def obsUpdateRow(fldPar, donePar, startTime, mmt, prevPos=None):
"""Calculate observing weight for given observation.
Input:
fldpar : DataFrame for a single observation. Comes from
readAllFLDFiles (i.e. not a single dictionary)
donepar : Dataframe with stats pertaining to a fields doneness
starTime : datetime string for beginning time for observation
"""
# The idea here is to loop through all of the fields and calculate
# the weight for this startTime.
weightList = [] # Will store array of dicts with weight info
for objID in fldPar["objid"]:
# Is this already observed?
donefld = donePar[donePar['objid'] == objID]
fld = fldPar[fldPar["objid"] == objID]
if donefld['complete'].values[0] == 1:
continue
# Initialize the weight dictionary
obsWeight = {}
obsWeight['objid'] = objID
objEphem = MMTEphem.ObjEphem(fld['ra'].values[0], fld['dec'].values[0],
startTime, mmt)
fitWeight, endTime, fitVisits = willItFitWeight(
fld, startTime, mmt, objEphem, donefld)
moonFlag = calcMoonFlag(fld, startTime, endTime, mmt)
# Does this observation have one very close by
dist_weight = 1
if prevPos is not None:
dist = angSep(
hms2dec(fld['ra'].values[0]) * 15.0,
hms2dec(fld['dec'].values[0]), prevPos[0], prevPos[1])
if dist < 10. / 3600.:
dist_weight = 1000
obsWeight['fitWeight'] = fitWeight
obsWeight['obsTime'] = (endTime - startTime).total_seconds()
obsWeight['moonFlag'] = moonFlag
obsWeight['nVisits'] = fitVisits
obsWeight['ra'] = fld['ra'].values[0]
obsWeight['dec'] = fld['dec'].values[0]
# Priority Weight
priorFlag = 1.0 / float(fld['priority'])**3
# Now combine the weights
weightTAC = 1.0 - 1.0 * donefld['doneTime'].values[0] \
/ donefld['totalTime'].values[0]
if weightTAC < 0:
weightTAC = 0.001
totalWeight = fitWeight * moonFlag * \
(weightTAC) *dist_weight*priorFlag
# We need to account for a few extra things.
# 1. I want fields that have had previously observed
# fields to be the top priority if they are observable.
# This modification will weight fields with no observations
# at one (1+0) and those partially observed at 10
partCompWeight = int(donefld['doneVisit'].values[0] > 0)
#totalWeight = totalWeight * (1+0.5*partCompWeight)
# Now account for weighting from preivous iteration of the
# code. This should smooth things out
totalWeight = totalWeight / donefld['prevWeight'].values[0]
obsWeight['totalWeight'] = totalWeight
# Append to weight listing
weightList.append(obsWeight)
obsWeights = pd.DataFrame(weightList)
# Doing the O(n) problem here
hasMax = []
# Check to see there are actually targets!
if len(obsWeights) == 0:
return None, None
maxWeight = max(obsWeights['totalWeight'])
for ii in range(len(obsWeights)):
if obsWeights['totalWeight'].values[ii] == maxWeight:
hasMax.append(ii)
# Now, check there are some with non-zero weight and
# Observe a random one
if maxWeight == 0:
# No targets were done.
return None, None
else:
# This allows for random choice.
randIndex = hasMax[randint(0, len(hasMax) - 1)]
diffTime = obsWeights['obsTime'].values[randIndex]
# Increment the schedule
schedule = []
schedule.append(startTime)
schedule.append(diffTime)
schedule.append(obsWeights['objid'].values[randIndex])
schedule.append(obsWeights['nVisits'].values[randIndex])
outPos = [
hms2dec(obsWeights['ra'].values[randIndex]) * 15.0,
hms2dec(obsWeights['dec'].values[randIndex])
]
# Update the done masks
index = [i for i, x in enumerate(donePar['objid'] == schedule[2]) if x]
# Donetime tracks total time for this PI. We weight by
# this, so let's increment all entries in donefld
PI = donePar.loc[index, 'PI'].values[0]
for ii in range(len(fldPar)):
if donePar["PI"].values[ii] == PI:
donePar.loc[ii, 'doneTime'] += diffTime / 3600.
donePar.loc[ii, 'currentWeight'] = \
donePar.loc[ii, 'doneTime'] / \
donePar.loc[ii, 'totalTime']
donePar.loc[index, 'doneVisit'] += \
obsWeights['nVisits'].values[randIndex]
# Check to see if the target is now done
requested = int(
fldPar[fldPar['objid'] == schedule[2]]["repeats"].values[0])
if int(donePar.loc[index, 'doneVisit']) >= requested:
donePar.loc[index, 'complete'] = 1
return schedule, outPos
