def METHOD_NAME():
    """
    look at events (make a small pandas dataframe).
    this is specific to Oliver's test file.  
    *** Let's worry about making this fast LATER. ***
    """
    hf = h5py.File("daq_testdata2.h5")
    data = hf['daqdata']
    nevt = data['evtno'].size
    cols = list(data.keys())
    # handle single-valued stuff
    tmp = []
    for c in cols:
        # skip bool columns for now (see test_bool below)
        if any(x in c for x in ["inverted", "muveto"]):
            continue
        # waveform data is handled below
        if not isinstance(data[c], h5py.Dataset):
            continue
        # for now, slice into the whole array and turn into pd.Series
        ser = pd.Series(data[c][...], name=c)
        tmp.append(ser)
    # combine the Series's into a DataFrame
    df = pd.concat(tmp, axis=1)
    print(df)
    # get waveform metadata
    wfdt = data["waveform_hf/dt"][0] # in the test file these are always the same
    wft0 = data["waveform_hf/t0"][0] 
    wfdt_unit = data["waveform_hf/dt"].attrs["units"]
    wft0_unit = data["waveform_hf/dt"].attrs["units"]
    print(f"wfdt: {wfdt} {wfdt_unit}  wft0: {wft0} {wft0_unit}")
    # show waveforms.  for fun, select waveforms from the first multi-hit event.
    ievt = 28
    evtno = df['daqevtno'].values
    wfsel = np.where(evtno == ievt)[0]
    # create a waveform block compatible w/ pygama
    # and yeah, i know, for loops are inefficient. i'll optimize when it matters
    wfs = []
    wfidx = data["waveform_hf/values/cumulative_length"] # where each wf starts
    wfdata = data["waveform_hf/values/flattened_data"] # adc values
    for iwf in wfsel: 
        ilo = wfidx[iwf]
        ihi = wfidx[iwf+1] if iwf+1 < nevt else nevt
        wfs.append(wfdata[ilo : ihi])
    wfs = np.vstack(wfs)
    print(wfs.shape) # wfs on each row.  will work w/ pygama.
    # plot waveforms, flip polarity for fun
    for i in range(wfs.shape[0]):
        wf = wfs[i,:]
        plt.plot(np.arange(len(wf)), wf * -1)
    plt.xlabel("clock ticks", ha='right', x=1)
    plt.ylabel("adc", ha='right', y=1)
    plt.tight_layout()
    # plt.show()
    plt.savefig(f"testdata_evt{ievt}.png")