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# Examples Gallery

Welcome to the notebook gallery for **coco-pipe**.
Here you will find sample scripts and examples demonstrating how to use the toolbox.


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the &quot;Visualizing Dynamics&quot; pillar of the coco_pipe strategic vision. We generate a synthetic dynamical system (a noisy limit cycle) and visualize its temporal flow using Streamlines.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_velocity_embedding_thumb.png
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  :doc:`/auto_examples/plot_velocity_embedding`

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      <div class="sphx-glr-thumbnail-title">Visualizing Dynamics: Velocity Embeddings</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the &quot;Advanced Exploration and Benchmarking&quot; pillar of the coco_pipe strategic vision. We move beyond &quot;looking good&quot; and use rigorous metrics (Trustworthiness, Continuity, LCMC) to quantify embedding distortion.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_quality_metrics_thumb.png
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  :doc:`/auto_examples/plot_quality_metrics`

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      <div class="sphx-glr-thumbnail-title">Benchmarking Dimensionality Reduction: The Epistemology of Embeddings</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Minimal descriptors example with explicit NumPy inputs.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_descriptors_example_thumb.png
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  :doc:`/auto_examples/descriptors_example`

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      <div class="sphx-glr-thumbnail-title">Minimal descriptors example with explicit NumPy inputs.</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the V2.1 Reporting API capabilities: 1.  Auto-Visualization (Raw Data Scroller) 2.  Auto-Provenance (Run Info) 3.  Quality Checks (Missingness, Flatlines) 4.  Scalability (Global Compressed Store)">

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  .. image:: /auto_examples/images/thumb/sphx_glr_demo_report_thumb.png
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  :doc:`/auto_examples/demo_report`

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      <div class="sphx-glr-thumbnail-title">Report Generation Demo</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the iterative balancing process for datasets.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_demo_iterative_balancing_thumb.png
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  :doc:`/auto_examples/demo_iterative_balancing`

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      <div class="sphx-glr-thumbnail-title">Iterative Balancing Demo</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This script demonstrates how to use the enhanced coco_pipe.dim_reduction module to benchmark different dimensionality reduction techniques on the Digits dataset.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_benchmark_dim_reduction_thumb.png
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  :doc:`/auto_examples/benchmark_dim_reduction`

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      <div class="sphx-glr-thumbnail-title">Benchmark Dimensionality Reduction Methods</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the &quot;Scientific Imperative&quot; for domain-specific reduction as outlined in the coco_pipe strategic vision. We compare generic methods (PCA, UMAP) against physics-informed methods (TRCA, DMD) on synthetic oscillatory data that mimics neural signals.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_scientific_dimred_thumb.png
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  :doc:`/auto_examples/plot_scientific_dimred`

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      <div class="sphx-glr-thumbnail-title">Scientific vs. Generic Dimensionality Reduction</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the full CoCo pipeline: 1.  Load (Tabular) 2.  Preprocess (StandardScaler) 3.  Reduce (PCA &amp; UMAP) 4.  Report (Comparison)">

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  .. image:: /auto_examples/images/thumb/sphx_glr_demo_pipeline_thumb.png
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  :doc:`/auto_examples/demo_pipeline`

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      <div class="sphx-glr-thumbnail-title">End-to-End Pipeline Demo</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Demonstrates the DataContainer and other core IO structures.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_demo_structures_thumb.png
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  :doc:`/auto_examples/demo_structures`

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      <div class="sphx-glr-thumbnail-title">Data Structures Demo</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Compare PHATE and UMAP dimensionality reduction methods on MNIST and">

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  .. image:: /auto_examples/images/thumb/sphx_glr_compare_phate_umap_thumb.png
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  :doc:`/auto_examples/compare_phate_umap`

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      <div class="sphx-glr-thumbnail-title">Compare PHATE and UMAP dimensionality reduction methods on MNIST and</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This script creates grid visualizations comparing different dimension reduction methods (PCA, t-SNE, UMAP, and Pacmap) with different parameter settings for MNIST and Fashion-MNIST.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_compare_dim_reduction_thumb.png
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  :doc:`/auto_examples/compare_dim_reduction`

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      <div class="sphx-glr-thumbnail-title">Compare different dimension reduction methods on MNIST and Fashion-MNIST datasets.</div>
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.. toctree::
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   /auto_examples/plot_velocity_embedding
   /auto_examples/plot_quality_metrics
   /auto_examples/descriptors_example
   /auto_examples/demo_report
   /auto_examples/demo_iterative_balancing
   /auto_examples/benchmark_dim_reduction
   /auto_examples/plot_scientific_dimred
   /auto_examples/demo_pipeline
   /auto_examples/demo_structures
   /auto_examples/compare_phate_umap
   /auto_examples/compare_dim_reduction


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      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_