deploy: d624efe

_sources/written/background/parsing.rst.txt

@@ -1,3 +1,5 @@
+.. _parsers_and_containers:
+
 Parsing code output and using containers
 ========================================
 

_sources/written/background/phonon_dos.rst.txt

@@ -1,3 +1,5 @@
+.. _pdos_from_vacf:
+
 Velocity autocorrelation function and phonon DOS
 ================================================
 

_sources/written/background/rbf.rst.txt

@@ -2,10 +2,19 @@
 
 .. _rbf:
 
-
 Radial Basis Function interpolation an regression
 =================================================
 
+.. note::
+   While the material here is a useful reference and the code has been used in
+   production, we actually recommend to use Gaussian process regression
+   instead, e.g. :class:`sklearn.gaussian_process.GaussianProcessRegressor`,
+   or, if you want to replicate :mod:`~pwtools.rbf`, then use KRR
+   (:class:`sklearn.kernel_ridge.KernelRidge`). See
+   https://github.com/elcorto/gp_playground for a detailed `comparison of GPs
+   and KRR
+   <https://elcorto.github.io/gp_playground/content/gp_krr_hyperopt/intro.html>`_.
+
 Some background information on the method implemented in :mod:`~pwtools.rbf`.
 For code examples, see the doc string of :class:`~pwtools.rbf.core.Rbf` and
 ``examples/rbf``.
@@ -164,3 +173,4 @@ Other implementations
 ---------------------
 
 * :class:`scipy.interpolate.Rbf`
+* :class:`sklearn.kernel_ridge.KernelRidge`

_sources/written/features.rst.txt

@@ -3,50 +3,53 @@
 Features
 ========
 
-* container classes for single unit cells (:class:`~pwtools.crys.Structure`)
+* Container classes for single unit cells (:class:`~pwtools.crys.Structure`)
   and structure sequences such as molecular dynamics trajectories, relaxation
-  runs or NEB paths (:class:`~pwtools.crys.Trajectory`)
+  runs or NEB paths (:class:`~pwtools.crys.Trajectory`). See
+  :ref:`parsers_and_containers`.
 
-* classes to set up calculations (parameter studies) based on template input
-  files for any kind of computational backend (:mod:`~pwtools.batch`)
+* Classes to set up calculations (parameter studies) based on template input
+  files for any kind of computational backend (:mod:`~pwtools.batch`). See
+  :ref:`param_study`.
 
-* simple sqlite3 interface with convenience data extraction methods
-  (:mod:`~pwtools.sql`)
+* Simple sqlite3 interface with convenience data extraction methods
+  (:mod:`~pwtools.sql`).
 
-* parsing of PWscf (QE_), CPMD_ , CP2K_ and LAMMPS_
-  output into Python objects for easy access (:mod:`~pwtools.parse`)
+* Parsing of PWscf (QE_), CPMD_ , CP2K_ and LAMMPS_
+  output into Python objects for easy access (:mod:`~pwtools.parse`). See
+  :ref:`parsers_and_containers`.
 
-* structure io: read cif, pdb, write axsf, cif, xyz  (:mod:`~pwtools.io`)
+* Structure io: read cif, pdb, write axsf, cif, xyz  (:mod:`~pwtools.io`)
 
-* pythonic interface to external molecular viewers for interactive use:
-  xcrysden_, avogadro_, jmol_, VMD_ (:mod:`~pwtools.visualize`)
+* Pythonic interface to external molecular viewers for interactive use:
+  xcrysden_, avogadro_, jmol_, VMD_ (:mod:`~pwtools.visualize`).
 
-* interface to the Elk_ code's EOS fitting tool and own implementation (Vinet
-  EOS) (:mod:`~pwtools.eos`)
+* EOS fitting tools (:mod:`~pwtools.eos`)
 
-* thermodynamic properties in the quasi-harmonic approximation from phonon
-  density of states, QHA implementation (:mod:`~pwtools.thermo`)
+* Thermodynamic properties in the quasi-harmonic approximation from phonon
+  density of states, QHA implementation (:mod:`~pwtools.thermo`). See
+  :ref:`qha`.
 
 * MD analysis: radial pair distribution function (own implementation and VMD_
-  interface), RMS, RMSD (:mod:`~pwtools.crys`)
+  interface), RMS, RMSD (:mod:`~pwtools.crys`).
 
-* velocity autocorrelation function and phonon DOS from MD trajectories
-  (:mod:`~pwtools.pydos`)
+* Velocity autocorrelation function and phonon DOS from MD trajectories
+  (:mod:`~pwtools.pydos`). See :ref:`pdos_from_vacf`.
 
-* unit cell related tools: super cell building, coordinate transformation,
-  k-grid tools, ... (:mod:`~pwtools.crys`)
+* Unit cell related tools: super cell building, coordinate transformation,
+  k-grid tools, ... (:mod:`~pwtools.crys`).
 
-* thin wrappers for spglib_ functions (:mod:`~pwtools.symmetry`)
+* Thin wrappers for spglib_ functions (:mod:`~pwtools.symmetry`)
 
-* functions and classes to extend numpy/scipy, e.g. N-dim polynomial fitting
+* Functions and classes to extend numpy/scipy, e.g. N-dim polynomial fitting
   and a number of convenient 1D classes (polynomial, spline) with a common
-  API (:mod:`~pwtools.num`)
+  API (:mod:`~pwtools.num`).
 
 * N-dim radial basis function interpolation and fitting (:mod:`~pwtools.rbf`)
 
-* basic signal processing / fft related tools (:mod:`~pwtools.signal`)
+* Basic signal processing / fft related tools (:mod:`~pwtools.signal`)
 
-* tools to handle matplotlib plots in scripts (:mod:`~pwtools.mpl`)
+* Tools to handle matplotlib plots in scripts (:mod:`~pwtools.mpl`)
 
 * QE and LAMMPS calculators for ASE (:mod:`~pwtools.calculators`)
 

written/background/parsing.html

@@ -103,7 +103,7 @@ <h3 id="searchlabel">Quick search</h3>
           <div class="body" role="main">
 
   <section id="parsing-code-output-and-using-containers">
-<h1>Parsing code output and using containers<a class="headerlink" href="#parsing-code-output-and-using-containers" title="Link to this heading">¶</a></h1>
+<span id="parsers-and-containers"></span><h1>Parsing code output and using containers<a class="headerlink" href="#parsing-code-output-and-using-containers" title="Link to this heading">¶</a></h1>
 <section id="available-parsers">
 <span id="parser-classes"></span><h2>Available parsers<a class="headerlink" href="#available-parsers" title="Link to this heading">¶</a></h2>
 <p>A core feature of pwtools is a set of parsers for commonly used atomistic

written/background/phonon_dos.html

@@ -103,7 +103,7 @@ <h3 id="searchlabel">Quick search</h3>
           <div class="body" role="main">
 
   <section id="velocity-autocorrelation-function-and-phonon-dos">
-<h1>Velocity autocorrelation function and phonon DOS<a class="headerlink" href="#velocity-autocorrelation-function-and-phonon-dos" title="Link to this heading">¶</a></h1>
+<span id="pdos-from-vacf"></span><h1>Velocity autocorrelation function and phonon DOS<a class="headerlink" href="#velocity-autocorrelation-function-and-phonon-dos" title="Link to this heading">¶</a></h1>
 <section id="correlation-and-power-spectrum">
 <h2>Correlation and power spectrum<a class="headerlink" href="#correlation-and-power-spectrum" title="Link to this heading">¶</a></h2>
 <p>Here are some (textbook) notes about correlation, which you should read in

written/background/rbf.html

@@ -106,6 +106,16 @@ <h3 id="searchlabel">Quick search</h3>
 
   <section id="radial-basis-function-interpolation-an-regression">
 <span id="rbf"></span><h1>Radial Basis Function interpolation an regression<a class="headerlink" href="#radial-basis-function-interpolation-an-regression" title="Link to this heading">¶</a></h1>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>While the material here is a useful reference and the code has been used in
+production, we actually recommend to use Gaussian process regression
+instead, e.g. <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor" title="(in scikit-learn v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.gaussian_process.GaussianProcessRegressor</span></code></a>,
+or, if you want to replicate <code class="xref py py-mod docutils literal notranslate"><span class="pre">rbf</span></code>, then use KRR
+(<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html#sklearn.kernel_ridge.KernelRidge" title="(in scikit-learn v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.kernel_ridge.KernelRidge</span></code></a>). See
+<a class="reference external" href="https://github.com/elcorto/gp_playground">https://github.com/elcorto/gp_playground</a> for a detailed <a class="reference external" href="https://elcorto.github.io/gp_playground/content/gp_krr_hyperopt/intro.html">comparison of GPs
+and KRR</a>.</p>
+</div>
 <p>Some background information on the method implemented in <code class="xref py py-mod docutils literal notranslate"><span class="pre">rbf</span></code>.
 For code examples, see the doc string of <a class="reference internal" href="../../generated/api/pwtools.rbf.core.Rbf.html#pwtools.rbf.core.Rbf" title="pwtools.rbf.core.Rbf"><code class="xref py py-class docutils literal notranslate"><span class="pre">Rbf</span></code></a> and
 <code class="docutils literal notranslate"><span class="pre">examples/rbf</span></code>.</p>
@@ -231,6 +241,7 @@ <h2>Data scaling<a class="headerlink" href="#data-scaling" title="Link to this h
 <h2>Other implementations<a class="headerlink" href="#other-implementations" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.Rbf.html#scipy.interpolate.Rbf" title="(in SciPy v1.14.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">scipy.interpolate.Rbf</span></code></a></p></li>
+<li><p><a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html#sklearn.kernel_ridge.KernelRidge" title="(in scikit-learn v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.kernel_ridge.KernelRidge</span></code></a></p></li>
 </ul>
 </section>
 </section>