Update glacierdynamics.MassRedistributionCurveModel to work with any timestep

[btobers]

@drounce This PR is branched from your 133-mb_daily branch. I went through the MassRedistributionCurveModel code and implemented some changes to properly index into the model indices following the changes to the mass balance model. I'll leave this for you to review and then we can pull it into branch 133-mb_daily to finalize the work of running daily simulations. I also created two simple tests in this PR which run the simulation script using the mass redistribution curve model or no dynamics - previously our tests were only running OGGM dynamics.

[btobers]

Some other updates that are included in this PR:

1. This PR also introduces a new method under the PyGEMMassBalanceModel which returns the start and stop time indices for a given model year - since these indices are needed in a variety of places

   def get_step_inds(self, year):
       """
       Get time step start and stop indices for the specified model year.
       Both indices are inclusive, so standard Python slicing would use
       [t_start : t_stop + 1].
       """
       step_idxs = np.where(self.dates_table.year == int(year))[0]
       if step_idxs.size == 0:
           raise ValueError(f'Year {year} not found in dates_table.')
       return step_idxs[0], step_idxs[-1]
   

2. Removed the now unused daysinmonth() from pygem_modelsetup.py.

3. Cleaned up annual loop in constant-area runs. The following block was removed from run_simulation.py:

   mb_mwea = (
     mb_annual
     * 365
     * 24
     * 3600
     * pygem_prms['constants']['density_ice']
     / pygem_prms['constants']['density_water']
   
   glac_wide_mb_mwea = (
     mb_mwea * mbmod.glacier_area_initial
     ).sum() / mbmod.glacier_area_initial.sum()
     mb_all.append(glac_wide_mb_mwea)
   

   and was replaced with the following:

   t_start, t_stop = mbmod.get_step_inds(year)
   mb_all.append(
     mbmod.glac_wide_massbaltotal[t_start : t_stop + 1].sum()
     / mbmod.glacier_area_initial.sum()
   

   Note, this is only relevant for debugging:

   if debug:
     print(
     'iter:',
     n_iter,
     'massbal (mean, std):',
     np.round(np.mean(mb_all), 3),
     np.round(np.std(mb_all), 3),
     'massbal (med):',
     np.round(np.median(mb_all), 3),
     )