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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _two_func_initializer(freqs, signal):
 """ This is a helper function for heuristic estimation of the initial parameters used in fitting dual peak functions _do_two_lorentzian_fit _do_two_gaussian_fit """

   # Use the signal for a rough estimate of the parameters for initialization:
   r_signal = np.real(signal)
   # The local maxima have a zero-crossing in their derivative, so we start by
   # calculating the derivative:
   diff_sig = np.diff(r_signal)
   # We look for indices that have zero-crossings (in the right direction - we
   # are looking for local maxima, not minima!)
   local_max_idx = []
   for ii in range(len(diff_sig)-1):
      if diff_sig[ii]>0 and diff_sig[ii+1]<0:
        local_max_idx.append(ii)

   # Array-ify it before moving on:
   local_max_idx = np.array(local_max_idx)
   # Our guesses for the location of the interesting local maxima is the two
   # with the largest signals in them: 
   max_idx = local_max_idx[np.argsort(r_signal[local_max_idx])[::-1][:2]]
   # We sort again, so that we can try to get the first one to be the left peak:
   max_idx = np.sort(max_idx)
   if len(max_idx)==1:
      max_idx = [max_idx[0], max_idx[0]]
   # And thusly: 
   max_idx_1 = max_idx[0]
   max_idx_2 = max_idx[1]
   # A few of the rest just follow:
   max_sig_1 = r_signal[max_idx_1]
   max_sig_2 = r_signal[max_idx_2]
   initial_amp_1 = max_sig_1
   initial_amp_2 = max_sig_2
   initial_f0_1 = freqs[max_idx_1]
   initial_f0_2 = freqs[max_idx_2]
   half_max_idx_1 = np.argmin(np.abs(np.real(signal) - max_sig_1/2))
   initial_hwhm_1 = np.abs(initial_f0_1 - freqs[half_max_idx_1])
   half_max_idx_2 = np.argmin(np.abs(np.real(signal) - max_sig_2/2))
   initial_hwhm_2 = np.abs(initial_f0_2 - freqs[half_max_idx_2])
   # Everything should be treated as real, except for the phase!
   initial_ph_1 = np.angle(signal[max_idx_1])
   initial_ph_2 = np.angle(signal[max_idx_2])
   # We only fit one offset and one drift, for both functions together! 
   initial_off = np.min(np.real(signal))
   initial_drift = 0

   initial_a_1 = (np.sum(np.real(signal)[max_idx_1:max_idx_1 +
                                         np.abs(half_max_idx_1)*2]) ) * 2

   initial_a_2 = (np.sum(np.real(signal)[max_idx_2:max_idx_2 +
                                         np.abs(half_max_idx_2)*2]) ) * 2

   return (initial_f0_1,
           initial_f0_2,
           initial_amp_1,
           initial_amp_2,
           initial_a_1,
           initial_a_2,
           initial_hwhm_1,
           initial_hwhm_2,
           initial_ph_1,
           initial_ph_2,
           initial_off,
           initial_drift)