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<li><a class="reference internal" href="#">Computation times</a></li>
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<section id="computation-times">
<span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">¶</a></h1>
<p><strong>00:05.791</strong> total execution time for 37 files <strong>from all galleries</strong>:</p>
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<tr class="row-odd"><th class="head"><p>Example</p></th>
<th class="head"><p>Time</p></th>
<th class="head"><p>Mem (MB)</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_ATAR_MNE_RAW.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-atar-mne-raw-py"><span class="std std-ref">ATAR Algorithm with MNE RAW Object</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_ATAR_MNE_RAW.py</span></code>)</p></td>
<td><p>00:05.141</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_EDF_FILE.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-edf-file-py"><span class="std std-ref">EEG Data from EDF File</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_EDF_FILE.py</span></code>)</p></td>
<td><p>00:00.650</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_ATAR_algorithm_tuning.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-atar-algorithm-tuning-py"><span class="std std-ref">ATAR: Automatic and Tunable Artifact Removal Algorithm</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_ATAR_algorithm_tuning.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_ICA_eeg_example.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-ica-eeg-example-py"><span class="std std-ref">Independed Principle Component analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_ICA_eeg_example.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_eeg_artifact_removal_ATAR_algorithm.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-eeg-artifact-removal-atar-algorithm-py"><span class="std std-ref">EEG Artifact removal using ATAR</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_eeg_artifact_removal_ATAR_algorithm.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_eeg_artifact_removal_ICA.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-eeg-artifact-removal-ica-py"><span class="std std-ref">EEG Artifact removal using ICA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_eeg_artifact_removal_ICA.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_eeg_artifact_removal_ICA_ATAR.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-eeg-artifact-removal-ica-atar-py"><span class="std std-ref">EEG Artifact: ATAR and ICA</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_eeg_artifact_removal_ICA_ATAR.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_eeg_rhythmic_features.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-eeg-rhythmic-features-py"><span class="std std-ref">EEG Computing Rhythmic Features - PhyAAt - Semanticity</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_eeg_rhythmic_features.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/electroencephalogram/plot_sp_eeg_topomaps.html#sphx-glr-auto-examples-electroencephalogram-plot-sp-eeg-topomaps-py"><span class="std std-ref">EEG Topographic Map</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/electroencephalogram/plot_sp_eeg_topomaps.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/information_theory/plot_it_dispersion_entropy_eeg_emb2.html#sphx-glr-auto-examples-information-theory-plot-it-dispersion-entropy-eeg-emb2-py"><span class="std std-ref">Dispersion Entropy with Embedding dim</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/information_theory/plot_it_dispersion_entropy_eeg_emb2.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/information_theory/plot_it_dispersion_entropy_top_patterns.html#sphx-glr-auto-examples-information-theory-plot-it-dispersion-entropy-top-patterns-py"><span class="std std-ref">Dispersion Entropy with top patterns</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/information_theory/plot_it_dispersion_entropy_top_patterns.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/information_theory/plot_it_entropy_discreet_example.html#sphx-glr-auto-examples-information-theory-plot-it-entropy-discreet-example-py"><span class="std std-ref">Entropy - Discreet Source</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/information_theory/plot_it_entropy_discreet_example.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/information_theory/plot_it_entropy_eeg_multi_channel.html#sphx-glr-auto-examples-information-theory-plot-it-entropy-eeg-multi-channel-py"><span class="std std-ref">Entropy - EEG Signal - Multi-Channel</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/information_theory/plot_it_entropy_eeg_multi_channel.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/information_theory/plot_it_entropy_real_valued_example.html#sphx-glr-auto-examples-information-theory-plot-it-entropy-real-valued-example-py"><span class="std std-ref">Entropy - Real-Valued Source</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/information_theory/plot_it_entropy_real_valued_example.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/information_theory/plot_it_sample_approx_entropy_comp.html#sphx-glr-auto-examples-information-theory-plot-it-sample-approx-entropy-comp-py"><span class="std std-ref">Sample and Approximate Entropy: Comparison</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/information_theory/plot_it_sample_approx_entropy_comp.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/machine_learning/plot_ml_decision_tree_catogorical_features.html#sphx-glr-auto-examples-machine-learning-plot-ml-decision-tree-catogorical-features-py"><span class="std std-ref">Decision Trees without converting Catogorical Features</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/machine_learning/plot_ml_decision_tree_catogorical_features.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/machine_learning/plot_ml_decision_tree_classification.html#sphx-glr-auto-examples-machine-learning-plot-ml-decision-tree-classification-py"><span class="std std-ref">Classification Trees: Depth & Decision boundaries</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/machine_learning/plot_ml_decision_tree_classification.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/machine_learning/plot_ml_decision_tree_shrinking_capability_cls.html#sphx-glr-auto-examples-machine-learning-plot-ml-decision-tree-shrinking-capability-cls-py"><span class="std std-ref">Decision Trees with shrinking capability - Classification example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/machine_learning/plot_ml_decision_tree_shrinking_capability_cls.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/machine_learning/plot_ml_decision_tree_shrinking_capability_reg.html#sphx-glr-auto-examples-machine-learning-plot-ml-decision-tree-shrinking-capability-reg-py"><span class="std std-ref">Decision Trees with shrinking capability - Regression example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/machine_learning/plot_ml_decision_tree_shrinking_capability_reg.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/machine_learning/plot_ml_decision_tree_visualisation.html#sphx-glr-auto-examples-machine-learning-plot-ml-decision-tree-visualisation-py"><span class="std std-ref">Decision Trees with visualisations while buiding tree</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/machine_learning/plot_ml_decision_tree_visualisation.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/machine_learning/plot_ml_logistic_regression_examples.html#sphx-glr-auto-examples-machine-learning-plot-ml-logistic-regression-examples-py"><span class="std std-ref">Logistic Regression - Visualisation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/machine_learning/plot_ml_logistic_regression_examples.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/machine_learning/plot_ml_naive_bayes_examples.html#sphx-glr-auto-examples-machine-learning-plot-ml-naive-bayes-examples-py"><span class="std std-ref">Naive Bayes classifier - Visualisation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/machine_learning/plot_ml_naive_bayes_examples.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/multielectrode-array/plot_mea_minimal_setting_example.html#sphx-glr-auto-examples-multielectrode-array-plot-mea-minimal-setting-example-py"><span class="std std-ref">MEA: Minimal Setting: Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/multielectrode-array/plot_mea_minimal_setting_example.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/multielectrode-array/plot_mea_stepwise_example.html#sphx-glr-auto-examples-multielectrode-array-plot-mea-stepwise-example-py"><span class="std std-ref">MEA: Step-wise Analysis: Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/multielectrode-array/plot_mea_stepwise_example.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_0_0_9_6.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-0-9-6-py"><span class="std std-ref">Release Highlights for spkit 0.0.9.6</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_0_0_9_6.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/release_highlights/plot_release_highlights_0_0_9_7.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-0-9-7-py"><span class="std std-ref">Release Highlights for spkit 0.0.9.7</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/release_highlights/plot_release_highlights_0_0_9_7.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/signal_processing/plot_sp_RFB_for_period_estimation_demo_examples.html#sphx-glr-auto-examples-signal-processing-plot-sp-rfb-for-period-estimation-demo-examples-py"><span class="std std-ref">Ramanujan Filter Banks - Demos</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/signal_processing/plot_sp_RFB_for_period_estimation_demo_examples.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/signal_processing/plot_sp_RFB_for_period_estimation_from_signal.html#sphx-glr-auto-examples-signal-processing-plot-sp-rfb-for-period-estimation-from-signal-py"><span class="std std-ref">Ramanujan Filter Banks Example</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/signal_processing/plot_sp_RFB_for_period_estimation_from_signal.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/signal_processing/plot_sp_RFB_for_period_estimation_with_sparse_penalty.html#sphx-glr-auto-examples-signal-processing-plot-sp-rfb-for-period-estimation-with-sparse-penalty-py"><span class="std std-ref">Ramanujan Dictionary - with sparse penalty</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/signal_processing/plot_sp_RFB_for_period_estimation_with_sparse_penalty.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/signal_processing/plot_sp_analysis_sythesis_models.html#sphx-glr-auto-examples-signal-processing-plot-sp-analysis-sythesis-models-py"><span class="std std-ref">Analysis and Synthesis Models</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/signal_processing/plot_sp_analysis_sythesis_models.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/signal_processing/plot_sp_fractional_fourier_transform.html#sphx-glr-auto-examples-signal-processing-plot-sp-fractional-fourier-transform-py"><span class="std std-ref">Fractional Fourier Transform: FRFT</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/signal_processing/plot_sp_fractional_fourier_transform.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/signal_processing/plot_sp_sinusodal_model_analysis_synthesis.html#sphx-glr-auto-examples-signal-processing-plot-sp-sinusodal-model-analysis-synthesis-py"><span class="std std-ref">Sinusoidal Model: Analysis and Synthesis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/signal_processing/plot_sp_sinusodal_model_analysis_synthesis.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/signal_processing/plot_sp_sinusodal_model_example_sound.html#sphx-glr-auto-examples-signal-processing-plot-sp-sinusodal-model-example-sound-py"><span class="std std-ref">Sinusoidal Model : Synthesis : Audio</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/signal_processing/plot_sp_sinusodal_model_example_sound.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/statistics/plot_stats_group_boxplotes.html#sphx-glr-auto-examples-statistics-plot-stats-group-boxplotes-py"><span class="std std-ref">Auditory Attention: Plot Group Data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/statistics/plot_stats_group_boxplotes.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/statistics/plot_stats_testing_2groups_demo.html#sphx-glr-auto-examples-statistics-plot-stats-testing-2groups-demo-py"><span class="std std-ref">Testing two groups</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/statistics/plot_stats_testing_2groups_demo.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/wavelet_analysis/plot_sp_scalogram_cwt_example.html#sphx-glr-auto-examples-wavelet-analysis-plot-sp-scalogram-cwt-example-py"><span class="std std-ref">Scalogram CWT</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/wavelet_analysis/plot_sp_scalogram_cwt_example.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/wavelet_analysis/plot_sp_wavelet_filtering_example.html#sphx-glr-auto-examples-wavelet-analysis-plot-sp-wavelet-filtering-example-py"><span class="std std-ref">Wavelet Filtering</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/wavelet_analysis/plot_sp_wavelet_filtering_example.py</span></code>)</p></td>
<td><p>00:00.000</p></td>
<td><p>0.0</p></td>
</tr>
</tbody>
</table>
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