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<!DOCTYPE html>
<html class="writer-html5" lang="en" >
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<title>API — MatrixProfile</title>
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<a href="index.html" class="icon icon-home" alt="Documentation Home"> matrixprofile
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1.1.6
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<p class="caption"><span class="caption-text">Contents:</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="Quickstart.html">Quickstart Guide</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-analyze">matrixprofile.analyze</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-compute">matrixprofile.compute</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-visualize">matrixprofile.visualize</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-preprocess-preprocess">matrixprofile.preprocess.preprocess</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-preprocess-impute-missing">matrixprofile.preprocess.impute_missing</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-preprocess-add-noise-to-series">matrixprofile.preprocess.add_noise_to_series</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-discover-motifs">matrixprofile.discover.motifs</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-discover-discords">matrixprofile.discover.discords</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-discover-snippets">matrixprofile.discover.snippets</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-discover-regimes">matrixprofile.discover.regimes</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-discover-statistics">matrixprofile.discover.statistics</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-discover-hierarchical-clusters">matrixprofile.discover.hierarchical_clusters</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-stomp">matrixprofile.algorithms.stomp</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-mpx">matrixprofile.algorithms.mpx</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-skimp">matrixprofile.algorithms.skimp</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-mass2">matrixprofile.algorithms.mass2</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-mpdist">matrixprofile.algorithms.mpdist</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-pairwise-dist">matrixprofile.algorithms.pairwise_dist</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-maximum-subsequence">matrixprofile.algorithms.maximum_subsequence</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-prescrimp">matrixprofile.algorithms.prescrimp</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-algorithms-scrimp-plus-plus">matrixprofile.algorithms.scrimp_plus_plus</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-transform-apply-av">matrixprofile.transform.apply_av</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-transform-make-default-av">matrixprofile.transform.make_default_av</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-transform-make-complexity-av">matrixprofile.transform.make_complexity_av</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-transform-make-meanstd-av">matrixprofile.transform.make_meanstd_av</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-transform-make-clipping-av">matrixprofile.transform.make_clipping_av</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-utils-empty-mp">matrixprofile.utils.empty_mp</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-utils-pick-mp">matrixprofile.utils.pick_mp</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-io-to-disk">matrixprofile.io.to_disk</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-io-from-disk">matrixprofile.io.from_disk</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-io-to-json">matrixprofile.io.to_json</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-io-from-json">matrixprofile.io.from_json</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-io-to-mpf">matrixprofile.io.to_mpf</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-io-from-mpf">matrixprofile.io.from_mpf</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-datasets-fetch-available">matrixprofile.datasets.fetch_available</a></li>
<li class="toctree-l2"><a class="reference internal" href="#matrixprofile-datasets-load">matrixprofile.datasets.load</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="examples.html">Examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="Algorithms.html">Algorithms</a></li>
<li class="toctree-l1"><a class="reference internal" href="help.html">Getting Help</a></li>
<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a></li>
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<li class="toctree-l1"><a class="reference internal" href="citations.html">Citations</a></li>
<li class="toctree-l1"><a class="reference external" href="https://github.com/matrix-profile-foundation/matrixprofile">Code Repository (Github)</a></li>
<li class="toctree-l1"><a class="reference external" href="https://matrixprofile.org">Website</a></li>
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<div class="section" id="api">
<h1>API<a class="headerlink" href="#api" title="Permalink to this headline">¶</a></h1>
<div class="section" id="matrixprofile-analyze">
<h2>matrixprofile.analyze<a class="headerlink" href="#matrixprofile-analyze" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.analyze">
<code class="sig-prename descclassname">matrixprofile.</code><code class="sig-name descname">analyze</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">query</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">windows</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_pct</span><span class="o">=</span><span class="default_value">1.0</span></em>, <em class="sig-param"><span class="n">threshold</span><span class="o">=</span><span class="default_value">0.98</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">preprocessing_kwargs</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/analyze.html#analyze"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.analyze" title="Permalink to this definition">¶</a></dt>
<dd><p>Runs an appropriate workflow based on the parameters passed in. The goal
of this function is to compute all fundamental algorithms on the provided
time series data. For now the following is computed:</p>
<ol class="arabic simple">
<li><p>Matrix Profile - exact or approximate based on sample_pct given that a
window is provided. By default is the exact algorithm.</p></li>
<li><p>Top Motifs - The top 3 motifs are found.</p></li>
<li><p>Top Discords - The top 3 discords are found.</p></li>
<li><p>Plot MP, Motifs and Discords</p></li>
</ol>
<p>When a window is not provided or more than a single window is provided,
the PMP is computed:</p>
<ol class="arabic simple">
<li><p>Compute UPPER window when no window(s) is provided</p></li>
<li><p>Compute PMP for all windows</p></li>
<li><p>Top Motifs</p></li>
<li><p>Top Discords</p></li>
<li><p>Plot PMP, motifs and discords.</p></li>
</ol>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to analyze.</p></li>
<li><p><strong>query</strong> (<em>array_like</em><em>, </em><em>Optional</em>) – The query to analyze. Note that when computing the PMP the query is
ignored!</p></li>
<li><p><strong>windows</strong> (<em>int</em><em> or </em><em>array_like</em><em>, </em><em>Optional</em>) – The window(s) to compute the MatrixProfile. Note that it may be an int
for a single matrix profile computation or an array of ints for
computing the pan matrix profile.</p></li>
<li><p><strong>sample_pct</strong> (<em>float</em><em>, </em><em>default = 1</em>) – A float between 0 and 1 representing how many samples to compute for
the MP or PMP. When it is 1, the exact algorithm is used.</p></li>
<li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>Default 0.98</em>) – The correlation coefficient used as the threshold. It should be between
0 and 1. This is used to compute the upper window size when no
window(s) is given.</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>Default = 1</em>) – Number of cpu cores to use.</p></li>
<li><p><strong>preprocessing_kwargs</strong> (<em>dict</em><em>, </em><em>default = None</em>) – <p>A dictionary object to sets parameters for preprocess function.
A valid preprocessing_kwargs should have the following structure:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'window'</span><span class="p">:</span> <span class="n">The</span> <span class="n">window</span> <span class="n">size</span> <span class="n">to</span> <span class="n">compute</span> <span class="n">the</span> <span class="n">mean</span><span class="o">/</span><span class="n">median</span><span class="o">/</span><span class="n">minimum</span><span class="o">/</span><span class="n">maximum</span> <span class="n">value</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'method'</span><span class="p">:</span> <span class="n">A</span> <span class="n">string</span> <span class="n">indicating</span> <span class="n">the</span> <span class="n">data</span> <span class="n">imputation</span> <span class="n">method</span><span class="p">,</span> <span class="n">which</span> <span class="n">should</span> <span class="n">be</span>
<span class="gp">>>> </span> <span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'median'</span><span class="p">,</span> <span class="s1">'min'</span> <span class="ow">or</span> <span class="s1">'max'</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'direction'</span><span class="p">:</span> <span class="n">A</span> <span class="n">string</span> <span class="n">indicating</span> <span class="n">the</span> <span class="n">data</span> <span class="n">imputation</span> <span class="n">direction</span><span class="p">,</span> <span class="n">which</span> <span class="n">should</span> <span class="n">be</span>
<span class="gp">>>> </span> <span class="s1">'forward'</span><span class="p">,</span> <span class="s1">'fwd'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">,</span> <span class="s1">'backward'</span><span class="p">,</span> <span class="s1">'bwd'</span><span class="p">,</span> <span class="s1">'b'</span><span class="o">.</span> <span class="n">If</span> <span class="n">the</span> <span class="n">direction</span> <span class="ow">is</span>
<span class="gp">>>> </span> <span class="n">forward</span><span class="p">,</span> <span class="n">we</span> <span class="n">use</span> <span class="n">previous</span> <span class="n">data</span> <span class="k">for</span> <span class="n">imputation</span><span class="p">;</span> <span class="k">if</span> <span class="n">the</span> <span class="n">direction</span> <span class="ow">is</span>
<span class="gp">>>> </span> <span class="n">backward</span><span class="p">,</span> <span class="n">we</span> <span class="n">use</span> <span class="n">subsequent</span> <span class="n">data</span> <span class="k">for</span> <span class="n">imputation</span><span class="o">.</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'add_noise'</span><span class="p">:</span> <span class="n">A</span> <span class="n">boolean</span> <span class="n">value</span> <span class="n">indicating</span> <span class="n">whether</span> <span class="n">noise</span> <span class="n">needs</span> <span class="n">to</span> <span class="n">be</span> <span class="n">added</span> <span class="n">into</span> <span class="n">the</span>
<span class="gp">>>> </span> <span class="n">time</span> <span class="n">series</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
<p>To disable preprocessing procedure, set the preprocessing_kwargs to
None/False/””/{}.</p>
</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>tuple</strong> – The appropriate PMP or MP profile object and associated figures.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>(profile, figures)</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-compute">
<h2>matrixprofile.compute<a class="headerlink" href="#matrixprofile-compute" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.compute">
<code class="sig-prename descclassname">matrixprofile.</code><code class="sig-name descname">compute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">windows</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">query</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_pct</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">threshold</span><span class="o">=</span><span class="default_value">0.98</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em>, <em class="sig-param"><span class="n">preprocessing_kwargs</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/compute.html#compute"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.compute" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the exact or approximate MatrixProfile based on the sample percent
specified. Currently, MPX and SCRIMP++ is used for the exact and
approximate algorithms respectively. When multiple windows are passed, the
Pan-MatrixProfile is computed and returned.</p>
<p>By default, only passing in a time series (ts), the Pan-MatrixProfile is
computed based on the maximum upper window algorithm with a correlation
threshold of 0.98.</p>
<p class="rubric">Notes</p>
<p>When multiple windows are passed and the Pan-MatrixProfile is computed, the
query is ignored!</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to analyze.</p></li>
<li><p><strong>windows</strong> (<em>int</em><em>, </em><em>array_like</em>) – The window(s) to compute the MatrixProfile. Note that it may be an int
for a single matrix profile computation or an array of ints for
computing the pan matrix profile.</p></li>
<li><p><strong>query</strong> (<em>array_like</em><em>, </em><em>optional</em>) – The query to analyze. Note that when computing the PMP the query is
ignored!</p></li>
<li><p><strong>sample_pct</strong> (<em>float</em><em>, </em><em>default 1</em>) – A float between 0 and 1 representing how many samples to compute for
the MP or PMP. When it is 1, the exact algorithm is used.</p></li>
<li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>default 0.98</em>) – The correlation coefficient used as the threshold. It should be between
0 and 1. This is used to compute the upper window size when no
window(s) is given.</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>default = 1</em>) – Number of cpu cores to use.</p></li>
<li><p><strong>preprocessing_kwargs</strong> (<em>dict</em><em>, </em><em>default = None</em>) – <p>A dictionary object to sets parameters for preprocess function.
A valid preprocessing_kwargs should have the following structure:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'window'</span><span class="p">:</span> <span class="n">The</span> <span class="n">window</span> <span class="n">size</span> <span class="n">to</span> <span class="n">compute</span> <span class="n">the</span> <span class="n">mean</span><span class="o">/</span><span class="n">median</span><span class="o">/</span><span class="n">minimum</span><span class="o">/</span><span class="n">maximum</span> <span class="n">value</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'method'</span><span class="p">:</span> <span class="n">A</span> <span class="n">string</span> <span class="n">indicating</span> <span class="n">the</span> <span class="n">data</span> <span class="n">imputation</span> <span class="n">method</span><span class="p">,</span> <span class="n">which</span> <span class="n">should</span> <span class="n">be</span>
<span class="gp">>>> </span> <span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'median'</span><span class="p">,</span> <span class="s1">'min'</span> <span class="ow">or</span> <span class="s1">'max'</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'direction'</span><span class="p">:</span> <span class="n">A</span> <span class="n">string</span> <span class="n">indicating</span> <span class="n">the</span> <span class="n">data</span> <span class="n">imputation</span> <span class="n">direction</span><span class="p">,</span> <span class="n">which</span> <span class="n">should</span> <span class="n">be</span>
<span class="gp">>>> </span> <span class="s1">'forward'</span><span class="p">,</span> <span class="s1">'fwd'</span><span class="p">,</span> <span class="s1">'f'</span><span class="p">,</span> <span class="s1">'backward'</span><span class="p">,</span> <span class="s1">'bwd'</span><span class="p">,</span> <span class="s1">'b'</span><span class="o">.</span> <span class="n">If</span> <span class="n">the</span> <span class="n">direction</span> <span class="ow">is</span>
<span class="gp">>>> </span> <span class="n">forward</span><span class="p">,</span> <span class="n">we</span> <span class="n">use</span> <span class="n">previous</span> <span class="n">data</span> <span class="k">for</span> <span class="n">imputation</span><span class="p">;</span> <span class="k">if</span> <span class="n">the</span> <span class="n">direction</span> <span class="ow">is</span>
<span class="gp">>>> </span> <span class="n">backward</span><span class="p">,</span> <span class="n">we</span> <span class="n">use</span> <span class="n">subsequent</span> <span class="n">data</span> <span class="k">for</span> <span class="n">imputation</span><span class="o">.</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'add_noise'</span><span class="p">:</span> <span class="n">A</span> <span class="n">boolean</span> <span class="n">value</span> <span class="n">indicating</span> <span class="n">whether</span> <span class="n">noise</span> <span class="n">needs</span> <span class="n">to</span> <span class="n">be</span> <span class="n">added</span> <span class="n">into</span> <span class="n">the</span>
<span class="gp">>>> </span> <span class="n">time</span> <span class="n">series</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
<p>To disable preprocessing procedure, set the preprocessing_kwargs to
None/False/””/{}.</p>
</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>dict</strong> – The profile computed.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>profile</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-visualize">
<h2>matrixprofile.visualize<a class="headerlink" href="#matrixprofile-visualize" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.visualize">
<code class="sig-prename descclassname">matrixprofile.</code><code class="sig-name descname">visualize</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">profile</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/visualize.html#visualize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.visualize" title="Permalink to this definition">¶</a></dt>
<dd><p>Automatically creates plots for the provided data structure. In some cases
many plots are created. For example, when a MatrixProfile is passed with
corresponding motifs and discords, the matrix profile, discords and motifs
will be plotted.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>profile</strong> (<em>dict_like</em>) – A MatrixProfile, Pan-MatrixProfile or Statistics data structure.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>list</strong> – A list of matplotlib figures.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>figures</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-preprocess-preprocess">
<h2>matrixprofile.preprocess.preprocess<a class="headerlink" href="#matrixprofile-preprocess-preprocess" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.preprocess.preprocess">
<code class="sig-prename descclassname">matrixprofile.preprocess.</code><code class="sig-name descname">preprocess</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">window</span></em>, <em class="sig-param"><span class="n">impute_method</span><span class="o">=</span><span class="default_value">'mean'</span></em>, <em class="sig-param"><span class="n">impute_direction</span><span class="o">=</span><span class="default_value">'forward'</span></em>, <em class="sig-param"><span class="n">add_noise</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/preprocess.html#preprocess"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.preprocess.preprocess" title="Permalink to this definition">¶</a></dt>
<dd><p>Preprocesses the given time series by adding noise and imputing missing data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to be preprocessed.</p></li>
<li><p><strong>window</strong> (<em>int</em>) – The window size to compute the mean/median/minimum value/maximum
value.</p></li>
<li><p><strong>method</strong> (<em>string</em><em>, </em><em>Default = 'mean'</em>) – A string indicating the data imputation method, which should be
‘mean’, ‘median’, ‘min’ or ‘max’.</p></li>
<li><p><strong>direction</strong> (<em>string</em><em>, </em><em>Default = 'forward'</em>) – A string indicating the data imputation direction, which should be
‘forward’, ‘fwd’, ‘f’, ‘backward’, ‘bwd’, ‘b’. If the direction is
forward, we use previous data for imputation; if the direction is
backward, we use subsequent data for imputation.</p></li>
<li><p><strong>add_noise</strong> (<em>bool</em><em>, </em><em>Default = True</em>) – A boolean value indicating whether noise needs to be added into the time series.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>temp</strong> – The time series after being preprocessed.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>array_like</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-preprocess-impute-missing">
<h2>matrixprofile.preprocess.impute_missing<a class="headerlink" href="#matrixprofile-preprocess-impute-missing" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.preprocess.impute_missing">
<code class="sig-prename descclassname">matrixprofile.preprocess.</code><code class="sig-name descname">impute_missing</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">window</span></em>, <em class="sig-param"><span class="n">method</span><span class="o">=</span><span class="default_value">'mean'</span></em>, <em class="sig-param"><span class="n">direction</span><span class="o">=</span><span class="default_value">'forward'</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/preprocess.html#impute_missing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.preprocess.impute_missing" title="Permalink to this definition">¶</a></dt>
<dd><p>Imputes missing data in time series.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to be handled.</p></li>
<li><p><strong>window</strong> (<em>int</em>) – The window size to compute the mean/median/minimum value/maximum
value.</p></li>
<li><p><strong>method</strong> (<em>string</em><em>, </em><em>Default = 'mean'</em>) – A string indicating the data imputation method, which should be
‘mean’, ‘median’, ‘min’ or ‘max’.</p></li>
<li><p><strong>direction</strong> (<em>string</em><em>, </em><em>Default = 'forward'</em>) – A string indicating the data imputation direction, which should be
‘forward’, ‘fwd’, ‘f’, ‘backward’, ‘bwd’, ‘b’. If the direction is
forward, we use previous data for imputation; if the direction is
backward, we use subsequent data for imputation.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>temp</strong> – The time series after being imputed missing data.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>array_like</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-preprocess-add-noise-to-series">
<h2>matrixprofile.preprocess.add_noise_to_series<a class="headerlink" href="#matrixprofile-preprocess-add-noise-to-series" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.preprocess.add_noise_to_series">
<code class="sig-prename descclassname">matrixprofile.preprocess.</code><code class="sig-name descname">add_noise_to_series</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">series</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/preprocess.html#add_noise_to_series"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.preprocess.add_noise_to_series" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds noise to the given time series.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>series</strong> (<em>array_like</em>) – The time series subsequence to be added noise.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>temp</strong> – The time series subsequence after being added noise.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>array_like</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-discover-motifs">
<h2>matrixprofile.discover.motifs<a class="headerlink" href="#matrixprofile-discover-motifs" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.discover.motifs">
<code class="sig-prename descclassname">matrixprofile.discover.</code><code class="sig-name descname">motifs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">profile</span></em>, <em class="sig-param"><span class="n">exclusion_zone</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">3</span></em>, <em class="sig-param"><span class="n">max_neighbors</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">radius</span><span class="o">=</span><span class="default_value">3</span></em>, <em class="sig-param"><span class="n">use_cmp</span><span class="o">=</span><span class="default_value">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#matrixprofile.discover.motifs" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the top K number of motifs (patterns) given a matrix profile or a pan
matrix profile. By default the algorithm will find up to 3 motifs (k) and
up to 10 of their neighbors with a radius of 3 * min_dist using the
regular matrix profile. If the profile is a Matrix Profile data structure,
you can also use a Corrected Matrix Profile to compute the motifs.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>profile</strong> (<em>dict</em>) – The output from one of the matrix profile algorithms.</p></li>
<li><p><strong>exclusion_zone</strong> (<em>int</em><em>, </em><em>Default to algorithm ez</em>) – Desired number of values to exclude on both sides of the motif. This
avoids trivial matches. It defaults to half of the computed window
size. Setting the exclusion zone to 0 makes it not apply.</p></li>
<li><p><strong>k</strong> (<em>int</em><em>, </em><em>Default = 3</em>) – Desired number of motifs to find.</p></li>
<li><p><strong>max_neighbors</strong> (<em>int</em><em>, </em><em>Default = 10</em>) – The maximum number of neighbors to include for a given motif.</p></li>
<li><p><strong>radius</strong> (<em>int</em><em>, </em><em>Default = 3</em>) – The radius is used to associate a neighbor by checking if the
neighbor’s distance is less than or equal to dist * radius</p></li>
<li><p><strong>use_cmp</strong> (<em>bool</em><em>, </em><em>Default = False</em>) – Use the Corrected Matrix Profile to compute the motifs (only for
a Matrix Profile data structure).</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>dict</strong> – The original input profile with the addition of the “motifs” key. The
motifs key consists of the following structure.</p>
<p>A list of dicts containing motif indices and their corresponding
neighbor indices.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">[</span>
<span class="gp">>>> </span> <span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'motifs'</span><span class="p">:</span> <span class="p">[</span><span class="n">first_index</span><span class="p">,</span> <span class="n">second_index</span><span class="p">],</span>
<span class="gp">>>> </span> <span class="s1">'neighbors'</span><span class="p">:</span> <span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">...</span><span class="n">max_neighbors</span><span class="p">]</span>
<span class="gp">>>> </span> <span class="p">}</span>
<span class="gp">>>> </span><span class="p">]</span>
</pre></div>
</div>
<p>The index is a single value when a MatrixProfile is passed in otherwise
the index contains a row and column index for Pan-MatrixProfile.</p>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>profile</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-discover-discords">
<h2>matrixprofile.discover.discords<a class="headerlink" href="#matrixprofile-discover-discords" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.discover.discords">
<code class="sig-prename descclassname">matrixprofile.discover.</code><code class="sig-name descname">discords</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">profile</span></em>, <em class="sig-param"><span class="n">exclusion_zone</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">k</span><span class="o">=</span><span class="default_value">3</span></em><span class="sig-paren">)</span><a class="headerlink" href="#matrixprofile.discover.discords" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the top K number of discords (anomalies) given a mp or pmp,
exclusion zone and the desired number of discords. The exclusion zone
nullifies entries on the left and right side of the first and subsequent
discords to remove non-trivial matches. More specifically, a discord found
at location X will more than likely have additional discords to the left or
right of it.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>profile</strong> (<em>dict</em>) – A MatrixProfile or Pan-MatrixProfile structure.</p></li>
<li><p><strong>exclusion_zone</strong> (<em>int</em><em>, </em><em>Default mp algorithm ez</em>) – Desired number of values to exclude on both sides of the anomaly.</p></li>
<li><p><strong>k</strong> (<em>int</em>) – Desired number of discords to find.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>dict</strong> – The original profile object with an additional ‘discords’ key. Take
note that a MatrixProfile discord contains a single value while the
Pan-MatrixProfile contains a row and column index.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>profile</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-discover-snippets">
<h2>matrixprofile.discover.snippets<a class="headerlink" href="#matrixprofile-discover-snippets" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.discover.snippets">
<code class="sig-prename descclassname">matrixprofile.discover.</code><code class="sig-name descname">snippets</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">snippet_size</span></em>, <em class="sig-param"><span class="n">num_snippets</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">window_size</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/snippets.html#snippets"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.discover.snippets" title="Permalink to this definition">¶</a></dt>
<dd><p>The snippets algorithm is used to summarize your time series by
identifying N number of representative subsequences. If you want to
identify typical patterns in your time series, then this is the algorithm
to use.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series.</p></li>
<li><p><strong>snippet_size</strong> (<em>int</em>) – The size of snippet desired.</p></li>
<li><p><strong>num_snippets</strong> (<em>int</em><em>, </em><em>Default 2</em>) – The number of snippets you would like to find.</p></li>
<li><p><strong>window_size</strong> (<em>int</em><em>, </em><em>Default</em><em> (</em><em>snippet_size / 2</em><em>)</em>) – The window size.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>list</strong> – A list of snippets as dictionary objects with the following structure.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="n">index</span><span class="p">:</span> <span class="n">the</span> <span class="n">index</span> <span class="n">of</span> <span class="n">the</span> <span class="n">snippet</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">snippet</span><span class="p">:</span> <span class="n">the</span> <span class="n">snippet</span> <span class="n">values</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">neighbors</span><span class="p">:</span> <span class="n">the</span> <span class="n">starting</span> <span class="n">indices</span> <span class="n">of</span> <span class="nb">all</span> <span class="n">subsequences</span> <span class="n">similar</span> <span class="n">to</span> <span class="n">the</span> <span class="n">current</span> <span class="n">snippet</span>
<span class="gp">>>> </span> <span class="n">fraction</span><span class="p">:</span> <span class="n">fraction</span> <span class="n">of</span> <span class="n">the</span> <span class="n">snippet</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>snippets</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-discover-regimes">
<h2>matrixprofile.discover.regimes<a class="headerlink" href="#matrixprofile-discover-regimes" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.discover.regimes">
<code class="sig-prename descclassname">matrixprofile.discover.</code><code class="sig-name descname">regimes</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">profile</span></em>, <em class="sig-param"><span class="n">num_regimes</span><span class="o">=</span><span class="default_value">3</span></em><span class="sig-paren">)</span><a class="headerlink" href="#matrixprofile.discover.regimes" title="Permalink to this definition">¶</a></dt>
<dd><p>Given a MatrixProfile, compute the corrected arc curve and extract
the desired number of regimes. Regimes are computed with an exclusion
zone of 5 * window size per the authors.</p>
<dl class="simple">
<dt>The author states:</dt><dd><p>This exclusion zone is based on an assumption that regimes will have
multiple repetitions; FLUSS is not able to segment single gesture
patterns.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>profile</strong> (<em>dict</em>) – Data structure from a MatrixProfile algorithm.</p></li>
<li><p><strong>num_regimes</strong> (<em>int</em>) – The desired number of regimes to find.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>dict</strong> – The original MatrixProfile object with additional keys containing.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'cac'</span><span class="p">:</span> <span class="n">The</span> <span class="n">corrected</span> <span class="n">arc</span> <span class="n">curve</span>
<span class="gp">>>> </span> <span class="s1">'cac_ez'</span><span class="p">:</span> <span class="n">The</span> <span class="n">exclusion</span> <span class="n">zone</span> <span class="n">used</span>
<span class="gp">>>> </span> <span class="s1">'regimes'</span><span class="p">:</span> <span class="n">Array</span> <span class="n">of</span> <span class="n">starting</span> <span class="n">indices</span> <span class="n">indicating</span> <span class="n">a</span> <span class="n">regime</span><span class="o">.</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>profile</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-discover-statistics">
<h2>matrixprofile.discover.statistics<a class="headerlink" href="#matrixprofile-discover-statistics" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.discover.statistics">
<code class="sig-prename descclassname">matrixprofile.discover.</code><code class="sig-name descname">statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">window_size</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/statistics.html#statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.discover.statistics" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute global and moving statistics for the provided 1D time
series. The statistics computed include the min, max, mean, std. and median
over the window specified and globally.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series.</p></li>
<li><p><strong>window_size</strong> (<em>int</em>) – The size of the window to compute moving statistics over.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>dict</strong> – The global and rolling window statistics.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="n">ts</span><span class="p">:</span> <span class="n">the</span> <span class="n">original</span> <span class="n">time</span> <span class="n">series</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="nb">min</span><span class="p">:</span> <span class="n">the</span> <span class="k">global</span> <span class="n">minimum</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="nb">max</span><span class="p">:</span> <span class="n">the</span> <span class="k">global</span> <span class="n">maximum</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">mean</span><span class="p">:</span> <span class="n">the</span> <span class="k">global</span> <span class="n">mean</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">std</span><span class="p">:</span> <span class="n">the</span> <span class="k">global</span> <span class="n">standard</span> <span class="n">deviation</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">median</span><span class="p">:</span> <span class="n">the</span> <span class="k">global</span> <span class="n">median</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">moving_min</span><span class="p">:</span> <span class="n">the</span> <span class="n">moving</span> <span class="n">minimum</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">moving_max</span><span class="p">:</span> <span class="n">the</span> <span class="n">moving</span> <span class="n">maximum</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">moving_mean</span><span class="p">:</span> <span class="n">the</span> <span class="n">moving</span> <span class="n">mean</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">moving_std</span><span class="p">:</span> <span class="n">the</span> <span class="n">moving</span> <span class="n">standard</span> <span class="n">deviation</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">moving_median</span><span class="p">:</span> <span class="n">the</span> <span class="n">moving</span> <span class="n">median</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">window_size</span><span class="p">:</span> <span class="n">the</span> <span class="n">window</span> <span class="n">size</span> <span class="n">provided</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">class</span><span class="p">:</span> <span class="n">Statistics</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>statistics</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><strong>ValueError</strong> – If window_size is not an int.
If window_size > len(ts)
If ts is not a list or np.array.
If ts is not 1D.</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-discover-hierarchical-clusters">
<h2>matrixprofile.discover.hierarchical_clusters<a class="headerlink" href="#matrixprofile-discover-hierarchical-clusters" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.discover.hierarchical_clusters">
<code class="sig-prename descclassname">matrixprofile.discover.</code><code class="sig-name descname">hierarchical_clusters</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">window_size</span></em>, <em class="sig-param"><span class="n">t</span></em>, <em class="sig-param"><span class="n">threshold</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">method</span><span class="o">=</span><span class="default_value">'single'</span></em>, <em class="sig-param"><span class="n">depth</span><span class="o">=</span><span class="default_value">2</span></em>, <em class="sig-param"><span class="n">criterion</span><span class="o">=</span><span class="default_value">'distance'</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/hierarchical_clustering.html#hierarchical_clusters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.discover.hierarchical_clusters" title="Permalink to this definition">¶</a></dt>
<dd><p>Cluster M time series into hierarchical clusters using agglomerative
approach. This function is more or less a convenience wrapper around
SciPy’s scipy.cluster.hierarchy functions, but uses the MPDist algorithm
to compute distances between each pair of time series.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Memory usage could potentially high depending on the length of your
time series and how many distances are computed!</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> (<em>array_like</em>) – An M x N matrix where M is the time series and N is the observations at
a given time.</p></li>
<li><p><strong>window_size</strong> (<em>int</em>) – The window size used to compute the MPDist.</p></li>
<li><p><strong>t</strong> (<em>scalar</em>) – For criteria ‘inconsistent’, ‘distance’ or ‘monocrit’, this is the
threshold to apply when forming flat clusters.
For ‘maxclust’ criteria, this would be max number of clusters
requested.</p></li>
<li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>Default 0.05</em>) – The percentile in which the MPDist is taken from. By default it is
set to 0.05 based on empircal research results from the paper.
Generally, you should not change this unless you know what you are
doing! This value must be a float greater than 0 and less than 1.</p></li>
<li><p><strong>method</strong> (<em>str</em><em>, </em><em>Default single</em>) – The linkage algorithm to use.
Options: {single, complete, average, weighted}</p></li>
<li><p><strong>depth</strong> (<em>int</em><em>, </em><em>Default 2</em>) – A non-negative value more than 0 to specify the number of levels below
a non-singleton cluster to allow.</p></li>
<li><p><strong>criterion</strong> (<em>str</em><em>, </em><em>Default distance</em>) – <p>Options: {inconsistent, distance, maxclust, monocrit}
The criterion to use in forming flat clusters.</p>
<blockquote>
<div><dl>
<dt><code class="docutils literal notranslate"><span class="pre">inconsistent</span></code> :</dt><dd><p>If a cluster node and all its
descendants have an inconsistent value less than or equal
to <cite>t</cite>, then all its leaf descendants belong to the
same flat cluster. When no non-singleton cluster meets
this criterion, every node is assigned to its own
cluster. (Default)</p>
</dd>
<dt><code class="docutils literal notranslate"><span class="pre">distance</span></code> :</dt><dd><p>Forms flat clusters so that the original
observations in each flat cluster have no greater a
cophenetic distance than <cite>t</cite>.</p>
</dd>
<dt><code class="docutils literal notranslate"><span class="pre">maxclust</span></code> :</dt><dd><p>Finds a minimum threshold <code class="docutils literal notranslate"><span class="pre">r</span></code> so that
the cophenetic distance between any two original
observations in the same flat cluster is no more than
<code class="docutils literal notranslate"><span class="pre">r</span></code> and no more than <cite>t</cite> flat clusters are formed.</p>
</dd>
<dt><code class="docutils literal notranslate"><span class="pre">monocrit</span></code> :</dt><dd><p>Forms a flat cluster from a cluster node c
with index i when <code class="docutils literal notranslate"><span class="pre">monocrit[j]</span> <span class="pre"><=</span> <span class="pre">t</span></code>.
For example, to threshold on the maximum mean distance
as computed in the inconsistency matrix R with a
threshold of 0.8 do:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">MR</span> <span class="o">=</span> <span class="n">maxRstat</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">R</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">cluster</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s1">'monocrit'</span><span class="p">,</span> <span class="n">monocrit</span><span class="o">=</span><span class="n">MR</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</div></blockquote>
</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>Default 1</em>) – The number of cpu cores used to compute the MPDist.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>clusters</strong> – Clustering statistics, distances and labels.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="n">pairwise_distances</span><span class="p">:</span> <span class="n">MPDist</span> <span class="n">between</span> <span class="n">pairs</span> <span class="n">of</span> <span class="n">time</span> <span class="n">series</span> <span class="k">as</span>
<span class="gp">>>> </span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">linkage_matrix</span><span class="p">:</span> <span class="n">clustering</span> <span class="n">linkage</span> <span class="n">matrix</span> <span class="k">as</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">inconsistency_statistics</span><span class="p">:</span> <span class="n">inconsistency</span> <span class="n">stats</span> <span class="k">as</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">assignments</span><span class="p">:</span> <span class="n">cluster</span> <span class="n">label</span> <span class="n">associated</span> <span class="k">with</span> <span class="nb">input</span> <span class="n">X</span> <span class="n">location</span> <span class="k">as</span>
<span class="gp">>>> </span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">cophenet</span><span class="p">:</span> <span class="nb">float</span> <span class="n">the</span> <span class="n">cophenet</span> <span class="n">statistic</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">cophenet_distances</span><span class="p">:</span> <span class="n">cophenet</span> <span class="n">distances</span> <span class="n">between</span> <span class="n">pairs</span> <span class="n">of</span> <span class="n">time</span>
<span class="gp">>>> </span> <span class="n">series</span> <span class="k">as</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span>
<span class="gp">>>> </span> <span class="n">class</span><span class="p">:</span> <span class="n">hclusters</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>dict</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-algorithms-stomp">
<h2>matrixprofile.algorithms.stomp<a class="headerlink" href="#matrixprofile-algorithms-stomp" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.algorithms.stomp">
<code class="sig-prename descclassname">matrixprofile.algorithms.</code><code class="sig-name descname">stomp</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">window_size</span></em>, <em class="sig-param"><span class="n">query</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/stomp.html#stomp"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.algorithms.stomp" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes matrix profiles for a single dimensional time series using the
parallelized STOMP algorithm (by default). Ray or Python’s multiprocessing
library may be used. When you have initialized Ray on your machine,
it takes priority over using Python’s multiprocessing.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to compute the matrix profile for.</p></li>
<li><p><strong>window_size</strong> (<em>int</em>) – The size of the window to compute the matrix profile over.</p></li>
<li><p><strong>query</strong> (<em>array_like</em>) – Optionally, a query can be provided to perform a similarity join.</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>Default = 1</em>) – Number of cpu cores to use.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>dict</strong> – A MatrixProfile data structure.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'mp'</span><span class="p">:</span> <span class="n">The</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'pi'</span><span class="p">:</span> <span class="n">The</span> <span class="n">matrix</span> <span class="n">profile</span> <span class="mi">1</span><span class="n">NN</span> <span class="n">indices</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'rmp'</span><span class="p">:</span> <span class="n">The</span> <span class="n">right</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'rpi'</span><span class="p">:</span> <span class="n">The</span> <span class="n">right</span> <span class="n">matrix</span> <span class="n">profile</span> <span class="mi">1</span><span class="n">NN</span> <span class="n">indices</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'lmp'</span><span class="p">:</span> <span class="n">The</span> <span class="n">left</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'lpi'</span><span class="p">:</span> <span class="n">The</span> <span class="n">left</span> <span class="n">matrix</span> <span class="n">profile</span> <span class="mi">1</span><span class="n">NN</span> <span class="n">indices</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'metric'</span><span class="p">:</span> <span class="n">The</span> <span class="n">distance</span> <span class="n">metric</span> <span class="n">computed</span> <span class="k">for</span> <span class="n">the</span> <span class="n">mp</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'w'</span><span class="p">:</span> <span class="n">The</span> <span class="n">window</span> <span class="n">size</span> <span class="n">used</span> <span class="n">to</span> <span class="n">compute</span> <span class="n">the</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'ez'</span><span class="p">:</span> <span class="n">The</span> <span class="n">exclusion</span> <span class="n">zone</span> <span class="n">used</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'join'</span><span class="p">:</span> <span class="n">Flag</span> <span class="n">indicating</span> <span class="k">if</span> <span class="n">a</span> <span class="n">similarity</span> <span class="n">join</span> <span class="n">was</span> <span class="n">computed</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'sample_pct'</span><span class="p">:</span> <span class="n">Percentage</span> <span class="n">of</span> <span class="n">samples</span> <span class="n">used</span> <span class="ow">in</span> <span class="n">computing</span> <span class="n">the</span> <span class="n">MP</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'data'</span><span class="p">:</span> <span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'ts'</span><span class="p">:</span> <span class="n">Time</span> <span class="n">series</span> <span class="n">data</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'query'</span><span class="p">:</span> <span class="n">Query</span> <span class="n">data</span> <span class="k">if</span> <span class="n">supplied</span>
<span class="gp">>>> </span> <span class="p">}</span>
<span class="gp">>>> </span> <span class="s1">'class'</span><span class="p">:</span> <span class="s2">"MatrixProfile"</span>
<span class="gp">>>> </span> <span class="s1">'algorithm'</span><span class="p">:</span> <span class="s2">"stomp_parallel"</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>profile</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><strong>ValueError</strong> – If window_size < 4.
If window_size > query length / 2.
If ts is not a list or np.array.
If query is not a list or np.array.
If ts or query is not one dimensional.</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-algorithms-mpx">
<h2>matrixprofile.algorithms.mpx<a class="headerlink" href="#matrixprofile-algorithms-mpx" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.algorithms.mpx">
<code class="sig-prename descclassname">matrixprofile.algorithms.</code><code class="sig-name descname">mpx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">query</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">cross_correlation</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/mpx.html#mpx"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.algorithms.mpx" title="Permalink to this definition">¶</a></dt>
<dd><p>The MPX algorithm computes the matrix profile without using the FFT.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to compute the matrix profile for.</p></li>
<li><p><strong>w</strong> (<em>int</em>) – The window size.</p></li>
<li><p><strong>query</strong> (<em>array_like</em>) – Optionally a query series.</p></li>
<li><p><strong>cross_correlation</strong> (<em>bool</em><em>, </em><em>Default=False</em>) – Determine if cross_correlation distance should be returned. It defaults
to Euclidean Distance.</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>Default = 1</em>) – Number of cpu cores to use.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>dict</strong> – A MatrixProfile data structure.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'mp'</span><span class="p">:</span> <span class="n">The</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'pi'</span><span class="p">:</span> <span class="n">The</span> <span class="n">matrix</span> <span class="n">profile</span> <span class="mi">1</span><span class="n">NN</span> <span class="n">indices</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'rmp'</span><span class="p">:</span> <span class="n">The</span> <span class="n">right</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'rpi'</span><span class="p">:</span> <span class="n">The</span> <span class="n">right</span> <span class="n">matrix</span> <span class="n">profile</span> <span class="mi">1</span><span class="n">NN</span> <span class="n">indices</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'lmp'</span><span class="p">:</span> <span class="n">The</span> <span class="n">left</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'lpi'</span><span class="p">:</span> <span class="n">The</span> <span class="n">left</span> <span class="n">matrix</span> <span class="n">profile</span> <span class="mi">1</span><span class="n">NN</span> <span class="n">indices</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'metric'</span><span class="p">:</span> <span class="n">The</span> <span class="n">distance</span> <span class="n">metric</span> <span class="n">computed</span> <span class="k">for</span> <span class="n">the</span> <span class="n">mp</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'w'</span><span class="p">:</span> <span class="n">The</span> <span class="n">window</span> <span class="n">size</span> <span class="n">used</span> <span class="n">to</span> <span class="n">compute</span> <span class="n">the</span> <span class="n">matrix</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'ez'</span><span class="p">:</span> <span class="n">The</span> <span class="n">exclusion</span> <span class="n">zone</span> <span class="n">used</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'join'</span><span class="p">:</span> <span class="n">Flag</span> <span class="n">indicating</span> <span class="k">if</span> <span class="n">a</span> <span class="n">similarity</span> <span class="n">join</span> <span class="n">was</span> <span class="n">computed</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'sample_pct'</span><span class="p">:</span> <span class="n">Percentage</span> <span class="n">of</span> <span class="n">samples</span> <span class="n">used</span> <span class="ow">in</span> <span class="n">computing</span> <span class="n">the</span> <span class="n">MP</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'data'</span><span class="p">:</span> <span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'ts'</span><span class="p">:</span> <span class="n">Time</span> <span class="n">series</span> <span class="n">data</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'query'</span><span class="p">:</span> <span class="n">Query</span> <span class="n">data</span> <span class="k">if</span> <span class="n">supplied</span>
<span class="gp">>>> </span> <span class="p">}</span>
<span class="gp">>>> </span> <span class="s1">'class'</span><span class="p">:</span> <span class="s2">"MatrixProfile"</span>
<span class="gp">>>> </span> <span class="s1">'algorithm'</span><span class="p">:</span> <span class="s2">"mpx"</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>profile</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-algorithms-skimp">
<h2>matrixprofile.algorithms.skimp<a class="headerlink" href="#matrixprofile-algorithms-skimp" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.algorithms.skimp">
<code class="sig-prename descclassname">matrixprofile.algorithms.</code><code class="sig-name descname">skimp</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">windows</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">show_progress</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">cross_correlation</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">pmp_obj</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_pct</span><span class="o">=</span><span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/skimp.html#skimp"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.algorithms.skimp" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the Pan Matrix Profile (PMP) for the given time series. When the
time series is only passed, windows start from 8 and increase by increments
of 2 up to length(ts) / 2. Also, the PMP is only computed using 10% of the
windows unless sample_pct is set to a different value.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When windows is explicitly provided, sample_pct no longer takes affect. The
MP for all windows provided will be computed.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series.</p></li>
<li><p><strong>show_progress</strong> (<em>bool</em><em>, </em><em>default = False</em>) – Show the progress in percent complete in increments of 5% by printing
it out to the console.</p></li>
<li><p><strong>cross_correlation</strong> (<em>bool</em><em>, </em><em>default = False</em>) – Return the MP values as Pearson Correlation instead of Euclidean
distance.</p></li>
<li><p><strong>pmp_obj</strong> (<em>dict</em><em>, </em><em>default = None</em>) – Repurpose already computed window sizes with this provided PMP. It
should be the output of a PMP algorithm such as skimp or maximum
subsequence.</p></li>
<li><p><strong>sample_pct</strong> (<em>float</em><em>, </em><em>default = 0.1</em><em> (</em><em>10%</em><em>)</em>) – Number of window sizes to compute MPs for. Decimal percent between
0 and 1.</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>Default = 1</em>) – Number of cpu cores to use.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>dict</strong> – A Pan-MatrixProfile data structure.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'pmp'</span><span class="p">:</span> <span class="n">the</span> <span class="n">pan</span> <span class="n">matrix</span> <span class="n">profile</span> <span class="k">as</span> <span class="n">a</span> <span class="mi">2</span><span class="n">D</span> <span class="n">array</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'pmpi'</span><span class="p">:</span> <span class="n">the</span> <span class="n">pmp</span> <span class="n">indices</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'data'</span><span class="p">:</span> <span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'ts'</span><span class="p">:</span> <span class="n">time</span> <span class="n">series</span> <span class="n">used</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="p">},</span>
<span class="gp">>>> </span> <span class="s1">'windows'</span><span class="p">:</span> <span class="n">the</span> <span class="n">windows</span> <span class="n">used</span> <span class="n">to</span> <span class="n">compute</span> <span class="n">the</span> <span class="n">pmp</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'sample_pct'</span><span class="p">:</span> <span class="n">the</span> <span class="n">sample</span> <span class="n">percent</span> <span class="n">used</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'metric'</span><span class="p">:</span><span class="n">The</span> <span class="n">distance</span> <span class="n">metric</span> <span class="n">computed</span> <span class="k">for</span> <span class="n">the</span> <span class="n">pmp</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'algorithm'</span><span class="p">:</span> <span class="n">the</span> <span class="n">algorithm</span> <span class="n">used</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'class'</span><span class="p">:</span> <span class="n">PMP</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>profile</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><strong>ValueError :</strong> – <ol class="arabic simple">
<li><p>ts is not array_like.
2. windows is not an iterable
3. show_progress is not a boolean.
4. cross_correlation is not a boolean.
5. sample_pct is not between 0 and 1.</p></li>
</ol>
</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-algorithms-mass2">
<h2>matrixprofile.algorithms.mass2<a class="headerlink" href="#matrixprofile-algorithms-mass2" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.algorithms.mass2">
<code class="sig-prename descclassname">matrixprofile.algorithms.</code><code class="sig-name descname">mass2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">query</span></em>, <em class="sig-param"><span class="n">extras</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">threshold</span><span class="o">=</span><span class="default_value">1e-10</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/mass2.html#mass2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.algorithms.mass2" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the distance profile for the given query over the given time
series.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to search.</p></li>
<li><p><strong>query</strong> (<em>array_like</em>) – The query.</p></li>
<li><p><strong>extras</strong> (<em>boolean</em><em>, </em><em>default False</em>) – Optionally return additional data used to compute the matrix profile.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>np.array, dict</strong> – An array of distances np.array() or dict with extras.</p>
<p>With extras:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s1">'distance_profile'</span><span class="p">:</span> <span class="n">The</span> <span class="n">distance</span> <span class="n">profile</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'product'</span><span class="p">:</span> <span class="n">The</span> <span class="n">FFT</span> <span class="n">product</span> <span class="n">between</span> <span class="n">ts</span> <span class="ow">and</span> <span class="n">query</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'data_mean'</span><span class="p">:</span> <span class="n">The</span> <span class="n">moving</span> <span class="n">average</span> <span class="n">of</span> <span class="n">the</span> <span class="n">ts</span> <span class="n">over</span> <span class="nb">len</span><span class="p">(</span><span class="n">query</span><span class="p">),</span>
<span class="gp">>>> </span> <span class="s1">'query_mean'</span><span class="p">:</span> <span class="n">The</span> <span class="n">mean</span> <span class="n">of</span> <span class="n">the</span> <span class="n">query</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="s1">'data_std'</span><span class="p">:</span> <span class="n">The</span> <span class="n">moving</span> <span class="n">std</span><span class="o">.</span> <span class="n">of</span> <span class="n">the</span> <span class="n">ts</span> <span class="n">over</span> <span class="nb">len</span><span class="p">(</span><span class="n">query</span><span class="p">),</span>
<span class="gp">>>> </span> <span class="s1">'query_std'</span><span class="p">:</span> <span class="n">The</span> <span class="n">std</span><span class="o">.</span> <span class="n">of</span> <span class="n">the</span> <span class="n">query</span>
<span class="gp">>>> </span><span class="p">}</span>
</pre></div>
</div>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>distance_profile</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><strong>ValueError</strong> – If ts is not a list or np.array.
If query is not a list or np.array.
If ts or query is not one dimensional.</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="matrixprofile-algorithms-mpdist">
<h2>matrixprofile.algorithms.mpdist<a class="headerlink" href="#matrixprofile-algorithms-mpdist" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="matrixprofile.algorithms.mpdist">
<code class="sig-prename descclassname">matrixprofile.algorithms.</code><code class="sig-name descname">mpdist</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">ts</span></em>, <em class="sig-param"><span class="n">ts_b</span></em>, <em class="sig-param"><span class="n">w</span></em>, <em class="sig-param"><span class="n">threshold</span><span class="o">=</span><span class="default_value">0.05</span></em>, <em class="sig-param"><span class="n">n_jobs</span><span class="o">=</span><span class="default_value">1</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/matrixprofile/algorithms/mpdist.html#mpdist"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#matrixprofile.algorithms.mpdist" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the MPDist between the two series ts and ts_b. For more details
refer to the paper:</p>
<p>Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow
Data Mining in More Challenging Scenarios. Shaghayegh Gharghabi,
Shima Imani, Anthony Bagnall, Amirali Darvishzadeh, Eamonn Keogh. ICDM 2018</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>ts</strong> (<em>array_like</em>) – The time series to compute the matrix profile for.</p></li>
<li><p><strong>ts_b</strong> (<em>array_like</em>) – The time series to compare against.</p></li>
<li><p><strong>w</strong> (<em>int</em>) – The window size.</p></li>
<li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>Default 0.05</em>) – The percentile in which the distance is taken from. By default it is
set to 0.05 based on empircal research results from the paper.
Generally, you should not change this unless you know what you are
doing! This value must be a float greater than 0 and less than 1.</p></li>