code: def fetch_LIGO_large(data_home=None, download_if_missing=True):
      """Loader for LIGO large dataset
  
      Parameters
      ----------
      data_home : optional, default=None
          Specify another download and cache folder for the datasets. By default
          all astroML data is stored in '~/astroML_data'.
  
      download_if_missing : optional, default=True
          If False, raise a IOError if the data is not locally available
          instead of trying to download the data from the source site.
  
      Returns
      -------
      data : ndarray
      dt : float
          data represents ~2000s of amplitude data from LIGO hanford;
          dt is the time spacing between measurements in seconds.
      """
      data_home = get_data_home(data_home)
  
      local_file = os.path.join(data_home, LOCAL_FILE_LARGE)
  
      if os.path.exists(local_file):
          data = np.load(local_file)
  
      else:
          if not download_if_missing:
              raise IOError(
                  "data not present on disk. "
                  "set download_if_missing=True to download"
              )
  
          print(
              "downloading LIGO bigdog data from %s to %s"
              % (DATA_URL_LARGE, local_file)
          )
  
          zipped_buf = download_with_progress_bar(
              DATA_URL_LARGE, return_buffer=True
          )
          gzf = GzipFile(fileobj=zipped_buf, mode="rb")
          print("uncompressing file...")
          extracted_buf = BytesIO(gzf.read())
          data = np.loadtxt(extracted_buf)
          np.save(local_file, data)
  
      return data, 1.0 / 4096
  
file_path: astroML/astroML/datasets/LIGO_bigdog.py
imports: import os
  from io import BytesIO
  from gzip import GzipFile
  import numpy
  from .None import get_data_home
  from .tools import download_with_progress_bar
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_LIGO_large(data_home, download_if_missing) and returns: data,  unknown