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<h1>Source code for pyFTS.data.lorentz</h1><div class="highlight"><pre>
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<span></span><span class="sd">"""</span>
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<span class="sd">Lorenz, Edward Norton (1963). "Deterministic nonperiodic flow". Journal of the Atmospheric Sciences. 20 (2): 130–141.</span>
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<span class="sd">https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2</span>
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<span class="sd">dx/dt = a(y -x)</span>
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<span class="sd">dy/dt = x(b - z) - y</span>
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<span class="sd">dz/dt = xy - cz</span>
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<span class="sd">"""</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
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<span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</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">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Get a simple univariate time series data.</span>
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<span class="sd"> :param var: the dataset field name to extract</span>
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<span class="sd"> :return: numpy array</span>
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<span class="sd"> """</span>
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<span class="k">return</span> <span class="n">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">,</span> <span class="n">iterations</span><span class="p">)[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">values</span></div>
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<div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
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<span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</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">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
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<span class="sd">'''</span>
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<span class="sd"> Return a dataframe with the multivariate Lorenz Map time series (x, y, z).</span>
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<span class="sd"> :param a: Equation coefficient. Default value: 10</span>
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<span class="sd"> :param b: Equation coefficient. Default value: 28</span>
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<span class="sd"> :param c: Equation coefficient. Default value: 8.0/3.0</span>
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<span class="sd"> :param dt: Time differential for continuous time integration. Default value: 0.01</span>
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<span class="sd"> :param initial_values: numpy array with the initial values of x,y and z. Default: [0.1, 0, 0]</span>
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<span class="sd"> :param iterations: number of iterations. Default: 1000</span>
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<span class="sd"> :return: Panda dataframe with the x, y and z values</span>
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<span class="sd"> '''</span>
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<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">initial_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
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<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">initial_values</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
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<span class="n">z</span> <span class="o">=</span> <span class="p">[</span><span class="n">initial_values</span><span class="p">[</span><span class="mi">2</span><span class="p">]]</span>
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<span class="k">for</span> <span class="n">t</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">iterations</span><span class="p">):</span>
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<span class="n">dxdt</span> <span class="o">=</span> <span class="n">a</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
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<span class="n">dydt</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="o">-</span> <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
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<span class="n">dzdt</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">*</span> <span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">c</span> <span class="o">*</span> <span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
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<span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">dxdt</span><span class="p">)</span>
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<span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">dydt</span><span class="p">)</span>
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<span class="n">z</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">dzdt</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'x'</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">:</span><span class="n">y</span><span class="p">,</span> <span class="s1">'z'</span><span class="p">:</span> <span class="n">z</span><span class="p">})</span></div>
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</pre></div>
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