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- <h1>Source code for ipfml.utils</h1><div class="highlight"><pre>
- <span></span><span class="sd">"""</span>
- <span class="sd">Utils functions of ipfml package (array normalization)</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>
- <span class="kn">import</span> <span class="nn">math</span>
- <span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="k">import</span> <span class="n">simps</span>
- <div class="viewcode-block" id="normalize_arr"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.normalize_arr">[docs]</a><span class="k">def</span> <span class="nf">normalize_arr</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
- <span class="sd">"""Normalize data of 1D array shape</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: array data of 1D shape</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Normalized 1D array</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> arr = np.arange(11)</span>
- <span class="sd"> >>> arr_normalized = utils.normalize_arr(arr)</span>
- <span class="sd"> >>> arr_normalized[1]</span>
- <span class="sd"> 0.1</span>
- <span class="sd"> """</span>
- <span class="n">output_arr</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">max_value</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
- <span class="n">min_value</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">arr</span><span class="p">:</span>
- <span class="n">output_arr</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">v</span> <span class="o">-</span> <span class="n">min_value</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">max_value</span> <span class="o">-</span> <span class="n">min_value</span><span class="p">))</span>
- <span class="k">return</span> <span class="n">output_arr</span></div>
- <div class="viewcode-block" id="normalize_arr_with_range"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.normalize_arr_with_range">[docs]</a><span class="k">def</span> <span class="nf">normalize_arr_with_range</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="nb">min</span><span class="p">,</span> <span class="nb">max</span><span class="p">):</span>
- <span class="sd">'''Normalize data of 1D array shape</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: array data of 1D shape</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Normalized 1D Numpy array</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> arr = np.arange(11)</span>
- <span class="sd"> >>> arr_normalized = utils.normalize_arr_with_range(arr, 0, 20)</span>
- <span class="sd"> >>> arr_normalized[1]</span>
- <span class="sd"> 0.05</span>
- <span class="sd"> '''</span>
- <span class="n">output_arr</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">arr</span><span class="p">:</span>
- <span class="n">output_arr</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">v</span> <span class="o">-</span> <span class="nb">min</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="nb">max</span> <span class="o">-</span> <span class="nb">min</span><span class="p">))</span>
- <span class="k">return</span> <span class="n">output_arr</span></div>
- <div class="viewcode-block" id="normalize_2D_arr"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.normalize_2D_arr">[docs]</a><span class="k">def</span> <span class="nf">normalize_2D_arr</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
- <span class="sd">"""Return array normalize from its min and max values</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: 2D Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Normalized 2D Numpy array</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import utils, processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> img_mscn = processing.rgb_to_mscn(img)</span>
- <span class="sd"> >>> img_normalized = utils.normalize_2D_arr(img_mscn)</span>
- <span class="sd"> >>> img_normalized.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="c1"># getting min and max value from 2D array</span>
- <span class="n">max_value</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
- <span class="n">min_value</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
- <span class="c1"># normalize each row</span>
- <span class="n">output_array</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span>
- <span class="k">for</span> <span class="n">row_index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="p">):</span>
- <span class="n">values</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[</span><span class="n">row_index</span><span class="p">,</span> <span class="p">:]</span>
- <span class="n">output_array</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <span class="n">normalize_arr_with_range</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">min_value</span><span class="p">,</span> <span class="n">max_value</span><span class="p">))</span>
- <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">output_array</span><span class="p">)</span></div>
- <div class="viewcode-block" id="integral_area_trapz"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.integral_area_trapz">[docs]</a><span class="k">def</span> <span class="nf">integral_area_trapz</span><span class="p">(</span><span class="n">y_values</span><span class="p">,</span> <span class="n">dx</span><span class="p">):</span>
- <span class="sd">"""Returns area under curves from provided data points using Trapezium rule</span>
- <span class="sd"> Args:</span>
- <span class="sd"> y_values: y values of curve</span>
- <span class="sd"> dx: number of unit for x axis</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Area under curves obtained from these points</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])</span>
- <span class="sd"> >>> area = utils.integral_area_trapz(y_values, dx=5)</span>
- <span class="sd"> >>> area</span>
- <span class="sd"> 452.5</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">trapz</span><span class="p">(</span><span class="n">y_values</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">)</span></div>
- <div class="viewcode-block" id="integral_area_simps"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.integral_area_simps">[docs]</a><span class="k">def</span> <span class="nf">integral_area_simps</span><span class="p">(</span><span class="n">y_values</span><span class="p">,</span> <span class="n">dx</span><span class="p">):</span>
- <span class="sd">"""Returns area under curves from provided data points using Simpsons rule</span>
- <span class="sd"> Args:</span>
- <span class="sd"> y_values: y values of curve</span>
- <span class="sd"> dx: number of unit for x axis</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Area under curves obtained from these points</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])</span>
- <span class="sd"> >>> area = utils.integral_area_simps(y_values, dx=5)</span>
- <span class="sd"> >>> area</span>
- <span class="sd"> 460.0</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">simps</span><span class="p">(</span><span class="n">y_values</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_indices_of_highest_values"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.get_indices_of_highest_values">[docs]</a><span class="k">def</span> <span class="nf">get_indices_of_highest_values</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
- <span class="sd">"""Returns indices of n highest values from list or 1D numpy array</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: List of numpy array</span>
- <span class="sd"> n: number of highest elements wanted</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> `n` indices of highest values</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> arr = np.arange(10)</span>
- <span class="sd"> >>> indices = utils.get_indices_of_highest_values(arr, 2)</span>
- <span class="sd"> >>> indices</span>
- <span class="sd"> array([9, 8])</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[</span><span class="o">-</span><span class="n">n</span><span class="p">:][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_indices_of_lowest_values"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.get_indices_of_lowest_values">[docs]</a><span class="k">def</span> <span class="nf">get_indices_of_lowest_values</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
- <span class="sd">"""Returns indices of n highest values from list or 1D numpy array</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: List of numpy array</span>
- <span class="sd"> n: number of highest elements wanted</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> `n` indices of highest values</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> arr = np.arange(10)</span>
- <span class="sd"> >>> indices = utils.get_indices_of_lowest_values(arr, 2)</span>
- <span class="sd"> >>> indices</span>
- <span class="sd"> array([0, 1])</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[::</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="n">n</span><span class="p">:][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_entropy"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.get_entropy">[docs]</a><span class="k">def</span> <span class="nf">get_entropy</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
- <span class="sd">"""Returns the computed entropy from arr</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> entropy score computed</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> arr = np.arange(10)</span>
- <span class="sd"> >>> entropy = utils.get_entropy(arr)</span>
- <span class="sd"> >>> int(entropy)</span>
- <span class="sd"> 0</span>
- <span class="sd"> """</span>
- <span class="n">arr</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">arr</span><span class="p">)</span>
- <span class="n">eigen_values</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">sum_eigen_values</span> <span class="o">=</span> <span class="p">(</span><span class="n">arr</span> <span class="o">*</span> <span class="n">arr</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">arr</span><span class="p">:</span>
- <span class="n">eigen_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span> <span class="o">*</span> <span class="n">val</span><span class="p">)</span>
- <span class="n">v</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">eigen_values</span><span class="p">:</span>
- <span class="n">v</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span> <span class="o">/</span> <span class="n">sum_eigen_values</span><span class="p">)</span>
- <span class="n">entropy</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">v</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">val</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
- <span class="n">entropy</span> <span class="o">+=</span> <span class="n">val</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
- <span class="n">entropy</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
- <span class="n">entropy</span> <span class="o">/=</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
- <span class="k">return</span> <span class="n">entropy</span></div>
- <div class="viewcode-block" id="get_entropy_without_i"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.get_entropy_without_i">[docs]</a><span class="k">def</span> <span class="nf">get_entropy_without_i</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
- <span class="sd">"""Returns the computed entropy from arr without contribution of i</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: numpy array</span>
- <span class="sd"> i: column index</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> entropy score computed</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> arr = np.arange(10)</span>
- <span class="sd"> >>> entropy = utils.get_entropy_without_i(arr, 3)</span>
- <span class="sd"> >>> int(entropy)</span>
- <span class="sd"> 0</span>
- <span class="sd"> """</span>
- <span class="n">arr</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">v</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> <span class="k">if</span> <span class="n">index</span> <span class="o">!=</span> <span class="n">i</span><span class="p">])</span>
- <span class="k">return</span> <span class="n">get_entropy</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_entropy_contribution_of_i"><a class="viewcode-back" href="../../ipfml/ipfml.utils.html#ipfml.utils.get_entropy_contribution_of_i">[docs]</a><span class="k">def</span> <span class="nf">get_entropy_contribution_of_i</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
- <span class="sd">"""Returns the entropy contribution i column</span>
- <span class="sd"> Args:</span>
- <span class="sd"> arr: numpy array</span>
- <span class="sd"> i: column index</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> entropy contribution score computed</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml import utils</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> arr = np.arange(10)</span>
- <span class="sd"> >>> entropy = utils.get_entropy_contribution_of_i(arr, 3)</span>
- <span class="sd"> >>> int(entropy)</span>
- <span class="sd"> 0</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">get_entropy</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> <span class="o">-</span> <span class="n">get_entropy_without_i</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span></div>
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