Merge branch 'master' of https://github.com/desihub/desisurvey

py/desisurvey/NTS.py

@@ -368,6 +368,16 @@ def next_tile(self, conditions=None, exposure=None, constraints=None,
         s2n = 50.0 * texp_remaining/texp_tot
         exptime = texp_remaining
         maxtime = self.ETC.MAX_EXPTIME
+        maxtimecond = getattr(self.config, 'maximum_time_in_conditions',
+                              None)
+        if maxtimecond is not None:
+            maxtimecond = getattr(maxtimecond, sched_program, None)
+            if maxtimecond is not None:
+                maxtimecond = maxtimecond().to(u.day).value
+        if maxtimecond is None:
+            maxtimecond = np.inf
+        maxtime = np.min([maxtime, maxtimecond])
+
         days_to_seconds = 60*60*24
         fivemin = 5/60/24  # 5 minutes... pretty arbitrary.
         if ((mjd <= self.scheduler.night_ephem['dusk']-fivemin) or

py/desisurvey/optimize.py

@@ -134,6 +134,10 @@ def __init__(self, condition, lst_edges, lst_hist, subset=None, start=None, stop
         texp_nom = u.Quantity([
             getattr(config.nominal_exposure_time, program)()
             for program in tiles.tileprogram[tile_sel]])
+        moon_up_factor = getattr(config, 'moon_up_factor', None)
+        if moon_up_factor is not None:
+            moon_up_factor = getattr(moon_up_factor, condition)()
+            texp_nom *= moon_up_factor
         if completed is not None:
             completed = astropy.table.Table.read(completed)
             nobtained = np.zeros(tiles.ntiles, dtype='f4')
@@ -146,6 +150,7 @@ def __init__(self, condition, lst_edges, lst_hist, subset=None, start=None, stop
                 completed['NNIGHT_NEEDED_'+condition][mask])
             boost = np.clip(nneeded-nobtained, 0, np.inf)
             texp_nom *= boost[tile_sel]
+
         self.dlst_nom = 360 * texp_nom.to(u.day).value / 0.99726956583
 
         # Save arrays for the tiles to plan.
@@ -193,6 +198,10 @@ def __init__(self, condition, lst_edges, lst_hist, subset=None, start=None, stop
         self.use_plan(save_history=False)
         self.min_total_time = self.plan_hist.sum()
 
+        self.plan_tiles = self.get_plan(np.zeros(self.ntiles))
+        self.use_plan()
+        self.plan_hist_ha0 = self.plan_hist.copy()
+
         # Initialize HA assignments for each tile.
         if init == 'zero':
             self.ha = np.zeros(self.ntiles)
@@ -220,6 +229,8 @@ def __init__(self, condition, lst_edges, lst_hist, subset=None, start=None, stop
                 lst_cdf = np.zeros_like(edges)
                 lst_cdf[1:] = np.cumsum(hist)
                 lst_cdf /= lst_cdf[-1]
+                lst_cen = (edges[1:]+edges[:-1])/2
+                lst_cdf = (lst_cdf[1:]+lst_cdf[:-1])/2
                 # Calculate the CDF of planned LST usage relative to the same
                 # central LST, assuming HA=0. Instead of spreading each exposure
                 # over multiple LST bins, add its entire HA=0 exposure time at
@@ -229,9 +240,10 @@ def __init__(self, condition, lst_edges, lst_hist, subset=None, start=None, stop
                 sort_idx = np.argsort(tile_ra)
                 tile_cdf = np.cumsum(exptime[sort_idx])
                 tile_cdf /= tile_cdf[-1]
+                tile_cdf = (np.concatenate([[0], tile_cdf[:-1]])+tile_cdf)/2
                 # Use linear interpolation to find an LST for each tile that
                 # matches the plan CDF to the available LST CDF.
-                new_lst = np.interp(tile_cdf, lst_cdf, edges)
+                new_lst = np.interp(tile_cdf, lst_cdf, lst_cen)
                 # Calculate each tile's HA as the difference between its HA=0
                 # LST and its new LST after CDF matching.
                 ha = np.empty(self.ntiles)

py/desisurvey/scripts/surveyinit.py

@@ -98,6 +98,9 @@ def parse(options=None):
     parser.add_argument(
         '--completed', default=None,
         help='filename with information on already completed tiles')
+    parser.add_argument(
+        '--include-weather', default=True, type=bool,
+        help='Use past weather to discount available LST when planning.')
 
     if options is None:
         args = parser.parse_args()
@@ -137,6 +140,8 @@ def calculate_initial_plan(args):
     for year in years:
         fractions.append(
             desimodel.weather.dome_closed_fractions(first, last, replay='Y{}'.format(year)))
+    from scipy.ndimage import gaussian_filter
+    fractions = gaussian_filter(fractions, 7, mode='wrap')
     weather = 1 - np.mean(fractions, axis=0)
     # Save the weather fractions as the primary HDU.
     hdr['FIRST'] = first.isoformat()
@@ -152,8 +157,14 @@ def calculate_initial_plan(args):
     # Calculate the distribution of available LST in each program
     # during the nominal survey [start, stop).
     ilo, ihi = (start - first).days, (stop - first).days
+    print('weather', args.include_weather)
+    if args.include_weather:
+        tweather = weather[ilo:ihi]
+    else:
+        tweather = None
     lst_hist, lst_bins = ephem.get_available_lst(
-        nbins=args.nbins, weather=weather[ilo:ihi], include_twilight=args.include_twilight)
+        nbins=args.nbins, weather=tweather,
+        include_twilight=args.include_twilight)
 
     # Initialize the output results table.
     conditions = ['DARK', 'GRAY', 'BRIGHT']
@@ -227,6 +238,7 @@ def calculate_initial_plan(args):
                  .format(condition, plan_sum, avail_sum, 1e2 * margin))
         # Save planned LST usage.
         table['PLAN'] = opt.plan_hist
+        table['HA0'] = opt.plan_hist_ha0
         hdus.append(fits.BinTableHDU(table, name=condition))
 
         # Calculate exposure times in (solar) seconds.