Update Sphinx documentation, commit 1d5aecc [skip ci].

_sources/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 =========
 
-4.0.0 (unreleased)
+4.0.0 (2024-03-03)
 ------------------
 
 New

notebooks/Batch mode overview.html

@@ -1385,7 +1385,7 @@ <h2>Ensemble propagations<a class="headerlink" href="#ensemble-propagations" tit
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-<img alt="../_images/3fcb0dba9ae6130454c35f9b22c5f4845eca5fc17829324d579ced015f6e1ee8.png" src="../_images/3fcb0dba9ae6130454c35f9b22c5f4845eca5fc17829324d579ced015f6e1ee8.png" />
+<img alt="../_images/dda7e7d64ba84418544f527fb34357f81aa4c8a18f38d97641fb303edc86cf6d.png" src="../_images/dda7e7d64ba84418544f527fb34357f81aa4c8a18f38d97641fb303edc86cf6d.png" />
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 </section>

notebooks/Customising the adaptive integrator.html

@@ -637,8 +637,8 @@ <h2>Compact mode<a class="headerlink" href="#compact-mode" title="Link to this h
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 5.33 s, sys: 41.8 ms, total: 5.37 s
-Wall time: 5.37 s
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 5.55 s, sys: 51.2 ms, total: 5.6 s
+Wall time: 5.6 s
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@@ -653,8 +653,8 @@ <h2>Compact mode<a class="headerlink" href="#compact-mode" title="Link to this h
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 184 ms, sys: 3.95 ms, total: 188 ms
-Wall time: 188 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 198 ms, sys: 4.57 ms, total: 203 ms
+Wall time: 202 ms
 </pre></div>
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notebooks/NeuralHamiltonianODEs.html

@@ -637,10 +637,10 @@ <h1>Neural Hamiltonian ODEs<a class="headerlink" href="#neural-hamiltonian-odes"
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7fcb95468d60&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f5d79f74e20&gt;]
 </pre></div>
 </div>
-<img alt="../_images/c319fa9461e062a358de060b595ffc230781d6f6dd0d23b43ca4857bbea48cdf.png" src="../_images/c319fa9461e062a358de060b595ffc230781d6f6dd0d23b43ca4857bbea48cdf.png" />
+<img alt="../_images/9dd04d04285666282132b66406fd58f74e3ffe40088657334e1b65f0d4c12414.png" src="../_images/9dd04d04285666282132b66406fd58f74e3ffe40088657334e1b65f0d4c12414.png" />
 </div>
 </div>
 <p>Clearly, the power and interest of this technique, applied to Hamiltonian systems, lies in the possibility to define some good training for the FFNN weights and biases so that the final system converges to something useful</p>

notebooks/NeuralODEs.html

@@ -695,7 +695,7 @@ <h3>Taylor Integrator<a class="headerlink" href="#taylor-integrator" title="Link
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.4642505645751953 seconds --- to build (jit) the Taylor integrator
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.586190938949585 seconds --- to build (jit) the Taylor integrator
 </pre></div>
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@@ -736,7 +736,7 @@ <h3>Taylor Integrator<a class="headerlink" href="#taylor-integrator" title="Link
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.0001513957977294922 seconds --- to propagate using the Taylor scheme
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.0003941059112548828 seconds --- to propagate using the Taylor scheme
 </pre></div>
 </div>
 </div>
@@ -755,7 +755,7 @@ <h3>Taylor Integrator<a class="headerlink" href="#taylor-integrator" title="Link
 </div>
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-<img alt="../_images/dd52c37323dba31bc92e751724518b9a8e95c4fd1b7e699330b2a9a546dea943.png" src="../_images/dd52c37323dba31bc92e751724518b9a8e95c4fd1b7e699330b2a9a546dea943.png" />
+<img alt="../_images/4de05280da500d983f5e11fd92f5d1b1a2c59989e0f80405c9537a1b06e43957.png" src="../_images/4de05280da500d983f5e11fd92f5d1b1a2c59989e0f80405c9537a1b06e43957.png" />
 </div>
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 </section>
@@ -794,7 +794,7 @@ <h3>Scipy Counterpart<a class="headerlink" href="#scipy-counterpart" title="Link
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.0014193058013916016 seconds --- to propagate
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.0015683174133300781 seconds --- to propagate
 </pre></div>
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@@ -809,7 +809,7 @@ <h3>Scipy Counterpart<a class="headerlink" href="#scipy-counterpart" title="Link
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-<img alt="../_images/dc1159f42a489bcef4d98929926690a4b8f7f76dfabb1f38c228c922022b03b1.png" src="../_images/dc1159f42a489bcef4d98929926690a4b8f7f76dfabb1f38c228c922022b03b1.png" />
+<img alt="../_images/77f3238b08310fce0be35275680351a3984679c9130da08129ee60e281372fd9.png" src="../_images/77f3238b08310fce0be35275680351a3984679c9130da08129ee60e281372fd9.png" />
 </div>
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 <p>We see a net advantage in timings using the Taylor integration scheme. Note we are here not using batch propagation, which would add an additional 2-4 factor speedup in performances.</p>

notebooks/Optimal Control of the Lotka-Volterra equations.html

@@ -1099,9 +1099,9 @@ <h2>Implementing a single shooting method<a class="headerlink" href="#implementi
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_10357/2413872175.py:11: 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.)
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_10354/2413872175.py:11: 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.)
   ta.pars[6] = np.heaviside(switching_function(x_0,y_0,lx_0,ly_0, ps), 1.)
-/tmp/ipykernel_10357/2413872175.py:14: 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.)
+/tmp/ipykernel_10354/2413872175.py:14: 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.)
   ta.state[0] = x_0; ta.state[1] = y_0; ta.state[2] = lx_0; ta.state[3] = ly_0
 </pre></div>
 </div>
@@ -1143,7 +1143,7 @@ <h2>Implementing a single shooting method<a class="headerlink" href="#implementi
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_10357/3578120629.py:12: 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.)
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_10354/3578120629.py:12: 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.)
   ta.pars[6] = np.heaviside(switching_function(x_0,y_0,lx_0,ly_0, ps), 1.)
 </pre></div>
 </div>

notebooks/Periodic orbits in the CR3BP.html

@@ -736,7 +736,7 @@ <h2>Putting all together and integrating some initial conditions<a class="header
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 10.702593088150024 seconds --- to build the Taylor integrator
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 12.994643211364746 seconds --- to build the Taylor integrator
 </pre></div>
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@@ -767,7 +767,7 @@ <h2>Putting all together and integrating some initial conditions<a class="header
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.050169944763183594 seconds --- to propagate
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--- 0.049716949462890625 seconds --- to propagate
 </pre></div>
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@@ -825,7 +825,7 @@ <h2>Putting all together and integrating some initial conditions<a class="header
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7feef50c7c40&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7fa6ad5dfa30&gt;
 </pre></div>
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 <img alt="../_images/d28617762dce1b6a17d27870baf3198cb01123078b376f3bb3909aeda7e54a96.png" src="../_images/d28617762dce1b6a17d27870baf3198cb01123078b376f3bb3909aeda7e54a96.png" />

notebooks/ensemble_mode.html

@@ -637,11 +637,11 @@ <h2>A simple example<a class="headerlink" href="#a-simple-example" title="Link t
  Taylor order            : 20
  Dimension               : 2
  Time                    : 20
- State                   : [0.044478155030506665, 0.06317206891537967],
+ State                   : [0.03847826276763499, 0.04683603466449001],
  &lt;taylor_outcome.time_limit: -4294967299&gt;,
- 0.20354329753224296,
- 0.21816196374011906,
- 96,
+ 0.21034484024984398,
+ 0.22310267911273451,
+ 93,
  None,
  None)
 </pre></div>
@@ -693,7 +693,7 @@ <h2>A simple example<a class="headerlink" href="#a-simple-example" title="Link t
 </div>
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-<img alt="../_images/873e90a9fc93b378cae7d19ef529c12db62bdf6228c5254169be3fa7a569691a.png" src="../_images/873e90a9fc93b378cae7d19ef529c12db62bdf6228c5254169be3fa7a569691a.png" />
+<img alt="../_images/8a03e4cc1e86d825b12d0762b6710a82a6321ef94fe11cb3c5fa070902c20b6e.png" src="../_images/8a03e4cc1e86d825b12d0762b6710a82a6321ef94fe11cb3c5fa070902c20b6e.png" />
 </div>
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 </section>