text: 
  from .load import load
  from pytplot import get_data, store_data, options
  import numpy as np
  
  def induction(site=None, 
                trange=['2019-02-01','2019-02-02'],
                suffix='',  
                get_support_data=False, 
                varformat=None,
                varnames=[],
                downloadonly=False,
                notplot=False,
                no_update=False,
                time_clip=False):
      """
      This function loads data from the Magnetic Induction Coil Array (MICA)
      
      Parameters:
          site: str
              abbreviated name of station. sites include:
              NAL, LYR, LOR, ISR, SDY, IQA, SNK, MCM, SPA, JBS, NEV, HAL, PG2[3,4,5]
  
          trange: list of str
              time range of interest [starttime, endtime] with the format 
              'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day 
              ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
  
          suffix: str
              The tplot variable names will be given this suffix.  By default, 
              no suffix is added.
  
          get_support_data: bool
              Data with an attribute "VAR_TYPE" with a value of "support_data"
              will be loaded into tplot.  By default, only loads in data with a 
              "VAR_TYPE" attribute of "data".
  
          varformat: str
              The file variable formats to load into tplot.  Wildcard character
              "*" is accepted.  By default, all variables are loaded in.
  
          varnames: list of str
              List of variable names to load (if not specified,
              all data variables are loaded)
  
          downloadonly: bool
              Set this flag to download the CDF files, but not load them into 
              tplot variables
  
          notplot: bool
              Return the data in hash tables instead of creating tplot variables
  
          no_update: bool
              If set, only load data from your local cache
  
          time_clip: bool
              Time clip the variables to exactly the range specified in the trange keyword
  
      Returns:
          List of tplot variables created.
  
      """
  
      out_vars = load(site=site, trange=trange, suffix=suffix, get_support_data=get_support_data, varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update)
      
      if downloadonly == True or notplot == True or out_vars == None:
          return out_vars
  
      # remove values > 1000; taken from IDL SPEDAS version
      for out_var in out_vars:
          if out_var[0:7] == 'spectra':
              times, data, freq = get_data(out_var)
              w_fill = np.where(data > 1000.)
              data[w_fill] = np.nan
              store_data(out_var, data={'x': times, 'y': data, 'v': freq})
              options(out_var, 'spec', True)
              options(out_var, 'Colormap', 'jet')
              options(out_var, 'zlog', True)
              
      return out_vars
  