Reducing Suprime-cam Data


(Kyle Pittman) #21

That gives me:

bash-4.2$ processCcd.py DATA --calib DATA/CALIB --rerun processCcdOutput --id field=N5846_1
root INFO: Loading config overrride file '/home/lsst/obs_subaru/config/processCcd.py'
root INFO: Loading config overrride file '/home/lsst/obs_subaru/config/suprimecam/processCcd.py'
Traceback (most recent call last):
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/14.0-54-gb4e84373/bin/processCcd.py", line 25, in <module>
    ProcessCcdTask.parseAndRun()
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_base/14.0-6-ge2c9487+58/python/lsst/pipe/base/cmdLineTask.py", line 581, in parseAndRun
    parsedCmd = argumentParser.parse_args(config=config, args=args, log=log, override=cls.applyOverrides)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_base/14.0-6-ge2c9487+58/python/lsst/pipe/base/argumentParser.py", line 614, in parse_args
    namespace.butler = dafPersist.Butler(inputs=inputs, outputs=outputs)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/daf_persistence/14.0-17-g2e0c876/python/lsst/daf/persistence/butler.py", line 521, in __init__
    self._getCfgs(repoDataList)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/daf_persistence/14.0-17-g2e0c876/python/lsst/daf/persistence/butler.py", line 821, in _getCfgs
    cfg, repoData.repoArgs))
RuntimeError: The RepositoryArgs and RepositoryCfg must match for writable repositories, RepositoryCfg:RepositoryCfg(root='/home/lsst/DATA/rerun/processCcdOutput', mapper=<class 'lsst.obs.suprimecam.suprimecamMapper.SuprimecamMapper'>, mapperArgs={}, parents=[RepositoryCfg(root='/home/lsst/DATA', mapper=<class 'lsst.obs.suprimecam.suprimecamMapper.SuprimecamMapper'>, mapperArgs={}, parents=[], policy=None)], policy=None), RepositoryArgs:RepositoryArgs(root='/home/lsst/DATA/rerun/processCcdOutput', cfgRoot=None, mapper=None, mapperArgs={'calibRoot': '/home/lsst/DATA/CALIB'}, tags=set(), mode='rw', policy=None)

(Paul Price) #22

That’s because the rerun was created with the old (presumably wrong) configuration. Kill it and start over.

rm -r DATA/rerun/processCcdOutput

(Kyle Pittman) #23

That worked. Now I am getting:

processCcd FATAL: Failed on dataId={'field': 'N5846_1', 'dateObs': '2009-04-21', 'pointing': 1937, 'filter': 'W-S-G+', 'visit': 109045, 'ccd': 0, 'taiObs': '2009-04-21', 'expTime': 460.0}: ColortermNotFoundError: No colorterm dict found with photoCatName 'ps1_pv3_3pi_20170110'
Traceback (most recent call last):
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_base/14.0-6-ge2c9487+58/python/lsst/pipe/base/cmdLineTask.py", line 408, in __call__
    result = task.run(dataRef, **kwargs)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_base/14.0-6-ge2c9487+58/python/lsst/pipe/base/timer.py", line 150, in wrapper
    res = func(self, *args, **keyArgs)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/14.0-54-gb4e84373/python/lsst/pipe/tasks/processCcd.py", line 199, in run
    icSourceCat=charRes.sourceCat,
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_base/14.0-6-ge2c9487+58/python/lsst/pipe/base/timer.py", line 150, in wrapper
    res = func(self, *args, **keyArgs)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/14.0-54-gb4e84373/python/lsst/pipe/tasks/calibrate.py", line 433, in run
    icSourceCat=icSourceCat,
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/14.0-54-gb4e84373/python/lsst/pipe/tasks/calibrate.py", line 536, in calibrate
    photoRes = self.photoCal.run(exposure, sourceCat=sourceCat, expId=exposureIdInfo.expId)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_base/14.0-6-ge2c9487+58/python/lsst/pipe/base/timer.py", line 150, in wrapper
    res = func(self, *args, **keyArgs)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/14.0-54-gb4e84373/python/lsst/pipe/tasks/photoCal.py", line 471, in run
    arrays = self.extractMagArrays(matches=matches, filterName=filterName, sourceKeys=sourceKeys)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_base/14.0-6-ge2c9487+58/python/lsst/pipe/base/timer.py", line 150, in wrapper
    res = func(self, *args, **keyArgs)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/14.0-54-gb4e84373/python/lsst/pipe/tasks/photoCal.py", line 326, in extractMagArrays
    filterName=filterName, photoCatName=self.config.photoCatName, doRaise=True)
  File "/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/pipe_tasks/14.0-54-gb4e84373/python/lsst/pipe/tasks/colorterms.py", line 183, in getColorterm
    "No colorterm dict found with photoCatName %r" % photoCatName)
lsst.pipe.tasks.colorterms.ColortermNotFoundError: No colorterm dict found with photoCatName 'ps1_pv3_3pi_20170110'

I have added the code from this post to my config/processCcd.py (both the code under LSST-style format and common configuration). More specifically, I added:

config.calibrate.astromRefObjLoader.retarget(LoadIndexedReferenceObjectsTask)
config.calibrate.astromRefObjLoader.ref_dataset_name = "ps1_pv3_3pi_20170110"
config.calibrate.photoRefObjLoader.retarget(LoadIndexedReferenceObjectsTask)
config.calibrate.photoRefObjLoader.ref_dataset_name = "ps1_pv3_3pi_20170110"
config.calibrate.photoCal.photoCatName = "ps1_pv3_3pi_20170110"

and

colorterms = config.calibrate.photoCal.colorterms
from lsst.pipe.tasks.colorterms import ColortermDict, Colorterm
colorterms.data["ps1*"] = ColortermDict(data={
       'g': Colorterm(primary="g", secondary="r", c0=-0.00928, c1=-0.0824),
       'r': Colorterm(primary="r", secondary="i", c0=-0.00282, c1=-0.0498, c2=-0.0149),
       'i': Colorterm(primary="i", secondary="z", c0=0.00186, c1=-0.140, c2=-0.0196),
       'z': Colorterm(primary="z", secondary="i", c0=-4.03e-4, c1=0.0967, c2=0.0210),
       'B': Colorterm(primary="g", secondary="r", c0=0.02461907, c1=0.20098328, c2=0.00858468),
       'V': Colorterm(primary="g", secondary="r", c0=-0.03117934, c1=-0.63134136, c2=0.05056544),
       'R': Colorterm(primary="r", secondary="i", c0=-0.01179613, c1=-0.25403307, c2=0.00696479),
       'I': Colorterm(primary="i", secondary="r", c0=0.01078282, c1=0.26727768, c2=0.00747123),
})

the latter coming from obs_subaru/config/suprimecam/colorterms.py.


(Paul Price) #24

The color terms in obs_subaru/config/suprimecam/colorterms.py are for the SDSS catalog, not PS1.

I think you want to set calibrate.photoCal.applyColorTerms=False to disable the application of color terms.


(Kyle Pittman) #25

I am now running into a segmentation fault in processCcd.py. The full error is (apologies for the giant block of text):

processCcd INFO: Processing {'field': 'N5846_1', 'dateObs': '2009-04-21', 'pointing': 1937, 'filter': 'W-S-G+', 'visit': 109045, 'ccd': 8, 'taiObs': '2009-04-21', 'expTime': 460.0}
processCcd.isr INFO: Performing ISR on sensor {'field': 'N5846_1', 'dateObs': '2009-04-21', 'pointing': 1937, 'filter': 'W-S-G+', 'visit': 109045, 'ccd': 8, 'taiObs': '2009-04-21', 'expTime': 460.0}
daf.persistence.butler INFO: NoResults or IOError (continuing search) while evaluating bypass function for Dataset type:raw_md Data ID:DataId(initialdata={'field': 'N5846_1', 'dateObs': '2009-04-21', 'pointing': 1937, 'filter': 'W-S-G+', 'visit': 109045, 'ccd': 8, 'taiObs': '2009-04-21', 'expTime': 460.0}, tag=set()) at location lsst.afw.image.DecoratedImageU at FitsStorage(N5846_1/2009-04-21/01937/W-S-G+/SUPA01090458.fits)
processCcd.isr.crosstalk INFO: Applying crosstalk corrections to CCD 8 based on Yagi+2012
processCcd.isr INFO: Set 0 BAD pixels to 1855.65
processCcd.isr INFO: Adding a distortion model to the WCS
processCcd.isr INFO: Flattened sky level: 1855.938904 +/- 28.985080
processCcd.isr INFO: Measuring sky levels in 8x16 grids: 1854.532196
processCcd.isr INFO: Sky flatness in 8x16 grids - pp: 0.028817 rms: 0.005004
processCcd.isr INFO: Setting rough magnitude zero point: 35.656895
processCcd.charImage INFO: Processing {'field': 'N5846_1', 'dateObs': '2009-04-21', 'pointing': 1937, 'filter': 'W-S-G+', 'visit': 109045, 'ccd': 8, 'taiObs': '2009-04-21', 'expTime': 460.0}
processCcd.charImage WARN: Source catalog detected and measured with placeholder or default PSF
processCcd.charImage.repair INFO: Identified 1205 cosmic rays.
processCcd.charImage.detection INFO: Detected 1436 positive peaks in 345 footprints and 12 negative peaks in 4 footprints to 50 sigma
processCcd.charImage.detection INFO: Resubtracting the background after object detection
processCcd.charImage.measurement INFO: Measuring 345 sources (345 parents, 0 children)
Caught signal 11, backtrace follows:
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/utils/14.0-10-gaad1ee2+1/lib/libutils.so(+0x15214) [0x7f8d8288f214]
/lib64/libc.so.6(+0x35270) [0x7f8d8bca7270]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(+0xefb08) [0x7f8d1e6f1b08]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(Eigen::internal::general_matrix_matrix_triangular_product<long, double, 1, false, double, 0, false, 0, 2, 0>::run(long, long, double const*, long, double const*, long, double*, long, double const&)+0x430) [0x7f8d1e6f4920]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(lsst::meas::modelfit::Optimizer::_computeDerivatives()+0x1742) [0x7f8d1e6ea5b2]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(lsst::meas::modelfit::Optimizer::_stepImpl(int, lsst::meas::modelfit::OptimizerHistoryRecorder const*, lsst::afw::table::CatalogT<lsst::afw::table::BaseRecord>*)+0x1217) [0x7f8d1e6ef8b7]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(lsst::meas::modelfit::Optimizer::_runImpl(lsst::meas::modelfit::OptimizerHistoryRecorder const*, lsst::afw::table::CatalogT<lsst::afw::table::BaseRecord>*)+0x111) [0x7f8d1e6f06e1]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(+0x53afa) [0x7f8d1e655afa]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(lsst::meas::modelfit::CModelAlgorithm::_applyImpl(lsst::meas::modelfit::CModelResult&, lsst::afw::image::Exposure<float, int, float> const&, lsst::shapelet::MultiShapeletFunction const&, lsst::afw::geom::Point<double, 2> const&, lsst::afw::geom::ellipses::Quadrupole const&, double, double, int) const+0x888) [0x7f8d1e65f648]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/lib/libmeas_modelfit.so(lsst::meas::modelfit::CModelAlgorithm::measure(lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&) const+0x3fb) [0x7f8d1e6602db]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/python/lsst/meas/modelfit/cmodel/cmodel.so(void pybind11::cpp_function::initialize<pybind11::cpp_function::initialize<void, lsst::meas::modelfit::CModelAlgorithm, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg, pybind11::arg>(void (lsst::meas::modelfit::CModelAlgorithm::*)(lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&) const, pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&, pybind11::arg const&)::{lambda(lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&)#1}, void, lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg, pybind11::arg>(pybind11::cpp_function::initialize<void, lsst::meas::modelfit::CModelAlgorithm, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg, pybind11::arg>(void (lsst::meas::modelfit::CModelAlgorithm::*)(lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&) const, pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&, pybind11::arg const&)::{lambda(lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&)#1}&&, void (*)(lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#3}::operator()(pybind11::detail::function_call) const+0xe2) [0x7f8d166e9ad2]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/python/lsst/meas/modelfit/cmodel/cmodel.so(void pybind11::cpp_function::initialize<pybind11::cpp_function::initialize<void, lsst::meas::modelfit::CModelAlgorithm, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg, pybind11::arg>(void (lsst::meas::modelfit::CModelAlgorithm::*)(lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&) const, pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&, pybind11::arg const&)::{lambda(lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&)#1}, void, lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg, pybind11::arg>(pybind11::cpp_function::initialize<void, lsst::meas::modelfit::CModelAlgorithm, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg, pybind11::arg>(void (lsst::meas::modelfit::CModelAlgorithm::*)(lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&) const, pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&, pybind11::arg const&)::{lambda(lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&)#1}&&, void (*)(lsst::meas::modelfit::CModelAlgorithm const*, lsst::afw::table::SourceRecord&, lsst::afw::image::Exposure<float, int, float> const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call)+0xe) [0x7f8d166e9c8e]
/opt/lsst/software/stack/stack/miniconda3-4.3.21-10a4fa6/Linux64/meas_modelfit/14.0-3-gda7b0c7+14/python/lsst/meas/modelfit/cmodel/cmodel.so(pybind11::cpp_function::dispatcher(_object*, _object*, _object*)+0xdc6) [0x7f8d166ee0e6]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x102) [0x7f8d8cc25302]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyCFunction_FastCallDict+0x102) [0x7f8d8cc25302]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(+0x14895b) [0x7f8d8ccaa95b]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x2c40) [0x7f8d8ccadd40]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyEval_EvalFrameDefault+0x2c40) [0x7f8d8ccadd40]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(+0x146514) [0x7f8d8cca8514]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x285) [0x7f8d8cca9515]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyFunction_FastCallDict+0x285) [0x7f8d8cca9515]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x166) [0x7f8d8cbcdce6]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyObject_FastCallDict+0x166) [0x7f8d8cbcdce6]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0xcc) [0x7f8d8cbcdf3c]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyObject_Call_Prepend+0xcc) [0x7f8d8cbcdf3c]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(PyObject_Call+0x56) [0x7f8d8cbcdfd6]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x3ec9) [0x7f8d8ccaefc9]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyEval_EvalFrameDefault+0x3ec9) [0x7f8d8ccaefc9]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(+0x147100) [0x7f8d8cca9100]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x10c) [0x7f8d8cca939c]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyFunction_FastCallDict+0x10c) [0x7f8d8cca939c]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x166) [0x7f8d8cbcdce6]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyObject_FastCallDict+0x166) [0x7f8d8cbcdce6]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0xcc) [0x7f8d8cbcdf3c]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyObject_Call_Prepend+0xcc) [0x7f8d8cbcdf3c]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(PyObject_Call+0x56) [0x7f8d8cbcdfd6]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x3ec9) [0x7f8d8ccaefc9]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(_PyEval_EvalFrameDefault+0x3ec9) [0x7f8d8ccaefc9]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(+0x147100) [0x7f8d8cca9100]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(+0x148b2a) [0x7f8d8ccaab2a]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0((null)+0x31cc) [0x7f8d8ccae2cc]
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/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(+0x147100) [0x7f8d8cca9100]
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/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(+0x9f1a1) [0x7f8d8cc011a1]
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/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(PyEval_EvalCode+0x3b) [0x7f8d8cca95cb]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(PyRun_FileExFlags+0x130) [0x7f8d8ccdbee0]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(PyRun_SimpleFileExFlags+0x173) [0x7f8d8ccdd4a3]
/opt/lsst/software/stack/python/miniconda3-4.3.21/bin/../lib/libpython3.6m.so.1.0(Py_Main+0xe25) [0x7f8d8ccf88d5]
python(main+0x16d) [0x400c1d]
/lib64/libc.so.6((null)+0xf5) [0x7f8d8bc93c05]
/lib64/libc.so.6(__libc_start_main+0xf5) [0x7f8d8bc93c05]
python() [0x4009e9]
Segmentation fault

what’s particularly confusing for me is that the step it crashed on ran fine for 8 prior images, specifically 'ccd': 0-7. I found forum posts stating that most segfaults are due to a mismatch of library versions, so I verified my versions (obs_subaru DM-13148, LSST stack 7-stack-lsst_distrib-w_2018_11).


(Paul Price) #26

Most often segfaults are due to mismatched versions, but there are other possibilities. What setup commands have you run, and what packages have you built personally?
Can you run processCcd.py under gdb (e.g., gdb --args $(which python) $(which processCcd.py) ...) and get a backtrace?


(Kyle Pittman) #27

I’m not sure what happened, but in the process of making a new docker container with the correct permissions to run gdb, the segfault was fixed and processCcd.py ran all the way through. I assume starting over fixed any library mismatches.


(Kyle Pittman) #28

Hello again,
I was hoping to clerify the next steps to coadd and mosaic the Suprime-cam data. This tutorial just runs makeDiscreteSkyMap.py and makeCoaddTempExp.py, but I’ve seen coaddDriver.py mentioned in relation to the other two and want to double check. Also, when I ran makeDiscreteSkyMap.py with
makeDiscreteSkyMap.py DATA --id field=N5846_1 --rerun processCcdOutput:coadd --config skyMap.projection="TAN" and got

daf.persistence.butler INFO: NoResults or IOError (continuing search) while evaluating bypass function for Dataset type:calexp_md Data ID:DataId(initialdata={'field': 'N5846_1', 'pointing': 1937, 'filter': 'W-S-G+', 'visit': 109076, 'ccd': 9, 'dateObs': '2009-04-21', 'taiObs': '2009-04-21', 'expTime': 460.0}, tag=set()) at location lsst.afw.image.ExposureF at FitsStorage(01937/W-S-G+/corr/CORR01090769.fits)

for each image, and I wasn’t sure if this was expected behavior, though it did run. I also experienced this when running makeCoaddTempExp.py, though it did not finish, and stoped with the message ‘Killed’. I ran makeCoaddTempExp.py with
makeCoaddTempExp.py DATA --rerun coadd --selectId filter=W-S-G+ --id filter=W-S-G+ tract=0 patch=0,0^0,1^0,2^0,3^1,0^1,1^1,2^1,3^2,0^2,1^2,2^2,3^3,0^3,1^3,2^3,3 --config doApplyUberCal=False doApplySkyCorr=False
Thank you!


(Paul Price) #29

coaddDriver.py combines makeCoaddTempExp.py, assembleCoadd.py and detectCoaddSources.py in a form where it can be run over multiple nodes (in the same way as singleFrameDriver.py). Do be warned, however, that spreading it too wide (I typically use a maximum of about 20 cores) can overwhelm some cluster filesystems due to the multitude of small reads in the early stage.

The NoResults or IOError is not something you should worry about greatly. It probably means that a CCD didn’t pass successfully through a previous stage.

You may want to run meas_mosaic (mosaic.py) on your data, but I’m not sure if it still works on Suprime-Cam. Here’s an example command-line from HSC that you can adapt:

mosaic.py /tigress/HSC/HSC/ --rerun price/ikeda-20180430 --calib /tigress/pprice/ikeda-20180410/CALIB_s18a --id tract=8766 ccd=0..8^10..103 visit=84654^84656^84658^84662^84664^84836^137270^137272^137274^137276^137278^138618

I’ve had reports from others about processes failing with the Killed message, but I have not recently experienced the problem myself. I believe this is the work of the system’s out-of-memory assassin, but I can’t think what might be causing it. I would be grateful to anyone who could help me reproduce the problem.


(Kyle Pittman) #30

I’ve been playing around with makeCoaddTempExp.py and I noticed that it crashes and gives the Killed message at the same spot, no matter how many patches I try to coadd. If I run
makeCoaddTempExp.py DATA --rerun coadd --selectId filter=W-S-G+ --id filter=W-S-G+ tract=0 patch=0,0^0,1^0,2^0,3^1,0^1,1^1,2^1,3^2,0^2,1^2,2^2,3^3,0^3,1^3,2^3,3 --config doApplyUberCal=False doApplySkyCorr=False
or
makeCoaddTempExp.py DATA --rerun coadd --selectId filter=W-S-G+ --id filter=W-S-G+ tract=0 patch=0,3 --config doApplyUberCal=False doApplySkyCorr=False
it will crash at the same spot, trying to coadd patch 0,3.

makeCoaddTempExp INFO: Selected 10 calexps for patch DataId(initialdata={'filter': 'W-S-G+', 'tract': 0, 'patch': '0,3'}, tag=set())
makeCoaddTempExp INFO: Processing 10 existing calexps for patch DataId(initialdata={'filter': 'W-S-G+', 'tract': 0, 'patch': '0,3'}, tag=set())
makeCoaddTempExp INFO: Processing 9 warp exposures for patch DataId(initialdata={'filter': 'W-S-G+', 'tract': 0, 'patch': '0,3'}, tag=set())
makeCoaddTempExp INFO: Processing Warp 0/9: id=DataId(initialdata={'visit': 109045, 'filter': 'W-S-G+', 'patch': '0,3', 'tract': 0}, tag=set())
makeCoaddTempExp INFO: Processing calexp 1 of 1 for this Warp: id={'filter': 'W-S-G+', 'pointing': 1937, 'visit': 109045, 'ccd': 6, 'field': 'N5846_1', 'dateObs': '2009-04-21', 'taiObs': '2009-04-21', 'expTime': 460.0}
makeCoaddTempExp.warpAndPsfMatch.psfMatch INFO: compute Psf-matching kernel
makeCoaddTempExp.warpAndPsfMatch.psfMatch INFO: Adjusted dimensions of reference PSF model from (23, 23) to (341, 341)
Killed

I’m wondering if the Adjusted dimensions of reference PSF model from (23, 23) to (341, 341) has something to do with it, as that’s where it is crashing each time and the other patches only adjusted from 23 to ~50-70 instead of 341. Do you have any suggestions on how I can track down the issue? Or is the answer to reduce the data on something with more available memory? Thanks


(Paul Price) #31

Can you try deactivating the PSF matching (doPsfMatch=False) to see if that allows it to complete?


(Kyle Pittman) #32

Unfortunately, deactivating the PST matching did not work. I have found that it is patches 0,3 ^ 1,3 ^ 2,3 ^ 3,3 which are not able to run.

Edit: I just decided to run on a machine with more available memory and it worked. I really appreciate the continued help with this!


(Paul Price) #33

How much memory per core did you have before, and how much now? And how many jobs are you running in parallel (-j)?


(Kyle Pittman) #34

Initially I was using a Docker container with only 2GB of allocated memory, and I am now using 8GB. This is still not enough for a couple of the patches, so I will be switching to something with more. I have not been using the -j option, so I guess whatever the default is.


(Paul Price) #35

Ah! 2 GB is much less than I would recommend. You want at least 4 GB per core, and sometimes (depending on the source density), even twice that amount is sometimes too small.


(Kyle Pittman) #36

Hello,
I recently started working on this again after a short break, and I was wondering if there is a task or function in the pipeline that will mosaic the coadded patches made by makeCoaddTempExp.py and assembleCoadd.py into a final image, or would that be something I have to do with other software? I’ve been looking around but haven’t found a lot of information.
Thank you!


(Paul Price) #37

(Sorry for the delay; I was on vacation.)

I’m not aware of anything formal in our stack that will mosaic the coadded patches, as we have focussed on reducing survey data where the full mosaic would be impractical to handle. There may be some informal solutions invented by various people, as the code isn’t difficult.

An alternative solution would be to use a different skymap, so that the tract of interest contains a single patch: see the patchInnerDimensions configuration parameter.


(Kyle Pittman) #38

Where can I find more information on patchInnerDimensions? I have been having trouble finding documentaion on the various options for makeDiscreteSkyMap.py. I do not think I can use the patchInnerDimensions option currently, because I am getting the error makeDiscreteSkyMap.py: error: no config field: patchInnerDimensions. Is it included by default, or is there an additional package I need to install?


(Paul Price) #39

Try adding --show config=*patchInnerDimensions* to your command-line. I think you probably want to set:

config.skyMap["discrete"].patchInnerDimensions = [XXX, YYY]

(setting XXX and YYY to the appropriate integers).

But I think you would have more control if you set the skymap explicitly, rather than allowing it to depend upon the data processing. In this case, you’d use makeSkyMap.py with a configuration override file like the following:

config.skyMap = "discrete"  # Choose to use discrete skymap
# Configuration for discrete skymap:
# Tract 0: ACTJ0022M0036
# Tract 1: M31
config.skyMap["discrete"].raList = [5.5, 10.7]
config.skyMap["discrete"].decList = [-0.6, 41.3]
config.skyMap["discrete"].radiusList = [0.5, 0.8]
config.skyMap["discrete"].pixelScale = 0.2
config.skyMap["discrete"].patchInnerDimensions = [XXX, YYY]  # Set XXX,YYY to integers

Do bear in mind that using large patches may cause problems with memory exhaustion down the road. I only recommend using large patches for making pretty pictures, not for any science operations.