def get_prediction_states(self, tbegin):
"""
Get the states used for the prediction. This includes both the
states from the review load backstop file and all the
states between the latest telemetry data and the beginning
of that review load backstop file.
The Review Backstop commands already obtained.
Telemtry from 21 days back to the latest in Ska obtained.
So now the task is to backchain through the loads and assemble
any states missing between the end of telemetry through the start
of the review load.
Parameters
----------
tbegin : string
The starting date/time from which to obtain states for
prediction. This is tlm['date'][-5]) or, in other words, the
date used is 5 enteries back from the end of the fetched telemetry
"""
"""
Get state0 as last cmd_state that starts within available telemetry.
The original logic in get_state0() is to return a state that
is absolutely, positively reliable by insisting that the
returned state is at least ``date_margin`` days old, where the
default is 10 days. That is too conservative (given the way
commanded states are actually managed) and not what is desired
here, which is a recent state from which to start thermal propagation.
Instead we supply ``date_margin=None`` so that get_state0 will
find the newest state consistent with the ``date`` criterion
and pcad_mode == 'NPNT'.
"""
# If an OFLS directory has been specified, get the backstop commands
# stored in the backstop file in that directory
# Ok ready to start the collection of continuity commands
#
# Make a copy of the Review Load Commands. This will have
# Continuity commands concatenated to it and will be the final product
import copy
bs_cmds = copy.copy(self.bs_cmds)
bs_start_time = bs_cmds[0]['time']
present_ofls_dir = copy.copy(self.backstop_file)
# So long as the earliest command in bs_cmds is after the state0
# time, keep concatenating continuity commands to bs_cmds based upon
# the type of load.
# Note that as you march back in time along the load chain, "ofls_dir" will change.
# First we need a State0 because cmd_states.get_states cannot translate
# backstop commands into commanded states without one. cmd_states.get_state0
# is written such that if you don't give it a database object it will
# create one for itself and use that. Here, all that really matters
# is the value of 'tbegin', the specification of the date parameter to be used
# and the date_margin.
state0 = cmd_states.get_state0(tbegin,
self.db,
datepar='datestart',
date_margin=None)
# WHILE
# The big while loop that backchains through previous loads and concatenates the
# proper load sections to the review load.
while state0['tstart'] < bs_start_time:
# Read the Continuity information of the present ofls directory
cont_load_path, present_load_type, scs107_date = self.BSC.get_continuity_file_info(
present_ofls_dir)
#---------------------- NORMAL ----------------------------------------
# If the load type is "normal" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
if present_load_type.upper() == 'NORMAL':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(
cont_load_path)
# Combine the continuity commands with the bs_cmds
bs_cmds = self.BSC.CombineNormal(cont_bs_cmds, bs_cmds)
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
#---------------------- TOO ----------------------------------------
# If the load type is "too" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
elif present_load_type.upper() == 'TOO':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(
cont_load_path)
# Combine the continuity commands with the bs_cmds
bs_cmds = self.BSC.CombineTOO(cont_bs_cmds, bs_cmds)
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
#---------------------- STOP ----------------------------------------
# If the load type is "STOP" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
# Take into account the SCS-107 commands which shut ACIS down
# and any LTCTI run
elif present_load_type.upper() == 'STOP':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(
cont_load_path)
# CombineSTOP the continuity commands with the bs_cmds
bs_cmds = self.BSC.CombineSTOP(cont_bs_cmds, bs_cmds,
scs107_date)
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
#---------------------- SCS-107 ----------------------------------------
# If the load type is "STOP" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
# Take into account the SCS-107 commands which shut ACIS down
# and any LTCTI run
elif present_load_type.upper() == 'SCS-107':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(
cont_load_path)
# Store the continuity bs commands as a chunk in the chunk list
# Obtain the CONTINUITY load Vehicle-Only file
vo_bs_cmds, vo_bs_name = self.BSC.get_vehicle_only_bs_cmds(
cont_load_path)
# Combine107 the continuity commands with the bs_cmds
bs_cmds = self.BSC.Combine107(cont_bs_cmds, vo_bs_cmds,
bs_cmds, scs107_date)
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
# Convert the assembled backstop command history into commanded states
# from state0 through the end of the Review Load backstop commands.
# get_states trims the list to any command whose time is AFTER the state0 START
# time and then converts each relevant backstop command, in that resultant list,
# into a pseudo-commanded states state
states = cmd_states.get_states(state0, bs_cmds)
# Get rid of the 2099 placeholder stop date
states[-1].datestop = bs_cmds[-1]['date']
states[-1].tstop = bs_cmds[-1]['time']
self.logger.debug('state0 at %s is\n%s' %
(DateTime(state0['tstart']).date, pformat(state0)))
return states, state0
def make_week_predict(opt, tstart, tstop, bs_cmds, tlm, db):
print "In make_week_predict"
# Try to make initial state0 from cmd line options
state0 = dict((x, getattr(opt, x))
for x in ('pitch', 'simpos', 'ccd_count', 'fep_count',
'vid_board', 'clocking', 'T_dea'))
print state0
state0.update({'tstart': tstart - 30,
'tstop': tstart,
'datestart': DateTime(tstart - 30).date,
'datestop': DateTime(tstart).date,
'q1': 0.0, 'q2': 0.0, 'q3': 0.0, 'q4': 1.0,
}
)
print state0
# If cmd lines options were not fully specified then get state0 as last
# cmd_state that starts within available telemetry. Update with the
# mean temperatures at the start of state0.
if None in state0.values():
state0 = cmd_states.get_state0(tlm['date'][-5], db,
datepar='datestart')
ok = ((tlm['date'] >= state0['tstart'] - 700) &
(tlm['date'] <= state0['tstart'] + 700))
state0.update({'T_dea': np.mean(tlm['1deamzt'][ok])})
# TEMPORARY HACK: core model doesn't actually support predictive
# active heater yet. Initial temperature determines active heater
# state for predictions now.
if state0['T_dea'] < 15:
state0['T_dea'] = 15.0
logger.debug('state0 at %s is\n%s' % (DateTime(state0['tstart']).date,
pformat(state0)))
# Get commands after end of state0 through first backstop command time
cmds_datestart = state0['datestop']
cmds_datestop = bs_cmds[0]['date']
# Get timeline load segments including state0 and beyond.
timeline_loads = db.fetchall("""SELECT * from timeline_loads
WHERE datestop > '%s'
and datestart < '%s'"""
% (cmds_datestart, cmds_datestop))
logger.info('Found {} timeline_loads after {}'.format(
len(timeline_loads), cmds_datestart))
# Get cmds since datestart within timeline_loads
db_cmds = cmd_states.get_cmds(cmds_datestart, db=db, update_db=False,
timeline_loads=timeline_loads)
# Delete non-load cmds that are within the backstop time span
# => Keep if timeline_id is not None or date < bs_cmds[0]['time']
db_cmds = [x for x in db_cmds if (x['timeline_id'] is not None or
x['time'] < bs_cmds[0]['time'])]
logger.info('Got %d cmds from database between %s and %s' %
(len(db_cmds), cmds_datestart, cmds_datestop))
# Get the commanded states from state0 through the end of backstop commands
states = cmd_states.get_states(state0, db_cmds + bs_cmds)
states[-1].datestop = bs_cmds[-1]['date']
states[-1].tstop = bs_cmds[-1]['time']
logger.info('Found %d commanded states from %s to %s' %
(len(states), states[0]['datestart'], states[-1]['datestop']))
# Create array of times at which to calculate DEA temps, then do it.
logger.info('Calculating DEA thermal model')
print state0
model = calc_model(opt.model_spec, states, state0['tstart'], tstop,
state0['T_dea'])
# Make the DEA limit check plots and data files
plt.rc("axes", labelsize=10, titlesize=12)
plt.rc("xtick", labelsize=10)
plt.rc("ytick", labelsize=10)
temps = {'dea': model.comp['1deamzt'].mvals}
plots = make_check_plots(opt, states, model.times, temps, tstart)
viols = make_viols(opt, states, model.times, temps)
write_states(opt, states)
write_temps(opt, model.times, temps)
return dict(opt=opt, states=states, times=model.times, temps=temps,
plots=plots, viols=viols)
def get_prediction_states(self, tbegin):
"""
Get the states used for the prediction.
Parameters
----------
tbegin : string
The starting date/time from which to obtain states for
prediction.
"""
"""
Get state0 as last cmd_state that starts within available telemetry.
The original logic in get_state0() is to return a state that
is absolutely, positively reliable by insisting that the
returned state is at least ``date_margin`` days old, where the
default is 10 days. That is too conservative (given the way
commanded states are actually managed) and not what is desired
here, which is a recent state from which to start thermal propagation.
Instead we supply ``date_margin=None`` so that get_state0 will
find the newest state consistent with the ``date`` criterion
and pcad_mode == 'NPNT'.
"""
state0 = cmd_states.get_state0(tbegin,
self.db,
datepar='datestart',
date_margin=None)
self.logger.debug('state0 at %s is\n%s' %
(DateTime(state0['tstart']).date, pformat(state0)))
# Get commands after end of state0 through first backstop command time
cmds_datestart = state0['datestop']
cmds_datestop = self.bs_cmds[0]['date']
# Get timeline load segments including state0 and beyond.
timeline_loads = self.db.fetchall("""SELECT * from timeline_loads
WHERE datestop >= '%s'
and datestart < '%s'""" %
(cmds_datestart, cmds_datestop))
self.logger.info('Found {} timeline_loads after {}'.format(
len(timeline_loads), cmds_datestart))
# Get cmds since datestart within timeline_loads
db_cmds = cmd_states.get_cmds(cmds_datestart,
db=self.db,
update_db=False,
timeline_loads=timeline_loads)
# Delete non-load cmds that are within the backstop time span
# => Keep if timeline_id is not None (if a normal load)
# or date < bs_cmds[0]['time']
# If this is an interrupt load, we don't want to include the end
# commands from the continuity load since not all of them will be valid,
# and we could end up evolving on states which would not be present in
# the load under review. However, once the load has been approved and is
# running / has run on the spacecraft, the states in the database will
# be correct, and we will want to include all relevant commands from the
# continuity load. To check for this, we find the current time and see
# the load under review is still in the future. If it is, we then treat
# this as an interrupt if requested, otherwise, we don't.
current_time = DateTime().secs
interrupt = self.interrupt and self.bs_cmds[0]["time"] > current_time
db_cmds = [
x for x in db_cmds
if ((x['timeline_id'] is not None and not interrupt)
or x['time'] < self.bs_cmds[0]['time'])
]
self.logger.info('Got %d cmds from database between %s and %s' %
(len(db_cmds), cmds_datestart, cmds_datestop))
# Get the commanded states from state0 through the end of backstop commands
states = cmd_states.get_states(state0, db_cmds + self.bs_cmds)
states[-1].datestop = self.bs_cmds[-1]['date']
states[-1].tstop = self.bs_cmds[-1]['time']
self.logger.info(
'Found %d commanded states from %s to %s' %
(len(states), states[0]['datestart'], states[-1]['datestop']))
return states, state0
def make_week_predict(opt, tstart, tstop, bs_cmds, tlm, db):
logger.debug("In make_week_predict")
# Try to make initial state0 from cmd line options
state0 = dict((x, getattr(opt, x))
for x in ('pitch', 'simpos', 'ccd_count', 'fep_count',
'vid_board', 'clocking', 'T_psmc','T_pin1at',
'dh_heater'))
state0.update({'tstart': tstart - 30,
'tstop': tstart,
'datestart': DateTime(tstart - 30).date,
'datestop': DateTime(tstart).date,
'q1': 0.0, 'q2': 0.0, 'q3': 0.0, 'q4': 1.0,
}
)
logger.debug("Completed state0 update")
# If cmd lines options were not fully specified then get state0 as last
# cmd_state that starts within available telemetry. Update with the
# mean temperatures at the start of state0.
if None in state0.values():
state0 = cmd_states.get_state0(tlm['date'][-5], db,
datepar='datestart')
ok = ((tlm['date'] >= state0['tstart'] - 700) &
(tlm['date'] <= state0['tstart'] + 700))
state0.update({'T_psmc': np.mean(tlm['1pdeaat'][ok])})
# state0.update({'T_pin1at': np.mean(tlm['1pin1at'][ok]) + 3.0 })
state0.update({'T_pin1at': np.mean(tlm['1pdeaat'][ok]) - 10.0 })
# TEMPORARY HACK: core model doesn't actually support predictive
# active heater yet. Initial temperature determines active heater
# state for predictions now.
if state0['T_psmc'] < 15:
state0['T_psmc'] = 15.0
logger.info('state0 at %s is\n%s' % (DateTime(state0['tstart']).date,
pformat(state0)))
# Get commands after end of state0 through first backstop command time
cmds_datestart = state0['datestop']
cmds_datestop = bs_cmds[0]['date']
# Get timeline load segments including state0 and beyond.
timeline_loads = db.fetchall("""SELECT * from timeline_loads
WHERE datestop > '%s'
and datestart < '%s'"""
% (cmds_datestart, cmds_datestop))
logger.info('Found {} timeline_loads after {}'.format(
len(timeline_loads), cmds_datestart))
# Get cmds since datestart within timeline_loads
db_cmds = cmd_states.get_cmds(cmds_datestart, db=db, update_db=False,
timeline_loads=timeline_loads)
# Delete non-load cmds that are within the backstop time span
# => Keep if timeline_id is not None or date < bs_cmds[0]['time']
db_cmds = [x for x in db_cmds if (x['timeline_id'] is not None or
x['time'] < bs_cmds[0]['time'])]
logger.info('Got %d cmds from database between %s and %s' %
(len(db_cmds), cmds_datestart, cmds_datestop))
# Get the commanded states from state0 through the end of backstop commands
states = cmd_states.get_states(state0, db_cmds + bs_cmds)
states[-1].datestop = bs_cmds[-1]['date']
states[-1].tstop = bs_cmds[-1]['time']
logger.info('Found %d commanded states from %s to %s' %
(len(states), states[0]['datestart'], states[-1]['datestop']))
# htrbfn='/home/edgar/acis/thermal_models/dhheater_history/dahtbon_history.rdb'
htrbfn='dahtbon_history.rdb'
logger.info('Reading file of dahtrb commands from file %s' % htrbfn)
htrb=Ska.Table.read_ascii_table(htrbfn,headerrow=2,headertype='rdb')
dh_heater_times=Chandra.Time.date2secs(htrb['time'])
dh_heater=htrb['dahtbon'].astype(bool)
# Create array of times at which to calculate PSMC temps, then do it.
logger.info('Calculating PSMC thermal model')
logger.info('state0 at start of calc is\n%s' % (pformat(state0)))
model = calc_model(opt.model_spec, states, state0['tstart'], tstop,
state0['T_psmc'],None,state0['T_pin1at'], None,
dh_heater,dh_heater_times)
# Make the PSMC limit check plots and data files
plt.rc("axes", labelsize=10, titlesize=12)
plt.rc("xtick", labelsize=10)
plt.rc("ytick", labelsize=10)
temps = dict(psmc=model.comp['1pdeaat'].mvals,pin=model.comp['pin1at'].mvals)
plots = make_check_plots(opt, states, model.times, temps, tstart)
viols = make_viols(opt, states, model.times, temps)
write_states(opt, states)
write_temps(opt, model.times, temps)
return dict(opt=opt, states=states, times=model.times, temps=temps,
plots=plots, viols=viols)
def get_prediction_states(self, tbegin):
"""
Get the states used for the prediction. This includes both the
states from the review load backstop file and all the
states between the latest telemetry data and the beginning
of that review load backstop file.
The Review Backstop commands already obtained.
Telemtry from 21 days back to the latest in Ska obtained.
So now the task is to backchain through the loads and assemble
any states missing between the end of telemetry through the start
of the review load.
Parameters
----------
tbegin : string
The starting date/time from which to obtain states for
prediction. This is tlm['date'][-5]) or, in other words, the
date used is 5 enteries back from the end of the fetched telemetry
"""
"""
Get state0 as last cmd_state that starts within available telemetry.
The original logic in get_state0() is to return a state that
is absolutely, positively reliable by insisting that the
returned state is at least ``date_margin`` days old, where the
default is 10 days. That is too conservative (given the way
commanded states are actually managed) and not what is desired
here, which is a recent state from which to start thermal propagation.
Instead we supply ``date_margin=None`` so that get_state0 will
find the newest state consistent with the ``date`` criterion
and pcad_mode == 'NPNT'.
"""
# If an OFLS directory has been specified, get the backstop commands
# stored in the backstop file in that directory
# Ok ready to start the collection of continuity commands
#
# Make a copy of the Review Load Commands. This will have
# Continuity commands concatenated to it and will be the final product
import copy
bs_cmds = copy.copy(self.bs_cmds)
bs_start_time = bs_cmds[0]['time']
present_ofls_dir = copy.copy(self.backstop_file)
# So long as the earliest command in bs_cmds is after the state0
# time, keep concatenating continuity commands to bs_cmds based upon
# the type of load.
# Note that as you march back in time along the load chain, "ofls_dir" will change.
# First we need a State0 because cmd_states.get_states cannot translate
# backstop commands into commanded states without one. cmd_states.get_state0
# is written such that if you don't give it a database object it will
# create one for itself and use that. Here, all that really matters
# is the value of 'tbegin', the specification of the date parameter to be used
# and the date_margin.
state0 = cmd_states.get_state0(tbegin, self.db, datepar='datestart',
date_margin=None)
# WHILE
# The big while loop that backchains through previous loads and concatenates the
# proper load sections to the review load.
while state0['tstart'] < bs_start_time:
# Read the Continuity information of the present ofls directory
cont_load_path, present_load_type, scs107_date = self.BSC.get_continuity_file_info(present_ofls_dir)
#---------------------- NORMAL ----------------------------------------
# If the load type is "normal" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
if present_load_type.upper() == 'NORMAL':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(cont_load_path)
# Combine the continuity commands with the bs_cmds
bs_cmds = self.BSC.CombineNormal(cont_bs_cmds, bs_cmds)
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
#---------------------- TOO ----------------------------------------
# If the load type is "too" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
elif present_load_type.upper() == 'TOO':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(cont_load_path)
# Combine the continuity commands with the bs_cmds
bs_cmds = self.BSC.CombineTOO(cont_bs_cmds, bs_cmds)
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
#---------------------- STOP ----------------------------------------
# If the load type is "STOP" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
# Take into account the SCS-107 commands which shut ACIS down
# and any LTCTI run
elif present_load_type.upper() == 'STOP':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(cont_load_path)
# CombineSTOP the continuity commands with the bs_cmds
bs_cmds = self.BSC.CombineSTOP(cont_bs_cmds, bs_cmds, scs107_date )
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
#---------------------- SCS-107 ----------------------------------------
# If the load type is "STOP" then grab the continuity command
# set and concatenate those commands to the start of bs_cmds
# Take into account the SCS-107 commands which shut ACIS down
# and any LTCTI run
elif present_load_type.upper() == 'SCS-107':
# Obtain the continuity load commands
cont_bs_cmds, cont_bs_name = self.BSC.get_bs_cmds(cont_load_path)
# Store the continuity bs commands as a chunk in the chunk list
# Obtain the CONTINUITY load Vehicle-Only file
vo_bs_cmds, vo_bs_name = self.BSC.get_vehicle_only_bs_cmds(cont_load_path)
# Combine107 the continuity commands with the bs_cmds
bs_cmds = self.BSC.Combine107(cont_bs_cmds, vo_bs_cmds, bs_cmds, scs107_date )
# Reset the backstop collection start time for the While loop
bs_start_time = bs_cmds[0]['time']
# Now point the operative ofls directory to the Continuity directory
present_ofls_dir = cont_load_path
# Convert the assembled backstop command history into commanded states
# from state0 through the end of the Review Load backstop commands.
# get_states trims the list to any command whose time is AFTER the state0 START
# time and then converts each relevant backstop command, in that resultant list,
# into a pseudo-commanded states state
states = cmd_states.get_states(state0, bs_cmds)
# Get rid of the 2099 placeholder stop date
states[-1].datestop = bs_cmds[-1]['date']
states[-1].tstop = bs_cmds[-1]['time']
self.logger.debug('state0 at %s is\n%s' % (DateTime(state0['tstart']).date,
pformat(state0)))
return states, state0
def get_prediction_states(self, tbegin):
"""
Get the states used for the prediction.
Parameters
----------
tbegin : string
The starting date/time from which to obtain states for
prediction.
"""
"""
Get state0 as last cmd_state that starts within available telemetry.
The original logic in get_state0() is to return a state that
is absolutely, positively reliable by insisting that the
returned state is at least ``date_margin`` days old, where the
default is 10 days. That is too conservative (given the way
commanded states are actually managed) and not what is desired
here, which is a recent state from which to start thermal propagation.
Instead we supply ``date_margin=None`` so that get_state0 will
find the newest state consistent with the ``date`` criterion
and pcad_mode == 'NPNT'.
"""
state0 = cmd_states.get_state0(tbegin, self.db, datepar='datestart',
date_margin=None)
self.logger.debug('state0 at %s is\n%s' % (DateTime(state0['tstart']).date,
pformat(state0)))
# Get commands after end of state0 through first backstop command time
cmds_datestart = state0['datestop']
cmds_datestop = self.bs_cmds[0]['date']
# Get timeline load segments including state0 and beyond.
timeline_loads = self.db.fetchall("""SELECT * from timeline_loads
WHERE datestop >= '%s'
and datestart < '%s'"""
% (cmds_datestart, cmds_datestop))
self.logger.info('Found {} timeline_loads after {}'.format(
len(timeline_loads), cmds_datestart))
# Get cmds since datestart within timeline_loads
db_cmds = cmd_states.get_cmds(cmds_datestart, db=self.db, update_db=False,
timeline_loads=timeline_loads)
# Delete non-load cmds that are within the backstop time span
# => Keep if timeline_id is not None (if a normal load)
# or date < bs_cmds[0]['time']
# If this is an interrupt load, we don't want to include the end
# commands from the continuity load since not all of them will be valid,
# and we could end up evolving on states which would not be present in
# the load under review. However, once the load has been approved and is
# running / has run on the spacecraft, the states in the database will
# be correct, and we will want to include all relevant commands from the
# continuity load. To check for this, we find the current time and see
# the load under review is still in the future. If it is, we then treat
# this as an interrupt if requested, otherwise, we don't.
current_time = DateTime().secs
interrupt = self.interrupt and self.bs_cmds[0]["time"] > current_time
db_cmds = [x for x in db_cmds
if ((x['timeline_id'] is not None and not interrupt) or
x['time'] < self.bs_cmds[0]['time'])]
self.logger.info('Got %d cmds from database between %s and %s' %
(len(db_cmds), cmds_datestart, cmds_datestop))
# Get the commanded states from state0 through the end of backstop commands
states = cmd_states.get_states(state0, db_cmds + self.bs_cmds)
states[-1].datestop = self.bs_cmds[-1]['date']
states[-1].tstop = self.bs_cmds[-1]['time']
self.logger.info('Found %d commanded states from %s to %s' %
(len(states), states[0]['datestart'],
states[-1]['datestop']))
return states, state0
def make_week_predict(opt, tstart, tstop, bs_cmds, tlm, db):
# Try to make initial state0 from cmd line options
state0 = dict((x, getattr(opt, x)) for x in ("pitch", "simpos", "power", "T_dea", "T_pin"))
state0.update(
{
"tstart": tstart - 30,
"tstop": tstart,
"datestart": DateTime(tstart - 30).date,
"datestop": DateTime(tstart).date,
}
)
# If cmd lines options were not fully specified then get state0 as last
# cmd_state that starts within available telemetry. Update with the
# mean temperatures at the start of state0.
if None in state0.values():
state0 = cmd_states.get_state0(tlm[-5].date, db, datepar="datestart")
ok = (tlm.date >= state0["tstart"] - 150) & (tlm.date <= state0["tstart"] + 150)
state0.update({"T_dea": np.mean(tlm["1pdeaat"][ok]), "T_pin": np.mean(tlm["1pin1at"][ok])})
logger.debug("state0 at %s is\n%s" % (DateTime(state0["tstart"]).date, pformat(state0)))
if opt.old_cmds:
cmds_datestart = DateTime(state0["tstop"]).date
cmds_datestop = DateTime(bs_cmds[0]["time"]).date
db_cmds = cmd_states.get_cmds(cmds_datestart, cmds_datestop, db)
else:
# Get the commands after end of state0 through first backstop command time
cmds_datestart = state0["datestop"]
cmds_datestop = bs_cmds[0]["date"] # *was* DateTime(bs_cmds[0]['time']).date
# Get timeline load segments including state0 and beyond.
timeline_loads = db.fetchall(
"""SELECT * from timeline_loads
WHERE datestop > '%s' and datestart < '%s'"""
% (cmds_datestart, cmds_datestop)
)
logger.info("Found %s timeline_loads after %s" % (len(timeline_loads), cmds_datestart))
# Get cmds since datestart within timeline_loads
db_cmds = cmd_states.get_cmds(cmds_datestart, db=db, update_db=False, timeline_loads=timeline_loads)
# Delete non-load cmds that are within the backstop time span
# => Keep if timeline_id is not None or date < bs_cmds[0]['time']
db_cmds = [x for x in db_cmds if (x["timeline_id"] is not None or x["time"] < bs_cmds[0]["time"])]
logger.info("Got %d cmds from database between %s and %s" % (len(db_cmds), cmds_datestart, cmds_datestop))
# Get the commanded states from state0 through the end of the backstop commands
states = cmd_states.get_states(state0, db_cmds + bs_cmds)
states[-1].datestop = bs_cmds[-1]["date"]
states[-1].tstop = bs_cmds[-1]["time"]
logger.info(
"Found %d commanded states from %s to %s" % (len(states), states[0]["datestart"], states[-1]["datestop"])
)
# Add power column based on ACIS commanding in states
states = Ska.Numpy.add_column(states, "power", get_power(states))
# Create array of times at which to calculate PSMC temperatures, then do it.
times = np.arange(state0["tstart"], tstop, opt.dt)
logger.info("Calculating PSMC thermal model")
T_pin, T_dea = twodof.calc_twodof_model(states, state0["T_pin"], state0["T_dea"], times, characteristics.model_par)
# Make the PSMC limit check plots and data files
plt.rc("axes", labelsize=10, titlesize=12)
plt.rc("xtick", labelsize=10)
plt.rc("ytick", labelsize=10)
temps = dict(dea=T_dea, pin=T_pin)
plots = make_check_plots(opt, states, times, temps, tstart)
viols = make_viols(opt, states, times, temps)
write_states(opt, states)
write_temps(opt, times, temps)
return dict(opt=opt, states=states, times=times, temps=temps, plots=plots, viols=viols)