Tickets/opsim 1140

rubin_sim/maf/batches/col_map_dict.py

@@ -26,8 +26,6 @@ def col_map_dict(dict_name=None):
         col_map["seeingGeom"] = "seeingFwhmGeom"
         col_map["skyBrightness"] = "skyBrightness"
         col_map["moonDistance"] = "moonDistance"
-        col_map["fieldId"] = "fieldId"
-        col_map["proposalId"] = "proposalId"
         col_map["slewactivities"] = {}
         col_map["metadataList"] = [
             "airmass",
@@ -41,7 +39,7 @@ def col_map_dict(dict_name=None):
             "saturation_mag",
         ]
         col_map["metadataAngleList"] = ["rotSkyPos"]
-        col_map["note"] = "note"
+        col_map["scheduler_note"] = "scheduler_note"
 
     elif dict_name == "opsimv4":
         col_map = {}

rubin_sim/maf/batches/ddf_batch.py

@@ -474,7 +474,7 @@ def ddfBatch(
             for f in filterlist:
                 fieldsqls[f] = sqls[f]
         else:
-            fieldsql = f"note like '%{fieldname}%'"
+            fieldsql = f"scheduler_note like '%{fieldname}%'"
             for f in filterlist:
                 if len(sqls[f]) > 0:
                     fieldsqls[f] = fieldsql + " and " + sqls[f]

rubin_sim/maf/batches/glance_batch.py

@@ -144,7 +144,7 @@ def glanceBatch(
         bundle_list.append(bundle)
 
     # alt az of long gaps
-    sql = "note = 'long'"
+    sql = "scheduler_note = 'long'"
     metric = metrics.CountMetric(colmap["mjd"], metric_name="Nvisits long")
     bundle = metric_bundles.MetricBundle(
         metric,
@@ -156,7 +156,7 @@ def glanceBatch(
     )
     bundle_list.append(bundle)
 
-    sql = "note like 'blob_long%'"
+    sql = "scheduler_note like 'blob_long%'"
     metric = metrics.CountMetric(colmap["mjd"], metric_name="Nvisits blob long")
     bundle = metric_bundles.MetricBundle(
         metric,
@@ -168,7 +168,7 @@ def glanceBatch(
     )
     bundle_list.append(bundle)
 
-    sql = "note like '%neo%' or note like '%near_sun%'"
+    sql = "scheduler_note like '%neo%' or scheduler_note like '%near_sun%'"
     metric = metrics.CountMetric(colmap["mjd"], metric_name="Nvisits twilight near sun")
     bundle = metric_bundles.MetricBundle(
         metric,
@@ -182,7 +182,7 @@ def glanceBatch(
 
     # alt,az pf ToO
 
-    sql = "note like 'ToO%'"
+    sql = "scheduler_note like 'ToO%'"
     metric = metrics.CountMetric(colmap["mjd"], metric_name="Nvisits long")
     bundle = metric_bundles.MetricBundle(
         metric,
@@ -277,7 +277,7 @@ def glanceBatch(
         bundle_list.append(bundle)
 
     # Make a cumulative plot of a WFD spot
-    sql = "note not like '%NEO%' and note not like '%near_sun%'"
+    sql = "scheduler_note not like '%NEO%' and scheduler_note not like '%near_sun%'"
     uslicer = slicers.UserPointsSlicer(ra=0, dec=-20)
     metric = metrics.CumulativeMetric()
     metricb = metric_bundles.MetricBundle(
@@ -362,16 +362,16 @@ def glanceBatch(
     )
     bundle_list.append(bundle)
 
-    # stats from the note column
-    if "note" in colmap.keys():
+    # stats from the scheduler_note column
+    if "scheduler_note" in colmap.keys():
         displayDict = {"group": "Basic Stats", "subgroup": "Percent stats"}
-        metric = metrics.StringCountMetric(col=colmap["note"], percent=True, metric_name="Percents")
+        metric = metrics.StringCountMetric(col=colmap["scheduler_note"], percent=True, metric_name="Percents")
         sql = ""
         slicer = slicers.UniSlicer()
         bundle = metric_bundles.MetricBundle(metric, slicer, sql, display_dict=displayDict)
         bundle_list.append(bundle)
         displayDict["subgroup"] = "Count Stats"
-        metric = metrics.StringCountMetric(col=colmap["note"], metric_name="Counts")
+        metric = metrics.StringCountMetric(col=colmap["scheduler_note"], metric_name="Counts")
         bundle = metric_bundles.MetricBundle(metric, slicer, sql, display_dict=displayDict)
         bundle_list.append(bundle)
 
@@ -381,7 +381,7 @@ def glanceBatch(
     displayDict["subgroup"] = ""
     for ddf in ddf_surveys:
         label = ddf.replace("DD:", "")
-        sql = 'note like "%s%%"' % ("DD:" + label)
+        sql = 'scheduler_note like "%s%%"' % ("DD:" + label)
         slicer = slicers.UniSlicer()
         metric = metrics.CumulativeMetric()
         metricb = metric_bundles.MetricBundle(
@@ -515,7 +515,7 @@ def glanceBatch(
         lat_lon_deg=colmap["raDecDeg"],
     )
     for filtername in filternames:
-        sql = "filter='%s' and note like 'ToO%%'" % filtername
+        sql = "filter='%s' and scheduler_note like 'ToO%%'" % filtername
         metric = metrics.CountMetric(col=colmap["mjd"], metric_name="N ToO")
         bundle = metric_bundles.MetricBundle(
             metric,
@@ -528,7 +528,9 @@ def glanceBatch(
         )
         bundle_list.append(bundle)
 
-    too_sqls = ["note like 'ToO, %" + "t%i'" % hour for hour in [0, 1, 2, 4, 24, 48]] + ["note like 'ToO, %'"]
+    too_sqls = ["scheduler_note like 'ToO, %" + "t%i'" % hour for hour in [0, 1, 2, 4, 24, 48]] + [
+        "scheduler_note like 'ToO, %'"
+    ]
     slicer = slicers.UniSlicer()
     for sql in too_sqls:
         metric = metrics.CountMetric(col="night")

rubin_sim/maf/batches/info_batch.py

@@ -61,7 +61,7 @@ def info_bundle_dicts(allsky_slicer, wfd_slicer, opsim="opsim", colmap=batches.c
             colmap,
             opsim,
             slicer=allsky_slicer,
-            extraSql='night > 365*3.5 and night < 365*4.5 and note not like "%DD%"',
+            extraSql='night > 365*3.5 and night < 365*4.5 and scheduler_note not like "%DD%"',
             extraInfoLabel="Yr 3-4",
             runLength=1,
         )
@@ -87,7 +87,7 @@ def info_bundle_dicts(allsky_slicer, wfd_slicer, opsim="opsim", colmap=batches.c
             extraInfoLabel="WFD",
         )
     )
-    dd_constraint = "note like '%DD%'"
+    dd_constraint = "scheduler_note like '%DD%'"
     bdict.update(
         batches.nvisitsPerSubset(
             colmap,

rubin_sim/maf/batches/radar_limited.py

@@ -529,7 +529,7 @@ def radar_limited(
     for yr_cut in yrs:
         ptsrc_lim_mag_i_band = mag_cuts[yr_cut]
         sqlconstraint = "night <= %s" % (yr_cut * 365.25 + 0.5)
-        sqlconstraint += ' and note not like "DD%"'
+        sqlconstraint += ' and scheduler_note not like "DD%"'
         info_label = f"{bandpass} band non-DD year {yr_cut}"
         ThreebyTwoSummary_simple = metrics.StaticProbesFoMEmulatorMetricSimple(
             nside=nside, year=yr_cut, metric_name="3x2ptFoM_simple"
@@ -581,7 +581,7 @@ def radar_limited(
     subgroupCount += 1
     displayDict["subgroup"] = f"{subgroupCount}: WL"
     displayDict["order"] = 0
-    sqlconstraint = 'note not like "DD%" and (filter="g" or filter="r" or filter="i")'
+    sqlconstraint = 'scheduler_note not like "DD%" and (filter="g" or filter="r" or filter="i")'
     info_label = "gri band non-DD"
     minExpTime = 15
     m = metrics.WeakLensingNvisits(
@@ -612,7 +612,7 @@ def radar_limited(
     # Do the weak lensing per year
     for year in [10]:
         sqlconstraint = (
-            'note not like "DD%"'
+            'scheduler_note not like "DD%"'
             + ' and (filter="g" or filter="r" or filter="i") and night < %i' % (year * 365.25)
         )
         m = metrics.WeakLensingNvisits(
@@ -648,7 +648,7 @@ def radar_limited(
         bundleList.append(bundle)
 
         sqlconstraint = (
-            'note not like "DD%"'
+            'scheduler_note not like "DD%"'
             + ' and (filter="r" or filter="i" or filter="z") and night < %i' % (year * 365.25)
         )
         m = metrics.WeakLensingNvisits(
@@ -741,7 +741,7 @@ def radar_limited(
     bundle = mb.MetricBundle(
         metric,
         snslicer,
-        "note not like '%DD%'",
+        "scheduler_note not like '%DD%'",
         plot_dict=plotDict,
         display_dict=displayDict,
         info_label="DDF excluded",
@@ -925,7 +925,7 @@ def radar_limited(
     bundle = mb.MetricBundle(
         metric,
         kneslicer,
-        "note not like 'DD%'",
+        "scheduler_note not like 'DD%'",
         run_name=runName,
         info_label="single model",
         summary_metrics=lightcurve_summary(),
@@ -947,7 +947,7 @@ def radar_limited(
     bundle = mb.MetricBundle(
         metric_allkne,
         kneslicer_allkne,
-        "note not like 'DD%'",
+        "scheduler_note not like 'DD%'",
         run_name=runName,
         info_label="all models",
         summary_metrics=lightcurve_summary(),

rubin_sim/maf/batches/science_radar_batch.py

@@ -568,7 +568,7 @@ def science_radar_batch(
     for yr_cut in yrs:
         ptsrc_lim_mag_i_band = mag_cuts[yr_cut]
         sqlconstraint = "night <= %s" % (yr_cut * 365.25 + 0.5)
-        sqlconstraint += ' and note not like "DD%"'
+        sqlconstraint += ' and scheduler_note not like "DD%"'
         info_label = f"{bandpass} band non-DD year {yr_cut}"
         ThreebyTwoSummary_simple = metrics.StaticProbesFoMEmulatorMetricSimple(
             nside=nside, year=yr_cut, metric_name="3x2ptFoM_simple"
@@ -614,7 +614,7 @@ def science_radar_batch(
     plotDict = {"n_ticks": 5}
     # Have to include all filters in query to check for filter coverage.
     # Galaxy numbers calculated using 'bandpass' images only though.
-    sqlconstraint = 'note not like "DD%"'
+    sqlconstraint = 'scheduler_note not like "DD%"'
     info_label = f"{bandpass} band galaxies non-DD"
     metric = maf.DepthLimitedNumGalMetric(
         nside=nside,
@@ -654,7 +654,7 @@ def science_radar_batch(
     subgroupCount += 1
     displayDict["subgroup"] = f"{subgroupCount}: WL"
     displayDict["order"] = 0
-    sqlconstraint = 'note not like "DD%" and (filter="g" or filter="r" or filter="i")'
+    sqlconstraint = 'scheduler_note not like "DD%" and (filter="g" or filter="r" or filter="i")'
     info_label = "gri band non-DD"
     minExpTime = 15
     m = metrics.WeakLensingNvisits(
@@ -686,7 +686,7 @@ def science_radar_batch(
     for year in np.arange(1, 10):
         displayDict["order"] = year
         sqlconstraint = (
-            'note not like "DD%"'
+            'scheduler_note not like "DD%"'
             + ' and (filter="g" or filter="r" or filter="i") and night < %i' % (year * 365.25)
         )
         m = metrics.WeakLensingNvisits(
@@ -722,7 +722,7 @@ def science_radar_batch(
         bundleList.append(bundle)
 
         sqlconstraint = (
-            'note not like "DD%"'
+            'scheduler_note not like "DD%"'
             + ' and (filter="r" or filter="i" or filter="z") and night < %i' % (year * 365.25)
         )
         m = metrics.WeakLensingNvisits(
@@ -781,7 +781,7 @@ def science_radar_batch(
     # Kuiper per year in gri and riz
     for year in np.arange(1, 10):
         sqlconstraint = (
-            'note not like "DD%"'
+            'scheduler_note not like "DD%"'
             + ' and (filter="g" or filter="r" or filter="i") and night < %i' % (year * 365.25)
         )
         metric1 = metrics.KuiperMetric("rotSkyPos", metric_name="Kuiper_rotSkyPos_gri_year%i" % year)
@@ -835,7 +835,7 @@ def science_radar_batch(
     bundle = mb.MetricBundle(
         metric,
         snslicer,
-        "note not like '%DD%'",
+        "scheduler_note not like '%DD%'",
         plot_dict=plotDict,
         display_dict=displayDict,
         info_label="DDF excluded",
@@ -858,7 +858,7 @@ def science_radar_batch(
 
     # Calculate the number of expected QSOs, in each band
     for f in filterlist:
-        sql = filtersqls[f] + ' and note not like "%DD%"'
+        sql = filtersqls[f] + ' and scheduler_note not like "%DD%"'
         md = filterinfo_label[f] + " and non-DD"
         summaryMetrics = [metrics.SumMetric(metric_name="Total QSO")]
         zmin = 0.3
@@ -1359,7 +1359,7 @@ def science_radar_batch(
     bundle = mb.MetricBundle(
         metric,
         kneslicer,
-        "note not like 'DD%'",
+        "scheduler_note not like 'DD%'",
         run_name=runName,
         info_label="single model",
         summary_metrics=lightcurve_summary(),
@@ -1381,7 +1381,7 @@ def science_radar_batch(
     bundle = mb.MetricBundle(
         metric_allkne,
         kneslicer_allkne,
-        "note not like 'DD%'",
+        "scheduler_note not like 'DD%'",
         run_name=runName,
         info_label="all models",
         summary_metrics=lightcurve_summary(),

rubin_sim/maf/metrics/sn_n_sn_metric.py

@@ -86,7 +86,7 @@ def __init__(
         nexp_col="numExposures",
         vistime_col="visitTime",
         seeing_col="seeingFwhmEff",
-        note_col="note",
+        note_col="scheduler_note",
         season=[-1],
         coadd_night=True,
         zmin=0.1,

rubin_sim/maf/metrics/use_metrics.py

@@ -8,7 +8,7 @@
 class UseMetric(BaseMetric):  # pylint: disable=too-few-public-methods
     """Metric to classify visits by type of visits"""
 
-    def __init__(self, note_col="note", **kwargs):
+    def __init__(self, note_col="scheduler_note", **kwargs):
         self.note_col = note_col
         super().__init__(col=[note_col], metric_dtype="object", **kwargs)
 

rubin_sim/maf/slicers/time_interval_slicers.py

@@ -147,7 +147,7 @@ def __init__(
         self,
         mjd_column_name="observationStartMJD",
         duration_column_name="visitTime",
-        note_column_name="note",
+        note_column_name="scheduler_note",
         badval=np.NaN,
         verbose=False,
     ):
@@ -171,7 +171,7 @@ def setup_slicer(self, sim_data, maps=None):
             columns={
                 self.mjd_column_name: "mjd",
                 self.duration_column_name: "duration",
-                self.note_column_name: "note",
+                self.note_column_name: "scheduler_note",
             },
             inplace=True,
         )

rubin_sim/maf/stackers/__init__.py

@@ -1,7 +1,6 @@
 from .base_stacker import *
 from .coord_stackers import *
 from .date_stackers import *
-from .dither_stackers import *
 from .general_stackers import *
 from .get_col_info import *
 from .label_stackers import *

rubin_sim/maf/stackers/coord_stackers.py

@@ -7,7 +7,24 @@
 from rubin_scheduler.utils import calc_lmst
 
 from .base_stacker import BaseStacker
-from .dither_stackers import wrap_ra
+
+
+def wrap_ra(ra):
+    """
+    Wrap only RA values into 0-2pi (using mod).
+
+    Parameters
+    ----------
+    ra : numpy.ndarray
+        RA in radians
+
+    Returns
+    -------
+    numpy.ndarray
+        Wrapped RA values, in radians.
+    """
+    ra = ra % (2.0 * np.pi)
+    return ra
 
 
 def ra_dec2_alt_az(ra, dec, lat, lon, mjd, altonly=False):