Make a quantile-quantile plot comparing events and a model.

    *kev*
      A 1D, sorted array of event energy bins measured in keV.
    *obs*
      A 1D array giving the number or rate of events in each bin.
    *mdl*
      A 1D array giving the modeled number or rate of events in each bin.
    *unit*
      Text describing the unit in which *obs* and *mdl* are measured; will
      be shown on the plot axes.
    *key_text*
      Text describing the quantile-quantile comparison quantity; will be
      shown on the plot legend.
    Returns:
      An :class:`omega.RectPlot` instance.

    *TODO*: nothing about this is Sherpa-specific. Same goes for some of the
    plotting routines in :mod:`pkwit.environments.casa.data`; might be
    reasonable to add a submodule for generic X-ray-y plotting routines.

def make_qq_plot(kev, obs, mdl, unit, key_text):
    """Make a quantile-quantile plot comparing events and a model.

    *kev*
      A 1D, sorted array of event energy bins measured in keV.
    *obs*
      A 1D array giving the number or rate of events in each bin.
    *mdl*
      A 1D array giving the modeled number or rate of events in each bin.
    *unit*
      Text describing the unit in which *obs* and *mdl* are measured; will
      be shown on the plot axes.
    *key_text*
      Text describing the quantile-quantile comparison quantity; will be
      shown on the plot legend.
    Returns:
      An :class:`omega.RectPlot` instance.

    *TODO*: nothing about this is Sherpa-specific. Same goes for some of the
    plotting routines in :mod:`pkwit.environments.casa.data`; might be
    reasonable to add a submodule for generic X-ray-y plotting routines.

    """
    import omega as om

    kev = np.asarray(kev)
    obs = np.asarray(obs)
    mdl = np.asarray(mdl)

    c_obs = np.cumsum(obs)
    c_mdl = np.cumsum(mdl)
    mx = max(c_obs[-1], c_mdl[-1])

    p = om.RectPlot()
    p.addXY([0, mx], [0, mx], '1:1')
    p.addXY(c_mdl, c_obs, key_text)

    # HACK: this range of numbers is chosen to give reasonable sampling for my
    # sources, which are typically quite soft.
    locs = np.array([0, 0.05, 0.08, 0.11, 0.17, 0.3, 0.4, 0.7, 1]) * (kev.size - 2)
    c0 = mx * 1.05
    c1 = mx * 1.1

    for loc in locs:
        i0 = int(np.floor(loc))
        frac = loc - i0
        kevval = (1 - frac) * kev[i0] + frac * kev[i0+1]
        mdlval = (1 - frac) * c_mdl[i0] + frac * c_mdl[i0+1]
        obsval = (1 - frac) * c_obs[i0] + frac * c_obs[i0+1]
        p.addXY([mdlval, mdlval], [c0, c1], '%.2f keV' % kevval, dsn=2)
        p.addXY([c0, c1], [obsval, obsval], None, dsn=2)

    p.setLabels('Cumulative model ' + unit, 'Cumulative data ' + unit)
    p.defaultKeyOverlay.vAlign = 0.3
    return p