def runpipeline(pl, convdict, conf):
"""runs the quicklook pipeline as configured
Args:
pl: is a list of [pa,qas] where pa is a pipeline step and qas the corresponding
qas for that pa
convdict: converted dictionary e.g : conf["IMAGE"] is the real psf file
but convdict["IMAGE"] is like desispec.image.Image object and so on.
details in setup_pipeline method below for examples.
conf: a configured dictionary, read from the configuration yaml file.
e.g: conf=configdict=yaml.load(open('configfile.yaml','rb'))
"""
qlog = qllogger.QLLogger("QuickLook", 20)
log = qlog.getlog()
hb = QLHB.QLHeartbeat(log, conf["Period"], conf["Timeout"])
inp = convdict["rawimage"]
paconf = conf["PipeLine"]
qlog = qllogger.QLLogger("QuickLook", 0)
log = qlog.getlog()
for s, step in enumerate(pl):
log.info("Starting to run step %s" % (paconf[s]["StepName"]))
pa = step[0]
pargs = mapkeywords(step[0].config["kwargs"], convdict)
try:
hb.start("Running %s" % (step[0].name))
inp = pa(inp, **pargs)
except Exception as e:
log.critical("Failed to run PA %s error was %s" %
(step[0].name, e))
sys.exit("Failed to run PA %s" % (step[0].name))
qaresult = {}
for qa in step[1]:
try:
qargs = mapkeywords(qa.config["kwargs"], convdict)
hb.start("Running %s" % (qa.name))
res = qa(inp, **qargs)
log.debug("%s %s" % (qa.name, inp))
qaresult[qa.name] = res
except Exception as e:
log.warning("Failed to run QA %s error was %s" % (qa.name, e))
if len(qaresult):
yaml.dump(qaresult, open(paconf[s]["OutputFile"], "wb"))
hb.stop("Step %s finished. Output is in %s " %
(paconf[s]["StepName"], paconf[s]["OutputFile"]))
else:
hb.stop("Step %s finished." % (paconf[s]["StepName"]))
hb.stop("Pipeline processing finished. Serializing result")
return inp
def setup_pipeline(config):
"""
Given a configuration from QLF, this sets up a pipeline [pa,qa] and also returns a
conversion dictionary from the configuration dictionary so that Pipeline steps (PA) can
take them. This is required for runpipeline.
"""
import astropy.io.fits as fits
import desispec.io.fibermap as fibIO
import desispec.io.sky as skyIO
import desispec.io.fiberflat as ffIO
import desispec.fiberflat as ff
import desispec.io.image as imIO
import desispec.image as im
import desispec.io.frame as frIO
import desispec.frame as dframe
from desispec.quicklook import procalgs
from desispec.boxcar import do_boxcar
qlog=qllogger.QLLogger("QuickLook",20)
log=qlog.getlog()
if config is None:
return None
log.info("Reading Configuration")
if "RawImage" not in config:
log.critical("Config is missing \"RawImage\" key.")
sys.exit("Missing \"RawImage\" key.")
inpname=config["RawImage"]
if "FiberMap" not in config:
log.critical("Config is missing \"FiberMap\" key.")
sys.exit("Missing \"FiberMap\" key.")
fibname=config["FiberMap"]
proctype="Exposure"
if "Camera" in config:
camera=config["Camera"]
if "DataType" in config:
proctype=config["DataType"]
debuglevel=20
if "DebugLevel" in config:
debuglevel=config["DebugLevel"]
log.setLevel(debuglevel)
hbeat=QLHB.QLHeartbeat(log,config["Period"],config["Timeout"])
if config["Timeout"]> 200.0:
log.warning("Heartbeat timeout exceeding 200.0 seconds")
dumpintermediates=False
if "DumpIntermediates" in config:
dumpintermediates=config["DumpIntermediates"]
biasimage=None #- This will be the converted dictionary key
biasfile=None
if "BiasImage" in config:
biasfile=config["BiasImage"]
darkimage=None
darkfile=None
if "DarkImage" in config:
darkfile=config["DarkImage"]
pixelflatfile=None
pixflatimage=None
if "PixelFlat" in config:
pixelflatfile=config["PixelFlat"]
fiberflatimagefile=None
fiberflatimage=None
if "FiberFlatImage" in config:
fiberflatimagefile=config["FiberFlatImage"]
arclampimagefile=None
arclampimage=None
if "ArcLampImage" in config:
arclampimagefile=config["ArcLampImage"]
fiberflatfile=None
fiberflat=None
if "FiberFlatFile" in config:
if config["Flavor"] == 'arcs':
pass
else:
fiberflatfile=config["FiberFlatFile"]
skyfile=None
skyimage=None
if "SkyFile" in config:
skyfile=config["SkyFile"]
psf=None
if config["Flavor"] == 'arcs':
if not os.path.exists(os.path.join(os.environ['QL_SPEC_REDUX'],'calib2d','psf',config["Night"])):
os.mkdir(os.path.join(os.environ['QL_SPEC_REDUX'],'calib2d','psf',config["Night"]))
pass
elif "PSFFile" in config:
#from specter.psf import load_psf
import desispec.psf
psf=desispec.psf.PSF(config["PSFFile"])
#psf=load_psf(config["PSFFile"])
if "basePath" in config:
basePath=config["basePath"]
hbeat.start("Reading input file {}".format(inpname))
inp=fits.open(inpname) #- reading raw image directly from astropy.io.fits
hbeat.start("Reading fiberMap file {}".format(fibname))
fibfile=fibIO.read_fibermap(fibname)
fibhdr=fibfile.meta
convdict={"FiberMap":fibfile}
if psf is not None:
convdict["PSFFile"]=psf
if biasfile is not None:
hbeat.start("Reading Bias Image {}".format(biasfile))
biasimage=imIO.read_image(biasfile)
convdict["BiasImage"]=biasimage
if darkfile is not None:
hbeat.start("Reading Dark Image {}".format(darkfile))
darkimage=imIO.read_image(darkfile)
convdict["DarkImage"]=darkimage
if pixelflatfile:
hbeat.start("Reading PixelFlat Image {}".format(pixelflatfile))
pixelflatimage=imIO.read_image(pixelflatfile)
convdict["PixelFlat"]=pixelflatimage
if fiberflatimagefile:
hbeat.start("Reading FiberFlat Image {}".format(fiberflatimagefile))
fiberflatimage=imIO.read_image(fiberflatimagefile)
convdict["FiberFlatImage"]=fiberflatimage
if arclampimagefile:
hbeat.start("Reading ArcLampImage {}".format(arclampimagefile))
arclampimage=imIO.read_image(arclampimagefile)
convdict["ArcLampImage"]=arclampimage
if fiberflatfile:
hbeat.start("Reading FiberFlat {}".format(fiberflatfile))
fiberflat=ffIO.read_fiberflat(fiberflatfile)
convdict["FiberFlatFile"]=fiberflat
if skyfile:
hbeat.start("Reading SkyModel file {}".format(skyfile))
skymodel=skyIO.read_sky(skyfile)
convdict["SkyFile"]=skymodel
if dumpintermediates:
convdict["DumpIntermediates"]=dumpintermediates
hbeat.stop("Finished reading all static files")
img=inp
convdict["rawimage"]=img
pipeline=[]
for step in config["PipeLine"]:
pa=getobject(step["PA"],log)
if len(pipeline) == 0:
if not pa.is_compatible(type(img)):
log.critical("Pipeline configuration is incorrect! check configuration {} {}".format(img,pa.is_compatible(img)))
sys.exit("Wrong pipeline configuration")
else:
if not pa.is_compatible(pipeline[-1][0].get_output_type()):
log.critical("Pipeline configuration is incorrect! check configuration")
log.critical("Can't connect input of {} to output of {}. Incompatible types".format(pa.name,pipeline[-1][0].name))
sys.exit("Wrong pipeline configuration")
qas=[]
for q in step["QAs"]:
qa=getobject(q,log)
if not qa.is_compatible(pa.get_output_type()):
log.warning("QA {} can not be used for output of {}. Skipping expecting {} got {} {}".format(qa.name,pa.name,qa.__inpType__,pa.get_output_type(),qa.is_compatible(pa.get_output_type())))
else:
qas.append(qa)
pipeline.append([pa,qas])
return pipeline,convdict
def runpipeline(pl,convdict,conf,mergeQA=False):
"""
Runs the quicklook pipeline as configured
Args:
pl: is a list of [pa,qas] where pa is a pipeline step and qas the corresponding
qas for that pa
convdict: converted dictionary e.g : conf["IMAGE"] is the real psf file
but convdict["IMAGE"] is like desispec.image.Image object and so on.
details in setup_pipeline method below for examples.
conf: a configured dictionary, read from the configuration yaml file.
e.g: conf=configdict=yaml.load(open('configfile.yaml','rb'))
mergedQA: if True, outputs the merged QA after the execution of pipeline. Perhaps, this
should always be True, but leaving as option, until configuration and IO settles.
"""
qlog=qllogger.QLLogger("QuickLook",20)
log=qlog.getlog()
hb=QLHB.QLHeartbeat(log,conf["Period"],conf["Timeout"])
inp=convdict["rawimage"]
paconf=conf["PipeLine"]
qlog=qllogger.QLLogger("QuickLook",0)
log=qlog.getlog()
passqadict=None #- pass this dict to QAs downstream
QAresults=[] #- merged QA list for the whole pipeline. This will be reorganized for databasing after the pipeline executes
for s,step in enumerate(pl):
log.info("Starting to run step {}".format(paconf[s]["StepName"]))
pa=step[0]
pargs=mapkeywords(step[0].config["kwargs"],convdict)
try:
hb.start("Running {}".format(step[0].name))
oldinp=inp #- copy for QAs that need to see earlier input
inp=pa(inp,**pargs)
except Exception as e:
log.critical("Failed to run PA {} error was {}".format(step[0].name,e))
sys.exit("Failed to run PA {}".format(step[0].name))
qaresult={}
for qa in step[1]:
try:
qargs=mapkeywords(qa.config["kwargs"],convdict)
hb.start("Running {}".format(qa.name))
qargs["dict_countbins"]=passqadict #- pass this to all QA downstream
if qa.name=="RESIDUAL" or qa.name=="Sky_Residual":
res=qa(inp[0],inp[1],**qargs)
else:
if isinstance(inp,tuple):
res=qa(inp[0],**qargs)
else:
res=qa(inp,**qargs)
if qa.name=="COUNTBINS" or qa.name=="CountSpectralBins": #TODO -must run this QA for now. change this later.
passqadict=res
log.debug("{} {}".format(qa.name,inp))
qaresult[qa.name]=res
except Exception as e:
log.warning("Failed to run QA {} error was {}".format(qa.name,e))
if len(qaresult):
if conf["DumpIntermediates"]:
f = open(paconf[s]["OutputFile"],"w")
f.write(yaml.dump(yamlify(qaresult)))
hb.stop("Step {} finished. Output is in {} ".format(paconf[s]["StepName"],paconf[s]["OutputFile"]))
else:
hb.stop("Step {} finished.".format(paconf[s]["StepName"]))
QAresults.append([pa.name,qaresult])
hb.stop("Pipeline processing finished. Serializing result")
#- merge QAs for this pipeline execution
if mergeQA is True:
from desispec.quicklook.util import merge_QAs
log.info("Merging all the QAs for this pipeline execution")
merge_QAs(QAresults)
if isinstance(inp,tuple):
return inp[0]
else:
return inp
def runpipeline(pl, convdict, conf):
"""runs the quicklook pipeline as configured
Args:
pl: is a list of [pa,qas] where pa is a pipeline step and qas the corresponding
qas for that pa
convdict: converted dictionary e.g : conf["IMAGE"] is the real psf file
but convdict["IMAGE"] is like desispec.image.Image object and so on.
details in setup_pipeline method below for examples.
conf: a configured dictionary, read from the configuration yaml file.
e.g: conf=configdict=yaml.load(open('configfile.yaml','rb'))
"""
qlog = qllogger.QLLogger("QuickLook", 20)
log = qlog.getlog()
hb = QLHB.QLHeartbeat(log, conf["Period"], conf["Timeout"])
inp = convdict["rawimage"]
paconf = conf["PipeLine"]
qlog = qllogger.QLLogger("QuickLook", 0)
log = qlog.getlog()
passqadict = None #- pass this dict to QAs downstream
for s, step in enumerate(pl):
log.info("Starting to run step %s" % (paconf[s]["StepName"]))
pa = step[0]
pargs = mapkeywords(step[0].config["kwargs"], convdict)
try:
hb.start("Running %s" % (step[0].name))
oldinp = inp #- copy for QAs that need to see earlier input
inp = pa(inp, **pargs)
except Exception as e:
log.critical("Failed to run PA %s error was %s" %
(step[0].name, e))
sys.exit("Failed to run PA %s" % (step[0].name))
qaresult = {}
for qa in step[1]:
try:
qargs = mapkeywords(qa.config["kwargs"], convdict)
hb.start("Running %s" % (qa.name))
qargs[
"dict_countbins"] = passqadict #- pass this to all QA downstream
if qa.name == "RESIDUAL" or qa.name == "Sky_Residual":
res = qa(oldinp, inp[1], **qargs)
else:
if isinstance(inp, tuple):
res = qa(inp[0], **qargs)
else:
res = qa(inp, **qargs)
if qa.name == "COUNTBINS" or qa.name == "CountSpectralBins": #TODO -must run this QA for now. change this later.
passqadict = res
log.debug("%s %s" % (qa.name, inp))
qaresult[qa.name] = res
except Exception as e:
log.warning("Failed to run QA %s error was %s" % (qa.name, e))
if len(qaresult):
#- TODO - This dump of QAs for each PA should be reorganised. Dumping everything now.
yaml.dump(qaresult, open(paconf[s]["OutputFile"], "wb"))
hb.stop("Step %s finished. Output is in %s " %
(paconf[s]["StepName"], paconf[s]["OutputFile"]))
else:
hb.stop("Step %s finished." % (paconf[s]["StepName"]))
hb.stop("Pipeline processing finished. Serializing result")
if isinstance(inp, tuple):
return inp[0]
else:
return inp
def runpipeline(pl, convdict, conf):
"""
Runs the quicklook pipeline as configured
Args:
pl: is a list of [pa,qas] where pa is a pipeline step and qas the corresponding
qas for that pa
convdict: converted dictionary e.g : conf["IMAGE"] is the real psf file
but convdict["IMAGE"] is like desispec.image.Image object and so on.
details in setup_pipeline method below for examples.
conf: a configured dictionary, read from the configuration yaml file.
e.g: conf=configdict=yaml.load(open('configfile.yaml','rb'))
"""
qlog = qllogger.QLLogger()
log = qlog.getlog()
hb = QLHB.QLHeartbeat(log, conf["Period"], conf["Timeout"])
inp = convdict["rawimage"]
singqa = conf["singleqa"]
paconf = conf["PipeLine"]
qlog = qllogger.QLLogger()
log = qlog.getlog()
passqadict = None #- pass this dict to QAs downstream
schemaMerger = QL_QAMerger(conf['Night'], conf['Expid'], conf['Flavor'],
conf['Camera'], conf['Program'], convdict)
QAresults = []
if singqa is None:
for s, step in enumerate(pl):
log.info("Starting to run step {}".format(paconf[s]["StepName"]))
pa = step[0]
pargs = mapkeywords(step[0].config["kwargs"], convdict)
schemaStep = schemaMerger.addPipelineStep(paconf[s]["StepName"])
try:
hb.start("Running {}".format(step[0].name))
oldinp = inp #- copy for QAs that need to see earlier input
inp = pa(inp, **pargs)
if step[0].name == 'Initialize':
schemaStep.addMetrics(inp[1])
except Exception as e:
log.critical("Failed to run PA {} error was {}".format(
step[0].name, e),
exc_info=True)
sys.exit("Failed to run PA {}".format(step[0].name))
qaresult = {}
for qa in step[1]:
try:
qargs = mapkeywords(qa.config["kwargs"], convdict)
hb.start("Running {}".format(qa.name))
qargs[
"dict_countbins"] = passqadict #- pass this to all QA downstream
if qa.name == "RESIDUAL" or qa.name == "Sky_Residual":
res = qa(inp[0], inp[1], **qargs)
else:
if isinstance(inp, tuple):
res = qa(inp[0], **qargs)
else:
res = qa(inp, **qargs)
if qa.name == "COUNTBINS" or qa.name == "CountSpectralBins":
passqadict = res
if "qafile" in qargs:
qawriter.write_qa_ql(qargs["qafile"], res)
log.debug("{} {}".format(qa.name, inp))
qaresult[qa.name] = res
schemaStep.addParams(res['PARAMS'])
schemaStep.addMetrics(res['METRICS'])
except Exception as e:
log.warning("Failed to run QA {}. Got Exception {}".format(
qa.name, e),
exc_info=True)
hb.stop("Step {} finished.".format(paconf[s]["StepName"]))
QAresults.append([pa.name, qaresult])
hb.stop("Pipeline processing finished. Serializing result")
else:
import numpy as np
qa = None
qas = [[],
[
'Bias_From_Overscan', 'Get_RMS', 'Count_Pixels',
'Calc_XWSigma'
], 'Trace_Shifts', 'CountSpectralBins',
['Sky_Continuum', 'Sky_Peaks'], ['Calculate_SNR'],
['Sky_Rband', 'Integrate_Spec']]
singleqaperpa = [
'Bias_From_Overscan', 'Check_HDUs', 'Trace_Shifts',
'CountSpectralBins'
]
for palg in range(len(qas)):
if singqa in qas[palg]:
pa = pl[palg][0]
pac = paconf[palg]
if singqa in singleqaperpa:
qa = pl[palg][1][0]
else:
for qalg in range(len(qas[palg])):
if qas[palg][qalg] == singqa:
qa = pl[palg][1][qalg]
if qa is None:
log.critical("Unknown input QA... Valid QAs are: {}".format(qas))
sys.exit()
log.info("Starting to run step {}".format(pac["StepName"]))
pargs = mapkeywords(pa.config["kwargs"], convdict)
schemaStep = schemaMerger.addPipelineStep(pac["StepName"])
qaresult = {}
try:
qargs = mapkeywords(qa.config["kwargs"], convdict)
hb.start("Running {}".format(qa.name))
if singqa == "Sky_Residual":
res = qa(inp[0], inp[1], **qargs)
else:
if isinstance(inp, tuple):
res = qa(inp[0], **qargs)
else:
res = qa(inp, **qargs)
if singqa == "CountSpectralBins":
passqadict = res
if "qafile" in qargs:
qawriter.write_qa_ql(qargs["qafile"], res)
log.debug("{} {}".format(qa.name, inp))
schemaStep.addMetrics(res['METRICS'])
except Exception as e:
log.warning("Failed to run QA {}. Got Exception {}".format(
qa.name, e),
exc_info=True)
if len(qaresult):
if conf["DumpIntermediates"]:
f = open(pac["OutputFile"], "w")
f.write(yaml.dump(yamlify(qaresult)))
log.info("{} finished".format(qa.name))
#- merge QAs for this pipeline execution
#- RS: don't write merged file if running single QA
if singqa is None:
log.debug("Dumping mergedQAs")
from desispec.io import findfile
specprod_dir = os.environ[
'QL_SPEC_REDUX'] if 'QL_SPEC_REDUX' in os.environ else ""
destFile = findfile('ql_mergedQA_file',
night=conf['Night'],
expid=conf['Expid'],
camera=conf['Camera'],
specprod_dir=specprod_dir)
schemaMerger.writeTojsonFile(destFile)
log.info("Wrote merged QA file {}".format(destFile))
if isinstance(inp, tuple):
return inp[0]
else:
return inp
def runpipeline(pl, convdict, conf, mergeQA=False):
"""
Runs the quicklook pipeline as configured
Args:
pl: is a list of [pa,qas] where pa is a pipeline step and qas the corresponding
qas for that pa
convdict: converted dictionary e.g : conf["IMAGE"] is the real psf file
but convdict["IMAGE"] is like desispec.image.Image object and so on.
details in setup_pipeline method below for examples.
conf: a configured dictionary, read from the configuration yaml file.
e.g: conf=configdict=yaml.load(open('configfile.yaml','rb'))
mergedQA: if True, outputs the merged QA after the execution of pipeline. Perhaps, this
should always be True, but leaving as option, until configuration and IO settles.
"""
qlog = qllogger.QLLogger()
log = qlog.getlog()
hb = QLHB.QLHeartbeat(log, conf["Period"], conf["Timeout"])
inp = convdict["rawimage"]
singqa = conf["singleqa"]
paconf = conf["PipeLine"]
qlog = qllogger.QLLogger()
log = qlog.getlog()
passqadict = None #- pass this dict to QAs downstream
schemaMerger = QL_QAMerger(conf['Night'], conf['Expid'], conf['Flavor'],
conf['Camera'], conf['Program'])
QAresults = [
] #- merged QA list for the whole pipeline. This will be reorganized for databasing after the pipeline executes
if singqa is None:
for s, step in enumerate(pl):
log.info("Starting to run step {}".format(paconf[s]["StepName"]))
pa = step[0]
pargs = mapkeywords(step[0].config["kwargs"], convdict)
schemaStep = schemaMerger.addPipelineStep(paconf[s]["StepName"])
try:
hb.start("Running {}".format(step[0].name))
oldinp = inp #- copy for QAs that need to see earlier input
inp = pa(inp, **pargs)
except Exception as e:
log.critical("Failed to run PA {} error was {}".format(
step[0].name, e),
exc_info=True)
sys.exit("Failed to run PA {}".format(step[0].name))
qaresult = {}
for qa in step[1]:
try:
qargs = mapkeywords(qa.config["kwargs"], convdict)
hb.start("Running {}".format(qa.name))
qargs[
"dict_countbins"] = passqadict #- pass this to all QA downstream
if qa.name == "RESIDUAL" or qa.name == "Sky_Residual":
res = qa(inp[0], inp[1], **qargs)
else:
if isinstance(inp, tuple):
res = qa(inp[0], **qargs)
else:
res = qa(inp, **qargs)
if qa.name == "COUNTBINS" or qa.name == "CountSpectralBins": #TODO -must run this QA for now. change this later.
passqadict = res
if "qafile" in qargs:
qawriter.write_qa_ql(qargs["qafile"], res)
log.debug("{} {}".format(qa.name, inp))
qaresult[qa.name] = res
schemaStep.addParams(res['PARAMS'])
schemaStep.addMetrics(res['METRICS'])
except Exception as e:
log.warning("Failed to run QA {}. Got Exception {}".format(
qa.name, e),
exc_info=True)
if len(qaresult):
if conf["DumpIntermediates"]:
f = open(paconf[s]["OutputFile"], "w")
f.write(yaml.dump(yamlify(qaresult)))
hb.stop("Step {} finished. Output is in {} ".format(
paconf[s]["StepName"], paconf[s]["OutputFile"]))
else:
hb.stop("Step {} finished.".format(paconf[s]["StepName"]))
QAresults.append([pa.name, qaresult])
hb.stop("Pipeline processing finished. Serializing result")
else:
import numpy as np
qa = None
qas = [
'Bias_From_Overscan', ['Get_RMS', 'Calc_XWSigma', 'Count_Pixels'],
'CountSpectralBins', ['Sky_Continuum', 'Sky_Peaks'],
['Sky_Residual', 'Integrate_Spec', 'Calculate_SNR']
]
for palg in range(len(qas)):
if singqa in qas[palg]:
pa = pl[palg][0]
pac = paconf[palg]
if singqa == 'Bias_From_Overscan' or singqa == 'CountSpectralBins':
qa = pl[palg][1][0]
else:
for qalg in range(len(qas[palg])):
if qas[palg][qalg] == singqa:
qa = pl[palg][1][qalg]
if qa is None:
log.critical("Unknown input... Valid QAs are: {}".format(qas))
sys.exit()
log.info("Starting to run step {}".format(pac["StepName"]))
pargs = mapkeywords(pa.config["kwargs"], convdict)
schemaStep = schemaMerger.addPipelineStep(pac["StepName"])
qaresult = {}
try:
qargs = mapkeywords(qa.config["kwargs"], convdict)
hb.start("Running {}".format(qa.name))
if singqa == "Sky_Residual":
res = qa(inp[0], inp[1], **qargs)
else:
if isinstance(inp, tuple):
res = qa(inp[0], **qargs)
else:
res = qa(inp, **qargs)
if singqa == "CountSpectralBins":
passqadict = res
if "qafile" in qargs:
qawriter.write_qa_ql(qargs["qafile"], res)
log.debug("{} {}".format(qa.name, inp))
schemaStep.addMetrics(res['METRICS'])
except Exception as e:
log.warning("Failed to run QA {}. Got Exception {}".format(
qa.name, e),
exc_info=True)
if len(qaresult):
if conf["DumpIntermediates"]:
f = open(pac["OutputFile"], "w")
f.write(yaml.dump(yamlify(qaresult)))
log.info("{} finished".format(qa.name))
#- merge QAs for this pipeline execution
if mergeQA is True:
# from desispec.quicklook.util import merge_QAs
# log.info("Merging all the QAs for this pipeline execution")
# merge_QAs(QAresults,conf)
log.debug("Dumping mergedQAs")
from desispec.io import findfile
ftype = 'ql_mergedQA_file'
specprod_dir = os.environ[
'QL_SPEC_REDUX'] if 'QL_SPEC_REDUX' in os.environ else ""
if conf['Flavor'] == 'arcs':
ftype = 'ql_mergedQAarc_file'
destFile = findfile(ftype,
night=conf['Night'],
expid=conf['Expid'],
camera=conf['Camera'],
specprod_dir=specprod_dir)
# this will overwrite the file. above function returns same name for different QL executions
# results will be erased.
schemaMerger.writeToFile(destFile)
log.info("Wrote merged QA file {}".format(destFile))
schemaMerger.writeTojsonFile(destFile)
log.info("Wrote merged QA file {}".format(
destFile.split('.yaml')[0] + '.json'))
if isinstance(inp, tuple):
return inp[0]
else:
return inp