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apt_inputs.py
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apt_inputs.py
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#! /usr/bin/env python
'''
Given APT output files, read in data relevant to the data simulator,
organize, and create input files for the simulator.
Inputs:
xml file - Name of xml file exported from APT.
pointing file - Name of associated pointing file exported from APT.
siaf - Name of csv version of SIAF.
Optional inputs:
epoch_list - Name of ascii file which lists observation labels and
associated starting observation time, as well as telescope
roll angle (PAV3). If you wish to have observations back-
to-back, give them all the same starting time.
Outputs:
output_csv - Ascii table containing all relevant information needed
to construct a ramp simulator input file for each
observation/dither/detector combination.
Dependencies:
argparse, lxml, astropy, numpy, collections
JWST Calibration pipeline (only for the use of set_telescope_pointing.py)
in order to translate PAV3 values to local roll angles for each detector.
rotations.py - Colin Cox's module of WCS-related functions
HISTORY:
July 2017 - V0: Initial version. Bryan Hilbert
'''
import argparse
from lxml import etree
from astropy.table import Table, Column
from astropy.io import ascii
import numpy as np
import sys,os
import collections
import yaml
import rotations
import set_telescope_pointing_separated as set_telescope_pointing
class AptInput:
def __init__(self):
self.input_xml = '' #e.g. 'GOODSS_ditheredDatasetTest.xml'
self.output_csv = None #e.g. 'GOODSS_ditheredDatasetTest.csv'
self.pointing_file = '' #e.g. 'GOODSS_ditheredDatasetTest.pointing'
self.siaf = ''
self.observation_table = ''
self.output_csv = None
def read_xml(self,infile):
#read in APT xml file. WFSS and/or direct exposures can be
#in the file
#open file, get tree
with open(infile) as f:
tree = etree.parse(f)
namespaces = tree.getroot().nsmap.copy()
namespaces['apt'] = namespaces[None]
del namespaces[None]
#creat dictionary to hold all relevant exposure info
dict = {}
dict['PI_Name'] = []
dict['ProposalID'] = []
dict['Title'] = []
dict['Proposal_category'] = []
dict['Science_category'] = []
dict['Mode'] = []
dict['Module'] = []
dict['Subarray'] = []
dict['PrimaryDitherType'] = []
dict['PrimaryDithers'] = []
dict['SubpixelDitherType'] = []
dict['SubpixelPositions'] = []
dict['ShortFilter'] = []
dict['LongFilter'] = []
dict['ReadoutPattern'] = []
dict['Groups'] = []
dict['Integrations'] = []
dict['ShortPupil'] = []
dict['LongPupil'] = []
dict['Grism'] = []
dict['CoordinatedParallel'] = []
#get high level information: proposal info
#P.I. Name, etc
propid_default = 42424
proptitle_default = 'Looking for my towel'
scicat_default = 'Planets and Planet Formation'
piname_default = 'D.N. Adams'
apt = '{http://www.stsci.edu/JWST/APT}'
propinfo = tree.find(apt+'ProposalInformation')
try:
proptitle = propinfo.find(apt+'Title').text
except:
proptitle = proptitle_default
try:
propid = propinfo.find(apt+'ProposalID').text
except:
propid = propid_default
propcat = 'GO'
try:
scicat = propinfo.find(apt+'ScientificCategory').text
except:
scicat = scicat_default
try:
piinfo = propinfo.find(apt+'PrincipalInvestigator')
piadd = piinfo.find(apt+'InvestigatorAddress')
firstname = piadd.find(apt+'FirstName').text
lastname = piadd.find(apt+'LastName').text
piname = firstname + ' ' + lastname
except:
piname = piname_default
#Also look at mosaic details. All we really need to know
#is how many tiles will be taken.
#more streamlined version with valid (i belive) assumptions
#about format of xml file:
#within an observation: only 1 instrument, 1 template, 1 nci:NircamImaging or 1 ncwfss:NircamWfss
obspath = "//apt:Observation"
obsresults = tree.xpath(obspath, namespaces=namespaces)
obsgrouppath = "//apt:ObservationGroup"
obsgroupresults = tree.xpath(obsgrouppath,namespaces=namespaces)
apt = '{http://www.stsci.edu/JWST/APT}'
nci = "{http://www.stsci.edu/JWST/APT/Template/NircamImaging}"
ncwfss = "{http://www.stsci.edu/JWST/APT/Template/NircamWfss}"
mos = "{http://www.stsci.edu/JWST/APT/MosaicParameters}"
for obs in obsresults:
obs_tuple_list = []
numbele = obs.find(apt+'Number')
number = numbele.text
labelele = obs.find(apt+'Label')
if labelele is not None:
label = labelele.text
else:
label = 'None'
targele = obs.find(apt+'TargetID')
targsplit = targele.text.split()
target = ''
for i in range(1,len(targsplit)):
target += targsplit[i]
instele = obs.find(apt+'Instrument')
if instele.text == 'NIRCAM':
template = obs.find(apt+'Template')
coordparallel = obs.find(apt+'CoordinatedParallel').text
imaging_temp = template.find(nci+'NircamImaging')
if imaging_temp is not None:
typeflag = 'Imaging'
grismval = 'N/A'
short_pupil = 'CLEAR'
mod = imaging_temp.find(nci+'Module').text
subarr = imaging_temp.find(nci+'Subarray').text
pdithtype = imaging_temp.find(nci+'PrimaryDitherType').text
try:
pdither = imaging_temp.find(nci+'PrimaryDithers').text
except:
pdither = '1'
sdithtype = imaging_temp.find(nci+'SubpixelDitherType').text
try:
sdither = imaging_temp.find(nci+'SubpixelPositions').text
except:
try:
stemp = imaging_temp.find(nci+'CoordinatedParallelSubpixelPositions').text
sdither = np.int(stemp[0])
except:
sdither = '1'
filtele = imaging_temp.find(nci+'Filters')
filtconfigeles = filtele.findall(nci+'FilterConfig')
for fcele in filtconfigeles:
sfilt = fcele.find(nci+'ShortFilter').text
lfilt = fcele.find(nci+'LongFilter').text
rpatt = fcele.find(nci+'ReadoutPattern').text
grps = fcele.find(nci+'Groups').text
ints = fcele.find(nci+'Integrations').text
#separate pupil and filter in case of filter that is
#mounted in the pupil wheel
if '+' in sfilt:
p = sfilt.find('+')
short_pupil = sfilt[0:p]
sfilt = sfilt[p+1:]
else:
short_pupil = 'CLEAR'
if '+' in lfilt:
p = lfilt.find('+')
long_pupil = lfilt[0:p]
lfilt = lfilt[p+1:]
else:
long_pupil = 'CLEAR'
tup_to_add = (piname,propid,proptitle,propcat,
scicat,typeflag,mod,subarr,pdithtype,
pdither,sdithtype,sdither,sfilt,lfilt,
rpatt,grps,ints,short_pupil,
long_pupil,grismval,coordparallel)
dict = self.add_exposure(dict,tup_to_add)
obs_tuple_list.append(tup_to_add)
wfss_temp = template.find(ncwfss+'NircamWfss')
if wfss_temp is not None:
mod = wfss_temp.find(ncwfss+'Module').text
subarr = wfss_temp.find(ncwfss+'Subarray').text
grismval = wfss_temp.find(ncwfss+'Grism').text
if grismval == 'BOTH':
grismval = ['GRISMR','GRISMC']
else:
grismval = [grismval]
#pdithtype = wfss_temp.find(ncwfss+'PrimaryDitherType').text
#pdither = wfss_temp.find(ncwfss+'PrimaryDithers').text
#sdither = wfss_temp.find(ncwfss+'SubpixelPositions').text
#sdithtype = wfss_temp.find(ncwfss+'SubpixelPositions').text
explist = wfss_temp.find(ncwfss+'ExposureList')
expseqs = explist.findall(ncwfss+'ExposureSequences')
#if BOTH was specified for the grism,
#then we need to repeat the sequence of
#grism/direct/grism/direct/outoffield for each grism
for gnum in range(len(grismval)):
for expseq in expseqs:
#sequence = grism,direct,grism,direct,outoffield
#if grism == both, sequence is done for grismr,
#then repeated for grismc
grismvalue = grismval[gnum]
#need to switch the order of the grism and direct
#exposures in order for them to be chronological
grismexp = expseq.find(ncwfss+'GrismExposure')
typeflag = 'WFSS'
sfilt = grismexp.find(ncwfss+'ShortFilter').text
lfilt = grismexp.find(ncwfss+'LongFilter').text
rpatt = grismexp.find(ncwfss+'ReadoutPattern').text
groups = grismexp.find(ncwfss+'Groups').text
integrations = grismexp.find(ncwfss+'Integrations').text
pdithtype = wfss_temp.find(ncwfss+'PrimaryDitherType').text
pdither = wfss_temp.find(ncwfss+'PrimaryDithers').text
sdither = wfss_temp.find(ncwfss+'SubpixelPositions').text
sdithtype = wfss_temp.find(ncwfss+'SubpixelPositions').text
#separate pupil and filter in case of filter
#that is mounted in the pupil wheel
if '+' in sfilt:
p = sfilt.find('+')
short_pupil = sfilt[0:p]
sfilt = sfilt[p+1:]
else:
short_pupil = 'CLEAR'
long_pupil = grismvalue
tup_to_add = (piname,propid,proptitle,propcat,
scicat,typeflag,mod,subarr,
pdithtype,pdither,sdithtype,
sdither,sfilt,lfilt,rpatt,groups,
integrations,short_pupil,long_pupil,
grismvalue,coordparallel)
dict = self.add_exposure(dict,tup_to_add)
obs_tuple_list.append(tup_to_add)
directexp = expseq.find(ncwfss+'DiExposure')
typeflag = 'Imaging'
pdither = '1' #direct image has no dithers
sdither = '1' #direct image has no dithers
sdithtype = '1' #direct image has no dithers
grismvalue = 'N/A'
sfilt = directexp.find(ncwfss+'ShortFilter').text
lfilt = directexp.find(ncwfss+'LongFilter').text
rpatt = directexp.find(ncwfss+'ReadoutPattern').text
grps = directexp.find(ncwfss+'Groups').text
ints = directexp.find(ncwfss+'Integrations').text
#separate pupil and filter in case of filter
#that is mounted in the pupil wheel
if '+' in sfilt:
p = sfilt.find('+')
short_pupil = sfilt[0:p]
sfilt = sfilt[p+1:]
else:
short_pupil = 'CLEAR'
if '+' in lfilt:
p = lfilt.find('+')
long_pupil = lfilt[0:p]
lfilt = lfilt[p+1:]
else:
long_pupil = 'CLEAR'
direct_tup_to_add = (piname,propid,proptitle,propcat,
scicat,typeflag,mod,subarr,pdithtype,
pdither,sdithtype,sdither,sfilt,lfilt,
rpatt,grps,ints,short_pupil,long_pupil,
grismvalue,coordparallel)
dict = self.add_exposure(dict,direct_tup_to_add)
obs_tuple_list.append(tup_to_add)
#Now we need to add the two out-of-field exposures, which are
#not present in the APT file (but are in the associated pointing
#file from APT. We can just
#duplicate the entries for the direct images taken immediately
#prior. BUT, will there ever be a case where there is no preceding
#direct image?
dict = self.add_exposure(dict,direct_tup_to_add)
dict = self.add_exposure(dict,direct_tup_to_add)
obs_tuple_list.append(tup_to_add)
obs_tuple_list.append(tup_to_add)
#Now we need to look for mosaic details, if any
mosaicele = obs.find(apt+'MosaicParameters')
mostile = mosaicele.findall(apt+'MosaicTiles')
numtiles = len(mostile)
if numtiles > 1:
print("Found {} mosaic tiles for observation {}".format(numtiles,obs))
for i in range(numtiles-1):
for tup in obs_tuple_list:
dict = self.add_exposure(dict,tup)
return dict
def add_exposure(self,dictionary,tup):
#add an exposure to the dictionary
dictionary['PI_Name'].append(tup[0])
dictionary['ProposalID'].append(tup[1])
dictionary['Title'].append(tup[2])
dictionary['Proposal_category'].append(tup[3])
dictionary['Science_category'].append(tup[4])
dictionary['Mode'].append(tup[5])
dictionary['Module'].append(tup[6])
dictionary['Subarray'].append(tup[7])
dictionary['PrimaryDitherType'].append(tup[8])
dictionary['PrimaryDithers'].append(tup[9])
dictionary['SubpixelDitherType'].append(tup[10])
dictionary['SubpixelPositions'].append(tup[11])
dictionary['ShortFilter'].append(tup[12])
dictionary['LongFilter'].append(tup[13])
dictionary['ReadoutPattern'].append(tup[14])
dictionary['Groups'].append(tup[15])
dictionary['Integrations'].append(tup[16])
dictionary['ShortPupil'].append(tup[17])
dictionary['LongPupil'].append(tup[18])
dictionary['Grism'].append(tup[19])
dictionary['CoordinatedParallel'].append(tup[20])
return dictionary
def read_wfss_xml(self,infile):
#read APT xml file for WFSS mode observations
#first, set up variables
MyList = collections.OrderedDict()
MyList['Module'] = []
MyList['Subarray'] = []
MyList['Grism'] = []
MyList['PrimaryDitherType'] = []
MyList['PrimaryDithers'] = []
MyList['SubpixelPositions'] = []
MyList['TargID'] = []
MyFilterList = collections.OrderedDict()
MyFilterList['Module'] = []
MyFilterList['Subarray'] = []
MyFilterList['Grism'] = []
MyFilterList['PrimaryDitherType'] = []
MyFilterList['PrimaryDithers'] = []
MyFilterList['SubpixelPositions'] = []
MyFilterList['Mode'] = []
MyFilterList['ShortFilter'] = []
MyFilterList['LongFilter'] = []
MyFilterList['ReadoutPattern'] = []
MyFilterList['Groups'] = []
MyFilterList['Integrations'] = []
MyTargList = []
#read in the full file
f = open(self.input_xml)
fullfile = f.readlines()
f.close()
#now find the lines corresponding to the beginning of each
#exposure list.
wfss_start = np.array([]).astype(np.int)
wfss_end = np.array([]).astype(np.int)
explist_start = np.array([]).astype(np.int)
directlist_start = np.array([]).astype(np.int)
grismlist_start = np.array([]).astype(np.int)
targlines = np.array([]).astype(np.int)
#default values in case of missing data in APT file
propid = '42424'
title = 'I need to find my towel'
piname = 'D.N. Adams'
pistart = 0
piend = -1
prop_category = 'GO'
science_category = 'extrasolar towels'
for linenum in range(len(fullfile)):
if "<ncwfss:NircamWfss>" in fullfile[linenum]:
wfss_start = np.append(wfss_start,linenum)
if "</ncwfss:NircamWfss>" in fullfile[linenum]:
wfss_end = np.append(wfss_end,linenum)
if "<ncwfss:ExposureList>" in fullfile[linenum]:
explist_start = np.append(explist_start,linenum)
if "<ncwfss:DiExposure>" in fullfile[linenum]:
directlist_start = np.append(directlist_start,linenum)
if "<ncwfss:GrismExposure>" in fullfile[linenum]:
grismlist_start = np.append(grismlist_start,linenum)
#get the proposal ID number
if "<ProposalID>" in fullfile[linenum]:
propid = self.extract_value(fullfile[linenum])
if "<Title>" in fullfile[linenum]:
title = self.extract_value(fullfile[linenum])
if "<PrincipalInvestigator>" in fullfile[linenum]:
pistart = linenum
if "</PrincipalInvestigator>" in fullfile[linenum]:
piend = linenum
if "<ProposalCategory>" in fullfile[linenum]:
prop_category = self.extract_value(fullfile[linenum+1])[:-1]
if "<ScientificCategory>" in fullfile[linenum]:
science_category = self.extract_value(fullfile[linenum])
#if "<Target ID>" in fullfile[linenum]:
# targlines.append(linenum)
if pistart > 0:
for lnum in range(pistart,piend):
if "<FirstName>" in fullfile[lnum]:
first = self.extract_value(fullfile[lnum])
if "<LastName>" in fullfile[lnum]:
last = self.extract_value(fullfile[lnum])
piname = first + ' ' + last
#now, work on each wfss_start entry individually.
#each one of these will have grism exposures, optional
#direct exposure (singular), and out of field exposures (2).
#We need to keep these all grouped together so that we end
#with an exposure list that is in chronological order
for wfssstart,wfssend in zip(wfss_start,wfss_end):
for listele,addline in zip(MyList.keys(),range(1,7)):
gt = fullfile[wfssstart+addline].find('>')
lt = fullfile[wfssstart+addline].find('<',gt)
MyList[listele] = fullfile[wfssstart+addline][gt+1:lt]
#associate a target ID with each
#prevtarg = np.where(targlines < wfssstart)
#targ = self.extract_value(fullfile[prevtarg][-1]).split()
#fulltarg = ''
#for i in range(1,len(targ)):
# fulltarg = fulltarg + targ[i]
##MyTargList.append(fulltarg.strip())
#now get info on the grism and optional direct images
#that are only within the current wfss_start entry
#first get all the grism exposure info
gline = ((grismlist_start > wfssstart) &
(grismlist_start < wfssend))
#make sure there is a WFSS entry in this exposure list
if np.sum(gline) > 0:
gentries = grismlist_start[gline]
#loop over grism entry start lines
for gindex in range(len(gentries)):
grismstart = gentries[gindex]
if gindex != 0:
prev_grism = gentries[gindex-1]
else:
prev_grism = wfssstart
MyFilterList['Mode'].append('WFSS')
for listele,addline in zip(MyFilterList.keys()[7:12],range(7,12)):
gt = fullfile[grismstart+addline-6].find('>')
lt = fullfile[grismstart+addline-6].find('<',gt)
MyFilterList[listele].append(fullfile[grismstart+addline-6][gt+1:lt])
for key in MyList:
MyFilterList[key].append(MyList[key])
MyTargList.append(fulltarg.strip())
#now get any direct exposure info that is tied to this
#grism exposure
dline = ((directlist_start > prev_grism) &
(directlist_start < grismstart))
#make sure there is a direct image entry in this exposure list
if np.sum(dline) > 0:
for directstart in directlist_start[dline]:
MyFilterList['Mode'].append('Imaging')
MyTargList.append(fulltarg.strip())
for listele,addline in zip(MyFilterList.keys()[7:12],range(7,12)):
gt = fullfile[directstart+addline-6].find('>')
lt = fullfile[directstart+addline-6].find('<',gt)
MyFilterList[listele].append(fullfile[directstart+addline-6][gt+1:lt])
for key in MyList:
if key not in ['PrimaryDithers','SubpixelPositions']:
MyFilterList[key].append(MyList[key])
else:
MyFilterList[key].append('1')
#now we need to add the two OUT OF FIELD expoures,
#which are not in the xml file. They use the same
#readout pattern/groups/ints as the direct image.
#This is seen within APT itself, but is not in the
#xml file. Since everything is the same as the direct
#image taken immediately prior, we can just duplicate
#the dictionary entries for the direct image.
for key in MyFilterList:
MyFilterList[key].extend((MyFilterList[key][-1],MyFilterList[key][-1]))
#add proposal info
MyFilterList['ProposalID'] = []
if len(MyFilterList['Module']) > 0:
MyFilterList['ProposalID'] = [np.int(propid)]*len(MyFilterList['Module'])
MyFilterList['Title'] = [title]*len(MyFilterList['Module'])
MyFilterList['PI_Name'] = [piname]*len(MyFilterList['Module'])
MyFilterList['Proposal_category'] = [prop_category]*len(MyFilterList['Module'])
MyFilterList['Science_category'] = [science_category]*len(MyFilterList['Module'])
#now we need to deal with the pupil values.
swpupillist = ['CLEAR'] * len(MyFilterList['Mode'])
lwpupillist = ['CLEAR'] * len(MyFilterList['Mode'])
for i in range(len(MyFilterList['Mode'])):
if MyFilterList['Mode'][i] == 'WFSS':
lwpupillist[i] = MyFilterList['Grism'][i]
else:
if '+' in MyFilterList['LongFilter'][i]:
p = MyFilterList['LongFilter'][i].find('+')
pup = MyFitlerList['LongFilter'][i][0:p]
f1 = MyFitlerList['LongFilter'][i][p+1:]
lwpupillist[i] = pup
MyFilterList['LongFilter'][i] = f1
if '+' in MyFilterList['ShortFilter'][i]:
p = MyFilterList['ShortFilter'][i].find('+')
pup = MyFilterList['ShortFilter'][i][0:p]
f1 = MyFilterList['ShortFilter'][i][p+1:]
swpupillist[i] = pup
MyFilterList['ShortFilter'][i] = f1
MyFilterList['ShortPupil'] = swpupillist
MyFilterList['LongPupil'] = lwpupillist
#add in target names
#print('list lengths: ',len(MyTargList),len(MyFilterList['ShortPupil'])))
#stop
#MyFilterList['TargID'] = MyTargList
#add subpixeldithertype, to be consistent with imaging output
MyFilterList['SubpixelDitherType'] = MyFilterList['SubpixelPositions']
return MyFilterList
def extract_value(self,line):
#extract text from xml line
gt = line.find('>')
lt = line.find('<',gt)
return line[gt+1:lt]
def read_imaging_xml(self,infile):
#read APT xml file for imaging mode obs
#first, a cheat. get proposal id by reading in file as ascii,
#because I can't figure out the xml way to do it
#read in the full file
f = open(self.input_xml)
fullfile = f.readlines()
f.close()
#get proposal information
#default values in case of missing data in APT file
propid = '42424'
title = 'Looking for my towel'
piname = 'D.N. Adams'
pistart = 0
piend = -1
prop_category = 'GO'
science_category = 'extrasolar towels'
for linenum in range(len(fullfile)):
if "<ProposalID>" in fullfile[linenum]:
propid = self.extract_value(fullfile[linenum])
if "<Title>" in fullfile[linenum]:
title = self.extract_value(fullfile[linenum])
if "<PrincipalInvestigator>" in fullfile[linenum]:
pistart = linenum
if "</PrincipalInvestigator>" in fullfile[linenum]:
piend = linenum
if "<ProposalCategory>" in fullfile[linenum]:
lt = fullfile[linenum+1].find('<')
gt = fullfile[linenum+1].find('>')
prop_category = fullfile[linenum+1][lt+1:gt][:-1]
if "<ScientificCategory>" in fullfile[linenum]:
science_category = self.extract_value(fullfile[linenum])
if pistart > 0:
for lnum in range(pistart,piend):
if "<FirstName>" in fullfile[lnum]:
first = self.extract_value(fullfile[lnum])
if "<LastName>" in fullfile[lnum]:
last = self.extract_value(fullfile[lnum])
piname = first + ' ' + last
path = "//apt:Observation[apt:Instrument[contains(string(), '{}')]]/apt:Template/nci:NircamImaging".format('NIRCAM')
targpath = "//apt:Observation"
# READ XML file
with open(infile) as f:
tree = etree.parse(f)
# APT makes extensive use of XML namespaces
# (e.g. 'xmlns:nsmsasd="http://www.stsci.edu/JWST/APT/Template/NirspecMSAShortDetect"')
# so we have to as well
namespaces = tree.getroot().nsmap.copy()
# There is no 'default' namespace for XPath (used below), but the lxml parser
# does respect a default namespace, so we have to update its name from
# 'None' to 'apt'
namespaces['apt'] = namespaces[None]
del namespaces[None]
# Find your specific Observation
results = tree.xpath(path, namespaces=namespaces)
targresults = tree.xpath(targpath, namespaces=namespaces)
#set up variables for output
MyList = {'Module': [], 'Subarray': [], 'PrimaryDitherType': [],
'PrimaryDithers': [], 'SubpixelDitherType': [],
'SubpixelPositions': []}
MyFilterList = {'ShortFilter': [], 'LongFilter': [],
'ReadoutPattern': [], 'Groups': [], 'Integrations': []}
finalList = {'Mode':[], 'Module': [], 'Subarray': [],
'PrimaryDitherType': [], 'PrimaryDithers': [],
'SubpixelDitherType': [], 'SubpixelPositions': [],
'ShortFilter': [], 'LongFilter': [], 'ReadoutPattern': [],
'Groups': [], 'Integrations': []}
for ExposureList in results:
#reset the lists in the dictionaries to be empty at
#the beginning of each Template
MyList = {'Module': [], 'Subarray': [], 'PrimaryDitherType': [],
'PrimaryDithers': [], 'SubpixelDitherType': [],
'SubpixelPositions': []}
MyFilterList = {'ShortFilter': [], 'LongFilter': [],
'ReadoutPattern': [], 'Groups': [], 'Integrations': []}
for item in MyList:
entryList = ExposureList.xpath('nci:%s' % item,namespaces=namespaces)
for entry in entryList:
MyList[item].append(entry.text)
for item in MyFilterList:
entryList = ExposureList.xpath('nci:Filters/nci:FilterConfig/nci:%s' % item,namespaces=namespaces)
for entry in entryList:
MyFilterList[item].append(entry.text)
#Add in a mode keyword so that we can easily separate
#imaging from wfss entries. This will be useful once
#these outputs are passed to the tool for making simulator
#input files
MyList['Mode'] = ['Imaging']*len(MyList['Module'])
#duplicate entries in the MyList dictionary so that the length
#matches the myFilterList dictionary
n_module = len(MyList['Module'])
n_filter = len(MyFilterList['ShortFilter'])
reps = n_filter - n_module
for key in MyFilterList:
finalList[key] = finalList[key] + MyFilterList[key]
for key in MyList:
finalList[key] = finalList[key] + MyList[key]*(reps+1)
#check the filters. In the case where a pupil wheel-mounted filter
#is used, the filter name will be "filter1+filter2". Separate into
#filter and pupil entries
shortplist = ['CLEAR']*len(finalList['ShortFilter'])
longplist = ['CLEAR']*len(finalList['LongFilter'])
for key in ['ShortFilter','LongFilter']:
for i in range(len(finalList['ShortFilter'])):
filt = finalList[key][i]
if '+' in filt:
p = filt.find('+')
pupil = filt[0:p]
f1 = filt[p+1:]
if key == 'ShortFilter':
finalList[key][i] = f1
shortplist[i] = pupil
if key == 'LongFilter':
finalList[key][i] = f1
longplist[i] = pupil
finalList['ShortPupil'] = shortplist
finalList['LongPupil'] = longplist
#for consistency with the output from the WFSS reader
finalList['Grism'] = ['N/A'] * len(finalList['Mode'])
#add proposal info lines
finalList['ProposalID'] = [np.int(propid)]*len(finalList['Module'])
finalList['Title'] = [title]*len(finalList['Module'])
finalList['PI_Name'] = [piname]*len(finalList['Module'])
finalList['Proposal_category'] = [prop_category]*len(finalList['Module'])
finalList['Science_category'] = [science_category]*len(finalList['Module'])
#for code checking
ascii.write(Table(finalList), 'test_imaging.csv', format='csv', overwrite=True)
return finalList
def expand_for_dithers(self,indict):
#Expand a given dictionary to create one entry
#for each dither
#define the dictionary to hold the expanded entries
#in here we should also reset the primary and subpixel dither
#numbers to 1, to avoid confusion.
expanded = {}
for key in indict:
expanded[key] = []
#loop over entries in dict and duplicate by the
#number of dither positions
keys = np.array(indict.keys())
for i in range(len(indict['PrimaryDithers'])):
#entry = np.array([item[i] for item in dict.values()])
arr = np.array([item[i] for item in indict.values()])
entry = dict(zip(keys,arr))
#subpix = entry[keys == 'SubpixelPositions']
subpix = entry['SubpixelPositions']
if subpix == '0':
subpix = [[1]]
if subpix == '4-Point':
subpix = [[4]]
if subpix == '9-Point':
subpix = [[9]]
#in WFSS, SubpixelPositions will be either '4-Point' or '9-Point'
#primary = entry[keys == 'PrimaryDithers']
primary = entry['PrimaryDithers']
if primary == '0':
primary = [1]
reps = np.int(subpix[0][0]) * np.int(primary[0])
for key in keys:
for j in range(reps):
expanded[key].append(indict[key][i])
return expanded
def base36encode(self,integer):
chars, encoded = '0123456789abcdefghijklmnopqrstuvwxyz', ''
while integer > 0:
integer, remainder = divmod(integer, 36)
encoded = chars[remainder] + encoded
return encoded.zfill(2)
def get_pointing_info(self,file,propid):
#read in information from APT's pointing file
tar = []
tile = []
exp = []
dith = []
aperture = []
targ1 = []
targ2 = []
ra = []
dec = []
basex = []
basey = []
dithx = []
dithy = []
v2 = []
v3 = []
idlx = []
idly = []
level = []
type_str = []
expar = []
dkpar = []
ddist = []
observation_number = []
visit_number = []
visit_id = []
visit_grp = []
activity_id = []
observation_label = []
observation_id = []
seq_id = []
act_counter = 1
with open(file) as f:
for line in f:
if len(line) > 1:
elements = line.split()
#look for lines that give visit/observation numbers
if line[0:2] == '* ':
paren = line.rfind('(')
if paren == -1:
obslabel = line[2:]
obslabel = obslabel.strip()
else:
obslabel = line[2:paren-1]
obslabel = obslabel.strip()
if line[0:2] == '**':
v = elements[2]
obsnum,visitnum = v.split(':')
obsnum = str(obsnum).zfill(3)
visitnum = str(visitnum).zfill(3)
try:
#skip the line at the beginning of each
#visit that gives info on the target,
#but is not actually an observation
#These lines have 'Exp' values of 0,
#while observations have a value of 1
#(that I've seen so far)
#
#Also, skip non-NIRCam lines. Check for NRC in aperture name
if ((np.int(elements[1]) > 0) & ('NRC' in elements[4])):
act = self.base36encode(act_counter)
activity_id.append(act)
observation_label.append(obslabel)
observation_number.append(obsnum)
visit_number.append(visitnum)
vid = str(propid)+visitnum+obsnum
visit_id.append(vid)
vgrp = '01'
visit_grp.append(vgrp)
seq = '1'
seq_id.append(seq)
tar.append(np.int(elements[0]))
tile.append(np.int(elements[1]))
exnum = str(elements[2]).zfill(5)
exp.append(exnum)
dith.append(np.int(elements[3]))
ap = elements[4]
if ('GRISMR_WFSS' in elements[4]):
ap = ap.replace('GRISMR_WFSS','FULL')
elif ('GRISMC_WFSS' in elements[4]):
ap = ap.replace('GRISMC_WFSS','FULL')
aperture.append(ap)
targ1.append(np.int(elements[5]))
targ2.append(elements[6])
ra.append(elements[7])
dec.append(elements[8])
basex.append(elements[9])
basey.append(elements[10])
dithx.append(np.float(elements[11]))
dithy.append(np.float(elements[12]))
v2.append(np.float(elements[13]))
v3.append(np.float(elements[14]))
idlx.append(np.float(elements[15]))
idly.append(np.float(elements[16]))
level.append(elements[17])
type_str.append(elements[18])
expar.append(np.int(elements[19]))
dkpar.append(np.int(elements[20]))
ddist.append(np.float(elements[21]))
observation_id.append('V'+vid+'P00000000'+vgrp+seq+act)
act_counter += 1
except:
pass
pointing = {'exposure':exp, 'dither':dith, 'aperture':aperture,
'targ1':targ1, 'targ2':targ2, 'ra':ra, 'dec':dec,
'basex':basex, 'basey':basey, 'dithx':dithx,
'dithy':dithy, 'v2':v2, 'v3':v3, 'idlx':idlx,
'idly':idly, 'obs_label':observation_label,
'obs_num':observation_number,'visit_num':visit_number,
'act_id':activity_id,'visit_id':visit_id,'visit_group':visit_grp,
'sequence_id':seq_id,'observation_id':observation_id}
return pointing
def combine_dicts(self,dict1,dict2):
#Now combine the dictionaries from the xml file and the pointing file
combined = dict1.copy()
combined.update(dict2)
return combined
def expand_for_detectors(self,dict):
#Expand dictionary to have one line per detector, rather than the
#one line per module that is in the input
finaltab = {}
for key in dict:
finaltab[key] = []
finaltab['detector'] = []
for i in range(len(dict['PrimaryDithers'])):
module = dict['Module'][i]
if module == 'ALL':
detectors = ['A1','A2','A3','A4','A5','B1','B2','B3','B4','B5']
elif module == 'A':
detectors = ['A1','A2','A3','A4','A5']
elif module == 'B':
detectors = ['B1','B2','B3','B4','B5']
for key in dict:
finaltab[key].extend(([dict[key][i]]*len(detectors)))
finaltab['detector'].extend(detectors)
return finaltab
def ra_dec_update(self):
#given the v2,v3 values in each entry, calculate RA,Dec
#read in siaf
distortionTable = ascii.read(self.siaf,header_start=1)
aperture_ra = []
aperture_dec = []
for i in range(len(self.exposure_tab['Module'])):
#first find detector
#need ra,dec and v2,v3 pairs from entry
#to calculate ra,dec at each detector's reference location
detector = 'NRC' + self.exposure_tab['detector'][i]
sub = self.exposure_tab['Subarray'][i]
aperture = detector + '_' + sub
pointing_ra = np.float(self.exposure_tab['ra'][i])
pointing_dec = np.float(self.exposure_tab['dec'][i])
pointing_v2 = np.float(self.exposure_tab['v2'][i])
pointing_v3 = np.float(self.exposure_tab['v3'][i])
pav3 = np.float(self.exposure_tab['pav3'][i])
#calculate local roll angle
local_roll = set_telescope_pointing.compute_local_roll(pav3,pointing_ra,
pointing_dec,
pointing_v2,
pointing_v3)
#create attitude_matrix
attitude_matrix = rotations.attitude(pointing_v2,pointing_v3,
pointing_ra,pointing_dec,local_roll)
#find v2,v3 of the reference location for the detector
aperture_v2,aperture_v3 = self.ref_location(distortionTable,aperture)
#calculate RA, Dec of reference location for the detector
ra,dec = rotations.pointing(attitude_matrix,aperture_v2,aperture_v3)
#if dec < 0:
# print("ra, dec: {}, {}".format(ra,dec))
# print('attitude matrix {}'.format(attitude_matrix))
# print('aperture v2,v3: {}, {}'.format(aperture_v2.data,aperture_v3.data))
# print(pointing_v2,pointing_v3,pointing_ra,pointing_dec,local_roll,pav3)
# stop
aperture_ra.append(ra)
aperture_dec.append(dec)
self.exposure_tab['ra_ref'] = aperture_ra
self.exposure_tab['dec_ref'] = aperture_dec
def ref_location(self,siaf,det):
#find v2,v3 of detector reference location
match = siaf['AperName'] == det
if np.any(match) == False:
print("Aperture name {} not found in input CSV file.".
format(aperture))
sys.exit()
v2 = siaf[match]['V2Ref']
v3 = siaf[match]['V3Ref']
return v2,v3
def create_input_table(self):
# Expand paths to full paths
self.input_xml = os.path.abspath(self.input_xml)
self.pointing_file = os.path.abspath(self.pointing_file)
self.siaf = os.path.abspath(self.siaf)
if self.output_csv is not None:
self.output_csv = os.path.abspath(self.output_csv)
if self.observation_table is not None:
self.observation_table = os.path.abspath(self.observation_table)
#read in xml file using the new function
tab = self.read_xml(self.input_xml)
ascii.write(Table(tab), 'as_read_in.csv', format='csv', overwrite=True)
#expand the dictionary for multiple dithers. Expand such that there
#is one entry in each list for each exposure, rather than one entry
#for each set of dithers
xmltab = self.expand_for_dithers(tab)