Updates index, removing badges

Author: miguelgondu

docs/poli-docs/_build/.doctrees/environment.pickle

@@ -1,7 +1,5 @@
 # poli 🧪: a library of discrete objective functions
 
-[![Testing (conda, python 3.9)](https://github.com/MachineLearningLifeScience/poli/actions/workflows/python-tox-testing-including-conda.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli/actions/workflows/python-tox-testing-including-conda.yml) [![Test (conda, python 3.9)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml)
-
 [poli](https://github.com/MachineLearningLifeScience/poli) is a library of discrete objective functions for benchmarking optimization algorithms. Examples include:
 - 🔬 **stability** of mutations from a wildtype protein (using [foldx](https://foldxsuite.crg.eu/) or [rasp](https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg)).
 - 🧪 **docking scores** of ligands to proteins (using [dockstring](https://github.com/dockstring/dockstring), [pyscreener](https://github.com/coleygroup/pyscreener) and [pytdc](https://tdcommons.ai/functions/oracles/)).
@@ -13,7 +11,7 @@ Some of `poli`'s features:
 -  A numpy interface. Inputs are `np.array`s of strings, outputs are `np.array`s of floats.
 - `SMILES` and `SELFIES` support for small molecule manipulation.
 
-This documentation also discusses [`poli-baselines`](https://github.com/MachineLearningLifeScience/poli-baselines), a collection of optimizers of these discrete black box functions which is currently in alpha stage.
+This documentation also discusses [`poli-baselines`](https://github.com/MachineLearningLifeScience/poli-baselines), a collection of optimizers of these discrete black box functions.
 
 ## Getting started
 

docs/poli-docs/_build/.doctrees/index.doctree

@@ -563,7 +563,6 @@ <h2> Contents </h2>
 
   <section class="tex2jax_ignore mathjax_ignore" id="poli-a-library-of-discrete-objective-functions">
 <h1>poli 🧪: a library of discrete objective functions<a class="headerlink" href="#poli-a-library-of-discrete-objective-functions" title="Permalink to this heading">#</a></h1>
-<p><a class="reference external" href="https://github.com/MachineLearningLifeScience/poli/actions/workflows/python-tox-testing-including-conda.yml"><img alt="Testing (conda, python 3.9)" src="https://github.com/MachineLearningLifeScience/poli/actions/workflows/python-tox-testing-including-conda.yml/badge.svg" /></a> <a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml"><img alt="Test (conda, python 3.9)" src="https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml/badge.svg" /></a></p>
 <p><a class="reference external" href="https://github.com/MachineLearningLifeScience/poli">poli</a> is a library of discrete objective functions for benchmarking optimization algorithms. Examples include:</p>
 <ul class="simple">
 <li><p>🔬 <strong>stability</strong> of mutations from a wildtype protein (using <a class="reference external" href="https://foldxsuite.crg.eu/">foldx</a> or <a class="reference external" href="https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg">rasp</a>).</p></li>
@@ -577,7 +576,7 @@ <h1>poli 🧪: a library of discrete objective functions<a class="headerlink" hr
 <li><p>A numpy interface. Inputs are <code class="docutils literal notranslate"><span class="pre">np.array</span></code>s of strings, outputs are <code class="docutils literal notranslate"><span class="pre">np.array</span></code>s of floats.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">SMILES</span></code> and <code class="docutils literal notranslate"><span class="pre">SELFIES</span></code> support for small molecule manipulation.</p></li>
 </ul>
-<p>This documentation also discusses <a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines"><code class="docutils literal notranslate"><span class="pre">poli-baselines</span></code></a>, a collection of optimizers of these discrete black box functions which is currently in alpha stage.</p>
+<p>This documentation also discusses <a class="reference external" href="https://github.com/MachineLearningLifeScience/poli-baselines"><code class="docutils literal notranslate"><span class="pre">poli-baselines</span></code></a>, a collection of optimizers of these discrete black box functions.</p>
 <section id="getting-started">
 <h2>Getting started<a class="headerlink" href="#getting-started" title="Permalink to this heading">#</a></h2>
 <p>A good place to start is the next chapter! <a class="reference internal" href="getting_started/getting_started.html"><span class="doc std std-doc">Go to Getting Started</span></a>.</p>

docs/poli-docs/_build/.jupyter_cache/global.db

@@ -1,7 +1,5 @@
 # poli 🧪: a library of discrete objective functions
 
-[![Testing (conda, python 3.9)](https://github.com/MachineLearningLifeScience/poli/actions/workflows/python-tox-testing-including-conda.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli/actions/workflows/python-tox-testing-including-conda.yml) [![Test (conda, python 3.9)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml/badge.svg)](https://github.com/MachineLearningLifeScience/poli-baselines/actions/workflows/python-tox-testing.yml)
-
 [poli](https://github.com/MachineLearningLifeScience/poli) is a library of discrete objective functions for benchmarking optimization algorithms. Examples include:
 - 🔬 **stability** of mutations from a wildtype protein (using [foldx](https://foldxsuite.crg.eu/) or [rasp](https://github.com/KULL-Centre/_2022_ML-ddG-Blaabjerg)).
 - 🧪 **docking scores** of ligands to proteins (using [dockstring](https://github.com/dockstring/dockstring), [pyscreener](https://github.com/coleygroup/pyscreener) and [pytdc](https://tdcommons.ai/functions/oracles/)).
@@ -13,7 +11,7 @@ Some of `poli`'s features:
 -  A numpy interface. Inputs are `np.array`s of strings, outputs are `np.array`s of floats.
 - `SMILES` and `SELFIES` support for small molecule manipulation.
 
-This documentation also discusses [`poli-baselines`](https://github.com/MachineLearningLifeScience/poli-baselines), a collection of optimizers of these discrete black box functions which is currently in alpha stage.
+This documentation also discusses [`poli-baselines`](https://github.com/MachineLearningLifeScience/poli-baselines), a collection of optimizers of these discrete black box functions.
 
 ## Getting started