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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 _channel_loop(tr, parameters, max_trigger_length=60, despike=False, debug=0):
 """ Internal loop for parellel processing. :type tr: obspy.core.trace :param tr: Trace to look for triggers in. :type parameters: list :param parameters: List of TriggerParameter class for trace. :type max_trigger_length: float :type despike: bool :type debug: int :return: trigger :rtype: list """

    for par in parameters:
        if par['station'] == tr.stats.station and \
           par['channel'] == tr.stats.channel:
            parameter = par
            break
    else:
        msg = 'No parameters set for station ' + str(tr.stats.station)
        warnings.warn(msg)
        return []

    triggers = []
    if debug > 0:
        print(tr)
    tr.detrend('simple')
    if despike:
        median_filter(tr)
    if parameter['lowcut'] and parameter['highcut']:
        tr.filter('bandpass', freqmin=parameter['lowcut'],
                  freqmax=parameter['highcut'])
    elif parameter['lowcut']:
        tr.filter('highpass', freq=parameter['lowcut'])
    elif parameter['highcut']:
        tr.filter('lowpass', freq=parameter['highcut'])
    # find triggers for each channel using recursive_sta_lta
    df = tr.stats.sampling_rate
    cft = recursive_sta_lta(tr.data, int(parameter['sta_len'] * df),
                            int(parameter['lta_len'] * df))
    if max_trigger_length:
        trig_args = {'max_len_delete': True}
        trig_args['max_len'] = int(max_trigger_length *
                                   df + 0.5)
    if debug > 3:
        plot_trigger(tr, cft, parameter['thr_on'], parameter['thr_off'])
    tmp_trigs = trigger_onset(cft, float(parameter['thr_on']),
                              float(parameter['thr_off']),
                              **trig_args)
    for on, off in tmp_trigs:
        cft_peak = tr.data[on:off].max()
        cft_std = tr.data[on:off].std()
        on = tr.stats.starttime + \
            float(on) / tr.stats.sampling_rate
        off = tr.stats.starttime + \
            float(off) / tr.stats.sampling_rate
        triggers.append((on.timestamp, off.timestamp,
                         tr.id, cft_peak,
                         cft_std))
    return triggers