def run_tutorial(min_magnitude=2, shift_len=0.2, num_cores=4, min_cc=0.5):
    """Functional, tested example script for running the lag-calc tutorial."""
    if num_cores > cpu_count():
        num_cores = cpu_count()
    client = Client('NCEDC')
    t1 = UTCDateTime(2004, 9, 28)
    t2 = t1 + 86400
    print('Downloading catalog')
    catalog = client.get_events(
        starttime=t1, endtime=t2, minmagnitude=min_magnitude,
        minlatitude=35.7, maxlatitude=36.1, minlongitude=-120.6,
        maxlongitude=-120.2, includearrivals=True)
    # We don't need all the picks, lets take the information from the
    # five most used stations - note that this is done to reduce computational
    # costs.
    catalog = catalog_utils.filter_picks(
        catalog, channels=['EHZ'], top_n_picks=5)
    # There is a duplicate pick in event 3 in the catalog - this has the effect
    # of reducing our detections - check it yourself.
    for pick in catalog[3].picks:
        if pick.waveform_id.station_code == 'PHOB' and \
                        pick.onset == 'emergent':
            catalog[3].picks.remove(pick)
    print('Generating templates')
    templates = template_gen.from_client(
        catalog=catalog, client_id='NCEDC', lowcut=2.0, highcut=9.0,
        samp_rate=50.0, filt_order=4, length=3.0, prepick=0.15,
        swin='all', process_len=3600)
    # In this section we generate a series of chunks of data.
    start_time = UTCDateTime(2004, 9, 28, 17)
    end_time = UTCDateTime(2004, 9, 28, 20)
    process_len = 3600
    chunks = []
    chunk_start = start_time
    while chunk_start < end_time:
        chunk_end = chunk_start + process_len
        if chunk_end > end_time:
            chunk_end = end_time
        chunks.append((chunk_start, chunk_end))
        chunk_start += process_len

    all_detections = []
    picked_catalog = Catalog()
    template_names = [str(template[0].stats.starttime)
                      for template in templates]
    for t1, t2 in chunks:
        print('Downloading and processing for start-time: %s' % t1)
        # Download and process the data
        bulk_info = [(tr.stats.network, tr.stats.station, '*',
                      tr.stats.channel, t1, t2) for tr in templates[0]]
        # Just downloading a chunk of data
        st = client.get_waveforms_bulk(bulk_info)
        st.merge(fill_value='interpolate')
        st = pre_processing.shortproc(
            st, lowcut=2.0, highcut=9.0, filt_order=4, samp_rate=50.0,
            debug=0, num_cores=num_cores)
        detections = match_filter.match_filter(
            template_names=template_names, template_list=templates, st=st,
            threshold=8.0, threshold_type='MAD', trig_int=6.0, plotvar=False,
            plotdir='.', cores=num_cores)
        # Extract unique detections from set.
        unique_detections = []
        for master in detections:
            keep = True
            for slave in detections:
                if not master == slave and\
                   abs(master.detect_time - slave.detect_time) <= 1.0:
                    # If the events are within 1s of each other then test which
                    # was the 'best' match, strongest detection
                    if not master.detect_val > slave.detect_val:
                        keep = False
                        break
            if keep:
                unique_detections.append(master)
        all_detections += unique_detections

        picked_catalog += lag_calc.lag_calc(
            detections=unique_detections, detect_data=st,
            template_names=template_names, templates=templates,
            shift_len=shift_len, min_cc=min_cc, interpolate=False, plot=False,
            parallel=True, debug=3)
    # Return all of this so that we can use this function for testing.
    return all_detections, picked_catalog, templates, template_names
Functional, tested example script for running the lag-calc tutorial.