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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 calc_dmgrid(d, maxloss=0.05, dt=3000., mindm=0., maxdm=0.):
 """ Function to calculate the DM values for a given maximum sensitivity loss. maxloss is sensitivity loss tolerated by dm bin width. dt is assumed pulse width in microsec. """


    # parameters
    tsamp = d['inttime']*1e6  # in microsec
    k = 8.3
    freq = d['freq'].mean()  # central (mean) frequency in GHz
    bw = 1e3*(d['freq'][-1] - d['freq'][0])
    ch = 1e3*(d['freq'][1] - d['freq'][0])  # channel width in MHz

    # width functions and loss factor
    dt0 = lambda dm: n.sqrt(dt**2 + tsamp**2 + ((k*dm*ch)/(freq**3))**2)
    dt1 = lambda dm, ddm: n.sqrt(dt**2 + tsamp**2 + ((k*dm*ch)/(freq**3))**2 + ((k*ddm*bw)/(freq**3.))**2)
    loss = lambda dm, ddm: 1 - n.sqrt(dt0(dm)/dt1(dm,ddm))
    loss_cordes = lambda ddm, dfreq, dt, freq: 1 - (n.sqrt(n.pi) / (2 * 6.91e-3 * ddm * dfreq / (dt*freq**3))) * erf(6.91e-3 * ddm * dfreq / (dt*freq**3))  # not quite right for underresolved pulses

    if maxdm == 0:
        return [0]
    else:
        # iterate over dmgrid to find optimal dm values. go higher than maxdm to be sure final list includes full range.
        dmgrid = n.arange(mindm, maxdm, 0.05)
        dmgrid_final = [dmgrid[0]]
        for i in range(len(dmgrid)):
            ddm = (dmgrid[i] - dmgrid_final[-1])/2.
            ll = loss(dmgrid[i],ddm)
            if ll > maxloss:
                dmgrid_final.append(dmgrid[i])

    return dmgrid_final