def write_qa_prod(outroot, qaprod, indent=True):
"""Write QA for a given production
Args:
outroot : str
filename without format extension
qa_prod : QA_Prod object
Returns:
outfile: str
output filename
"""
from desiutil.io import combine_dicts
log = get_logger()
outfile = outroot + '.json'
outfile = makepath(outfile, 'qa')
# Loop on exposures
odict = {}
for qaexp in qaprod.qa_exps:
# Get the exposure dict
idict = write_qa_exposure('foo', qaexp, ret_dict=True)
odict = combine_dicts(odict, idict)
ydict = yamlify(odict) # This works well for JSON too
# Simple json
with open(outfile, 'wt') as fh:
json.dump(ydict, fh, indent=indent)
log.info('Wrote QA_Prod file: {:s}'.format(outfile))
return outfile
def write_qa_exposure(outroot, qaexp, ret_dict=False):
"""Write QA for a given exposure
Args:
outroot : str
filename without format extension
qa_exp : QA_Exposure object
ret_dict : bool, optional
Return dict only? [for qa_prod, mainly]
Returns:
outfile or odict : str or dict
"""
# Generate the dict
odict = {qaexp.night: {qaexp.expid: {}}}
odict[qaexp.night][qaexp.expid]['flavor'] = qaexp.flavor
cameras = qaexp.data['frames'].keys()
for camera in cameras:
odict[qaexp.night][qaexp.expid][camera] = qaexp.data['frames'][camera]
# Return dict only?
if ret_dict:
return odict
# Simple yaml
ydict = yamlify(odict)
outfile = outroot+'.yaml'
outfile = makepath(outfile, 'qa')
with open(outfile, 'w') as yamlf:
yamlf.write( yaml.dump(ydict))#, default_flow_style=True) )
return outfile
def write_qa_frame(outfile, qaframe, verbose=False):
"""Write QA for a given frame
Args:
outfile : str
filename
qa_exp : QA_Frame object, with the following attributes
qa_data: dict of QA info
"""
log = get_logger()
outfile = makepath(outfile, 'qa')
# Generate the dict
odict = {
qaframe.night: {
qaframe.expid: {
qaframe.camera: {},
'flavor': qaframe.flavor
}
}
}
odict[qaframe.night][qaframe.expid][qaframe.camera] = qaframe.qa_data
ydict = yamlify(odict)
# Simple yaml
with open(outfile, 'w') as yamlf:
yamlf.write(yaml.dump(ydict))
if verbose:
log.info("Wrote QA frame file: {:s}".format(outfile))
return outfile
def write_qa_ql(outfile, qaresult):
"""Write QL output files
Args:
outfile : str
filename to be written (yaml)
qaresult : dict
QAresults from run_qa()
Returns:
outfile : str
"""
#import yaml
#from desiutil.io import yamlify
# Take in QL input and output to yaml
qadict = yamlify(qaresult)
f = open(outfile, "w")
f.write(yaml.dump(qadict))
f.close()
g = open(outfile.split('.yaml')[0] + '.json', "w")
json.dump(qadict, g, sort_keys=True, indent=4)
g.close()
return outfile
def write_qa_frame(outfile, qaframe):
"""Write QA for a given frame
Args:
outfile : str
filename
qa_exp : QA_Frame object, with the following attributes
qa_data: dict of QA info
"""
outfile = makepath(outfile, 'qa')
# Generate the dict
odict = {
qaframe.night: {
qaframe.expid: {
qaframe.camera: {},
'flavor': qaframe.flavor
}
}
}
odict[qaframe.night][qaframe.expid][qaframe.camera] = qaframe.qa_data
ydict = yamlify(odict)
# Simple yaml
with open(outfile, 'w') as yamlf:
yamlf.write(yaml.dump(ydict)) #, default_flow_style=True) )
return outfile
def write_qa_prod(outroot, qaprod):
"""Write QA for a given production
Args:
outroot : str
filename without format extension
qa_prod : QA_Prod object
Returns:
outfile or odict : str or dict
"""
from desiutil.io import combine_dicts
outfile = outroot+'.yaml'
outfile = makepath(outfile, 'qa')
# Loop on exposures
odict = {}
for qaexp in qaprod.qa_exps:
# Get the exposure dict
idict = write_qa_exposure('foo', qaexp, ret_dict=True)
odict = combine_dicts(odict, idict)
ydict = yamlify(odict)
# Simple yaml
with open(outfile, 'w') as yamlf:
yamlf.write( yaml.dump(ydict))#, default_flow_style=True) )
log.info('Wrote QA_Prod file: {:s}'.format(outfile))
return outfile
def write_qa_multiexp(outroot, qa_mexp, indent=True, skip_rebuild=False):
"""Write QA for a given production
Args:
outroot : str
filename without format extension
qa_prod : QA_Prod object
skip_rebuild : bool, optional
Do not rebuild the data dict
Returns:
outfile: str
output filename
"""
log = get_logger()
outfile = outroot + '.json'
outfile = makepath(outfile, 'qa')
if not skip_rebuild:
qa_mexp.build_data()
ydict = yamlify(qa_mexp.data) # This works well for JSON too
# Simple json
with open(outfile, 'wt') as fh:
json.dump(ydict, fh, indent=indent)
log.info('Wrote QA_Prod file: {:s}'.format(outfile))
return outfile
def write_qa_exposure(outroot, qaexp, ret_dict=False):
"""Write QA for a given exposure
Args:
outroot : str
filename without format extension
qa_exp : QA_Exposure object
ret_dict : bool, optional
Return dict only? [for qa_prod, mainly]
Returns:
outfile or odict : str or dict
"""
# Generate the dict
odict = {qaexp.night: {qaexp.expid: {}}}
odict[qaexp.night][qaexp.expid]['flavor'] = qaexp.flavor
odict[qaexp.night][qaexp.expid]['meta'] = qaexp.meta
cameras = list(qaexp.data['frames'].keys())
for camera in cameras:
odict[qaexp.night][qaexp.expid][camera] = qaexp.data['frames'][camera]
# Return dict only?
if ret_dict:
return odict
# Simple yaml
ydict = yamlify(odict)
outfile = outroot+'.yaml'
outfile = makepath(outfile, 'qa')
with open(outfile, 'w') as yamlf:
yamlf.write( yaml.dump(ydict))#, default_flow_style=True) )
return outfile
def write_qa_ql(outfile, qaresult):
"""Write QL output files
Args:
outfile : str
filename to be written (yaml)
qaresult : dict
QAresults from run_qa()
Returns:
outfile : str
"""
#import yaml
#from desiutil.io import yamlify
# Take in QL input and output to yaml
#SE: No yaml creation as of May 2018
qadict = yamlify(qaresult)
#f=open(outfile,"w")
#f.write(yaml.dump(qadict))
#f.close()
g=open(outfile,"w")
json.dump(qadict, g, sort_keys=True, indent=4)
g.close()
return outfile
def write_qa_ql(outfile, qaresult):
"""Write QL output files
Args:
outfile : str
filename to be written (yaml)
qaresult : dict
QAresults from run_qa()
Returns:
outfile : str
"""
#import yaml
#from desiutil.io import yamlify
# Take in QL input and output to yaml
#SE: No yaml creation as of May 2018
qadict = yamlify(qaresult)
#f=open(outfile,"w")
#f.write(yaml.dump(qadict))
#f.close()
g = open(outfile, "w")
json.dump(qadict, g, sort_keys=True, indent=4)
g.close()
return outfile
def writeTojsonFile(self, fileName):
g = open(fileName, 'w')
myDict = yamlify(self.__schema)
#reOrderDict(myDict)
# remove lists ... after this step there is no list of dictionaries
EditDic(myDict)
# this step modifies Takse, renames them, and re-arrange Metrics and corresponding Paramas
myDict = taskMaker(myDict)
json.dump(myDict, g, sort_keys=True, indent=4)
g.close()
def writeTojsonFile(self,fileName):
g=open(fileName,'w')
myDict = yamlify(self.__schema)
#reOrderDict(myDict)
# remove lists ... after this step there is no list of dictionaries
EditDic(myDict)
# this step modifies Takse, renames them, and re-arrange Metrics and corresponding Paramas
myDict = taskMaker(myDict)
json.dump(myDict, g, sort_keys=True, indent=4)
g.close()
def write_qa_brick(outfile, qabrick):
"""Write QA for a given exposure
Args:
outfile : filename
qabrick : QA_Brick object
_data: dict of QA info
"""
outfile = makepath(outfile, 'qa')
# Simple yaml
ydict = yamlify(qabrick.data)
with open(outfile, 'w') as yamlf:
yamlf.write( yaml.dump(ydict))#, default_flow_style=True) )
return outfile
def write_qa_brick(outfile, qabrick):
"""Write QA for a given exposure
Args:
outfile : filename
qabrick : QA_Brick object
_data: dict of QA info
"""
outfile = makepath(outfile, 'qa')
# Simple yaml
ydict = yamlify(qabrick.data)
with open(outfile, 'w') as yamlf:
yamlf.write(yaml.dump(ydict))#, default_flow_style=True) )
return outfile
def merge_QAs(qaresult):
"""
Per QL pipeline level merging of QA results
qaresult: list of [pa,qa]; where pa is pa name; qa is the list of qa results.
This list is created inside the QL pipeline.
"""
mergedQA = {}
for s, result in enumerate(qaresult):
pa = result[0].upper()
night = result[1].values()[0]['NIGHT']
expid = int(result[1].values()[0]['EXPID'])
camera = result[1].values()[0]['CAMERA']
flavor = result[1].values()[0]['FLAVOR']
if night not in mergedQA:
mergedQA[night] = {} #- top level key
if expid not in mergedQA[night]:
mergedQA[night][expid] = {}
if camera not in mergedQA[night][expid]:
mergedQA[night][expid][camera] = {}
if 'flavor' not in mergedQA[night][expid]:
mergedQA[night][expid]['flavor'] = flavor
mergedQA[night][expid][camera][pa] = {}
mergedQA[night][expid][camera][pa]['PARAMS'] = {}
mergedQA[night][expid][camera][pa]['METRICS'] = {}
#- now merge PARAM and QA metrics for all QAs
for qa in result[1]:
if 'PARAMS' in result[1][qa]:
mergedQA[night][expid][camera][pa]['PARAMS'].update(
result[1][qa]['PARAMS'])
if 'METRICS' in result[1][qa]:
mergedQA[night][expid][camera][pa]['METRICS'].update(
result[1][qa]['METRICS'])
from desiutil.io import yamlify
qadict = yamlify(mergedQA)
f = open('mergedQA-{}-{:08d}.yaml'.format(camera, expid),
'w') #- IO/file naming should move from here.
f.write(yaml.dump(qadict))
f.close()
return
def write_qa_ql(outfile, qaresult):
"""Write QL output files
Args:
outfile : str
filename to be written (yaml)
qaresult : dict
QAresults from run_qa()
Returns:
outfile : str
"""
import yaml
from desiutil.io import yamlify
# Take in QL input and output to yaml
qadict = yamlify(qaresult)
f = open(outfile, "w")
f.write(yaml.dump(qadict))
return outfile
def write_qa_frame(outfile, qaframe):
"""Write QA for a given frame
Args:
outfile : str
filename
qa_exp : QA_Frame object, with the following attributes
qa_data: dict of QA info
"""
outfile = makepath(outfile, 'qa')
# Generate the dict
odict = {qaframe.night: {qaframe.expid: {qaframe.camera: {}, 'flavor': qaframe.flavor}}}
odict[qaframe.night][qaframe.expid][qaframe.camera] = qaframe.qa_data
ydict = yamlify(odict)
# Simple yaml
with open(outfile, 'w') as yamlf:
yamlf.write( yaml.dump(ydict))#, default_flow_style=True) )
return outfile
def write_qa_multiexp(outroot, mdict, indent=True):
"""Write QA for a given production
Args:
outroot : str
filename without format extension
mdict : dict
Returns:
outfile: str
output filename
"""
log=get_logger()
outfile = outroot+'.json'
outfile = makepath(outfile, 'qa')
ydict = yamlify(mdict) # This works well for JSON too
# Simple json
with open(outfile, 'wt') as fh:
json.dump(ydict, fh, indent=indent)
log.info('Wrote QA Multi-Exposure file: {:s}'.format(outfile))
return outfile
def write_qa_multiexp(outroot, mdict, indent=True):
"""Write QA for a given production
Args:
outroot : str
filename without format extension
mdict : dict
Returns:
outfile: str
output filename
"""
log = get_logger()
outfile = outroot + '.json'
outfile = makepath(outfile, 'qa')
ydict = yamlify(mdict) # This works well for JSON too
# Simple json
with open(outfile, 'wt') as fh:
json.dump(ydict, fh, indent=indent)
log.info('Wrote QA Multi-Exposure file: {:s}'.format(outfile))
return outfile
def getYaml(self):
yres=yamlify(self.__schema)
reOrderDict(yres)
return yaml.dump(yres)
def writeTojsonFile(self,fileName):
g=open(fileName.split('.yaml')[0]+'.json',"w")
myDict = yamlify(self.__schema)
reOrderDict(myDict)
json.dump(myDict, g, sort_keys=True, indent=4)
g.close()
def getJson(self):
import json
return json.dumps(yamlify(self.__schema))
def main(args):
import os.path
import sys
import yaml
import numpy as np
import pdb
import matplotlib
matplotlib.use('agg')
import desispec.io
from desiutil.log import get_logger
from desisim.spec_qa import redshifts as dsqa_z
from desiutil.io import yamlify
import desiutil.depend
log = get_logger()
# Initialize
if args.qaprod_dir is not None:
qaprod_dir = args.qaprod_dir
else:
qaprod_dir = desispec.io.meta.qaprod_root()
if args.load_simz_table is not None:
from astropy.table import Table
log.info("Loading simz info from {:s}".format(args.load_simz_table))
simz_tab = Table.read(args.load_simz_table)
else:
# Grab list of fibermap files
fibermap_files = []
zbest_files = []
nights = desispec.io.get_nights()
for night in nights:
for exposure in desispec.io.get_exposures(night, raw=True, rawdata_dir=args.rawdir):
# Ignore exposures with no fibermap, assuming they are calibration data.
fibermap_path = desispec.io.findfile(filetype='fibermap', night=night,
expid=exposure, rawdata_dir=args.rawdir)
if not os.path.exists(fibermap_path):
log.debug('Skipping exposure %08d with no fibermap.' % exposure)
continue
# Load data
fibermap_data = desispec.io.read_fibermap(fibermap_path)
# Skip calib
objtype = fibermap_data['OBJTYPE'][0]
if objtype in ['FLAT', 'FLT', 'ARC', 'BIAS', 'BIA']:
continue
elif objtype in ['SKY','TGT','BAD']:
pass
else:
err_message = 'Unrecognized OBJTYPE {}'.format(objtype)
log.critical(err_message)
raise ValueError(err_message)
# Append fibermap file
fibermap_files.append(fibermap_path)
# Slurp the zbest_files
zbest_files += dsqa_z.find_zbest_files(fibermap_data)
# Cut down zbest_files to unique ones
zbest_files = list(set(zbest_files))
if len(zbest_files) == 0:
log.fatal('No zbest files found')
sys.exit(1)
# Write? Table
simz_tab, zbtab = dsqa_z.load_z(fibermap_files, zbest_files=zbest_files)
dsqa_z.match_truth_z(simz_tab, zbtab)
if args.write_simz_table is not None:
simz_tab.write(args.write_simz_table, overwrite=True)
# Meta data
meta = dict(
DESISIM = desiutil.depend.getdep(simz_tab.meta, 'desisim'),
SPECPROD = os.getenv('SPECPROD', 'unknown'),
PIXPROD = os.getenv('PIXPROD', 'unknown'),
)
# Include specter version if it was used to generate input files
# (it isn't used for specsim inputs so that dependency may not exist)
try:
meta['SPECTER'] = desiutil.depend.getdep(simz_tab.meta, 'specter')
except KeyError:
pass
# Run stats
log.info("Running stats..")
summ_dict = dsqa_z.summ_stats(simz_tab)
if args.yaml_file is not None:
log.info("Generating yaml file of stats: {:s}".format(args.yaml_file))
# yamlify
# Write yaml
desispec.io.util.makepath(args.yaml_file)
with open(args.yaml_file, 'w') as outfile:
outfile.write(yaml.dump(yamlify(meta), default_flow_style=False))
outfile.write(yaml.dump(yamlify(summ_dict), default_flow_style=False))
log.info("Generating QA files")
# Summary for dz of all types
outfile = qaprod_dir+'/QA_dzsumm.png'
desispec.io.util.makepath(outfile)
dsqa_z.dz_summ(simz_tab, outfile=outfile)
# Summary of individual types
#outfile = args.qafig_root+'_summ_fig.png'
#dsqa_z.summ_fig(simz_tab, summ_dict, meta, outfile=outfile)
for objtype in ['BGS', 'MWS', 'ELG','LRG', 'QSO_T', 'QSO_L']:
outfile = qaprod_dir+'/QA_zfind_{:s}.png'.format(objtype)
desispec.io.util.makepath(outfile)
dsqa_z.obj_fig(simz_tab, objtype, summ_dict, outfile=outfile)
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
def main(args):
import os.path
import sys
import yaml
import numpy as np
import pdb
import matplotlib
matplotlib.use('agg')
import desispec.io
from desiutil.log import get_logger
from desisim.spec_qa import redshifts as dsqa_z
from desiutil.io import yamlify
import desiutil.depend
from desimodel.footprint import radec2pix
log = get_logger()
if args.load_simz_table is not None:
from astropy.table import Table
log.info("Loading simz info from {:s}".format(args.load_simz_table))
simz_tab = Table.read(args.load_simz_table)
else:
# Grab list of fibermap files
fibermap_files = []
zbest_files = []
nights = desispec.io.get_nights()
for night in nights:
for exposure in desispec.io.get_exposures(night,
raw=True,
rawdata_dir=args.rawdir):
# Ignore exposures with no fibermap, assuming they are calibration data.
fibermap_path = desispec.io.findfile(filetype='fibermap',
night=night,
expid=exposure,
rawdata_dir=args.rawdir)
if not os.path.exists(fibermap_path):
log.debug('Skipping exposure %08d with no fibermap.' %
exposure)
continue
'''
# Search for zbest files
fibermap_data = desispec.io.read_fibermap(fibermap_path)
flavor = fibermap_data.meta['FLAVOR']
if flavor.lower() in ('arc', 'flat', 'bias'):
log.debug('Skipping calibration {} exposure {:08d}'.format(flavor, exposure))
continue
brick_names = set(fibermap_data['BRICKNAME'])
import pdb; pdb.set_trace()
for brick in brick_names:
zbest_path=desispec.io.findfile('zbest', groupname=brick, specprod_dir=args.reduxdir)
if os.path.exists(zbest_path):
log.debug('Found {}'.format(os.path.basename(zbest_path)))
zbest_files.append(zbest_path)
else:
log.warn('Missing {}'.format(os.path.basename(zbest_path)))
#pdb.set_trace()
'''
# Load data
fibermap_data = desispec.io.read_fibermap(fibermap_path)
# Skip calib
if fibermap_data['OBJTYPE'][0] in ['FLAT', 'ARC', 'BIAS']:
continue
elif fibermap_data['OBJTYPE'][0] in ['SKY', 'STD', 'SCIENCE']:
pass
else:
pdb.set_trace()
# Append fibermap file
fibermap_files.append(fibermap_path)
# Search for zbest files with healpy
ra_targ = fibermap_data['RA_TARGET'].data
dec_targ = fibermap_data['DEC_TARGET'].data
# Getting some NAN in RA/DEC
good = np.isfinite(ra_targ) & np.isfinite(dec_targ)
pixels = radec2pix(64, ra_targ[good], dec_targ[good])
uni_pixels = np.unique(pixels)
for uni_pix in uni_pixels:
zbest_files.append(
desispec.io.findfile('zbest',
groupname=uni_pix,
nside=64))
# Cut down zbest_files to unique ones
zbest_files = list(set(zbest_files))
if len(zbest_files) == 0:
log.fatal('No zbest files found')
sys.exit(1)
# Write? Table
simz_tab = dsqa_z.load_z(fibermap_files, zbest_files)
if args.write_simz_table is not None:
simz_tab.write(args.write_simz_table, overwrite=True)
# Meta data
meta = dict(
DESISIM=desiutil.depend.getdep(simz_tab.meta, 'desisim'),
SPECTER=desiutil.depend.getdep(simz_tab.meta, 'specter'),
SPECPROD=os.getenv('SPECPROD', 'unknown'),
PIXPROD=os.getenv('PIXPROD', 'unknown'),
)
# Run stats
log.info("Running stats..")
summ_dict = dsqa_z.summ_stats(simz_tab)
if args.qafile is not None:
log.info("Generating yaml file: {:s}".format(args.qafile))
# yamlify
# Write yaml
with open(args.qafile, 'w') as outfile:
outfile.write(yaml.dump(yamlify(meta), default_flow_style=False))
outfile.write(
yaml.dump(yamlify(summ_dict), default_flow_style=False))
if args.qafig_root is not None:
log.info("Generating QA files")
# Summary for dz of all types
outfile = args.qafig_root + '_dzsumm.png'
#dsqa_z.dz_summ(simz_tab, outfile=outfile)
# Summary of individual types
#outfile = args.qafig_root+'_summ_fig.png'
#dsqa_z.summ_fig(simz_tab, summ_dict, meta, outfile=outfile)
for objtype in ['BGS', 'MWS', 'ELG', 'LRG', 'QSO_T', 'QSO_L']:
outfile = args.qafig_root + '_zfind_{:s}.png'.format(objtype)
dsqa_z.obj_fig(simz_tab, objtype, summ_dict, outfile=outfile)
def main(args):
import os.path
import sys
import yaml
import numpy as np
import pdb
import matplotlib
matplotlib.use('agg')
import desispec.io
from desiutil.log import get_logger
from desisim.spec_qa import redshifts as dsqa_z
from desiutil.io import yamlify
import desiutil.depend
log = get_logger()
# Initialize
if args.qaprod_dir is not None:
qaprod_dir = args.qaprod_dir
else:
qaprod_dir = desispec.io.meta.qaprod_root()
if args.load_simz_table is not None:
from astropy.table import Table
log.info("Loading simz info from {:s}".format(args.load_simz_table))
simz_tab = Table.read(args.load_simz_table)
else:
# Grab list of fibermap files
fibermap_files = []
zbest_files = []
nights = desispec.io.get_nights()
for night in nights:
for exposure in desispec.io.get_exposures(night,
raw=True,
rawdata_dir=args.rawdir):
# Ignore exposures with no fibermap, assuming they are calibration data.
fibermap_path = desispec.io.findfile(filetype='fibermap',
night=night,
expid=exposure,
rawdata_dir=args.rawdir)
if not os.path.exists(fibermap_path):
log.debug('Skipping exposure %08d with no fibermap.' %
exposure)
continue
# Load data
fibermap_data = desispec.io.read_fibermap(fibermap_path)
# Skip calib
objtype = fibermap_data['OBJTYPE'][0]
if objtype in ['FLAT', 'FLT', 'ARC', 'BIAS', 'BIA']:
continue
elif objtype in ['SKY', 'TGT', 'BAD']:
pass
else:
err_message = 'Unrecognized OBJTYPE {}'.format(objtype)
log.critical(err_message)
raise ValueError(err_message)
# Append fibermap file
fibermap_files.append(fibermap_path)
# Slurp the zbest_files
zbest_files += dsqa_z.find_zbest_files(fibermap_data)
# Cut down zbest_files to unique ones
zbest_files = list(set(zbest_files))
if len(zbest_files) == 0:
log.fatal('No zbest files found')
sys.exit(1)
# Write? Table
simz_tab, zbtab = dsqa_z.load_z(fibermap_files,
zbest_files=zbest_files)
dsqa_z.match_truth_z(simz_tab, zbtab)
if args.write_simz_table is not None:
simz_tab.write(args.write_simz_table, overwrite=True)
# Meta data
meta = dict(
DESISIM=desiutil.depend.getdep(simz_tab.meta, 'desisim'),
SPECPROD=os.getenv('SPECPROD', 'unknown'),
PIXPROD=os.getenv('PIXPROD', 'unknown'),
)
# Include specter version if it was used to generate input files
# (it isn't used for specsim inputs so that dependency may not exist)
try:
meta['SPECTER'] = desiutil.depend.getdep(simz_tab.meta, 'specter')
except KeyError:
pass
# Run stats
log.info("Running stats..")
summ_dict = dsqa_z.summ_stats(simz_tab)
if args.yaml_file is not None:
log.info("Generating yaml file of stats: {:s}".format(args.yaml_file))
# yamlify
# Write yaml
desispec.io.util.makepath(args.yaml_file)
with open(args.yaml_file, 'w') as outfile:
outfile.write(yaml.dump(yamlify(meta), default_flow_style=False))
outfile.write(
yaml.dump(yamlify(summ_dict), default_flow_style=False))
log.info("Generating QA files")
# Summary for dz of all types
outfile = qaprod_dir + '/QA_dzsumm.png'
desispec.io.util.makepath(outfile)
dsqa_z.dz_summ(simz_tab, outfile=outfile)
# Summary of individual types
#outfile = args.qafig_root+'_summ_fig.png'
#dsqa_z.summ_fig(simz_tab, summ_dict, meta, outfile=outfile)
for objtype in ['BGS', 'MWS', 'ELG', 'LRG', 'QSO_T', 'QSO_L']:
outfile = qaprod_dir + '/QA_zfind_{:s}.png'.format(objtype)
desispec.io.util.makepath(outfile)
dsqa_z.obj_fig(simz_tab, objtype, summ_dict, outfile=outfile)
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("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