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<h1>Source code for pyFTS.data.artificial</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">Facilities to generate synthetic stochastic processes</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<div class="viewcode-block" id="SignalEmulator"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator">[docs]</a><span class="k">class</span> <span class="nc">SignalEmulator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Emulate a complex signal built from several additive and non-additive components</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">SignalEmulator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">components</span> <span class="o">=</span> <span class="p">[]</span>
<span class="w"> </span><span class="sd">"""Components of the signal"""</span>
<div class="viewcode-block" id="SignalEmulator.stationary_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.stationary_gaussian">[docs]</a> <span class="k">def</span> <span class="nf">stationary_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mu</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Creates a continuous Gaussian signal with mean mu and variance sigma.</span>
<span class="sd"> :param mu: mean</span>
<span class="sd"> :param sigma: variance</span>
<span class="sd"> :keyword additive: If False it cancels the previous signal and start this one, if True</span>
<span class="sd"> this signal is added to the previous one</span>
<span class="sd"> :keyword start: lag index to start this signal, the default value is 0</span>
<span class="sd"> :keyword it: Number of iterations, the default value is 1</span>
<span class="sd"> :keyword length: Number of samples generated on each iteration, the default value is 100</span>
<span class="sd"> :keyword vmin: Lower bound value of generated data, the default value is None</span>
<span class="sd"> :keyword vmax: Upper bound value of generated data, the default value is None</span>
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
<span class="sd"> """</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'mu'</span><span class="p">:</span> <span class="n">mu</span><span class="p">,</span> <span class="s1">'sigma'</span><span class="p">:</span> <span class="n">sigma</span><span class="p">}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'constant'</span><span class="p">,</span>
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span> <span class="n">kwargs</span><span class="p">})</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="SignalEmulator.incremental_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.incremental_gaussian">[docs]</a> <span class="k">def</span> <span class="nf">incremental_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mu</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Creates an additive gaussian interference on a previous signal</span>
<span class="sd"> :param mu: increment on mean</span>
<span class="sd"> :param sigma: increment on variance</span>
<span class="sd"> :keyword start: lag index to start this signal, the default value is 0</span>
<span class="sd"> :keyword it: Number of iterations, the default value is 1</span>
<span class="sd"> :keyword length: Number of samples generated on each iteration, the default value is 100</span>
<span class="sd"> :keyword vmin: Lower bound value of generated data, the default value is None</span>
<span class="sd"> :keyword vmax: Upper bound value of generated data, the default value is None</span>
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
<span class="sd"> """</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'mu'</span><span class="p">:</span> <span class="n">mu</span><span class="p">,</span> <span class="s1">'sigma'</span><span class="p">:</span> <span class="n">sigma</span><span class="p">}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'incremental'</span><span class="p">,</span>
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span> <span class="n">kwargs</span><span class="p">})</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="SignalEmulator.periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.periodic_gaussian">[docs]</a> <span class="k">def</span> <span class="nf">periodic_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">period</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Creates an additive periodic gaussian interference on a previous signal</span>
<span class="sd"> :param type: 'linear' or 'sinoidal'</span>
<span class="sd"> :param period: the period of recurrence</span>
<span class="sd"> :param mu: increment on mean</span>
<span class="sd"> :param sigma: increment on variance</span>
<span class="sd"> :keyword start: lag index to start this signal, the default value is 0</span>
<span class="sd"> :keyword it: Number of iterations, the default value is 1</span>
<span class="sd"> :keyword length: Number of samples generated on each iteration, the default value is 100</span>
<span class="sd"> :keyword vmin: Lower bound value of generated data, the default value is None</span>
<span class="sd"> :keyword vmax: Upper bound value of generated data, the default value is None</span>
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
<span class="sd"> """</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span><span class="nb">type</span><span class="p">,</span> <span class="s1">'period'</span><span class="p">:</span><span class="n">period</span><span class="p">,</span>
<span class="s1">'mu_min'</span><span class="p">:</span> <span class="n">mu_min</span><span class="p">,</span> <span class="s1">'sigma_min'</span><span class="p">:</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="s1">'mu_max'</span><span class="p">:</span> <span class="n">mu_max</span><span class="p">,</span> <span class="s1">'sigma_max'</span><span class="p">:</span> <span class="n">sigma_max</span><span class="p">}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'periodic'</span><span class="p">,</span>
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span> <span class="n">kwargs</span><span class="p">})</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="SignalEmulator.blip"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.blip">[docs]</a> <span class="k">def</span> <span class="nf">blip</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,</span>
<span class="sd"> and insert it on a random location of the signal.</span>
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
<span class="sd"> """</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'blip'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'blip'</span><span class="p">,</span>
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span><span class="n">kwargs</span><span class="p">})</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="SignalEmulator.run"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.run">[docs]</a> <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Render the signal</span>
<span class="sd"> :return: a list of float values</span>
<span class="sd"> """</span>
<span class="n">signal</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">last_it</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">last_num</span> <span class="o">=</span> <span class="mi">10</span>
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">component</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="p">):</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="n">component</span><span class="p">[</span><span class="s1">'parameters'</span><span class="p">]</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="n">component</span><span class="p">[</span><span class="s1">'args'</span><span class="p">]</span>
<span class="n">additive</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'additive'</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'start'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">it</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'it'</span><span class="p">,</span> <span class="n">last_it</span><span class="p">)</span>
<span class="n">num</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'length'</span><span class="p">,</span> <span class="n">last_num</span><span class="p">)</span>
<span class="n">vmin</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'vmin'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
<span class="n">vmax</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'vmax'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">if</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'constant'</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_gaussian_linear</span><span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">],</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma'</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">it</span><span class="o">=</span><span class="n">it</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'incremental'</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_gaussian_linear</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">],</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma'</span><span class="p">],</span>
<span class="n">it</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'periodic'</span><span class="p">:</span>
<span class="n">period</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'period'</span><span class="p">]</span>
<span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'mu_min'</span><span class="p">],</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma_min'</span><span class="p">]</span>
<span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'mu_max'</span><span class="p">],</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma_max'</span><span class="p">]</span>
<span class="k">if</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'sinoidal'</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_sinoidal_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span>
<span class="n">it</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_linear_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span>
<span class="n">it</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'blip'</span><span class="p">:</span>
<span class="n">_mx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span>
<span class="n">_mn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span>
<span class="n">_mx</span> <span class="o">+=</span> <span class="mi">2</span><span class="o">*</span><span class="n">_mx</span> <span class="k">if</span> <span class="n">_mx</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">_mx</span>
<span class="n">_mn</span> <span class="o">+=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">_mn</span> <span class="k">if</span> <span class="n">_mn</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">2</span><span class="o">*</span><span class="n">_mn</span>
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">_mx</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">_mx</span><span class="p">,</span> <span class="n">vmax</span><span class="p">)</span> <span class="k">if</span> <span class="n">vmax</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">max</span><span class="p">(</span><span class="n">_mx</span><span class="p">,</span> <span class="n">vmax</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">_mn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">_mn</span><span class="p">,</span> <span class="n">vmin</span><span class="p">)</span> <span class="k">if</span> <span class="n">vmin</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">min</span><span class="p">(</span><span class="n">_mn</span><span class="p">,</span> <span class="n">vmin</span><span class="p">)</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">))</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">_mx</span><span class="p">]</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span> <span class="o">>=</span> <span class="mf">.5</span> <span class="k">else</span> <span class="p">[</span><span class="o">-</span><span class="n">_mn</span><span class="p">]</span>
<span class="n">last_num</span> <span class="o">=</span> <span class="n">num</span>
<span class="n">last_it</span> <span class="o">=</span> <span class="n">it</span>
<span class="n">signal</span> <span class="o">=</span> <span class="n">_append</span><span class="p">(</span><span class="n">additive</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">signal</span><span class="p">,</span> <span class="n">tmp</span><span class="p">)</span>
<span class="k">return</span> <span class="n">signal</span></div></div>
<div class="viewcode-block" id="generate_gaussian_linear"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_gaussian_linear">[docs]</a><span class="k">def</span> <span class="nf">generate_gaussian_linear</span><span class="p">(</span><span class="n">mu_ini</span><span class="p">,</span> <span class="n">sigma_ini</span><span class="p">,</span> <span class="n">mu_inc</span><span class="p">,</span> <span class="n">sigma_inc</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generate data sampled from Gaussian distribution, with constant or linear changing parameters</span>
<span class="sd"> :param mu_ini: Initial mean</span>
<span class="sd"> :param sigma_ini: Initial variance</span>
<span class="sd"> :param mu_inc: Mean increment after 'num' samples</span>
<span class="sd"> :param sigma_inc: Variance increment after 'num' samples</span>
<span class="sd"> :param it: Number of iterations</span>
<span class="sd"> :param num: Number of samples generated on each iteration</span>
<span class="sd"> :param vmin: Lower bound value of generated data</span>
<span class="sd"> :param vmax: Upper bound value of generated data</span>
<span class="sd"> :return: A list of it*num float values</span>
<span class="sd"> """</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">mu_ini</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma_ini</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">it</span><span class="p">):</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmax</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
<span class="n">mu</span> <span class="o">+=</span> <span class="n">mu_inc</span>
<span class="n">sigma</span> <span class="o">+=</span> <span class="n">sigma_inc</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="generate_linear_periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_linear_periodic_gaussian">[docs]</a><span class="k">def</span> <span class="nf">generate_linear_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generates a periodic linear variation on mean and variance</span>
<span class="sd"> :param period: the period of recurrence</span>
<span class="sd"> :param mu_min: initial (and minimum) mean of each period</span>
<span class="sd"> :param sigma_min: initial (and minimum) variance of each period</span>
<span class="sd"> :param mu_max: final (and maximum) mean of each period</span>
<span class="sd"> :param sigma_max: final (and maximum) variance of each period</span>
<span class="sd"> :param it: Number of iterations</span>
<span class="sd"> :param num: Number of samples generated on each iteration</span>
<span class="sd"> :param vmin: Lower bound value of generated data</span>
<span class="sd"> :param vmax: Upper bound value of generated data</span>
<span class="sd"> :return: A list of it*num float values</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">period</span> <span class="o">></span> <span class="n">it</span><span class="p">:</span>
<span class="k">raise</span><span class="p">(</span><span class="s2">"The 'period' parameter must be lesser than 'it' parameter"</span><span class="p">)</span>
<span class="n">mu_inc</span> <span class="o">=</span> <span class="p">(</span><span class="n">mu_max</span> <span class="o">-</span> <span class="n">mu_min</span><span class="p">)</span><span class="o">/</span><span class="n">period</span>
<span class="n">sigma_inc</span> <span class="o">=</span> <span class="p">(</span><span class="n">sigma_max</span> <span class="o">-</span> <span class="n">sigma_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">period</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">mu_min</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma_min</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">signal</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">it</span><span class="p">):</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmax</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
<span class="k">if</span> <span class="n">k</span> <span class="o">%</span> <span class="n">period</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">signal</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">signal</span>
<span class="n">mu</span> <span class="o">+=</span> <span class="p">(</span><span class="n">mu_inc</span> <span class="k">if</span> <span class="n">signal</span> <span class="k">else</span> <span class="o">-</span><span class="n">mu_inc</span><span class="p">)</span>
<span class="n">sigma</span> <span class="o">+=</span> <span class="p">(</span><span class="n">sigma_inc</span> <span class="k">if</span> <span class="n">signal</span> <span class="k">else</span> <span class="o">-</span><span class="n">sigma_inc</span><span class="p">)</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">sigma</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="generate_sinoidal_periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_sinoidal_periodic_gaussian">[docs]</a><span class="k">def</span> <span class="nf">generate_sinoidal_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generates a periodic sinoidal variation on mean and variance</span>
<span class="sd"> :param period: the period of recurrence</span>
<span class="sd"> :param mu_min: initial (and minimum) mean of each period</span>
<span class="sd"> :param sigma_min: initial (and minimum) variance of each period</span>
<span class="sd"> :param mu_max: final (and maximum) mean of each period</span>
<span class="sd"> :param sigma_max: final (and maximum) variance of each period</span>
<span class="sd"> :param it: Number of iterations</span>
<span class="sd"> :param num: Number of samples generated on each iteration</span>
<span class="sd"> :param vmin: Lower bound value of generated data</span>
<span class="sd"> :param vmax: Upper bound value of generated data</span>
<span class="sd"> :return: A list of it*num float values</span>
<span class="sd"> """</span>
<span class="n">mu_range</span> <span class="o">=</span> <span class="n">mu_max</span> <span class="o">-</span> <span class="n">mu_min</span>
<span class="n">sigma_range</span> <span class="o">=</span> <span class="n">sigma_max</span> <span class="o">-</span> <span class="n">sigma_min</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">mu_min</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma_min</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">it</span><span class="p">):</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmax</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
<span class="n">mu</span> <span class="o">+=</span> <span class="n">mu_range</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">period</span> <span class="o">*</span> <span class="n">k</span><span class="p">)</span>
<span class="n">sigma</span> <span class="o">+=</span> <span class="n">sigma_range</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">period</span> <span class="o">*</span> <span class="n">k</span><span class="p">)</span>
<span class="n">sigma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">sigma</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="generate_uniform_linear"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_uniform_linear">[docs]</a><span class="k">def</span> <span class="nf">generate_uniform_linear</span><span class="p">(</span><span class="n">min_ini</span><span class="p">,</span> <span class="n">max_ini</span><span class="p">,</span> <span class="n">min_inc</span><span class="p">,</span> <span class="n">max_inc</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Generate data sampled from Uniform distribution, with constant or linear changing bounds</span>
<span class="sd"> :param mu_ini: Initial mean</span>
<span class="sd"> :param sigma_ini: Initial variance</span>
<span class="sd"> :param mu_inc: Mean increment after 'num' samples</span>
<span class="sd"> :param sigma_inc: Variance increment after 'num' samples</span>
<span class="sd"> :param it: Number of iterations</span>
<span class="sd"> :param num: Number of samples generated on each iteration</span>
<span class="sd"> :param vmin: Lower bound value of generated data</span>
<span class="sd"> :param vmax: Upper bound value of generated data</span>
<span class="sd"> :return: A list of it*num float values</span>
<span class="sd"> """</span>
<span class="n">_min</span> <span class="o">=</span> <span class="n">min_ini</span>
<span class="n">_max</span> <span class="o">=</span> <span class="n">max_ini</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">it</span><span class="p">):</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmax</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
<span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
<span class="n">_min</span> <span class="o">+=</span> <span class="n">min_inc</span>
<span class="n">_max</span> <span class="o">+=</span> <span class="n">max_inc</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="white_noise"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.white_noise">[docs]</a><span class="k">def</span> <span class="nf">white_noise</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">500</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Simple Gaussian noise signal</span>
<span class="sd"> :param n: number of samples</span>
<span class="sd"> :return:</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span></div>
<div class="viewcode-block" id="random_walk"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.random_walk">[docs]</a><span class="k">def</span> <span class="nf">random_walk</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">'gaussian'</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Simple random walk</span>
<span class="sd"> :param n: number of samples</span>
<span class="sd"> :param type: 'gaussian' or 'uniform'</span>
<span class="sd"> :return:</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'gaussian'</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_gaussian_linear</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_uniform_linear</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">ret</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">return</span> <span class="n">ret</span></div>
<span class="k">def</span> <span class="nf">_append</span><span class="p">(</span><span class="n">additive</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">before</span><span class="p">,</span> <span class="n">new</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">additive</span><span class="p">:</span>
<span class="n">before</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
<span class="k">return</span> <span class="n">before</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">):</span>
<span class="n">new</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
<span class="n">l1</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">before</span><span class="p">)</span>
<span class="n">l2</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
<span class="k">if</span> <span class="n">l2</span> <span class="o"><</span> <span class="n">l1</span><span class="p">:</span>
<span class="n">new</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">l1</span> <span class="o">-</span> <span class="n">l2</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
<span class="k">elif</span> <span class="mi">0</span> <span class="o"><</span> <span class="n">l1</span> <span class="o"><</span> <span class="n">l2</span><span class="p">:</span>
<span class="n">new</span> <span class="o">=</span> <span class="n">new</span><span class="p">[:</span><span class="n">l1</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">before</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">before</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
<span class="k">return</span> <span class="n">tmp</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
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