How to use the presto.singlepulse.sp_pgplot.ppgplot.pgbox function in presto

sp_pgplot.ppgplot.pgsvp(0.07, 0.7, 0.01, 0.05)
        sp_pgplot.ppgplot.pgmtxt('T', -2.1, 0.01, 0.0, "%s" % fn)

        # DM vs SNR
        if not man_params:
            dm_arr = np.float32(spdobj.dmVt_this_dms)
            sigma_arr = np.float32(spdobj.dmVt_this_sigmas)
            time_arr = np.float32(spdobj.dmVt_this_times)
            if integrate_spec:
                sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
            else:
                sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
            sp_pgplot.ppgplot.pgswin(np.min(dm_arr), np.max(dm_arr), 0.95 * np.min(sigma_arr), 1.05 * np.max(sigma_arr))
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\\u-3\\d)")
            sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-noise")
            sp_pgplot.ppgplot.pgpt(dm_arr, sigma_arr, 20)
        else:
            dm_arr = np.array([])
            sigma_arr = np.array([])
            time_arr = np.array([])
            if integrate_spec:
                sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
            else:
                sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
            sp_pgplot.ppgplot.pgslw(3)

sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
        if not integrate_spec:
            sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
        sp_pgplot.plot_waterfall(array, rangex=[datastart - start, datastart - start + datanumspectra * datasamp],
                                 rangey=[min_freq, max_freq], image='apjgrey')

        #### Plot Dedispersed Time series - Zerodm filter - Off
        Dedisp_ts = array[::-1].sum(axis=0)
        times = np.arange(datanumspectra) * datasamp
        if integrate_ts:
            sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.80, 0.90)
            sp_pgplot.ppgplot.pgswin(datastart - start, datastart - start + duration, np.min(Dedisp_ts),
                                     1.05 * np.max(Dedisp_ts))
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgline(times, Dedisp_ts)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgsci(1)

            errx1 = np.array([0.60 * (datastart - start + duration)])
            erry1 = np.array([0.60 * np.max(Dedisp_ts)])
            erry2 = np.array([np.std(Dedisp_ts)])
            errx2 = np.array([pulse_width])
            sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
            sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)

        #### Plot Spectrum - Zerodm filter - Off
        if integrate_spec:
            spectrum_window = spec_width * pulse_width
            window_width = int(spectrum_window / datasamp)

sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time (s)")
            sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "DM (pc cm\u-3\d)")
    else:
        #sp_pgplot.ppgplot.pgpap(10.25, 10.0/5.0)
        sp_pgplot.ppgplot.pgpap(8.0, 1.5)
        # Dedispersed waterfall plot - zerodm - OFF
        array = spdobj.data_nozerodm_dedisp.astype(np.float64)
        sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.44, 0.75)
        sp_pgplot.ppgplot.pgswin(datastart - start, datastart -start+datanumspectra*datasamp, min_freq, max_freq)
        sp_pgplot.ppgplot.pgsch(0.8)
        sp_pgplot.ppgplot.pgslw(3)
        sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
        sp_pgplot.ppgplot.pgslw(3)
        sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
        sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp], rangey = [min_freq, max_freq], image = 'apjgrey')
         
        #### Plot Dedispersed Time series - Zerodm filter - Off
        Dedisp_ts = array[::-1].sum(axis = 0)
        times = np.arange(datanumspectra)*datasamp
        if integrate_ts:
            sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.75, 0.83)
            sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+duration, np.min(Dedisp_ts), 1.05*np.max(Dedisp_ts))
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgline(times,Dedisp_ts)
            sp_pgplot.ppgplot.pgslw(3)

sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
            sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
            sp_pgplot.ppgplot.pgsch(0.7)
            sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
            sp_pgplot.ppgplot.pgsch(0.8)
        if disp_pulse: 
            # Sweeped waterfall plot Zerodm - OFF
            array = spdobj.data_nozerodm.astype(np.float64)
            sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.44, 0.65)
            sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(4)
            sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
            sp_pgplot.ppgplot.pgsch(3)
            sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
            delays = spdobj.dmsweep_delays
            freqs = spdobj.dmsweep_freqs
            sp_pgplot.ppgplot.pgslw(5)
            sweepstart = sweeped_start- 0.2*sweep_duration
            sp_pgplot.ppgplot.pgsci(0)
            sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgslw(3)
            
            # Sweeped waterfall plot Zerodm - ON
            array = spdobj.data_zerodm.astype(np.float64)
            sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.05, 0.25)
            sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
            sp_pgplot.ppgplot.pgsch(0.8)

sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
        
        #### Plot Spectrum - Zerodm filter - On
        if integrate_spec:
            spectrum_window = spec_width*pulse_width
            window_width = int(spectrum_window/datasamp)
            #burst_bin = int(datanumspectra*loc_pulse/downsamp)
            burst_bin = int(nbins*loc_pulse/downsamp)
            on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
            Dedisp_spec = on_spec.sum(axis=1)
            freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec)) 
            sp_pgplot.ppgplot.pgsvp(0.70, 0.90, 0.05, 0.36)
            sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
            sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
            sp_pgplot.ppgplot.pgsch(0.7)
            sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
            sp_pgplot.ppgplot.pgsch(0.8)
        if disp_pulse: 
            # Sweeped waterfall plot Zerodm - OFF
            array = spdobj.data_nozerodm.astype(np.float64)
            sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.44, 0.65)
            sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(4)
            sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
            sp_pgplot.ppgplot.pgsch(3)
            sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')

sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
        
        #### Plot Spectrum - Zerodm filter - On
        if integrate_spec:
            spectrum_window = spec_width*pulse_width
            window_width = int(spectrum_window/datasamp)
            #burst_bin = int(datanumspectra*loc_pulse/downsamp)
            burst_bin = int(nbins*loc_pulse/downsamp)
            on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
            Dedisp_spec = on_spec.sum(axis=1)
            freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec)) 
            sp_pgplot.ppgplot.pgsvp(0.4, 0.47, 0.1, 0.4)
            sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
            sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
            sp_pgplot.ppgplot.pgsch(0.7)
            sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
            sp_pgplot.ppgplot.pgsch(0.8)
        
        if disp_pulse:
            # Sweeped waterfall plot Zerodm - OFF
            array = spdobj.data_nozerodm.astype(np.float64)
            sp_pgplot.ppgplot.pgsvp(0.20, 0.40, 0.50, 0.70)
            sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(4)
            sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
            sp_pgplot.ppgplot.pgsch(3)

sp_pgplot.ppgplot.pgsch(0.8)
        sp_pgplot.ppgplot.pgslw(3)
        sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
        sp_pgplot.ppgplot.pgslw(3)
        sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
        sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp], rangey = [min_freq, max_freq], image = 'apjgrey')
         
        #### Plot Dedispersed Time series - Zerodm filter - Off
        Dedisp_ts = array[::-1].sum(axis = 0)
        times = np.arange(datanumspectra)*datasamp
        if integrate_ts:
            sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.75, 0.83)
            sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+duration, np.min(Dedisp_ts), 1.05*np.max(Dedisp_ts))
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgline(times,Dedisp_ts)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgsci(1)
            errx1 = np.array([0.60 * (datastart-start+duration)])
            erry1 = np.array([0.60 * np.max(Dedisp_ts)])
            erry2 = np.array([np.std(Dedisp_ts)])
            errx2 = np.array([pulse_width])
            sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
            sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
        
        #### Plot Spectrum - Zerodm filter - Off
        if integrate_spec:
            spectrum_window = spec_width*pulse_width
            window_width = int(spectrum_window/datasamp)
            #burst_bin = int(datanumspectra*loc_pulse/downsamp)

sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time (s)")
            sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "DM (pc cm\\u-3\\d)")
    else:
        # sp_pgplot.ppgplot.pgpap(10.25, 10.0/5.0)
        sp_pgplot.ppgplot.pgpap(8.0, 1.5)
        # Dedispersed waterfall plot - zerodm - OFF
        array = spdobj.data_nozerodm_dedisp.astype(np.float64)
        sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.44, 0.75)
        sp_pgplot.ppgplot.pgswin(datastart - start, datastart - start + datanumspectra * datasamp, min_freq, max_freq)
        sp_pgplot.ppgplot.pgsch(0.8)
        sp_pgplot.ppgplot.pgslw(3)
        sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
        sp_pgplot.ppgplot.pgslw(3)
        sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
        sp_pgplot.plot_waterfall(array, rangex=[datastart - start, datastart - start + datanumspectra * datasamp],
                                 rangey=[min_freq, max_freq], image='apjgrey')

        #### Plot Dedispersed Time series - Zerodm filter - Off
        Dedisp_ts = array[::-1].sum(axis=0)
        times = np.arange(datanumspectra) * datasamp
        if integrate_ts:
            sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.75, 0.83)
            sp_pgplot.ppgplot.pgswin(datastart - start, datastart - start + duration, np.min(Dedisp_ts),
                                     1.05 * np.max(Dedisp_ts))
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)

sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)
            sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
            sp_pgplot.ppgplot.pgsci(1)
            sp_pgplot.ppgplot.pgline(Dedisp_spec, freqs)
            sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
            sp_pgplot.ppgplot.pgsch(0.7)
            sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
            sp_pgplot.ppgplot.pgsch(0.8)

        # Dedispersed waterfall plot - Zerodm ON
        sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.1, 0.40)
        sp_pgplot.ppgplot.pgswin(datastart - start, datastart - start + datanumspectra * datasamp, min_freq, max_freq)
        sp_pgplot.ppgplot.pgsch(0.8)
        sp_pgplot.ppgplot.pgslw(3)
        sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
        sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time - %.2f s" % datastart)
        sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
        if not integrate_spec:
            sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
        array = spdobj.data_zerodm_dedisp.astype(np.float64)
        sp_pgplot.plot_waterfall(array, rangex=[datastart - start, datastart - start + datanumspectra * datasamp],
                                 rangey=[min_freq, max_freq], image='apjgrey')
        #### Plot Dedispersed Time series - Zerodm filter - On
        dedisp_ts = array[::-1].sum(axis=0)
        times = np.arange(datanumspectra) * datasamp
        if integrate_ts:
            sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.40, 0.50)
            sp_pgplot.ppgplot.pgswin(datastart - start, datastart - start + duration, np.min(dedisp_ts),
                                     1.05 * np.max(dedisp_ts))
            sp_pgplot.ppgplot.pgsch(0.8)
            sp_pgplot.ppgplot.pgslw(3)