.952075Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.951495Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.918326Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.918231Z",
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+     "shell.execute_reply": "2024-06-03T14:24:01.112532Z"
     }
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    "outputs": [
@@ -460,10 +459,10 @@
      "start_time": "2020-03-21T21:31:33.930103Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.954121Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.953956Z",
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-     "shell.execute_reply": "2024-06-03T10:32:49.957179Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.172595Z",
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     }
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    "outputs": [],
@@ -511,10 +510,10 @@
      "start_time": "2020-03-21T21:31:35.459426Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.959488Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.959336Z",
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-     "shell.execute_reply": "2024-06-03T10:32:49.962981Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.200433Z",
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+     "shell.execute_reply": "2024-06-03T14:24:01.203147Z"
     }
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    "outputs": [],
@@ -542,10 +541,10 @@
      "start_time": "2020-03-21T21:31:36.671546Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.965219Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.965067Z",
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-     "shell.execute_reply": "2024-06-03T10:32:49.967347Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.205079Z",
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    "outputs": [],
@@ -571,10 +570,10 @@
      "start_time": "2020-03-21T21:31:38.576793Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.969783Z",
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-     "iopub.status.idle": "2024-06-03T10:32:49.992390Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.991924Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.209130Z",
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     }
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    "outputs": [
@@ -640,10 +639,10 @@
      "start_time": "2020-03-21T21:31:41.254299Z"
     },
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-     "iopub.execute_input": "2024-06-03T10:32:49.994375Z",
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@@ -667,10 +666,10 @@
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+     "iopub.execute_input": "2024-06-03T14:24:01.226141Z",
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    "outputs": [],
@@ -691,10 +690,10 @@
      "start_time": "2020-03-21T21:31:43.260810Z"
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-     "iopub.execute_input": "2024-06-03T10:32:50.004811Z",
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+     "shell.execute_reply": "2024-06-03T14:24:21.391810Z"
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     "scrolled": true
    },
@@ -712,10 +711,10 @@
      "start_time": "2020-03-21T21:32:06.511175Z"
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     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.359682Z",
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-     "iopub.status.idle": "2024-06-03T10:33:25.370328Z",
-     "shell.execute_reply": "2024-06-03T10:33:25.369929Z"
+     "iopub.execute_input": "2024-06-03T14:24:21.393955Z",
+     "iopub.status.busy": "2024-06-03T14:24:21.393845Z",
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+     "shell.execute_reply": "2024-06-03T14:24:21.398841Z"
     }
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    "outputs": [
@@ -754,10 +753,10 @@
      "start_time": "2020-03-21T21:32:18.385960Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.372310Z",
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-     "shell.execute_reply": "2024-06-03T10:33:25.375382Z"
+     "iopub.execute_input": "2024-06-03T14:24:21.400577Z",
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@@ -774,10 +773,10 @@
      "start_time": "2020-03-21T21:32:19.323843Z"
     },
     "execution": {
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+     "iopub.execute_input": "2024-06-03T14:24:21.403895Z",
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@@ -800,10 +799,10 @@
      "start_time": "2020-03-21T21:32:20.180098Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.382499Z",
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+     "iopub.execute_input": "2024-06-03T14:24:21.407142Z",
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+     "shell.execute_reply": "2024-06-03T14:24:21.409155Z"
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@@ -830,10 +829,10 @@
      "start_time": "2020-03-21T21:32:20.943081Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.388130Z",
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    "outputs": [
@@ -877,10 +876,10 @@
      "start_time": "2020-03-21T21:32:41.070283Z"
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+     "iopub.execute_input": "2024-06-03T14:24:26.855582Z",
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+     "shell.execute_reply": "2024-06-03T14:24:26.857106Z"
     }
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    "outputs": [],
@@ -911,10 +910,10 @@
      "start_time": "2020-03-21T21:32:50.996511Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:34.921618Z",
-     "iopub.status.busy": "2024-06-03T10:33:34.921438Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.755388Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.754775Z"
+     "iopub.execute_input": "2024-06-03T14:24:26.858929Z",
+     "iopub.status.busy": "2024-06-03T14:24:26.858842Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.112930Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.112641Z"
     }
    },
    "outputs": [
@@ -923,28 +922,28 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T   ∑∫gₐ(t)dt          J       ΔJ_T         ΔJ  secs\n",
-      "0     1.22e-01    0.00e+00   1.22e-01        n/a        n/a     6\n"
+      "0     1.22e-01    0.00e+00   1.22e-01        n/a        n/a     4\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1     7.49e-02    2.34e-02   9.83e-02  -4.67e-02  -2.33e-02    20\n"
+      "1     7.49e-02    2.34e-02   9.83e-02  -4.67e-02  -2.33e-02    11\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2     7.41e-02    4.06e-04   7.45e-02  -8.12e-04  -4.06e-04    20\n"
+      "2     7.41e-02    4.06e-04   7.45e-02  -8.12e-04  -4.06e-04    11\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3     7.33e-02    3.78e-04   7.37e-02  -7.55e-04  -3.77e-04    21\n"
+      "3     7.33e-02    3.78e-04   7.37e-02  -7.55e-04  -3.77e-04    11\n"
      ]
     }
    ],
@@ -976,10 +975,10 @@
      "start_time": "2020-03-21T21:34:37.817889Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.757492Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.757332Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.764707Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.764229Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.114621Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.114525Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.121494Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.121174Z"
     }
    },
    "outputs": [],
@@ -1010,10 +1009,10 @@
      "start_time": "2020-03-21T21:34:38.622364Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.766601Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.766453Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.769798Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.769427Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.123221Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.123112Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.125280Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.125040Z"
     }
    },
    "outputs": [
@@ -1054,10 +1053,10 @@
      "start_time": "2020-03-21T21:34:47.074533Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.771684Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.771538Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.774100Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.773642Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.126643Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.126550Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.128783Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.128329Z"
     }
    },
    "outputs": [],
@@ -1074,10 +1073,10 @@
      "start_time": "2020-03-21T21:34:48.073785Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.776217Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.775846Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.907336Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.906697Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.130233Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.130133Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.197221Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.196834Z"
     }
    },
    "outputs": [
@@ -1105,10 +1104,10 @@
      "start_time": "2020-03-21T21:34:49.038956Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.909439Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.909277Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.912273Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.911713Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.198929Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.198788Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.200859Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.200553Z"
     }
    },
    "outputs": [],
@@ -1130,10 +1129,10 @@
      "start_time": "2020-03-21T21:34:49.807447Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.914185Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.914032Z",
-     "iopub.status.idle": "2024-06-03T10:35:34.124544Z",
-     "shell.execute_reply": "2024-06-03T10:35:34.123926Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.202971Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.202851Z",
+     "iopub.status.idle": "2024-06-03T14:25:34.786184Z",
+     "shell.execute_reply": "2024-06-03T14:25:34.785903Z"
     },
     "scrolled": true
    },
@@ -1151,10 +1150,10 @@
      "start_time": "2020-03-21T21:35:25.571194Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:34.126947Z",
-     "iopub.status.busy": "2024-06-03T10:35:34.126760Z",
-     "iopub.status.idle": "2024-06-03T10:35:34.137510Z",
-     "shell.execute_reply": "2024-06-03T10:35:34.137017Z"
+     "iopub.execute_input": "2024-06-03T14:25:34.787921Z",
+     "iopub.status.busy": "2024-06-03T14:25:34.787819Z",
+     "iopub.status.idle": "2024-06-03T14:25:34.793068Z",
+     "shell.execute_reply": "2024-06-03T14:25:34.792816Z"
     }
    },
    "outputs": [
@@ -1179,10 +1178,10 @@
      "start_time": "2020-03-21T21:35:26.698539Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:34.139820Z",
-     "iopub.status.busy": "2024-06-03T10:35:34.139458Z",
-     "iopub.status.idle": "2024-06-03T10:35:34.142794Z",
-     "shell.execute_reply": "2024-06-03T10:35:34.142333Z"
+     "iopub.execute_input": "2024-06-03T14:25:34.795116Z",
+     "iopub.status.busy": "2024-06-03T14:25:34.794991Z",
+     "iopub.status.idle": "2024-06-03T14:25:34.796931Z",
+     "shell.execute_reply": "2024-06-03T14:25:34.796672Z"
     }
    },
    "outputs": [],
@@ -1212,10 +1211,10 @@
      "start_time": "2020-03-21T21:35:27.831020Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:34.144957Z",
-     "iopub.status.busy": "2024-06-03T10:35:34.144764Z",
-     "iopub.status.idle": "2024-06-03T10:35:46.772654Z",
-     "shell.execute_reply": "2024-06-03T10:35:46.772043Z"
+     "iopub.execute_input": "2024-06-03T14:25:34.798436Z",
+     "iopub.status.busy": "2024-06-03T14:25:34.798332Z",
+     "iopub.status.idle": "2024-06-03T14:25:42.069102Z",
+     "shell.execute_reply": "2024-06-03T14:25:42.068846Z"
     }
    },
    "outputs": [
@@ -1245,10 +1244,10 @@
      "start_time": "2020-03-21T21:35:41.361375Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:46.776326Z",
-     "iopub.status.busy": "2024-06-03T10:35:46.776127Z",
-     "iopub.status.idle": "2024-06-03T10:35:46.779205Z",
-     "shell.execute_reply": "2024-06-03T10:35:46.778773Z"
+     "iopub.execute_input": "2024-06-03T14:25:42.071910Z",
+     "iopub.status.busy": "2024-06-03T14:25:42.071772Z",
+     "iopub.status.idle": "2024-06-03T14:25:42.073791Z",
+     "shell.execute_reply": "2024-06-03T14:25:42.073503Z"
     }
    },
    "outputs": [],
@@ -1270,44 +1269,16 @@
      "start_time": "2020-03-21T21:35:42.567254Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:46.781268Z",
-     "iopub.status.busy": "2024-06-03T10:35:46.781088Z",
-     "iopub.status.idle": "2024-06-03T10:35:47.472461Z",
-     "shell.execute_reply": "2024-06-03T10:35:47.471816Z"
+     "iopub.execute_input": "2024-06-03T14:25:42.075128Z",
+     "iopub.status.busy": "2024-06-03T14:25:42.075024Z",
+     "iopub.status.idle": "2024-06-03T14:25:42.258918Z",
+     "shell.execute_reply": "2024-06-03T14:25:42.258583Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1341,7 +1312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/07_example_PE.ipynb.txt b/master/_sources/notebooks/07_example_PE.ipynb.txt
index cca5e8cf..73857eef 100644
--- a/master/_sources/notebooks/07_example_PE.ipynb.txt
+++ b/master/_sources/notebooks/07_example_PE.ipynb.txt
@@ -17,10 +17,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.265098Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.264891Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.831750Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.831209Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.187140Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.186638Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.737586Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.737321Z"
     }
    },
    "outputs": [
@@ -29,15 +29,15 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
+      "scipy      : 1.12.0\n",
       "qutip      : 4.7.6\n",
-      "krotov     : 1.3.0\n",
+      "krotov     : 1.3.0+dev\n",
       "weylchamber: 0.4.0\n",
-      "scipy      : 1.10.1\n",
       "matplotlib : 3.7.5\n",
-      "numpy      : 1.24.4\n",
+      "numpy      : 1.26.4\n",
       "\n"
      ]
     }
@@ -147,10 +147,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.857901Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.857653Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.861621Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.861218Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.759869Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.759602Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.761728Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.761475Z"
     }
    },
    "outputs": [],
@@ -192,10 +192,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.863555Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.863407Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.866369Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.865876Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.763170Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.763069Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.764762Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.764550Z"
     }
    },
    "outputs": [],
@@ -216,10 +216,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.868409Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.868250Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.871343Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.870899Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.766044Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.765962Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.767977Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.767752Z"
     },
     "lines_to_next_cell": 2
    },
@@ -240,10 +240,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.873210Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.873057Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.007363Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.006895Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.769336Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.769220Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.846777Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.845740Z"
     }
    },
    "outputs": [
@@ -274,10 +274,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.009535Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.009374Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.015670Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.015232Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.856159Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.855355Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.870140Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.869001Z"
     }
    },
    "outputs": [],
@@ -332,10 +332,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.017693Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.017543Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.022414Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.021961Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.880759Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.880069Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.888306Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.887598Z"
     }
    },
    "outputs": [],
@@ -358,10 +358,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.024736Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.024255Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.027920Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.027409Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.895949Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.895424Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.902649Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.901109Z"
     }
    },
    "outputs": [],
@@ -410,10 +410,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.029893Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.029743Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.033537Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.033103Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.911504Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.911124Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.927263Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.920852Z"
     }
    },
    "outputs": [],
@@ -428,10 +428,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.035420Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.035241Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.038954Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.038504Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.936431Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.934388Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.949888Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.949033Z"
     }
    },
    "outputs": [
@@ -466,10 +466,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.040991Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.040767Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.047675Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.047245Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.957180Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.956511Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.984752Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.983508Z"
     }
    },
    "outputs": [
@@ -585,10 +585,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.049736Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.049586Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.052938Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.052502Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.994382Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.993854Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.999948Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.999317Z"
     }
    },
    "outputs": [
@@ -600,34 +600,34 @@
       "\n",
       "make_PE_krotov_chi_constructor(canonical_basis, unitarity_weight=0)\n",
       "    Return a constructor for the χ's in a PE optimization.\n",
-      "    \n",
+      "\n",
       "    Return a `chi_constructor` that determines the boundary condition of the\n",
       "    backwards propagation in an optimization towards a perfect entangler in\n",
       "    Krotov's method, based on the foward-propagtion of the Bell states. In\n",
       "    detail, the function returns a callable function that calculates\n",
-      "    \n",
+      "\n",
       "    .. math::\n",
-      "    \n",
+      "\n",
       "        \\ket{\\chi_{i}}\n",
       "        =\n",
       "        \\frac{\\partial F_{PE}}{\\partial \\bra{\\phi_i}}\n",
       "        \\Bigg|_{\\ket{\\phi_{i}(T)}}\n",
-      "    \n",
+      "\n",
       "    for all $i$ with $\\ket{\\phi_{0}(T)}, ..., \\ket{\\phi_{3}(T)}$ the forward\n",
       "    propagated Bell states at final time $T$, cf. Eq. (33b) in Ref. [1].\n",
       "    $F_{PE}$ is the perfect-entangler functional\n",
       "    :func:`~weylchamber.perfect_entanglers.F_PE`. For the details of the\n",
       "    derivative see Appendix G in Ref. [2].\n",
-      "    \n",
+      "\n",
       "    References:\n",
-      "    \n",
+      "\n",
       "    [1] `M. H. Goerz, et al., Phys. Rev. A 91, 062307 (2015)\n",
       "    <https://doi.org/10.1103/PhysRevA.91.062307>`_\n",
-      "    \n",
+      "\n",
       "    [2] `M. H. Goerz, Optimizing Robust Quantum Gates in Open Quantum Systems.\n",
       "    PhD thesis, University of Kassel, 2015\n",
       "    <https://michaelgoerz.net/research/diss_goerz.pdf>`_\n",
-      "    \n",
+      "\n",
       "    Args:\n",
       "        canonical_basis (list[qutip.Qobj]): A list of four basis states that\n",
       "            define the canonical basis $\\ket{00}$, $\\ket{01}$, $\\ket{10}$, and\n",
@@ -635,7 +635,7 @@
       "        unitarity_weight (float): A weight in [0, 1] that determines how much\n",
       "            emphasis is placed on  maintaining population in the logical\n",
       "            subspace.\n",
-      "    \n",
+      "\n",
       "    Returns:\n",
       "        callable: a function ``chi_constructor(fw_states_T, **kwargs)`` that\n",
       "        receives the result of a foward propagation of the Bell states\n",
@@ -657,10 +657,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.054772Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.054623Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.060997Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.060556Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.004313Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.004148Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.014026Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.013137Z"
     }
    },
    "outputs": [],
@@ -751,10 +751,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.063011Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.062831Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.067603Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.067167Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.018838Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.018718Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.025022Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.024128Z"
     }
    },
    "outputs": [],
@@ -825,10 +825,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.069631Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.069479Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.072436Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.072000Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.028478Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.028369Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.032169Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.030853Z"
     }
    },
    "outputs": [],
@@ -860,10 +860,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.074329Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.074178Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.078086Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.077651Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.036940Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.036397Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.041896Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.040303Z"
     }
    },
    "outputs": [],
@@ -895,10 +895,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.080021Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.079827Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.082494Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.082048Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.046945Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.046817Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.051515Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.050551Z"
     }
    },
    "outputs": [],
@@ -917,10 +917,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.084335Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.084148Z",
-     "iopub.status.idle": "2024-06-03T10:39:15.447707Z",
-     "shell.execute_reply": "2024-06-03T10:39:15.447189Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.053193Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.053110Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.474389Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.474076Z"
     }
    },
    "outputs": [
@@ -946,11 +946,11 @@
       "    S(t) (ranges): [0.000000, 1.000000]\n",
       "    iter_start: 0\n",
       "    iter_stop: 20\n",
-      "    duration: 1.7 secs (started at 2024-06-03 12:38:39)\n",
+      "    duration: 1.4 secs (started at 2024-06-03 10:32:26)\n",
       "    optimized pulses (ranges): [0.00, 0.30]\n",
       "    ∫gₐ(t)dt: 0.00e+00\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): None, [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): None, [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -967,11 +967,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 1\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:38:40)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:27)\n",
       "    optimized pulses (ranges): [0.00, 0.32]\n",
       "    ∫gₐ(t)dt: 2.58e-01\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -988,11 +988,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 2\n",
-      "    duration: 4.4 secs (started at 2024-06-03 12:38:45)\n",
+      "    duration: 3.2 secs (started at 2024-06-03 10:32:30)\n",
       "    optimized pulses (ranges): [0.00, 0.34]\n",
       "    ∫gₐ(t)dt: 2.20e-01\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1009,11 +1009,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 3\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:38:49)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:33)\n",
       "    optimized pulses (ranges): [0.00, 0.35]\n",
       "    ∫gₐ(t)dt: 1.46e-01\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1030,11 +1030,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 4\n",
-      "    duration: 4.4 secs (started at 2024-06-03 12:38:53)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:36)\n",
       "    optimized pulses (ranges): [0.00, 0.37]\n",
       "    ∫gₐ(t)dt: 7.72e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1051,11 +1051,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 5\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:38:58)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:39)\n",
       "    optimized pulses (ranges): [0.00, 0.37]\n",
       "    ∫gₐ(t)dt: 3.88e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1072,11 +1072,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 6\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:39:02)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:43)\n",
       "    optimized pulses (ranges): [0.00, 0.38]\n",
       "    ∫gₐ(t)dt: 2.03e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1093,11 +1093,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 7\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:39:06)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:46)\n",
       "    optimized pulses (ranges): [0.00, 0.39]\n",
       "    ∫gₐ(t)dt: 1.13e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1114,11 +1114,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 8\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:39:11)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:49)\n",
       "    optimized pulses (ranges): [0.00, 0.39]\n",
       "    ∫gₐ(t)dt: 6.68e-03\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1152,10 +1152,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:15.449812Z",
-     "iopub.status.busy": "2024-06-03T10:39:15.449623Z",
-     "iopub.status.idle": "2024-06-03T10:39:15.453232Z",
-     "shell.execute_reply": "2024-06-03T10:39:15.452842Z"
+     "iopub.execute_input": "2024-06-03T14:32:52.475866Z",
+     "iopub.status.busy": "2024-06-03T14:32:52.475776Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.478006Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.477763Z"
     }
    },
    "outputs": [
@@ -1164,11 +1164,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:38:39\n",
+       "- Started at 2024-06-03 10:32:26\n",
        "- Number of objectives: 4\n",
        "- Number of iterations: 8\n",
        "- Reason for termination: Reached convergence: achieved perfect entangler\n",
-       "- Ended at 2024-06-03 12:39:15 (0:00:36)"
+       "- Ended at 2024-06-03 10:32:52 (0:00:26)"
       ]
      },
      "execution_count": 19,
@@ -1195,44 +1195,16 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:15.455211Z",
-     "iopub.status.busy": "2024-06-03T10:39:15.455036Z",
-     "iopub.status.idle": "2024-06-03T10:39:16.163814Z",
-     "shell.execute_reply": "2024-06-03T10:39:16.163218Z"
+     "iopub.execute_input": "2024-06-03T14:32:52.479449Z",
+     "iopub.status.busy": "2024-06-03T14:32:52.479366Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.652610Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.652339Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
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aFMREREQkRXv48CHdunVj6NChALz//vusXbsWT0/PRI0T9yDnGjVq6AwxEbEqBTERERFJsa5fv07dunWZM2cOadKk4ffff+eXX35J0izWqVOnCA4OJm3atLz66qtamigiVqW5dhEREUmR9u3bR4sWLbh8+TKenp4sXryY+vXrJ3k802xYlSpVcHd3VxATEavSjJiIiIikOEuWLKFmzZpcvnyZUqVK4e/vn6wQBv9r1FG9enUA89JE7RETEWtQEBMREZEUw2g0MmrUKNq1a0d4eDiNGzfGz8+PEiVKJHvsuPvDAM2IiYhVKYiJiIhIivDgwQO6du3KiBEjAPjggw9YvXo1WbNmTfbYoaGhHD58GPjfjJiCmIhYk+baRURExOFdvXqVVq1aERgYiKurK7/99ht9+vSx2PgBAQEYjUaKFClCnjx5ANS+XkSsSu8sIiIi4tD27NlDy5YtuXLlCtmyZWPJkiXUrVvXoteIe5CzidrXi4g1aWmiiIiIOKxFixZRq1Ytrly5QpkyZQgICLB4CIP4BzmbaGmiiFiTgpiIiIg4HKPRyMiRI+nQoQMPHjygadOm7Nq1i2LFiln8WrGxsfj5+QHxZ8QUxETEmrQ0UURERBxKeHg4b775JgsXLgRg0KBBfP/991YLRMeOHSM0NJT06dNTvnx58/3aIyYi1qR3FhEREXEYV65coWXLluzZswc3Nzd+//13evXqZdVrmpYlenl5xQtd2iMmItakICYiIiIOITAwkJYtW3Lt2jWyZ8/O33//Te3ata1+3ccPcjbR0kQRsSbtERMRERG7W7BgAbVr1+batWuUK1eOwMBAm4QwePIgZxMFMRGxJgUxERERsZvY2Fg+//xzOnXqxMOHD3n99dfZuXMnL730kk2uHxISwvHjxwGoVq1avO+ZliZqj5iIWIPeWURERMQuwsLC6NGjB0uWLAHg448/5rvvvrPpDJSpW2KJEiXIkSNHvO9pRkxErElBTERERGzu8uXLtGjRgn379uHm5saff/5Jz549bV7H0w5yNlEQExFrUhATERERm/L396dVq1Zcv36dnDlzsnTpUnx8fOxSy9MOcjZR+3oRsSbtERMRERGbmTt3LnXq1OH69eu88sorBAQE2C2ERUdH4+/vDzx9Rkzt60XEmhTERERExOpiY2MZNmwYXbt2JSIighYtWrBjxw6KFClit5oOHz5MWFgYmTJlomzZsk98X0sTRcSaFMRERETEqu7fv0+7du0YPXo0AJ9++il///03mTJlsmtdpmWJ1apVe2rYUhATEWvSomcRERGxmosXL9KiRQsOHDiAu7s7kydPpnv37vYuC3j2Qc4m2iMmItakdxYRERGxil27dtG6dWtu3LhBrly5WLp06VP3YtnLsw5yNtEeMRGxJgWxRDAajZw/f55Dhw5x+fJl7ty5Q9q0afH09KREiRJUrVqVdOnS2btMERERu5s1axa9e/cmMjKSChUqsHz5cgoXLmzvssxu3rzJmTNnAPD29n7qY7Q0UUSsSUHs/7333ntMmjQJgMKFC3P+/HkAbt++zbJly1i3bh3//fcft27deuYYbm5uvP7663z44YfUqVPHFmWLiIg4lNjYWIYOHcqYMWMAaNWqFbNmzSJjxox2riw+02xYuXLlyJo161MfoyAmItakZh3/b9WqVeavfX19AejXrx958uThrbfeYuHChc8NYQBRUVEsW7aMunXr0qNHD+7evWvVmkVERBzJvXv3aN26tTmEDR06lCVLljhcCIMX7w8D7RETEevSOwuwb98+Ll++bL5tCmL+/v5ERkY+8fg0adKQN29ecufOTVRUFBcuXCA0NDTeY2bOnMnx48fZuHGjQ/4AEhERsaQLFy7g6+vLoUOHSJs2LVOmTKFr1672LuuZnneQs4n2iImINWlGDFixYoX560yZMlG3bt0nHpM1a1b69u3L6tWruX37NpcuXWL37t0cOHCA4OBgNm3aRK1ateI9JyAggJ49e1q5ehEREfvasWMHVatW5dChQ+TJk4ctW7Y4dAiLjIwkMDAQeHajDtDSRBGxLgUxYOXKleavmzRpgru7u/l2kSJF+Ouvv7h69Sq//fYbzZo1e+LckzRp0lC3bl02bdpEnz594n1vyZIlbNq0ybovQERExE5mzJhB/fr1CQoKomLFigQEBDyz+YWjOHDgAA8fPsTT05OSJUs+83EKYiJiTU4fxK5evcrevXvNt03LEgFGjhzJiRMn6NWrFx4eHi8cK02aNEycOJEqVarEu/+vv/6yXMEiIiIOICYmhsGDB9OzZ08iIyNp06YN27dvp2DBgvYu7YXi7g9zcXn2r0KmpYnaIyYi1uD0QWzVqlUYjUbgUZB6/fXXzd97/fXX482OJUSaNGkYPHhwvPvWr1+f/EJFREQcxL1792jVqhVjx44FYMSIESxatIgMGTLYubKEScj+MNCMmIhYl9N/xBN3f1j16tXJnj17ssd8fK9YcHAw4eHhpE+fPtlji4iI2NO5c+do0aIFhw8fJl26dEybNo1OnTrZu6xEedFBziYKYiJiTXafEfvyyy8xGAwW/7N58+YXXjs8PJyNGzeab8ddlpgcnp6eT9z3eFdFW4iNjeXu3bvcvHmTixcvEhUVZfMaREQk9di2bRteXl4cPnyYvHnzsmXLlhQXwq5cucLFixdxcXHBy8vruY9V+3oRsSa7BzF7+vfff3n48KH5dosWLSwy7pUrV564zxIzbYm1f/9+smTJQu7cuSlcuDAXLlyweQ0iIpI6TJ06lQYNGnDr1i0qV65MYGDgC4OMIzLNhpUvX/6Fx8uofb2IWJNTB7G43RKLFy9O6dKlLTLutm3b4t0uXLhwoveaWULatGnj3Y6IiLB5DSIikrLFxMTw0Ucf0atXL6Kiomjfvj1bt24lf/789i4tSRJykLOJliaKiDXZfa69e/fu1KxZE4CxY8eyYcMG8/fGjRtHhQoVnvncDRs2mDcKA3Tr1o3u3bsDPPd5AEajkVWrVplvW2o2DB59ahhXs2bNLDZ2YqRLly7e7bizfyIiIi8SGhpK586dWbt2LfBoO8Hnn3+OwWCwc2VJl9BGHaAgJiLWZfcgVrRoUYoWLQrA7Nmz432vcuXKTz1c2eTy5ctPjNWwYcMEXTcwMJDr16+bb1tqf9iaNWvYunVrvPvsdaizgpiIiCTVmTNn8PX15dixY3h4eDBjxgzat29v77KS5eHDh+zZswd4caMO0B4xEbEup12aGHdZoqenp3lWLjlCQkJ455134t3XqlUrm62hP3v2LLGxsebbCmIiIpIUW7Zswdvbm2PHjpEvXz62bt2a4kMYwN69e4mKiiJnzpzmD4GfR3vERMSaFMSApk2bJvvTrtjYWN544414s3RZsmRhwoQJyRo3oSIiInj55ZfJmzcv3bp1Y9asWU90alQQExGRF5k8eTINGzYkODiYqlWrEhgYSJUqVexdlkWY9ofVqFEjQcsrtTRRRKzJKefaL168yIEDB8y3LbEs8ZNPPjGvoTf5448/KFiwYLLHTogdO3bw4MEDHjx4wOzZs59Y5glw9+5dm9QiIiIpT3R0NB9//DE///wzAJ06dWLq1Kl4eHjYuTLLScz+MFAQExHrcsoZsbizYa6urrz22mvJGm/ChAn8+OOP8e4bPHgwHTt2TNa4iRG3ycmz9OzZk2bNmjF+/HiuXbtmg6pERCQlCA0NpXnz5uYQ9tVXXzF37txUFcKMRmO8GbGEMC1N1B4xEbEGp3xniRvEateuTdasWZM81ty5c/nwww/j3dezZ0++++67JI+ZFF988QUNGjRgw4YNrF+/nkOHDj3xmMjISNauXcvatWupXLkyefPmtWmNIiLieE6fPo2vry/Hjx8nffr0zJw5k7Zt29q7LIu7cOEC169fx9XVNcFLLTUjJiLW5HQzYvfv32fz5s3m28lZlrhq1Sp69OiB0Wg039emTRv++usvm7f29fDwoFGjRowdO5aDBw9y5coVpk+f/tTzyzJlykS1atVsWp+IiDie//77Dy8vL44fP06BAgXYvn17qgxh8L9lia+++mqCZ/oUxETEmpwuiK1fvz7ewcZJPT9s06ZNtG/f3rxsAaBRo0bMmzfPId6w8+XLR48ePciZM6f5vmbNmlG3bl1ee+013Nzc7FidiIjY26RJk2jSpAm3b9/G29ubgIAAXn31VXuXZTWJOcjZRO3rRcSanO6dJe6yxLJlyyaofe3j/P39adGiRbwuhDVq1GDp0qVPnYGyp7gt7H18fBg6dGi8GTwREXEu0dHRDBw4kF9//RWALl26MGXKlCeOPEltTDNiCd0fBmpfLyLW5VRBLDY2ljVr1phvJ2VZ4sGDB2natCn379833/fqq6+yZs0aMmTIYJE6LSnuD1ZTcLT1skkREXEMt2/fpmPHjvzzzz8AfPPNN3z22Wep/udCWFgY+/fvB5I2I6YgJiLW4FRBzM/Pj6CgIPPtxC5LPHHiBI0aNeL27dvm+8qUKcP69evJkiWLxeq0pKcFMRERcT4nT57E19eXkydPkj59embPnk3r1q3tXZZN7N69m5iYGPLly5eoY2UUxETEmpwqiK1YscL8dc6cORPVsOLChQs0bNiQmzdvmu976aWX+Oeff+Ltw3I0CmIiIvLvv//Svn177ty5Q8GCBVmxYgUVK1a0d1k2k9iDnE20R0xErMmpmnXE3R/WrFkzXFwS9vKvXbtGgwYNuHz5svm+/Pnzs3HjRvLnz2/xOi1JQUxExLlNnDiR1157jTt37lC9enUCAwOdKoRB4g9yNtEeMRGxphQdxB7/hMr0ydXTnD17lqNHj5pvJ3RZYkhICI0aNeLMmTPm+3LmzMk///zDSy+9lMiKbU9BTETEOUVFRdG3b1/69etHTEwM3bp147///iN37tz2Ls2mjEZjkhp1gJYmioh1pei59sc7PF2/fv2Zj407G5Y2bVoaN278wvHv3bvHa6+9xpEjR8z3Zc2alQ0bNlCmTJkkVGx7adOmNX8dt22/iIikXiEhIXTo0IGNGzdiMBj49ttvGTx4cKpvyvE0p0+f5tatW7i7uye6Pb+CmIhYU4oOYnny5Il3e8WKFXz11VfkzZv3icfG3R9Wr149MmbM+MLxW7RoQWBgYLz7Bg0axK1bt/j3338TVWvlypXx9PRM1HMsQTNiIiLO5fjx4/j6+nL69GkyZMjA3Llzk3xmZmpgmg2rUqVKvA8nE0J7xETEmlL0O0uZMmUwGAzmc7Fu3rxJiRIlKF26NEWKFGHx4sUAhIaGsm3bNvPzEtq2fvPmzU/c9/nnnyep1k2bNlG3bt0kPTc5FMRERJzHhg0b6NChA6GhoRQqVIiVK1dSvnx5e5dlV0k5yNlEe8RExJpS9B6x7NmzU7NmzXj3hYWFsWfPHk6fPm2+b926dURFRZlvJ+X8sJRKQUxEJPUzGo388ssvNG3alNDQUHx8fAgMDHT6EAZJO8jZREsTRcSaUnQQA/j1119fuPE47v6wihUrJuoMkZROQUxEJHWLiorivffe44MPPiA2NpYePXqwceNGcuXKZe/S7O7u3bscOnQISNqMmIKYiFiTQy1NnD59OtOnT0/Uc8qXL8+pU6dYs2YN586dAx51NTRtyI2Ojmbt2rXmxydmNsy05DElUxATEUm9goODadeuHZs3b8ZgMPD999/z0UcfOWVTjqcJCAjAaDRSuHDhp+4ffxHT0kTtERMRa0gV7yyZMmWiY8eOT/3ejh07CAkJMd92pmWJoCAmIpJaHTt2DF9fX86cOUPGjBmZN28ezZs3t3dZDiXuQc5JoRkxEbGmVBHEnifussS8efNSpUoVO1ZjewpiIiKpz7p16+jYsSN3796lSJEirFy5kpdfftneZTmcpB7kbKIgJiLWlOL3iL1I3CDWvHlzp1uuoXPERERSD6PRyPjx43n99de5e/cutWrVIiAgQCHsKWJjY/Hz8wOSPyOmpYkiYg2p/p3lxIkT9i7BrjQjJiKSOkRGRtKvXz/++usvAHr16sXEiRNxd3e3c2WO6fjx49y5cwcPD48kd49U+3oRsaZUH8ScnYKYiEjKd+vWLdq2bcvWrVtxcXFh3LhxfPjhh063yiMxTMsSvby8cHNzS9IYWpooItakIJbKxQ1iERERGI1G/eAWEUlBjhw5gq+vL+fOnSNTpkzMnz+fZs2a2bssh5ecg5xNFMRExJpS/R4xZxc3iIH2iYmIpCSrV6+mevXqnDt3jqJFi+Ln56cQlkDJOcjZRHvERMSaFMRSuceDmJYniog4PqPRyA8//ICvry/37t2jTp06+Pv7U7ZsWXuXliKEhIRw7NgxAKpVq5bkcbRHTESsSUEslVMQExFJWSIiIujVqxcff/wxRqORt99+mw0bNpAjRw57l5Zi+Pv7A1CiRAly5syZ5HG0NFFErElz7amcgpiISMoRFBREmzZt2L59Oy4uLvz000/0799fe3sTyRL7w2JjY81fK4iJiDUoiKVycc8RA+0RExFxVIcOHcLX15cLFy6QJUsWFixYQJMmTexdVoqU3IOc4X/LEkF7xETEOrQ0MZXTjJiIiONbuXIlNWrU4MKFCxQvXhw/Pz+FsCSKiYkxL020RKMO0IyYiFiHglgqpyAmIuK4jEYj33//PS1btuT+/fvUq1cPf39/Spcube/SUqzDhw9z//59MmXKRLly5ZI8joKYiFibglgqpyAmIuKYIiIi6NmzJ59++ilGo5F3332X9evXky1bNnuXlqKZliV6e3snK0DFDWJamigi1qB3llROQUxExPHcvHmT1q1bs3PnTtKkScP48ePp16+fmnJYgCUadUD8PWKaERMRa1AQS+UUxEREHMvBgwfx9fXl4sWLZMmShUWLFtGoUSN7l5VqWOIgZ4g/I+biogVEImJ5emdJ5dzd3ePdVhATEbGf5cuXU6NGDS5evEiJEiXw9/dXCLOgmzdvcvr0aeDR0sTkMAUxFxcXzVSKiFUoiKVyBoMh3qyYgpiIiO0ZjUa+/fZbWrduTVhYGA0bNsTf359SpUrZu7RUxc/PD4CyZcvi6emZrLFMQUz7w0TEWhTEnEDcs8R0jpiIiG09fPiQ7t27M3ToUIxGI/369WPNmjXJDgryJEvtD4P/7RHT/jARsRZ9zOME0qVLR2hoKKAZMRERW7p+/TqtW7fGz8+PNGnS8Msvv/Dee+/Zu6xUyxIHOZuYZsQUxETEWhTEnICWJoqI2N6+ffto2bIlly5dwtPTk0WLFtGgQQN7l5VqRUVFERgYCCS/UQcoiImI9WlpohNQEBMRsa2///6bmjVrcunSJUqVKoW/v79CmJUdOHCABw8ekDVrVovsvTMtTdQeMRGxFgUxJ6AgJiJiG0ajkW+++Ya2bdsSHh5O48aN8fPzo0SJEvYuLdWLuyzREu3mNSMmItamIOYEFMRERKzvwYMHdO3aleHDhwPwwQcfsHr1arJmzWrfwpyEJRt1gIKYiFif5tudgIKYiIh1Xbt2jVatWhEQEICrqyu//fYbffr0sXdZTsVSBzmbKIiJiLUpiDkBBTEREevZu3cvLVq04MqVK2TLlo3FixdTr149e5flVK5evcqFCxdwcXHBy8vLImNqj5iIWJuWJjoBnSMmImIdixcvpmbNmly5coUyZcrg7++vEGYHptmwV155hUyZMllkTM2IiYi1KYg5Ac2IiYhYltFo5KuvvqJ9+/Y8ePCApk2bsmvXLooXL27v0pySpfeHgYKYiFif5tudgIKYiIjlhIeH8+abb7Jw4UIABg4cyNixY/ULux1Zen8Y/C+IaWmiiFiL3l2cgIKYiIhlXLlyhVatWrF7925cXV35/fff6d27t73LcmoRERHs2bMHsOyMmGmPmAK2iFiLgpgTUBATEUm+3bt307JlS65evUr27NlZsmQJderUsXdZTm/v3r1ERkaSI0cOihUrZrFxtTRRRKxNe8ScgIKYiEjyLFiwgFq1anH16lXKli1LQECAQpiDiLss0WAwWGxcBTERsTYFMSegICYikjSxsbF88cUXdOrUiYcPH9KsWTN27dpF0aJF7V2a/D9rNOoA7RETEevTu4sTUBATEUm88PBwevToweLFiwH46KOPGDNmjGZIHIjRaLRKow7QHjERsT4FMSegc8RERBLn8uXLtGzZkr179+Lm5sYff/zBm2++ae+y5DEXL17k6tWruLq6UqVKFYuOraWJImJtCmJOQDNiIiIJ5+/vT6tWrbh+/To5cuRg6dKl1KxZ095lyVOYZsMqVqxI+vTpLTq2gpiIWJv2iDkBBTERkYSZO3cuderU4fr167z88ssEBgYqhDkwa+0Pg/8tTdQeMRGxFgUxJ6AgJiLyfLGxsQwfPpyuXbsSERGBr68vO3fupEiRIvYuTZ7DWvvDQDNiImJ9CmJOIG4Qi4mJMX/KJyIiEBYWRvv27fnmm28AGDx4MEuXLiVTpkx2rkyeJzw8nP379wPWmRFTEBMRa9N8uxOIG8Tg0axYxowZ7VSNiIjjuHTpEi1atGD//v24u7vz559/0qNHD3uXJQmwe/duoqOjyZs3L4UKFbL4+GpfLyLWpncXJ6AgJiLyJD8/P1q1asWNGzfIlSsXS5cutcoSN7EO0/4wSx/kbKL29SJibVqa6ASeFsRERJzZ7NmzqVu3Ljdu3KB8+fIEBAQohKUwpv1h1liWCFqaKCLWpyDmBOKeIwY6S0xEnFdsbCyfffYZ3bp1IyIiglatWrFjxw4KFy5s79IkEax5kLOJgpiIWJuCmBPQjJiICNy/f582bdrw3XffAfDZZ5+xZMkSLdVOgc6cOUNQUBDu7u5UqlTJKtfQHjERsTa9uzgBBTERcXYXLlygRYsWHDx4kLRp0zJlyhS6du1q77IkiUyzYZUrV35i1YelaI+YiFibZsRSoXfffZdJkyZx4cIFQEFMRJzbzp078fLy4uDBg+TOnZvNmzcrhKVw1jzI2URLE0XE2jQjlsqcOXOGP/74w3y7XLly1KtXL95jFMRExFnMnDmTt99+m8jISCpWrMjy5cut0upcbMva+8NAQUxErE8zYqnM2rVr490+cuQIv/76a7z7VqxYwdWrV21ZloiITcXExPDpp5/So0cPIiMjad26Ndu3b1cISwXu3bvHoUOHAOvOiJmWJmqPmIhYi4JYKpMzZ06qV6/+3DNVJkyYQP78+Xn11VfZt2+fDasTEbG+e/fu0bp1a77//nsAhg8fzuLFi8mQIYOdKxNLCAgIIDY2lsKFC5MvXz6rXUczYiJibQpiqUzHjh3ZuXMnN2/eZM6cOXTt2pXs2bM/9bH79+8nf/78Nq5QRMR6zp8/T40aNVi5ciVp06Zl7ty5fP3117i46MddamGL/WGgICYi1qf59lQqR44cdOnShS5duhATE0PGjBmf2BtWtWpVcuXKZacKRUQsa/v27bRu3Zpbt26RJ08eli9fjpeXl73LEguz9kHOJmpfLyLWpncXJ5AmTRoyZMhgDmKjR48mb968WqYjIqnGtGnTeOedd4iKiqJSpUosX76cAgUK2LsssbDY2Fj8/PwA6zbqALWvFxHrUxBzEnFb2KdNm5aePXvarxgREQuJiYlh8ODB/PjjjwC0b9+e6dOnkz59ejtXJtZw4sQJbt++jYeHBxUqVLDqtbQ0UUSsTYvmnUTcIKb29SKSGty9e5cWLVqYQ9gXX3zB/PnzFcJSMdOyxKpVq+Lm5mbVaymIiYi1aUbMSSiIiUhqcvbsWVq0aMGRI0dIly4d06dPp2PHjvYuS6zMVo06QHvERMT69O7iJBTERCS12LJlC23btiU4OJh8+fKxfPlyqlSpYu+yxAZscZCzifaIiYi1aWmik1AQE5HU4K+//qJhw4YEBwdTpUoVAgICFMKcxO3btzl69CgA1apVs/r1tDRRRKxNQcxJKIiJSEoWHR3NwIEDefvtt4mOjqZjx45s2bJFZyE6EX9/fwCKFy9uk6NXFMRExNq0NNFJpE2b1vx1RESEHSsREUmc0NBQOnXqxLp16wD46quvGD58OAaDwc6ViS3Zcn8YaI+YiFif3l2chGbERCQlOn36NL6+vhw/fhwPDw9mzpxJu3bt7F2W2IGtDnI20R4xEbE2BTEnoSAmIinNpk2baNeuHSEhIeTPn58VK1ZQqVIle5cldhATE2NemmiLRh2ma4KCmIhYj/aIOQkFMRFJSf744w8aN25MSEgIXl5eBAYGKoQ5sSNHjnDv3j0yZszIyy+/bJNrKoiJiLUpiDkJBTERSQmio6P54IMPePfdd4mOjqZz585s3ryZvHnz2rs0sSPTskRvb2+bBSPT0kTtERMRa9G7i5NQEBMRR3fnzh06duzIhg0bABg1ahRDhw5VUw6xeaMO0IyYiFifgpiTUBATEUd26tQpfH19OXHiBOnTp2fWrFm0adPG3mWJg7DlQc4mCmIiYm0KYk5CQUxEHNXGjRtp3749t2/fpmDBgqxYsYKKFSvauyxxEEFBQZw6dQqwzUHOJmpfLyLWpj1iTkLniImII5o4cSJNmjTh9u3bVKtWjYCAAIUwicfPzw+AMmXK4OnpabPrqn29iFibgpiT0IyYiDiSqKgo+vXrR79+/YiJieGNN95g06ZN5MmTx96liYOxx/4w0NJEEbE+zbc7CQUxEXEUt2/fpn379mzcuBGDwcDo0aP59NNP1ZRDnsoe+8NAQUxErE9BzEkoiImIIzhx4gS+vr6cOnWKDBkyMGfOHFq2bGnvssRBRUVFERgYCNhvRkx7xETEWvTu4iTiBrGIiAiMRqM+fRYRm/rnn39o3749oaGhFCpUiBUrVlChQgV7lyUO7ODBg4SHh5M1a1ZKly5t02trj5iIWJv2iDmJuEEM1LBDRGzHaDTy66+/0rRpU0JDQ6lRowYBAQEKYfJCpmWJ1apVw8XFtr+yaGmiiFibgpiTeDyIaXmiiNhCVFQUffv2pX///sTExNC9e3f+++8/cufObe/SJAWwV6MOUBATEevT0kQnoSAmIrYWHBxM+/bt2bRpEwaDgTFjxvDxxx9rWbQkmL0adcD/liZqj5iIWIveXZxE3HPEQEsTRcS6jh07hq+vL2fOnCFjxozMnTsXX19fe5clKci1a9c4f/48BoMBLy8vm19fM2IiYm1amugkNCMm4th69uyJwWB45p+0adOSK1cuvLy86N+/Pzt27LB3yc+0bt06qlWrxpkzZyhSpAg7d+5UCJNEM82GvfLKK2TOnNnm11cQExFrUxBzEgpiIilbZGQkQUFBBAYG8uuvv1KzZk3q16/P5cuX7V2amdFo5Oeff+b111/n7t271KpVi4CAAF555RV7lyYpkD33h4Ha14uI9endxUkoiImkHJ6enk8sxXr48CHnz5/nwoUL5vs2bdpE9erV8ff3J1++fLYuM57IyEj69evHX3/9BcCbb77JpEmTcHd3t2tdknLZc38YqH29iFifgpiTUBATSTnKly/PunXrnvq9vXv30rdvX/z9/QG4fPkyb7/9NqtXr7ZlifHcunWLdu3asWXLFgwGA2PHjmXQoEFqyiFJFhERwe7duwH7z4gpiImItWhpopNQEBNJHSpVqsSmTZsoX768+b41a9Zw4MABu9Rz9OhRvL292bJlC5kyZWLVqlV89NFHCmGSLPv27SMyMpIcOXJQvHhxu9SgICYi1qYg5iQUxERSDw8PD7755pt4961du9bmdaxZs4Zq1apx9uxZXnrpJXbt2kWzZs1sXoekPqZlidWrV7dbqNceMRGxNgUxJ/H4Pg0FMZGUrWHDhri5uZlvHzp0yGbXNhqN/Pjjj/j6+nLv3j1q165NQEAA5cqVs1kNkrrZu1EHaI+YiFifgpiTMLW/NtE5YiIpW7p06ciRI4f5dnBwsE2uGxkZSe/evfnoo4+IjY2ld+/e/PPPP/FqEUkOo9FoDmL2atQBWpooItan+XYnki5dOnMA04yYSMoXGRlp/toW3QmDgoJo27Yt27Ztw8XFhR9//JEPPvhA+8HEoi5dusTVq1dJkyYNVapUsVsdCmIiYm0KYk4kXbp0hIaGAgpiIindrVu3CAkJMd/OlSuXVa93+PBhfH19OX/+PJkzZ2bBggW89tprVr2mOCfT/rCKFSuSIUMGu9WhPWIiYm1amuhE4jbsUBATSdkWLlyI0Wg03/b29rbatVatWkX16tU5f/48xYoVw8/PTyFMrMYR9oeB9oiJiPUpiDkRBTGR1OHkyZOMGDHCfDtt2rQ0b97c4tcxGo2MHTuWFi1acP/+ferWrYu/vz9lypSx+LVETOx9kLOJliaKiLUpiDkRBTGRlCsiIoITJ07w3Xff4e3tHW9ZYv/+/cmbN6/Fr/fWW28xePBgjEYj77zzDhs2bCB79uwWvY5IXA8ePGDfvn2AfWfEYmNjzTPOCmIiYi1a+OxEFMREUoYtW7YkuAFG06ZN+fbbby16/Zs3b9KmTRt27NiBi4sL48eP5/3331dTDrG63bt3Ex0dTd68eSlcuLDd6jDNhoH2iImI9WhGzIkoiImkHjly5GDMmDGsWrXKor8oHjx4EC8vL3bs2EGWLFlYu3Yt/fv3VwgTm3CEg5whfhDTjJiIWIs+5nEiOkdMJGXw9PTEy8sr3n1p06YlS5YsFClShGrVqtGgQYN4/6YtYfny5XTt2pWwsDBKlCjBypUrKVWqlEWvIfI8jtKoQ0FMRGxBQcyJaEZMJGUoX74869ats9n1jEYjY8aMYejQoRiNRho0aMDChQvJli2bzWoQMRqNDteoA7Q0UUSsR0sTnYiCmIg87uHDh/To0YPPPvsMo9FI3759Wbt2rUKY2NzZs2e5efMmbm5uVKpUya61mFrXg2bERMR69DGPE1EQE5G4rl+/TuvWrfHz8yNNmjRMmDCBvn372rsscVKm2bDKlSvH+3llD1qaKCK2oCDmRBTERMRk//79tGjRgkuXLpE1a1YWLVpEw4YN7V2WODFH2R8G/wtiBoNBjWpExGq0NNGJKIiJCMDSpUvx8fHh0qVLlCxZEn9/f4UwsTtH2R8G/wti2h8mItakIOZEFMREnJvRaGT06NG0adOG8PBwGjVqhJ+fHyVLlrR3aeLk7t27x8GDBwHHmBEz7RHTskQRsSYFMSeiICbivB48eMAbb7zBsGHDAOjfvz9r1qzB09PTzpWJQGBgILGxsRQqVIj8+fPbuxzzjJiCmIhYk+bcnYjOERNxTteuXaNVq1YEBATg6urKr7/+yjvvvGPvskTM4h7k7AgUxETEFhTEnIhmxEScz969e2nZsiWXL1/G09OTJUuWUK9ePXuXJRKPIzXqgP8tTdQeMRGxJi1NdCIKYiLOZcmSJdSsWZPLly9TunRpAgICFMLE4cTGxuLn5wc4RqMO0IyYiNiGgpgTURATcVzTp0/HaDRiNBrZvHlzssYyGo189dVXtGvXjgcPHtCkSRP8/PwoXry4ZYoVsaCTJ08SEhJCunTpqFChgr3LARTERMQ2NOfuRBTERFK/Bw8e8NZbbzF//nwABgwYwLhx47TEShyWaX9Y1apVcXd3t3M1j6h9vYjYgt5hnEjcIBYTE0N0dLR+yIikIlevXqVVq1YEBgbi6urKxIkTefvtt+1dlshzOdr+MFD7ehGxDf0W7kTiBjF4NCuWMWNGO1UjIpa0e/duWrZsydWrV8mePTtLliyhTp069i5L5IUc6SBnEy1NFBFb0B4xJ/K0ICYiKd/ChQupXbs2V69epWzZsgQEBCiESYpw584djhw5AjjWjJiCmIjYgoKYE4l7jhjoLDGRlC42NpYvv/ySjh078uDBA5o1a8auXbsoWrSovUsTSRB/f38AihUrRq5cuexczf9oj5iI2ILeYVIxo9GIwWAw39aMmEjqER4eTs+ePVm0aBEAH330EWPGjNEn+JKiOOL+MNAeMRGxDc2IpVLR0dGULFmSLl26MH/+fO7cuaMgJpJK/PTTT2TNmpVFixbh6urKlClTGDdunH5plBTHEfeHgZYmiohtaEYsldq1axenT5/m9OnTzJs3D1dXV6pWrRrvMQpiIilPQEAAgwcPNn9iHx0dzZgxYzh16hTNmjWjevXqWk4lKUJMTIx5aaKjzYgpiImILWhGLJVasWJFvNvR0dHmTx5Nxo8fz65du8w/cETEsc2fP586deqYQ5jJyZMn+e6776hduzY5cuSgQ4cOTJs2jWvXrtmpUpEXO3r0KHfv3iVDhgy8/PLL9i4nHu0RExFbUBBLpTp16sTgwYMpU6bMMx8ze/ZsatSoQd68eTl+/LgNqxORxIiNjWXEiBF07tyZhw8fmvd+1q1bl7Zt28Z7bGhoKIsWLeKtt94iX758VKpUieHDh7Njx44nApyIPZk+HPT29na4wKM9YiJiCwpiqVTlypUZM2YMR48e5dSpU/z444/UqlXrqY+NjIykWLFiNq5QRBIiLCyMDh06MGrUKADat2+P0WgEIEeOHCxevJj9+/fTtGlT83NKly5N1apVMRgM7Nu3j2+++YaaNWuSK1cuOnXqxIwZM7hx44ZdXo+IiaM26gAtTRQR21AQcwLFixdn4MCB/Pfff/HuT58+PQBNmzbFzc3NHqWJyHNcunSJWrVqsWTJEtzd3Zk2bRrFixc3f98UyCpUqMCaNWvYvHkzNWrUYMGCBQQEBHD9+nUmTZpEhw4dyJYtG7dv32bBggX07NmTPHnyUKVKFT7//HMtURa7cNRGHaAgJiK24VhrAcSqXF1dSZMmjfkHzO+//06BAgXInDmznSsTkcf5+/vTsmVLbty4Qc6cOVm6dCk+Pj5Uq1bN/JjY2Nh4z6lTpw47duww386VKxcHDhxg165djB07lpIlS7J+/XrWrl3Lnj17zH++/vprsmXLRpMmTWjWrBlNmjQhZ86cNnut4nxu3brFyZMnAeL9nXYUpqWJjrZkUkRSF82IOZm4Leyjo6OpX78+VapUsWNFIvK4OXPmUKdOHW7cuEH58uUJDAzEx8eH0NBQAgMDzY97PIg97uHDh6xdu5ZLly7Rq1cv3n33XapWrUpgYCDXrl1j+vTpdOjQgaxZsxISEsK8efPo1q0buXPnxtvbmy+//JKAgIAXXkcksfz8/IBHy2izZctm52qepBkxEbEFBTEnEzeIqX29iGOJjY1l6NChvPHGG0RERNCyZUt27NhB4cKFAdi2bRuxsbHkypXL/PjnSZcuHUePHuX777/H09OTI0eO0LJlS2rVqsXp06fp0aMHCxYsICgoiO3btzN06FBeffVVjEYjAQEBjBw5Em9vb3Lnzk23bt2YO3cuwcHBVv/vIKmfI+8PAwUxEbENBTEnoyAm4pju379P27Zt+fbbbwEYMmQIf//9NxkzZjQ/xrTPs2nTppw4cYJJkya9cFwPDw8++eQTzp49y5AhQ/Dw8GDHjh3UqlWLv/76C3i0/MrHx4dvvvmGvXv3cvXqVaZOnUq7du3IkiULt27dYvbs2XTt2pWcOXNSvXp1vvrqK3bv3q3ZMkkSR94fBmpfLyK2oSDmZBTERBzPxYsXqVmzJsuWLcPd3Z2ZM2fy7bff4uIS/y06bhArWbIk+fLlS/A1smbNyrfffsvp06fp06cP2bJlo1WrVubvxw1UefPm5c0332TRokUEBQWxZcsWhgwZQvny5TEajfj5+fHFF19QtWpV8ubNS48ePZg/fz4hISHJ+w8hTiE6OpqAgADAcWfE1L5eRGxBQczJKIiJOJZdu3ZRtWpVDhw4QK5cudi8eTPdunV74nG3bt3iwIEDwKPzw5IqX758/PHHH5w7d44cOXIAj7ovNm/enIEDB3Lr1q14j3dzc6N27dp8++23HDhwgEuXLjF58mTatGlDpkyZuHnzJjNnzqRz587kzJkz3syaZsscX8+ePTEYDPH+mJYNJkTdunXNz/v4448T9JyDBw8SHh5OlixZnnvWpT1paaKI2IKCmJNREBNxHLNmzaJu3brcvHmTChUqEBgY+MwZgs2bNwPw8ssvc/36dYYNG8bUqVOTfO243VIDAgJYu3Yt48ePp2jRonz99dfcv3//qc8rUKAAvXv3ZsmSJdy6dYtNmzbxySef8PLLLxMbG8vOnTsZPnw4lStXJn/+/OaZtTt37iS5VrGt4cOHW3V807LEatWqPTHr6ygUxETEFhzzHVCsRkFMxP5iYmIYMmQI3bt3JzIyktatW7N9+3YKFSr0zOeYliXWq1ePo0ePMnr0aObOnWuRery8vFi/fj2vvvoq9+7d4/PPP6d48eJMnDiRqKioZz7P3d2dunXr8v3333Po0CEuXrzIH3/8QatWrciYMSPXr183d2bMkSOHeWZt//795jPQxPFs2rSJjRs3Wm18R2/UAdojJiK2oSDmZNKmTWv+OiIiwo6ViDine/fu0bp1a8aMGQPAsGHDWLx4cbymHE9jCmL169c3zyJYaumfwWCgcePG7N69m3nz5lGsWDFu3LhBv379KFOmDKdPn07QOAULFqRPnz4sXbqU4OBgNm7cyEcffUTZsmWJiYlh27Zt5s6MBQoUoFevXixZsoTQ0FCLvA6xnGHDhlltbEdv1AHaIyYitqEg5mQ0IyZiP+fPn8fHx4eVK1eSNm1a5syZw6hRo164POvKlSucOHECg8FAnTp1LB7ETFxcXOjUqRNHjx7lt99+I3fu3Li4uJjb5yeGu7s79evXZ9y4cRw5coTz58/z+++/4+vrS/r06eN1ZsyRIwd169ZlzJgxHDp0SLNldlK8eHHz1/7+/qxcudLi17h+/Trnzp3DYDDg7e1t8fEtRUsTRcQWFMScjIKYiH1s374dLy8vDh06RJ48ediyZQtdunRJ0HM3bdoEQKVKlfD09LRaEDNxd3enb9++nD59mqVLl+Lm5gY8mkXv1q2bueNdYhQuXJh3332XFStWEBISwoYNGxg4cCClSpUiOjo6XmfGQoUKmWfW7t27Z+mXJ89QoUIFWrdubb79+eefWzwUm2bDXn755Xj7FB2NgpiI2IKCmJNREBOxvenTp1O/fn2CgoKoVKkSgYGBiZoNiLssEbB6EDPJmDEj5cqVM9/+448/mD17Nt7e3rRr144TJ04kady0adPSqFEjfvzxR44fP86ZM2f49ddfef311/Hw8ODy5cvmzozZs2ePN7Om2TLr+vrrr81/v/bv38+iRYssOn5K2B8G/1uaqD1iImJNCmIWduPGDfz9/Tl69KhD7sFSEBOxnZiYGD755BPefPNNoqKiaNeuHVu3bqVAgQIJHsNoNJobJ9g6iD2uRYsWdO/eHYPBwJIlSyhXrhx9+vThypUryRq3aNGi9OvXj1WrVhESEsK6desYMGAAJUqUICoqKl5nxiJFivDuu++yfPnyZ3Z2lKQrV64cnTp1Mt/+4osvzLNDlpAS9oeBZsRExDYUxBIgMjKSSZMm0bFjR5o2bcrAgQOf+CT4xIkT1KtXj3z58lGjRg1eeeUVcubMybvvvsvt27ftVPmTFMREbOPu3bu0atWKcePGATBixAgWLFhAhgwZEjXOuXPnuHjxIq6urtSsWROwXxArUqQIM2bM4MCBAzRv3pyYmBgmT55MiRIl+OyzzyzyC3u6dOlo0qQJ48eP5+TJk5w6dYoJEybQtGlT0qVLF68zY/bs2WnYsCE//vgjx44d02yZhYwcOdI8E3T8+HFmzZplkXEjIyPZvXs34PgzYgpiImILCmIvcP36dSpVqkS/fv1YvHgxGzZsYMKECVSsWJH169cDcPXqVerVq8fWrVsxGo3mP/fv32fy5MlUr16dmzdv2vmVPKIgJmJ9Z8+epUaNGqxatYp06dIxf/58vvrqqySdmWRalujt7W3urFi7dm327t1rsV+QE+uVV15h5cqVbNu2jRo1avDgwQP2799vlV9aixcvTv/+/VmzZg0hISGsWbOG999/n6JFixIZGRmvM2PRokXp27cvq1atIiwszOK1OIvixYvTs2dP8+2RI0c+9xiDhNq3bx8RERFkz56dEiVKJHs8a1L7ehGxBQWxF2jXrh1Hjx6NF7CMRiMRERF06dKFoKAg3nnnHa5fv066dOlo1KgRnTt3platWri5uWE0Gjl16hS9e/e290sBFMRErG3r1q14eXlx5MgR8ubNy9atW+nYsWOSxzM16jAtSwTIkiULr776qt1/ma1Zsybbt29n+fLljB071nz/9evXmTJlinmfjaV4eHjQtGlTfvnlF06fPs2JEycYP348jRs3Jm3atPE6M2bPnj3ezJpmyxLn888/Nx93cv78ef76669kj2lalli9enUMBkOyx7Mmta8XEVtQEHuOlStXsnPnTgwGA76+vuzcuZPDhw/z6aefAnDnzh2+//571q1bR5MmTTh//jzr169nzpw5bNmyhRMnTlClShWMRiOrV68mMDDQzq9I54iJWNOUKVNo2LAhwcHBVK5cmcDAQKpWrZrk8YxG4xONOhyNwWCgRYsWvPzyy+b7vv76a3r37s0rr7zC0qVLrRKCDAYDJUuWZMCAAaxfv57g4GBWrlxJ3759KVKkCBEREfE6M8adWQsPD7d4PalNwYIFeeedd8y3R40alewP71JKow7Q0kQRsQ0FsedYsGABgPmXiWrVqlG2bFm+/fZbOnfujNFo5KeffqJAgQIsWbKEnDlzxnt+kSJFWL16NZ6engDMnz/f5q/hcZoRE7G8mJgYBg0aRO/evYmKiqJDhw5s3bqV/PnzJ2vc48ePm2fbq1WrZr7/3LlzjB49msmTJye3dKsoXbo02bJl4/jx47Rp04YaNWqwZcsWq14zQ4YMNG/enN9++42zZ89y7NgxfvzxRxo2bIi7uztnz541d2bMnj07TZs2ZcKECQk+rNoZDR06lPTp0wOPluD/9ttvyRovpTTqAAUxEbENBbHn2L17NwaDgd69ez+xt6NPnz7Ao0+s+/TpY/5h9bicOXPSo0cPjEYj/v7+Vq/5RRTERCwrNDSU5s2b89NPPwGP9tPMnz//me8JiWGaDfPx8Yn3b/fMmTMMGzaMX375JdnXsIb+/ftz9uxZhg0bRvr06fHz86Nu3bo0a9aMAwcOWP36BoOB0qVLM3DgQP755x+Cg4NZvnw57777LoUKFeLhw4fxOjOWKFGCAQMGsG7dOh48eGD1+lKK3Llz079/f/Pt7777LsmdKi9dusTly5dJkyZNsmaJbUV7xETEFhTEnuPq1avAo0MuH1e6dGnz1y9aZlGnTh0Ah/jkVUFMxHLOnDlD9erVWbduHR4eHixcuJDPP//cYvtfnrUs0fQpva27JiZGlixZGDVqFKdPn+a9997D1dWVtWvXMm3aNJvXkjFjRlq0aMHvv//O+fPnOXLkCGPHjqV+/fq4ublx+vRpc2fG7Nmz8/rrr/Prr79y9uxZm9fqaAYPHmw+ePnWrVuMHz8+SeOYZsMqVKiQ6M6h9qA9YiJiCwpiz2HaQ5UlS5YnvpcrVy7z19mzZ3/uOKblSaGhoRasLmkUxEQsY/PmzXh5eXHs2DHy58/Ptm3baN++vcXGj42NfWqjDrBf+/qkyJs3LxMnTuTYsWN0796dYcOGmb935swZm3eUNRgMlC1blo8//piNGzcSHBzM0qVL6dOnDwUKFODBgwesWbOG/v37U6xYMUqVKsXAgQPZsGGDU75nZsuWjY8++sh8e9y4cUk6kiUl7Q8DLU0UEdtQEHsO096up/3QifuJ94veqE1LGxzhDT1uEIuIiFAnMZEk+PPPP2nUqBEhISFUrVqVgIAAKleubNFrHDhwgNu3b5MpUyaqVKkS73spKYiZFC9enBkzZsTbS/vOO+9QrFgxvvzyS+7du2eXujJlykSrVq34448/uHjxIocOHWLMmDHUrVsXV1dXTp48yfjx42nSpAnZs2fH19fXPLPmLAYOHGj+wDE0NDReh8yESkn7w0BBTERsQ0HsOUw/eK5fv56scUxB7vFmHvYQN4iBOieKJEZ0dDQDBgzgnXfeITo6mk6dOrFlyxby5ctn8WuZliXWrl37iX0qKTGIPS40NJTQ0FDu37/PyJEjKVasGBMmTLDre5LBYODll19m8ODBbNq0ieDgYJYsWUKvXr3Ily8f4eHhrFq1ir59+/LSSy9RtmxZPvroIzZu3Jiq30szZcpk7hYMMGHChETNZD548IC9e/cCKWdGzLQ0UXvERMSaFMSeo2jRosCjDmVPExQUxM2bN+PtF3uao0ePAo82Ptvb40HMGZfaiCTFnTt3aN68ORMmTAAetWifO3cuHh4eVrne89rWp4YgliVLFgICAli4cCElSpQgKCiIAQMGULp0aebMmeMQry1z5sy0adOGv/76i8uXL7N//36+/fZbateuTZo0aeJ1ZsyePXu8mbXU5v333ydv3rwAhIWFMXr06AQ/d8+ePURHR5MnTx6KFClipQotSzNiImILCmLPUblyZYxGI3v27Hnq97Nnz0727Nmf6Kj4uE2bNpn3Jdhb3HPEQDNiIglx6tQpqlWrxvr160mfPj2LFy9m+PDhVjuUNioqiq1btwKpN4jBoxmo9u3bc+TIESZNmkTevHk5f/48b7zxBvPmzbN3efEYDAYqVKjAkCFD2LJlC7du3WLRokW8+eab5MmTh7CwMHNnxsKFC/Pyyy/zySefsGnTJiIjI+1dfrJ5eHjE2983adIkLl++nKDnpqSDnE0UxETEFhTEnsO052Pz5s1JHiM4OJg1a9YAjrE2XjNiIonz33//4e3tzYkTJyhQoADbt2+nbdu2Vr3m7t27uX//PtmyZaN8+fJPfL9s2bJs376dRYsWWbUOW3Fzc+Odd97h1KlTfPPNN1SpUoUOHTqYv+8IjY4elzVrVtq1a8fUqVO5cuUKe/fu5ZtvvsHHxwcXFxeOHDnCuHHjqF+/Pjly5KBNmzZMnjw5weHFEb399tsULlwYePQh3tdff52g56W0Rh2gICYitqEg9hxNmzbl0qVL7N+/P8ljrF+/Hm9vb2rXrk3Tpk0tV1wSKYiJJNzvv/9O48aNuX37Nt7e3gQGBvLqq69a/bqmZYn16tV76ox7pkyZ8PHxsXiDEHvLkCEDQ4cOJSAgADc3NwAiIyOpVKkSrVu35tixY3au8OlcXFx49dVXGTp0KNu3bycoKIj58+fTo0cPcuXKxb1798ydGQsWLEj58uXNM2tRUVH2Lj/B3N3d+fzzz823p06dyoULF577HKPRmOIadYD2iImIbSiIPYerqyv58+c3t59Pii5durBp0yY2bdqUrHEsRUFM5MWio6N5//336du3LzExMXTt2pXNmzeTJ08em1z/efvDnEHc5Wvbtm3j/PnzLFu2jJdffplevXpx6dIlO1b3YtmyZaNjx45Mnz6da9eusXv3br766ivz0ry4nRlz5MhhnlkznV3pyHr06EHJkiWBR/9OXtQ98ty5c9y4cQM3N7cU9cGBZsRExBYUxJyMgpjI892+fZumTZvy22+/ATB69GhmzZr1xL8da3n48CE7duwAnh3EgoKCGD9+PH/88YdNarKnBg0acOjQIVq1akVsbCxTp06lRIkSfPLJJ4SEhNi7vBdycXGhcuXKjBgxgp07dxIUFMTcuXPp1q0bOXLk4O7du+bOjPnz5483s2aalXEkadKkYeTIkQl+vGk2rFKlSjb7N2QJCmIiYgsKYk5GQUzk2U6ePEm1atX4999/yZAhA0uXLuWzzz6zaYOBXbt2ERERQd68eSlVqtRTH3Pt2jUGDhzIF198YbO67Kls2bIsXbqUnTt3Urt2bSIiIhg3bhxFixblypUr9i4vUbJnz07nzp2ZOXMmN27cICAggC+//BJvb28MBoO5M2OtWrXImTMnHTp0YPr06ck+RsWSOnbs+NS9i0+TEveHwf+CmJYmiog1KYg5GQUxkaf7999/8fb25uTJkxQsWJAdO3bQqlUrm9cRd1niswJgaumamFjVq1dn8+bNrF69mldeeYVq1arFW/Kd0g6od3FxoWrVqnzxxRf4+flx48YNZs+eTZcuXciePTt37twxd2bMmzcvlStXZvjw4ezcudMcFOzBYDAkuFFHStwfBv/bI6YZMRGxJoMxpf3ksqDo6Gjzp3UAxYsXT9LBrFeuXOHMmTPm27Vq1XLYFr0RERHxwtiyZcto2bKlHSsSsb/ffvuNAQMGEBMTQ/Xq1Vm6dKndzv3z8fFh586dTJkyhbfeeuupjzl27Bhly5Yle/bs3Lp1y8YVOoaYmBju3LlD9uzZAbhx4wavv/46Q4YMoW3btg77HpxQMTExBAYGsnbtWtasWcPu3bvjfd/T05MmTZrQtGlTXnvtNXLlymWnSp/t/v37ZMmShdjYWC5dukSBAgXsXVKC1atXj82bNzN//nw6duxo73JEJJVy6hmxv//+m3r16lGvXj0aNGhAeHh4ksYJCwujfv365rFWr15t4Uotx93dPd5tnSMmziwqKoq+ffvy/vvvExMTQ/fu3dm0aZPdQti9e/cICAgAnt+ow1lnxOJKkyaNOYQB/PDDD+zZs4f27dvj7e1tnllMqdKkSUO1atUYOXIkgYGBXL9+nRkzZtCpUyc8PT25ffu2uTNj7ty5482s2XO2LK7AwEBiY2MpWLBgigphoD1iImIbTh3EpkyZgtFoxGg00rx5c4oXL56kcUqWLEmzZs3MY02ZMsXClVqOwWCId6izliaKswoJCeG1117j999/x2AwMGbMGKZPn/7Eoee2ZGrQ8NJLL1GkSJFnPk5B7EkjRozgiy++IEOGDAQGBtKgQQOaNGnC3r177V2aReTOnZvu3bszb948bt68yY4dOxg+fDiVKlUCiNeZMXfu3HTt2pU5c+bYdcY07kHOKY32iImILThtEAsPD2fr1q0YDAYMBgOdOnVK1nhdunQxf71x40aHPhsm7tJEBTFxRsePHzfPmmTMmJFly5YxePBguy9nS2jbegWxJ2XKlIkvv/ySM2fO8P777+Pm5saGDRuoXLkyPXr0SHH7x57H1dWVGjVq8PXXX7Nnzx6uXbvGtGnT6NChA1myZCE4OJi5c+fyxhtvkCtXLry9vRk5ciQBAQE2/TtjWvqf0vaHgfaIiYhtOG0Q279/PxEREeYfzg0aNEjWeHGfHxYWxsGDB5M1njUpiIkzW79+PdWqVeP06dMUKVKEnTt30qJFC3uXBSiIWULu3Ln55ZdfOH78uPkDsqxZs9o9ZFtTnjx56NmzJwsWLODWrVts27aNzz77jIoVK2I0GuN1ZsyTJw/dunVj3rx5BAcHW62muAc5p+QZMQUxEbEmpw1iJ06cMH+dL18+cuTIkazxcubMGa/Rx/Hjx5M1njUpiIkzMhqN/PzzzzRr1ozQ0FB8fHzw9/fnlVdesXdpwKOlkvv27QMeNQp4njx58rB+/XpWrFhhi9JSpKJFizJnzhz27dvH8OHDzffv3buXESNGcPfuXTtWZz2urq7UrFmT0aNHs2/fPq5cucKUKVNo27YtmTNnJigoyNyZMVeuXPFm1iwZ7E+ePElISAjp0qWjYsWKFhvXVhTERMQWnDaImQ4CNRgMFus2FXeDf1BQkEXGtAYFMXE2UVFRvPvuu3z44YfExsbSs2dPNm7c6FCd5rZs2YLRaKRMmTLkzZv3uY/18PCgcePGL5w5E6hYsSI5c+Y03x4yZAijRo2iaNGi/PTTT6m+YVG+fPl46623WLx4Mbdu3WLz5s18+umnvPLKK8TGxrJr1y4+//xzqlSpQt68ec0za7dv307WdU2zYVWqVHmiSVRKYFqaqD1iImJNThvEIiMjzV9b6hOvuOMktQOjLSiIiTMJDg6mcePG/PnnnxgMBsaNG8fUqVPt2pTjaRK6LFGSzmg08t5771G6dGmCg4MZNGgQJUuWZMaMGQ7TadCa3NzcqFOnDt999x0HDx7k0qVL/Pnnn7Ru3ZpMmTJx8+ZNc2fGHDlyxJtZS+weu5R6kLOJZsRExBacNoiZ2h4bjUZu3rxpkTHjzoJlzZrVImNag4KYOIujR4/i5eXF5s2byZQpEytXruSjjz5yyP1CiQli4eHhTJ48mT///NPaZaUqBoOB1q1bc+jQISZPnkz+/Pm5ePEiPXv2pGLFimzYsMHeJdpUgQIFePvtt/n777+5desW//33H5988gnlypUjNjaWHTt2MGzYMCpVqmSeWVu0aBF37tx54dgp9SBnEwUxEbEFpw1icZeqXL58Odlh7ObNm1y8eNH8C17c8R1N3JmA1L4sR5zX2rVrqV69OmfPnuWll15i165dvP766/Yu66muX7/O0aNHMRgM1KlT54WPv3v3Ln369OHdd9+1QXWpj6urK7179+bUqVOMGTOGrFmzcvjwYU6fPm3v0uzG3d2devXq8f3333P48GEuXLjApEmTaNmyJRkyZOD69evmzow5cuSgdu3afPfddxw4cOCJ2bLQ0FCOHDkCpPwZMS1NFBFrctogVqVKFeDRJ6RGo5HFixcna7xFixaZzxEDKF++fLJrtBbNiElqZjQa+emnn2jevDl3796ldu3aBAQEUK5cOXuX9kybNm0CHu1nintI8bOYuibGfc+RxPPw8GDw4MGcPXuWb775ht69e5u/t3XrVg4fPmzH6uyrUKFCvPPOOyxbtozg4GD+/fdfBg0aRJkyZYiJiYnXmbFgwYL07t2bv//+m7t37+Lv74/RaKRo0aJ2Oxw9udS+XkRswWmDWMGCBSlRogTw6JeZUaNGJbmLVmhoKN988415Nqxw4cKUKlXKYrVamoKYpFaRkZG8/fbbDBo0iNjYWHr16sU///yT7K6o1pbY/WGmIAYoiFmAp6cnQ4cONTeViIqK4q233qJ8+fL07NmTCxcu2LlC+0qbNi0NGjTghx9+4OjRo5w7d46JEyfSvHlz0qdPH68zY/bs2Wnfvj3AC5vOODItTRQRW3DaIAaYD/k0GAzcuHGDli1b8uDBg0SN8eDBA1q2bMn169fNY3Xv3t1KFVuGgpikRkFBQTRs2JApU6bg4uLCTz/9xOTJk1NExzZTEHtR23qTuL8c6iwxy7t79675DK4ZM2ZQsmRJBg0axK1bt+xdmkMoUqQI7733HitXriQ4OJj169fz4YcfUqpUKaKjo80fapqOY0iJFMRExBacOogNGDAg3iflW7dupVKlSmzZsiVBz9+8eTOvvvoq27ZtM8+GZcuWjY8++sgq9VqKgpikNocPH8bLy4tt27aROXNmVq1axYcffuiQTTked/78ec6ePUuaNGmoVatWgp4Td0ZMQczysmfPzuLFi/H396devXpERkby008/UaxYMUaNGkVYWJi9S3QY6dKlo3Hjxvz0008MGDAg3t/N8PDwFNsARXvERMQWnPodJkOGDEyePJm2bdua91qcOHGC+vXr8/LLL9O0aVOqVKlCrly5yJgxI/fv3+fmzZvs3r2btWvXcvjwYfMsmNFoxMXFhcmTJ5MpUyZ7v7TnUhCT1GT16tV06tSJ+/fvU7RoUVauXEnZsmXtXVaCmfaHVa1alcyZMyfoOQpituHl5cXGjRvZsGEDQ4YMYf/+/YwYMYLq1avToEEDe5fnMKKjoxk0aBC//PLLE9/r0aMHhw4dcvjlwY/THjERsQWnDmIALVu25Pvvv+fjjz82f3puNBo5dOjQczdqm/ZlmEKYwWDghx9+oFWrVrYoO1kUxCQ1MBqN/PDDDwwePBij0UjdunVZvHhxgppdOBJTEEvM+WEKYrZjMBho0qQJjRo1YsGCBfz333/xQtjRo0cpXbp0vP8nziQ0NJSOHTuyfv16AFq3bs3SpUuBR4dJX716lV69erFs2bIUMUNtoqWJImILzvmT4zGDBg3i77//JkuWLOZQFTeUPf4HMD/GaDSSLVs2li9fzoABA+z5MhJMQUxSuoiICHr16sUnn3yC0WikT58+rF+/PsWFMKPRmKSDnNOmTcvff//NsmXLUsQeuNTAxcWFzp07M3nyZPN9QUFBeHt7U7VqVf755x87VmcfZ8+epXr16qxfv5706dObf46aDBgwAHd3d1asWJHizrxTEBMRW1AQ+3+tWrXi+PHjDBkyxBzIntWNzPQ9T09Phg8fzvHjx2nevLmNK046nSMmKdnNmzdp2LAh06ZNw8XFhZ9//plJkyalyEBy6tQprly5gru7e6IOvnV1daV169a0bNlSe1jsaO/evRgMBvbu3Uvjxo1p2LAhu3fvtndZNrFt2za8vLw4duwY+fPnZ/v27bRu3dp8kDPASy+9xHfffQfAwIEDOX78uL3KTTTT0kT9+xIRa9I7TBy5cuVi9OjRjBw5koCAALZt28aZM2cICQnh3r17ZMqUiWzZslGiRAlq1apF1apVU+SbtGbEJKU6dOgQvr6+XLhwgSxZsrBw4UIaN25s77KSzDQbVqNGDTw8POxcjSRWkyZNOHPmDKNHj2bixIls3LiRqlWr0r59e0aNGkXJkiXtXaJVTJ8+nT59+hAVFUWVKlVYvnw5+fLlIzg4mBMnTpgf5+LiwoABA1i7di3//PMPXbp0YdeuXfE+DHRUmhETEVtIeSnCBtzc3PDx8cHHx8fepViFgpikRCtWrKBr167cv3+f4sWLs3LlSkqXLm3vspIlKcsS4dG+sPnz5xMbG0v79u1TxC+2qVXOnDnNHQO/+OILZs2axaJFi1i1ahVXrlzB09PT3iVaTGxsLJ999hnff/89AB06dGDatGmkT58eAD8/P+DRz5iHDx/i4uKCi4sL06dPp3z58uzbt48RI0aYn+/IFMRExBa0NNEJKYhJSmI0GhkzZgytWrXi/v371K9fH39//xQfwmJjY5PUqMOka9eudOvWjXv37lm6NEmCIkWKMGPGDA4cOEDz5s3p1atXvBCW0t9r79+/T5s2bcwh6vPPP2fevHnmEAaYlyWaOgebGpjky5ePKVOmADB27Fg2btxoy9ITzWg0mpvgpMRVLyKSciiIOSEFMUkpHj58SM+ePRkyZAhGo5H33nuPdevWkS1bNnuXlmyHDx/m1q1bZMiQgapVqybquXG7z6lromN55ZVXWLlyJT/99JP5vn379lGgQAHGjRvHgwcP7Fhd0ly8eJGaNWuyfPly0qZNy9y5cxk5cuQTnSJ37twJPDoaBuJ392zZsiXvvPMOAN27dyc4ONhG1SeeaTYMNCMmItalIOaEFMQkJbhx4wb169dn5syZpEmThl9//ZWJEyfi5uZm79IswrQssVatWoluNBK3s6uCmGOKO5Py559/EhwczCeffELJkiWZOnWquRmEo/P398fLy4sDBw6QO3duNm/eTOfOnZ94XHR0NAEBAcDTgxjAjz/+SKlSpbh69Spvv/32Mxti2ZuCmIjYioKYE4obxGJiYlLMLwTiPA4cOICXlxe7du0ia9asrF27ln79+tm7LItK6v4wE9MvuQpiju/XX39l2rRpFCxYkMuXL9OrVy/Kly/PsmXLHDaMAMyfP586depw48YNypcvT0BAANWqVXvqYw8dOkRYWBiZM2c271l8PIilT5+euXPn4ubmxtKlS83LFR2NgpiI2IqCmBN4/Ad93CAGmhUTx7Js2TJ8fHy4ePEiJUuWxN/fn0aNGtm7LIuKjo5my5YtQNKDmOkXRAUxx5cmTRp69uzJyZMn+eGHH8iWLRvHjh2jdevWtG7d2t7lPcFoNPLFF1/QuXNnIiIi8PX1Zfv27RQqVOiZzzHtD6tWrZr57+TTDrmuVKkS33zzDfDonLGTJ09a4RUkT9wgpj1iImJNCmKp3MOHDylatChvvfUWK1eu5OHDh090WNNZYuIIjEYj3377La1btyYsLIyGDRvi5+eXKluA7927l7t375I1a1YqVqyYpDE0I5bypEuXjkGDBnH27FmGDRtG+vTpadiwob3LiufBgwd06tSJr776CoBPPvmEpUuXmhtwPItpf1j16tVf2HHwo48+on79+oSHh9OlSxciIyMt+AqSL+4qEc2IiYg1KYilcv/++y/nz59n2rRptGjRghw5cjBq1Kh4j9GMmNjbw4cP6datG0OHDgXg/fffZ+3atamq9XdcpmWJdevWTfIvegpiKVeWLFkYNWoUp0+fpk+fPub7Fy9eTLdu3Th37pxd6rp27Rp16tRh4cKFuLm5MXXqVL7//vsE/R01zYjVqFHjuTNipvtnzpyJp6cne/bs4YsvvrDci7AALU0UEVtREEvlli5dGu92WFgYmzdvjnfftGnTuH79ug2rEvmf69evU7duXebMmUOaNGn4/fff+eWXX1L1kqDk7g8DmDJlCnPmzCFHjhyWKktsLG/evOZGLbGxsQwbNozZs2dTqlQpBgwYQFBQkM1q2bdvH15eXgQGBpI9e3b+/fdf3nzzzQQ998aNG5w9exaDwYC3t/cLgxhA/vz5+euvvwAYM2bMEz+X7CluEHveaxARSS69w6RyvXr1on///hQsWPCZjxkxYgT58uXDx8fHpj/4Rfbt20fVqlXx9/fH09OTDRs28O6779q7LKuKiIhg+/btQPKCWKdOnejSpQsZM2a0VGliRy4uLsydO5eGDRsSFRXFhAkTKFq0KCNHjrT6WXHLli2jZs2aXL58mTJlyuDv70/t2rUT/HzTbFi5cuXIkiVLgoIYQJs2bejduzdGo5Fu3boREhKS9BdhQaYglpo/DBIRx6AglsrVqFGDCRMmcOHCBXbv3s2wYcMoXrz4E48zGo1cuHCB7Nmz26FKcUZLliwx//JXunRpAgICkhVMUgp/f38ePHhArly5KFu2rL3LEQdSuXJl/vnnH/755x8qV67M/fv3+fLLLylWrBiLFi2y+PVMh6W3adOG8PBwGjduzK5duyhWrFiixom7PwxIcBAD+OmnnyhRogSXL1/mnXfecYgukqY9YlqWKCLWpiDmJAwGA5UrV2bUqFHPXALSqlUrLcMQqzMajYwaNYp27doRHh5OkyZN2LVr11M/IEiN4i5LjHswc2KtXbuWpUuXcvfuXUuVJg6iYcOGBAQEsGDBAooXL05QUBBZs2a16DUiIiJ48803zYelv//++6xevZosWbIkeqy4+8MgcUEsY8aMzJkzB1dXVxYvXsyMGTMSfX1Le1GzERERS9Fv3U7o8fb18+fP57fffkvwfgCRpHrw4AFdu3ZlxIgRAHzwwQesWrXK4r9kOjJL7A8D6N69O23atOHy5cuWKEscjIuLCx06dODo0aMsXbo03hEO06ZNY+3atUmePQoKCqJhw4bMmDGDNGnS8NtvvyV5X2ZkZCS7d+8GkjYjBlC1alW+/vpr4FGjntOnTye6DktSEBMRW1EQc0KPB7GMGTPSt29fKleubKeKxBlcvXqVOnXqMG/ePFxdXfnjjz/4+eefnWofRlhYGH5+fkDyg5i6JjoHNzc3WrVqZb4dFBTEgAEDaNasGfXq1cPf3z9R4x05cgRvb2+2b99OlixZWLt2LX379k1yffv37+fhw4dky5bNfNREYoMYPGqTX6dOHcLCwujatStRUVFJrim5TEsTnem9SUTsQ0HMCekcMbG1PXv2mDuyZcuWjX/++Sde225nsWPHDqKioihUqBBFixZN1lgKYs7Jzc2NPn36kDZtWrZs2UK1atVo06YNx48ff+Fz161bR40aNTh37hzFihXDz88v2Yelm5YlVq9e3bzUNilBLE2aNMyaNYusWbMSEBDAyJEjk1VXcmhGTERsRUHMCbm6usb7AaNzxMSaFi1aRK1atbhy5QplypQhICCAunXr2rssu7DU/jBQEHNWWbNmZdy4cZw8eZI333wTFxcXli5dSrly5ejduzfXrl174jlGo5EJEybw+uuvc/fuXerUqYO/vz+lS5dOdj2PN+qApAUxgIIFC/Lnn38CMHr0aLZu3Zrs+pJCQUxEbEVBzEnFXZ6oICbWYDQaGTlyJB06dODBgwc0bdo0SR3ZUhNL7Q8DBTFnV6hQIaZOncrBgwdp2bIlsbGxzJgxg7CwsHiPi4qKom/fvgwYMIDY2Fh69erFhg0bLNYh9/FGHZD0IAbQvn17evbsaW5pf+fOHYvUmRhqXy8itqIg5qQUxMSawsPD6dSpE19++SUAgwYNYuXKlUnqyJZa3Llzhz179gBQr169ZI+nICbw6OyuZcuWsWPHDn788cd43UfnzZtH48aNmTRpEgaDgXHjxjF58mTzIdLJdfnyZS5duoSLiwtVq1Y135+cIAYwYcIEihUrxsWLF3nvvfds3tJe7etFxFb0cY+TUhATa7ly5QotW7Zkz549uLm58fvvv9OrVy97l2V3W7duJTY2lpIlS1KgQIFkj6cgJnHVqFEj3qzUihUr6NKlC/BoX/D8+fPjNf2wBNNsWIUKFeIdLJ7cpX2ZMmVizpw5+Pj4MH/+fJo1a0a3bt2SX3ACaWmiiNiKZsSclIKYWENgYCBVq1Zlz549ZM+enX///Vch7P9ZclkiPNpDM3nyZIoUKWKR8ST12LRpE2+88Yb5dkREBEOGDGHJkiUWnV162v4wSP6MGIC3t7d5Rr1fv36cPXs2yWMlloKYiNiKgpiTUhATS1uwYAG1a9fm2rVrlCtXjsDAQGrXrm3vshyGpYNY586d6d27N7ly5bLIeJI6TJ48mcaNG3Pv3j28vb356quvyJEjBydOnKBdu3ZUq1aNTZs2WeRaT9sfBpYJYgCfffYZNWvW5N69e7zxxhvmJYPWpj1iImIrCmJOSkFMLCU2NpbPP/+cTp068fDhQ15//XV27tzJSy+9ZO/SHMbNmzc5dOgQgNN2jBTriomJYdCgQfTp04fo6Gi6dOnC5s2bGTFiBGfOnGHEiBFkyJCBgIAAOnbsSHh4eLKu9/DhQ/bu3QtYZ0YMHs1IzZ49m8yZM7Nr1y5GjRqVrPESSnvERMRWFMScVNyzxHSOmCRVWFgYHTp04Ouvvwbg448/Zvny5WTOnNnOlTmWzZs3A1C+fHly5sxpkTF37drFhg0bCAkJsch4knLdvXuXli1b8tNPPwHw9ddfM3v2bPMHbpkzZ+arr77izJkzvP/++4wcOZL06dMDj7qbXrhwIdHX3LNnD1FRUeTOnfuJD10sFcQAChcuzKRJk4BHr2vHjh3JHvNFtDRRRGxFQcxJaUZMkuvy5cvUqlWLJUuW4ObmxrRp0xg7dqx+eXkKSy9LBOjduzdNmjTh4MGDFhtTUp7z58/j4+PD6tWr8fDwYOHChQwfPvyp59Tlzp2bX375hffee898399//03x4sV5//33uXHjRoKv+7SDnE0sGcTg0TLcbt26ERsbyxtvvEFoaKhFxn0WBTERsRUFMSelICbJ4e/vT9WqVdm3bx85c+Zk06ZN9OzZ095lOSxrBDF1TZSdO3fi5eXF4cOHyZs3L1u3bqV9+/aJGuO///4jOjqa3377jWLFivH5559z9+7dBF0bntwfBpYPYgC//vorL730EufPn+f999+32LhPY1qaqD1iImJtCmJOSkFMkmru3LnUqVOH69ev88orrxAQEICPj4+9y3JYly5d4tSpU7i4uFi0eYmCmHObPXs29erVIygoiFdffZWAgACqVKmS6HF+++03/vvvP7y8vAgLC+Prr7+mWLFijB8//pnL1o1GY7wZscdZI4hlzpyZ2bNnm/eNzZ0712JjP04zYiJiKwpiTkpBTBIrNjaWYcOG0bVrVyIiImjRogU7duxQ+/QXMHWoq1KlikUPtFYQc06mf4fdunUjMjKSNm3asG3btmSdTVevXj38/PxYvHgxpUqV4tatWwwcOJAOHTo89fHnz5/n+vXruLq6Urly5afWCJYNYvBo9m3EiBEAvPfee5w/f96i45soiImIrSiIOSkFMUmM+/fv065dO0aPHg3Ap59+ytKlS8mUKZOdK3N8piBmyWWJoCDmjMLCwmjfvr353+HQoUNZtGgRGTJkSPbYBoOBtm3bcvjwYf7880/y5ctH3759zd+Pjo42n0Fmmg2rVKkSHh4eT4xlrSAGMGzYMGrUqMHdu3et1tJe7etFxFYUxJyUgpgk1MWLF6lZsyZLly7F3d2dGTNm8N1331nll6zUxmg0mveH1atXz6JjK4g5lytXrlC7dm3+/vtv3N3dmTlzJt98843F/x26urry9ttvc+bMGRo3bmy+f8yYMdSpU4ddu3Y98yBnE2sGMVdXV2bPnk2mTJnYsWMH3377rcWvofb1ImIr+k3KSSmISULs2rULLy8vDhw4QK5cudi0aRPdu3e3d1kpxtmzZ7l48SJubm4W30enIOY8du/eTdWqVdm7dy85c+bkv//+o1u3bla9Zrp06czdECMjI/nll1/Ytm0bNWrUYPbs2cDTG3WYZs3AOkEM4KWXXmLixIkAjBw5Ej8/P4uOr6WJImIrCmJOSueIyYvMmjWLunXrcuPGDSpUqEBAQMBTf/GSZzPNhlWrVs0iy8fi+vDDDxk/fjxly5a16LjiWBYvXkzt2rW5du0a5cqVs0tzHHd3d3bv3k3v3r0xGAzm9vGLFy/m0qVL8R4b94MBawaZrl270rlzZ2JiYujatWuCOj0mlIKYiNiKgpiT0oyYPEtsbCxDhgyhe/fuREZG0qpVK7Zv307hwoXtXVqKY4229SadO3dmwIABFC1a1OJji/0ZjUZGjRpF+/btefDgAc2aNWPnzp12a45ToEABJk+ezPTp0833LVq0iBIlSjBnzhzzfaYQA9abEYNHe9omTpxI4cKFOXv2LB988IHFxtYeMRGxFQUxJ6UgJk9z7949WrduzZgxY4BHzQCWLFlCxowZ7VxZyhN3f5g1gpikXg8fPuSNN94wdwj88MMPWbFiBZkzZ7ZzZY/2qgE0aNCAunXrEhMTg5eXl/n7cWfErL2PNGvWrMyaNQsXFxdmzJjBggULLDKu9oiJiK0oiDkpBTF53IULF/Dx8WHFihWkTZuW2bNnW6UZgLM4evQoN2/exMPDA29vb4uPf/jwYbZv305QUJDFxxb7uXHjBvXq1WPu3Lm4urryxx9/8NNPPzlMKDA16mjevDn//fcfBw4coESJEubvDxkyxPy1Ld47atWqxdChQwF45513uHjxYrLH1NJEEbEV/YblpBTEJK4dO3ZQtWpVDh06RJ48ediyZQtdu3a1d1kpmmk2rGbNmvH2ZFpK//79qVWrlrk9vqR8Bw8exMvLCz8/Pzw9PVm/fj19+vSxd1lmjx/kbDAY4u1RPHz4MD///LP59tKlS23STObzzz/H29ub0NBQunXrFm95ZFIoiImIrSiIOSkFMTGZPn069erVIygoiIoVKxIQEGCVGRxnY+1lieqamLqsXLkSHx8fLl68SMmSJfHz83O4Ja2nTp0iODiYtGnT8uqrrz7x/ZIlS/L999+bb3fv3h0vLy/+/fdfq9bl5ubGnDlzyJgxI1u3bjUvrU4q7RETEVtREHNScYNYREREvJbD4hxiYmIYPHgwb775JlFRUbRp04bt27dTsGBBe5eW4sXExLB582ZAQUyez2g08sMPP9CyZUvu379PgwYN8PPzo2TJkvYu7Qmm2bAqVarg7u7+xPfd3d15++23zbczZMjAnj17aNSoEY0aNeLChQtWq61YsWL88ssvAHzxxRcEBAQkeSztERMRW1EQc1Jxgxiohb2zuXfvHq1atWLs2LEAjBgxgkWLFlm8xbqz2r9/P3fu3CFz5sxUqlTJKtdQEEv5IiMjefvtt/n4448xGo28++67rF27Fk9PT3uX9lQvOsgZ4v99PHnyJB988AFubm7s37/f6q+rR48edOjQgejoaLp27cr9+/eTNI6WJoqIrSiIOanH96woiDmPc+fOUaNGDVatWkW6dOmYN28eX331lZpyWJBpWWKdOnWstrxJQSxlCw4OpnHjxkyZMgUXFxd+/vlnJk6ciJubm71LeybTjNjzzhOM+/cxT548/Pzzz5w4cYLZs2ebuz4ajUa+++47rl27ZtH6DAYDkyZNomDBgpw+fZoBAwYkaRwFMRGxFf3m5aQenxHTPjHnsG3bNry8vDh8+DB58+Zly5YtdOrUyd5lpTq2aFuvIJZyHT9+HG9vb7Zs2UKmTJlYtWoVH3zwAQaDwd6lPVNoaCiHDx8GEj4jZno9L730Ek2aNDHfv2zZMj777DOKFy/OsGHDzAdEW4KnpyezZs3CYDAwdepUFi9enOgxTEsTtUdMRKxNQcxJKYg5n6lTp9KgQQNu3bpF5cqVCQwMjHf+j1hGZGQk27ZtAxTE5En//PMP1apV48yZM7z00kvs2rWLpk2b2rusFwoICMBoNPLSSy+RJ0+eZz7O9PfRYDA8M1jmy5ePatWqER4ezujRoylatCg//PCDxX4O1alTx9xGv0+fPly6dClRz9eMmIjYioKYk1IQcx4xMTF89NFH9OrVi6ioKNq3b8/WrVvJnz+/vUtLlQIDAwkLCyNHjhy8/PLLVrtOt27d+Oabb6hcubLVriGWNXHiRJo2bUpoaCg1a9bE39+fcuXK2busBInbtv55TEHseSHG29ubnTt3snTpUsqUKUNISAgff/wxJUuWZNq0acluPw/w5ZdfUqVKFW7fvk337t0TNaaCmIjYioKYk1IQcw6hoaH4+vry448/Ao9+OVmwYAHp06e3c2Wpl2lZYr169ay6765Dhw4MHTqUChUqWO0aYhnR0dH079+ffv36ERMTQ48ePfj333/JmTOnvUtLMFOjjuftD4P/BbEX/d03GAy0atWKQ4cOMXXqVAoUKMClS5f4+eefLbJE093dnblz55I+fXo2b97MDz/8kODnJrV9/ebNm80zgQaDgQwZMnDv3r1EjSEizkVBzEkpiKV+Z86coXr16qxduxYPDw8WLlzIF1984dD7UFIDW+wPk5QjNDSU5s2b8+uvv2IwGPjuu++YNm2aVQ75tpbY2Fj8/PyAhM+IJfRDiDRp0vDmm29y8uRJxo0bx7hx48zPDQ8PZ/v27Umuu0SJEkyYMAGAYcOGsWfPngQ9L6nt62fMmBHvdnh4OIsWLUrUGCLiXBTEnJSCWOq2ZcsWvLy8OHbsGPny5WPr1q20b9/e3mWleg8ePDDPHFg7iJ0/f559+/YRFBRk1etI0pk+DFm/fj3p06fn77//5tNPP01xH4YcO3aM0NBQ0qdPT/ny5Z/7WNNsUmJngz08PPjoo49o2LCh+b4JEyZQq1YtmjdvzqFDhxJfOPDWW2/Rpk0boqOj6dKlC2FhYS98TlKWJoaFhT21Mcjj4UxEJC4FMSelIJZ6TZ48mYYNGxISEkLVqlUJDAykSpUq9i7LKezcuZPIyEjy589PiRIlrHqtTz/9lEqVKrFgwQKrXkeSZuvWrXh7e3Ps2DEKFCjA9u3badWqlb3LShLT/jAvL68XLtdL7IzY89y+fZs0adKwevVqKlSoQPfu3Tl//nyixjAYDEyePJn8+fNz8uRJBg0a9MLnJCWI/f333+Zzy3x8fMiUKRPwqFPtuXPnElWziDgPBTEnpXPEUp/o6Gg+/PBD+vTpQ3R0NJ06dWLLli3ky5fP3qU5jbjLEq0966GuiY5r2rRpNGzYkODgYKpWrUpAQACvvvqqvctKsoQc5GxiySA2ZswYjh49Svv27TEajcyaNYtSpUoxcOBAbt26leBxsmXLxsyZMzEYDPz5558sXbr0uY9Pyh6x6dOnm7/u3bs3bdq0AR6dmTZz5swEjyMizkVBzEk9HsQ0I5aymfah/PzzzwB89dVXzJ07Fw8PDztX5lxsuT9MQczxxMTEMHjwYN566y2ioqLo0KEDW7ZsIW/evPYuLVkScpCziSWDGEDJkiVZuHAhgYGBNGjQgMjISMaPH8/AgQMTNU79+vX5+OOPgUdB6erVq898bGL3iF28eJFNmzYBj5ZYtm3bljfeeMP8/ZkzZ2I0GhNVr4g4BwUxJ2UwGOKFMQWxlOv06dNUq1bNvA9l8eLFjBgxIsXtQ0np7t69S2BgIPCoY6K1KYg5lvv379OmTRvGjh0LwBdffMH8+fNT/IchISEhHD9+HIBq1aq98PGWDmImVapU4d9//2XDhg14eXkxfPhw8/fu3LlDZGTkC8cYNWoUlSpVIiQkhO7duz/z305ilybOmjXLHLRatGhBpkyZqF+/vvmIkLNnzyar6YiIpF4KYk4s7j4xBbGU6b///sPLy4vjx4+b96G0bdvW3mU5pW3bthETE0OxYsUoXLiw1a+nIOY4Ll68SM2aNVmxYgVp06Zl3rx5fPnll6niwxBTt8SSJUuSI0eOFz7eWkHMpFGjRvj7+1OqVCnzfQMHDqRMmTLMmzfvuf8e3N3dmTNnDh4eHmzcuJGffvrpqY9LbBCLu/Swa9euwKPX37lzZ/P9atohIk+jIObEFMRStkmTJtGkSRNu376Nt7d3it+HktLZum19coNYTEwMGzduZODAgXh5eVGoUCHSpUtHtmzZKFOmDM2bN2fixIlcvnzZkmWnOv7+/nh5eXHgwAFy587Nli1b6NSpk73LspiEHuRsYu0g9rj79++zYcMGzp49S5cuXahSpQrr169/5lLA0qVLmwPYZ599xv79+594jGlpYkL2iO3cuZOTJ08CkCNHDl577TXz9+IuT1y0aBEPHjxI8OsSEeegIObEFMRSJtPhsO+99565JfPmzZtT/D6UlM7WQcw025KUILZ27VrKly9Pw4YNGT9+PIGBgVy6dImIiAhu377N8ePHWb16Nf369eOll15iwIABhISEWPolpHjz5s2jTp063LhxgwoVKhAQEIC3t7e9y7KohB7kbGLrIJYxY0ZOnDjBqFGjyJw5M/v27eO1116jfv36BAQEPPU5ffr0oWXLlkRFRdG5c2fCw8PjfT8xM2JxZ7o6dOiAm5ub+XaFChV45ZVXgEdLl1/UJEREnI+CmBNTEEt5bt++TbNmzfj1118B+Oabb5g9e/YTxxGIbQUHB5s/WbfF/jCA5s2bM2zYsATPVMCjDm4DBgygWbNmHD161Hy/q6srJUuWpE6dOlSuXJmcOXOavxcdHc2ECRN4+eWXk3yWU2oTGxvLF198QZcuXYiIiKBFixZs376dQoUK2bs0i4qOjjaHGUedEYNHYWzYsGGcOXOGQYMG4e7uzubNm/H29mbOnDlPPN5gMPDXX3+RN29ejh8/bm7iYZLQIPbw4UMWLlxovm1alhhX3FkxLU8UkScYxWlVqFDBCBgB42effWbvcuQFTpw4YSxZsqQRMKZPn974999/27sk+X+LFy82AsZy5crZu5Rnio2NNXbt2tX8bx4wZs+e3fjLL78Yg4KCnnjszp07jW3atIn3+KxZsxoDAgLs9AocQ1hYmLFDhw7m/yaffPKJMTo62t5lWcW+ffuMgDFz5swJfo27d+82AsaCBQtaubpnu3DhgrFnz57GnDlzGu/cuWO+PzY2Nt7jNmzYYP7/uHz5cvP97du3NwLGX3/99bnXmT9/vvn5RYsWfepjLl26ZHRxcTECRhcXF+Ply5eT8cpEJLXRjJgTi9s1UeeIObZ///0Xb29vTp48ScGCBdmxYwetW7e2d1ny/2y9LDEpfv7553izA15eXhw7doz333//iSYMBoOB6tWrs2TJEmbOnGmeGbhz5w4dOnTg7t27Nq3dUVy7do26deuycOFC3NzcmDp1Kt9//32iDv5NSUz7w7y9vRP8Gu0xI/a4QoUKMW3aNE6fPk2WLFmAR7PBjRo1YsiQIdy+fRt41PjDdMBzr169uHbtGpDw9vVxZ7ieNhsGUKBAAerWrQs8+m8za9aspL8wEUl1FMScmJYmpgwTJ07ktdde486dO1SvXp3AwEAqVqxo77IkDnsEsZs3b3LixAmCgoJe+Njjx48zZMgQ8+3SpUuzfv36eEsQn6Vbt278/vvv5tvnz59nwIABSSs6Bdu3bx9Vq1YlMDCQ7Nmz8++///Lmm2/auyyrSsxBziaOEMRMMmfObP5606ZNbNy4kTFjxlCsWDG+//57Hjx4wOjRo6lQoQK3bt2iZ8+exMbGJmhp4rVr19iwYYP5dtwliI/T8kQReRb7v1OK3SiIObaoqCj69u1Lv379iImJoVu3bvz333/kzp3b3qVJHFevXuX48eMYDAbq1Kljs+t+/fXXlC5dml9++eWFjx07dqx51ttgMDB58mSyZs2a4Gu9/fbbNG7c2Hx71qxZXLhwIdE1p1RLly6lZs2aXLlyhTJlyuDv70/t2rXtXZbVJeYgZxNHCmJx1atXjxUrVlCuXDlu377Np59+SokSJZg1axYzZ84kXbp0bNiwgQkTJiQoiM2ePdv8uCpVqlCyZMlnPrZt27bm8+SOHz/+zCYiAJcuXeLnn3+mZcuWFClShLRp05IxY0bKlSvHgAEDOHv2bFJevog4KMd6pxSbUhBzXCEhITRt2pTff/8dg8HAd999x4wZM9SUwwFt2rQJgEqVKuHp6Wmz6ya0fX1QUFC8JYlNmzalZs2aib7e6NGjzV/HxMQwYcKERI+R0hiNRr777jvatGlDeHg4jRs3ZteuXRQrVszepVndzZs3OXPmDECiOkGawomjBTGDwYCvry8HDhxg+vTpFCpUiCtXrvD222/TsWNHhg0bBsCnn37KnTt3gOe3r487s/W82TB4NDPXokWLpz43rkuXLlG4cGE+/PBDVqxYwYULF0iXLh2RkZEcPXrU3DQnboMQEUnZHOudUmxKQcwxHT9+HG9vbzZu3EiGDBlYtmwZn376aao4HDY1MgUxW+8PS2gQW79+fbw9oG+99VaSrle5cmVzK26AFStWJGmclCIiIoKePXvy2WefAfD++++zevVq856j1M40G1auXLlEzZ466oyYSZo0aejRowcnTpzgxx9/JHv27Li5uTFkyBCaN29OZGSkuQPqs2bE9uzZw5EjR8y3hw8fTtasWZ/7Z/ny5ebHz58/n8jIyCfGNYXY1157jXnz5hEUFERoaCjh4eFs2bKF8uXL8+DBA9544w11MBVJJRzznVJsQkHM8WzYsIFq1apx+vRpChUqxM6dO+N9kiqOx16NOhIaxLZv327+2mAw0KhRoyRfM+5zT58+zY0bN5I8liMLCgqiQYMG5kYlv/32G7/88kuCDvhNLRJ7kLOJowcxk3Tp0jFw4EDOnDnDggULcHV1ZcqUKeTKlYuwsDDg2UFs+vTp8W7fv3+f0NDQ5/6J+zM2JCSElStXPjGup6cne/fuZe3atXTq1MncRMfV1ZXatWuzYcMGcubMSVRUlPlQahFJ2Rz7nVKsSkHMcRiNRn755ReaNm1KaGgoPj4+BAYGUr58eXuXJs9x7tw5zp07h6ura5KW+yVHQoPYnj17zF+XKFEiXgODxKpcufIzx04tjhw5gpeXFzt27CBLliysXbuWvn372rssm0vsQc4mKSWIwaMAlSVLFsqUKQNAv379uH//vvn7U6ZMeeI5kZGRzJs3L9nXftryxCxZsjy3EVPu3Llp1qwZkDr/7Yk4I+f5eE+eoCDmGKKioujfvz9//PEHAD169OCPP/6Id7yAOCbTskRvb28yZsxo02ublqq+KIjdvHnT/HXhwoWTdc3Hnx937NRg7dq1dOzYkXv37lGsWDFWrVpF6dKl7V2WzUVFRREYGAik/Bmx2NhYLl26xLFjx574kzNnTo4dO2Z+7IULFwgPDzffLlWq1BPjrV69muDgYODRETBBQUFkypQpQbXs3LkTHx8f4NHftZs3b5IrV65EvR7TLJmpxb6IpGwKYk5M54jZX3BwMO3atWPz5s0YDAa+//57PvroI+0HSyHseX5YQmfEQkJCzF8nd3/T48+PO3ZKZjQamTBhAoMGDSI2NpY6deqwZMkSsmfPbu/S7GL//v08fPiQbNmyPbcb4NPYK4hFRUVx+vRpLl++HG8JrY+PD35+fk99zr1794iKisLNzQ2A7777jj59+nDmzBneeustfv311yeeE3cmq3HjxgkOYfAo1ObPn58rV64QHR3N3Llz+fDDDxP8fIAtW7YA8PLLLyfqeSLimBTEnJhmxOzr2LFj+Pr6cubMGTJmzMi8efNo3ry5vcuSBDIajXYNYj4+PgwYMIBatWo993FxP2RJ7izr489/8OBBssZzBI/PSPfq1YuJEyfi7u5u58rsx7Q/rFq1aokOVLYIYkePHmXfvn0cO3aMo0ePcuzYMU6fPk10dDRp06YlLCzMvL/rpZdeYs+ePZQoUYIyZcrE+1OqVClzCAM4c+YMZ86cwdPTk++///6J6wYFBbFmzRrz7Xbt2iWqboPBQNu2bc0dR2fMmJGoILZs2TJ2794NkOrPsBNxFgpiTkxBzH7WrVtHx44duXv3LkWKFGHlypX6hDOFOXHiBNeuXSNt2rRUq1bN5tf39fXF19f3hY/LmjWr+dDnu3fvJuuajz8/MbMBjuj27du0b9+ejRs3YjAYGDduHAMHDnT6Gemk7g8DywWx4OBg8xLCU6dOMWbMGPP/l88//5wlS5Y88ZwMGTJQpkwZQkJCzIeV//bbb8yYMSNe4Hqae/fuMXz4cPP4T5sNnTt3LlFRUQC4u7snqZFS+/btzUFs//79HDx4MEF7gS9evEifPn0AaNmyJa+99lqiry0ijkdBzIkpiNme0Wjk559/5qOPPiI2NpZatWqxZMkS8y8NknKYZsN8fHwc+nw3T09PcxBL7lLCx5+fkpfunTp1iubNm3Py5EnNSD8mqR0T4X9B7HmHIT/NqlWrWLVqlTl8mf7OmvTv35+CBQsCj/ZkBgUFPTHDVaBAgSdCdELP9vv222+5efMmJUqUeGZzlrjLEhs0aJCotv4mPj4+5M2bl2vXrpnH/OGHH577nDt37uDr60tQUBBFixZl6tSpib6uiDgmBTEnpiBmW5GRkfTr14+//voL0BKolM6eyxLh0Sf4d+/eJX369M/9ZbNo0aKcPHkSgMOHD2M0GpM84/P42UWmbnMpzX///Ue7du24ffs2hQoVYuXKlepQ+v+uXLnCxYsXcXFxwcvLK9HPf9qMWHR0NGfOnHmiWcaqVavInTs3ANu2bTMvDzUpVKgQZcqUoWzZsvHG++STT/jkk0+S8vKe6vz58/z4448AjB079qnvyYcOHWLfvn3m24ldlmhiWp5o2n82Z84cxowZ88yjEe7fv0/Tpk05ePAg+fLl459//iFbtmxJuraIOB4FMScRGRnJ+++/j6+vL40aNSJdunQKYjZ069Yt2rZty9atW3FxcWHcuHF8+OGHTr8EKqWKjY2120HOJuPHj+fzzz/nnXfeYdKkSc98nI+PD+vWrQMgNDSU48ePJzlABQQEmL9Oly5digxif/75J/369SM6Oprq1auzdOlScxiQ/82GlS9fPtGdQB88eGA+qNjFxYXp06czduxYTp06ZV7SF9fRo0fN/+1fe+01XF1d4+3fslUn0iFDhhAREUG9evWeudww7myYq6srrVq1SvL12rdvbw5iN27cYN26dU+djQ0PD+f111/Hz8+PnDlz8u+//1K0aNEkX1dEHI+CmJPYuHEjkydPZvLkyWTKlIkWLVrEa5urIGY9R44cwdfXl3PnzpEpUybmz59vPgtGUqaDBw8SEhJCxowZqVKlil1qSGjXxLp168a7PW/ePL766qtEX+/+/fusWrXKfLt+/fp4eHgkehx7iYmJ4eOPP2b8+PEAdO3alb/++suhl5Umxu2wSK6GPiA8MoaomEd/J9zTuJAhrSv5sniQJf3z90iZmPaHPW9Z4u3bt5/aDv78+fOMGjUKePT3MzIykqNHjwKQPn16SpcuHW8pYdxZyHr16lGvXr0kvfbk2LlzJwsWLMBgMPDjjz8+88OxcePGMW7cOItcs3bt2hiNxuc+5uHDh/j6+rJ161Y8PT35559/UuQHHyLyfApiTmLx4sXmr+/du8ecOXPifT8mJoa7d+8m67BXedLq1avp3Lkz9+7do2jRoqxcuZKyZcvauyxJJtOyxNq1a7+wCYC1JDSI+fj4ULp0aY4fPw7A1KlT+eyzzxIdoqZPn05Y2P+xd97xNZ1/HH/flR1JjNg7RsyiZqndWrFJbC0tWlWltWurvatarSqKxF5V1Cxaq1YRW4xYIZFxk9x1zu+P25yfK0HGzSDP+/W6L856nu9Ncu85n+e79Mp29+7dU2hx5hEVFUWXLl2UineTJ09m1KhRr71H2iLJ3Hsax52IWGINlkTHY7HwNNZEaEQc7k5aCuV0IX8OJ9TqF7/vZ/PDQkNDCQ4OpkKFCuTLlw+An376iY8++uiF14eGhgLWv88WLVrw+++/4+vrS+HChbNMb7EEJEniiy++AODDDz98aTPljMRgMNC2bVv27dtHjhw52LlzJ5UrV85sswQCQToghFg2oWjRohQuXJg7d+688Jz8+fPTsmVLOnXqRLt27V4Ysy54NbIsM2fOHL766itkWaZ+/fqsX79eacYpeL3J7PwwSH5DZ5VKxeDBg+nfvz9gfVCeMGEC06ZNS/Zcjx49YuzYscp28eLF6dy5cyqsznhCQkLw8/Pj/PnzODs7s2LFilTn92QlYo1mztx+SqzRgsFkITrexJNYE/p4M7JKxkGjxt1JRw4nHV4uOqLjzQTfi+JOeCxvFfbESff/YhpPnjzhyJEjnDt3Tgk//eSTT4iJiQGsYXk9e/YEUELjChcunKhYhq+vL/v27eO7775DrVZTqFAhChUqlME/meQTGBjI8ePHcXV1VTx5mY3JZKJz587s2rULV1dXduzYkapcPYFA8HognrSzCWPHjuXrr7/mxIkTrFu3jvXr1xMSEmJzTmxsLOvWrePIkSN06NAhcwx9AzAYDAwYMIBly5YB8NFHH/Htt9+KohxvCCaTSWmqmplCLLkeMbCu9i9dupQTJ04A1oIElStXpkuXLq+8NiYmhrZt2xIREaHsW7hwYYqr4mUGR44coV27doSFhVGgQAG2bNmSaaGk9iQ63sQ/tyIIizYQ+jSOuxGx6A0WJMk23E2nVePhrCOnqwOeTmpMUY+5F3qXpXdvE9CkJo3r1wXgxIkTtGnTxubamJgYNBoNPj4+Nvvr1q1LdHT0C/O3Mquhc0qJjY1lxIgRAIwcOVLx+GUmFouFbt26sXXrVpydndm2bRvvvPNOZpslEAjSESHEshEqlYoaNWpQo0YNZsyYwc8//0zfvn0TndehQ4csfxPNqoSFhdG+fXsOHz6MWq1m7ty5fPbZZ699CJTg//zzzz/ExMTg5eWVqeFCKRFiOp2ONWvWUKVKFaKjo5EkiR49enDlyhVGjhz5wkWCs2fP8uGHH3Lq1Cll3+eff07Lli3t8ybSkZUrV9K3b1+MRiNVq1Zl69atFCxYMLPNSjPxJgv7Lj3i6sMYog0mwqIMmCUZk0VCrVLh4qDBQavGFB/LuTMXCI+KJSo6mugnDzBHPcYcfhfZbAJZpt47dXDQqilXrpwSlnfmzBmqV6/OsmXLKFWqVKK/DQcHh5cuKr0uQmzOnDncuXOHIkWKMGTIkMw2B7AuHKxbtw6w/hxftVDy4MGDjDBLIBCkI0KIZVNUKlWiePgBAwawZ8+eNyJsJzP4999/8fPz49atW3h4eBAUFMT777+f2WYJ7ExCWGLDhg0z9WEzJUIMoGTJkuzevZtWrVrx5MkTLBYL48eP58cff6Rz587UrFmTfPnyodfrCQkJYdu2bezZswez2ayM0adPH+bOnZsu78deSJLEmDFjmDp1KgDt27dnxYoVuLq6ZrJl9uGPiw+5EBrJ06gYrt57zJNIPdGxccTHRJE/rzfFixcn1mghNtbI+QvBSPExoNagdsmBa143chYpQvGcThQvVYYrD6OpUNCDIkWKcPr0aTp06MCZM2fo2LEj5cuXT5V9r4MQu3fvnhKaO23atCxTdObZz7LBYODhw4eZaI1AIMgIhBDLxjxfLezTTz9l0aJFmWTN6822bdvo2rUrMTEx+Pj4sG3bNsqWLZvZZgnSgayQHwZQsWJFPvroI2rWrJnsa2rVqsXhw4fp2bOnEqYYGhr6SnGVI0cOpk+fruSZZVX0ej09e/Zk48aNAIwaNYpJkyZlaVHwMiwWC3q9XimidPDsDUZMX8qTJ0+QvYqgcbP2k5LNRkBGExlLrjgTjjo1jk7OlPStiJubG3m83CmWLxcxFg06tRofb1fK5MvBw6h4fLzdcNJpkGU5WRUTX8XrIMS+/vpr9Ho9tWrVIiAgILPNUWjQoMErqykKBII3CyHEsjHPC7H4+HgRQpdCZFlm5syZjBgxAlmWadiwIevXrxcNN99Q4uPjOXLkCJD5Qqxp06Y0bdo0xdeVLVuWY8eOsWbNGhYtWsTRo0df6lUrWbIkR48ezfKFZkJDQ2ndujWnTp3CwcGBpUuXvjaVHQ0GA1euXElUDv7y5ct06dKFZcuWERZt4GyYkYeRcWjz+KB180Kn1eCklnFzdsPD1YUcHjlwdrDm7pksMmV8y6FWqdCoVUSbNOTJ4ciDyDhuh8dR0MsFN0ctoU/jKJnHjVu3bvHgwQO0Wm2a8uiyuhA7ffq0kr87d+5ccc8TCASZihBi2ZikhJgg+RgMBj7++GNWrFgBQP/+/VmwYEGmlTMXpD9Hjx4lPj6efPnyvdYeT5VKRdeuXenatStPnjzh77//5sGDB4SFhWEwGNi5cyfHjh0D4Pr16/zwww+MHj06k61+MSdPnqR169bcv3+fPHnysHnzZurUqZPZZiUiKiqK4OBgLBaLYp/BYMDd3T3JhscAV69eBeD4zSecC42mcp3G6GUNKo0WF50GR52GBCeKASDejIuDBs1/JerNkoQsq4nHwpMYA66OWp7GGXkQGYePtzv3nsZRPJerUra+SpUqaQrVy8pCTJZlhgwZgizLBAQEUKtWrcw2SSAQZHOEEMvGCCGWeh49ekS7du3466+/0Gg0zJs3j08//VSsrr7hPBuWmNm/a6PRiF6vR6vV4u7unupxcuXKRatWrWz2DRkyhIYNGypFOsaMGUO+fPno06dPmmxOD9atW0fPnj2Jj4+nQoUKbNu2jWLFimW2WRw8eJCLFy8SHBys/Hvv3j3AWnnw0KFDADg6OlKoUCHCw8OTLAdfrFgxbj6OYf/lRxjMEhpnV8yxJlQWGRxUmCy2oWwmi5kYgxknrRo3Jy06jRqTRSLhNBcHLchwOyKWYrldwQTRBrNdwhLh/0IsK1bV3LJlCwcOHMDJySlF7RsEAoEgvRBCLBvj6Ohos20wGDLJkteLc+fO4efnx+3bt/Hw8GDdunWpChETvH5klfwwgGXLltG/f3/atWun5ETZixw5crBr1y7q1aunNILu168f3t7e+Pn52XWu1CLLMpMnT1b6m7Vs2ZLVq1dnWFN6SZK4deuWEkYoyzJffvmlcrxbt25Kc+NnyZ8/f6JS6adOncLDwyNJca83mNl65h4mi0xMvInIODMWScbFQYOjRoWLoxbtf94nsyQRZ5QwWiTizRIGvREvZwccdWr0BgtqlYo4kxmNWsVTvYk4kwV3RzVGs6R4xNLqScyqHjGj0chXX30FWBcaihYtmskWCQQCgRBi2RrhEUs5W7ZsoVu3buj1ekqVKsW2bdsoU6ZMZpslyABiYmKUcL2sIMRSWjUxpeTOnZs//viDunXrcuvWLSwWC/7+/uzduzfNXpO0Eh8fT58+fVi9ejUAX3zxBTNnzkx3L8yCBQs4evSokr8VFxenHMufP7+NEHvvvfcICwuz8W6VLVsWT0/PROMmtS+B4zfDeRRtIN5k4UGk9V8ZsFhk4swS8WYTGrUKJ60aV0ctudy0mCwSUXFmjBaJiDgjOVUOOGrVPI01odOocdCqkCR4GmvC3VFHdIyeM2fOAPbziGU1IbZo0SKuXbtGvnz5lP5hAoFAkNkIIZaN0Wq1aDQaLBYLIITYy5BlmWnTpjF69GhkWaZJkyasXbsWLy+vzDZNkEEcPnwYs9lMsWLFKF68eGabk+5CDKBQoULs2bOHX3/9Vdl38uRJatWqlWmhmQ8ePKBt27YcO3YMrVbLd999x0cffZTmcaOjo7l06ZJNsYzo6Gj27t2rnLNx40almTdYe2qVLl2acuXK4evriyRJyu/l559/TrNNUfEmboXHEqE3cvlBNDEGax6Zk06j/PxlZMySTIxRIsZoxlGrwd1RS05XHRGxJgxmiaexJvK4OyLLMkazhEVS4ai1esIAzv97FovFQsGCBSlcuHCabM6KQuzJkydMnDgRgMmTJ6cplFcgEAjsiRBi2RwnJyf0ej0ghNiLiI+P56OPPlIeRj/99FPmzp0rinJkM7JSWCJkjBAD8PHxYfz48ek6R3I5d+4crVq14s6dO3h5ebFhwwYaNmyY7OtlWSY8PJxcuXIp+4YMGcK6deu4e/duktfo9XqlB9mHH35I8+bNFQ9X8eLF0WrT7zZ6+UE0l+5HEvIkFoPpv9wrtQqjWSIeCwazGrVahVqlwkGjQqdRYzBbMJgtuDpo8XDW8jjGiEWWiTNZ0KhVRMWbcHPS4qhSk6CVTp+0tjKoXbt2mgV2VhRi48eP5+nTp1SuXJnevXtntjkCgUCgIIRYNkcIsZfz4MED2rVrx9GjR9FoNCxcuJABAwZktlmCTCC7CrGswrZt2+jSpQt6vZ7SpUuzfft2SpUqleS5kiRx584dm0IZCa+nT58SGxur5MhGRkYqIixv3ryJimU8u+DSs2fP9H+j/2EwWdh29h6PY4zEmcyYJAmzJCMDapUKWQaLZK3AISFjtoBKJeGoVeOoVaM3mpFkGRedhhijhTijGZ1GTXScGRdHDS46a26ZTqvm5NHDQNrzwwAlwiKrCLHg4GAWL14MwJw5c7JkERGBQJB9EUIsm/NsnpgQYracPn2aNm3aKKvv69evzzIP4YKMJSIiQqkgmBIPTHqSXYSYLMvMnj2bYcOGIcsyjRs3Zt26dXh5eWEymbh27RrBwcG0bt1a8U717dtX6RX1PCqVips3byrtBwYPHkyfPn0oW7Zslur/9/v5BzyONmCWZKLizJgtElq1ChlrWKEsg1oFDlo1lv9CDmVZJt5kwSzJuDpoiDNZSPBvGS0yFsmCSZKwmGXcnTRo1SoKeDhx9L9CHfbI/ctqHrEvv/wSi8VC69atxfe3QCDIcgghls0RQixpNm7cSI8ePYiNjaVMmTJs27bthavvgjefgwcPIssyZcuWpUCBApltDpA9hJjRaGTAgAFKvlWjRo14++236dOnD8HBwVy7dg2z2QzApUuXlMI5Pj4+6HQ6SpcuncjDVbp0aVxcXJQ5KlasmPFv7BXcCY/lysNoZOBxtAGLJKFWq3Bz0GIwS1bxJcnIMkiyNWfMUavGZLGGIJotEnojuDpoiDVZ0KhUSLJMrElCpVbh5KDGSafFWachPvwejx8/xtHRkSpVqqTZ9qwkxHbv3s2OHTvQarXMnDkzs80RCASCRAghls0RQswWWZb55ptvGDNmDGCtfBYUFPTSqmaCN5+sFpYIULx4cbp27UqFChUy2xS78fjxYyWE8NSpU5w7d46///4btVrNe++9x86dO5XfRQJubm74+voqIdZg9XINGzYsXfO30pPzoZFIskxknIl4kwWNWo2zg4YczjrCYwyYLDLOOjUGs4RFkok3SjjoVDho1ahVoDdaxZjRYt0XZ7RgtEhoVCpcdGpyuTnioFVT0tuN0wf2A1CtWrVELU1SQ1YRYmazmaFDhwIwcOBASpcunan2CAQCQVK8nncpgd149sab3fuIxcXF0adPH9asWQPAoEGDmD179mv7MCewH1lRiNWpU8cuOT2ZyV9//cXy5csV8fX48eNE57i7uxMUFITFYiE2NjaRh6tQoUKJCkw86/F63YiMM3EnIha1SkVUnAlJBgetijijGWQZg1nCbJExmSWcdGpkZCQZ4o0yapUKrUaFg0ZNvMlCnNGCJIHeaEYNaB015HRzxEGjoZCXM775c/CDnRo5J5BVhNjSpUs5f/48Xl5efP3115lqi0AgELwI8YSZzREeMSv379+nbdu2HD9+HK1Wy6JFi/j4448z2yxBFuDhw4dcuHABgAYNGmSuMa8RZrOZ69ev2xTKCA4OZtasWdSvXx+AGzdusGTJEpvrvL29CQ8Px2w2U7BgQXbt2kX58uUBaNWqVYa/j4zm0v0oAMJjjETEGok3SaiQMVlkVEio/9OcBrOESZLQqFXKCxmMZhlZlrFIMmZkJFnGZJHQadTkdHXARafBUaemSbm8OOk0dmvknEBWEGKRkZGK+Bo/fnyWyv0TCASCZxFCLJsjhBicOnWK1q1bExoaSs6cOVm/fn2WKcggyHz277eGbr311ls2Zc8zG0mSlPwoBweHTLMjNjYWWZaVEu/79u3js88+4+rVq5hMpkTnnzt3ThFitWrVYvTo0Yp368CBAwwbNgyLxULdunXZuHEjefLkydD3k9mEPo0l5LGeU3ciiDdJ6DQqJFmFZLYQa7SWoJdkGRkZi2TNEbNIoFHLOGqtBThk2SqETBYJo9kq1hx1anK6WEMS/SrnJ7ebI1FRUZw/fx54szxiU6dOJSwsjDJlyogqtwKBIEsjhFg2J7sLsfXr19OzZ0/i4uLw9fVl69at+Pj4ZLZZgixEghDLSmGJAFu2bKF9+/a88847HD58ON3nCw8PT+TdCg4O5tatW3z33Xf0798fAGdnZy5evAiAq6srZcuWtQklrFmzpjKmj48PkydPxmw2M3jwYBYtWgRAr169+OGHH+ySs/Q6Icsyp2495cZjvVKa3mC2lq23SBISIEsyKkAGJElGpQJJJSNJKkwWM05aNVqNWvGcSTLo1CpyOOnwdNFRo5gXbxe1eoiOHz+OJEkUK1aM/Pnz2+U9JAixzCoTf/PmTebOnQvArFmzRL9HgUCQpRFCLJuTXYWYLMtMmjSJcePGAdC8eXPWrFmDh4dHJlsmyGpkxfwwSJ+qibIsExoaSnBwMAULFqRcuXIAHD58mHr16r3wupCQEOX/lSpVYufOnUr+1qs8I0+fPsXf35/du3ejUqmYOnUqw4YNS3Nj4deRw9ceE/JEjyzLxBjMGMwSKkCtVlnFFQAyqFRIkowFkABJtgoyNSrizRKOQMKPz0GrJoeTjpyuOgp5OeP3VkHlZ/u3HcvWJ5DZHrHhw4djNBpp0qQJLVu2zBQbBAKBILkIIZbNyY5CLDY2lg8++IC1a9cC8MUXXzBz5kzR6FOQiNu3b3Pt2jU0Gs1LhUhmkFYhptfr2bt3r03T40uXLhEdHQ1Y+y8llPxOKAtfuHBhG+9WuXLl8PX1JXfu3Mq4rq6uvP/++8my4fr167Rq1YpLly7h4uLCqlWraNu2barez+vOnfBYDl99jEat4mGU0SrCVOCi0+Cs04AKYg1mJNnqDVNpAEnGKEmYLdZ9WrW1WIcsg05jLXGfkBvm5eJIrRK58HT5fxjrX/8V6rBn0ZfMFGKHDx9m3bp1qNVqZs+enS3FvEAgeL0QQiybYw8hZrFYOHDgANu3b+fIkSM8ePCAR48e4eLiQt68eSlZsiQtWrSgdevWFCpUyF6mp4rQ0FDatm3LyZMn0Wq1fP/99/Tp0ydTbRJkXRLCEqtXr06OHDky2RpbkiPE4uLiuHLlihJG6OvrS0BAAGD1RLVp0ybRNRqNBh8fH5uWDXny5CE6Oho3Nze72f/nn3/Svn17njx5QqFChdi6datd+li9rhy7GQ5YS8/HGCyoVdZ+X+5O1tA6iyTj7KglzmhGkgAZZJUKnVqNjITJIhNvltDKKtSoMFks6DRq8uVwIr+HMwW8nKlZ4v85jpIkcfToUeDN8IhJksQXX3wBQJ8+fahUqVKGzi8QCASpQQixbE5ahdjvv//Ol19+qeSEPIvBYCAiIoJLly7x22+/8fnnn/PJJ58wbty4TKlidfLkSdq0acO9e/fIlSsXGzZsUIoGCARJkVXDEiFpIRYbG8u4ceMU4XXz5k1kWVaOt2vXThFiBQoUoG7duhQpUsTGy+Xj45Nk8Q97irBly5bRr18/TCYTNWrUYPPmzXbLUXodiYwzcfWR1RMZb7QAkMvVAYPZgsEskdvNgah4E0gqXB20GCwSJrP0n2vMKsYk2ZpLhgxarTV0MZebjnweTjho1TQrnw8n3f+9/pcuXeLp06e4uLjYVbRklhBbvXo1J0+exN3dnUmTJmXo3AKBQJBahBDL5qS2j5gsywwePJgFCxbY7NdqtZQoUYL8+fMTExPD7du3CQsLA6zlrBcsWMC6devYtWsXFStWtM+bSAZBQUH07t2b+Ph4ypUrx7Zt2yhRokSGzS94/ZBlOUsJMVmWuX//viKytm/fDsCtW7eUc5ycnFi0aBFxcXHKPi8vLyWM8NnwSpVKxaFDhzLuDWD1no8YMYJZs2YB4O/vz7Jly3B2ds5QO7IaZ+48BdkqyFQqFWqVCi8XHeGxMkazRKzRgpeLA5GxJswSOGk1OGo1mC1W8SXJMhq1ilij1ZOm06gxqyTUKmuRjjL53KlSxNNmzoT8sOrVq9u1oEVmCLHY2FhGjhwJwKhRo8ibN2+GzS0QCARpQQixbE5qPGKyLNOjRw9WrVql7MuVKxfjx48nICDAJl9ElmWOHj3KrFmz2LhxI2Dt2fXuu++ye/duqlevbqd3kjSSJDFhwgQmTpwIQIsWLVizZk2WCzMTZD2uXbvG3bt3cXBwyNDGyRaLhfDwcKVsuyzLNGjQgDNnzhAVFZXo/GcXU9RqNWPHjsXDw0PxcHl7e2eJXJmYmBi6devG1q1bARg3bhzjxo3LErZlNlcfWr1hj6INqFWQN4cjarXV+2U0G4k1mtFpVHi5OhBvshBvsmCyWPO/nnFyIcsQ918jZ0edmpyujpTO507Fgh7oNLbC6C87N3JOwGKxevQyUojNmjWLu3fvUrRoUQYPHpxh8woEAkFaEUIsm5MaITZ//nwbEVajRg22b9+eZL8flUpF7dq12bBhAytXruSDDz7AYrHw9OlTOnfuzNmzZ9NNFMXGxtKrVy/Wr18PwNChQ5k+fbooyiFIFgnesDp16qSLx8ZgMHDlyhWlUEbC68qVK1SoUIGTJ08C1s/QkydPiIqKQq1WU7JkSdzd3Tl16hRqtZpvvvnGZtwRI0bY3da0cvv2bfz8/Dh37hxOTk4sW7ZMCZEUQIzB2g8uzmj9t5CXC4+i43F20GA0a4kxmIiMM2E0S7g5afFycLDmhJksmC0Ssow1LFH1X9EOjYq8OZzwcNaiVasoXyBxNVh7N3JOIKM9YqGhoUyfPh2A6dOn29zTBAKBIKsjhFg2J6VC7NKlSzYPemXLlmXXrl02if0vokePHsTHx/Pxxx8D1pLXn3/+OcuWLUu54a/g7t27tGnThlOnTqHT6fjhhx/44IMP7D6P4M3FXmGJkZGRXLp0ibCwMFq1aqXsr1y5MpcvX07ymlu3biHLsuIt+vHHH3F3d6dUqVI8evRICesdO3YsPXv2TJN96c3Ro0dp27YtDx8+JG/evGzZssWml5gAjGareLH2C5OJjDURHmMkPNaILINJkrBYrOGHTrFq3J20OOm0qFWg1agxmCyYJQmtWoVGrcLZQYO7kxazJPNu6Tx4uNiGHkZERBAcHAxYm2rbk4wWYqNHjyY2NpY6derQuXPnDJlTIBAI7IUQYtmclAqxmTNnKrlkKpWKH3/8MVkiLIGPPvqI9evXs3v3bgBWrlzJ+PHjKVq0aMoMfwnHjh2jbdu2PHjwgNy5c7Np0ybq1q1rt/EFbz6SJKWqkfPRo0c5deqUjYfr3r17AHh6ehIeHq6IqzJlyvDw4UObQhkJr6JFi9qE7CWEj0mSxAcffEBkZCQ1atRg1KhR9nrL6cKaNWv44IMPMBgMVK5cmW3btlG4cOHMNivLodOoiYozEBZjIDLWRFS8NRTRUash3mRBp1ajVslYLDIxBgt6gwVHnQkXnQZ1QudmrLlhzjqs5e4BH283Sud1TzRfQrXEUqVKJRnJkBYyUoj9888/LF++HIC5c+eKMFeBQPDaIYRYNiclQiwsLMwmJLF58+apEjjffPONIsQsFgsLFixg9uzZKR4nKVavXs2HH36IwWCgQoUKbNu2jWLFitllbEH24cKFC4SFheHi4mKTx2ixWLh165Yism7fvm1TsGbChAns3Lkz0XgFChTA19eX2NhYXF1dAatIcXZ2TtHD48KFC9m7dy/Ozs6sXLnSrkUW7MnzuZlt2rTh119/tWvlxTeJqDgTx0PC0Rss/+V5mYkzgsFi9YSZJRkV1pBDB60aSZYxmWWiJTO5XB1wdtDi4qAhXG/E/F/vsCI5XfDxTizCIH0aOSeQUUJMlmWGDBkCQLdu3ahRo0a6zicQCATpgRBi2ZyUCLFdu3bZVFb88MMPUzVntWrVqFixIv/++y8AW7duTbMQkySJsWPHMmXKFAD8/PxYtWoV7u5JP4gIBC8jISyxXr16rF27lt9++43g4GAuX76c6HMybtw4cuWy9mdq0KABWq02kYfLwyNxjo6Li0uKbLp48SLDhw8HrMUJSpcunZq3lu483zB92LBhTJ06NVMa/L4O/HMrgvP3IrFIMmqVtdhGrFHGQWv9ealU1kbNkgxGi4zRbEGrVuGgU6NRqYiKN+PurENGxmCScHXQ8FZhTzycdRTLlfTfWHo0ck4go4TYpk2b+PPPP3FyckqUJykQCASvC0KIZXOeFWIGg8EmL+V5Dh8+rPxfpVLRtGnTVM/btGlTRYhdu3aNR48e4e3tnaqx9Ho9PXv2VKoyDhs2jG+++UYU5RC8kujoaJswwoRXqVKlAGtY4rFjxwgMDFSucXR0pHTp0orIerZP1/DhwxWxZG9y587N+++/j8FgYMCAAekyR1q5d+8ebdu25cSJE+h0OpYsWULv3r0z26wsy/WwGH49egsvFwdCI+L+84jJ1oIbajVuTho0amsFDrMkE2eyEG+SkGQZg8mCs4O1GEd4jJH8Hk7kdHMgh5MOD2cdWo2KCgUTLwBYLBaOHTsGpK9HLD2/fw0GA1999RUAX375JUWKFEm3uQQCgSA9EUIsm/Ns6WsAo9GYaF8C//zzj/L/UqVKpanaYbVq1Wy2T548SYsWLVI8zp07d2jdujVnzpzBwcGBJUuW0KtXr1TbJXjzkGWZsLAwvLy8lFC+efPmMXv2bO7evZvkNQl5XY0aNSIqKorChQsrwqt48eKZIvK9vb3ZvHkzsbGxWTIX5tSpU7Ru3ZrQ0FBy5crFpk2bbPqWCWyRZZlVR28rAktvtAAyTjoNKgAVaNRqtP/lgGk14KTTYJYkouLMmP5r6uzm6IAsy9yJiMPb3ZF8HtbFtQoFPGwaOCdw/vx5YmJicHd3p3z58nZ/XxnhEVu4cCE3btwgf/786bbwIRAIBBmBEGLZnOdL/cbHx79QiD169Ej5f1qLazx//bNjJ5dnq7F5e3uzadOmDO33JMhaSJLE7du3k/RwhYeHc+rUKapUqaKcnyDC8uXLZxNGqFKpGDhwIB4eHlSpUgWNRpOpDZ3Dw8PJmTMnYPVEJ+SYZSU2btxIjx49iI2NxdfXl+3bt4uG6a/g4v0owmIMWCSJm4/11lBEWYW7k5boeDMmi0xknAk3By2Ouv+LGq1ajZeLjqdxJgwmiScxBrQaFSpUeDjryOXqACqoVtQryXkT8sNq1qyZLgsK6S3EwsLCmDRpEgBTpkwReYcCgeC1RgixbE5SQiypfBawPhAm8KJzksvz1z87dnL49ddf6du3LwaDgUqVKrF161a7Vl4UZF2MRiPXrl0jODiYunXrkjdvXgDmzJmjhCs9j0ql4tatW4oQ69ixIzVr1qRs2bJ4edk+sCb0JGrQoEGmh7fGxMRQo0YNatWqxaJFi9L8ubM3siwzbdo0pXrj+++/T1BQUJazMyty+OpjAJ7EGJFkcHfU4uGsteZ8OemIjjdhssjEGMzEGsFRp0GnUaNSgSTLaNUqYiwSMqBWaXBxVFvDGIF6PnnwzpF0P630zA+D9Bdi48ePJyoqiipVqojoB4FA8NojhFg2Jykh9iKeLdTxIq9Zcnn++uQ2k5YkidGjRzNt2jQA2rZty8qVK8Wq6BtKaGgo+/bts/FuXbt2DYvFAsC6devo2LEjYC0Hr9PpbPK3fH19KVeuHKVLl7ZpylyoUCEKFSqU5Jz26h9mD4YOHcr169cxGo2ZbUoiDAYDH3/8MStWrADgs88+Y86cOWi14rbyKsL1Ri7ciwJZJiza+r3qncOJ3G4OWMJj0RvM5HDWEWe0EG+yIMkQZ7QQh8VmHK1aba2oqAY3Ry33I+PpULUQ7/jkeuHc6VkxEdJXiF28eJEffvgBsC68iAIwAoHgdUfcMbM5KRFinp6ehIWFARAVFZWmeZ+//nmvRFLExMTQvXt3tmzZAsDIkSOZPHmyuBm/5jx+/NhGaPn7+ytNZo8ePZpkw2I3Nzd8fX1tyrc3a9aM2NjYNAkBo9HIoUOHgMwXYr/99htLliwBYPny5VnKyxQWFka7du04cuQIGo2GhQsXZtkCIlmRm49jkAG90UK8WUKtgpyuDqhUKorkdOHe0zgi40w4O2hwdtBgNEvEmy1IEsjIqLA2bnZ11BIZZ0SSZCQZPJx1VCrk8cIcwkePHnHt2jWAdGuqnZ5CbOjQoVgsFtq2bUuDBg3sPr5AIBBkNEKIZXNSIsS8vLwUIZbSUMLnef76hByYF3Hr1i1at27NuXPncHR0ZOnSpXTr1i1NNggyhytXrjB79mxFeD1+/NjmeL58+RQhVrFiRerXr5+oHHzBggUTPWzao6fWsWPHiIuLI0+ePOlSyCC5hIWF0adPHwCGDBlCw4YNM82W5zl//jx+fn6EhITg4eHBunXr0lRBNTsSHW8GwGi2ihYXB60SVqhSqSjo6Yy7k45wvZFYoxkHrVopZ594LBVqtYr8Hk7kcnP8r+hH0iQ0ci5XrlyyFr9SQ3oJsZ07d7Jz5050Oh0zZ86069gCgUCQWQghls1JiRArUaIEV65cAawPYy8rdf8qEkrXPzv2izhy5Ajt2rUjLCyMvHnzsnnzZuVBXZC1MJlMXL9+PVGxjL59+9K/f3/A2mcqwdOTQLFixRSR9ezvtnTp0hw4cCDD7H82LDGzKhPKsky/fv14+PAh5cuXV3rjZQV27NhBQEAA0dHRlCxZku3bt1O2bNnMNuu1w2SxtjyQ/mt9oH7+T02lIoezjhzOOgwmC+F6I3qjGct/ni+1CrQaNe6OWswWGZMkKxUSTRbphfOmd1gioIQN21OImc1mhg4dClhDYH18fOw2tkAgEGQmQohlc1IixN555x127twJQGRkJJcuXcLX1zdV8x4/flz5v4uLi001u2dZvnw5H3/8MUajkbfeeostW7aInjFZAL1eT3x8vNLI+Pr16/j5+XH16lXMZnOi86tWrar8v0yZMowZM0YRXmXKlElxc+P0Iivkh61YsYJNmzah0+lYuXJlos9oZiDLMvPnz2fo0KFIkkSDBg1Yv3698vsXpAzn/0RTghfMbJFfeK6jTkN+T+ckj8myTOjTOOtY/y0cJFWyPoH0LtQB6eMR+/HHH7l48SK5cuXi66+/ttu4AoFAkNkIIZbNeb5oxrMFOZ6nfv36Nttr1qxh4sSJKZ4zJiaG7du3K9u1a9dOlNdjsVgYNWoUM2bMAKBdu3asXLkyS5bufpMJDw8nODiYixcv2ni4bt26xaeffsq3334LWHtcBQcHA+Dq6krZsmWVQhm+vr689dZbypjOzs5K+emsRGxsrOIxyEwhVqBAAfLnz8+gQYNeuECRkZhMJgYOHKh4Mfv27cuiRYtwcHDIZMteXzxdrGG0zv/1DIszWTCYLThqU1alMzrejCSDRg0OWqsQ83ROOkTXZDJx4sQJIH09YvYWYk+fPmXs2LGAtWKip6enXcYVCASCrIAQYtmclHjE6tatS+nSpZXwxJ9//pmRI0faVKNLDr/88gt6vV7Z/uijj2yOR0dH061bN7Zt2wbAmDFjmDBhgijKkU7IskxoaCjBwcE4OTkpTXgjIiJe6vF4thmyu7s7e/fuxcfHh0KFCr2Wv6sjR45gMpkoXLgwJUuWzDQ7mjZtyvnz57NEcY7w8HA6derEvn37UKlUzJo1iy+++CJLNpR+nSiV1w2n/3K+3J2sJesfRxsp6JWy79KwGOvCWS5XB9RqNQU8nMjjnnRF27NnzxIXF4eXlxdlypRJ2xt4CfYWYlOmTOHx48eULVuWfv362WVMgUAgyCoIIZbNSUkZeZVKxRdffKFURwsNDWXChAlKKfnk8OjRI2V1E6BIkSJ06NBB2Q4JCcHPz4/z58/j6OjIsmXL6NKlS7LHF7wcSZLYtm2bjXfr0qVLREdHA9Y+UAnhp15eXuTNmxdHR8dExTJ8fX3JnTu3zdiZXWUwrWR2flhcXJyyqPGq4jUZwZUrV2jVqhVXr17Fzc2NNWvW0KpVq8w2643AUauhZolcHLwSRh53R6LizYRFx5PbzQGNWmUtSa+ylqfXJEogsxJrMBMZawIgj7t1Qa1uqdwv/NtN8PbWqlUrXRdKEoSYPXrwXb9+nfnz5wMwe/ZsuxTkEQgEgqyEEGLpyPXr1zl+/Dh3797FaDTi5eVF2bJlqVOnTpbI+wCruHJ0dFRCEl/Vz6tPnz789NNP/PPPPwDMnDmTypUrJ0ssxcTE0LZtWyIiIpR9CxcuVMISDx8+TLt27Xj8+DH58uVjy5Yt1KhRI7VvLdsSFxfH5cuXFaHl6urK8OHDAevvu3fv3jx9+tTmGq1Wi4+PD8WLF7fZf+vWrTT3jHtdyMz8sHPnztGkSRNmzZpFjx49Mt3jtG/fPjp27EhERARFihRh27ZtVKpUKVNtetOo65Obg1fC8HDWoVXD01gTh64+RqdVoeL/v38XBw35PJzIm8MJncYqoOJNFq6FWUvgezprcdJpcNSqqV7sxQI+I/LDwL4eseHDh2MymXjvvfdo3rx5mscTCASCrIYQYunA5s2bmTRpEqdOnUryuJubG71792bcuHGJvAqZgZOTU7KFmE6nY82aNVStWpWYmBgkSaJHjx5cuXKFkSNHvjBv5OzZs3z44Yc2P5NPPvmE1q1bA7Bs2TL69euHyWSiatWqbNmy5YUNdwWJGTduHCdPniQ4OJiQkBBk+f/J/z4+PjZCrF27dhgMBhvvlo+PT5K/u+wiwiIjIzl58iRAhpeKNxgMdO/enbCwMNavX0+PHj0ydP7nWbJkCZ9++ilms5natWuzadMm8ubNm6k2vYnk83Ait6sD+y4/4mmsiXiThIyMxahCp1WhVasAFbFGCzfC9IQ8jiW3mwNuTloexxixSDLOOjXFclvzZuv65H5poY6MqJgI9hNif/75Jxs2bECtVjN79uxMX5wQCF537t27h6enZ5YpjiWwIoSYHTEYDPTp04dVq1a99LyYmBi+/fZbgoKCWL9+Pe+++24GWZg0Tk5OREZGAq8WYgClSpVi165dtGrVioiICCwWC+PHj+fHH3+kc+fO1KxZk3z58qHX6wkJCWHbtm3s2bPHppper169mD9/PhaLhWHDhjFnzhwAOnXqxC+//CK+KP5DlmXu3buXqBw8wP79+5XzduzYoQgJsIa2JYisChUq2Iz5888/Z4zxrxF//vknkiRRqlQpChcunKFzf/311/z777/kyZOHH3/8MdMeOC0WC0OHDlVCwbp168ZPP/2UZbz3bxpbz97jREg4eoMZlUqFVqOyijFZxvRfKzCVylpZMUGUhTzRo/6vtL2Xs46S3m5o1GpK5nGlZaX8L5zr3r173Lp1C7Vane5RBvYQYpIkMWTIEMCaQ/z8d5hAIEg+oaGhTJs2jR9//JEpU6YorSAEWQMhxOyEJEn4+/uzZcsWm/0ajYYiRYrg4eHBzZs3FcED1qatzZs3Z8+ePem+Svkynn3QSo4QA2t4y+HDh+nVq5ciAEJDQ5k7d+5Lr3N2dmbUqFGMHj2a6OhounTpwo4dOwCrV2fs2LGvZaGHtGKxWLh3756NCOjZsydbtmwhKioq0flarRaTyaTkTHz++efExMQo4itPnjxiBTkFZFZY4p9//smsWbMAqycqszxPUVFRBAQE8PvvvwMwefJkRo0aJf6G0ol1J++w8ugt1GoVzjoNkXHxSBLWgEQVJDi0Zdla2t5sseaMqVUqUFlDE/Pmc0erUVMkpzMf1SuhhC0mRYI3rGLFiri7u6fre7OHEFu5ciX//PMPOXLkSFVlXoEgO2Eymdi+fTtNmzbFzc1N2X/nzh2mTZvGTz/9hNFoBGDGjBkMGDBALHZnIYQQsxMzZ85MJML69+/P119/TYECBQDrDWrLli0MHjyY27dvA9aS2Z07d87UKmmpEWIA5cqV4/jx46xevZrvvvuOo0ePKjfh5/H29qZt27aMGTOGwoULc+PGDfz8/Lh48SJOTk788ssv+Pv7p/m9ZHXi4+O5cuVKIg/XlStXUKlUxMTEKEnuRqORqKgoNBoNJUuWTFQs49kHne7du2fWW3ojyAwhFhUVRc+ePZFlmQ8++IC2bdtm2NzPcvPmTfz8/Lhw4QLOzs6sWLGCjh07Zoot2YGDV8JYefQWAE9iDITrTf8V5JDRqNVo1WrUKrBIVgFmka0vsAoznUaNWqUi+H4U3WoW4ZOGPq8se59RYYmQdiGm1+sZNWoUAKNHj8bb29tutgkEbyKHDx+mffv2ODg40LBhQ+rUqcOVK1dYt26dIsASCAsLY+/evfj5+WWStYLnEULMDjx58oQpU6bY7Js6dSojRoyw2adWq2nXrh01atSgbt26hISEANYy4HPmzGHChAkZZbINz+YBvayPWFKoVCq6detGt27dePLkCX///TcPHjzg8ePHODk5kTdvXnx8fKhWrZpyYz548CAdOnTgyZMnFChQgC1btvD222/b9T1lNpGRkQQHB3P58mV69uypeBa6devGxo0bk7zGycmJ+/fvK7lx48aN4+uvv8bHxyfb5GplBmFhYZw7dw6ABg0aZNi8gwcP5tatWxQrVox58+Zl2LzPcuTIEdq2bcvjx4/f2M9iViLeZOGHg9cBiNAbCddbqx7mcnXA08UBg1kiJt5MnMmCVqMiQV9JsozBJGGRZOJMFnK6OuDloiPeJCWr91hGFeqAtAuxmTNncu/ePYoXL87nn39uT9MEgjeShL6sRqORXbt2sWvXriTPCwgIYMyYMZQvXz4jzRO8AiHE7MCMGTOU8t8A7777rlIcISkKFizITz/9RJMmTZR9c+fOZdCgQS/t25RepNYj9jy5cuV6ZXnrn376iQEDBmA2m3n77bfZvHkzBQsWTPWcWYGzZ89y+PBhGw/X/fv3leONGzdWxFXZsmXx9PS08WwlND0uWrSozcOLr69vhr+X7MiBAwcAa9hWRq2+S5KEl5cXGo2GFStWkCNHjgyZ91lWrlxJ3759MRqNVK1ala1bt772n8Wszp9XwogxmDGZJR7HWFeqc7s74OViLZTj4qDBxUGDySKhN1gwWySk/8IU3RxlYgwWTBYJo1nCSafh6M1wHscYyO324oUag8GgVLnN6h6xu3fvMmPGDMB6XxULUALBqzl+/Pgrz/Hy8sLBwYHg4GAKFy6cKfec9EaWZUJCQvj333+5e/cuT58+xdHRES8vL0qVKkX16tWzZM6zEGJpRJIkli1bZrNv/Pjxr8ytaNy4MfXq1ePQoUOAtYnx2rVrlR5dGYm9hNjLMJvNfPXVV8rKv7+/Pz///PNrEadssVgICQmxEVozZsxQKl6uWrWKmTNnJrquQIEC+Pr62jSvHjduHJMnTxa5N1mIhKInGRmWmFAJbuDAgYlaBqQ3kiQxZswYpk6dCkD79u1ZsWIFrq6uGWpHdkOWZXb8a12giYyzesJcHDSKCHsWnUaNp0tiIePlKhPyWI/RLBFrtODioGHXhQd0q1n0hfOeOnUKo9FInjx5MqRReVqE2KhRo4iLi6Nu3bo2/SUFAkFibty4wTfffKOEHr+MiIgIVqxYwYoVK/D19eXixYsZYKH9GDBgAN9//z0ARYsWVSLKIiIi2Lx5Mzt37mTfvn08fvz4hWPodDpatmzJ4MGDqV+/fkaYnSyEEEsjf/31F2FhYcp2iRIlkh3e1KdPH0WIgbXs/ZsoxCIjIwkICFAaBU+cOJExY8ZkaTGya9cufvnlFy5evMiVK1cS/Vx69uyp/J5r166Nn5+fjZerbNmySeb8vai8vyDzyMj8MFmWkSRJyQPMaBGm1+vp0aMHmzZtAqwPvpMmTcqWBXIymuthMdx4rEeSITLeKsQ8XVLWoFirVpHDSUtknJnIONN/QuwhXaoXQf2Cxs/P5odlxHeuxWIt+ZjSv6kTJ06wcuVKAObMmZOl7w8CQWZy/fp1pkyZwooVK5TPWwJqtZqOHTvy9ttv888//7Bz506bInEA77//fkaaaxcSwi8BJb/t008/tSlE8ipMJhObN29m8+bN9OzZk4ULF2YJz6AQYmnkt99+s9lu2rRpsm8gTZs2tdk+cOAAer0+w1em01OIXbt2DT8/Py5dupRlCgFERUVx6dKlRAUzVqxYQa1atQAICQkhMDBQucbR0ZEyZcooQuvZHmft2rWjXbt2Gf4+BGknNDSUy5cvo1arM6SNxNKlS1m2bBkrVqzIEO/Es9y9e5fWrVtz+vRpHBwcWLp0qSjykoFcvG8NX481mJEk0GpUuDi8Or/reTycdUTGmdEbzEiyzNNYI4+iDeTzSDrkJiPzwyB1HjFZlvniiy8A6NGjB9WrV08X2wSC15lr164xefJkfv3110QCTKPR0L17d0aPHk2pUqWU/SaTiUOHDrF9+3a2bdumPJO9Tpw+fZq7d+8q2wn2Hzt2LEkRptFoyJ8/P3nz5sVkMnHr1q1EYnTFihVcunSJvXv32lSazAyEEEsjZ86csdlOyc2uQIECFCtWTHGxGo1GLl68mOE3ofQSYvv376djx46Eh4dTsGBBtm7dStWqVe02/suQZZlHjx7h4uKilGvevHkzAwcOJDQ0NMlrLly4oAix+vXrM2PGDEV4FStWTPFiCN4cEsISq1WrhqenZ7rOdf36dQYPHoxer2fLli1Kn6SM4MSJE7Rp04b79+/j7e3N5s2bM7VlRnYk+j8vmPm/pC9HrTpVXh8HrRrVfyXuLRKoNdaxkxJisixnaMVESJ0Q27BhA0eOHMHZ2ZlvvvkmvUwTCF5Lrly5wuTJk1m1alWiytQajYaePXsyatQofHx8El2r0+lo1KgRjRo1Yvbs2Vy5ciXDIzHSytatW5X/u7u7Jxl15unpSdeuXWnZsiX16tWzadNhsVg4dOgQY8eOtYlCO378OL1792b9+vXpav+rEEIsjSQ0102gXLlyKbq+XLlyihBLGO9NEGI//PADAwcOxGw2U6NGDTZv3kz+/C9uOJpaJEni1q1bibxbwcHBSkx0jx49AHBzc1NEWL58+RIVy6hSpYoybtmyZSlbtqzd7RVkLTIqLNFisdCzZ0/0ej3169dn8ODB6Trfs6xbt46ePXsSHx9PhQoV2L59O0WLvjinSJA+mC0JJeit/6pTGXqnUqmwFrtPGEuFwZx025Dbt29z7949tFpthlXDTHhQTO7CVXx8PMOGDQNg2LBhNtEGAkF25tKlS0yePJk1a9YkEmBarZZevXoxatQoSpQokazxVCoVZcqUSQ9T05Vt27Yp/3///fdtUjyKFSvGmDFj6Nq1K87Ozkler9FoaNCgAfv37+eTTz5hyZIlyrENGzawf/9+GjZsmH5v4BUIIZYG4uLilH5gCTzbkDc5PH/+5cuX02xXSrGnEDObzQwZMoSFCxcC0KVLF5YuXfrCD0hyMRqNXLt2jeDgYMqWLauUX921axctWrRI8hqVSmVTvbBmzZr89ddf+Pr6prv3Q5D1kWWZvXv3AukvxGbMmMFff/2Fu7s7y5cvz5CcLFmWmTx5MmPHjgWgZcuWrFmzJt0b+gqSJiEMMSGXK8EzllIkWVYqKWr+G8vNMelbeYI37K233sqwwkgp9YgtWLCAmzdvUqBAAb766qv0NE0geC0IDg5WBFjCwk0CWq2WDz74gJEjR752nq3UcO/ePU6dOqVsPxtWOWHCBJo2bZrs3HuNRsN3333HqVOnOHnypLL/p59+EkLsdeXx48c2HxKdTpfi8tfPl4t+9OiRXWxLCWnpI/YsT58+xd/fn927dwMwefJkRo0aleLwm4iICLZv327j3bp27ZoSEz1u3DhFiPn6+uLg4EDp0qUTNTwuXbq0jQB0d3cX4VgChZs3b3L79m10Oh3vvPNOus1z+vRpRQx9++23GeKNio+Pp0+fPqxevRqAIUOGMGPGDBFem4nkcbd+zzrprL+DeJO1PL1WkzJRHhNvBkCnUaH577s1l1vSDyIZHZYIKRNijx49YvLkyQB88803onKnIFtz8eJFJk2aRFBQUCIBptPpFAFWrFixzDEwE9i+fbvys9BoNLRs2VI59uz/k4tGo2HYsGF07txZ2feivmsZhRBiaSAmJsZm28XFJcWi4/kbz/Nj2htZlomKirKp6GcPj9jVq1fx8/Pj8uXLuLi4sHLlStq3b//C88PCwmyEVq1atQgICFCO9ezZM9E17u7u+Pr62ojdokWLotfr0WrFn7IgZSSEJdaqVSvdHgDj4+Pp3r07ZrOZ9u3bK2Gy6cmDBw9o27Ytx44dQ6vVsnjxYvr27Zvu8wpeztvFcuLwn+hy1qmJM0lExpvJ5ZqySqoJpe9zOOtABVUKe+LulHT1xYwu1AEpE2Jjx44lOjqaatWqZchnQyDIipw/f55Jkyaxbt26JAVYnz59GDlyJEWKFMkkCzOPZ/PDateubZdeu/Xq1bPZfvLkCbGxsZnWTkk8vaaB50VTahrFPR+yl95C7OzZs9SsWZPmzZsTEBCAn59fmoXY3r176dSpExERERQuXJitW7fy1ltv2ZwTERHB8OHDFeH15MkTm+NPnjxRhFiJEiVo3LhxIi9XgQIFEgldlUolRJggVWREflhYWBiOjo7kzZuXH374Id1Lcp89exY/Pz/u3LmDl5cXGzZsyNSQC8H/cXPU0qBMHnZffIiHiwNxkfFExhrxcNIm2ysWE28m3iShUlmrJwI0r5h07m1sbKxSTCoresTOnz/Pjz/+CMDcuXNFCwVBtuPff/9l4sSJSRaLcHBwoG/fvowYMSLFKS/2ZPz48UyYMMHu4+7fv/+VrZ5iY2OV9AHAbtUevby8Eu2LjIwUQux15HnRkpoeUc+GBYI17yw9CQoKwmg0smXLFrZs2YKzs7NNpZ2UCDFZllm8eDGDBg3CYrFQunRp2rVrx6xZswgODqZOnTpKrpiLiws///yzEl6oUqkoWrSoUijj2dLhWq2WPXv22OkdCwSJkWU5Q4RY4cKFOXr0KNeuXVMagKcXW7dupWvXruj1ekqXLs327dttyhgLMp9mFfKz++JD3By1OGhVGM0y957GU8DT6ZViLNZo4WGU9fvZw1mHRq0it5sj1YvlTPL8kydPYjabKVCgQIaupCdHiMmyzNChQ5EkiQ4dOiRaoRYI3mTOnj3LxIkT2bhxY6Jjjo6OfPTRRwwfPjzbF67Zs2ePzTNp69at7TJuUpWz7eFpSy1CiKWB5z1gyW0q9yzP52SlxquWXGRZtumNBVbh9++//yrb9+7dY8eOHTRt2hSd7sXNRk0mEz4+PjbFSq5cucL06dOV7WeFqaOjIzNmzFCqFZYpUybTVh8EguDgYB4+fIizszM1a9a0+/iyLCveLwcHhxRXU03pXLNmzWL48OHIskyTJk1Yu3Ztkqt+gszFx9uNqkW8OHU7ggKeztwJj8NglrgdHkdOVx3uTjqlAEcCRrNEZJyJp7HWkEQXBw253awLeO2rFkx0fgIZ3cg5geQIsd9//53du3fj4OBgc88QCN5kTp8+zcSJE9m8eXOiY46OjvTr149hw4Ylqh2QXXm2WqKPj4/dKlk/W8IerCkuqXGk2AshxNLA803gUhPW97wHLD0by6lUKn7//XeCgoJYs2ZNkhUa4+LiaNmyJTlz5qRnz57MnTs3ybHOnj2bqGKkl5cXlSpVolGjRlSqVIkKFSrYHM/IvkkCwctI8IbVrVs3kVfaHnzwwQcUKVKEr7/++qULGmnFaDTSv39/li1bBsCAAQOYP39+us4pSBtfNCnNV+vP8iAqnsJezoQ+jcNkkQmLNvI4xoiroxadRoUsg8EsEWf8f+NWNycteXM4oVJBg9J5aPmCsETInPwweLUQM5lMDB06FIBBgwYl2di8d+/eLF++3GZf9+7dWblyZYps8fPzY/v27Tb7hg4dyqxZs1I0jkCQFk6dOsXEiRPZsmVLomNOTk6KACtQoEAmWPdyevbsSd26dQGYOXOmUowNYNasWVSuXPmF1+7evZuZM2cq2z169FDy/192HVgXGJ/97NrLGwbw888/22y/qPJ2RiGEWBp4XjTFxsbarIQnB71e/9Ix7U3ZsmUZN24cY8eO5dy5cwQGBvLTTz/x+PFjm/PCw8N5+PDhC8epVq0a33//PWvXruXq1avcuXOHiIgIDh48yOnTp2nbti0ODg4UKVIkU1caBIKkSM+wxLVr1yol6tu0aUO1atXsPgdYq7Z26NCBP//8E7Vazbx58xg4cGCGej8EKcfDRceENuUZu+UCD6PiKZLThah4M5FxJoxmSamK+Cyujho8nHW4OmhBBbVK5OKzxqVe+LvOjEbOCbxKiC1ZsoRLly6RO3duxowZk+xxN23aRExMTLLvkY8ePWLnzp3JHl8gsDf//PMPEyZMsPHsJODs7Ez//v356quv0qXHqr0oUaKE0qfs119/tTlWrVq1l+Z53b17N9FYTZo0Sda8J06c4MGDB8q2vfLDduzYwZ9//mmzr3fv3nYZO7WI7Ng0kDt3bpsboclkSnH5+edjVVNa/j61qFQqKleuzNSpU5k3b16S5/j7+7/0+n79+rF3715u3brF8ePHGTJkCIUKFSIqKooVK1bQsmVL8ufPz0cffcTevXsxmxM/YAgEGY3FYuHAgQOA/YVYaGgo/fv3B2DUqFHpJsKCg4OpWbMmf/75Jzly5OC3337js88+EyLsNSG/hzMzO1aifIEcqNUqPF10FM3lQiEvZ3K5OeDposPLVUduNweK5XahgKczro5aVCpo+1ZBRjQri+4lOWXXr18nLCwMBwcHqlatmoHv7OVCLCIignHjxgEwceJEm+q9r0Kv17Nhw4Zkn79q1SpxzxFkCidOnKBVq1a8/fbbiUSYs7MzQ4cO5ebNm8yZMydLi7DM5Nmfm5eXl+KVSwvh4eH069fPZl/btm2pUaNGmsdOC0KIpQFnZ+dESdDPh+u9iufPt1cMbEp4Pi9t3759DBo0iGbNmiXrepVKRfXq1Zk9eza3bt3i0KFDDBw4EG9vb8LDw/npp59o0qQJBQsWZODAgRw6dChRl3iBIKM4e/YsERER5MiRw64PqbIs06dPHyIiIqhatarSO8ze7N69m9q1a3Pjxg2KFy/O33//nezPqiDr4OniwDftKjK5bQVql8yFSqXC2UFDTlcH8rg7ktvNES9XB3QaNW6OWtq+VZDF3avxYd3iSlPoF5HgDatWrVq6hN6+jISCTEkJscmTJ/PkyRPKlSvHRx99lKzxnu2ZtGLFimTbkXBuQmEogSC9OXbsGC1atKBGjRr89ttvNsdcXFz46quvCAkJYdasWeTNmzeTrHw9eFaINW/ePM3VsSVJonv37jZeOg8PDxYsWJCmce2BCE1MI2XLluXWrVvK9sWLF6levXqyrw8ODk40XkbzvBCrUaNGqkteq9Vq6tatS926dZk3bx4HDx4kMDCQDRs28OjRIxYtWsSiRYsoWLAgnTt3JiAggOrVq4uVfEGGkRCW+O6779q19cHixYvZtWsXTk5OrFy5Ml3ytL799lsGDx6MxWKhbt26bNy4kTx58th9HkHGoFKpqFTIk0qFPHkSY+DQ1cc8jIonxmBGo1bh5qilpLcbtUvkUhpBJ4eE/LDMaGD/Io/Y1atXlSq6s2fPTvZnr1q1ajg7OxMcHMyBAwe4c+fOK8t5//vvv0rp/rp166JWq23u0wKBPfn777+ZMGFCko2BXV1dGThwIEOHDhXf1cnk9u3bnD17Vtm2R1jiV199xe+//26z74cffsjU1gAJCI9YGnm+X1bCDTA53L9/n5CQEGVbp9Ola3W1F/G8EHuVV+/5hoMvQqPR0KhRI5YsWcKDBw/YsWMHvXr1IkeOHISGhjJ37lxq1qxJyZIlGTlyJGfOnEn22AJBakmP/LDLly/z5ZdfAjB9+nS7f47NZjOffvopn332GRaLhV69erFnzx5xY3+DyOXmSNsqBelXvyRD3yvD4Cal6VuvBA3LeKdIhMH/PWIZXagD/i/ENBpbm4cNG4bJZKJZs2Yp9uAmNHuWJClRnkpSPFvoI6E4gEBgb44cOcJ7771HnTp1EokwNzc3Ro4cSUhICNOmTRPf1SngWW+YVqtNc8THggULmDNnjs2+YcOGvTT9JiMRQiyNtGrVymZ7z549yRYTz1afAWjYsGG6F+tIirCwMJvt8uXL07RpU5YuXUpERARgFV8HDx6kU6dODBo0KMVz6HQ6mjdvzi+//MLDhw/ZvHkzAQEBuLi4cPPmTaZNm0aVKlXw9fVl3LhxiTyFAoE9MJlMSqKuPYXYhQsXUKlUNG7cmIEDB9ptXICnT5/SokULvvvuO1QqFdOnT2fZsmUZHnImyBxijWZCn8Zx7VEM9yPjiDdZXnp+dHS00pIkq3jEDhw4wObNm9FoNMyePTvFY3bv3l0Z71WVEy0WC6tWrQKsi4ydOnVK8XwCwcs4fPgwTZs2pW7duvzxxx82x9zd3Rk9ejQhISF888036d4/8k3kWSH27rvv4unpmeqxVq9ezeDBg2329e7dm2nTpqV6THsjQhPTSJ06dcidO7dSdfDGjRscOHAgWaF9S5cutdlu06ZNutj4Kg4ePGizLcsye/bsYc+ePfTv3x9fX18iIiKU2FpXV1cmT56cokTrZ3FycqJNmza0adMGvV7Pb7/9RmBgIDt27ODy5ctMnDiRiRMnUqlSJfz9/fH390+yxLFAkFJOnDiBXq8nV65cVKxY0W7jtm/fnooVK+Ls7PzS/kkp5dq1a/j5+XHp0iVcXFxYtWoVbdu2tdv4gqyJwWxhX/AjNpy6y7m7kTbH1CoV1Yp60bFaIeqVyp2oEfTx48eRJImiRYtmSjns54WYxWJRWpf069cvVd7iwoUL06BBA/bt20dwcDAnTpx4YQrA7t27lWprbdq0SfV9SiB4nj///JMJEyYoURXPkiNHDgYNGsQXX3xBzpxJN1kXvJqYmBilmBakLSxx+/bt9OrVy8Y50r59e3766acslQ4jPGJpRK1WJyp9OWHChFd6xfbu3WvTVM7d3Z3OnTunh4mvpE+fPlSuXDnJlRuz2cy///5rk+Co1+v56aef7DK3q6srnTt3ZuPGjTx69EiptqjVajl37hyjR4/Gx8dHKQZy584du8wryJ4k3EAbNmxoV8EEUKpUKQoVKmS38Q4ePEjNmjW5dOkShQoV4siRI0KEveHEmyx8f/A6fgsPM27rBUWEyfw/JFySZU6EhDN8wznaLDrCyr9DMFn+X/wos8rWJ/C8EFuxYgWnT5/Gw8OD8ePHp3rcXr16Kf9/WdGOZ8MSn71GIEgtCYvr9evXTyTCPDw8GDt2LCEhIUyaNEmIsDSya9cuDAaDsp3a/mH79++nU6dONpVTmzZtypo1axKFTWc2QojZgeHDh9uEFB48eJDp06e/8PzQ0FD69u1rs+/zzz/PNBf222+/zZkzZ7h//z5LliyhVKlSr7xm3Lhx9OzZk99//x2TyWQXO3LkyEGPHj3Yvn07Dx8+VKotqtVqTp48yZdffkmRIkWoW7cuCxcutOkxIRAkB3vmh8XGxtKsWbNEPUnswc8//0zTpk0JDw+nRo0aHD9+PFE+quDNIlxvZMCv//Dz4Zs8jTUhyTJ6o5lwvZEnMQae6I08jjEQrjcSa7QgyTJh0QYW7rvG0LVniTFYHzgyq5FzAs8KsZiYGEaNGgXAmDFj0pQn06FDB1xdXQEIDAxM8r4TGRmpNM3Nly8f7733XqrnE2RvZFlm//791K9fn4YNG9p4aQA8PT0ZP348ISEhTJgwAS8vr8wx9A3j2bDEcuXKKT3MUsKxY8do3bo18fHxyr46deqwadOmLNnXVggxO5A7d27lZpPAyJEj+eSTT7h3756yT5IkNm/eTJ06dWyKdBQoUIChQ4dmlLlJcvXqVUqWLMnHH3/M1atXX3m+Xq9n5cqVtGjRgnz58tGvXz/279+vlC5OKzlz5qRPnz788ccf3Lt3j0WLFvHuu++iUqk4cuQIgwYNomDBgkoxkOcbUgsEzxMXF6c8pNpDiA0bNoxdu3bRvXt3mxW8tGCxWPjqq6/o06cPJpMJf39/Dhw4IHrNvOFExprot/IkF+5FIcsQFW8iXG8k7j/B9SySLBP7n0CLjjcjA0dvPGHQmtPEGkwcPXoUyBoesenTp/PgwQNKlizJZ599lqZxXV1dad++PWBtZr5jx45E56xdu1Z5+OratWuWW/kWZB1kWSYyMjLJ/Xv37qV+/fo0atQo0UKbp6cnEydOJCQkhHHjxqUpf0lgiyRJNp/r1IQlnjt3jubNmxMTE6Psq1KlCjt27FAWcrIaQojZieHDhycq3LF48WKKFClCyZIlqVq1Krly5aJdu3Y2VQmdnZ1Zu3Ztpn+Yixcvnqi3l6OjIz179uTo0aOcO3eOUaNGJbk6ER4ezpIlS2jUqBGFChXi888/5+jRo3argJg3b14++eQTDh48yJ07d5Rqi5IksX//fvr160e+fPlo3rw5y5cvT/LLVSD4+++/MRgMFChQgNKlS6dprF27drFo0SLAmutpj8IZ0dHRtGvXjlmzZgFWr/OaNWtwdnZO89iCrIvJIvHl+rPcehKLJMs8jTNiNFu/i3UaNe5OWnK6OpDL1YGcrg64OWrR/hf2ZzBbiIw1IctwPjSSwSv/JiIiAmdnZypXrpwp7yfhPvLw4UPlb3nGjBl2+Yw8WwExqfDEZ/eJaomCl3Hx4kVy585N06ZNWbhwISEhIfzxxx/Uq1ePJk2a2KSOgLWp8KRJkwgJCeHrr78WuYfpwNGjR22Kx6U0LPHy5cs0bdpUKTIH4Ovry65du7L070sIMTuhVqtZt24dAQEBNvstFgs3btzg9OnTPH361OZYrly52LFjB++8804GWpo0Wq2W/v37A1CkSBGmTp3KnTt3WL58OTVr1qRixYpMmTKFa9eucfz4cYYMGULBggUTjfPgwQMWLFhA7dq1KV68OCNGjLBrWfqCBQsyePBgjh49yo0bN5g2bRpvvfUWFouFnTt30rt3b7y9vWnbti1r1qyxWRURZG+eDUtMS6JueHg4H3zwAQCfffYZTZs2TbNtt27dom7dumzbtg0nJyfWrFnD+PHjs1RCsSB92PHvfc7eeYosQ2ScCYsko1ap8HDW4eGsw1GrQa1SoVKpUKtUOOk0eLroyOGkQ6VSYZYkIuNNyMBfN5/iVLQy1atXT5c+dskhQYjNmzeP+Ph43n33Xdq1a2eXsRMW+8CaiB8eHq4cu3HjBocPHwagcuXKmSZEBa8HW7ZswWw2s2fPHgYNGkTx4sV57733OHLkiM15OXPmZMqUKYSEhDBmzJgs/UD/urN161bl/3ny5KFWrVrJvvbWrVs0adKER48eKfuKFy/OH3/8keVbBwghZkcSHqDWr1//0nwOV1dXPvnkEy5evEiDBg0yzL5X8dFHH7FlyxZu3LjBiBEjkvzjValUSuGM27dv8+effzJgwIAk89tu3brF9OnTlbL048eP59KlS3azt3jx4gwfPpzTp09z6dIlJkyYgK+vL0ajkS1bttC1a1e8vb2VYiBxcXF2m1vw+mGP/DBZlhkwYAD379+nTJkydimBe/ToUWrUqMG5c+fImzcvBw4cSLSgI3gzkWWZdSethZBijWYbEabTvPz27KBV4+GstYoxi0S8yYLRaMS1QuNMyw+D/wuxbdu2oVKpmDNnjt0WFNRqNd27dwfAaDQSGBioHBPeMEFKSMglfBFqtZqaNWvy008/MXToUHLkyJFBlmVfns0Pa9GiRbILat2/f5/GjRvbFJUrWLAge/fuTdJhkNUQQiwd6NChA6dPn+bq1av8+uuvTJ8+nUmTJrFw4UL27NnD48ePWbRoEd7e3pltqg3e3t60bt062XH1arWaevXq8d1333H//n127drFBx98kOSK0eXLlxWhVKVKFaZPn26TJ5dWypQpw9ixY7lw4YJSbbFkyZLExcWxbt06OnTogLe3t1IMxGg02m1uQdYnOjqa48ePA2kTYmvWrGHt2rVotVp+/fVXXFxc0mTX6tWradCgAY8ePaJy5cqcOHGCmjVrpmlMwevD+dAorjyMRgbi/wtHdHXUolEnT7ho1WpcHKzf1/EmCyaTCaeilSlTJXPywwCbEPeePXtSrVo1u46fVHiiLMtKfzGNRkO3bt3sOqfgzUGWZX777TebQg5JIUkSx44do3379uTJk4fu3bvbLbInu6DV2nbIelkNgRs3bnDx4kVlO7lhieHh4TRt2pTr168r+/LkycMff/xB8eLFU2hx5iCEWDri4+NDt27dGDZsGGPGjGHgwIE0btwYJyenzDbN7mi1Wt577z1+/vlnHj58yJYtW+jSpUuSD6pnzpxhxIgRFC9enNq1azN//nyboiZpQaVSUbFiRSZPnszVq1eVaouFCxcmJiaGX3/9FT8/P/LmzasUA3m2vKkgfenduzeq/8KsMtIbfOjQISwWCyVKlKBo0aKpHiehCfvXX3/N22+/nepxJEli7NixdOvWDYPBQJs2bTh8+DCFCxdO9ZiC149Np0MBa66XLFu9YQ6alHmPnLRqVCoVFknmPy3HI5ci9jY12SQUrnFycuKbb76x+/i+vr7KZ+/YsWNcuXKFw4cPc+PGDQDee+898ubNa/d5Ba83siyzbds2atSoQatWrTh37pzNcXd3dypUqJBkQYfo6GgeP34sQsVTyPPPui+rdP2sN8zR0TFZFU+jo6Np1qwZFy5cUPZ5enqye/dufH19U2Fx5iAaOgvsjqOjI61bt6Z169bo9Xq2b9+uNGx+3hN19OhRjh49yhdffEGDBg0ICAigffv2dinlr1KpqFatGtWqVWP69OkcPXqUoKAg1q5dy4MHD/j555/5+eefyZMnDx07dsTf35969erZvb+UIPOxV9n6ZcuW0aZNmzQ1mYyNjaV3796sW7cOsFZfnDp1qvi7y4acv2ctLGQwWRWUk06d4oc9lUqFo1ZNnNGCSuuARiVzK1J69YVp5OnTp1y4cIELFy5w/vx55d+E7/gBAwakW0PpXr16cfLkScDqFXv2AU/0DhM8S4IAmzBhAqdOnUp0PG/evAwbNoz+/fvj4uJCfHw8+/btY8uWLWzdulX520ptP6vsTL58+Wy2t27dysSJE5OsAvxsfljDhg1tWkK9iNatW3PixAmbfUOGDOHx48fs2bMnRbZWq1Yt01oQCCEmSFdcXV3x9/fH39+fyMhINm/eTGBgIH/88YeNmzqhZ8f+/fv59NNPadq0KQEBAbRt29YusdlqtZo6depQp04d5syZw6FDhwgMDGT9+vWEhYWxePFiFi9eTIECBejUqRMBAQHUrFlTrIC9IdhLiKlUqjQVHrh37x5t2rTh5MmT6HQ6lixZkqghvCD7EBVn7YWVUKJem0oxrlWrkJFBpcLBwYHIOPv0dgSIiori4sWLvP3220qo0cCBA5WqoUmhUqn45JNP7GbD83Tp0oUhQ4ZgMplYsWKFUinXw8ODNm3apNu8gtcHWZbZsmULEydO5PTp04mO58uXj2HDhtGvXz+byB0nJydatGhBixYtWLx4MSdPnmTLli1CiKUCX19fVCqVEtL56NEjSpUqRdmyZSlWrBjr168HrP3/nq1SmdyFzud7uwGMHTs2Vbbu378/02o2iCVYQYbh4eFBr169+P3337l//z6LFy+mfv36icSO2Wzm999/p1evXnh7e9O+fXvWrl1LbGysXezQaDQ0aNCA77//nvv377Nz504lt+3evXvMnz9fqfo4fPhwTp06JWLDX2OePHnCmTNnAOtKW0q5ePEivXv3timJmxpOnTpFjRo1OHnyJLly5WLv3r1ChGVzTBZbz1Vq131UKkD+vxAzWlLuEdPr9Zw4cYJffvmFr776ihYtWlC0aFE8PDyoXbu2TQ5GQgJ84cKFadasGUOHDmXevHlKWNfy5cvx8fFJ3ZtJBrly5aJFixYA3Llzh6ioKAA6der0Rob+C5KPJEls3LiRKlWq0K5du0QiLH/+/MybN48bN27wxRdfvDTPV61WU6NGDaZMmaJU6xQkn1y5clG3bl2bfXq9nn/++Ydr164p+3bu3GnToD0tESevI0KICTKFPHny0L9/fw4cOGDTG+x5DAYDmzZtwt/fH29vb7p168a2bdvs1kBXp9Px/vvvK7ltW7dupWvXrri6unLr1i1mzJhBtWrVbIqBCF4vDh48iCzLlCtXLlGoxKswGo10796d5cuX88UXX6Taho0bN1KvXj1CQ0Px9fXl+PHj1KtXL9XjCd4M3JysJeYTFqMsUuoWfCySVYchS+gcdLg7vjjYJS4ujlOnTrFy5UoeP36s7J85cyY1atTggw8+YNasWfz+++9Kz8sCBQrYlIUeMGAAkZGR3L59m99//51Zs2Zx4cIF9Ho9b7/9doYUy0iqMqKolph9kSRJqVjdoUMHzp49a3O8QIECLFiwgBs3bvD555+L/owZxLfffvvKnM1n88PeeuutbJcrLUITsznXr1/n+PHj3L17F6PRiJeXF2XLlqVOnToZtrKY0Bts8ODB3Lhxg6CgIAIDAxMl0+r1elavXs3q1avx9PSkffv2dOnShQYNGiSqzpMaHB0d8fPzw8/Pj9jYWHbs2EFgYCC//fYbV69eZdKkSUyaNIkKFSoo4ZalSpVK87yC9GX//v1A6sISE8JacubMmarCA7IsM23aNEaNGgXA+++/T1BQkOhFIwCggIcT95/GoVOrMFvAaJZw0iWvau2zGEz/FRySLOi0OvJ5WL+7Q0NDOXjwoJLLdeHCBa5fv654+Ldv307Lli0BKF++PN7e3pQvX54KFSpQvnx55fV87oSnp6fN9rlz51i6dCkAc+fOzZB8x1atWpEzZ06ll1jx4sUTrb4L3nwSBNikSZM4f/58ouMFCxZk5MiR9OnTR3hL08gvv/zCL7/8kqJrKlWqxNWrV9mxYwc3b94ErAvxVapUAf4fAZVASrxhb0qkkhBi2ZTNmzczadKkJJNXAdzc3Ojduzfjxo2zS+GM5FKiRAlGjhzJyJEjuXjxIkFBQaxZs4arV6/anPf06VOl2Ia3t7eS11WnTh27PAS4uLjQsWNHOnbsSHR0NFu3biUoKIidO3dy/vx5zp8/z9dff03VqlUJCAigc+fOaarGJ0g/Upsf9tdffzF16lQAfvjhhxQXHjAYDHz00UdKWe1BgwYxe/ZsuywaCN4MmpbLyz+3InDSaYgzWTBaJCySnOzy9QAmiwWTJAMyasmC2WyiWQWr53f//v306NEj0TW5cuWifPnyNk2fO3bsSKdOnVL8HmRZZsiQIUiSRKdOnTJMDDk4OPDkyZMMmUuQ9bBYLKxbt45JkybZlD1PoHDhwowcOZIPP/wQR0fHTLBQkIC7uzv+/v5JHjty5IhNY/bsFpYIIjQx22EwGOjevTvt2rV7oQgDiImJ4dtvv6VcuXL8+eefGWjh/ylXrhwTJkzg8uXLnDp1imHDhlGkSOKyzI8ePWLRokXUq1ePokWL8uWXX3Ly5Em7rZa4u7vTrVs3tm7dysOHD/n5559577330Gg0il3FihWjTp06di3FL0g7Dx484OLFi6hUKurXr5/s62JiYujRoweSJNG9e3c6duyYonkfPXpEo0aNWLlyJRqNhsWLFzN//nwhwgQ2NKuQT+kbltDAWW80v/S7y2KxEB0TTUREBI/CwgiPikWWZGSzCbPZhM4cS52S1sWzypUr88477/Dxxx8zf/589uzZw4MHDwgLC+PgwYM2JaJTW5jot99+Y+/evTg4ODB9+vRUjSEQJBeLxcKaNWuoWLEiXbp0SSTCihQpwvfff8/Vq1cZMGCAEGFZnGfDEvPnz5+mtjCvK0KIZSMkScLf359Vq1bZ7NdoNBQvXpy33norUchUWFgYzZs35++//85IU21QqVRKE+ibN29y5MgRPvvssyTjju/evcvs2bOpXr06pUuX5uuvv7ZrXpeXlxcffPABu3btUgqONGjQAJVKxd9//83gwYMpVKiQUgwkLCzMbnMLUk5CWGKVKlXImTNnsq8bOnQoN27coHDhwixcuDBFc54/f56aNWvy119/4enpyc6dO+nfv3+KxhBkD1wctLSomO+//1tDEo1miRiDibi4eGJiYoiIiLBpPitJFqKjoomLi0NS60CtBWRkUzyOTo40Ku6meNQqVqzI4cOH+eGHHxg0aBCNGzcmb968dqsGazKZ+PLLLwEYPHjwa9NAVfD6YbFYWLVqFRUqVKBr164EBwfbHC9atChLlizh6tWr9OvXTwiw14RnhVirVq2yZaVqIcSyETNnzmTLli02+/r378/t27e5ceMGp0+fJjw8nI0bN9p4nmJjY+ncubNSIjgzSShDv2DBAkJDQ9m7dy8fffRRkv0frl27xuTJk6lQoQIVK1ZkypQpNpV60kpCwZH9+/dz9+5dpdqiLMscPHiQAQMGkD9/ft5//32WLVuW5qp7gpSTmrDE8PBwfvvtN8Ba/e35fJiXsWPHDurUqUNISAg+Pj4cPXqUJk2apMhmQfaia82iaFQy+ugoZGMcFotErMFCZJyJaH0ccXFxNsWJNBotTi5uOLh7oXFwRq1WIxn0yJKFovnyMCIgbS0aUsLixYu5fPkyefLkYfTo0Rk2ryD7YDabWblyJeXKlaN79+5cunTJ5nixYsX48ccfuXLlCh999BEODg6ZZKkgNVy+fBlZlpFlmSVLlmS2OZmCEGLZhCdPnjBlyhSbfVOnTlV6ZyWgVqtp164df/31F8WKFVP23717lzlz5mSUuclCo9HQqFEjlixZwoMHD9i+fTvdu3dPshHg+fPnGTNmDKVKlaJ69erMmTOHu3fv2s2WAgUKMGjQIP766y9CQkKUaosWi4Xdu3fz4YcfkjdvXlq3bs2qVauIjo6229yCF5MaIZYzZ07OnTvHr7/+muxy97IsM3fuXPz8/IiOjqZBgwYcPXqUMmXKpMpuwZuDLMtKdcGZM2fSu3dvqlevzrhx4wAo6OnMyKbFiI2NxRQfi2TQAzJqrRaNszs695zIWidiDGai401ExJmRtI6gUqNWqXDWWsMSdRoVcwKq4uGie7lBdiI8PJzx48cDMGnSJLv0exQIEjCbzSxfvhxfX1969uzJlStXbI6XKFGCpUuXcuXKFfr27SsEmOC1RSW/KWVHBC9l+PDhzJgxQ9l+9913OXDgwEvdwHv37rVZzXd3d+fmzZvkypUrXW1NK3FxcUrFw+3bt9uE9TxPvXr1CAgIoGPHjnh7e9vdlqtXr7J27VoCAwNtKjo5OTnRsmVLAgICaNGixUt7mbxJ9O7dm+XLlwNQv379JBsy2ouQkBCKFy+OVqslPDwcd3f3dJnHZDLx6aef8uOPPwLQt29fFi1aJB4MshmyLBMbG6v00oqMjKRZs2ZcuHAhyYWX5s2bs2PHDmW735QfOGYoiEarBZWGeJOFeLOUZL6YWqXCSafBSadGHxND1NNwqnONzd9PTb83+BxffPEF8+bNo0KFCpw+fVrkPwrsgslk4tdff2XKlCk2vesSKFmyJGPGjKFbt242xWYEgtcVIcSyAZIkkS9fPpt8pX379iVrtf/dd9+16Xj+3XffMWDAgHSxMz1IqHi4Zs0adu3ahdlsTvI8tVpN48aNCQgIoH379ikKR0suFy5cUErzP1sF0tXVlTZt2uDv78/777//Rse2Z6QQW7ZsGR9++CF16tThyJEjrzx/9erVmEwmevbsmew49fDwcDp27Mj+/ftRqVTMnj2bwYMHZ8s49+yCLMs8fPjQpiR8wqthw4Zs3LhROc/T05OoqCi0Wi1lypSxKQlfuXLlRE2Pj954wvitFwjXG5V9BrOEJMnIgArQqFU4aP8fzPL0fgi31k/juynWEt0ZwZUrVyhfvjxms5ndu3fTtGnTDJlX8OZiMplYsWIF33zzDTdu3Eh0vFSpUowZM4auXbsK0S94s5AFbzyHDh2y1jb+71WiRAlZkqRkXfvLL7/YXPvee++ls7Xpx5MnT+Qff/xRbty4saxWq23e17MvnU4nt27dWl69erUcHR1tdzskSZJPnTolDxs2TC5atKjN3B4eHnLv3r3lnTt3ykaj0e5zZza9evVS3mv9+vXTda7u3bvLgDxmzJhXnnvz5k3Z3d1dBuR169Yla/zLly/LpUqVkgHZzc1N3r59e1pNFmQxHj16JF+8eFHZliRJLly48Au/O8qWLWtz/Z49e+QLFy6k6LMcZzTLW8+Eyr1+PiZXn/zHC1/9VpyUfz97V3Zxs/7dXrhwwW7v+1W0bt1aBuQWLVpk2JyCNxODwSD/+OOPcrFixZL8TJUuXVpeuXKlbDKZMttUgSBdEEIsGzBixAibL7Z+/fol+9rQ0FCbax0cHOSYmJh0tDZjuH//vrxw4UK5Tp06L3yoAmRnZ2e5c+fO8saNG+W4uDi72yFJkvz333/LgwcPlgsUKGAzd65cueR+/frJ+/btk81ms93nzgwySohJkqT8PPft2/fSc81ms1yvXj0ZkN95551k/az37Nkje3p6yoBctGhR+dy5c/YyXZAJPHnyRP7zzz/lxYsXywMHDpQbNmwo58mTRwbkihUr2pxbtWpVWaVSyT4+PnLbtm3l0aNHy6tXr5bPnj0rx8fH29WuC6GR8s+Hb8izd1+WJ2+/IM/ZfVle8ddN+doj6wLRyZMnZUD29PSULRaLXed+EXv37pUBWaPR2IhUgSAlGAwG+Ycffki0GJnwKlOmjLxq1ao35t4nELwIIcSyAc2aNbP5glu+fHmKrn9+per48ePpZGnmEBISIs+YMUOuWrXqS0VZjhw55F69esm///57unirLBaLfPDgQXnAgAHKQ2DCK1++fPJnn30mHzlyJMMeuNKDjBJily5dkgHZ0dHxlQJ6xowZilfr+vXrrxz7+++/lzUajQzItWvXlh8+fGgvswXpzNOnT+UjR47IGzZssNlfvnz5l3q5no0guHXrlhwbG5vRpifJwoULZUBu1qxZhsxnNpvlSpUqyYA8cODADJlT8GYRHx8vL168WC5SpEiSnzdfX1959erVQoAJsg1CiGUDnl9xOnHiRIqub9GiRZqE3OvE5cuX5YkTJ8q+vr4vFWUJ3qr9+/enyw3DZDLJu3fvlvv06aN4XhJehQsXlr/88kv55MmTyQ4xzSpklBD77rvvZEBu1KjRS887e/as7ODgIAPyTz/99NJzTSaT/Pnnnyv2d+vWLV28pAL7cPbsWfnnn3+Whw4dKr///vtyoUKFlN+di4uLzYKGv7+/XKRIEbl58+byl19+Kf/yyy/yiRMnsrz3v0uXLjIgT5w4MUPm++mnnxQP3OPHjzNkTsGbQXx8vLxo0SKbz+Gzr/Lly8uBgYFCgAmyHUKIveHExsbKKpXK5gvvwYMHKRqjX79+NtePGjUqnazNOkiSJJ89e1YeOXKkXLx48ZeKsgIFCsiDBw+Wjx49mi7CyGAwyNu2bZO7d+8uu7m52cxdsmRJefTo0fK5c+deC1GWUUKsY8eOMiBPnjz5hefEx8fLFStWlAG5devWL/35PX36VG7evLli+5QpU16Ln/ebjl6vl0+ePCkvX75cHjNmjM3vJCGP6flXwYIF5ffee0+OiIhQzn1dH/4SohX++OOPdJ8rKipKzps3rwzIc+bMSff5BG8GcXFx8sKFC+WCBQsm+XmsUKGCvHbt2tc60kMgSAtCiL3h3L592+ZLT6fTpfgBcuLEiTZj9O3bN52szZpIkiQfO3ZM/uKLLxLlcT3/KlasmDxixAj5zJkzNj9ns9ksb9myRR4xYkSabImNjZU3bNggd+rUSXZ2draZu1y5cvKECRPkS5cupfUtpxsZIcQsFoucK1cuGZD/+uuvF563fft2WaVSyXny5HlpeOGNGzfkcuXKyWDNGVy/fn16mC1IBvv27ZNHjhwpt27dWi5ZsmSiRabQ0FDl3OnTp8uNGjWSBw0aJP/www/y4cOHbcTX6869e/dkQFar1XJkZGS6zzdq1CgZkH18fGSDwZDu8wleb2JjY+X58+e/8J5ZqVIlef369UKACbI9Qoi94Vy8eNHmy8/DwyPFY8yePdtmjICAAPsb+ppgsVjkAwcOyP3795dz5879UlFWtmxZeeTIkfKoUaPkkiVLKvvtVd0sOjpaXr16tdymTRslvC7h9dZbb8nTpk2Tb9y4YZe57EVGCLEzZ87IYM35elUu34EDB+SdO3e+8PihQ4eU33OBAgXkkydP2ttcwTMYDAb533//ldesWSOPGTNGbteunfzkyRPl+JAhQxJ9znLnzi3Xr19f/uSTT+Q7d+5kovUZy4YNG5QH2vQmJCREdnR0lAF506ZN6T6f4PUlNjZWnjdvnpw/f/4k74tvvfWWvHHjRiHABIL/EELsDef48eM2X4J58+ZN8RgJ+TYJr1atWqWDpa8fRqNR3rlzp9y7d285R44cLxVlz75SUrUyuURERMi//PKL3KxZM6WQRMKrZs2a8pw5c+S7d+/afd6UkhFCbM6cOTKkvbT28uXLFYFbrVq1LPHzexP57bff5E6dOsm+vr6yVqtN9Hk5ePCgzbn9+vWTFyxYIO/bty9bFkqJjjfJ/9wKl7t9+Y3s7FNT7vTJKPlOuD5d5wwICJABuUGDBiIkV5Aker1enjNnjpwvX74k73tVqlSRN2/eLP5+BILnEF3x3nDi4+Ntth0cHFI8xvMNhuPi4tJk05uCTqfj/fff5/3332fx4sXs3LmTBQsW8Oeff2KxWF543a5du1iwYAGdOnUif/78drHF09OTXr160atXLx4/fszGjRsJCgpi//79HDt2jGPHjjF06FDq1q1LQEAAHTt2xNvb2y5zZzX27dsHQKNGjZI8PmPGDNq1a0epUqWSPC5JEqNHj2batGkAdOjQgRUrVuDi4pI+Br/BWCwWrl+/nqjx8bJly6hWrRoAt27dYt26dco17u7uVKhQQWl8XLx4ceVYixYtaNGiRYa/j6zAv3cjWXk0hK1n7xFvkkBTCe8OlTgO1J2+n2pFvehRqyjNK+bDUaux27x///03gYGBqFQq5s6dK5qVZ1MkSUKtVifar9fr+f7775k5cyYPHz5MdLxq1aqMHz+eVq1aib8dgSAJVLIsy5lthCD9OHHiBDVq1FC28+bNy4MHD1I0xuLFi/nkk0+U7ZYtW7J9+3a72fi6I8syv/zyC/PmzePcuXPJvk6tVtOgQQMCAgJo3749uXLlsrttDx48YP369QQGBnLkyBGbuRs1aoS/vz/t27cnZ86cdp87KXr37s3y5csBqF+/PgcOHLDr+GazmZw5cxIdHc2pU6eoUqWKzfHNmzfTrl073N3duXHjBrlz57Y5rtfr6dGjB5s2bQJg9OjRTJw4MckHEMH/kSQJi8WCTqcDYOfOnYwcOZLg4GAMBkOi83/++Wc++OADAC5dusRvv/2mCK9ChQqJB7ZnOHPnKeO3XuDMnafJOj+XqwP965ekT93iqNVp+znKskydOnU4evQoH374IUuXLk3TeILXl7fffpv8+fPTrl07/Pz8cHFx4bvvvmPmzJmEhYUlef64ceNo2bKl+DwLBC9BCLE3nODgYMqVK6dse3h48PTp0xSNMWfOHIYOHaps+/v7ExgYaC8T3whq167N0aNHbfZptVratWtH2bJlOX78OHv27Hmhp0yr1fLee+8REBBAmzZtyJEjh91tvHPnDmvXriUoKIgTJ05k6NwJPCvEVCpVij20ly9fpmjRoi88fuzYMWrVqoWXlxePHz+2EVAPHz6kQoUKPH78mOHDhyserwTu3r2Ln58fZ86cwcHBgaVLl9K9e/cU2femI0kSt2/fTuThunjxIkuXLqVLly4A/PHHH7z33nsAODs74+vrqwitChUqUKNGDfLkyZOZb+W1YMe/9xkcdAajWUrxta0rF2Bmp0pp8o6tWbOGrl274urqytWrV+3mwRe8Xly5coUyZcoo2yqVCo1Gg9lsTnRujRo1GDduHM2bNxcCTCBIBiI08Q3Hzc3NZjs2NhZZllP0BanX6186pgAGDx5MQEAAALlz56Z///4MGDCAAgUKKOeEhYUp3qlDhw7x7BqI2Wxmx44d7NixA0dHR1q2bEmXLl1o2bIlzs7OdrGxcOHCDB06lKFDh3L9+nWCgoIICgri3LlzNnO3aNGCgIAAWrZsiaurq13mTgpZlpP0lrzqmpeREJbYsGFDGxEmyzJ9+/bl8ePHVKpUiQkTJthcd+LECVq3bs2DBw/w9vZm8+bN1K5dO0W2vUnIsszdu3dxcHAgb968ABw6dIjmzZsn+j5I4MKFC8r/q1evzpYtWyhfvjzFihVDo7FfqFx2Yef5B3y6+hSpXSrdevYeRrPEom5V0aTCMxYXF8fw4cMBGDFihBBh2ZjNmzfbbMuynEiEFSpUiFGjRtGvXz8RQSAQpADxaXnDyZ07t43oMplMPHr0KEVjhIaG2my/qblFaaF9+/Y0a9aMpUuXcvv2bSZNmmQjwgDy5MnDgAEDOHjwILdv32bOnDk2YaMJGAwGNm7cSKdOnfD29qZ79+5s374do9FoN3tLlizJqFGjOHv2LBcvXmTcuHGUKVMGg8HApk2b8Pf3x9vbmy5durB58+ZEuYZZlWeF2LMsXbqU7du34+DgwK+//mqT97h27VreffddHjx4QIUKFTh+/Hi2EWGyLHP//n327NnD/Pnz+eijj6hTpw6enp4UKVKEJUuWKOcWLFgQvV6PTqejQoUK+Pv7M2nSJDZu3Mjly5cZP368cq6npyetW7emZMmSQoSlggv3Ivk88HSqRVgCOy88YNbuy6m6ds6cOdy5c0dZwBFkT6Kiorh///4rFwTv3r3LJ598QunSpfnyyy+5fft2BlkoELzeiNDEbECxYsW4deuWsn38+HGqV6+e7OtbtGjB77//rmwvX76cnj172tXG7MyNGzcIDAwkMDCQf//994XneXl50b59e7p06UKDBg3s/oAryzLnzp0jMDCQoKAgbt68qRzLkSMHbdu2JSAggCZNmii5QFkJg8GAp6cn8fHxXLhwQQnJvX79OpUrV0av1zNr1izloVKWZSZNmsS4ceMAa+7jmjVrcHd3z7T3kJ48evSICxcu4OHhQdWqVYHEocvPotFo+Oyzz5g7dy5gDUu8fPkyPj4+WfL3/ybR6+fjHLySOO8mNWjUKg4Pb0h+j+R71h88eICPjw96vZ5Vq1bRtWtXu9gieH2IjIxk4cKFzJkzh4iIiETHPTw8iI6ORpKSDpu9evUqPj4+6W2mQPDaI4RYNqBZs2bs2rVL2f7ll1/o1atXsq8vXrw4ISEhyvaxY8eS9OQI0s6FCxcICgpizZo1XLt27YXn5c2bl06dOhEQEEDt2rXtHgoiyzInTpxQwhef9YrmzJmTDh064O/vny6CMLUcPHiQBg0akDdvXu7fv694gvv168eSJUuoX78++/btQ61WExcXR58+fVizZg0AQ4YMYcaMGVnmvaQFk8nE33//bZPDdf78eR4/fgxAz549lTw9k8lEjhw5KFy4sE2lwgoVKlC6dOlUVVkVpI1bT/TUn3nArmMOalyKIU1LJ/v8vn37snTpUmrWrMnff/8tcn2yEU+fPmXBggXMnTs3yXzyevXqMX78eBo2bMiTJ0/Yvn07mzZtYvfu3UrkRIUKFV66qCgQCP6PEGLZgBEjRjB9+nRl++OPP+aHH35I1rX379+3CbHT6XSEh4eLPLF0RpZlTp8+rXjK7ty588JzCxcujL+/P126dKFKlSp2f2iSJIm//vqLwMBA1q1bZxPamjdvXjp27EhAQAB16tTJ1NyAcePGMXHiRLp06cLq1auV/UajkalTp9K7d2+KFi3KgwcPaNu2LceOHUOr1bJ48WL69u2baXanlsjISEVkeXh44O/vD1hze9zc3BKtVKtUKooXL07btm2ZPXu2st9oNArBlYX4ZkcwS/68Ydcx87g78teIRug0r/58njlzhqpVqyLLMkeOHKFOnTp2tUWQNXn69Cnz5s1j3rx5REZGJjpev359xo0bR4MGDZK8x+j1enbt2sWmTZuoVKkSX331VUaYLRC89gghlg04fPgw9erVU7ZLlCjBtWvXkvXAvnz5cnr37q1sv/feezbeNUH6I0mS0stn7dq1L83xK1WqFAEBAQQEBLww5CwtmM1mDh48SGBgIBs3biQ8PFw5VqhQITp37kxAQABvv/12hq+i16tXj8OHD/Pjjz++UFidPXsWMerkWgAATWhJREFUPz8/7ty5g5eXFxs2bEiUT5YVSWiRcP78ec6fP8+FCxdsvJR16tSxaU/QuHFjnJycFO9W+fLlKVu2bLoWXxGkHYsk8/bkP4iINdl97B97vk3Tcnlfeo4syzRu3Jj9+/eL6rjZhIiICObOncv8+fOJiopKdLxhw4aMGzeO+vXrZ4J1AsGbjxBi2QBJksibN68SmgTWogbJeQB99913OXTokLK9aNEim55igozlWSG0YcOGJGP3E6hYsSIBAQH4+/tTsmRJu9tiNBrZs2cPQUFBbNq0iejoaOVYiRIl8Pf3x9/fn0qVKqW7KNPr9Xh6emI2m7l+/Tr58+fn+++/Z+DAgUo+09atW+natSt6vZ7SpUuzffv2FzZ1zgz0ej3BwcFKOKFWq+Wbb75RjhctWjRRAnyhQoUoX748tWrVsimWIXg9ufc0jjrT9qXL2EOalmZQ45f/vW/dupU2bdrg6Oj4ylYRgteb8PBw5s6dy4IFC5IUYI0bN2bcuHE2i7gCgcD+CCGWTfjqq6+YNWuWsl2/fn3279//0gfkvXv30qRJE2X7RU1wBZmD0Whk9+7dBAYGsmXLFmJiYl54bo0aNQgICKBz584ULFjQ7rbEx8ezc+dOAgMD2bZtG7GxscqxsmXLKqLM19fX7nMD7Nq1i2bNmlG0aFFu3rzJkCFDmDdvHi1btmTbtm3MmjWL4cOHI8syTZo0Ye3atXh5eaWLLSlh+vTpHDlyhAsXLnDz5k2b8vzPN18fPXo0er1eyeMqV64cnp6emWC1IL0Ivh9F8/mHXn1iKvjwneKM9Xuxl9xoNFKhQgWuXr3KyJEjbRYBBG8OT548Yc6cOSxcuNBm8SyBJk2aMG7cOOrWrZsJ1gkE2Q8hxLIJjx8/pnjx4jYP61OnTmXEiBFJnh8aGkrdunVtinSMGTOGSZMmpbepglQQGxvLjh07CAwMZPv27S/sz6VSqahXrx4BAQF07NgxXZrq6vV6tm/fTlBQEDt27LCxpVKlSoqXrkSJEnabc/jw4cyYMYMPPviAbt26KQsImzdvZsuWLSxbtgyAAQMGMH/+/Ayp+mcwGLhy5YoSSnjhwgUiIiI4cOCAck6jRo3Yv3+/sp0nTx5FaJUvX1705MlmXLgXScsFh9Nl7N51ijG+dfkXHp83bx5ffPEF3t7eXLt27Y2tHppdefz4MbNnz+bbb79NctHuvffeY9y4cSInUCDIYIQQy0ZMnTqVUaNG2ewbMGAAY8aMUQpySJLE1q1b+fzzz23CoAoUKMCFCxfECvxrQFRUFFu3bmXNmjXs3r07UePNBDQaDY0bN6ZLly60bds2XX63UVFRbNmyhcDAwES2VK9eHX9/fzp37kzhwoXTNE/16tU5efIk33//PZMnT+bu3bv06tWLGzducOjQIdRqNfPmzWPgwIF2D5M0m81otVpl++uvv2b9+vVcvXoVi8WS6PzIyEhy5MgBWHuYhYWFKcIrPYSx4PXhbkQsdafvf/WJqWBwk1IMbpJ05cQnT57g4+PD06dPWbJkCR999FG62CDIeMLCwpg1axaLFi1Kshl7s2bNGDduHLVq1coE6wQCgRBi2QhJkmjTpg3bt2+32a/RaChatCgeHh7cvHkzUclaZ2dn/vjjD955550MtFZgD548ecLGjRsJDAxk//79vOjj7uDgQPPmzQkICMDPzy9dijo8efKETZs2ERQUxL59+2yq+r3zzjuKly5fvnwpGjciIoLcuXMjSRLt2rVj06ZNFClSBI1Gw82bN8mRIwdBQUE0a9YsTfYn5J89Wxb+woULXLt2jfDwcKXh6ccff8yPP/4IWHvtPOvhqlChAnXr1rVpKC0QJGCySFSesJtYY2IBn1bmB7xFm7eSDkseNGgQCxcupFKlSpw6deqNaOOQ3Xn06BEzZ87ku+++swkVT6BFixaMHTuWmjVrZoJ1AoEgASHEshnx8fF88MEHya6GlStXLtavX0+DBg3S1zBBunP//n3WrVtHYGAgf//99wvPc3Fxwc/Pj4CAAJo3b54uouHhw4ds2LCBwMBAm2IwarWaBg0a4O/vT4cOHciVK9crx9qyZQtt27alQIEC3Lt3D5VKhYuLC3q9nhIlSrBt27YUVZC0WCzcvHmTYsWKKZ6u0aNHM3v27BeGfJ46dYoqVaoAcPr0acXLVaBAAdGDSZAiRmw4R+CJF7erSA3ujlqOjW6Mi4M20bFLly5RoUIFLBYLe/bsoXHjxnadW5CxPHjwgJkzZ7J48WLi4uISHW/VqhVjx46levXqmWCdQCB4HiHEsikbNmxg8uTJnDlzJsnjrq6u9OrVi3HjxuHt7Z2xxgnSnZCQENauXUtgYCCnT59+4XkeHh60a9eOgIAAGjVqlC65VXfv3lUE4vHjx5X9Wq2Wpk2b4u/vT9u2bfHw8Ejy+sGDBzN//nxcXV3R6/WoVCpkWaZevXps3LjxhcVlJEni1q1biTxcwcHBxMXFcfHiRaW4yIwZMxg+fDguLi6UK1cukZercOHCQnAJ7ML50EhaLbRvntjL8sP8/PzYvn07fn5+bN261a7zCjKOBw8eMGPGDL7//vskBVjr1q0ZO3Ys1apVywTrBALBixBCLJtz7do1jh07RmhoKEajEU9PT3x9fXnnnXdwcnLKbPMEGcDly5cJCgpizZo1XLp06YXn5c6dW2neXK9evXQpInHjxg3Wrl1LUFCQzSLBy0InK1WqxL///kvdunU5fNj6ANu7d2++//57HB0dkWWZO3fucOHCBWrWrEnOnDkBmDJlCmPGjEnSDkdHR7Zu3cp7770HWD14er2eYsWKieIZgnSn3XdHOH37qd3G2zOkPj7ebon379lD06ZN0Wq1nD9/njJlythtTkHGcO/ePWbMmMEPP/xAfHx8ouNt27Zl7NixisdeIBBkLYQQEwgEgLWZ67lz5wgMDCQwMNCmYubzFChQAH9/fwICAqhevXq6eIMSBGJgYCDBwcHKfmdnZ/z8/PD396datWoUK1ZMOaZSqejTpw/lypVTPFwXL15U+uRs27aNVq1aAbBx40a6dOlCmTJlbLxb5cuXp0SJEiJPRpBpHL76mB4/H8Med+f2VQoyx/+tRPstFgtVqlTh33//ZdCgQcyfPz/tkwkyjNDQUKZPn86SJUuSDJlu164dY8eO5a233sp44wQCQbIRQkwgECRClmWOHz9OYGAgQUFB3L9//4XnFi9enICAAAICAqhYsaLdRZksy5w/f16x5fr168oxBwcHjEYjYA2n/fjjj5k7d26iMbRaLaVLl2bKlCm0bdsWsPZNUqvVNhUPBYKsws+HbzJx+8U0jVGliCdrPqqFky7xosKSJUvo168fXl5eXLt2TfEUC7I2d+/eZdq0afz0009JCrAOHTowduxYKlWqlAnWCQSClCKEmEAgeCkWi4VDhw4RGBjI+vXrefLkyQvP9fX1pUuXLvj7+1O6dNKlstOCLMv8888/BAUFERQUxJ07/y9q4O7uTpMmTTh+/DjVq1enYsWKiperVKlSODg42N0egSA9WXzgOtN3vjhc+GVUK+rF0l5v4+mS+O8+KiqKUqVK8ejRI+bNm8fnn3+eVlMF6cydO3eYOnUqS5cuVRafElCpVHTs2JGvv/6aihUrZpKFAoEgNQghJhAIko3JZGLv3r0EBgayadMmJeQvKapWrao0by5SpIjdbZEkialTpzJz5kzA2p8rgTx58tCpUyf8/f2pW7euyOsSvLb8du4+I9efIsr46nMBNGoVnd8uzDi/ckl6wgBGjhzJtGnTKF26NOfPn8+QBueC1HH79m1FgJlMJptjKpWKzp078/XXX1O+/IubdQsEgqyLEGICgSBVxMfHs3PnTgIDA9m6dWuSlboSqFOnDl26dElVn7DkYLFY+PPPPwkMDGTDhg02XruCBQvSqVMnAgICqFGjhqhuKHjtmDj5G6av2kmJ93sT7ZQ3yXPy5nCkS40iBFQvQj6PFxdaCgkJoWzZshgMBrZs2ULr1q3Ty2xBGggJCWHq1KksW7YsSQEWEBDAmDFjUtSaQyAQZD2EEBMIBGkmJiaGbdu2ERgYyO+//57owSEBtVpNw4YNCQgIoH379umSl5LgtQsKCmLjxo02XrtixYrh7++Pv78/b731lhBlgteCli1bsmPHDubPn0+LgA85d/cpT2NNmCWJHE46Cnm5ULNETnSaV3t+/f39Wbt2LY0aNWLPnj3iM5DFuHnzJt988w2//PILZrPZ5pharaZLly6MGTOGsmXLZpKFAoHAngghJhAI7EpERASbN28mMDCQvXv3YrFYkjxPq9Xy/vvvExAQQJs2bXB3d7e7LQaDgV27dileO71erxwrXbq0UvlRrCoLsiqSJJEnTx7Cw8OV/MfUcuTIEerWrYtKpeL06dNUrlzZjpYK0sKNGzeYMmUKK1asSFKAde3alTFjxogWAwLBG4YQYgKBIN149OgR69evJzAwkEOHDr3wPCcnJ1q1akVAQAAtWrTA2dnZ7rbExsby22+/ERQUxG+//WbTc6dChQpKPpuPj4/d5xYIUsulS5fw9fXF2dmZyMjIVOdzSZJE7dq1OX78OH379uXHH3+0s6WC1HDt2jWmTJnCypUrEy1aaTQaunXrxujRo9Ol+JFAIMh8hBATCAQZwp07d1i3bh2BgYGcOHHihee5ubnRtm1bAgICaNq0aaJqh48ePWLv3r106dIl1bZER0ezdetWAgMD2bVrl00oZbVq1fD396dz584ULVo01XMIBPZg2bJlfPjhh9SrV48///wz1eOsWrWK7t274+bmxtWrV9MlV1OQfK5evcrkyZNZtWpVkgKsR48ejB49WiwMCQRvOEKICQSCDOf69etK4+jz58+/8LycOXPSoUMHAgICyJcvH/Pnz2f58uUYjUauXr1KyZIl02xLREQEmzZtIjAwkH379tk8FNWuXZuAgAA6depE/vz50zyXQJBSPv74Y3788UeGDx/OtGnTUjVGbGwsZcqU4e7du0yZMoVRo0bZ2UpBcrl8+TKTJ09m9erVSJJkc0yj0dCrVy9GjRpll+82gUCQ9RFCTCAQZCrnz58nKCiIwMBArl27luzrPv30U7799lu72vLo0SM2bNhAUFAQf/75JwlfjyqVivr16xMQEECHDh3InTu3XecVCF5EhQoVuHDhAps3b6ZNmzapGmPSpEmMHTuWIkWKcOnSpXQJ/RVYwz9VKlWSBVAuXbrE5MmTWbNmTSIBptVq6d27N6NGjaJ48eIZZa5AIMgCCCEmEAiyBLIsc+rUKQIDA1mzZg2hoaEvPb9MmTKsXr2aKlWqpEvlt3v37imhlEePHlX2azQamjRpgr+/P+3atcPT09PucwsEAE+fPiVnzpzIsszDhw/x9vZO8Rj37t2jVKlSxMbGsmbNGgICAtLBUgHA5s2b+eqrr+jQoQMdOnTg7bff5tKlS0yaNInAwECef9zS6XR88MEHjBw5kmLFimWO0QKBIFMRQkwgEGQZ9Ho9v/zyC3PmzOHGjRvJuqZ06dIEBAQQEBCAr69vutgVEhLC2rVrCQoK4tSpU8p+BwcHpfKjn59fulR+FGRfdu3aRbNmzShZsmSKvMXP8uGHH7Js2TJq167NkSNHRLn6dKRHjx78+uuvyraLiwuxsbGJztPpdPTp04cRI0aIPFSBIJsjhJhAIMgyvPvuu0lWV3R3d6dEiRLcvHnTpi/Y81SqVEkRZekV4nP16lUllPLChQvK/oTKj/7+/rRs2VKEfwnSzPjx45kwYQLdu3dn5cqVKb7+1KlTvP3228iyzN9//02tWrXSwUoBgNFoxNvbm8jIyBeeo1aradmyJfPmzaNEiRIZaJ1AIMiqvLr7o0AgEGQQvXv3ttkuVqwYCxYs4N69e5w5c4ZHjx6xdetWunbtiqura6Lrz507x6hRoyhRogS1atVi3rx5rwxxTCmlSpVizJgxnD9/nn///ZcxY8bg4+NDfHw869evp1OnTnh7e9O9e3e2bduGwWCw6/yC7MNff/0FQJ06dVJ8rSzLDBkyBFmW6dKlixBh6cy///77yibLkiSxbds2atSoQZ8+fdi1a1cGWScQCLIqwiMmEAiyDAaDgaJFi1KkSBG++uor2rVrh1arTfLchL5ggYGB/Pbbby8UPCqVinfffZeAgAA6duyYLoU2ZFnm9OnTiqfs9u3byjFPT0/atWtHQEAAjRo1euH7EQiexWKxkDNnTqKiojh9+jRvvfVWiq7ftGkT7du3x8nJicuXL1OkSJH0MTSbc+bMGSZOnMimTZsSHVOr1eh0uhd+NzVs2JB9+/alt4kCgSALI4SYQCDIUoSGhlKgQIEU5bJERUWxZcsW1qxZwx9//IHZbE7yPI1GQ9OmTQkICKBt27Z4eHjYy2wFWZY5duwYgYGBrF27lvv37yvHcufOTceOHfH396devXpoNBq7zy94M/j333+pVKkSbm5uREREpEjAGwwGypcvz/Xr1xk9ejSTJ09OR0uzJ6dPn2bixIls3rw50TEnJyf69evHsGHDyJMnD/v3/6+9Ow+P6ez/B/6erBIhiEiIRMROtZbYRe3SBrVnfFulD1VUqxoe2qo0XVBbi2hRVaplQuwRu1D7VkpRtUZpLAmRVZKZ+fz+8JvzZGSRyMxke7+ua67rmXPuc+7PyEyfec+5z31HYd26ddi4cSPu3buntAsNDcW7775rwaqJqKhhECOiEiU2Nhbr16+HRqPBvn37ssxUZmBnZ4dXX30VarUaPXv2zHaoY0HpdDocPHgQGo0G4eHhiI2NVfZVrVoVAwcOhFqtRuvWrTmJAhlZsmQJ3nnnHXTu3Bl79uzJ17Fz585FUFAQ3N3dcfnyZTg5OZmpytLn1KlT+Pzzz7F58+Ys+xwcHDBq1ChMnDgx23UHdTodDh06pISyI0eOoFq1apYom4iKKAYxIiqxcpqC/mlly5ZF7969oVar0aNHD9jb25u8Fq1Wi7179yIsLAzr169HfHy8ss/LywuBgYEIDAxEs2bNGMoIw4YNw4oVKzBlyhR88cUXeT4uNjYWtWvXxqNHj/Djjz/iP//5jxmrLD1OnjyJkJAQREREZNnn4OCA0aNHY+LEiXB3d8/T+USEn3MiYhAjotLh+vXrWLNmDTQaDc6cOZNjO2dnZ/Tr18+s93Slp6dj586d0Gg02LRpE5KSkpR9tWvXRmBgINRqNV544QWT903FQ7169fD3339j69atePXVV/N83NixY7Fw4UI0adIEJ0+e5PDXAjp+/DhCQkIQGRmZZZ+joyPGjBmDCRMmwM3NrRCqI6LijkGMiEqdv/76C2FhYVi9ejUuXbqUYztXV1dl+GC7du1gZWX6iWZTU1MRGRmJsLAwREREIDU1VdnXsGFDqNVqBAYGom7duibvm4qm2NhYuLq6AgDi4uJQqVKlPB134cIFvPjii9DpdNi7dy86depkzjJLtKNHjyIkJATbt2/Psq9s2bJ49913ERQU9FyLbBMRGTCIEVGpJSL4448/oNFooNFoEB0dnWNbDw8P5UqVr6+vWYYVJSUlYcuWLdBoNNi2bRsyMjKUfU2bNoVarcagQYPg7e1t8r6p6IiIiECvXr1Qv359XLx4Mc/Hvfrqq9i2bRvatWuHQ4cOKdsdHR1x584dLjieB4cPH0ZISAh27tyZZZ+TkxPGjh2LoKAgs8y+SkSlD4MYERGMZzsMCwvDnTt3cmzr4+MDtVqNwYMHm234YHx8PDZu3AiNRoPdu3dDp9Mp+1q3bo3AwEAMHDgQHh4eZumfCs8nn3yCadOm4a233sKyZcvydMyOHTvg7+8PW1tb9OrVC+vXrzfaz/vFcnfo0CGEhIRg165dWfY5OTnh/fffx/jx4xnAiMikGMSIiJ6i0+nw22+/KbMdPnjwIMe2jRo1UoYP1qlTxyz1xMbGYt26dQgLCzOaCVKlUsHPzw9qtRr9+/fnMKkSolOnTti3bx9++OEHjBgx4pnttVotmjRpgvPnz2Ps2LFYvny50X2HANChQwfs37/fXCUXWwcOHEBISEi2M1OWK1cO48aNw/jx4/M8PJSIKD8YxIio2BMR3LhxA+fOncOtW7cQHx8Pe3t7VKxYEXXq1EGLFi1QpkyZ5zp3RkYGdu/eDY1Ggw0bNiAxMTHHts2bN1dCmaen5/O+nFzFxMQgPDwcGo0Ghw8fVrZbWVmhS5cuCAwMRN++ffnFsZjSarVwdnZGSkoK/vzzTzRq1OiZxyxatAijR49GpUqV8NVXX2H06NEAgHbt2uHs2bNITEyESqXC1atXUbNmTXO/hGJh//79CAkJQVRUVJZ95cuXx7hx4/DBBx/wc0RE5iVEREXYqFGjBIAAkBo1aijbHzx4IMuWLZNBgwZJ5cqVlTbZPWxtbaVPnz6yb9++AtWSmpoq69evl4EDB4qDg0OufbZv315CQ0Plzp07BfwXyFl0dLTMmjVLfH19s7zegIAAWblypTx69Mhs/ZPpnTp1SgCIs7Oz6HS6Z7aPj48XV1dXASDz58+Xzp07K++Dn376SYYOHao8/+yzzyzwCoq2qKgoefnll7P9zDo7O0twcLA8ePCgsMssMvLyHszLOfR6vdH5DM+JSjsGMSIq0qpXr658URo7dqyIiIwZM0bs7OxyDUI5Pd58802ThJOEhAT59ddfpVevXmJra5tjf1ZWVtK1a1dZunSpWb/gXb58Wb766itp3LixUf/29vbSr18/CQsLk+TkZLP1T6YRGhoqAKRHjx55aj9x4kQBIPXr15crV66ISqUSAOLg4CAJCQmya9cu5b3g4+NTKr8A6/V62bNnj3To0CHbz2iFChUkJCREHj58WNilFkkPHjyQ06dPy+3bt/P1/tHpdKLVapXnqamp5iiPqFjj0EQiKrJOnz6NZs2aKc937NiB7t27w9fXF6dOncrS3traGlWrVoWbmxsyMjIQHR2NR48eZWnXsmVL7NmzB05OTiap8+HDh9iwYQM0Gg327NkDvV6fbTtbW1v4+/tDrVajd+/eJuv/aRcuXEBYWBg0Gg3+/vtvZXvZsmXRq1cvqNVq+Pv7m2XhaiqY119/HatWrUJISAimTp2aa9tr166hQYMGSE9PR0REBM6cOYMpU6YAAAIDA6HRaKDX6+Hl5YXbt28DAH777Tf4+fmZ/XUUBSKCPXv2ICQkBAcPHsyyv2LFivjwww/x3nvvwdnZuRAqLJri4+Nx4cIFXLx4EXv27MHevXvh6OgIe3t7rFy5Er6+vrkeL09+5FeW+7h79y5+/vlnnD17FhkZGXjw4AE6deqEnj17onHjxpZ4SURFV6HGQCKiXHz22WfKr9blypWTtLQ0ERFp3ry50a/ZY8aMka1bt0pCQoLR8VqtVqKiosTPzy/Lr+D9+/c3S8137tyR0NBQad++fa5X5hwcHGTgwIGybt06SUlJMUster1eTp8+LZMnTxZvb2+j/suXLy9Dhw6VyMhISU9PN0v/lH81a9YUALJz585nth0wYIAAkG7duoler5e6desqf9/Nmzcr7SZMmKBsHz58uDnLLxL0er3s2LFD2rZtm+1nr1KlSvLVV19x2O5TkpKSZPfu3TJu3Dhp2rSpqFSqLI+PP/5YHj9+nKfzxcTEyPvvvy9WVlbZnsvFxUW++eYbo6tmRKUNgxgRFVmZA9eAAQOMtnt7e8vSpUvzFGK0Wq2MHDkyyxeyvXv3mrN8uXnzpsyePTvLPVxPP8qVKydDhgwxayjS6/Vy7NgxGT9+vFSrVs2ofxcXFxk5cqTs2bOHX4oKUUxMjAAQlUr1zJDw22+/KUNfz549K4cOHVL+npUrVzZ6H505c8YogJsr+Bc2vV4v27Ztk9atW2f7OXNxcZFp06Zl+cGmNDMMNUxJSZFvvvlGfH19sw1Nnp6eYmdnJ82aNZMrV67kes7k5GT58ccfxdvbWzm+a9euMnz4cOnXr5+0adNG2V6pUiVZtmwZ/7tDpRaDGBEVSbdv31budwEgK1asUPZFREQoV8fySqvVZglE//d//2fqsnN0+fJl+eKLL6Rhw4a5hrJKlSrJyJEjZe/evWb7cqLT6eS3336TMWPGSJUqVYz6d3d3l7Fjx8rBgwdNcqM+5d369esFgDRu3DjXdjqdTvmRYuTIkSIiRj80jBkzJssxme8d/PXXX81Sf2HR6/WydetWadWqVbafqcqVK8uMGTMkMTGxsEstku7evSuvv/66UfCyt7cXNzc3ee2112TNmjWyb98+OXnypNy9e/eZ93otWbJEateuLSqVSjp06CDr1q2TuLg45QeABw8eyOjRo8XFxUVUKpX4+/vLmTNnLPFSiYocBjEiKpIWL16sfJGytraW2NjYAp9zzZo1WX4hLwznzp2TTz75RGrVqpVrKHN3d5f3339fjhw5YrZJFjIyMmTXrl0yYsQIqVixolH/1atXl6CgIDl+/HipnOTB0gwTbxjCVU5+/vln5UrqnTt3JDU1VSpUqKD83Q4dOpTlmK+//lrZ3717d3O9BIvS6/WyZcsWadGiRbafH1dXV5k5cyYDWC7u3r0rr776qlEIc3JyknfffVf++OOPfJ9vzpw5ylDEXr16ydGjR41+0DH8uHTt2jX5z3/+IyqVSipUqCAbNmww1UsiKlYYxIioSAoICFC+ULVv394k5zQM/cr8KMyZBPV6vZw4cUI+/PBD8fDwyDWU1ahRQyZNmiSnT582WyhKS0uTrVu3ypAhQ6RcuXJG/fv4+MhHH30kf/zxB0OZmbRr104AyPLly3Nsk5SUpLxXZsyYISIiGo3G6O+UnX/++UesrKyU4Yy3bt0yy2uwBL1eL5s3bzYaupz5UaVKFZk9e7YkJSUVdqlFluEzPHnyZLG1tRUbGxtRqVTi6+srhw8fNmqb1yvzjx49Ej8/P1GpVNKlSxe5fPlyru3nzZsn5cuXF5VKJbNnzzaqi6i0YBAjomwFBwfnGgye9xEVFfXMvpOTk6VMmTLKMV9//bVJXtPjx4+z1PPvv/+a5NwFlXm4oGFdqJwe9erVk+DgYLl48aLZ6jGsmRYYGJhlzbT69evLZ599Ztb+S5u0tDSxt7cXAHLp0qUc2xkmsPH29laGiL3yyivK3+bTTz/N8djMa4xNnz7d5K/B3PR6vWzcuFGaNWuW4xXkuXPncpmGPDp37pw4OzsrV8K8vLzkxIkTyv78DE02tJ07d6506NBB/vrrrxzbGsLW+vXrlb4nTpz4nK+CqHhjECOibBVmENu0aZPRMab6wn/16tUs9eT3XjNLyMjIkJ07d8p//vMfcXZ2zvXfs0mTJjJjxgy5fv262epJTEyU1atXS58+fbKs3/bSSy/J9OnT5dq1a2brvzQ4evSoAE+Gy+Z0VeDWrVvi6OgoACQsLExERP7991+xtrZW/h65hbhly5YZheniQqfTyfr166VJkybZfgaqVq0q3377bYmdhMQc9Hq9jBs3TlQqldja2oqtra0sXbq0QOcTEXn48KEcP37caFtO9u/fL7a2tqJSqeSnn34SEdMsIE1UnDxZ5IGIqAjZsmWL8r9r166N+vXrm+S8Bw4cMHpeo0YN2NnZmeTcpmRjY4Nu3brhxx9/xN27d7Fp0yYMHjwYjo6OWdqeOXMGkydPRs2aNdGmTRvMmzcP//77r0nrcXJyglqtxoYNG3Dv3j2sWLECr7zyCmxsbPDHH3/go48+go+PD1q1aoW5c+fi1q1bJu2/NDhy5AgAoE2bNlCpVNm2+fjjj5GSkoJ27dph4MCBAIBffvkFOp0OAODr64u6devm2Ef//v3h4OAAAPjrr79w/PjxHNsmJiZiy5YtCA4ORkBAANzc3KBSqaBSqbBv377neYn5ptfrsW7dOjRt2hT9+vXDmTNnjPZXq1YN8+bNw9WrVzFu3DjltdGzpaSkYMeOHQAAEYGbmxsCAgKe+3yG92yFChXQokULo205OXLkCLRaLcqWLQs3NzcAUNYeIyo1CjsJElHRdPXqVdm1a5fs2rVLunfvbvQL9OzZs5V92T0Mkw4YHkOGDFH2PXjwINd+9Xq9uLu7K8d++OGHJntNHTp0MKpr9OjRJju3JSQlJUlYWJj07dtXGcaW3UOlUkmnTp1k8eLFcv/+fbPVExsbK0uWLJEuXboo9x8ZHu3bt5fQ0FC5c+eO2fovSQYOHCgAZNq0adnuP3HihPJva7jiICLSqFEjZfu33377zH4CAwOV9tnNrmiwYcOGAl3VLgidTidr1qwxmukx88PDw0MWLFjwzNn7KGdnzpyRcuXKib29vahUKvHz87NY34YrZf369VOmsI+Li7NY/0RFCYMYET3T0KFD8/VF7KeffjJqHxwcnOe+jh07ZpYvfVu3bs3yhe7YsWMmOXdhiI+Pl+XLl4u/v7/R0LSnHzY2NvLKK6/IihUrclybSq/X5zqkLS9yWsjayspKunTpIkuWLDHJzJclVfXq1XN8v+v1emVR8jfeeEPZfvLkSaN/aycnJ3F2ds71kfney0qVKuU4NHfDhg3i6uoq/v7+MmXKFFm1apXZg5hWqxWNRmMULjM/qlevLgsXLszzgsKUs99//91opsQJEyaISN4n5igIrVYr8fHx0qBBA1GpVKJWqy3WN1FRwyBGRM9kySA2ZcoU5biKFStKRkZGwYoXkbi4OOWLruHRp0+fAp+3qLh//74sWrRIOnbsaLT22tMPe3t76du3r4SFhUlycrLodDoJDw+X5s2bi4ODg9y7d88k9dy8eVPmzJkjLVu2zDEUxsfHm6SvkuDmzZsCPFmmIbuZ/sLDwwWAODg4yM2bN5XtY8eOzfX+wbw8wsPDs63p6S/FiYmJBQ5iOQUorVYrq1evlgYNGmRbo6enp3z//fcMYCa0ceNGKV++vHJFrH///hbp13AP2Pbt28Xe3l5sbW2N1ogkKm04GJeIipTM94cZ7kMqCL1ejzfeeMPoviVnZ2fMnz+/QOctSipXrox33nkHUVFRuHXrFr755hu0atUqS7u0tDRs2LABgYGBqFixIipWrIgBAwbg1KlTSE1NNdm/iaenJz788EMcO3YMV69exfTp0/HSSy9Bq9Vi27ZtGDp0KNzc3NC3b19oNBokJyebpN/iynB/2EsvvYSyZcsa7UtLS8N///tfAMCECRPg6ekJAEhPT8fq1asL3PeKFSuy3W5tbV3gcz9t0KBB8PX1xcyZM3H9+nXodDqsWrUKL7zwAgYPHoyLFy8ata9RowYWL16MK1euYNSoUbC3tzd5TaWNiAAAfHx8kJiYiIyMDADAw4cPTX5vaXYM94Bt3rwZ6enpcHJygp+fn9n7JSqqGMSIqMi4efMm/vjjD+V5r169CnzOiRMnYtu2bUbbFi9erHyhLWmqVauGDz74AEePHsW1a9eUEPS09PR0JCQkGG1bvnw50tPTTVqPj48PJk+ejDNnzuDixYv47LPPUL9+faSlpWHjxo0YPHgwqlSpgsDAQGzYsAGPHz82af/FgSGItW3bNsu++fPn49q1a6hataoSyABg69atiIuLAwDY29sjISEB8mSUyzMfhw4dUs6zbds23Lt3z8yvEHj06BG2b9+OU6dOYdKkSfDx8UHZsmXx+uuv46+//jJq6+3tjR9++AF///03Ro4cWSQn1CmuDBNoVK9eHU2aNFGC2f379y3yPgCABw8eKO/B1q1bo2bNmhbpl6goYhAjoiIj89UwGxsb+Pv7F+h88+fPx9y5c422/fe//0VgYGCBzltc1KxZE5MnT0ZUVBTef//9bGddzOzWrVvw9PTE2LFjcfDgQej1epPWU79+fQQHB+PChQtGsy2mpKRgzZo16NevH6pUqYI333wTkZGRJg+FRdXhw4cBPJkxMbP79+/jyy+/BABMmzYNTk5Oyr7MV7K6d++OcuXK5bm/Nm3awMPDAwCg1WqxatWq5649r7Zs2ZLl75mWlmb0vGLFipg+fTr+/vtvjBgxggHMjHQ6HRo1aqQ8//PPP/H7778rwcwcDP89OXnypHL1c9iwYUb7MouPjzdbLURFBYMYERUZmYNYhw4dUKFChec+16pVq/DBBx8YbRs2bBhmzJjx3OcsjmbMmAEvLy/Mnz8fKSkpRvuyG/Z57949LFy4EH5+fqhRowYmTJiAkydPmvQLmkqlwosvvohp06bhypUrOH78OIKCglC9enUkJiZi5cqVCAgIgLu7O95++23s3r0bWq3WZP0XJampqTh9+jSArEEsODgYCQkJaNasGd58801l+/379xEZGak8HzBgQL76VKlU6N+/v/I8p+GJpqLVahEfH48qVark2u7hw4f46KOP0KZNG3z99deIjo42a12lWeXKlfHCCy/A2dlZ2abRaPDgwYPnPuetW7dyvaJt+G/Itm3bkJGRgWrVqqF169bQ6/VG09bfvHkTs2bNwhtvvIGIiIjnroeoOGAQI6IiISkpyWh9ooIMS4yIiMDQoUONwkO/fv2wdOnSZ65tU9Lo9XokJSUZbWvZsqUyDPDw4cN4//334e7unuXYW7duYc6cOWjRogXq1q2LTz/9FOfPnzdpfSqVCi1atMDs2bMRHR2NgwcPYuzYsXBzc8PDhw+xdOlSdOvWDR4eHnj33Xdx4MABk1+pK0ynTp1CRkYG3N3d4e3trWw/f/48Fi9eDACYO3eu0RfVVatWKff22NnZoXfv3vnu17AOGfBkLbqzZ88+5yvIWUZGBpYtW4Z69erhvffeyzL0zdHRMdt70U6dOoXJkydj3bp1Jq+J/nf1yd/fH9WrV1e27969W7l3K782bNiAN954A9evX8+xjbW1NR49eoS9e/cCABo2bAgvLy/lvR0dHY05c+agW7dumDRpEiIjI3Nd646oRLD8/CBEVNxYYtZEw8xwhsfVq1efq9a9e/caTdENQLp165bjNN0l3cOHD6VcuXICQLp06SK7d+9W1vHJTKvVyp49e+Ttt9+WSpUq5TrT3gsvvCBffvmlXL582Wx1G+oZOXJklno8PDxk/PjxcvTo0WxfS3Eyc+ZMASB9+/Y12t6jR49st4uING3aVPm3eOWVV56rX71eL1WrVlXO86z1+vIza2J6erosXbpUatasme37p27duvLzzz9LRkaGxMbGytKlS6VHjx5iY2Nj1C46Ovq5Xhvl3XvvvSdly5ZVprFv1aqV7N2795nHZZ5Vc//+/dKiRQtRqVQyffr0XNd3i4iIEDs7O1GpVLJ8+XIREbl27ZrMnDlTatWqpdRRq1YtWbZsWcFfIFERxyBGRM9kiSCWuY+GDRs+V51Hjx4VJycno77btm2b7ZTgpckvv/xitAjws6SlpcnWrVtlyJAhWf49n374+vrKnDlz5J9//jFb/enp6bJt2zYZOnSolC9f3qh/b29vmTRpkpw+fbpYhrI+ffoIAJk1a5ayLTIyUgCIra1tlrB79uxZo9f/448/Pnffmae/d3Nzy3WpiLwEsbS0NFmyZIl4e3tn+16pV6+e/PLLLzmuF5U5lFlygeHSyDCN/OnTp6VNmzZGa4p16dJFLl68KCJPAruhrUjWZQ2ioqKkadOmyrEvvfRStusFGt5b48aNE5VKJbVr15aTJ0/KtGnTpGbNmsrxtWvXlpUrV2ZbK1FJxCBGRM9k7iCm0+nE1dVVaT9p0qR81/jHH39IxYoVjfpt2rQp16sqoJSUFFm3bp0MGDAgy5XGpx9+fn6ycOFCuXv3rtnqSU1NlY0bN4parRZHR8csX/SnTp0q58+fN1v/pqTX68XNzU0AyMGDB0XkyRdWw3paQUFBWY4JCgpSXq+NjY3ExcU9d//79+83+vfbsmVLjm1zC2JpaWmyaNEi8fLyyvZ90aBBA1m1alW+Fuzl4r6WExoaKl5eXqJSqcTGxkZUKpX4+fnJgQMHcjzm3Llz8s4774iVlZUSolxdXWXhwoU5HpOcnCwvvviiqFQqcXBwEE9PT+XYunXryq+//mrU3hRrSBIVdQxiRPRM5g5ihw4dMmp/6NChfNX3119/SZUqVbJ8+TPVAsX0REJCgvzyyy/Ss2dPsbW1zTGQWVlZSbdu3eTHH3+Uhw8fmq2epKQkCQsLk379+om9vb1RDY0bN5avvvpKrly5Yrb+C+rq1avKlS/DcK6FCxcKAHFxcTHrv11+ZRfEHj9+LN999514enpm+z5o2LChaDQahqoiynAFOTY2VmbPni0qlUqsra3F2tpaVCqV2NnZydChQ2Xt2rVy9OhROX36tGzfvl2GDBki3t7eyhBDw+Ojjz6ShISEHPvbtm2bODs7i62trXJMvXr1ZNWqVUbtGMCoNGEQI6JnMncQmzRpktLW1dU1X0NRbty4IdWrVzfqr2bNmnLr1q08n4PyLy4uTpYuXSpdu3YVKyurHEOZra2t9O7dW1atWiWJiYlmq+fRo0eycuVKCQgIyBISfX19ZdasWUXunqNffvlFAEirVq1E5Mn9fC4uLgJAQkNDC7k6Y5mD2I4dOyQ0NDTL587waNSokYSFhXFIWTEzZMgQqVChgqhUKqP7xgzBqVq1akbBy/Bo166dbN26NcfzGgLf9evXeQWM6CkMYkT0TOYOYg0bNlTaDh06NM91/fvvv1KrVi2jvjw8POTatWt5PgcVXExMjCxYsEDatWuX69BFBwcHGTRokKxfvz7XG/oLyhASu3XrliUktm3bVubPny8xMTFm6z+v3n33XQEg48ePF5H/DTts0KBBkftSmjmIVa5cOdu/b+PGjWXt2rUMYMWM4YplbGysrFixQhwcHIyCVpkyZYyulBkejRo1kgkTJsjRo0eV92tOf3vD9i+++IJXwIgyYRAjomfKbxBbuXKlUfspU6bk2NYwPMvwWLduXZ5qiouLk0aNGhkd6+rqKhcuXMjPSyMTi46OllmzZknz5s1zDWXly5eXoUOHyrZt2yQ9Pd1s9dy9e1e+++476dChg6hUKqV/lUolnTp1kkWLFsn9+/fN1n9uDLMfrlmzRi5fvqxcyYuMjCyUep52//59uX//vty8eVOmTp2a49/yxRdflHXr1jGAlRDnzp2TKVOmSIMGDZR7xjIPQ3z55ZclJCREtm3blu3EHNnJ7r3BAEYkohIx4zLqRFQiDBs2zGjR16ioKHTs2DHH9uHh4UbrFI0YMQI//PBDtm3nzZunLLxsb2+P2NhYODk55VpPYmIiunTpghMnTijbKlSogKioKDRp0uTZL4gs4vLly9BoNFi9ejUuXryYYzsXFxcMGDAAarUafn5+2a4tZQq3b9/G2rVrERYWhqNHjyrbra2t0bVrV6jVavTp06dAC4nnVVJSEipUqACdTod//vkH48aNw/r169GjRw9s377d7P3nRV7X3Lt69Sp8fHzMXA1Z2r1793Dr1i1cuHABtra2iI+PR6dOnVCpUiU4OjrC0dHxuc779ALORKUZgxgRPVN+g9jBgwfh5+enPK9SpQrOnDmDqlWrZmnbpUsXZYFPf39/bNu27Zn1dOrUyWjxZwD4/PPP0aZNm2ce+7TmzZujYsWK+T6O8k5E8Oeff0Kj0UCj0eDatWs5tq1atSoGDRqEwYMHo2XLlmZbgPvGjRtYs2YNNBoNTp8+rWy3s7ODv78/AgMD0bt372f+KPC8oqKi0LlzZ3h6emLlypXo2LEjrKyscPbsWTRq1MgsfeZVSkoKFi1ahKCgoDy1v379utFi1FT8iUiePnt5bUdEOSjEq3FEVEzkd2hibGys0TAwAFK2bFlp3ry59O/fX2kXHx9vNLFCblMfZ4Zchrzl9/Gs10Kmpdfr5fjx4zJ+/HipVq1arn8bb29vmTx5spw5c8asa4RdunRJPv/8c6N7FfH/72kbOHCghIeHS0pKikn7/OqrrwSADBw4UJo1ayYAZNSoUSbtI7+SkpJk1qxZWWYgNTyaN28uW7ZsKZbrtZFpcPgpkWnx2jARmZyLiwvat29vtC05ORmnTp3ClStXlG3bt29HRkaG8rxXr14Wq5EKh0qlQosWLTB37lz8888/2L9/P0aPHo3KlStnaXvjxg3MmDEDTZo0QcOGDfH555/j0qVLJq+pbt26+PTTT3H+/HmcO3cOn3zyCWrXro3U1FSsXbsWAwYMQJUqVfDGG28gIiIC6enpBe7z8OHDAAAbGxv8/vvvKF++PD7//PMCn/d5JCUlYebMmahZsyYmTpyIe/fuGe1v0aIFIiIicOLECfTs2ZNXQEoxDikkMi1+oojILEJDQ+Hm5pZrmy1btij/u0mTJvD09DR3WVSEWFlZoUOHDvjuu+8QExODHTt24K233oKzs3OWtn/99ReCg4NRv359NGvWDDNnzkR0dLTJa3rhhRfw5Zdf4u+//8bJkycxceJEeHl5ISkpCb/++it69eoFNzc3DB8+HDt37oRWq813HyKCI0eOAAB27twJAJgyZQpcXV1N+lqeJTExETNmzEDNmjUxadIk3L9/32h/q1atEBkZiWPHjiEgIIABjIjIxHiPGBGZTWJiIiIjI3H9+nUAgKurK5o2bYpmzZpBq9XCzc0NDx48AAB8+umnhXZFgIqWtLQ0bN++HRqNBps3b0ZKSkqObdu0aQO1Wo2BAwdmew+iKej1ehw7dgwajQZr165FTEyMss/V1RX9+/eHWq1G+/bt8zTRyKVLl1C/fn3Y2NhAq9XCx8cHFy5cgL29vVnqf1pCQgJCQ0Mxd+5cxMXFZdnfpk0bBAcHo3v37gxfRERmxCBGRIVi//79RhN+HD9+HC1atCi8gqhISk5ORkREBDQaDSIjI3McFmhlZYWOHTtCrVajX79+cHFxMUs9Op0OBw4cQFhYGMLDwxEbG6vsM0w0olar0apVqxxDzPLly/HWW2/BysoKer0e4eHh6N+/v1nqzSwhIQELFizA3LlzlR9AMmvXrh2Cg4PRtWtXBjAiIgtgECOiQjFhwgTMmTMHwJMvsLdv3+aXP8pVfHw8Nm7cCI1Gg927d0On02XbzsbGBt27d4darcZrr72G8uXLm6WejIwM7N27F2FhYVi/fj0ePXqk7KtRowYCAwMRGBiIpk2bGr2333nnHSxZsgQA4Ofnh/3795v1vf/o0SPMnz8f33zzDR4+fJhlv5+fH4KDg9G5c2d+BomILIhBjIgKRb169fD3338DAN5++23liylRXty/fx/r1q2DRqPBb7/9hpz+r8ze3h4BAQEYPHgwAgIC4ODgYJZ60tLSsHPnToSFhWHTpk1ISkpS9tWpUweBgYFQq9Vo1KgRateujatXrwIATpw4AV9fX7PUFB8fj3nz5uHbb79FfHx8lv0vv/wygoOD0bFjRwYwIqJCwCBGVMJlXjyTC2lSSWRYqFmj0eDYsWM5tnNycsJrr70GtVqN7t27w87Oziz1pKamIjIyEhqNBhEREXj8+LGyr169esrMjwMHDsSaNWtM3v/Dhw/x7bffYt68eUZX6Qw6duyoBDAiIio8DGJEJZA8tchmRkYGbG1tC7EiIsu4du0awsLCoNFocPbs2RzbVaxYEf369cPgwYPRsWPHPE2y8TwSExOxZcsWaDSaLMs1REdHw8vLy2R9PXjwQAlgCQkJWfZ37twZwcHB6NChg8n6JCKi58cgRlRCJSYm4vfff8eRI0dw584dXL16FXXr1kWdOnXg6emJ6tWrw8fHB+XKlSvsUonM4sKFCwgLC8Pq1atx+fLlHNu5ublh4MCBUKvVaNOmjdmuGj98+BDjx4/Hr7/+ChcXF9y5c8ck542Li8M333yD+fPnIzExMcv+rl27Ijg4OMvafkREVLgYxIhKCMNVsLS0NKxevRrff/89zpw5Y/QLvEGVKlVQt25dtG7dGp07d0b79u3h5OSk3GfD+0WoJBERnD59GhqNBhqNBv/880+ObT09PREYGIjBgwdnmWTDlPVkZGQUeGhkbGws5s6diwULFhjdk2bQvXt3BAcHo23btgXqh4iIzINBjKgEefjwIYYOHYqIiAhlm52dXY5TfgOAl5cX+vfvjw8//BAeHh6WKJOo0Oj1ehw9ehQajQZr1qzB3bt3c2xbp04dqNVqqNVqNGzY0IJV5u7+/fuYM2cOQkNDkZycnGV/jx49EBwcjDZt2hRCdURElFcMYkTFnGECjrNnz+Lzzz/H+vXrUaZMGWWCAHt7e5QrVw5+fn5ITEzE+fPnkZKSgpSUFGi1WuUqmKurK6ZNm4YhQ4aYbRIDoqJEq9Vi//790Gg0WLduXbZTuxs0btwYarUagYGBqFWrlgWr/J979+5h9uzZ+O6777INYK+88gqCg4PRqlWrQqiOiIjyi0GMqIR47bXXsGXLFlhbWyvrK3Xq1AmjRo1C//79kZCQgAoVKkCr1eLAgQPYvHkz1qxZg5iYGABP1l6qVKkS1q9fz6FMVOqkp6dj165d0Gg02LhxY7ZD/QxatmwJtVqNQYMGWeQq8t27dzFr1ix8//33SElJybI/ICAAU6dORcuWLc1eCxERmQ6DGFExp9Vq8fPPP+Ptt9+GiChBLCQkBO+99x4qVKigtNXpdEazwyUmJiI0NBRLly7F9evXYWVlhV27dqFTp06F8EqIiobU1FRs3boVGo0GW7duNZp+PjOVSgU/Pz+o1WoMGDAArq6uOZ7z6ZlMM4uLi4OLi0uW7Xfu3FECWGpqapb9vXr1wtSpU822DhkREZkXgxhRMWUYknjy5EkEBQXhwIEDAJ7cE9azZ0+Eh4fnuG6YXq8HAFhZWSEjIwNr1qzBiBEj0LhxY0RERKBKlSoWfS1ERVVCQgI2b94MjUaDHTt2QKvVZtvO2toaXbp0weDBg9GnTx/lB5C0tDQsX74cCxYswL59+1C5cmWj42JjY1GtWjW0atVKCXR6vR4zZ87EokWLsg2Br732GqZOnYpmzZqZ/PUSEZHlMIgRFVOGX9i//PJLzJkzR1m4tUuXLvjiiy/QunXrLFfAcnPw4EHExcUhICAANjY25iydqFiKi4vDhg0boNFoEBUVpfyg8TQ7Ozt0794drq6u2LVrF27dugUAmDx5MqZPn27UdvHixRg1apTyXKVSQaVSZXvuvn37YurUqWjSpInpXhQRERUaBjGiYiw+Ph69evXCoUOHoFKpICJYunQphgwZkucFnDMPmUpJSYGjo6M5SyYqEe7cuYO1a9dCo9Hg8OHDeTrGyckJN27cMBqG2LlzZ0RFReV6nK+vL+bNm8d7N4mIShjzrFpJRGZl+LX82LFjuHHjBoAngapevXro1KlTnkMYYLxmGEMYUd64u7vjvffew6FDh3Djxg189dVXqF69eq7H6PV6BAUFYceOHcjIyMA///yDGjVqPPOq9cmTJ9GlSxcMGDAA4eHh2d4vRkRExQ+viBEVY9OnT8f06dORnJwMEcH777+PWbNm5SuIEdHzS05OxuLFizFz5sxc1yR7WpkyZZCenp7tEMSKFSvmOpX+n3/+iUaNGj1Xvdm5evUqjh8/jlu3biE9PR0VK1ZE/fr10bZtW5QpU8Zk/RARkTHeCEJUTKWmpuLOnTtG02y3bNmS93cRWdD8+fPx8ccfZ9nerFkzDB06FDExMQgLC8P169eN9j89CYdKpUJgYCA+/fRTNGzYEFeuXEFYWBg0Gg3+/PNPpd0LL7xgshC2ceNGfPHFF/j999+z3e/k5IRhw4YhODg4yyQjRERUcLwiRlRMabVavPPOO/jpp58AAFWrVsWyZcvQo0cPk/aT+R4yvV6vTCZARE/u06xRowYSEhIAPLmfKzg4GAEBAcrnRERw4sQJrF69GmFhYcrafcCTADZ48GBMmTIFDRo0yLaP8+fPIywsDKtXr8awYcPwySefFKjmtLQ0DB8+HL/++mue2ru6uiI8PBwdOnQoUL9ERGSMQYyoGDKEoxYtWuDUqVOwsbGBs7MzwsPD8fLLL+e6ZlF+zm+Qnp4OOzs7ozZarRZWVlbZTo9PVJpMnToVO3fuRHBwMPz9/XP97Ol0OoSHh+P111+HtbU1Fi5ciBEjRuSpHxFBRkZGls9ifuj1evTr1w+bNm0y2m5tbQ0vLy84Ozvj+vXryiysBo6Ojti9ezfatGnz3H0TEZExBjGiYiolJQWvvfYaoqKiICIQEVy/fh01atQwWRALCgpCYmIiPDw8YGdnB3d3d7Rt2xb16tUz4SshKt4yMjJgY2OTr89cbGwsnJ2dYWVlleclJkzh66+/xuTJk422jRo1Cp9++imqVasG4ElY27RpEz744APcvHlTaVe9enX8+eefcHZ2tli9REQlGYMYUTFkWKh5/PjxmDdvHqytrWFnZ4ewsDD07NnTJH3cvXsX9evXV34Zd3JyUu5Hq1evHnr37o0pU6agXLlyJumPiMwrLi4ONWvWRGJiorJt+vTpWYKZwe3bt9G+fXtlZlbgydW/kJAQc5dKRFQqcEwRUTFkGA7o4+MD4MmwotTUVOzfvx/Ak6GEBXX79m3069cP3bp1g7OzM5KSklC+fHnY2tri0qVLmDVrFmJjYwvcDxFZxsyZM41CWIcOHTBp0qQc23t4eGDp0qVG27755hvExcWZrUYiotKEV8SIirHo6Gi89NJLypUqV1dXnD59Gu7u7iY5f1xcHOzt7WFlZYXbt2/jiy++wKZNm5CYmIi+ffti3bp1JumHiMxLr9fD3d0d9+/fV7bt3bsXnTp1euaxHTp0wIEDB5Tn3333HUaPHm2WOomIShNeESMqxjw8PPD6669Dr9fD1tYWd+/exfDhw5WhRHq9Hjqd7rnP7+LiAicnJzg6OqJOnToAnsy4BgB9+vQpaPlEZCGHDx82CmE+Pj7o2LFjno4dPny40fONGzeasDIiotKLQYyoGLOxsUFQUBC8vb2RlpYGlUqFvXv3YtasWYiJiTGaCECv18NwATw/F8INC85eunQJJ06cQHp6OsqXL48uXbqY/gURkVls3brV6Hm3bt3yPLlIt27djJ7v27cPycnJJquNiKi0YhAjKsZEBD4+PliwYAFq1aoF4MkVq++//x4NGjTAu+++i507dyItLQ1WVlZQqVTPPaNiVFQU7t27BwDw9/dXZlgjoqLvzJkzRs/btm2b52OrVasGb29v5Xl6ejouXLhgosqIiEovm8IugIienyFQBQQE4Pbt2/j444/x4MEDAEBiYiKWLl2K1atXo3z58mjYsKFyP9nFixcxcuRIBAQEoGzZsrn2YZgYZNeuXcq9aP379zfjqyIiU7t48aLR84YNG+br+IYNGxrNnnjx4kW0aNHCFKUREZVaDGJEJcTIkSPh7++P2bNnY8uWLYiOjkZGRgbi4+MRHx+PmzdvYvv27cpVseHDhz8zhBmmyb9w4QLOnTuHjIwMVK5cGS+//LKFXhURFVRqaqrRemAA4Onpma9zPN3+0qVLBa6LiKi0YxAjKgEMCzp7eXnhs88+Q0BAAHbv3o1Tp07h0qVLiImJMWrr4eEBX1/fPJ9/165dyo3+r776KqpUqWLy10BE5hEbG2t0X6itrW2+P8MeHh5Gzw3DlImI6PkxiBGVACqVShmmWKlSJfTo0UNZiPX27duIi4vDpUuXcPnyZVSvXh2tWrWCl5fXM89rGJa4Z88epKSkAOCwRKLixjCk2MDR0THf94k+ffX86XMSEVH+MYgRlTCGyTjKli2LRo0aoVGjRkb7tVotbGye/dE3DEs8c+YMLly4gIyMDFSrVg3t2rUzV+lEZAZPh6YyZcrk+xwODg65npOIiPKPsyYSlTCZf+nOPBzJsJ5YXkJYZjt37kRsbCwAoGfPnqhUqZIJqiQiS3n8+LHRczs7u3yfw97e3uh5ampqgWoiIiIGMaISLXMoM6wnlleGYYl79+5VhiX269fPdMURkUU8fQUsPT093+cwLOSe0zmJiCj/GMSIKAvDIs7Hjx/HpUuXoNVq4e3tjdatWxdyZUSUX05OTkbPn75ClhdPXwF7+pxERJR/DGJElIUhiO3YsUNZl6x3794oX758YZZFRM/h6dCUkpJiNGw5L5KTk3M9JxER5R+DGBFBq9Xi3r17SExMBPC/+8j27dunDEvs27dvodVHRM+vcuXKRsOUMzIy8j39/O3bt42ecwkLIqKC46yJRKWYYYbFS5cuYcmSJXB0dESbNm3QqlUrnD17FleuXIFOp0O9evXg6+urtCei4sPBwQFeXl6Ijo5Wtt28eRNubm55PsfTC0LXr1/fZPUREZVWDGJEpZghVB09ehQLFiwAALi7u6N169aIj49XFnEOCAjIso4QUUn22WefISQkxOTnjYqKQseOHU1+3mepX7++URC7cOECWrRokefjL168mOV8RERUMByaSETo3r07Ro4cCTc3N9y5cwcbN27Evn37oNPpYGVlhcePHyMqKgrR0dHPNeMaERWuJk2aGD0/fPhwno+NiYnBjRs3lOe2trZo2LChiSojIiq9GMSICJ6enli0aBFiYmLw22+/Yfjw4ShXrhwyMjKg1+vx3XffYfjw4Zg0aRIWLlyII0eOFHbJRJQPPXv2NHq+e/fuPE/YsXPnTqPnnTp14mQdREQmoJL8Tp1ERCWOXq+HXq/Pstjzzp07sWLFCmzYsMFoyuugoCDMmjXL0mUSWcy1a9dw7do1AMCsWbOMwsjs2bPx0ksv5Xjszp07jT4fQ4YMwZtvvgkAaN68OSpWrGimqnOm1+vh5uamLM4OPFkjsFOnTs88tkOHDjhw4IDyfOHChRgzZoxZ6iQiKk14jxgRwcrKSlnAWafTQURgY2OD7t27o3v37hARbN26FcuXL8f69evRvHlzAODkHVRi+fj4wMfHBwDwyy+/GO1r3rx5rvd53bp1K8u5unbtavIa88PKygrDhg3D7NmzlW0hISHo2LFjrp/hPXv2GIWwcuXKYdCgQWatlYiotODQRCIyYm1trVwZ0+l00Ol0UKlU6NmzJ8LDw5GWlqZ8EWMIIyo+Jk2aZDSkcP/+/fj6669zbH/79m2MGDHCaNu4ceNQuXJls9VIRFSaMIgRUY6sra1hbW0N4H+hzNbWVrl6RkTFR+XKlfHxxx8bbfvoo48wZswY/Pvvv8o2vV6PjRs3om3btkaTdFSrVg1BQUGWKpeIqMTjtykiypPMoYyIiqdJkyZlmbjj+++/h5eXF2rVqoVmzZrBxcUFffv2NVo7zMHBAWvWrEGFChUsXDERUcnFIEZERFRKWFlZYe3atVCr1UbbdTodrl27htOnTyM+Pt5on4uLCyIjI9GuXTsLVkpEVPIxiBEREZUiZcqUwerVqxEeHp5lfbHMypYtizFjxuDChQuFsgg1EVFJx1kTiYiISqH+/fujf//+uHLlCo4dO4bbt28jPT0dFSpUQIMGDdCuXTuUKVOmsMskIiqxGMSIiIhKsdq1a6N27dqFXQYRUanDoYlEREREREQWxiBGRERERERkYQxiREREREREFsYgRkREREREZGEMYkRERERERBbGIEZERGRCNjbGExLrdLpCqoSIiIoyBjEiIiITenrtrTt37hRSJUREVJQxiBEREZmQu7u70fPNmzcjJiamkKohIqKiikGMiIjIhBo0aACVSqU8v3fvHurUqQNfX18MGDCgECsjIqKihEGMiIjIhFxcXNC+fXujbcnJyTh16hSuXLlSSFUREVFRwyBGRERkYqGhoXBzcyvsMoiIqAhTiYgUdhFEREQlTWJiIiIjI3H9+nUAgKurK5o2bYpmzZoVcmVERFQUMIgRERERERFZGIcmEhERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhTGIERERERERWRiDGBERERERkYUxiBEREREREVkYgxgREREREZGFMYgRERERERFZGIMYERERERGRhf0/ILZum9YaqR4AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1003.94x708.661 with 1 Axes>"
       ]
@@ -1266,10 +1238,10 @@
      "n": "17"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:16.165868Z",
-     "iopub.status.busy": "2024-06-03T10:39:16.165683Z",
-     "iopub.status.idle": "2024-06-03T10:39:16.270845Z",
-     "shell.execute_reply": "2024-06-03T10:39:16.270346Z"
+     "iopub.execute_input": "2024-06-03T14:32:52.654203Z",
+     "iopub.status.busy": "2024-06-03T14:32:52.654108Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.716145Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.715845Z"
     }
    },
    "outputs": [
@@ -1309,7 +1281,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/08_example_ensemble.ipynb.txt b/master/_sources/notebooks/08_example_ensemble.ipynb.txt
index 7b30c1bb..860ecf0e 100644
--- a/master/_sources/notebooks/08_example_ensemble.ipynb.txt
+++ b/master/_sources/notebooks/08_example_ensemble.ipynb.txt
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:50.496774Z",
-     "iopub.status.busy": "2024-06-03T10:35:50.496597Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.021701Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.021120Z"
+     "iopub.execute_input": "2024-06-03T14:25:44.483473Z",
+     "iopub.status.busy": "2024-06-03T14:25:44.483339Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.123828Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.123485Z"
     }
    },
    "outputs": [
@@ -33,15 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "numpy     : 1.24.4\n",
-      "sys       : 3.8.16 (default, May 26 2024, 19:41:26) \n",
-      "[GCC 12.2.0]\n",
       "matplotlib: 3.7.5\n",
-      "scipy     : 1.10.1\n",
+      "krotov    : 1.3.0+dev\n",
+      "numpy     : 1.26.4\n",
+      "scipy     : 1.12.0\n",
+      "sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
       "qutip     : 4.7.6\n",
       "\n"
      ]
@@ -129,10 +128,10 @@
      "start_time": "2020-03-22T05:01:29.429149Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.048051Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.047795Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.051930Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.051469Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.145515Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.147686Z"
     }
    },
    "outputs": [],
@@ -168,10 +167,10 @@
      "start_time": "2020-03-22T05:01:29.443630Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.053786Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.053638Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.056472Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.055951Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.149334Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.149232Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.151126Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.150878Z"
     }
    },
    "outputs": [],
@@ -188,10 +187,10 @@
      "start_time": "2020-03-22T05:01:29.452425Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.058349Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.058202Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.063690Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.063274Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.152558Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.152452Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.154961Z"
     }
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    "outputs": [],
@@ -229,10 +228,10 @@
      "start_time": "2020-03-22T05:01:29.470251Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.065522Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.069107Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.156516Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.158758Z"
     }
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    "outputs": [],
@@ -258,10 +257,10 @@
      "start_time": "2020-03-22T05:01:29.482710Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.071412Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.074565Z"
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     }
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    "outputs": [],
@@ -280,10 +279,10 @@
      "start_time": "2020-03-22T05:01:29.497857Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.076812Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.078831Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.164288Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.165714Z"
     }
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    "outputs": [],
@@ -301,10 +300,10 @@
      "start_time": "2020-03-22T05:01:29.505632Z"
     },
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-     "iopub.execute_input": "2024-06-03T10:35:51.081065Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.083027Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.167760Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.169225Z"
     }
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    "outputs": [],
@@ -321,10 +320,10 @@
      "start_time": "2020-03-22T05:01:29.514140Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.085565Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.090468Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.171021Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.174447Z"
     }
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    "outputs": [
@@ -372,10 +371,10 @@
      "start_time": "2020-03-22T05:01:29.525825Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.092752Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.092600Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.095498Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.094995Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.176475Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.176365Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.179752Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.179421Z"
     }
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    "outputs": [],
@@ -394,10 +393,10 @@
      "start_time": "2020-03-22T05:01:29.534230Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.097329Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.097180Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.099969Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.181134Z",
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     }
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@@ -416,10 +415,10 @@
      "start_time": "2020-03-22T05:01:29.542275Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.102344Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.138025Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.184598Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.184472Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.254725Z"
     }
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    "outputs": [],
@@ -440,10 +439,10 @@
      "start_time": "2020-03-22T05:01:29.862369Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.140344Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.143123Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.256920Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.258871Z"
     }
    },
    "outputs": [],
@@ -468,17 +467,17 @@
      "start_time": "2020-03-22T05:01:29.879489Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.145578Z",
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-     "shell.execute_reply": "2024-06-03T10:35:51.297325Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.260666Z",
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+     "shell.execute_reply": "2024-06-03T14:25:45.427308Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
      "data": {
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",
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8/zgL9i4AYEzrMRankdpOxY1IOVzXNQYvm8G6xOMkJGdaHUfEcl/t/opCZyGtI1rToW4Hq+NILafiRqQc6gf7cXlbs9l95ioNLJbazely8tn2zwC4vtX1GIZhcSKp7VTciJTT6ItjAfhqwyEKijWwWGqvFckrSMxKJNA7kCubXGl1HBEVNyLl1bdFPSJDfDmeW8QPCWlWxxGxzMlWm+HNhhPgHWBxGhEVNyLlZrcZjOoSA8Dnaw9anEbEGik5Kfx44EcArm95vcVpREwqbkQq4JoTxc2SHemkZeVbnEak6n2x8wucLifxkfE0D29udRwRQMWNSIU0rx9E50ZhOJwuvvrtkNVxRKpUkbOIL3aYu3+PbjXa4jQip6i4Eamga+NPdU25XC6L04hUnZ8O/kR6XjoRfhEMbDTQ6jgiJVTciFTQVR2i8fGysT01i81JWvNGao/Zu2YD5kBib7u3xWlETlFxI1JBof7eDG4XBcDnaw9YnEakaqTnpvPTwZ8AGNl8pMVpREpTcSPiBie7prTmjdQWX+/+GofLQcd6HWka1tTqOCKlqLgRcYM+zeuWrHmzZHu61XFEKpXL5WLOrjkAjGoxytowImeh4kbEDew2g+EdowH4er1mTYln+y3tN/Zl7sPfy5/BcYOtjiNyBhU3Im5ydaeGAHyfkEpWfpHFaUQqz8mBxIPjBhPoHWhxGpEzqbgRcZN20SE0qxdIQbGT77akWh1HpFLkFOWwcN9CQAOJpfqyvLh54403iIuLw8/Pj+7du7Nq1arznj916lRatWqFv78/sbGxPPTQQ+Tna2VYsZ5hGCWtN19tUNeUeKYFexeQV5xHXEgcnet3tjqOyFlZWtx8+umnTJw4kSeffJJ169bRsWNHBg8eTFra2TchnDFjBo899hhPPvkkCQkJvPfee3z66af87W9/q+LkImd3ctzNL7sOk55VYHEaEfc72SU1ssVIDMOwOI3I2Vla3Lz00kvceeedjB8/nrZt2/LWW28REBDA+++/f9bzf/31V3r37s2NN95IXFwcgwYNYsyYMedt7SkoKCAzM7PUh0hliasbSMdYczuG+ZuSrY4j4lZ7ju9hQ/oG7Iad4c2GWx1H5JwsK24KCwtZu3YtAweeWrLbZrMxcOBAli9fftbX9OrVi7Vr15YUM3v27GH+/PkMHTr0nPeZMmUKoaGhJR+xsbHu/UJEfufqE603X61PsjiJiHudbLXpG9OXuv51LU4jcm6WFTeHDx/G4XAQGRlZ6nhkZCQpKSlnfc2NN97IM888Q58+ffD29qZZs2b079//vN1SkyZNIiMjo+TjwAGtICuV66oODbAZsC7xOIlHcq2OI+IWRc4ivt79NaCBxFL9WT6guCyWLFnCs88+y5tvvsm6dev48ssvmTdvHn//+9/P+RpfX19CQkJKfYhUpvohfvRqZv5V+81GDSwWz7Ds4DKO5h+ljl8d+sb0tTqOyHlZVtzUrVsXu91OamrpKbOpqalERUWd9TVPPPEEt9xyC3fccQcXXXQRI0eO5Nlnn2XKlCk4nc6qiC1yQYZ3Mrum5vyWpJ3CxSPM3nlik8zmw/G2aZNMqd4sK258fHyIj49n8eLFJcecTieLFy+mZ8+eZ31Nbm4uNlvpyHa7HUBvIFKtXNE+Ch8vGzvTsklIzrI6jkiFpOWm8VOSuUnmiOYjrA0jcgEs7ZaaOHEi7777Lh9++CEJCQncfffd5OTkMH78eADGjh3LpEmTSs4fNmwY06ZNY+bMmezdu5dFixbxxBNPMGzYsJIiR6Q6CPHz5tJW9QENLJaa7+vdX+N0OelcvzNNQ7VJplR/XlbefPTo0aSnpzN58mRSUlLo1KkTCxYsKBlknJiYWKql5vHHH8cwDB5//HGSkpKoV68ew4YN45///KdVX4LIOV3dKZoFW1KYuzGZx4a01pogUiOdvkmmBhJLTWG4all/TmZmJqGhoWRkZGhwsVSq/CIH8X9fRE6hgy/u7kV843CrI4mU2drUtYxbMA5/L3+WXL+EAO8AqyNJLVWW9+8aNVtKpCbx87YzqJ05OP4bbccgNdSXO78E4Iq4K1TYSI2h4kakEg3r2ACAeZuScThrVSOpeIDswmwW7V8EwKgWoyxOI3LhVNyIVKI+zesR6u9NelYBK/cesTqOSJks2GduktkktAkd63W0Oo7IBVNxI1KJfLxsDGmvrimpmU6ubTOyuTbJlJpFxY1IJRt2Yq+pbzenUFisxSalZth1bBcbD2/EbtgZ1myY1XFEykTFjUgl69G0DnWDfDmeW8Qvuw5bHUfkgny5yxxI3C+mnzbJlBpHxY1IJbPbDK68SF1TUnMUOYqYu3suoIHEUjOpuBGpAie7pr7bmkp+kcPiNCLn9+OBHzlWcIx6/vXo07CP1XFEykzFjUgV6NIonOhQP7ILilmyPc3qOCLndbJLaniz4XjZLF3IXqRcVNyIVAGbzeCqE60332xItjiNyLml5KTwa9KvAIxsoe0WpGZScSNSRYZ1MIubxdtSyS4otjiNyNnN2TUHFy7iI+NpHNLY6jgi5aLiRqSKtG8YQlydAPKLnCxOSLU6jsgZnC5nySaZGkgsNZmKG5EqYhhGycBizZqS6mhVyiqSspMI8g7i8saXWx1HpNxU3IhUoeEnipulO9LJyC2yOI1IaSc3yRzSZAj+Xv4WpxEpPxU3IlWoRWQwraOCKXK4WLglxeo4IiUyCjJYvH8xoC4pqflU3IhUsZKuqY3qmpLqY96eeRQ6C2kZ3pJ2ddpZHUekQlTciFSxqzo0AOCXXYc5nF1gcRoR0+xd5iaZo1qM0iaZUuOpuBGpYo3rBNIxJhSnC77dpDVvxHpbj2xl29FteNu8ubLJlVbHEakwFTciFhimBf2kGjk5kPiyRpcR5hdmbRgRN1BxI2KBK090Ta3ad5TkjDyL00htlluUy9w92iRTPIuKGxELNAj1p1tcBADzNqr1Rqwzf+98copyaBzSmO4NulsdR8QtVNyIWGRYR7P1Rgv6iZU+2/4ZANe1vA6bobcE8Qz6SRaxyJCLGmAzYMPBDPYfybE6jtRCWw5vIeFoAt42b4Y3G251HBG3UXEjYpG6Qb70bl4XgLnqmhILfLbDbLUZFDeIcL9wi9OIuI+KGxELndwpXF1TUtUyCzP5du+3AFzf8nqL04i4l4obEQsNbheFt91gW0oWO1KzrI4jtcjc3XPJK86jeVhzOtfvbHUcEbdScSNiodAAby5pWQ+AuWq9kSricrmYtWMWYA4k1orE4mlU3IhY7NReU8m4XC6L00htsDZ1LbuO78Lfy59hzYZZHUfE7VTciFhsYJtI/Lxt7D2cw5ZDmVbHkVpgxrYZAAxtMpRgn2CL04i4n4obEYsF+npxWetIQAOLpfIdyj7E4sTFANzU5iaL04hUDhU3ItXAyQX95m5MxulU15RUnpnbZuJ0OeneoDstwltYHUekUqi4EakG+reqT5CvF0nH8/jtwDGr44iHyi3K5fOdnwNwc5ubLU4jUnlU3IhUA37edga1Pdk1pQX9pHLM3TOXrMIsYoNj6RfTz+o4IpVGxY1INXFy1tTcjck41DUlbuZyufgk4RMAxrQeo32kxKPpp1ukmujdvC5hAd4czi5g5Z4jVscRD7M8eTl7MvYQ4BXAiOYjrI4jUqlU3IhUEz5eNoa0jwLgm42aNSXudbLVZkTzEZr+LR5PxY1INXJyr6lvN6dQWOy0OI14ip3HdrLs4DIMDG5sc6PVcUQqnYobkWqke9M61A3y5XhuEb/sOmx1HPEQ07dMB2Bg44E0DmlsbRiRKqDiRqQasdsMrupgrnmjBf3EHZKzk5m/Zz4At7W/zeI0IlVDxY1INXNyQb+FW1LIL3JYnEZquo+2fkSxq5huUd1oX7e91XFEqoSKG5FqpnNsOA3D/MkpdLBwS4rVcaQGyyjI4IudXwBqtZHaRcWNSDVjsxlc06UhAJ+vPWhxGqnJZmybQV5xHq3CW9ErupfVcUSqjIobkWro2vhYAH7edZhDx/MsTiM1UVZhFv/d+l8Abr/odgzDsDiRSNVRcSNSDTWqE0D3JhG4XPDlOrXeSNl9kvAJWYVZNA1tyqDGg6yOI1KlVNyIVFPXxscAZteUy6XtGOTCZRVm8dHWjwC4q+Nd2G12ixOJVC0VNyLV1NCLGhDgY2ffkVzW7NdO4XLh1GojtZ2KG5FqKtDXiysvMqeFz1pzwOI0UlOo1UZExY1ItXaya2rexmRyC4stTiM1wQebP1CrjdR6Km5EqrFuTSJoXCeAnEIH8zdpzRs5v9Sc1JIZUvd3uV+tNlJrqbgRqcYMw+DaLmbrzWfqmpI/MG3DNPId+XSq14lLYy+1Oo6IZVTciFRz13aNwWbAqr1H2ZmaZXUcqaZ2H9/N7F2zAfhL179oXRup1VTciFRzDUL9uaxNJACfrEy0OI1UV1PXTcXpcnJp7KV0qt/J6jgillJxI1ID3NyjMQBfrDuogcVyhl+TfmXJgSXYDTsPxD9gdRwRy6m4EakB+javS6OIALLyi/lmwyGr40g1UuQoYsqqKQCMaT2GpqFNLU4kYj0VNyI1gM1mcGP3RoC6pqS0j7Z+xL7MfdTxq8M9ne6xOo5ItaDiRqSGuC4+Bh+7jY0HM9h48LjVcaQaSMlJ4e2NbwPmIOJgn2CLE4lUD5YXN2+88QZxcXH4+fnRvXt3Vq1add7zjx8/zoQJE2jQoAG+vr60bNmS+fPnV1FaEevUCfJl6EVRAHy8Yr/FacRqLpeL51c9T15xHl3qd+GqpldZHUmk2rC0uPn000+ZOHEiTz75JOvWraNjx44MHjyYtLS0s55fWFjI5Zdfzr59+/j888/Zvn077777Lg0bNqzi5CLWODmw+OsNh8jIK7I4jVhp4f6FfJ/4PV6GF3/r/jdN/RY5jZeVN3/ppZe48847GT9+PABvvfUW8+bN4/333+exxx474/z333+fo0eP8uuvv+Lt7Q1AXFzcee9RUFBAQUFByfPMzEz3fQEiVSy+cTitIoPZnprFrDUHuKOvBo/WRkfzj/LsimcBuLPDnbSKaGVxIqkqDoeDoiLP/cPGx8cHm63i7S6WFTeFhYWsXbuWSZMmlRyz2WwMHDiQ5cuXn/U1X3/9NT179mTChAl89dVX1KtXjxtvvJFHH30Uu/3sy4xPmTKFp59+ulK+BpGqZhgG43rHMenLTXzwyz7G9YrDy25577JUsSkrp3Cs4Bgtw1ty50V3Wh1HqoDL5SIlJYXjx49bHaVS2Ww2mjRpgo+PT4WuY1lxc/jwYRwOB5GRkaWOR0ZGsm3btrO+Zs+ePfzwww/cdNNNzJ8/n127dnHPPfdQVFTEk08+edbXTJo0iYkTJ5Y8z8zMJDY21n1fiEgVG9m5IS8s3E7S8TwWbknlyg4NrI4kVWjB3gUs2LcAu2Hn773/jrfd2+pIUgVOFjb169cnICDAI7shnU4nhw4dIjk5mUaNGlXoayxXcZOamsrDDz/M4sWLSUtLw+Vylfq8w+Eod6DzcTqd1K9fn3feeQe73U58fDxJSUm88MIL5yxufH198fX1rZQ8Ilbw87Zzc4/GvLp4J//5eY+Km1rkQNYBnl5utkTfftHttK3T1uJEUhUcDkdJYVOnTh2r41SqevXqcejQIYqLi0uGn5RHuYqbcePGkZiYyBNPPEGDBg3KVV3VrVsXu91OampqqeOpqalERUWd9TUNGjTA29u7VBdUmzZtSElJobCwsMLNWCI1xS09GvPWkt38lnictfuPEd843OpIUsmKHEU8svQRsouy6Vy/M3d3vNvqSFJFTo6xCQgIsDhJ5Tv5Pu5wOKq+uPn555/56aef6NSpU7lv7OPjQ3x8PIsXL2bEiBGA2TKzePFi7r333rO+pnfv3syYMQOn01ky4GjHjh00aNBAhY3UKvWCfRnROZrP1hzkvZ/3EN843upIUsmmrpvKliNbCPEJ4fm+z+Nls3Q+iFjAE7uifs9dX2O5RiLGxsae0RVVHhMnTuTdd9/lww8/JCEhgbvvvpucnJyS2VNjx44tNeD47rvv5ujRozzwwAPs2LGDefPm8eyzzzJhwoQKZxGpaW7vY86UWrA5hX2HcyxOI5Vp3p55fLT1IwD+0fsfNAhSV6TI+ZSruJk6dSqPPfYY+/btq9DNR48ezYsvvsjkyZPp1KkT69evZ8GCBSWDjBMTE0lOTi45PzY2loULF7J69Wo6dOjA/fffzwMPPHDWaeMinq5VVDD9W9XD6YK3lu62Oo5Uko3pG5n8y2QAxrcfz4BGAyxOJFL9Ga5yNMGEh4eTm5tLcXExAQEBZ/SLHT161G0B3S0zM5PQ0FAyMjIICQmxOo5Ihazdf5Rrpi3H226w5JEBNAzztzqSuFFKTgpj5o3hcN5h+sf0Z+qAqdhtZ1/2QjxXfn4+e/fupUmTJvj5+Vkdp1Kd72sty/t3uTptp06dWp6XiYibxTeOoGfTOizfc4R3lu7m6avbWx1J3CSjIIN7Ft/D4bzDtAhvwXP9nlNhIzXOsmXLeOGFF1i7di3JycnMnj27ZJxtZSpXcXPrrbe6O4eIlNN9lzZn+Z4j/G/1ASZc2pz6wZ79l11tkF2YzV2L7mLnsZ3U86/Ha5e+RqB3oNWxRMosJyeHjh07cttttzFq1Kgqu2+5h9s7HA7mzJlDQkICAO3atWP48OHnXClYRCpHz2Z16NIojHWJx3l32R7+70qtfVKT5RXnce8P97L5yGbCfMN4d9C7NAzS/nlSmsvlIq+octaU+yP+3vYLntU0ZMgQhgwZUsmJzlSu4mbXrl0MHTqUpKQkWrUy9zSZMmUKsbGxzJs3j2bNmrk1pIicm2EY3HdZC8Z/sJqPlu/n9j5NiQpV601NlFmYyX2L72Nd2jqCvYN5+/K3aRam36dyprwiB20nL7Tk3lufGUyAT/VeiqBcs6Xuv/9+mjVrxoEDB1i3bh3r1q0jMTGRJk2acP/997s7o4j8gf4t69G1cTgFxU5e/WGn1XGkHNJz0xm3YFxJYfPmwDe1ArFIOZWr9Fq6dCkrVqwgIiKi5FidOnV47rnn6N27t9vCiciFMQyDR4e05rq3lvPp6gPc0acJTesFWR1LLtCOYzu4/4f7ScpOop5/PaYNnKadvuW8/L3tbH1msGX3ru7KVdz4+vqSlZV1xvHs7GytFCxikYvjIri0dX1+2JbGvxft4I0bu1gdSS7Agr0LmPzrZPKK84gNjuXty98mNlib+8r5GYZR7buGrFSubqmrrrqKP/3pT6xcuRKXy4XL5WLFihXcddddDB8+3N0ZReQCPTK4FYYB8zYms+lghtVx5DwKHAU8v+p5Hln2CHnFefRo0INPhn6iwkbEDcpV3Lz66qs0a9aMnj174ufnh5+fH71796Z58+a88sor7s4oIheoTYMQRnQyZ9Y8M3eLW7ZJEffbcngL139zPR8nfAzAbe1v462BbxHupw1QxbNkZ2ezfv161q9fD8DevXtZv349iYmJlXrfcrVphYWF8dVXX7Fz5062bdsGmLtzN2/e3K3hRKTsHhncigWbU1i97xhfrT/EiM6aRlxdZBVm8faGt/k44WMcLgd1/OrwVK+n6B/b3+poIpVizZo1DBhwasuQiRMnAuZ6edOnT6+0+1aow65Fixa0aNHCXVlExA2iw/y599LmvLBwO8/OT2Bg20iCfNU3b6UiZxFzds3h9d9e52i+uT3NkLgh/K373wjzC7M2nEgl6t+/vyUtyBf8G2/ixIn8/e9/JzAwsKTyOpeXXnqpwsFEpPzu6NuEWWsOsO9ILq8t3smkoW2sjlQrFTgKmLNzDh9s+YCk7CQAmoQ24ZGuj9A3pq/F6UQ81wUXN7/99htFRUUlj0Wk+vL1sjN5WFtum76G937eyzXxMbSMDLY6Vq2xL2MfX+z8gq93f13SUhPhF8GdF93J6Naj8bZ5/8EVRKQiLri4+fHHH8/6WESqp0tbRzKwTSTfJ6TyyKwNfHF3L7zs5ZpDIBdgf+Z+FicuZvH+xWw8vLHkeFRgFOPbjWdUi1H4eWnlaJGqUK6O+Ntuu41XXnmF4ODSfwnm5ORw33338f7777slnIhUzD9HtmfV3iNsOJjBuz/t5e7+WsrfHYqdxSRmJbI+bT1rUtawJnUNyTnJJZ+3GTb6NOzDNS2uoV9MP7xsGvMkUpUMVzlG+tjtdpKTk6lfv36p44cPHyYqKori4mK3BXS3zMxMQkNDycjIICQkxOo4IpVu1poDPPL5RnzsNubd34cW6p76Q0WOIjIKM8gsyCQ1N5WUnBSSc5I5mHWQncd3suf4HgqdhaVe42V40TWqKwMbDeTSRpdSL6CeRenF0+Tn57N3716aNGmCn59nt/6d72sty/t3mf6cyMzMLFm0Lysrq9SNHQ4H8+fPP6PgERFrXRsfw7xNySzZns7DszYw665e+HjVzO4pl8tFZmEmKTkpZBRkkFmYaX4UZJJXnEeRs+jUh6OIQmchRc4iip3FFDmKKHYVm49PHPv947ziPDILzWv9EX8vf9pEtCE+Mp6uUV3pVK8TAd4BVfBdEJE/UqbiJiwsDMMwMAyDli1bnvF5wzB4+umn3RZORCrOMAymjLqIwS8vY8PBDP61YBuPX1W9N2R0uVyk5qaScCSBbUe3se3oNhKzEknOSSanKKdKMhgYBPkEUd+/PlFBUTQIbEB0YDTNwprRIrwFDYMaYjNqZpEo4unKVNz8+OOPuFwuLr30Ur744otSG2f6+PjQuHFjoqOj3R5SRCqmQag/L1zXkT//dy3/+Xkv3ZpEMKhdlNWxSskuzOanpJ9Yfmg5y5OXk5KTcs5zI/wiCPUNJcQnxPzwDSHAKwAfuw/eNm/zw+5d8tjL5nXGYy+b1xmP/ex+JdcL8g7Cbqv+GwSKyJnKVNxccsklgLl8cmxsLDab/moRqSkGt4vi9j5NeO/nvTw8awPzGoQQG2FtN4rD6WDpwaXM3TOXZQeXUeAoKPmcl+FF07CmtI5oTeuI1jQNbUp0UDQNAhto1pGInFe5hvA3btwYgNzcXBITEyksLD2wrkOHDhVPJiJu9+gVrVm7/xjrDxznT/9dy6y7elqyenFOUQ6zd87m44SPSxa3A4gLiaN/bH96NOhBl8gu+Hv5V3k2Ean5yvVbLT09nfHjx/Ptt9+e9fMOh6NCoUSkcvh42Xjjpi5c/fovJCRnct+Mdbw7tmuVrX9T5Cziix1fMG3DtJLF7cJ8wxjRfARDmwyldURrDMOokiwiUvmmTJnCl19+ybZt2/D396dXr148//zztGrVqlLvW67faA8++CDHjx9n5cqV+Pv7s2DBAj788ENatGjB119/7e6MIuJGDcP8+c+tXfHztvHj9nSe/mZrlez9siJ5BaO+GsU/V/6To/lHaRTciCd6PMF3137HX7r+hTZ12qiwEfEwS5cuZcKECaxYsYJFixZRVFTEoEGDyMmp3IkB5Wq5+eGHH/jqq6/o2rUrNpuNxo0bc/nllxMSEsKUKVO48sor3Z1TRNyoU2wYU0d35u5P1vLfFfupG+TLAwMrZxPc3KJcXl77MjO3zwQg3DecezrdwzUtr9E2BCLl5XJBUa419/YOgAv8Q2TBggWlnk+fPp369euzdu1a+vXrVxnpgHIWNzk5OSXr2YSHh5Oenk7Lli256KKLWLdunVsDikjluKJ9FI9f2Za/z93Ky9/vwGbAfZe5t8DZfnQ7E5dMJDErEYDRrUbzQJcHCPbRQoIiFVKUC89aNDv5b4fAJ7BcL83IyAAoNdu6MpSruGnVqhXbt28nLi6Ojh078vbbbxMXF8dbb71FgwYN3J1RRCrJ7X2aUORw8ty32/j3oh0YBkwY0Nwt3UOL9i/i/37+P/KK84gMiOSZ3s/QK7qXG1KLSE3kdDp58MEH6d27N+3bt6/Ue5WruHnggQdITjb3UXnyySe54oor+OSTT/Dx8WH69OnuzCcileyuS5rhcLp4YeF2XvxuB+lZBUwe1g67rXwFjsvl4p2N7/D6+tcB6NGgBy9e8iKhvqHujC1Su3kHmC0oVt27HCZMmMDmzZv5+eef3RzoTOUqbm6++eaSx/Hx8ezfv59t27bRqFEj6tat67ZwIlI1Jgxojo/dxj/nJ/Dh8v0kHc/j1TGdCfAp268Il8vFS2tfYvqW6QDc3OZm/tL1L9o4UsTdDKPcXUNWuPfee5k7dy7Lli0jJiam0u/nlvmfAQEBdOnSRYWNSA12Z7+mvHlTF3y9bHyfkMaoN39lV1rWBb/e5XLx/OrnSwqbx7o9xqPdHlVhI1KLuVwu7r33XmbPns0PP/xAkyZNquS+F/xbZ+LEiRd80ZdeeqlcYUTEWkMvakBkiB9//u8atqVkMey1X3j66nZcFx9z3nE4LpeLf63+F58kfALA5J6Tua7ldVUVW0SqqQkTJjBjxgy++uorgoODSUkxt1UJDQ3F37/yFum84OLmt99+u6DztE6FSM0W3zic+Q/0ZeKnG/h512H++vlGvtuSwlPD2xETfva+9v9u/S8fJ3wMwDO9nmFki5FVGVlEqqlp06YB0L9//1LHP/jgA8aNG1dp973g4ubHH3+stBAiUr3UD/bjo9u6MW3pbqZ+v4PvE9L4ZdcRHhjYgnG94vDzPrWh5Pf7v+fFNS8CMDF+ogobESlRFQuEno12vhSRs7LZDCYMaM78+/vSLS6CvCIHz327jf4vLOHjFfspLHay5fAWHvvpMVy4GN1qNOPajbM6tohI+WZLDRgw4LzdTz/88EO5A4lI9dIiMphP/9yDL9Yl8dJ32zmUkc/jczbz2o8bscdOpcBRQL+YfjzW7TF1S4tItVCu4qZTp06lnhcVFbF+/Xo2b97Mrbfe6o5cIlKNGIbBtfExDOvYgP+tTOT1JbvICP4P3o40nIUR5B0czfxNaQxsU7/M08dFRNytXL+FXn755bMef+qpp8jOzq5QIBGpvny97Izr3QRCf+Lfa7eCy4u8pBv5IT+LHxJ+w9fLRrcmEfRpXpeezerQKioYXy/7H19YRMSN3Pon1s0330y3bt148cUX3XlZEalGth/dziu/mX/gTOr+CJ0HX8XcjYeYuzGZ/Udy+WnnYX7aeRgAb7tBq6hgLmoYSrN6QTSKCKBRnQBiwgMI9LGrG0tEKoVbi5vly5fj5+fnzkuKSDVS7Cxm8q+TKXYWMyB2AGNaj8EwDNo0COHhQa3YlZbNTzsP8/Ouw/yWeIxjuUVsTspkc1LmGdfy9bJRJ9CHiCAfIgJ9Cfb1ws/bjr+PDX9vO/7edvx87Ph52fH1tuHrZcfXy4aPlw1fL/P5ycd+p30+2M8bfx+1FonUZuUqbkaNGlXqucvlIjk5mTVr1vDEE0+4JZiIVD8fbvmQrUe2EuwTzBM9nijV8mIYBi0ig2kRGcxtfZrgcrk4eCyPzUkZbDmUyd4jORw4msv+I7lk5BVRUOzkUEY+hzLy3Z7T39tORKAPEYE+1AnyITY8gEYRAcRGBNC8fiBN6gaVe+8sEan+ylXchIaW3gDPZrPRqlUrnnnmGQYNGuSWYCJSvezN2Mub698E4K8X/5V6AfXOe75hGMSeKCiGXNSg1OdyCoo5mlPIkZxCjmQXcCSnkNyCYvKLneQVOsgvcpBX5CCv0Py3sNhJQbHzxL8OCs72vMh87HRBXpGDpON5JB3PO2u2AB87bRuE0L5hKD2aRtCzaV1CA7zd840SEcuVq7j54IMP3J1DRKoxl8vF08ufptBZSO/o3lzd7OoKXS/Q14tAXy9iI8q3u/C5uFwusguKOZZTxNHcQo7mFJCWWcCBY7kkHs0j8UgOO1KzyS10sGb/MdbsP8b0X/dhM6BjbBhD2zfgyg4NiA6rvGXhRaTyVWjMzZo1a0hISACgbdu2xMfHuyWUiFQvC/ctZG3qWvzsfkzuObnaDgQ2DINgP2+C/bxpVOfshZPD6WJPejabD2WwPvE4P+86zO70HH5LPM5vicf55/wEujWJYGzPxgxuF4W3XWuditQ05SpuDh48yJgxY/jll18ICwsD4Pjx4/Tq1YuZM2dWyXbmIlI18orz+PfafwNw20W3ER0UbXGiirHbTo0NGtnZ/F2VnJHH9wlpzN1wiFX7jrJqr/kRGeLL7X2aMLZn6S0nROTCTJs2jWnTprFv3z4A2rVrx+TJkxkyZEil3rdcf5LccccdFBUVkZCQwNGjRzl69CgJCQk4nU7uuOMOd2cUEQtN3zKdlJwUGgQ28NjtFRqE+nNLj8Z8+ueeLH/sMu6/rAV1g3xJzSzg2fnb6PevH/nv8n0UOZxWRxWpUWJiYnjuuedYu3Yta9as4dJLL+Xqq69my5YtlXpfw1WOXa38/f359ddf6dy5c6nja9eupW/fvuTm5rotoLtlZmYSGhpKRkYGISEhVscRqdZSclIYNnsY+Y58Xuj3Alc0ucLqSFWmoNjBV78d4pXFO0sGJrdpEMJzoy6iY2yYteGkVsnPz2fv3r00adKkZLkVl8tFXvHZB8xXNn8v/wp1TUdERPDCCy9w++23n/G5s32tJ5Xl/btc3VKxsbEUFRWdcdzhcBAdXbObrEXklNd+e418Rz5d6ndhcNxgq+NUKV8vO9dfHMvVnaP5dPUBXlq0g4TkTEa8+QvjesXx6BWt1VUllskrzqP7jO6W3HvljSsJ8C77ZACHw8GsWbPIycmhZ8+elZDslHJ1S73wwgvcd999rFmzpuTYmjVreOCBB7Q6sYiH2JOxh7l75gLwcNeHq+0g4srm62VnbM84Fk+8hJGdG+JywQe/7GPkm7+y93CO1fFEqr1NmzYRFBSEr68vd911F7Nnz6Zt27aVes9ydUuFh4eTm5tLcXExXl5m48/Jx4GBgaXOPXr0qHuSuom6pUQuzF+X/pVv931L/9j+vHbpa1bHqTZ+3J7Gw59t4EhOIUG+Xvzr2g4M/d06PiLuVNO7pQoLC0lMTCQjI4PPP/+c//znPyxduvSsBY6l3VJTp04tz8tEpIbYfnQ73+77FoB7O91rcZrqZUCr+sx/oC/3/e83Vu09yj2frOPxK9twR9+mVkeTWsQwjHJ1DVnBx8eH5s2bAxAfH8/q1at55ZVXePvttyvtnuUqbm699VZ35xCRauTkSsSD4wbTKqKVxWmqn8gQP2bc0Z1/zEtg+q/7+Me8BJIz8vm/oW2waVsHkfNyOp0UFBRU6j3KvYifw+Fgzpw5JYv4tWvXjuHDh2O3a4CdSE229chWfjjwAzbDxj0d77E6TrXlZbfx5LC2RIf58ez8bbz3814y8or41zUdVOCInDBp0iSGDBlCo0aNyMrKYsaMGSxZsoSFCxdW6n3LVdzs2rWLoUOHkpSURKtW5l91U6ZMITY2lnnz5tGsWTO3hhSRqvP+5vcBuCLuCpqGqavlfAzD4E/9mhEZ4sfEzzbw+dqD+HrZ+MeI9rV2ALbI6dLS0hg7dizJycmEhobSoUMHFi5cyOWXX16p9y1XcXP//ffTrFkzVqxYQUREBABHjhzh5ptv5v7772fevHluDSkiVeNA5gEW7V8EwO0XnbkGhZzd1Z0aAvDgp+v5ZGUiPl42Jl/VVgWO1HrvvfeeJfctV3GzdOnSUoUNQJ06dXjuuefo3bu328KJSNWavmU6TpeTvg370jK8pdVxapSrOzWkoNjJXz/fyAe/7CMqxI8/X6JWbBErlGudG19fX7Kyss44np2djY+PT4VDiUjVO5x3mDm75gBwW/vbrA1TQ13fNZbJV5nTW59bsI1FW1MtTiRSO5WruLnqqqv405/+xMqVK3G5XLhcLlasWMFdd93F8OHD3Z1RRKrAjIQZFDoL6VCvA/GR8VbHqbHG947jpu6NcLnggZm/kZCcaXUkkVqnXMXNq6++SrNmzejZsyd+fn74+fnRq1cvmjdvziuvvFLm673xxhvExcXh5+dH9+7dWbVq1QW9bubMmRiGwYgRI8p8TxE5Jbcol5nbZwJmq43GipSfYRg8NbwdvZrVIbfQwR0friEj98ztakSk8pSruAkLC+Orr75ix44dzJo1i1mzZrFjxw5mz55NaGhoma716aefMnHiRJ588knWrVtHx44dGTx4MGlpaed93b59+3j44Yfp27dveb4EETnN3D1zySrMolFwIwbEDrA6To3nbbfx5k1daFwngKTjeTz6xUbKsRi8SClOp+fvSu+u/07Kvc7Ne++9x8svv8zOnTsBaNGiBQ8++CB33HFHma7z0ksvceeddzJ+/HgA3nrrLebNm8f777/PY489dtbXOBwObrrpJp5++ml++uknjh8/Xt4vQ6TWc7lc/G/b/wC4ofUN2Ixy/c0jvxMW4MNrYzpzzbRfWbAlhU9WJnJzj8ZWx5IayMfHB5vNxqFDh6hXrx4+Pj4e2brqcrlIT0/HMAy8vb0rdK1yFTeTJ0/mpZde4r777ivZ2XP58uU89NBDJCYm8swzz1zQdQoLC1m7di2TJk0qOWaz2Rg4cCDLly8/5+ueeeYZ6tevz+23385PP/103nsUFBSUWgkxM1P93yKnW52yml3Hd+Hv5c+I5iOsjuNROsSE8egVrfnHvAT+PncrXePCaR2lPe2kbGw2G02aNCE5OZlDhw5ZHadSGYZBTExMhRcELldxM23aNN59913GjBlTcmz48OF06NCB++6774KLm8OHD+NwOIiMjCx1PDIykm3btp31NT///DPvvfce69evv6B7TJkyhaeffvqCzhWpjU622gxvNpxgn2CL03ie23o34eddh1myPZ0HZ67nm/v64G1X65iUjY+PD40aNaK4uBiHw2F1nErj7e3tlp0OylXcFBUV0bVr1zOOx8fHU1xcXOFQ55KVlcUtt9zCu+++S926dS/oNZMmTWLixIklzzMzM4mNja2siCI1SnJ2Mj8c+AGAG1rdYHEaz2SzGbx4XUcuf2kp21KyeHvpbu69tIXVsaQGOtldU9Eum9qgXH8+3HLLLUybNu2M4++88w433XTTBV+nbt262O12UlNLrwWRmppKVFTUGefv3r2bffv2MWzYMLy8vPDy8uKjjz7i66+/xsvLi927d5/xGl9fX0JCQkp9iIjpsx2f4XQ56R7Vnebhza2O47HqBvny5LB2ALy6eBe70rItTiTi2So0oPi7776jR48eAKxcuZLExETGjh1bqqXkpZdeOuc1fHx8iI+PZ/HixSXTuZ1OJ4sXL+bee+894/zWrVuzadOmUscef/xxsrKyeOWVV9QiI1IGRc4ivtz5JWAOJJbKdXWnaOasT2LJ9nQe/WIjs/7cUxtsilSSchU3mzdvpkuXLgAlrSV169albt26bN68ueS8CxnNPXHiRG699Va6du1Kt27dmDp1Kjk5OSWzp8aOHUvDhg2ZMmUKfn5+tG/fvtTrw8LCAM44LiLnt+zgMo7mH6WOXx0uib3E6jgezzAM/jnyIga9tJS1+48xY5VmT4lUlnIVNz/++KPbAowePZr09HQmT55MSkoKnTp1YsGCBSWDjBMTE7HZNPhOxN1Ottpc3fxqvG3qw68KDcP8eXhwK57+Ziv//m47wzpEExqg772IuxmuWrayVGZmJqGhoWRkZGj8jdRaqTmpDPpiEE6Xk29GfENcaJzVkWqNYoeToa/+xI7UbMb3jisZiyMi51eW9281iYjUQl/t/gqny0l8ZLwKmyrmZbcx+SqzoPlo+X52pp65CbGIVIyKG5FaxulylnRJjWoxyuI0tVOfFnUZ1DYSh9PFM3O3amsGETdTcSNSy6xOWU1SdhJB3kFc3vhyq+PUWv93ZRt87DZ+2nmYZTsPWx1HxKOouBGpZb7Y+QUAVza9En8vf4vT1F6N6wRyS09zttSLC7er9UbEjVTciNQiGQUZLN6/GICRLUZanEbu6d+MQB87m5IyWLglxeo4Ih5DxY1ILfLt3m8pdBbSKrwVbSPaWh2n1qsT5MvtfZsC8OJ3O3A41Xoj4g4qbkRqkbl75gLmJpkXssimVL47+jYhLMCbXWnZzP4tyeo4Ih5BxY1ILXEg8wAb0jdgM2wMaTLE6jhyQoifN3df0gyAVxbvoNjhtDiRSM2n4kaklpi712y16dGgB/UC6lmcRk43tmccdQJ9OHA0j683HLI6jkiNp+JGpBZwuVzM3zMfgKuaXmVxGvk9fx87t/VpAsCbS3bj1NgbkQpRcSNSC2w5soV9mfvw9/LnskaXWR1HzuKWno0J9vNiV1o2323VzCmRilBxI1ILnBxIPCB2AAHeARankbMJ8fPm1p5xALz+4y6teyNSASpuRDxckbOIb/d+C6hLqrq7rU8T/L3tbE7KZOmOdKvjiNRYKm5EPNyKQys4mn+UCL8Iekb3tDqOnEdEoA83dm8EwDvL9licRqTmUnEj4uFOdkldEXcFXjYvi9PIHxnfOw67zeDX3UfYeijT6jgiNZKKGxEPllecx48HfgTMvaSk+osJD+CK9lEAvPfzXovTiNRMKm5EPNjPST+TV5xHw6CGXFT3IqvjyAW648S08K83JJGWmW9xGpGaR8WNiAdbtG8RAIMaD9J2CzVI50bhdGkURpHDxX9X7Lc6jkiNo+JGxEPlF+ez5OASAC5vfLm1YaTM7jixoebHK/aTX+SwOI1IzaLiRsRD/XLoF/KK82gQ2ID2ddtbHUfKaFDbSGLC/TmWW8TX67Ulg0hZqLgR8VCL9ptdUpc3vlxdUjWQl93GTd0bA/DxSnVNiZSFihsRD1TgKGDJgSWAuqRqsuu7xuBjt7HxYAYbDhy3Oo5IjaHiRsQDLT+0nJyiHOoH1KdDvQ5Wx5FyqhPky9CLzGnhH2tgscgFU3Ej4oFOdkkNajwIm6H/zGuym3uYXVPfbDxERm6RxWlEagb91hPxMIWOQn5MNBfuU5dUzRffOJzWUcHkFzn5fN1Bq+OI1AgqbkQ8zIrkFWQVZVHPvx6d6neyOo5UkGEYJa03n6zcr93CRS6AihsRD3OyS2pg44HqkvIQIzo3JNDHzp70HJbvPmJ1HJFqT7/5RDxIkbOIHxJ/ANQl5UmCfL0Y2aUhgFYsFrkAKm5EPMiq5FVkFmZSx68OXep3sTqOuNHJrqlFW1M5nF1gcRqR6k3FjYgHOb1Lym6zW5xG3Kl1VAgdY8ModrqYvS7J6jgi1ZqKGxEPUeQsYnHiYkBdUp7q+q4xAHy25oAGFouch4obEQ+xJmUNxwuOE+4bTnxkvNVxpBIM6xiNn7eNnWnZ/KYVi0XOScWNiIc42SV1WePL8LJ5WZxGKkOInzdD2zcAYNaaAxanEam+VNyIeIBiZ7G6pGqJ67rGAvDNhmRyC4stTiNSPam4EfEA61LXcTT/KKG+oVwcdbHVcaQSdW8SQaOIALILipm/KcXqOCLVkoobEQ/w3f7vALis0WV427wtTiOVyWYzSg0sFpEzqbgRqeEcTgff7/8eUJdUbXFNfAw2A1btPcrewzlWxxGpdlTciNRwv6X9xpH8IwT7BNM9qrvVcaQKNAj1p1/LeoAGFoucjYobkRru5CypS2MvxduuLqna4tp4s2vqq/WHcDq15o3I6VTciNRgTpezpEtqUNwgi9NIVRrYJpJgPy+Sjuexcu9Rq+OIVCsqbkRqsA3pG0jLSyPIO4geDXpYHUeqkJ+3nSsvMte8+XLdQYvTiFQvKm5EarDv9pmzpAbEDsDH7mNxGqlqo7qYXVPzNyWTV+iwOI1I9aHiRqSGcrqcJeNtNEuqduraOJzYCH9yCh18t1Vr3oicpOJGpIbadHgTqbmpBHoH0qthL6vjiAVsNoORnc3Wmy+1U7hICRU3IjXUon1mq80lMZfga/e1OI1YZWTnhgD8tDOdtMx8i9OIVA8qbkRqIJfLVdIlNaixZknVZk3qBtKlURhOF3y94ZDVcUSqBRU3IjXQliNbOJRzCH8vf3o37G11HLHYyYHFX6hrSgRQcSNSI53cS6pfTD/8vPwsTiNWu6pDA3zsNhKSM0lIzrQ6jojlVNyI1DAul6tkCri6pAQgLMCHS1vXB2D2b2q9EVFxI1LDJBxNICk7CT+7H30a9rE6jlQTo7qYA4tn/5ZEscNpcRoRa6m4EalhTg4k7hvTlwDvAIvTSHXRv1V9wgO8Sc8q4JfdR6yOI2IpFTciNYi6pORcfLxsDO8YDcBsbccgtZyKG5EaZMexHSRmJeJr96VvTF+r40g1M/LErKkFW1LILii2OI2IdVTciNQgC/ctBKB3dG8CvQMtTiPVTceYUJrWCyS/yMm3m5KtjiNiGRU3IjVEqYX74tQlJWcyDINrTrTeaNaU1GYqbkRqiO3HtrMvcx++dl/6x/a3Oo5UU1d3MsfdLN9zhKTjeRanEbFGtShu3njjDeLi4vDz86N79+6sWrXqnOe+++679O3bl/DwcMLDwxk4cOB5zxfxFCe7pPo27KsuKTmnmPAAejSNwOWCOWq9kVrK8uLm008/ZeLEiTz55JOsW7eOjh07MnjwYNLS0s56/pIlSxgzZgw//vgjy5cvJzY2lkGDBpGUpP+IxXO5XK6S4mZw3GCL00h1N+q0rimXy2VxGpGqZ7gs/snv3r07F198Ma+//joATqeT2NhY7rvvPh577LE/fL3D4SA8PJzXX3+dsWPH/uH5mZmZhIaGkpGRQUhISIXzi1SFrUe2MnruaPzsfiwdvVTr28h5ZeUX0fUf31NQ7OTre3vTISbM6kgiFVaW929LW24KCwtZu3YtAwcOLDlms9kYOHAgy5cvv6Br5ObmUlRURERExFk/X1BQQGZmZqkPkZqmpEtKC/fJBQj282ZwuygAvtRmmlILWVrcHD58GIfDQWRkZKnjkZGRpKSkXNA1Hn30UaKjo0sVSKebMmUKoaGhJR+xsbEVzi1SldQlJeVxcjuGrzccokjbMUgtY/mYm4p47rnnmDlzJrNnz8bP7+w7I0+aNImMjIySjwMHDlRxSpGK2XJkC0nZSfh7+dMvpp/VcaSG6NO8LnWDfDmaU8jS7elWxxGpUpYWN3Xr1sVut5OamlrqeGpqKlFRUed97Ysvvshzzz3Hd999R4cOHc55nq+vLyEhIaU+RGqSk602l8Rcgr+Xv8VppKbwstsYcWJauNa8kdrG0uLGx8eH+Ph4Fi9eXHLM6XSyePFievbsec7X/etf/+Lvf/87CxYsoGvXrlURVcQS6pKSihh5omtqUUIqGblFFqcRqTqWd0tNnDiRd999lw8//JCEhATuvvtucnJyGD9+PABjx45l0qRJJec///zzPPHEE7z//vvExcWRkpJCSkoK2dnZVn0JIpVm0+FNJOck4+/lT5+GfayOIzVM2wYhtI4KprDYyTxtxyC1iJfVAUaPHk16ejqTJ08mJSWFTp06sWDBgpJBxomJidhsp2qwadOmUVhYyLXXXlvqOk8++SRPPfVUVUYXcZ/07ZDwNez9CY7tg7zj4BvEgrBgsEP/8Hb42X2tTik1jGEYjOrSkGfnb2P2bwe5sXsjqyOJVAnL17mpalrnRqqVA6tgyRTY/cMZn3ICg2KjSfXy4pXUdC71j4G+f4EOo8FmeaOr1BCpmfn0nLIYpwuWPtKfxnW0urXUTDVmnRuRWqsgC755EN673CxsDDu0GARXvgTjF8CE1fx27VukenkRiJ3eRTY4shPm3AX/uRRSNlv9FUgNERniR+/mdQENLJbaQ8WNSFVLS4C3+sDaD8znnW6C+9bCTbPg4tuhcU+o15K5WTsAuLz5MHz/sg0GPgU+wXDoN3j3Ulj5DtSuhlcpp2u0HYPUMipuRKrSzu/hP5eb42pCG8Gt38CINyGiSanTCh2FJbOkrmp6FfiFQJ+H4P510PIKcBTAt4/A1/eBQ7Ng5PwGtYskwMfO/iO5rEs8ZnUckUpn+YBikVoj4RuYNR6cRdC4D4z+LwScfduQn5J+Iqswi/oB9ekaedpyB0H1YcxMWPEmfPc4/PZfyEyC0R+Dj8ZSyNkF+HgxpH0Dvlh3kC/WJREf5Q0H10DGQcg7Br5BEBQFMRdDUD2r44pUmIobkaqQMBdmjQNnMbS/Bka8BV4+5zx93p55AFzZ5ErsNnvpTxoG9JwAEc3g8/HmmJ0Zo+HGT1XgyDld27EujvUzGblhCa6N2zFcjrOfGNkeuoyFjjeAX2jVhhRxE82WEqls+3+Fj0aYXUkXXQ8jpoH93H9XZBZm0v/T/hQ5i/h82Oe0imh17msfWAX/HQWFWRDXF27+Arw0ZVxO43TAhv/h+vFZjMzTBhSHNoJ6rcA/DApzzK7StK2nPu8XCv3/Zo4Ds3tXdWqRM5Tl/VstNyKVKX0H/G+MWdi0uhJGvgW/b4n5nUX7FlHkLKJFeIvzFzYAsd3gltnw35Gw7yeYfRdc856miovp8E7zZyJpDQaQ5VOPd3IuIb3pSJ67/aozz885Alu+hFXvwOEdsOBRWP8xXPsB1G1R5fFFyku/AUUqS34mzLwR8o9DTDe45j9/WNgAzN0zFzgxkPhCxF4MN3wMNm/zjWnRExUILR5j/QxzVl7SGvANgUH/IPXW5bzmGMXnu20czi448zWBdaDbnXDPCrhqKvhHQMomePsS2PR5lX8JIuWl4kakMrhc8NU95to0IQ3hhhngE/CHL0vOTmZN6hoMDIY2GXrh92va35x1BbD8dVj5dvlyS83ndMDC/4M5d0NxPjQdAPcsh1730bxhPTrGhlHsdDF73XnWvLHZoet4uPtXaNIPinLgi9vh55e1/IDUCCpuRCrDL1PN2VF2H7j+owuegTJvrzmQ+OKoi4kKjCrbPTtcDwOfNh8vmAR7l5Xt9VLzFebA/24wC1yAfo/AzV9CaEzJKTdcHAvA/1Yn/vGaNyEN4JY50GOC+fz7p8yWQRU4Us2puBFxt/2/wuJnzMdD/gUxF7Zzvcvl4uvdXwNwZdMry3fv3g9AhxvA5TBnZx1PLN91pOYpyIJProOd34GXH1z7Plz6+Bnjr4Z1jCbAx86e9BzW7L+ANW9sdrjiWRg8xXz+62vw/ZMqcKRaU3Ej4k55x+HLP4HLCR1vhPhxF/zSDekb2JuxF38vfwY1HlS++xsGDJsKDTpB7hGYeRMU5pbvWlJz5GeYs+b2/2KOrxn7tbnkwFkE+XoxrEM0ADNXHbjwe/S8B4a+aD7+5RWzdVKkmlJxI+JO8x+GjAMQ3gSG/sssNi7Qlzu/BGBQ40EE+QSVP4O3v7moX0BdSNkICx4r/7Wk+ivINgubg6vALwzGfgWNup/3JaO7mV1T8zYdIjO/DCtcd7sTBj9rPv7+Kdj8Zfkyi1QyFTci7rLxM9g0y9wEc9S74Bt8wS/NKcphwb4FAIxqMariWcJi4dr3AAPWfaiZLp6quAA+vdmcEeUfbm7n0bDLH76sc2wYLSODyC9y8vX6Q2W7Z88J0P1u8/HsuyBxZTmCi1QuFTci7pCZDPMeNh9f8qg5PbsMFuxdQF5xHnEhcXSu39k9mZr2NweUAnzzABzZ7Z7rSvXgdJhdoHt+BO9AuOkLaNDhgl5qGAajL24EwMzV5RiXNfif5rpNjgKYOcbcxkGkGlFxI1JRLhfMfQgKMiC6C/T9S5kv8eUus3l/ZIuRGGXoyvpDlzwKjXtDYbY5wLj4LGubSM20YBJsnWPOyLvhE4iJL9PLR3ZuiI/dxuakTDYnZZTt3jY7XPMuNOhoju367Fb9bEm1ouJGpKI2fwE7vjUX0bv6jfNurXA2u4/vZmP6RuyGneHNhrs3m93LXDzQP8Icf/OdFvjzCKv/A6tOrGU06h1oNqDMl4gI9GFwe3O5gXK13vgEmssc+IWZ3WIL/6/s1xCpJCpuRCoi5zB8+1fzcb9HILJtmS/x+Q5zPEy/mH7U9a/rznSmkGgYeeKNcNXbsPVr999Dqs7uH2D+iZ+5y56EdiPLfakxJ9a8mb0uiayyDCw+KTzOHF8GsPpd2Dir3FlE3EnFjUhFzH/EbJaPbA99Hirzy3OLcpmzaw4Ao1uNdnO407QcBL3uNx9/fa/Wv6mp0nfAZ+PMdYw6jinXz9zpejarQ/P6QeQUOvjyfCsWn0/LQdDvRLE190E4uqdCmUTcQcWNSHklzDX3cjLscPXr4OVT5kvM3TOX7KJsGgU3omd0z0oIeZrLJkPDruaaKJ/fDo5y/KUu1sk9Cv8bbY7tiu0Bw14p01IDZ2MYBmN7Ngbgo+X7/njF4nPp/xg06mWO7friTv1sieVU3IiUR94xmDfRfNz7fogu+wwnl8vFzO0zAbPVxmZU8n+Odm9zerhvqLkmyo/PVu79xH2KC+GzsWarSFgjcx0jL1+3XHpk54YE+tjZnZ7Dr7uPlO8iNrs59sc31Bx/s/RfbskmUl4qbkTKY+HjkJ0KdVrAJeVbJG9t6lp2HtuJn92Pq5tf7eaA5xAeB8NfMR///LI5fkOqN5fLXBxy30/gEwRjPr3gvcouRLCfN9fEm3tPffjrvvJfKCwWhr1sPv7pRXMbEhGLqLgRKatdi2H9x4Bhdkd5+5XrMidbba5seiWhvqFuDPgH2o2E+PGAC778M2SnVd29pexWvGkuxGjYzP2iyjFo/Y/c0sPsmvo+IZWk43nlv1D7a8xtR1xOcw2evOPuCShSRipuRMqiIBu+edB83O1P0KhHuS6TmpPK4v2LARjTeoybwpXBFVOgflvISYPZfwans+ozyB/bsRC+e9x8POgf0HJwpdymRWQwvZrVwemCjyrSegPmtiPhceY2JPMfcUc8kTJTcSNSFj/8HTISIbSROUC3nD5J+IRiVzFd6nehVUQrNwa8QN7+ZiuAl7/ZNfXrK1WfQc4vdas58NvlhC5jocc9lXq72/s0AWDGysTyTQs/yTcYRv3HbGna9Jm5DpRIFVNxI3KhElfCyhPrxQybCr7l29wyqzCLz3Z8BsBt7W9zU7hyqN8GhjxvPl78dziwyrosUlp2ujkzqjAL4vrC0H9XeGbUHxnQqj7N6gWSVVDMp6vLsFv42cRefGrrj7kPQUY5p5mLlJOKG5ELUZRvrg+DCzrdBM0vK/elZu2YRU5RDs3DmtM3pq/7MpZHl7HQbpS5bsrnt2uMRHVQlA+f3mSuRRTR1FwFuBzLDJSVzWbwp35NAXj/570UOSrYVdnvEXM7kvwMmHOXuj6lSqm4EbkQP70Ih3dAUKS5aWA5FToK+XjrxwCMazeu8qd//xHDMFuhwhqb3W1f32fOzhFrOJ3w1T1wYCX4hZozowIiquz2V3dqSN0gXw5l5DN/U3LFLmb3Nlcv9g6Avctg5TT3hBS5ACpuRP5IyiZz2jTA0BfBP7zcl5q3Zx7peenUD6jP0CZD3RSwgvxC4doPwOYFCV/DmvetTlR7LXnWHKNi84Lr/wv1Wlbp7f287YzrZc6cenvpnvIv6ndS3ean/hj4/mlI3VLBhCIXRsWNyPk4iuGre8FZDG2GQ9vyb2xZ7Czm/c1m4TC27Vi87d7uSllxMfEw8Cnz8YJJkLTW0ji10voZsOwF8/GwV6DpJZbEuLlHY/y97WxNzmTJjvSKXzB+PLS8AhwF5urF2j1cqoCKG5HzWf46JK83WzeGvlihS83bM499mfsI8w3j2pbXuiefO/WYAK2Gmm9CM2/W+jdVae9P8PWJvb/6TITON1sWJSzAp2RLhqnf76x4641hwPDXIKAupG0xZxyKVDIVNyLnkroFfjzRpD74WQiOLPelihxFTNtgjjm4rf1tBHoHuiOhe9ls5u7hdVpA1iGYNU57BFWFtAT49GZwFkHbEXDpE1Yn4s5+TfH3trPhwHH3tN4E1TcLHIBfXzfH4IhUIhU3ImdTXGCu3usohJZDzBlSFTB712ySspOo41eHG1rf4KaQlcAvBG6YAT7BsP8XWPg3qxN5tmP74b8jIf84xFwMI98yi0yL1Q3y5RZ3tt4AtB4KXW4FXDD7bs3Mk0pl/X9FItXRkimQugkC6sDwVyu0xkiBo4C3N5rr49zZ4U78vfzdlbJy1GtpboIIsOodWPuhtXk8VXYa/HcEZCVDvTZw42fm4orVxJ/6NcXP2+a+1hswW0AjmkLmQXO/LJFKouJG5Pf2L4efp5qPh71iNqlXwMxtM0nLTSMqMIrrWl5X8XxVofVQ6H+i1WbuQ7Dze2vzeJr8DPj4mlO7fN/yZZVO+b4QdYN8GdszDoCXvtuB0+mG1hvfIHN6uGGHTbNg0+cVv6bIWai4ETldQZa519LJxfraDKvQ5Y7kHeHtDWarzV0d7sLHXvmLsbnNJX+FjmPMBf4+GwuHfrM6kWfIz4RProeUjRBYD26ZAyHRVqc6qz/1a0qQrxebkjL4aoObVhmO6Xra6sUTIeOge64rchoVNyInuVxmK8Xx/ebeUVc8V+FLvvbba2QVZdEmog0jmo+oeMaqZBgw7FVo2h+Kcsw35GP7rE5Vs+VnwMej4MAKcwbezV9AnWZWpzqnukG+3N3fzPfCgu3kFzncc+F+D0PDrlCQAbO1erG4n4obkZPWfWg2lRt2uOZdc3BtBWw5vIUvd34JwKTuk7Db7O5IWbW8fMzF5CIvMncQ//gac98jKbu8Y/DRCDi4GvzCYOzX0KCj1an+0O19mtAwzJ9DGfm89/Ne91zU7m2O6/IOgH0/nVrfR8RNVNyIgLkK8fy/mo8vmwyNelTock6Xk2dXPYsLF1c1vYrO9Tu7IaRF/ELgplkQGgtHdsFHwyHniNWpapacI/DR1XBoHfhHwK3fQHQnq1NdED9vO3+9wty5/s0fd5GWle+eC9dpBlf+23y8ZArsXOSe64qg4kbEnJL62a3m4nUtBkOv+yt8yS92fsHG9I34e/nzUPxDFc9otZAGMPYrCIqCtK3mG3XuUatT1QzH9sH7gyB5g7mQ3bi50KCD1anKZFiHaDrGhJJT6OC5+dvcd+FON0LX2wAXfHE7HHVTy5DUeipupHZzFMPnt8HR3RAS45Z1RpKzk/n3GvMv0gmdJlA/oGKzraqNOs3MN+bA+uY0+Q+HQ1aq1amqt0Pr4T+Xmy1eobEwfj5EtrM6VZnZbAZPDW+HYcCXvyWxzF1Tw8Ec2xZzsTke6dNboDDXfdeWWkvFjdRui56A3YvNvv8xMyo8HdflcvH0iqfJKcqhY72O3NzGumX0K0XdFqULnPcHmdOZ5UxbZsMHQ82xSpEXwe2LoF4rq1OVW+dG4dx6Ymr432ZvIrew2D0X9vKF6z8yZ46lboJvHtDO9FJhKm6k9lr7Iax403w88i23DO6csW0GvyT9go/Nh2d6P1MzBxH/kXqt4PaFEB5ndrm8N1jTxE/ndMD3T5nbVxTlQLNLzRabkAZWJ6uwhwe3omGYPweP5fHSdzvcd+GQaLhu+on1bz6DJRWfqSi1m4obqZ22zTOnfYO5WF3bqyt8yYQjCSXdUX/p+heahjat8DWrrYimcNt3EHViFtX7Q7QgG0Bmsrmdws8vm8973Q83zqrwzLvqIsjXi3+MbA/A+7/sZc0+N467iusDV534vi19Dn772H3XllpHxY3UPnt/glnjzcXpOt1kLlZXQRkFGTy89GGKnEUMiB3AmNZj3BC0mguOhHHzoMUgKM4zB4R+94Q5jqk22jYPpvWCvUvNbs5r3oNBfwe7l9XJ3GpAq/qM6twQpwsemLmejDw3bq4afyv0PbEtwzcPaGVsKTcVN1K7HFwL/xtjzoxqdaW5SF0F9o0CKHYW88jSR0jMSiQ6MJpnej2DUcFr1hh+oTBmJvSZaD7/9VX4YEjtmvWSc8TcCHLmjZB3FKI6wJ+WwkXXWp2s0jx9dTsaRQSQdDyPv325yT0ba5506eNw0fXgLIZPb4I9S913bak1VNxI7bHvF3MKc2EWxPWFa9+v8F/VLpeL51c9z/Lk5fh7+fPqpa8S5hfmnrw1hc0OA580x0z4hsDBVfBWX/jtE88eGOpywYaZ8MbFsGEGYEDPe+GO783NRz1YsJ83r47pjJfNYN6mZN79yY2Dyg0Drn4DWg6B4nz43w3mf7siZaDiRmqHXd+bq+ueLGzG/A+8/Sp82bc2vsXM7TMxMPhnn3/SKqLmzoapsHYj4a6foVFP8/v81T3w4TBI3251Mvfb/yv85zJzH7LcI1C/rTkbavA/zdk/tUCn2DAmD2sLwHPfbmPJ9jT3XdzLB67/EJpfDkW58Ml1sGeJ+64vHk/FjXi+dR/BjBvMcSEtBpur7foGV/iyH275kDfXm7OtHuv2GJc3vrzC16zxwhub43AGPgVe/ubS+tN6w4K/Qc5hq9NV3ME1MGO02fWWtBZ8gswVrf+8DGIvtjpdlbulR2NuuDgWpwsmfLKOjQePu+/iXr4w+mNoOsCcdfbxtbBxlvuuLx7NcLm1s7T6y8zMJDQ0lIyMDEJCPGMGg5yDoxgW/g1Wmbty0/4aGPGW+VdhBbhcLt7a+FZJYXNPp3u4u+PdFU3reY7tg28fhR0LzOc+QdDjHuhxd4XXE6pSTgfs/gGWv36q9cCwQfw46D8JgjxkkcZyKix2Mu6DVfy6+wgRgT589ueeNK8f5L4bFBeYLWRbZpvPBz4NvR+o8Fg5qXnK8v6t4kY80/ED5m7D+382nw/4P+j3SIV/IRY5ivjnyn/yxc4vALiv8338qcOfKprWc7lcsGsxLH4aUjaax7z8oeNo6H4X1G9jbb7zOZ5obqS6drr5GMDmBR1GQ5+HzAUNBYDsgmLGvLOCTUkZ1A3y5ZM7utMqquKtoyWcTvju/06tS9V2BAx/zWOm2MuFUXFzHipuPNzJQZ7f/hUKMsE7EEa9DW2GVfjSablp/HXZX1mbuhYDg0e7PcpNbW5yQ+hawOmEhK/hp3+fKnIAojubxUK7kRAcZV2+k47th21zYfOXkLTm1HG/UHPZgB53Q1gj6/JVY0eyC7j5vVUkJGcSFuDNe7deTHzjcPfeZOU7ZmusswgimpmD2GvYPl1SfipuzkPFjQdL327+4tt1Ym2MmG7mysN1mlXosi6Xix8Sf+Cp5U9xvOA4gd6B/Kvfv+gX088NoWsZl8scjLviTbO7ynnamjgNOppjouJ6Q8N4t4yL+sMsGQfg4GpzuvHepWZXWgnDXFiu4w3QbhT4BFRuHg+QkVvErR+sYv2B4/jYbUwZdRHXxMe49yYHVpurP2ceNFvSej8A/f7qlgkCUr2puDkPFTceKCsVfn4JVr1rLsxn84b+j0HvBys81ftA5gGeW/0cyw4uA6BNRBue7/c8TUKbuCF4LZdzGDZ/ARs/K91KAoBhzkBq2BnqtoQ6zc2PkGhz7E5ZuhcLcyEr2Sxcju011+BJ3Wzu0p137He3tUOjHma3R9urzYUKpUxyCoqZ+Nl6Fm4xN1W9Lj6GJ4e3I8jXjYsZ5hyBuQ+arYFg/mwM+ge0vEJjcTyYipvzUHHjQY7shl9fg/UzzEX5AFoNNX/JVbC1JiUnhQ+3fMhn2z+j0FmIl82Lce3GcXfHu/GxV2xAspxFdhrsXGRuYnpgNWQknvtcLz9zk0X/cPOxly/Yfcw3teICcBSa66PkHoPcw+ZU4nOxeZvjfuL6QNP+0LhX5bcY1QJOp4upi3fy2g87cbkgJtyfp4a1Y2BbNxeLCd/AvIchO8V8HtPNXASwST8VOR5Ixc15qLip4fIzzV9o6z+B/act7BXTDQZMMjcpLCeny8m61HXM3jWb+XvmU+wyu0x6NujJpO6T1FpTlbJS4MAqSN0CR3ad+Nhtrp9THl5+5kaf4U0goonZGhTdyWwdqiXr0lhh1d6jPPTpepKO5wHQt0VdHhzY0r1jcfIzzL28VrxlLvcAENkeut0JF10HPoHuu5dYqsYVN2+88QYvvPACKSkpdOzYkddee41u3bqd8/xZs2bxxBNPsG/fPlq0aMHzzz/P0KFDL+heKm5qmOJCSN0E+5fDzoXmeI2ScRoGtLjc7H5q3Ktcf6kVOApYl7qO5YeWs3DfQg7lHCr5XLeobtzW/jZ6RfeqPdspVHeFOWZ3Vs5hc6uD4vwTrTVFgMtswfHyBbuv2bITWAcC6pgrJ+t/Q0vkFBTz2g+7eO/nPRQ5zLebLo3CuOHiRlzZoQGB7uquykqBZS+aG26eLHK8A8zfEW2vNv/w8XfzAGepUjWquPn0008ZO3Ysb731Ft27d2fq1KnMmjWL7du3U7/+metH/Prrr/Tr148pU6Zw1VVXMWPGDJ5//nnWrVtH+/bt//B+Km6qKUexOS6i5K/0XXBoPSSvN9/ATle3pTnDpuMYCG14QZd3uVwcLzjOgawD7M/cz9YjW9l6ZCtbjmyh4GSXFhDkHcTljS/n2pbX0qGeZmGIuEvikVze+HEXX6w7SLHTfNvx97bTq1kd+raoS6/mdWlaNxAvewXXls07Zm79sfo/5hirEoa5i33j3ua/ke2gXivw9q/Y/aTK1Kjipnv37lx88cW8/vrrADidTmJjY7nvvvt47LHHzjh/9OjR5OTkMHfu3JJjPXr0oFOnTrz11lt/eL/KKm4KC7I4fGQ7uFy4cAGukn11zOeYnyv5dp/6fKnXQKlzXC7naY9PvO4s5+E87bwT1zz1+ORnnGfNVPLcdTK388THifufyGBmMT/vOuMcF5Rc33Xqc06HWZwU5eEqzjf/oirKx1WUZzYn5x3DlXfUfHwqVWm+wVCvDa6GXSC2O4Q2pNhZTIGjgILiAvId+RQ4CsgvNv/NK87jWP4xjhUc41j+MY7mHyUlJ4Xsouyz/m9X378+PaJ70C+mH5fEXIKfl2ZdiFSWtMx8vliXxGdrDrD3cE6pz/l62WgdFUzjOoE0CPUjKtSPyBA/AnzsBPh44e9tx9/Hjt1m4HKd+K3pMn83Ol3gdLlwOM2PYocT38ObCNs7j4gDiwjIPHP/Kxc2cv2jyPWLIscvkhzfSPK8gimwBZJvDyTfFkSB4UsRdoqx4cSO07DjMrxxGTYMXBg4zX9dpz3Ghe3E70vbyXNcrpLPAScemwzj5G8+o+Tz4DrV2Hi2X4wGmJsMnLiScfpVDVyGgWFQ8vzki1wnLurCMO9mGOe8BoZ57FzXOHl/jN99VYZBeEgwg3t2Pt+PQpmV5f3bjcPXy66wsJC1a9cyadKkkmM2m42BAweyfPnys75m+fLlTJw4sdSxwYMHM2fOnLOeX1BQQEHBqb/MMzMzKx78LBJ2zuXmtc9WyrU9mj/g7wf8QUHhTIQDiXBgToVuVz+gPo2CG9EqohVt67SlfZ32NAltom4nkSpSP8SPu/s3465LmrI1OZNlOw6zbEc6Gw4eJ7fQwYaDGWw4mOHGO14CXEI9jtHTlkBn205aGwdobUsk3MgmMO8QgXmHqOfGOwps82oDPVdYdn9Li5vDhw/jcDiIjCw9gj4yMpJt27ad9TUpKSlnPT8lJeWs50+ZMoWnn37aPYHPw2Z44ecsXV6f6+3SOM855/tcWV9jnPH/y5rHOFnIn/dKxunHjNLPzdeb1b1hGKc9toHNZk69tdnB8DKf/+56pxcdpx+3GTb8vPzwtfviZ/fD18u35LGflx9hvmFE+EUQ7hdOuF84kQGRNAxqqFYZkWrCMAzaRYfSLjqUu/s3w+l0sf9oLgnJmSQdyyM5I5+UzDzSswrILXSQV+ggr8hBbqEDp9PFyYYGm838zWAYBjYD7DYDL5vtxL8GthP/2m0h7LfFccA2lPk2A7sBdTlOpDOVus7D1HGkE+E8SoAzmwBXDv5O88PHVYjd5cCGA7urGJvLgc3lwMDJqbYYA5dhK2mncRnm7zInNlyGUXIcw8B5ji0dXaf9fnOd5fesq9Tv1VPtJLhOtgadPMN12nHz+anXnDh2tte4SrcqnbrOqfuVHDtxjuE6y31xYbggMNCNW3CUg6XFTVWYNGlSqZaezMxMYmNj3X6fi9pdx+p217n9uiIitYHNZtCkbiBN6mp2kydw/7ts2Vha3NStWxe73U5qamqp46mpqURFnX0p9qioqDKd7+vri6+vpnqKiIjUFhUcll4xPj4+xMfHs3jx4pJjTqeTxYsX07Nnz7O+pmfPnqXOB1i0aNE5zxcREZHaxfJuqYkTJ3LrrbfStWtXunXrxtSpU8nJyWH8+PEAjB07loYNGzJlyhQAHnjgAS655BL+/e9/c+WVVzJz5kzWrFnDO++8Y+WXISIiItWE5cXN6NGjSU9PZ/LkyaSkpNCpUycWLFhQMmg4MTERm+1UA1OvXr2YMWMGjz/+OH/7299o0aIFc+bMuaA1bkRERMTzWb7OTVXTIn4iIiI1T1nevy0dcyMiIiLibipuRERExKOouBERERGPouJGREREPIqKGxEREfEoKm5ERETEo6i4EREREY+i4kZEREQ8ioobERER8SiWb79Q1U4uyJyZmWlxEhEREblQJ9+3L2RjhVpX3GRlZQEQGxtrcRIREREpq6ysLEJDQ897Tq3bW8rpdHLo0CGCg4MxDMOt187MzCQ2NpYDBw5o36pKpO9z1dD3uWro+1x19L2uGpX1fXa5XGRlZREdHV1qQ+2zqXUtNzabjZiYmEq9R0hIiP7DqQL6PlcNfZ+rhr7PVUff66pRGd/nP2qxOUkDikVERMSjqLgRERERj6Lixo18fX158skn8fX1tTqKR9P3uWro+1w19H2uOvpeV43q8H2udQOKRURExLOp5UZEREQ8ioobERER8SgqbkRERMSjqLgRERERj6Lixk3eeOMN4uLi8PPzo3v37qxatcrqSB5n2bJlDBs2jOjoaAzDYM6cOVZH8khTpkzh4osvJjg4mPr16zNixAi2b99udSyPM23aNDp06FCy0FnPnj359ttvrY7l8Z577jkMw+DBBx+0OopHeeqppzAMo9RH69atLcuj4sYNPv30UyZOnMiTTz7JunXr6NixI4MHDyYtLc3qaB4lJyeHjh078sYbb1gdxaMtXbqUCRMmsGLFChYtWkRRURGDBg0iJyfH6mgeJSYmhueee461a9eyZs0aLr30Uq6++mq2bNlidTSPtXr1at5++206dOhgdRSP1K5dO5KTk0s+fv75Z8uyaCq4G3Tv3p2LL76Y119/HTD3r4qNjeW+++7jscceszidZzIMg9mzZzNixAiro3i89PR06tevz9KlS+nXr5/VcTxaREQEL7zwArfffrvVUTxOdnY2Xbp04c033+Qf//gHnTp1YurUqVbH8hhPPfUUc+bMYf369VZHAdRyU2GFhYWsXbuWgQMHlhyz2WwMHDiQ5cuXW5hMxD0yMjIA841XKofD4WDmzJnk5OTQs2dPq+N4pAkTJnDllVeW+l0t7rVz506io6Np2rQpN910E4mJiZZlqXUbZ7rb4cOHcTgcREZGljoeGRnJtm3bLEol4h5Op5MHH3yQ3r170759e6vjeJxNmzbRs2dP8vPzCQoKYvbs2bRt29bqWB5n5syZrFu3jtWrV1sdxWN1796d6dOn06pVK5KTk3n66afp27cvmzdvJjg4uMrzqLgRkXOaMGECmzdvtrTv3JO1atWK9evXk5GRweeff86tt97K0qVLVeC40YEDB3jggQdYtGgRfn5+VsfxWEOGDCl53KFDB7p3707jxo357LPPLOlmVXFTQXXr1sVut5OamlrqeGpqKlFRURalEqm4e++9l7lz57Js2TJiYmKsjuORfHx8aN68OQDx8fGsXr2aV155hbffftviZJ5j7dq1pKWl0aVLl5JjDoeDZcuW8frrr1NQUIDdbrcwoWcKCwujZcuW7Nq1y5L7a8xNBfn4+BAfH8/ixYtLjjmdThYvXqy+c6mRXC4X9957L7Nnz+aHH36gSZMmVkeqNZxOJwUFBVbH8CiXXXYZmzZtYv369SUfXbt25aabbmL9+vUqbCpJdnY2u3fvpkGDBpbcXy03bjBx4kRuvfVWunbtSrdu3Zg6dSo5OTmMHz/e6mgeJTs7u9RfAXv37mX9+vVERETQqFEjC5N5lgkTJjBjxgy++uorgoODSUlJASA0NBR/f3+L03mOSZMmMWTIEBo1akRWVhYzZsxgyZIlLFy40OpoHiU4OPiM8WKBgYHUqVNH48jc6OGHH2bYsGE0btyYQ4cO8eSTT2K32xkzZowleVTcuMHo0aNJT09n8uTJpKSk0KlTJxYsWHDGIGOpmDVr1jBgwICS5xMnTgTg1ltvZfr06Ral8jzTpk0DoH///qWOf/DBB4wbN67qA3motLQ0xo4dS3JyMqGhoXTo0IGFCxdy+eWXWx1NpMwOHjzImDFjOHLkCPXq1aNPnz6sWLGCevXqWZJH69yIiIiIR9GYGxEREfEoKm5ERETEo6i4EREREY+i4kZEREQ8ioobERER8SgqbkRERMSjqLgRERERj6LiRkRERDyKihsRqRGWLFmCYRgcP37c6igiUs1phWIRqZb69+9Pp06dmDp1KgCFhYUcPXqUyMhIDMOwNpyIVGvaW0pEagQfHx+ioqKsjiEiNYC6pUSk2hk3bhxLly7llVdewTAMDMNg+vTppbqlpk+fTlhYGHPnzqVVq1YEBARw7bXXkpuby4cffkhcXBzh4eHcf//9OByOkmsXFBTw8MMP07BhQwIDA+nevTtLliyx5gsVkUqhlhsRqXZeeeUVduzYQfv27XnmmWcA2LJlyxnn5ebm8uqrrzJz5kyysrIYNWoUI0eOJCwsjPnz57Nnzx6uueYaevfuzejRowG499572bp1KzNnziQ6OprZs2dzxRVXsGnTJlq0aFGlX6eIVA4VNyJS7YSGhuLj40NAQEBJV9S2bdvOOK+oqIhp06bRrFkzAK699lr++9//kpqaSlBQEG3btmXAgAH8+OOPjB49msTERD744AMSExOJjo4G4OGHH2bBggV88MEHPPvss1X3RYpIpVFxIyI1VkBAQElhAxAZGUlcXBxBQUGljqWlpQGwadMmHA4HLVu2LHWdgoIC6tSpUzWhRaTSqbgRkRrL29u71HPDMM56zOl0ApCdnY3dbmft2rXY7fZS551eEIlIzabiRkSqJR8fn1IDgd2hc+fOOBwO0tLS6Nu3r1uvLSLVh2ZLiUi1FBcXx8qVK9m3bx+HDx8uaX2piJYtW3LTTTcxduxYvvzyS/bu3cuqVauYMmUK8+bNc0NqEakOVNyISLX08MMPY7fbadu2LfXq1SMxMdEt1/3ggw8YO3Ysf/nLX2jVqhUjRoxg9erVNGrUyC3XFxHraYViERER8ShquRERERGPouJGREREPIqKGxEREfEoKm5ERETEo6i4EREREY+i4kZEREQ8ioobERER8SgqbkRERMSjqLgRERERj6LiRkRERDyKihsRERHxKP8P8A4CiiK+hIAAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -518,10 +517,10 @@
      "start_time": "2020-03-22T05:01:30.122273Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.300117Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.299935Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.303410Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.303009Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.435742Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.435519Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.439042Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.438751Z"
     }
    },
    "outputs": [],
@@ -553,10 +552,10 @@
      "start_time": "2020-03-22T05:01:30.131210Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.305356Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.305181Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.307897Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.307440Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.444350Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.444208Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.451939Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.446690Z"
     },
     "lines_to_next_cell": 2
    },
@@ -590,10 +589,10 @@
      "start_time": "2020-03-22T05:01:30.144877Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.309838Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.309663Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.312620Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.312127Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.462713Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.462451Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.469328Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.468323Z"
     }
    },
    "outputs": [],
@@ -612,10 +611,10 @@
      "start_time": "2020-03-22T05:01:30.156668Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.314469Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.314295Z",
-     "iopub.status.idle": "2024-06-03T10:35:52.298480Z",
-     "shell.execute_reply": "2024-06-03T10:35:52.297878Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.473923Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.473631Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.602503Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.602171Z"
     },
     "lines_to_next_cell": 2
    },
@@ -637,10 +636,10 @@
      "start_time": "2020-03-22T05:01:40.684478Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:52.300925Z",
-     "iopub.status.busy": "2024-06-03T10:35:52.300744Z",
-     "iopub.status.idle": "2024-06-03T10:35:52.305295Z",
-     "shell.execute_reply": "2024-06-03T10:35:52.304889Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.604288Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.604178Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.606958Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.606628Z"
     }
    },
    "outputs": [],
@@ -675,44 +674,16 @@
      "start_time": "2020-03-22T05:01:40.696343Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:52.307166Z",
-     "iopub.status.busy": "2024-06-03T10:35:52.306993Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.004439Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.003888Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.608495Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.608382Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.795133Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.794824Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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y/r3sCLILK9DGoyl+mt4Ho7r5Sl0a3QODElmE7i2csWPGg3i0sxc0OhHv7ziPiSvjkFtcIXVpREQoKK3ElNXHseDX89DqRIwI8cFPUX3QztNB6tLoHzAokcVwslMiemw3vD+yM2wUMuy/mIMhnx7AH5dypS6NiKzYqfRbGPr5AexJugGVQob3R3bGJ2NC0MSGU23mgEGJLIogCBgb5o+fp/dFO8+myCmqwPhvj+LDXedRqdVJXR4RWRFRFLH68BU8seQwruWXwd/FHlum9cbYMH8IgiB1eVRLDEpkkQKrFmt7KtQfoghE703GmKWHkVVQLnVpRGQFisorMX39Sbzx0zmotTpEdPTEthf7olNzJ6lLozpiUCKLZaeSY8Gozoge2w0OtgqcSLuFYV/8gRNp+VKXRkQWLCmzEI99cRDbT2dCIRPw+r+CsOTp7nC0VUpdGhmBQYks3tAu3tj+woMI9HRATlEF/r30CDYdT5e6LCKyQN/HpWNE9EGk5pbA28kWG5/vhWf7tuRUmxljUCKr4O9qj83/6Y3BQZ5Qa3WY/cNpvLMtERr2LRFRPajQaDHnh1OYs/k0KjQ69At0x/YXH0T3Fs5Sl0b3iUGJrEZTGwWWPN0dLz7cFgDwzR+pmLgyDgWlXM2biIyXX6LG+K+P4fvj1yATgNkRgfg2sidcmqikLo3qAYMSWRWZTMCsQe3w1bhusFPKceBSLoZH/8GNdYnIKMk5xRj55UEcu5IHBxsFVkwMRVT/NpDJONVmKRiUyCoN6eyNzdN6o3kzO1y5WYoR0YcQk5QtdVlEZEYOJedi1JeHcOVmKXyd7bD5P70R3s5d6rKonjEokdUK8nHEz9P7ILSlC4orNJi8+ji+3HcZositT4jo3r6PS8eEb46hoKwSXf2bYStX2bZYDEpk1Vyb2uC7Z8MwLky/3tIHOy9gxoYElKm1UpdGRCZIpxOx8NfzmLP5NDQ6EcOCfbB+ygNwa2ojdWnUQBiUyOqpFDK8N7Iz3hnRCQqZgJ9PZeCJpYeQcatM6tKIyISUqbX4z9oTWLI/GQDw4sNt8dm/Q2CrlEtcGTUkqw9KI0eOhLOzM0aPHi11KSSx8Q+0wHeTw+Bsr8TZ6/oF4+Kv5kldFhGZgBuF5Riz7DB2nsuCSi7DJ2OCMWtQO66PZAWsPijNmDEDq1evlroMMhEPtHLFz9P7or2XA3KLK/DvZUewMS5N6rKISEKJGYUYHn0Qp68VwNleibVTwjCyq6/UZVEjsfqg1K9fPzg4sAGP/uTnYo/N03pjSCcvVGpFvLr5DD7cdZ5N3kRWKCYpG6OXHEJmQTlauzfB1qg+6BngInVZ1IhMOijFxsZi2LBh8PHxgSAI2Lp16x23iY6ORkBAAGxtbREWFoZjx441fqFkcZrYKBA9thtmVC1OGb03Ga/9eBZaHcMSkTUQRRHf/pGKKauPo1StRe/WrtgyrQ9auDaRujRqZAqpC7iXkpISBAcHY9KkSRg1atQd12/cuBGzZs3CkiVLEBYWhsWLFyMiIgIXLlyAh4cHACAkJAQajeaO+/7222/w8fGpUz0VFRWoqKgwfF9YWFjHn4jMiUwm4KVB7eDhaIP/bT2L9cfSUFCmxidjQmCjYPMmkaXSaHV465dz+O6Iftr9qVA/vD28E5Rykx5boAZi0kFpyJAhGDJkSI3XL1q0CFOmTMHEiRMBAEuWLMH27dvx7bffYu7cuQCAhISEeqtnwYIFmD9/fr09HpmHcWEt4GyvwswNCdhxJgsFZXFYOr4HmtqY9OFDREYoVWsw7bsT2H8xB4IAvDakAyY/yE1trZnZxmO1Wo34+HgMHDjQcJlMJsPAgQNx+PDhBnnOefPmoaCgwPCVns4d6K3Fo5298e0zPWGvkuPg5ZsYu/wI8krUUpdFRPWoqLwSz3wbh/0Xc2CnlGPJ090x5aFWDElWzmyDUm5uLrRaLTw9PW+73NPTE1lZWbV+nIEDB+KJJ57Ajh074Ovre8+QZWNjA0dHx9u+yHr0beuG9VMegLO9EqevFWD0kkO4zrWWiCzCrVI1nv7mmH7PNlsFvpschoiOXlKXRSbAbINSfdmzZw9ycnJQWlqKa9euoVevXlKXRCYs2K8ZNk3tDW8nW6TklGD0V4e4oS6RmcstrsBTy4/iVPotONsrsX7KA+jewlnqsshEmG1QcnNzg1wuR3b27RuZZmdnw8uLfwVQw2nj0RSbp/VGa/cmyCwoxxNLDuNU+i2pyyIiI2QXlmPM0sNIyiyEW1MbbHiuFzo1d5K6LDIhZhuUVCoVunfvjpiYGMNlOp0OMTExHBWiBufTzA6bpvZGsK8T8ksr8dTyI/jjUq7UZRFRHVzLL8WTSw8jOacE3k62+P75BxDoxXX16HYmHZSKi4uRkJBgOHMtNTUVCQkJSEvTn7I5a9YsLF++HKtWrUJSUhKmTZuGkpISw1lwDSU6OhpBQUHo2bNngz4PmTaXJiqsnfIA+rRxRalai0kr47DjTKbUZRFRLVzJLcGTSw7j6s1S+LnY4fvne6GVe1OpyyITJIgmvNzwvn370L9//zsuj4yMxMqVKwEAX3zxBT788ENkZWUhJCQEn332GcLCwhqlvsLCQjg5OaGgoICN3VasQqPFSxv1SwcIAvDeiM4YG+bfcE9YVARs3w44OgL29g33PESNobQUKCwEhg4FGmmXhEvZRRj39VHcKKpAK/cmWDf5AXg52TbKc5NpqMvnt0kHJVPHoETVtDoRr/90FuuO6kc7Z0cE4j/9WjfMacUMSmRJGjkonb1egAnfHkNeiRrtvRyw5tkwuDvYNPjzkmmpy+e3SU+9EZkLuUzAeyM6YXr/NgCAD3ddwLvbk6DjlidEJuNkWr5hDbQuvk7Y8NwDDEn0jxiUiOqJIAh4JSIQr/8rCADwzR+peP2ns9xMl8gEHE25iae/PorCcg16tHDGd5PD0MxeJXVZZAYYlIjq2bN9W+LjJ4IhCMDao2l4f0cSwxKRhGIv5iByxTGUVG1uu/rZUDjaKqUui8wEgxJRA3i8uy8WjuoMAFh+IBWfxlySuCIi67Q7MRuTVx1HeaUO/QPdq7Yi4j6NVHsMSkbg8gBUG2N6+uPNYfppuMV7LmFZbLLEFRFZl22nMzDtu3iotToM6eSFpeN7wFYpl7osMjMMSkaIiopCYmIi4uLipC6FTNzEPi0xOyIQAPD+jvNYc/iKtAURWYndidl4cf1JaHQiRnZtjs+f6gqVgh95VHf8rSFqYFH92yCqf2sAwOs/ncMP8dckrojIsh1LzcP0dSegE4HR3X3x8RPBUMj5cUfG4W8OUSN4ZXAgnukdAACY88MpbD/NFbyJGkJSZiGeXRWHCo0OAzt4YuGozpDJGmA9M7IaDEpEjUAQBLw5LAj/7ukHnQjM2HASv5/P/uc7ElGtpeeVYsK3x1BUrkFogAu+GNuVI0l03/gbRNRIBEHAeyM747FgH2h0IqZ+dwKHLnMjXaL6kFtcgfHfHEVOUQXaezlgeSQbt6l+MCgRNSK5TMDHTwZjUJAn1BodJq8+jvireVKXRWTWisor8cyKY7hysxS+znZYNSkUTnZcJ4nqB4MSUSNTymX4YmxXPNjWDaVqLZ75Ng5nrxdIXRaRWarQaPH8mnicvV4I1yYqrHk2DJ6O3OCW6g+DkhG4jhLdLxuFHMvG90BogAuKKjQY/81RXMwukrosIrOi1YmYtfEUDiXfRBOVHCsnhqKlWxOpyyILw6BkBK6jRPXBTiXHN8/0QLCvE/JLKzHu66NIzS2RuiwisyCKIt76+Ry2n8mESi7Dsgk90NnXSeqyyAIxKBFJyMFWiVWTQtHeywE5RRUYt/wIruWXSl0Wkcn7NOYS1hy5CkEAPhkTgj5t3KQuiSwUgxKRxJrZ6/sqWrk3QUZBOZ7++ihuFJZLXRaRyVpz5CoW79Hvn/j28E4Y2sVb4orIkjEoEZkAdwcbrJ0cBl9nO1y5WYrJq4+jvFIrdVlEJmf76Uy88dNZAMCMh9ti/AMtJK6ILB2DEpGJ8Hayw9rJYXC2V+L0tQLM/uE0RFGUuiwik3Hoci5e2pgAUQSefsAfMwe2lboksgIMSkQmpIVrE3w5rjsUMgG/nMrAl/uSpS6JyCScuVaAKauPQ63V4dHOXpj/WCcIArcmoYbHoERkYnq1dsX84R0BAB/uuoBd57IkrohIWqm5JXhmxTGUqLXo3doVn4wJgZz7t1EjYVAiMkHjwlogspe+9+KljQlIyiyUuCIiaeQWV2DCt0dxs0SNTs0dsXR8d9gouDUJNR4GJSNwwUlqDK//Kwh92riiVK3F5FXHcbO4QuqSiBqVRqvDC+tOIj2vDAGu9lg5MRQOttyahBoXg5IRuOAkNQaFXIbosd0Q4GqP67fKMO27E1BrdFKXRdRoPvztAg6n6Ffd/jqyB9ya2khdElkhBiUiE9bMXoWvI3vAwUaBY1fy8MZPZ3kmHFmFX89kYun+FADAB6OD0cbDQeKKyFoxKBGZuDYeDvhsbFfIBGBDXDpWHs+QuiSiBnX5RhFe2XQKAPDcQ624oCRJikGJyAz0D/TAvCEdAADv7E5GbCGbWckyFVdo8PyaeJSotejVyhVzIgKlLomsHIMSkZmY/GBLjO7uC50IRF2xQ3Ipp+DIsoiiiNmbTiE5pwTeTrb4fGxXKOT8mCJp8TeQyEwIgoD3RnZCd19HFOkETDkHFFQyLJHlWBqbgl/PZkEpF/DluG5s3iaTwKBEZEZsFHIseTwIPkodUsqA6QkV0OgYlsj8HbqSjw92ngcAvDmsI7r6O0tcEZEegxKRmXFvqsLylmWwkwEHbmrx/gW11CUR3ZcMtYDpP56HTgRGd/fFuDB/qUsiMmBQIjJDHe11WFTV4/rtVQ02XquUtiAiI1XoREy7Yoe80kp0au6Id0dwDzcyLQxKRGZqiLuAl9roVyn+3zk14vK1EldEVHdvXQZOlcrRzE6Br8Z1h62SZ3SSaWFQMgK3MCFT8WJrJYZ6yVEpAlNPliO9lCt3k/n4/lol1mcBAkR8Orw9/FzspS6J6A4MSkbgFiZkKgRBwEedbdDRUYabamD6qQpUsrmbzMDpAi3+l6jvr5vlpUZ4axeJKyK6OwYlIjNnJxewrKsNHBXAqQIdPk9mvxKZtjy1iGknK6DWAQNdgChPnpBApotBicgCNLeT4d2O+jVnolMqceIW+5XINGlFETNOleN6uYgAewEftwdk7N0mE8agRGQhHvNWYIS3HFoReOl0BUo0nIIj07PoUiUO3NTBTg4s7WoLJwVTEpk2BiUiCzI/yAY+tgKulop49zynM8i0/JatQXSKfmp4YUcbBDrwI4hMH39LiSyIk1LAx51tIABYf02D3Tc0UpdEBABIK9Xh5TMVAICJLRQY7qOQuCKi2mFQIrIwvVzlmBKgX19p7tkK5FRwCo6kpRNFzD5TgSIN0NNZhtcCVVKXRFRrDEpEFujldkq0d9AvGfDq2QqIIsMSSWdtugZH8/V9SR93toGS3dtkRhiUiCyQjUzAp11soJIBv+dosTadU3AkjfRSHRZU7Uc4t50K/vb82CHzwt9YIgsV6CDDnHb6KY73LqiRUsJVu6lxiaKIuecqUKoFQp1lGO/PviQyPwxKRBZsUgsF+rjKUKbVLxnAVbupMa2/psHBmzrYyoAPOtlAxs1uyQwxKBFZMFnVFidctZsa2/UyHd6vWqLilXYqBDThxw2ZJ/7mGoGb4pI58baV4b2qVbu/SK5EfD5X7aaGJYoi5p5Vo1gLdGsmw8QWnHIj88WgZARuikvmZljVqt06ALPOcNVualibrmtw4KYWKhnwYScbyDnlRmaMQYnISswPskHzqlW73+Gq3dRAssp1ht+vl9sq0bopP2bIvPE3mMhKOCkFfNxFv2r3hmsa7MrmkgFUv0RRxGvn1CjSAMFOMkyuWviUyJwxKBFZkQdc5Hiupf7Da97ZCtyo4JIBVH9+zNDg9xwtVALwUWdOuZFlYFAisjKz2irRwUGGvErg1bNqrtpN9eJGuQ5vJemn3Ga0UaItp9zIQvA3mcjK2MgELK5atXsvV+2meiCKIv6bqEahBujsKMPzLTnlRpaDQYnICgU6yPBq1ard755XI7mYU3BkvJ8ztdh9QwulAHzY2QYK7uVGFoRBichKTaxatbtcB8zhxrlkpJwKEW8lVQAAprfWb8ZMZEn4G01kpapX7baXA/G3dPg5kwtRUt29kViB/Eqgg4MM/2nFKTeyPAxKRFbM21aGqKoPtwUX1CjlQpRUB9uzNPg1WwuFAHzYWQUlp9zIAjEoEVm5ZwOU8LUTkFUhYkkq94Kj2rmpFvFGon7K7T+tlOjkKJe4IqKGwaBEZOVs5QL+F6hv7F6aWon0UjZ20z97K7ECN9VAYFMB01tzyo0sF4MSESHCU45eLjJU6ICFF7m9Cd3bzmwNfsnSQl51lpuKU25kwRiUiAiCIODNDjaQAdiepcXhm2zspru7pRbxv3P6MP1cgBJdnDjlRpatzkGprKwM169fv+Pyc+fO1UtBRCSN9g4yjPNXAADmn1dDy+UC6C4WJ6uRqxbRpomAGW045UaWr05B6YcffkDbtm0xdOhQdOnSBUePHjVcN378+Hovjoga16w2KjgpgfNFOmzgit30N2mlOqxN0/9evNXBBrZyTrmR5atTUHr33XcRHx+PhIQErFixAs8++yzWrVsHAFa1WF10dDSCgoLQs2dPqUshqlfOKgEvtdE3dn90SY2CSus5rumffXRJjUoReNBVjr5unHIj61CnoFRZWQlPT08AQPfu3REbG4ulS5fi7bffhmBFu0RHRUUhMTERcXFxUpdCVO/G+SnQtqmA/Erg08ts7Ca9MwVaw6KkrwZyyo2sR52CkoeHB06fPm343sXFBbt370ZSUtJtlxOR+VLKBLzR3gYAsDpNg8vcB44A/F/V2ZAjvOVcM4msSp2C0po1a+Dh4XHbZSqVCuvXr8f+/fvrtTAiks6DbnIM8pBDIwJvn1db1dQ63Sk2V4M/buqgEoCX26qkLoeoUdUpKPn6+sLLy+uu1/Xp06deCiIi0/DfQBVUAhCbq8XvOVwuwFrpRBELL+hXbH/aXwE/e64qQ9aFv/FEdFcBTWSYFKDvRXnnvBpqHUeVrNEvmVokFungoACmt+ZoElkfhTF3atmypVHN2zNnzsSLL75ozFMSkQSmt1Zic4YGV0pFrLyqwXMt2cRrTSp0Ij68pO9NmtpSCReV9Zy0Q1TNqKC0cuVKo54sICDAqPsRkTSaKgTMaavE7LNqfHZZjZE+Crjb8MPSWqxN0+BamQgPGwETWzAkk3UyKiiFh4fXdx1EZKIeb67Ad+kanCrQ4aNLavxfJxupS6JGUFgp4vNk/WjSS22UsFcwIJN1qtcepby8POh0PJWYyJLIBAFvtNf3pnx/TYMzBWzstgbLUiuRXwm0biLgieZG/U1NZBHuOyglJiZi4cKF6N27N9zd3eHh4YEJEyZg8+bNKCkpqY8aiUhi3Z3lGOkjhwhgfhKXC7B02eU6fH1Ff6bbnHYqKGQcTSLrZVRQunDhAl5++WW0bdsWDzzwAOLi4jB16lRkZ2djx44daNGiBd5++224ublhyJAh+Oqrr+q7biJqZK+2U8FODhy/pcMvWRxVsmSLL1eiXAd0bybDYA8uLknWzaigdOjQIZSUlOCzzz5Dbm4uNm/ejAkTJsDNzQ2hoaF45513cOrUKSQlJeGRRx7Bli1b6rtuImpkXrYyRLXSN/QuuKBGqYajSpbocrEO31/Xb3w7N1BlVdtTEd2NIHIM3WiFhYVwcnJCQUEBHB0dpS6HrEVREbB9O+DoCNjbN+pTl2tFDPyjDNfKRLzYWolZXKXZ4jx/shy7srUY6CHH191sG/4JS0uBwkJg6FDAwaHhn48Idfv85oKTRFRrtnIB/w3Uh6OlqZW4VsaTNyxJfL4Wu7K1kEE/1UpE9xmUkpKSsGLFCpw/fx4AcP78eUybNg2TJk3C77//Xi8FEpFpecRTjl4uMlTogIUX1FKXQ/VEFEUsrNr49glfBdo25d/RRMB9BKWdO3ciJCQEr7zyCrp27YqdO3fioYcewuXLl3H16lUMHjyYYYnIAgmCgDc66NdS2p6lRXIxR5UswZ4cLeLydbCR6ddNIiI9o4PS22+/jdmzZ+PmzZtYsWIFxo4diylTpmD37t2IiYnB7NmzsXDhwvqslYhMRAcHGQZ66JcLqD6NnMyXRifig6rRpEktlPCy5WgSUTWjj4Zz587hmWeeAQA8+eSTKCoqwujRow3Xjxs3DqdPn77vAonINE2t2vdt83UNblRwVMmcbcnQ4FKxiGZKYGorjiYR/dV9/dlQfdqoTCaDra0tnJycDNc5ODigoKDg/qojIpPVw1mO7s1kUIvAyqsaqcshI5VpRSy6pB8VnN5KBScllwMg+iujg1JAQAAuXbpk+P7w4cPw9/c3fJ+WlgZvb+/7q46ITNrzVaNKa9IqUcx1lczSyquVyKoQ0dxWwNP+3KqE6O+MDkrTpk2DVvvn6rydOnWCQvHnQfbrr79iwIAB91cdEZm0gR5ytGoioEgDbEjnqJK5yVeL+DJFP5r0clslbOUcTSL6O6P/fJg6deo9r3///feNfWgiMhMyQcDzLZV49awa31ytxIQWCqi4L5jZ+DJFjSIN0N5BhuE+HE0iuhue2kBE92WEjwLuNgIyy0X8kslRJXNxrUyHVVW9ZXPbKSHnViVEd8WgZITo6GgEBQWhZ8+eUpdCJDkbmYCJLfSjEUtTK8FdkczDstRKqEWgl4sM4W7c+JaoJkaNtbZs2dKojRJnzpyJF1980ZinNClRUVGIiooy7BVDZO3G+SkRnVyJi8Ui9uVq0d+d0zimrEgjYnPVxrfTW3PjW6J7MerdbOXKlUY9WUBAgFH3IyLT5qQUMNZPgeVXNFiSUsmgZOJ+uKZBiRZo00RAbxdOLBDdi1HvZuHh4fVdBxGZuUkBSqy4qsHRfB0SbmkR0ozTOaZIJ4pYnaY/0y2yhZKjSUT/wOg/JdLS0pCWlob09HQuLElE8LaVYbi3/m+vZanc1sRUHcjVIrVUhIMCGMUz3Yj+0X0tONmyZUsEBATAxcUF/v7+eOutt6BWczdxImv1XNUClL9ma3GlhNuamKJVafrepNHNFWii4GgS0T8x+s+JvXv3AgBEUURGRgaOHz+Ojz76CLt378aePXtgZ2dXb0USkXkIdJBhgLscv+dosfxKJd7raCN1SfQXV0t12JujXyh4gj/3dCOqDaNHlMLDwxEeHo5+/fph7NixWLRoEc6dO4f09HS8/fbb9VkjEZmR6m1NNl3XIKeCSwWYktVXKyECCHeTo2UTNnET1YbRR0phYeEdX87Oznj55ZexadMmFBUVobCwsD5rJSIzEOosQ4iTDGodDE3DJL1SjYjvq5YEeKYFe5OIasvoo6VZs2b3PFvCyckJgiDcth8cEVk+QRAwtaUSUxMqsDqtElNbKtkLYwJ+zNCgSAME2AtcYJKoDu67R+nvdu7ciVWrVmH9+vVGF0VE5m2Qpxwt7QWklorYeE2DSQHsh5GSKIpYVTW6N95fCRmXBCCqNaOD0t3WUiopKcHMmTMxdOhQrrVEZMXkgoApLZV47Zwa31ypxHh/BZTcLFcyh/N0uFgswk6uP9uNiGrP6CMmLS0NgP4vlczMTJw4cQIffPABFAoF3n///XorkIjM0ygfBRZdUuN6uYjtWVqM4Jo9kqnuFRvlo4CTkoGVqC6MfucKCAgw9CiJogilUonRo0fjk08+gbu7e70VSETmyVYuYGILJT68VIklqZUY7i3nKtASuF6mw2/Z+l7RSC4JQFRnRgel33//HYIgQBAEODg4IDAwEPb29vVZGxGZuaf9lYhOqcT5Ih1ic7UI5x5wje67NA10AHq7yNDOgUsCENWVUe9aWVlZ6NWrF2xsareYXEpKClq1amXMUxGRGXNSCvi3rwLfXtVgaWolg1IjK9eK2HBNP+02oQVHk4iMYdSfFz/88ANcXFwwcuRIrFixAjk5OXfc5ujRo3jttdfQsWNHBAcH33ehRGSeng1QQi4Ah/J0OF3A5UIa0y+ZGuRXAs1tBQx055IARMYwKihNnz4dp06dwoMPPoiVK1fC19cXffv2xfvvv48pU6bA29sbI0aMwI0bN7Bw4cK7Bikisg7N7WR4zFv/Ib2Um+U2Gv2SAPoFJp/2V0DBsw6JjGL0OHibNm0wa9YszJo1Czdv3sS2bduwY8cOBAQEYPPmzejVqxcbN4kIAPBcSxV+zCjDr1laXC3VoYU9e2Ua2olbOpwt1MFGBvzbl9NuRMaql4YBV1dXREZGIjIysj4ejogsTAcHGcLd5Nifq8XXVyrxThA3y21oK6/qR+8e81bAWcU/WomMxT/riKhRVG+W+/01DW6quVluQ7pRrsOv1UsCcF83ovvCoEREjaKXiwxdHGWo0AGrrrJXqSGtTddAIwI9msnQyZFN3ET3w6g/NVq2bGlU/9HMmTPx4osvGvOURGTmBEHA862UiEqowJq0SkxvrYSKDcb1Tq0Tse6avombSwIQ3T+jgtLKlSuNerKAgACj7kdEliHCQw43lYBctYg/crUY4MFpofr2a5YWORUiPGwEDPHkaBLR/TLqXYob3hKRMRQyAf/ykmNlmgY/ZWoYlBrAqqp93cb5cSNiovrAHiUialSPVW2Ou/uGFmVaNnXXpzMFWpy4pYNSAJ7yYwglqg8MSkTUqLo6yeBnJ6BUC+y5wZW669PKq/repEe95PCw4ds7UX3gkUREjUoQBAzz1o92/Jypkbgay3FTLeKXLP3rGckmbqJ6U69B6eLFi9Bo+MZHRPf2WFVQ2pejRUElp9/qw4b0Sqh1QBdHGbo68W9govpSr0dThw4dkJKSUp8PSUQWqL2DDIFNBVSKwM5s/nF1vzQ6EWvTq0eTFNw+iqge1WtQEkX+ZUhEtfMYp9/qze4bWmSUi3BRAkO92MRNVJ84PktEkqjuUzp8U4cb5TqJqzFv1fu6PeWnhK2co0lE9YlBiYgk4W8vQ4iTDDoA27J49puxzhfpcDRfB7kAPO3P0SSi+sagRESSGc7pt/u2IV0/mhThIYe3Ld/SieobjyoiksxQbzlkABIKdEgr5fRbXelEETuz9aNxo5tzNImoITAoEZFkPGxk6OWqfxv6haNKdXa6QIesChFN5EBvV+7rRtQQrDoopaeno1+/fggKCkKXLl2wadMmqUsisjo8+814u6pGk/q5y9nETdRA6jUovfrqq3B1da3Ph2xQCoUCixcvRmJiIn777TfMnDkTJSUlUpdFZFUe8VRAJQAXikWcL+L0W13suqEPl4O5uTBRg6nXoLRgwQKzCkre3t4ICQkBAHh5ecHNzQ15eXnSFkVkZZyUAsLd9dNGHFWqvcvFOqSUiFAKQH93TrsRNRSTnnqLjY3FsGHD4OPjA0EQsHXr1jtuEx0djYCAANja2iIsLAzHjh0z6rni4+Oh1Wrh5+d3n1UTUV1VT7/9kqnhwrW1tKtqRfPernI4KjntRtRQTDoolZSUIDg4GNHR0Xe9fuPGjZg1axbefPNNnDhxAsHBwYiIiMCNGzcMtwkJCUGnTp3u+MrIyDDcJi8vDxMmTMCyZcsa/GciojsN9JDDXg6kl4k4WcDpt9r47Ya+PynCk6NJRA3JpCe2hwwZgiFDhtR4/aJFizBlyhRMnDgRALBkyRJs374d3377LebOnQsASEhIuOdzVFRUYMSIEZg7dy569+79j7etqKgwfF9YWFjLn4SI7sVOLmCwhxxbM7X4OUODbs344X8vmeU6nCrQQYA+ZBJRw2mwEaX09HRMmjSpoR4earUa8fHxGDhwoOEymUyGgQMH4vDhw7V6DFEU8cwzz2DAgAEYP378P95+wYIFcHJyMnxxmo6o/lRPv23L0kKj4/TbvfxWdbZbt2YyeNiY9MQAkdlrsCMsLy8Pq1ataqiHR25uLrRaLTw9PW+73NPTE1lZWbV6jIMHD2Ljxo3YunUrQkJCEBISgjNnztR4+3nz5qGgoMDwlZ6efl8/AxH9qa+bHM2UQK5axJE8Tr/dy29VZ7tFeJr0pACRRTD6KPv555/veX1KSoqxD91o+vbtC52u9m/INjY2sLGxacCKiKyXSiZgiKcC669p8HOmBn3dOKV0N7f+EiTZn0TU8IwOSiNGjIAgCPc8Q0UQGu5MDDc3N8jlcmRnZ992eXZ2Nry8vBrseYmo4Qz30QelX7M1eKejCjYyns31dzE5GmhFoH1TAS3sOe1G1NCMPsq8vb2xZcsW6HS6u36dOHGiPuu8g0qlQvfu3RETE2O4TKfTISYmBr169WrQ5yaihhHqLIOXjYAiDbAvRyt1OSapejXuwZx2I2oURgel7t27Iz4+vsbr/2m0qTaKi4uRkJBgOHMtNTUVCQkJSEtLAwDMmjULy5cvx6pVq5CUlIRp06ahpKTEcBYcEZkXmSDgX95cfLImZVoRsbnVQYnTbkSNweg/SWbPnn3P7T7atGmDvXv3GvvwAIDjx4+jf//+hu9nzZoFAIiMjMTKlSsxZswY5OTk4I033kBWVhZCQkKwc+fOOxq861t0dDSio6Oh1fIvXqL69pi3Al9f0SDmhhbFGhFNFZx+qxabq0W5DmhuK6CjA6fdiBqDIHIZXKMVFhbCyckJBQUFcHR0lLocshZFRcD27YCjI2BvL3U19U4URQw4UIbUUhGfdLHBSB9OMVWbdboCWzI0mNRCgTc6WMiJJaWlQGEhMHQo4OAgdTVkJery+c0/SYjIpAiCgGFVaypx+u1PlToRMTlcFoCosTEoEZHJqV588kCuFvlqDnoDwLF8HQoqARcl0MOZb91EjYVHGxGZnDZNZQhykEEjAjuyOKoE/LkJ7kAPBeQNuPQKEd2OQYmITNJwH/1ZXT9x+g06UTRsW8JFJokaF4MSEZmkf3npp9/i8nXILLfuLU3OFOqQVSGiiRzo48qgRNSYGJSMEB0djaCgIPTs2VPqUogsVnM7GXo6yyAC2JZp3UtxVC8y2c9dDls5p92IGhODkhGioqKQmJiIuLg4qUshsmiP8ew3AH/2Jw324NluRI2NQYmITNajXgrIBf3UU0qJdU6/XS7WIblEhFIA+rtz2o2osTEoEZHJclUJ6Otq3VuaVI8m9XKVw1HJaTeixsagREQm7bG/7P1mjRsJ/Haj6mw3D44mEUmBQYmITNpgTwVsZEBKiYhzRdY1/ZZVrsOpAh0EAIO4LACRJBiUiMikOSgEPFzVm/NzhnWd/Va9dlK3ZjJ42PDtmkgKPPKIyORVn/32S5YGOiuaftt1g3u7EUmNQckIXEeJqHH1c5fDTg5klou4VGwdQemWWsSRPP1U42D2JxFJhkHJCFxHiahx2coF9Gimf7s6kmcd02+/52igFYHApgICmvCtmkgqPPqIyCw84KIfVbGWoLSr+mw3TrsRSYpBiYjMQnVQOpqntfg+pTKtiP05+qA0mGe7EUmKQYmIzEJnJxns5EBeJSy+Tyk2V4tyHdDcVkBHB75NE0mJRyARmQWV7M8+pcMWPv1WvQluhKccgsDVuImkxKBERGbDGvqUKnUiYnKqNsFlfxKR5BiUiMhsWEOfUly+DgWVgIsS6OnMt2giqfEoJCKz0aWqTym/ErhooX1K1ZvgDvRQQM5pNyLJMSgZgQtOEklDKRPQo5nlTr+JovjnJrg8243IJDAoGYELThJJ5wEXy1148nShDpnlIprIgT6uDEpEpoBBiYjMiiX3KVWf7dbPXQ5bOafdiEwBgxIRmZW/9ildsLA+pd+q+pMGe/BsNyJTwaBERGbltj6lm5Yz/ZZcrMPlEhFKAejvzmk3IlPBoEREZsfQp5RvOUFp1w39aFIvVzkclZx2IzIVDEpEZHZ6uVpen9KBXH3oG+TB0SQiU8KgRERmp7OjDPZy4JaF9ClV6kQkFOgAAGHODEpEpoRBiYjMjlImoIez5fQpnS/SoUwLOCqANk057UZkShiUiMgsWVKf0vF8/WhSt2ZyyLgaN5FJYVAiIrNkSespxd/Sh70e3NuNyOTwqDQCtzAhkt5f+5TOF+mkLue+xN/6c0SJiEwLg5IRuIUJkfRu61PKM9+glFGm37ZELgAhTnxLJjI1PCqJyGz1soB9345XjSYFOchgr2B/EpGpYVAiIrNl6FPKN98+pRNVzejd2Z9EZJJ4ZBKR2erkKEMTOVBgxn1K1SNK3dmfRGSSGJSIyGyZe59SiUZEUlXA4xlvRKaJRyYRmbUHzLhP6VSBDloR8LEV4G3Lt2MiU8Qjk4jMmjn3KVWvn9StGd+KiUwVj04iMmt/7VNKMrM+pfj86mk39icRmSoGJSIya0qZgJ5m2KekE0WcqBpR6s4RJSKTxaOTiMyeOfYpXS4WUagB7ORABwe+FROZKh6dRGT2qvuUjplRn9LxqtGkECcZFDIuNElkqhiUiMjsmWOfEvuTiMwDg5IRuCkukWlRmGGfEs94IzIPPEKNwE1xiUyPOfUp5VaIuFKqnyLsxhW5iUwagxIRWQTDekp5WmhNvE+p+my3dk0FOCnZn0RkyhiUiMgidHKUoakcKNSYfp9SPPd3IzIbDEpEZBEUMgE9XcyjTyk+v2r9JO7vRmTyeJQSkcUwhz6lCp2I04UcUSIyFwxKRGQxDOspmXCf0tkCHdQ6wFUFBNizP4nI1DEoEZHF6Ohg+n1K1f1J3ZrJIQgMSkSmjkGJiCyGOfQpVa+f1IP9SURmgUcqEVkUQ5/STdPrUxJF8c9GbvYnEZkFBiUisii9qtdTyje9PqW0MhG5akAl6JczICLTxyOViCxKkIMMDgqgSAMkFZrW9NvxqtGkTk4y2MrZn0RkDhiUiMiimPK+b9WN3D047UZkNhiUiMjiPGBo6DatPqUTVSNK3djITWQ2eLQSkcWpbug2pT6lgkoRF4r1tbCRm8h8MCgRkcUxxT6lhFtaiABa2Atwt2F/EpG5YFAiIotjin1K3AiXyDwxKBkhOjoaQUFB6Nmzp9SlEFENqpcJOGwifUrVC012b8a3XSJzwiPWCFFRUUhMTERcXJzUpRBRDar7lI6ZQJ+SRifiZPWIkjNHlIjMCYMSEVmkIMc/+5QSJe5TOl+sQ6kWcFAA7ZqyP4nInDAoEZFFkgsCQk2kTyk+X//8XZvJIeNGuERmhUGJiCyWqaynZNgIl/1JRGaHRy0RWSxT6VOqHlFifxKR+WFQIiKLZQp9SpnlOlwvFyEDEOLEt1wic8OjlogsllwQ0KNqFOdYvjRB6UTV2W4dHGVoomB/EpG5YVAiIosW6vzn9JsUjudz/SQic8Yjl4gsWvWZb3F5WogS9Cmd4IrcRGaNQYmILFpnJxlsZEBeJZBc0rhBqUwr4lxhdSM3326JzBGPXCKyaCqZgG5V015HG3mZgFMFOmhEwMtGQHNb9icRmSMGJSKyeNUb5MY1cp9SfHV/krMMAheaJDJLDEpEZPHCXKQ58y2e/UlEZo9BiYgsXlcnGRQCkFEu4lpZ44QlnSgaVuRmfxKR+eLRS0QWz14hoJNj1TIBjdSnlFIioqASsJUBQQ58qyUyVzx6icgqVE+/xTXS9Fv1+knBTjIoZexPIjJXDEpEZBV6Vk1/HW2khm5DfxL3dyMyawxKRGQVejrLIUA/JZZT0fDrKVX3J/XgitxEZo1HMBFZBSelgMCm+imw4w08qpSnFpFStbhlN57xRmTWGJSIyGqEVvUpNfT024mq0aQ2TQQ0U7E/icicMSgRkdUw7PvWwA3dx/PZn0RkKRiUiMhqhFY1dCcW6lBY2XB9StUjSt3Zn0Rk9ngUE5HV8LCVIcBegIg/m63rm1on4lQBV+QmshQMSkaIjo5GUFAQevbsKXUpRFRH1fu+HctrmOm3c4U6VOgAZyXQqgn7k4jMHYOSEaKiopCYmIi4uDipSyGiOqqefjvWQA3d8fl/jiZxI1wi88egRERWpXqF7tMFOpRr679PKY77uxFZFB7JRGRV/OwEeNoIqBSBk7fqd/qtSCNif44+KPV2ZX8SkSVgUCIiqyIIQoNNv/2apUG5Tt+b1MWRb69EloBHMhFZnVDDBrn1G5R+uK4BAIxurmB/EpGFYFAiIqtTvfBk/C0dKnX106eUVqrDsXwdBAAjfRT18phEJD0GJSKyOm2bCmimBMq0+tP568PmqtGkvq4yeNvyrZXIUvBoJiKrIxME9KheT6ketjPRiSK2ZOiD0uPNlff9eERkOhiUiMgqhRkWnrz/PqW4fB3Sy0Q0lQMRnjzbjciSMCgRkVXqWXXmW9wtLXTi/fUpVU+7DfVWwE7OJm4iS8KgRERWqaOjDPZyoKASuFhsfFAq1YjYnlU17cYmbiKLw6BERFZJKRPQvVnVqNJ9LBOw64YWJVrA304wjFIRkeXgUU1EVqt6g9yj99GntPl6JQBgFNdOIrJIDEpEZLWqF548lq+DaESfUkaZDgdv6s+a47QbkWViUCIiqxXiJINSAG5UiEgrq3tQ+jFDAxFAqLMMfvZ8OyWyRDyyichq2coFBDvp3wbrOv0miqLhbLfRzTmaRGSpGJSIyKr9ue9b3RaePFmgQ0qpCDs58KgXgxKRpWJQIiKrVn2m2rE6nvlWPZr0iKccTRVs4iayVAxKRGTVujeTQwbgaqmI7PLajSqVa0X8klm9dhK3LCGyZAxKRGTVHJUCOjhWjyrVLijtuaFFoQbwthXQy5Vvo0SWjEc4EVm90DpOv22u2gB3lI8Ccq6dRGTRGJSIyOpVb5AbV4sz325U6BCbq7/dKK6dRGTxGJSIyOr1qApK54tF3FLfez2lnzK00IpAVycZWjflWyiRpeNRTkRWz81GQOsm+im0uFs1jyqJoogfqrYseZxrJxFZBQYlIiIAoYbpt5obus8V6nChWIRKBgzzZlAisgYMSkRE+HPhyaP3aOj+oaqJe5CHHE5KNnETWQMGJSIi/Hnm27lCHUo0d/YpqXUifs7gliVE1oZBiYgIQHM7GZrbCtCIwMlbd06/7cvRIq8ScLcR8KCrXIIKiUgKDEpERFVCXWpeT+mHqi1LRnjLoZBx2o3IWjAoERFV6VnV0P33oJSnFrE3R3/Z4825ZQmRNWFQIiKqUn3m28lbOlTo/uxT+jlTg0oR6OQoQ3sHvm0SWRMe8UREVVo3EeCqAip0wNmCP/uUNl+v3gCXTdxE1oZBiYioiiAIhum36mUCLhTpcKZQB4UAPMagRGR1GJSIiP6ievrtWNXCk9WjSf3d5XBVsYmbyNowKBER/UX1ekrxt7RQ60T8mFk17ca1k4isklUHpVu3bqFHjx4ICQlBp06dsHz5cqlLIiKJdXCUoakcKNIAy1MrkVMhwlkJDHDn2klE1siq/0RycHBAbGws7O3tUVJSgk6dOmHUqFFwdXWVujQikohcENDDWY59uVp8nqzfAHe4twIqrp1EZJWsekRJLpfD3t4eAFBRUQFRFCGKd25dQETWpWfV9Ft51YlvnHYjsl4mHZRiY2MxbNgw+Pj4QBAEbN269Y7bREdHIyAgALa2tggLC8OxY8fq9By3bt1CcHAwfH19MXv2bLi5udVT9URkrsJc/pxma9dUQCdHk36rJKIGZNJHf0lJCYKDgxEdHX3X6zdu3IhZs2bhzTffxIkTJxAcHIyIiAjcuHHDcJvq/qO/f2VkZAAAmjVrhlOnTiE1NRXr1q1DdnZ2jfVUVFSgsLDwti8isjydnWRQVb07Pt5cAUHgtBuRtTLp8eQhQ4ZgyJAhNV6/aNEiTJkyBRMnTgQALFmyBNu3b8e3336LuXPnAgASEhJq9Vyenp4IDg7GgQMHMHr06LveZsGCBZg/f37dfggiMjs2MgHPtlDiUJ4WT3DLEiKrZtIjSveiVqsRHx+PgQMHGi6TyWQYOHAgDh8+XKvHyM7ORlFREQCgoKAAsbGxCAwMrPH28+bNQ0FBgeErPT39/n4IIjJZrwaq8FMvO7hw7SQiq2bSI0r3kpubC61WC09Pz9su9/T0xPnz52v1GFevXsVzzz1naOJ+4YUX0Llz5xpvb2NjAxsbm/uqm4iIiMyH2Qal+hAaGlrrqTkiIiKyPmY79ebm5ga5XH5H83V2dja8vLwkqoqIiIgsidkGJZVKhe7duyMmJsZwmU6nQ0xMDHr16iVhZURERGQpTHrqrbi4GJcvXzZ8n5qaioSEBLi4uMDf3x+zZs1CZGQkevTogdDQUCxevBglJSWGs+AaSnR0NKKjo6HVahv0eYiIiEhagmjCS1Hv27cP/fv3v+PyyMhIrFy5EgDwxRdf4MMPP0RWVhZCQkLw2WefISwsrFHqKywshJOTEwoKCuDo6Ngoz0mEoiJg+3bA0RGoWlmeyGyVlgKFhcDQoYCDg9TVkJWoy+e3SQclU8egRJJgUCJLwqBEEqjL57fZ9igRERERNTQGJSIiIqIaMCgRERER1YBBiYiIiKgGDEpGiI6ORlBQEHr27Cl1KURERNSAGJSMEBUVhcTERMTFxUldChERETUgBiUiIiKiGpj0ytymrnoJqsLCQokrIatSVKRfe0aj0f+XyJyVlwNqtX4tJS7rR42k+nO7NktJMijdh6KiIgCAn5+fxJUQERFRXRUVFcHJyemet+HK3PdBp9MhIyMDDg4OEARB6nIkV1hYCD8/P6Snp3Ol8gbE17lx8HVuHHydGw9f6z+JooiioiL4+PhAJrt3FxJHlO6DTCaDr6+v1GWYHEdHR6s/CBsDX+fGwde5cfB1bjx8rfX+aSSpGpu5iYiIiGrAoERERERUAwYlqjc2NjZ48803YWNjI3UpFo2vc+Pg69w4+Do3Hr7WxmEzNxEREVENOKJEREREVAMGJSIiIqIaMCgRERER1YBBiYiIiKgGDEpUJ9HR0QgICICtrS3CwsJw7Nixe95+8eLFCAwMhJ2dHfz8/PDSSy+hvLy8kao1X3V5nSsrK/H222+jdevWsLW1RXBwMHbu3NmI1Zqn2NhYDBs2DD4+PhAEAVu3bv3H++zbtw/dunWDjY0N2rRpg5UrVzZ4neaurq9zZmYmxo4di3bt2kEmk2HmzJmNUqe5q+vrvGXLFgwaNAju7u5wdHREr169sGvXrsYp1swwKFGtbdy4EbNmzcKbb76JEydOIDg4GBEREbhx48Zdb79u3TrMnTsXb775JpKSkvDNN99g48aNeO211xq5cvNS19f5f//7H5YuXYrPP/8ciYmJmDp1KkaOHImTJ082cuXmpaSkBMHBwYiOjq7V7VNTUzF06FD0798fCQkJmDlzJiZPnswPl39Q19e5oqIC7u7u+N///ofg4OAGrs5y1PV1jo2NxaBBg7Bjxw7Ex8ejf//+GDZsGN837kYkqqXQ0FAxKirK8L1WqxV9fHzEBQsW3PX2UVFR4oABA267bNasWWKfPn0atE5zV9fX2dvbW/ziiy9uu2zUqFHiuHHjGrROSwJA/PHHH+95mzlz5ogdO3a87bIxY8aIERERDViZZanN6/xX4eHh4owZMxqsHktV19e5WlBQkDh//vz6L8jMcUSJakWtViM+Ph4DBw40XCaTyTBw4EAcPnz4rvfp3bs34uPjDdNGKSkp2LFjBx599NFGqdkcGfM6V1RUwNbW9rbL7Ozs8McffzRordbm8OHDt/27AEBERESN/y5E5kSn06GoqAguLi5Sl2JyuCku1Upubi60Wi08PT1vu9zT0xPnz5+/633Gjh2L3Nxc9O3bF6IoQqPRYOrUqZx6uwdjXueIiAgsWrQIDz30EFq3bo2YmBhs2bIFWq22MUq2GllZWXf9dyksLERZWRns7Owkqozo/n300UcoLi7Gk08+KXUpJocjStRg9u3bh/fffx9ffvklTpw4gS1btmD79u145513pC7Nonz66ado27Yt2rdvD5VKhenTp2PixImQyXh4E9E/W7duHebPn4/vv/8eHh4eUpdjcjiiRLXi5uYGuVyO7Ozs2y7Pzs6Gl5fXXe/z+uuvY/z48Zg8eTIAoHPnzigpKcFzzz2H//73v/wgvwtjXmd3d3ds3boV5eXluHnzJnx8fDB37ly0atWqMUq2Gl5eXnf9d3F0dORoEpmtDRs2YPLkydi0adMdU8ukx08qqhWVSoXu3bsjJibGcJlOp0NMTAx69ep11/uUlpbeEYbkcjkAQOQWg3dlzOtczdbWFs2bN4dGo8HmzZsxfPjwhi7XqvTq1eu2fxcA2L179z/+uxCZqvXr12PixIlYv349hg4dKnU5JosjSlRrs2bNQmRkJHr06IHQ0FAsXrwYJSUlmDhxIgBgwoQJaN68ORYsWAAAGDZsGBYtWoSuXbsiLCwMly9fxuuvv45hw4YZAhPdqa6v89GjR3H9+nWEhITg+vXreOutt6DT6TBnzhwpfwyTV1xcjMuXLxu+T01NRUJCAlxcXODv74958+bh+vXrWL16NQBg6tSp+OKLLzBnzhxMmjQJv//+O77//nts375dqh/BLNT1dQaAhIQEw31zcnKQkJAAlUqFoKCgxi7fbNT1dV63bh0iIyPx6aefIiwsDFlZWQD0J4I4OTlJ8jOYLKlPuyPz8vnnn4v+/v6iSqUSQ0NDxSNHjhiuCw8PFyMjIw3fV1ZWim+99ZbYunVr0dbWVvTz8xP/85//iPn5+Y1fuJmpy+u8b98+sUOHDqKNjY3o6uoqjh8/Xrx+/boEVZuXvXv3igDu+Kp+bSMjI8Xw8PA77hMSEiKqVCqxVatW4ooVKxq9bnNjzOt8t9u3aNGi0Ws3J3V9ncPDw+95e/qTIIqcAyEiIiK6G/YoEREREdWAQYmIiIioBgxKRERERDVgUCIiIiKqAYMSERERUQ0YlIiIiIhqwKBEREREVAMGJSIiIqIaMCgRWaF+/fph5syZJvM45kAQBGzduhUAcOXKFQiCYNhqg+7trbfeQkhIiNRlEBmFQYmI/tG+ffsgCAJu3bp12+VbtmzBO++8I01REvLz80NmZiY6deokWQ2mGtb+GiiJLAE3xSWyIGq1GiqVqtGez8XFpdGey5TI5XJ4eXlJXUatNPbvBJGl4YgSkRnr168fpk+fjpkzZ8LNzQ0REREAgLNnz2LIkCFo2rQpPD09MX78eOTm5tb4OGvWrEGPHj3g4OAALy8vjB07Fjdu3ACgH7no378/AMDZ2RmCIOCZZ54xPH/11Ntrr72GsLCwOx47ODgYb7/9tuH7r7/+Gh06dICtrS3at2+PL7/88p4/o06nwwcffIA2bdrAxsYG/v7+eO+99wzXnzlzBgMGDICdnR1cXV3x3HPPobi4+LbX6O/TgyNGjDD8DAAQEBCAd955B0899RSaNGmC5s2bIzo6usaa/j6aUz3iFhMTgx49esDe3h69e/fGhQsXbrvfu+++Cw8PDzg4OGDy5MmYO3fuPaek8vPzMW7cOLi7u8POzg5t27bFihUrAAAtW7YEAHTt2hWCIKBfv34AgGeeeQYjRozAe++9Bx8fHwQGBgIA0tPT8eSTT6JZs2ZwcXHB8OHDceXKFcNzVd/vo48+gre3N1xdXREVFYXKykrDbTIzMzF06FDY2dmhZcuWWLduHQICArB48WLD6wgAI0eOhCAIhu+rrVmzBgEBAXBycsK///1vFBUV1fizE5kKBiUiM7dq1SqoVCocPHgQS5Yswa1btzBgwAB07doVx48fx86dO5GdnY0nn3yyxseorKzEO++8g1OnTmHr1q24cuWKIUj4+flh8+bNAIALFy4gMzMTn3766R2PMW7cOBw7dgzJycmGy86dO4fTp09j7NixAIC1a9fijTfewHvvvYekpCS8//77eP3117Fq1aoaa5s3bx4WLlyI119/HYmJiVi3bh08PT0BACUlJYiIiICzszPi4uKwadMm7NmzB9OnT6/z6/jhhx8iODgYJ0+exNy5czFjxgzs3r27To/x3//+Fx9//DGOHz8OhUKBSZMmGa5bu3Yt3nvvPfzf//0f4uPj4e/vj6+++uqej1f9M//6669ISkrCV199BTc3NwDAsWPHAAB79uxBZmYmtmzZYrhfTEwMLly4gN27d2Pbtm2orKxEREQEHBwccODAARw8eBBNmzbFI488ArVabbjf3r17kZycjL1792LVqlVYuXIlVq5cabh+woQJyMjIwL59+7B582YsW7bMEKgBIC4uDgCwYsUKZGZmGr4HgOTkZGzduhXbtm3Dtm3bsH//fixcuLBOry+RJEQiMlvh4eFi165db7vsnXfeEQcPHnzbZenp6SIA8cKFC4b7zZgxo8bHjYuLEwGIRUVFoiiK4t69e0UAYn5+/h3P/9fHCQ4OFt9++23D9/PmzRPDwsIM37du3Vpct27dHfX26tXrrnUUFhaKNjY24vLly+96/bJly0RnZ2exuLjYcNn27dtFmUwmZmVl1fizDh8+XIyMjDR836JFC/GRRx657TZjxowRhwwZYvgegPjjjz+KoiiKqampIgDx5MmToij++frs2bPntjoAiGVlZaIoimJYWJgYFRV123P06dNHDA4OvuvPJoqiOGzYMHHixIl3ve7vNVSLjIwUPT09xYqKCsNla9asEQMDA0WdTme4rKKiQrSzsxN37dpluF+LFi1EjUZjuM0TTzwhjhkzRhRFUUxKShIBiHFxcYbrL126JAIQP/nkk7u+TtXefPNN0d7eXiwsLDRcNnv27Nt+N4hMFUeUiMxc9+7db/v+1KlT2Lt3L5o2bWr4at++PQDcNtrzV/Hx8Rg2bBj8/f3h4OCA8PBwAEBaWlqdahk3bhzWrVsHABBFEevXr8e4ceMA6Ed/kpOT8eyzz95W27vvvltjXUlJSaioqMDDDz9c4/XBwcFo0qSJ4bI+ffpAp9PdMe31T3r16nXH90lJSXV6jC5duhj+39vbGwAMIy4XLlxAaGjobbf/+/d/N23aNGzYsAEhISGYM2cODh06VKs6OnfufFtf0qlTp3D58mU4ODgYXncXFxeUl5ff9tp37NgRcrn8tp/hr/UrFAp069bNcH2bNm3g7Oxcq5oCAgLg4OBw18cmMmVs5iYyc38NCQBQXFyMYcOG4f/+7//uuG31h/dfVU9fRUREYO3atXB3d0daWhoiIiJum5apjaeeegqvvvoqTpw4gbKyMqSnp2PMmDGGugBg+fLld/Qy/fXD+a/s7Ozq9Px3I5PJIIribZf9te+mPimVSsP/C4IAQN9jZawhQ4bg6tWr2LFjB3bv3o2HH34YUVFR+Oijj+55v7v9TnTv3h1r166947bu7u53rR/Q/wz3U/9fNeRjEzUkjigRWZhu3brh3LlzCAgIQJs2bW77+vsHKACcP38eN2/exMKFC/Hggw+iffv2d/ylXz06odVq7/ncvr6+CA8Px9q1a7F27VoMGjQIHh4eAABPT0/4+PggJSXljrqqG5P/rm3btrCzs0NMTMxdr+/QoQNOnTqFkpISw2UHDx6ETCYzNDG7u7sjMzPTcL1Wq8XZs2fveKwjR47c8X2HDh3u+fPWRWBg4G09OwDu+P5u3N3dERkZie+++w6LFy/GsmXLANT+3wTQ/05cunQJHh4ed7z2Tk5Ota5fo9Hg5MmThssuX76M/Pz8226nVCprVRORuWBQIrIwUVFRyMvLw1NPPYW4uDgkJydj165dmDhx4l0/wPz9/aFSqfD5558jJSUFP//88x1rI7Vo0QKCIGDbtm3Iycm57ayyvxs3bhw2bNiATZs2Gabdqs2fPx8LFizAZ599hosXL+LMmTNYsWIFFi1adNfHsrW1xauvvoo5c+Zg9erVSE5OxpEjR/DNN98YnsvW1haRkZE4e/Ys9u7dixdeeAHjx483NHwPGDAA27dvx/bt23H+/HlMmzbtjvWgAH3A+uCDD3Dx4kVER0dj06ZNmDFjxj1f67p44YUX8M0332DVqlW4dOkS3n33XZw+fdow8nQ3b7zxBn766SdcvnwZ586dw7Zt2wzhzcPDA3Z2doZm/YKCghofZ9y4cXBzc8Pw4cNx4MABpKamYt++fXjxxRdx7dq1WtXfvn17DBw4EM899xyOHTuGkydP4rnnnoOdnd1tP0NAQABiYmKQlZV1R4giMkcMSkQWxsfHBwcPHoRWq8XgwYPRuXNnzJw5E82aNYNMduch7+7ujpUrV2LTpk0ICgrCwoUL75jaad68OebPn4+5c+fC09PznmeVjR49Gjdv3kRpaSlGjBhx23WTJ0/G119/jRUrVqBz584IDw/HypUraxxRAvRnfr388st444030KFDB4wZM8Yw4mVvb49du3YhLy8PPXv2xOjRo/Hwww/jiy++MNx/0qRJiIyMxIQJExAeHo5WrVoZljv4q5dffhnHjx9H165d8e6772LRokWG5Rbqw7hx4zBv3jy88sor6NatG1JTU/HMM8/A1ta2xvuoVCrMmzcPXbp0wUMPPQS5XI4NGzYAABQKBT777DMsXboUPj4+GD58eI2PY29vj9jYWPj7+2PUqFHo0KEDnn32WZSXl8PR0bHWP8Pq1avh6emJhx56CCNHjsSUKVPg4OBw28/w8ccfY/fu3fDz80PXrl1r/dhEpkoQ/z55T0RkZQICAjBz5sxG345l0KBB8PLywpo1axr1eevLtWvX4Ofnhz179tTYcE9k7tjMTUTUCEpLS7FkyRJERERALpdj/fr12LNnT53XapLS77//juLiYnTu3BmZmZmYM2cOAgIC8NBDD0ldGlGDYVAiImoEgiBgx44deO+991BeXo7AwEBs3rwZAwcOlLq0WqusrMRrr72GlJQUODg4oHfv3li7du0dZ7QRWRJOvRERERHVgM3cRERERDVgUCIiIiKqAYMSERERUQ0YlIiIiIhqwKBEREREVAMGJSIiIqIaMCgRERER1YBBiYiIiKgG/w9478qYbBliqAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -764,10 +735,10 @@
      "start_time": "2020-03-22T05:01:41.456749Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.006801Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.006612Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.009449Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.008942Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.796847Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.796728Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.798557Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.798264Z"
     }
    },
    "outputs": [],
@@ -791,10 +762,10 @@
      "start_time": "2020-03-22T05:01:41.474312Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.011672Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.011362Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.015977Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.015507Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.800022Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.799922Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.802170Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.801873Z"
     }
    },
    "outputs": [],
@@ -820,10 +791,10 @@
      "start_time": "2020-03-22T05:01:41.492908Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.017910Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.017759Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.022102Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.021715Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.803857Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.803723Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.806284Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.806018Z"
     }
    },
    "outputs": [
@@ -880,10 +851,10 @@
      "start_time": "2020-03-22T05:01:41.506536Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.024240Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.024021Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.027633Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.027129Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.807661Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.807536Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.809769Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.809484Z"
     }
    },
    "outputs": [],
@@ -932,10 +903,10 @@
      "start_time": "2020-03-22T05:01:41.518139Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.029577Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.029430Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.033214Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.032725Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.811207Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.811109Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.813402Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.813115Z"
     }
    },
    "outputs": [],
@@ -976,10 +947,10 @@
      "start_time": "2020-03-22T05:01:41.528504Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.035095Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.034948Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.038293Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.037801Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.814998Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.814872Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.816773Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.816511Z"
     }
    },
    "outputs": [],
@@ -1012,10 +983,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.040255Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.040108Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.963747Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.963225Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.818298Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.818156Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.197787Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.197474Z"
     }
    },
    "outputs": [
@@ -1156,10 +1127,10 @@
      "start_time": "2020-03-22T05:06:03.287117Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.965960Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.965774Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.973424Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.972921Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.199381Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.199290Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.204322Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.204085Z"
     }
    },
    "outputs": [
@@ -1224,10 +1195,10 @@
      "start_time": "2020-03-22T05:06:03.349278Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.975332Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.975156Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.979223Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.978634Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.205591Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.205477Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.207655Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.207421Z"
     }
    },
    "outputs": [
@@ -1261,10 +1232,10 @@
      "start_time": "2020-03-22T05:06:03.360976Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.981072Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.980922Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.984264Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.983734Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.208917Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.208835Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.210624Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.210397Z"
     }
    },
    "outputs": [],
@@ -1294,10 +1265,10 @@
      "start_time": "2020-03-22T05:06:03.369715Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.986158Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.986009Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.316731Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.316154Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.212020Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.211928Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.395371Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.395105Z"
     }
    },
    "outputs": [
@@ -1381,10 +1352,10 @@
      "start_time": "2020-03-22T05:06:04.277559Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.318698Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.318537Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.353835Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.353316Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.396873Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.396752Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.479090Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.478761Z"
     }
    },
    "outputs": [],
@@ -1403,16 +1374,16 @@
      "start_time": "2020-03-22T05:06:04.616827Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.355730Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.355576Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.472245Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.471702Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.480926Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.480801Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.552270Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.551962Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1455,10 +1426,10 @@
      "start_time": "2020-03-22T05:06:04.827766Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.474223Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.474067Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.477318Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.476861Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.553941Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.553833Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.555755Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.555477Z"
     }
    },
    "outputs": [
@@ -1483,10 +1454,10 @@
      "start_time": "2020-03-22T05:06:04.834702Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.479202Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.478995Z",
-     "iopub.status.idle": "2024-06-03T10:38:32.900613Z",
-     "shell.execute_reply": "2024-06-03T10:38:32.899972Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.557457Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.557323Z",
+     "iopub.status.idle": "2024-06-03T14:27:38.444412Z",
+     "shell.execute_reply": "2024-06-03T14:27:38.444160Z"
     }
    },
    "outputs": [
@@ -1495,28 +1466,28 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T  ∫gₐ(ϵ₁)dt  ∫gₐ(ϵ₂)dt  ∫gₐ(ϵ₃)dt  ∫gₐ(ϵ₄)dt   ∑∫gₐ(t)dt          J       ΔJ_T         ΔJ  secs\n",
-      "0     5.90e-04   0.00e+00   0.00e+00   0.00e+00   0.00e+00    0.00e+00   5.90e-04        n/a        n/a     1\n"
+      "0     5.90e-04   0.00e+00   0.00e+00   0.00e+00   0.00e+00    0.00e+00   5.90e-04        n/a        n/a     0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "13    3.24e-04   6.29e-05   9.90e-06   5.10e-05   9.21e-06    1.33e-04   4.57e-04  -2.66e-04  -1.33e-04     2\n"
+      "13    3.24e-04   6.29e-05   9.90e-06   5.10e-05   9.21e-06    1.33e-04   4.57e-04  -2.66e-04  -1.33e-04     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "14    1.83e-04   3.16e-05   7.05e-06   2.56e-05   6.48e-06    7.08e-05   2.53e-04  -1.42e-04  -7.08e-05     2\n"
+      "14    1.83e-04   3.16e-05   7.05e-06   2.56e-05   6.48e-06    7.08e-05   2.53e-04  -1.42e-04  -7.08e-05     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15    1.06e-04   1.60e-05   5.02e-06   1.29e-05   4.56e-06    3.85e-05   1.44e-04  -7.70e-05  -3.85e-05     2\n"
+      "15    1.06e-04   1.60e-05   5.02e-06   1.29e-05   4.56e-06    3.85e-05   1.44e-04  -7.70e-05  -3.85e-05     1\n"
      ]
     }
    ],
@@ -1558,10 +1529,10 @@
      "start_time": "2020-03-22T05:06:16.592668Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:32.903152Z",
-     "iopub.status.busy": "2024-06-03T10:38:32.902952Z",
-     "iopub.status.idle": "2024-06-03T10:38:33.885938Z",
-     "shell.execute_reply": "2024-06-03T10:38:33.885370Z"
+     "iopub.execute_input": "2024-06-03T14:27:38.445976Z",
+     "iopub.status.busy": "2024-06-03T14:27:38.445864Z",
+     "iopub.status.idle": "2024-06-03T14:27:40.599983Z",
+     "shell.execute_reply": "2024-06-03T14:27:40.599672Z"
     }
    },
    "outputs": [],
@@ -1584,10 +1555,10 @@
      "start_time": "2020-03-22T05:06:25.332449Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:33.888096Z",
-     "iopub.status.busy": "2024-06-03T10:38:33.887938Z",
-     "iopub.status.idle": "2024-06-03T10:38:34.894530Z",
-     "shell.execute_reply": "2024-06-03T10:38:34.893871Z"
+     "iopub.execute_input": "2024-06-03T14:27:40.601494Z",
+     "iopub.status.busy": "2024-06-03T14:27:40.601404Z",
+     "iopub.status.idle": "2024-06-03T14:27:42.952722Z",
+     "shell.execute_reply": "2024-06-03T14:27:42.952404Z"
     }
    },
    "outputs": [],
@@ -1608,16 +1579,16 @@
      "start_time": "2020-03-22T05:06:34.911667Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:34.896840Z",
-     "iopub.status.busy": "2024-06-03T10:38:34.896653Z",
-     "iopub.status.idle": "2024-06-03T10:38:35.177508Z",
-     "shell.execute_reply": "2024-06-03T10:38:35.177098Z"
+     "iopub.execute_input": "2024-06-03T14:27:42.954198Z",
+     "iopub.status.busy": "2024-06-03T14:27:42.954102Z",
+     "iopub.status.idle": "2024-06-03T14:27:43.121127Z",
+     "shell.execute_reply": "2024-06-03T14:27:43.120859Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1665,7 +1636,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_sources/notebooks/09_example_numpy.ipynb.txt b/master/_sources/notebooks/09_example_numpy.ipynb.txt
index 01b915dd..297da2cc 100644
--- a/master/_sources/notebooks/09_example_numpy.ipynb.txt
+++ b/master/_sources/notebooks/09_example_numpy.ipynb.txt
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:09.544235Z",
-     "iopub.status.busy": "2024-06-03T10:32:09.544062Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.077876Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.077317Z"
+     "iopub.execute_input": "2024-06-03T14:23:56.491044Z",
+     "iopub.status.busy": "2024-06-03T14:23:56.490913Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.496001Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.495645Z"
     }
    },
    "outputs": [
@@ -33,13 +33,13 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "scipy     : 1.10.1\n",
+      "numpy     : 1.26.4\n",
+      "krotov    : 1.3.0+dev\n",
       "matplotlib: 3.7.5\n",
-      "numpy     : 1.24.4\n",
+      "scipy     : 1.12.0\n",
       "\n"
      ]
     }
@@ -129,10 +129,10 @@
      "start_time": "2020-03-24T21:13:40.847535Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.107387Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.107103Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.111392Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.110906Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.519194Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.518953Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.522368Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.522085Z"
     }
    },
    "outputs": [],
@@ -164,10 +164,10 @@
      "start_time": "2020-03-24T21:13:40.865354Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.113342Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.113151Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.115611Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.115135Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.523800Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.523713Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.525491Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.525175Z"
     },
     "lines_to_next_cell": 2
    },
@@ -196,10 +196,10 @@
      "start_time": "2020-03-24T21:13:40.876732Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.117573Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.117415Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.119834Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.119351Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.527047Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.526938Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.528584Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.528326Z"
     }
    },
    "outputs": [],
@@ -223,10 +223,10 @@
      "start_time": "2020-03-24T21:13:40.883324Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.121782Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.121623Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.124415Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.123936Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.530097Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.530012Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.531903Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.531594Z"
     }
    },
    "outputs": [],
@@ -251,10 +251,10 @@
      "start_time": "2020-03-24T21:13:40.891777Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.126403Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.126212Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.131789Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.131306Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.533523Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.533391Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.536997Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.536700Z"
     }
    },
    "outputs": [
@@ -306,10 +306,10 @@
      "start_time": "2020-03-24T21:13:40.905293Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.133844Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.133654Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.136951Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.136474Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.538677Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.538564Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.540781Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.540487Z"
     }
    },
    "outputs": [],
@@ -341,10 +341,10 @@
      "start_time": "2020-03-24T21:13:40.917919Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.138951Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.138763Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.141562Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.141086Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.542211Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.542051Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.543896Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.543647Z"
     }
    },
    "outputs": [],
@@ -371,10 +371,10 @@
      "start_time": "2020-03-24T21:13:40.926588Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.143527Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.143369Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.146007Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.145531Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.545255Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.545135Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.546902Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.546662Z"
     }
    },
    "outputs": [],
@@ -400,10 +400,10 @@
      "start_time": "2020-03-24T21:13:40.933147Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.148001Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.147811Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.150343Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.149867Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.548325Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.548228Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.549880Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.549500Z"
     }
    },
    "outputs": [],
@@ -425,13 +425,24 @@
      "n": "12"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.152322Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.152132Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.223699Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.223236Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.551258Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.551171Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.582962Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.582650Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/3854291581.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  return complex(state.conj().T @ (proj @ state)).real\n",
+      "/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/3854291581.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  return complex(state.conj().T @ (proj @ state)).real\n"
+     ]
+    }
+   ],
    "source": [
     "guess_dynamics = objectives[0].propagate(\n",
     "    tlist, propagator=expm, e_ops=[proj0, proj1], expect=expect\n",
@@ -452,10 +463,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.225618Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.225388Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.228433Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.227969Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.584562Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.584425Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.586642Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.586302Z"
     }
    },
    "outputs": [],
@@ -484,10 +495,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.230359Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.230203Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.377278Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.376788Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.588919Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.588769Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.788571Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.787424Z"
     }
    },
    "outputs": [
@@ -526,10 +537,10 @@
      "start_time": "2020-03-24T21:13:41.397827Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.379362Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.379205Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.382230Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.381780Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.796425Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.795886Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.803552Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.802616Z"
     }
    },
    "outputs": [],
@@ -550,10 +561,10 @@
      "start_time": "2020-03-24T21:13:41.406924Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.384142Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.383964Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.386559Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.386109Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.811656Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.811251Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.823075Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.817756Z"
     }
    },
    "outputs": [],
@@ -586,10 +597,10 @@
      "start_time": "2020-03-24T21:13:41.415715Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.388498Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.388350Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.391084Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.390634Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.829078Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.828463Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.834886Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.832323Z"
     }
    },
    "outputs": [],
@@ -617,10 +628,10 @@
      "start_time": "2020-03-24T21:13:41.427434Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.393128Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.392908Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.395372Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.394924Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.839276Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.838954Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.848829Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.848176Z"
     }
    },
    "outputs": [],
@@ -658,10 +669,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.397329Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.397181Z",
-     "iopub.status.idle": "2024-06-03T10:32:11.392602Z",
-     "shell.execute_reply": "2024-06-03T10:32:11.392046Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.858243Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.854303Z",
+     "iopub.status.idle": "2024-06-03T14:23:58.390263Z",
+     "shell.execute_reply": "2024-06-03T14:23:58.389983Z"
     }
    },
    "outputs": [
@@ -701,6 +712,14 @@
       "4     7.37e-01    3.84e-02   7.76e-01  -8.52e-02  -4.68e-02     0\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/124890641.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  return complex(psi1.conj().T @ psi2)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -774,10 +793,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:11.394706Z",
-     "iopub.status.busy": "2024-06-03T10:32:11.394522Z",
-     "iopub.status.idle": "2024-06-03T10:32:11.398301Z",
-     "shell.execute_reply": "2024-06-03T10:32:11.397920Z"
+     "iopub.execute_input": "2024-06-03T14:23:58.391772Z",
+     "iopub.status.busy": "2024-06-03T14:23:58.391640Z",
+     "iopub.status.idle": "2024-06-03T14:23:58.393703Z",
+     "shell.execute_reply": "2024-06-03T14:23:58.393450Z"
     }
    },
    "outputs": [
@@ -786,11 +805,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:32:10\n",
+       "- Started at 2024-06-03 10:23:57\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 10\n",
        "- Reason for termination: Reached 10 iterations\n",
-       "- Ended at 2024-06-03 12:32:11 (0:00:01)"
+       "- Ended at 2024-06-03 10:23:58 (0:00:01)"
       ]
      },
      "execution_count": 19,
@@ -820,7 +839,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/master/_static/_sphinx_javascript_frameworks_compat.js b/master/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 00000000..81415803
--- /dev/null
+++ b/master/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/master/_static/basic.css b/master/_static/basic.css
index b3bdc004..f316efcb 100644
--- a/master/_static/basic.css
+++ b/master/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -130,7 +130,7 @@ ul.search li a {
     font-weight: bold;
 }
 
-ul.search li div.context {
+ul.search li p.context {
     color: #888;
     margin: 2px 0 0 30px;
     text-align: left;
@@ -222,7 +222,7 @@ table.modindextable td {
 /* -- general body styles --------------------------------------------------- */
 
 div.body {
-    min-width: 450px;
+    min-width: 360px;
     max-width: 800px;
 }
 
@@ -237,14 +237,8 @@ a.headerlink {
     visibility: hidden;
 }
 
-a.brackets:before,
-span.brackets > a:before{
-    content: "[";
-}
-
-a.brackets:after,
-span.brackets > a:after {
-    content: "]";
+a:visited {
+    color: #551A8B;
 }
 
 h1:hover > a.headerlink,
@@ -335,12 +329,16 @@ p.sidebar-title {
     font-weight: bold;
 }
 
+nav.contents,
+aside.topic,
 div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
 
+nav.contents,
+aside.topic,
 div.topic {
     border: 1px solid #ccc;
     padding: 7px;
@@ -379,6 +377,8 @@ div.body p.centered {
 
 div.sidebar > :last-child,
 aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
@@ -386,6 +386,8 @@ div.admonition > :last-child {
 
 div.sidebar::after,
 aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -428,10 +430,6 @@ table.docutils td, table.docutils th {
     border-bottom: 1px solid #aaa;
 }
 
-table.footnote td, table.footnote th {
-    border: 0 !important;
-}
-
 th {
     text-align: left;
     padding-right: 5px;
@@ -508,6 +506,63 @@ table.hlist td {
     vertical-align: top;
 }
 
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
 
 /* -- other body styles ----------------------------------------------------- */
 
@@ -558,19 +613,26 @@ ul.simple p {
     margin-bottom: 0;
 }
 
-dl.footnote > dt,
-dl.citation > dt {
+aside.footnote > span,
+div.citation > span {
     float: left;
-    margin-right: 0.5em;
 }
-
-dl.footnote > dd,
-dl.citation > dd {
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
     margin-bottom: 0em;
 }
-
-dl.footnote > dd:after,
-dl.citation > dd:after {
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
     content: "";
     clear: both;
 }
@@ -587,10 +649,6 @@ dl.field-list > dt {
     padding-right: 5px;
 }
 
-dl.field-list > dt:after {
-    content: ":";
-}
-
 dl.field-list > dd {
     padding-left: 0.5em;
     margin-top: 0em;
@@ -616,6 +674,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -634,14 +702,6 @@ dl.glossary dt {
     font-size: 1.1em;
 }
 
-.optional {
-    font-size: 1.3em;
-}
-
-.sig-paren {
-    font-size: larger;
-}
-
 .versionmodified {
     font-style: italic;
 }
@@ -682,8 +742,9 @@ dl.glossary dt {
 
 .classifier:before {
     font-style: normal;
-    margin: 0.5em;
+    margin: 0 0.5em;
     content: ":";
+    display: inline-block;
 }
 
 abbr, acronym {
@@ -691,6 +752,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
@@ -707,6 +776,7 @@ span.pre {
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
+    white-space: nowrap;
 }
 
 div[class*="highlight-"] {
@@ -770,8 +840,12 @@ div.code-block-caption code {
 
 table.highlighttable td.linenos,
 span.linenos,
-div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
-    user-select: none;
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
 }
 
 div.code-block-caption span.caption-number {
@@ -786,16 +860,6 @@ div.literal-block-wrapper {
     margin: 1em 0;
 }
 
-code.descname {
-    background-color: transparent;
-    font-weight: bold;
-    font-size: 1.2em;
-}
-
-code.descclassname {
-    background-color: transparent;
-}
-
 code.xref, a code {
     background-color: transparent;
     font-weight: bold;
diff --git a/master/_static/doctools.js b/master/_static/doctools.js
index 61ac9d26..4d67807d 100644
--- a/master/_static/doctools.js
+++ b/master/_static/doctools.js
@@ -2,320 +2,155 @@
  * doctools.js
  * ~~~~~~~~~~~
  *
- * Sphinx JavaScript utilities for all documentation.
+ * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
-  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
-    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
-    "profile", "profileEnd"];
-  window.console = {};
-  for (var i = 0; i < names.length; ++i)
-    window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
-  if (!x) {
-    return x
-  }
-  return decodeURIComponent(x.replace(/\+/g, ' '));
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-  if (typeof s === 'undefined')
-    s = document.location.search;
-  var parts = s.substr(s.indexOf('?') + 1).split('&');
-  var result = {};
-  for (var i = 0; i < parts.length; i++) {
-    var tmp = parts[i].split('=', 2);
-    var key = jQuery.urldecode(tmp[0]);
-    var value = jQuery.urldecode(tmp[1]);
-    if (key in result)
-      result[key].push(value);
-    else
-      result[key] = [value];
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
   }
-  return result;
 };
 
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-  function highlight(node, addItems) {
-    if (node.nodeType === 3) {
-      var val = node.nodeValue;
-      var pos = val.toLowerCase().indexOf(text);
-      if (pos >= 0 &&
-          !jQuery(node.parentNode).hasClass(className) &&
-          !jQuery(node.parentNode).hasClass("nohighlight")) {
-        var span;
-        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-        if (isInSVG) {
-          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-        } else {
-          span = document.createElement("span");
-          span.className = className;
-        }
-        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling));
-        node.nodeValue = val.substr(0, pos);
-        if (isInSVG) {
-          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-          var bbox = node.parentElement.getBBox();
-          rect.x.baseVal.value = bbox.x;
-          rect.y.baseVal.value = bbox.y;
-          rect.width.baseVal.value = bbox.width;
-          rect.height.baseVal.value = bbox.height;
-          rect.setAttribute('class', className);
-          addItems.push({
-              "parent": node.parentNode,
-              "target": rect});
-        }
-      }
-    }
-    else if (!jQuery(node).is("button, select, textarea")) {
-      jQuery.each(node.childNodes, function() {
-        highlight(this, addItems);
-      });
-    }
-  }
-  var addItems = [];
-  var result = this.each(function() {
-    highlight(this, addItems);
-  });
-  for (var i = 0; i < addItems.length; ++i) {
-    jQuery(addItems[i].parent).before(addItems[i].target);
-  }
-  return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-  jQuery.uaMatch = function(ua) {
-    ua = ua.toLowerCase();
-
-    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-      /(msie) ([\w.]+)/.exec(ua) ||
-      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-      [];
-
-    return {
-      browser: match[ 1 ] || "",
-      version: match[ 2 ] || "0"
-    };
-  };
-  jQuery.browser = {};
-  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
-
 /**
  * Small JavaScript module for the documentation.
  */
-var Documentation = {
-
-  init : function() {
-    this.fixFirefoxAnchorBug();
-    this.highlightSearchWords();
-    this.initIndexTable();
-    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
-      this.initOnKeyListeners();
-    }
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
   },
 
   /**
    * i18n support
    */
-  TRANSLATIONS : {},
-  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
-  LOCALE : 'unknown',
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
 
   // gettext and ngettext don't access this so that the functions
   // can safely bound to a different name (_ = Documentation.gettext)
-  gettext : function(string) {
-    var translated = Documentation.TRANSLATIONS[string];
-    if (typeof translated === 'undefined')
-      return string;
-    return (typeof translated === 'string') ? translated : translated[0];
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
   },
 
-  ngettext : function(singular, plural, n) {
-    var translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated === 'undefined')
-      return (n == 1) ? singular : plural;
-    return translated[Documentation.PLURALEXPR(n)];
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
   },
 
-  addTranslations : function(catalog) {
-    for (var key in catalog.messages)
-      this.TRANSLATIONS[key] = catalog.messages[key];
-    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
-    this.LOCALE = catalog.locale;
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
   },
 
   /**
-   * add context elements like header anchor links
+   * helper function to focus on search bar
    */
-  addContextElements : function() {
-    $('div[id] > :header:first').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this headline')).
-      appendTo(this);
-    });
-    $('dt[id]').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this definition')).
-      appendTo(this);
-    });
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
   },
 
   /**
-   * workaround a firefox stupidity
-   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   * Initialise the domain index toggle buttons
    */
-  fixFirefoxAnchorBug : function() {
-    if (document.location.hash && $.browser.mozilla)
-      window.setTimeout(function() {
-        document.location.href += '';
-      }, 10);
-  },
-
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords : function() {
-    var params = $.getQueryParameters();
-    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
-    if (terms.length) {
-      var body = $('div.body');
-      if (!body.length) {
-        body = $('body');
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
       }
-      window.setTimeout(function() {
-        $.each(terms, function() {
-          body.highlightText(this.toLowerCase(), 'highlighted');
-        });
-      }, 10);
-      $('<p class="highlight-link"><a href="javascript:Documentation.' +
-        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
-          .appendTo($('#searchbox'));
-    }
-  },
-
-  /**
-   * init the domain index toggle buttons
-   */
-  initIndexTable : function() {
-    var togglers = $('img.toggler').click(function() {
-      var src = $(this).attr('src');
-      var idnum = $(this).attr('id').substr(7);
-      $('tr.cg-' + idnum).toggle();
-      if (src.substr(-9) === 'minus.png')
-        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
-      else
-        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
-    }).css('display', '');
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
-        togglers.click();
-    }
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords : function() {
-    $('#searchbox .highlight-link').fadeOut(300);
-    $('span.highlighted').removeClass('highlighted');
-  },
-
-  /**
-   * make the url absolute
-   */
-  makeURL : function(relativeURL) {
-    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
-  },
+    };
 
-  /**
-   * get the current relative url
-   */
-  getCurrentURL : function() {
-    var path = document.location.pathname;
-    var parts = path.split(/\//);
-    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
-      if (this === '..')
-        parts.pop();
-    });
-    var url = parts.join('/');
-    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
   },
 
-  initOnKeyListeners: function() {
-    $(document).keydown(function(event) {
-      var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box, textarea, dropdown or button
-      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
-          && !event.shiftKey) {
-        switch (event.keyCode) {
-          case 37: // left
-            var prevHref = $('link[rel="prev"]').prop('href');
-            if (prevHref) {
-              window.location.href = prevHref;
-              return false;
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
             }
-          case 39: // right
-            var nextHref = $('link[rel="next"]').prop('href');
-            if (nextHref) {
-              window.location.href = nextHref;
-              return false;
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
             }
+            break;
         }
       }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
     });
-  }
+  },
 };
 
 // quick alias for translations
-_ = Documentation.gettext;
+const _ = Documentation.gettext;
 
-$(document).ready(function() {
-  Documentation.init();
-});
+_ready(Documentation.init);
diff --git a/master/_static/documentation_options.js b/master/_static/documentation_options.js
index 7cf0629b..0dacfce1 100644
--- a/master/_static/documentation_options.js
+++ b/master/_static/documentation_options.js
@@ -1,12 +1,13 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '1.3.0',
-    LANGUAGE: 'None',
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '1.3.0+dev (bd14c16)',
+    LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
     FILE_SUFFIX: '.html',
     LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
     SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
 };
\ No newline at end of file
diff --git a/master/_static/graphviz.css b/master/_static/graphviz.css
index b340734c..027576e3 100644
--- a/master/_static/graphviz.css
+++ b/master/_static/graphviz.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- graphviz extension.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/master/_static/jquery-3.5.1.js b/master/_static/jquery-3.5.1.js
deleted file mode 100644
index 50937333..00000000
--- a/master/_static/jquery-3.5.1.js
+++ /dev/null
@@ -1,10872 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.5.1
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2020-05-04T22:49Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-      // Support: Chrome <=57, Firefox <=52
-      // In some browsers, typeof returns "function" for HTML <object> elements
-      // (i.e., `typeof document.createElement( "object" ) === "function"`).
-      // We don't want to classify *any* DOM node as a function.
-      return typeof obj === "function" && typeof obj.nodeType !== "number";
-  };
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.5.1",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-					[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( _i, name ) {
-	class2type[ "[object " + name + "]" ] = name.toLowerCase();
-} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.5
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2020-03-14
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem.namespaceURI,
-		docElem = ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-};
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the master Deferred
-			master = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						master.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( master.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return master.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
-		}
-
-		return master.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-					value :
-					value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var
-	rkeyEvent = /^key/,
-	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
-	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-					dataPriv.get( this, "events" ) || Object.create( null )
-				)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-							return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-							return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-						return result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-
-	which: function( event ) {
-		var button = event.button;
-
-		// Add which for key events
-		if ( event.which == null && rkeyEvent.test( event.type ) ) {
-			return event.charCode != null ? event.charCode : event.keyCode;
-		}
-
-		// Add which for click: 1 === left; 2 === middle; 3 === right
-		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
-			if ( button & 1 ) {
-				return 1;
-			}
-
-			if ( button & 2 ) {
-				return 3;
-			}
-
-			if ( button & 4 ) {
-				return 2;
-			}
-
-			return 0;
-		}
-
-		return event.which;
-	}
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px";
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-						swap( elem, cssShow, function() {
-							return getWidthOrHeight( elem, dimension, extra );
-						} ) :
-						getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-				) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-					jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-			/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-						animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-			doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-						"" :
-						dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-					return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = (
-					dataPriv.get( cur, "events" ) || Object.create( null )
-				)[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {
-		xml = undefined;
-	}
-
-	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
-		jQuery.error( "Invalid XML: " + data );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} )
-		.filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} )
-		.map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-	originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-					jQuery( callbackContext ) :
-					jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script
-			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			if ( typeof props.top === "number" ) {
-				props.top += "px";
-			}
-			if ( typeof props.left === "number" ) {
-				props.left += "px";
-			}
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
-		function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	} );
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
-	define( "jquery", [], function() {
-		return jQuery;
-	} );
-}
-
-
-
-
-var
-
-	// Map over jQuery in case of overwrite
-	_jQuery = window.jQuery,
-
-	// Map over the $ in case of overwrite
-	_$ = window.$;
-
-jQuery.noConflict = function( deep ) {
-	if ( window.$ === jQuery ) {
-		window.$ = _$;
-	}
-
-	if ( deep && window.jQuery === jQuery ) {
-		window.jQuery = _jQuery;
-	}
-
-	return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
-	window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/master/_static/jquery.js b/master/_static/jquery.js
index b0614034..c4c6022f 100644
--- a/master/_static/jquery.js
+++ b/master/_static/jquery.js
@@ -1,2 +1,2 @@
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e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
diff --git a/master/_static/language_data.js b/master/_static/language_data.js
index 863704b3..367b8ed8 100644
--- a/master/_static/language_data.js
+++ b/master/_static/language_data.js
@@ -5,15 +5,15 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
 
-var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
+/* Non-minified version is copied as a separate JS file, if available */
 
 /**
  * Porter Stemmer
@@ -197,101 +197,3 @@ var Stemmer = function() {
   }
 }
 
-
-
-
-var splitChars = (function() {
-    var result = {};
-    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
-         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
-         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
-         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
-         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
-         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
-         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
-         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
-         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
-         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
-    var i, j, start, end;
-    for (i = 0; i < singles.length; i++) {
-        result[singles[i]] = true;
-    }
-    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
-         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
-         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
-         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
-         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
-         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
-         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
-         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
-         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
-         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
-         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
-         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
-         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
-         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
-         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
-         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
-         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
-         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
-         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
-         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
-         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
-         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
-         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
-         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
-         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
-         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
-         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
-         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
-         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
-         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
-         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
-         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
-         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
-         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
-         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
-         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
-         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
-         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
-         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
-         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
-         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
-         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
-         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
-         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
-         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
-         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
-         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
-         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
-         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
-    for (i = 0; i < ranges.length; i++) {
-        start = ranges[i][0];
-        end = ranges[i][1];
-        for (j = start; j <= end; j++) {
-            result[j] = true;
-        }
-    }
-    return result;
-})();
-
-function splitQuery(query) {
-    var result = [];
-    var start = -1;
-    for (var i = 0; i < query.length; i++) {
-        if (splitChars[query.charCodeAt(i)]) {
-            if (start !== -1) {
-                result.push(query.slice(start, i));
-                start = -1;
-            }
-        } else if (start === -1) {
-            start = i;
-        }
-    }
-    if (start !== -1) {
-        result.push(query.slice(start));
-    }
-    return result;
-}
-
-
diff --git a/master/_static/searchtools.js b/master/_static/searchtools.js
index 1a90152e..92da3f8b 100644
--- a/master/_static/searchtools.js
+++ b/master/_static/searchtools.js
@@ -4,22 +4,24 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
+"use strict";
 
-if (!Scorer) {
-  /**
-   * Simple result scoring code.
-   */
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
   var Scorer = {
     // Implement the following function to further tweak the score for each result
-    // The function takes a result array [filename, title, anchor, descr, score]
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
     // and returns the new score.
     /*
-    score: function(result) {
-      return result[4];
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
     },
     */
 
@@ -28,9 +30,11 @@ if (!Scorer) {
     // or matches in the last dotted part of the object name
     objPartialMatch: 6,
     // Additive scores depending on the priority of the object
-    objPrio: {0:  15,   // used to be importantResults
-              1:  5,   // used to be objectResults
-              2: -5},  // used to be unimportantResults
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
     //  Used when the priority is not in the mapping.
     objPrioDefault: 0,
 
@@ -39,452 +43,548 @@ if (!Scorer) {
     partialTitle: 7,
     // query found in terms
     term: 5,
-    partialTerm: 2
+    partialTerm: 2,
   };
 }
 
-if (!splitQuery) {
-  function splitQuery(query) {
-    return query.split(/\s+/);
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
   }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
 }
 
 /**
  * Search Module
  */
-var Search = {
-
-  _index : null,
-  _queued_query : null,
-  _pulse_status : -1,
-
-  htmlToText : function(htmlString) {
-      var virtualDocument = document.implementation.createHTMLDocument('virtual');
-      var htmlElement = $(htmlString, virtualDocument);
-      htmlElement.find('.headerlink').remove();
-      docContent = htmlElement.find('[role=main]')[0];
-      if(docContent === undefined) {
-          console.warn("Content block not found. Sphinx search tries to obtain it " +
-                       "via '[role=main]'. Could you check your theme or template.");
-          return "";
-      }
-      return docContent.textContent || docContent.innerText;
-  },
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
 
-  init : function() {
-      var params = $.getQueryParameters();
-      if (params.q) {
-          var query = params.q[0];
-          $('input[name="q"]')[0].value = query;
-          this.performSearch(query);
-      }
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
   },
 
-  loadIndex : function(url) {
-    $.ajax({type: "GET", url: url, data: null,
-            dataType: "script", cache: true,
-            complete: function(jqxhr, textstatus) {
-              if (textstatus != "success") {
-                document.getElementById("searchindexloader").src = url;
-              }
-            }});
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
   },
 
-  setIndex : function(index) {
-    var q;
-    this._index = index;
-    if ((q = this._queued_query) !== null) {
-      this._queued_query = null;
-      Search.query(q);
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
     }
   },
 
-  hasIndex : function() {
-      return this._index !== null;
-  },
+  hasIndex: () => Search._index !== null,
 
-  deferQuery : function(query) {
-      this._queued_query = query;
-  },
+  deferQuery: (query) => (Search._queued_query = query),
 
-  stopPulse : function() {
-      this._pulse_status = 0;
-  },
+  stopPulse: () => (Search._pulse_status = -1),
 
-  startPulse : function() {
-    if (this._pulse_status >= 0)
-        return;
-    function pulse() {
-      var i;
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
       Search._pulse_status = (Search._pulse_status + 1) % 4;
-      var dotString = '';
-      for (i = 0; i < Search._pulse_status; i++)
-        dotString += '.';
-      Search.dots.text(dotString);
-      if (Search._pulse_status > -1)
-        window.setTimeout(pulse, 500);
-    }
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
     pulse();
   },
 
   /**
    * perform a search for something (or wait until index is loaded)
    */
-  performSearch : function(query) {
+  performSearch: (query) => {
     // create the required interface elements
-    this.out = $('#search-results');
-    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
-    this.dots = $('<span></span>').appendTo(this.title);
-    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
-    this.output = $('<ul class="search"/>').appendTo(this.out);
-
-    $('#search-progress').text(_('Preparing search...'));
-    this.startPulse();
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
 
     // index already loaded, the browser was quick!
-    if (this.hasIndex())
-      this.query(query);
-    else
-      this.deferQuery(query);
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
   },
 
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query : function(query) {
-    var i;
-
-    // stem the searchterms and add them to the correct list
-    var stemmer = new Stemmer();
-    var searchterms = [];
-    var excluded = [];
-    var hlterms = [];
-    var tmp = splitQuery(query);
-    var objectterms = [];
-    for (i = 0; i < tmp.length; i++) {
-      if (tmp[i] !== "") {
-          objectterms.push(tmp[i].toLowerCase());
-      }
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
 
-      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
-        // skip this "word"
-        continue;
-      }
       // stem the word
-      var word = stemmer.stemWord(tmp[i].toLowerCase());
-      // prevent stemmer from cutting word smaller than two chars
-      if(word.length < 3 && tmp[i].length >= 3) {
-        word = tmp[i];
-      }
-      var toAppend;
+      let word = stemmer.stemWord(queryTermLower);
       // select the correct list
-      if (word[0] == '-') {
-        toAppend = excluded;
-        word = word.substr(1);
-      }
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
       else {
-        toAppend = searchterms;
-        hlterms.push(tmp[i].toLowerCase());
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
       }
-      // only add if not already in the list
-      if (!$u.contains(toAppend, word))
-        toAppend.push(word);
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
     }
-    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
 
-    // console.debug('SEARCH: searching for:');
-    // console.info('required: ', searchterms);
-    // console.info('excluded: ', excluded);
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
 
-    // prepare search
-    var terms = this._index.terms;
-    var titleterms = this._index.titleterms;
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
 
-    // array of [filename, title, anchor, descr, score]
-    var results = [];
-    $('#search-progress').empty();
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
 
-    // lookup as object
-    for (i = 0; i < objectterms.length; i++) {
-      var others = [].concat(objectterms.slice(0, i),
-                             objectterms.slice(i+1, objectterms.length));
-      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
     }
 
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
     // lookup as search terms in fulltext
-    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
     if (Scorer.score) {
-      for (i = 0; i < results.length; i++)
-        results[i][4] = Scorer.score(results[i]);
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
     }
 
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort(function(a, b) {
-      var left = a[4];
-      var right = b[4];
-      if (left > right) {
-        return 1;
-      } else if (left < right) {
-        return -1;
-      } else {
-        // same score: sort alphabetically
-        left = a[1].toLowerCase();
-        right = b[1].toLowerCase();
-        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
       }
-    });
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
-    //console.info('search results:', Search.lastresults);
+    // console.info("search results:", Search.lastresults);
 
     // print the results
-    var resultCount = results.length;
-    function displayNextItem() {
-      // results left, load the summary and display it
-      if (results.length) {
-        var item = results.pop();
-        var listItem = $('<li></li>');
-        var requestUrl = "";
-        var linkUrl = "";
-        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
-          // dirhtml builder
-          var dirname = item[0] + '/';
-          if (dirname.match(/\/index\/$/)) {
-            dirname = dirname.substring(0, dirname.length-6);
-          } else if (dirname == 'index/') {
-            dirname = '';
-          }
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
-          linkUrl = requestUrl;
-
-        } else {
-          // normal html builders
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-        }
-        listItem.append($('<a/>').attr('href',
-            linkUrl +
-            highlightstring + item[2]).html(item[1]));
-        if (item[3]) {
-          listItem.append($('<span> (' + item[3] + ')</span>'));
-          Search.output.append(listItem);
-          setTimeout(function() {
-            displayNextItem();
-          }, 5);
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
-          $.ajax({url: requestUrl,
-                  dataType: "text",
-                  complete: function(jqxhr, textstatus) {
-                    var data = jqxhr.responseText;
-                    if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
-                    }
-                    Search.output.append(listItem);
-                    setTimeout(function() {
-                      displayNextItem();
-                    }, 5);
-                  }});
-        } else {
-          // no source available, just display title
-          Search.output.append(listItem);
-          setTimeout(function() {
-            displayNextItem();
-          }, 5);
-        }
-      }
-      // search finished, update title and status message
-      else {
-        Search.stopPulse();
-        Search.title.text(_('Search Results'));
-        if (!resultCount)
-          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
-        else
-            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
-        Search.status.fadeIn(500);
-      }
-    }
-    displayNextItem();
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**
    * search for object names
    */
-  performObjectSearch : function(object, otherterms) {
-    var filenames = this._index.filenames;
-    var docnames = this._index.docnames;
-    var objects = this._index.objects;
-    var objnames = this._index.objnames;
-    var titles = this._index.titles;
-
-    var i;
-    var results = [];
-
-    for (var prefix in objects) {
-      for (var name in objects[prefix]) {
-        var fullname = (prefix ? prefix + '.' : '') + name;
-        var fullnameLower = fullname.toLowerCase()
-        if (fullnameLower.indexOf(object) > -1) {
-          var score = 0;
-          var parts = fullnameLower.split('.');
-          // check for different match types: exact matches of full name or
-          // "last name" (i.e. last dotted part)
-          if (fullnameLower == object || parts[parts.length - 1] == object) {
-            score += Scorer.objNameMatch;
-          // matches in last name
-          } else if (parts[parts.length - 1].indexOf(object) > -1) {
-            score += Scorer.objPartialMatch;
-          }
-          var match = objects[prefix][name];
-          var objname = objnames[match[1]][2];
-          var title = titles[match[0]];
-          // If more than one term searched for, we require other words to be
-          // found in the name/title/description
-          if (otherterms.length > 0) {
-            var haystack = (prefix + ' ' + name + ' ' +
-                            objname + ' ' + title).toLowerCase();
-            var allfound = true;
-            for (i = 0; i < otherterms.length; i++) {
-              if (haystack.indexOf(otherterms[i]) == -1) {
-                allfound = false;
-                break;
-              }
-            }
-            if (!allfound) {
-              continue;
-            }
-          }
-          var descr = objname + _(', in ') + title;
-
-          var anchor = match[3];
-          if (anchor === '')
-            anchor = fullname;
-          else if (anchor == '-')
-            anchor = objnames[match[1]][1] + '-' + fullname;
-          // add custom score for some objects according to scorer
-          if (Scorer.objPrio.hasOwnProperty(match[2])) {
-            score += Scorer.objPrio[match[2]];
-          } else {
-            score += Scorer.objPrioDefault;
-          }
-          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
-        }
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
       }
-    }
 
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
     return results;
   },
 
-  /**
-   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
-   */
-  escapeRegExp : function(string) {
-    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-  },
-
   /**
    * search for full-text terms in the index
    */
-  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
-    var docnames = this._index.docnames;
-    var filenames = this._index.filenames;
-    var titles = this._index.titles;
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
 
-    var i, j, file;
-    var fileMap = {};
-    var scoreMap = {};
-    var results = [];
+    const scoreMap = new Map();
+    const fileMap = new Map();
 
     // perform the search on the required terms
-    for (i = 0; i < searchterms.length; i++) {
-      var word = searchterms[i];
-      var files = [];
-      var _o = [
-        {files: terms[word], score: Scorer.term},
-        {files: titleterms[word], score: Scorer.title}
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
       ];
       // add support for partial matches
       if (word.length > 2) {
-        var word_regex = this.escapeRegExp(word);
-        for (var w in terms) {
-          if (w.match(word_regex) && !terms[word]) {
-            _o.push({files: terms[w], score: Scorer.partialTerm})
-          }
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
         }
-        for (var w in titleterms) {
-          if (w.match(word_regex) && !titleterms[word]) {
-              _o.push({files: titleterms[w], score: Scorer.partialTitle})
-          }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
         }
       }
 
       // no match but word was a required one
-      if ($u.every(_o, function(o){return o.files === undefined;})) {
-        break;
-      }
+      if (arr.every((record) => record.files === undefined)) return;
+
       // found search word in contents
-      $u.each(_o, function(o) {
-        var _files = o.files;
-        if (_files === undefined)
-          return
-
-        if (_files.length === undefined)
-          _files = [_files];
-        files = files.concat(_files);
-
-        // set score for the word in each file to Scorer.term
-        for (j = 0; j < _files.length; j++) {
-          file = _files[j];
-          if (!(file in scoreMap))
-            scoreMap[file] = {};
-          scoreMap[file][word] = o.score;
-        }
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
       });
 
       // create the mapping
-      for (j = 0; j < files.length; j++) {
-        file = files[j];
-        if (file in fileMap && fileMap[file].indexOf(word) === -1)
-          fileMap[file].push(word);
-        else
-          fileMap[file] = [word];
-      }
-    }
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
 
     // now check if the files don't contain excluded terms
-    for (file in fileMap) {
-      var valid = true;
-
+    const results = [];
+    for (const [file, wordList] of fileMap) {
       // check if all requirements are matched
-      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
-        searchterms.filter(function(term){return term.length > 2}).length
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
       if (
-        fileMap[file].length != searchterms.length &&
-        fileMap[file].length != filteredTermCount
-      ) continue;
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
 
       // ensure that none of the excluded terms is in the search result
-      for (i = 0; i < excluded.length; i++) {
-        if (terms[excluded[i]] == file ||
-            titleterms[excluded[i]] == file ||
-            $u.contains(terms[excluded[i]] || [], file) ||
-            $u.contains(titleterms[excluded[i]] || [], file)) {
-          valid = false;
-          break;
-        }
-      }
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
 
-      // if we have still a valid result we can add it to the result list
-      if (valid) {
-        // select one (max) score for the file.
-        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
-        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
-        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
-      }
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
     }
     return results;
   },
@@ -492,31 +592,28 @@ var Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, hlwords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
+   * of stemmed words.
    */
-  makeSearchSummary : function(htmlText, keywords, hlwords) {
-    var text = Search.htmlToText(htmlText);
-    var textLower = text.toLowerCase();
-    var start = 0;
-    $.each(keywords, function() {
-      var i = textLower.indexOf(this.toLowerCase());
-      if (i > -1)
-        start = i;
-    });
-    start = Math.max(start - 120, 0);
-    var excerpt = ((start > 0) ? '...' : '') +
-      $.trim(text.substr(start, 240)) +
-      ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<div class="context"></div>').text(excerpt);
-    $.each(hlwords, function() {
-      rv = rv.highlightText(this, 'highlighted');
-    });
-    return rv;
-  }
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
 };
 
-$(document).ready(function() {
-  Search.init();
-});
+_ready(Search.init);
diff --git a/master/_static/sphinx_highlight.js b/master/_static/sphinx_highlight.js
new file mode 100644
index 00000000..8a96c69a
--- /dev/null
+++ b/master/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/master/_static/underscore-1.12.0.js b/master/_static/underscore-1.12.0.js
deleted file mode 100644
index 3af6352e..00000000
--- a/master/_static/underscore-1.12.0.js
+++ /dev/null
@@ -1,2027 +0,0 @@
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-  typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = global || self, (function () {
-    var current = global._;
-    var exports = global._ = factory();
-    exports.noConflict = function () { global._ = current; return exports; };
-  }()));
-}(this, (function () {
-  //     Underscore.js 1.12.0
-  //     https://underscorejs.org
-  //     (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
-  //     Underscore may be freely distributed under the MIT license.
-
-  // Current version.
-  var VERSION = '1.12.0';
-
-  // Establish the root object, `window` (`self`) in the browser, `global`
-  // on the server, or `this` in some virtual machines. We use `self`
-  // instead of `window` for `WebWorker` support.
-  var root = typeof self == 'object' && self.self === self && self ||
-            typeof global == 'object' && global.global === global && global ||
-            Function('return this')() ||
-            {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
-  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var push = ArrayProto.push,
-      slice = ArrayProto.slice,
-      toString = ObjProto.toString,
-      hasOwnProperty = ObjProto.hasOwnProperty;
-
-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _(obj) {
-    if (obj instanceof _) return obj;
-    if (!(this instanceof _)) return new _(obj);
-    this._wrapped = obj;
-  }
-
-  _.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _.prototype.valueOf = _.prototype.toJSON = _.prototype.value;
-
-  _.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _) a = a._wrapped;
-    if (b instanceof _) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath(path) {
-    return isArray(path) ? path : [path];
-  }
-  _.toPath = toPath;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath$1(path) {
-    return _.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath$1(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has$1(obj, path) {
-    path = toPath$1(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath$1(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_.iteratee !== iteratee) return _.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    // If a variable is not specified, place data values in local scope.
-    if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n';
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(settings.variable || 'obj', '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    var argument = settings.variable || 'obj';
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath$1(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath$1(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria;
-      var b = right.criteria;
-      if (a !== b) {
-        if (a > b || a === void 0) return 1;
-        if (a < b || b === void 0) return -1;
-      }
-      return left.index - right.index;
-    }), 'value');
-  }
-
-  // An internal function used for aggregate "group by" operations.
-  function group(behavior, partition) {
-    return function(obj, iteratee, context) {
-      var result = partition ? [[], []] : {};
-      iteratee = cb(iteratee, context);
-      each(obj, function(value, index) {
-        var key = iteratee(value, index, obj);
-        behavior(result, value, key);
-      });
-      return result;
-    };
-  }
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
-
-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
-    if (isString(obj)) {
-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
-
-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
-    if (isFunction$1(iteratee)) {
-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N.
-  function initial(array, n, guard) {
-    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
-  }
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  function first(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[0];
-    return initial(array, array.length - n);
-  }
-
-  // Returns everything but the first entry of the `array`. Especially useful on
-  // the `arguments` object. Passing an **n** will return the rest N values in the
-  // `array`.
-  function rest(array, n, guard) {
-    return slice.call(array, n == null || guard ? 1 : n);
-  }
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array.
-  function last(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[array.length - 1];
-    return rest(array, Math.max(0, array.length - n));
-  }
-
-  // Trim out all falsy values from an array.
-  function compact(array) {
-    return filter(array, Boolean);
-  }
-
-  // Flatten out an array, either recursively (by default), or up to `depth`.
-  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten$1(array, depth) {
-    return flatten(array, depth, false);
-  }
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  var difference = restArguments(function(array, rest) {
-    rest = flatten(rest, true, true);
-    return filter(array, function(value){
-      return !contains(rest, value);
-    });
-  });
-
-  // Return a version of the array that does not contain the specified value(s).
-  var without = restArguments(function(array, otherArrays) {
-    return difference(array, otherArrays);
-  });
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // The faster algorithm will not work with an iteratee if the iteratee
-  // is not a one-to-one function, so providing an iteratee will disable
-  // the faster algorithm.
-  function uniq(array, isSorted, iteratee, context) {
-    if (!isBoolean(isSorted)) {
-      context = iteratee;
-      iteratee = isSorted;
-      isSorted = false;
-    }
-    if (iteratee != null) iteratee = cb(iteratee, context);
-    var result = [];
-    var seen = [];
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var value = array[i],
-          computed = iteratee ? iteratee(value, i, array) : value;
-      if (isSorted && !iteratee) {
-        if (!i || seen !== computed) result.push(value);
-        seen = computed;
-      } else if (iteratee) {
-        if (!contains(seen, computed)) {
-          seen.push(computed);
-          result.push(value);
-        }
-      } else if (!contains(result, value)) {
-        result.push(value);
-      }
-    }
-    return result;
-  }
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  var union = restArguments(function(arrays) {
-    return uniq(flatten(arrays, true, true));
-  });
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays.
-  function intersection(array) {
-    var result = [];
-    var argsLength = arguments.length;
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var item = array[i];
-      if (contains(result, item)) continue;
-      var j;
-      for (j = 1; j < argsLength; j++) {
-        if (!contains(arguments[j], item)) break;
-      }
-      if (j === argsLength) result.push(item);
-    }
-    return result;
-  }
-
-  // Complement of zip. Unzip accepts an array of arrays and groups
-  // each array's elements on shared indices.
-  function unzip(array) {
-    var length = array && max(array, getLength).length || 0;
-    var result = Array(length);
-
-    for (var index = 0; index < length; index++) {
-      result[index] = pluck(array, index);
-    }
-    return result;
-  }
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  var zip = restArguments(unzip);
-
-  // Converts lists into objects. Pass either a single array of `[key, value]`
-  // pairs, or two parallel arrays of the same length -- one of keys, and one of
-  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
-  function object(list, values) {
-    var result = {};
-    for (var i = 0, length = getLength(list); i < length; i++) {
-      if (values) {
-        result[list[i]] = values[i];
-      } else {
-        result[list[i][0]] = list[i][1];
-      }
-    }
-    return result;
-  }
-
-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
-  // [the Python documentation](https://docs.python.org/library/functions.html#range).
-  function range(start, stop, step) {
-    if (stop == null) {
-      stop = start || 0;
-      start = 0;
-    }
-    if (!step) {
-      step = stop < start ? -1 : 1;
-    }
-
-    var length = Math.max(Math.ceil((stop - start) / step), 0);
-    var range = Array(length);
-
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diff --git a/master/_static/underscore.js b/master/_static/underscore.js
deleted file mode 100644
index 166240ef..00000000
--- a/master/_static/underscore.js
+++ /dev/null
@@ -1,6 +0,0 @@
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-  <title>Index &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
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+  <title>Index &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
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@@ -45,7 +41,7 @@
             Krotov
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               <div class="version">
-                1.3.0
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -254,7 +250,7 @@ <h2 id="E">E</h2>
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-      <li><a href="API/krotov.result.html#krotov.result.Result.end_local_time_str">end_local_time_str() (krotov.result.Result property)</a>
+      <li><a href="API/krotov.result.html#krotov.result.Result.end_local_time_str">end_local_time_str (krotov.result.Result property)</a>
 </li>
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 </li>
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       <li><a href="API/krotov.result.html#krotov.result.Result.states">states (krotov.result.Result attribute)</a>
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@@ -674,7 +670,7 @@ <h2 id="Z">Z</h2>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
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diff --git a/master/index.html b/master/index.html
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@@ -37,7 +45,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -47,7 +55,7 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="02_contributing.html">Contributing</a></li>
@@ -123,8 +131,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="welcome-to-the-krotov-package-s-documentation">
-<h1>Welcome to the Krotov package’s documentation!<a class="headerlink" href="#welcome-to-the-krotov-package-s-documentation" title="Permalink to this headline">¶</a></h1>
+  <section id="welcome-to-the-krotov-package-s-documentation">
+<h1>Welcome to the Krotov package’s documentation!<a class="headerlink" href="#welcome-to-the-krotov-package-s-documentation" title="Link to this heading">¶</a></h1>
 <a class="reference external image-reference" href="https://github.com/qucontrol/krotov"><img alt="Source code on Github" src="https://img.shields.io/badge/github-qucontrol/krotov-blue.svg" /></a>
 <a class="reference external image-reference" href="https://qucontrol.github.io/krotov"><img alt="Documentation" src="https://img.shields.io/badge/docs-gh--pages-blue.svg" /></a>
 <a class="reference external image-reference" href="https://pypi.python.org/pypi/krotov"><img alt="Krotov on the Python Package Index" src="https://img.shields.io/pypi/v/krotov.svg" /></a>
@@ -135,7 +143,7 @@ <h1>Welcome to the Krotov package’s documentation!<a class="headerlink" href="
 <a class="reference external image-reference" href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs%2Fnotebooks"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" /></a>
 <a class="reference external image-reference" href="https://doi.org/10.21468/SciPostPhys.7.6.080"><img alt="DOI" src="https://img.shields.io/badge/DOI-10.21468/SciPostPhys.7.6.080-blue.svg" /></a>
 <div class="toctree-wrapper compound">
-<p class="caption"><span class="caption-text">Contents:</span></p>
+<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="01_overview.html">Krotov Python Package</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="01_overview.html#purpose">Purpose</a></li>
@@ -172,6 +180,7 @@ <h1>Welcome to the Krotov package’s documentation!<a class="headerlink" href="
 </li>
 <li class="toctree-l1"><a class="reference internal" href="04_features.html">Features</a></li>
 <li class="toctree-l1"><a class="reference internal" href="05_history.html">History</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="05_history.html#dev-unreleased">2.0.0-dev (unreleased)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05_history.html#id1">1.3.0 (2024-06-03)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05_history.html#id2">1.2.1 (2021-01-13)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="05_history.html#id3">1.2.0 (2020-08-17)</a></li>
@@ -252,22 +261,22 @@ <h1>Welcome to the Krotov package’s documentation!<a class="headerlink" href="
 <li class="toctree-l1"><a class="reference internal" href="99_bibliography.html">References</a></li>
 </ul>
 </div>
-</div>
-<div class="section" id="api">
-<h1>API<a class="headerlink" href="#api" title="Permalink to this headline">¶</a></h1>
+</section>
+<section id="api">
+<h1>API<a class="headerlink" href="#api" title="Link to this heading">¶</a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="API/krotov.html">API of the Krotov package</a></li>
 </ul>
 </div>
-</div>
-<div class="section" id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
+</section>
+<section id="indices-and-tables">
+<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">¶</a></h1>
 <ul class="simple">
 <li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
 <li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
 </ul>
-</div>
+</section>
 
 
            </div>
@@ -280,7 +289,7 @@ <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Pe
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/01_example_simple_state_to_state.html b/master/notebooks/01_example_simple_state_to_state.html
index 37654c2f..e6569f99 100644
--- a/master/notebooks/01_example_simple_state_to_state.html
+++ b/master/notebooks/01_example_simple_state_to_state.html
@@ -1,27 +1,33 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
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+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Optimization of a State-to-State Transfer in a Two-Level-System &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>Optimization of a State-to-State Transfer in a Two-Level-System &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
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+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
     <script src="../_static/js/theme.js"></script>
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@@ -41,7 +47,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -51,7 +57,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -147,9 +153,9 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/01_example_simple_state_to_state.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/01_example_simple_state_to_state.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-of-a-State-to-State-Transfer-in-a-Two-Level-System">
-<h1>Optimization of a State-to-State Transfer in a Two-Level-System<a class="headerlink" href="#Optimization-of-a-State-to-State-Transfer-in-a-Two-Level-System" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/01_example_simple_state_to_state.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/01_example_simple_state_to_state.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-of-a-State-to-State-Transfer-in-a-Two-Level-System">
+<h1>Optimization of a State-to-State Transfer in a Two-Level-System<a class="headerlink" href="#Optimization-of-a-State-to-State-Transfer-in-a-Two-Level-System" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -172,21 +178,21 @@ <h1>Optimization of a State-to-State Transfer in a Two-Level-System<a class="hea
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
+numpy     : 1.26.4
+krotov    : 1.3.0+dev
+scipy     : 1.12.0
 qutip     : 4.7.6
-scipy     : 1.10.1
 matplotlib: 3.7.5
-krotov    : 1.3.0
-numpy     : 1.24.4
 
 </pre></div></div>
 </div>
 <p><span class="math notranslate nohighlight">\(\newcommand{tr}[0]{\operatorname{tr}} \newcommand{diag}[0]{\operatorname{diag}} \newcommand{abs}[0]{\operatorname{abs}} \newcommand{pop}[0]{\operatorname{pop}} \newcommand{aux}[0]{\text{aux}} \newcommand{opt}[0]{\text{opt}} \newcommand{tgt}[0]{\text{tgt}} \newcommand{init}[0]{\text{init}} \newcommand{lab}[0]{\text{lab}} \newcommand{rwa}[0]{\text{rwa}} \newcommand{bra}[1]{\langle#1\vert} \newcommand{ket}[1]{\vert#1\rangle} \newcommand{Bra}[1]{\left\langle#1\right\vert} \newcommand{Ket}[1]{\left\vert#1\right\rangle} \newcommand{Braket}[2]{\left\langle #1\vphantom{#2}\mid{#2}\vphantom{#1}\right\rangle} \newcommand{op}[1]{\hat{#1}} \newcommand{Op}[1]{\hat{#1}} \newcommand{dd}[0]{\,\text{d}} \newcommand{Liouville}[0]{\mathcal{L}} \newcommand{DynMap}[0]{\mathcal{E}} \newcommand{identity}[0]{\mathbf{1}} \newcommand{Norm}[1]{\lVert#1\rVert} \newcommand{Abs}[1]{\left\vert#1\right\vert} \newcommand{avg}[1]{\langle#1\rangle} \newcommand{Avg}[1]{\left\langle#1\right\rangle} \newcommand{AbsSq}[1]{\left\vert#1\right\vert^2} \newcommand{Re}[0]{\operatorname{Re}} \newcommand{Im}[0]{\operatorname{Im}}\)</span></p>
 <p>This first example illustrates the basic use of the <code class="docutils literal notranslate"><span class="pre">krotov</span></code> package by solving a simple canonical optimization problem: the transfer of population in a two level system.</p>
-<div class="section" id="Two-level-Hamiltonian">
-<h2>Two-level-Hamiltonian<a class="headerlink" href="#Two-level-Hamiltonian" title="Permalink to this headline">¶</a></h2>
+<section id="Two-level-Hamiltonian">
+<h2>Two-level-Hamiltonian<a class="headerlink" href="#Two-level-Hamiltonian" title="Link to this heading">¶</a></h2>
 <p>We consider the Hamiltonian <span class="math notranslate nohighlight">\(\op{H}_{0} = - \frac{\omega}{2} \op{\sigma}_{z}\)</span>, representing a simple qubit with energy level splitting <span class="math notranslate nohighlight">\(\omega\)</span> in the basis <span class="math notranslate nohighlight">\(\{\ket{0},\ket{1}\}\)</span>. The control field <span class="math notranslate nohighlight">\(\epsilon(t)\)</span> is assumed to couple via the Hamiltonian <span class="math notranslate nohighlight">\(\op{H}_{1}(t) = \epsilon(t) \op{\sigma}_{x}\)</span> to the qubit, i.e., the control field effectively drives transitions between both qubit states.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -258,9 +264,9 @@ <h2>Two-level-Hamiltonian<a class="headerlink" href="#Two-level-Hamiltonian" tit
 <img alt="../_images/notebooks_01_example_simple_state_to_state_10_0.png" src="../_images/notebooks_01_example_simple_state_to_state_10_0.png" />
 </div>
 </div>
-</div>
-<div class="section" id="Optimization-target">
-<h2>Optimization target<a class="headerlink" href="#Optimization-target" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization-target">
+<h2>Optimization target<a class="headerlink" href="#Optimization-target" title="Link to this heading">¶</a></h2>
 <p>The <code class="docutils literal notranslate"><span class="pre">krotov</span></code> package requires the goal of the optimization to be described by a list of <code class="docutils literal notranslate"><span class="pre">Objective</span></code> instances. In this example, there is only a single objective: the state-to-state transfer from initial state <span class="math notranslate nohighlight">\(\ket{\Psi_{\init}} = \ket{0}\)</span> to the target state <span class="math notranslate nohighlight">\(\ket{\Psi_{\tgt}} = \ket{1}\)</span>, under the dynamics of the Hamiltonian <span class="math notranslate nohighlight">\(\op{H}(t)\)</span>:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -309,9 +315,9 @@ <h2>Optimization target<a class="headerlink" href="#Optimization-target" title="
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Simulate-dynamics-under-the-guess-field">
-<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Simulate-dynamics-under-the-guess-field">
+<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Link to this heading">¶</a></h2>
 <p>Before running the optimization procedure, we first simulate the dynamics under the guess field <span class="math notranslate nohighlight">\(\epsilon_{0}(t)\)</span>. The following solves equation of motion for the defined objective, which contains the initial state <span class="math notranslate nohighlight">\(\ket{\Psi_{\init}}\)</span> and the Hamiltonian <span class="math notranslate nohighlight">\(\op{H}(t)\)</span> defining its evolution. This delegates to QuTiP’s usual <code class="docutils literal notranslate"><span class="pre">mesolve</span></code> function.</p>
 <p>We use the projectors <span class="math notranslate nohighlight">\(\op{P}_0 = \ket{0}\bra{0}\)</span> and <span class="math notranslate nohighlight">\(\op{P}_1 = \ket{1}\bra{1}\)</span> for calculating the population:</p>
 <div class="nbinput nblast docutils container">
@@ -362,9 +368,9 @@ <h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate
 <img alt="../_images/notebooks_01_example_simple_state_to_state_25_0.png" src="../_images/notebooks_01_example_simple_state_to_state_25_0.png" />
 </div>
 </div>
-</div>
-<div class="section" id="Optimize">
-<h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimize">
+<h2>Optimize<a class="headerlink" href="#Optimize" title="Link to this heading">¶</a></h2>
 <p>In the following we optimize the guess field <span class="math notranslate nohighlight">\(\epsilon_{0}(t)\)</span> such that the intended state-to-state transfer <span class="math notranslate nohighlight">\(\ket{\Psi_{\init}} \rightarrow \ket{\Psi_{\tgt}}\)</span> is solved, via the <code class="docutils literal notranslate"><span class="pre">krotov</span></code> package’s central <code class="docutils literal notranslate"><span class="pre">optimize_pulses</span></code> routine. It requires, besides the previously defined <code class="docutils literal notranslate"><span class="pre">objectives</span></code>, information about the optimization functional <span class="math notranslate nohighlight">\(J_T\)</span> (implicitly, via <code class="docutils literal notranslate"><span class="pre">chi_constructor</span></code>, which calculates the states <span class="math notranslate nohighlight">\(\ket{\chi} = \frac{J_T}{\bra{\Psi}}\)</span>).</p>
 <p>Here, we choose <span class="math notranslate nohighlight">\(J_T = J_{T,\text{ss}} = 1 - F_{\text{ss}}\)</span> with <span class="math notranslate nohighlight">\(F_{\text{ss}} = \Abs{\Braket{\Psi_{\tgt}}{\Psi(T)}}^2\)</span>, with <span class="math notranslate nohighlight">\(\ket{\Psi(T)}\)</span> the forward propagated state of <span class="math notranslate nohighlight">\(\ket{\Psi_{\init}}\)</span>. Even though <span class="math notranslate nohighlight">\(J_T\)</span> is not explicitly required for the optimization, it is nonetheless useful to be able to calculate and print it as a way to provide some feedback about the optimization progress. Here, we pass as an <code class="docutils literal notranslate"><span class="pre">info_hook</span></code> the function
 <code class="docutils literal notranslate"><span class="pre">krotov.info_hooks.print_table</span></code>, using <code class="docutils literal notranslate"><span class="pre">krotov.functionals.J_T_ss</span></code> (which implements the above functional; the <code class="docutils literal notranslate"><span class="pre">krotov</span></code> library contains implementations of all the “standard” functionals used in quantum control). This <code class="docutils literal notranslate"><span class="pre">info_hook</span></code> prints a tabular overview after each iteration, containing the value of <span class="math notranslate nohighlight">\(J_T\)</span>, the magnitude of the integrated pulse update, and information on how much <span class="math notranslate nohighlight">\(J_T\)</span> (and the full Krotov functional <span class="math notranslate nohighlight">\(J\)</span>) changes between iterations. It also stores the
@@ -434,16 +440,16 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <div class="highlight"><pre>
 Krotov Optimization Result
 --------------------------
-- Started at 2024-06-03 12:28:57
+- Started at 2024-06-03 10:31:56
 - Number of objectives: 1
 - Number of iterations: 18
 - Reason for termination: Reached convergence: J_T &lt; 1e-3
-- Ended at 2024-06-03 12:29:28 (0:00:31)
+- Ended at 2024-06-03 10:32:22 (0:00:26)
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Simulate-the-dynamics-under-the-optimized-field">
-<h2>Simulate the dynamics under the optimized field<a class="headerlink" href="#Simulate-the-dynamics-under-the-optimized-field" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Simulate-the-dynamics-under-the-optimized-field">
+<h2>Simulate the dynamics under the optimized field<a class="headerlink" href="#Simulate-the-dynamics-under-the-optimized-field" title="Link to this heading">¶</a></h2>
 <p>Having obtained the optimized control field, we can simulate the dynamics to verify that the optimized field indeed drives the initial state <span class="math notranslate nohighlight">\(\ket{\Psi_{\init}} = \ket{0}\)</span> to the desired target state <span class="math notranslate nohighlight">\(\ket{\Psi_{\tgt}} = \ket{1}\)</span>.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
@@ -557,8 +563,8 @@ <h2>Simulate the dynamics under the optimized field<a class="headerlink" href="#
 </div>
 </div>
 <p>The initial guess (dashed) and final optimized (solid) control amplitude and resulting dynamics are shown with full opacity, whereas the curves corresponding intermediate iterations are shown with decreasing transparency.</p>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -572,7 +578,7 @@ <h2>Simulate the dynamics under the optimized field<a class="headerlink" href="#
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/01_example_simple_state_to_state.ipynb b/master/notebooks/01_example_simple_state_to_state.ipynb
index b7ed4c81..ada25919 100644
--- a/master/notebooks/01_example_simple_state_to_state.ipynb
+++ b/master/notebooks/01_example_simple_state_to_state.ipynb
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.114927Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.113597Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.657268Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.656634Z"
+     "iopub.execute_input": "2024-06-03T14:31:55.328265Z",
+     "iopub.status.busy": "2024-06-03T14:31:55.327934Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.201526Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.201160Z"
     }
    },
    "outputs": [
@@ -33,14 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
+      "numpy     : 1.26.4\n",
+      "krotov    : 1.3.0+dev\n",
+      "scipy     : 1.12.0\n",
       "qutip     : 4.7.6\n",
-      "scipy     : 1.10.1\n",
       "matplotlib: 3.7.5\n",
-      "krotov    : 1.3.0\n",
-      "numpy     : 1.24.4\n",
       "\n"
      ]
     }
@@ -123,10 +123,10 @@
      "start_time": "2019-07-14T20:35:02.380331Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.682891Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.682662Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.686515Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.685952Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.223555Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.223348Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.226065Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.225754Z"
     }
    },
    "outputs": [],
@@ -158,10 +158,10 @@
      "start_time": "2019-07-14T20:35:02.389898Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.688481Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.688335Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.691208Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.690793Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.227590Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.227478Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.229589Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.229260Z"
     }
    },
    "outputs": [],
@@ -188,10 +188,10 @@
      "start_time": "2019-07-14T20:35:02.399999Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.693184Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.693040Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.695372Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.695003Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.231217Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.231070Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.233357Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.232936Z"
     },
     "lines_to_next_cell": 2
    },
@@ -214,10 +214,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.697366Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.697145Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.700210Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.699654Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.235044Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.234897Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.237077Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.236790Z"
     }
    },
    "outputs": [],
@@ -246,10 +246,10 @@
      "n": "11"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.702077Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.701934Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.839686Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.839088Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.238533Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.238417Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.355081Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.347586Z"
     }
    },
    "outputs": [
@@ -295,10 +295,10 @@
      "start_time": "2019-07-14T20:35:02.631007Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.841887Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.841730Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.846200Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.845750Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.369351Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.368050Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.412091Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.403810Z"
     }
    },
    "outputs": [
@@ -344,10 +344,10 @@
      "start_time": "2019-07-14T20:35:02.642363Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.848173Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.848029Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.850815Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.850363Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.431875Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.430962Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.445428Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.437356Z"
     },
     "lines_to_next_cell": 2
    },
@@ -381,10 +381,10 @@
      "start_time": "2019-07-14T20:35:02.651951Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.852963Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.852701Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.855192Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.854803Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.454736Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.453874Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.480447Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.470708Z"
     },
     "lines_to_next_cell": 0
    },
@@ -429,10 +429,10 @@
      "start_time": "2019-07-14T20:35:02.658095Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.857281Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.857137Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.860434Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.859883Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.484702Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.484254Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.487715Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.487348Z"
     }
    },
    "outputs": [],
@@ -455,10 +455,10 @@
      "n": "12"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.862394Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.862192Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.921201Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.920671Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.489575Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.489477Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.524433Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.524060Z"
     }
    },
    "outputs": [],
@@ -489,10 +489,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.923206Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.923060Z",
-     "iopub.status.idle": "2024-06-03T10:28:56.926296Z",
-     "shell.execute_reply": "2024-06-03T10:28:56.925804Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.526936Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.526787Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.529206Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.528817Z"
     }
    },
    "outputs": [],
@@ -521,10 +521,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:56.928214Z",
-     "iopub.status.busy": "2024-06-03T10:28:56.928070Z",
-     "iopub.status.idle": "2024-06-03T10:28:57.034270Z",
-     "shell.execute_reply": "2024-06-03T10:28:57.033676Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.530896Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.530759Z",
+     "iopub.status.idle": "2024-06-03T14:31:56.595226Z",
+     "shell.execute_reply": "2024-06-03T14:31:56.594947Z"
     }
    },
    "outputs": [
@@ -600,10 +600,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:28:57.036409Z",
-     "iopub.status.busy": "2024-06-03T10:28:57.036253Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.428987Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.428497Z"
+     "iopub.execute_input": "2024-06-03T14:31:56.596878Z",
+     "iopub.status.busy": "2024-06-03T14:31:56.596763Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.156636Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.156360Z"
     }
    },
    "outputs": [
@@ -772,10 +772,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.431053Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.430862Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.434488Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.434023Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.158163Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.158052Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.160158Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.159889Z"
     }
    },
    "outputs": [
@@ -784,11 +784,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:28:57\n",
+       "- Started at 2024-06-03 10:31:56\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 18\n",
        "- Reason for termination: Reached convergence: J_T < 1e-3\n",
-       "- Ended at 2024-06-03 12:29:28 (0:00:31)"
+       "- Ended at 2024-06-03 10:32:22 (0:00:26)"
       ]
      },
      "execution_count": 15,
@@ -831,10 +831,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.436510Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.436335Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.464276Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.463660Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.161579Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.161476Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.201351Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.200891Z"
     }
    },
    "outputs": [],
@@ -857,16 +857,16 @@
      "n": "19"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.466530Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.466134Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.604413Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.603904Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.203077Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.202958Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.270730Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.270433Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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RuGmCwk37UVxZw7E8I+Tkl1ZTUFZNUUUNdofTuDmd+FgthPj7EuxvIzTAh+jQAOIiAogLCyQs0Mftx8u0ltySKu5+cyPpWSV0jgjkw5+PIcYNBnBLG3E6oeQ0FJ6A4pNQmgPVZcaUZKcDfALAxx8CO0BoHITEGBs1BnXUwNeLOXMMVj8HO/8JzrNbqQz8gdFaE5FoamneQuGmCQo34i3ySqv4wYL1HM0rIzkmlH/9v9GEB7nXbC9xEYcDsnbCgS/gxEajy6Q87/LfJyAcopKhU2+I7gfxw4yxI37uvYRDq8o7BOtehh3/AMfZbVF6TYLxs40F+aTNKNw0QeFGvMmJgnJufXUduSVVjEqK5O/3pmi3cU+Ssw+2vgt7Pjq3CWMdiw3Cu0BYZwiNNQKKT6Axk8deBTUVUJ4PJdnGa8vygEZ+HVhsEN0XOg+DxNHQdSx06NomH880TiccXwfrX4H9n1H/felxLYz/DXQZYWp53krhpgkKN+Jt9p0u5r8WrKekqpY7UxL53+9p2fd2zemEg1/AVy/CiQ3njvsGQ4/x0P0ao8Ulpv/l7VtUU2nM+MndD3kH4PQ3cGrrhaEJIDzR2Oyx61jjvkM3z+jOqi6HvR/Dptcgc/u5470nw9iHoeto82oThZumKNyIN1qRns2P392C0wl/vG0Q/zUyweySpDmOrIK0p+HUFuO5xQbJk2HYdCPU+LTC2kbFmUbIObnZaM3I3H6ue6ZOaPy5sNN1LET1aj9hx+k0PtP2v8GuD6Cq2DjuEwCDb4fR9xufR0yncNMEhRvxVv+XdpAXlh/Az2bl/Z+OZnBChNklyaUqzYFlj8HufxvPfQJh1H1wxc8hLK5ta6kqNcb1HP8ajn1tBB/Ht5ZeCO4EXcdA1yuN++h+7jU9ui7Q7P3YuJ05eu5rHZJg6N0w/B4IjjKrQmmEwk0TFG7EWzkcTv7f37eyfG82CZGBLH1onNttJyGN2LkIPvtvqCw0xsuM+DFc9SsIjTG7MkN1OZzcZLTqHF9ntPDUfmuxzIAII+R0GQnxQyBuCARFtm2dpblGy9eRlcZ98alzX/MJhD43Gi1gSePcK4hJPYWbJijciDcrqaxh8ktfcfJMBd8f3oXnf6BVVN1WTYURarb91XgeOxCmvGwM7HVntVVwahscX2uEnYyNUFN24XnhiRA3CGIHQccexi2yBwS08Ody3TT4/MPGrLHMbUY957fOAPgGQa+J0H8q9LwO/LUWlLtTuGmCwo14u83HCpj22nocTvjzncO4YWAbd2vIdztzHBbdBVnfABa45jEY90uwtcOWNnuNMTj5+NdGV9DpHVBw5OLnB3cy1twJjoaQaGPtHb9g8A00WlisVuM97TXGrK+KQmPWV1meEWoKjkBNeePvHTvQ2KG7x3hj5pev56xO7g0UbpqgcCMCz3++n1dWHiI80JfPH7mK2HAt8Oc2Tn8D//g+lGYbv9hv+4sxBdmTVBYZn/P0TsjZa7SyFByGslzXvL/FZiysF90POg81Zo/FD237rjBxKYWbJijciECN3cGtf17HrlNFXNcvhjema90Ot3D0K1h4hzFjJ2YA3LHIWKvGW1QWG91HJVlGuCvNgfICoyWmttK4dzrA6gs2P6MlKzACgqKMwb/B0RDZ3Qg2Pn5mfxpxMYWbJijciBj2Z5Vw48tfUetwsuCu4Vw/INbskrzbwS9h4e1grzZmGf3wH8YvbhEBLu/3t4aEi3ip5NhQfnp1DwCe+GQ3xZWN76QubeDYWlh0pxFs+twEd/1bwUakBRRuRLzYA9f2JKljENnFVTz/+X6zy/FOJ7fAe9OMbpfek+EH71zeysIicgGFGxEvFuBr45mz2zH8bcNxdp0sMrkiL5N/GP5+G1SXQrerjWDTHmdEibgZhRsRLzemZxRTh8TjdMJTn+7By4bhmaei0GixqSyEziPg9n+qxUbERRRuRIT/mdyHAF8rm4+dYemuRjZKFNey18IHMyH/IIR1gR++Z6zlIiIuoXAjIsSFB9YPLn5m6T4qa+wmV+Thls+BwyuMVXJv/6f7bKUg4iEUbkQEgJ9c1Z3YsABOFVbw5tqj3/0CaZ70JbBhvvH41teNLQhExKUUbkQEgCA/H/5ncjIAr646zJmyapMr8kCFJ2Dxz43Hox+AvlPMrUfEQynciEi9WwZ3pm9cGKVVtSxYfdjscjyLvRb+fa8xgDh+GEx4wuyKRDyWwo2I1LNaLfxqUm8A3ll3jOziSpMr8iBfvQAnNoB/GHz/LW0PINKKFG5EpIHxydEMS4ygqtbBKysOmV2OZ8jeA2ueMx7f+AJEdjO3HhEPp3AjIg1YLBZ+NakPAAs3Z3CioNzkito5e60xzsZRA8k3wsAfmF2RiMdTuBGRC4zu0ZEre0ZRY3eq9aal1r0Ep3dAQATc9CJYLGZXJOLxFG5EpFG/uM4Ye/Ph9pNkFlaYXE07lXcIVj1rPL7+WQjVzusibUHhRkQaNbxrB0Z370iN3cnra46YXU7743TCZ/9t7PTdMxUG/9DsikS8hsKNiFzU/eN7AvDPTRnklVaZXE07k/4pHE4Dmx9M/qO6o0TakMKNiFzU2J4dGZxgzJzSqsWXobocls02Ho95CDr2MLceES+jcCMiF2WxWHjgbOvN39Yfp6i8xuSK2om1f4KiE8ammONmmV2NiNdRuBGRJk3oE01yTCilVbW8tynD7HLcX9FJWPey8fj6Z7Tbt4gJFG5EpElWq4X7ruoOwDvrjlJd6zC5Ije38hmorYTEMdD3ZrOrEfFKCjci8p2mDI6jU6g/2cVVLN112uxy3Ff2HtjxnvF44tMaRCxiEoUbEflO/j42ZozuCsBf1h7B6XSaXJGb+vJ3gBP63QJdRphdjYjXUrgRkUtyR0pXAnyt7D5VzMajBWaX436OroGDX4DVRzt+i5jM9HAzf/58kpKSCAgIICUlhU2bNjV5/rx580hOTiYwMJCEhAR+8YtfUFmpnYtFWltksB+3DesCwF++0rTwBpxOWPF74/HwmZr6LWIyU8PNokWLmDVrFk888QTbtm1j8ODBTJo0iZycnEbPf++993jsscd44okn2LdvH2+++SaLFi3i17/+dRtXLuKdfnSlsZt1Wno2GfnaULPekZVwYiP4BMBVj5pdjYjXMzXcvPjii9x3333MnDmTfv36sWDBAoKCgnjrrbcaPX/dunWMHTuWO+64g6SkJCZOnMjtt9/eZGtPVVUVxcXFDW4i0jw9OoUwrlcUTif8Y9Nxs8txD07nuf2jhs/U/lEibsC0cFNdXc3WrVtJTU09V4zVSmpqKuvXr2/0NWPGjGHr1q31YebIkSMsXbqUG2644aLXmTt3LuHh4fW3hIQE134QES9z1xXGwOL3t5ykqtZucjVu4PxWmysfMbsaEcHEcJOXl4fdbicmJqbB8ZiYGLKyshp9zR133MFTTz3FlVdeia+vLz169OCaa65psltq9uzZFBUV1d9OnDjh0s8h4m0m9IkmLjyAgrJqPtvV+P+rXkOtNiJuyfQBxZdj1apVPPPMM/z5z39m27ZtfPjhhyxZsoSnn376oq/x9/cnLCyswU1Ems/HZuX2UYkA/G2Dl3dNHV2tVhsRN2RauImKisJms5Gdnd3geHZ2NrGxjf/18/jjj3P33Xdz7733MnDgQL73ve/xzDPPMHfuXBwOrZoq0lZ+ODIBH6uFrcfPsDfTi8exrZ1n3A+brlYbETdiWrjx8/Nj+PDhpKWl1R9zOBykpaUxevToRl9TXl6O1dqwZJvNBqBFxUTaUHRYAJP6G7/M/77RS1tvMncY420sNhj9gNnViMh5TO2WmjVrFm+88Qbvvvsu+/bt42c/+xllZWXMnDkTgOnTpzN79uz686dMmcKrr77KwoULOXr0KMuXL+fxxx9nypQp9SFHRNrGnVcYXVOLt5+ipNILdwuv2xxzwK3Qoau5tYhIAz5mXnzatGnk5uYyZ84csrKyGDJkCMuWLasfZJyRkdGgpea3v/0tFouF3/72t5w6dYpOnToxZcoU/vd//9esjyDitUZ370iPTsEczi3jo+2nmD46yeyS2k7BUdjzkfF4zEPm1iIiF7A4vaw/p7i4mPDwcIqKijS4WKSF3v76KE/+Zy/948NY8tA4s8tpO0t+CZv/Aj0mwN0fml2NiFe4nN/f7Wq2lIi4l6lDOuNns7Ins5g9mUVml9M2ygtg+z+Mx2MfNrcWEWmUwo2INFuHYD+u62d0I7+/5aTJ1bSRbX+F2gqIHQjdrjK7GhFphMKNiLTI90cYm2ku3nHK81csttca3VEAKT8Fi8XcekSkUQo3ItIiV/XqRGxYAIXlNaTta3zTW49x4DMoOgGBkTDgNrOrEZGLULgRkRaxWS3cOqwzAP/a4uHbm2x8zbgffg/4BppaiohcnMKNiLTYD0YYG9KuOZBLVlGlydW0kuw9cOwrY9G+kT82uxoRaYLCjYi0WLeoYEYmdcDhhA+3e+jA4k2vG/d9b4LwLubWIiJNUrgREZeoa715f8tJz9sOpeIM7FxkPB71/8ytRUS+k8KNiLjEjQPjCPKzcTSvjG0ZZ8wux7W2/92Y/h0zELqOMbsaEfkOCjci4hLB/j5cP8DYTPOj7adMrsaFnE7Y8rbxeNR9mv4t0g4o3IiIy0wdYsyaWvLNaWrsDpOrcZHjX0PBYfAL0fRvkXZC4UZEXGZMj45EhfhzpryGNQdyzS7HNba+a9wP/D74h5hbi4hcEoUbEXEZH5uVKYPjAFi8I9PkalygvAD2fmw8Hjbd3FpE5JIp3IiIS31vqNE1tXxvFqVVtSZX00Lf/AvsVcZA4vhhZlcjIpdI4UZEXGpg53C6RwVTWePgiz1ZZpfTfE4nbDvbJTV8hgYSi7QjCjci4lIWi4Vbzg4sbtezpk5ugZy94BNgjLcRkXZD4UZEXO6WIfEAfH0oj5ySdrodQ12rTb+pENjB1FJE5PIo3IiIyyVFBTMkIQKHEz7dedrsci5fVQns/tB4PHyGubWIyGVTuBGRVjH1bOvNxzvaYdfUnsVQUwYde0LiaLOrEZHLpHAjIq3ixkHxWC2w82QRJwrKzS7n8uxcaNwPuUMDiUXaIYUbEWkVnUL9uaJ7RwCW7mpHXVOFGXB8LWCBgf9ldjUi0gwKNyLSam4cZCzot6Q9hZtvzu7+3W0cRCSYW4uINIvCjYi0mkn9Y7Fa4JuTRWTkt4OuKafzXJfU4NvNrUVEmk3hRkRaTVSIP6N7GF1T7aL15tRWyD8EvkHQd4rZ1YhIMynciEirunGgMWuqXYy72flP477vFPAPNbcWEWk2hRsRaVWT+sdgs1rYdaqI4/llZpdzcbVVsPvfxuPBPzS3FhFpEYUbEWlVHUP8Gd29HXRNHfwCKs5AaBx0u9rsakSkBRRuRKTV1c+a+saNw03dQOJB/wVWm7m1iEiLKNyISKub1D8Wm9XCnsxijuW5YddURaHRcgMwSF1SIu2dwo2ItLrIYD/GuPOsqfQlYK+G6H4Q08/sakSkhRRuRKRN3DjQjbum6gYSD7jV3DpExCUUbkSkTdR1Te09XcxRd+qaKsuDI6uMx/0VbkQ8gcKNiLSJDud1TbnVmjd7PwanHeKHQsceZlcjIi6gcCMibeb6AbEAfLEny+RKzrP7Q+N+wG3m1iEiLqNwIyJt5rp+MVgssPNkEaeLKswuB4oz4fjXxuP+3zO3FhFxGYUbEWkz0aEBDE/sAMAXe7JNrgbYsxhwQsIVEN7F7GpExEUUbkSkTU3qb3RNfe4OXVP1s6TUJSXiSRRuRKRN1YWbjUcLOFNWbV4hZ47BqS1gsUK/W8yrQ0RcTuFGRNpUYscg+saFYXc4+XKfiV1TdQOJk8ZBaIx5dYiIyynciEibm9TfCBOfmznuRrOkRDyWwo2ItLm6rqk1B3Mpq6pt+wJyD0D2LrD6QN8pbX99EWlVCjci0ub6xIbStWMQ1bUOVh/IbfsC9i427ntcC0GRbX99EWlVCjci0uYsFou5s6b2fmLcayCxiEdSuBERU9SNu1mxL4fqWkfbXbjgiNElZbFB8g1td10RaTMKNyJiiqEJHegU6k9JVS3rDue13YXrWm26jVOXlIiHUrgREVNYrRYm9jNh1tS+s+FGA4lFPJbCjYiYpm7czfK92Tgczta/YNFJOLUVsEAfhRsRT6VwIyKmuaJ7R0L9fcgrrWLnycLWv+C+T437xCu0cJ+IB1O4ERHT+PlYuTq5E0DbrFZc3yV1c+tfS0RMo3AjIqa67uy4my/35rTuhUpz4Pg647HG24h4NIUbETHVNb2jsVkt7M8uISO/vPUulP4p4IT4oRCR0HrXERHTKdyIiKnCg3wZlWRMyW7Vrql9/zHu1SUl4vEUbkTEdKl1XVOtFW4qzsDRNcZjrUos4vEUbkTEdKl9owHYeLSAovIa119g/2fgqIXo/tCxh+vfX0TcisKNiJiua8dgekWHYHc4WXWgFQYW1+8lpS4pEW+gcCMibuFc15SLw011GRxeYTzuc5Nr31tE3JLp4Wb+/PkkJSUREBBASkoKmzZtavL8wsJC7r//fuLi4vD396d3794sXbq0jaoVkdaS2tcIN6v2u3gjzSOrwV4FEYkQ09917ysibsvUcLNo0SJmzZrFE088wbZt2xg8eDCTJk0iJ6fxv9yqq6u57rrrOHbsGB988AH79+/njTfeoHPnzm1cuYi42pCECKJC/CiprGXzsQLXvfGBz4z73pPBYnHd+4qI2zI13Lz44ovcd999zJw5k379+rFgwQKCgoJ46623Gj3/rbfeoqCggMWLFzN27FiSkpK4+uqrGTx48EWvUVVVRXFxcYObiLgfm9XCtX2MgcXL97po1pTDAQc+Nx4nX++a9xQRt2dauKmurmbr1q2kpqaeK8ZqJTU1lfXr1zf6mk8++YTRo0dz//33ExMTw4ABA3jmmWew2+0Xvc7cuXMJDw+vvyUkaPEuEXdV1zX15b5snE4XbKR5ejuUZoNfCHQd2/L3E5F2wbRwk5eXh91uJyam4eZ1MTExZGVlNfqaI0eO8MEHH2C321m6dCmPP/44L7zwAr///e8vep3Zs2dTVFRUfztx4oRLP4eIuM6VvaLw97Fy8kwF+7NLWv6G+5cZ9z2uBR//lr+fiLQLzQo32dnZ3H333cTHx+Pj44PNZmtway0Oh4Po6Ghef/11hg8fzrRp0/jNb37DggULLvoaf39/wsLCGtxExD0F+flwZc8oAL50RddU3Xib5Mktfy8RaTd8mvOie+65h4yMDB5//HHi4uKwNGOQXlRUFDabjezshj/AsrOziY2NbfQ1cXFx+Pr6NghQffv2JSsri+rqavz8/C67DhFxL6n9YkhLz2H5vhweuLZX89+o6CRk7QIs0Guiy+oTEffXrHCzdu1avvrqK4YMGdLsC/v5+TF8+HDS0tKYOnUqYLTMpKWl8cADDzT6mrFjx/Lee+/hcDiwWo1GpwMHDhAXF6dgI+IhJpwdVLzzRCE5xZVEhwU0740OnO2SShgFwVEuqk5E2oNmdUslJCS4ZLDfrFmzeOONN3j33XfZt28fP/vZzygrK2PmzJkATJ8+ndmzZ9ef/7Of/YyCggIefvhhDhw4wJIlS3jmmWe4//77W1yLiLiH6LAABidEAJCW3oIF/erG2/TWLCkRb9OscDNv3jwee+wxjh071qKLT5s2jeeff545c+YwZMgQduzYwbJly+oHGWdkZHD69On68xMSEvj888/ZvHkzgwYN4qGHHuLhhx/msccea1EdIuJeUs+23qQ1dyPN6rJzG2VqvI2I17E4m9EE06FDB8rLy6mtrSUoKAhfX98GXy8ocOECXC5WXFxMeHg4RUVFGlws4qb2ZhZzw8tfEeBrZceciQT4XuZEhfQlsPAOiOgKD+/U4n0iHuByfn83a8zNvHnzmvMyEZFL0jculPjwADKLKvn6UB4T+sZ894vOt/+8WVIKNiJep1nhZsaMGa6uQ0SknsViYULfGP624Thf7su5vHBz/qrEvSe1ToEi4taaFW4A7HY7ixcvZt++fQD079+fm2++uVXXuRER73Ft32j+tuE4K9KzcToHXPqSE5nboSwH/EKh65WtW6RIG7Pb7dTU1JhdRqvx8/Ornw3dEs0KN4cOHeKGG27g1KlTJCcnA8Y2BwkJCSxZsoQePXq0uDAR8W6ju3ckyM9GdnEVezKLGdA5/NJeWLdwX89rwUdLRIhncDqdZGVlUVhYaHYprcpqtdKtW7cWL+/SrHDz0EMP0aNHDzZs2EBkZCQA+fn53HXXXTz00EMsWbKkRUWJiAT42riyZxRf7M3my33Zlx5u6qeAa5aUeI66YBMdHU1QUFCzFs91dw6Hg8zMTE6fPk1iYmKLPmOzws3q1asbBBuAjh078uyzzzJ2rDanExHXSO0bwxd7s0nbl8Mjqb2/+wWFJyB7F1isWpVYPIbdbq8PNh07djS7nFbVqVMnMjMzqa2tvWAm9uVoVseWv78/JSUXbmpXWlqqlYJFxGXG94nGYoFdp4rILq787hfUrUrcZRQEe/YvAfEedWNsgoKCTK6k9dVlCLvd3qL3aVa4uemmm/jJT37Cxo0bcTqdOJ1ONmzYwE9/+lNuvvnmFhUkIlKnU6g/g7tEAJC27xJWK64LN8lalVg8jyd2RX2bqz5js8LNyy+/TI8ePRg9ejQBAQEEBAQwduxYevbsyUsvveSSwkREAFL7XuJqxVWl51Yl1ngbEa/WrDE3ERERfPzxxxw8eJD09HTA2J27Z8+eLi1ORGRC3xie/+IAaw/lUVFtJ9DvIstNHFkF9mrokASdktuyRBFxMy2aTN6rVy+mTJnClClTFGxEpFX0iQ2lc0QgVbUOvj6Ud/ET66aA99aqxCLuZv78+SQlJREQEEBKSgqbNm1q1etdcsvNrFmzePrppwkODmbWrFlNnvviiy+2uDAREahbrTiav64/Tlp6Nqn9Glmt2OGAA18Yj7UqsYhbWbRoEbNmzWLBggWkpKQwb948Jk2axP79+4mOjm6Va15yuNm+fXv9iO3t27e3SjEiIo25ts/ZcLMvB4fDidX6rZaZzG3GqsT+YdBVy1GIuJMXX3yR++67j5kzZwKwYMEClixZwltvvcVjjz3WKte85HCzcuXKRh+LiLS2K86uVpxTYqxWPLDLtxb0q9sos4dWJRbv4HQ6qahp2XTp5gr0tV3yrKbq6mq2bt3K7Nmz649ZrVZSU1NZv359a5XYvAHFP/rRj3jppZcIDQ1tcLysrIwHH3yQt956yyXFiYiAsVrxuF5RfL7HWK34gnBTPwVcs6TEO1TU2Ok353NTrr33qUkE+V1afMjLy8NutxMT07A7OSYmpn5CUmto1oDid999l4qKiguOV1RU8Ne//rXFRYmIfFvdzuBp6d+aEl6YAdm7tSqxiNS7rJab4uLi+kX7SkpKCAgIqP+a3W5n6dKlrTY4SES827VnVyvefaqYrKJKYsPP/vw5cPav14QUCIq8+BuIeJBAXxt7nzJn8Hyg70WWY2hEVFQUNpuN7OyGf5RkZ2cTGxvr6tLqXVa4iYiIwGKxYLFY6N37wn1eLBYLTz75pMuKExGpExXiz5CECLZnFJKWns2dKV2NL9SNt+mtVYnFe1gslkvuGjKTn58fw4cPJy0tjalTpwLGBplpaWk88MADrXbdy/rOrFy5EqfTybXXXsu///3vBhtn+vn50bVrV+Lj411epIgIGBtpbs8oJG1fjhFuqkrg2FfGFzXeRsQtzZo1ixkzZjBixAhGjRrFvHnzKCsrq5891RouK9xcffXVABw9epSEhASs1hatASgiclkm9I3muc/383XdasX1qxJ3g6hL2DVcRNrctGnTyM3NZc6cOWRlZTFkyBCWLVt2wSBjV2pWm1bXrkZzcHl5ORkZGVRXVzf4+qBBg1pemYjItyTHGKsVnyqsYO2hPK6rmyXV+3qtSizixh544IFW7Yb6tmaFm9zcXGbOnMlnn33W6NdbulW5iEhjLBYLqX2jeXf9cVbsPc11R88OJtYu4CJynmb1Kz3yyCMUFhayceNGAgMDWbZsGe+++y69evXik08+cXWNIiL1rj07JTx73zooyzVWJU4cY3JVIuJOmtVys2LFCj7++GNGjBiB1Wqla9euXHfddYSFhTF37lxuvPFGV9cpIgLAFd0jCfazMaxqg/ETrOcErUosIg00q+WmrKysfj2bDh06kJubC8DAgQPZtm2b66oTEfkWfx8b43p1YoL17M+a3polJSINNSvcJCcns3//fgAGDx7Ma6+9xqlTp1iwYAFxcXEuLVBE5NumdK2lr/UEdqzQ6zqzyxERN9OsbqmHH36Y06dPA/DEE09w/fXX849//AM/Pz/eeecdV9YnInKBq9kKwBZHbxJrAtGfVCJyvmaFm7vuuqv+8fDhwzl+/Djp6ekkJiYSFRXlsuJERBoTcnw5AGn2oSTuy+GuK7qaXJGIuBOXrMIXFBTEsGHDFGxEpPVVlcCxtQCkOYaRti/7O14gIt7mkltuZs2adclv+uKLLzarGBGR73R4BdirqQ5L4nBOPCcO51NeXdsu9tkRkbZxyT8Ntm/ffknnWbRKqIi0prO7gPv2nUyXmiBOnqlg7cE8JvZvvR2GRaR9ueRws3LlytasQ0Tkuzns9eHGkjyZ1Joo3ll3jLR9OQo3Im5ozZo1PPfcc2zdupXTp0/z0Ucf1e8O3pq086WItB+ntkJ5HviHQ9cxTOhrrLe1Yn8ODofT5OJE5NvKysoYPHgw8+fPb9PrNquTevz48U12P61YsaLZBYmIXNT+s/vZ9ZwANl9GdTNWK84tqWLXqSIGJ0SYWp6INDR58mQmT277hTabFW6GDBnS4HlNTQ07duxg9+7dzJgxwxV1iYhcqG4X8GTjh6W/j42renfis91ZpO3LVrgR7+F0Qk25Odf2DQI3H1/brHDzpz/9qdHjv/vd7ygtLW1RQSIijTpzHHL2gsUGPVPrD0/oG8Nnu7P4cl8OsyYmm1igSBuqKYdn4s259q8zwS/YnGtfIpeOubnrrrt46623XPmWIiKGulabxCsgKLL+8PjkTlgssPd0MZmFFSYVJyLuxKULQ6xfv56AgABXvqWIiKFuvE3v6xsc7hjiz7DEDmw9foa09Bzu1mrF4g18g4wWFLOu7eaaFW5uvfXWBs+dTienT59my5YtPP744y4pTESkXmVx/arEdeNtzjehb7QRbvZlK9yId7BY3L5ryEzNCjfh4eENnlutVpKTk3nqqaeYOHGiSwoTEal3eAU4aiCyO3TsecGXU/vG8Mdl+1mn1YpF3EppaSmHDh2qf3706FF27NhBZGQkiYmJrXbdZv0EePvtt11dh4jIxZ1duI/ekxudpdErOoSEyEBOFFTw1cE8JmlBPxG3sGXLFsaPH1//vG4rpxkzZvDOO++02nVb9OfNli1b2LdvHwD9+vVj+PDhLilKRKSeww4Hz4ab5OsbPcVisTChTwzvrDvGin05CjcibuKaa67B6Wz7BTabFW5OnjzJ7bffztdff01ERAQAhYWFjBkzhoULF9KlSxdX1igi3uzkFijPN1YlThx90dNS+xrhJi3dWK3YanXvdThEpPU0ayr4vffeS01NDfv27aOgoICCggL27duHw+Hg3nvvdXWNIuLNDpydJdUrFWy+Fz1tVLdIQvx9yCut4ptTRW1UnIi4o2aFm9WrV/Pqq6+SnHxuwazk5GT+7//+jzVr1risOBER9p9d36Z300u4+/lYuap3FABp+7JbuyoRcWPNCjcJCQnU1NRccNxutxMfb9KKiSLiec4cg9x9xqrEvVK/8/QJfWIA+HJfTisXJiLurFnh5rnnnuPBBx9ky5Yt9ce2bNnCww8/zPPPP++y4kTEy9W12iSOhsAO33n6+D7RWC2w73Qxp7RasXgYMwbmtjVXfcZmhZt77rmHHTt2kJKSgr+/P/7+/qSkpLBt2zZ+9KMfERkZWX8TEWm2uvE2F5kl9W2RwX4MSzRC0Ap1TYmH8PU1xpqVl5u0UWYbqq6uBsBms7XofZo1W2revHktuqiIyHeqLIZjXxuPv2O8zfkm9I1hy/EzfLkvh7tHJ7VObSJtyGazERERQU6O0d0aFBSExc135W4Oh8NBbm4uQUFB+Pi0bCHOZr16xowZLbqoiMh3Opx2dlXiHhB14arEF5PaN5o/LEtn/eF8yqpqCfbXasXS/sXGGms31QUcT2W1WklMTGxxeGv2//V2u53FixfXL+LXv39/br755hY3JYmIAOdWJW5kL6mm9IwOITEyiIyCcr46mMf1A7Sgn7R/FouFuLg4oqOjG53Q4yn8/PywWps1YqaBZoWbQ4cOccMNN3Dq1Kn66eBz584lISGBJUuW0KNHjxYXJiJezGGHg18Yj3tf2nibOhaLhQl9o3n762OsSM9WuBGPYrPZ1IhwCZoVjx566CF69OjBiRMn2LZtG9u2bSMjI4Nu3brx0EMPubpGEfE2JzcbqxIHhEPiFZf98tS+xpTwFem5OByeP8NERBpqVsvN6tWr2bBhQ4PZUB07duTZZ59l7NixLitORLzU/rOzpHpe1+SqxBczMimS0LOrFe88WcjQxO+eRi4inqNZLTf+/v6UlJRccLy0tBQ/P78WFyUiXu7A2fVtLnO8TR1jteJOAKRpQT8Rr9OscHPTTTfxk5/8hI0bN+J0OnE6nWzYsIGf/vSn3Hzzza6uUUS8Sf5hyE0Hqw/0nNDst5nQNxqAL7XejYjXaVa4efnll+nRowejR48mICCAgIAAxowZQ8+ePXnppZcu+/3mz59PUlISAQEBpKSksGnTpkt63cKFC7FYLEydOvWyrykibqqu1abrmEtalfhixicbqxWnZ5Vw8oznL34mIuc0a8xNREQEH3/8MYcOHWLv3r0A9OvXj549L30tijqLFi1i1qxZLFiwgJSUFObNm8ekSZPYv38/0dHRF33dsWPHePTRRxk3blxzPoKIuKu68TbJN7TobToE+zG8awc2HzvDivQcpmtBPxGv0ezJ5G+++SZTp07lBz/4AT/4wQ+YOnUqf/nLXy77fV588UXuu+8+Zs6cSb9+/ViwYAFBQUG89dZbF32N3W7nzjvv5Mknn6R79+7N/Qgi4m7KC+D4OuPxZU4Bb8yEvtpIU8QbNSvczJkzh4cffpgpU6bw/vvv8/777zNlyhR+8YtfMGfOnEt+n+rqarZu3Upq6rndfq1WK6mpqaxfv/6ir3vqqaeIjo7mxz/+8Xdeo6qqiuLi4gY3EXFTh74Epx2i+0Fktxa/XerZcTcbDudTWlXb4vcTkfahWd1Sr776Km+88Qa33357/bGbb76ZQYMG8eCDD/LUU09d0vvk5eVht9uJiYlpcDwmJob09PRGX7N27VrefPNNduzYcUnXmDt3Lk8++eQlnSsiJtu/1Lhv5iypb+vRKYSuHYM4nl/OWq1WLOI1mtVyU1NTw4gRIy44Pnz4cGprW++vo5KSEu6++27eeOMNoqKiLuk1s2fPpqioqP524sSJVqtPRFqgthoOfmk8buF4mzoWi4UJfYw/ntI0a0rEazSr5ebuu+/m1Vdf5cUXX2xw/PXXX+fOO++85PeJiorCZrORnd3wh052dnb9JmHnO3z4MMeOHWPKlCn1xxwOBwA+Pj7s37//gq0f/P398ff3v+SaRMQkx9dCdQkER0P8MJe9bWrfaN76+igr9+fgcDixWj1vN2URaajZG2e++eabfPHFF1xxhbE0+saNG8nIyGD69OnMmjWr/rxvB6Dz+fn5MXz4cNLS0uqnczscDtLS0njggQcuOL9Pnz7s2rWrwbHf/va3lJSU8NJLL5GQkNDcjyMiZqufJXU9uGDjvDoju0USGuBDXmk120+cYXjXyO9+kYi0a80KN7t372bYMOMvq8OHDwNGK0xUVBS7d++uP+9StiyfNWsWM2bMYMSIEYwaNYp58+ZRVlbGzJkzAZg+fTqdO3dm7ty5BAQEMGDAgAavj4iIALjguIi0I06ny6aAf5uvzcq1faL5eEcmy3ZnKdyIeIFmhZuVK1e6rIBp06aRm5vLnDlzyMrKYsiQISxbtqx+kHFGRoZLtj8XETeWvQeKToBPIHS72uVvP3lALB/vyOSz3Vn8+oa+l/SHl4i0Xxan0+lVW+YWFxcTHh5OUVERYWFhZpcjIgCrn4OVvzdabW7/p8vfvqLazrCnl1NRY+fTB69kQOdwl19DRFrX5fz+VpOIiJjPxVPAvy3Qz8Y1ycZGmp/tPt0q1xAR96FwIyLmKj4NmdsAi0tWJb6YujVuPtudhZc1WIt4HYUbETFX3UaZXUZAyMX3k2upa/tE42ezciS3jEM5pa12HRExn8KNiJirfpZU63RJ1QkN8OXKXsbin5/tzmrVa4mIuRRuRMQ81WVwZJXx2MVTwBtzfteUiHguhRsRMc+hNLBXQYck6NSn1S93Xd8YbFYL+04Xczy/rNWvJyLmULgREfPs+49x3+cmaIO1ZzoE+3FFd2MRP7XeiHguhRsRMUdtNRz43Hjcd0rT57rQ9QPiAFimcCPisRRuRMQcx76CqiJjo8wuo9rsspP6x2CxwI4ThZwuqmiz64pI21G4ERFz1HdJ3eDSjTK/S3RoACO6dgDUeiPiqRRuRKTtORznViVuwy6pOnVdU0t3abViEU+kcCMibe/kZijNBv9wSLqqzS9/w0BjSvjmY2fUNSXigRRuRKTtpZ/tkuo9EXz82vzyceGBjEoyZk0t+UatNyKeRuFGRNqW03luvI0JXVJ1pgw2uqb+szPTtBpEpHUo3IhI28reA2eOgU8A9Ew1rYzJA+OwWmDnySIt6CfiYRRuRKRtpX9q3Pe4FvyCTSsjKsSfsT2NvaY+VdeUiEdRuBGRtnX+qsQmmzIoHlDXlIinUbgRkbZTcBSyd4PF1uq7gF+KSf1j8bVZSM8q4WB2idnliIiLKNyISNup65JKGgtBkebWAoQH+XJ1704A/EddUyIeQ+FGRNrO3k+M+z7mzZL6tpvOdk19ujMTp9NpcjUi4goKNyLSNopOwslNgMXUKeDfltovBn8fK0fyytiTWWx2OSLiAgo3ItI29n5s3CeOhrA4c2s5T4i/DxP6RgPwn280sFjEEyjciEjb2PORcd//e+bW0Ygp9V1Tp9U1JeIBFG5EpPUVnjD2k8IC/W42u5oLjO8TTbCfjVOFFWw9fsbsckSkhRRuRKT11XVJdR0LobHm1tKIAF9b/U7hH24/ZXI1ItJSCjci0vrqu6SmmlpGU24b1hkwZk1V1thNrkZEWkLhRkRa15njcGoLxiwp9+uSqnNF947EhwdQXFnLivQcs8sRkRZQuBGR1lXXJZV0JYTGmFtLE6xWC7cMNVpvPtx20uRqRKQlFG5EpHXtXWzcu3GXVJ1bz4abVftzySutMrkaEWkuhRsRaT1njsOprWCxunWXVJ1eMaEM6hJOrcOpzTRF2jGFGxFpPXWtNl3HQki0qaVcqlvru6Y0a0qkvVK4EZHW48YL913MlMHx+Fgt7DpVxAHtFC7SLinciEjrOHMMMre3my6pOh1D/Lkm2WhlUuuNSPukcCMirWPX+8Z90jgI6WRuLZepbs2bj7afpNbuMLkaEblcCjci4npOJ3xzNtwM+i9za2mGa/tGExnsR3ZxFav255pdjohcJoUbEXG9rG8gbz/Y/KHvFLOruWz+Prb6gcULN2eYXI2IXC6FGxFxvW/+ZdwnT4aAcHNraaYfjkoAYEV6DllFlSZXIyKXQ+FGRFzLYYddHxiP22GXVJ2e0aGMTOqAwwkfbD1hdjkichkUbkTEtY59BaVZEBABPa8zu5oW+eHIRAAWbTmBw+E0uRoRuVQKNyLiWnUDiftPBR8/U0tpqRsGxhEa4MOJggrWHc43uxwRuUQKNyLiOjWVsO8T4/HA9tslVSfQz8b3zg4s/qcGFou0Gwo3IuI6B5ZBVTGEdYHE0WZX4xLTRhoDi7/Yk0W+NtMUaRcUbkTEdeoW7hv0A7B6xo+X/vHhDOoSTo3dyQdbT5pdjohcAs/46SMi5ivLM1puwCO6pM53Z4oxsPhvG45j18BiEbencCMirvHNv8BRC/FDIaaf2dW41C1DOhMR5MvJMxWk7cs2uxwR+Q4KNyLSck4nbP+78XjoXebW0goCfG3108LfXX/M3GJE5Dsp3IhIy53eATl7jO0WBtxmdjWt4q4rErFa4OtD+RzILjG7HBFpgsKNiLRcXatN3ykQ2MHcWlpJlw5BTOwXC8C7646ZW4yINEnhRkRapqby3CypoXeaW0srmzEmCYAPt52iqLzG3GJE5KIUbkSkZdI/hcoiY22bblebXU2ruqJ7JMkxoVTU2Hlf+02JuC2FGxFpmR3/MO6H3AFWm7m1tDKLxcI9Y5MAY2CxpoWLuCeFGxFpvsITcHil8XjIHebW0kamnp0WfqKggs/3ZJldjog0QuFGRJpvxz8AJySNg8huZlfTJgL9bEy/oisAr60+jNOp1hsRd6NwIyLNY6+Fre8aj4fNMLeWNjZ9TBL+PlZ2nixi/RHtFi7ibhRuRKR5DnwGJZkQFAX9bja7mjYVFeLPf40wNtR8bfURk6sRkW9TuBGR5tn8F+N+2N3g429uLSa4b1x3rBZYfSCXvZnFZpcjIudxi3Azf/58kpKSCAgIICUlhU2bNl303DfeeINx48bRoUMHOnToQGpqapPni0gryD8MR1YBFhg+0+xqTJHYMYgbBsYB8NqawyZXIyLnMz3cLFq0iFmzZvHEE0+wbds2Bg8ezKRJk8jJyWn0/FWrVnH77bezcuVK1q9fT0JCAhMnTuTUqVNtXLmIF9vylnHfayJ06GpuLSb66dU9APjPzkwO55aaXI2I1LE4TR7qn5KSwsiRI3nllVcAcDgcJCQk8OCDD/LYY4995+vtdjsdOnTglVdeYfr06d95fnFxMeHh4RQVFREWFtbi+kW8Tk0FvNAHKgvhjn9B70lmV2Sqe9/dwpf7svne0M78adoQs8sR8ViX8/vb1Jab6upqtm7dSmpqav0xq9VKamoq69evv6T3KC8vp6amhsjIyEa/XlVVRXFxcYObiLTA7g+NYBOeCD1Tv/N0T/dIai8APt5xSq03Im7C1HCTl5eH3W4nJiamwfGYmBiysi5tcaz/+Z//IT4+vkFAOt/cuXMJDw+vvyUkJLS4bhGvVjeQeMRMj1+R+FIM6BxOat8YHE54ZcUhs8sREdxgzE1LPPvssyxcuJCPPvqIgICARs+ZPXs2RUVF9bcTJ7QfjEizndgMmdvA5gdD7za7Greh1hsR92JquImKisJms5Gdnd3geHZ2NrGxsU2+9vnnn+fZZ5/liy++YNCgQRc9z9/fn7CwsAY3EWmmDfON+4E/gJBO5tbiRs5vvXnxiwNmlyPi9UwNN35+fgwfPpy0tLT6Yw6Hg7S0NEaPHn3R1/3xj3/k6aefZtmyZYwYMaItShWRM8dh78fG4yt+bm4tbuiXE3tjscCSXafZnnHG7HJEvJrp3VKzZs3ijTfe4N1332Xfvn387Gc/o6ysjJkzjbUzpk+fzuzZs+vP/8Mf/sDjjz/OW2+9RVJSEllZWWRlZVFaqqZgkVa16XVwOqD7NRA7wOxq3E7fuDBuHdoFgLmfpWvPKRETmR5upk2bxvPPP8+cOXMYMmQIO3bsYNmyZfWDjDMyMjh9+nT9+a+++irV1dV8//vfJy4urv72/PPPm/URRDxfZfG5faRGP2BuLW7slxN74+djZdPRAlakN75Wl4i0PtPXuWlrWudGpBnWz4fPfw1RyfDzDWA1/e8itzX3s328tvoIvaJD+OzhcfjY9L0ScYV2s86NiLQDtdWw/s/G4yt+pmDzHX5+TU8ignw5mFPKe5syzC5HxCvpp5SINO2bRVB8EkJiYPDtZlfj9sIDfZl1XW8Anv98P/mlVSZXJOJ9FG5E5OIcdlj7J+Px6AfAt/H1pKShO1O60i8ujOLKWv64bL/Z5Yh4HYUbEbm4vYuh4DAEdoARPzK7mnbDZrXw9NT+ACzackJTw0XamMKNiDTO6YSvXjQep/wM/EPMraedGd41ktuGGVPDH/94N7V2h8kViXgPhRsRadyBZZC9G/xCYNR9ZlfTLj02uQ9hAT7sPlXM618dMbscEa+hcCMiF3I4YOX/Go9H/hiCIs2tp53qFOrPnClG99S85Qc5lFNickUi3kHhRkQutO9jyNoFfqEw9hGzq2nXbhvWmWuSO1Ftd/Do+99gd3jV0mIiplC4EZGG7LWw8hnj8ej71WrTQhaLhbm3DiTU34cdJwp5fY26p0Ram8KNiDT0zSLIO2DMkBp9v9nVeIS48EAev6kfAC98sV+zp0RamcKNiJxTWw2rnzUeX/kLCNAWJa7ygxFduHFgHLUOJw/+cztFFTVmlyTisRRuROScTa9DYYaxGvFIzZByJYvFwtzbBtKlQyAnz1Tw6w93aedwkVaicCMihrI8WP1H4/G1j4NfkLn1eKCwAF9euWMYPlYLS3ad5s21R80uScQjKdyIiGHlM1BVBLGDYMgdZlfjsYYkRPDrG/oC8MzSfazcn2NyRSKeR+FGRCB7L2x923h8/Vyw2sytx8PNHJvEtBEJOJzw0HvbOZit9W9EXEnhRsTbOZ3w+WxwOqDvzZB0pdkVeTyLxcLTUwcwqlskJVW1/OjdzWQXV5pdlojHULgR8Xa7PoAjq8DmD9c9ZXY1XsPPx8qCu4bTtWMQJwoquOsvG8kvrTK7LBGPoHAj4s3KC2DZY8bjq38Fkd3MrcfLRAb78fcfpxAXHsDBnFKmv7VJU8RFXEDhRsSbLX8cyvOgU18Y87DZ1XilhMgg/n5vClEhfuzJLOauv2wkTy04Ii2icCPirY5+Bdv/bjye8hL4+Jlbjxfr0SmEv/04hchgP3adKuL7r67jREG52WWJtFs+ZhcgIiaoLIaPf248HvEjSEwxtx6hb1wYH/x0NHe/uYlj+eXc+uo63pg+giEJEWaX1iYKy6s5ll/O8fwysosryS2pIq+0moKyaipq7FTV2KmsceBwOvGxWfG1WfC1WQkP9CUy2I+OwX50CvUnMTKIblHBJHYMwt9Hs/68lcXpZUtkFhcXEx4eTlFREWFhWlpevNRHP4Od70FEIvz0a22z4EayiyuZ8dYm0rNK8LVZmHNTP+66oisWi8Xs0lyissZOelYJu04VsedUEelZJRzLL6Ow3LVjjSwW6BYVzOAuEQzsHM7ghAgGdQnH16YOi/bqcn5/K9yIeJs9H8H794DFCvcsha6jza5IvqW4sob/fv8blu3JAmDK4HievqU/EUHtq+vQ6XRy8kwFG48WsOloPt+cLOJgTil2R+O/dmLC/OnaMZj48ACiQvyJCvUnMtiPID8bgb42AnxtWCxQa3dS63BQXeugsLyG/DKjhSeruJLj+WUcyyuntKr2gvcP9rNxRfeOXNkriqt7d6J7p5DW/haICyncNEHhRrxa0Ul4dSxUFsK4R2HC42ZXJBfhdDp5c+1R5n6Wjt3hJCrEjzlT+jNlUJzbtuI4nU6O5JWx6WgBG4/ks+loAZlFF67f0zHYjwGdwxnQOYx+ceF0iwomKSqIID/XjJRwOp3kllaxJ7OYb04U8c3JQrafKKSgrLrBeckxodwwMI4bBsbSKybUJdeW1qNw0wSFG/FaNZXw9mTI3AbxQ+HHy8Hma3ZV8h22ZZzhfz74hoM5pQCM7dmRRycmMzSxg8mVgcPh5GBOKRuP5rPxSAEbjxZcMNPLx2phUJdwRnXryLDECAZ2CSc2LKDNA5rD4WTv6WLWHspj7cE8NhzJp/a8FqR+cWH8cFQCtwzuTHiQ/r9wRwo3TVC4Ea/kdMInDxizowI7wE9WQYcks6uSS1RVa2fBqiPMX3mIarsDgNS+0dw3rjujukW2WVBwOJykZ5Ww4Ug+G48aLTNnvjVWxs/HytCECFK6RZLSvSNDEyNc1iLjSkXlNSzfl81nu06z5mAuNXbjV6G/j5UbBsYxc2wSg7pEmFukNKBw0wSFG/FKm9+EJbOMcTZ3/Rt6XGt2RdIMJwrKeSntIB9uO0ldo0PP6BB+ODKBif1iSezo2p3ciypq2HWyiJ0nC9meUcjmYwUXLDIY6GtjeNcO9WFmUJdwAnzb1yylM2XVLN5xikWbT5CedW6fr1HdIrlvXHcm9InGanXPrkBvonDTBIUb8ToHv4R/TgNHLaT+Dq78hdkVSQsdzi3ljTVH+GRnJuXV9vrjPToFc2XPKAaenSHUtWPQdwYNp9NJcUUtmUUVHM4t5VCOcdubWcyRvLILzg/2szEiKZKU7pGkdOvIwM7h+Pl4xgwkp9PJzpNF/HXdMT7ZmVnfbdU9KpifXNWd24Z30WwrEyncNEHhRrzKyS3w7hSoKYeBP4Bb3zDmyIpHKKms4eMdmXz6TSabj51pdBZSZLAf0aH+BPkZs41sVgtVNQ6qau3kl1WTU1JFda3jotdIjAxiUJdwBneJYGS3SAbEh+HjBb/gs4oqeWfdMd7beJziSmPmVWJkEA9N6MXUIfFe8T1wNwo3TVC4Ea+RewDemgQVBUY31O2LtAqxByuqqGHtwTy2ZZxh16ki9mYWNzod+mIignzpHhVMz+gQekaH0DsmlEFdIogM9u5/M2VVtfxzUwYLVh8mr9SYbdU9KpiHU3sxZVC8uqvakMJNExRu2omKM5B/2LiVZhv7H5XlG1OYHbXgsIPTDlZf8A8BvxDwD4XQWAjrbNwiEo3n3thSkb0X/jbV+N7FD4MZ/zG+T+I1nE4nRRU1ZBZWklNSSWWNnapaB3aHkwBfG/4+ViKCfIkODaBTqH+7GyfT1sqra/nb+uMsWH24fhD1gM5h/PbGflzRvaPJ1XkHhZsmKNy4obI8o/vk5Gbjlr3HCDOu4B8OnZIhug/EDYYuIyG6P9jcb/aGy5zaBn+/1QiI0f1hxicQHGV2VSIeobSqlne+Psprq49QcrZlbFL/GGZP7ktSVLDJ1Xk2hZsmKNy4AXstnNwEB5fDoeWQtavx80LjoGNPCIuHoCgIioTACLD5gcUGVhvYa6C6FKpKoLIISk5D0SkozoTik+BsZCyBb5DRmpEwCrpdBYmjwTegVT9ymzm8AhZNh+oS6Dwc7vzA+L6JiEvllVYx78sDvLcxA4cTfG0W7hmTxMOpvQnx9+A/nkykcNMEhRuTOByQsQ52vQ97PzZaFc4XlWy0qnQZAfFDoGOvlnej1FZB3kHITYecfcbidSe3QlVRw/N8AiDxCug+HnqMh5iBYG1ngwWdTvj6JUh70gh0SePg9n8aXXUi0moOZJfwzNJ9rNqfCxhbSDx+Uz9uHOi+K0m3Vwo3TVC4aWNnjsO2d2HHP6Ek89zxwA7QMxV6Xgc9J7Rdt4nDAXkHjJaj4+vhyEqjted8wZ2MunqlGgNxA81fCbZJFWfg018Ye0YBDLkLbnzBc1qjRNqBlftz+N0nezieXw7AuF5RPHlzf+1f5UIKN01QuGkDDofR3bT5TTj4BXD2n5h/OPSbYkxJThpndCuZzemE3P1GyDmyCo6tNbq56lis0GWUEXR6Xgexg9yrVSd9qRFsSrPA6gPXPwsj7/XOQdQiJqussbNg9WH+vOow1bUO/GxWfnJVdx64tqcGbLuAwk0TFG5akb3G6HZaOw/y9p873v0aGPEj6H09+PibVd2lqa2GjPVGODu43OjSOl9IjNHi1Os6oxsrMMKUMsk7BCueMrr4wOjGm/oqJIw0px4RqXc8v4zffbKHlWe7qrpFBTP31oGaVdVCCjdNULhpBdVlsO2vsO4VYxAvgH8YDJsOw2dCVE9z62uJwgw49KURdI6shprzVmy12CAh5bxWnYGt32KSf9gYW7P978ZUeIsVxjwI18wG38DWvbaIXDKn08nne7J54pPdZBcbm4nePiqBxyb3JTxQG3M2h8JNExRuXKi2Cra+A2uegzLjLxSCo2H0z42WmoBwU8tzudoqo1Xn4NlWnfNbp8CY3dVzghF0ul3lullKVSXG9ba9a3Sd1el9PVz7OMQOcM11RMTliitr+MNn6fxjYwYA0aH+PHXLAK4fEGtyZe2Pwk0TFG5cwGGHb/4Fq54xWjYAIrrClY/A4Du8ZyDrmePnuq+OrjG2ODhfh27GzK+4IRDVGyK7GTtxN9XC4nRCaY4xPT7rGzi6Go59DY66zQotRpfYuF8aM7xEpF3YeCSf2R/uqt+v6/r+sTx5S39iwrzk56ULKNw0QeGmBZxO2L8U0p6G3H3GsZBYuPq/jS4omxc3tdZUGlPd61p18g9e/NygKGOsTkCE8T1z2I3wUpZvrChsr7rwNR26wYBbYdgM6NC1tT6FiLSiyho7r6w4xILVh6l1OAkN8OHXN/TlhyMTNG38EijcNEHhppmy98Ky/zFaKMD4xXzlIzDq/4FfkJmVuafyAji9EzK3Gy0wBUeg4NiFa+w0ymIsXhg70Fj3p9dE47l++Il4hL2ZxTz24Td8c9L4eXBF90jm3jqIblrhuEkKN01QuLlM5QWwaq4xrdtpNxa8u+LnMPZh82YKtVdOp7EmTXGmsZpyZZGxT5bFakzjDuoIIdHGjCxv6doT8VJ2h5O3vz7K81/sp7LGgZ+PlYcn9OK+cd3x83Gj5SbciMJNExRuLpHDbgwWXvF7Y1dpgL43w8Tfq1tERMRFThSU8+uPdvHVQWM/veSYUJ69bSBDE9188VATKNw0QeHmEhxfB5/997k9nzr1hcl/gO5Xm1uXiIgHcjqdLN5xiqf+s5cz5TVYLDBjdBKPTkrWPlXnUbhpgsJNE4pOwvI5sPvfxvOAcBj/GxjxY8/eRVtExA0UlFXz+0/38uH2UwDEhwfw9NQBTOgbY3Jl7kHhpgkKN42oqYR1/wdrXzw7ndkCw++Ba3/bdns+iYgIAGsO5PKbxbs4UVABwI2D4nhiSj+iQ717LJ7CTRMUbs7jdEL6p/D5b6DwuHEscbTRBRU32NzaRES8WHl1LfO+PMhfvjqCwwlhAT48OimZO0Yl4mPzzgHHCjdNULg5K2s3LHsMjn1lPA+Nh4lPw4DbNOVYRMRN7D5VxGMffsPuU8UA9I0L43dT+pHihftUKdw0wevDTVkerPxfYyaU0wE2f2NvonGzwE9rLIiIuJtau4P3NmXwwhcHKKowViu/eXA8s2/oQ1y49+wpp3DTBK8NN/Ya2PQGrHr23EJy/abCdU9pareISDtQUFbN81/s55+bMnA6IcjPxk+v7sGPr+xGsBfMqlK4aYLXhRunE/b9B9KeOrclQOxAuP4PkDTW3NpEROSy7T5VxBOf7GHr8TMARIX48eC1vbh9VKJHLwCocNMErwk3TiccWWmEmsztxrHgTsYu0kPvAqvN3PpERKTZnE4nn+zM5IUvDpBRYGzamxAZyKzrenPz4M7YrJ43dlLhpgleEW4yNhgrC9cNFvYNhtH3w5gHjLVrRETEI1TXOli05QQvpx0kt8TYdLdrxyD+31U9uHVYZwJ8PecPWYWbJnhsuHE44OAX8PU8yFhvHLP5GQvwjfslhHQytTwREWk95dW1vP31Mf7y1RHOlBuDjjuF+nPPmCSmjUwgKsTf5ApbTuGmCR4XbiqLYNf7xsaWOXuNYzY/GPxDuOpXEJFobn0iItJmyqtrWbjpBG98dYTTRZUA+NosXD8gjjtTEknpFomlnS73cTm/v91i5NH8+fNJSkoiICCAlJQUNm3a1OT577//Pn369CEgIICBAweydOnSNqrUTTjsxv5Pi38OL/SBJb80go1fKIx5CB7+Bm7+PwUbEREvE+Tnw4+u7MbqX43n+R8MZkhCBDV2J//ZmckPX9/AlX9Yye8/3cvW4wXU2h1ml9tqTG+5WbRoEdOnT2fBggWkpKQwb9483n//ffbv3090dPQF569bt46rrrqKuXPnctNNN/Hee+/xhz/8gW3btjFgwIDvvF67bbkpzTHG0hxOg/SlUJZz7mud+sCwGTDkDgiMMK1EERFxP7tPFfHepgw+3n6Ksmp7/fEQfx9GJnVgeNcO9I4JpU9sGHERAfi66QrI7apbKiUlhZEjR/LKK68A4HA4SEhI4MEHH+Sxxx674Pxp06ZRVlbGp59+Wn/siiuuYMiQISxYsOA7r9dq4aa2CkqzjVlKTgfgPPv4/OcO4/n5j8//Wm01VBZCRSFUnIGiE5B/GPL2w5ljDa/nHw59bzJCTcIorSosIiJNqqyxs/pALku+Oc3qA7n1CwKez2KBjsH+RIf6ExXqT7CfjUA/G0F+NoL8fPC1WbBaLFgsFmwWC1YLWK0WLBawnn0OEBMWwC1DOru0/sv5/W3qqj/V1dVs3bqV2bNn1x+zWq2kpqayfv36Rl+zfv16Zs2a1eDYpEmTWLx4caPnV1VVUVVVVf+8uLi45YU35vROePO61nlvACwQ3Q+6jobkGyBpHPj4teL1RETEkwT42pjUP5ZJ/WOxO5zsO13MhiP57MksZn9WCYdySqm2O8grrSKvtApON/9awxIjXB5uLoep4SYvLw+73U5MTMPt3GNiYkhPT2/0NVlZWY2en5WV1ej5c+fO5cknn3RNwU2x2MAnACxWwGLcWyxnW1TO3l/wtW89t/lCQIQxXTswAsI6Q2R36NgD4oaoy0lERFzCZrUwoHM4AzqfWx7E4XBSUF5NdnElOcVV5JdVU1FdS1m1nfJqOxXVtdQ6nDidYHc4cTidOJzGmjsOpxO7A5wYnUFJHc3dzsfj12uePXt2g5ae4uJiEhISXH+hLsPht9muf18REZE2YLVaiArxJyrEn/7xZlfTMqaGm6ioKGw2G9nZDUNBdnY2sbGxjb4mNjb2ss739/fH37/9z+8XERGRS2PqkGg/Pz+GDx9OWlpa/TGHw0FaWhqjR49u9DWjR49ucD7A8uXLL3q+iIiIeBfTu6VmzZrFjBkzGDFiBKNGjWLevHmUlZUxc+ZMAKZPn07nzp2ZO3cuAA8//DBXX301L7zwAjfeeCMLFy5ky5YtvP7662Z+DBEREXETpoebadOmkZuby5w5c8jKymLIkCEsW7asftBwRkYGVuu5BqYxY8bw3nvv8dvf/pZf//rX9OrVi8WLF1/SGjciIiLi+Uxf56attdtF/ERERLxYu9t+QURERMRVFG5ERETEoyjciIiIiEdRuBERERGPonAjIiIiHkXhRkRERDyKwo2IiIh4FIUbERER8SgKNyIiIuJRTN9+oa3VLchcXFxsciUiIiJyqep+b1/KxgpeF25KSkoASEhIMLkSERERuVwlJSWEh4c3eY7X7S3lcDjIzMwkNDQUi8Xi0vcuLi4mISGBEydOaN+qVqTvc9vQ97lt6PvcdvS9bhut9X12Op2UlJQQHx/fYEPtxnhdy43VaqVLly6teo2wsDD9j9MG9H1uG/o+tw19n9uOvtdtozW+z9/VYlNHA4pFRETEoyjciIiIiEdRuHEhf39/nnjiCfz9/c0uxaPp+9w29H1uG/o+tx19r9uGO3yfvW5AsYiIiHg2tdyIiIiIR1G4EREREY+icCMiIiIeReFGREREPIrCjYvMnz+fpKQkAgICSElJYdOmTWaX5HHWrFnDlClTiI+Px2KxsHjxYrNL8khz585l5MiRhIaGEh0dzdSpU9m/f7/ZZXmcV199lUGDBtUvdDZ69Gg+++wzs8vyeM8++ywWi4VHHnnE7FI8yu9+9zssFkuDW58+fUyrR+HGBRYtWsSsWbN44okn2LZtG4MHD2bSpEnk5OSYXZpHKSsrY/DgwcyfP9/sUjza6tWruf/++9mwYQPLly+npqaGiRMnUlZWZnZpHqVLly48++yzbN26lS1btnDttddyyy23sGfPHrNL81ibN2/mtddeY9CgQWaX4pH69+/P6dOn629r1641rRZNBXeBlJQURo4cySuvvAIY+1clJCTw4IMP8thjj5lcnWeyWCx89NFHTJ061exSPF5ubi7R0dGsXr2aq666yuxyPFpkZCTPPfccP/7xj80uxeOUlpYybNgw/vznP/P73/+eIUOGMG/ePLPL8hi/+93vWLx4MTt27DC7FEAtNy1WXV3N1q1bSU1NrT9mtVpJTU1l/fr1JlYm4hpFRUWA8YtXWofdbmfhwoWUlZUxevRos8vxSPfffz833nhjg5/V4loHDx4kPj6e7t27c+edd5KRkWFaLV63caar5eXlYbfbiYmJaXA8JiaG9PR0k6oScQ2Hw8EjjzzC2LFjGTBggNnleJxdu3YxevRoKisrCQkJ4aOPPqJfv35ml+VxFi5cyLZt29i8ebPZpXislJQU3nnnHZKTkzl9+jRPPvkk48aNY/fu3YSGhrZ5PQo3InJR999/P7t37za179yTJScns2PHDoqKivjggw+YMWMGq1evVsBxoRMnTvDwww+zfPlyAgICzC7HY02ePLn+8aBBg0hJSaFr167861//MqWbVeGmhaKiorDZbGRnZzc4np2dTWxsrElVibTcAw88wKeffsqaNWvo0qWL2eV4JD8/P3r27AnA8OHD2bx5My+99BKvvfaayZV5jq1bt5KTk8OwYcPqj9ntdtasWcMrr7xCVVUVNpvNxAo9U0REBL179+bQoUOmXF9jblrIz8+P4cOHk5aWVn/M4XCQlpamvnNpl5xOJw888AAfffQRK1asoFu3bmaX5DUcDgdVVVVml+FRJkyYwK5du9ixY0f9bcSIEdx5553s2LFDwaaVlJaWcvjwYeLi4ky5vlpuXGDWrFnMmDGDESNGMGrUKObNm0dZWRkzZ840uzSPUlpa2uCvgKNHj7Jjxw4iIyNJTEw0sTLPcv/99/Pee+/x8ccfExoaSlZWFgDh4eEEBgaaXJ3nmD17NpMnTyYxMZGSkhLee+89Vq1axeeff252aR4lNDT0gvFiwcHBdOzYUePIXOjRRx9lypQpdO3alczMTJ544glsNhu33367KfUo3LjAtGnTyM3NZc6cOWRlZTFkyBCWLVt2wSBjaZktW7Ywfvz4+uezZs0CYMaMGbzzzjsmVeV5Xn31VQCuueaaBsfffvtt7rnnnrYvyEPl5OQwffp0Tp8+TXh4OIMGDeLzzz/nuuuuM7s0kct28uRJbr/9dvLz8+nUqRNXXnklGzZsoFOnTqbUo3VuRERExKNozI2IiIh4FIUbERER8SgKNyIiIuJRFG5ERETEoyjciIiIiEdRuBERERGPonAjIiIiHkXhRkRERDyKwo2ItAurVq3CYrFQWFhodiki4ua0QrGIuKVrrrmGIUOGMG/ePACqq6spKCggJiYGi8VibnEi4ta0t5SItAt+fn7ExsaaXYaItAPqlhIRt3PPPfewevVqXnrpJSwWCxaLhXfeeadBt9Q777xDREQEn376KcnJyQQFBfH973+f8vJy3n33XZKSkujQoQMPPfQQdru9/r2rqqp49NFH6dy5M8HBwaSkpLBq1SpzPqiItAq13IiI23nppZc4cOAAAwYM4KmnngJgz549F5xXXl7Oyy+/zMKFCykpKeHWW2/le9/7HhERESxdupQjR45w2223MXbsWKZNmwbAAw88wN69e1m4cCHx8fF89NFHXH/99ezatYtevXq16ecUkdahcCMibic8PBw/Pz+CgoLqu6LS09MvOK+mpoZXX32VHj16APD973+fv/3tb2RnZxMSEkK/fv0YP348K1euZNq0aWRkZPD222+TkZFBfHw8AI8++ijLli3j7bff5plnnmm7DykirUbhRkTaraCgoPpgAxATE0NSUhIhISENjuXk5ACwa9cu7HY7vXv3bvA+VVVVdOzYsW2KFpFWp3AjIu2Wr69vg+cWi6XRYw6HA4DS0lJsNhtbt27FZrM1OO/8QCQi7ZvCjYi4JT8/vwYDgV1h6NCh2O12cnJyGDdunEvfW0Tch2ZLiYhbSkpKYuPGjRw7doy8vLz61peW6N27N3feeSfTp0/nww8/5OjRo2zatIm5c+eyZMkSF1QtIu5A4UZE3NKjjz6KzWajX79+dOrUiYyMDJe879tvv8306dP55S9/SXJyMlOnTmXz5s0kJia65P1FxHxaoVhEREQ8ilpuRERExKMo3IiIiIhHUbgRERERj6JwIyIiIh5F4UZEREQ8isKNiIiIeBSFGxEREfEoCjciIiLiURRuRERExKMo3IiIiIhHUbgRERERj/L/AUrSvNtGH2vjAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -891,10 +891,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.606505Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.606350Z",
-     "iopub.status.idle": "2024-06-03T10:29:28.612791Z",
-     "shell.execute_reply": "2024-06-03T10:29:28.612413Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.272302Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.272192Z",
+     "iopub.status.idle": "2024-06-03T14:32:22.275871Z",
+     "shell.execute_reply": "2024-06-03T14:32:22.275613Z"
     }
    },
    "outputs": [],
@@ -973,16 +973,16 @@
      "start_time": "2019-07-14T21:07:20.914008Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:28.614631Z",
-     "iopub.status.busy": "2024-06-03T10:29:28.614482Z",
-     "iopub.status.idle": "2024-06-03T10:29:29.389014Z",
-     "shell.execute_reply": "2024-06-03T10:29:29.388467Z"
+     "iopub.execute_input": "2024-06-03T14:32:22.277161Z",
+     "iopub.status.busy": "2024-06-03T14:32:22.277080Z",
+     "iopub.status.idle": "2024-06-03T14:32:23.061812Z",
+     "shell.execute_reply": "2024-06-03T14:32:23.061489Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-of-a-State-to-State-Transfer-in-a-Lambda-System-in-the-RWA">
-<h1>Optimization of a State-to-State Transfer in a Lambda System in the RWA<a class="headerlink" href="#Optimization-of-a-State-to-State-Transfer-in-a-Lambda-System-in-the-RWA" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-of-a-State-to-State-Transfer-in-a-Lambda-System-in-the-RWA">
+<h1>Optimization of a State-to-State Transfer in a Lambda System in the RWA<a class="headerlink" href="#Optimization-of-a-State-to-State-Transfer-in-a-Lambda-System-in-the-RWA" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -175,21 +180,21 @@ <h1>Optimization of a State-to-State Transfer in a Lambda System in the RWA<a cl
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
-krotov    : 1.3.0
-scipy     : 1.10.1
+krotov    : 1.3.0+dev
 matplotlib: 3.7.5
-numpy     : 1.24.4
+scipy     : 1.12.0
+numpy     : 1.26.4
 qutip     : 4.7.6
 
 </pre></div></div>
 </div>
 <p><span class="math notranslate nohighlight">\(\newcommand{tr}[0]{\operatorname{tr}} \newcommand{diag}[0]{\operatorname{diag}} \newcommand{abs}[0]{\operatorname{abs}} \newcommand{pop}[0]{\operatorname{pop}} \newcommand{aux}[0]{\text{aux}} \newcommand{opt}[0]{\text{opt}} \newcommand{tgt}[0]{\text{tgt}} \newcommand{init}[0]{\text{init}} \newcommand{lab}[0]{\text{lab}} \newcommand{rwa}[0]{\text{rwa}} \newcommand{bra}[1]{\langle#1\vert} \newcommand{ket}[1]{\vert#1\rangle} \newcommand{Bra}[1]{\left\langle#1\right\vert} \newcommand{Ket}[1]{\left\vert#1\right\rangle} \newcommand{Braket}[2]{\left\langle #1\vphantom{#2}\mid{#2}\vphantom{#1}\right\rangle} \newcommand{ketbra}[2]{\vert#1\rangle\!\langle#2\vert} \newcommand{op}[1]{\hat{#1}} \newcommand{Op}[1]{\hat{#1}} \newcommand{dd}[0]{\,\text{d}} \newcommand{Liouville}[0]{\mathcal{L}} \newcommand{DynMap}[0]{\mathcal{E}} \newcommand{identity}[0]{\mathbf{1}} \newcommand{Norm}[1]{\lVert#1\rVert} \newcommand{Abs}[1]{\left\vert#1\right\vert} \newcommand{avg}[1]{\langle#1\rangle} \newcommand{Avg}[1]{\left\langle#1\right\rangle} \newcommand{AbsSq}[1]{\left\vert#1\right\vert^2} \newcommand{Re}[0]{\operatorname{Re}} \newcommand{Im}[0]{\operatorname{Im}}\)</span></p>
 <p>This example is illustrates the use of complex-valued control fields. This is accomplished by rewriting the Hamiltonian as the sum of two independent controls (real and imaginary parts). We consider a 3-level system in a <span class="math notranslate nohighlight">\(\Lambda\)</span> configuration, and seek control pulses that implement a (phase-sensitive) state-to-state transition <span class="math notranslate nohighlight">\(\ket{1} \rightarrow \ket{3}\)</span>.</p>
-<div class="section" id="The-rotating-wave-Hamiltonian">
-<h2>The rotating wave Hamiltonian<a class="headerlink" href="#The-rotating-wave-Hamiltonian" title="Permalink to this headline">¶</a></h2>
+<section id="The-rotating-wave-Hamiltonian">
+<h2>The rotating wave Hamiltonian<a class="headerlink" href="#The-rotating-wave-Hamiltonian" title="Link to this heading">¶</a></h2>
 <p>The system consists of three levels <span class="math notranslate nohighlight">\(\ket{1}\)</span>, <span class="math notranslate nohighlight">\(\ket{2}\)</span> and <span class="math notranslate nohighlight">\(\ket{3}\)</span> with energy levels <span class="math notranslate nohighlight">\(E_{1}, E_{2}\)</span> and <span class="math notranslate nohighlight">\(E_{3}\)</span> which interact with a pair of laser pulses <span class="math notranslate nohighlight">\(\epsilon_{P}(t)\)</span> (“pump laser”) and <span class="math notranslate nohighlight">\(\epsilon_{S}(t)\)</span> (“Stokes laser”), respectively, see Chapter 15.4.2 in <a class="reference external" href="http://www.weizmann.ac.il/chemphys/tannor/Book/">“Introduction to Quantum Mechanics: A Time-Dependent Perspective” by David Tannor</a> for details.</p>
 <p>In the lab frame, the Hamiltonian reads</p>
 <div class="math notranslate nohighlight">
@@ -236,7 +241,7 @@ <h2>The rotating wave Hamiltonian<a class="headerlink" href="#The-rotating-wave-
     0                           &amp; -\frac{1}{2} \Omega_S^*(t) &amp; \Delta_S
 \end{pmatrix}\,,\end{split}\]</div>
 <p>with the detunings <span class="math notranslate nohighlight">\(\Delta_P \equiv E_1 + \omega_P - E_2\)</span>, <span class="math notranslate nohighlight">\(\Delta_S \equiv E3 + \omega_S -E_2\)</span> and the complex-valued control fields <span class="math notranslate nohighlight">\(\Omega_P(t) \equiv \Omega_{P}^{(1)}(t) + i \Omega_{P}^{(2)}(t)\)</span> and <span class="math notranslate nohighlight">\(\Omega_S(t) \equiv \Omega_{S}^{(1)}(t) + i \Omega_{S}^{(2)}(t)\)</span>, illustrated in the following diagram:</p>
-<p><img alt="Lambda system considered in this notebook" src="../_images/energylevels.png" /></p>
+<img alt="Lambda system considered in this notebook" src="../_images/energylevels.png" />
 <p>Most textbooks (including Tannor’s) only allow control fields of the form <span class="math notranslate nohighlight">\(\epsilon_{P,S}(t) \propto \Omega_{P,S}(t) \cos{(\omega_{P,S} t)}\)</span> with the pulse envelopes <span class="math notranslate nohighlight">\(\Omega_{P,S}(t) \in \mathbb{R}^+\)</span>. This will result in the same <span class="math notranslate nohighlight">\(\Op{H}_\text{RWA}\)</span> as above, but with the positive real-valued envelopes instead of the complex-valued <span class="math notranslate nohighlight">\(\Omega_{P,S}(t)\)</span>. However, this restriction is unnecessary: complex-valued control fields in the RWA are more general and entirely physical,
 with the relation to the real-valued field in the lab frame as defined above. The spectra of the optimized pulses are free to deviate from the frequencies of the rotating frame, limited only by the numerical resolution of the time grid and the RWA.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">krotov</span></code> package requires that all control pulses are real-valued. Therefore, the real and imaginary parts of <span class="math notranslate nohighlight">\(\Omega_{P}\)</span> and <span class="math notranslate nohighlight">\(\Omega_{S}\)</span> are treated as independent Hamiltonians, and we write</p>
@@ -256,9 +261,9 @@ <h2>The rotating wave Hamiltonian<a class="headerlink" href="#The-rotating-wave-
 \Op{H}_{S,\text{re}} &amp;= -\frac{1}{2} \left(\ketbra{2}{3} + \ketbra{3}{2}\right)\,, \\
 \Op{H}_{S,\text{im}} &amp;= -\frac{i}{2} \left(\ketbra{2}{3} - \ketbra{3}{2}\right)\,.
 \end{align}\end{split}\]</div>
-</div>
-<div class="section" id="Guess-controls">
-<h2>Guess controls<a class="headerlink" href="#Guess-controls" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Guess-controls">
+<h2>Guess controls<a class="headerlink" href="#Guess-controls" title="Link to this heading">¶</a></h2>
 <p>We choose the initial guess for the four control fields based on the intuition of the “stimulated Raman adiabatic passage” (STIRAP) scheme. STIRAP allows to transfer the population in <span class="math notranslate nohighlight">\(\ket{1}\)</span> <span class="math notranslate nohighlight">\(\ket{3}\)</span> without having to pass through <span class="math notranslate nohighlight">\(\ket{2}\)</span>; it requires the Stokes-pulse to precede but overlap the pump-pulse.</p>
 <p>Here, we leave it up to Krotov’s method to find appropriate pulses for a STIRAP-like transfer (without requiring that the <span class="math notranslate nohighlight">\(\ket{2}\)</span> level remains unpopulated). We start from a low intensity real-valued <span class="math notranslate nohighlight">\(\Omega_S(t)\)</span> pulse with a Blackman shape, followed by an overlapping real-valued <span class="math notranslate nohighlight">\(\Omega_P(t)\)</span> of the same shape. The entire scheme is in the time interval [0, 5].</p>
 <div class="nbinput nblast docutils container">
@@ -326,9 +331,9 @@ <h2>Guess controls<a class="headerlink" href="#Guess-controls" title="Permalink
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Target-state-in-the-rotating-frame">
-<h2>Target state in the rotating frame<a class="headerlink" href="#Target-state-in-the-rotating-frame" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Target-state-in-the-rotating-frame">
+<h2>Target state in the rotating frame<a class="headerlink" href="#Target-state-in-the-rotating-frame" title="Link to this heading">¶</a></h2>
 <p>The basis states of the <span class="math notranslate nohighlight">\(\Lambda\)</span>-system are defined as</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
@@ -378,9 +383,9 @@ <h2>Target state in the rotating frame<a class="headerlink" href="#Target-state-
 Objective[|Ψ₀(3)⟩ to |Ψ₁(3)⟩ via [H₀[3,3], [H₁[3,3], u₁(t)], [H₂[3,3], u₂(t)], [H₃[3,3], u₃(t)], [H₄[3,3], u₄(t)]]]
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Simulate-dynamics-under-the-guess-field">
-<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Simulate-dynamics-under-the-guess-field">
+<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Link to this heading">¶</a></h2>
 <p>We use a time grid with 500 steps between <span class="math notranslate nohighlight">\(t=0\)</span> and <span class="math notranslate nohighlight">\(T=5\)</span>:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -490,9 +495,9 @@ <h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate
 </div>
 </div>
 <p>We find that our guess pulses are too disjoint to implement the STIRAP scheme. Thus, the Stokes pulse has no effect, whilst the pump pulse merely transfers population out of <span class="math notranslate nohighlight">\(\ket{1}\)</span> into <span class="math notranslate nohighlight">\(\ket{2}\)</span> and back again.</p>
-</div>
-<div class="section" id="Optimize">
-<h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimize">
+<h2>Optimize<a class="headerlink" href="#Optimize" title="Link to this heading">¶</a></h2>
 <p>In order to invoke <code class="docutils literal notranslate"><span class="pre">optimize_pulses</span></code>, we must define the required parameters for each control, a pulse shape (used to ensure that the controls remain 0 at <span class="math notranslate nohighlight">\(t=0\)</span> and <span class="math notranslate nohighlight">\(t=T\)</span>), and the parameter <span class="math notranslate nohighlight">\(\lambda_a\)</span> that determines the overall magnitude of the pulse updates in each iteration.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
@@ -551,19 +556,19 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 iter.      J_T   g_a_int_1   g_a_int_2   g_a_int_3   g_a_int_4    g_a_int          J  Delta J_T    Delta J  secs
-0     1.01e+00    0.00e+00    0.00e+00    0.00e+00    0.00e+00   0.00e+00   1.01e+00        n/a        n/a     1
-1     6.72e-01    8.60e-02    2.87e-04    8.17e-02    3.72e-04   1.68e-01   8.40e-01  -3.37e-01  -1.68e-01     2
-2     4.02e-01    7.20e-02    4.21e-04    6.22e-02    4.20e-04   1.35e-01   5.37e-01  -2.70e-01  -1.35e-01     2
-3     2.22e-01    4.91e-02    4.64e-04    3.99e-02    3.88e-04   8.98e-02   3.12e-01  -1.80e-01  -8.98e-02     2
-4     1.17e-01    2.89e-02    3.87e-04    2.29e-02    3.01e-04   5.25e-02   1.69e-01  -1.05e-01  -5.25e-02     2
-5     6.00e-02    1.56e-02    2.69e-04    1.23e-02    2.10e-04   2.84e-02   8.84e-02  -5.69e-02  -2.84e-02     2
-6     3.05e-02    8.08e-03    1.71e-04    6.37e-03    1.39e-04   1.48e-02   4.52e-02  -2.95e-02  -1.48e-02     2
-7     1.54e-02    4.08e-03    1.06e-04    3.24e-03    9.10e-05   7.51e-03   2.30e-02  -1.50e-02  -7.51e-03     2
-8     7.85e-03    2.04e-03    6.65e-05    1.63e-03    5.99e-05   3.79e-03   1.16e-02  -7.59e-03  -3.79e-03     2
-9     4.02e-03    1.02e-03    4.31e-05    8.14e-04    4.01e-05   1.91e-03   5.94e-03  -3.83e-03  -1.91e-03     2
-10    2.09e-03    5.05e-04    2.88e-05    4.07e-04    2.73e-05   9.68e-04   3.05e-03  -1.94e-03  -9.68e-04     2
-11    1.10e-03    2.52e-04    1.99e-05    2.03e-04    1.88e-05   4.94e-04   1.59e-03  -9.87e-04  -4.94e-04     2
-12    5.90e-04    1.26e-04    1.40e-05    1.02e-04    1.31e-05   2.54e-04   8.45e-04  -5.09e-04  -2.54e-04     2
+0     1.01e+00    0.00e+00    0.00e+00    0.00e+00    0.00e+00   0.00e+00   1.01e+00        n/a        n/a     0
+1     6.72e-01    8.60e-02    2.87e-04    8.17e-02    3.72e-04   1.68e-01   8.40e-01  -3.37e-01  -1.68e-01     1
+2     4.02e-01    7.20e-02    4.21e-04    6.22e-02    4.20e-04   1.35e-01   5.37e-01  -2.70e-01  -1.35e-01     1
+3     2.22e-01    4.91e-02    4.64e-04    3.99e-02    3.88e-04   8.98e-02   3.12e-01  -1.80e-01  -8.98e-02     1
+4     1.17e-01    2.89e-02    3.87e-04    2.29e-02    3.01e-04   5.25e-02   1.69e-01  -1.05e-01  -5.25e-02     1
+5     6.00e-02    1.56e-02    2.69e-04    1.23e-02    2.10e-04   2.84e-02   8.84e-02  -5.69e-02  -2.84e-02     1
+6     3.05e-02    8.08e-03    1.71e-04    6.37e-03    1.39e-04   1.48e-02   4.52e-02  -2.95e-02  -1.48e-02     1
+7     1.54e-02    4.08e-03    1.06e-04    3.24e-03    9.10e-05   7.51e-03   2.30e-02  -1.50e-02  -7.51e-03     1
+8     7.85e-03    2.04e-03    6.65e-05    1.63e-03    5.99e-05   3.79e-03   1.16e-02  -7.59e-03  -3.79e-03     1
+9     4.02e-03    1.02e-03    4.31e-05    8.14e-04    4.01e-05   1.91e-03   5.94e-03  -3.83e-03  -1.91e-03     1
+10    2.09e-03    5.05e-04    2.88e-05    4.07e-04    2.73e-05   9.68e-04   3.05e-03  -1.94e-03  -9.68e-04     1
+11    1.10e-03    2.52e-04    1.99e-05    2.03e-04    1.88e-05   4.94e-04   1.59e-03  -9.87e-04  -4.94e-04     1
+12    5.90e-04    1.26e-04    1.40e-05    1.02e-04    1.31e-05   2.54e-04   8.45e-04  -5.09e-04  -2.54e-04     1
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -582,11 +587,11 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <div class="highlight"><pre>
 Krotov Optimization Result
 --------------------------
-- Started at 2024-06-03 12:32:15
+- Started at 2024-06-03 10:29:08
 - Number of objectives: 1
 - Number of iterations: 12
 - Reason for termination: Reached convergence: J_T &lt; 0.001
-- Ended at 2024-06-03 12:32:45 (0:00:30)
+- Ended at 2024-06-03 10:29:29 (0:00:21)
 </pre></div></div>
 </div>
 <p>We dump the result of the optimization to disk for later use in the <a class="reference internal" href="08_example_ensemble.html"><span class="doc">Ensemble Optimization for Robust Pulses</span></a>.</p>
@@ -702,19 +707,8 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <div class="nboutput docutils container">
 <div class="prompt empty docutils container">
 </div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmtt10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmb10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmss10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;DejaVu Sans Display&#39;] not found. Falling back to DejaVu Sans.
-</pre></div></div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_2.png" src="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_2.png" />
+<img alt="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_1.png" src="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_1.png" />
 </div>
 </div>
 <div class="nboutput docutils container">
@@ -729,7 +723,7 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_4.png" src="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_4.png" />
+<img alt="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_3.png" src="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_45_3.png" />
 </div>
 </div>
 <p>Lastly, we check the population dynamics to verify that we indeed implement the desired state-to-state transfer:</p>
@@ -757,8 +751,8 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <img alt="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_48_0.png" src="../_images/notebooks_02_example_lambda_system_rwa_complex_pulse_48_0.png" />
 </div>
 </div>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -772,7 +766,7 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb b/master/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb
index 35c2a4e9..c0eabb9c 100644
--- a/master/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb
+++ b/master/notebooks/02_example_lambda_system_rwa_complex_pulse.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.234562Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.234410Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.737411Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.736828Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.143219Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.142398Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.934955Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.934649Z"
     }
    },
    "outputs": [
@@ -24,13 +24,13 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "scipy     : 1.10.1\n",
+      "krotov    : 1.3.0+dev\n",
       "matplotlib: 3.7.5\n",
-      "numpy     : 1.24.4\n",
+      "scipy     : 1.12.0\n",
+      "numpy     : 1.26.4\n",
       "qutip     : 4.7.6\n",
       "\n"
      ]
@@ -267,10 +267,10 @@
      "n": "6"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.763940Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.763603Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.767750Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.767233Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.957846Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.957636Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.960968Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.960638Z"
     }
    },
    "outputs": [],
@@ -309,10 +309,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.769643Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.769495Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.774715Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.774217Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.962402Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.962262Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.965179Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.964936Z"
     }
    },
    "outputs": [],
@@ -346,10 +346,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.776668Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.776335Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.780392Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.779969Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.966449Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.966349Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.968911Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.968658Z"
     }
    },
    "outputs": [],
@@ -376,10 +376,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.782260Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.782114Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.785703Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.785203Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.970425Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.970263Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.972638Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.972392Z"
     }
    },
    "outputs": [],
@@ -409,10 +409,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.787521Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.787374Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.790048Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.789547Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.973845Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.973764Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.975527Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.975301Z"
     }
    },
    "outputs": [],
@@ -426,10 +426,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.791868Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.791714Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.794112Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.793725Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.976763Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.976687Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.978454Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.978125Z"
     }
    },
    "outputs": [],
@@ -458,10 +458,10 @@
      "n": "5"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.795968Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.795822Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.801179Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.800807Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.979849Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.979774Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.983133Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.982878Z"
     }
    },
    "outputs": [
@@ -509,10 +509,10 @@
      "n": "4"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.803094Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.802948Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.805464Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.805083Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.984518Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.984435Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.985912Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.985694Z"
     }
    },
    "outputs": [],
@@ -541,10 +541,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.807345Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.807170Z",
-     "iopub.status.idle": "2024-06-03T10:32:14.810290Z",
-     "shell.execute_reply": "2024-06-03T10:32:14.809793Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.987100Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.987022Z",
+     "iopub.status.idle": "2024-06-03T14:29:07.988980Z",
+     "shell.execute_reply": "2024-06-03T14:29:07.988755Z"
     }
    },
    "outputs": [],
@@ -573,10 +573,10 @@
      "n": "11"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:14.812103Z",
-     "iopub.status.busy": "2024-06-03T10:32:14.811957Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.045265Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.044695Z"
+     "iopub.execute_input": "2024-06-03T14:29:07.990334Z",
+     "iopub.status.busy": "2024-06-03T14:29:07.990246Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.191599Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.190621Z"
     }
    },
    "outputs": [
@@ -627,10 +627,10 @@
      "n": "12"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.047348Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.047190Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.050451Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.049922Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.197491Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.196869Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.202478Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.201756Z"
     }
    },
    "outputs": [],
@@ -661,10 +661,10 @@
      "n": "3"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.052462Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.052313Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.055616Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.055234Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.206707Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.206571Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.209506Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.208887Z"
     }
    },
    "outputs": [],
@@ -688,10 +688,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.057681Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.057509Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.371749Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.371256Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.214148Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.213771Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.378228Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.377856Z"
     }
    },
    "outputs": [],
@@ -713,10 +713,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.373831Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.373681Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.377295Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.376808Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.380028Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.379894Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.382566Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.382102Z"
     }
    },
    "outputs": [],
@@ -746,10 +746,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.379336Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.378953Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.497059Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.496546Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.384249Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.384131Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.453493Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.453209Z"
     }
    },
    "outputs": [
@@ -799,10 +799,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.499151Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.498995Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.501830Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.501340Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.455244Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.455137Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.457235Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.456924Z"
     }
    },
    "outputs": [],
@@ -819,10 +819,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.503663Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.503443Z",
-     "iopub.status.idle": "2024-06-03T10:32:15.506400Z",
-     "shell.execute_reply": "2024-06-03T10:32:15.505904Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.458641Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.458539Z",
+     "iopub.status.idle": "2024-06-03T14:29:08.460482Z",
+     "shell.execute_reply": "2024-06-03T14:29:08.460170Z"
     }
    },
    "outputs": [],
@@ -856,10 +856,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:15.508319Z",
-     "iopub.status.busy": "2024-06-03T10:32:15.508173Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.112419Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.111851Z"
+     "iopub.execute_input": "2024-06-03T14:29:08.461832Z",
+     "iopub.status.busy": "2024-06-03T14:29:08.461726Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.129548Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.129125Z"
     }
    },
    "outputs": [
@@ -868,91 +868,91 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T   g_a_int_1   g_a_int_2   g_a_int_3   g_a_int_4    g_a_int          J  Delta J_T    Delta J  secs\n",
-      "0     1.01e+00    0.00e+00    0.00e+00    0.00e+00    0.00e+00   0.00e+00   1.01e+00        n/a        n/a     1\n"
+      "0     1.01e+00    0.00e+00    0.00e+00    0.00e+00    0.00e+00   0.00e+00   1.01e+00        n/a        n/a     0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1     6.72e-01    8.60e-02    2.87e-04    8.17e-02    3.72e-04   1.68e-01   8.40e-01  -3.37e-01  -1.68e-01     2\n"
+      "1     6.72e-01    8.60e-02    2.87e-04    8.17e-02    3.72e-04   1.68e-01   8.40e-01  -3.37e-01  -1.68e-01     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2     4.02e-01    7.20e-02    4.21e-04    6.22e-02    4.20e-04   1.35e-01   5.37e-01  -2.70e-01  -1.35e-01     2\n"
+      "2     4.02e-01    7.20e-02    4.21e-04    6.22e-02    4.20e-04   1.35e-01   5.37e-01  -2.70e-01  -1.35e-01     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3     2.22e-01    4.91e-02    4.64e-04    3.99e-02    3.88e-04   8.98e-02   3.12e-01  -1.80e-01  -8.98e-02     2\n"
+      "3     2.22e-01    4.91e-02    4.64e-04    3.99e-02    3.88e-04   8.98e-02   3.12e-01  -1.80e-01  -8.98e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4     1.17e-01    2.89e-02    3.87e-04    2.29e-02    3.01e-04   5.25e-02   1.69e-01  -1.05e-01  -5.25e-02     2\n"
+      "4     1.17e-01    2.89e-02    3.87e-04    2.29e-02    3.01e-04   5.25e-02   1.69e-01  -1.05e-01  -5.25e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5     6.00e-02    1.56e-02    2.69e-04    1.23e-02    2.10e-04   2.84e-02   8.84e-02  -5.69e-02  -2.84e-02     2\n"
+      "5     6.00e-02    1.56e-02    2.69e-04    1.23e-02    2.10e-04   2.84e-02   8.84e-02  -5.69e-02  -2.84e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6     3.05e-02    8.08e-03    1.71e-04    6.37e-03    1.39e-04   1.48e-02   4.52e-02  -2.95e-02  -1.48e-02     2\n"
+      "6     3.05e-02    8.08e-03    1.71e-04    6.37e-03    1.39e-04   1.48e-02   4.52e-02  -2.95e-02  -1.48e-02     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7     1.54e-02    4.08e-03    1.06e-04    3.24e-03    9.10e-05   7.51e-03   2.30e-02  -1.50e-02  -7.51e-03     2\n"
+      "7     1.54e-02    4.08e-03    1.06e-04    3.24e-03    9.10e-05   7.51e-03   2.30e-02  -1.50e-02  -7.51e-03     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8     7.85e-03    2.04e-03    6.65e-05    1.63e-03    5.99e-05   3.79e-03   1.16e-02  -7.59e-03  -3.79e-03     2\n"
+      "8     7.85e-03    2.04e-03    6.65e-05    1.63e-03    5.99e-05   3.79e-03   1.16e-02  -7.59e-03  -3.79e-03     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9     4.02e-03    1.02e-03    4.31e-05    8.14e-04    4.01e-05   1.91e-03   5.94e-03  -3.83e-03  -1.91e-03     2\n"
+      "9     4.02e-03    1.02e-03    4.31e-05    8.14e-04    4.01e-05   1.91e-03   5.94e-03  -3.83e-03  -1.91e-03     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10    2.09e-03    5.05e-04    2.88e-05    4.07e-04    2.73e-05   9.68e-04   3.05e-03  -1.94e-03  -9.68e-04     2\n"
+      "10    2.09e-03    5.05e-04    2.88e-05    4.07e-04    2.73e-05   9.68e-04   3.05e-03  -1.94e-03  -9.68e-04     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11    1.10e-03    2.52e-04    1.99e-05    2.03e-04    1.88e-05   4.94e-04   1.59e-03  -9.87e-04  -4.94e-04     2\n"
+      "11    1.10e-03    2.52e-04    1.99e-05    2.03e-04    1.88e-05   4.94e-04   1.59e-03  -9.87e-04  -4.94e-04     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12    5.90e-04    1.26e-04    1.40e-05    1.02e-04    1.31e-05   2.54e-04   8.45e-04  -5.09e-04  -2.54e-04     2\n"
+      "12    5.90e-04    1.26e-04    1.40e-05    1.02e-04    1.31e-05   2.54e-04   8.45e-04  -5.09e-04  -2.54e-04     1\n"
      ]
     }
    ],
@@ -987,10 +987,10 @@
      "n": "17"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.114577Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.114397Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.118223Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.117659Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.131611Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.131435Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.134135Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.133861Z"
     }
    },
    "outputs": [
@@ -999,11 +999,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:32:15\n",
+       "- Started at 2024-06-03 10:29:08\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 12\n",
        "- Reason for termination: Reached convergence: J_T < 0.001\n",
-       "- Ended at 2024-06-03 12:32:45 (0:00:30)"
+       "- Ended at 2024-06-03 10:29:29 (0:00:21)"
       ]
      },
      "execution_count": 20,
@@ -1028,10 +1028,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.120239Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.119937Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.122575Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.122206Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.136014Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.135896Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.137898Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.137629Z"
     }
    },
    "outputs": [],
@@ -1057,10 +1057,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.124539Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.124370Z",
-     "iopub.status.idle": "2024-06-03T10:32:45.483149Z",
-     "shell.execute_reply": "2024-06-03T10:32:45.482639Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.139511Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.139395Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.327709Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.327446Z"
     }
    },
    "outputs": [
@@ -1134,10 +1134,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:45.485258Z",
-     "iopub.status.busy": "2024-06-03T10:32:45.485074Z",
-     "iopub.status.idle": "2024-06-03T10:32:46.190606Z",
-     "shell.execute_reply": "2024-06-03T10:32:46.190081Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.329382Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.329269Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.504967Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.504672Z"
     }
    },
    "outputs": [
@@ -1148,37 +1148,9 @@
       "Physical electric pump pulse in the lab frame:\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1195,7 +1167,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1252,10 +1224,10 @@
      "n": "19"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:46.192581Z",
-     "iopub.status.busy": "2024-06-03T10:32:46.192427Z",
-     "iopub.status.idle": "2024-06-03T10:32:46.227403Z",
-     "shell.execute_reply": "2024-06-03T10:32:46.227008Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.506506Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.506353Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.575616Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.575302Z"
     }
    },
    "outputs": [],
@@ -1278,16 +1250,16 @@
      "n": "20"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:46.229183Z",
-     "iopub.status.busy": "2024-06-03T10:32:46.229034Z",
-     "iopub.status.idle": "2024-06-03T10:32:46.344415Z",
-     "shell.execute_reply": "2024-06-03T10:32:46.343885Z"
+     "iopub.execute_input": "2024-06-03T14:29:29.577145Z",
+     "iopub.status.busy": "2024-06-03T14:29:29.577053Z",
+     "iopub.status.idle": "2024-06-03T14:29:29.647757Z",
+     "shell.execute_reply": "2024-06-03T14:29:29.647509Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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         <script type="text/x-mathjax-config">MathJax.Hub.Config({"extensions": ["tex2jax.js"], "jax": ["input/TeX", "output/SVG"], "TeX": {"extensions": ["AMSmath.js", "AMSsymbols.js"], "Macros": {"tr": ["{\\operatorname{tr}}", 0], "diag": ["{\\operatorname{diag}}", 0], "abs": ["{\\operatorname{abs}}", 0], "pop": ["{\\operatorname{pop}}", 0], "ee": ["{\\text{e}}", 0], "ii": ["{\\text{i}}", 0], "aux": ["{\\text{aux}}", 0], "opt": ["{\\text{opt}}", 0], "tgt": ["{\\text{tgt}}", 0], "init": ["{\\text{init}}", 0], "lab": ["{\\text{lab}}", 0], "rwa": ["{\\text{rwa}}", 0], "bra": ["{\\langle#1\\vert}", 1], "ket": ["{\\vert#1\\rangle}", 1], "Bra": ["{\\left\\langle#1\\right\\vert}", 1], "Braket": ["{\\left\\langle #1\\vphantom{#2} \\mid #2\\vphantom{#1}\\right\\rangle}", 2], "ketbra": ["{\\vert#1\\rangle\\!\\langle#2\\vert}", 2], "Ket": ["{\\left\\vert#1\\right\\rangle}", 1], "mat": ["{\\mathbf{#1}}", 1], "op": ["{\\hat{#1}}", 1], "Op": ["{\\hat{#1}}", 1], "dd": ["{\\,\\text{d}}", 0], "daggered": ["{^{\\dagger}}", 0], "transposed": ["{^{\\text{T}}}", 0], "Liouville": ["{\\mathcal{L}}", 0], "DynMap": ["{\\mathcal{E}}", 0], "identity": ["{\\mathbf{1}}", 0], "Norm": ["{\\left\\lVert#1\\right\\rVert}", 1], "norm": ["{\\lVert#1\\rVert}", 1], "Abs": ["{\\left\\vert#1\\right\\vert}", 1], "avg": ["{\\langle#1\\rangle}", 1], "Avg": ["{\\left\\langle#1\\right\\rangle}", 1], "AbsSq": ["{\\left\\vert#1\\right\\vert^2}", 1], "Re": ["{\\operatorname{Re}}", 0], "Im": ["{\\operatorname{Im}}", 0], "Real": ["{\\mathbb{R}}", 0], "Complex": ["{\\mathbb{C}}", 0], "Integer": ["{\\mathbb{N}}", 0]}}, "tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "tex2jax_ignore|mathjax_ignore|document", "processClass": "tex2jax_process|mathjax_process|math|output_area"}})</script>
+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -43,7 +47,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -53,7 +57,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -148,9 +152,9 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-of-a-Dissipative-State-to-State-Transfer-in-a-Lambda-System">
-<h1>Optimization of a Dissipative State-to-State Transfer in a Lambda System<a class="headerlink" href="#Optimization-of-a-Dissipative-State-to-State-Transfer-in-a-Lambda-System" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-of-a-Dissipative-State-to-State-Transfer-in-a-Lambda-System">
+<h1>Optimization of a Dissipative State-to-State Transfer in a Lambda System<a class="headerlink" href="#Optimization-of-a-Dissipative-State-to-State-Transfer-in-a-Lambda-System" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -178,24 +182,24 @@ <h1>Optimization of a Dissipative State-to-State Transfer in a Lambda System<a c
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
-krotov    : 1.3.0
-qutip     : 4.7.6
+numpy     : 1.26.4
 matplotlib: 3.7.5
-numpy     : 1.24.4
-scipy     : 1.10.1
+qutip     : 4.7.6
+scipy     : 1.12.0
+krotov    : 1.3.0+dev
 
 </pre></div></div>
 </div>
 <p><span class="math notranslate nohighlight">\(\newcommand{tr}[0]{\operatorname{tr}} \newcommand{diag}[0]{\operatorname{diag}} \newcommand{abs}[0]{\operatorname{abs}} \newcommand{pop}[0]{\operatorname{pop}} \newcommand{aux}[0]{\text{aux}} \newcommand{opt}[0]{\text{opt}} \newcommand{tgt}[0]{\text{tgt}} \newcommand{init}[0]{\text{init}} \newcommand{lab}[0]{\text{lab}} \newcommand{rwa}[0]{\text{rwa}} \newcommand{bra}[1]{\langle#1\vert} \newcommand{ket}[1]{\vert#1\rangle} \newcommand{Bra}[1]{\left\langle#1\right\vert} \newcommand{Ket}[1]{\left\vert#1\right\rangle} \newcommand{Braket}[2]{\left\langle #1\vphantom{#2}\mid{#2}\vphantom{#1}\right\rangle} \newcommand{ketbra}[2]{\vert#1\rangle\!\langle#2\vert} \newcommand{op}[1]{\hat{#1}} \newcommand{Op}[1]{\hat{#1}} \newcommand{dd}[0]{\,\text{d}} \newcommand{Liouville}[0]{\mathcal{L}} \newcommand{DynMap}[0]{\mathcal{E}} \newcommand{identity}[0]{\mathbf{1}} \newcommand{Norm}[1]{\lVert#1\rVert} \newcommand{Abs}[1]{\left\vert#1\right\vert} \newcommand{avg}[1]{\langle#1\rangle} \newcommand{Avg}[1]{\left\langle#1\right\rangle} \newcommand{AbsSq}[1]{\left\vert#1\right\vert^2} \newcommand{Re}[0]{\operatorname{Re}} \newcommand{Im}[0]{\operatorname{Im}} \newcommand{toP}[0]{\omega_{12}} \newcommand{toS}[0]{\omega_{23}}\)</span></p>
 <p>This example illustrates the use of Krotov’s method with a non-Hermitian Hamiltonian. It considers the same system as the <a class="reference internal" href="02_example_lambda_system_rwa_complex_pulse.html"><span class="doc">previous example</span></a>, a transition <span class="math notranslate nohighlight">\(\ket{1} \rightarrow \ket{3}\)</span> in a three-level system in a <span class="math notranslate nohighlight">\(\Lambda\)</span>-configuration. However, here we add a non-Hermitian decay term to model loss from the intermediary level <span class="math notranslate nohighlight">\(\ket{2}\)</span>. At a technical level, this examples also illustrates how to use <code class="docutils literal notranslate"><span class="pre">args</span></code> in
 time-dependent control fields (“QuTiP style”), as opposed to hard-coding parameters or setting them through a closure, as was done in the previous examples.</p>
-<div class="section" id="The-effective-Hamiltonian">
-<h2>The effective Hamiltonian<a class="headerlink" href="#The-effective-Hamiltonian" title="Permalink to this headline">¶</a></h2>
+<section id="The-effective-Hamiltonian">
+<h2>The effective Hamiltonian<a class="headerlink" href="#The-effective-Hamiltonian" title="Link to this heading">¶</a></h2>
 <p>We consider the system as in the following diagram:</p>
-<p><img alt="Lambda system considered in this notebook" src="../_images/energylevels.png" /></p>
+<img alt="Lambda system considered in this notebook" src="../_images/energylevels.png" />
 <p>with the Hamiltonian</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}\Op{H}_{\text{lab}} = \begin{pmatrix}
@@ -356,9 +360,9 @@ <h2>The effective Hamiltonian<a class="headerlink" href="#The-effective-Hamilton
 H1 is Hermitian: True
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Define-the-optimization-target">
-<h2>Define the optimization target<a class="headerlink" href="#Define-the-optimization-target" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Define-the-optimization-target">
+<h2>Define the optimization target<a class="headerlink" href="#Define-the-optimization-target" title="Link to this heading">¶</a></h2>
 <p>We optimize for the phase-sensitive transition <span class="math notranslate nohighlight">\(\ket{1} \rightarrow \ket{3}\)</span>. As we are working in the rotating frame, the target state must be adjusted with an appropriate phase factor:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -394,9 +398,9 @@ <h2>Define the optimization target<a class="headerlink" href="#Define-the-optimi
 [Objective[|Ψ₀(3)⟩ to |Ψ₁(3)⟩ via [A₀[3,3], [H₁[3,3], u₁(t)], [H₂[3,3], u₂(t)], [H₃[3,3], u₃(t)], [H₄[3,3], u₄(t)]]]]
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Simulate-dynamics-under-the-guess-field">
-<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Simulate-dynamics-under-the-guess-field">
+<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Link to this heading">¶</a></h2>
 <p>We use a time grid with 500 steps between <span class="math notranslate nohighlight">\(t=0\)</span> and <span class="math notranslate nohighlight">\(T=5\)</span>:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
@@ -481,9 +485,9 @@ <h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate
 </div>
 </div>
 <p>The population dynamics and the norm-plot show the effect the non-Hermitian term in the Hamiltonian, resulting in a 30% loss.</p>
-</div>
-<div class="section" id="Optimize">
-<h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimize">
+<h2>Optimize<a class="headerlink" href="#Optimize" title="Link to this heading">¶</a></h2>
 <p>For each control, we define the update shape and the <span class="math notranslate nohighlight">\(\lambda_a\)</span> parameter that determines the magnitude of the update:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
@@ -742,22 +746,11 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmtt10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmb10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmss10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;DejaVu Sans Display&#39;] not found. Falling back to DejaVu Sans.
-</pre></div></div>
-</div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_03_example_lambda_system_rwa_non_hermitian_41_1.png" src="../_images/notebooks_03_example_lambda_system_rwa_non_hermitian_41_1.png" />
+<img alt="../_images/notebooks_03_example_lambda_system_rwa_non_hermitian_41_0.png" src="../_images/notebooks_03_example_lambda_system_rwa_non_hermitian_41_0.png" />
 </div>
 </div>
 <p>We have used here that the return value of the routine <code class="docutils literal notranslate"><span class="pre">print_fidelity</span></code> that was passed to the <code class="docutils literal notranslate"><span class="pre">optimize_pulses</span></code> routine as an <code class="docutils literal notranslate"><span class="pre">info_hook</span></code> is automatically accumulated in <code class="docutils literal notranslate"><span class="pre">result.info_vals</span></code>.</p>
@@ -906,8 +899,8 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <img alt="../_images/notebooks_03_example_lambda_system_rwa_non_hermitian_47_3.png" src="../_images/notebooks_03_example_lambda_system_rwa_non_hermitian_47_3.png" />
 </div>
 </div>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -921,7 +914,7 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb b/master/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb
index c36b9556..bf7397d2 100644
--- a/master/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb
+++ b/master/notebooks/03_example_lambda_system_rwa_non_hermitian.ipynb
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.058730Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.057958Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.630385Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.629760Z"
+     "iopub.execute_input": "2024-06-03T14:27:51.989457Z",
+     "iopub.status.busy": "2024-06-03T14:27:51.989274Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.490207Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.489897Z"
     }
    },
    "outputs": [
@@ -33,14 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "qutip     : 4.7.6\n",
+      "numpy     : 1.26.4\n",
       "matplotlib: 3.7.5\n",
-      "numpy     : 1.24.4\n",
-      "scipy     : 1.10.1\n",
+      "qutip     : 4.7.6\n",
+      "scipy     : 1.12.0\n",
+      "krotov    : 1.3.0+dev\n",
       "\n"
      ]
     }
@@ -217,10 +217,10 @@
      "start_time": "2019-11-10T17:36:51.300148Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.656109Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.655815Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.660910Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.660332Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.511590Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.511407Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.514485Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.514201Z"
     }
    },
    "outputs": [],
@@ -273,10 +273,10 @@
      "start_time": "2019-11-10T17:36:51.319722Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.663022Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.662601Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.667769Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.667344Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.515988Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.515900Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.518841Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.518614Z"
     }
    },
    "outputs": [],
@@ -322,10 +322,10 @@
      "start_time": "2019-11-10T17:36:51.337196Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.669836Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.669665Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.673029Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.672559Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.520217Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.520134Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.522134Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.521849Z"
     }
    },
    "outputs": [],
@@ -366,10 +366,10 @@
      "start_time": "2019-11-10T17:36:51.346901Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.674987Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.674817Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.679145Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.678761Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.523434Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.523352Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.525901Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.525628Z"
     },
     "lines_to_next_cell": 2
    },
@@ -399,10 +399,10 @@
      "n": "3"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.681058Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.680916Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.684359Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.683870Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.527187Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.527108Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.529146Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.528908Z"
     },
     "lines_to_next_cell": 2
    },
@@ -445,10 +445,10 @@
      "start_time": "2019-11-10T17:36:51.366724Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.686271Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.686056Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.690338Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.689864Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.530531Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.530448Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.533144Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.532910Z"
     }
    },
    "outputs": [],
@@ -479,10 +479,10 @@
      "start_time": "2019-11-10T17:36:51.381216Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.692216Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.692073Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.697846Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.697458Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.534402Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.534325Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.537683Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.537387Z"
     },
     "lines_to_next_cell": 2
    },
@@ -521,10 +521,10 @@
      "start_time": "2019-11-10T17:36:51.393811Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.700054Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.699578Z",
-     "iopub.status.idle": "2024-06-03T10:30:22.702321Z",
-     "shell.execute_reply": "2024-06-03T10:30:22.701934Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.538935Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.538849Z",
+     "iopub.status.idle": "2024-06-03T14:27:52.540403Z",
+     "shell.execute_reply": "2024-06-03T14:27:52.540172Z"
     }
    },
    "outputs": [],
@@ -549,10 +549,10 @@
      "start_time": "2019-11-10T17:36:51.401596Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:22.704273Z",
-     "iopub.status.busy": "2024-06-03T10:30:22.704047Z",
-     "iopub.status.idle": "2024-06-03T10:30:24.970693Z",
-     "shell.execute_reply": "2024-06-03T10:30:24.970052Z"
+     "iopub.execute_input": "2024-06-03T14:27:52.541671Z",
+     "iopub.status.busy": "2024-06-03T14:27:52.541593Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.211487Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.211213Z"
     }
    },
    "outputs": [],
@@ -581,10 +581,10 @@
      "start_time": "2019-11-10T17:36:54.723039Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:24.973053Z",
-     "iopub.status.busy": "2024-06-03T10:30:24.972852Z",
-     "iopub.status.idle": "2024-06-03T10:30:24.977850Z",
-     "shell.execute_reply": "2024-06-03T10:30:24.977365Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.213177Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.213083Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.216103Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.215851Z"
     }
    },
    "outputs": [],
@@ -628,10 +628,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:24.979660Z",
-     "iopub.status.busy": "2024-06-03T10:30:24.979518Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.221995Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.221476Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.217420Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.217337Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.347849Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.347559Z"
     },
     "lines_to_end_of_cell_marker": 2
    },
@@ -684,10 +684,10 @@
      "start_time": "2019-11-10T17:36:55.327833Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.224163Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.223974Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.226801Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.226397Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.349456Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.349350Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.351109Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.350866Z"
     }
    },
    "outputs": [],
@@ -710,10 +710,10 @@
      "start_time": "2019-11-10T17:36:55.336694Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.228750Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.228576Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.231894Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.231464Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.352356Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.352263Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.354219Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.354014Z"
     }
    },
    "outputs": [],
@@ -758,10 +758,10 @@
      "start_time": "2019-11-10T17:36:55.351667Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.233940Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.233725Z",
-     "iopub.status.idle": "2024-06-03T10:30:25.236392Z",
-     "shell.execute_reply": "2024-06-03T10:30:25.235916Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.355606Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.355510Z",
+     "iopub.status.idle": "2024-06-03T14:27:54.357240Z",
+     "shell.execute_reply": "2024-06-03T14:27:54.357006Z"
     }
    },
    "outputs": [],
@@ -781,10 +781,10 @@
      "start_time": "2019-11-10T17:36:55.360831Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:25.238191Z",
-     "iopub.status.busy": "2024-06-03T10:30:25.238048Z",
-     "iopub.status.idle": "2024-06-03T10:32:00.125630Z",
-     "shell.execute_reply": "2024-06-03T10:32:00.125113Z"
+     "iopub.execute_input": "2024-06-03T14:27:54.358559Z",
+     "iopub.status.busy": "2024-06-03T14:27:54.358481Z",
+     "iopub.status.idle": "2024-06-03T14:29:00.830132Z",
+     "shell.execute_reply": "2024-06-03T14:29:00.829877Z"
     }
    },
    "outputs": [
@@ -1107,10 +1107,10 @@
      "n": "17"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:00.128080Z",
-     "iopub.status.busy": "2024-06-03T10:32:00.127688Z",
-     "iopub.status.idle": "2024-06-03T10:32:00.456986Z",
-     "shell.execute_reply": "2024-06-03T10:32:00.456418Z"
+     "iopub.execute_input": "2024-06-03T14:29:00.831745Z",
+     "iopub.status.busy": "2024-06-03T14:29:00.831640Z",
+     "iopub.status.idle": "2024-06-03T14:29:01.012675Z",
+     "shell.execute_reply": "2024-06-03T14:29:01.012396Z"
     }
    },
    "outputs": [
@@ -1192,10 +1192,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:00.459065Z",
-     "iopub.status.busy": "2024-06-03T10:32:00.458854Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.622010Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.621465Z"
+     "iopub.execute_input": "2024-06-03T14:29:01.014157Z",
+     "iopub.status.busy": "2024-06-03T14:29:01.014047Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.589048Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.588654Z"
     }
    },
    "outputs": [],
@@ -1220,10 +1220,10 @@
      "n": "19"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.624294Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.624102Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.836207Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.835626Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.590858Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.590739Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.743541Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.743222Z"
     }
    },
    "outputs": [
@@ -1275,10 +1275,10 @@
      "start_time": "2019-11-10T17:39:10.406834Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.838287Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.838127Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.845799Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.845259Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.745086Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.744975Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.750856Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.750594Z"
     }
    },
    "outputs": [],
@@ -1312,10 +1312,10 @@
      "n": "21"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.847734Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.847585Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.850705Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.850230Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.752257Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.752150Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.754116Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.753861Z"
     }
    },
    "outputs": [
@@ -1345,10 +1345,10 @@
      "n": "22"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.852487Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.852343Z",
-     "iopub.status.idle": "2024-06-03T10:32:02.855401Z",
-     "shell.execute_reply": "2024-06-03T10:32:02.854825Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.755654Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.755549Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.757552Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.757286Z"
     }
    },
    "outputs": [],
@@ -1382,42 +1382,14 @@
      "n": "23"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:02.857327Z",
-     "iopub.status.busy": "2024-06-03T10:32:02.857183Z",
-     "iopub.status.idle": "2024-06-03T10:32:03.522361Z",
-     "shell.execute_reply": "2024-06-03T10:32:03.521853Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.758894Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.758806Z",
+     "iopub.status.idle": "2024-06-03T14:29:02.903500Z",
+     "shell.execute_reply": "2024-06-03T14:29:02.903193Z"
     },
     "lines_to_next_cell": 0
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -1456,10 +1428,10 @@
      "n": "25"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:03.524726Z",
-     "iopub.status.busy": "2024-06-03T10:32:03.524287Z",
-     "iopub.status.idle": "2024-06-03T10:32:03.880818Z",
-     "shell.execute_reply": "2024-06-03T10:32:03.880259Z"
+     "iopub.execute_input": "2024-06-03T14:29:02.905100Z",
+     "iopub.status.busy": "2024-06-03T14:29:02.904982Z",
+     "iopub.status.idle": "2024-06-03T14:29:03.086960Z",
+     "shell.execute_reply": "2024-06-03T14:29:03.086642Z"
     }
    },
    "outputs": [
@@ -1516,10 +1488,10 @@
      "start_time": "2019-11-10T17:39:11.633126Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:03.882753Z",
-     "iopub.status.busy": "2024-06-03T10:32:03.882595Z",
-     "iopub.status.idle": "2024-06-03T10:32:06.052386Z",
-     "shell.execute_reply": "2024-06-03T10:32:06.050825Z"
+     "iopub.execute_input": "2024-06-03T14:29:03.088715Z",
+     "iopub.status.busy": "2024-06-03T14:29:03.088575Z",
+     "iopub.status.idle": "2024-06-03T14:29:04.638557Z",
+     "shell.execute_reply": "2024-06-03T14:29:04.638201Z"
     },
     "lines_to_next_cell": 2
    },
@@ -1545,10 +1517,10 @@
      "n": "26"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:06.058093Z",
-     "iopub.status.busy": "2024-06-03T10:32:06.057700Z",
-     "iopub.status.idle": "2024-06-03T10:32:06.280643Z",
-     "shell.execute_reply": "2024-06-03T10:32:06.280081Z"
+     "iopub.execute_input": "2024-06-03T14:29:04.640300Z",
+     "iopub.status.busy": "2024-06-03T14:29:04.640188Z",
+     "iopub.status.idle": "2024-06-03T14:29:04.776702Z",
+     "shell.execute_reply": "2024-06-03T14:29:04.776442Z"
     }
    },
    "outputs": [
@@ -1599,10 +1571,10 @@
      "start_time": "2019-11-10T17:39:14.994569Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:06.282827Z",
-     "iopub.status.busy": "2024-06-03T10:32:06.282663Z",
-     "iopub.status.idle": "2024-06-03T10:32:06.507575Z",
-     "shell.execute_reply": "2024-06-03T10:32:06.507040Z"
+     "iopub.execute_input": "2024-06-03T14:29:04.778194Z",
+     "iopub.status.busy": "2024-06-03T14:29:04.778081Z",
+     "iopub.status.idle": "2024-06-03T14:29:04.910860Z",
+     "shell.execute_reply": "2024-06-03T14:29:04.910609Z"
     }
    },
    "outputs": [
@@ -1615,7 +1587,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1632,7 +1604,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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diff --git a/master/notebooks/04_example_dissipative_qubit_reset.html b/master/notebooks/04_example_dissipative_qubit_reset.html
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+  <title>Optimization of Dissipative Qubit Reset &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
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@@ -44,7 +47,7 @@
             Krotov
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-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -151,9 +154,9 @@
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-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/04_example_dissipative_qubit_reset.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/04_example_dissipative_qubit_reset.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-of-Dissipative-Qubit-Reset">
-<h1>Optimization of Dissipative Qubit Reset<a class="headerlink" href="#Optimization-of-Dissipative-Qubit-Reset" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/04_example_dissipative_qubit_reset.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/04_example_dissipative_qubit_reset.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-of-Dissipative-Qubit-Reset">
+<h1>Optimization of Dissipative Qubit Reset<a class="headerlink" href="#Optimization-of-Dissipative-Qubit-Reset" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -177,21 +180,21 @@ <h1>Optimization of Dissipative Qubit Reset<a class="headerlink" href="#Optimiza
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
+krotov    : 1.3.0+dev
+numpy     : 1.26.4
 qutip     : 4.7.6
+scipy     : 1.12.0
 matplotlib: 3.7.5
-krotov    : 1.3.0
-scipy     : 1.10.1
-numpy     : 1.24.4
 
 </pre></div></div>
 </div>
 <p><span class="math notranslate nohighlight">\(\newcommand{tr}[0]{\operatorname{tr}} \newcommand{diag}[0]{\operatorname{diag}} \newcommand{abs}[0]{\operatorname{abs}} \newcommand{pop}[0]{\operatorname{pop}} \newcommand{aux}[0]{\text{aux}} \newcommand{int}[0]{\text{int}} \newcommand{opt}[0]{\text{opt}} \newcommand{tgt}[0]{\text{tgt}} \newcommand{init}[0]{\text{init}} \newcommand{lab}[0]{\text{lab}} \newcommand{rwa}[0]{\text{rwa}} \newcommand{bra}[1]{\langle#1\vert} \newcommand{ket}[1]{\vert#1\rangle} \newcommand{Bra}[1]{\left\langle#1\right\vert} \newcommand{Ket}[1]{\left\vert#1\right\rangle} \newcommand{Braket}[2]{\left\langle #1\vphantom{#2} \mid #2\vphantom{#1}\right\rangle} \newcommand{op}[1]{\hat{#1}} \newcommand{Op}[1]{\hat{#1}} \newcommand{dd}[0]{\,\text{d}} \newcommand{Liouville}[0]{\mathcal{L}} \newcommand{DynMap}[0]{\mathcal{E}} \newcommand{identity}[0]{\mathbf{1}} \newcommand{Norm}[1]{\lVert#1\rVert} \newcommand{Abs}[1]{\left\vert#1\right\vert} \newcommand{avg}[1]{\langle#1\rangle} \newcommand{Avg}[1]{\left\langle#1\right\rangle} \newcommand{AbsSq}[1]{\left\vert#1\right\vert^2} \newcommand{Re}[0]{\operatorname{Re}} \newcommand{Im}[0]{\operatorname{Im}}\)</span></p>
 <p>This example illustrates an optimization in an <em>open</em> quantum system, where the dynamics is governed by the Liouville-von Neumann equation. Hence, states are represented by density matrices <span class="math notranslate nohighlight">\(\op{\rho}(t)\)</span> and the time-evolution operator is given by a general dynamical map <span class="math notranslate nohighlight">\(\DynMap\)</span>.</p>
-<div class="section" id="Define-parameters">
-<h2>Define parameters<a class="headerlink" href="#Define-parameters" title="Permalink to this headline">¶</a></h2>
+<section id="Define-parameters">
+<h2>Define parameters<a class="headerlink" href="#Define-parameters" title="Link to this heading">¶</a></h2>
 <p>The system consists of a qubit with Hamiltonian <span class="math notranslate nohighlight">\(\op{H}_{q}(t) = - \frac{\omega_{q}}{2} \op{\sigma}_{z} - \frac{\epsilon(t)}{2} \op{\sigma}_{z}\)</span>, where <span class="math notranslate nohighlight">\(\omega_{q}\)</span> is an energy level splitting that can be dynamically adjusted by the control <span class="math notranslate nohighlight">\(\epsilon(t)\)</span>. This qubit couples strongly to another two-level system (TLS) with Hamiltonian <span class="math notranslate nohighlight">\(\op{H}_{t} = - \frac{\omega_{t}}{2} \op{\sigma}_{z}\)</span> with static energy level splitting <span class="math notranslate nohighlight">\(\omega_{t}\)</span>. The coupling strength between
 both systems is given by <span class="math notranslate nohighlight">\(J\)</span> with the interaction Hamiltonian given by <span class="math notranslate nohighlight">\(\op{H}_{\int} = J \op{\sigma}_{x} \otimes \op{\sigma}_{x}\)</span>.</p>
 <p>The Hamiltonian for the system of qubit and TLS is</p>
@@ -220,9 +223,9 @@ <h2>Define parameters<a class="headerlink" href="#Define-parameters" title="Perm
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Define-the-Liouvillian">
-<h2>Define the Liouvillian<a class="headerlink" href="#Define-the-Liouvillian" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Define-the-Liouvillian">
+<h2>Define the Liouvillian<a class="headerlink" href="#Define-the-Liouvillian" title="Link to this heading">¶</a></h2>
 <p>The dynamics of the qubit-TLS system state <span class="math notranslate nohighlight">\(\op{\rho}(t)\)</span> is governed by the Liouville-von Neumann equation</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}\begin{split}
@@ -294,9 +297,9 @@ <h2>Define the Liouvillian<a class="headerlink" href="#Define-the-Liouvillian" t
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Define-the-optimization-target">
-<h2>Define the optimization target<a class="headerlink" href="#Define-the-optimization-target" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Define-the-optimization-target">
+<h2>Define the optimization target<a class="headerlink" href="#Define-the-optimization-target" title="Link to this heading">¶</a></h2>
 <p>The initial state of qubit and TLS are assumed to be in thermal equilibrium with the heat bath (although only the TLS is directly interacting with the bath). Both states are given by</p>
 <div class="math notranslate nohighlight">
 \[  \op{\rho}_{\alpha}^{th} =
@@ -403,9 +406,9 @@ <h2>Define the optimization target<a class="headerlink" href="#Define-the-optimi
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Simulate-the-dynamics-of-the-guess-field">
-<h2>Simulate the dynamics of the guess field<a class="headerlink" href="#Simulate-the-dynamics-of-the-guess-field" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Simulate-the-dynamics-of-the-guess-field">
+<h2>Simulate the dynamics of the guess field<a class="headerlink" href="#Simulate-the-dynamics-of-the-guess-field" title="Link to this heading">¶</a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
@@ -507,9 +510,9 @@ <h2>Simulate the dynamics of the guess field<a class="headerlink" href="#Simulat
 </div>
 </div>
 <p>The population dynamics of qubit and TLS ground state show that both are oscillating and especially the qubit’s ground state population reaches a maximal value at intermediate times <span class="math notranslate nohighlight">\(t &lt; T\)</span>. This maximum is indeed the maximum that is physically possible. It corresponds to a perfect swap of the initial qubit and TLS purities. However, we want to reach this maximum at final time <span class="math notranslate nohighlight">\(T\)</span> (not before), so the guess control is not yet working as desired.</p>
-</div>
-<div class="section" id="Optimize">
-<h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimize">
+<h2>Optimize<a class="headerlink" href="#Optimize" title="Link to this heading">¶</a></h2>
 <p>Our optimization target is the ground state <span class="math notranslate nohighlight">\(\ket{\Psi_{q}^{\tgt}} = \ket{0}\)</span> of the qubit, irrespective of the state of the TLS. Thus, our optimization functional reads</p>
 <div class="math notranslate nohighlight">
 \[  J_T = 1 -
@@ -593,7 +596,7 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
     S(t) (ranges): [0.000000, 1.000000]
     iter_start: 0
     iter_stop: 5
-    duration: 0.2 secs (started at 2024-06-03 12:39:19)
+    duration: 0.1 secs (started at 2024-06-03 10:27:46)
     optimized pulses (ranges): [0.00, 2.00]
     ∫gₐ(t)dt: 0.00e+00
     λₐ: 1.00e-01
@@ -602,47 +605,47 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
     τ: (7.97e-01:0.00π)
     qubit error: 1.1e-01
 Iteration 1
-    duration: 1.5 secs (started at 2024-06-03 12:39:20)
+    duration: 0.6 secs (started at 2024-06-03 10:27:46)
     optimized pulses (ranges): [0.00, 2.06]
     ∫gₐ(t)dt: 8.64e-03
     λₐ: 1.00e-01
-    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None
+    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None
     fw_states_T norm: 1.000000
     τ: (7.98e-01:0.00π)
     qubit error: 1.0e-01
 Iteration 2
-    duration: 1.5 secs (started at 2024-06-03 12:39:21)
+    duration: 0.7 secs (started at 2024-06-03 10:27:47)
     optimized pulses (ranges): [0.00, 2.36]
-    ∫gₐ(t)dt: 4.73e-02
+    ∫gₐ(t)dt: 4.72e-02
     λₐ: 1.00e-01
-    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None
+    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None
     fw_states_T norm: 1.000000
     τ: (7.92e-01:0.00π)
     qubit error: 5.5e-02
 Iteration 3
-    duration: 1.5 secs (started at 2024-06-03 12:39:23)
+    duration: 0.7 secs (started at 2024-06-03 10:27:47)
     optimized pulses (ranges): [0.00, 2.44]
-    ∫gₐ(t)dt: 6.87e-03
+    ∫gₐ(t)dt: 6.88e-03
     λₐ: 1.00e-01
-    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None
+    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None
     fw_states_T norm: 1.000000
     τ: (7.76e-01:0.00π)
     qubit error: 4.8e-02
 Iteration 4
-    duration: 1.5 secs (started at 2024-06-03 12:39:24)
+    duration: 0.7 secs (started at 2024-06-03 10:27:48)
     optimized pulses (ranges): [0.00, 2.42]
-    ∫gₐ(t)dt: 7.32e-04
+    ∫gₐ(t)dt: 7.33e-04
     λₐ: 1.00e-01
-    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None
+    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None
     fw_states_T norm: 1.000000
     τ: (7.82e-01:0.00π)
     qubit error: 4.8e-02
 Iteration 5
-    duration: 1.6 secs (started at 2024-06-03 12:39:26)
+    duration: 0.7 secs (started at 2024-06-03 10:27:49)
     optimized pulses (ranges): [0.00, 2.43]
-    ∫gₐ(t)dt: 1.25e-04
+    ∫gₐ(t)dt: 1.23e-04
     λₐ: 1.00e-01
-    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None
+    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None
     fw_states_T norm: 1.000000
     τ: (7.80e-01:0.00π)
     qubit error: 4.7e-02
@@ -664,16 +667,16 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <div class="highlight"><pre>
 Krotov Optimization Result
 --------------------------
-- Started at 2024-06-03 12:39:19
+- Started at 2024-06-03 10:27:46
 - Number of objectives: 1
 - Number of iterations: 5
 - Reason for termination: Reached 5 iterations
-- Ended at 2024-06-03 12:39:27 (0:00:08)
+- Ended at 2024-06-03 10:27:49 (0:00:03)
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Simulate-the-dynamics-of-the-optimized-field">
-<h2>Simulate the dynamics of the optimized field<a class="headerlink" href="#Simulate-the-dynamics-of-the-optimized-field" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Simulate-the-dynamics-of-the-optimized-field">
+<h2>Simulate the dynamics of the optimized field<a class="headerlink" href="#Simulate-the-dynamics-of-the-optimized-field" title="Link to this heading">¶</a></h2>
 <p>The plot of the optimized field shows that the optimization slightly shifts the field such that qubit and TLS are no longer perfectly in resonance.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
@@ -710,8 +713,8 @@ <h2>Simulate the dynamics of the optimized field<a class="headerlink" href="#Sim
 <img alt="../_images/notebooks_04_example_dissipative_qubit_reset_45_0.png" src="../_images/notebooks_04_example_dissipative_qubit_reset_45_0.png" />
 </div>
 </div>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -725,7 +728,7 @@ <h2>Simulate the dynamics of the optimized field<a class="headerlink" href="#Sim
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/04_example_dissipative_qubit_reset.ipynb b/master/notebooks/04_example_dissipative_qubit_reset.ipynb
index 3ca71d22..fa7bfd24 100644
--- a/master/notebooks/04_example_dissipative_qubit_reset.ipynb
+++ b/master/notebooks/04_example_dissipative_qubit_reset.ipynb
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:18.998818Z",
-     "iopub.status.busy": "2024-06-03T10:39:18.998624Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.507620Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.507068Z"
+     "iopub.execute_input": "2024-06-03T14:27:45.304871Z",
+     "iopub.status.busy": "2024-06-03T14:27:45.304735Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.012221Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.011869Z"
     }
    },
    "outputs": [
@@ -33,14 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
+      "krotov    : 1.3.0+dev\n",
+      "numpy     : 1.26.4\n",
       "qutip     : 4.7.6\n",
+      "scipy     : 1.12.0\n",
       "matplotlib: 3.7.5\n",
-      "krotov    : 1.3.0\n",
-      "scipy     : 1.10.1\n",
-      "numpy     : 1.24.4\n",
       "\n"
      ]
     }
@@ -156,10 +156,10 @@
      "n": "2"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.534004Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.533759Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.536805Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.536261Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.034965Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.034733Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.036866Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.036616Z"
     }
    },
    "outputs": [],
@@ -205,10 +205,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.538744Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.538596Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.547969Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.547428Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.038150Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.038050Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.043402Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.043167Z"
     }
    },
    "outputs": [],
@@ -305,10 +305,10 @@
      "n": "6"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.550005Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.549856Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.553297Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.552921Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.045003Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.044914Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.047008Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.046802Z"
     }
    },
    "outputs": [],
@@ -346,10 +346,10 @@
      "n": "7"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.555244Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.555097Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.558549Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.558060Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.048337Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.048246Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.050395Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.050181Z"
     }
    },
    "outputs": [],
@@ -387,10 +387,10 @@
      "n": "8"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.560542Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.560364Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.563350Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.562884Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.051684Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.051603Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.053489Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.053291Z"
     }
    },
    "outputs": [],
@@ -423,10 +423,10 @@
      "n": "9"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.565291Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.565141Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.570491Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.570126Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.054823Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.054739Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.058150Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.057884Z"
     }
    },
    "outputs": [
@@ -469,10 +469,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.572561Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.572257Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.574843Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.574470Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.059506Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.059411Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.061166Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.060923Z"
     }
    },
    "outputs": [],
@@ -507,10 +507,10 @@
      "n": "11"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.576798Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.576646Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.579503Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.579013Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.062393Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.062320Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.064439Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.063910Z"
     }
    },
    "outputs": [],
@@ -538,10 +538,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.581579Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.581264Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.583854Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.583352Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.066273Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.066158Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.067792Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.067560Z"
     }
    },
    "outputs": [],
@@ -561,10 +561,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.585737Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.585587Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.587899Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.587515Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.069225Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.069145Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.070811Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.070612Z"
     }
    },
    "outputs": [],
@@ -586,10 +586,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.589724Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.589575Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.592757Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.592265Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.072118Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.072001Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.074152Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.073905Z"
     }
    },
    "outputs": [],
@@ -626,10 +626,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.594718Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.594540Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.735869Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.735373Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.075405Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.075326Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.169070Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.168707Z"
     }
    },
    "outputs": [
@@ -661,10 +661,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.737943Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.737731Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.744435Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.743960Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.174207Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.173948Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.182369Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.181589Z"
     }
    },
    "outputs": [],
@@ -693,10 +693,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.746275Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.746126Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.790154Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.789778Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.188678Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.188498Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.233985Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.226614Z"
     }
    },
    "outputs": [],
@@ -720,10 +720,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.792085Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.791911Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.908482Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.907976Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.236709Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.236564Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.355393Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.354576Z"
     }
    },
    "outputs": [
@@ -807,10 +807,10 @@
      "start_time": "2019-02-12T04:47:27.118668Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.911006Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.910814Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.913931Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.913550Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.357352Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.357219Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.359780Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.359366Z"
     }
    },
    "outputs": [],
@@ -859,10 +859,10 @@
      "n": "18"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.915863Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.915672Z",
-     "iopub.status.idle": "2024-06-03T10:39:19.918699Z",
-     "shell.execute_reply": "2024-06-03T10:39:19.918211Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.361247Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.361152Z",
+     "iopub.status.idle": "2024-06-03T14:27:46.363293Z",
+     "shell.execute_reply": "2024-06-03T14:27:46.363069Z"
     }
    },
    "outputs": [],
@@ -888,10 +888,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:19.920612Z",
-     "iopub.status.busy": "2024-06-03T10:39:19.920464Z",
-     "iopub.status.idle": "2024-06-03T10:39:27.823290Z",
-     "shell.execute_reply": "2024-06-03T10:39:27.822652Z"
+     "iopub.execute_input": "2024-06-03T14:27:46.364685Z",
+     "iopub.status.busy": "2024-06-03T14:27:46.364602Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.835176Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.834803Z"
     }
    },
    "outputs": [
@@ -909,7 +909,7 @@
       "    S(t) (ranges): [0.000000, 1.000000]\n",
       "    iter_start: 0\n",
       "    iter_stop: 5\n",
-      "    duration: 0.2 secs (started at 2024-06-03 12:39:19)\n",
+      "    duration: 0.1 secs (started at 2024-06-03 10:27:46)\n",
       "    optimized pulses (ranges): [0.00, 2.00]\n",
       "    ∫gₐ(t)dt: 0.00e+00\n",
       "    λₐ: 1.00e-01\n",
@@ -930,11 +930,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 1\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:20)\n",
+      "    duration: 0.6 secs (started at 2024-06-03 10:27:46)\n",
       "    optimized pulses (ranges): [0.00, 2.06]\n",
       "    ∫gₐ(t)dt: 8.64e-03\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.98e-01:0.00π)\n"
      ]
@@ -951,11 +951,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 2\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:21)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:47)\n",
       "    optimized pulses (ranges): [0.00, 2.36]\n",
-      "    ∫gₐ(t)dt: 4.73e-02\n",
+      "    ∫gₐ(t)dt: 4.72e-02\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.92e-01:0.00π)\n"
      ]
@@ -972,11 +972,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 3\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:23)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:47)\n",
       "    optimized pulses (ranges): [0.00, 2.44]\n",
-      "    ∫gₐ(t)dt: 6.87e-03\n",
+      "    ∫gₐ(t)dt: 6.88e-03\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.76e-01:0.00π)\n"
      ]
@@ -985,19 +985,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    qubit error: 4.8e-02\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "    qubit error: 4.8e-02\n",
       "Iteration 4\n",
-      "    duration: 1.5 secs (started at 2024-06-03 12:39:24)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:48)\n",
       "    optimized pulses (ranges): [0.00, 2.42]\n",
-      "    ∫gₐ(t)dt: 7.32e-04\n",
+      "    ∫gₐ(t)dt: 7.33e-04\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.82e-01:0.00π)\n"
      ]
@@ -1014,11 +1008,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 5\n",
-      "    duration: 1.6 secs (started at 2024-06-03 12:39:26)\n",
+      "    duration: 0.7 secs (started at 2024-06-03 10:27:49)\n",
       "    optimized pulses (ranges): [0.00, 2.43]\n",
-      "    ∫gₐ(t)dt: 1.25e-04\n",
+      "    ∫gₐ(t)dt: 1.23e-04\n",
       "    λₐ: 1.00e-01\n",
-      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.2 MB), None, None\n",
+      "    storage (bw, fw, fw0): [1 * ndarray(2500)] (1.1 MB), None, None\n",
       "    fw_states_T norm: 1.000000\n",
       "    τ: (7.80e-01:0.00π)\n"
      ]
@@ -1062,10 +1056,10 @@
      "n": "20"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:27.825609Z",
-     "iopub.status.busy": "2024-06-03T10:39:27.825411Z",
-     "iopub.status.idle": "2024-06-03T10:39:27.829561Z",
-     "shell.execute_reply": "2024-06-03T10:39:27.828992Z"
+     "iopub.execute_input": "2024-06-03T14:27:49.837292Z",
+     "iopub.status.busy": "2024-06-03T14:27:49.837153Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.839825Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.839511Z"
     }
    },
    "outputs": [
@@ -1074,11 +1068,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:39:19\n",
+       "- Started at 2024-06-03 10:27:46\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 5\n",
        "- Reason for termination: Reached 5 iterations\n",
-       "- Ended at 2024-06-03 12:39:27 (0:00:08)"
+       "- Ended at 2024-06-03 10:27:49 (0:00:03)"
       ]
      },
      "execution_count": 20,
@@ -1115,16 +1109,16 @@
      "n": "21"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:27.831673Z",
-     "iopub.status.busy": "2024-06-03T10:39:27.831506Z",
-     "iopub.status.idle": "2024-06-03T10:39:27.931477Z",
-     "shell.execute_reply": "2024-06-03T10:39:27.930868Z"
+     "iopub.execute_input": "2024-06-03T14:27:49.841504Z",
+     "iopub.status.busy": "2024-06-03T14:27:49.841391Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.897493Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.897183Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1156,16 +1150,16 @@
      "n": "22"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:27.933785Z",
-     "iopub.status.busy": "2024-06-03T10:39:27.933596Z",
-     "iopub.status.idle": "2024-06-03T10:39:28.092799Z",
-     "shell.execute_reply": "2024-06-03T10:39:28.092180Z"
+     "iopub.execute_input": "2024-06-03T14:27:49.899157Z",
+     "iopub.status.busy": "2024-06-03T14:27:49.898997Z",
+     "iopub.status.idle": "2024-06-03T14:27:49.997285Z",
+     "shell.execute_reply": "2024-06-03T14:27:49.996999Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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@@ -45,7 +47,7 @@
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-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -151,9 +153,9 @@
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-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/05_example_transmon_xgate.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/05_example_transmon_xgate.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-of-an-X-Gate-for-a-Transmon-Qubit">
-<h1>Optimization of an X-Gate for a Transmon Qubit<a class="headerlink" href="#Optimization-of-an-X-Gate-for-a-Transmon-Qubit" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/05_example_transmon_xgate.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/05_example_transmon_xgate.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-of-an-X-Gate-for-a-Transmon-Qubit">
+<h1>Optimization of an X-Gate for a Transmon Qubit<a class="headerlink" href="#Optimization-of-an-X-Gate-for-a-Transmon-Qubit" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -180,23 +182,22 @@ <h1>Optimization of an X-Gate for a Transmon Qubit<a class="headerlink" href="#O
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
-krotov    : 1.3.0
-qutip     : 4.7.6
-numpy     : 1.24.4
-sys       : 3.8.16 (default, May 26 2024, 19:41:26)
-[GCC 12.2.0]
+sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]
+krotov    : 1.3.0+dev
 matplotlib: 3.7.5
-scipy     : 1.10.1
+scipy     : 1.12.0
+qutip     : 4.7.6
+numpy     : 1.26.4
 
 </pre></div></div>
 </div>
 <p><span class="math notranslate nohighlight">\(\newcommand{tr}[0]{\operatorname{tr}} \newcommand{diag}[0]{\operatorname{diag}} \newcommand{abs}[0]{\operatorname{abs}} \newcommand{pop}[0]{\operatorname{pop}} \newcommand{aux}[0]{\text{aux}} \newcommand{opt}[0]{\text{opt}} \newcommand{tgt}[0]{\text{tgt}} \newcommand{init}[0]{\text{init}} \newcommand{lab}[0]{\text{lab}} \newcommand{rwa}[0]{\text{rwa}} \newcommand{bra}[1]{\langle#1\vert} \newcommand{ket}[1]{\vert#1\rangle} \newcommand{Bra}[1]{\left\langle#1\right\vert} \newcommand{Ket}[1]{\left\vert#1\right\rangle} \newcommand{Braket}[2]{\left\langle #1\vphantom{#2} \mid #2\vphantom{#1}\right\rangle} \newcommand{op}[1]{\hat{#1}} \newcommand{Op}[1]{\hat{#1}} \newcommand{dd}[0]{\,\text{d}} \newcommand{Liouville}[0]{\mathcal{L}} \newcommand{DynMap}[0]{\mathcal{E}} \newcommand{identity}[0]{\mathbf{1}} \newcommand{Norm}[1]{\lVert#1\rVert} \newcommand{Abs}[1]{\left\vert#1\right\vert} \newcommand{avg}[1]{\langle#1\rangle} \newcommand{Avg}[1]{\left\langle#1\right\rangle} \newcommand{AbsSq}[1]{\left\vert#1\right\vert^2} \newcommand{Re}[0]{\operatorname{Re}} \newcommand{Im}[0]{\operatorname{Im}}\)</span></p>
 <p>In the previous examples, we have only optimized for state-to-state transitions, i.e., for a single objective. This example shows the optimization of a simple quantum gate, which requires multiple objectives to be fulfilled simultaneously (one for each state in the logical basis). We consider a superconducting “transmon” qubit and implement a single-qubit Pauli-X gate.</p>
-<div class="section" id="The-transmon-Hamiltonian">
-<h2>The transmon Hamiltonian<a class="headerlink" href="#The-transmon-Hamiltonian" title="Permalink to this headline">¶</a></h2>
+<section id="The-transmon-Hamiltonian">
+<h2>The transmon Hamiltonian<a class="headerlink" href="#The-transmon-Hamiltonian" title="Link to this heading">¶</a></h2>
 <p>The effective Hamiltonian of a single transmon depends on the capacitive energy <span class="math notranslate nohighlight">\(E_C=e^2/2C\)</span> and the Josephson energy <span class="math notranslate nohighlight">\(E_J\)</span>, an energy due to the Josephson junction working as a nonlinear inductor periodic with the flux <span class="math notranslate nohighlight">\(\Phi\)</span>. In the so-called transmon limit, the ratio between these two energies lies around <span class="math notranslate nohighlight">\(E_J / E_C \approx 45\)</span>. The Hamiltonian for the transmon is</p>
 <div class="math notranslate nohighlight">
 \[\op{H}_{0} = 4 E_C (\hat{n}-n_g)^2 - E_J \cos(\hat{\Phi})\]</div>
@@ -333,9 +334,9 @@ <h2>The transmon Hamiltonian<a class="headerlink" href="#The-transmon-Hamiltonia
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Optimization-target">
-<h2>Optimization target<a class="headerlink" href="#Optimization-target" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization-target">
+<h2>Optimization target<a class="headerlink" href="#Optimization-target" title="Link to this heading">¶</a></h2>
 <p>The key insight for the realization of a quantum gate <span class="math notranslate nohighlight">\(\Op{O}\)</span> is that (by virtue of linearity)</p>
 <div class="math notranslate nohighlight">
 \[\ket{\Psi(t=0)} \rightarrow \ket{\Psi(t=T)}
@@ -365,9 +366,9 @@ <h2>Optimization target<a class="headerlink" href="#Optimization-target" title="
  Objective[|Ψ₁(17)⟩ to |Ψ₀(17)⟩ via [H₀[17,17], [H₁[17,17], u₁(t)]]]]
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Dynamics-of-the-guess-pulse">
-<h2>Dynamics of the guess pulse<a class="headerlink" href="#Dynamics-of-the-guess-pulse" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Dynamics-of-the-guess-pulse">
+<h2>Dynamics of the guess pulse<a class="headerlink" href="#Dynamics-of-the-guess-pulse" title="Link to this heading">¶</a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
@@ -417,9 +418,9 @@ <h2>Dynamics of the guess pulse<a class="headerlink" href="#Dynamics-of-the-gues
 <img alt="../_images/notebooks_05_example_transmon_xgate_22_1.png" src="../_images/notebooks_05_example_transmon_xgate_22_1.png" />
 </div>
 </div>
-</div>
-<div class="section" id="Optimization">
-<h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization">
+<h2>Optimization<a class="headerlink" href="#Optimization" title="Link to this heading">¶</a></h2>
 <p>We define the desired shape of the update and the factor <span class="math notranslate nohighlight">\(\lambda_a\)</span>, and then start the optimization</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
@@ -466,12 +467,12 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 iter.      J_T    g_a_int          J  Delta J_T    Delta J  secs
-0     1.00e+00   0.00e+00   1.00e+00        n/a        n/a     3
-1     2.80e-01   3.61e-01   6.41e-01  -7.20e-01  -3.59e-01     7
-2     2.12e-01   3.42e-02   2.46e-01  -6.81e-02  -3.39e-02     7
-3     1.35e-01   3.88e-02   1.74e-01  -7.72e-02  -3.83e-02     7
-4     9.79e-02   1.87e-02   1.17e-01  -3.71e-02  -1.84e-02     7
-5     7.13e-02   1.34e-02   8.47e-02  -2.65e-02  -1.32e-02     7
+0     1.00e+00   0.00e+00   1.00e+00        n/a        n/a     2
+1     2.80e-01   3.61e-01   6.41e-01  -7.20e-01  -3.59e-01     5
+2     2.12e-01   3.42e-02   2.46e-01  -6.81e-02  -3.39e-02     5
+3     1.35e-01   3.88e-02   1.74e-01  -7.72e-02  -3.83e-02     5
+4     9.79e-02   1.87e-02   1.17e-01  -3.71e-02  -1.84e-02     5
+5     7.13e-02   1.34e-02   8.47e-02  -2.65e-02  -1.32e-02     5
 </pre></div></div>
 </div>
 <p>(this takes a while …)</p>
@@ -550,27 +551,16 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 </pre></div>
 </div>
 </div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmtt10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmb10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmss10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;DejaVu Sans Display&#39;] not found. Falling back to DejaVu Sans.
-</pre></div></div>
-</div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_05_example_transmon_xgate_31_1.png" src="../_images/notebooks_05_example_transmon_xgate_31_1.png" />
-</div>
+<img alt="../_images/notebooks_05_example_transmon_xgate_31_0.png" src="../_images/notebooks_05_example_transmon_xgate_31_0.png" />
 </div>
 </div>
-<div class="section" id="Optimized-pulse-and-dynamics">
-<h2>Optimized pulse and dynamics<a class="headerlink" href="#Optimized-pulse-and-dynamics" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimized-pulse-and-dynamics">
+<h2>Optimized pulse and dynamics<a class="headerlink" href="#Optimized-pulse-and-dynamics" title="Link to this heading">¶</a></h2>
 <p>We obtain the following optimized pulse:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
@@ -710,11 +700,11 @@ <h2>Optimized pulse and dynamics<a class="headerlink" href="#Optimized-pulse-and
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Time discretization error = 1.9e-05
+Time discretization error = 2.9e-05
 </pre></div></div>
 </div>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -728,7 +718,7 @@ <h2>Optimized pulse and dynamics<a class="headerlink" href="#Optimized-pulse-and
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/05_example_transmon_xgate.ipynb b/master/notebooks/05_example_transmon_xgate.ipynb
index e28b0836..94110ab7 100644
--- a/master/notebooks/05_example_transmon_xgate.ipynb
+++ b/master/notebooks/05_example_transmon_xgate.ipynb
@@ -16,10 +16,10 @@
      "start_time": "2020-03-21T21:17:42.571725Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.338767Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.338588Z",
-     "iopub.status.idle": "2024-06-03T10:29:32.853063Z",
-     "shell.execute_reply": "2024-06-03T10:29:32.852499Z"
+     "iopub.execute_input": "2024-06-03T14:31:16.323104Z",
+     "iopub.status.busy": "2024-06-03T14:31:16.322439Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.135543Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.135244Z"
     }
    },
    "outputs": [
@@ -28,16 +28,15 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "qutip     : 4.7.6\n",
-      "numpy     : 1.24.4\n",
-      "sys       : 3.8.16 (default, May 26 2024, 19:41:26) \n",
-      "[GCC 12.2.0]\n",
+      "sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
+      "krotov    : 1.3.0+dev\n",
       "matplotlib: 3.7.5\n",
-      "scipy     : 1.10.1\n",
+      "scipy     : 1.12.0\n",
+      "qutip     : 4.7.6\n",
+      "numpy     : 1.26.4\n",
       "\n"
      ]
     }
@@ -163,10 +162,10 @@
      "start_time": "2020-03-21T21:17:56.572502Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.878973Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.878699Z",
-     "iopub.status.idle": "2024-06-03T10:29:32.882224Z",
-     "shell.execute_reply": "2024-06-03T10:29:32.881711Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.157920Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.157739Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.159978Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.159714Z"
     },
     "lines_to_next_cell": 2
    },
@@ -188,10 +187,10 @@
      "start_time": "2020-03-21T21:17:57.275685Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.884079Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.883914Z",
-     "iopub.status.idle": "2024-06-03T10:29:32.886974Z",
-     "shell.execute_reply": "2024-06-03T10:29:32.886611Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.161331Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.161219Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.163292Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.163075Z"
     }
    },
    "outputs": [],
@@ -217,10 +216,10 @@
      "start_time": "2020-03-21T21:17:57.957925Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:32.888796Z",
-     "iopub.status.busy": "2024-06-03T10:29:32.888650Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.028652Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.028190Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.164724Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.164612Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.266454Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.265154Z"
     }
    },
    "outputs": [
@@ -255,10 +254,10 @@
      "start_time": "2020-03-21T21:17:59.814723Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.030668Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.030486Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.034761Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.034268Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.271474Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.270543Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.282541Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.276920Z"
     }
    },
    "outputs": [],
@@ -295,10 +294,10 @@
      "start_time": "2020-03-21T21:18:00.539883Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.036682Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.036370Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.039637Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.039270Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.286426Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.286021Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.294392Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.291689Z"
     }
    },
    "outputs": [],
@@ -326,10 +325,10 @@
      "start_time": "2020-03-21T21:18:01.468153Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.041664Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.041267Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.046599Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.046227Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.298554Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.297671Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.315070Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.312721Z"
     }
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    "outputs": [
@@ -374,10 +373,10 @@
      "start_time": "2020-03-21T21:18:02.813232Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.048475Z",
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-     "iopub.status.idle": "2024-06-03T10:29:33.051477Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.051094Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.328660Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.328300Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.332172Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.331055Z"
     }
    },
    "outputs": [],
@@ -423,10 +422,10 @@
      "start_time": "2020-03-21T21:18:04.764412Z"
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-     "iopub.execute_input": "2024-06-03T10:29:33.053365Z",
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-     "iopub.status.idle": "2024-06-03T10:29:33.059132Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.058762Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.336580Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.336262Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.343732Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.342734Z"
     },
     "lines_to_next_cell": 2,
     "scrolled": true
@@ -468,10 +467,10 @@
      "start_time": "2020-03-21T21:18:05.911538Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.061042Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.060869Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.351168Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.350676Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.347951Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.347584Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.512515Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.512227Z"
     }
    },
    "outputs": [],
@@ -490,10 +489,10 @@
      "start_time": "2020-03-21T21:18:07.421285Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.353166Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.352929Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.356387Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.355886Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.514026Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.513936Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.516242Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.516027Z"
     }
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    "outputs": [],
@@ -518,16 +517,16 @@
      "start_time": "2020-03-21T21:18:08.195251Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.358203Z",
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-     "iopub.status.idle": "2024-06-03T10:29:33.574590Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.574061Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.517518Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.517440Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.649044Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.648758Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -574,10 +573,10 @@
      "start_time": "2020-03-21T21:18:11.065096Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.576774Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.576582Z",
-     "iopub.status.idle": "2024-06-03T10:29:33.579917Z",
-     "shell.execute_reply": "2024-06-03T10:29:33.579394Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.650603Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.650498Z",
+     "iopub.status.idle": "2024-06-03T14:31:17.652441Z",
+     "shell.execute_reply": "2024-06-03T14:31:17.652219Z"
     }
    },
    "outputs": [],
@@ -600,10 +599,10 @@
      "start_time": "2020-03-21T21:18:15.565391Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:29:33.581739Z",
-     "iopub.status.busy": "2024-06-03T10:29:33.581593Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.159622Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.159083Z"
+     "iopub.execute_input": "2024-06-03T14:31:17.653824Z",
+     "iopub.status.busy": "2024-06-03T14:31:17.653686Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.351456Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.351150Z"
     }
    },
    "outputs": [
@@ -612,42 +611,42 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T    g_a_int          J  Delta J_T    Delta J  secs\n",
-      "0     1.00e+00   0.00e+00   1.00e+00        n/a        n/a     3\n"
+      "0     1.00e+00   0.00e+00   1.00e+00        n/a        n/a     2\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1     2.80e-01   3.61e-01   6.41e-01  -7.20e-01  -3.59e-01     7\n"
+      "1     2.80e-01   3.61e-01   6.41e-01  -7.20e-01  -3.59e-01     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2     2.12e-01   3.42e-02   2.46e-01  -6.81e-02  -3.39e-02     7\n"
+      "2     2.12e-01   3.42e-02   2.46e-01  -6.81e-02  -3.39e-02     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3     1.35e-01   3.88e-02   1.74e-01  -7.72e-02  -3.83e-02     7\n"
+      "3     1.35e-01   3.88e-02   1.74e-01  -7.72e-02  -3.83e-02     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4     9.79e-02   1.87e-02   1.17e-01  -3.71e-02  -1.84e-02     7\n"
+      "4     9.79e-02   1.87e-02   1.17e-01  -3.71e-02  -1.84e-02     5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5     7.13e-02   1.34e-02   8.47e-02  -2.65e-02  -1.32e-02     7\n"
+      "5     7.13e-02   1.34e-02   8.47e-02  -2.65e-02  -1.32e-02     5\n"
      ]
     }
    ],
@@ -688,10 +687,10 @@
      "start_time": "2020-03-21T21:19:54.217987Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.161727Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.161569Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.166455Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.166082Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.353103Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.352994Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.356622Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.356341Z"
     },
     "lines_to_next_cell": 2
    },
@@ -732,10 +731,10 @@
      "start_time": "2020-03-21T21:19:55.178133Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.168260Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.168112Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.171865Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.171357Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.358438Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.358312Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.360725Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.360475Z"
     }
    },
    "outputs": [
@@ -769,10 +768,10 @@
      "start_time": "2020-03-21T21:19:57.099117Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.173733Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.173502Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.176385Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.175901Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.362151Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.362041Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.363960Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.363690Z"
     }
    },
    "outputs": [],
@@ -794,41 +793,13 @@
      "start_time": "2020-03-21T21:19:58.026972Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.178193Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.178047Z",
-     "iopub.status.idle": "2024-06-03T10:30:14.900021Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.899389Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.365326Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.365199Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.560241Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.559934Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -867,10 +838,10 @@
      "start_time": "2020-03-21T21:20:00.433074Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:14.902243Z",
-     "iopub.status.busy": "2024-06-03T10:30:14.902057Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.000492Z",
-     "shell.execute_reply": "2024-06-03T10:30:14.999982Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.562113Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.561995Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.620363Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.620067Z"
     }
    },
    "outputs": [
@@ -905,16 +876,16 @@
      "start_time": "2020-03-21T21:20:02.755327Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.002529Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.002374Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.107341Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.106830Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.622021Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.621890Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.688084Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.687782Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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Th+MNDQ347rvvJCnKXZjXAeIsMCIiIq/icBfY4cOHzZ8fO3YMxcXF5q/1ej22b9+OuLg4aatzsUA1W4CIiIi8kcMBKC0tDQqFAgqFAhMnTuxwe48ePfDKK69IWpyrmQdBcwwQERGRV3E4AOXn50MQBPTv3x/79+9Hr169zLep1WpERUVBpVLJUqSrXJ0Gzy4wIiIib+JwAOrXrx8AwGAwyFaMu+FCiERERN7JoQD02WefYfr06fD398dnn33W6X1NK0N7A64DRERE5J0cCkAzZ85EcXExoqKiMHPmTJv3UygU0Ou9p7uIK0ETERF5J4cCUNtuL1/qAmvbAiQIAhQKhYsrIiIiIimIXgfIlwSqjWOADALQ2OI7wY+IiMjbid4KAwCysrKQlZWF0tLSDi1CmzZtkqQwdxCkvvrtqW1sQYC/d81yIyIi8lWiA9Dq1auxZs0ajBkzBrGxsV7dLaRUKhCoVqG+SY+6xhZEBmtcXRIRERFJQHQA2rBhA95++23cd999ctTjdoI0fqhv0nMgNBERkRcRPQaoqakJ1157rRy1uKWrA6G9Z3YbERGRrxMdgH7/+9/j3XfflaMWt8TFEImIiLyP6C6whoYGvPHGG/jmm2+QkpICf39/i9vXrVsnWXHuwDQQml1gRERE3kN0ADp8+DDS0tIAAHl5eRa3eeOAaFMXWD03RCUiIvIaogPQrl275KjDbQVyQ1QiIiKvw4UQ7QjmGCAiIiKvI7oF6Oabb+60q2vnzp1OFeRuTGOAGICIiIi8h+gAZBr/Y9Lc3Izc3Fzk5eVh7ty5UtXlNrghKhERkfcRHYBeeOEFq8efeuop1NbWOl2Qu2m7ISoRERF5B8nGAN17771etQ+YSRAHQRMREXkdyQJQdnY2AgICpHo6t2FaCJHT4ImIiLyH6C6wO++80+JrQRBQVFSEAwcOYOXKlZIV5i44CJqIiMj7iA5AWq3W4mulUokhQ4ZgzZo1mDJlimSFuQsOgiYiIvI+ogPQP//5TznqcFvcDJWIiMj7cCFEO7gZKhERkfdhALLD3ALU1AJBEFxcDREREUmBAcgO0xgggwBcaWY3GBERkTdweQBav349EhISEBAQgPT0dOzfv7/T+1dVVWHhwoWIjY2FRqPB4MGD8eWXX8pWXw9/FUw7f3AcEBERkXdwaQDasmULlixZglWrViEnJwepqamYOnUqSktLrd6/qakJkydPxvnz5/Hhhx/i5MmT2LhxI+Li4mSrUalUINCf44CIiIi8iehZYLZ8+umnqK6uxpw5cxx+zLp16zB//nzMmzcPALBhwwZs27YNmzZtwrJlyzrcf9OmTaisrMQPP/wAf39/AEBCQoIk9XcmSOOHuiY9p8ITERF5CclagJYuXWoOMo5oamrCwYMHkZGRcbUYpRIZGRnIzs62+pjPPvsM48ePx8KFCxEdHY3k5GQ888wz0Ottd001NjZCp9NZfIjF/cCIiIi8i2QB6MSJE50GkfbKy8uh1+sRHR1tcTw6OhrFxcVWH3Pu3Dl8+OGH0Ov1+PLLL7Fy5Uo8//zz+Otf/2rzdTIzM6HVas0f8fHxDtdoEtRmJhgRERF5PpcPghbDYDAgKioKb7zxBkaPHo1Zs2bhiSeewIYNG2w+Zvny5aiurjZ/FBYWin5d01pA3BCViIjIO3RpDNDly5fx1ltv4fjx4wCAYcOG4f7770dERITDzxEZGQmVSoWSkhKL4yUlJYiJibH6mNjYWPj7+0OlUpmPDRs2DMXFxWhqaoJare7wGI1GA41G43Bd1rALjIiIyLuIbgH69ttvkZiYiJdffhmXL1/G5cuX8corryAxMRHffvutw8+jVqsxevRoZGVlmY8ZDAZkZWVh/PjxVh9z3XXX4cyZMzAYDOZjp06dQmxsrNXwI5UgBiAiIiKvIjoALVy4EL/73e+Qn5+PrVu3YuvWrTh37hzuuusuLFy4UNRzLVmyBBs3bsS//vUvHD9+HAsWLEBdXZ15MPWcOXOwfPly8/0XLFiAyspKLF68GKdOncK2bdvwzDPPiH5dsQLV3A+MiIjIm4juAjtz5gw+/PBDi24olUqFJUuW4N///reo55o1axbKysrw5JNPori4GGlpadi+fbt5YHRBQQGUyqsZLT4+Hl9//TUee+wxpKSkIC4uDosXL8bSpUvFnoYowab9wDgImoiIyCuIDkCjRo3C8ePHMWTIEIvjx48fR2pqqugCFi1ahEWLFlm9bffu3R2OjR8/Hvv27RP9Os4wdYFxHSAiIiLv4FAAOnz4sPnzRx55BIsXL8aZM2cwbtw4AMC+ffuwfv16rF27Vp4qXYyDoImIiLyLQwEoLS0NCoXCYjf0//3f/+1wv9mzZ2PWrFnSVecmOAiaiIjIuzgUgPLz8+Wuw62xC4yIiMi7OBSA+vXrBwBobm7GH/7wB6xcuRKJiYmyFuZOTIOg65s4C4yIiMgbiJoG7+/vj48++kiuWtyWaRo8W4CIiIi8g+h1gGbOnIlPPvlEhlLcFwdBExEReRfR0+AHDRqENWvW4Pvvv8fo0aMRFBRkcfsjjzwiWXHu4uogaHaBEREReQPRAeitt95CWFgYDh48iIMHD1rcplAovDQAXV0IURAEKBQKF1dEREREzhAdgHxxRpipC0wQjAOhTS1CRERE5JlEjwHyRT38VVC2NvpwHBAREZHn61JTxoULF/DZZ5+hoKAATU1NFretW7dOksLciUKhQJDaDzWNLajjVHgiIiKPJzoAZWVl4bbbbkP//v1x4sQJJCcn4/z58xAEAaNGjZKjRrcQqFEZAxBbgIiIiDye6C6w5cuX4/HHH8eRI0cQEBCAjz76CIWFhbjxxhvx29/+Vo4a3QJXgyYiIvIeogPQ8ePHMWfOHACAn58frly5guDgYKxZswbPPvus5AW6C64FRERE5D1EB6CgoCDzuJ/Y2FicPXvWfFt5ebl0lbmZIK4GTURE5DVEjwEaN24c9u7di2HDhuGWW27Bn/70Jxw5cgRbt27FuHHj5KjRLXAxRCIiIu8hOgCtW7cOtbW1AIDVq1ejtrYWW7ZswaBBg7xyBpiJaUNUdoERERF5PtEBqH///ubPg4KCsGHDBkkLclfmFqAmBiAiIiJP59AYIEEQ5K7D7QVxEDQREZHXcCgADR8+HJs3b+6w6GF7p0+fxoIFC7B27VpJinMnVwdBcwwQERGRp3OoC+yVV17B0qVL8fDDD2Py5MkYM2YMevfujYCAAFy+fBnHjh3D3r17cfToUSxatAgLFiyQu+5uF8QxQERERF7DoQA0adIkHDhwAHv37sWWLVvwzjvv4JdffsGVK1cQGRmJkSNHYs6cObjnnnsQHh4ud80uwXWAiIiIvIeoQdATJkzAhAkT5KrFrXElaCIiIu/B3eAdFMxZYERERF6DAchBphageg6CJiIi8ngMQA4KVBsHQbMLjIiIyPMxADmIg6CJiIi8BwOQg66uBK2HwcCFIYmIiDxZlwLQ2bNnsWLFCtx9990oLS0FAHz11Vc4evSopMW5E1MLEADUN3McEBERkScTHYD27NmDESNG4Mcff8TWrVvNG6P+/PPPWLVqleQFuosAfyWUCuPn7AYjIiLybKID0LJly/DXv/4VO3bsgFqtNh+fOHEi9u3bJ2lx7kShUHAtICIiIi8hOgAdOXIEd9xxR4fjUVFRKC8vl6QodxXMqfBEREReQXQACgsLQ1FRUYfjhw4dQlxcnCRFuStOhSciIvIOogPQXXfdhaVLl6K4uBgKhQIGgwHff/89Hn/8ccyZM0eOGt0Gp8ITERF5B9EB6JlnnsHQoUMRHx+P2tpaJCUl4YYbbsC1116LFStWyFGj2wjidhhEREReQdRmqACgVquxceNGrFy5Enl5eaitrcXIkSMxaNAgOepzKxwETURE5B1EByCTvn37om/fvlLW4vbYBUZEROQdHApAS5YscfgJ161b1+Vi3F2QxjQImrPAiIiIPJlDAejQoUMWX+fk5KClpQVDhgwBAJw6dQoqlQqjR4+WvkI3cnVHeLYAEREReTKHAtCuXbvMn69btw4hISH417/+hfDwcADA5cuXMW/ePFx//fXyVOkmgtQcBE1EROQNRM8Ce/7555GZmWkOPwAQHh6Ov/71r3j++eclLc7d9GhdB+hkcQ2yz1ZAz01RiYiIPJLoAKTT6VBWVtbheFlZGWpqaiQpyh1tzyvCqzvPAAByCqpw98Z9mPDsTmzP67goJBEREbk30QHojjvuwLx587B161ZcuHABFy5cwEcffYQHHngAd955pxw1utz2vCIs+E8Oqq80Wxwvrm7Agv/kMAQRERF5GNHT4Dds2IDHH38cs2fPRnOzMRD4+fnhgQcewN///nfJC3Q1vUHA6s+PwVpnlwBAAWD158cwOSkGKtN28UREROTWFIIgdGkgS11dHc6ePQsAGDBgAIKCgiQtTC46nQ5arRbV1dUIDQ21e//ssxW4e6P9Xe7fmz8O4wf0lKJEIiIiakfs+7c9XV4IMSgoCCkpKU4X4O5KaxokvR8RERG5nugAdPPNN0OhsN3Vs3PnTqcKcjdRIQGS3o+IiIhcT3QASktLs/i6ubkZubm5yMvLw9y5c6Wqy22MTYxArDYAxdUNVscBKQDEaAMwNjGiu0sjIiKiLhIdgF544QWrx5966inU1tY6XZC7USkVWDUjCQv+kwMFYBGCTO1gq2YkcQA0ERGRBxE9Dd6We++9F5s2bZLq6dzKtORYvH7vKMRoLbu5YrQBeP3eUZiWHOuiyoiIiKgrujwIur3s7GwEBHjvOJhpybGYnBSDmev34shFHRbcOACPTx3Clh8iIiIPJDoAtV/sUBAEFBUV4cCBA1i5cqVkhbkjlVKBAb2CceSiDmGB/gw/REREHkp0AAoNDbWYBaZUKjFkyBCsWbMGU6ZMkbQ4dxQZrAEAVNQ1ubgSIiIi6irRAejtt9+WoQzPERliDEDlNY0uroSIiIi6SvQg6P79+6OioqLD8aqqKvTv379LRaxfvx4JCQkICAhAeno69u/f79DjNm/eDIVCgZkzZ3bpdbvC1AJUVssARERE5KlEB6Dz589Dr9d3ON7Y2IiLFy+KLmDLli1YsmQJVq1ahZycHKSmpmLq1KkoLS21W8fjjz+O66+/XvRrOiMyWA0AKK9lFxgREZGncrgL7LPPPjN//vXXX0Or1Zq/1uv1yMrKQkJCgugC1q1bh/nz52PevHkAjJutbtu2DZs2bcKyZcusPkav1+Oee+7B6tWr8d1336Gqqkr063aVqQWonC1AREREHsvhAGTqZlIoFB1WfPb390dCQgKef/55US/e1NSEgwcPYvny5eZjSqUSGRkZyM7Otvm4NWvWICoqCg888AC+++67Tl+jsbERjY1Xw4pOpxNVY3umAFRZ1wSDQYCSM8GIiIg8jsMByGAwAAASExPx008/ITIy0ukXLy8vh16vR3R0tMXx6OhonDhxwupj9u7di7feegu5ubkOvUZmZiZWr17tbKlmPVu7wPQGAZfrm9CzNRARERGR5xA9Big/P1+S8NMVNTU1uO+++7Bx40aHa1i+fDmqq6vNH4WFhU7V4K9SIizQHwCnwhMREXkqh1qAXn75ZTz44IMICAjAyy+/3Ol9H3nkEYdfPDIyEiqVCiUlJRbHS0pKEBMT0+H+Z8+exfnz5zFjxgzzMVPLlJ+fH06ePIkBAwZYPEaj0UCjkbaVJjJYg6r6ZpTXNGJwdIikz01ERETycygAvfDCC7jnnnsQEBBgczNUwDg+SEwAUqvVGD16NLKyssxjjAwGA7KysrBo0aIO9x86dCiOHDlicWzFihWoqanBSy+9hPj4eIdf2xmRwWqcKeVUeCIiIk/lUADKz8+3+rkUlixZgrlz52LMmDEYO3YsXnzxRdTV1Zlnhc2ZMwdxcXHIzMxEQEAAkpOTLR4fFhYGAB2Oy+nqTDB2gREREXkiyTZD7apZs2ahrKwMTz75JIqLi5GWlobt27ebB0YXFBRAqZRs03pJcCo8ERGRZ1MIgiDYu9OSJUscfsJ169Y5VZDcdDodtFotqqurERoa2qXneHXnafzff0/ht6P74O+/TZW4QiIiImpPivfvthxqATp06JBDT9Z2k1Rvxg1RiYiIPJtDAWjXrl1y1+FR2AVGRETk2ZwaXFNYWOj0ujqeiDvCExEReTbRAailpQUrV66EVqtFQkICEhISoNVqsWLFCjQ3N8tRo9tpuyGqA0OoiIiIyM2IngX2xz/+EVu3bsVzzz2H8ePHAwCys7Px1FNPoaKiAq+//rrkRbobUxdYk94AXUMLtD38XVwRERERiSE6AL377rvYvHkzpk+fbj6WkpKC+Ph43H333T4RgAL8VQjW+KG2sQXltY0MQERERB5GdBeYRqNBQkJCh+OJiYlQq9VS1OQRTN1gFVwMkYiIyOOIDkCLFi3C008/jcbGqwOAGxsb8be//c3q9hXeijPBiIiIPJfoLrBDhw4hKysLffr0QWqqcRHAn3/+GU1NTZg0aRLuvPNO8323bt0qXaVuhgGIiIjIc4kOQGFhYfj1r39tcay7NiF1J5EhrTPBOBWeiIjI44gOQP/85z/lqMPjmFqAyjgGiIiIyOO41y6jHqQnu8CIiIg8lugWoIqKCjz55JPYtWsXSktLYTAYLG6vrKyUrDh31su8GCIDEBERkacRHYDuu+8+nDlzBg888ACio6N9ZgPU9swborILjIiIyOOIDkDfffcd9u7da54B5qs4C4yIiMhziR4DNHToUFy5ckWOWjyKaUPU+iY96ptaXFwNERERiSE6AL322mt44oknsGfPHlRUVECn01l8+IogtQoB/sZvX3kNu8GIiIg8SZfWAdLpdJg4caLFcUEQoFAooNfrJSvOnSkUCvQM0uBi1RWU1Taib89AV5dEREREDhIdgO655x74+/vj3Xff9elB0ICxG+xi1RWOAyIiIvIwogNQXl4eDh06hCFDhshRj0fpxQ1RiYiIPJLoMUBjxoxBYWGhHLV4HM4EIyIi8kyiW4D++Mc/YvHixfjzn/+MESNGwN/f3+L2lJQUyYpzdwxAREREnkl0AJo1axYA4P777zcfUygUPjcIGgAiuRo0ERGRRxIdgPLz8+WowyOZ1gLiNHgiIiLPIjoA9evXT446PFLPIHaBEREReSLRAcjk2LFjKCgoQFOTZevHbbfd5nRRnqJXCLvAiIiIPJHoAHTu3DnccccdOHLkiHnsDwDzekC+NQbI2AKka2hBY4seGj+ViysiIiIiR4ieBr948WIkJiaitLQUgYGBOHr0KL799luMGTMGu3fvlqFE96Xt4Q9/lTH4cS0gIiIizyE6AGVnZ2PNmjWIjIyEUqmEUqnEhAkTkJmZiUceeUSOGt2WaTsMgN1gREREnkR0ANLr9QgJCQEAREZG4tKlSwCMg6NPnjwpbXUeIJLjgIiIiDyO6DFAycnJ+Pnnn5GYmIj09HQ899xzUKvVeOONN9C/f385anRr5hYgToUnIiLyGKID0IoVK1BXVwcAWLNmDX71q1/h+uuvR8+ePbFlyxbJC3R35tWg69gCRERE5ClEB6CpU6eaPx84cCBOnDiByspKhIeH++TO8OYuMLYAEREReYwurwPUVkREhBRP45F6cT8wIiIijyN6EDRZ4oaoREREnocByEkMQERERJ6HAchJPc07wnMMEBERkadgAHKSqQXocn0TWvQGF1dDREREjmAAclJEkBpKBSAIQGU9W4GIiIg8AQOQk1RKBSKCOBWeiIjIkzAASYADoYmIiDwLA5AEGICIiIg8CwOQBK7OBGMAIiIi8gQMQBK42gLEMUBERESegAFIAuwCIyIi8iwMQBKI5GKIREREHoUBSAKRIa0tQDVsASIiIvIEDEAS4I7wREREnoUBSAKmWWAVdU0wGAQXV0NERET2MABJoGeQsQVIbxBQdaXZxdUQERGRPQxAElD7KaHt4Q8AqGA3GBERkdtjAJKIaSZYGQMQERGR22MAkggXQyQiIvIcDEAS4VR4IiIiz+EWAWj9+vVISEhAQEAA0tPTsX//fpv33bhxI66//nqEh4cjPDwcGRkZnd6/u3AqPBERkedweQDasmULlixZglWrViEnJwepqamYOnUqSktLrd5/9+7duPvuu7Fr1y5kZ2cjPj4eU6ZMwcWLF7u5cks9g7ghKhERkadweQBat24d5s+fj3nz5iEpKQkbNmxAYGAgNm3aZPX+77zzDh5++GGkpaVh6NChePPNN2EwGJCVldXNlVsyd4FxDBAREZHbc2kAampqwsGDB5GRkWE+plQqkZGRgezsbIeeo76+Hs3NzYiIiLB6e2NjI3Q6ncWHHEyDoDkNnoiIyP25NACVl5dDr9cjOjra4nh0dDSKi4sdeo6lS5eid+/eFiGqrczMTGi1WvNHfHy803Vbww1RiYiIPIfLu8CcsXbtWmzevBkff/wxAgICrN5n+fLlqK6uNn8UFhbKUoupBaisthGCwO0wiIiI3JmfK188MjISKpUKJSUlFsdLSkoQExPT6WP/7//+D2vXrsU333yDlJQUm/fTaDTQaDSS1NsZUwBqajGgprEFoQH+sr8mERERdY1LW4DUajVGjx5tMYDZNKB5/PjxNh/33HPP4emnn8b27dsxZsyY7ijVrh5qFYLUKgBcC4iIiMjdubwLbMmSJdi4cSP+9a9/4fjx41iwYAHq6uowb948AMCcOXOwfPly8/2fffZZrFy5Eps2bUJCQgKKi4tRXFyM2tpaV52CmWlX+E9yLyH7bAX03BmeiIjILbm0CwwAZs2ahbKyMjz55JMoLi5GWloatm/fbh4YXVBQAKXyak57/fXX0dTUhN/85jcWz7Nq1So89dRT3Vm6he15RSiqbgAAvJx1Gi9nnUasNgCrZiRhWnKsy+oiIiKijhSCj43Y1el00Gq1qK6uRmhoqCTPuT2vCAv+k4P230hF67+v3ztK9hCkNwjYn1+J0poGRIUEYGxiBFRKhf0HEhEReQCp379d3gLk6fQGAas/P9Yh/ACAAGMIWv35MUxOipEtkGzPK8Lqz4+ZW6AAsPWJiIioEy4fA+Tp9udXWgSP9gQARdUN2J9fKcvrm1qf2tdQXN2ABf/Jwfa8Illel4iIyJMxADmptMZ2+OnK/cSw1/oEGFufOBibiIjIEgOQk6JCrC/A2NX7ieHq1iciIiJPxQDkpLGJEYjVBsDW6B4FjONxxiZa36vMGa5sfSIiIvJkDEBOUikVWDUjCQA6hCDT16tmJMkyANqVrU9ERESejAFIAtOSY/H6vaMQo7UMGjHaAFmnwLuy9YmMY7Cyz1bg09yLXPiSiMjDcBq8RKYlx2JyUgwyvzyON/fmI7l3KD5dNEHWtXhMrU8P/Senw21ytz75Oi49IB+uaUVE3YEBSEIqpQIzR8bhzb35KKisR3f8zp6WHIvbUmPx2c+W091j+GYsG1sLX5qWHuiOhS+9lbPBkuGJiBzFACSxwdEhUKuU0DW0oKCyHv16Bsn+mqWtm69GhWhQWtOI6wb0xL8fSOcvfhm4w8KX3srZYMlWOSISg2OAJKb2U2JYbAgA4PCFatlfr76pBTm/VAEAfn99IgCguqGZb74y4dID8nB2TSsuCEpEYjEAyWBEHy0A4MhF+QPQT+cvo0lvQFxYD0wcatxA9lxZHXxsi7duw6UH5OFMsOSCoETUFQxAMkiJCwMAHL5QJftrfX+mHABw3cCe6NczEH5KBeqb9CjW8Q1YDlx6QB7OBEu2yhFRVzAAySAl3tgClHdRB4PMf3VeDUCR8Fcp0bdnIADgbGmdrK/rq7j0gDycCZZslSOirmAAksHAXsEI8FeitrEF+RXyBZHKuiYcvaQDAFw7IBIA0D8yGABwrrxWttf1ZW0XvrSFSw+IZwqWtnQWLNkqR0RdwQAkAz+VEsN7t44DknEg9A9nja0/Q2NC0CtEAwAYEGWcdXa2lAFILtOSY/HaPaOgaJdxNH5KToHvos6Cpb01rdgqR0RdwQAkkxFxxgAk50ywtt1fJgN6mVqA2AUmp2GxoRAEwE+pwONThgAADIKA6wf1cnFlnmvq8BhEtQb5tuytqM5WOSLqCgYgmaT0MQWgKtleY29rAJpgEYDYAtQdDhVeBmCc8bfw5gHo1zMQzXoBe06Vubgyz3W6tBalNY3wVyrw5pwxUKuMgeWtudfYbVUzbUejVln+SgsP9GerHBFZxQAkE1MAOnpJhxa9QfLnL6ioR2HlFfgpFRZN+6YxQJeqG1Df1CL565KRae2lUX3DoVAoMHV4DADg66PFLqzKs32dZ/zeXT+4FzKSojEmwfhzbQqb9kwaFm1efd30h8Cdo+IYfojIKgYgmSRGBiNIrcKVZj3OlknfHfV96/ifUX3DEaS5uqB3eJAaEUFqAMb1gEgepjflUX3DAQBThxvXYNp5ohRNLdIHXl+wvTU8TmsNk2P6Gb+3B39xLADlXaxGQ4sBYYH+WHDTQABAbqH8a3ERkWdiAJKJSqnA8Dj5usH2Whn/Y2LuBivz/G4wd9xxvb6pBceLagAAI/uGGf+ND0dksAY1DS3Yd65Cktdxx3OXS2FlPY5e0kGpACYNiwIAjG5tAXI0AJnW+RnTLwKjW8PTkYvVDKREZBX3ApNRSpwW+/MrceRiNX47Jl6y5zUYBPzQZgHE9vpHBuOn85c9vgXIXfd2OnyhGnqDgJjQAPQO6wEAUCoVmJwUjff2F+Dro8W4YbBzg6Hd9dzlYuo6HJsYgZ7BxoHQI/uGQaEAfqmoR1lNo3mmoy0/nTcGoPTECCT0DERYoD+q6ptxvEiH1PgwWesnIs/DFiAZjegjz0ywY0U6XK5vRpBaZfUXu3kqvAe3ALnz3k6HCqoAXG39MTF1g+04VuLUApjufO5y+bpd9xcAhAb4Y0i0cV89e61ABoOAn84b73NNYgQUCgVGtv7fOFTgWAsSEfkWBiAZpfYJA2AMLM0SDoQ2TX8f178n/FUdL6F5KryHtgC5+95OOQWW439Mrh0QiRCNH0prGnGosKpLz+3u5y6HsppGHGgNOFPaBCAAGGUeB9T5NhanSmtQfaUZgWoVhvcOBQCMbL0+Xb0WROTdGIBk1K9nIEIC/NDUYsCpkhrJnvf7s8YxJtbG/wBA/15XV4OWeysOObjz3k6CIJhbgEb1C7O4Te2nxM1DjeNX/tvF2WDufO5y2XGsBIIApPbRmrsUTUwDoQ/YaQEyfT9G9ws3/1FgaqHLYQsQEVnBACQjhUJhng4v1YrQjS167M83BqAJg6wHoPjwHvBXKdDQbMCl6iuSvG53cue9nS5cvoLy2kb4qxTm1b7bmtLaDfb10WIIgvjw6c7nLhfT7K+pyTEdbhvTzzgQOu9iNRqa9Taf48fWAHRNwtUlIVLjjWOICiuvoKymUcqSicgLMADJbIRpZ/iL0gSgnF+q0NBsQK8QDQZFBVu9j59KiX49jeOAPLEbzJ33djK1JiT11iLAX9Xh9puGREHtp8T5inqc7sJilO587nKovtKM7NYlHaYO7xiA4iN6IDJYg2a9gCM2/g8JgoCfWgNQ2zWxQgP8zf9HctkNRkTtMADJTOoVoc3bXwzoCUX7zaja6M6p8FJP13ZmY0y5mQdA25hVFKzxM6/MbVrYT4yxiRGICXXPc3eUmJ+HXSdK0awXMCgq2Dx2rS2FQnG1G+y89a6sXyrqjStIqxRIa3ddRsa3jgNiNxgRtcNp8DIz7Ql2srgGDc16q60GjtAbBOzPr8TnP18CAIwf0HH6e1vGcUAlsrcAyTFdW6VUYNn0oVi8OdfmfVy1t5N5AHS/cJv3mTo8GjtPlOLrY8X446RBop5fpVRg4tBeeHd/oc37uPO+VmJ/Hra3hsRpVrq/TMYkhGP70WKbM8H2t05/T+0T1uH/18i+YdhyoNAcXImITNgCJLM+4T0QHuiPZr2Ak8VdGwi9Pa8IE57dibs37sMvlfUAgOf/e6rT6dCmv6blbAFydLp2V1qILlw2jl1StXufVyqA9bNds7dTQ7Mexy7pAACj2k2BbyujdUuGvIs6XLhcL+o1ahtb8N9jJQCA0ICOf58smz7UbdcBEjt9/0qT3rx3mrXuLxNT2MwpuGx1XJVpAPQ1VlrFTDPBfr5QJcuWNETkuRiAZKZQKDCidTp8V8YB2XpTKatp7HRNGFMXmFwtQI5O1/7y8NXwtnhzLu7euA8Tnt3ZaXirqm/Chj1nAQDP/SYF780fh//7bQoC/JQwCEBsmGvGvxy5WI0Wg4BeIRrEtZut1FbPYI15H6sdrWHGUW/sOYvy2iYkRgZh/xMZeG/+OLx0Vxqua23x+yqv2C1n9nVl+v63p8twpVmPuLAe5qnr1iT31kLtp0RlXRPyyzv+PJsWQLTWLTgoKhghGj/UN+lxqsRz18UiIukxAHWDlDjTTLAqUY9zZk0Y01T4Yl0Dahul3xTV0enaD78rfkG/13afRU1DC4bGhOCOkX0wfkBP/GZ0PCa3thJ81YWxNVI4ZF7/J6zT8VcAMCXJOBvs/QOFDrd8FVc34I3vzgEAlk4bggB/FcYP6Inb0+KwblYagtQq5BZW4cODFyQ4G2mJmb5vahF881vjuU5Oiu70+6n2UyK1dSxd++nwJboG/FJRD6UC5u0v2lIqFebFQh3dVJWIfAMDUDfo6orQzqwJo+3hj8jWLQXOydAN5sw07M7C26WqK3j7h/MAgKXTh0LZZqzLLcmmAFTUpSnm1ojpnjPtAD+yr+3xPyamsSjHi2ocbvlat+MkGpoNGNMvvEOXUHRoAB7NGAwAWLv9BKrrm+3W0J0c/XnYcazY3CL4U2uY+fznS3ZXtx7dOh3+YLuB0Kaf/WGxoQgN8Lf6WNN6QBwHRERtMQB1A9OK0KdLa3GlyfZaJu05uyZMfxm7wZydhm0rvL34zSk0tRiQnhiBm9rtp3XTkCj08FehsPIKjraOxXFG27FV9kKKIAg2V4C29rwrP8nrcLyzlq/jRTp80Nqy85dbh1ltEfmf6xIwKCoYlXVNeO7rE261UaqjPw+bvj/fIdRX1jXZ3eLDvDN8gfUA1NmsuKsBiC1ARHQVA1A3iA7VoFeIBnqDgGNF1t+4rbVEOLsmjJwDoU1T1Z2di1RcfcV83h8eKMQHB4whYOn0oR1CQA+1CjcPNYaiL484tx+W2AG7l6obUFrTCD+lwjyzzxox3ZZtr/nSjw5DEIBbU2JtBix/lRKrbx8OAHjnxwJR46rk5szPgyNbfJgGQp8prUVVfZP5uHn8T4LtAJTWOhX+bFmd27WcEZHrcBp8N1AoFEiJ0yLrRCmOXKjqMFbB2tThmNAAjG631UKH5wUQ08maMHIOhFYpFVg1IwkP/SfHal2Otkc8ve04KuuaLI6lxWtthoBpybH48kgxvsorxp+nDrE7FscaeyFFAeOb8eSkGPN085zW7pphsaHooba9lIGj3Zav7jyDzT8VdLjvuMTOlzfQXbH+Bm4Kbq/f65oZcs7+PLRtEbS2xENEkBr9ewXhXFkdDv5yGZOGRaOqvgknWmdWWpsB1vaxCT0Dcb6iHocKL+OmIVEizoyIvBVbgLrJ8DjjLJdtR4osuixstkToGrDtyNXBvu3f5k1fd7YmjNxT4aclx2JwdMfF62K0AXht9kiHWgTahx8AyC2sttmaMXGocaXl/PI6nOzi/mpdGVtl3v+rk+nvgOPdli98c8pqDU9+mmfz3E3BzVbNgGs3Sp2SFIPoEE2H4zHaADxwXYJDz9HZ98/cDdYaRk27v/fvFWQe72aLKVBzHBARmbAFqBtszyvC/8v+BYDxl/bdG/chVhuAlbcOw9Pbjnf613FYD388c8cIPL2tXQuRA4sNmneFL6+D3iBIvnheqa7BvN3Dy3elQYCxO25sYgRUSgWUSgUW/CdHVIsQYL0FxiRY44cbB/fCjmMl+PJIMYbG2J4+bbPuLoytMo3/sTcAWootKmydu5jgZm+hTDnsOV2GkppGBKtVeHn2KNQ0NJt/HvbnV+Kt78/bfY7Ovn+j+4Xj/QMXzDPBTN1f6Q6sij2ybxi2HrrIneGJyIwBSGamFp72AaC4ugEPv3vI7uOrrjQjPEiNvUsnYn9+JUprGixCRmfiwntA7adEU4sBl6quID4i0Ikz6WjbkSIIgrFV5La0uA63T0uOxev3jurQvRcR5I/KOttjMey9kd8yIgY7jpXgqyNFWDJ5sOi6xY6tamjW4+gl4ww+ewOgTWNhiqsbRIU+k87O3d03St20Nx8AcNfYvpg41LKbyd73xV53LnB1JtjPhVVo1husboBqiym45hZchsEgWMwuFMO0IruY/4dE5J4YgGTkyIBYR5TWNEClVIj+q16lVCCxZxBOltTgTFmt5AHoi8PGrpoZqb1t3mdaciwmJ8VYvGkU6xrw2JZcu89v64184tBo+KsUOF1ai9MlNRgUHSKq7rGJEQhUq1BvY0Ze+zfjo5d0aNYLiAxWIz7C9gKIwNWxMNZavsS0hFk7d3feKPV0SQ2+O10OpQKYe21Ch9vtfV8A+1t89I8MQligP6rqm/HT+UocbV1Y1JF90YbEhCDAXwldQwvOlddhoI2NhDsjx7YvROQ6HAMkI3tdFo5y5g1NrqnwFy7X4+Avl6FQALeO6PyXvym83Z4Wh/EDena62Wdbts5b28PfvOFoVxZF/H/Z522GH8D45vzErcPMb8am6dNp8eEODbo2tXzFtNvQNUYbgMcyHNsbzNq525tp5cqNUv/ZunbT5KRom0G7s++LI4O3lUoFRre25Lz5XT5aDALiwnqgT7j9YO+vUiIlLgzA1e5MMcTOGiQi98cWIBk52xXhSLeAPXINhN7W2vozLrEnohwMNCZSdIdMHxGLXSfL8FVeMR7pZMPR9l0WNVeaseYL40DimWm98aONkLrrRBmmDY/BT+cvm1u60vranv7enrWWL9P5bP6psEvn3lkrClq/dsVGqVX1TdiaY1y+4P7rEju9r63vi6M1j04IR9aJUuw8UQoASOgZ6PD4tpF9w7D/fCUOFVThd2PiHXo9oGuzBonI/TEAyUhMy01XuwXsGRBlagGSNgB9fti4K/2vUsU3/UvRHTIlKRp/USpwvEiH/PI6JEYGdbiPtS4Lk7uuiUfmnSNgEGDxZlzb0IyH3snBRzkXsP1oEeoar7YUbdp7HgN7BTvc3WGr29KZc7c1rspE4297ir5c3ttfiIZmA5JiQx0K613pzjVp0VvGkO/PVmDCszsd6oYaaZ4JJq4FyN0HnxNR17ALTEaOdlm8Nrvr3QL29I80tQBJ1wV2rqwWeRd1UCkVmN7F+pztDgkLVJvfbL6y0v1gq8vCZMKgSCgUig7dc5OHx2D22L4AYBF+AOCyAysWO8LZc5+WHIu9SyeaN0p9b/44/E/ruJvlHx1BtY21gsRwdIuQZr0B/84+DwC4f0Jil9ZlctT2vCK8sONUh+OOdkOZVoQ+VVIjan88dx98TkRdwxYgGTna0jEtORZTk7veLdAZ0xigsppG6Bqabe6XJIapS2jCwEhEBKm7/DzOdodMT47Fd6fL8cGBC4gL62HRzWSrywIwfu//tu04pifHdngtvUHAN8et7+AuZXeHs+fevhUlLT4Me06VIb+8Dn/94hj+/tvULtcmZrDv10eLUVTdgMhgNWZ0oTXQUVJ0Q0WHBqC3NgCXqhuwYfcZXDewV4fvubVZXuGBjv2fcXbwOWeYEXUvBiCZ2eqyaL+OjzPdAp0JCfBHVIgGpTWNOFdWh7TWnbG7ShAEfPazsfurs9lfjnLmvP1UxjeH/PI6LN6cC8D4Rn3XNfFd7rLozu4OKa95D7UKz/0mBb/7RzY+OHgB05JjEKj2s/lmauvNtrNlG0wrTbcNbq/uPA0AuCe9HzR+8nW/SXFdtucVobJ1G41Xd53Fq7vOWgQ7a8GvV4gGPfztN5RHhWgwul84ss9WdCnAcIYZUfdjAOoGzv6176wBvYJRWtOIs6W1TgegkyU1OFNaC7VKiSnDo6UpsAu25xVh6YeHOxwvqm7AC9+cdug5rHVZeHJ3xzUJEZh3bSI2fZ+P+f8+gLa9Vvbe6O0tzGlqZVm29Qie+uwYinWW5987TN6p985eF3vB7sEbEvHGt/kdbi+raQQA83patpYy0DU0Y3xmFirarGzuaIBxJHQyBBFJj2OAukn7sSbd2bRtngpf7vxA6M9bW39uGtJLku60ruisO0QMa10W7rzWjiNS440z1doP2TG9mWZ+eczmdO6H3z1kt5Wlqr65Q/gBgGUfHZF1Krgz18Ve95kAYON3HcNPW2E9/K2O1YsK0SAiUI2GZoNF+AEcG5skZvNcexwdt0VERmwB8gHmqfClzg2EFgQBn/9sf/FDuTm7vlJnU82lmKLvKnqDgLVfnbB6m+lcbL3RS/FWKedUcGeuiyM/L/ayQmlNo9UV2Uf3C8f1z+20+pj2Y5NMtbRtBZaqy5VdaETiMQD5gAGtq97mXarCp7kXO3TBOTr48vCFahRU1qOHvwqThrluR20x3U9ip5pLMUXfVaR4o+8quaeCO3NdpOqutLYie/bZCpToGm0+xvR9eXXnGWz+qaBDQJno4P+jzs6hO7rQ7P2O4ABu8kQMQD6gsLIeAHDhcoPFYOFVM5IAwO5fjqZfbm98exYAMGlYFALVrvvRcbQ75LGMwR3edBzZRNbRgevuxh3GJclZQ1evi1Tdldaex9HzfeGbjtP3i6ob8M6+Aodf21rIAGzPeGzbAjVxaDQO/nK5SwHGXuuSu7c+ORPOGOy8m0IQBJ/qKNbpdNBqtaiurkZoqPidxD2Nrb8OO9uXyvTf+/V7RwHoGJDCA/2ReecIl/1y0xsETHh2p93ukL1LJwLo2O3grb/8ss9W4O6N+yR5LjH7lrX13vxxsi8GKPa62Pt5AQClAhAE6+fc9uep/etI+T23Ra1S4q8zk/HCN6c6hIy7rol3aNB/RJAalTYGaHcWYADY/P0BwObg8ba/Q6Ylx7oshNgLZ84EP0dqc+Z2T/v90x2kfv9mAPJipl/8XRkvowCgDfRHdX2z3V9urmAKdoD17hBfnDkj1Rv9yluT8PS2dq0soRo0tBis/jy0fay1kOAO7P28mN7Ibd1u6+fJke+5o8SGzq6GVNNjgc4DjACYN5+1Ramw3a3a2c9Td4SQzv74M537Zz8XdSn42frjUEzLmL3gKXf4cobc4czW8zMAOcmXApCcf526wxueuze9u4JUb/TWfgHtOFbs0aFTrq6czr7njv5yvf+6BHyVV9zhte+fkIi1Xx6HXqbf0p0FGDnJHUJMSzqI/ePPkeDnyB+H9lrG7AVPW68LSBO+gK63PsndMtbZ89fV1uA344d4VwBav349/v73v6O4uBipqal45ZVXMHbsWJv3/+CDD7By5UqcP38egwYNwrPPPotbbrnFodfypQD0ae5F85gfuXRHl0dn2EzckZxjNjw9dMr1V7Ot74ujXVTvzR9nnhXWfpaY3F1s7kaKEOLqNzV7NXQ1eEoRvuwFKHu3ydky1tnzCwAMjfUofPF33hOAtmzZgjlz5mDDhg1IT0/Hiy++iA8++AAnT55EVFTHGRI//PADbrjhBmRmZuJXv/oV3n33XTz77LPIyclBcnKy3dfzpQDUHeMTXrorDbenxcn6GiSenM3jDJ3W2Rqk7Oh4NWvfQzF/xLjDGz+5nr1uya4Ex+5oGXOky9XrAlB6ejquueYavPrqqwAAg8GA+Ph4/PGPf8SyZcs63H/WrFmoq6vDF198YT42btw4pKWlYcOGDXZfz5cCkJTjE2xxdQsQkbtzZryao3/EWJvxGBHkj8o65zfG7UxnY8qI2nM2pEsdgFy6EnRTUxMOHjyIjIwM8zGlUomMjAxkZ2dbfUx2drbF/QFg6tSpNu/f2NgInU5n8eErTGunAFd/2ZoobHze9uuwQH+7O9m744KARO7ENH2//SrSMdoAu+OmTAtA2vt/uGjiQOxdOhHvzR+Hl+5Kw3vzx2Hf8oxOHwsYA0xnz236HWDtd4QCwPzrE81ft7+dqD13C8ouDUDl5eXQ6/WIjrbcUyo6OhrFxcVWH1NcXCzq/pmZmdBqteaP+Ph4aYr3EJ398t1w7yhs6OS2tXeOAGD7l5u7LghI5G6mJcd2CCh7l060O27KkT9iTP8P22+3o/ZTdvpYRwLM2jtHdBrelt+SZPP212aPtBvAuoPY17cX/IDO/zgEOg+WjtxO3cPrF0Jcvnw5lixZYv5ap9P5ZAjqbDPWzm7zxAUBidxR+1WkHeXMwpyOPHZk33C7z93Z74jOfr8olQqrq3fb03Y8CWB9dl2YjfEmpvt0NgX/ttTYTmdDmv74s/V9AdDpquTzrzeOdenK7YKVzx09b4DdkmK4NABFRkZCpVKhpKTE4nhJSQliYmKsPiYmJkbU/TUaDTQajTQFe7DOfvl2dpurd7InIuf+H9p7rCPPbS+82brdVgDrjhBiCnBTk62fm7PBz9lg2dntzpy3vXBlL0DZ4mwoBeyvQ2bv+aXmFoOgx44di1deeQWAcRB03759sWjRIpuDoOvr6/H555+bj1177bVISUnhIGgiIjfU2WKFzqwp4+yyDM7OaHTVStDOLrJob90qW+HKkSn0zq5D1tnze906QFu2bMHcuXPxj3/8A2PHjsWLL76I999/HydOnEB0dDTmzJmDuLg4ZGZmAjBOg7/xxhuxdu1a3Hrrrdi8eTOeeeYZToMnIvJAcocQbyXXYoOAc6tQS7EOmU+tBP3qq6+aF0JMS0vDyy+/jPT0dADATTfdhISEBLz99tvm+3/wwQdYsWKFeSHE5557jgshEhEROUjOfcjkWofMKwNQd2IAIiIi8jxSv3+7dBo8ERERkSswABEREZHPYQAiIiIin8MARERERD6HAYiIiIh8DgMQERER+RwGICIiIvI5DEBERETkcxiAiIiIyOe4dDd4VzAtfK3T6VxcCRERETnK9L4t1QYWPheAKioqAADx8fEuroSIiIjEqqiogFardfp5fC4ARUREAAAKCgok+QZ6Cp1Oh/j4eBQWFvrUHmg8b563L+B587x9QXV1Nfr27Wt+H3eWzwUgpdI47Emr1frUD45JaGgoz9uH8Lx9C8/bt/jqeZvex51+HkmehYiIiMiDMAARERGRz/G5AKTRaLBq1SpoNBpXl9KteN48b1/A8+Z5+wKetzTnrRCkmk9GRERE5CF8rgWIiIiIiAGIiIiIfA4DEBEREfkcBiAiIiLyOT4XgNavX4+EhAQEBAQgPT0d+/fvd3VJsnrqqaegUCgsPoYOHerqsiT37bffYsaMGejduzcUCgU++eQTi9sFQcCTTz6J2NhY9OjRAxkZGTh9+rRripWQvfP+n//5nw7Xf9q0aa4pVkKZmZm45pprEBISgqioKMycORMnT560uE9DQwMWLlyInj17Ijg4GL/+9a9RUlLiooql4ch533TTTR2u+UMPPeSiiqXx+uuvIyUlxbzw3/jx4/HVV1+Zb/fGaw3YP29vvNbtrV27FgqFAo8++qj5mFTX26cC0JYtW7BkyRKsWrUKOTk5SE1NxdSpU1FaWurq0mQ1fPhwFBUVmT/27t3r6pIkV1dXh9TUVKxfv97q7c899xxefvllbNiwAT/++COCgoIwdepUNDQ0dHOl0rJ33gAwbdo0i+v/3nvvdWOF8tizZw8WLlyIffv2YceOHWhubsaUKVNQV1dnvs9jjz2Gzz//HB988AH27NmDS5cu4c4773Rh1c5z5LwBYP78+RbX/LnnnnNRxdLo06cP1q5di4MHD+LAgQOYOHEibr/9dhw9ehSAd15rwP55A953rdv66aef8I9//AMpKSkWxyW73oIPGTt2rLBw4ULz13q9Xujdu7eQmZnpwqrktWrVKiE1NdXVZXQrAMLHH39s/tpgMAgxMTHC3//+d/OxqqoqQaPRCO+9954LKpRH+/MWBEGYO3eucPvtt7uknu5UWloqABD27NkjCILx+vr7+wsffPCB+T7Hjx8XAAjZ2dmuKlNy7c9bEAThxhtvFBYvXuy6orpJeHi48Oabb/rMtTYxnbcgePe1rqmpEQYNGiTs2LHD4jylvN4+0wLU1NSEgwcPIiMjw3xMqVQiIyMD2dnZLqxMfqdPn0bv3r3Rv39/3HPPPSgoKHB1Sd0qPz8fxcXFFtdeq9UiPT3d6689AOzevRtRUVEYMmQIFixYgIqKCleXJLnq6moAVzc7PnjwIJqbmy2u+dChQ9G3b1+vuubtz9vknXfeQWRkJJKTk7F8+XLU19e7ojxZ6PV6bN68GXV1dRg/frzPXOv2523irdd64cKFuPXWWy2uKyDt/22f2Qy1vLwcer0e0dHRFsejo6Nx4sQJF1Ulv/T0dLz99tsYMmQIioqKsHr1alx//fXIy8tDSEiIq8vrFsXFxQBg9dqbbvNW06ZNw5133onExEScPXsWf/nLXzB9+nRkZ2dDpVK5ujxJGAwGPProo7juuuuQnJwMwHjN1Wo1wsLCLO7rTdfc2nkDwOzZs9GvXz/07t0bhw8fxtKlS3Hy5Els3brVhdU678iRIxg/fjwaGhoQHByMjz/+GElJScjNzfXqa23rvAHvvdabN29GTk4Ofvrppw63Sfl/22cCkK+aPn26+fOUlBSkp6ejX79+eP/99/HAAw+4sDLqDnfddZf58xEjRiAlJQUDBgzA7t27MWnSJBdWJp2FCxciLy/PK8e2dcbWeT/44IPmz0eMGIHY2FhMmjQJZ8+exYABA7q7TMkMGTIEubm5qK6uxocffoi5c+diz549ri5LdrbOOykpySuvdWFhIRYvXowdO3YgICBA1tfymS6wyMhIqFSqDiPFS0pKEBMT46Kqul9YWBgGDx6MM2fOuLqUbmO6vr5+7QGgf//+iIyM9Jrrv2jRInzxxRfYtWsX+vTpYz4eExODpqYmVFVVWdzfW665rfO2Jj09HQA8/pqr1WoMHDgQo0ePRmZmJlJTU/HSSy95/bW2dd7WeMO1PnjwIEpLSzFq1Cj4+fnBz88Pe/bswcsvvww/Pz9ER0dLdr19JgCp1WqMHj0aWVlZ5mMGgwFZWVkW/anerra2FmfPnkVsbKyrS+k2iYmJiImJsbj2Op0OP/74o09dewC4cOECKioqPP76C4KARYsW4eOPP8bOnTuRmJhocfvo0aPh7+9vcc1PnjyJgoICj77m9s7bmtzcXADw+GvensFgQGNjo9dea1tM522NN1zrSZMm4ciRI8jNzTV/jBkzBvfcc4/5c8mut3Rjtt3f5s2bBY1GI7z99tvCsWPHhAcffFAICwsTiouLXV2abP70pz8Ju3fvFvLz84Xvv/9eyMjIECIjI4XS0lJXlyapmpoa4dChQ8KhQ4cEAMK6deuEQ4cOCb/88osgCIKwdu1aISwsTPj000+Fw4cPC7fffruQmJgoXLlyxcWVO6ez866pqREef/xxITs7W8jPzxe++eYbYdSoUcKgQYOEhoYGV5fulAULFgharVbYvXu3UFRUZP6or6833+ehhx4S+vbtK+zcuVM4cOCAMH78eGH8+PEurNp59s77zJkzwpo1a4QDBw4I+fn5wqeffir0799fuOGGG1xcuXOWLVsm7NmzR8jPzxcOHz4sLFu2TFAoFMJ///tfQRC881oLQufn7a3X2pr2s92kut4+FYAEQRBeeeUVoW/fvoJarRbGjh0r7Nu3z9UlyWrWrFlCbGysoFarhbi4OGHWrFnCmTNnXF2W5Hbt2iUA6PAxd+5cQRCMU+FXrlwpREdHCxqNRpg0aZJw8uRJ1xYtgc7Ou76+XpgyZYrQq1cvwd/fX+jXr58wf/58rwj81s4ZgPDPf/7TfJ8rV64IDz/8sBAeHi4EBgYKd9xxh1BUVOS6oiVg77wLCgqEG264QYiIiBA0Go0wcOBA4c9//rNQXV3t2sKddP/99wv9+vUT1Gq10KtXL2HSpEnm8CMI3nmtBaHz8/bWa21N+wAk1fVWCIIgdLGlioiIiMgj+cwYICIiIiITBiAiIiLyOQxARERE5HMYgIiIiMjnMAARERGRz2EAIiIiIp/DAEREREQ+hwGIiIiIfA4DEBEREfkcBiAiIiLyOQxARERE5HMYgIjIo3388cfQaDQoKyvrcNuf//xnjBw50gVVEZG742aoROTRZs6cCQD45JNPOtyWk5OD0aNH4+eff0ZKSkr3FkZEbo0tQETksQwGA3bu3InJkydbvX3UqFHQarXYsWNHN1dGRO6OAYiIPFZpaSlqamowcOBA87H77rsPH374ofnrAQMG4PTp064oj4jcGAMQEXm8lpYW8+e5ubkoKCgwf61UKqFQKFxRFhG5MQYgIvJYUVFRCAwMxKlTpwAAjY2NOH36NI4dOwbA2EWWn5+PhIQEF1ZJRO7Iz9UFEBF1lVKpxO23345XX30VN954Iz766CNMnDgRH3/8MR544AHs2rULlZWVmDFjhqtLJSI3wwBERB7tpZdewpw5c3Dttddi8ODB+PDDD/HFF19g+vTpCAgIwOuvv46kpCRXl0lEbobT4ImIiMjncAwQERER+RwGICIiIvI5DEBERETkcxiAiIiIyOcwABEREZHPYQAiIiIin8MARERERD6HAYiIiIh8DgMQERER+RwGICIiIvI5DEBERETkc/4/VxEKXsQTQUwAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -974,10 +945,10 @@
      "start_time": "2020-03-21T21:20:04.164574Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.109294Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.109140Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.275426Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.274900Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.689566Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.689446Z",
+     "iopub.status.idle": "2024-06-03T14:31:49.957380Z",
+     "shell.execute_reply": "2024-06-03T14:31:49.956982Z"
     }
    },
    "outputs": [],
@@ -997,16 +968,16 @@
      "start_time": "2020-03-21T21:20:05.686139Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.277553Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.277392Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.395549Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.395000Z"
+     "iopub.execute_input": "2024-06-03T14:31:49.958925Z",
+     "iopub.status.busy": "2024-06-03T14:31:49.958776Z",
+     "iopub.status.idle": "2024-06-03T14:31:50.033475Z",
+     "shell.execute_reply": "2024-06-03T14:31:50.033065Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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DZOtg4oahEc0VdvzrP87BE7esxc/+bYVYHXXmHEHcHB/Uc3kMnXH7ghj1EPHS4LRF/UwUNyxyw2Aw1BLwAn6SOUBx/qSlWCl4FlBTYhObslHWtlYAAHZ3jsIfDMNiYjq0EOkRojYlNhNKbNGt/YutNHLDxA2DwVAJTUkZLYC1VN+1aAg7Y2Yps6uLUW43YzIQxv7uMb2Xw9AJ0Uw8xW8DAMVC5IZ5bhgMhmpEM3ENkEd91Zi4yVI4jsOqmSR6s/3kiM6rYehFrxC5qZ+SkgIgRnKYuGEwGKoRe9zkT0oKYOImq1ndUg4AeP/ksM4rYejFgMsHAKgutk77GUtLMRiMlMnDHjcAEzdZzemCuNlxagQ8z+u8GoYeDE6QHjZVJdPFjVQtxcQNg8FQiZuKm/yplAKYuMlqljSWwWIyYMjtR9ugW+/lMHRgYIJEbqoSRW5YWorBYKhlgqWlGBnGajJiRZMTAPPdFCqDLipupk/9poZifygMXzCU0XUxGIw8wSO0G7FX6bsOjWHiJstZxXw3Bc2gELmpjpGWKrZInRxY9IbBYKjCI5xb7JX6rkNjmLjJciJ9N4zCg3puYhmKDQZOTE2xiikGg6EKj9AFn4kbRiZZNYOUg58YdItX8YzCwB8MY8xLuhPH8twArGKKwWCkiFe4cLZX6LsOjWHiJssps5sxv7YEALCdpaYKiiE3EbMmA4eyInPMx7BGfgwGIyXEtBQTN4wMI/W7YampQmLQRVJSlcUWGAyxO4eycnAGg6GawCQQECpxi5i4YWSYNcKcqW1tbEJ4ITEoRG4qHbFTUgAiPDdsMjiDwVCIV4jacEbAVqbvWjSGiZscYG0rMXod6B7HODuJFQyjHhK5qXBMLwOnOISKKY+flYIzGAyFiGbiiryaKwUwcZMT1JXZMLPSjjDPqqYKiRE3EbJOe2y/DQAUWYwAgMkAEzcMBkMh1G+TZykpgImbnGFNC/nwvdfGTMWFAo3cJBI3NjMRN14WuWEwGErJ0zJwgImbnIH6bt5n4qZgGPGQyE25PX5aqkgQNx4WuWEwGErx5melFMDETc6wotkJADjQM45wmA3RLARGvTQtlUDcWMi/MIvcMBgMxXjys8cNwMRNztBa5YDFZIDHH0L7sEfv5TAygJiWitPjBpAiN8xzw2AwFEPTUsxzoz0PPPAAWlpaYLPZsHbtWrz33nsJH3///fdj/vz5KCoqQnNzM772ta9hcnIyQ6vVD5PRgAV1pJnfwZ5xnVfDyAQjgrgpdyQyFJNqKS8TNwwGQyne/JwrBegsbp566ils2rQJd911F3bu3Inly5fjwgsvRH9/f8zHP/HEE/jmN7+Ju+66CwcPHsSjjz6Kp556Ct/61rcyvHJ9WFhXCoCkphj5j1Qtldxzw9JSDAZDMZGl4HmGruLmvvvuwy233IIbb7wRixYtwkMPPQS73Y7HHnss5uPffvttrF+/Hp/85CfR0tKCCy64ANdee23CaI/P58P4+HjUV66ysJ5Ebg71unReCSMT0LRUQkMx9dywyA2DwVAKKwXXHr/fjx07dmDDhg3SYgwGbNiwAe+8807M55x55pnYsWOHKGZOnDiBF154AR/96Efjvs4999yDsrIy8au5uVnbN5JBWquLAQCnhtw6r4SRbvzBMNxCNIZ5bhgMRlpgaSntGRwcRCgUQm1tbdT9tbW16O3tjfmcT37yk/je976Hs846C2azGbNnz8a5556bMC11++23Y2xsTPzq6OjQ9H1kkpkVdgBA+7AHPJ/5iqkJXxADLjaZPBOMeknUhuOA0gTiRuxzw8QNg8FQSp4OzQSywFCshC1btuCHP/whfv3rX2Pnzp145pln8Pzzz+Puu++O+xyr1YrS0tKor1ylwVkEAwdMBsIZFxk7To1g3T2bccY9m/HPvT0Zfe1CZFTocVNqM8MYZ2gmwDw3DAZDJaEg4BNsGkXl+q4lDZj0euGqqioYjUb09fVF3d/X14e6urqYz7njjjtw3XXX4eabbwYALF26FG63G5/73Ofw7W9/GwZDTmk1xVhMBjQ4i9A54sWpYQ9qSm0ZeV2e53Hn3/bBNUkmT3/37wewYVEtzMb8/n3rybjQ46YsQdQGkMYvMHHDYDAU4Yvwn+bZ0ExAx8iNxWLBqlWrsHnzZvG+cDiMzZs3Y926dTGf4/F4pgkYo5Ec3PVI0+jBzEqSmjo1lLleNztOjWB/9zgMHGA0cOgdn8S7J9iE8nRChWSJLfH1RxFLSzEYDDVMjpKt2QEYE19E5SK6Xnpv2rQJjzzyCB5//HEcPHgQX/ziF+F2u3HjjTcCAK6//nrcfvvt4uMvu+wyPPjgg3jyySfR1taGV155BXfccQcuu+wyUeTkOzMifDeZ4sn3iU/p6lXN+PhpjQCA1w8PZOz1CxE6/T2ZuGGeGwaDoQrvKNkWOfVcRdrQLS0FANdccw0GBgZw5513ore3FytWrMCLL74omozb29ujIjXf+c53wHEcvvOd76CrqwvV1dW47LLL8IMf/ECvt5BxmsqJuOkcyYy4CYV5vHqI9B26YmUjese9+NP2TuzqGM3I6xcqNHJTapOXlpoMhBEO8zAk8OcwGAyGyOQY2eZhSgrQWdwAwMaNG7Fx48aYP9uyZUvU9yaTCXfddRfuuuuuDKwsO6kVfDaZMhTv6hjFsNuPEpsJq1vK0TZIeq4c6BlHKMwnNLsy1CNFbpKIG7MUsfQFw6LYYTAYjITkubhhjtAco7bUCgDoG8/MyInNB4nh+9z5NTAbDZhV5YDNTGZcnWT9dtKGXM+NLULcsNQUg8GQDfXc2Jx6riJtMHGTY9DITd94ZiI3mw+SlNSGhTUA6IwrUk6/vzt3uz1nOy4hcpOoxw1ADN4WE+tSzGAwFMIiN4xsoraEiJsxbyDtXWk7hj043OeC0cDhnHnV4v2LG4QZV0zcpI1xL/XcJM8c21k5eN7D8zz+tL0DP3npEAYnWCNNhgbkuaGYiZsco7TIJF6pp9t3Q43Eq2aWRw1vnFNDxkC0DU4o3ueOU8P43t8P4J3jrJQ8ES6Z1VIAG8FQCDy7qwtff3oPHnjtOK579D34g2G9l8TIdVjkhpFNcByXMd/NvwS/DU1JUVqqHACAk4PKKrYO9ozj2t9sw2NvteG6R7dhX9eYNgvNQyTPTfL+E6zXTf7zyBtt4u2DPeN46v12HVfDyAuYuGFkGzQ1lU7fzYQviG0nyNyRjyyMnv/VWimImyE3wmH5zRP/+8VD8IfIFWcwzOPhN05otNr8Q24pOCCZij0sLZWX9LsmcaBnHBwHfG3DPADAzzcfgy/I/t6MFGCGYka2IZmK0xe52Xp0EP5QGC2VdswSIjWUpvIimAwcfMEwemWuoWPYgy2HB8BxwC+uXQmAVGKxVEps5DbxAwCbmfwb+9jvMi/ZeWoEALCgrhRfOm826stsGJzw4e+72Yw3RgqwyA0j26guIWmpgTQaC984SjoQnzu/BhwX3cvGZDSgWeiUfHJQXjn4c7u7AQDrZlXismX1qC21wuMPYWf7iIarzh/kloIDgNUkeG6YDyMvOdjjAkCM/GajAZ8+YyYA4HdvtRXM2BlGGmDihpFtVDiIuXfE7U/L/nmexxtHiLj50LyqmI9pEWZctcnsdfPcLiJurljRCI7jsGommUK7t5P5bqYSCvOY8AlpqSSl4ABgZZGbvOZIHxE3C+pKAACfXDMDVpMB+7vH8f5JdnHAUAmrlmJkG+WCuBlOk7hpG3Sjc8QLi9GAM2ZVxnxMa5VQMTWQXNx0jXpxuM8FAwdcuJhMfF/SSK4W9jJT8TSosAHkRm4EccMiN3lJmxAdnVVN0sPlDguuXElmvP3urba4z2MwEsIiN4xso0Ioyx7xpEfc0KjN6pZy2C2xT66tVUJaSkbkhu5v5YxylNlJJGIpEzdxGfcSv43FZBBTTomgj2HiJv/geR5dI14AQLMwVw4APrO+BQDw0v5edI169VgaI5cJeIGQYGtg4oaRLdC01FCaIjdvCT1ozp5bHfcxtBy8TYbnhk4Q/1DE/qi4OTXkwZhwMmcQpEopeaPfpMgNS0vlG+PeIFxCJK8pQtwsqCvFGbMqEOaBZ3Z06rU8Rq5CozacAbCU6LuWNMHETQ6STs8Nz/P4oH0UALCmtTzu41qEcvD2YQ9CCcrBg6Ew3jo+CCDav+O0W9BQRqq+jvUrbwaYz4ijF2SUgQORnhsWuck3OkdJL6lKh2XaUNRPrGoGADzzQRczFjOUQcWNtRQw5KcMyM93ledQcTPqDSQUFmroHPFicMIHs5HD4ob44coGZxEsJgMCISlsHotdHaNwTQbhtJuxrMkZ9TOpGSAbwBmJkkopgKWl8pmhCXIBQyskI7l4SR2KzEa0DbrZnDeGMqiZOE9TUgATNzmJU/Ct8DwwqrHvZlfHKABgYX1p1MTpqRgNnFgxdSLBGAbqtzlrThWMhuiSclHcsOniUUg9bmRGblhaKm8ZchNfRFXxdHHjsJqwfg6JhtLWDQyGLGjkJk8rpQAmbnISs9GAMqFEWGtT8W5B3KxodiZ9bKuMyMvrR2lKarp/RywnZ5GbKETPTZG8yA0VoSxyk38Musj/d2WxJebPz5lP/q+or43BkIVPiPRZS/VdRxph4iZHEU3FE9qKm8NCT41F9ck/9GI5eBxxMuL2Y0/nKIBoMzGlpZJFbmIhDs20KozcMM9N3jEoRG4qHdMjNwBwjvB/tePUCJsKz5APEzeMbEU0FWscuTnaR1JMc2uTO+hpOfiJOOJm67FB8Dwwv7YEdYJ5OPr50gBOZoiUUO65YWmpfIVevMSL3DRXFKGq2IpgmMehXua7YcjEJ1gJrMX6riONMHGTo5TbtS8HH/MGxFlRc2qSf+iTRW6SdTmmIxwmfMG0lbXnItRzI6c7MQBYWVoqbxmaoJ6b2OKG4zgsbiBX38xUzJCNj0ToYc3PMnCAiZuchXputOwRQ0uy60pt4v4TQSMvXaPeaQMweZ4XTY6x/DYA8YrUlpJwe2eCiqtCY1xl5IYNIc0/qOiPl5YCECFuWENMhkz8QuTGwiI3jCwjHeLmqOC3mVsr7wNfVWxBidUEnif9bqL21T+BvnEfrCYDTm+piLsP2nW1c8QT9zGFhpSWkuu5YZGbfCVZWgqA2LKBRW4YshE9Nyxyw8gyqLgZ11Dc0CGYs6vliRuO4zBbSF8d7nVF/YympNbOqkxYUt5UXgQA6BhmkRsK/Zsq99wwcZNP8DyPwYn4peCUhfXkBHWkz8W8awx5iJ4bJm4YWQbtdaNl5KZTEBhUcMhhSSMJie+bMiPqDVoCPje234ZCfTcsciOhvkMxS0upYTIQwpf/uBPXPboNAy6f3ssRcftDomBNFLlpKreD44DJQBgDE9mzfkYWwzw3jGwlHWmpDkFgUMEhhyVCSHxfRL7f7Qti2wkynyqe34ZChRTz3Eio7VDsZ5EbVfz+7ZN4fm8P3jw6iJ/964jeyxGhDTotJkPcAbb05/WlpBqRRUAZsmCeG0a2khZxI/hmIqcPJ2OJMABzX9e4GBLfcngAvmAYMyvtmJuk6oq+VgeL3IhIgzOVdihm4kYNz+3qFm//c28PwhqPNFGLkgGqTSwCylACi9wwspVSjcXNhC+IEQ/ZV3OF/LTUvNoSWIwGjHkDODlEDqz/3NcDALhocR04jkv0dHHScdeIl/kFAARCYXiF9JLcDsViWor1uVHMmDeAAz2SEXfEE8iappLUeyVH5IoXCcNM3DBkwDw3jGyFRm5GPdqIG3rF57SbZVfpACQkvnKGEwBp2jfq8eOVA30AgIuX1id9fr3TBgNHog7Z5HfQC3q1DgDFVoWDM1mHYsXQDtrNFUXi53hvV3aUVCtJT9ILkqlViwxGTPxC5IalpRjZBhU3rsmgJpPBaa5eSUqKQn01L+7rwVPvd8AXDGNRfSmWNyWfOGs2GlBfJlRMMd+NaCa2W4wwGeX9e9rMuZGW2np0EH/b1ZVVETpa5be0sQwL6og5/nh//EGwmcTlkz9AdUYFjdyw/yFGEniepaUY2Utkkz16QkyF7lFyUGxwTh+TkIzLlzcAAN46NoR7/nkIAHDDmTOTpqQojaKpmF11KjUTAxGG4lA4a/wiU9nVMYpPP7oNtz25C28dG9J7OSK0ceWc6mJxkCtNr+qNks9Co5P8D3WPMXHDSELQB4SFCDEbv8DINkgFBTmpaeG7oWMXaBRFCc0Vdnz8tCbx+4X1pbhyZVOCZ0x5vtjIjx2YlfgsKNRQDBCBk428fXxQvP1WxG29OT5AxM3smmLMFMTNqSxJ7Sjpd1RdQvrgDLLULiMZvoieZHmclpJ/ecjIOsqKzPD4Q5qIm74xIm5qSuM3C0vE969YguaKIox5A/jSuXNgMcnXzU0sciOidPQCEC1ufIFwwqaJevFB+6h4e29ndnhaAElQz6x0wGwkkcbOLBE3SjpVVwnixu0PwesPociSfZ8BRpZA/TZmB2DI388JEzc5TFmRGT1jk9qIGxcRN3WlytNSAFBkMeKrG+apei7rUixBU4xKTN0mowFGA4dQmBcqpuQ/N1OciqhA6smS1EkwFEafELFsKLOJHqchtx/+YFiRQE8H4wpaApRYTbCaDPAFwxic8CnqVcUoMArAbwOwtFROo2U5eO9YauImFViXYgmxt4nMieAUaXhm9qWleJ6PEq70s6Y3fS4fwjxgNnKoKrai3G4Wozf9Lv3XKAnd5NegHMeJIxr6WWqKkQixDDx/U1IAEzc5jZbl4H3j5IBYW5Z5cUMjN12j3qw1xGaKcQUntEikRn7Z1+tmyO0Xe/cAJHWihQk+VXoEE31dmQ0GAweO41BTQj7/NKKjJ0rN5aLvho1gYCSCRW4Y2Y5WXYonfEFM+MiBtFaHyE1dqQ0mA4dAiBfTY4WKmmopILsng/eMCn6uEqvYbTcbxEP3GE1JSSb6ujIqbvQXCOMKU5Q0csPEDSMhBTB6AWDiJqdxajQZnJ5oiq0m2Y3jtMRkNKBeKEEv9IoppUMzKdncpZimeGpKrahwkAGQIxo1n0wFqf2BJG7o+obdfl3WFImS8QsAUF1C1j7o0n/tjCzGJ3Tktpbqu440w8RNDqNV5IZWStWqrJTSAqkcvLB9N0pPaBQxLZWFnhvaebq62Cp9ZrNA3NC0VH1EKrbcTlO9+gsEpebyaha5YcihQDw3rFoqhymzayNuBoSDIc3Z6wGrmCIoTUVQsjktJYqbEitCgqVKy4GvaqFpqfqIyE25PXsiS5K5XN5hmpaDszEmjIT483+uFMDETU6jVeRmcIJcpdKcvR40scgNgFQ8N9mblooUz14hsjSaBeKGRjhqIkS9UxQ3+kZuQmEeHj/5WzpkpoorHeR96JlSOzEwgTDPY05Nfp84cxpf/s+VApi4yWm0KgUfEg7yeoobOviv0CM3akvBaeO+bIzcDAkn20qHVfysZkPkZkgU9RbxPiktpe/6IqvL5PrgnBpFctXyy81H8dNXjgAAPn/OLNx+8UJd1sFIglgtld/ihnluchitSsEHRXFjSfLI9CFGbkYLO3KjpOV+JNnsuaH+lXKHGc4i8hkbywJPC41wVDiyL3LjEaoXDVx0B+pEiMcDb+bX/kH7iChsAODh109g69HsGbPBiEAUN8xQzMhSyjSqlqJXsJV6Rm4EcdMzOolgls5HSjc8z4tX3WVKm/hlcbXUiJu8J6fdIvpHaPddvZgMhMT2B5VZGLlx05SUxSR7AK1WaWo1PLjlOADgqtMaccO6mQCAh14/nvF1MGTgF7qFWxz6riPNMHGTw9CDmcsXRCiF5neDWZCWqimxwmzkEAzz4hDPQsMbCCEo/B0Vl4JnsaFYjNzYLaJ/xO3TV9zQVJnFaEBJRNqn3JEdkRv6+1EyI4oWGEwGwpgMZE7kDk348OqhfgDAF86ZjZvPngWOA7YeG0TboDvJsxkZJyBEx835PaKDiZscJvLqPpXozWAM70GmMRg4cQzDiYHEB8TJQAgPv34c1zz8Dq769Vu454WDWdPSPxXoFbfJwIkT3+UiGYqzUNwI78tZZBb9I26/vuJmeIKmpCxRkZFI30oqFwypotRMDJD5UkYDeS+pRnOV8Pfd3QiGeSxtLMO82hI0V9hx3vwaAMBfdnRmbB0MmbDIDSPbMRsNcAgnQbWhaJ7nsyJyAwAL6kiFxaHe8biP6Rr14tJfbsU9/zyEbW3D2Nk+ioffOIELfvY6thzuz9RS08K4l5zwy4rMslMRFGm2VHalpXzBkHiiLrdbYLeQk/WET991DrrJZ75yiqCnniCez6xAmAoVf0pELsdxYn+kTFajvXp4AABw+fIG8T56++UDvRlbB0MmLHLDyAVSzbNP+ILi1f7UA32mWVhHDG4He1wxfz7mCeBTj7yLY/0TqC214u6PLcbPrlmO5U1lGJ8M4nN/2IG3j+euiZH+DZVWSgGANUurpah3xcARk7TDStbp0TstFRG5icRiMojRJT1TU94Iz40SqCE6U76byUAI77UNAQA+NK9avP+8+TUwGTgc6ZvASZaayi78grixMHHDyGJSLQenB3m7xSheVevFwnoqbmJHbr7+l904OeRBo7MIf/3Sely3rgVXrmzC0188ExctroM/FMZtT+4SS9szQSjMa/Z6KYkbsVoquyI3VNw47RYYDJyUltJd3MSPVpY7yO9fz0Z+9PdjtypLT5ZqOExXDjvbRzAZCKO6xIp5tVJpcZndjLWzKgAArxzoy8haGDIJCGLTzNJSjCwm1chNtqSkAGBhAxE3x/onplX9vHKgDy/t74PJwOHh61ZFzQMyGw342TUrMLemGAMuH77+9B7wvDK/xL6uMXzuD9tx8+Pv40B3/LRYJG5fEB97YCtWff9f+P1bbYpeLxY0DaJ09AKQvZ4bGv2gXhYpLaWz50bsvTM9WknN3HpOLveojdxkuGKKlnufNadqWir1gkV1AJi4yToCQi8xFrlhZDOpixtaBq5vSgoAGspsqHBYEAzz2N0xJt7v8QfxX8/tBwDcfPYsLGksm/bcIosRv/zkSliMBmw+1I+/ftAV93XCYR6BUBihMI9htx93/+MALv/VVrx8oA//OtiP6x7dhhEZXV6fer8D+7qIEPrJS4fF0QlqUVsGDkjVUtnmuaGVUvSkKxmKQ4oFqJbQaqnyGOJGqujS73dJPTdKqqWAyN5XmUmpvXVMEjdT+chCYire0T6S8v8GQyPCISAoFF8wzw0jm8mnyA3HceJB8o0jA+L9v9h8DF2jXjQ6i3DrR+bEff6CulLctmEuAOC7fz8gTqOmvHN8CJ985F0svPNFzP32PzH7Wy/gtLtfwaNb2xDmgQsX16Kl0o4htx+PyYjE/GNPt3jb7Q/htUOpGZrpCUCd54b8K/uzrEcQTe3QmU3UcxMK87pGmej/C40oRUJLwyd8OkZufDRyo0zc0PeTCTO01x/CPiHKecbsymk/byq3Y3a1A6Ewj7eP5a4XLq8IRDRJZeKGkc2kKm5itaDXE2pKfOMoETdH+1z47ZsnAADf+9jipL6gz39oFpY2lmHMG8Cmp3YjEArDHwzjnn8exLWPvIu3jw9NO6kuaSzF7248HQ9ftxr/eeF8AKSENZygFHh8MoBdHaMAgEuX1ZM1H0ntAJ5a5CY7OxRHem4ARP399PTdSCnA6b9rGrlx6dhokKal7ApKwYHILsXpFzf7uscQCvOoKbGiIWKyeiT0//n1iIsVho5QMzE4wFyU8KG5DpstleOI4kalgTCbIjcA8KG5VeA4YE/nGA50j+OOv+1DMMzj/EW1+MjC2qTPNxkN+MnVy3DlA29j67FBXPbLrfAHwzghVGxcu6YZnz1rFqpLrAiHeXBcdOn1hoW1KDIb0T02iWMDE5hXG3sA4L7OMYR5Ms38qtMa8Y89PdjTOZrSe6el4Eob+AHZO1uKjgKgEQWjgYPFaIA/FMZkFkRuYgnJYpv+aSmPkJZSGrnJZJfiXe2jAIAVzc64rQs+NK8av3vrJN44Mgie5xW3OGBojGgmtgN5/rdgkZscpyzFYXlDtN9HDO+BHtSU2vCRBUTEfPQXb2LHqREUW02467JFsvexoK4UD376NDgsRhzqdeHEoBsVDgse+vRpuOeqZZhTU4yyIjPKHRY47dFN3GxmI1bNLAcAbDsxFPc1dncST9DyJicWNxAP0PGBiZQ8L5pEbrJs/EIswUZTaHr6gxINKM2GtBQdv6C0gpHOJJvIQNRplyDmlzc74z7mjNZKWEwGdI16cXxgIu1rYiShQMrAASZucp6UPTcuIS1Vkh2RGwD4ziULxfdlMRrw60+dJg7WlMu582vw2n+ei+9cshA/+cQyvPYf5+KiJfWynru2lZSwvts2HPcxR/pIL55FDaWoKbGi0mFBmAcO9cbu0SOHcU0MxdkVuXGJPiLpJE2jTHqKm0RC0mHVv6KL9gFyKCwFz+Tadwtp2ZUJxE2RxSj+P205zFJTulMgDfwAlpbKeVLtc0M7tWZLWgoAWqoceOVrH8Jbxwdx2oxyzKxU14+hptSGm8+epfh5a4SD8Xttw3FD6fQqdE5NMTiOw6KGUrx5dBAHusexIsHBPhHjMYSAXLI2ciNEEEoiIjdFZn2FWDAUFk/+scrui636d1GWqqWUfRaKMyRuBid86BzxguOApU3TqxcjOWdeNd48Oog3jg6q+n9kaEiBjF4AWOQm50m1r8Wgi4qb7EhLUWpKbbhyZZNqYZMKy5udMHDAgMuHgRgN+niex/F+Im5mV5PGZYsaEjcglENKaSlzdva5oZGbEltk5EbfhoORRuFYaaliMbWjX1pK6lCsLHIjpqXSLG5o1GZOdXGUcI0FNRVvOzGUda0KCo4CitzoLm4eeOABtLS0wGazYe3atXjvvfcSPn50dBRf/vKXUV9fD6vVinnz5uGFF17I0GqzD3oiVFP66Q+GxSvrbIrc6I3NbERLFRFVh2KMgugb98HtD8Fk4DCzkhwk5ggiJ5UpyIkqeJIhTgXPsrRUrPdE01JenU50VEQ6LEaYjdMPgZmKfiRCreem2Ep+z+n23FBxk8hvQ5lbU4z6Mht8wTC2JUj1MjIA89xkhqeeegqbNm3CXXfdhZ07d2L58uW48MIL0d8fu1+I3+/H+eefj5MnT+Lpp5/G4cOH8cgjj6CxsTHDK88eqLhx+YKKpxhTM7HJwKk6oeYzdIjn4RgeGpqSmlFpF0+OVAypFTeBUFg8oamJ3IjRkCxLS7nEtFRE5EZnf1CyfkLZkJainhul0+Fp1MmVZmH2gQJxw3EcPjSXRG/eZCXh+lIgoxcAncXNfffdh1tuuQU33ngjFi1ahIceegh2ux2PPfZYzMc/9thjGB4exrPPPov169ejpaUF55xzDpYvXx73NXw+H8bHx6O+8onIA7TS6A01E1cWk7k/DIkFdIhnjAnlVNzMqpJm6bQI6bPuMa8qgRGZKilRNX4hO0vBqZCIFGx6V0slS/+JaSkdq6VoVEuxuBGEmT8YTpvQ5Xlelpk4knVCk7/3T42kZU0MmbDITfrx+/3YsWMHNmzYIC3GYMCGDRvwzjvvxHzOc889h3Xr1uHLX/4yamtrsWTJEvzwhz9EKBT/n/iee+5BWVmZ+NXc3Kz5e9ETs9Eg5uWV+m6yrcdNNkEjN7HSUqLfpka6+qkqtsBhMYLngY5hz7TnJIP+7YqtJphipEqSQQ3FkwF9xxpEEgiFxehMpGATDcU6RZnGkqT/xMiNjk38qLihKTy5FEc0/UtXn562QTfGJ4OwmgyYXxe7D9RUVreQ9gr7u8bEHj4MHWCem/QzODiIUCiE2troxmy1tbXo7e2N+ZwTJ07g6aefRigUwgsvvIA77rgDP/3pT/H9738/7uvcfvvtGBsbE786Ojo0fR/ZgNqupANM3MSFRm6O9U8gOGWkwfEBEtqlZmKAhN5paurkoHpxo2ZoJiBFbsI8EFSYnkwXkdGoyJOuTedqKbH3TpK0lF5N/HieF383SsWN0cCJ4jFd4my30N9mSWNZTM9SLBqdRagvsyEY5sXmfwwdYOImMX19fbjuuuvQ0NAAk8kEo9EY9ZUuwuEwampq8Jvf/AarVq3CNddcg29/+9t46KGH4j7HarWitLQ06ivfUFsOziI38WkqL4LDYoQ/FJ7mo6FpqUhxA0ipqZNDyn030vRsdVVrNNUDZE9qajzCuBsZjbJlSVoqXsk97RXjD6UvtZOIyL+f0sGZQKTvJj1pNTrUdnmTU/ZzOI7D6hbSYmFnO0tN6UYBpaVUXSZ+5jOfQXt7O+644w7U19eraqldVVUFo9GIvr6+qPv7+vpQV1cX8zn19fUwm81RAmrhwoXo7e2F3++HxZJd5cyZQm0jP3GuVElh/t4SYTBwmF9Xgp3tozjY68JcYQzDhC+InjEykHN2dbQpr6WKHDDUmIqHUpzObokQD75AKCpSohfxugDr3cQvlg8oksjf3cRkENbi9F2wxYKWgQOAzaT8+rPEasKAy5e2yJNkJk7c32YqSxtL8ffd3TiQQrsERooUkKFY1RFw69atePPNN7FixQrVL2yxWLBq1Sps3rwZV1xxBQASmdm8eTM2btwY8znr16/HE088gXA4DIOB/NMfOXIE9fX1BStsAPXiRozcOFjkJhYL6kuJuOkZx+XLGwAAbUJKqqrYMi3KQnvynBpSnpYaTnEMhiFiZlPWRG5i9LgB9Bc3yTw3RgMHu8UIjz+ECV8QlRmObFK/jcVoUOW/Sqch2hcM4aAwCXxlc7mi5y6qJ2LoYAwfGyNDFFDkRlVaqrm5WRPT4qZNm/DII4/g8ccfx8GDB/HFL34RbrcbN954IwDg+uuvx+233y4+/otf/CKGh4dx22234ciRI3j++efxwx/+EF/+8pdTXksuo7bXjShuWOQmJgvrpzfmOzZADsyzpqSkAKA1hXJwKXKj/kSabY38xNELtniRG33WKadZIq1S8vgzL8Co6ItMNSqhOI1TzQ/2uOAPhVHhsKC5QtlU6YX1JPp5csitaw+hgqaAPDeqIjf3338/vvnNb+Lhhx9GS0uL6he/5pprMDAwgDvvvBO9vb1YsWIFXnzxRdFk3N7eLkZoACKqXnrpJXzta1/DsmXL0NjYiNtuuw3f+MY3VK8hH6ATl0cF34ZcxLlSzHMTk0XCwThS3NDZUfNjTAunDf1oOTg1+cphyE3+FhUpDDC1moxwIZg1vW6ocXd65EZfz42cGV5FOoobGrkpUmgmpqSzCeEHgl8m0STweFQWW1FXakPv+CQO945j1cwKzdfHSEIBjV9QJW6uueYaeDwezJ49G3a7HWZz9EFieFh+F8qNGzfGTUNt2bJl2n3r1q3Du+++q2i9+U65cEIcdjNDsZbMFyqm+sZ9GHb7UeGwiE395sUoga0utorpjM4R7zTDcSKGJlIfgyGVg2dH5CZeszyxiZ9OESaxa3KiyI2ZHBr1EGD0NdWYiYHI8RHpEDejAOT3t5nKgvoSQdxMMHGjByxyk5j7779f42UwUoF6ZmjHYTkEQ2EMe1jkJhHFVhNmVNjRPuzBwZ5xrJ9TJYqbBTHEDcdxmFFhx6FeF9qHPIrEzbAYudEgLZUl83vGY3QnBiLGL+gQFQGkdSWK3Nh0TUsJZeAKIn+RpDVy00EiNytnKPPbUFqrHNhyeEBVRSFDAwrIc6NK3Nxwww1ar4ORArTChp4g5TDs8YPnAQOXWiok31lYXyKKmyUNZWKl1LwYaSmApKYO9bpwSuHBW6u0FJALnht9R0UkKwUHALuZipvMe0Oo6LOpjdykyXMzOOFDxzCZBL5MYaUUhbZLSGUGGyMFWLVUckKhEJ599lkcPHgQALB48WJcfvnlae1zw4gNNaFSU6ocqN+mwmGBkY1eiMvC+lK8tL8PB7rHcaiReG8aymxxr/rFiimFXYrFsnwN0lLZIm4kz032lILzPB9XdEVCDcV6rFHy3Kg0FKdpMjhtvjenulj1LDra6FKp+GdoBIvcJObYsWP46Ec/iq6uLsyfPx8AGXPQ3NyM559/HrNnz9Z0kYzE0PLhwQkfeJ6XZfSjKSyWkkrMCsFb8P6pYcypJWmmRMMCZ1SQg4aScnCPPyie0FKL3GTX8ExRREyJkBTpWC3lC4YRCJFKz+IE3aD1TEupHb1AKUnT+AgpJeVUvY/WiHYJ4TDPZtplmgLy3Ki6NLj11lsxe/ZsdHR0YOfOndi5cyfa29vR2tqKW2+9Ves1MpJA01K+oDRZOhnMTCyP1S0VMBo4dAx78cS2dvG+eNCKKSVXpjRqYzEZUmq+R0+GviwzFE+N3Og5ODMymuGwJE9LeXVYoy/VailBtLk1TqmJZmKVfhsAaHDaYDJw8AXD6Bmf1GhlDFnwPBM3yXj99dfx4x//GBUV0kG+srISP/rRj/D6669rtjiGPOwWkxhGp1U3yRhwpV6dUwgUW004U5ho3DniBQBsWFgT9/HUU9Ax4kVY5own6uOpK7Wp6vZNyba0lNihOF4TPx0iTG5B3NgtxoTpWFqppIfpOfVScCImtfTchMI89nSSsQsrVFZKAYDJaJCim8x3k1mCPoAXjg0FkJZSJW6sVitcruldJicmJgq6U7CeVIipKXm+G/GEWqasEVchct0ZM8XbZ82pEn01sagvI1em/mAYvTKvTLtHiWhqcNpSWqdV586/U4kXuaFVQF5/5kUYPeEni5Dp2udG+L2kaijW0nNzrH8CE74g7BZjXDO9XBqc5JhDj0GMDBGISJUXgKFYlbi59NJL8bnPfQ7btm0Dz/PgeR7vvvsuvvCFL+Dyyy/Xeo0MGVBTsdyKqb5xGi1gaalkXLC4Dv912SJ8cu0M3Hv18oSPNRkNaConB2+5vpsuUdykJjRzJ3KjX8k6jdwkEze0z40eaSka0VJbCl6Shj43tHnfsqaylAsQ6suIiO8Z86a8LoYCaAM/owUw6j97Lt2oeoe/+MUvcMMNN2DdunViA79gMIjLL78cP//5zzVdIEMeVULkRm5aikVulPGZ9a2yHzuj0oGTQx60D7uxTkhpJYJGbho1Ezf6R25IVVLswZk0KqJHWopGMxKZiQGgyEJ+l7qkpfy0iZ+6ailHGiI3dJL3aSn4bSiSuGGRm4xSQH4bQKW4cTqd+Nvf/oajR4/i0KFDAMh07jlz5mi6OIZ8qktIBKbfJU/c9AoHFnqgYWhHS6UdbwA4KTNy061Z5CZ7+tx4/CGEBM/R1CZ+dIJ5IMRnvGKGnvATmYkBoEj4uR59biY1HL8QDvMw+MeB7Y8By64BShtU7XOnYCbWQtzQC6peJm4ySwGNXgBS6HMDAHPnzsXcuXO1WgsjBeiJsWskeag3GAqLIoiJG+2hhsl22eKGHORTjtyI6R79xQ2N2pgM3LSTtMUkRST8oTBshsz1xpIbuZGqpTL/u5xMsRQ8MuXmCYRQ/M9vALv/Dzj8T+CmlwCFpvUxTwDH+icApFYGTqkXvGXdTNxkFha5ic2mTZtw9913w+FwYNOmTQkfe99996W8MIYyqM+jczT5CXVwwo9QmIfRwKU0hZoRG1oxdUJmNYhWkRubGLnRPy0lmYlN0yrApokblSdxNcj13EjVUjp0KE5R3NjMBhg4IMwDE55JFO/+P/KDjm3ASBtQMUvR/nZ1jgIgbQ60OF7QC6pe5rnJLAXUwA9QIG4++OADBAIB8TYju2hUELmhRr7aEivrTpwG5grN/o4PTIgiMh4jbj9cwgk39Wqp7DEUu+IMzQSktBQA+DO81olcqJYSokVq01Icx6HYasL4ZBC+/mPRP+zcoVjc7Dylnd8GAOpLybFqxBOA1x9SPSCUoZACGr0AKBA3r732WszbjOygSUiFdI9OJvUxUCNfLUtJpYXmcjtsZgMmA2G0D3vQWhX/YHKkj7RUaHQWwZ7EB5IMaSp4FkRuvLGHZgLk5GsxGuAPhTMvbnzkd+NIWi2lXxO/SX9qkRsAorjh+/ZH/6B3N7DsakX7kszETtXriaS0iPTl8vhD6B2fTPj/wdCQAovcqLLj33TTTTH73Ljdbtx0000pL4qhHBqF8YfCGEhSMUUn8s6sKIwPeaYxGDjMqSHRGype4nFE8DLMq5U/QTwe2WQoFtNS1tgziGhqKvPiRkqXJULPJn60ikxttRQQId5GO6J/MNymaD/hMI9dHaMAUutMHAnHcWIBxIDMAgiGBhSY50bVf8/jjz8Or3d6+sPr9eIPf/hDyotiKMdkNKCulERiOkcS+27aBoi4mVWd+gmVEZu5NaTR2dEk4maf0PV1QX1pyq+ZTX1uxoX0T7wBo6K4CWV2rW4auUmSCrHr2sQv9ciNKG4mesm2eiHZDp9QtJ/jAxNwTQZRZDZiQV1qzfsiqRa8O4MyW1cwNICKmwKpllIkbsbHxzE2NkZ6WLhcGB8fF79GRkbwwgsvoKYmfmt6RnqhpuL2JBOp2wSjKwsHpw/quznUm1jcbD81DABYpcFVsVXH5nhTGfcmjpBQ302mIzcusVoq8VRrWgquR1oq1fELgOQpMrr7yB0zzyTb4TYyY0gmOyOa95mM6iNJU6li4ibz0LSUuTB6mylK8judTnAcB47jMG/evGk/5zgO3/3udzVbHEMZ82pLsK1tOOkJ9QQTN2lnaWMZAIjzeGLRMezB8QE3DBywukUDcZNFaal4DfwoFp2iTFK1VGLhQIWFPxhOagrXGjotPVXPDQCYPf3kjsZVwPZHgaAX8I4A9vjDXyPROiVFqSoRxsWwtFTmEA3FhZGWUiRuXnvtNfA8jw9/+MP4y1/+EjU402KxYObMmWhoUNckipE6ixpIamN/1zgA4KX9vfj1a8dw2sxy3H7xQlhMBox6/OKIBiZu0sfyZic4jkTRBid8MaevP/U+8UOsba2E0576TDZbFlVLRZaCx0I3z41YLZU4cmOPSFt5/MFp87HSSapN/AApLWWdHCR3OJuBogrAOwy4emSLm33CsWRZU5nqtcSC/j8MyJyFJwvPMPDCfwKTY8D5dwO1i7Tbdz7gL6y0lCJxc8455wAA2tra0NzcDINBuzAlI3XotN4dp0bwxpEBbHxiJwIhHrs7x2AzG/GNixbguOC3qS21Jq0YYain1GbGnOpiHO2fwK72UWxYVCv+jOd5PPFeOx5+4zgA4IYzZ8bbjSLEyE0WpaVK44gCMS2VYc+N2KE4SeTGGtGLxxcMQzu3SXLEtFQKJdI0MmUNCJFDeyXpTuwdBsZ7gNrFSfcRCIVxWIgCL2lIk7jRMnLzz68D+/5CbvcfAr78LmDN5F8uy2GG4uTMnDkTBoMBHo8Hhw4dwp49e6K+GPqwoK4EdaU2eAMhXP/YewiEeNE4+eibbege9WKP0JBrkQYGVkZiaDfX9wVfDUAa7N365C58+6/7EAjxuGRpPS5cXKfJ62WToVhuWirz1VLxS9Qj4ThOXGMmS+sDobA4tkLt4EyARm542IIk8oKicqCkntx2dcvax9G+CfhDYZTYTGiu0NanobnnZqIf2P9X6fvxTmDbw9rsO18osPELqsTNwMAALr30UpSUlGDx4sVYuXJl1BdDHziOw3XrpCjAimYndtxxPta2VsAfCuNXrx3Ddo0bcjHic/bcagDAi/t6wfM8eJ7H15/eg7/v7obZyOE7lyzEL69dOa2Dr1qkJn5ZELnJwrQUz/Oi50ZO1NImipvMrTHSwGxLsRS8GF4YIOyvqBwoFcTNeI+sfezvJlGfRfWlmn1GKbQUXDNxc2wzEA4C9cuBqx4h9739S8A3oc3+8wEWuUnOV7/6VYyOjmLbtm0oKirCiy++iMcffxxz587Fc889p/UaGQr4wjmz8a2PLsDnz5mFx29cA5vZiP+4YD4A4Ilt7Xh+DzmwrZ9bpecyC4KPLKxBkdmIU0Me7Okcw/++ewp/29UNo4HDb284HTefPUvToZFSWiqLIjdx0lJWsRQ8c0LMFwwjKERFknUoBiRDbybFIm3gZ+CiOzkrpcRmgpMTTuwmG6mQKRH8kDIjN/u7SdRnSaO2KSkguhScV1C9FZeTW8l29keAJR8HKmYDk6NkplYuMnAYePhDwGs/1G6fBdbET5Xp4tVXX8Xf/vY3rF69GgaDATNnzsT555+P0tJS3HPPPbjkkku0XidDJkYDh899aHbUfWtaK3D23Cq8eZSYC2dU2LGiyanD6goLu8WEDYtq8ffd3fji/+5An+Av+MZF83HOvGrNXy+b0lLZWApOU1JA8qnggCRu9Ijc2MzGlKIlDosJTgjipkiI0qqM3Cxu0D6FTaulJgNhuP0hWWIzIb2CHaJxFWAwAms/Tzw42x4CVn8WyDV/6PP/AfTsJl9rPg84KlPfZ4GNX1D1F3e73WI/m/LycgwMDAAAli5dip07d2q3OoZm/Nfli9HoLEKpzYQffXypphEDRnw2njcHFqMB3WOTCIV5fGJVE245W9lsH7nQk7E/wrehFzQtlbSJXybFjRBNcliMsj7/oljMoOdmMsW5UhSH1QQnJ5zMbE6yVRC54XkeB3uImXixxmZigAh/Wt03nGrFVCgADBwit6lResUnAWspMHQMOL45tf3HYrwb+P2lwJOfIqX1WhIOEVFDOfmGNvtlkZvkzJ8/H4cPH0ZLSwuWL1+Ohx9+GC0tLXjooYdQX1+v9RoZGjC7uhiv/79zAUDTZlyMxMyvK8Efb1mLJ7a1Y2F9CT571izN/QuUyBPiZCCkWzWcPxgWT9Jxq6V0iDJNiA385P1exMhNBtNSqU4EpxQLnhuyM0GclAjG9Yn+pM/vHpvEhC8Ik4HDrOr0XOlXOqzoGvViyO3DjMoUTriDR4GQH7CUAE7Bc2gtAVZeB7z7APDug8Dc87VZNOUfm4CTb5Lb5S3AhT/Qbt9DxwHfuPT9wBFt9ltgnhtVR7/bbrsNPT0ktHnXXXfhoosuwh//+EdYLBb8/ve/13J9DA1hokYfTm+pwOkt8vqKpEJk+bJXR3FDJ4ID8YWEHqXgEwrMxIDUNyijaSl/6mXgAPm9F3OCuLEKY1YcQirUPQiEwwlTNXQmWmuVA+Y0HTcqHBZ0jXrFvluq6dtHtrWLo9/TmluAd39NIjcDR4Dq6Y1n4xLwAlvuIVGZC34A2CJSc6MdwJF/St/v+RNwwfcBrS5aRqbM/xo4qM1+C6xaStXR79Of/rR4e9WqVTh16hQOHTqEGTNmoKqKGVUZDD0wGDhxGrkeAx8p42KjPFPczr56pqXk+jt0MRQHaeQmNUFRbDXCgUnyjUUQN3bBt8GHiNk2QSO/Y310oGv6+sRUOIjvZihVcdMvnPynNu2raAXmfxQ4/Dzx3lx6n/x9vnkf8NbPye1wCLji19LPDr9Ato2rgb79gLufpMVqFqp/D5GMnCRbzgDwYWDklDb7DQhit0AiN5pIcrvdjtNOO40JGwZDZ4pEE6x+4oZGbkoTpH/0EDduvzJxY9WhFJxWS2nhuXEIaSmeXqmbLFKKyj2Q8Pk0cjNXg2n18agUxE3KkZsxYfJ5ecv0n53xBbLd/X/yvTE8D+z6o/T9nqeiTdiH/kG2i68kBmYA6P5A0ZITQsXMDGEemEueATwh4RAQEsruC0TcyI7cbNq0SfZO77tPgUJmMBiaUWQ2YgQBXQY+Usa9tFFe/JEFuogbYSK4XUalFABYdRCKWnluHFYTijkSuQmZHdKB3lFNxhO4B4Dq+XGff6SfRG7odPt0UKGVuBkVxE1Z8/SftZwN1C4hqaud/wOsvzX5/gYOAeNdgKmI/I56dhGBc9ZXyYiHk2+Rxy34KBFWp7aSCI5WjHeSbdNqsu+JPiAUBIwppJlpSgpghuKpfPCBPGWaLrMkg8FIjk3wauiblhIiN0XxDy9WY+YNxXKHZlJoh+CMRm40GJoJkFJwGrnxG6eIm6FjCSM3PM/jmBC5mZfGyE1FsZCWSrVaakwQA7HEDccR783fbwP2/kmeuOkRysobVgIrrgWe+wqw6wlg/W3AkZdIWq9mMVAxC6gRUmH9GvliAMAlTHKvWwpwRvJ67n4yPkMt1EwMjvQ9KgBki5vXXnstnetgMBgaQNMZekZuXGJ34iyL3AhpKbtCQ3EmPTdeDYZmAqTfVZmRpCF8hiKI1+oOwTrgHoz73O6xSbj9IZgMHFrSOFyXpqVGPCmIm1BAKm13xhA3ALDgUuDvXwV69wJjXUBZY+J99gtRmNpFwKIrgBe+DgweBrp2Anv/TH626HKyrWgl29F29e9hKhO9ZFvaCBTXkLSUqzc1cRNpJi6QAAQrn2Ew8ohs8NzQtJQsz00Gq6WkyI1cz40ekRttxA0AlBqIuPFyEWkIsWIqfuTmaAYqpQCgwkG6FKdkKHb1ENOt0QI4amI/xlEFNK8ht4+8mHyfNMVUu5hUSVEhs/U+4IRwkb/0arJ1ziDbsQ7i1UkVnpciNyW1kgncOxz/OXIosDJwQGW11HnnnZcw/fTqq6+qXhCDwVAPLSHW1XMjpqUSRG506FDs9lPPjcy0lFgKnsHIjV+baikAKDH4gBDg5SKGXsoQN22D5Cp/dnX6UlJApOcmhflS1G9T2pi4C/Hc84GObcCJLcDpn028z74DZFsT0RBwz1OSkbhpDVA5W3pdzgAEJ0n/oJJa1W8FAOBzAUGhqqm4Vuou7R1Nbb8F1sAPUCluVqxYEfV9IBDArl27sG/fPtxwww1arIvBYKiAejW8fv1GMNC5Uokmb1voHCxdPDdZXApODcUp9rkBgBKhz40bER4LBeImnSkpIKJaKhXPjei3aUr8uJnrybbjPRIdiXdx7h2R0ly0tLvlQ8SYfPJNImQ+cqf0eKOZCJyxDpKaSlXc0Iouk42kkKi48aQauSms0QuASnHzs5/9LOb9//Vf/4WJCTaFlcHQi2zw3NC5UvG6EwN6paWUVUuJnhsdZktpkZaifW4m+MjITXLPDRU3rVXpvcqnhmK3PwTfqe2wPnk10HIW8Infya8MGhO8LjQ9FI+GlYDBRPwso+1A+czYjxs+QbbFdVLjPoMB+OSfgH1Pk2hO06ro55Q1C+LmFNB8urx1x4OKGypqaC+iVNNSBRi50TSh+ulPfxqPPfaYlrtkMBgKyArPjSJDcebW6RY7FMsTDqLnJgcNxQBg50nkxsUri9ycHKLiJr1pqRKrCWYjiaCE3/oFOYEffA5oe13+TuRGbsxFQP1ycrvjvfiPoz1mpvbMsdiB066fLmwASVhpYSqeHCVbOg+sSBA3KUduCs9zo6m4eeedd2CzFUaZGYORjRRlRSm4YChOUAquh+fG46eDM7N3/IJPo1JwALBRcRO2SncmETe+YAhdI+R5LWmO3HAch3K7BQAP86mI4ZDH/iV/J4l63EylSYiqdCcY7jxKxU2cyE4stBQ3cSM3KQ7nLLDRC4DKtNRVV10V9T3P8+jp6cH27dtxxx13aLIwBoOhHFuOpKWsOqSllM+W0q+JX8qRG56HLUyu1keCMcTN5BgQ9JOuxRF0DHsQ5snk9OpiK9JNhcMC3tUHky/i5N27V/4O5EZuANLMD0jccI+OPojV7TgepcKw6Ik++c+Jx1RxIxqKWeRGKarETVlZWdT3BoMB8+fPx/e+9z1ccMEFmiyMwWAoJxs8N/IMxXpEbsjvRGlaKpOmZ7FaKlVDcdAHI8i+RoMRAsbmlBrDeQan9U5pGyQnwdZqR0YaslYWW1Bh6Iy+s/9AYtMvheel0QvJPDcAKe0GSLfiePunaSmngshNsWAiTou40SgtVYCeG1Xi5ne/+53W62AwGBpQZBFSKVnRoTi7mvgpjdxYdSgFFwdnmlJ0DPilwo7RUMTfwWAgpuKJPpKamiZuyPNaKjOTvqhwWGHjBHPzzPXAqbcAzxA5yScY7AmAPIZGJEqTNOYDgOoFpNrJMxS/bFuM3KgRN/3ynxMPWvJd5CRbrdJStFrKkl4fVTaRwrAKYPv27Th4kLSdXrRoEVatimG2YjAYGYNGbkrcJ4GHNpKeHc5mYM4GYNm/p17NkYRwmBdFRMJqqQx7bnielyI3cj03Jh3SUnRwZqqRG0HceHkLxv1TIhR2Qdx4hqY9TYzcpLkMnFLpsMCBUfJNeSsweISIrrGO5OKGelwcNYBZhtfTYgcqZgNDR0n0Zqq4CYekNJeiyI3QPHCiX17EKRFi5MYpbLWqlqKl4IUTuVF1edDZ2Ymzzz4ba9aswW233YbbbrsNp59+Os466yx0dnYm3wGDwUgLxCfC46ae7xHvAh8iV6Pv/xZ4dANpQx9O38na5QuKjVplpaUy5LnxBcMIhcnC5Kal9DAUa9ah2EfEzQRsYpWYiD1+qoNGbjIlbiocFtRwwgm9pE4yBlOjcCKU+G0oYmoqhu9mvAsIBwCDWdmoA9oZOeQjXqZUiOe5mRwjwzPVUoBpKVXi5uabb0YgEMDBgwcxPDyM4eFhHDx4EOFwGDfffLPWa2QwGDIpshhxOncYMwPHScOuz78JXPsUsPxaAByw43fAK3cm3Y9a6Fwpi9GQsOKHiptM+VkiT/Dy+9xkvomfVlPBabrGw9swMRlP3EyP3JwUIjfpbuBHqXBYUMuNkm9K6qT5UGNyxA3128iolKIkMhWLfptmwKDg92+2ATbBh5pqaoqKo6niBpDKxNVQgGkpVeLm9ddfx4MPPoj58+eL982fPx+//OUv8cYbbyR4JoPBSCdFZiM+ZBSmGi/4KFC/DJh/EXDlQ8DHf0vuf+eBxBUjKTAmVEqV2eOnpIDMp6VoA78isxFGg7y0Aa3oymQTP62mglNx44UVrmmRG2Fe0ZTIjccfRO84afw3K4NpqdQjN0rETYLIzagKMzFFK1MxjdzQPjdGkyRIfOPq9+svvGopVeKmubkZgUBg2v2hUAgNDSlMLmUwGClRZDbidMNh8k3rOdE/XPoJMuUYPPDOr9Py+mMeclxwJjATA9Gl4LwWAweTQCeCyzUTAxGl4Ho08UvVcxMgvWomYRFL80VEcRMduaFRG6fdDKc9ukQ8XZC01Cj5prguYhCljJ4x1HOjSNwsItvBw2SieCQjKnrcULQWN5ERG2sJ2fpc6vcr9rlh4iYhP/nJT/CVr3wF27dvF+/bvn07brvtNtx7772aLY7BYCjDZjZgHidc9dYvm/6ANbeQ7cG/A8EUBhbGYZRGbpKIG5qW4nkgGM6AuFHYnRiQxE0gxIt+nXQSCvNiJCtlzw2N3PBWsXpNJJ64EToTZ6pSCgAqHWZUU0NxJjw3ZTNIujbkl0YtUFKK3ESYilMhprgRxkBMphC5YWkpeXzmM5/Brl27sHbtWlitVlitVqxduxY7d+7ETTfdhIqKCvGLwWBkjuLgMCq4CYTBAZVzpz9gxplASQPgG1PWCVYmozRykywtFVHqnInUlFthpRQQPZk7ExVTkd6elKeCC5EbLyxwTQajxVlRbM8NnSmVqZQUAFQaPbByRHgG7NWSf0ZOt181nhuDQRqIOTU1lUrkhpqKU4ncBH1SabvmkZvCS0upKgW///77NV4Gg8HQgjIvOeB381VoihWCNhiARZcD2x4Cjm0GFlyi6euLnpuixGkN6rkBiLhxpLkZrprIDW3iBxDjc7rXGDkyw2bSJi3lBfk7TEwGJR9UHM9NpqaBR1IaID1uhvgShHxADe1X4x0GApPxS7wDXmmEhJK0FEDETdd2oP9g9P3i6IUWZfsDZA0kTQrtcQNOitYAEeImFc9N4aWlVImbG264Qet1MBgMDXBMkivHLr4SdaEwTMYYEYCZ64m4aX9X89cf9foBJE9LmYwGGDggzGemHFxpAz8AMBo4mI0cAiE+I5Eb6rexmgwwyDQ9x0WIAAQ4Ig7GvIEIcRM7cnNSnAaeOXFjnCQCa5gvRcjtR01tOWCyAcFJwNUDVLTGfuJYF9ma7dFRDjlQU3H/Aem+gPB6AOBsUbY/QBpr4UlF3ET0uDFE/N/S6eSpRG5oWsrMZkslJRQK4dlnnxWb+C1evBiXX345jMbUB74xGAx12DzkAN3FV2GBL4QyewxxM2Md2fYfIAdUpSeHBIzJTEsBJDU1GQhnJC3loeJGQVoKIBGUQCiYEXGjWaUUIEZuQiYb4Ee074ZGbrzDUU3n2nQQNzRaMQoHAhN+oI4jPWaGTwDj3QnETcTATKVN82oEU3FkWoruz1KcvHlgLGRMW08KLfWe+v+oSeSG9rkpHHGjKrF77NgxLFy4ENdffz2eeeYZPPPMM/j0pz+NxYsX4/jx41qvkcFgyMQ00Q0A6OYr4fJNr2gEABRXA5VzAPBA+zZNX5+mpWSJG2Pmet24Fc6Volgz2MiPpqXsqVZKAWLkhjcVAUB0xRQVN8FJ8XFj3gCG3CTqlsm0FI1WjPEO8fVRIlTc0khKLERxo8BMTKHiZuSklK6JnCmlpsOwmJZKQdzEMhMDgFXooaPWUBwKkgaDABM3ybj11lsxe/ZsdHR0YOfOndi5cyfa29vR2tqKW2+9Ves1MhgMubh6AQB9fLnY2yUmTcIYht49mr48NRQnS0sBgFWIUGTEUCxEbuQ28KNIwzMzl5ZKuVIKECM31EA6FiluLA7AKBiIhNQUTUlVl1hRrCB1lzJCtGIMDgxTcUO7A493xX+eOCZBod8GIOLeUQ2ABwYOkfuGjpJtvEhRMkRxM70xomym9rihpGoopikpoKAMxaqb+P34xz+OqoaqrKzEj370I7z++uuaLY7BYCjETQ2apZiIF7kBoicka4jcUnAgopFfBjw3VNwoPXFncgSDZt2JAanqxixEbiLTUhw3zXejS0oKENNSY3xxhLipJ9vxBJGb0RQiN4BUMUVNxTRFRaM6SqFpqYBbigYpRRyaqXFaiqakOCNgSrMrPotQJW6sVitcrukqcmJiAhZLZpo/MRiMGAhh8SG+DBOJIjeJOrWmwLiYlkp+HLBmcDI4TUvZFaalMtnIT7OhmYAYuTFaiVgZS9LIT48ycABRaSlJ3AgVU4kiN7RUXE1PGgCooZ9/wVRM/w/o/4VSLMXECA2or5iKl5ZK1VAciPDbpDLUM8dQJW4uvfRSfO5zn8O2bdvA8zx4nse7776LL3zhC7j88su1XiODwZALFTcomT4wMRI6Y2fouHRlpwGjHnKCStahGIgYnpnBtJTSyI00giEThmIN01LC39QglP6Oe+PNlyInVD3KwAHETkuVCJGbRJ6bkZNkq1bc1C0l267tZJAsjeCoFTccR6atA9qLm1TTUgU4ERxQKW5+8YtfYPbs2Vi3bh1sNhtsNhvOPPNMzJkzBz//+c8V7++BBx5AS0sLbDYb1q5di/fee0/W85588klwHIcrrrhC8WsyGHlHKCCeLIb40ukDEyMpronwHRyM/zgFBEJhMUIit1oKAPyh9AsHMXKjtFpKHJ6Zm2kpk01Z5EavtNQoX4wht2B6FSM33bGfE/RLUR01PWkAoGU92XbtIKnZoJdEXipmqdsfIPlu1JaDR5aCR5Jqh2Kxx03hmIkBlaXgTqcTf/vb33Ds2DEcOEDCeosWLcKcOXMU7+upp57Cpk2b8NBDD2Ht2rW4//77ceGFF+Lw4cOoqamJ+7yTJ0/iP//zP3H22WereQsMRv4hNGULg8MoisXeLnGpXkAiPYNHgcZVKb985Am0xKbAc5PRyI3KtFQm+tykIS1lKSLt9hONYOB5XpceNwCktFSUoZhGbnpJVGXqhO6xDgA8YCqSxh4opbyFzLEabQfe+gW5r3axsmngU0m1HDxuKThNS6kUN+LoBRa5kcWjjz6KK664AldffTWuvvpqXHHFFfjtb3+reD/33XcfbrnlFtx4441YtGgRHnroIdjtdjz22GNxnxMKhfCpT30K3/3udzFrVgpKm8HIJ4SDn8/gAA9DcnFTOZtsh7Rp30ArpUptJlmTt2nkJiOl4CqrpfQwFBelOnoBiBA3RKzQv41IxAiGwQk/XL4gOA6YUZHhEyBNS0V6boprifmVD8UWCmJKakZqHpKWD5HtvqfJdta56vcFpC5ukqalUjQUF1ADP0CluLnzzjtx22234bLLLsOf//xn/PnPf8Zll12Gr33ta7jzzjtl78fv92PHjh3YsGGDtCCDARs2bMA777wT93nf+973UFNTg89+9rNJX8Pn82F8fDzqi8HIS4Swtd9ErtYTem4AoEIQN8PaiJsxoTux3InS5kxGblRMBQcyWwquqedGSEsVF5MTo5jyoUREbujAzEZnkTYpMSVERG5GPAGEwzyJntAp27FMxamMSYhkyZXR3y+6IrX9pTqCQY6hmFcxwLUARy8AKtNSDz74IB555BFce+214n2XX345li1bhq985Sv43ve+J2s/g4ODCIVCqK2tjbq/trYWhw4divmcrVu34tFHH8WuXbtkvcY999yD7373u7Iey2DkNL4xAJK4cekUuZFTBg5Eem4y0aFYZRM/0VCcgTWKaSkN+swIkZuS4lIA4xia8Ef/PKJL8fH+CQA6pKTCYVGQj/HFCPE8xrwBlDsspNeNq5uUgzdOeV4qAy4jmf0RImgOPAusvgmoX5ba/lIWN6NkG6/PTThIGi8K5f2yCRSm50ZV5CYQCGD16tXT7l+1ahWCwSQH1BRwuVy47rrr8Mgjj6CqqkrWc26//XaMjY2JXx0dHWlbH4OhK8KJImQmB0P5kZsT6q4Ip6CkOzEgiZtABiI3E2rHL2SyFDwNkZvSUnLVPzThBx/5NxarpYZxpI+Im3m1Jam/rhJ8YwDImkI20oV3aFqvmximYpqWSjVyw3HA1b8H/t9x4NKfpbYvILW0VDgc33NjKQYgpN/UmIpZWko+1113HR588MFp9//mN7/Bpz71Kdn7qaqqgtFoRF9f9Jj4vr4+1NXVTXv88ePHcfLkSVx22WUwmUwwmUz4wx/+gOeeew4mkynm6Aer1YrS0tKoLwYjLxFy8iELOUklrJYChJMDR56XyjRjAaWRG2uGmvgFQ2HR16M8LZW5yM2kGLnRznPjLHMCIL/jqEheRFrqSB8pMZ6faXFD0zBmB8qLyYl3wDWlYsoVQ9yMRoxKSBWOkyIuqZKKuPGNA7zwGZtaLcVFTAlXUw4u9rlhaSlZPProo3j55ZdxxhlnAAC2bduG9vZ2XH/99di0aZP4uPvuuy/uPiwWC1atWoXNmzeL5dzhcBibN2/Gxo0bpz1+wYIF2Lt3b9R93/nOd+ByufDzn/8czc0qWnEzGPkCvaoTcvRJDcVmGxk8ONYODB0jbelTQEl3YiBzfW5oGTiQQloqg6XgKUduwiFxlpCtqBjFVhMmfEEMunwopVVsER2KD3vJ52ZeXabFzSjZFjnRUFqEE4NudI8KYyNKEkVuNEpLaU0qaSlR6NljdxG2lpBIlxpTsZ9E5gotLaVK3Ozbtw+nnXYaAIjRkqqqKlRVVWHfPqmdOyfDyb5p0ybccMMNWL16NdasWYP7778fbrcbN954IwDg+uuvR2NjI+655x7YbDYsWbIk6vlOpxMApt3PYBQc9MAnXOW5kkVuAKByFhE3w8eBmetSevlhwbRa6ZBnKM6UuPEIZmKzkRMNwnKxZrIUXKs+N3SuFACYi1BZbMGEL4ghtx+zqH6lkZuQH57JMQBFmFtTnNrrKoWmYWxONDqJj6SLihsauRmbYij2jpJp5oA2kRstsUf0uYmYti6LeCkpiq0UGIdKcVOYaSlV4ua1117TbAHXXHMNBgYGcOedd6K3txcrVqzAiy++KJqM29vbYTBoEKZlMPIdIXJjsjsBxGjcFouK2cCJLZqYikfcAQA8zh54EthsAM76GmCNf8IUp4KnOS2ltgwcyHDkRqs+N5HixmRDVbEVp4Y8GJqIqJgy20nTuuAkyrkJlDsrFKfsUiaiOqixXBA3I8La6QDLkbbo5wwcJtvSRqmKKFugkZuQn4gQwUcki3iVUpRUuhSztJR+bNy4MWYaCgC2bNmS8Lm///3vtV8Qg5GLCFd1VocTAKS+IYmojDAVp8iQ24d/N76G04/+FjgKcpK96IdxH5+xtJRQKaVm2rXVnIOl4PRkZioCDAYxkjYQWTHFcSR6M96FcrhQk2m/DRCVlqKRm85RYe20U/B4F4k80BMzneJdPT9z65SLuQiwlAB+F0lNaSpuUuhSXKAdillIhMHIFyZJKbitxAmApDm8/iQn5XLhCpmaNFNgZMKPm4z/lO7Y9b/E/xGHTPW5kSI3ykWDzaRHEz+NxI0gCCqLiYcjKnIDiL6bCs6Veb8NEJ2Wmhq5KSqXxAGtjgKkyE31gowsUTGi70ahqVjoLp48cpOCuCmwtBQTNwxGvkAjN8XlMBtJvn/EkyR6Q8tpI08gKinxnMQ8Qxd4zghwBiK2uj+I+/hMRW7EMvAsj9yInpuU01LUY0HETXWxELlxxW7kVw4XFughbiJmKdHITffoJGnkx3HRrQoodA5a1osbhaZiKm6o0XsqqaSlWOSGwWDkNELImrOVoVzoEpw0NeWcQbbeESlNoAKe57F4kggZf+NaYM755AcJxA31swTS7LmhzfGUVkoBmfbckNdIPXIjRD+EZm/1NOUz4o16WNhGogQVnAvLmpypvaYaItJSdWU2GDhSsj5AI0w0NRXZQbufpqWyVdyoLAenJumiOOJG7FKsInJDBZFVBwGrI0zcMBj5QkS1VIXgs0gaubEWSwfkFFJT45NBnMGRSknD7HOBOqF6sW9f3OdkqkOx2gZ+QOTgzEyIG/XpsyimiJuZwryojmFP1MNGQE6YtWY3Wip1MJtGpKXMRoM41+qY0DFZFDfU7D7eQ/recAagdlFm1yqXtEVuhBSdKs8NFTdZZsBOM0zcMBj5QkSfG9mRG0CT1NTwhA9rDSRlYJ5zHlAjnHz6Y49RATI3Fdyjcq4UEBm5SW9aiud57T03QlpqhiBcOkY8CIWlLsVdPiJ+5jj8stp2aI4YuSERJNohmTYVFE3DfftJd+XO98j3NYuzNwpBLxQ8CsVNsshNKp4bMXKT4VJ/nWHiplAIh4H9zwI7/yDlYBn5RUTkptxBmrWNZEjcTPQeQwU3AT9MQP2KCKNye9znZGoq+ITKuVJA5joU+0NhUN2RuudmSlqqrAhmI4dAiEfPmJSaOuYmArjJ6pm2i4wQkZYCgPmC72d/t/A5rl8OAPB378WCb/8Dz/ztGXJ/8+kZXKRC7CkaiuNFbiKHZyqB51laipHnPL8J+PMNwHNfAR6/DAjKOOkx1DM5Dmy+G/j7bUDf/vS/XihA+msAgMUhRW48MnrdaCBu+K4dAICTplmAySJ5eVw9cT9rGWvip0FaKt0CbNIv7V/ryI3RwKG5nNxuHyI/43keHwyQ16k16yRuxLQUidycNoNsd5wiRmNfWQvcnB0W3oe5/Em0ekmK0127KuNLlY1az41niGyTRW6UpqWCk2TgZuQ+CgQmbgqB9x8FdvxO+r5rB7D9Uf3Wk4uEgvIPLOEQ8MQ1wJv3Ajt+Dzx6QfoFTmQ0zuIQPTejyTw3gCbixtq3GwDQYRNSCY4q0mcFPDDeGfM5lgzNlnJrkZZKc4dimpIyGTixRF41UyI3gJSaOiX4bo4PuNHmtQEASnkVqQ4tiKiWAoDTZpbDwAFtg26cGnLj9r/uxztB8nl6aNlxLDcQ780fe2fosVp5qPXc0N8F7Rw9FatKQ7EY6eFYKTgjzzj5FvDPr5PbH7lLmn773m80mQRdEHTtBH62GPjRDOBf303++H1/AdrfJtN8axaT2S5Pfkpqg54O6AmNMwBGi0rPjXpDcckwmfnWX7pYWAcHOIVZb6MdMZ9jznATPzVGXWuGIjfpmAgeKW5aKsmJ7agwAfzNowMY4YkHw0BTIpkkFJBmHgmem7IiM86cTcTBxx54C8/s7MLr/EoAQNPh38EAHh+E5+CXO7zJ56bphRi5USBuQgFJtGidlopMSRVYp//CereFgKsPeOsXwGv3AK//BPjj1SQsufgq0g5/6dWA0UJ6R9CGWIz4BP3Anz8DTPQC4IGt9wFHX0n8nG0Pke3624DP/IO0ih9pA177QfrWGYiYH8NxqCohjdv6xieTP5fO6BltT9h0Ly7hEKpcxEw8XrFUur+MipvYvhtrhpv4qelQbIuo6AqH03cxQJstpuy3ASIiN1IF1PJmUm2zq4NECP6+uxsjvJCm8A5n/kInsu1ARCffz55NvFp0wvzC82+UKoUA/K3oCrgmg9h8sC8jy1SMI2K+VFjm55pGbcDF72qstkMxFTeWwjITA0zc5BdjXcDDHwJeuQN4/UfAa98HAm6g9RzgYw+Qq2lrCfkeAA6/oO96c4G9fyIl0sW1wMpPk/vejD/pHr17SdrPaAFW3UiuxGi07N1fA5070rPOKV1pm8pj9zaJSWkDYDAD4UDsKczJGDwKS9gLN29FuHKudD/13YzFjtxYMtTnhqal7Ck08QPSG73RNHLjnx65WdlMoiP7usaxt3MMO9tHMcoJ4ibkl6IomYKe0G1lgEF6z+fNr8GPP74M5y+qxb1XL8cnz1kGXPt/wKzzgPPvhn3FJwAAL+7rzex65ULTSnw4QrQkQexO7Iz6XURBxY3fpewCpEDNxAATN/nFy98hEYbyVnJiXfJx4IIfAJ/6c/TQtPkXk+2xf+mzzlyB54G3f0Vur/sycN63AXAk5TQW20eCwy+S7ZzzgWIhRD3vQmDpv5ED3nMb02PmnnJCowbS3vHJ5JERg1ESImp8N907AQD7+FZUFEsnVHGfcSI3mZ8tpb5aCkhvObhmc6WA6CiewMxKO2ZXO+APhXHZr7YCANbMayLDMwHJ0JopEkzB/rfTm/HI9avxiVVN5I6W9cD1zwLrb8XFSxsAAFsOD2RkUrtijGbpPck1FdOy8Xh+GyB6SKgSIcrEDSPnGe8BDjxLbv/bH4DL7gc+8Rhw5kbAZI1+7Mz1ZNv9ATHKMmLTs5u0ezfZgFWfIRGOmWeSn+3/a+znHH2ZbOddEH3/RT8iB6/+A8DWn2m/1kD0/JiqYgtsZgN4HugelRG9ScVU3EXEzd5wq+j1AQCU1JOtK/ZVdqaa+ImRGxXVUmajAUYD6QGTzsiNR6uJ4EBMQzHHcbh2TbQR9ysfniOdUDPtu0k2KDIOSxpLUVVshTcQwp7OsTQsTAMcNWQ7ITN1Rv8/6P9LLExWEg0GlKWmqBBi4oaRsxz6B4kMNK0B6pclfmzVPBLmDHikWS2M6ez5E9nOv1jKhS++kmwPPDf98e4hoPN9cpuOH6A4KoGLf0xuv/4j4LUfKq+oSAQ9oQkROo7j0CREb2SlplIRN0LkZk94NiqLI8RNMT3I98d8Gq2WSrdZNxXPDSBFb9IZKUiLoTgyWgvg+nUtuHpVE2ZVOfDDK5di1cwKycCaI+KG4zisbSVrfq8tw9EmuZSS6JLsFK8obuoSP05NxZTY+4p5bhi5yoktZEtTTokwGIAGUoWAzu1pW5JqeB7Y9wzw3K3A9t+pM7mmSjhEqp4AklKi0N9v1/bpJ4TjmwHwQO1SoKxx+j6XfBxYfRMRoa//N3DfImDz97SJnsXwWVDfTceIjCotteIm6AffS/qP7OZnobrYJv2suJZs41zBZq7PjfpqKSAzvW4mNY3cRPe5oVhMBvzk6uV49T/PxSfXClEc2lcl02kpleIGANYI4mZbmw5VXnIoFf7347RAmIarh2zp/0s81AzPFNNShTV6AWDiJn8QUgNoXivv8U2rheelyeCaClt/Bjx9I7DzceAfXwX+9+PpLaOORdsbxL9UVA7M2SDdX9ZEyrv5MHBsc/RzaEpq7pSoDYXjgEvuAz7+KNBwGhDyAW/+lBjAU2VKWgqQfDed6RQ3/QfAhXwY4+04xdeiuiQiBUojN56hmAIuss8Nn6ZqHZ7nxbRUqpGbdHYpTrehOC40LeXNjcgNAJzeQsTNzlMjaa1gUw29sBnrkvd4OWkpQPLdKElLMc8NI6dx9UoD5YSW5UmpE0p2+w+kb11qGI4omV58Fbn6PPEa8JebMxvB2ftnsl10Bem4GwkVL1TMAGRt1KA9d4rfJhKOA5Z+ArjlVeAKoWT83QdTL8ufkpYCiIkUkHqbJISKG6XDM4Wp33vCs1BiNUdHHuyV5DMJPuasHRq54XkgmKaTlDcQEscaqKmWAiLSUmk0FFNxY0uToTguoudGp8iNzan4qfPrSmAzG+D2h3ByKAtHyYiRG5nihkY205KWYuKGkcvQqE3VfPm5VTrYcOCw/H4MmeC935C+PLPOI4bo6/4KGK3A4eeBlxVEOPxu4J0HgP+7FvjTDWSmVkCG9wQgV77UU7Ps36b/nIqXY/+SBFfndnLAtpUBTTJm33AcsOJaYP4lAHjg/d/KW1vcNdPIjXS1vrzZCQD4oGM0eWSkXOh14x4AfAqqMQRxs5efherSKcZ1g1FqahYjNWWJqERKVzn4xCSJ2hg4wJFqWiqdkRva58aswSE5RhO/uOgtblREbowGDgvqyIlenEOVTdDIjWzPjZCWSou4Ef6XWZ8bRk4inGDQeJr851TMIr1N/BNx+5BknHBYqkJa8zkiAGacAVxJIxwPEJGSjP6DwIPrgZe+RXr5HHiWzNS6bxHwyp1ElLS9CRx/FTj1zvQw787HST+J8hag+Yzp+29eSxqLeYeltN6Rf5Lt7A8DRgURglWfIdv9z6YmMsUKGelqfUlDGUwGDgMuH7rHkjTzs5VJJxol0ZuIyE11sXX6zx3xTcWWiDED6fLduCLMxGonX2diMrgnxdRZFDGieHHJMUMxZXEDOdHv687CiikauYnXLmIq6UxLifO74jQHzGOYuMkHho6Rbc1C+c8xmknVFAAMHNJ+TWroeJdcxVjLgDkfke5fchVw7u3k9j82Sb1kYnH4n8BvzycdgUubgPPvBs77DlA2g4iRt35OPDyPXwr8z5XA7y4CfjIH+OsXgIEj5KC79X6yr7O+FrtludEEzPkwuX30ZZJX2f8s+X7hZcre86xzSerN3Q8MppCainG1XmQxYkE9CUfvah9Nvg+lvpvApJjW3BtuRU2pbfpjElRMmYwGCFXW6RM3QuSmxGZWvQ+riURuJtMYuXH7qelZC3ET21AcE90iN6Nkq1rckJP1gWyM3FBxMzkaPfMtFj6XVK4t21Cs4D0nmzaexzBxkw8Mk4FyqJil7Hk1C8i2P0vKwalnZf5F03vznPMNUm0UDgBPfYoMpIxMtYQCwJYfkTSU3wW0nA18/g1g/a3AOf8PuG0X8O9PEA9N9UKSwqtdQkYEhHzA7v8DHlgD/GKl1Ahx+bXx10pTU0deAnr3EDFlsgFzL1T2nk0WoFGYctz+rrLnRkIPopZonwWdtLz1mIyyc6Xipm8/EA7CY3KiC1WxIzeiuElcMZWuSiSalkolImI1ZyByQyeXq2g0OA1FhuLcjNwsEiI3B3sUzlrKBLZSKVKSbF4bNR1by5JbCsS0lIL3nOLvOZfR4DKBoSs8DwydILcrZit7brUgbgaPaLsmtZx6m2xbzp7+M44DrnyYGFT3/hn4+23A9sdIGijoJ31+aDpl9WeBi/+bRKcoBiOw4BLyFQnPk9TSm/cRX493BCiuA6753+kCK5I55wOckQibv36B3DfvQnX9JJrXAiffBDreA1bfqPz5QMx5QgBw/qJa/OGdU3hpfy/u/thimBJNnFYqboT+Nqes84AJDjVTPTeArF43k4Fw2hr5TfjIjKISWwrixpT+UnDNIjfhEBHrgDxDMS0Fz6FqKQCYU0P+zwYnfBjzBFBmVx+ZSwuVc0m7iMEjQO2i+I8TL0xbk+9TTVpK/D0XXuSGiZtcxz1IIhXgpJOTXKqEOUDZIG4Ck5J/hXYBnorRTARO7WJgy3+TDsI9u6Wf2yuBC38ILP93+a/LcaQs/tonSKXWeBcp007mVyiuJpGkvX+SKs7O+LL8141khuDr6UghchPHRLpuViXK7WYMu/1458QQzp5bHX8fSsVNzy4AwGHDHACI47kRXi9GtRQAWExGAMG0paXGaeQmFXFjTn8TP+q5STlyQz8HgHJDMc+T/4d0Ew4nHL8gh2KrCfVlNvSMTeLYgIs0JMwmqucTcZOsCnJIEDeVMi5MlaaleF4SrSxyw8g5qPIvawLMMTwPiaBDDgePZu7AFo/unWSAX3Ft4vSawUi8MCuvI0bh/kNE9DScRqIyckyU8aholXcFRbnwB2RQ5sAhkjabIbPH0FRoQ8XhEySloOY9xElLmYwGXLy0Hk9sa8ffdnVrK266dwEAdoXI7yxm5IaKmzhzdixG8plLl7jRIi1ly0TkxqdR5CayIlCJuKHDMzNRMuwbJ32iADIsUiVzaoqJuOmfyD5xQ/2MyXx09Odyou5K01L+CVJ5ChSk54aJm1xnSKXfBhCuFjhyFeUZAhxVWq5MGcKJEk2nyxNZjirg9JvTuqSkFNcAX3wb4EPRKTClOKrIlZV3hJjDk43PiEWctBQAXLmyEU9sa8eL+3px98eWxO+CK/a6aSdX17HM1BTfhOjVetvbDADRDfwo9DMVZ9REuudLTfg0MBRTz00aDcUecf6VRpEbs13e/5HFTrxiwUniu8mEuKGpErMjceo3CbOri/Hm0UEc68/wRHM5VM8n24GIqPhYF2lK6uoFPnIn6ZfVLUSe5fQnoz4eGvVKBvVRmWzyhG6ewQzFuQ4tN6QTmJVgLgKc5MSke2qqfz/Z1i7Wdx1KMRhSEzYUeqU3dFTd88UOxdPFzaoZ5WgqL8KEL4h/HUwwzK+0ifiIgpPJh/51vgfwIfClTTjiJVeUNSUxIofJIjeCuAmkrVpKC89N+pv4uVMcESHiV1ApRcl0xZRGJlfqu8lqcTN4hHTnDoeBP11Hqit79wBPfoq0oqAp7YYVyfdJf1+eEXlrKGAzMcDETe5Du2CWNal7fmRqSk/6clTcaEWqfwd6UouR0jIYOFyxgpSn/m1Xgq6pRpP0OaLpzngIlV3eBpKKMxk4OItiiDxR3AzG7OMjVkulOXLTGO4FRtX1c8pEEz/Jc6NRWkqRuMlwxZR40nWmtBtR3AxkobhxziQVUCEf0LeXzKnr2kGa6dHRK/9zJYn6Vs2Xd/ymIkWu+buAzcQAEze5D+2CmawBVDzE3LCOkZtwSCpHr12i3zr0pCpFcSOe1GKHny9ZRj4fW48NJjbG0rEcNE0YD6GybbCCNI6sLrHCYIiRBrELaSk+FDOcLs6XSmOfm6+ZnsYN268Afr5MVSfoTDTxk6qlUk1LTe9UnZRMV0ylaCamUHHTOeJNq9lbFQajVChw5GXgte+T22d9FbjhOdKOgrIiQcuJSKgIDXhIAUYyCthMDDBxk/vQ1t20cZRSqkili9gIUC1dO4AH1gL3LZa6DMtl+ARJhZjtyiu+8oVURWaMwZmRLKgrQW2pFZOBMLafTBDWpuFx2vU6Fn43KVsH0OYgj29wxjmZmiySVyBGairdk8Grx/bhNtMz5Bs+DPzzm4oFZLqb+AVDYfH9O7QyFCsxpWc6LeUWXifFk26lwwKn3QyeB45nY/Rm/kVku+WHxKRfUg+s/SLxNX3ySdJOYt1G8iUHa5kwqw1SVCYR9DF2Jm4YuQhNS5WqjNxokZYKeIEnP02qhsY7yZDLRCfHqfTtI9vqBeSKpxCJNPOqIclJjeM4fEiolHr9SOyeM68fGcBd2wWDp9DDJiYnXidhdecMHA2Rz119WYJKvQS+G4sgHNIlbjaMkgGoXTM/Rk4m4QDw9i8V7cOW5iZ+noiogz3VUnB/fO9VXDIuboTPAe2BpBKO4zC7mkRvTgxk4QDNJR+XfrcA6ZZO+2CVtwCffppUXMr17BkMylJTHua5YeQqAa+kzksb1O2DRgxGTpJmeGrY/SSZSl7SALSeQ8oPX/pOdAfhRPQJprpC9dsAUs59clTZ4EqAGBZDwt8uwUntnPlU3EwXGSNuPzY+sRPP9gst4IdPxPdgHBHGX8y7CF3CzKrGeJEbILG4oaXg6fDc+D043fcOAGBg8WeBs/+D3L/7ybjVW7FIdxM/j2AmNhm4qHlbqsgFz41bENeO1MQNAMyuJpHKrIzc2MqATz8DLP034LJfAEs/kfo+ixT8rVhaipGzUL+N2Q7YnOr2UVJHTG58iIwQUMPBv5Pt2s8BH3uATPE+tRU4uVXe80UzcYH6bQDSfZT2sRhPYPqNRSDiqjXBSe2sOVUwcMCRvgl0j0ZPSH/mgy64JoMYQzGOhoUU5/FXY7zWpPT3nnehuJ+4aSkgYTl4WtNSp96CBQF08lXg6pYRD0T9ChJ12vu07N2I1VJp8nW4I8rA1Q73FFEyEZyS8ciN8DnQoPXErGyO3AAkzfvxR4BVN2jTR8yuwB9FB3Imm1mVpzBxk8tEmonV/uNwHFAp+G7UpKZ8LjI6AADmf5SUlq/8FPn+/Ufk7YOmpRK1KS8EaPRG6ZR2erXOGRL2DXHaLVje7AQAbD0aLTSe30M+S1eubMQrYTLrKnzohek7Ofh3cmAtbQRaz0X3KIncJBY3iSI3aRQ3wqyyN0JLUVJkJp/1FcJnc/f/yd6NWC2V5shNypVSgLKhmRS90lL0c5ECs6qyOHKTDpREbmg7h5K69K0ni2HiJpcRzcQqU1KUVMysbW+QlEh5i7Sf1Z8l24P/AFxJ+qX4XNJMqJoCTksBEeJGYeQm0meRROSePYdcLb8ZMUhzaMKHDzpGAQD/eeF8vGVaAwDgj7wUnSILh8hUdYB0iDaaIiI36jw3teE+/NH8A5x++CcxS8VTgW97AwDwRni5NH5hyccBg4mMjpA5MNaa5uGebq0a+AHqDMWij0Nm/5RUoXPGNBA3s2ukyE04LDMNnssIkRveM4wdp4axt3Ms/mPFyA0TN4xcQzQTq6yUotAyZDUVU0LVDGadK51Y65aQSdd8iIxISAQ9wZTUA47KxI/Nd+jfkTZmlIuCq/Wz55ETytajA+LJ4LXDA+B5YHFDKRqdReAaV6EtXAtjYALY9qD05Hd/TXp22MqANbdgMhDCkJt4fRJ6bmg5eAxxs2Hoj1hv3I8VXU+kNltrKgEvMbgD2BmeixKrYNp0VEqT23c/KWtXUofiNBmKtepxA+SIoZimpVIXNzMq7DAZOHgDIfSOyyiPznUEIbrz8HF8/MF3cNmvtuLxt0/GfiyL3DBylnEauVFZKUUR01IqIje0KqrhtOj7l15Ntnv/nPj5YkqqwKM2QETkRqm4kX+1vqLZiWKrCSOeAPZ3kwF8L+8nV3gfWUAMnkubK/CzoGB+fPM+0htm893Ay3eQ+z58B+CoQucIEVUOixFlsRr4UUTPzZSTJ89jwdhb0veH/5l0/bLpPwCOD2OQL8WwoVyseAIgDVbd+2cSjUpCutNSmnUnBlQaiqcMz0wnAa8w6Bdk+GyKmI0GzKgk7zVrfTdaIkRuTnZIx4j/fvEQht1TikG8I2S2FMDEDSMH0SxyQ9NSR5Ud3HheavZGhz9SFl9JPCCd75Np2/GgZuKaAvfbAJK4GVcobhRcrZuNBpwxi5zMNh/qg8cfxBtHSUTlwiXkILissQx/D6/DdvMqEhV6/j+AN+8FwJN5XsJMr7ZBIm5aqx2JjbDx0lKuHpQEIwRP756k65dN714AwIHwTBTbzNHrm3chMeCPd5G0ahLEtFSaIzcpD80EkjZzjAk1qYb80mcpXdDPgNEiGehTZFYVSU0VhO9GEKJl/DiWNZVhSWMpPP4QfvvmiejHjQipfkf1tGG6hQITN7mMVp6bqQM05TJ8AvCNkeqomoXRPyupA1rOJrf3/SX+PlillAS9wqKeBLkovFr/6FLyOs/s7MJrhwYwGQijuaIIi+rJyWZpUxl4GHC9+1YEzvkWSTHOOg/4xGPAR+8V049tg+Rk0lKZ5OAZT9z0TBEzMj0wshD2fYBvmT4R3GQl4hsA9j+TdFdiE780dlEGUptcLpJgxlhczMLwTCD9qalIM7EW1UMAZteQz9+JQhA3Qif6Om4Ely6rx8bziKXgf949JY4aASD5GAu1KSqYuMltUh29QFE7QJNeadcujt2ISkxNxSm75XnW4yYSWrKZbGjlVBSW/168pB7FVhPahz348hOkWd8lSxvE6EajswjldjM8YTMOzf0CcMurwPXPEjNuxAlJjNxUyRQ3k6PRvZRGTgIg1UwAyPv2jsp6D0kRBhIeDDfHngi++AqyPfR80tSULc2eG1HcpDDcU0SNoZjjMue70dBMTJktRm7yPy0VKhaaZnJDOGtONS5YVItZVQ64JoN46v2IKsthIZLjnKnDKrMDJm5ylVBQOgmmmpYC1HUqHhCE0NSoDWXhZST8PHBQitBEMnKSRH4MZik1VshQceMdAYI++c+jqQSZ4eciixH/trpZ/N5mNuD6ddJBkOM4LGogUZz93fGrMU4OktdNGrkpKpfaxkeePIWry0P8DLgNJeQ+pT1+4iEc3E/wDSiJFRGZuZ6syzMkzsmKR7qb+NEr7lQml4uoMRQDmZsvRS/ItDhmCRRS5OZUkBiKKzkX5leZYTBwuPnsWXDChYEtD8Pfvp08UIyIF266n4mbXGWij8zKMZi0uQoSBzcqiNzQx8YTJkVO0vIeiB296dlNtrWLyAyiQsfmJH9PIGZlUVxUmEj/44J5uGRpPebVFuP+a1ZM61OzuIHMg6Km41icHBLETbLIjcEQu2JKGDXRwVdjxCR8hunJLxV8E6LwP8XXxI6IGM3APGH2T6xmhRFQz00wzCOYhk7KrskAAMQWYUpRYygGMtelWBQ3KabSI6Cem+6xSdG/lK/sHeLg4UkvK6OL/C6vWtmAJ20/wjeDD8L42AX42zN/xPBxQeTULdNrqbrDxE2uEpmSMmjwZ1RTDj54WHhugqjL0o+T7b6/TDcr9+wi2/oV8l8znzEYpJb0Snw3KnwWDqsJD3zqNLz8tXNw0ZLpac3FSSI3E74geoTRC7OSiRsgtu9GMD128NUYMgo/V1opFguh0/ak2YlxFMev5Gr9ENkmMRXTaikgPdEbKXIjc8ZQItR0KAYyl5YSiyC0EzflDgsqHOTiKN8rpg70uNDDC0JU+F3a+ndjAchn3ogQPrbnS6jwtCEEA/qKF+i1VN1h4iZXcWl8BaQ0LRUOA4OCEKqeH/9x8y4mk6pHTwGd26N/JlZarVCy0vymWIW48QsnNCU+iyRQc/HBHhdCMZqjHewhEZ26UhvKHTKiblNHMPC8mJbq5KsxaKCRGw3SUkJKathKUh9xxQ01vHfvBCbjp9/oiAggPeKGem40SUtRcaO0QiZjkRuNKjynUCidivd1j0WIG+EcIJjih5o2oM8qpZe3h+fhB1vkz1DLN5i4yVW0MhNTaORG7gDNsXYg6CWemkSmNYsdWPBRcntfRGoqHJZ65LDIjYQaU7HaVEQCZlUXw2Y2wBsIoW1w+tXwASFdRSM8SZkauZkcBXxkH518NfoNgvhR2p05FoK46TeTE2hpPHHjbAbKW0l6t31b3N0ZDRzMwoDPdMyX0rRayp9q5CbNJ8M0pKUAiNPBj/blt7g52jeBHl74W411kuPo/mcBAJXrP4PaG/8IFNcibCrCfwf+Hc/v7UF/ITQ3jAETN7mK1ldAJfWApYR0FR6SEb2hZuLKOYAxyUF5idAQbv9fpcqU3t3kBGcpAeqWql523kEbm7nTm5ZKhtHAYUFd/NQUvW+RWnEjpKR8tipMwoo+CAdsDSM3PQYi/BM2GGwmoyZEoR0HWxpNxaLnRou0FG3cZilW9jz7lMhaOuD5tImbxY3kc7gvgQE+Fh5/ELs7RtM2FFVLPP4g+l0+9ECI3Ix1AF3bSV8sSzHxN9YtBb66F4avH0e4aQ1CYR4vCk06Cw0mbnKVcY163FA4DqhfTm537Uz++GRm4khmf5hUpkz0AUdfJvdRE2frh2KXkRcqYuRG37QUQLoZA8D2k9NnDh3oURq5mXLyFFJSvmJStdUdjvYRpITg2+ngiaBKKG5o88kk4kYcwRDU/iSoWbVUOCSlpZQ2yCtR2YZACZOj0vo0FjdLGokBfl/XGHiZjUiP9U/gvHu34GMPvIVLfvHm9C6/WcapIfK76zHPIHf0HyQXjAAZWmwWehWZrIDFgQsWk7/p28cyNFYjy2DiJlcRr4A0SksBQNNqsu18P/ljqZk4kd+GYrIAKz9Nbr8rzCo6tplsZ5+nbI35jqq0lIpJ0DJY20oEx3tt0T6M8ckADvaQFvpLm5zydhYnchMoIV2ZuyN9BKmOABBSW50hUjabWNwIY0OSiRvayC+QxZ4bn0u6bVUYuVHbY0kJ1CxeVKE8bZaEhXWlMHDA4IQffePJ2yiEwjw2PrFTfOzxATd++vJhTddEGZzwaVJlR8WNyymUd/fuBfYJTShpU8oI1rSQ/6n3Tw7LFnz5BBM3uUo6jHlU3HTtSP7YAQWRGwBY8znS66TtdSJwTr0FgAPmXqBqqXmLGkNxmsTNGkHcHO5zRV3Vvnt8CKEwj5ZKe+KBmZFMFTdCGXiolFyF9gSFSEPAI6VW1CII/5MBJ4Ak4qZuKflcTvQmLENP1/DMYCgMj5/sM2XPDf29Gczk6l0JVNy4+tI3X4pWYlbO1nzXRRYj5taQXkl7u5Knpv76QRcO9brgtJvxwCeJwP3Lzk4xRagF/mAYn/vDdqz+/r9w7r1bcKTPlfxJCTgltF4w18whkbmAh3xuLSUkOj6FpU1lsJgMGHL7Y/rm8h0mbnIRnpdGL2hlKAaAptPJtv9AwuoR8Lw4cVlW5AYAnDOANZ8nt1/8JtnOuwgoL9wOmjFRk5ZS05VWBpXFVsyrJRGAN49KJdxbj5HU0llzq+TvjEYY6dW70J2YdxJxMxKykqo6QPn4iUgmx8TBjMcniWBKKG4sdkmgx2o0KZCuRn50aCagQYdinyBulEZtAOlzF/RGR4C0hFZipqlh58oZTgDAuycSp2H8wTB+vplcnH3hnNn46NI6zK52YDIQxpbDCvpLJeG3W0/g5QMkEtY54sXn/2dHSt6ek0LkprmqDJh/sfSD5f8upaQisJqMWCFEVmOllvMdJm5yEc8QGXIHaCtuSuqIQZgPJ+794R4g+XNw0kRxOXzkDqkyqrgWuOieFBabp6jpc6O2K60MLloszaECgHCYx+aDZG1nzVHQPJLOuHH3k/UOHwcA8BXkKt4XCKmLWk1FiL7wtjL0ThKxkFDcAFKH7QSzrcThmRqLm3EhUmAxGUQBpRoaubGWKH+uxS75dNKVmhJ9enPTsvv1c4jY3no0sSn6T9s70DHsRVWxFdevmwmO43DufPLZe/u4NobqYCiMx98+CQD41kcXoKbEirZBN375qoIO8FOgkZuZFXbgvG+R9h1NpwPnfCPuc5Y1ES8S9cgVEkzc5CI0fO6o1r6z75wNZHv0lfiPGRBy0+UzleXOLQ7g5n8Bn9sC3PoBUNGqepl5Cz3B+13yJzSnKS0FAFedRjwxbx4dQOeIB1uO9KNr1IsSmwnnzlcgborKASs50GLouJiWMlYRcTMZDGvj+xDSteGSRrE/T3JxI3gYEogbOl9K66oaaiYu1aLHjVBaD4sKcQNEiMt0i5v0RG7Wz6kCx5E0arzy58lACL96laTHNp43W5zEvn4OqdZ7SyPz7eZD/egb96HSYcENZ7bgex8jg4EfeaNNHFuiFOq5aamyk4uFje+T42lx/P/DeXXks5BqSiwXYeImF0lTOSUAaVzCsX+RHgqxEDsTy0xJRWI0kwoVpU3GCgVrCWASBKPcCEaa0lIAGa2wfk4lwjzw1Sd34bt/JwMpr10zI6pzryxoCvLkViAcBExFMDvJZzgU5hFWE7WaimAmDjhIxMliNIjCJC5i5OZA3IekKy2laY+bVNJSAFAsTKV3paF0OBxOe1qqwmHBUqFq6u97emI+5jdvnEDv+CQaymy4du0M8f41rZUwGTi0D3vQOeJJeS0v7SO/w6tOa4TVZMSFi2vxoXnV8IfCuOu5/QjHaIyZiMlACN1j5P98Jp3lJmOq+vxaJm4YuUSaunwCAFrWkyu/8S6g/Z3Yj6Fm4mo27FJzOC7CfCszRJ7GtBQA3HHpItjMBmw/NYJTQx40lNnwpXNVmEKpuKFtACpaYTVLUZWQXXjfKUVuiPD32ki6trTILE47j0u1IG4GDscV9FJaSuvIDUlLaTIRPJW0FJDeyI2rm0QYDSYpRZkGrhYGwv7vu6emCYjDvS78eguJ2nzzowuj0oDFVhMWCl2593Yq65UzlXCYxxtCauy8BeR3ynEc7rpsEcxGDq8fGcCtT36Ap3d04l8H+mRFAztHPOB5ss5KOR3BBeYKnrnBCT8GJxQM480DmLjJRdJhJqaYi4BFHyO39zwZ+zGpRG4YyRF7wsg0N6YxLQUAC+pK8X+3nIGLFtfhqpWNeOrz6+C0q0iH0pOaKG5miaIBAAJFWogbIvxdVnJSKSuSIRoqWgGjlZhpR0/GfAiNUmldCj7qIeLGWaRBeplGbpQ28KOksxycpqQqZqW1r9VVKxtRYjWhbdCNx95qE+8/2DOOzz7+PiYDYZw9twqXLZt+7FyishHgVA71ujA44UOR2YhVM8vF+2dXF+MnnyC9xP6xpwf/+efduPkP23HR/W+ga9SbcJ80JTWz0p5crEdgt5gwo4IcF470Flb0hombXIRWnKQjLQUQ9z0A7P0L4B2d/nMxcsPETVqgV9ByxE0oKJnL05jqWzmjHA9dtwr3XbMCzRUqRRQVN7xwpVoxCwYDB4uRHIb8orhJxVBMxM2oiQhEWSLMYJQ+y3F8N+mK3FBxU2bX4IRPPTdq01IlEeXgWpPmlBTFYTXhtg3EsPz95w/i8l9txRUPvIWP/uJNdI54MbPSjp//+8qYAmFxA20EmJr5lpqSz5hVMc0kfsXKRvzx5rX4xKomfGheNapLrDg55MHXntqVsBcNrZRqqVT+Pz6nhnweThRYOTgTN7mIUEabtvBuy1nEZBlwAzt+H/0z95A0tDPNB6qCRYzcyDjJByL8ARo3RtOcqTPEhL5KVDhMWoX3rUFaakAYxFkhN4SfxHcj9bnROHLjJeKmXAtxI45eUJuWykDkJk2VUpF89qxW3PrhOeA4YE/nGHZ1jILngQsW1eKZL54Z9zOhpstxLHYLaa3IqE0k6+dU4d6rl+MPN63BM188EzazAe+1DePt4/HNzLRSakal8gsLGrlpH07dS5RLZIW4eeCBB9DS0gKbzYa1a9fivffei/vYRx55BGeffTbKy8tRXl6ODRs2JHx8XiJ0d0V5mqqNOA5Y92Vye9vD0YM0u4TJ3pVzgSJnel6/0KHGWjmeG2omBgeYpve6yCroeA/KjDMBAFYh5eO1CvOlNDAU9wjjHCrkps9EcXMo5o/pbKlJjSM3Yx7yv6VpWkq15yYT4ib9F0Qcx2HTBfOx9Rsfxs//fQV+9cmVePPr5+E3169GZXH85oYL6kpgNHAYcsvrchyPXR2kp8yK5tjiJpLmCjuuXkV8Qk/v6Iz7OClyo1zczBSeQwVSoaC7uHnqqaewadMm3HXXXdi5cyeWL1+OCy+8EP39sQ9wW7ZswbXXXovXXnsN77zzDpqbm3HBBRegq0uDmTS5QGBSipykswHe0qvJwc7VLc0vAYBOQdzQbsYM7XEoSM/QoZkWh6zqCV0xmoELf0hun/FlsYSVRm48loiIVbxKvURMjosN/NpDTgBARbFccZO4HDxdkZsR6rnRMnKjuloqneImM2mpSBqdRfjYikZcuqxBVirVZjZiVhVJ+xxU2RdmaMKHjmFywbFU6DGTjCtWksKQl/f3xjUXiz1uVKSlJHHDIjcZ5b777sMtt9yCG2+8EYsWLcJDDz0Eu92Oxx57LObj//jHP+JLX/oSVqxYgQULFuC3v/0twuEwNm/enOGV64TQHwSWYsBemb7XMVmBNbeQ21t/Jp1supi4STtTRxUkgg7NzPaUFGXdl4H/OAxc+APxLioc3EYnuSMcBLwqOqrSFgm2MvQLDfwUR24Gj0RHKuka01QKTtNSqgzaU6GdhdUaimmBgmeIXERpxeS4VAShpOmnDtCKKbVN7/YIKanZ1Y7k/ZUETpvhRKOzCG5/CK8fmf4/HwiF0TlCBJMaz82MCvKc9mFPQc2Y0lXc+P1+7NixAxs2bBDvMxgM2LBhA955J04Z8hQ8Hg8CgQAqKipi/tzn82F8fDzqK6cZpSmplvRfqZ9+C2m8NnAQOPBXIOgDOoShmnRUA0N7ihWIG5qWSlOlVFooqYv67NKUj5c3kaGKgLrowTg12jdhSJiFVS7Xc1PWTD7r4YCUQonAlqbZUlJaSgtDsSBulE4Ep9grpB5LWkxnpwwJUZvi2qxPZVNxozZy80HHKABgebNT9nM4jsNHFpJU9FvHpqeiO0e8CIV52MwG1JQonBkGoLmiCBwHePwhDE5k9+RzLdFV3AwODiIUCqG2tjbq/traWvT2ymsk9Y1vfAMNDQ1RAimSe+65B2VlZeJXc3NzyuvWFWomdmZgJlORE1j3JXL7tXuAw/8kYf/iOqB2afpfv1BRFLkRUhG5JG6mEDWUMpXUSERzyxFBNMjuCcJxQB3pIovevdPXmCbPDU1LlTuyIC3FcYBTOD7SCLEW6JCSUsuihtQiN7sFcbNSgbgBgDNnk5RsLHHTNkj+ri2VDhgMyi9orSYj6kuJH699uHB8N7qnpVLhRz/6EZ588kn89a9/hc0W20x5++23Y2xsTPzq6OjI8Co1hh4oMjW64IwvkvTX0FHgzzeQ+5Z8HDDk9Ecnu6GGYs8wKfVOBG3gp/aElgVEzW1KZb7UGG1u2YARNxUNCtI9dYJgjyFuiixCdMmvdSm4Hzb40LLvAeCtn6c2kZsOu7XJ83rEpEwQN2Pxza2KEcVN+iulUmVhPTFjnxx0K/5b8zyP3Z2jAJRFbgBg3axKGDjg+IAbvWPRKcETA+R/fFa1+lYPMwrQd6PrGaqqqgpGoxF9fdFXaX19fairq0v43HvvvRc/+tGP8PLLL2PZsmVxH2e1WlFaWhr1ldPQUlVqgEw3tjLgyt8AEK4YbE6pkoqRHuwVAGcAwBP/QyKouFHrs8gCpAZ5qUZuBHFT1oQhN6l2UdLNVRQ3fdPFTa3nKL5uehKVnuPK1xWHUJjH+GQQ3zH9Lyrfvxd45U5gz1Pqd0h9SkXJq3TiQiM3YxpeBGawUipVakpsqCq2IMyTGVVKODXkwagnAIvJgAV1ys4zZXaz2Gdn6lTzNqE/TWuVenFDvTpM3GQIi8WCVatWRZmBqTl43bp1cZ/34x//GHfffTdefPFFrF5dYMbWAaFUtWZB5l5z7gbghr8D534L+OwrQFkaxj4wJAxGySyeLDUl9jbJ3VldsSM36tNSfnud2ElYUeSmNiItFRlBGW7D+jc+jS+ZnsOdg1+P3dhSBePeAKzw4yrjVunOD/5X3c7CYW3EDY3cjGopbnIncgOo993QqM2i+lJYTMpPrWtbid9sW9tw1P1U3MyqUn8BQyM3hdTrRvfcwqZNm/DII4/g8ccfx8GDB/HFL34RbrcbN954IwDg+uuvx+233y4+/r//+79xxx134LHHHkNLSwt6e3vR29uLiYkJvd5C5nAPSie76gyKGwBoPRs49xtsnlSmEH03SdIz/hRb7mcBUZVIYuRGRVpKiNyMWYhAshgNcFgUDPesXkBmH3lHog21b/8SpiA5wZTy48DePytfWwxGPH6sM+yHnYvoqdL+jvxp8JH4XQAvVHLZnOoXVaZx5CYUBIaFaFcORG4AIk4A5eLmg/ZRAMAKhSkpytpZ5IJmW1t05IampVpTSUsVYCM/3cXNNddcg3vvvRd33nknVqxYgV27duHFF18UTcbt7e3o6ZEmvD744IPw+/34xCc+gfr6evHr3nvv1estZA7ag8M5M6ev1BkyELsUJ2nklweeG1qJFJWWktOdORKeFz03w0byu6twWBTN4YHZJs1L69lNtt4RYPf/AQC2hIQmhHQ2VoqMeAL4iOED8s3qm8h7DweBvv3Kd0ajNqYi8j7UIhqKT6nfRySjp8h4EFMRUNqkzT7TjFgO3q0ucrNyhlPV665pqQDHETHT7yK+mzFvAL3j5PasFNJSzeVE3HQUkLjRYBRt6mzcuBEbN26M+bMtW7ZEfX/y5Mn0LyhboUbHTPltGPohdilOlpaKaOKXo0RHblQaiifHxAZ+XeEqAH2oKVVeNovm04H+/UDbm8CCS4CdfwACHkxWLMQveq7EucbdQPu7REyl2IphcMKHMwzCBcuc84mJ9+jLRFg1r1G2My1SUgAZbAmQtQR9pN9VKtCUVOWcnClCoOLmUK8L4TAvq0LJHwxjvyCGljc5Vb1umd2MBXWlONgzjvfahnHpsgbs7yIm8abyopR6IdEmhv0uHyYDIdHnls/kxqeNQejYRrasgV7+I7dLcaqN27KAqKGUag3FtLrHXokeLzkZqekJgtkfJtvjr5KUyrbfkOWsuAV7+VkI8gbAOyw1pUuBkZFhzOaE8vWm1VIjwSEVpmWtxE1xLZlNxYeB4bbkj09GBmdKacWsagcsRgMmfEGxeV4yDvaMwx8Mw2k3ix2B1SD6bk4Q381eQdwsbUyhAg5kdhlN0cp9T7kOEze5As9L4qZ5rb5rYaQfsZGfzLRUDkdu6FWkLxDhufEMAaGA/J1Qj0hZE/qFuUDVJSrSM63nkEq1wcPAtgdJY0B7FUJLPo4ATDjJC1WcA7FnUCnB2LcHBo7HqLmGRKxo7yray0oJWokbjgMqZ5PbQ8dS2xeQU5VSFLPRgHl15GKBiotkiCXgTU5lqdApSKbioajXX5KiuOE4TozedIwURmqKiZtcYayTXC1yRqBxld6rYaQb2YbifCgFJ4chrz9ETs6cEDKX08SQQiM3Zc3odxFxoypyU+QUB3ri5e+Q7eqbUGQn4vEoL1QKDhxWvu8pOIZImnmgREgzl7eQrSpxM0q2WnQAplEW2lk4FXKsUoqyeiYRGW8dlzG8FsD7J4m4VOu3oaxpJb6bI30T6Bj2iOMcUo3cAFJqqrNAfDdM3OQKJ4Vy0fplgCV3u9EyZCK3S3EelIIXWYj1zxMIEV+GmnJwMXLTjAHBjKnKcwMAZ31Nul1cC6z7MoqE6NIpXogsaVAqXTVOelZNVAr9dai4GT2lvJmfGLlxprwuVFJxU5iRGwA4ey4xpW89mlzchMM83hY6C9NOw2qpLLZinVA1df+/jqJ92AOzkcOKFEUTIJmKC6ViiombXOHoS2Q7J/aYCUaeQQ3FE3LFTe5Gbuxi91+hG7MaU/FoRFpKjNyorBqauwH49yfI5PIb/wkUOWE2GmAycOjmhf5DGpRKN3lJaitYK1RhlTUD4ICAR1nUCtAuLQVIaanBFCM37iHiTwKyfmDmVM6YVQmzkUP7sEfsMxOPQ70uDLn9KDIbVZeBR3KlMCX8Lzs7xbWU2lIfz9FcQeaG0anl+Q4TN7lAKAAcE8pP516o71oYmUEsBR9IfBWfB6XgVNx4aLt7NaZiMS0leW5UpaUoCy4BLvqhdKIHUGQ2opsX/i6pihvvKBpCxExsajqN3GeyAGVCubTS1JSYltJA3NBmhn37SXNAtdCoTdmMnIs2O6wmnCFEUP6yI/EoCjrJe01rharmfVP56NL6qM/uTWdpM2pHLAdnnhtG1tCxDfCNka61jafpvRpGJqBpqZBPqoiKhS8P0lLmqeJGTVqKnIDCpc0YmBDEjdq0VByKLMaIyE2Ks5eEPjod4WqUV0WMmhFNxQr7zNDITSoN/ChVc0lfGv8EMHxC/X5ysFIqkmvXzAAA/P7tk3j7+CB6xyYxPhltcud5Hs8IEZaLliQeGSQXh9WEx29ag8uXN+D7VyzBefNrNNmvaCgukLRUVvS5YSThiJCSmnsBac3PyH8sdpJq8k+Q6I0tzqyaPDAU2wXPjXda5EZmWioUEEuzh801CIV7wXFAVbH24qaLj4ioBbyAuUjVvnydH8AKYC/finMiI0x0tImrW9kOqRAs1uBEaDACtYuBru1Azy6gSmVKKUf9NpSLFtdh9cxybD81gk8+QipVOQ44o7USd1y6CIsaSvFBxyiO9k/AZjbgkmX1mr32wvpS/OLalZrtDyC9cgBgfDKIMW8AZUUaTKLPYljkJheIFDeMwiEyNRULns8Lzw2duO0JUM+NwrTUeBcAHjBa0eknEayaEivMRm0Pb0VmI8bgQMgkpFjGFQqQCAIdOwEAR7hZcFgjrjHpe3cp7PPj6iXbEo1OsPWCD6h3j/p95GilFMVg4PDwdatwybJ6OO1mmAwceB5458QQLv/VVvzXc/txx7P7AACXLmvQxBeTThxWkzhIthCiNyxyk+2MtpOeG5xRajDGKAwcNcR7ES+CEZwEeCHakcNpKclQPDUtJTNyI5qJG9E1SlJSTeXaezyICOPgLapHses48d1EeHKUYBBEQ2/xlBlxJUJqY6JX/s7C4YjITa2q9UyDipvOHer3keORG4BULz3wSckK0DHswQ+eP4gX9/fi92+fBAA47WZsOj833mNThR1Dbj86Rzwp987Jdpi4yXZObCHbptO1KfNk5A7JvCeRAxbzQNyoNhRTX0jFLHQKZslGp7p0USJEb1BRnSBuVPpuJsdhd5Huv67yxdE/UxO58Q4D4UD081NlptDrp/N9IDCpfF5VYFKaT5XD4mYqzRV2PHTdKrx2uB9/fPcUrCYjbv3IXDSk4fOWDmZU2LG7Y7QgKqaYuMl2qLiZdY6uy2DoQGkD2cZLf9CUlKkop71YNC3lDYTA8zw4pZ4b2o+lYja6RslBm/oLtISKG7dFSBe6FERXIhFmxHXxlSitnGJCFd+7gn3TddgrScWVFlTOIWuZ6CPem5azlD1/+AQZ4WAt08YHlGWcN79GM6NvJmkW/i8KoWKKeW6ymXAYOPE6uT3rXF2XwtAB6p+IN8doUmgNH89snCNQQzHPA5OBiOGZ/gmpGiwRNHJTOVucm9OYBnFjE0TYuFkQN0rnX1GESqn94RY0lE1ZJ01LKYncaO23AYhzlgqatjeVPz+yUirFAaMM7aAVU4XQyI+Jm2ym/wDgGQTMDqCRDcssOJJFbrTsbaIjRRETij3+IDFHmwXPTLLxE4A0aLJiNrqouElDmsAurHPMKJSDqx2e2bMLALA33Ir6qeukkRu/KzrtmAga5dEqJUWhF1RHXlT+XNFMnD8pqXxA7HXDxA1DV2hKauaZ2oWbGblDssiNlr1NdMRo4MTJ4B5/iFzpyzUVh8PACPGv8BWzItJS6TIUA6OiuEktcrOPb0WDc4qXxVoiCTu5aS/6+dAycgMA8y4mQ0R7dikfN0EHi+ZopVS+QrsUd454wSsd8ZFjMHGTzYh+m3P1XAVDL5JFbiZHyTbHIzdARMVUQGGvm/EuUjVmMGHQVIsJXxAcl17PzRAn/L6V+GIofjd4IWWzL9w6PcLEccoN1WJaSuPITXE10HwGuX3wOWXP7Sdzs1CzSNs1MVKiwVkEAwf4gmEMCGNK8hUmbrKVoB849Ra5zcRNYUKvxP0TwOT49J9rOQlaZ6jvRnGXYhohqJyDE0NkYGajswg2s/YGa7rGfgjixtWrfMBl7z5wfBh9vBNDnBN1ZTGqkETfjUzxREc1OGcoW4scllxFtjv/R/57Dfolz00tEzfZhNloQH1ZYZiKmbjJVjrfJwP0HNXs6qdQsRYDVsEsHCs1lSdpKSCikZ9fYSM/MUKwECeEAYezqtPT0LDYRsRNX0joDxLyS38DuQgpqb3hVjSV22E1xRBhSiM3EZ4jzVn2byRNNnCQjIGRtZ5jQDhIPrtlzdqviZESNKqZ7+XgTNxkA/v/Crz6/eh5MsdeIdvWcwAD+zMVLGJqqmv6z/IxLUUjNzRqNRbjfUfSf5BsaxbhxACprJpVlZ6eP8VWwXMTMEi/c6UVU4KZeD/fipZ461QSuQn6pX4yKhsKJsRWJkVvtj8m7zkRgpNVSmUfMwpkxhQ7a+rNwX8Af/4M8MZPgN9fKpX3HnmZbOexKeAFDU01xBqkmEdpqWnDM+kAydH2xE/s20+2NYtwYoBEbmbXpCdyQ8ckTPiCQDEVIAorpqiZONyC1so4pmclkZvRU6SfjNmuvaGYsvomst33jDxjccTfhJF9iAM0WVqKkVa23ifdHmsnIme0A+jfTyoV5mzQb20M/SlvIVvqq4iEpkTyIHJDhYMYuaGiLpG4CYeAgcPkdkRaanbaIjeCuJkMqutHE/CKkaa94Va0ahG5iUxJpStK0rgKaP0Q6YL81v3JH08jN7WLEz+OoQu0YoqlpRjpY7Qd6NpBRMzHHiD3vfsQ8NoPye3mMwB7hX7rY+gPFTejMSI3NC2Vj56bciFyM95Jpn7HYvAIEPIBZge8jmacGiLiZk5tmjw3grhx+yPFjYLITd8BgA9hlCtDLyrip6WURG6GBXFTOUv+OtTwoa+T7c7/AcaTvOc+VimVzdC0FP1/yVeYuNGTtjfItnE1sOJTZDBmOADsfoLcv/pG/dbGyA4SRm5GyTYPIje0QZ6HloI7agCjlaRcYvmNADHFg/plODzgQZgHqootqClROAdJJtRQ7PYFlZt+AaDnAwDAnlALAA6z4xmflURuuneRbfVC+etQQ8tZwIx1REwmit5MDJAINDgWuclS6Oeue2ySfJbzFCZu9KT9XbJtWU9CypfcJx00Z50LLPm4bktjZAmyxI0zM2tJIzQtJR5sDQbAKVTaxEtN0RN7/XIc6iGl8gvq0jeKwiGUgrsmgxENFhX0uhHE2J5wCxwWY/wuytTP4x0mhuFE0Aqm5jXy16EGjgPO+Qa5/f6jsT+PAND5HtnWLMyLz2U+4rRbUFVMmsIeH5Ax3iRHYeJGT4QBemhYSbYVrcDG94HPvwF8+pmcHobI0AhqrPWOSGZzgPhNfHS2lDPjy9KaUiEqMu6NuJJMZKYGIiI3K3BQEDcL60vStUQxLeULhhF0CH14lIgbQYztDbdifl0JDIY4Hhl7BWAwk9uJIkOuXiFdyQFNp8tfh1pmn0cuusIB4NUfxH4MvWBLt9hipARtl3Csn4kbhtaEglIZa+0S6X5bGVC/nAkbBsFaTHodAZJ5FAA8w8INLi+ukEuLyMncNRnhr0lUMRUOA717yO365TjY4wKQ5siNIG4AwGulwzNlipugT/x/38+3Yn6idcrtUtwhRElqF2dueOqG/yLbvX+SxGXUmmgkaW1m1sNQxZwaJm4Y6WLoKMlfW4qB8la9V8PIZqoXkC0Vw4B0UnVUAUZz5tekMaU28h7GJ2NEbmKJm+HjpHOzqQihyrnY102iWEsay9K2RovJAIswA2vCIghOV5+8zr39B4FwABOGEnTyVckjTCVyxE2GUlKRNKwElnyC3P7Xd6N/5h4kzUcBYOb6zK2JoZg5LHLDSBu9+8i2dglr0sdIDK06oSW2gJQOof6MHKdETEtFRm6ouImRlqJ+m7olODY4CY8/BIfFKF6Rpm2dQvTGZRKGZwa90enCeEQ07wO45BGmYhmmYr2iJB/+DkmbHd8MHH1Fuv/Q88QAXr9cqnZjZCVi5IZ5bhiaI/aCYOWSjCTUJhA3JfkhbqS0VGTkRjhBxvLcCGIB9Suwu2MUAInaGOP5WDSCpqZcIRNgFaJEciqmhBTOB4GZMBo4LE0WYUo2WyswKQm8TIubilZg7efJ7ee+IvVb2vc02S68LLPrYSiGiptTQx74g2GdV5MemLjRi6FjZFs1T991MLIfMXITkZai/VW0ngStE1JaKiJyQ8cJuLoB35QrTNFMvBy7OkcBACuaneldJCIa+flCynrdRJiJF9aXiH194pKsHLxnFzH2OmqkirpMct63gco55L0//Vlg319IawvOCCz9t8yvh6GI+jIbHBYjQmEeJ/O03w0TN3pBzaGVc/RdByP7oZ4bVw/pIwJI0QxnfoT/Y6al7BWAXTDuDh2V7g+HJXHTIEVulmdS3EwGJWGZrEtxKCCOJNjHt2Bls4y+RMkMxZFVSXrMb7LYgSt/A5hsJD31tDCiYfVNLCWVA3Ach3l1xPd1qNel82rSAxM3ehAOR3QWTcOwO0Z+YSuVmrS1v0O2wyfINk/M6DQt5faHEAxFhMlpZHMwQtyMtAG+ccBoxaRzrnhwzoS4cQjDM92+iF43ySqmBg4BIR/cnAOn+FqsnOFM/kLJIje0UkrPqqSmVcB1zwqRIw5YfBVwwd36rYehiEX1xPd1oHtc55WkB1PyhzA0x9UNBCcBgwkom6H3ahi5QMt6YOAgcOotYNHl5AQPABVpbrufIWjkBiCDKZ120mQM1fOA9relGVKA5LepXYw9PR6EwjyqS6xoKEtPZ+JIioX02URkl+JkvW6EKNPe0EwAHFbOSDFyw/OSmXjGGTJWnUZmrgNu3UWiUyaLvmthKGJRAxE3+7tlGOJzEBa50QPqtylvBYxMXzJkMPNMsj31FuAeknweVfmR1jQbDeJk8KhGfmLk5oh0X4Tf5v2TpN/P6S3l4DKQnqEibMwbkD8mgYqbcAucdjNa4k0Dj3ohYd8T/aRhYyTDJwDPIGC0kMokveE4JmxykMjIDS+nnUGOwcSNHlBxw/w2DLnMPItse/cCh/5BblfOIU0f84TSIsF3E2kqrp5PtpGVYt1kRhPql+O9NiJuVs/MzIDZCiGiNOrxyxc3EWbi9bOr5IkwRw0ADuBDpH9MJDRq07ASMFnlL57BiGBBXSkMHDDk9mPA5dN7OZrDxI0eDDG/DUMhJbXADCF68/dbyTYTLfczSEmsiqn6FWQ7dIzM0goFgc4dAIBQ4+nYeYqUIa9pzYy4cdrJGkc8AakXTSLPTTgkjlnZx7fi7LlV8l7IaAJKG8jtsY7on+nRvI+RdxRZjOIYhv09+ee7YeJGD1ilFEMNp382+vvFV+qzjjQRc76Uo0oqde7eCfTtBQJuwFqGQ+FGuHxBFFtNWFCXvplSkVQ4SORmJCpyk6BL8eARIOjFBG9DG1+Hs+SKGyCiz8/J6PuzwUzMyAvy2VTMxI0e0LJWFrlhKGHxVZKgmXM++cojaMVUVDk4ADSuItuuHUC7FLXYfooYIVfOcMJkzMyhrNweIW6o6TfgBnxxymkFv81+vgWt1SVoKpfhtxFfjM7Wimhi6B2V+h0xccNIkSWNRNzsEXpF5RPMzZppgn6pR0nlXH3XwsgtDAbg6t8DF/03iWjk2dgOGhUZcvujf9C4mjSJa3+X9FUBgBlr8Z5gJl7TkpmUFBCRlnIHyFBTSwngd5GqpljDKwW/zf5wCz40t1rhi8WYit61HQBPihFoF2MGQyUrhJ5LH7SPguf5jJjyM0V+HR1zgZGTxCRoKc6b1vmMDFNSm5dT46uLiTl2mrlx9nlke+xfopman3M+3hfMxKdnyG8DTElLARGN/GL7bsJC2frecCsuXKzw/z3WVPR2NnWboR1LhZEl/S4fesYm9V6OpjBxk2kiU1J5pJIZjFSpLiHiZnBiiripXiCNoACAqnk4bpyFfpcPFpMhI2MXKLT/jscfwmQgJDXyiyVuwmGEu/cAALqK5ik3PcdKSzEzMUNDiixG0a+2S+j0nS8wcZNpxDJwlpJiMCKh4mZa5IbjgPPvJk0vDSbggu/jzWNDAEhKymbOXBSr1GYSh3OOegIRzfZiiJvh4zAF3fDyFixYskr5UE8xctNBqsRCQeI7AvRv3sfIG+jFARM3jNSgbeRZpRSDEYWYlpoauQGAuRuAWz8ANm4H5l2IrUdJ7xdF1UcawHEcysVycD9QKkRuxjqnPdbXQYTIAX4mPrq8SfmLlTYCZgcZkDl8nFSL+ScAm1OaN8ZgpAgVNx+0j+i7EI1h4ibTiNPAWeSGwYgkbuSG4pwBVLQiEArj3RMkcnPWnMyKG2BKxRQdf0FnfUVwcs/bZGuZq870bDAANcJMsb59wIkt5Pasc/LSc8XQBzoOZG/XGAKRc91yHCZuMg2L3DAYMakSIjdj3gB8wVDcx33QPgq3P4RKh0Xs05FJRHHjDgAVQjsH2rsqAn8n6aRcPvt0GJSmpCj1y8i2cwdw/DVye9a56vbFYMRgVpUDJTYTJgNhHM6jCeFM3GQS7wiZCQOwHjcMxhTKiswwG4kIGJrwx33cKweIv+XsuVXqRUMKVJcSEdY3Pin9H4+eIp4YgX2dI5jpJxcyK8849/+3d/dRUZX7HsC/MwMzvIigqIODIMhBESFAEVI088iSWua5ZCZ5KUn76x5UkOzkS2p1VNKOHV+XRncd7HYlrJZQkXkPkdLhKC/yYpIpecDGNEBIHF4UcGbfPwZIEjupwzzD9vtZywWzZ7P3d/ZS94/nefbz3PvJRkd1HfAj4GKh+Xu/39/78Yh+QalU9HRNneyaXkEOWNxYU3erjctIQGOdGVWJBgqlUtHTenOnrilJkvD3M+aVsu/60WoLGeXmCAD44ep1wEVnnnvHdLPXU03ZXxZgsOI6OhX2GOIdfO8n851hHkTdUgdIJvOSG90zNhNZyMNj3AEAx//VKDiJ5bC4saZa82Oh0AaJzUFko0Z0jbu505wb5+qa8X1jG9R2Sjwy9i4nxbOQUUO6i5s287iY7nE3XV1Tl5uuo7HqBACgc3gQoLK/95MNGg4EL/j59SN/uvdjEd1BVNfYtcLqRhhN8lghnMWNNXUtoAeP+/hNjkjGvN2dAQDfN7b2+f7/VZpbbR7xHwZnjZgJ1ruXUPjh6nXzhu6uqYYqAMD+4xcwEecAAE5+Ufd/wif+CszeCMR/BIydff/HI/qFIN1guGjsYLhxUzbrTLG4sabaSvNXFjdEfRozzFzc1DT0Xdz8vWu8zWxBXVLAL1puAGBkiPnr5XIYbnQio0iPcKW50LHITML2DsDUZYC/vNYSI9thp1Iicoz5ib7j/2oQnMYyWNxYi8kI1H1j/t7jIbFZiGzUmOHm4qb6yu3Fjb6xDd9cNkCpAGYFiFtXybOruDHcuIlr1ztvWdjzJA4WX4Si3YBxyovmbVwmgQaIKX7mrqn8qiuCk1gGixtrqT8D3LxuXlNqqK/oNEQ2yber5aa6j5abrPJLAMzjA9y7Bh6L4KS2g3vXGlOXrl4HdGHmN65ewKGCU3hE+TWUkMyzkHevPUVk42YHmv+uFlY3or554K8zxeLGWi4UmL96T+EEXER30F3cNLS0w3Cjs2e7JEnIrjAXN0+GeQrJditvd/O4m/NXWgDHIT3zVulaTuMJTZl5p3GPi4pHdNe8hjohzNsNJgnIOfWj6Dj3jcWNtXQXNz7TxOYgsmEuDvY9MxV/V9fSs73iYhNqGlrhaK8S9gj4rSbozJMHVl66BgAw+TwCAHhCVYiZqlPmnQLmCMlGdK+6f3HYf/wC2m8aYTJJqLx0DcfO1Zu7YAcQMY8bPGiMnbcUN9PFZiGycSGjXPHFt/Uo11/FpNHmqeEPlpjHsMRM0Ap7SupWD3m6AdCj9Hvzejz/dJiO6fgbnlT9E7gJwNXLPCcN0QAyf9Io7Mw7D/1PbViYVojG1g5832geOK+xU+KlmHF4YZovFArrT555t9hyYw3nvwBuNAHOw39+soKI+jSxq6AprDbPltrU1tHTJRX/8GhhuW4V1bVgZ7n+KmoaWvGnksEoMt2ymOXMtex+pgHHSW2HrfODYadUoEzfhO8b2+CsVsHTzRHtN03Y+Nm3eCW7EqYBMBeOTRQ3e/bsgY+PDxwcHBAZGYni4uJf3f/DDz9EQEAAHBwcEBwcjMOHD1sp6T2qyDB/DV4AqMT/1klkyx7xN0/OV3D+Cto6buK//1GDG50mjB85GOFdhY9onm6OCPEyj0+Y+Zdj+NHQjtcHvYKO6I1A3AEgdKHoiET35PcBWny6bBpeihmHXQvDULw2GgUvz8T6JwKhUAAHivR48cNTuGnji2wKL24OHjyIlJQUbNiwAWVlZQgJCUFMTAzq6+v73P/48eNYuHAhXnjhBZSXlyM2NhaxsbGorKy0cvLf6MevgbM55u9D/1NsFqIBYIJuMHzcnXCj04S1WZV45x/mFbeTZvnbVHP4imh/dMfR2Cnx2oIoqKctA8Y/ITYY0X0aP3IwEmf+DnNDdHDW2EGhUGDJNF9sjwuFSqlAVvkl/NeBMtzovPMCt6IpJEkS2r4UGRmJyZMnY/fu3QAAk8kELy8vLFu2DKtWrbpt/7i4OLS2tiInJ6dn28MPP4zQ0FDs27fv357PYDDA1dUV165dw+DBFlxR+GY70FIPmDrNc9oYO82Pf3/xKnDtIhD4H8CC/7Hc+Yhk7P1iPVYfOt3zOup37vjfFyJtqrgBgILvGlBc04jHgkYiUGf9FcqJrO2LM3X4Y0YZOm6aMGqII+aFecJvxCA42PfuhnVztEdk15pVlnI392+hfSQdHR0oLS3F6tWre7YplUpER0fjxIkTff7MiRMnkJKS0mtbTEwMsrOz+9y/vb0d7e0/L8JnMPTT1NKXy4G/xfT93lA/YM5b/XNeIhmKC/fCudpmfFT6AyaOHoK/LgixucIGAKb5D8O0rvE3RA+C6EAt9i+ejBUHK/DD1evY+eX5Pveb6O2GQ3+0wPIj90hocdPQ0ACj0QittvdEV1qtFmfPnu3zZ2pra/vcv7a2ts/9U1NT8dprr1km8K9R2QMqNaC0N4+rUdoDg7TmuS6ilgMOrv2fgUgmlEoFXv3DBLz6hwmioxDRL0z1G4ajKx9Fztc/orC6EZebrqPT+HMnkCRJGOfhIjDhA/Ao+OrVq3u19BgMBnh5eVn+RJ6TgHXymLaaiIjo1zip7bAg3AsLwvvhfmoBQoubYcOGQaVSoa6urtf2uro6eHj0PVGXh4fHXe2v0Wig0Yibqp2IiIisS+jTUmq1GpMmTUJeXl7PNpPJhLy8PEyZMqXPn5kyZUqv/QEgNzf3jvsTERHRg0V4t1RKSgoSEhIQHh6OiIgIbN++Ha2trVi8eDEAYNGiRfD09ERqaioAICkpCTNmzMC2bdswZ84cZGZm4uTJk0hLSxP5MYiIiMhGCC9u4uLicOXKFaxfvx61tbUIDQ3FkSNHegYN6/V6KJU/NzBNnToVGRkZeOWVV7BmzRr4+/sjOzsbQUFBoj4CERER2RDh89xYW7/Nc0NERET95m7u38JnKCYiIiKyJBY3REREJCssboiIiEhWWNwQERGRrLC4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFeHLL1hb94TMBoNBcBIiIiL6rbrv279lYYUHrrhpbm4GAHh5eQlOQkRERHerubkZrq6uv7rPA7e2lMlkwuXLl+Hi4gKFQmHRYxsMBnh5eeHixYtct6of8TpbB6+zdfA6Ww+vtXX013WWJAnNzc3Q6XS9FtTuywPXcqNUKjFq1Kh+PcfgwYP5D8cKeJ2tg9fZOnidrYfX2jr64zr/uxabbhxQTERERLLC4oaIiIhkhcWNBWk0GmzYsAEajUZ0FFnjdbYOXmfr4HW2Hl5r67CF6/zADSgmIiIieWPLDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNxYyJ49e+Dj4wMHBwdERkaiuLhYdCTZSU1NxeTJk+Hi4oIRI0YgNjYW586dEx1L1t544w0oFAokJyeLjiJLly5dwrPPPgt3d3c4OjoiODgYJ0+eFB1LVoxGI9atWwdfX184OjrCz88Pf/7zn3/T+kR0Z1999RXmzp0LnU4HhUKB7OzsXu9LkoT169dj5MiRcHR0RHR0NL777jur5WNxYwEHDx5ESkoKNmzYgLKyMoSEhCAmJgb19fWio8lKfn4+EhMTUVhYiNzcXHR2dmL27NlobW0VHU2WSkpK8Pbbb+Ohhx4SHUWWrl69iqioKNjb2+Pzzz/HmTNnsG3bNgwZMkR0NFnZsmUL9u7di927d+Pbb7/Fli1bsHXrVuzatUt0tAGttbUVISEh2LNnT5/vb926FTt37sS+fftQVFQEZ2dnxMTE4MaNG9YJKNF9i4iIkBITE3teG41GSafTSampqQJTyV99fb0EQMrPzxcdRXaam5slf39/KTc3V5oxY4aUlJQkOpLsvPzyy9K0adNEx5C9OXPmSEuWLOm1bd68eVJ8fLygRPIDQMrKyup5bTKZJA8PD+nNN9/s2dbU1CRpNBrp/ffft0omttzcp46ODpSWliI6Orpnm1KpRHR0NE6cOCEwmfxdu3YNADB06FDBSeQnMTERc+bM6fX3mizrk08+QXh4OJ5++mmMGDECYWFheOedd0THkp2pU6ciLy8PVVVVAIBTp06hoKAAjz/+uOBk8lVTU4Pa2tpe/3+4uroiMjLSavfFB27hTEtraGiA0WiEVqvttV2r1eLs2bOCUsmfyWRCcnIyoqKiEBQUJDqOrGRmZqKsrAwlJSWio8hadXU19u7di5SUFKxZswYlJSVYvnw51Go1EhISRMeTjVWrVsFgMCAgIAAqlQpGoxGbNm1CfHy86GiyVVtbCwB93he73+tvLG5oQEpMTERlZSUKCgpER5GVixcvIikpCbm5uXBwcBAdR9ZMJhPCw8OxefNmAEBYWBgqKyuxb98+FjcW9MEHH+DAgQPIyMjAhAkTUFFRgeTkZOh0Ol5nGWO31H0aNmwYVCoV6urqem2vq6uDh4eHoFTytnTpUuTk5ODo0aMYNWqU6DiyUlpaivr6ekycOBF2dnaws7NDfn4+du7cCTs7OxiNRtERZWPkyJEIDAzstW38+PHQ6/WCEsnTSy+9hFWrVuGZZ55BcHAwnnvuOaxYsQKpqamio8lW971P5H2Rxc19UqvVmDRpEvLy8nq2mUwm5OXlYcqUKQKTyY8kSVi6dCmysrLw5ZdfwtfXV3Qk2Zk1axZOnz6NioqKnj/h4eGIj49HRUUFVCqV6IiyERUVddtUBlVVVRg9erSgRPLU1tYGpbL3rU6lUsFkMglKJH++vr7w8PDodV80GAwoKiqy2n2R3VIWkJKSgoSEBISHhyMiIgLbt29Ha2srFi9eLDqarCQmJiIjIwMff/wxXFxcevpuXV1d4ejoKDidPLi4uNw2hsnZ2Rnu7u4c22RhK1aswNSpU7F582YsWLAAxcXFSEtLQ1pamuhosjJ37lxs2rQJ3t7emDBhAsrLy/HWW29hyZIloqMNaC0tLTh//nzP65qaGlRUVGDo0KHw9vZGcnIyNm7cCH9/f/j6+mLdunXQ6XSIjY21TkCrPJP1ANi1a5fk7e0tqdVqKSIiQiosLBQdSXYA9PknPT1ddDRZ46Pg/efTTz+VgoKCJI1GIwUEBEhpaWmiI8mOwWCQkpKSJG9vb8nBwUEaM2aMtHbtWqm9vV10tAHt6NGjff5/nJCQIEmS+XHwdevWSVqtVtJoNNKsWbOkc+fOWS2fQpI4TSMRERHJB8fcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcENGAcOzYMSgUCjQ1NYmOQkQ2jjMUE5FNevTRRxEaGort27cDADo6OvDTTz9Bq9VCoVCIDUdENo0LZxLRgKBWq+Hh4SE6BhENAOyWIiKb8/zzzyM/Px87duyAQqGAQqHA/v37e3VL7d+/H25ubsjJycG4cePg5OSE+fPno62tDe+++y58fHwwZMgQLF++HEajsefY7e3tWLlyJTw9PeHs7IzIyEgcO3ZMzAclon7Blhsisjk7duxAVVUVgoKC8PrrrwMAvvnmm9v2a2trw86dO5GZmYnm5mbMmzcPTz75JNzc3HD48GFUV1fjqaeeQlRUFOLi4gAAS5cuxZkzZ5CZmQmdToesrCw89thjOH36NPz9/a36OYmof7C4ISKb4+rqCrVaDScnp56uqLNnz962X2dnJ/bu3Qs/Pz8AwPz58/Hee++hrq4OgwYNQmBgIGbOnImjR48iLi4Oer0e6enp0Ov10Ol0AICVK1fiyJEjSE9Px+bNm633IYmo37C4IaIBy8nJqaewAQCtVgsfHx8MGjSo17b6+noAwOnTp2E0GjF27Nhex2lvb4e7u7t1QhNRv2NxQ0QDlr29fa/XCoWiz20mkwkA0NLSApVKhdLSUqhUql773VoQEdHAxuKGiGySWq3uNRDYEsLCwmA0GlFfX4/p06db9NhEZDv4tBQR2SQfHx8UFRXhwoULaGho6Gl9uR9jx45FfHw8Fi1ahEOHDqGmpgbFxcVITU3FZ599ZoHURGQLWNwQkU1auXIlVCoVAgMDMXz4cOj1eoscNz09HYsWLcKLL76IcePGITY2FiUlJfD29rbI8YlIPM5QTERERLLClhsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrLC4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhW/h938x67rnRK2AAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1028,17 +999,17 @@
      "start_time": "2020-03-21T21:20:06.250554Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.397416Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.397256Z",
-     "iopub.status.idle": "2024-06-03T10:30:15.511438Z",
-     "shell.execute_reply": "2024-06-03T10:30:15.510886Z"
+     "iopub.execute_input": "2024-06-03T14:31:50.035372Z",
+     "iopub.status.busy": "2024-06-03T14:31:50.035191Z",
+     "iopub.status.idle": "2024-06-03T14:31:50.106452Z",
+     "shell.execute_reply": "2024-06-03T14:31:50.106153Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1074,10 +1045,10 @@
      "start_time": "2020-03-21T21:20:07.755573Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:15.513438Z",
-     "iopub.status.busy": "2024-06-03T10:30:15.513277Z",
-     "iopub.status.idle": "2024-06-03T10:30:19.132873Z",
-     "shell.execute_reply": "2024-06-03T10:30:19.132312Z"
+     "iopub.execute_input": "2024-06-03T14:31:50.108228Z",
+     "iopub.status.busy": "2024-06-03T14:31:50.108088Z",
+     "iopub.status.idle": "2024-06-03T14:31:53.038747Z",
+     "shell.execute_reply": "2024-06-03T14:31:53.038395Z"
     }
    },
    "outputs": [],
@@ -1108,10 +1079,10 @@
      "start_time": "2020-03-21T21:20:15.951751Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:30:19.135386Z",
-     "iopub.status.busy": "2024-06-03T10:30:19.135210Z",
-     "iopub.status.idle": "2024-06-03T10:30:19.138920Z",
-     "shell.execute_reply": "2024-06-03T10:30:19.138289Z"
+     "iopub.execute_input": "2024-06-03T14:31:53.040706Z",
+     "iopub.status.busy": "2024-06-03T14:31:53.040458Z",
+     "iopub.status.idle": "2024-06-03T14:31:53.042707Z",
+     "shell.execute_reply": "2024-06-03T14:31:53.042470Z"
     }
    },
    "outputs": [
@@ -1119,7 +1090,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time discretization error = 1.9e-05\n"
+      "Time discretization error = 2.9e-05\n"
      ]
     }
    ],
@@ -1153,7 +1124,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
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   "toc": {
    "base_numbering": 1,
diff --git a/master/notebooks/06_example_3states.html b/master/notebooks/06_example_3states.html
index 631b56f1..b947b128 100644
--- a/master/notebooks/06_example_3states.html
+++ b/master/notebooks/06_example_3states.html
@@ -1,32 +1,33 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Optimization of a Dissipative Quantum Gate &mdash; Krotov 1.3.0 documentation</title><link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
-    <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/graphviz.css" type="text/css" />
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-      <link rel="stylesheet" href="../_static/nbsphinx-code-cells.css" type="text/css" />
+  <title>Optimization of a Dissipative Quantum Gate &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=649a27d8" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
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+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
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         <script type="text/x-mathjax-config">MathJax.Hub.Config({"extensions": ["tex2jax.js"], "jax": ["input/TeX", "output/SVG"], "TeX": {"extensions": ["AMSmath.js", "AMSsymbols.js"], "Macros": {"tr": ["{\\operatorname{tr}}", 0], "diag": ["{\\operatorname{diag}}", 0], "abs": ["{\\operatorname{abs}}", 0], "pop": ["{\\operatorname{pop}}", 0], "ee": ["{\\text{e}}", 0], "ii": ["{\\text{i}}", 0], "aux": ["{\\text{aux}}", 0], "opt": ["{\\text{opt}}", 0], "tgt": ["{\\text{tgt}}", 0], "init": ["{\\text{init}}", 0], "lab": ["{\\text{lab}}", 0], "rwa": ["{\\text{rwa}}", 0], "bra": ["{\\langle#1\\vert}", 1], "ket": ["{\\vert#1\\rangle}", 1], "Bra": ["{\\left\\langle#1\\right\\vert}", 1], "Braket": ["{\\left\\langle #1\\vphantom{#2} \\mid #2\\vphantom{#1}\\right\\rangle}", 2], "ketbra": ["{\\vert#1\\rangle\\!\\langle#2\\vert}", 2], "Ket": ["{\\left\\vert#1\\right\\rangle}", 1], "mat": ["{\\mathbf{#1}}", 1], "op": ["{\\hat{#1}}", 1], "Op": ["{\\hat{#1}}", 1], "dd": ["{\\,\\text{d}}", 0], "daggered": ["{^{\\dagger}}", 0], "transposed": ["{^{\\text{T}}}", 0], "Liouville": ["{\\mathcal{L}}", 0], "DynMap": ["{\\mathcal{E}}", 0], "identity": ["{\\mathbf{1}}", 0], "Norm": ["{\\left\\lVert#1\\right\\rVert}", 1], "norm": ["{\\lVert#1\\rVert}", 1], "Abs": ["{\\left\\vert#1\\right\\vert}", 1], "avg": ["{\\langle#1\\rangle}", 1], "Avg": ["{\\left\\langle#1\\right\\rangle}", 1], "AbsSq": ["{\\left\\vert#1\\right\\vert^2}", 1], "Re": ["{\\operatorname{Re}}", 0], "Im": ["{\\operatorname{Im}}", 0], "Real": ["{\\mathbb{R}}", 0], "Complex": ["{\\mathbb{C}}", 0], "Integer": ["{\\mathbb{N}}", 0]}}, "tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "tex2jax_ignore|mathjax_ignore|document", "processClass": "tex2jax_process|mathjax_process|math|output_area"}})</script>
+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -46,7 +47,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -56,7 +57,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -152,9 +153,9 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/06_example_3states.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/06_example_3states.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-of-a-Dissipative-Quantum-Gate">
-<h1>Optimization of a Dissipative Quantum Gate<a class="headerlink" href="#Optimization-of-a-Dissipative-Quantum-Gate" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/06_example_3states.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/06_example_3states.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-of-a-Dissipative-Quantum-Gate">
+<h1>Optimization of a Dissipative Quantum Gate<a class="headerlink" href="#Optimization-of-a-Dissipative-Quantum-Gate" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -182,16 +183,15 @@ <h1>Optimization of a Dissipative Quantum Gate<a class="headerlink" href="#Optim
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
-numpy     : 1.24.4
 qutip     : 4.7.6
-scipy     : 1.10.1
+krotov    : 1.3.0+dev
+sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]
 matplotlib: 3.7.5
-krotov    : 1.3.0
-sys       : 3.8.16 (default, May 26 2024, 19:41:26)
-[GCC 12.2.0]
+numpy     : 1.26.4
+scipy     : 1.12.0
 
 </pre></div></div>
 </div>
@@ -199,8 +199,8 @@ <h1>Optimization of a Dissipative Quantum Gate<a class="headerlink" href="#Optim
 <p>This example illustrates the optimization for a quantum gate in an open quantum system, where the dynamics is governed by the Liouville-von Neumann equation. A naive extension of a gate optimization to Liouville space would seem to imply that it is necessary to optimize over the full basis of Liouville space (16 matrices, for a two-qubit gate). However, <a class="reference external" href="https://michaelgoerz.net/research/Goerz_NJP2014.pdf">Goerz et al., New J. Phys. 16, 055012 (2014)</a> showed that is not necessary, but that a
 set of 3 density matrices is sufficient to track the optimization.</p>
 <p>This example reproduces the “Example II” from that paper, considering the optimization towards a <span class="math notranslate nohighlight">\(\sqrt{\text{iSWAP}}\)</span> two-qubit gate on a system of two transmons with a shared transmission line resonator.</p>
-<div class="section" id="The-two-transmon-system">
-<h2>The two-transmon system<a class="headerlink" href="#The-two-transmon-system" title="Permalink to this headline">¶</a></h2>
+<section id="The-two-transmon-system">
+<h2>The two-transmon system<a class="headerlink" href="#The-two-transmon-system" title="Link to this heading">¶</a></h2>
 <p>We consider the Hamiltonian from Eq (17) in the paper, in the rotating wave approximation, together with spontaneous decay and dephasing of each qubit. Alltogether, we define the Liouvillian as follows:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -351,9 +351,9 @@ <h2>The two-transmon system<a class="headerlink" href="#The-two-transmon-system"
 <img alt="../_images/notebooks_06_example_3states_22_0.png" src="../_images/notebooks_06_example_3states_22_0.png" />
 </div>
 </div>
-</div>
-<div class="section" id="Optimization-objectives">
-<h2>Optimization objectives<a class="headerlink" href="#Optimization-objectives" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization-objectives">
+<h2>Optimization objectives<a class="headerlink" href="#Optimization-objectives" title="Link to this heading">¶</a></h2>
 <p>Our target gate is <span class="math notranslate nohighlight">\(\Op{O} = \sqrt{\text{iSWAP}}\)</span>:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
@@ -433,9 +433,9 @@ <h2>Optimization objectives<a class="headerlink" href="#Optimization-objectives"
 </pre></div></div>
 </div>
 <p>The use of <code class="docutils literal notranslate"><span class="pre">normalize_weights=False</span></code> is because we have included the purities in the weights, as discussed above.</p>
-</div>
-<div class="section" id="Dynamics-under-the-Guess-Pulse">
-<h2>Dynamics under the Guess Pulse<a class="headerlink" href="#Dynamics-under-the-Guess-Pulse" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Dynamics-under-the-Guess-Pulse">
+<h2>Dynamics under the Guess Pulse<a class="headerlink" href="#Dynamics-under-the-Guess-Pulse" title="Link to this heading">¶</a></h2>
 <p>For numerical efficiency, both for the analysis of the guess/optimized controls, we will use a stateful density matrix propagator:</p>
 <p>A true physical measure for the success of the optimization is the “average gate fidelity”. Evaluating the fidelity requires to simulate the dynamics of the full basis of Liouville space:</p>
 <div class="nbinput nblast docutils container">
@@ -542,9 +542,9 @@ <h2>Dynamics under the Guess Pulse<a class="headerlink" href="#Dynamics-under-th
 <img alt="../_images/notebooks_06_example_3states_47_0.png" src="../_images/notebooks_06_example_3states_47_0.png" />
 </div>
 </div>
-</div>
-<div class="section" id="Optimization">
-<h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization">
+<h2>Optimization<a class="headerlink" href="#Optimization" title="Link to this heading">¶</a></h2>
 <p>We now define the optimization parameters for the controls, the Krotov step size <span class="math notranslate nohighlight">\(\lambda_a\)</span> and the update-shape that will ensure that the pulse switch-on and switch-off stays intact.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
@@ -584,10 +584,10 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 iter.      J_T   ∑∫gₐ(t)dt          J       ΔJ_T         ΔJ  secs
-0     1.22e-01    0.00e+00   1.22e-01        n/a        n/a     6
-1     7.49e-02    2.34e-02   9.83e-02  -4.67e-02  -2.33e-02    20
-2     7.41e-02    4.06e-04   7.45e-02  -8.12e-04  -4.06e-04    20
-3     7.33e-02    3.78e-04   7.37e-02  -7.55e-04  -3.77e-04    21
+0     1.22e-01    0.00e+00   1.22e-01        n/a        n/a     4
+1     7.49e-02    2.34e-02   9.83e-02  -4.67e-02  -2.33e-02    11
+2     7.41e-02    4.06e-04   7.45e-02  -8.12e-04  -4.06e-04    11
+3     7.33e-02    3.78e-04   7.37e-02  -7.55e-04  -3.77e-04    11
 </pre></div></div>
 </div>
 <p>(this takes a while)…</p>
@@ -636,9 +636,9 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 - Ended at 2024-06-02 09:36:53 (12:53:06)
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Optimization-result">
-<h2>Optimization result<a class="headerlink" href="#Optimization-result" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization-result">
+<h2>Optimization result<a class="headerlink" href="#Optimization-result" title="Link to this heading">¶</a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
 </pre></div>
@@ -752,26 +752,15 @@ <h2>Optimization result<a class="headerlink" href="#Optimization-result" title="
 </pre></div>
 </div>
 </div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmtt10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmb10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmss10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;DejaVu Sans Display&#39;] not found. Falling back to DejaVu Sans.
-</pre></div></div>
-</div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_06_example_3states_66_1.png" src="../_images/notebooks_06_example_3states_66_1.png" />
-</div>
-</div>
+<img alt="../_images/notebooks_06_example_3states_66_0.png" src="../_images/notebooks_06_example_3states_66_0.png" />
 </div>
 </div>
+</section>
+</section>
 
 
            </div>
@@ -785,7 +774,7 @@ <h2>Optimization result<a class="headerlink" href="#Optimization-result" title="
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/06_example_3states.ipynb b/master/notebooks/06_example_3states.ipynb
index b168459b..d75f4faf 100644
--- a/master/notebooks/06_example_3states.ipynb
+++ b/master/notebooks/06_example_3states.ipynb
@@ -16,10 +16,10 @@
      "start_time": "2020-03-21T21:31:16.947553Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.206961Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.206139Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.725494Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.724916Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.376969Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.376836Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.861352Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.860942Z"
     }
    },
    "outputs": [
@@ -28,16 +28,15 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "numpy     : 1.24.4\n",
       "qutip     : 4.7.6\n",
-      "scipy     : 1.10.1\n",
+      "krotov    : 1.3.0+dev\n",
+      "sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
       "matplotlib: 3.7.5\n",
-      "krotov    : 1.3.0\n",
-      "sys       : 3.8.16 (default, May 26 2024, 19:41:26) \n",
-      "[GCC 12.2.0]\n",
+      "numpy     : 1.26.4\n",
+      "scipy     : 1.12.0\n",
       "\n"
      ]
     }
@@ -124,10 +123,10 @@
      "start_time": "2020-03-21T21:31:20.724436Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.752291Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.752017Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.758455Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.757921Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.882411Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.882238Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.886521Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.886280Z"
     },
     "lines_to_next_cell": 2
    },
@@ -179,10 +178,10 @@
      "start_time": "2020-03-21T21:31:22.096718Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.760380Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.760230Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.762898Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.762342Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.887847Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.887740Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.889367Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.889143Z"
     }
    },
    "outputs": [],
@@ -216,10 +215,10 @@
      "start_time": "2020-03-21T21:31:24.057786Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.764918Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.764733Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.768205Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.767589Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.890703Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.890597Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.892721Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.892434Z"
     }
    },
    "outputs": [],
@@ -246,10 +245,10 @@
      "start_time": "2020-03-21T21:31:24.964035Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.770016Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.769863Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.772690Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.772126Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.894184Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.894099Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.895767Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.895476Z"
     }
    },
    "outputs": [],
@@ -273,10 +272,10 @@
      "start_time": "2020-03-21T21:31:26.257115Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.774616Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.774466Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.776836Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.776461Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.897109Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.897026Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.898648Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.898398Z"
     }
    },
    "outputs": [],
@@ -300,10 +299,10 @@
      "start_time": "2020-03-21T21:31:27.288042Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.778668Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.778519Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.781301Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.780802Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.900017Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.899936Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.901808Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.901551Z"
     }
    },
    "outputs": [],
@@ -329,10 +328,10 @@
      "start_time": "2020-03-21T21:31:28.060909Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.783146Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.782993Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.785342Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.784841Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.903239Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.903160Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.905094Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.904643Z"
     }
    },
    "outputs": [],
@@ -357,10 +356,10 @@
      "start_time": "2020-03-21T21:31:29.232057Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.787239Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.787014Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.795619Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.795235Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.906732Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.906637Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.912774Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.912364Z"
     }
    },
    "outputs": [],
@@ -386,10 +385,10 @@
      "start_time": "2020-03-21T21:31:30.666954Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.797523Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.797371Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.800583Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.800165Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.914704Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.914587Z",
+     "iopub.status.idle": "2024-06-03T14:24:00.916976Z",
+     "shell.execute_reply": "2024-06-03T14:24:00.916732Z"
     }
    },
    "outputs": [],
@@ -415,10 +414,10 @@
      "start_time": "2020-03-21T21:31:32.090987Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.802338Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.802185Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.952075Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.951495Z"
+     "iopub.execute_input": "2024-06-03T14:24:00.918326Z",
+     "iopub.status.busy": "2024-06-03T14:24:00.918231Z",
+     "iopub.status.idle": "2024-06-03T14:24:01.154341Z",
+     "shell.execute_reply": "2024-06-03T14:24:01.112532Z"
     }
    },
    "outputs": [
@@ -460,10 +459,10 @@
      "start_time": "2020-03-21T21:31:33.930103Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.954121Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.953956Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.957576Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.957179Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.172595Z",
+     "iopub.status.busy": "2024-06-03T14:24:01.172419Z",
+     "iopub.status.idle": "2024-06-03T14:24:01.191719Z",
+     "shell.execute_reply": "2024-06-03T14:24:01.190785Z"
     }
    },
    "outputs": [],
@@ -511,10 +510,10 @@
      "start_time": "2020-03-21T21:31:35.459426Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.959488Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.959336Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.963366Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.962981Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.200433Z",
+     "iopub.status.busy": "2024-06-03T14:24:01.200293Z",
+     "iopub.status.idle": "2024-06-03T14:24:01.203500Z",
+     "shell.execute_reply": "2024-06-03T14:24:01.203147Z"
     }
    },
    "outputs": [],
@@ -542,10 +541,10 @@
      "start_time": "2020-03-21T21:31:36.671546Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.965219Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.965067Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.967939Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.967347Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.205079Z",
+     "iopub.status.busy": "2024-06-03T14:24:01.204980Z",
+     "iopub.status.idle": "2024-06-03T14:24:01.207084Z",
+     "shell.execute_reply": "2024-06-03T14:24:01.206814Z"
     }
    },
    "outputs": [],
@@ -571,10 +570,10 @@
      "start_time": "2020-03-21T21:31:38.576793Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.969783Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.969630Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.992390Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.991924Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.209130Z",
+     "iopub.status.busy": "2024-06-03T14:24:01.208943Z",
+     "iopub.status.idle": "2024-06-03T14:24:01.220062Z",
+     "shell.execute_reply": "2024-06-03T14:24:01.219711Z"
     }
    },
    "outputs": [
@@ -640,10 +639,10 @@
      "start_time": "2020-03-21T21:31:41.254299Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:49.994375Z",
-     "iopub.status.busy": "2024-06-03T10:32:49.994225Z",
-     "iopub.status.idle": "2024-06-03T10:32:49.998709Z",
-     "shell.execute_reply": "2024-06-03T10:32:49.998206Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.222133Z",
+     "iopub.status.busy": "2024-06-03T14:24:01.222005Z",
+     "iopub.status.idle": "2024-06-03T14:24:01.224696Z",
+     "shell.execute_reply": "2024-06-03T14:24:01.224461Z"
     }
    },
    "outputs": [],
@@ -667,10 +666,10 @@
      "start_time": "2020-03-21T21:31:42.544173Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:50.000552Z",
-     "iopub.status.busy": "2024-06-03T10:32:50.000403Z",
-     "iopub.status.idle": "2024-06-03T10:32:50.002997Z",
-     "shell.execute_reply": "2024-06-03T10:32:50.002612Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.226141Z",
+     "iopub.status.busy": "2024-06-03T14:24:01.226034Z",
+     "iopub.status.idle": "2024-06-03T14:24:01.227807Z",
+     "shell.execute_reply": "2024-06-03T14:24:01.227574Z"
     }
    },
    "outputs": [],
@@ -691,10 +690,10 @@
      "start_time": "2020-03-21T21:31:43.260810Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:50.004811Z",
-     "iopub.status.busy": "2024-06-03T10:32:50.004662Z",
-     "iopub.status.idle": "2024-06-03T10:33:25.357518Z",
-     "shell.execute_reply": "2024-06-03T10:33:25.356919Z"
+     "iopub.execute_input": "2024-06-03T14:24:01.229193Z",
+     "iopub.status.busy": "2024-06-03T14:24:01.229095Z",
+     "iopub.status.idle": "2024-06-03T14:24:21.392136Z",
+     "shell.execute_reply": "2024-06-03T14:24:21.391810Z"
     },
     "scrolled": true
    },
@@ -712,10 +711,10 @@
      "start_time": "2020-03-21T21:32:06.511175Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.359682Z",
-     "iopub.status.busy": "2024-06-03T10:33:25.359502Z",
-     "iopub.status.idle": "2024-06-03T10:33:25.370328Z",
-     "shell.execute_reply": "2024-06-03T10:33:25.369929Z"
+     "iopub.execute_input": "2024-06-03T14:24:21.393955Z",
+     "iopub.status.busy": "2024-06-03T14:24:21.393845Z",
+     "iopub.status.idle": "2024-06-03T14:24:21.399079Z",
+     "shell.execute_reply": "2024-06-03T14:24:21.398841Z"
     }
    },
    "outputs": [
@@ -754,10 +753,10 @@
      "start_time": "2020-03-21T21:32:18.385960Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.372310Z",
-     "iopub.status.busy": "2024-06-03T10:33:25.372133Z",
-     "iopub.status.idle": "2024-06-03T10:33:25.375923Z",
-     "shell.execute_reply": "2024-06-03T10:33:25.375382Z"
+     "iopub.execute_input": "2024-06-03T14:24:21.400577Z",
+     "iopub.status.busy": "2024-06-03T14:24:21.400492Z",
+     "iopub.status.idle": "2024-06-03T14:24:21.402593Z",
+     "shell.execute_reply": "2024-06-03T14:24:21.402330Z"
     }
    },
    "outputs": [],
@@ -774,10 +773,10 @@
      "start_time": "2020-03-21T21:32:19.323843Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.377841Z",
-     "iopub.status.busy": "2024-06-03T10:33:25.377663Z",
-     "iopub.status.idle": "2024-06-03T10:33:25.380705Z",
-     "shell.execute_reply": "2024-06-03T10:33:25.380181Z"
+     "iopub.execute_input": "2024-06-03T14:24:21.403895Z",
+     "iopub.status.busy": "2024-06-03T14:24:21.403813Z",
+     "iopub.status.idle": "2024-06-03T14:24:21.405693Z",
+     "shell.execute_reply": "2024-06-03T14:24:21.405452Z"
     }
    },
    "outputs": [],
@@ -800,10 +799,10 @@
      "start_time": "2020-03-21T21:32:20.180098Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.382499Z",
-     "iopub.status.busy": "2024-06-03T10:33:25.382349Z",
-     "iopub.status.idle": "2024-06-03T10:33:25.386203Z",
-     "shell.execute_reply": "2024-06-03T10:33:25.385699Z"
+     "iopub.execute_input": "2024-06-03T14:24:21.407142Z",
+     "iopub.status.busy": "2024-06-03T14:24:21.407059Z",
+     "iopub.status.idle": "2024-06-03T14:24:21.409406Z",
+     "shell.execute_reply": "2024-06-03T14:24:21.409155Z"
     }
    },
    "outputs": [],
@@ -830,10 +829,10 @@
      "start_time": "2020-03-21T21:32:20.943081Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:25.388130Z",
-     "iopub.status.busy": "2024-06-03T10:33:25.387980Z",
-     "iopub.status.idle": "2024-06-03T10:33:34.914215Z",
-     "shell.execute_reply": "2024-06-03T10:33:34.913591Z"
+     "iopub.execute_input": "2024-06-03T14:24:21.410587Z",
+     "iopub.status.busy": "2024-06-03T14:24:21.410509Z",
+     "iopub.status.idle": "2024-06-03T14:24:26.853774Z",
+     "shell.execute_reply": "2024-06-03T14:24:26.853482Z"
     }
    },
    "outputs": [
@@ -877,10 +876,10 @@
      "start_time": "2020-03-21T21:32:41.070283Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:34.916585Z",
-     "iopub.status.busy": "2024-06-03T10:33:34.916387Z",
-     "iopub.status.idle": "2024-06-03T10:33:34.919528Z",
-     "shell.execute_reply": "2024-06-03T10:33:34.919127Z"
+     "iopub.execute_input": "2024-06-03T14:24:26.855582Z",
+     "iopub.status.busy": "2024-06-03T14:24:26.855445Z",
+     "iopub.status.idle": "2024-06-03T14:24:26.857471Z",
+     "shell.execute_reply": "2024-06-03T14:24:26.857106Z"
     }
    },
    "outputs": [],
@@ -911,10 +910,10 @@
      "start_time": "2020-03-21T21:32:50.996511Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:33:34.921618Z",
-     "iopub.status.busy": "2024-06-03T10:33:34.921438Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.755388Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.754775Z"
+     "iopub.execute_input": "2024-06-03T14:24:26.858929Z",
+     "iopub.status.busy": "2024-06-03T14:24:26.858842Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.112930Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.112641Z"
     }
    },
    "outputs": [
@@ -923,28 +922,28 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T   ∑∫gₐ(t)dt          J       ΔJ_T         ΔJ  secs\n",
-      "0     1.22e-01    0.00e+00   1.22e-01        n/a        n/a     6\n"
+      "0     1.22e-01    0.00e+00   1.22e-01        n/a        n/a     4\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1     7.49e-02    2.34e-02   9.83e-02  -4.67e-02  -2.33e-02    20\n"
+      "1     7.49e-02    2.34e-02   9.83e-02  -4.67e-02  -2.33e-02    11\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2     7.41e-02    4.06e-04   7.45e-02  -8.12e-04  -4.06e-04    20\n"
+      "2     7.41e-02    4.06e-04   7.45e-02  -8.12e-04  -4.06e-04    11\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3     7.33e-02    3.78e-04   7.37e-02  -7.55e-04  -3.77e-04    21\n"
+      "3     7.33e-02    3.78e-04   7.37e-02  -7.55e-04  -3.77e-04    11\n"
      ]
     }
    ],
@@ -976,10 +975,10 @@
      "start_time": "2020-03-21T21:34:37.817889Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.757492Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.757332Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.764707Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.764229Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.114621Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.114525Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.121494Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.121174Z"
     }
    },
    "outputs": [],
@@ -1010,10 +1009,10 @@
      "start_time": "2020-03-21T21:34:38.622364Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.766601Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.766453Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.769798Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.769427Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.123221Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.123112Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.125280Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.125040Z"
     }
    },
    "outputs": [
@@ -1054,10 +1053,10 @@
      "start_time": "2020-03-21T21:34:47.074533Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.771684Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.771538Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.774100Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.773642Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.126643Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.126550Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.128783Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.128329Z"
     }
    },
    "outputs": [],
@@ -1074,10 +1073,10 @@
      "start_time": "2020-03-21T21:34:48.073785Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.776217Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.775846Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.907336Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.906697Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.130233Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.130133Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.197221Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.196834Z"
     }
    },
    "outputs": [
@@ -1105,10 +1104,10 @@
      "start_time": "2020-03-21T21:34:49.038956Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.909439Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.909277Z",
-     "iopub.status.idle": "2024-06-03T10:34:44.912273Z",
-     "shell.execute_reply": "2024-06-03T10:34:44.911713Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.198929Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.198788Z",
+     "iopub.status.idle": "2024-06-03T14:25:06.200859Z",
+     "shell.execute_reply": "2024-06-03T14:25:06.200553Z"
     }
    },
    "outputs": [],
@@ -1130,10 +1129,10 @@
      "start_time": "2020-03-21T21:34:49.807447Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:34:44.914185Z",
-     "iopub.status.busy": "2024-06-03T10:34:44.914032Z",
-     "iopub.status.idle": "2024-06-03T10:35:34.124544Z",
-     "shell.execute_reply": "2024-06-03T10:35:34.123926Z"
+     "iopub.execute_input": "2024-06-03T14:25:06.202971Z",
+     "iopub.status.busy": "2024-06-03T14:25:06.202851Z",
+     "iopub.status.idle": "2024-06-03T14:25:34.786184Z",
+     "shell.execute_reply": "2024-06-03T14:25:34.785903Z"
     },
     "scrolled": true
    },
@@ -1151,10 +1150,10 @@
      "start_time": "2020-03-21T21:35:25.571194Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:34.126947Z",
-     "iopub.status.busy": "2024-06-03T10:35:34.126760Z",
-     "iopub.status.idle": "2024-06-03T10:35:34.137510Z",
-     "shell.execute_reply": "2024-06-03T10:35:34.137017Z"
+     "iopub.execute_input": "2024-06-03T14:25:34.787921Z",
+     "iopub.status.busy": "2024-06-03T14:25:34.787819Z",
+     "iopub.status.idle": "2024-06-03T14:25:34.793068Z",
+     "shell.execute_reply": "2024-06-03T14:25:34.792816Z"
     }
    },
    "outputs": [
@@ -1179,10 +1178,10 @@
      "start_time": "2020-03-21T21:35:26.698539Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:34.139820Z",
-     "iopub.status.busy": "2024-06-03T10:35:34.139458Z",
-     "iopub.status.idle": "2024-06-03T10:35:34.142794Z",
-     "shell.execute_reply": "2024-06-03T10:35:34.142333Z"
+     "iopub.execute_input": "2024-06-03T14:25:34.795116Z",
+     "iopub.status.busy": "2024-06-03T14:25:34.794991Z",
+     "iopub.status.idle": "2024-06-03T14:25:34.796931Z",
+     "shell.execute_reply": "2024-06-03T14:25:34.796672Z"
     }
    },
    "outputs": [],
@@ -1212,10 +1211,10 @@
      "start_time": "2020-03-21T21:35:27.831020Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:34.144957Z",
-     "iopub.status.busy": "2024-06-03T10:35:34.144764Z",
-     "iopub.status.idle": "2024-06-03T10:35:46.772654Z",
-     "shell.execute_reply": "2024-06-03T10:35:46.772043Z"
+     "iopub.execute_input": "2024-06-03T14:25:34.798436Z",
+     "iopub.status.busy": "2024-06-03T14:25:34.798332Z",
+     "iopub.status.idle": "2024-06-03T14:25:42.069102Z",
+     "shell.execute_reply": "2024-06-03T14:25:42.068846Z"
     }
    },
    "outputs": [
@@ -1245,10 +1244,10 @@
      "start_time": "2020-03-21T21:35:41.361375Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:46.776326Z",
-     "iopub.status.busy": "2024-06-03T10:35:46.776127Z",
-     "iopub.status.idle": "2024-06-03T10:35:46.779205Z",
-     "shell.execute_reply": "2024-06-03T10:35:46.778773Z"
+     "iopub.execute_input": "2024-06-03T14:25:42.071910Z",
+     "iopub.status.busy": "2024-06-03T14:25:42.071772Z",
+     "iopub.status.idle": "2024-06-03T14:25:42.073791Z",
+     "shell.execute_reply": "2024-06-03T14:25:42.073503Z"
     }
    },
    "outputs": [],
@@ -1270,44 +1269,16 @@
      "start_time": "2020-03-21T21:35:42.567254Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:46.781268Z",
-     "iopub.status.busy": "2024-06-03T10:35:46.781088Z",
-     "iopub.status.idle": "2024-06-03T10:35:47.472461Z",
-     "shell.execute_reply": "2024-06-03T10:35:47.471816Z"
+     "iopub.execute_input": "2024-06-03T14:25:42.075128Z",
+     "iopub.status.busy": "2024-06-03T14:25:42.075024Z",
+     "iopub.status.idle": "2024-06-03T14:25:42.258918Z",
+     "shell.execute_reply": "2024-06-03T14:25:42.258583Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
         <script type="text/x-mathjax-config">MathJax.Hub.Config({"extensions": ["tex2jax.js"], "jax": ["input/TeX", "output/SVG"], "TeX": {"extensions": ["AMSmath.js", "AMSsymbols.js"], "Macros": {"tr": ["{\\operatorname{tr}}", 0], "diag": ["{\\operatorname{diag}}", 0], "abs": ["{\\operatorname{abs}}", 0], "pop": ["{\\operatorname{pop}}", 0], "ee": ["{\\text{e}}", 0], "ii": ["{\\text{i}}", 0], "aux": ["{\\text{aux}}", 0], "opt": ["{\\text{opt}}", 0], "tgt": ["{\\text{tgt}}", 0], "init": ["{\\text{init}}", 0], "lab": ["{\\text{lab}}", 0], "rwa": ["{\\text{rwa}}", 0], "bra": ["{\\langle#1\\vert}", 1], "ket": ["{\\vert#1\\rangle}", 1], "Bra": ["{\\left\\langle#1\\right\\vert}", 1], "Braket": ["{\\left\\langle #1\\vphantom{#2} \\mid #2\\vphantom{#1}\\right\\rangle}", 2], "ketbra": ["{\\vert#1\\rangle\\!\\langle#2\\vert}", 2], "Ket": ["{\\left\\vert#1\\right\\rangle}", 1], "mat": ["{\\mathbf{#1}}", 1], "op": ["{\\hat{#1}}", 1], "Op": ["{\\hat{#1}}", 1], "dd": ["{\\,\\text{d}}", 0], "daggered": ["{^{\\dagger}}", 0], "transposed": ["{^{\\text{T}}}", 0], "Liouville": ["{\\mathcal{L}}", 0], "DynMap": ["{\\mathcal{E}}", 0], "identity": ["{\\mathbf{1}}", 0], "Norm": ["{\\left\\lVert#1\\right\\rVert}", 1], "norm": ["{\\lVert#1\\rVert}", 1], "Abs": ["{\\left\\vert#1\\right\\vert}", 1], "avg": ["{\\langle#1\\rangle}", 1], "Avg": ["{\\left\\langle#1\\right\\rangle}", 1], "AbsSq": ["{\\left\\vert#1\\right\\vert^2}", 1], "Re": ["{\\operatorname{Re}}", 0], "Im": ["{\\operatorname{Im}}", 0], "Real": ["{\\mathbb{R}}", 0], "Complex": ["{\\mathbb{C}}", 0], "Integer": ["{\\mathbb{N}}", 0]}}, "tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "tex2jax_ignore|mathjax_ignore|document", "processClass": "tex2jax_process|mathjax_process|math|output_area"}})</script>
+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -47,7 +47,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -57,7 +57,7 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -152,9 +152,9 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/07_example_PE.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/07_example_PE.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-towards-a-Perfect-Entangler">
-<h1>Optimization towards a Perfect Entangler<a class="headerlink" href="#Optimization-towards-a-Perfect-Entangler" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/07_example_PE.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/07_example_PE.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-towards-a-Perfect-Entangler">
+<h1>Optimization towards a Perfect Entangler<a class="headerlink" href="#Optimization-towards-a-Perfect-Entangler" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -182,15 +182,15 @@ <h1>Optimization towards a Perfect Entangler<a class="headerlink" href="#Optimiz
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
+scipy      : 1.12.0
 qutip      : 4.7.6
-krotov     : 1.3.0
+krotov     : 1.3.0+dev
 weylchamber: 0.4.0
-scipy      : 1.10.1
 matplotlib : 3.7.5
-numpy      : 1.24.4
+numpy      : 1.26.4
 
 </pre></div></div>
 </div>
@@ -210,8 +210,8 @@ <h1>Optimization towards a Perfect Entangler<a class="headerlink" href="#Optimiz
 </li>
 </ul>
 <p>for details.</p>
-<div class="section" id="Hamiltonian">
-<h2>Hamiltonian<a class="headerlink" href="#Hamiltonian" title="Permalink to this headline">¶</a></h2>
+<section id="Hamiltonian">
+<h2>Hamiltonian<a class="headerlink" href="#Hamiltonian" title="Link to this heading">¶</a></h2>
 <p>We consider a generic two-qubit Hamiltonian (motivated from the example of two superconducting transmon qubits, truncated to the logical subspace),</p>
 <div class="math notranslate nohighlight">
 \[\begin{equation}
@@ -355,9 +355,9 @@ <h2>Hamiltonian<a class="headerlink" href="#Hamiltonian" title="Permalink to thi
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Objectives-for-a-perfect-entangler">
-<h2>Objectives for a perfect entangler<a class="headerlink" href="#Objectives-for-a-perfect-entangler" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Objectives-for-a-perfect-entangler">
+<h2>Objectives for a perfect entangler<a class="headerlink" href="#Objectives-for-a-perfect-entangler" title="Link to this heading">¶</a></h2>
 <p>Our optimization target is the closest perfectly entangling gate, quantified by the perfect-entangler functional</p>
 <div class="math notranslate nohighlight">
 \[\begin{equation}
@@ -516,9 +516,9 @@ <h2>Objectives for a perfect entangler<a class="headerlink" href="#Objectives-fo
 </div>
 </div>
 <p>Again, the key point to take from this is that when defining a new or unusual functional, <strong>the ``chi_constructor`` must be congruent with the way the objectives are defined</strong>. As a user, you can choose whatever definition of objectives and implementation of <code class="docutils literal notranslate"><span class="pre">chi_constructor</span></code> is most suitable, as long they are compatible.</p>
-</div>
-<div class="section" id="Second-Order-Update-Equation">
-<h2>Second Order Update Equation<a class="headerlink" href="#Second-Order-Update-Equation" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Second-Order-Update-Equation">
+<h2>Second Order Update Equation<a class="headerlink" href="#Second-Order-Update-Equation" title="Link to this heading">¶</a></h2>
 <p>As the perfect-entangler functional <span class="math notranslate nohighlight">\(F_{PE}\)</span> is non-linear in the states, Krotov’s method requires the second-order contribution in order to guarantee monotonic convergence (see D. M. Reich, et al., J. Chem. Phys. 136, 104103 (2012) for details). The second order update equation reads</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}\begin{align}
@@ -587,9 +587,9 @@ <h2>Second Order Update Equation<a class="headerlink" href="#Second-Order-Update
 </div>
 </div>
 <p>This combines the evaluation of the function, <code class="docutils literal notranslate"><span class="pre">sigma(t)</span></code>, with the recalculation of <span class="math notranslate nohighlight">\(A\)</span> (or whatever parametrizations another <span class="math notranslate nohighlight">\(\sigma(t)\)</span> function might contain) in <code class="docutils literal notranslate"><span class="pre">sigma.refresh</span></code>, which <code class="docutils literal notranslate"><span class="pre">optimize_pulses</span></code> invokes automatically at the end of each iteration.</p>
-</div>
-<div class="section" id="Optimization">
-<h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization">
+<h2>Optimization<a class="headerlink" href="#Optimization" title="Link to this heading">¶</a></h2>
 <p>Before running the optimization, we define the shape function <span class="math notranslate nohighlight">\(S(t)\)</span> to maintain the smooth switch-on and switch-off, and the <span class="math notranslate nohighlight">\(\lambda_a\)</span> parameter that determines the overall magnitude of the pulse update in each iteration:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
@@ -682,83 +682,83 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
     S(t) (ranges): [0.000000, 1.000000]
     iter_start: 0
     iter_stop: 20
-    duration: 1.7 secs (started at 2024-06-03 12:38:39)
+    duration: 1.4 secs (started at 2024-06-03 10:32:26)
     optimized pulses (ranges): [0.00, 0.30]
     ∫gₐ(t)dt: 0.00e+00
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): None, [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): None, [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 1.447335
     gate conc.: 0.479492
 Iteration 1
-    duration: 4.3 secs (started at 2024-06-03 12:38:40)
+    duration: 3.1 secs (started at 2024-06-03 10:32:27)
     optimized pulses (ranges): [0.00, 0.32]
     ∫gₐ(t)dt: 2.58e-01
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 0.998102
     gate conc.: 0.645579
 Iteration 2
-    duration: 4.4 secs (started at 2024-06-03 12:38:45)
+    duration: 3.2 secs (started at 2024-06-03 10:32:30)
     optimized pulses (ranges): [0.00, 0.34]
     ∫gₐ(t)dt: 2.20e-01
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 0.582566
     gate conc.: 0.784635
 Iteration 3
-    duration: 4.3 secs (started at 2024-06-03 12:38:49)
+    duration: 3.1 secs (started at 2024-06-03 10:32:33)
     optimized pulses (ranges): [0.00, 0.35]
     ∫gₐ(t)dt: 1.46e-01
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 0.304191
     gate conc.: 0.892999
 Iteration 4
-    duration: 4.4 secs (started at 2024-06-03 12:38:53)
+    duration: 3.1 secs (started at 2024-06-03 10:32:36)
     optimized pulses (ranges): [0.00, 0.37]
     ∫gₐ(t)dt: 7.72e-02
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 0.153498
     gate conc.: 0.951891
 Iteration 5
-    duration: 4.3 secs (started at 2024-06-03 12:38:58)
+    duration: 3.1 secs (started at 2024-06-03 10:32:39)
     optimized pulses (ranges): [0.00, 0.37]
     ∫gₐ(t)dt: 3.88e-02
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 0.076100
     gate conc.: 0.980700
 Iteration 6
-    duration: 4.3 secs (started at 2024-06-03 12:39:02)
+    duration: 3.1 secs (started at 2024-06-03 10:32:43)
     optimized pulses (ranges): [0.00, 0.38]
     ∫gₐ(t)dt: 2.03e-02
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 0.035062
     gate conc.: 0.993840
 Iteration 7
-    duration: 4.3 secs (started at 2024-06-03 12:39:06)
+    duration: 3.1 secs (started at 2024-06-03 10:32:46)
     optimized pulses (ranges): [0.00, 0.39]
     ∫gₐ(t)dt: 1.13e-02
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: 0.012118
     gate conc.: 0.998971
 Iteration 8
-    duration: 4.3 secs (started at 2024-06-03 12:39:11)
+    duration: 3.1 secs (started at 2024-06-03 10:32:49)
     optimized pulses (ranges): [0.00, 0.39]
     ∫gₐ(t)dt: 6.68e-03
     λₐ: 1.00e+02
-    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)
+    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)
     fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000
     F_PE: -0.001422
     gate conc.: 1.000000
@@ -780,11 +780,11 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 <div class="highlight"><pre>
 Krotov Optimization Result
 --------------------------
-- Started at 2024-06-03 12:38:39
+- Started at 2024-06-03 10:32:26
 - Number of objectives: 4
 - Number of iterations: 8
 - Reason for termination: Reached convergence: achieved perfect entangler
-- Ended at 2024-06-03 12:39:15 (0:00:36)
+- Ended at 2024-06-03 10:32:52 (0:00:26)
 </pre></div></div>
 </div>
 <p>We can visualize how each iteration of the optimization brings the dynamics closer to the polyhedron of perfect entanglers (using the Weyl chamber coordinates that we calculated in the <code class="docutils literal notranslate"><span class="pre">info_hook</span></code> routine <code class="docutils literal notranslate"><span class="pre">print_fidelity</span></code>, and that were stored in <code class="docutils literal notranslate"><span class="pre">Result.info_vals</span></code>).</p>
@@ -800,22 +800,11 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 </pre></div>
 </div>
 </div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmtt10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmb10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmss10&#39;] not found. Falling back to DejaVu Sans.
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;DejaVu Sans Display&#39;] not found. Falling back to DejaVu Sans.
-</pre></div></div>
-</div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_07_example_PE_42_1.png" src="../_images/notebooks_07_example_PE_42_1.png" />
+<img alt="../_images/notebooks_07_example_PE_42_0.png" src="../_images/notebooks_07_example_PE_42_0.png" />
 </div>
 </div>
 <p>The final optimized control field looks like this:</p>
@@ -834,8 +823,8 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 <img alt="../_images/notebooks_07_example_PE_44_0.png" src="../_images/notebooks_07_example_PE_44_0.png" />
 </div>
 </div>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -849,7 +838,7 @@ <h2>Optimization<a class="headerlink" href="#Optimization" title="Permalink to t
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/07_example_PE.ipynb b/master/notebooks/07_example_PE.ipynb
index cca5e8cf..73857eef 100644
--- a/master/notebooks/07_example_PE.ipynb
+++ b/master/notebooks/07_example_PE.ipynb
@@ -17,10 +17,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.265098Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.264891Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.831750Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.831209Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.187140Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.186638Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.737586Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.737321Z"
     }
    },
    "outputs": [
@@ -29,15 +29,15 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
+      "scipy      : 1.12.0\n",
       "qutip      : 4.7.6\n",
-      "krotov     : 1.3.0\n",
+      "krotov     : 1.3.0+dev\n",
       "weylchamber: 0.4.0\n",
-      "scipy      : 1.10.1\n",
       "matplotlib : 3.7.5\n",
-      "numpy      : 1.24.4\n",
+      "numpy      : 1.26.4\n",
       "\n"
      ]
     }
@@ -147,10 +147,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.857901Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.857653Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.861621Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.861218Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.759869Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.759602Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.761728Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.761475Z"
     }
    },
    "outputs": [],
@@ -192,10 +192,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.863555Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.863407Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.866369Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.865876Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.763170Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.763069Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.764762Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.764550Z"
     }
    },
    "outputs": [],
@@ -216,10 +216,10 @@
      "n": "10"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.868409Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.868250Z",
-     "iopub.status.idle": "2024-06-03T10:38:38.871343Z",
-     "shell.execute_reply": "2024-06-03T10:38:38.870899Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.766044Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.765962Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.767977Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.767752Z"
     },
     "lines_to_next_cell": 2
    },
@@ -240,10 +240,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:38.873210Z",
-     "iopub.status.busy": "2024-06-03T10:38:38.873057Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.007363Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.006895Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.769336Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.769220Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.846777Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.845740Z"
     }
    },
    "outputs": [
@@ -274,10 +274,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.009535Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.009374Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.015670Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.015232Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.856159Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.855355Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.870140Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.869001Z"
     }
    },
    "outputs": [],
@@ -332,10 +332,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.017693Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.017543Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.022414Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.021961Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.880759Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.880069Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.888306Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.887598Z"
     }
    },
    "outputs": [],
@@ -358,10 +358,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.024736Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.024255Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.027920Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.027409Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.895949Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.895424Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.902649Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.901109Z"
     }
    },
    "outputs": [],
@@ -410,10 +410,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.029893Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.029743Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.033537Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.033103Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.911504Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.911124Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.927263Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.920852Z"
     }
    },
    "outputs": [],
@@ -428,10 +428,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.035420Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.035241Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.038954Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.038504Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.936431Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.934388Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.949888Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.949033Z"
     }
    },
    "outputs": [
@@ -466,10 +466,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.040991Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.040767Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.047675Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.047245Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.957180Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.956511Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.984752Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.983508Z"
     }
    },
    "outputs": [
@@ -585,10 +585,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.049736Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.049586Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.052938Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.052502Z"
+     "iopub.execute_input": "2024-06-03T14:32:25.994382Z",
+     "iopub.status.busy": "2024-06-03T14:32:25.993854Z",
+     "iopub.status.idle": "2024-06-03T14:32:25.999948Z",
+     "shell.execute_reply": "2024-06-03T14:32:25.999317Z"
     }
    },
    "outputs": [
@@ -600,34 +600,34 @@
       "\n",
       "make_PE_krotov_chi_constructor(canonical_basis, unitarity_weight=0)\n",
       "    Return a constructor for the χ's in a PE optimization.\n",
-      "    \n",
+      "\n",
       "    Return a `chi_constructor` that determines the boundary condition of the\n",
       "    backwards propagation in an optimization towards a perfect entangler in\n",
       "    Krotov's method, based on the foward-propagtion of the Bell states. In\n",
       "    detail, the function returns a callable function that calculates\n",
-      "    \n",
+      "\n",
       "    .. math::\n",
-      "    \n",
+      "\n",
       "        \\ket{\\chi_{i}}\n",
       "        =\n",
       "        \\frac{\\partial F_{PE}}{\\partial \\bra{\\phi_i}}\n",
       "        \\Bigg|_{\\ket{\\phi_{i}(T)}}\n",
-      "    \n",
+      "\n",
       "    for all $i$ with $\\ket{\\phi_{0}(T)}, ..., \\ket{\\phi_{3}(T)}$ the forward\n",
       "    propagated Bell states at final time $T$, cf. Eq. (33b) in Ref. [1].\n",
       "    $F_{PE}$ is the perfect-entangler functional\n",
       "    :func:`~weylchamber.perfect_entanglers.F_PE`. For the details of the\n",
       "    derivative see Appendix G in Ref. [2].\n",
-      "    \n",
+      "\n",
       "    References:\n",
-      "    \n",
+      "\n",
       "    [1] `M. H. Goerz, et al., Phys. Rev. A 91, 062307 (2015)\n",
       "    <https://doi.org/10.1103/PhysRevA.91.062307>`_\n",
-      "    \n",
+      "\n",
       "    [2] `M. H. Goerz, Optimizing Robust Quantum Gates in Open Quantum Systems.\n",
       "    PhD thesis, University of Kassel, 2015\n",
       "    <https://michaelgoerz.net/research/diss_goerz.pdf>`_\n",
-      "    \n",
+      "\n",
       "    Args:\n",
       "        canonical_basis (list[qutip.Qobj]): A list of four basis states that\n",
       "            define the canonical basis $\\ket{00}$, $\\ket{01}$, $\\ket{10}$, and\n",
@@ -635,7 +635,7 @@
       "        unitarity_weight (float): A weight in [0, 1] that determines how much\n",
       "            emphasis is placed on  maintaining population in the logical\n",
       "            subspace.\n",
-      "    \n",
+      "\n",
       "    Returns:\n",
       "        callable: a function ``chi_constructor(fw_states_T, **kwargs)`` that\n",
       "        receives the result of a foward propagation of the Bell states\n",
@@ -657,10 +657,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.054772Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.054623Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.060997Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.060556Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.004313Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.004148Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.014026Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.013137Z"
     }
    },
    "outputs": [],
@@ -751,10 +751,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.063011Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.062831Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.067603Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.067167Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.018838Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.018718Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.025022Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.024128Z"
     }
    },
    "outputs": [],
@@ -825,10 +825,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.069631Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.069479Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.072436Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.072000Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.028478Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.028369Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.032169Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.030853Z"
     }
    },
    "outputs": [],
@@ -860,10 +860,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.074329Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.074178Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.078086Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.077651Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.036940Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.036397Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.041896Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.040303Z"
     }
    },
    "outputs": [],
@@ -895,10 +895,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.080021Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.079827Z",
-     "iopub.status.idle": "2024-06-03T10:38:39.082494Z",
-     "shell.execute_reply": "2024-06-03T10:38:39.082048Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.046945Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.046817Z",
+     "iopub.status.idle": "2024-06-03T14:32:26.051515Z",
+     "shell.execute_reply": "2024-06-03T14:32:26.050551Z"
     }
    },
    "outputs": [],
@@ -917,10 +917,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:39.084335Z",
-     "iopub.status.busy": "2024-06-03T10:38:39.084148Z",
-     "iopub.status.idle": "2024-06-03T10:39:15.447707Z",
-     "shell.execute_reply": "2024-06-03T10:39:15.447189Z"
+     "iopub.execute_input": "2024-06-03T14:32:26.053193Z",
+     "iopub.status.busy": "2024-06-03T14:32:26.053110Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.474389Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.474076Z"
     }
    },
    "outputs": [
@@ -946,11 +946,11 @@
       "    S(t) (ranges): [0.000000, 1.000000]\n",
       "    iter_start: 0\n",
       "    iter_stop: 20\n",
-      "    duration: 1.7 secs (started at 2024-06-03 12:38:39)\n",
+      "    duration: 1.4 secs (started at 2024-06-03 10:32:26)\n",
       "    optimized pulses (ranges): [0.00, 0.30]\n",
       "    ∫gₐ(t)dt: 0.00e+00\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): None, [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): None, [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -967,11 +967,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 1\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:38:40)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:27)\n",
       "    optimized pulses (ranges): [0.00, 0.32]\n",
       "    ∫gₐ(t)dt: 2.58e-01\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -988,11 +988,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 2\n",
-      "    duration: 4.4 secs (started at 2024-06-03 12:38:45)\n",
+      "    duration: 3.2 secs (started at 2024-06-03 10:32:30)\n",
       "    optimized pulses (ranges): [0.00, 0.34]\n",
       "    ∫gₐ(t)dt: 2.20e-01\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1009,11 +1009,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 3\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:38:49)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:33)\n",
       "    optimized pulses (ranges): [0.00, 0.35]\n",
       "    ∫gₐ(t)dt: 1.46e-01\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1030,11 +1030,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 4\n",
-      "    duration: 4.4 secs (started at 2024-06-03 12:38:53)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:36)\n",
       "    optimized pulses (ranges): [0.00, 0.37]\n",
       "    ∫gₐ(t)dt: 7.72e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1051,11 +1051,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 5\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:38:58)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:39)\n",
       "    optimized pulses (ranges): [0.00, 0.37]\n",
       "    ∫gₐ(t)dt: 3.88e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1072,11 +1072,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 6\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:39:02)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:43)\n",
       "    optimized pulses (ranges): [0.00, 0.38]\n",
       "    ∫gₐ(t)dt: 2.03e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1093,11 +1093,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 7\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:39:06)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:46)\n",
       "    optimized pulses (ranges): [0.00, 0.39]\n",
       "    ∫gₐ(t)dt: 1.13e-02\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1114,11 +1114,11 @@
      "output_type": "stream",
      "text": [
       "Iteration 8\n",
-      "    duration: 4.3 secs (started at 2024-06-03 12:39:11)\n",
+      "    duration: 3.1 secs (started at 2024-06-03 10:32:49)\n",
       "    optimized pulses (ranges): [0.00, 0.39]\n",
       "    ∫gₐ(t)dt: 6.68e-03\n",
       "    λₐ: 1.00e+02\n",
-      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB), [4 * ndarray(250)] (0.5 MB)\n",
+      "    storage (bw, fw, fw0): [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB), [4 * ndarray(250)] (0.4 MB)\n",
       "    fw_states_T norm: 1.000000, 1.000000, 1.000000, 1.000000\n"
      ]
     },
@@ -1152,10 +1152,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:15.449812Z",
-     "iopub.status.busy": "2024-06-03T10:39:15.449623Z",
-     "iopub.status.idle": "2024-06-03T10:39:15.453232Z",
-     "shell.execute_reply": "2024-06-03T10:39:15.452842Z"
+     "iopub.execute_input": "2024-06-03T14:32:52.475866Z",
+     "iopub.status.busy": "2024-06-03T14:32:52.475776Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.478006Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.477763Z"
     }
    },
    "outputs": [
@@ -1164,11 +1164,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:38:39\n",
+       "- Started at 2024-06-03 10:32:26\n",
        "- Number of objectives: 4\n",
        "- Number of iterations: 8\n",
        "- Reason for termination: Reached convergence: achieved perfect entangler\n",
-       "- Ended at 2024-06-03 12:39:15 (0:00:36)"
+       "- Ended at 2024-06-03 10:32:52 (0:00:26)"
       ]
      },
      "execution_count": 19,
@@ -1195,44 +1195,16 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:15.455211Z",
-     "iopub.status.busy": "2024-06-03T10:39:15.455036Z",
-     "iopub.status.idle": "2024-06-03T10:39:16.163814Z",
-     "shell.execute_reply": "2024-06-03T10:39:16.163218Z"
+     "iopub.execute_input": "2024-06-03T14:32:52.479449Z",
+     "iopub.status.busy": "2024-06-03T14:32:52.479366Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.652610Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.652339Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1003.94x708.661 with 1 Axes>"
       ]
@@ -1266,10 +1238,10 @@
      "n": "17"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:39:16.165868Z",
-     "iopub.status.busy": "2024-06-03T10:39:16.165683Z",
-     "iopub.status.idle": "2024-06-03T10:39:16.270845Z",
-     "shell.execute_reply": "2024-06-03T10:39:16.270346Z"
+     "iopub.execute_input": "2024-06-03T14:32:52.654203Z",
+     "iopub.status.busy": "2024-06-03T14:32:52.654108Z",
+     "iopub.status.idle": "2024-06-03T14:32:52.716145Z",
+     "shell.execute_reply": "2024-06-03T14:32:52.715845Z"
     }
    },
    "outputs": [
@@ -1309,7 +1281,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
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diff --git a/master/notebooks/08_example_ensemble.html b/master/notebooks/08_example_ensemble.html
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+  <title>Ensemble Optimization for Robust Pulses &mdash; Krotov 1.3.0+dev (bd14c16) documentation</title>
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@@ -48,7 +47,7 @@
             Krotov
           </a>
               <div class="version">
-                1.3.0
+                1.3.0+dev
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 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
@@ -58,7 +57,7 @@
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 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -156,9 +155,9 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/08_example_ensemble.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/08_example_ensemble.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Ensemble-Optimization-for-Robust-Pulses">
-<h1>Ensemble Optimization for Robust Pulses<a class="headerlink" href="#Ensemble-Optimization-for-Robust-Pulses" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/08_example_ensemble.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/08_example_ensemble.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Ensemble-Optimization-for-Robust-Pulses">
+<h1>Ensemble Optimization for Robust Pulses<a class="headerlink" href="#Ensemble-Optimization-for-Robust-Pulses" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -186,25 +185,24 @@ <h1>Ensemble Optimization for Robust Pulses<a class="headerlink" href="#Ensemble
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
-krotov    : 1.3.0
-numpy     : 1.24.4
-sys       : 3.8.16 (default, May 26 2024, 19:41:26)
-[GCC 12.2.0]
 matplotlib: 3.7.5
-scipy     : 1.10.1
+krotov    : 1.3.0+dev
+numpy     : 1.26.4
+scipy     : 1.12.0
+sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]
 qutip     : 4.7.6
 
 </pre></div></div>
 </div>
 <p><span class="math notranslate nohighlight">\(\newcommand{tr}[0]{\operatorname{tr}} \newcommand{diag}[0]{\operatorname{diag}} \newcommand{abs}[0]{\operatorname{abs}} \newcommand{pop}[0]{\operatorname{pop}} \newcommand{aux}[0]{\text{aux}} \newcommand{opt}[0]{\text{opt}} \newcommand{tgt}[0]{\text{tgt}} \newcommand{init}[0]{\text{init}} \newcommand{lab}[0]{\text{lab}} \newcommand{rwa}[0]{\text{rwa}} \newcommand{bra}[1]{\langle#1\vert} \newcommand{ket}[1]{\vert#1\rangle} \newcommand{Bra}[1]{\left\langle#1\right\vert} \newcommand{Ket}[1]{\left\vert#1\right\rangle} \newcommand{Braket}[2]{\left\langle #1\vphantom{#2}\mid{#2}\vphantom{#1}\right\rangle} \newcommand{ketbra}[2]{\vert#1\rangle\!\langle#2\vert} \newcommand{op}[1]{\hat{#1}} \newcommand{Op}[1]{\hat{#1}} \newcommand{dd}[0]{\,\text{d}} \newcommand{Liouville}[0]{\mathcal{L}} \newcommand{DynMap}[0]{\mathcal{E}} \newcommand{identity}[0]{\mathbf{1}} \newcommand{Norm}[1]{\lVert#1\rVert} \newcommand{Abs}[1]{\left\vert#1\right\vert} \newcommand{avg}[1]{\langle#1\rangle} \newcommand{Avg}[1]{\left\langle#1\right\rangle} \newcommand{AbsSq}[1]{\left\vert#1\right\vert^2} \newcommand{Re}[0]{\operatorname{Re}} \newcommand{Im}[0]{\operatorname{Im}} \newcommand{toP}[0]{\omega_{12}} \newcommand{toS}[0]{\omega_{23}}\)</span></p>
 <p>This example revisits the <a class="reference internal" href="02_example_lambda_system_rwa_complex_pulse.html"><span class="doc">Optimization of a State-to-State Transfer in a Lambda System in the RWA</span></a>, attempting to make the control pulses robustness with respect to variations in the pulse amplitude, through “ensemble optimization”.</p>
-<div class="section" id="Control-objectives-for-population-transfer-in-the-Lambda-system">
-<h2>Control objectives for population transfer in the Lambda system<a class="headerlink" href="#Control-objectives-for-population-transfer-in-the-Lambda-system" title="Permalink to this headline">¶</a></h2>
+<section id="Control-objectives-for-population-transfer-in-the-Lambda-system">
+<h2>Control objectives for population transfer in the Lambda system<a class="headerlink" href="#Control-objectives-for-population-transfer-in-the-Lambda-system" title="Link to this heading">¶</a></h2>
 <p>As in the original example, we define the Hamiltonian for a Lambda system in the rotating wave approximation, like this:</p>
-<p><img alt="Lambda system considered in this notebook" src="../_images/energylevels.png" /></p>
+<img alt="Lambda system considered in this notebook" src="../_images/energylevels.png" />
 <p>We set up the control fields and the Hamiltonian exactly as before:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -325,9 +323,9 @@ <h2>Control objectives for population transfer in the Lambda system<a class="hea
 [Objective[|Ψ₀(3)⟩ to |Ψ₁(3)⟩ via [H₀[3,3], [H₁[3,3], u₁(t)], [H₂[3,3], u₂(t)], [H₃[3,3], u₃(t)], [H₄[3,3], u₄(t)]]]]
 </pre></div></div>
 </div>
-</div>
-<div class="section" id="Robustness-to-amplitude-fluctuations">
-<h2>Robustness to amplitude fluctuations<a class="headerlink" href="#Robustness-to-amplitude-fluctuations" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Robustness-to-amplitude-fluctuations">
+<h2>Robustness to amplitude fluctuations<a class="headerlink" href="#Robustness-to-amplitude-fluctuations" title="Link to this heading">¶</a></h2>
 <p>A potential source of error is fluctuations in the pulse amplitude between different runs of the experiment. To account for this, the <code class="docutils literal notranslate"><span class="pre">hamiltonian</span></code> function above include a parameter <code class="docutils literal notranslate"><span class="pre">mu</span></code> that scales the pulse amplitudes by the given factor.</p>
 <p>We can analyze the result of the <a class="reference internal" href="02_example_lambda_system_rwa_complex_pulse.html"><span class="doc">Optimization of a State-to-State Transfer in a Lambda System in the RWA</span></a> with respect to such fluctuations. We load the earlier optimization result from disk, and verify that the optimized controls produce the <span class="math notranslate nohighlight">\(\ket{1} \rightarrow \ket{3}\)</span> transition as desired.</p>
 <div class="nbinput nblast docutils container">
@@ -476,29 +474,18 @@ <h2>Robustness to amplitude fluctuations<a class="headerlink" href="#Robustness-
 </pre></div>
 </div>
 </div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-WARNING:matplotlib.font_manager:findfont: Font family [&#39;cmtt10&#39;] not found. Falling back to DejaVu Sans.
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-WARNING:matplotlib.font_manager:findfont: Font family [&#39;DejaVu Sans Display&#39;] not found. Falling back to DejaVu Sans.
-</pre></div></div>
-</div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_08_example_ensemble_27_1.png" src="../_images/notebooks_08_example_ensemble_27_1.png" />
+<img alt="../_images/notebooks_08_example_ensemble_27_0.png" src="../_images/notebooks_08_example_ensemble_27_0.png" />
 </div>
 </div>
 <p>The plot shows that as the pulse amplitude deviates from the optimal value, the error rises quickly: our previous optimization result is not robust.</p>
 <p>The highlighted region of ±10% is our “region of interest” within which we would like the control to be robust by applying optimal control.</p>
-</div>
-<div class="section" id="Setting-the-ensemble-objectives">
-<h2>Setting the ensemble objectives<a class="headerlink" href="#Setting-the-ensemble-objectives" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Setting-the-ensemble-objectives">
+<h2>Setting the ensemble objectives<a class="headerlink" href="#Setting-the-ensemble-objectives" title="Link to this heading">¶</a></h2>
 <p>They central idea of optimizing for robustness is to take multiple copies of the Hamiltonian, sampling over the space of variations to which would like to be robust, and optimize over the average of this ensemble.</p>
 <p>Here, we sample 5 values of <span class="math notranslate nohighlight">\(\mu\)</span> (including the unperturbed <span class="math notranslate nohighlight">\(\mu=1\)</span>) in the region of interest, <span class="math notranslate nohighlight">\(\mu \in [0.9, 1.1]\)</span>.</p>
 <div class="nbinput nblast docutils container">
@@ -544,9 +531,9 @@ <h2>Setting the ensemble objectives<a class="headerlink" href="#Setting-the-ense
 </pre></div></div>
 </div>
 <p>It is important that all five objectives reference the same four control pulses, as is the case here.</p>
-</div>
-<div class="section" id="Optimize">
-<h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimize">
+<h2>Optimize<a class="headerlink" href="#Optimize" title="Link to this heading">¶</a></h2>
 <p>We use the same update shape <span class="math notranslate nohighlight">\(S(t)\)</span> and <span class="math notranslate nohighlight">\(\lambda_a\)</span> value as in the original optimization:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
@@ -828,8 +815,8 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <img alt="../_images/notebooks_08_example_ensemble_54_0.png" src="../_images/notebooks_08_example_ensemble_54_0.png" />
 </div>
 </div>
-<div class="section" id="Robustness-analysis">
-<h3>Robustness analysis<a class="headerlink" href="#Robustness-analysis" title="Permalink to this headline">¶</a></h3>
+<section id="Robustness-analysis">
+<h3>Robustness analysis<a class="headerlink" href="#Robustness-analysis" title="Link to this heading">¶</a></h3>
 <p>When comparing the robustness of the “robust” optimized pulse to that obtained from the original optimization for the unperturbed Hamiltonian, we should make sure that we have converged to a comparable error: We would like to avoid the suspicion that the ensemble error is below our threshold only because the error for <span class="math notranslate nohighlight">\(\mu = 1\)</span> is so much lower. Therefore, we continue the original unperturbed optimization for a few more iterations, until we reach the same error
 <span class="math notranslate nohighlight">\(\approx 1.13 \times 10^{-4}\)</span> that we found as the result of the ensemble optimization, looking at <span class="math notranslate nohighlight">\(\mu=1\)</span> only:</p>
 <div class="nbinput docutils container">
@@ -878,10 +865,10 @@ <h3>Robustness analysis<a class="headerlink" href="#Robustness-analysis" title="
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 iter.      J_T  ∫gₐ(ϵ₁)dt  ∫gₐ(ϵ₂)dt  ∫gₐ(ϵ₃)dt  ∫gₐ(ϵ₄)dt   ∑∫gₐ(t)dt          J       ΔJ_T         ΔJ  secs
-0     5.90e-04   0.00e+00   0.00e+00   0.00e+00   0.00e+00    0.00e+00   5.90e-04        n/a        n/a     1
-13    3.24e-04   6.29e-05   9.90e-06   5.10e-05   9.21e-06    1.33e-04   4.57e-04  -2.66e-04  -1.33e-04     2
-14    1.83e-04   3.16e-05   7.05e-06   2.56e-05   6.48e-06    7.08e-05   2.53e-04  -1.42e-04  -7.08e-05     2
-15    1.06e-04   1.60e-05   5.02e-06   1.29e-05   4.56e-06    3.85e-05   1.44e-04  -7.70e-05  -3.85e-05     2
+0     5.90e-04   0.00e+00   0.00e+00   0.00e+00   0.00e+00    0.00e+00   5.90e-04        n/a        n/a     0
+13    3.24e-04   6.29e-05   9.90e-06   5.10e-05   9.21e-06    1.33e-04   4.57e-04  -2.66e-04  -1.33e-04     1
+14    1.83e-04   3.16e-05   7.05e-06   2.56e-05   6.48e-06    7.08e-05   2.53e-04  -1.42e-04  -7.08e-05     1
+15    1.06e-04   1.60e-05   5.02e-06   1.29e-05   4.56e-06    3.85e-05   1.44e-04  -7.70e-05  -3.85e-05     1
 </pre></div></div>
 </div>
 <p>Now, we can compare the robustness of the optimized pulses from the original unperturbed optimization (label “-1”), the continued unperturbed optimization (label “0”), and the ensemble optimization (label “1”):</p>
@@ -931,9 +918,9 @@ <h3>Robustness analysis<a class="headerlink" href="#Robustness-analysis" title="
 </div>
 </div>
 <p>We see that without the ensemble optimization, we only lower the error for exactly <span class="math notranslate nohighlight">\(\mu = 1\)</span>: the more we converge, the less robust the result. In contrast, the ensemble optimization results in considerably lower errors (order of magnitude!) throughout the highlighted “region of interest” and beyond.</p>
-</div>
-</div>
-</div>
+</section>
+</section>
+</section>
 
 
            </div>
@@ -947,7 +934,7 @@ <h3>Robustness analysis<a class="headerlink" href="#Robustness-analysis" title="
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/08_example_ensemble.ipynb b/master/notebooks/08_example_ensemble.ipynb
index 7b30c1bb..860ecf0e 100644
--- a/master/notebooks/08_example_ensemble.ipynb
+++ b/master/notebooks/08_example_ensemble.ipynb
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:50.496774Z",
-     "iopub.status.busy": "2024-06-03T10:35:50.496597Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.021701Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.021120Z"
+     "iopub.execute_input": "2024-06-03T14:25:44.483473Z",
+     "iopub.status.busy": "2024-06-03T14:25:44.483339Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.123828Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.123485Z"
     }
    },
    "outputs": [
@@ -33,15 +33,14 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "numpy     : 1.24.4\n",
-      "sys       : 3.8.16 (default, May 26 2024, 19:41:26) \n",
-      "[GCC 12.2.0]\n",
       "matplotlib: 3.7.5\n",
-      "scipy     : 1.10.1\n",
+      "krotov    : 1.3.0+dev\n",
+      "numpy     : 1.26.4\n",
+      "scipy     : 1.12.0\n",
+      "sys       : 3.12.0 (main, Jun  3 2024, 09:08:56) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
       "qutip     : 4.7.6\n",
       "\n"
      ]
@@ -129,10 +128,10 @@
      "start_time": "2020-03-22T05:01:29.429149Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.048051Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.047795Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.051930Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.051469Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.145515Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.145298Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.147955Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.147686Z"
     }
    },
    "outputs": [],
@@ -168,10 +167,10 @@
      "start_time": "2020-03-22T05:01:29.443630Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.053786Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.053638Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.056472Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.055951Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.149334Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.149232Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.151126Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.150878Z"
     }
    },
    "outputs": [],
@@ -188,10 +187,10 @@
      "start_time": "2020-03-22T05:01:29.452425Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.058349Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.058202Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.063690Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.063274Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.152558Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.152452Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.155197Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.154961Z"
     }
    },
    "outputs": [],
@@ -229,10 +228,10 @@
      "start_time": "2020-03-22T05:01:29.470251Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.065522Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.065372Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.069519Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.069107Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.156516Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.156402Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.159010Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.158758Z"
     }
    },
    "outputs": [],
@@ -258,10 +257,10 @@
      "start_time": "2020-03-22T05:01:29.482710Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.071412Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.071263Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.074980Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.074565Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.160692Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.160580Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.162951Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.162706Z"
     }
    },
    "outputs": [],
@@ -280,10 +279,10 @@
      "start_time": "2020-03-22T05:01:29.497857Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.076812Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.076662Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.079240Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.078831Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.164288Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.164176Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.165980Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.165714Z"
     }
    },
    "outputs": [],
@@ -301,10 +300,10 @@
      "start_time": "2020-03-22T05:01:29.505632Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.081065Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.080916Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.083439Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.083027Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.167760Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.167617Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.169516Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.169225Z"
     }
    },
    "outputs": [],
@@ -321,10 +320,10 @@
      "start_time": "2020-03-22T05:01:29.514140Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.085565Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.085190Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.090874Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.090468Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.171021Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.170916Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.174724Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.174447Z"
     }
    },
    "outputs": [
@@ -372,10 +371,10 @@
      "start_time": "2020-03-22T05:01:29.525825Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.092752Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.092600Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.095498Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.094995Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.176475Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.176365Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.179752Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.179421Z"
     }
    },
    "outputs": [],
@@ -394,10 +393,10 @@
      "start_time": "2020-03-22T05:01:29.534230Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.097329Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.097180Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.100490Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.099969Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.181134Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.181022Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.183208Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.182945Z"
     }
    },
    "outputs": [],
@@ -416,10 +415,10 @@
      "start_time": "2020-03-22T05:01:29.542275Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.102344Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.102195Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.138469Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.138025Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.184598Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.184472Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.255102Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.254725Z"
     }
    },
    "outputs": [],
@@ -440,10 +439,10 @@
      "start_time": "2020-03-22T05:01:29.862369Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.140344Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.140192Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.143619Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.143123Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.256920Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.256805Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.259119Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.258871Z"
     }
    },
    "outputs": [],
@@ -468,17 +467,17 @@
      "start_time": "2020-03-22T05:01:29.879489Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.145578Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.145303Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.297880Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.297325Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.260666Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.260549Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.431491Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.427308Z"
     },
     "lines_to_next_cell": 2
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -518,10 +517,10 @@
      "start_time": "2020-03-22T05:01:30.122273Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.300117Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.299935Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.303410Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.303009Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.435742Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.435519Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.439042Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.438751Z"
     }
    },
    "outputs": [],
@@ -553,10 +552,10 @@
      "start_time": "2020-03-22T05:01:30.131210Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.305356Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.305181Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.307897Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.307440Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.444350Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.444208Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.451939Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.446690Z"
     },
     "lines_to_next_cell": 2
    },
@@ -590,10 +589,10 @@
      "start_time": "2020-03-22T05:01:30.144877Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.309838Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.309663Z",
-     "iopub.status.idle": "2024-06-03T10:35:51.312620Z",
-     "shell.execute_reply": "2024-06-03T10:35:51.312127Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.462713Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.462451Z",
+     "iopub.status.idle": "2024-06-03T14:25:45.469328Z",
+     "shell.execute_reply": "2024-06-03T14:25:45.468323Z"
     }
    },
    "outputs": [],
@@ -612,10 +611,10 @@
      "start_time": "2020-03-22T05:01:30.156668Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:51.314469Z",
-     "iopub.status.busy": "2024-06-03T10:35:51.314295Z",
-     "iopub.status.idle": "2024-06-03T10:35:52.298480Z",
-     "shell.execute_reply": "2024-06-03T10:35:52.297878Z"
+     "iopub.execute_input": "2024-06-03T14:25:45.473923Z",
+     "iopub.status.busy": "2024-06-03T14:25:45.473631Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.602503Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.602171Z"
     },
     "lines_to_next_cell": 2
    },
@@ -637,10 +636,10 @@
      "start_time": "2020-03-22T05:01:40.684478Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:52.300925Z",
-     "iopub.status.busy": "2024-06-03T10:35:52.300744Z",
-     "iopub.status.idle": "2024-06-03T10:35:52.305295Z",
-     "shell.execute_reply": "2024-06-03T10:35:52.304889Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.604288Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.604178Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.606958Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.606628Z"
     }
    },
    "outputs": [],
@@ -675,44 +674,16 @@
      "start_time": "2020-03-22T05:01:40.696343Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:52.307166Z",
-     "iopub.status.busy": "2024-06-03T10:35:52.306993Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.004439Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.003888Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.608495Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.608382Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.795133Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.794824Z"
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:matplotlib.font_manager:findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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fOHXlhtSlERHV6nhGIUZ8dgB7z1+HrUKGD8Z2wcdPhsDRlksbmiIGJTJrcpmAqEGt8cOzvdCsqT0u55fh8S//xNcH0qDTcSqOiEyHKIr45o90PLn8ELKKKtDCowm2RvXFkz0CpC6N7oJBiSxC9+Zu2D6nP4Z38oFaK+Lt2GQ8szoe+SWVUpdGRARVhRozvzuOt7YlQaMTMaKzL36Z1RcdfJ2lLo3+AYMSWQwXBxt8Mb4b3h7dCUqFDHvOX8fwTw/gz5Q8qUsjIit25moRRn7+B3aczYGNXMCbj3bEsnFd4WRnI3VpdA8YlMiiCIKAf/dqjl9m9UVrL0dcK67E+G+O4KOd56HR6qQuj4isiCiKWH8kA499+Scu55ehWVN7bJrRB5F9giAIXGHbXDAokUVq7+OM/5vVD0+HBUAUgWV7UvCvrw4jV8XtT4io4ZVVaTDvh5N45afTqNLo8EB7L8TO6YfQgKZSl0Z1xKBEFsteKcd7j3XB5093hZOtAscuF2Lk53/gREah1KURkQW7mFuMUcsO4qcTVyGXCXj5ofZYObEHmjoopS6NjMCgRBZvZIgfts3ph7be+qm4p746jM0JV6Qui4gs0NYTV/HosoO4eK0EXk62WD81HDMHtuJmtmaMQYmsQnP3JtjyXF88GOyNKo0OL2w6iXdik9i3RET1Qq3V4X9bT2PuxkSUq7Xo29odsXP6I7ylu9Sl0X1iUCKr4WirwIp/d8fswa0BACsPpGPK6mMoKuNq3kRkvKIyNSatOorvDmdAEIA5g1tjzZRweDrZSl0a1QMGJbIqMpmAF4a2Q/S4brC3kWP/hesY/cVBbqxLREa5nF+Kx748iIMp+XBQyrFyQg/MG6rfLYAsA4MSWaURXXzx48zeaNbUHul5pRgTfRB7zl2TuiwiMiPHLhVgzBd/IvV6KXyc7bBpRm8MCfaWuiyqZwxKZLU6+rng51l9ERbkhuJKDaasjseXe1Mhitz6hIjubuuJqxi38ggKSqvQuZn+vaSjn4vUZVEDYFAiq+bhaIvvpobj6bBAiCLw/o5zmLsxERVqrdSlEZEJEkURS3ZdwNyNiajS6jCsozc2PtsL3s52UpdGDYRBiayeUiHDu2M64a1RHaGQCfg5MQtPrjiE7KJyqUsjIhNSodZizveJ+CzuIgDg2YiW+HJ8dzgoFRJXRg3J6oPSmDFj4OrqirFjx0pdCklIEARM6B2Etc+Ew9XBBqeuFGHk5weRcJmLUxIRkFdSiXErD+P/TmZBIRPw/uOdsXB4B66PZAWsPig9//zzWLNmjdRlkIno3codv8zqh/Y+TsgrqcTTXx3GD8cypS6LiCR0IbcYo6MP4njGDTjbKbDmmTA81TNQ6rKokVh9UBo4cCCcnJykLoNMSICbAzbP7INhHb1RpdVh/o+n8PFv59nkTWSF9l+4jse/+BNXCsvR3N0BP0X1RZ9WHlKXRY3IpIPS/v37MXLkSPj5+UEQBGzduvW220RHRyMoKAh2dnYIDw/H0aNHG79QsjhNbBX4cnx3zHmgDQDg899T8N+tZ6DVMSwRWYu1hy9jckw8iis1CAtyw0/P9UUrT0epy6JGZtIdaKWlpQgJCcGUKVPw2GOP3Xb9xo0bMW/ePCxfvhzh4eFYunQphg0bhvPnz8PLywsAEBoaCo1Gc9vP/vbbb/Dz86tTPZWVlaisrDR8r1Kp6viMyJzIZALmPdgWXk62ePXnM1h/JANFZWoseSoEtgq51OURUQPR6kS8E5uMbw+mAwAe69YM7z3Wmce9lTLpoDR8+HAMHz681uuXLFmCadOmYfLkyQCA5cuXIzY2Ft9++y0WLFgAAEhMTKy3et577z28+eab9XZ/ZB7+3as5mjrY4D8bExF7OhuqCjWW/7s7mtia9OFDREaoUGsxa/0J7E7OBQC8OLQtoga1hiCwadtamfTU291UVVUhISEBQ4YMMVwmk8kwZMgQHDp0qEEec+HChSgqKjJ8ZWayyddaPNLFD99O6gkHpRwHLuZh/NdHUFhaJXVZRFSPSis1mBITj93JubBVyLBsXFfMGtyGIcnKmW1QysvLg1arhbf3rcvFe3t7Iycn557vZ8iQIXjiiSewfft2+Pv73zVk2drawtnZ+ZYvsh7923hi3dRwNHWwQWLmDTzBtZaILIaqQo2J3x7Fn6n5aKKUY82UMDzSpW7tGWSZzDYo1Zfdu3fj+vXrKCsrw5UrV9C7d2+pSyIT1jXQFZue7Q0fZzukXCvB2C8PIe06N9QlMmeFpVUYv/IIEi4XwtlOge+mhiO8pbvUZZGJMNug5OHhAblcjtzc3Fsuz83NhY+Pj0RVkTVo4+2EH2f2RkuPJrh6oxxPLD+E01eKpC6LiIxwvbgS//rqME5fLYJbEyU2TO+FroGuUpdFJsRsg5JSqUT37t0RFxdnuEyn0yEuLo6jQtTg/F0dsGlGb3Ru5oL80io8vfIw/kzNk7osIqqD7KJyPLXiEM7nFsPLyRYbp/fixrZ0G5MOSiUlJUhMTDScuZaeno7ExERkZGQAAObNm4eVK1di9erVSE5OxsyZM1FaWmo4C66hREdHIzg4GD179mzQxyHT5u5oi/XTwtG7pTtKKjWY9G08dpy59/44IpJOZkEZnlxxCGl5pWjW1B4/PNsbbby5+DDdThBNeLnhvXv3YtCgQbddHhkZiZiYGADAsmXL8OGHHyInJwehoaH47LPPEB4e3ij1qVQquLi4oKioiI3dVqxCrcXz35/AzrO5kAnA4se64MmeAQ33gMXFQGws4OwMODg03OMQNYayMkClAkaMABppl4S06yUY//URZBdVoLm7A9ZNDYe/K48la1KXz2+TDkqmjkGJami0Ovz3pzPYWL0v3MLh7fFsRKuGeTAGJbIkjRyUzucUY/zXR5BXUonWXo5YNzUc3s52Df64ZFrq8vlt0lNvROZCIZdh8eOdMaM6HL336zm892sy94cjMiGnrxThqa8OIa+kEsG+ztg4vRdDEv0jBiWieiIIAhYMb49XHm4PAFixLw1v/l8SwxKRCUi4XIBxKw/jRpkaoQFNsWFaL7g72kpdFpkBBiWiejZ9QCt88HgXCAIQ8+clfLjzvNQlEVm1P1PyMOGbo/rNbVu44bup4XBxsJG6LDITDEpEDeDJngF4e3QnAMAXe1MRvSdF4oqIrNOe89cwOSYeZVVa9G/jgdWTw+DIfRqpDhiUjMDlAehejA9vjv+N6AAA+HDneXz7R7rEFRFZlx1ncjB9zTFUanQY0sELKyf2gL1SLnVZZGYYlIwQFRWFpKQkxMfHS10Kmbip/VviP0PaAgAWbUvC90czJK6IyDrsOXcNs9Yfh1orYkQXX3z57+6ws2FIorpjUCJqYHMeaI1nB7QEACz86TR+TrwqcUVEli3hciFmrkuARididKgfPvtXV9jI+XFHxuFvDlEDqzkb7t+9AiGKwLwfTmLnWa7gTdQQLuQWY0pMPCrUOgxq54kPnwiBXCZIXRaZMQYlokYgCAIWPdoJj3fzh1YnYvb6E9h34brUZRFZlKs3yjHxm6MoKleja2BTRI/vxpEkum/8DSJqJDKZgPcf74wRnX1RpdXh2bXHcCQtX+qyiCxCQWkVJnxzBDmqCrTxcsSqST3hoOTZbXT/GJSIGpFCLsMnT4VicHsvVKh1mBITj8TMG1KXRWTWSis1mBwTj7TrpfBzscOaZ8LQ1EEpdVlkIRiUiBqZUiHDF+O7oU8rd5RWaTHxmyNIylJJXRaRWarS6DDjuwSczLwBVwcbrHkmHL4u9lKXRRaEQckIXEeJ7pedjRwrJ/ZAt8CmUFVoMOGbI0i5ViJ1WURmRacT8eKmkzhwMQ/2NnJ8O6knWns5Sl0WWRgGJSNwHSWqD01sFVg1OQydmjkjv7QK//76CDILyqQui8gsiKKIRduS8MvJLChkApZP6I6uga5Sl0UWiEGJSEIu9jZYMyUcbbwckaOqwLivDyOnqELqsohM3hd7UxHz5yUAwMdPhiCirae0BZHFYlAikphbEyXWTQ1Hc3cHZBaUY/zXh5FXUil1WUQm6/ujGYbNpl97JBijQptJXBFZMgYlIhPg5WyHdVPD4edih9TrpZi25hgq1FqpyyIyOTvO5OCVn04DAKIGtcKUfi0krogsHYMSkYnwd3XAd1PD4WJvgxMZN/DKT6chiqLUZRGZjMNp+Zjz/QnoROBfPQPw4tB2UpdEVoBBiciEtPR0RPS4bpDLBGw5fhUrD6RJXRKRSTibVYRpq4+hSqPD0GBvvD26EwSBW5NQw2NQIjIx/dp44LVHggEA7/16DnvOXZO4IiJpZeSXIfLbeBRXahDWwg2fPd0VCm5NQo2Ev2lEJmhi7+Z4Oky/ie6cDSeQcq1Y6pKIJFFQWoUJ3x5BXkkl2vs4YeXEHrCzkUtdFlkRBiUjcMFJamiCIODNRzsirIUbiis1eGb1MRSWVkldFlGj0upEzNlwApfzyxDgZo81U8LgYm8jdVlkZRiUjMAFJ6kxKBUyLP93d/i72uNyfhmi1h+HWquTuiyiRvPxb+fxR4p+1e2vJ/aEl7Od1CWRFWJQIjJhbk2U+DqyB5oo5fgzNR9vbUuSuiSiRrHzbA6+2JsKAHh/bBe083GSuCKyVgxKRCauvY8zPnkqFIIArDl0GeuOZ0ldElGDSr1eghd+OAkAmNK3BR4N8ZO4IrJmDEpEZmBoRx/DmjGv70zFoWI2s5JlKq3UYMbaBJRUn+G28OH2UpdEVo5BichMPDewFR4N8YNGJ+K5S3bIKOdilGRZRFHE/B9P4eK1Eng722LZuK6w4TIAJDH+BhKZCUEQ8MHYLuji64hCrQzTzgIlGoYlshxfH0hH7Ols2MgFfDG+G7yc2LxN0mNQIjIjdjZyfDW2I7wUOpwvA+aeqoSO25yQBTh0+QYW7zgHAHj1kWB0b+4mcUVEegxKRGbGx9kWX7Uoh1IAdl/T4qOLaqlLIrov2VUCZm1JhlYn4rGuzTChV3OpSyIyYFAiMkOhTXT4sHo/0C/S1Pg5SyNtQURGqtSJmHnJHvllanTwdcY7YzpzDzcyKQxKRGZqlJeAmS30qxS/dKYSiTe0EldEVHdvpQKJZXK42Cmw4t/dYa/kGZ1kWhiUjMAtTMhUvNTWBkM85ajSAdNPVCKngit3k/n48aoa32UDAkQsHdUege4OUpdEdBsGJSNwCxMyFTJBwNIQW7R1FHCtUsTsk5XQsrmbzMAZlRb/Pavfv3CuTxUGtWbzNpkmBiUiM+eoELCymx0c5UB8oQ4r0tncTaatsErEjBOVqNQBD7gBs7254TOZLgYlIgvQ3EGG1zsoAQCfXFTjTBH7lcg0aUURz5+qxJVyEc0dBCxpD8jYu00mjEGJyEKMbabAcG851KJ+faUKLafgyPQsTVFjf54WdjJgeVc7uCiYksi0MSgRWQhBEPBuR1t42QpIKRWx+DynM8i07Lqmweep+qnhxZ1s0cGJH0Fk+vhbSmRBXJUCPuykn4KLydBg33Wur0SmIaNMh3mnKgEAkwIVGO2nkLgionvDoERkYSI8FYgM1H8IvXSmCoVVnIIjaelEEfPPVKJYA3RvKsMr7ZVSl0R0zxiUiCzQgnZKtG6iXzLglbOVELlkAEloXaYGhwt0sJcDn3SxhZLd22RGGJSILJC9XMDSLrZQCMCvuVps5hYnJJEr5TpDv9z8NkoEOvBjh8wLf2OJLFQnFzn+01q/xckbSVXILOOq3dS4RFHEwjOVKNUCPV1liGzOviQyPwxKRBZsRksb9HSVoUQL/OcUV+2mxrXxigYH8nWwlQEfdLKFjJvdkhliUCKyYHJBwJLOtnCUA8du6LA8jat2U+PIKtfh7XP6KbcX2yjRogk/bsg88TfXCNwUl8xJgIMMbwRXr9qdwlW7qeGJooiFZ6tQogW6usgwJYhTbmS+GJSMwE1xydw87qdftVsjAs+fqkQ5V+2mBvTjVQ325WmhlAEfdraFnFNuZMYYlIiswM2rdqdy1W5qQLkVOrxVPeX2n9Y2aO3Ijxkyb/wNJrISN6/avZqrdlMDEEUR/z1bBZUGCHGRYVqQjdQlEd03BiUiKxLhqcCk6lW7XzxThQKu2k316OdsLXZf18JG0J/lpuDCkmQBGJSIrEzNqt3XuWo31aNrlTq8kazfy21Oaxu044a3ZCH4m0xkZexuWrV7R64WP17lFBzdH1EU8WpSFW6ogY7OMsxowSk3shwMSkRWqJOLHPPaVK/anVyFDK7aTfdhW44WO3O1UAjAh52UsOGUG1kQBiUiK/VsCxuEucpQqgVePsMpODJOfpWI15P0U27PtbRBsLNc4oqI6heDEpGVkgsCPu5sC1sZcKhAhx25XIiS6u61pEoUqIH2jgJmteKUG1keBiUiKxbgIMOz1f0k75yvQgUXoqQ62JGjQWyOFnIB+KizLZScciMLxKBEZOVmtLCBr52AK+UiVl7iXnB0bwqrRPyvesptRgsbdHLhlBtZJgYlIivnoBCwoK1+Icov0tTIrmBjN/2zN5MrkVcFtHEUMKc1p9zIcjEoEREe9ZWjR1MZyrXA+9zehP7BrmsabM3WQgbgw062sOWUG1kwBiUigiAIeL2DEgKArdlaJBSysZvurEit36YEAKa1sEFoU065kWWrc1AqLy/H1atXb7v87Nmz9VIQEUmjs4scT/rrtzd5M7kKOi4XQHfwaUoVrlWKaOkg4D+cciMrUKeg9OOPP6JNmzYYMWIEunTpgiNHjhiumzBhQr0XR0SN68U2SjgpgFMqHVfspttklumwNkP/e/F6ByXs5JxyI8tXp6D09ttvIyEhAYmJiVi1ahWeeeYZrF+/HgCsarG66OhoBAcHo2fPnlKXQlSvPG0FzGmlb+z+4IIaxRrrOa7pn318sQpqEejrLsMAD065kXWoU1BSq9Xw9vYGAHTv3h379+/HihUrsGjRIgiC9fxlERUVhaSkJMTHx0tdClG9i2yuQAsHAXlVIpalcrkA0juj0mJrtr53bUFbpVW955N1q1NQ8vLywqlTpwzfu7m5YdeuXUhOTr7lciIyX0qZgFfb60eVvr2kRnoplwsg4P3z+tA80keOzlwziaxInYLS2rVr4eXldctlSqUSGzZswL59++q1MCKSziBPOSI85FCL+hW7ybr9kafFgXwtbATgpeo1t4isRZ2Ckr+/P3x8fO54Xd++feulICKSniDoR5UUArD7mhb789jYba10oojFF/RheXyAAoEOXFWGrAt/44nojlo7yhAZqF8uYFFyFdQ6NnZbo205WpxR6eAoB2a35mgSWR+FMT/UokULoxr55s6dizlz5hjzkEQkgTmtlfgpS4OUUhHfZWgwOYjr5liTKp2Ij6pHk55taQN3JRu4yfoYFZRiYmKMerCgoCCjfo6IpOFiI+DFtkq8crYKn6RUYZSfAm78sLQa6zM1yCgX4Wkr4JnmDMlknYwKShEREfVdBxGZqKf8FfguQ4OkYh2WXKzC2x1tpS6JGkGxRsRnKfrRpLmtbOCgYEAm61SvPUoFBQXQ6XgqMZElkVfvAwfoRxiSVNwHzhqsTFejQA20dBAMW9sQWaP7DkpJSUlYvHgx+vTpA09PT3h5eWHixInYvHkzSktL66NGIpJYuJscI3zk0AFYdK7Kqlbit0bXKnRYeUm/btL8tkrYyDiaRNbLqKB0/vx5vPDCC2jTpg169eqF+Ph4zJgxA7m5udi+fTuaN2+ORYsWwcPDA8OHD8eXX35Z33UTUSNb2E4JWxlwuECHHbkcVbJkn6aqUa4FurrIMMybi0uSdTMqKP35558oLS3FZ599hry8PGzevBkTJ06Eh4cHwsLC8NZbb+HkyZNITk7GQw89hC1bttR33UTUyPztZZjRQt/Q+875KlRoOapkiVJLdPj+in7drAXtuFUJkSByDN1oKpUKLi4uKCoqgrOzs9TlkLUoLgZiYwFnZ8DBoVEfulwrYvCBcmRXiHixjQ1mteK6OpZm5okK/JqrxQOecnzT3a7hH7CsDFCpgBEjACenhn88ItTt85sLThLRPbOXC1jYTh+OotPUyK7gyRuW5PgNLX7N1UIGfW8SEd1nUEpOTsaqVatw7tw5AMC5c+cwc+ZMTJkyBb///nu9FEhEpmWkjxw9XWUo1wIfXOA+cJZCFEUsrt7X7/FmCrRz4t/RRMB9BKUdO3YgNDQUL774Irp27YodO3ZgwIABSElJweXLlzF06FCGJSILJAgCXm+vH234OUuLS6UcVbIEv1/X4mihDrYy4D+tubgkUQ2jg9KiRYvw0ksvIT8/H6tWrcK4ceMwbdo07Nq1C3FxcXjppZewePHi+qyViExEJxc5Bnnqlwv4uvo0cjJfWlHE+9Wjg5Oa28DPnqNJRDWMPhrOnj2LSZMmAQCefPJJFBcXY+zYsYbrx48fj1OnTt13gURkmp6tPgNu01UN8ip5Tog523xVgwslIlxsgOdacjSJ6Gb39WdDzWmjMpkMdnZ2cHFxMVzn5OSEoqKi+6uOiExWuKsMIS4yVOqANRkcVTJXFVoRn6To//1mtVTCxYbLARDdzOigFBQUhIsXLxq+P3ToEAIDAw3fZ2RkwNfX9/6qIyKTJQiCYVRpTYYaZRqOKpmjmMtqZFeIaGYnYEIgtyoh+jujg9LMmTOh1f61Om+nTp2gUPx1kP36668YPHjw/VVHRCZtmLcczR0E3FADP1zVSF0O1dGNKhFfpOlHk+a1sYGdnKNJRH9n9J8PM2bMuOv17777rrF3TURmQi4ImBZkg/8lVWFluhr/DlBAwX3BzMYXaWqoNEB7RwGj/TiaRHQnPLWBiO7L2GYKuCuBqxUiYnO4B5y5uFquQ0x1b9nL7ZSQc6sSojtiUDJCdHQ0goOD0bNnT6lLIZKcnVzApOb6XqUV6WpwVyTz8FW6GlU6oJebDAM9uPEtUW2MGmtt0aKFURslzp07F3PmzDHmIU1KVFQUoqKiDHvFEFm7CQE2+CJNjaRiHf7I16E/P3hNWrFGxI/VPWWzWnLjW6K7MSooxcTEGPVgQUFBRv0cEZm2pkoB//JXYNVlDVakV6G/h73UJdFdbL6qQakWaNVEQF93TiwQ3Y1RQSkiIqK+6yAiM/dMkA3WZGjwR74OZ4q06OTCUSVTpBNFrLms702KDLThaBLRPzD6T4mMjAxkZGQgMzOTC0sSEfztZRjpow9HK9K5AKWp+iNfi7QyEY5y4LFmPNON6J/c14KTLVq0QFBQENzc3BAYGIg33ngDVVXcTZzIWk2vXoBye64WmWXcLNcUrb6s700a66+Ao4KjSUT/xOg/J/bs2QMAEEURWVlZOHbsGD766CPs2rULu3fvhr09exSIrE2wsxz93eU4kK/FN5fUeCPYVuqS6CYZZTr8fl2/hMPEQO7pRnQvjB5RioiIQEREBAYOHIhx48ZhyZIlOHv2LDIzM7Fo0aL6rJGIzMiM6k1Vv7+iQUEVlwowJWsz1BABDPCQo2UTNnET3QujjxSVSnXbl6urK1544QVs2rQJxcXFUKlU9VkrEZmBPm4ydHKWoUKn/2Am01CmEbHxin7abRL3dCO6Z0YfLU2bNr3r2RIuLi4QBOGW/eCIyPLVbJY7+2QlVl9WY3oLG9hzDzHJbc3WQKUBAu0FDPTkGYlE9+q+e5T+bseOHVi9ejU2bNhgdFFEZN6Ge8sRYC8gs1y/sOEE9sNISrxpSYCJgTaQcUkAontmdFC601pKpaWlmDt3LkaMGMG1loismEKm3yz3tWT9ZrlP+3OzXCkdKdThXIkIeznwhD+n3YjqwugjJiMjA4D+L5Xs7GwcP34cH3zwARQKBd599916K5CIzNMT/gp8klKFjHIRO3K1eMSXH9BSWV09mjTGTwEXGwZWorow+p0rKCjI0KMkiiJsbGwwduxYfPLJJ/D09Ky3AonIPNnLBUwMtMGnqWqsSFdjhI+cq0BLIKtch9+ucUkAImMZHZR+//13CIIAQRDg5OSEdu3awcHBoT5rIyIzN7G5DVakq3FapcOhAh36uLOJuLGty9RAKwK93GRo78QlAYjqyqiglJOTg969e8PW9t4Wk0tLS0PLli2NeSgiMmPuSgFP+iuwJkODFelqBqVGVqEVsSFTP+02iaNJREYx6s+LH3/8EW5ubhgzZgxWrVqF69ev33abI0eO4JVXXkHHjh0REhJy34USkXmaGmQDGYB9eVokF3Nbk8a0LUeDAjXgZydgiBdDKpExjApKs2bNwsmTJ9G/f3/ExMTA398f/fr1w7vvvotp06bB19cXo0ePxrVr17B48eI7Bikisg6BDjI8XL1Z7lfp3AuysYiiaNjXbXwAzzokMpYgiuJ97zGQn5+Pbdu2Yfv27QgKCsKoUaPQu3dvi2/cVKlUcHFxQVFREZydnaUuh6xFcTEQGws4OwNm0hd4ukiLkYcqIBeAfQPs4W/PXpmGdvyGFo8droBSBhwe6AA3pYm+H5eVASoVMGIE4OQkdTVkJery+V0v5+u6u7sjMjISkZGR9XF3RGRhOrvI0dddhoP5Onx7SY3XOnCz3IZWsyTAo74K0w1JRGaAf9YRUaN4tsVfm+Xe4Ga5DepapQ7bc/RLAkRyXzei+8KgRESNor+7HB2cZCjTAt9lcrPchrQhUwO1CHRrKkNnFzZxE90Po/7UaNGihVH9R3PnzsWcOXOMeUgiMnM1m+XOPVWJmMsazGhhwwbjBlClE7EuU9/EHcklAYjum1FBKSYmxqgHCwoKMurniMgyjPCRY1EykFcl4mC+FhGenBaqbztytbhWKcLTVsBwH44mEd0vo96luOEtERnDRibgYR8FvsvU4JdsBqWGsKa6iXucvwJKjtgR3Tf2KBFRo3q0enPc365pUKFlU3d9OqPS4tgNHRSCfu0kIrp/DEpE1Kh6uMrgZyegWAPsva6VuhyLUrPA5MM+cnjZ8e2dqD7wSCKiRiUTBIysHlX6OVsjcTWWo7BKNLyebOImqj/1GpQuXLgAjYZvfER0dyN99U3Gcde1KNZw+q0+fH9FjSod0MlZhm5N+TcwUX2p16OpQ4cOSEtLq8+7JCIL1NFJhpZNBFTpgN9y+cfV/dLoRHyXUTOapLD47aOIGlO9BqV62DaOiKyAIAiGpu5fstmndL/irmtxtUKEqw0M05pEVD84PktEkqgJSn/ka5HPLU3uS82+bv/yt4GdnKNJRPWJQYmIJNGyiQydnWXQisD2HE6/GetCsQ5/FuggA/Bv7utGVO8YlIhIMn9NvzEoGWvDFf1o0lBvOZrZ8y2dqL7xqCIiyTziK4cAIL5Qh6vlOqnLMTuiKGJnrr7Ha2wzjiYRNQQGJSKSjK+dDGGu+reh/+OoUp2dVumQVSHCQQ70c+e+bkQNwaqDUmZmJgYOHIjg4GB06dIFmzZtkrokIqvDs9+MVzOaNNBDziZuogZSr0Hp5Zdfhru7e33eZYNSKBRYunQpkpKS8Ntvv2Hu3LkoLS2Vuiwiq/KwjwIKAUgq1iGlhNNvdbGzeg2qYd6cdiNqKPUalN577z2zCkq+vr4IDQ0FAPj4+MDDwwMFBQXSFkVkZVyVAvp76KeN2NR971JLdEgpFaEQgIGenHYjaigmPfW2f/9+jBw5En5+fhAEAVu3br3tNtHR0QgKCoKdnR3Cw8Nx9OhRox4rISEBWq0WAQEB91k1EdXVqJvOfuPCtffmt2v6UNnbTQ4XG067ETUUkw5KpaWlCAkJQXR09B2v37hxI+bNm4fXX38dx48fR0hICIYNG4Zr164ZbhMaGopOnTrd9pWVlWW4TUFBASZOnIivvvqqwZ8TEd3uQS857GTApTIRp1WcfrsXNf1Jw7w5mkTUkEx6Ynv48OEYPnx4rdcvWbIE06ZNw+TJkwEAy5cvR2xsLL799lssWLAAAJCYmHjXx6isrMTo0aOxYMEC9OnT5x9vW1lZafhepVLd4zMhortpohDwgJccsTla/JKtQRcXfvjfTU6FDolFOggAhnrxtSJqSA02opSZmYkpU6Y01N2jqqoKCQkJGDJkiOEymUyGIUOG4NChQ/d0H6IoYtKkSRg8eDAmTJjwj7d/77334OLiYvjiNB1R/ak5+21bthY6Tr/d1a5r+tGkrk1l8LIz6YkBIrPXYEdYQUEBVq9e3VB3j7y8PGi1Wnh7e99yube3N3Jycu7pPg4ePIiNGzdi69atCA0NRWhoKE6fPl3r7RcuXIiioiLDV2Zm5n09ByL6y0BPOZwUQE6liKOFnH67m7/OduNoElFDM3rq7Zdffrnr9WlpacbedaPp168fdLp7f0O2tbWFra1tA1ZEZL1sZQKGeyvww1UNfs7SoJcbQ8CdFKlFHC7Qv28N9TLp7gkii2D0UTZ69GgIgnDXM1QEoeHOxPDw8IBcLkdubu4tl+fm5sLHx6fBHpeIGs6jvvqg9GuuBm8GK6GU8Wyuv/v9uhYaEWjrKKBFE067ETU0o48yX19fbNmyBTqd7o5fx48fr886b6NUKtG9e3fExcUZLtPpdIiLi0Pv3r0b9LGJqGH0dpfBQynghhr4I48rdd8JF5kkalxGB6Xu3bsjISGh1uv/abTpXpSUlCAxMdFw5lp6ejoSExORkZEBAJg3bx5WrlyJ1atXIzk5GTNnzkRpaanhLDgiMi9yQcAjPlx8sjYVWhH7qgPkMJ7tRtQojP6T5KWXXrrrdh+tW7fGnj17jL17AMCxY8cwaNAgw/fz5s0DAERGRiImJgZPPfUUrl+/jtdeew05OTkIDQ3Fjh07bmvwrm/R0dGIjo6GVsu/eInq26N+CsRkaPDbNS3KtSLsuYeZwf48Lcq1QDM7AR2dOe1G1BgEkcvgGk2lUsHFxQVFRUVwdnaWuhyyFsXFQGws4OwMODhIXU29E0URA/aXI7NcxOchthjpyymmGi+cqsTmLA0mNVfgjQ4WcmJJWRmgUgEjRgBOTlJXQ1aiLp/f/JOEiEyKIAiGcMTpt79odCLirlf3J/FsN6JGw6BERCanZvHJfde1KFJz0BsAjhbqcEMNuNoAPV351k3UWHi0EZHJae8kQztHAVUisCOXo0oA8Fv16zDESwEFl00gajQMSkRkkh7l9JuBKIr47Ro3wSWSAoMSEZmkmj6lQ/k6XKu07i1NTqt0yKoQ4SAH+rkzKBE1JgYlI0RHRyM4OBg9e/aUuhQiixXoIEOoiww6ALHZ1r0Ux2+5+ucf4SGHHZdLIGpUDEpGiIqKQlJSEuLj46UuhciijaoeVfrZyqffdl7jatxEUmFQIiKTNcJXDhmAxCIdMsqsc/otrVSHiyUiFAIwyJPTbkSNjUGJiEyWl60Mvd31b1P/Z6WjSjV7u/V2k8PFhtNuRI2NQYmITJq1n/22M5dnuxFJiUGJiEzaQ94KKAXgfImIc8XWNf2WU6FDYpH+OT/ITXCJJMGgREQmzcVGQER1b461jSrtql47qauLDN52fLsmkgKPPCIyeTdPv1nTPt41q3Fz2o1IOgxKRuA6SkSNa4iXHLYy4Eq5iNRS6whKRWoRhwr0025cFoBIOgxKRuA6SkSNy14uoFtT/dvVoQLrWHzy9+taaESgraOAFk34Vk0kFR59RGQWernpp58OW0lQ2pnLRSaJTAGDEhGZhZqgdKRAa/F9ShVaEfvy9IFwKM92I5IUgxIRmYXQpjLYyoC8Klh8n9KBfC3KtUAzOwGdnPk2TSQlHoFEZBZsZQK6W0mfUs0ikw96yyEIXI2bSEoMSkRkNqyhT0mjExFXswmuF/uTiKTGoEREZsMa+pSOFupQqAZcbYCernyLJpIaj0IiMhshVtCnVLPI5BAvBRQyTrsRSY1ByQhccJJIGpbepySKIn6r3rZkKFfjJjIJDEpG4IKTRNKx5D6lMyodsipEOMiB/u4MSkSmgEGJiMzKzUHJ0vqUas52i/CQw07OaTciU8CgRERmJaSpDHYyIL8KSLGwPqWd17gaN5GpYVAiIrNiKxPQvfpsMEuafksr1eFiiQiFAAzy5LQbkalgUCIis2OJfUo1Z7v1dpPBxYbTbkSmgkGJiMyOJfYp7a/e2+1BLjJJZFIYlIjI7HRxsaw+JY1ORGKRDgAQ7sZpNyJTwqBERGbn5j6lQ/nmP/12rliHMi3gpADaOHLajciUMCgRkVmypD6lYzf0o0ndmsoh4ya4RCaFQYmIzJIl9SklFOrDXg/u7UZkcnhUGoFbmBBJL6S6T6lADVwsMfOgVD2i1L0p+5OITA2DkhG4hQmR9JQywTACY87Tb1nl+m1L5II+/BGRaeFRSURmyxL6lI5XjyZ1cJKhiYL9SUSmhkGJiMyWJfQpHbuhD3ndm/LtmMgU8cgkIrPVxQL6lI4XVvcnubI/icgUMSgRkdm6uU/pkBlOv5VpRJwtrmnk5tsxkSnikUlEZs2c+5ROFumgFQFfOwHN7Pl2TGSKeGQSkVnrXR2UjhRooTOzPqWE6v6kbhxNIjJZPDqJyKx1dpHBXm6efUo16yf14PpJRCaLQYmIzJpSJqBHU/NbT0knioYVubtzRW4ik8Wjk4jMnjn2KaWWilBpAHu5fg0lIjJNPDqJyOz1MsM+pZrRpBAXGWxkXGiSyFQxKBGR2bu5T+mCmfQpHeP+bkRmgUHJCNwUl8i0mGOf0vHqEaUe7E8iMmk8Qo3ATXGJTI859SnlV4lIK9OPfHV14YgSkSljUCIii2BOfUrHq9dPauMooKmS/UlEpoxBiYgsQpfqPqVCM+hTOlbI/iQic8GgREQWwUYmGBZuNPXpt5oRJe7vRmT6eJQSkcXo5Wb6Dd2VOhEni6pHlFw5okRk6hiUiMhimEOf0lmVDlU6wM0GaOHA/iQiU8egREQW4+Y+pfMm2qeUUN2f1M1VDkFgUCIydQxKRGQxbulTyjfN6beE6v6kHuxPIjILPFKJyKL0dq/uUyo0vaAkiiISbrA/icicMCgRkUUx5T6lzHIR1ytF2AhAZ2e+/RKZAx6pRGRROjvL4CAHbphgn1LNaFInZxns5OxPIjIHDEpEZFFsZAJ6uJpmn9Kx6unA7tzfjchs8GglIotjWE/JxPqUakaUenBFbiKzwaBERBbHFPuUVGoR54trlgbgWy+RueDRSkQW5+Y+pXPV4URqiUVaiAAC7QV42fKtl8hc8GglIotzS59SgWkEJcNGuBxNIjIrPGKNEB0djeDgYPTs2VPqUoioFr1NbN+3vzbCZX8SkTlhUDJCVFQUkpKSEB8fL3UpRFQLQ59SofR9SlpRxAkuNElklhiUiMgidXKWoYkcKDKBPqVzxTqUagEnBdDWkesnEZkTBiUiskim1Kd0vHo0KdRFDjk3wiUyKwxKRGSxeplInxIXmiQyXzxqichimUqfEheaJDJfDEpEZLE639SnlCxRn1JuhQ5XykXIAIQ25VsukbnhUUtEFkshE9CtehQnvlCaoFQzmtTeSQZHBfuTiMwNgxIRWbTw6j6leIn2fWN/EpF545FLRBYtzLVm3zcdRAn6lNifRGTeGJSIyKJ1cZFBKQB5VSLSyxo3KFVoRZxVVW+Ey/4kIrPEI5eILJqdXDA0UTf29NvJIh00IuBlK8Dfnv1JROaIQYmILN7N02+NKaF6f7ceTWUQuNAkkVliUCIii9ezupH6aCOPKCVUn2nXjfu7EZktBiUisnjdXeWQAbhSLiKrvHFGlURRvGVEiYjME49eIrJ4jgoBnZxr+pQaJyilloq4oQZsZUCwM99qicwVj14isgo1029HGmn67Xj1aFKIiwxKGfuTiMwVgxIRWYUwt5oVuhsnKB2rHrnqzvWTiMwagxIRWYWe1Q3VF0tE5Fc1/HpKhv4krshNZNZ4BBORVXBTCmjjqJ8Ca+hRpcIqEaml+jDWlSNKRGaNQYmIrEbNekrxBQ0blGr6k1o2EeCmZH8SkTljUCIiq1ETlI428Jlv3N+NyHIwKBGR1Qhz07/lnVXpUKxpuD6lY9VTe93Zn0Rk9ngUE5HV8LWTIcBegA7A8QbqU1LrRJws4hlvRJaCQckI0dHRCA4ORs+ePaUuhYjqqKGn386qdKjUAU1t9D1KRGTeGJSMEBUVhaSkJMTHx0tdChHVUc30W0Od+VbTn9S9qRwyboRLZPYYlIjIqtSMKCXe0KFCW/99SjX9Sd24vxuRReCRTERWJchBgKetgCoRhl6i+lKiEbE3Tx+U+rizP4nIEjAoEZFVEQQBYa4NM/32a44G5VqghYOAUBe+vRJZAh7JRGR1aqbfjhTU74jS5iwNAODxZgoI7E8isggMSkRkdWo2yD1+QwuNrn76lDLLdDhcoIMAYIyfol7uk4ikx6BERFannaMAZwVQqgWSiutnVGlL9WhSH3cZmtnzrZXIUvBoJiKrIxME9KxZT6kept9EUTQEpcc5mkRkURiUiMgq1ayndKQeGrqP3dDhcpmIJnLgIW8GJSJLwqBERFapZkTpWKEWOvH++pR+vKofTXrYRwEHBZu4iSwJgxIRWaVOzjLYy4FCNZBSYnxQKteKiM3+62w3IrIsDEpEZJWUMsGwevb9TL/9lqtFiRbwt/9rfSYishw8qonIatVMv93PwpM1026P+Sm4txuRBWJQIiKrFXbTmW+iEX1K2RU6/JGvD1mcdiOyTAxKRGS1ujaVwUYAcipFZJbXPSj9lKWBCCDMVYbmDnw7JbJEPLKJyGrZywV0rt6T7Wgdp99EUcTmq2ziJrJ0DEpEZNXCjFx4MrFIh9RSEXYy/bIARGSZGJSIyKqFuxk3olQzmvSQtxxOXDuJyGIxKBGRVevWVA4BwKUyEdcq7m1UqVIn4v9yaqbdbBqwOiKSGoMSEVk1FxsBHZxqRpXuLSjFXdOiSA342Aro4863USJLxiOciKxeWB2n3wxrJzVTQM61k4gsGoMSEVk9Q0P3PYwoXa8UsS+veu0kPzZxE1k6BiUisno1K3SfL9bhRtXd11P6OUsDrQiEusjQypFvoUSWjkc5EVk9T1sBLZsIEAEcu1H79JsoivjxqhoA104ishYMSkRE+Gv6Lf4u029ni3U4VyJCKQCP+jIoEVkDBiUiIui3IQGAIwW1jyjVrJ30oLccLjZs4iayBgxKREQAwtz0I0pnVDqUaW7vU6rSifg5q3rtJDZxE1kNBiUiIgD+9jL42QnQiMCJotun3/Ze16JADXgoBQzwkEtQIRFJgUGJiKja3abfNlePJo3xk0Mh47QbkbVgUCIiqlYz/fb3hScLqkT8fq167SRuWUJkVRiUiIiq1Zz5duKGDlW6v/qUfsnWQC0CHZ1laO/Et00ia8IjnoioWqsmAtxsgEodcPqmPqWas93YxE1kfRiUiIiqCYJgmH47Uj39dqFYh9MqHRQCMIpBicjqMCgREd2k598Wnqxp4h7kKYe7kk3cRNaGQYmI6Cbh1We+HSvUokonYkvN2kncsoTIKll1ULpx4wZ69OiB0NBQdOrUCStXrpS6JCKSWAdnGRzlQLEGWJmuxvVKEa42wGBPrp1EZI2s+k8kJycn7N+/Hw4ODigtLUWnTp3w2GOPwd3dXerSiEgickFAd1c59uVp8XmqfgPcUb4KKLl2EpFVsuoRJblcDgcHBwBAZWUlRFGEKN6+dQERWZeahScrqk9847QbkfUy6aC0f/9+jBw5En5+fhAEAVu3br3tNtHR0QgKCoKdnR3Cw8Nx9OjROj3GjRs3EBISAn9/f7z00kvw8PCop+qJyFzVnPkGAG0dBXRyNum3SiJqQCZ99JeWliIkJATR0dF3vH7jxo2YN28eXn/9dRw/fhwhISEYNmwYrl27ZrhNTf/R37+ysrIAAE2bNsXJkyeRnp6O9evXIzc3t9Z6KisroVKpbvkiIsvTxUUGZfW74+PNFBAETrsRWSuTHk8ePnw4hg8fXuv1S5YswbRp0zB58mQAwPLlyxEbG4tvv/0WCxYsAAAkJibe02N5e3sjJCQEBw4cwNixY+94m/feew9vvvlm3Z4EEZkdW5mAZ5rb4M8CLZ7gliVEVs2kR5TupqqqCgkJCRgyZIjhMplMhiFDhuDQoUP3dB+5ubkoLi4GABQVFWH//v1o165drbdfuHAhioqKDF+ZmZn39ySIyGS93E6Jn3vbw41rJxFZNZMeUbqbvLw8aLVaeHt733K5t7c3zp07d0/3cfnyZUyfPt3QxD179mx07ty51tvb2trC1tb2vuomIiIi82G2Qak+hIWF3fPUHBEREVkfs5168/DwgFwuv635Ojc3Fz4+PhJVRURERJbEbIOSUqlE9+7dERcXZ7hMp9MhLi4OvXv3lrAyIiIishQmPfVWUlKClJQUw/fp6elITEyEm5sbAgMDMW/ePERGRqJHjx4ICwvD0qVLUVpaajgLrqFER0cjOjoaWq22QR+HiIiIpCWIJrwU9d69ezFo0KDbLo+MjERMTAwAYNmyZfjwww+Rk5OD0NBQfPbZZwgPD2+U+lQqFVxcXFBUVARnZ+dGeUwiFBcDsbGAszNQvbI8kdkqKwNUKmDECMDJSepqyErU5fPbpIOSqWNQIkkwKJElYVAiCdTl89tse5SIiIiIGhqDEhEREVEtGJSIiIiIasGgRERERFQLBiUjREdHIzg4GD179pS6FCIiImpADEpGiIqKQlJSEuLj46UuhYiIiBoQgxIRERFRLUx6ZW5TV7MElUqlkrgSsirFxfq1ZzQa/X+JzFlFBVBVpV9Licv6USOp+dy+l6UkGZTuQ3FxMQAgICBA4kqIiIioroqLi+Hi4nLX23Bl7vug0+mQlZUFJycnCIIgdTmSU6lUCAgIQGZmJlcqb0B8nRsHX+fGwde58fC1/osoiiguLoafnx9ksrt3IXFE6T7IZDL4+/tLXYbJcXZ2tvqDsDHwdW4cfJ0bB1/nxsPXWu+fRpJqsJmbiIiIqBYMSkRERES1YFCiemNra4vXX38dtra2Updi0fg6Nw6+zo2Dr3Pj4WttHDZzExEREdWCI0pEREREtWBQIiIiIqoFgxIRERFRLRiUiIiIiGrBoER1Eh0djaCgINjZ2SE8PBxHjx696+2XLl2Kdu3awd7eHgEBAfjPf/6DioqKRqrWfNXldVar1Vi0aBFatWoFOzs7hISEYMeOHY1YrXnav38/Ro4cCT8/PwiCgK1bt/7jz+zduxfdunWDra0tWrdujZiYmAav09zV9XXOzs7GuHHj0LZtW8hkMsydO7dR6jR3dX2dt2zZggcffBCenp5wdnZG7969sXPnzsYp1swwKNE927hxI+bNm4fXX38dx48fR0hICIYNG4Zr167d8fbr16/HggUL8PrrryM5ORnffPMNNm7ciFdeeaWRKzcvdX2d//e//2HFihX4/PPPkZSUhBkzZmDMmDE4ceJEI1duXkpLSxESEoLo6Oh7un16ejpGjBiBQYMGITExEXPnzsXUqVP54fIP6vo6V1ZWwtPTE//73/8QEhLSwNVZjrq+zvv378eDDz6I7du3IyEhAYMGDcLIkSP5vnEnItE9CgsLE6Oiogzfa7Va0c/PT3zvvffuePuoqChx8ODBt1w2b948sW/fvg1ap7mr6+vs6+srLlu27JbLHnvsMXH8+PENWqclASD+9NNPd73N/PnzxY4dO95y2VNPPSUOGzasASuzLPfyOt8sIiJCfP755xusHktV19e5RnBwsPjmm2/Wf0FmjiNKdE+qqqqQkJCAIUOGGC6TyWQYMmQIDh06dMef6dOnDxISEgzTRmlpadi+fTsefvjhRqnZHBnzOldWVsLOzu6Wy+zt7fHHH380aK3W5tChQ7f8uwDAsGHDav13ITInOp0OxcXFcHNzk7oUk8NNceme5OXlQavVwtvb+5bLvb29ce7cuTv+zLhx45CXl4d+/fpBFEVoNBrMmDGDU293YczrPGzYMCxZsgQDBgxAq1atEBcXhy1btkCr1TZGyVYjJyfnjv8uKpUK5eXlsLe3l6gyovv30UcfoaSkBE8++aTUpZgcjihRg9m7dy/effddfPHFFzh+/Di2bNmC2NhYvPXWW1KXZlE+/fRTtGnTBu3bt4dSqcSsWbMwefJkyGQ8vInon61fvx5vvvkmfvjhB3h5eUldjsnhiBLdEw8PD8jlcuTm5t5yeW5uLnx8fO74M6+++iomTJiAqVOnAgA6d+6M0tJSTJ8+Hf/973/5QX4HxrzOnp6e2Lp1KyoqKpCfnw8/Pz8sWLAALVu2bIySrYaPj88d/12cnZ05mkRm6/vvv8fUqVOxadOm26aWSY+fVHRPlEolunfvjri4OMNlOp0OcXFx6N279x1/pqys7LYwJJfLAQAitxi8I2Ne5xp2dnZo1qwZNBoNNm/ejFGjRjV0uVald+/et/y7AMCuXbv+8d+FyFRt2LABkydPxoYNGzBixAipyzFZHFGiezZv3jxERkaiR48eCAsLw9KlS1FaWorJkycDACZOnIhmzZrhvffeAwCMHDkSS5YsQdeuXREeHo6UlBS8+uqrGDlypCEw0e3q+jofOXIEV69eRWhoKK5evYo33ngDOp0O8+fPl/JpmLySkhKkpKQYvk9PT0diYiLc3NwQGBiIhQsX4urVq1izZg0AYMaMGVi2bBnmz5+PKVOm4Pfff8cPP/yA2NhYqZ6CWajr6wwAiYmJhp+9fv06EhMToVQqERwc3Njlm426vs7r169HZGQkPv30U4SHhyMnJweA/kQQFxcXSZ6DyZL6tDsyL59//rkYGBgoKpVKMSwsTDx8+LDhuoiICDEyMtLwvVqtFt944w2xVatWop2dnRgQECA+99xzYmFhYeMXbmbq8jrv3btX7NChg2hrayu6u7uLEyZMEK9evSpB1eZlz549IoDbvmpe28jISDEiIuK2nwkNDRWVSqXYsmVLcdWqVY1et7kx5nW+0+2bN2/e6LWbk7q+zhEREXe9Pf1FEEXOgRARERHdCXuUiIiIiGrBoERERERUCwYlIiIiolowKBERERHVgkGJiIiIqBYMSkRERES1YFAiIiIiqgWDEhEREVEtGJSIrNDAgQMxd+5ck7kfcyAIArZu3QoAuHTpEgRBMGy1QXf3xhtvIDQ0VOoyiIzCoERE/2jv3r0QBAE3bty45fItW7bgrbfekqYoCQUEBCA7OxudOnWSrAZTDWs3B0oiS8BNcYksSFVVFZRKZaM9npubW6M9limRy+Xw8fGRuox70ti/E0SWhiNKRGZs4MCBmDVrFubOnQsPDw8MGzYMAHDmzBkMHz4cjo6O8Pb2xoQJE5CXl1fr/axduxY9evSAk5MTfHx8MG7cOFy7dg2AfuRi0KBBAABXV1cIgoBJkyYZHr9m6u2VV15BeHj4bfcdEhKCRYsWGb7/+uuv0aFDB9jZ2aF9+/b44osv7vocdTodPvjgA7Ru3Rq2trYIDAzEO++8Y7j+9OnTGDx4MOzt7eHu7o7p06ejpKTkltfo79ODo0ePNjwHAAgKCsJbb72Fp59+Gk2aNEGzZs0QHR1da01/H82pGXGLi4tDjx494ODggD59+uD8+fO3/Nzbb78NLy8vODk5YerUqViwYMFdp6QKCwsxfvx4eHp6wt7eHm3atMGqVasAAC1atAAAdO3aFYIgYODAgQCASZMmYfTo0XjnnXfg5+eHdu3aAQAyMzPx5JNPomnTpnBzc8OoUaNw6dIlw2PV/NxHH30EX19fuLu7IyoqCmq12nCb7OxsjBgxAvb29mjRogXWr1+PoKAgLF261PA6AsCYMWMgCILh+xpr165FUFAQXFxc8K9//QvFxcW1PnciU8GgRGTmVq9eDaVSiYMHD2L58uW4ceMGBg8ejK5du+LYsWPYsWMHcnNz8eSTT9Z6H2q1Gm+99RZOnjyJrVu34tKlS4YgERAQgM2bNwMAzp8/j+zsbHz66ae33cf48eNx9OhRpKamGi47e/YsTp06hXHjxgEA1q1bh9deew3vvPMOkpOT8e677+LVV1/F6tWra61t4cKFWLx4MV599VUkJSVh/fr18Pb2BgCUlpZi2LBhcHV1RXx8PDZt2oTdu3dj1qxZdX4dP/zwQ4SEhODEiRNYsGABnn/+eezatatO9/Hf//4XH3/8MY4dOwaFQoEpU6YYrlu3bh3eeecdvP/++0hISEBgYCC+/PLLu95fzXP+9ddfkZycjC+//BIeHh4AgKNHjwIAdu/ejezsbGzZssXwc3FxcTh//jx27dqFbdu2Qa1WY9iwYXBycsKBAwdw8OBBODo64qGHHkJVVZXh5/bs2YPU1FTs2bMHq1evRkxMDGJiYgzXT5w4EVlZWdi7dy82b96Mr776yhCoASA+Ph4AsGrVKmRnZxu+B4DU1FRs3boV27Ztw7Zt27Bv3z4sXry4Tq8vkSREIjJbERERYteuXW+57K233hKHDh16y2WZmZkiAPH8+fOGn3v++edrvd/4+HgRgFhcXCyKoiju2bNHBCAWFhbe9vg3309ISIi4aNEiw/cLFy4Uw8PDDd+3atVKXL9+/W319u7d+451qFQq0dbWVly5cuUdr//qq69EV1dXsaSkxHBZbGysKJPJxJycnFqf66hRo8TIyEjD982bNxcfeuihW27z1FNPicOHDzd8D0D86aefRFEUxfT0dBGAeOLECVEU/3p9du/efUsdAMTy8nJRFEUxPDxcjIqKuuUx+vbtK4aEhNzxuYmiKI4cOVKcPHnyHa/7ew01IiMjRW9vb7GystJw2dq1a8V27dqJOp3OcFllZaVob28v7ty50/BzzZs3FzUajeE2TzzxhPjUU0+JoiiKycnJIgAxPj7ecP3FixdFAOInn3xyx9epxuuvvy46ODiIKpXKcNlLL710y+8GkaniiBKRmevevfst3588eRJ79uyBo6Oj4at9+/YAcMtoz80SEhIwcuRIBAYGwsnJCREREQCAjIyMOtUyfvx4rF+/HgAgiiI2bNiA8ePHA9CP/qSmpuKZZ565pba333671rqSk5NRWVmJBx54oNbrQ0JC0KRJE8Nlffv2hU6nu23a65/07t37tu+Tk5PrdB9dunQx/L+vry8AGEZczp8/j7CwsFtu//fv/27mzJn4/vvvERoaivnz5+PPP/+8pzo6d+58S1/SyZMnkZKSAicnJ8Pr7ubmhoqKilte+44dO0Iul9/yHG6uX6FQoFu3bobrW7duDVdX13uqKSgoCE5OTne8byJTxmZuIjN3c0gAgJKSEowcORLvv//+bbet+fC+Wc301bBhw7Bu3Tp4enoiIyMDw4YNu2Va5l48/fTTePnll3H8+HGUl5cjMzMTTz31lKEuAFi5cuVtvUw3fzjfzN7evk6PfycymQyiKN5y2c19N/XJxsbG8P+CIADQ91gZa/jw4bh8+TK2b9+OXbt24YEHHkBUVBQ++uiju/7cnX4nunfvjnXr1t12W09PzzvWD+ifw/3Uf7OGvG+ihsQRJSIL061bN5w9exZBQUFo3br1LV9//wAFgHPnziE/Px+LFy9G//790b59+9v+0q8ZndBqtXd9bH9/f0RERGDdunVYt24dHnzwQXh5eQEAvL294efnh7S0tNvqqmlM/rs2bdrA3t4ecXFxd7y+Q4cOOHnyJEpLSw2XHTx4EDKZzNDE7OnpiezsbMP1Wq0WZ86cue2+Dh8+fNv3HTp0uOvzrYt27drd0rMD4Lbv78TT0xORkZH47rvvsHTpUnz11VcA7v3fBND/Tly8eBFeXl63vfYuLi73XL9Go8GJEycMl6WkpKCwsPCW29nY2NxTTUTmgkGJyMJERUWhoKAATz/9NOLj45GamoqdO3di8uTJd/wACwwMhFKpxOeff460tDT88ssvt62N1Lx5cwiCgG3btuH69eu3nFX2d+PHj8f333+PTZs2Gabdarz55pt477338Nlnn+HChQs4ffo0Vq1ahSVLltzxvuzs7PDyyy9j/vz5WLNmDVJTU3H48GF88803hseys7NDZGQkzpw5gz179mD27NmYMGGCoeF78ODBiI2NRWxsLM6dO4eZM2feth4UoA9YH3zwAS5cuIDo6Ghs2rQJzz///F1f67qYPXs2vvnmG6xevRoXL17E22+/jVOnThlGnu7ktddew88//4yUlBScPXsW27ZtM4Q3Ly8v2NvbG5r1i4qKar2f8ePHw8PDA6NGjcKBAweQnp6OvXv3Ys6cObhy5co91d++fXsMGTIE06dPx9GjR3HixAlMnz4d9vb2tzyHoKAgxMXFIScn57YQRWSOGJSILIyfnx8OHjwIrVaLoUOHonPnzpg7dy6aNm0Kmez2Q97T0xMxMTHYtGkTgoODsXjx4tumdpo1a4Y333wTCxYsgLe3913PKhs7dizy8/NRVlaG0aNH33Ld1KlT8fXXX2PVqlXo3LkzIiIiEBMTU+uIEqA/8+uFF17Aa6+9hg4dOuCpp54yjHg5ODhg586dKCgoQM+ePTF27Fg88MADWLZsmeHnp0yZgsjISEycOBERERFo2bKlYbmDm73wwgs4duwYunbtirfffhtLliwxLLdQH8aPH4+FCxfixRdfRLdu3ZCeno5JkybBzs6u1p9RKpVYuHAhunTpggEDBkAul+P7778HACgUCnz22WdYsWIF/Pz8MGrUqFrvx8HBAfv370dgYCAee+wxdOjQAc888wwqKirg7Ox8z89hzZo18Pb2xoABAzBmzBhMmzYNTk5OtzyHjz/+GLt27UJAQAC6du16z/dNZKoE8e+T90REViYoKAhz585t9O1YHnzwQfj4+GDt2rWN+rj15cqVKwgICMDu3btrbbgnMnds5iYiagRlZWVYvnw5hg0bBrlcjg0bNmD37t11XqtJSr///jtKSkrQuXNnZGdnY/78+QgKCsKAAQOkLo2owTAoERE1AkEQsH37drzzzjuoqKhAu3btsHnzZgwZMkTq0u6ZWq3GK6+8grS0NDg5OaFPnz5Yt27dbWe0EVkSTr0RERER1YLN3ERERES1YFAiIiIiqgWDEhEREVEtGJSIiIiIasGgRERERFQLBiUiIiKiWjAoEREREdWCQYmIiIioFv8Po4DiCYjyyHgAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -764,10 +735,10 @@
      "start_time": "2020-03-22T05:01:41.456749Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.006801Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.006612Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.009449Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.008942Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.796847Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.796728Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.798557Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.798264Z"
     }
    },
    "outputs": [],
@@ -791,10 +762,10 @@
      "start_time": "2020-03-22T05:01:41.474312Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.011672Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.011362Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.015977Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.015507Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.800022Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.799922Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.802170Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.801873Z"
     }
    },
    "outputs": [],
@@ -820,10 +791,10 @@
      "start_time": "2020-03-22T05:01:41.492908Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.017910Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.017759Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.022102Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.021715Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.803857Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.803723Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.806284Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.806018Z"
     }
    },
    "outputs": [
@@ -880,10 +851,10 @@
      "start_time": "2020-03-22T05:01:41.506536Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.024240Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.024021Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.027633Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.027129Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.807661Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.807536Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.809769Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.809484Z"
     }
    },
    "outputs": [],
@@ -932,10 +903,10 @@
      "start_time": "2020-03-22T05:01:41.518139Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.029577Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.029430Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.033214Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.032725Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.811207Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.811109Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.813402Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.813115Z"
     }
    },
    "outputs": [],
@@ -976,10 +947,10 @@
      "start_time": "2020-03-22T05:01:41.528504Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.035095Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.034948Z",
-     "iopub.status.idle": "2024-06-03T10:35:53.038293Z",
-     "shell.execute_reply": "2024-06-03T10:35:53.037801Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.814998Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.814872Z",
+     "iopub.status.idle": "2024-06-03T14:25:47.816773Z",
+     "shell.execute_reply": "2024-06-03T14:25:47.816511Z"
     }
    },
    "outputs": [],
@@ -1012,10 +983,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:35:53.040255Z",
-     "iopub.status.busy": "2024-06-03T10:35:53.040108Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.963747Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.963225Z"
+     "iopub.execute_input": "2024-06-03T14:25:47.818298Z",
+     "iopub.status.busy": "2024-06-03T14:25:47.818156Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.197787Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.197474Z"
     }
    },
    "outputs": [
@@ -1156,10 +1127,10 @@
      "start_time": "2020-03-22T05:06:03.287117Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.965960Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.965774Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.973424Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.972921Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.199381Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.199290Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.204322Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.204085Z"
     }
    },
    "outputs": [
@@ -1224,10 +1195,10 @@
      "start_time": "2020-03-22T05:06:03.349278Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.975332Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.975156Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.979223Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.978634Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.205591Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.205477Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.207655Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.207421Z"
     }
    },
    "outputs": [
@@ -1261,10 +1232,10 @@
      "start_time": "2020-03-22T05:06:03.360976Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.981072Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.980922Z",
-     "iopub.status.idle": "2024-06-03T10:38:23.984264Z",
-     "shell.execute_reply": "2024-06-03T10:38:23.983734Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.208917Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.208835Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.210624Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.210397Z"
     }
    },
    "outputs": [],
@@ -1294,10 +1265,10 @@
      "start_time": "2020-03-22T05:06:03.369715Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:23.986158Z",
-     "iopub.status.busy": "2024-06-03T10:38:23.986009Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.316731Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.316154Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.212020Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.211928Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.395371Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.395105Z"
     }
    },
    "outputs": [
@@ -1381,10 +1352,10 @@
      "start_time": "2020-03-22T05:06:04.277559Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.318698Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.318537Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.353835Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.353316Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.396873Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.396752Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.479090Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.478761Z"
     }
    },
    "outputs": [],
@@ -1403,16 +1374,16 @@
      "start_time": "2020-03-22T05:06:04.616827Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.355730Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.355576Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.472245Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.471702Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.480926Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.480801Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.552270Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.551962Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1455,10 +1426,10 @@
      "start_time": "2020-03-22T05:06:04.827766Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.474223Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.474067Z",
-     "iopub.status.idle": "2024-06-03T10:38:24.477318Z",
-     "shell.execute_reply": "2024-06-03T10:38:24.476861Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.553941Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.553833Z",
+     "iopub.status.idle": "2024-06-03T14:27:32.555755Z",
+     "shell.execute_reply": "2024-06-03T14:27:32.555477Z"
     }
    },
    "outputs": [
@@ -1483,10 +1454,10 @@
      "start_time": "2020-03-22T05:06:04.834702Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:24.479202Z",
-     "iopub.status.busy": "2024-06-03T10:38:24.478995Z",
-     "iopub.status.idle": "2024-06-03T10:38:32.900613Z",
-     "shell.execute_reply": "2024-06-03T10:38:32.899972Z"
+     "iopub.execute_input": "2024-06-03T14:27:32.557457Z",
+     "iopub.status.busy": "2024-06-03T14:27:32.557323Z",
+     "iopub.status.idle": "2024-06-03T14:27:38.444412Z",
+     "shell.execute_reply": "2024-06-03T14:27:38.444160Z"
     }
    },
    "outputs": [
@@ -1495,28 +1466,28 @@
      "output_type": "stream",
      "text": [
       "iter.      J_T  ∫gₐ(ϵ₁)dt  ∫gₐ(ϵ₂)dt  ∫gₐ(ϵ₃)dt  ∫gₐ(ϵ₄)dt   ∑∫gₐ(t)dt          J       ΔJ_T         ΔJ  secs\n",
-      "0     5.90e-04   0.00e+00   0.00e+00   0.00e+00   0.00e+00    0.00e+00   5.90e-04        n/a        n/a     1\n"
+      "0     5.90e-04   0.00e+00   0.00e+00   0.00e+00   0.00e+00    0.00e+00   5.90e-04        n/a        n/a     0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "13    3.24e-04   6.29e-05   9.90e-06   5.10e-05   9.21e-06    1.33e-04   4.57e-04  -2.66e-04  -1.33e-04     2\n"
+      "13    3.24e-04   6.29e-05   9.90e-06   5.10e-05   9.21e-06    1.33e-04   4.57e-04  -2.66e-04  -1.33e-04     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "14    1.83e-04   3.16e-05   7.05e-06   2.56e-05   6.48e-06    7.08e-05   2.53e-04  -1.42e-04  -7.08e-05     2\n"
+      "14    1.83e-04   3.16e-05   7.05e-06   2.56e-05   6.48e-06    7.08e-05   2.53e-04  -1.42e-04  -7.08e-05     1\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15    1.06e-04   1.60e-05   5.02e-06   1.29e-05   4.56e-06    3.85e-05   1.44e-04  -7.70e-05  -3.85e-05     2\n"
+      "15    1.06e-04   1.60e-05   5.02e-06   1.29e-05   4.56e-06    3.85e-05   1.44e-04  -7.70e-05  -3.85e-05     1\n"
      ]
     }
    ],
@@ -1558,10 +1529,10 @@
      "start_time": "2020-03-22T05:06:16.592668Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:32.903152Z",
-     "iopub.status.busy": "2024-06-03T10:38:32.902952Z",
-     "iopub.status.idle": "2024-06-03T10:38:33.885938Z",
-     "shell.execute_reply": "2024-06-03T10:38:33.885370Z"
+     "iopub.execute_input": "2024-06-03T14:27:38.445976Z",
+     "iopub.status.busy": "2024-06-03T14:27:38.445864Z",
+     "iopub.status.idle": "2024-06-03T14:27:40.599983Z",
+     "shell.execute_reply": "2024-06-03T14:27:40.599672Z"
     }
    },
    "outputs": [],
@@ -1584,10 +1555,10 @@
      "start_time": "2020-03-22T05:06:25.332449Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:33.888096Z",
-     "iopub.status.busy": "2024-06-03T10:38:33.887938Z",
-     "iopub.status.idle": "2024-06-03T10:38:34.894530Z",
-     "shell.execute_reply": "2024-06-03T10:38:34.893871Z"
+     "iopub.execute_input": "2024-06-03T14:27:40.601494Z",
+     "iopub.status.busy": "2024-06-03T14:27:40.601404Z",
+     "iopub.status.idle": "2024-06-03T14:27:42.952722Z",
+     "shell.execute_reply": "2024-06-03T14:27:42.952404Z"
     }
    },
    "outputs": [],
@@ -1608,16 +1579,16 @@
      "start_time": "2020-03-22T05:06:34.911667Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:38:34.896840Z",
-     "iopub.status.busy": "2024-06-03T10:38:34.896653Z",
-     "iopub.status.idle": "2024-06-03T10:38:35.177508Z",
-     "shell.execute_reply": "2024-06-03T10:38:35.177098Z"
+     "iopub.execute_input": "2024-06-03T14:27:42.954198Z",
+     "iopub.status.busy": "2024-06-03T14:27:42.954102Z",
+     "iopub.status.idle": "2024-06-03T14:27:43.121127Z",
+     "shell.execute_reply": "2024-06-03T14:27:43.120859Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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-              <p class="caption"><span class="caption-text">Contents:</span></p>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../01_overview.html">Krotov Python Package</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../02_contributing.html">Contributing</a></li>
@@ -154,9 +152,9 @@
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-  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/v1.3.0/docs/notebooks/09_example_numpy.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/v1.3.0?filepath=docs/notebooks/09_example_numpy.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
-<div class="section" id="Optimization-with-numpy-Arrays">
-<h1>Optimization with numpy Arrays<a class="headerlink" href="#Optimization-with-numpy-Arrays" title="Permalink to this headline">¶</a></h1>
+  <p><span class="raw-html"><a href="http://nbviewer.jupyter.org/github/qucontrol/krotov/blob/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d//home/runner/work/krotov/krotov/docs/notebooks/09_example_numpy.ipynb" target="_blank"><img alt="Render on nbviewer" src="https://img.shields.io/badge/render%20on-nbviewer-orange.svg" style="vertical-align:text-bottom"></a>&nbsp;<a href="https://mybinder.org/v2/gh/qucontrol/krotov/bd14c16e4fd8eab38d0d105fbf9b695b4dddea7d?filepath=/home/runner/work/krotov/krotov/docs/notebooks/09_example_numpy.ipynb" target="_blank"><img alt="Launch Binder" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+<section id="Optimization-with-numpy-Arrays">
+<h1>Optimization with numpy Arrays<a class="headerlink" href="#Optimization-with-numpy-Arrays" title="Link to this heading">¶</a></h1>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
@@ -179,21 +177,21 @@ <h1>Optimization with numpy Arrays<a class="headerlink" href="#Optimization-with
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Python implementation: CPython
-Python version       : 3.8.16
-IPython version      : 8.12.3
+Python version       : 3.12.0
+IPython version      : 8.25.0
 
-krotov    : 1.3.0
-scipy     : 1.10.1
+numpy     : 1.26.4
+krotov    : 1.3.0+dev
 matplotlib: 3.7.5
-numpy     : 1.24.4
+scipy     : 1.12.0
 
 </pre></div></div>
 </div>
 <p><span class="math notranslate nohighlight">\(\newcommand{tr}[0]{\operatorname{tr}} \newcommand{diag}[0]{\operatorname{diag}} \newcommand{abs}[0]{\operatorname{abs}} \newcommand{pop}[0]{\operatorname{pop}} \newcommand{aux}[0]{\text{aux}} \newcommand{opt}[0]{\text{opt}} \newcommand{tgt}[0]{\text{tgt}} \newcommand{init}[0]{\text{init}} \newcommand{lab}[0]{\text{lab}} \newcommand{rwa}[0]{\text{rwa}} \newcommand{bra}[1]{\langle#1\vert} \newcommand{ket}[1]{\vert#1\rangle} \newcommand{Bra}[1]{\left\langle#1\right\vert} \newcommand{Ket}[1]{\left\vert#1\right\rangle} \newcommand{Braket}[2]{\left\langle #1\vphantom{#2} \mid #2\vphantom{#1}\right\rangle} \newcommand{op}[1]{\hat{#1}} \newcommand{Op}[1]{\hat{#1}} \newcommand{dd}[0]{\,\text{d}} \newcommand{Liouville}[0]{\mathcal{L}} \newcommand{DynMap}[0]{\mathcal{E}} \newcommand{identity}[0]{\mathbf{1}} \newcommand{Norm}[1]{\lVert#1\rVert} \newcommand{Abs}[1]{\left\vert#1\right\vert} \newcommand{avg}[1]{\langle#1\rangle} \newcommand{Avg}[1]{\left\langle#1\right\rangle} \newcommand{AbsSq}[1]{\left\vert#1\right\vert^2} \newcommand{Re}[0]{\operatorname{Re}} \newcommand{Im}[0]{\operatorname{Im}}\)</span></p>
 <p>The <code class="docutils literal notranslate"><span class="pre">krotov</span></code> package heavily builds on QuTiP. However, in rare circumstances the overhead of <code class="docutils literal notranslate"><span class="pre">qutip.Qobj</span></code> objects might limit numerical efficiency, in particular when QuTiP’s automatic sparse storage is inappropriate. If you know what you are doing, it is possible to replace <code class="docutils literal notranslate"><span class="pre">Qobj</span></code>s with low-level objects such as numpy arrays. This example revisits the <a class="reference internal" href="01_example_simple_state_to_state.html"><span class="doc">Optimization of a State-to-State Transfer in a Two-Level-System</span></a>, but exclusively uses numpy
 objects for states and operators.</p>
-<div class="section" id="Two-level-Hamiltonian">
-<h2>Two-level-Hamiltonian<a class="headerlink" href="#Two-level-Hamiltonian" title="Permalink to this headline">¶</a></h2>
+<section id="Two-level-Hamiltonian">
+<h2>Two-level-Hamiltonian<a class="headerlink" href="#Two-level-Hamiltonian" title="Link to this heading">¶</a></h2>
 <p>We consider again the standard Hamiltonian of a two-level system, but now we construct the drift Hamiltonian <code class="docutils literal notranslate"><span class="pre">H0</span></code> and the control Hamiltonian <code class="docutils literal notranslate"><span class="pre">H1</span></code> as numpy matrices:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -226,9 +224,9 @@ <h2>Two-level-Hamiltonian<a class="headerlink" href="#Two-level-Hamiltonian" tit
 </pre></div>
 </div>
 </div>
-</div>
-<div class="section" id="Optimization-target">
-<h2>Optimization target<a class="headerlink" href="#Optimization-target" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimization-target">
+<h2>Optimization target<a class="headerlink" href="#Optimization-target" title="Link to this heading">¶</a></h2>
 <p>By default, the <code class="docutils literal notranslate"><span class="pre">Objective</span></code> initializer checks that the objective is expressed with QuTiP objects. If we want to use low-level objects instead, we have to explicitly disable this:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
@@ -271,9 +269,9 @@ <h2>Optimization target<a class="headerlink" href="#Optimization-target" title="
 </pre></div></div>
 </div>
 <p>Note how all objects are numpy arrays, as indicated by the symbol <code class="docutils literal notranslate"><span class="pre">a</span></code>.</p>
-</div>
-<div class="section" id="Simulate-dynamics-under-the-guess-field">
-<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Simulate-dynamics-under-the-guess-field">
+<h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate-dynamics-under-the-guess-field" title="Link to this heading">¶</a></h2>
 <p>To simulate the dynamics under the guess pulse, we can use the objective’s <code class="docutils literal notranslate"><span class="pre">propagator</span></code> method. However, the propagator we use must take into account the format of the states and operators. We define a simple propagator that solve the dynamics within a single time step my matrix exponentiation of the Hamiltonian:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
@@ -319,7 +317,7 @@ <h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate
 </pre></div>
 </div>
 </div>
-<div class="nbinput nblast docutils container">
+<div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
@@ -329,6 +327,17 @@ <h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate
 </pre></div>
 </div>
 </div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/3854291581.py:2: DeprecationWarning: Conversion of an array with ndim &gt; 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  return complex(state.conj().T @ (proj @ state)).real
+/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/3854291581.py:2: DeprecationWarning: Conversion of an array with ndim &gt; 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  return complex(state.conj().T @ (proj @ state)).real
+</pre></div></div>
+</div>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
@@ -360,9 +369,9 @@ <h2>Simulate dynamics under the guess field<a class="headerlink" href="#Simulate
 </div>
 </div>
 <p>The result is the same as in the original example.</p>
-</div>
-<div class="section" id="Optimize">
-<h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="Optimize">
+<h2>Optimize<a class="headerlink" href="#Optimize" title="Link to this heading">¶</a></h2>
 <p>First, we define the update shape and step width as before:</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
@@ -431,7 +440,7 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
+<div class="nboutput docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
@@ -442,6 +451,22 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 2     8.83e-01    1.83e-02   9.02e-01  -4.11e-02  -2.28e-02     0
 3     8.23e-01    2.71e-02   8.50e-01  -6.06e-02  -3.35e-02     0
 4     7.37e-01    3.84e-02   7.76e-01  -8.52e-02  -4.68e-02     0
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/124890641.py:2: DeprecationWarning: Conversion of an array with ndim &gt; 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+  return complex(psi1.conj().T @ psi2)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
 5     6.26e-01    5.07e-02   6.77e-01  -1.11e-01  -6.05e-02     0
 6     4.96e-01    6.04e-02   5.56e-01  -1.31e-01  -7.02e-02     0
 7     3.62e-01    6.30e-02   4.25e-01  -1.34e-01  -7.09e-02     0
@@ -466,15 +491,15 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 <div class="highlight"><pre>
 Krotov Optimization Result
 --------------------------
-- Started at 2024-06-03 12:32:10
+- Started at 2024-06-03 10:23:57
 - Number of objectives: 1
 - Number of iterations: 10
 - Reason for termination: Reached 10 iterations
-- Ended at 2024-06-03 12:32:11 (0:00:01)
+- Ended at 2024-06-03 10:23:58 (0:00:01)
 </pre></div></div>
 </div>
-</div>
-</div>
+</section>
+</section>
 
 
            </div>
@@ -488,7 +513,7 @@ <h2>Optimize<a class="headerlink" href="#Optimize" title="Permalink to this head
 
   <div role="contentinfo">
     <p>&#169; Copyright 2024, Michael Goerz et al..
-      <span class="lastupdated">Last updated on Jun 03, 2024.
+      <span class="lastupdated">Last updated on Jun 04, 2024.
       </span></p>
   </div>
 
diff --git a/master/notebooks/09_example_numpy.ipynb b/master/notebooks/09_example_numpy.ipynb
index 01b915dd..297da2cc 100644
--- a/master/notebooks/09_example_numpy.ipynb
+++ b/master/notebooks/09_example_numpy.ipynb
@@ -21,10 +21,10 @@
      "n": "1"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:09.544235Z",
-     "iopub.status.busy": "2024-06-03T10:32:09.544062Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.077876Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.077317Z"
+     "iopub.execute_input": "2024-06-03T14:23:56.491044Z",
+     "iopub.status.busy": "2024-06-03T14:23:56.490913Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.496001Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.495645Z"
     }
    },
    "outputs": [
@@ -33,13 +33,13 @@
      "output_type": "stream",
      "text": [
       "Python implementation: CPython\n",
-      "Python version       : 3.8.16\n",
-      "IPython version      : 8.12.3\n",
+      "Python version       : 3.12.0\n",
+      "IPython version      : 8.25.0\n",
       "\n",
-      "krotov    : 1.3.0\n",
-      "scipy     : 1.10.1\n",
+      "numpy     : 1.26.4\n",
+      "krotov    : 1.3.0+dev\n",
       "matplotlib: 3.7.5\n",
-      "numpy     : 1.24.4\n",
+      "scipy     : 1.12.0\n",
       "\n"
      ]
     }
@@ -129,10 +129,10 @@
      "start_time": "2020-03-24T21:13:40.847535Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.107387Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.107103Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.111392Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.110906Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.519194Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.518953Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.522368Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.522085Z"
     }
    },
    "outputs": [],
@@ -164,10 +164,10 @@
      "start_time": "2020-03-24T21:13:40.865354Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.113342Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.113151Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.115611Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.115135Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.523800Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.523713Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.525491Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.525175Z"
     },
     "lines_to_next_cell": 2
    },
@@ -196,10 +196,10 @@
      "start_time": "2020-03-24T21:13:40.876732Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.117573Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.117415Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.119834Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.119351Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.527047Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.526938Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.528584Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.528326Z"
     }
    },
    "outputs": [],
@@ -223,10 +223,10 @@
      "start_time": "2020-03-24T21:13:40.883324Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.121782Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.121623Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.124415Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.123936Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.530097Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.530012Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.531903Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.531594Z"
     }
    },
    "outputs": [],
@@ -251,10 +251,10 @@
      "start_time": "2020-03-24T21:13:40.891777Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.126403Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.126212Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.131789Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.131306Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.533523Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.533391Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.536997Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.536700Z"
     }
    },
    "outputs": [
@@ -306,10 +306,10 @@
      "start_time": "2020-03-24T21:13:40.905293Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.133844Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.133654Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.136951Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.136474Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.538677Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.538564Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.540781Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.540487Z"
     }
    },
    "outputs": [],
@@ -341,10 +341,10 @@
      "start_time": "2020-03-24T21:13:40.917919Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.138951Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.138763Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.141562Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.141086Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.542211Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.542051Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.543896Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.543647Z"
     }
    },
    "outputs": [],
@@ -371,10 +371,10 @@
      "start_time": "2020-03-24T21:13:40.926588Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.143527Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.143369Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.146007Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.145531Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.545255Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.545135Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.546902Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.546662Z"
     }
    },
    "outputs": [],
@@ -400,10 +400,10 @@
      "start_time": "2020-03-24T21:13:40.933147Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.148001Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.147811Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.150343Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.149867Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.548325Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.548228Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.549880Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.549500Z"
     }
    },
    "outputs": [],
@@ -425,13 +425,24 @@
      "n": "12"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.152322Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.152132Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.223699Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.223236Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.551258Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.551171Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.582962Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.582650Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/3854291581.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  return complex(state.conj().T @ (proj @ state)).real\n",
+      "/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/3854291581.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  return complex(state.conj().T @ (proj @ state)).real\n"
+     ]
+    }
+   ],
    "source": [
     "guess_dynamics = objectives[0].propagate(\n",
     "    tlist, propagator=expm, e_ops=[proj0, proj1], expect=expect\n",
@@ -452,10 +463,10 @@
      "n": "13"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.225618Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.225388Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.228433Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.227969Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.584562Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.584425Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.586642Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.586302Z"
     }
    },
    "outputs": [],
@@ -484,10 +495,10 @@
      "n": "14"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.230359Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.230203Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.377278Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.376788Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.588919Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.588769Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.788571Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.787424Z"
     }
    },
    "outputs": [
@@ -526,10 +537,10 @@
      "start_time": "2020-03-24T21:13:41.397827Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.379362Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.379205Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.382230Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.381780Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.796425Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.795886Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.803552Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.802616Z"
     }
    },
    "outputs": [],
@@ -550,10 +561,10 @@
      "start_time": "2020-03-24T21:13:41.406924Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.384142Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.383964Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.386559Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.386109Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.811656Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.811251Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.823075Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.817756Z"
     }
    },
    "outputs": [],
@@ -586,10 +597,10 @@
      "start_time": "2020-03-24T21:13:41.415715Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.388498Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.388350Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.391084Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.390634Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.829078Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.828463Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.834886Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.832323Z"
     }
    },
    "outputs": [],
@@ -617,10 +628,10 @@
      "start_time": "2020-03-24T21:13:41.427434Z"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.393128Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.392908Z",
-     "iopub.status.idle": "2024-06-03T10:32:10.395372Z",
-     "shell.execute_reply": "2024-06-03T10:32:10.394924Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.839276Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.838954Z",
+     "iopub.status.idle": "2024-06-03T14:23:57.848829Z",
+     "shell.execute_reply": "2024-06-03T14:23:57.848176Z"
     }
    },
    "outputs": [],
@@ -658,10 +669,10 @@
      "n": "15"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:10.397329Z",
-     "iopub.status.busy": "2024-06-03T10:32:10.397181Z",
-     "iopub.status.idle": "2024-06-03T10:32:11.392602Z",
-     "shell.execute_reply": "2024-06-03T10:32:11.392046Z"
+     "iopub.execute_input": "2024-06-03T14:23:57.858243Z",
+     "iopub.status.busy": "2024-06-03T14:23:57.854303Z",
+     "iopub.status.idle": "2024-06-03T14:23:58.390263Z",
+     "shell.execute_reply": "2024-06-03T14:23:58.389983Z"
     }
    },
    "outputs": [
@@ -701,6 +712,14 @@
       "4     7.37e-01    3.84e-02   7.76e-01  -8.52e-02  -4.68e-02     0\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/mm/rr6dgkzj0l5_j_ntxcg_hlth0000gn/T/ipykernel_52079/124890641.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  return complex(psi1.conj().T @ psi2)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -774,10 +793,10 @@
      "n": "16"
     },
     "execution": {
-     "iopub.execute_input": "2024-06-03T10:32:11.394706Z",
-     "iopub.status.busy": "2024-06-03T10:32:11.394522Z",
-     "iopub.status.idle": "2024-06-03T10:32:11.398301Z",
-     "shell.execute_reply": "2024-06-03T10:32:11.397920Z"
+     "iopub.execute_input": "2024-06-03T14:23:58.391772Z",
+     "iopub.status.busy": "2024-06-03T14:23:58.391640Z",
+     "iopub.status.idle": "2024-06-03T14:23:58.393703Z",
+     "shell.execute_reply": "2024-06-03T14:23:58.393450Z"
     }
    },
    "outputs": [
@@ -786,11 +805,11 @@
       "text/plain": [
        "Krotov Optimization Result\n",
        "--------------------------\n",
-       "- Started at 2024-06-03 12:32:10\n",
+       "- Started at 2024-06-03 10:23:57\n",
        "- Number of objectives: 1\n",
        "- Number of iterations: 10\n",
        "- Reason for termination: Reached 10 iterations\n",
-       "- Ended at 2024-06-03 12:32:11 (0:00:01)"
+       "- Ended at 2024-06-03 10:23:58 (0:00:01)"
       ]
      },
      "execution_count": 19,
@@ -820,7 +839,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.12.0"
   },
   "toc": {
    "base_numbering": 1,
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7,28,29,31,34,35],proj2:[28,29,34],proj3:[28,29,34],proj:35,proj_00:[30,33],proj_01:[30,33],proj_10:[30,33],proj_11:[30,33],project:[1,32],projector:[27,28,31,33,35],promis:5,prompt:1,prone:10,propag:[3,4,5,6,7,9,10,13,16,17,18,19,20,21,23,24,27,28,29,30,31,32,33,34,35],propagate_guess:32,propagate_guess_for_expv:32,propagate_opt:32,propagate_opt_for_expv:32,propagt:33,proper:[1,14,15],properli:[1,6,23],properti:[0,4,7,9,10,23,27],propmt:1,proport:[6,27,33],proportion:27,propos:1,propto:[10,18,28],prospect:12,protect:[1,9,12,14],protocol:[10,21],provid:[0,1,4,5,7,10,11,18,21,22,27,30,33],pseudocod:[5,26],psi00:30,psi01:30,psi0:[15,19,31],psi10:30,psi11:30,psi1:[19,31,35],psi2:35,psi:[1,6,16,19,27,28,29,31,32,34],psi_00:33,psi_01:33,psi_10:33,psi_11:33,psi_:[27,28,30],psi_k:16,psi_target:[28,29,34],psi_tgt:15,publish:1,pull:26,puls:[4,5,6,8,11,12,13,14,15,16,17,18,20,21,23,24,25,26,27,28,29,30,33,35],pulse_im:[28,29],pulse_imaginari:[28,29,34],pulse_onto_tlist:[15,27],pulse_opt:[4,7,9,15,16,17,20,25,27,28,29,30,31,32,33,34,35],pulse_options_dict_to_list:15,pulse_r:[28,29,34],pulse_v:21,pulseopt:4,pulseoptim:10,pulses_map:[18,35],pump:[12,28,29,34],pure:[5,16,19,21,32],puriti:[16,30,32],purpos:[1,5,10,14,17,20,26,27,28,29,32,33],purview:5,push:[1,9,10],put:[1,5,21,29,30],py38:1,pyenv:1,pylab:[1,27,28,29,30,31,32,33,34,35],pylint:1,pypa:1,pypi:1,pyproject:1,pytest:1,python:[1,4,5,7,9,11,12,15,17,21,23,26,27,28,29,30,31,32,33,34,35],q1t1:32,q1t2:32,q2t1:32,q2t2:32,qdyn:[0,11],qed:12,qengin:[10,11,12],qip:16,qobj:[4,7,9,15,16,18,19,20,22,23,28,29,30,31,32,33,34,35],qquad:[6,10,30,33],quad:[6,16],quantifi:33,quantiti:[14,17],quantum:[0,5,7,8,10,12,13,16,18,19,26,27,28,29,30,31,33],quasi:[10,12],qubit:[4,6,8,11,12,19,26,27,32,33,35],qucontrol:[0,1,4,33],queue:21,quickli:34,qutip:[0,1,4,5,6,11,12,15,16,19,20,21,22,23,25,26,27,28,29,30,31,32,33,34,35],qutip_callback:25,r01:12,r175:12,rabitz:12,rach:12,radio:6,raimond:12,rais:[15,17,19,20,21,23],raman:28,ramp:25,random:[10,12,19],rang:[9,10,19,20,30,31,33,34],rangl:[1,6,10,16,18,27,28,29,30,31,32,33,34,35],rapidli:12,rapin:12,rare:[6,18,35],rate:[6,9,29,30],ratio:31,rc1:1,reach:[5,6,9,10,14,17,20,23,27,28,29,30,31,32,33,34,35],reaction:[6,12],read:[0,1,6,9,15,21,28,29,30,31,33],readabl:1,readi:1,real:[6,9,15,16,17,19,20,28,29,30,31,32,34,35],realiz:[0,6,9,10,14,31,33,34],reason:[27,28,29,30,31,32,33,34,35],rebas:1,recalcul:[24,33],receiv:[4,9,14,19,21,22,33],recent:[0,5,9,10,14],recogn:19,recommend:[1,9,14,16,19,22],recoupl:12,recov:[10,27],red:34,redefin:[1,4],reduc:[10,29,30],reduct:12,reentrant:[22,32],ref:[6,9,10,33],refer:[0,1,5,6,10,26,33,34],reflect:32,reflog:1,refregi:12,refresh:[24,33],region:34,regul:16,regular:[9,14],reich:[0,2,12,33],reif:12,reinhard:12,reinhold:12,reiss:12,reiter:12,rel:[1,10,31,32,33,34],relat:[0,4,7,9,14,15,26,28,33],relax:12,releas:[0,4,21,26],relesas:1,relev:[6,9,10],reli:[0,5,6,7,27],rem:12,remain:[9,25,28,29],remaind:6,rembold:12,remedi:10,rememb:[1,6,9],remot:[1,12],remov:6,renam:4,render:1,renorm:29,rensen:12,rep:12,repeatedli:16,replac:[1,4,6,10,15,19,23,35],repo:1,report:[2,4,26],repositori:1,repr:19,repres:[5,7,9,16,18,19,27,30],represent:[4,9,14,19,31],reproduc:[1,4,5,32],reput:5,request:26,requir:[1,5,6,9,10,15,16,18,19,20,24,25,27,28,29,31,32,33],res:34,research:[0,5,33],reset:[4,6,8,9,19,26,34],reset_symbol_count:19,reshuffl:4,resiz:1,resolut:28,resolv:[6,9],reson:[5,6,30,32],resourc:6,respect:[0,4,6,9,10,14,17,18,19,21,22,27,28,32,33,34],rest:1,restor:[9,23],restrict:[1,4,5,9,10,20,21,28],restructur:1,result:[1,4,6,7,10,13,14,15,16,18,19,20,21,22,24,26,27,28,29,30,31,33,34,35],result_queu:21,returnd:19,rev:[12,19,33],revert:1,review:[6,21],revisit:[34,35],revmodphi:12,rewrit:[1,9,28],rewritten:[6,9,16],rho00:32,rho01:32,rho0:[19,32],rho10:32,rho11:32,rho2:20,rho:[6,16,18,19,30,32],rho_1:20,rho_q:30,rho_q_th:30,rho_q_trg:30,rho_t_th:30,rho_t_trg:30,rho_th:30,rho_trg:30,rhs:35,rice:12,rich:6,riedel:12,right:[1,6,9,10,15,16,18,25,27,28,29,30,31,32,33,34,35],rightarrow:[6,9,18,27,28,29,31,32,34],rigor:[6,29],rise:34,roadmap:12,robust:[3,4,6,8,10,12,19,26,28,33],role:[20,32],rolston:12,root:1,rossignolo:12,rot:28,rotat:[6,9,29,32,34],routin:[1,4,7,9,10,13,14,15,16,17,18,19,20,21,22,27,29,32,33,34,35],rst:1,rtol:[22,30],rule:10,run:[0,4,6,10,14,19,21,26,27,28,29,30,32,33,34,35],runtim:[9,21,22,25],rvert:[1,27,28,29,30,31,32,33,34,35],rwa:[1,8,9,26,27,29,30,31,32,33,34,35],rwa_target_st:[28,29,34],ryd:9,rydberg:[5,6,9,12],s11128:12,s12567:12,s40507:12,s41467:12,s41534:12,s_i:17,s_l:6,sacrific:34,safeti:1,same:[1,3,6,9,10,14,15,16,17,18,19,20,21,22,23,25,28,29,31,32,34,35],sampl:[7,9,15,19,34],san:[28,29,31,32,33,34],sander:12,santoro:12,santra:12,sarkar:12,satisfi:16,scalar:[6,9,10,22],scale:[5,10,12,20,28,29,31,34],scale_control:34,scheme:[6,11,12,26,28,29],schirmer:12,schmidt:[6,12,16,20,32],schmiedmay:12,schoelkopf:12,schr:12,schult:12,schuster:12,schwarz:6,sci:12,scienc:[5,12],scientif:[0,5,12],scientist:12,scipi:[1,10,12,22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Python Package","Contributing","Credits","Features","History","Introduction","Krotov\u2019s Method","Using Krotov with QuTiP","Examples","How-Tos","Other Optimization Methods","Related Software","References","krotov package","krotov.convergence module","krotov.conversions module","krotov.functionals module","krotov.info_hooks module","krotov.mu module","krotov.objectives module","krotov.optimize module","krotov.parallelization module","krotov.propagators module","krotov.result module","krotov.second_order module","krotov.shapes module","Welcome to the Krotov package\u2019s documentation!","Optimization of a State-to-State Transfer in a Two-Level-System","Optimization of a State-to-State Transfer in a Lambda System in the RWA","Optimization of a Dissipative State-to-State Transfer in a Lambda System","Optimization of Dissipative Qubit Reset","Optimization of an X-Gate for a Transmon Qubit","Optimization of a Dissipative Quantum Gate","Optimization towards a Perfect Entangler","Ensemble Optimization for Robust Pulses","Optimization with numpy 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