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README.md

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+<!-- [![PyPI Version](https://img.shields.io/pypi/v/simplextree)](https://pypi.org/project/simplextree) -->
 
 `primate`, short for **Pr**obabalistic **I**mplicit **Ma**trix **T**race **E**stimator, is a Python package that provides estimators of quantities derived from [matrix functions](https://en.wikipedia.org/wiki/Analytic_function_of_a_matrix); that is, matrices parameterized by functions:
 

docs/basic/install.html

@@ -355,7 +355,8 @@ <h1 class="title">Installation</h1>
 <!-- https://stackoverflow.com/questions/122906s39/quantifiable-metrics-benchmarks-on-the-usage-of-header-only-c-libraries/13593041#13593041 -->
 <section id="platform-support" class="level3">
 <h3 class="anchored" data-anchor-id="platform-support">Platform support</h3>
-<p>Platform-specific wheels are currently built with cibuildwheel and uploaded to PyPI. These enable <code>primate</code> to be installed on supported architecture / CPython implementations without compilation. As of 12/29/23, native <code>primate</code> wheels are buitl for the following platforms:</p>
+<!-- Platform-specific wheels are currently built with cibuildwheel and uploaded to PyPI.  -->
+<p>For certain platforms, <code>primate</code> can be installed from PyPi without compilation. As of 12/29/23, native <code>primate</code> wheels are built for the following platforms:</p>
 <table class="table">
 <thead>
 <tr class="header">
@@ -402,11 +403,12 @@ <h3 class="anchored" data-anchor-id="platform-support">Platform support</h3>
 </tr>
 </tbody>
 </table>
-<p>Wheels are currently built with <a href="https://cibuildwheel.readthedocs.io/en/stable/">cibuildwheel</a>. Currently no support is offered for PyPy, 32-bit runtimes, or <a href="https://devguide.python.org/versions/#supported-versions">unsupported versions of CPython</a>. If your platform isn’t on this list, feel free to make an issue requesting support.</p>
+<p>Wheels are currently built with <a href="https://cibuildwheel.readthedocs.io/en/stable/">cibuildwheel</a>. Currently no support is offered for <a href="https://www.pypy.org/">PyPy</a>, 32-bit systems, or <a href="https://devguide.python.org/versions/#supported-versions">unsupported versions of CPython</a>.</p>
+<p>If your platform isn’t on this table but you would like it to be supported, feel free to <a href="https://github.com/peekxc/primate">make an issue</a>.</p>
 </section>
 <section id="compiling-from-source" class="level3">
 <h3 class="anchored" data-anchor-id="compiling-from-source">Compiling from source</h3>
-<p>To install the package from its source distribution, a C++20 compiler is required; the current builds are all built with some variant of <a href="https://clang.llvm.org/">clang</a>. For platform- and compiler-specific settings, consult the build scripts and CI configuration files.</p>
+<p>To install the package from its source distribution, a C++20 compiler is required; the current builds are all built with some variant of <a href="https://clang.llvm.org/">clang</a>, preferably version 15.0 or higher. For platform- and compiler-specific settings, consult the build scripts and CI configuration files.</p>
 </section>
 <section id="c-installation" class="level3">
 <h3 class="anchored" data-anchor-id="c-installation">C++ Installation</h3>
@@ -445,7 +447,7 @@ <h3 class="anchored" data-anchor-id="c-installation">C++ Installation</h3>
 <div id="quarto-appendix" class="default"><section id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><h2 class="anchored quarto-appendix-heading">Footnotes</h2>
 
 <ol>
-<li id="fn1"><p>Single-thread execution only; ARM-based OSX runners come stocked with Apple’s clang, which doesn’t natively ship with <code>libomp.dylib</code>, though this <a href="https://mac.r-project.org/openmp/">may be fixable</a>. Feel free to file an PR if you can get this working.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+<li id="fn1"><p>Single-thread execution only; ARM-based OSX runners compile with Apple’s clang, which doesn’t natively ship with <code>libomp.dylib</code>, though this <a href="https://mac.r-project.org/openmp/">may be fixable</a>. Feel free to file an PR if you can get this working.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
 <li id="fn2"><p>Single-thread execution only; <code>primate</code> depends on OpenMP 4.5+, which isn’t supported on any Windows compiler I’m aware of.<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
 </ol>
 </section></div></main> <!-- /main -->

docs/basic/integration.html

@@ -337,8 +337,7 @@
     <h2 id="toc-title">On this page</h2>
 
   <ul>
-  <li><a href="#python-usage" id="toc-python-usage" class="nav-link active" data-scroll-target="#python-usage">Python usage</a></li>
-  <li><a href="#c-usage" id="toc-c-usage" class="nav-link" data-scroll-target="#c-usage">C++ usage</a></li>
+  <li><a href="#c-usage" id="toc-c-usage" class="nav-link active" data-scroll-target="#c-usage">C++ usage</a></li>
   </ul>
 </nav>
     </div>
@@ -364,8 +363,6 @@ <h1 class="title">Integration</h1>
 </header>
 
 
-<section id="python-usage" class="level2">
-<h2 class="anchored" data-anchor-id="python-usage">Python usage</h2>
 <p><code>primate</code> supports a variety of matrix-types of the box, including numpy <code>ndarray</code>’s, compressed <a href="https://docs.scipy.org/doc/scipy/reference/sparse.html">sparse matrices</a> (a lá SciPy), and <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.LinearOperator.html">LinearOperators</a>—the latter enables the use of <em>matrix free</em> operators.</p>
 <p>Outside of the natively types above, the basic requirements for any operator <code>A</code> to be used with e.g.&nbsp;the <code>Lanczos</code> method in <code>primate</code> are:</p>
 <ol type="1">
@@ -402,7 +399,6 @@ <h2 class="anchored" data-anchor-id="python-usage">Python usage</h2>
 ) 
 ## True
 ``` -->
-</section>
 <section id="c-usage" class="level2">
 <h2 class="anchored" data-anchor-id="c-usage">C++ usage</h2>
 <p>Similarly, to get started calling any matrix-free function provided by <code>primate</code> on the C++ side, such <code>hutch</code> or <code>lanczos</code>, simply pass any type with <code>.shape()</code> and <code>.matvec()</code> member functions:</p>

docs/basic/performance.html

@@ -342,7 +342,7 @@ <h1 class="title">Performance</h1>
 <p><code>primate</code> provides a variety of efficient algorithms for estimating quantities derived from matrix functions. These algorithms are largely implemented in C++ to minimize overhead, and for some computational problems <code>primate</code> can out-perform the standard algorithms for estimating spectral quantities by several orders of magnitude. Nonetheless, there are some performance-related caveats to be aware of.</p>
 <!-- The main  -->
 <!-- To illustrate this, we give an example below using Toeplitz matrices.   -->
-<div id="a5cf844d" class="cell" data-execution_count="1">
+<div id="a4792004" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> scipy.linalg <span class="im">import</span> toeplitz</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> primate.trace <span class="im">import</span> hutch </span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -386,7 +386,7 @@ <h1 class="title">Performance</h1>
 <span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a>a, b <span class="op">=</span> lanczos(T, deg<span class="op">=</span><span class="dv">499</span>, orth<span class="op">=</span><span class="dv">150</span>)</span>
 <span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a>np.<span class="bu">sum</span>(np.<span class="bu">abs</span>(eigvalsh_tridiagonal(a,b)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="87320ce5" class="cell" data-execution_count="2">
+<div id="e2cabf3d" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> timeit </span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>timeit.timeit(<span class="kw">lambda</span>: hutch(A, maxiter<span class="op">=</span><span class="dv">20</span>, deg<span class="op">=</span><span class="dv">5</span>, fun<span class="op">=</span><span class="st">"log"</span>, quad<span class="op">=</span><span class="st">"fttr"</span>), number <span class="op">=</span> <span class="dv">1000</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>timeit.timeit(<span class="kw">lambda</span>: np.<span class="bu">sum</span>(np.log(np.linalg.eigvalsh(A))), number <span class="op">=</span> <span class="dv">1000</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>

docs/basic/todo.html

@@ -323,7 +323,7 @@
 
 
 
-<div id="7e43e6e6" class="cell" data-execution_count="1">
+<div id="690056ed" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># a, b = 0.8, 2</span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># x = np.random.uniform(low=0, high=10, size=40)</span></span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="co"># eps = np.random.normal(loc=0, scale=1.0, size=40)</span></span>