.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_datasets_plot_make_cbf.py: ===================================== Making a Cylinder-Bell-Funnel dataset ===================================== This example shows how to generate a Cylinder-Bell-Funnel dataset. This simulated dataset was introduced by N. Saito in his Ph.D. thesis entitled "Local feature extraction and its application". It is one of the most well-known datasets in time series classification. It is implemented as :func:`pyts.datasets.make_cylinder_bell_funnel`. .. image:: /auto_examples/datasets/images/sphx_glr_plot_make_cbf_001.png :class: sphx-glr-single-img .. code-block:: default # Author: Johann Faouzi # License: BSD-3-Clause import matplotlib.pyplot as plt from pyts.datasets import make_cylinder_bell_funnel X, y = make_cylinder_bell_funnel(n_samples=12, random_state=42) plt.figure(figsize=(12, 9)) for i, classe in enumerate(['cylinder', 'bell', 'funnel']): plt.subplot(3, 1, i + 1) for x in X[y == i]: plt.plot(x, color='C0', linewidth=0.9) plt.title('Class: {}'.format(classe), fontsize=16) plt.tight_layout() plt.subplots_adjust(hspace=0.4) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.444 seconds) .. _sphx_glr_download_auto_examples_datasets_plot_make_cbf.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_make_cbf.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_make_cbf.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_