.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/demo_report.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_demo_report.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_demo_report.py:


Report Generation Demo
======================

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)

Usage:
    python examples/demo_report.py

.. GENERATED FROM PYTHON SOURCE LINES 15-82







.. code-block:: Python


    import logging
    from pathlib import Path

    import numpy as np

    from coco_pipe.io.structures import DataContainer
    from coco_pipe.report import from_container

    # Configure logging
    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger(__name__)


    def generate_dummy_data() -> DataContainer:
        """Create a synthetic dataset with some quality issues."""
        n_samples = 100
        n_features = 50

        # 1. Signals (Sine waves with noise)
        t = np.linspace(0, 10, n_features)
        X = np.sin(t) + np.random.randn(n_samples, n_features) * 0.5

        # Inject Issue: Dead Channel (Flatline)
        X[:, 10] = 0.0

        # Inject Issue: Outlier
        X[50, 0] = 100.0

        # Inject Issue: Missing Data
        X[20:30, 5] = np.nan

        # Metadata
        ids = [f"sub-{i:03d}" for i in range(n_samples)]
        coords = {
            "group": ["patient" if i % 2 == 0 else "control" for i in range(n_samples)],
            "age": np.random.randint(20, 80, n_samples).tolist(),
        }

        return DataContainer(X=X, dims=("obs", "time"), ids=ids, coords=coords)


    def main():
        logger.info("Generating dummy data...")
        container = generate_dummy_data()

        logger.info("Generating Report (V2.1)...")
        # This single line triggers:
        # - Quality Checks (will flag missing data, flatline, outlier)
        # - Provenance Capture (Git hash, OS, etc.)
        # - Interactive Visualization (Raw Data Scroller)
        report = from_container(
            container,
            title="CoCo V2.1 Demo Report",
            config={"demo_mode": True, "notes": "Synthetic data with injected faults."},
        )

        output_path = Path("examples/outputs/demo_report.html")
        output_path.parent.mkdir(parents=True, exist_ok=True)

        logger.info(f"Saving report to {output_path}...")
        report.save(output_path)
        logger.info("Done! Open the report to see V2.1 features in action.")


    if __name__ == "__main__":
        main()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.015 seconds)


.. _sphx_glr_download_auto_examples_demo_report.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

      :download:`Download Jupyter notebook: demo_report.ipynb <demo_report.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: demo_report.py <demo_report.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: demo_report.zip <demo_report.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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