update docs

Author: erdogant

docs/pages/doctrees/Abstract.doctree

@@ -131,7 +131,7 @@ <h1>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">
 <dt>Aim</dt><dd><p>This library aims to detect peaks in both a 1-dimensional vector and 2-dimensional arrays (images) without making any assumption on the peak shape or baseline noise.
 To make sure that peaks can be detected across global and local heights, and in noisy data, multiple pre-processing and denoising methods are implemented or utilized.</p>
 </dd>
-<dt>Results</dt><dd><p>Three peak-detection methods are incorporated into this package, namely <em>Topology</em>, <em>Mask</em> and <em>Peakdetect</em>. The peaks can be ranked with among others <strong>persistence</strong> scores. The pre-processing approaches are among others, <strong>denoising</strong> methods, <strong>interpolation</strong> and <strong>smoothing</strong>, <strong>resizing</strong> and <strong>normalizing</strong> methods. In addition we implemented various plots to easily intepretate the results, such as the conversion of 2d-images to <strong>3d-mesh</strong> plots, <strong>persistence</strong> plot, and <strong>peak detection</strong> plots.</p>
+<dt>Results</dt><dd><p>Three peak-detection methods are incorporated into this package, namely <em>Topology</em>, <em>Mask</em> and <em>Peakdetect</em>. The peaks can be ranked with among others <strong>persistence</strong> scores. The pre-processing approaches are among others, <strong>denoising</strong> methods, <strong>interpolation</strong> and <strong>smoothing</strong>, <strong>resizing</strong> and <strong>normalizing</strong> methods. In addition various plots are implemented to easily intepretate the results, such as the conversion of 2d-images to <strong>3d-mesh</strong> plots, <strong>persistence</strong> plot, and <strong>peak detection</strong> plots.</p>
 </dd>
 </dl>
 <p>[1] Peak detection. Data Analysis and Signal Processing in Chromatography, <a class="reference external" href="https://doi.org/10.1016/S0922-3487(98)80027-0">https://doi.org/10.1016/S0922-3487(98)80027-0</a>, Volume 21, 1998, Pages 183-190</p>

docs/pages/doctrees/Caerus.doctree

@@ -139,14 +139,14 @@ <h1>Caerus<a class="headerlink" href="#caerus" title="Link to this heading"><
 <p>This method works only for one-dimensional data.</p>
 <section id="stock-market-data">
 <h2>Stock market data<a class="headerlink" href="#stock-market-data" title="Link to this heading"></a></h2>
-<p>For <strong>caerus</strong> method, we can set the minimum percentage (minperc) parameter to declare a starting position with window relevant.
+<p>For the <strong>Caerus</strong> method, the minimum percentage (minperc) parameter can be set to declare a starting position with window relevant.
 The window size is used to determine whether there is an increase in percentage. start location + window.
 The use of smaller window sizes (eg 50) are able to pickup better local-minima, whereas Larger window sizes (eg 1000) will more stress on global minma.
 The default value for minperc is set to 5 and window size is set to 50.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import library</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">findpeaks</span><span class="w"> </span><span class="kn">import</span> <span class="n">findpeaks</span>
 <span class="c1"># Initialize findpeaks with cearus method.</span>
-<span class="c1"># The default setting is that it only return peaks-vallyes with at least 5% difference. We can change this using params</span>
+<span class="c1"># The default setting is that it only return peaks-vallyes with at least 5% difference. Change this using params</span>
 <span class="n">fp</span> <span class="o">=</span> <span class="n">findpeaks</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;caerus&#39;</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;minperc&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="s1">&#39;window&#39;</span><span class="p">:</span><span class="mi">50</span><span class="p">})</span>
 <span class="c1"># Import example data</span>
 <span class="n">X</span> <span class="o">=</span> <span class="n">fp</span><span class="o">.</span><span class="n">import_example</span><span class="p">(</span><span class="s1">&#39;facebook&#39;</span><span class="p">)</span>

docs/pages/doctrees/Denoise.doctree

@@ -139,8 +139,19 @@
 <h1>Denoise<a class="headerlink" href="#denoise" title="Link to this heading"></a></h1>
 <p>Images can be corrupted by noise. To suppress and improve the image analysis various filtering techniques have been developed.
 Denoising the image is very usefull before the detection of peaks using <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.findpeaks.findpeaks.preprocessing" title="findpeaks.findpeaks.findpeaks.preprocessing"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.findpeaks.findpeaks.preprocessing()</span></code></a>.
-In <code class="docutils literal notranslate"><span class="pre">findpeaks</span></code> we incorporated some well-known filtering methods: <em>Frost</em> via <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.frost.frost_filter" title="findpeaks.filters.frost.frost_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.frost.frost_filter()</span></code></a>, <em>Lee</em> via <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.lee.lee_filter" title="findpeaks.filters.lee.lee_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.lee.lee_filter()</span></code></a>, and <em>Kuan</em> via <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.kuan.kuan_filter" title="findpeaks.filters.kuan.kuan_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.kuan.kuan_filter()</span></code></a>, but also other methods such as <em>fastnl</em>, <em>bilateral</em>, <em>mean</em> via <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.mean.mean_filter" title="findpeaks.filters.mean.mean_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.mean.mean_filter()</span></code></a> and <em>median</em> via <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.median.median_filter" title="findpeaks.filters.median.median_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.median.median_filter()</span></code></a> filters.
-Some of the methods are adopted from <code class="docutils literal notranslate"><span class="pre">pyradar</span></code> [1], for which the code is refactored and rewritten for Python 3. Other methods are adopted from <code class="docutils literal notranslate"><span class="pre">python-opencv</span></code>.</p>
+In <code class="docutils literal notranslate"><span class="pre">findpeaks</span></code> well-known filtering methods are implemented:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><em>Frost</em>: <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.frost.frost_filter" title="findpeaks.filters.frost.frost_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.frost.frost_filter()</span></code></a></p></li>
+<li><p><em>Lee</em>: <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.lee.lee_filter" title="findpeaks.filters.lee.lee_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.lee.lee_filter()</span></code></a>,</p></li>
+<li><p><em>Kuan</em>: <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.kuan.kuan_filter" title="findpeaks.filters.kuan.kuan_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.kuan.kuan_filter()</span></code></a>,</p></li>
+<li><p><em>mean</em>: <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.mean.mean_filter" title="findpeaks.filters.mean.mean_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.mean.mean_filter()</span></code></a></p></li>
+<li><p><em>median</em>: <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.filters.median.median_filter" title="findpeaks.filters.median.median_filter"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.filters.median.median_filter()</span></code></a></p></li>
+<li><p><em>fastnl</em></p></li>
+<li><p><em>bilateral</em></p></li>
+</ul>
+</div></blockquote>
+<p>Some of the methods are adopted from <code class="docutils literal notranslate"><span class="pre">pyradar</span></code> [1], for which the code is refactored and rewritten for Python 3. Other methods are adopted from <code class="docutils literal notranslate"><span class="pre">python-opencv</span></code>.</p>
 <p>It is generally known that noise can follow various distributions, and requires different approaches to effectly reduce the noise.</p>
 <table class="docutils align-center" id="id1">
 <caption><span class="caption-text">Noise distributions</span><a class="headerlink" href="#id1" title="Link to this table"></a></caption>
@@ -152,7 +163,7 @@ <h1>Denoise<a class="headerlink" href="#denoise" title="Link to this heading">
 <p><em>SAR</em> images are affected by <em>speckle</em> noise that inherently exists in and which degrades the image quality.
 It is caused by the back-scatter waves from multiple distributed targets. It is locally strong and it increases the mean Grey level of local area.
 Reducing the noise enhances the resolution but tends to decrease the spatial resolution too.</p>
-<p>Lets demonstrate the denoising by example. First we will import the example data:</p>
+<p>Lets demonstrate the denoising by example. First import the example data:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import library</span>
 <span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">findpeaks</span><span class="w"> </span><span class="kn">import</span> <span class="n">findpeaks</span>

docs/pages/doctrees/Topology.doctree

@@ -130,14 +130,13 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
 
-  <p>Some of the explanations of <strong>topology</strong>, and <strong>persitent homology</strong> are gathered from various sources on the internet (articles, stackoverflow, blog pages, and git pages), and combined on this page.
-All references are depicted at the bottom of the page. Note that all examples are made with <code class="docutils literal notranslate"><span class="pre">findpeaks</span></code>.
-The function with docstrings for the topology method can be found here <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.stats.topology" title="findpeaks.stats.topology"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.stats.topology()</span></code></a>.</p>
-<section id="topology">
+  <section id="topology">
 <h1>Topology<a class="headerlink" href="#topology" title="Link to this heading"></a></h1>
-<p>Topological data analysis (TDA) applies methods of topology in data analysis and found many applications in data science[1].
+<p>Topological data analysis applies methods of topology in data analysis and found many applications in data science[1].
 TDA builds upon the observation that data often possesses a certain intrinsic shape such as the shape of a point cloud, the shape of a signal or the shape of a geometric object.
 Persistent homology is probably the most prominent tool in TDA that gives us the means to describe and quantify topological properties of these shapes [1].</p>
+<p>All references are depicted at the bottom of the page. Note that all examples are made with <code class="docutils literal notranslate"><span class="pre">findpeaks</span></code>.
+The function with docstrings for the topology method can be found here <a class="reference internal" href="findpeaks.findpeaks.html#findpeaks.stats.topology" title="findpeaks.stats.topology"><code class="xref py py-func docutils literal notranslate"><span class="pre">findpeaks.stats.topology()</span></code></a>.</p>
 <section id="persistent-homology">
 <h2>Persistent Homology<a class="headerlink" href="#persistent-homology" title="Link to this heading"></a></h2>
 <p>In computational topology the idea of persistent homology leads to an efficient – fast as sorting numbers – solution.