MTScheduler: Add BLOCK-T345 json for science program

Co-authored-by: Lynne Jones <[email protected]>

Scheduler/feature_scheduler/maintel/fbs_config_sit_survey_block_t345.py

@@ -0,0 +1,194 @@
+# This file is part of ts_config_ocs.
+#
+# Developed for the Vera Rubin Observatory Telescope and Site System.
+# This product includes software developed by the LSST Project
+# (https://www.lsst.org).
+# See the COPYRIGHT file at the top-level directory of this distribution
+# for details of code ownership.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+import numpy as np
+from rubin_scheduler.scheduler import basis_functions, detailers, example, features
+from rubin_scheduler.scheduler.schedulers import CoreScheduler
+from rubin_scheduler.scheduler.surveys import BlobSurvey
+from rubin_scheduler.scheduler.utils import CurrentAreaMap, Footprint
+from rubin_scheduler.site_models import Almanac
+
+
+def get_scheduler():
+    nside = 32
+    science_program = "BLOCK-T345"
+    survey_start = 60653.5
+    camera_rot_limits = [-80, 80]
+
+    map_band_to_filtername = {
+        "u": "u_02",
+        "g": "g_01",
+        "r": "r_03",
+        "i": "i_06",
+        "z": "z_03",
+        "y": "y_04",
+    }
+
+    filtername = map_band_to_filtername["r"]
+    filtername2 = map_band_to_filtername["g"]
+
+    # Masks are fine - no band specific information.
+    mask_basis_functions = example.standard_masks(
+        nside=nside,
+        # Let's avoid the moon by this many degrees
+        moon_distance=30.0,
+        # Erik says to make wind speed minor so set limit high
+        wind_speed_maximum=50.0,
+        # I think these are the values appropriate for alt limits now?
+        min_alt=40,
+        max_alt=70,
+        min_az=0,
+        max_az=360,
+        # Avoid going into avoidance regions 30 minutes into the future
+        shadow_minutes=30,
+    )
+    # Mask basis functions have zero weights.
+    mask_basis_functions_weights = [0 for mask in mask_basis_functions]
+
+    # Set up footprint stuff here instead of in rubin_scheduler.
+    # Use the Almanac to find the position of the sun at the start of survey.
+    almanac = Almanac(mjd_start=survey_start)
+    sun_moon_info = almanac.get_sun_moon_positions(survey_start)
+    sun_ra_start = sun_moon_info["sun_RA"].copy()
+    # So this is a dictionary of filternames : int.
+    filterdict = {}
+    for i, f in enumerate(map_band_to_filtername.values()):
+        filterdict[f] = i
+    footprints = Footprint(
+        filters=filterdict,
+        mjd_start=survey_start,
+        sun_ra_start=sun_ra_start,
+        nside=nside,
+    )
+    # need to remap this to filtername not band
+    footprint = CurrentAreaMap(nside=nside)
+    footprint_hp, labels = footprint.return_maps()
+    new_dtype = np.dtype([(map_band_to_filtername[f], "<f8") for f in "ugrizy"])
+    footprint_hp_filter = footprint_hp.astype(new_dtype)
+    for f in footprint_hp_filter.dtype.names:
+        footprints.set_footprint(f, footprint_hp_filter[f])
+
+    # Now set up basis functions
+    m5_weight = 6.0
+    footprint_weight = 1.5
+    slewtime_weight = 3.0
+    stayfilter_weight = 3.0
+    repeat_weight = -20
+    # Add the same basis functions, but M5 and footprint
+    # basis functions need to be added twice, with half the weight.
+    rf1 = example.simple_rewards(
+        footprints=footprints,
+        filtername=filtername,
+        nside=nside,
+        m5_weight=m5_weight / 2.0,
+        footprint_weight=footprint_weight / 2.0,
+        slewtime_weight=slewtime_weight,
+        stayfilter_weight=stayfilter_weight,
+        repeat_weight=repeat_weight,
+    )
+    rf2 = example.simple_rewards(
+        footprints=footprints,
+        filtername=filtername2,
+        nside=nside,
+        m5_weight=m5_weight / 2.0,
+        footprint_weight=footprint_weight / 2.0,
+        slewtime_weight=0,
+        stayfilter_weight=0,
+        repeat_weight=0,
+    )
+    # Now clean up and combine these - and remove the separate
+    # BasisFunction for FilterLoadedBasisFunction.
+    reward_functions = [(i[0], i[1]) for i in rf1 if i[1] > 0] + [
+        (i[0], i[1]) for i in rf2 if i[1] > 0
+    ]
+    # Remove the M5Diff basis functions as unusable at present
+    reward_functions = [
+        (bf, weight)
+        for (bf, weight) in reward_functions
+        if not isinstance(bf, basis_functions.M5DiffBasisFunction)
+    ]
+
+    # unpack the basis functions and weights
+    reward_basis_functions_weights = [val[1] for val in reward_functions]
+    reward_basis_functions = [val[0] for val in reward_functions]
+
+    # Set up blob surveys.
+    pair_time = 20
+    if filtername2 is None:
+        survey_name = "simple pair %i, %s" % (pair_time, filtername)
+    else:
+        survey_name = "simple pair %i, %s%s" % (pair_time, filtername, filtername2)
+
+    # Set up detailers for each requested observation.
+    detailer_list = []
+    # Avoid camera rotator limits.
+    detailer_list.append(
+        detailers.CameraRotDetailer(
+            min_rot=np.min(camera_rot_limits), max_rot=np.max(camera_rot_limits)
+        )
+    )
+    # Reorder visits in a blob so that closest to current altitude is first.
+    detailer_list.append(detailers.CloseAltDetailer())
+    # Add a detailer to label visits as either first or second of the pair.
+    if filtername2 is not None:
+        detailer_list.append(detailers.TakeAsPairsDetailer(filtername=filtername2))
+
+    # Set up the survey.
+    ignore_obs = ["DD"]
+
+    BlobSurvey_params = {
+        "slew_approx": 7.5,
+        "filter_change_approx": 140.0,
+        "read_approx": 2.4,
+        "flush_time": pair_time * 3,
+        "smoothing_kernel": None,
+        "nside": nside,
+        "seed": 42,
+        "dither": True,
+        "twilight_scale": False,
+    }
+
+    pair_survey = BlobSurvey(
+        reward_basis_functions + mask_basis_functions,
+        reward_basis_functions_weights + mask_basis_functions_weights,
+        filtername1=filtername,
+        filtername2=filtername2,
+        exptime=150,
+        ideal_pair_time=pair_time,
+        survey_name=survey_name,
+        ignore_obs=ignore_obs,
+        nexp=1,
+        detailers=detailer_list,
+        science_program=science_program,
+        **BlobSurvey_params,
+    )
+
+    # Tucking this here so we can look at how many observations
+    # recorded for this survey and what was the last one.
+    pair_survey.extra_features["ObsRecorded"] = features.NObsCount()
+    pair_survey.extra_features["LastObs"] = features.LastObservation()
+
+    scheduler = CoreScheduler([pair_survey], nside=nside)
+    return nside, scheduler
+
+
+if __name__ == "config":
+    nside, scheduler = get_scheduler()

Scheduler/observing_blocks_maintel/AOS/BLOCK-T345.json

@@ -0,0 +1,79 @@
+{
+    "name": "AOSSurveyMode",
+    "program": "BLOCK-T345",
+    "constraints": [],
+    "scripts": [
+        {
+            "name": "maintel/track_target.py",
+            "standard": true,
+            "parameters": {
+                "target_name": "$name",
+                "slew_icrs": {
+                    "ra": "$ra",
+                    "dec": "$dec"
+                },
+                "rot_value": "$rot",
+                "rot_type": "PhysicalSky",
+                "az_wrap_strategy": "NOUNWRAP"
+            }
+        },
+        {
+            "name": "maintel/close_loop_comcam.py",
+            "standard": true,
+            "parameters": {
+                "exposure_time": 15,
+                "max_iter": 1,
+                "gain_sequence": [
+                    0.75
+                ],
+                "mode": "FAM",
+                "program": "$program",
+                "note": "closed_loop_aos_survey_mode",
+                "reason": "aos_survey_mode",
+                "filter": "$band_filter",
+                "used_dofs": [
+                    0,
+                    1,
+                    2,
+                    3,
+                    4,
+                    5,
+                    6,
+                    7,
+                    8,
+                    9,
+                    10,
+                    11,
+                    12,
+                    13,
+                    14,
+                    17,
+                    18,
+                    19,
+                    20,
+                    21,
+                    22,
+                    23,
+                    28,
+                    29,
+                    30,
+                    31,
+                    32,
+                    33,
+                    34,
+                    37,
+                    38,
+                    39,
+                    40,
+                    41,
+                    42,
+                    45,
+                    46
+                ],
+                "apply_corrections": true,
+                "use_ocps": true
+            }
+        }
+    ],
+    "configuration_schema": ""
+}

Scheduler/v7/maintel_fbs_sit_block_t345.yaml

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+maintel:
+  driver_type: feature_scheduler
+  mode: ADVANCE
+  startup_type: COLD
+  startup_database: /home/saluser/rubin_sim_data/fbs_observation_database_maintel.sql
+  instrument_name: CCCamera
+  models:
+    observatory_model:
+      camera:
+        filter_max_changes_burst_num: 1000000
+        filter_max_changes_avg_num: 30000
+      optics_loop_corr:
+        tel_optics_cl_alt_limit:
+          - 0
+          - 60
+          - 90
+      park:
+        filter_position: r_03
+  driver_configuration:
+    parameters:
+      night_boundary: -10.0
+    stop_tracking_observing_script_name: maintel/stop_tracking.py
+  feature_scheduler_driver_configuration:
+    observation_database_name: /home/saluser/rubin_sim_data/fbs_observation_database_maintel.sql
+    scheduler_config: /net/obs-env/auto_base_packages/ts_config_ocs/Scheduler/feature_scheduler/maintel/fbs_config_sit_survey_block_t345.py
+  telemetry:
+    streams:
+      - name: seeing
+        efd_table: lsst.sal.DIMM.logevent_dimmMeasurement
+        efd_columns:
+          - fwhm
+        efd_delta_time: 300.0
+        fill_value: 1.0
+      - name: wind_speed
+        efd_table: lsst.sal.ESS.airFlow
+        efd_columns:
+          - speed
+        efd_delta_time: 300.0
+        fill_value: 0.0
+        csc_index: 301
+      - name: wind_direction
+        efd_table: lsst.sal.ESS.airFlow
+        efd_columns:
+          - direction
+        efd_delta_time: 300.0
+        fill_value: 0.0
+        csc_index: 301