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110 changes: 21 additions & 89 deletions build/html/WARNINGS.log
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Expand Up @@ -17,6 +17,21 @@ block source: ModuleNotFoundError: No module named 'pyxu.opt.solver.GradientDe
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/intro/tomo.ipynb:710: WARNING: File not found: 'api/abc.html#pyxu.abc.Operator.argshift'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/intro/tomo.ipynb:722: WARNING: File not found: 'api/abc.html#pyxu.abc.DiffMap.estimate_diff_lipschitz'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/intro/tomo.ipynb:946: WARNING: File not found: 'api/abc.html#pyxu.abc.ProxDiffFunc'
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/pyxu/opt/solver/pds.py:docstring of pyxu.opt.solver.pds.ADMM:152: WARNING: Exception occurred in plotting opt-solver-3
from /home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/api/opt.solver.rst:
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/matplotlib/sphinxext/plot_directive.py", line 552, in _run_code
exec(code, ns)
File "<string>", line 29, in <module>
TypeError: SquaredL2Norm.__init__() got an unexpected keyword argument 'dim'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/examples/contributing.ipynb:42: WARNING: invalid syntax (<unknown>, line 5) in document 'examples/contributing'
Parsed source in `python` block:
concatenations: try:
from pyxu.opt.solver import GradientDescent
except ImportError as e:
print(e)
block source: !pip install pyxu-gradient-descent
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/examples/deblur.ipynb:278: WARNING: File not found: 'api/opt.solver.html#pyxu.opt.solver.PD3O'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/algebra.ipynb:32: WARNING: File not found: 'api/abc.html#pyxu.abc.Map.apply'
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/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/algebra.ipynb:48: WARNING: File not found: 'api/abc.html#pyxu.abc.DiffMap'
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Conjugate Gradient (CG) `🔗 <../api/opt.solver.html#pyxu.opt.solver.CG>`__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/algorithms.ipynb:29: WARNING: Title underline too short.

(Accelerated) Proximal Gradient Descent (PGD) `🔗 <../api/opt.solver.html#pyxu.opt.solver.PGD>`__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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(Accelerated) Proximal Gradient Descent (PGD) `🔗 <../api/opt.solver.html#pyxu.opt.solver.PGD>`__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/diff_ops.ipynb:18: WARNING: File not found: 'api/operator/linop.html#pyxu.operator.PartialDerivative'
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``Func``: The Foundation Stone 🧱
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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``DiffFunc``: Differentiable Functionals 🎯
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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``DiffFunc``: Differentiable Functionals 🎯
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Demystifying the Proximal Operator 🎭
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Demystifying the Proximal Operator 🎭
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Specialized Classes: ``ProxDiffFunc``, ``LinFunc``, ``QuadraticFunc`` 🎨
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Specialized Classes: ``ProxDiffFunc``, ``LinFunc``, ``QuadraticFunc`` 🎨
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Implicit Functionals: The Undercover Agents 🕵️‍♀️
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:142: WARNING: Title underline too short.

Practical Example: Deblurring through Least-Squares 🌠
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The Benefit: Automatic Propagation 🚀
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The Benefit: Automatic Propagation 🚀
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:194: WARNING: Title underline too short.

The Takeaway: Flexibility and Power 🌈
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:194: WARNING: Title underline too short.

The Takeaway: Flexibility and Power 🌈
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Implicit Functionals: The Undercover Agents 🕵️‍♀️
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:27: WARNING: File not found: 'api/operator.interop.html#pyxu.operator.interop.from_source'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:27: WARNING: File not found: 'api/abc.html#pyxu.abc.Map.apply'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:27: WARNING: File not found: 'api/abc.html#pyxu.abc.DiffFunc.grad'
Expand All @@ -132,14 +95,6 @@ The Takeaway: Flexibility and Power 🌈
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:137: WARNING: File not found: 'api/abc.html#pyxu.abc.ProxDiffFunc'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:137: WARNING: File not found: 'api/abc.html#pyxu.abc.LinFunc'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/funcs.ipynb:137: WARNING: File not found: 'api/abc.html#pyxu.abc.QuadraticFunc'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/fwdops.ipynb:142: WARNING: Title underline too short.

A Nod to Deep Learning 🚀
~~~~~~~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/fwdops.ipynb:142: WARNING: Title underline too short.

A Nod to Deep Learning 🚀
~~~~~~~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/fwdops.ipynb:11: WARNING: File not found: 'api/abc.html#pyxu.abc.LinOp'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/fwdops.ipynb:17: WARNING: File not found: 'api/operator.interop.html#general'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/fwdops.ipynb:17: WARNING: File not found: 'api/abc.html#pyxu.abc.Map.apply'
Expand Down Expand Up @@ -170,35 +125,12 @@ A Nod to Deep Learning 🚀
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/fwdops.ipynb:153: WARNING: File not found: 'api/abc.html#pyxu.abc.LinOp'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/fwdops.ipynb:153: WARNING: File not found: 'api/abc.html#pyxu.abc.DiffMap'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/hpc.ipynb:214: WARNING: File not found: 'api/runtime.html#pyxu.runtime.Precision'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/lipschitz.ipynb:81: WARNING: Title underline too short.

Hands-On Example 🎓
~~~~~~~~~~~~~~~~~~
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Hands-On Example 🎓
~~~~~~~~~~~~~~~~~~
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/lipschitz.ipynb:30: WARNING: File not found: 'api/abc.html#pyxu.abc.Map.lipschitz'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/lipschitz.ipynb:30: WARNING: File not found: 'api/abc.html#pyxu.abc.DiffMap.diff_lipschitz'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/lipschitz.ipynb:40: WARNING: File not found: 'api/abc.html#pyxu.abc.Map.estimate_lipschitz'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/lipschitz.ipynb:51: WARNING: File not found: 'api/abc.html#pyxu.abc.LinOp'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/lipschitz.ipynb:285: WARNING: File not found: 'api/index.html'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/guide/lipschitz.ipynb:285: WARNING: File not found: 'api/abc/arithmetic.html'
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/pyxu/opt/solver/pds.py:docstring of pyxu.opt.solver.pds.ADMM:152: WARNING: Exception occurred in plotting opt-solver-3
from /home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/api/opt.solver.rst:
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/matplotlib/sphinxext/plot_directive.py", line 552, in _run_code
exec(code, ns)
File "<string>", line 29, in <module>
TypeError: SquaredL2Norm.__init__() got an unexpected keyword argument 'dim'
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/examples/contributing.ipynb:42: WARNING: invalid syntax (<unknown>, line 5) in document 'examples/contributing'
Parsed source in `python` block:
concatenations: try:
from pyxu.opt.solver import GradientDescent
except ImportError as e:
print(e)
block source: !pip install pyxu-gradient-descent
/home/runner/work/pyxu-org.github.io/pyxu-org.github.io/pyxu/doc/examples/deblur.ipynb:278: WARNING: File not found: 'api/opt.solver.html#pyxu.opt.solver.PD3O'
WARNING: missing attribute to_sciop in object pyxu.abc.LinOp
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/pyxu/abc/operator.py:docstring of pyxu.abc.operator.LinOp.asarray:8: WARNING: Inline emphasis start-string without end-string.
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/pyxu/abc/operator.py:docstring of pyxu.abc.operator.LinOp.asarray:8: WARNING: Inline emphasis start-string without end-string.
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4 changes: 2 additions & 2 deletions guide/algebra.html
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Expand Up @@ -505,8 +505,8 @@ <h2>Arithmetic Operations on Operators<a class="headerlink" href="#Arithmetic-Op
<section id="How-Does-it-Work?-🛠️">
<h3>How Does it Work? 🛠️<a class="headerlink" href="#How-Does-it-Work?-🛠️" title="Link to this heading">#</a></h3>
<p>Every time you perform an arithmetic operation, Pyxu automatically infers the output type based on the properties of the operators involved in the operation. This type inference is super convenient because it saves you from manual calculations!</p>
<p>For example, Pyxu takes care of updating as needed methods like <code class="docutils literal notranslate"><span class="pre">apply()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.Map.apply">🔗</a>, <code class="docutils literal notranslate"><span class="pre">jacobian()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.DiffMap.jacobian">🔗</a>, <code class="docutils literal notranslate"><span class="pre">grad()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.DiffFunc.grad">🔗</a>, <code class="docutils literal notranslate"><span class="pre">prox()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.ProxFunc.prox">🔗</a>, and <code class="docutils literal notranslate"><span class="pre">adjoint()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.LinOp.adjoint">🔗</a> according to arithmetic rules. This means you can plug these composite operators directly into proximal gradient algorithms
without having to implement manually gradients or proximal steps yourself.</p>
<p>For example, Pyxu takes care of updating as needed methods like <code class="docutils literal notranslate"><span class="pre">apply()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.Map.apply">🔗</a>, <code class="docutils literal notranslate"><span class="pre">jacobian()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.DiffMap.jacobian">🔗</a>, <code class="docutils literal notranslate"><span class="pre">grad()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.DiffFunc.grad">🔗</a>, <code class="docutils literal notranslate"><span class="pre">prox()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.ProxFunc.prox">🔗</a>, and <code class="docutils literal notranslate"><span class="pre">adjoint()</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.LinOp.adjoint">🔗</a> according to arithmetic rules. This means you can plug these composite operators directly into proximal gradient
algorithms without having to implement manually gradients or proximal steps yourself.</p>
</section>
</section>
<section id="Behind-the-Scenes:-Arithmetic-Rules">
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2 changes: 1 addition & 1 deletion guide/diff_ops.html
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Expand Up @@ -292,7 +292,7 @@ <h2>Partial Derivatives<a class="headerlink" href="#Partial-Derivatives" title="
<div class="math notranslate nohighlight">
\[\mathbf{D} f [n] = f[n+1] - f[n]\]</div>
<p>As we will see below, this is the forward finite difference approximation. This could be implemented in <strong>matrix-form</strong>, in which case it would look like this:</p>
<center><p><img alt="a5dbce7a75d64c1b9b90e5bb203304a3" class="no-scaled-link" src="../_images/matrix-form.png" style="width: 400px;"/></p>
<center><p><img alt="32e5b5f8f4e347379dca69a23ae2cf76" class="no-scaled-link" src="../_images/matrix-form.png" style="width: 400px;"/></p>
</center><p>Or, it could be instead implemented via a for loop, in which the case of large input signals, would not require storing a large matrix:</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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Expand Up @@ -393,8 +393,8 @@ <h2>Naive Application of Filtered-Back Projection<a class="headerlink" href="#Na
</section>
<section id="Pyxu's-Modularity-to-the-Rescue">
<h2>Pyxu’s Modularity to the Rescue<a class="headerlink" href="#Pyxu's-Modularity-to-the-Rescue" title="Link to this heading">#</a></h2>
<p>We now turn our attention to how Pyxu addresses the limitations encountered when using <code class="docutils literal notranslate"><span class="pre">iradon()</span></code><a class="reference external" href="https://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.iradon">🔗</a> for reconstruction in non-ideal conditions. Unlike monolithic approaches, Pyxu’s microservice architecture thrives on modularity, allowing us to decompose the data model into a sequence of simple linear operators: the Radon transform (<span class="math notranslate nohighlight">\(R\)</span>), followed by filtering (<span class="math notranslate nohighlight">\(F\)</span>), masking (<span class="math notranslate nohighlight">\(M\)</span>),
and finally tapering (<span class="math notranslate nohighlight">\(T\)</span>).</p>
<p>We now turn our attention to how Pyxu addresses the limitations encountered when using <code class="docutils literal notranslate"><span class="pre">iradon()</span></code><a class="reference external" href="https://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.iradon">🔗</a> for reconstruction in non-ideal conditions. Unlike monolithic approaches, Pyxu’s microservice architecture thrives on modularity, allowing us to decompose the data model into a sequence of simple linear operators: the Radon transform (<span class="math notranslate nohighlight">\(R\)</span>), followed by filtering (<span class="math notranslate nohighlight">\(F\)</span>), masking
(<span class="math notranslate nohighlight">\(M\)</span>), and finally tapering (<span class="math notranslate nohighlight">\(T\)</span>).</p>
<div class="math notranslate nohighlight">
\[\Phi= T \circ M \circ F \circ R\]</div>
<p>Here is a code snippet detailing the construction of such an operator with Pyxu:</p>
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<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">SquaredL2Norm()</span></code><a class="reference external" href="../api/operator/func.html#pyxu.operator.SquaredL2Norm">🔗</a>: Represents the squared L2 norm of the residual between the observed and modeled data, scaled by the noise level <code class="docutils literal notranslate"><span class="pre">sigma</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">.argshift(-sino)</span></code><a class="reference external" href="../api/abc.html#pyxu.abc.Operator.argshift">🔗</a>: Sets the observed sinogram as the data.</p></li>
<li><p>`* Phi`: Compose the <span class="math notranslate nohighlight">\(L_2\)</span> norm with <span class="math notranslate nohighlight">\(\Phi\)</span>, the operator modeling the acquisition process.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">*</span> <span class="pre">Phi</span></code>: Compose the <span class="math notranslate nohighlight">\(L_2\)</span> norm with <span class="math notranslate nohighlight">\(\Phi\)</span>, the operator modeling the acquisition process.</p></li>
</ul>
<p><strong>Defining the Smooth Part of the Posterior</strong></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Posterior</span>
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