How to use the presto.psr_utils.delay_from_DM function in presto

Outputs:
                None

            *** Subbanding happens in-place ***
        """
        assert (self.numchans % nsub) == 0
        assert (subdm is None) or (subdm >= 0)
        nchan_per_sub = self.numchans // nsub
        sub_hifreqs = self.freqs[np.arange(nsub)*nchan_per_sub]
        sub_lofreqs = self.freqs[(1+np.arange(nsub))*nchan_per_sub-1]
        sub_ctrfreqs = 0.5*(sub_hifreqs+sub_lofreqs)
        
        if subdm is not None:
            # Compute delays
            ref_delays = psr_utils.delay_from_DM(subdm-self.dm, sub_ctrfreqs)
            delays = psr_utils.delay_from_DM(subdm-self.dm, self.freqs)
            rel_delays = delays-ref_delays.repeat(nchan_per_sub) # Relative delay
            rel_bindelays = np.round(rel_delays/self.dt).astype('int')
            # Shift channels
            self.shift_channels(rel_bindelays, padval)

        # Subband
        self.data = np.array([np.sum(sub, axis=0) for sub in \
                                np.vsplit(self.data, nsub)])
        self.freqs = sub_ctrfreqs
        self.numchans = nsub

Outputs:
                None

            *** Subbanding happens in-place ***
        """
        assert (self.numchans % nsub) == 0
        assert (subdm is None) or (subdm >= 0)
        nchan_per_sub = self.numchans // nsub
        sub_hifreqs = self.freqs[np.arange(nsub)*nchan_per_sub]
        sub_lofreqs = self.freqs[(1+np.arange(nsub))*nchan_per_sub-1]
        sub_ctrfreqs = 0.5*(sub_hifreqs+sub_lofreqs)
        
        if subdm is not None:
            # Compute delays
            ref_delays = psr_utils.delay_from_DM(subdm-self.dm, sub_ctrfreqs)
            delays = psr_utils.delay_from_DM(subdm-self.dm, self.freqs)
            rel_delays = delays-ref_delays.repeat(nchan_per_sub) # Relative delay
            rel_bindelays = np.round(rel_delays/self.dt).astype('int')
            # Shift channels
            self.shift_channels(rel_bindelays, padval)

        # Subband
        self.data = np.array([np.sum(sub, axis=0) for sub in \
                                np.vsplit(self.data, nsub)])
        self.freqs = sub_ctrfreqs
        self.numchans = nsub

scaleindep = None, zerodm = None, mask = mask,
                                      barytime=barytime,
                                      nsub = nsub, bandpass_corr = bandpass_corr)
                data, Data_nozerodm = waterfall_array(rawdatafile, spdcand.start,
                                           spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub,
                                           spdcand.subdm, spdcand.zerodm, spdcand.downsamp,
                                           spdcand.scaleindep, spdcand.width_bins,
                                           spdcand.mask, maskfile, spdcand.bandpass_corr)
                text_array = np.append(text_array, spdcand.sweep_duration)
                text_array = np.append(text_array, data.starttime)
                text_array = np.append(text_array, spdcand.bary_start_time)
                text_array = np.append(text_array, man_params)
                # Array to Construct the sweep
                if spdcand.sweep_dm is not None:
                    ddm = spdcand.sweep_dm-data.dm
                    delays = psr_utils.delay_from_DM(ddm, data.freqs)
                    delays -= delays.min()
                    delays_nozerodm = delays
                    freqs_nozerodm = data.freqs
                # Sweeped with zerodm-on 
                zerodm = True
                #downsamp_temp = 1
                data, Data_zerodm = waterfall_array(rawdatafile, spdcand.start,
                                           spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub,
                                           spdcand.subdm, zerodm, spdcand.downsamp,
                                           spdcand.scaleindep, spdcand.width_bins,
                                           spdcand.mask, maskfile, spdcand.bandpass_corr)
                # Saving the arrays into the .spd file.
                with open(temp_filename+".spd", 'wb') as f:
                    np.savez_compressed(f,
                                        Data_dedisp_nozerodm = Data_dedisp_nozerodm.astype(np.float16),
                                        Data_dedisp_zerodm = Data_dedisp_zerodm.astype(np.float16),

rawdatafile.frequencies[0], rawdatafile.frequencies[-1], rawdatafile,
                          loc_pulse=loc_pulse, dedisp=None, scaleindep=None, zerodm=None, mask=mask,
                          barytime=barytime, bandpass_corr=bandpass_corr)
    data, Data_nozerodm = waterfall_array(rawdatafile, spdcand.start,
                                 spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub,
                                 spdcand.subdm, spdcand.zerodm, spdcand.downsamp,
                                 spdcand.scaleindep, spdcand.width_bins,
                                 spdcand.mask, maskfile, spdcand.bandpass_corr)
    text_array = np.append(text_array, spdcand.sweep_duration)
    text_array = np.append(text_array, data.starttime)
    text_array = np.append(text_array, spdcand.bary_start_time)
    text_array = np.append(text_array, man_params)
    # Array to Construct the sweep
    if spdcand.sweep_dm is not None:
        ddm = spdcand.sweep_dm-data.dm
        delays = psr_utils.delay_from_DM(ddm, data.freqs)
        delays -= delays.min()
        delays_nozerodm = delays
        freqs_nozerodm = data.freqs
    # Sweeped with zerodm-on 
    zerodm = True
    #downsamp_temp = 1
    data, Data_zerodm = waterfall_array(rawdatafile, spdcand.start,
                                 spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub,
                                 spdcand.subdm, zerodm, spdcand.downsamp,
                                 spdcand.scaleindep, spdcand.width_bins,
                                 spdcand.mask, maskfile, spdcand.bandpass_corr)
    with open(temp_filename+".spd", 'wb') as f:
        np.savez_compressed(f,
                            Data_dedisp_nozerodm = Data_dedisp_nozerodm.astype(np.float16),
                            Data_dedisp_zerodm = Data_dedisp_zerodm.astype(np.float16),
                            Data_nozerodm = Data_nozerodm.astype(np.float16),

the given DM.
            
            Inputs:
                dm: The DM (in pc/cm^3) to use.
                padval: The padding value to use when shifting
                    channels during dedispersion. See documentation
                    of Spectra.shift_channels. (Default: 0)

            Outputs:
                None

            *** Dedispersion happens in place ***
        """
        assert dm >= 0
        ref_delay = psr_utils.delay_from_DM(dm-self.dm, np.max(self.freqs))
        delays = psr_utils.delay_from_DM(dm-self.dm, self.freqs)
        rel_delays = delays-ref_delay # Relative delay
        rel_bindelays = np.round(rel_delays/self.dt).astype('int')
        # Shift channels
        self.shift_channels(rel_bindelays, padval)

        self.dm=dm