final pre-release changes and v=0.1.0

README.md

@@ -41,7 +41,7 @@ For instance, if you wanted to draw samples from a 10-dimensional Rosenbrock dis
 import pocomc as pc
 import numpy as np
 
-ndim = 10  # Number of dimensions
+n_dim = 10  # Number of dimensions
 
 def log_prior(x):
     if np.any((x < -10.0) | (x > 10.0)):  # If any dimension is out of bounds, the log prior is -infinity
@@ -54,11 +54,11 @@ def log_likelihood(x):
             + (x[:,::2] - 1.0)**2.0, axis=1)
 
 
-nwalkers = 1000
+n_particles = 1000
 prior_samples = np.random.uniform(size=(nwalkers, ndim), low=-10.0, high=10.0)
 
-sampler = pc.Sampler(nwalkers,
-                     ndim,
+sampler = pc.Sampler(n_particles,
+                     n_dim,
                      log_likelihood,
                      log_prior,
                      vectorize_likelihood=True,

docs/build/html/_sources/examples.rst.txt

@@ -5,7 +5,8 @@ Examples
 This page highlights several examples on how ``pocomc``
 can be used in practice, illustrating both simple and more advanced
 aspects of the code. Jupyter notebooks containing more details are available
-`on Github <https://github.com/minaskar/pocomc/tree/examples>`_.
+`on Github <https://github.com/minaskar/pocomc/tree/examples>`_. ``seaborn`` 
+is required to be installed to generate some of the plots.
 
 Gaussian Shells
 ===============

docs/build/html/_sources/index.rst.txt

@@ -85,6 +85,6 @@ Copyright 2022 Minas Karamanis and contributors.
 Changelog
 =========
 
-**0.0.1 (30/04/22)**
+**0.1.0 (12/07/22)**
 
 - First version

docs/build/html/_sources/pages/quickstart.ipynb.txt

@@ -45,7 +45,7 @@
     "            + (x[:,::2] - 1.0)**2.0, axis=1)\n",
     "\n",
     "# Define our uniform prior.\n",
-    "def logprior(x):\n",
+    "def log_prior(x):\n",
     "    if np.any(x < -10.0) or np.any(x > 10.0):\n",
     "        return -np.inf\n",
     "    else:\n",
@@ -79,10 +79,10 @@
     "nparticles = 1000\n",
     "\n",
     "# Initialise sampler\n",
-    "sampler = pc.Sampler(nparticles,\n",
-    "                     ndim,\n",
-    "                     loglikelihood=log_likelihood,\n",
-    "                     logprior=logprior,\n",
+    "sampler = pc.Sampler(n_particles,\n",
+    "                     n_dim,\n",
+    "                     log_likelihood=log_likelihood,\n",
+    "                     log_prior=log_prior,\n",
     "                     vectorize_likelihood=True,\n",
     "                     bounds=(-10.0, 10.0))"
    ]

docs/build/html/api/plotting.html

@@ -340,7 +340,7 @@ <h1>Plotting<a class="headerlink" href="#plotting" title="Permalink to this head
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>results</strong> (<em>dict</em>) – Result dictionary produced using <code class="docutils literal notranslate"><span class="pre">pocoMC</span></code>.</p></li>
-<li><p><strong>full_run</strong> (<em>bool</em>) – Whether or not to include run diagnostics beyond the basic run. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
+<li><p><strong>full_run</strong> (<em>bool</em>) – If True, include run diagnostics beyond the basic run. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
 <li><p><strong>width</strong> (<em>float</em>) – Width of figure. Default: <code class="docutils literal notranslate"><span class="pre">10.0</span></code>.</p></li>
 <li><p><strong>height</strong> (<em>float</em>) – Height of each subplot. Default: <code class="docutils literal notranslate"><span class="pre">10.0</span></code>.</p></li>
 </ul>
@@ -381,9 +381,7 @@ <h1>Plotting<a class="headerlink" href="#plotting" title="Permalink to this head
 <dl class="py function">
 <dt class="sig sig-object py" id="pocomc.plotting.corner">
 <span class="sig-prename descclassname"><span class="pre">pocomc.plotting.</span></span><span class="sig-name descname"><span class="pre">corner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dims</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">corner_kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pocomc.plotting.corner" title="Permalink to this definition">#</a></dt>
-<dd><blockquote>
-<div><p>Corner plot of the 1-D and 2-D marginal posteriors.</p>
-</div></blockquote>
+<dd><p>Corner plot of the 1-D and 2-D marginal posteriors.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">

docs/build/html/examples.html

@@ -538,7 +538,8 @@ <h1>Examples<a class="headerlink" href="#examples" title="Permalink to this head
 <p>This page highlights several examples on how <code class="docutils literal notranslate"><span class="pre">pocomc</span></code>
 can be used in practice, illustrating both simple and more advanced
 aspects of the code. Jupyter notebooks containing more details are available
-<a class="reference external" href="https://github.com/minaskar/pocomc/tree/examples">on Github</a>.</p>
+<a class="reference external" href="https://github.com/minaskar/pocomc/tree/examples">on Github</a>. <code class="docutils literal notranslate"><span class="pre">seaborn</span></code>
+is required to be installed to generate some of the plots.</p>
 <section id="gaussian-shells">
 <h2>Gaussian Shells<a class="headerlink" href="#gaussian-shells" title="Permalink to this headline">#</a></h2>
 <p>The <code class="docutils literal notranslate"><span class="pre">Gaussian</span> <span class="pre">Shells</span></code> are a clear example of a bimodal distribution. Allthough

docs/build/html/index.html

@@ -437,7 +437,7 @@ <h1>Authors &amp; License<a class="headerlink" href="#authors-license" title="Pe
 </section>
 <section id="changelog">
 <h1>Changelog<a class="headerlink" href="#changelog" title="Permalink to this headline">#</a></h1>
-<p><strong>0.0.1 (30/04/22)</strong></p>
+<p><strong>0.1.0 (12/07/22)</strong></p>
 <ul class="simple">
 <li><p>First version</p></li>
 </ul>

docs/build/html/pages/quickstart.html

@@ -418,7 +418,7 @@ <h2>Likelihood function and prior distribution<a class="headerlink" href="#likel
             <span class="o">+</span> <span class="p">(</span><span class="n">x</span><span class="p">[:,::</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span><span class="o">**</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
 <span class="c1"># Define our uniform prior.</span>
-<span class="k">def</span> <span class="nf">logprior</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">log_prior</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mf">10.0</span><span class="p">)</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">x</span> <span class="o">&gt;</span> <span class="mf">10.0</span><span class="p">):</span>
         <span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
     <span class="k">else</span><span class="p">:</span>
@@ -440,10 +440,10 @@ <h2>Preconditioned Monte Carlo sampling<a class="headerlink" href="#precondition
 <span class="n">nparticles</span> <span class="o">=</span> <span class="mi">1000</span>
 
 <span class="c1"># Initialise sampler</span>
-<span class="n">sampler</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">Sampler</span><span class="p">(</span><span class="n">nparticles</span><span class="p">,</span>
-                     <span class="n">ndim</span><span class="p">,</span>
-                     <span class="n">loglikelihood</span><span class="o">=</span><span class="n">log_likelihood</span><span class="p">,</span>
-                     <span class="n">logprior</span><span class="o">=</span><span class="n">logprior</span><span class="p">,</span>
+<span class="n">sampler</span> <span class="o">=</span> <span class="n">pc</span><span class="o">.</span><span class="n">Sampler</span><span class="p">(</span><span class="n">n_particles</span><span class="p">,</span>
+                     <span class="n">n_dim</span><span class="p">,</span>
+                     <span class="n">log_likelihood</span><span class="o">=</span><span class="n">log_likelihood</span><span class="p">,</span>
+                     <span class="n">log_prior</span><span class="o">=</span><span class="n">log_prior</span><span class="p">,</span>
                      <span class="n">vectorize_likelihood</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                      <span class="n">bounds</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">))</span>
 </pre></div>

docs/build/jupyter_execute/pages/quickstart.ipynb

@@ -45,7 +45,7 @@
     "            + (x[:,::2] - 1.0)**2.0, axis=1)\n",
     "\n",
     "# Define our uniform prior.\n",
-    "def logprior(x):\n",
+    "def log_prior(x):\n",
     "    if np.any(x < -10.0) or np.any(x > 10.0):\n",
     "        return -np.inf\n",
     "    else:\n",
@@ -79,10 +79,10 @@
     "nparticles = 1000\n",
     "\n",
     "# Initialise sampler\n",
-    "sampler = pc.Sampler(nparticles,\n",
-    "                     ndim,\n",
-    "                     loglikelihood=log_likelihood,\n",
-    "                     logprior=logprior,\n",
+    "sampler = pc.Sampler(n_particles,\n",
+    "                     n_dim,\n",
+    "                     log_likelihood=log_likelihood,\n",
+    "                     log_prior=log_prior,\n",
     "                     vectorize_likelihood=True,\n",
     "                     bounds=(-10.0, 10.0))"
    ]