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<section id="migrating-from-v1-to-v2">
<span id="migration-guide"></span><h1>Migrating from v1 to v2<a class="headerlink" href="#migrating-from-v1-to-v2" title="Link to this heading">#</a></h1>
<p>With the release of <code class="code docutils literal notranslate"><span class="pre">Pyxu</span> <span class="pre">v2</span></code>, several major improvements and changes have been introduced. This guide will help you smoothly transition your code from <code class="code docutils literal notranslate"><span class="pre">v1</span></code> to <code class="code docutils literal notranslate"><span class="pre">v2</span></code>.</p>
<p>The most significant change is that <code class="code docutils literal notranslate"><span class="pre">Pyxu</span> <span class="pre">v2</span></code> no longer vectorizes <strong>N-dimensional</strong> signals. In <code class="code docutils literal notranslate"><span class="pre">v1</span></code>, vectorizing <strong>N-dimensional</strong> arrays caused Dask arrays to rechunk into 1-dimensional chunks, which required computing the array in a single node, thus breaking the distributed nature of Dask. In <code class="code docutils literal notranslate"><span class="pre">v2</span></code>, the arrays remain <strong>N-dimensional</strong> throughout, and Dask arrays are not “computed” at any point, preserving the benefits of distributed computing.</p>
<section id="key-changes">
<h2>Key Changes<a class="headerlink" href="#key-changes" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><strong>Signal Handling</strong>: Operators and solvers now work directly with <strong>N-dimensional</strong> data without needing to flatten and reshape.</p></li>
<li><p><strong>Functionals and Losses</strong>: In <code class="code docutils literal notranslate"><span class="pre">v1</span></code>, loss functionals could be defined from functionals with the <code class="code docutils literal notranslate"><span class="pre">asloss</span></code> method. We have changed this method to <code class="code docutils literal notranslate"><span class="pre">argshift</span></code> for clarity, avoiding ambiguity around sign usage.</p></li>
<li><p><strong>Stopping Criteria</strong>: The stopping criteria have been updated to use <code class="code docutils literal notranslate"><span class="pre">dim_rank</span></code>, which specifies the rank of the signal dimensions.</p></li>
</ul>
</section>
<section id="example-conversion">
<h2>Example Conversion<a class="headerlink" href="#example-conversion" title="Link to this heading">#</a></h2>
<p>Below is an example showing how to convert code from <code class="code docutils literal notranslate"><span class="pre">v1</span></code> to <code class="code docutils literal notranslate"><span class="pre">v2</span></code>.</p>
<p><strong>Common Setup for v1 and v2</strong>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/index.html#module-numpy" title="numpy"><span class="nn">numpy</span></a> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <a class="sphinx-codeautolink-a" href="https://matplotlib.org/stable/api/pyplot_summary.html#module-matplotlib.pyplot" title="matplotlib.pyplot"><span class="nn">matplotlib.pyplot</span></a> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">skimage</span>
<span class="kn">from</span> <span class="nn">pyxu.operator</span> <span class="kn">import</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">Convolve</span></a><span class="p">,</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.L21Norm" title="pyxu.operator.func.norm.L21Norm"><span class="n">L21Norm</span></a><span class="p">,</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Gradient" title="pyxu.operator.Gradient"><span class="n">Gradient</span></a><span class="p">,</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.SquaredL2Norm" title="pyxu.operator.func.norm.SquaredL2Norm"><span class="n">SquaredL2Norm</span></a><span class="p">,</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.PositiveOrthant" title="pyxu.operator.func.indicator.PositiveOrthant"><span class="n">PositiveOrthant</span></a>
<span class="kn">from</span> <span class="nn">pyxu.opt.solver</span> <span class="kn">import</span> <a class="sphinx-codeautolink-a" href="api/opt.solver.html#pyxu.opt.solver.PD3O" title="pyxu.opt.solver.pds.PD3O"><span class="n">PD3O</span></a>
<span class="kn">from</span> <span class="nn">pyxu.opt.stop</span> <span class="kn">import</span> <a class="sphinx-codeautolink-a" href="api/opt.stop.html#pyxu.opt.stop.RelError" title="pyxu.opt.stop.RelError"><span class="n">RelError</span></a>
<span class="c1"># Load and preprocess the data</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">skimage</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cat</span><span class="p">()</span> <span class="c1"># shape (300, 451, 3)</span>
<span class="n">data</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/generated/numpy.asarray.html#numpy.asarray" title="numpy.asarray"><span class="n">np</span><span class="o">.</span><span class="n">asarray</span></a><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># shape (3, 300, 451)</span>
<span class="c1"># Create the Gaussian blurring kernel</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="mi">7</span>
<span class="n">width</span> <span class="o">=</span> <span class="mi">13</span>
<span class="n">mu</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">gauss</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/constants.html#numpy.pi" title="numpy.pi"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">*</span> <span class="n">sigma</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span> <span class="o">*</span> <a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/generated/numpy.exp.html#numpy.exp" title="numpy.exp"><span class="n">np</span><span class="o">.</span><span class="n">exp</span></a><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">x</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sigma</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
<span class="n">kernel_1d</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/generated/numpy.fromfunction.html#numpy.fromfunction" title="numpy.fromfunction"><span class="n">np</span><span class="o">.</span><span class="n">fromfunction</span></a><span class="p">(</span><span class="n">gauss</span><span class="p">,</span> <span class="p">(</span><span class="n">width</span><span class="p">,))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># The shape of the input array will be used to define operators</span>
<span class="n">dim_shape</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
</pre></div>
</div>
<p><strong>v1 Code</strong>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Applying the blurring and adding noise</span>
<a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">conv</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">Convolve</span></a><span class="p">(</span>
<span class="n">arg_shape</span><span class="o">=</span><span class="n">dim_shape</span><span class="p">,</span> <span class="c1"># v1: using `arg_shape`</span>
<span class="n">kernel</span><span class="o">=</span><span class="p">[</span><a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">1</span><span class="p">]),</span> <span class="n">kernel_1d</span><span class="p">,</span> <span class="n">kernel_1d</span><span class="p">],</span>
<span class="n">center</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">width</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">width</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">conv</span></a><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim_shape</span><span class="p">)</span> <span class="c1"># Flattening and reshaping required in v1</span>
<span class="n">y</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html#numpy.random.normal" title="numpy.random.normal"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span></a><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
<span class="c1"># Setting up the MAP approach with total variation prior and positivity constraint</span>
<span class="n">sl2</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.SquaredL2Norm" title="pyxu.operator.func.norm.SquaredL2Norm"><span class="n">SquaredL2Norm</span></a><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">y</span><span class="o">.</span><span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">asloss</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">ravel</span><span class="p">())</span> <span class="c1"># v1: `dim` used with `.asloss()`</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">sl2</span> <span class="o">*</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">conv</span></a>
<a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.L21Norm" title="pyxu.operator.func.norm.L21Norm"><span class="n">l21</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.L21Norm" title="pyxu.operator.func.norm.L21Norm"><span class="n">L21Norm</span></a><span class="p">(</span><span class="n">arg_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="o">*</span><span class="n">dim_shape</span><span class="p">),</span> <span class="n">l2_axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># v1: `arg_shape` used</span>
<span class="n">grad</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Gradient" title="pyxu.operator.Gradient"><span class="n">Gradient</span></a><span class="p">(</span>
<span class="n">arg_shape</span><span class="o">=</span><span class="n">dim_shape</span><span class="p">,</span> <span class="c1"># v1: `arg_shape`</span>
<span class="n">directions</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
<span class="p">)</span>
<a class="sphinx-codeautolink-a" href="api/opt.stop.html#pyxu.opt.stop.RelError" title="pyxu.opt.stop.RelError"><span class="n">stop_crit</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/opt.stop.html#pyxu.opt.stop.RelError" title="pyxu.opt.stop.RelError"><span class="n">RelError</span></a><span class="p">(</span>
<span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
<span class="p">)</span>
<a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.PositiveOrthant" title="pyxu.operator.func.indicator.PositiveOrthant"><span class="n">positivity</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.PositiveOrthant" title="pyxu.operator.func.indicator.PositiveOrthant"><span class="n">PositiveOrthant</span></a><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">y</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="c1"># v1: `dim` used</span>
<a class="sphinx-codeautolink-a" href="api/opt.solver.html#pyxu.opt.solver.PD3O" title="pyxu.opt.solver.pds.PD3O"><span class="n">solver</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/opt.solver.html#pyxu.opt.solver.PD3O" title="pyxu.opt.solver.pds.PD3O"><span class="n">PD3O</span></a><span class="p">(</span><span class="n">f</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.PositiveOrthant" title="pyxu.operator.func.indicator.PositiveOrthant"><span class="n">positivity</span></a><span class="p">,</span> <span class="n">h</span><span class="o">=</span><a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.L21Norm" title="pyxu.operator.func.norm.L21Norm"><span class="n">l21</span></a><span class="p">,</span> <span class="n">K</span><span class="o">=</span><span class="n">grad</span><span class="p">)</span>
<span class="n">solver</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x0</span><span class="o">=</span><span class="n">y</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">stop_crit</span><span class="o">=</span><a class="sphinx-codeautolink-a" href="api/opt.stop.html#pyxu.opt.stop.RelError" title="pyxu.opt.stop.RelError"><span class="n">stop_crit</span></a><span class="p">)</span> <span class="c1"># Flattening required in v1</span>
<span class="c1"># Getting the deblurred image</span>
<span class="n">recons</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">solution</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">dim_shape</span><span class="p">)</span>
<span class="n">recons</span> <span class="o">/=</span> <span class="n">recons</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
</pre></div>
</div>
<p><strong>v2 Code</strong>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Applying the blurring and adding noise</span>
<a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">conv</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">Convolve</span></a><span class="p">(</span>
<span class="n">dim_shape</span><span class="o">=</span><span class="n">dim_shape</span><span class="p">,</span> <span class="c1"># v2: `dim_shape` replaces `arg_shape`</span>
<span class="n">kernel</span><span class="o">=</span><span class="p">[</span><a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">1</span><span class="p">]),</span> <span class="n">kernel_1d</span><span class="p">,</span> <span class="n">kernel_1d</span><span class="p">],</span>
<span class="n">center</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">width</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">width</span> <span class="o">//</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">conv</span></a><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="c1"># No need to flatten or reshape in v2</span>
<span class="n">y</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html#numpy.random.normal" title="numpy.random.normal"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span></a><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
<span class="c1"># Setting up the MAP approach with total variation prior and positivity constraint</span>
<span class="n">sl2</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.SquaredL2Norm" title="pyxu.operator.func.norm.SquaredL2Norm"><span class="n">SquaredL2Norm</span></a><span class="p">(</span><span class="n">dim_shape</span><span class="o">=</span><span class="n">dim_shape</span><span class="p">)</span><span class="o">.</span><span class="n">argshift</span><span class="p">(</span><span class="o">-</span><span class="n">y</span><span class="p">)</span> <span class="c1"># v2: `dim_shape` replaces `dim`, `.argshift()` replaces `.asloss()`</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">sl2</span> <span class="o">*</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Convolve" title="pyxu.operator.linop.stencil.stencil.Convolve"><span class="n">conv</span></a>
<a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.L21Norm" title="pyxu.operator.func.norm.L21Norm"><span class="n">l21</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.L21Norm" title="pyxu.operator.func.norm.L21Norm"><span class="n">L21Norm</span></a><span class="p">(</span><span class="n">dim_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="o">*</span><span class="n">dim_shape</span><span class="p">),</span> <span class="n">l2_axis</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># v2: `dim_shape` replaces `arg_shape`</span>
<span class="n">grad</span> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/linop.html#pyxu.operator.Gradient" title="pyxu.operator.Gradient"><span class="n">Gradient</span></a><span class="p">(</span>
<span class="n">dim_shape</span><span class="o">=</span><span class="n">dim_shape</span><span class="p">,</span> <span class="c1"># v2: `dim_shape` replaces `arg_shape`</span>
<span class="n">directions</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
<span class="p">)</span>
<a class="sphinx-codeautolink-a" href="api/opt.stop.html#pyxu.opt.stop.RelError" title="pyxu.opt.stop.RelError"><span class="n">stop_crit</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/opt.stop.html#pyxu.opt.stop.RelError" title="pyxu.opt.stop.RelError"><span class="n">RelError</span></a><span class="p">(</span>
<span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
<span class="n">dim_rank</span><span class="o">=</span><a class="sphinx-codeautolink-a" href="https://docs.python.org/3/library/functions.html#len" title="len"><span class="nb">len</span></a><span class="p">(</span><span class="n">dim_shape</span><span class="p">),</span> <span class="c1"># v2: New `dim_rank` parameter for dimensional rank</span>
<span class="p">)</span>
<a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.PositiveOrthant" title="pyxu.operator.func.indicator.PositiveOrthant"><span class="n">positivity</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.PositiveOrthant" title="pyxu.operator.func.indicator.PositiveOrthant"><span class="n">PositiveOrthant</span></a><span class="p">(</span><span class="n">dim_shape</span><span class="o">=</span><span class="n">dim_shape</span><span class="p">)</span> <span class="c1"># v2: `dim_shape` replaces `dim`</span>
<a class="sphinx-codeautolink-a" href="api/opt.solver.html#pyxu.opt.solver.PD3O" title="pyxu.opt.solver.pds.PD3O"><span class="n">solver</span></a> <span class="o">=</span> <a class="sphinx-codeautolink-a" href="api/opt.solver.html#pyxu.opt.solver.PD3O" title="pyxu.opt.solver.pds.PD3O"><span class="n">PD3O</span></a><span class="p">(</span><span class="n">f</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span> <span class="n">g</span><span class="o">=</span><a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.PositiveOrthant" title="pyxu.operator.func.indicator.PositiveOrthant"><span class="n">positivity</span></a><span class="p">,</span> <span class="n">h</span><span class="o">=</span><a class="sphinx-codeautolink-a" href="api/operator/func.html#pyxu.operator.L21Norm" title="pyxu.operator.func.norm.L21Norm"><span class="n">l21</span></a><span class="p">,</span> <span class="n">K</span><span class="o">=</span><span class="n">grad</span><span class="p">)</span>
<span class="n">solver</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x0</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">stop_crit</span><span class="o">=</span><a class="sphinx-codeautolink-a" href="api/opt.stop.html#pyxu.opt.stop.RelError" title="pyxu.opt.stop.RelError"><span class="n">stop_crit</span></a><span class="p">)</span> <span class="c1"># No flattening required in v2</span>
</pre></div>
</div>
</section>
<section id="migration-tips">
<h2>Migration Tips<a class="headerlink" href="#migration-tips" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><strong>dim_shape vs. dim</strong>: In <code class="code docutils literal notranslate"><span class="pre">v2</span></code>, wherever <code class="code docutils literal notranslate"><span class="pre">dim</span></code> was used in <code class="code docutils literal notranslate"><span class="pre">v1</span></code>, you now use <code class="code docutils literal notranslate"><span class="pre">dim_shape</span></code> to work with the full <strong>N-dimensional</strong> structure of the data.</p></li>
<li><p><strong>arg_shape vs. dim_shape</strong>: Similarly, <code class="code docutils literal notranslate"><span class="pre">arg_shape</span></code> is replaced by <code class="code docutils literal notranslate"><span class="pre">dim_shape</span></code> to emphasize the full shape of the data.</p></li>
<li><p><strong>argshift replaces asloss</strong>: <code class="code docutils literal notranslate"><span class="pre">argshift</span></code> is introduced in place of <code class="code docutils literal notranslate"><span class="pre">asloss</span></code> to avoid ambiguity around signs and provide a more intuitive interface.</p></li>
<li><p><strong>Flattening/Reshaping</strong>: In <code class="code docutils literal notranslate"><span class="pre">v2</span></code>, there is no need to flatten and reshape data when using operators like <code class="code docutils literal notranslate"><span class="pre">Convolve</span></code> and solvers like <code class="code docutils literal notranslate"><span class="pre">PD3O</span></code>. You can work directly with n-dimensional data.</p></li>
<li><p><strong>dim_rank</strong>: In stopping criteria, <code class="code docutils literal notranslate"><span class="pre">dim_rank</span></code> now specifies the rank of the signal dimensions, which was not explicitly required in <code class="code docutils literal notranslate"><span class="pre">v1</span></code>.</p></li>
</ul>
</section>
<section id="further-help">
<h2>Further Help<a class="headerlink" href="#further-help" title="Link to this heading">#</a></h2>
<p>If you encounter any issues during your migration, please consult the <code class="code docutils literal notranslate"><span class="pre">API</span> <span class="pre">Reference</span></code> and <code class="code docutils literal notranslate"><span class="pre">Example</span> <span class="pre">Gallery</span></code> or reach out to the community via our support channels.</p>
</section>
</section>
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