text: # epyHFO is an open-source Python package for automatic detection of high frequency oscillations (HFOs) in EEG
  # recordings. If you use the source code, please make sure to reference both the package and the paper:
  # * Migliorelli, C., Bachiller, A., Alonso, JF., Romero S., Aparicio J., Jacobs-Le Van, J., Mañanas, MA.,
  #   San Antonio-Arce V. (Apr 2020). SGM: a novel time-frequency algorithm based on unsupervised learning improves
  #   high-frequency oscillation detection in epilepsy. J Neural Eng. 22;17(2):026032. doi: 10.1088/1741-2552/ab8345.
  # * Migliorelli, C. (2020). epyHFO v1.0, https://github.com/cmiglio/epyHFO (Zenodo Doi: https://doi.org/10.5281/zenodo.3894472)
  # --------------------------------------------------------------------------------------------------------------------
  # Copyright (C) 2020, Centro de investigación Biomedica en red (CIBER-BBN), Universitat Politecnica de Catalunya (UPC)
  # Author: Carolina Migliorelli
  #
  # This software is distributed under the terms of the BSD 3-Clause License licence
  #
  #
  # FOR RESEARCH PURPOSES ONLY. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS
  # AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
  # NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
  # PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT OWNER OR
  # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
  # OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
  # GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  #
  # --------------------------------------------------------------------------------------------------------------------
  
  import sgm as sgm
  # Load files
  
  # Define which patients will be used
  patients = ['PAT_example']
  results_path = 'data_example/results/'
  config_path = 'data_example/config/'
  data_path = 'data_example/data/'
  
  sgm.extract_features(patients, results_path, config_path, data_path)
  sgm.obtain_gmm(patients, results_path, config_path)
  