Generation 3 Butler notes using S3

---

Contents

• Useful links

• butler Command line tasks

  • Creating a generation 3 butler repository
  • Adding an instrument to the GEN 3 butler repository
  • Ingest raw frames into from a directory into the butler registry
  • Importing data accross two GEN 3 repositories
  • Convert a Butler gen 2 repository into a gen 3 repository

• Butler using a jupyter notebook

  • LSST Stack Imports
  • Accessing the butler repository
  • Accessing the data registry
  • Plotting
  • Getting the URIs
  • Exporting
  • Importing

---

Useful links

• https://pipelines.lsst.io/modules/lsst.daf.butler/scripts/butler.py.html
• https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Basics/Gen3ButlerTutorial.ipynb
• https://dmtn-073.lsst.io/
• https://stackclub.readthedocs.io/en/latest/notebooks.html#getting-started
• https://docushare.lsstcorp.org/docushare/dsweb/Get/presentation-890/Gen3%20Middleware.pdf
• https://pipelines.lsst.io/modules/lsst.ap.pipe/getting-started.html?highlight=gen
• https://pipelines.lsst.io/search.html?q=gen+3&check_keywords=yes&area=default
• https://pipelines.lsst.io/v/weekly/py-api/lsst.daf.butler.Butler.html
• https://github.com/LSSTScienceCollaborations/StackClub
• https://notebook.community/hsinfang/lsst-notes/gen3butler/gen3notes
• https://github.com/lsst/testdata_jointcal/tree/master/scripts

---

butler Command line tasks

---

Creating a generation 3 butler repository

• Step 1 : Creating registry file (reg.yaml file)
  
  

  • Firstly, we need to create a SQL file for the registry (e.g vi test.sqlite3) (postgresql is often use for S3)
    
    

  • Then, we have to create an S3 bucket on echo which will be the butler repository. I used Rclone (https://rclone.org/docs/) to do this "rclone mkdir remote:bucket_name"
    
    

  • Now we have all of this, create a new file and call it reg.yaml. Within that file you have the path to the SQL file. Example
    
    

    • registry:
      

        db: sqlite:////home/test.sqlite3 

• Step 2 : Configuring the butler repository
  
  

  • Now we have got the butler.yaml file we can create an empty Gen3 Butler repository.
    
    

  • We do this by using "butler create" which is a command line task (https://pipelines.lsst.io/modules/lsst.daf.butler/scripts/butler.py.html)
    
    

  • We run:
    
    

    • » butler create s3://bucket_name --seed-config reg.yaml --override
      
      
    • where s3://bucket_name is the REPO which is the URI or path to the new repository
      
      

  • Now we have created a generation 3 Butler repository, if we check our s3 butler repository will see that the butler.yaml is now in the repository. We can also check the sql file and see all the tables loaded in which help query the dataset
    
    

---

Adding an instrument to the GEN 3 butler repository

• Step 1: Find the instrument class
  
  

  • For lsst use this link (https://pipelines.lsst.io/modules/lsst.obs.lsst/index.html, Ctrl-F lsst.obs.lsst Package) an example of class is "lsst.obs.lsst.LsstComCam"
    
    

• Step 2: Running the "register-instrument" Command
  
  

  • » butler register-instrument s3://bucket_name lsst.obs.subaru.HyperSuprimeCam
    
    

  • where s3://bucket_name is the REPO which is the URI or path to the new repository and lsst.obs.subaru.HyperSuprimeCam is the instrument class

---

Ingest raw frames into from a directory into the butler registry

• Step 1: Adding an instrument to the GEN 3 butler repository
  
  

  • Make sure that an instrument has been added the into the GEN 3 butler repository (Look the above for instructions to how to add an instrument to the butler repsitory)
    
    

• Step 2: Running the "ingest-raws" Command
  
  

  • » butler ingest-raws s3://bucket_name /home/lsst_stack/testdata_ci_hsc/raw
    
    

  • where s3://bucket_name is the REPO which is the URI or path to the new repository and /home/lsst_stack/testdata_ci_hsc/raw is the LOCATIONs specifies files to ingest and/or locations to search for files.

---

Convert a Butler gen 2 repository into a gen 3 repository

• Step 1: Set up a Gen 3 butler repository(Look above for instructions)
  
  

• Step 2: Running the "convert" Command
  
  

  • » butler convert s3://bucket_name --gen2root ~/lsst_stack/DATA --processes 2 --reruns "rerun/coadd,rerun/coaddPhot,rerun/processCcdOutputs,rerun/coaddForcedPhot" --calibs ~/lsst_stack/DATA/CALIB/
    
    

    • where s3://bucket_name is the REPO which is the URI or path to the new repository, --gen2root is the root path of the gen 2 repo to be converted and/home/lsst_stack/DATA is the path to the gen 2 repo, --processes sets the amount of proccessing cores use in the conversion, --reruns is the path to the rerun directories and --calibs is the path to the calib directory.
      
      

  • The tutorial for create a gen 2 repository is here https://pipelines.lsst.io/getting-started/data-setup.html
    
    

---

Importing data accross two GEN 3 repositories

• Step 1: Export the data
  
  

  • Firstly you will have export data from the repository with the data currently in it.(how to export data)
    
    

• Step 2: Running the "ingest-raws" Command
  
  

  • » butler import s3://bucket_name_new s3://bucket_name --export-file exports.yaml
    
    
  • where s3://bucket_name is the REPO which is the URI or path to the repository with the data and s3://bucket_name_new is the REPO which is the URI or path to the repository ehre you want to put your data .

Butler using a jupyter notebook

---

LSST Stack Imports

• butler class (https://pipelines.lsst.io/v/weekly/py-api/lsst.daf.butler.Butler.html)
  
• Image display class(https://pipelines.lsst.io/v/weekly/modules/lsst.afw.display/index.html)
  
• pylab for plotting
  

import lsst.daf.butler as dafButler
import lsst.afw.display as afwDisplay
import pylab as plt
import os,glob
import lsst.geom as geom

---

Accessing the butler repository

butler = dafButler.Butler("s3://joshuakitenge-DATA-ed")

---

Accessing the data registry

registry = butler.registry

The registry is a good tool for investigating a repo (more on the registry schema can be found here). For example, we can get a list of all collections, which includes the HSC/raw/all collection that we were using before

for c in registry.queryCollections():
    print(c)

HSC/raw/all
HSC/calib
HSC/calib/unbounded
HSC/calib/curated/1970-01-01T00:00:00
HSC/calib/curated/2013-01-31T00:00:00
HSC/calib/curated/2014-04-03T00:00:00
HSC/calib/curated/2014-06-01T00:00:00
HSC/calib/curated/2015-11-06T00:00:00
HSC/calib/curated/2016-04-01T00:00:00
HSC/calib/curated/2016-11-22T00:00:00
HSC/calib/curated/2016-12-23T00:00:00
refcats
HSC/calib/gen2/2014-11-12
HSC/calib/gen2/2014-07-14
HSC/calib/gen2/2013-11-03
HSC/calib/gen2/2013-06-17
shared/HSC/rerun/coadd
shared/HSC/rerun/coaddPhot
shared/HSC/rerun/processCcdOutputs
shared/HSC/rerun/coaddForcedPhot
shared/HSC

now that we "know" that HSC/raw/all exists, let's create our butler with this collection:

butler = dafButler.Butler("s3://joshuakitenge-DATA-ed",collections='shared/HSC/rerun/processCcdOutputs')
registry = butler.registry

We can also use the registry to get a list of all dataset types

for x in registry.queryDatasetTypes():
    print(x)

DatasetType('raw', {band, instrument, detector, physical_filter, exposure}, Exposure)
DatasetType('camera', {instrument}, Camera, isCalibration=True)
DatasetType('defects', {instrument, detector}, Defects, isCalibration=True)
DatasetType('bfKernel', {instrument}, NumpyArray, isCalibration=True)
DatasetType('transmission_optics', {instrument}, TransmissionCurve, isCalibration=True)
DatasetType('transmission_sensor', {instrument, detector}, TransmissionCurve, isCalibration=True)
DatasetType('transmission_filter', {band, instrument, physical_filter}, TransmissionCurve, isCalibration=True)
DatasetType('transmission_atmosphere', {instrument}, TransmissionCurve, isCalibration=True)
DatasetType('ps1_pv3_3pi_20170110', {htm7}, SimpleCatalog)
DatasetType('sky', {band, instrument, detector, physical_filter}, ExposureF, isCalibration=True)
DatasetType('dark', {instrument, detector}, ExposureF, isCalibration=True)
DatasetType('bias', {instrument, detector}, ExposureF, isCalibration=True)
DatasetType('flat', {band, instrument, detector, physical_filter}, ExposureF, isCalibration=True)
DatasetType('deepCoadd_skyMap', {skymap}, SkyMap)
DatasetType('deep_compareWarpAssembleCoadd_metadata', {band, skymap, tract, patch}, PropertySet)
DatasetType('deep_safeClipAssembleCoadd_config', {}, Config)
DatasetType('deep_makeCoaddTempExp_config', {}, Config)
DatasetType('packages', {}, Packages)
DatasetType('deep_makeCoaddTempExp_metadata', {band, skymap, tract, patch}, PropertySet)
DatasetType('deepCoadd_directWarp', {band, instrument, skymap, physical_filter, tract, visit_system, patch, visit}, ExposureF)
DatasetType('deepCoadd_psfMatchedWarp', {band, instrument, skymap, physical_filter, tract, visit_system, patch, visit}, ExposureF)
DatasetType('deepCoadd_nImage', {band, skymap, tract, patch}, ImageU)
DatasetType('deepCoadd', {band, skymap, tract, patch}, ExposureF)
DatasetType('deepCoadd_deblendedFlux', {band, skymap, tract, patch}, SourceCatalog)
DatasetType('deblendCoaddSources_metadata', {band, skymap, tract, patch}, PropertySet)
DatasetType('deepCoadd_measMatch', {band, skymap, tract, patch}, Catalog)
DatasetType('deepCoadd_meas', {band, skymap, tract, patch}, SourceCatalog)
DatasetType('detectCoaddSources_metadata', {band, skymap, tract, patch}, PropertySet)
DatasetType('deepCoadd_calexp_background', {band, skymap, tract, patch}, Background)
DatasetType('deepCoadd_calexp', {band, skymap, tract, patch}, ExposureF)
DatasetType('deepCoadd_measMatchFull', {band, skymap, tract, patch}, Catalog)
DatasetType('deepCoadd_det', {band, skymap, tract, patch}, SourceCatalog)
DatasetType('measureCoaddSources_metadata', {band, skymap, tract, patch}, PropertySet)
DatasetType('deepCoadd_ref', {skymap, tract, patch}, SourceCatalog)
DatasetType('deepCoadd_mergeDet', {skymap, tract, patch}, SourceCatalog)
DatasetType('measureCoaddSources_config', {}, Config)
DatasetType('detectCoaddSources_config', {}, Config)
DatasetType('mergeCoaddDetections_config', {}, Config)
DatasetType('deblendCoaddSources_config', {}, Config)
DatasetType('mergeCoaddMeasurements_config', {}, Config)
DatasetType('deepCoadd_ref_schema', {}, SourceCatalog)
DatasetType('deepCoadd_mergeDet_schema', {}, SourceCatalog)
DatasetType('deepCoadd_meas_schema', {}, SourceCatalog)
DatasetType('deepCoadd_det_schema', {}, SourceCatalog)
DatasetType('deepCoadd_peak_schema', {}, PeakCatalog)
DatasetType('deepCoadd_deblendedFlux_schema', {}, SourceCatalog)
DatasetType('deepCoadd_deblendedModel_schema', {}, SourceCatalog)
DatasetType('src', {band, instrument, detector, physical_filter, visit_system, visit}, SourceCatalog)
DatasetType('icSrc', {band, instrument, detector, physical_filter, visit_system, visit}, SourceCatalog)
DatasetType('srcMatch', {band, instrument, detector, physical_filter, visit_system, visit}, Catalog)
DatasetType('srcMatchFull', {band, instrument, detector, physical_filter, visit_system, visit}, Catalog)
DatasetType('processCcd_metadata', {band, instrument, detector, physical_filter, visit_system, visit}, PropertySet)
DatasetType('calexp', {band, instrument, detector, physical_filter, visit_system, visit}, ExposureF)
DatasetType('calexpBackground', {band, instrument, detector, physical_filter, visit_system, visit}, Background)
DatasetType('processCcd_config', {}, Config)
DatasetType('src_schema', {}, SourceCatalog)
DatasetType('icSrc_schema', {}, SourceCatalog)
DatasetType('deepCoadd_forced_src', {band, skymap, tract, patch}, SourceCatalog)
DatasetType('deepCoadd_forced_metadata', {band, skymap, tract, patch}, PropertySet)
DatasetType('deepCoadd_forced_config', {}, Config)
DatasetType('deepCoadd_forced_src_schema', {}, SourceCatalog)

We suspect that this is all datasetTypes that the processing has tried to create during the processing. There may be intermediate products that were created during processing, but no longer exist.

It is now possible to get all DatasetRef (including dataId) for a specific datasetType in a specific collection with a query like the one that follows

datasetRefs = list(registry.queryDatasets(datasetType='calexp',collections=['shared/HSC/rerun/processCcdOutputs']))
for ref in datasetRefs:
    print(ref.dataId)

{band: i, instrument: HSC, detector: 1, physical_filter: HSC-I, visit_system: 0, visit: 904014}
{band: i, instrument: HSC, detector: 12, physical_filter: HSC-I, visit_system: 0, visit: 904014}
{band: i, instrument: HSC, detector: 10, physical_filter: HSC-I, visit_system: 0, visit: 904010}
{band: i, instrument: HSC, detector: 25, physical_filter: HSC-I, visit_system: 0, visit: 903990}
{band: i, instrument: HSC, detector: 23, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 18, physical_filter: HSC-I, visit_system: 0, visit: 903990}
{band: i, instrument: HSC, detector: 6, physical_filter: HSC-I, visit_system: 0, visit: 904014}
{band: i, instrument: HSC, detector: 100, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: i, instrument: HSC, detector: 16, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: i, instrument: HSC, detector: 24, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 4, physical_filter: HSC-I, visit_system: 0, visit: 904010}
{band: i, instrument: HSC, detector: 23, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: i, instrument: HSC, detector: 17, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 16, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 100, physical_filter: HSC-I, visit_system: 0, visit: 904010}
{band: i, instrument: HSC, detector: 22, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: r, instrument: HSC, detector: 6, physical_filter: HSC-R, visit_system: 0, visit: 903346}
{band: r, instrument: HSC, detector: 4, physical_filter: HSC-R, visit_system: 0, visit: 903342}
{band: r, instrument: HSC, detector: 11, physical_filter: HSC-R, visit_system: 0, visit: 903344}
{band: r, instrument: HSC, detector: 12, physical_filter: HSC-R, visit_system: 0, visit: 903346}
{band: r, instrument: HSC, detector: 5, physical_filter: HSC-R, visit_system: 0, visit: 903344}
{band: r, instrument: HSC, detector: 18, physical_filter: HSC-R, visit_system: 0, visit: 903338}
{band: r, instrument: HSC, detector: 24, physical_filter: HSC-R, visit_system: 0, visit: 903336}
{band: r, instrument: HSC, detector: 1, physical_filter: HSC-R, visit_system: 0, visit: 903346}
{band: r, instrument: HSC, detector: 17, physical_filter: HSC-R, visit_system: 0, visit: 903336}
{band: r, instrument: HSC, detector: 23, physical_filter: HSC-R, visit_system: 0, visit: 903334}
{band: r, instrument: HSC, detector: 100, physical_filter: HSC-R, visit_system: 0, visit: 903342}
{band: r, instrument: HSC, detector: 100, physical_filter: HSC-R, visit_system: 0, visit: 903334}
{band: r, instrument: HSC, detector: 25, physical_filter: HSC-R, visit_system: 0, visit: 903338}
{band: r, instrument: HSC, detector: 10, physical_filter: HSC-R, visit_system: 0, visit: 903342}
{band: r, instrument: HSC, detector: 22, physical_filter: HSC-R, visit_system: 0, visit: 903334}
{band: r, instrument: HSC, detector: 0, physical_filter: HSC-R, visit_system: 0, visit: 903344}
{band: r, instrument: HSC, detector: 16, physical_filter: HSC-R, visit_system: 0, visit: 903334}

Ok, now that we know what collections exist (HSC/raw/all in particular), the datasetTypes that are defined for that collection, and the datasetRefs (which contain dataIds) for data products of the requested type. This is all the information that we need to get the dataset of interest.

From the list above, I choose index 16 and with this we will find the dataId

ref = datasetRefs[16]
print(ref.dataId)

{band: r, instrument: HSC, detector: 6, physical_filter: HSC-R, visit_system: 0, visit: 903346}

---

Plotting

• Using the dataset from above we can get the image (more on plotting in
  https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Basics/Gen3ButlerTutorial.ipynb
  https://github.com/LSSTScienceCollaborations/StackClub/blob/master/Basics/Calexp_guided_tour.ipynb)
  
• get(datasetRefOrType, …): Retrieve a stored dataset.

# To get the image,we pass the dataId
test = butler.get(ref)

#And plot!
afwDisplay.setDefaultBackend('matplotlib') 
fig = plt.figure(figsize=(10,8))
afw_display = afwDisplay.Display(1)
afw_display.scale('asinh', 'zscale')
afw_display.mtv(test)
plt.gca().axis('off')

(-0.5, 2047.5, -0.5, 4175.5)

datasetRefs = list(registry.queryDatasets(datasetType='src',collections=['shared/HSC/rerun/processCcdOutputs']))
for ref in datasetRefs:
    print(ref.dataId)

{band: i, instrument: HSC, detector: 4, physical_filter: HSC-I, visit_system: 0, visit: 904010}
{band: i, instrument: HSC, detector: 24, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 100, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: i, instrument: HSC, detector: 17, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 1, physical_filter: HSC-I, visit_system: 0, visit: 904014}
{band: i, instrument: HSC, detector: 16, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 100, physical_filter: HSC-I, visit_system: 0, visit: 904010}
{band: i, instrument: HSC, detector: 10, physical_filter: HSC-I, visit_system: 0, visit: 904010}
{band: i, instrument: HSC, detector: 16, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: i, instrument: HSC, detector: 12, physical_filter: HSC-I, visit_system: 0, visit: 904014}
{band: i, instrument: HSC, detector: 18, physical_filter: HSC-I, visit_system: 0, visit: 903990}
{band: i, instrument: HSC, detector: 23, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: i, instrument: HSC, detector: 25, physical_filter: HSC-I, visit_system: 0, visit: 903990}
{band: i, instrument: HSC, detector: 23, physical_filter: HSC-I, visit_system: 0, visit: 903988}
{band: i, instrument: HSC, detector: 6, physical_filter: HSC-I, visit_system: 0, visit: 904014}
{band: i, instrument: HSC, detector: 22, physical_filter: HSC-I, visit_system: 0, visit: 903986}
{band: r, instrument: HSC, detector: 10, physical_filter: HSC-R, visit_system: 0, visit: 903342}
{band: r, instrument: HSC, detector: 5, physical_filter: HSC-R, visit_system: 0, visit: 903344}
{band: r, instrument: HSC, detector: 100, physical_filter: HSC-R, visit_system: 0, visit: 903334}
{band: r, instrument: HSC, detector: 6, physical_filter: HSC-R, visit_system: 0, visit: 903346}
{band: r, instrument: HSC, detector: 18, physical_filter: HSC-R, visit_system: 0, visit: 903338}
{band: r, instrument: HSC, detector: 4, physical_filter: HSC-R, visit_system: 0, visit: 903342}
{band: r, instrument: HSC, detector: 100, physical_filter: HSC-R, visit_system: 0, visit: 903342}
{band: r, instrument: HSC, detector: 23, physical_filter: HSC-R, visit_system: 0, visit: 903334}
{band: r, instrument: HSC, detector: 17, physical_filter: HSC-R, visit_system: 0, visit: 903336}
{band: r, instrument: HSC, detector: 16, physical_filter: HSC-R, visit_system: 0, visit: 903334}
{band: r, instrument: HSC, detector: 0, physical_filter: HSC-R, visit_system: 0, visit: 903344}
{band: r, instrument: HSC, detector: 25, physical_filter: HSC-R, visit_system: 0, visit: 903338}
{band: r, instrument: HSC, detector: 24, physical_filter: HSC-R, visit_system: 0, visit: 903336}
{band: r, instrument: HSC, detector: 12, physical_filter: HSC-R, visit_system: 0, visit: 903346}
{band: r, instrument: HSC, detector: 11, physical_filter: HSC-R, visit_system: 0, visit: 903344}
{band: r, instrument: HSC, detector: 1, physical_filter: HSC-R, visit_system: 0, visit: 903346}
{band: r, instrument: HSC, detector: 22, physical_filter: HSC-R, visit_system: 0, visit: 903334}

ref = datasetRefs[16]
print(ref.dataId)

{band: r, instrument: HSC, detector: 10, physical_filter: HSC-R, visit_system: 0, visit: 903342}

How to create a table using the data

src = butler.get(ref)
src = src.copy(True)
src.asAstropy()

Table length=1356

id	coord_ra	coord_dec	parent	calib_detected	calib_psf_candidate	calib_psf_used	calib_psf_reserved	deblend_nChild	deblend_deblendedAsPsf	deblend_psfCenter_x	deblend_psfCenter_y	deblend_psf_instFlux	deblend_tooManyPeaks	deblend_parentTooBig	deblend_masked	deblend_skipped	deblend_rampedTemplate	deblend_patchedTemplate	deblend_hasStrayFlux	deblend_peak_center_x	deblend_peak_center_y	deblend_peakId	deblend_nPeaks	deblend_parentNPeaks	sky_source	base_NaiveCentroid_x	base_NaiveCentroid_y	base_NaiveCentroid_flag	base_NaiveCentroid_flag_noCounts	base_NaiveCentroid_flag_edge	base_NaiveCentroid_flag_resetToPeak	base_SdssCentroid_x	slot_Centroid_x	base_SdssCentroid_y	slot_Centroid_y	base_SdssCentroid_xErr	slot_Centroid_xErr	base_SdssCentroid_yErr	slot_Centroid_yErr	base_SdssCentroid_flag	base_CircularApertureFlux_flag_badCentroid	base_GaussianFlux_flag_badCentroid	base_LocalBackground_flag_badCentroid	base_NaiveCentroid_flag_badInitialCentroid	base_PsfFlux_flag_badCentroid	base_SdssShape_flag_badCentroid	base_Variance_flag_badCentroid	ext_photometryKron_KronFlux_flag_badInitialCentroid	ext_shapeHSM_HsmPsfMoments_flag_badCentroid	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid	ext_shapeHSM_HsmSourceMoments_flag_badCentroid	slot_Centroid_flag	base_SdssCentroid_flag_edge	base_CircularApertureFlux_flag_badCentroid_edge	base_GaussianFlux_flag_badCentroid_edge	base_LocalBackground_flag_badCentroid_edge	base_NaiveCentroid_flag_badInitialCentroid_edge	base_PsfFlux_flag_badCentroid_edge	base_SdssShape_flag_badCentroid_edge	base_Variance_flag_badCentroid_edge	ext_photometryKron_KronFlux_flag_badInitialCentroid_edge	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_edge	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_edge	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_edge	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_edge	slot_Centroid_flag_edge	base_SdssCentroid_flag_noSecondDerivative	base_CircularApertureFlux_flag_badCentroid_noSecondDerivative	base_GaussianFlux_flag_badCentroid_noSecondDerivative	base_LocalBackground_flag_badCentroid_noSecondDerivative	base_NaiveCentroid_flag_badInitialCentroid_noSecondDerivative	base_PsfFlux_flag_badCentroid_noSecondDerivative	base_SdssShape_flag_badCentroid_noSecondDerivative	base_Variance_flag_badCentroid_noSecondDerivative	ext_photometryKron_KronFlux_flag_badInitialCentroid_noSecondDerivative	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_noSecondDerivative	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_noSecondDerivative	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_noSecondDerivative	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_noSecondDerivative	slot_Centroid_flag_noSecondDerivative	base_SdssCentroid_flag_almostNoSecondDerivative	base_CircularApertureFlux_flag_badCentroid_almostNoSecondDerivative	base_GaussianFlux_flag_badCentroid_almostNoSecondDerivative	base_LocalBackground_flag_badCentroid_almostNoSecondDerivative	base_NaiveCentroid_flag_badInitialCentroid_almostNoSecondDerivative	base_PsfFlux_flag_badCentroid_almostNoSecondDerivative	base_SdssShape_flag_badCentroid_almostNoSecondDerivative	base_Variance_flag_badCentroid_almostNoSecondDerivative	ext_photometryKron_KronFlux_flag_badInitialCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_almostNoSecondDerivative	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_almostNoSecondDerivative	slot_Centroid_flag_almostNoSecondDerivative	base_SdssCentroid_flag_notAtMaximum	base_CircularApertureFlux_flag_badCentroid_notAtMaximum	base_GaussianFlux_flag_badCentroid_notAtMaximum	base_LocalBackground_flag_badCentroid_notAtMaximum	base_NaiveCentroid_flag_badInitialCentroid_notAtMaximum	base_PsfFlux_flag_badCentroid_notAtMaximum	base_SdssShape_flag_badCentroid_notAtMaximum	base_Variance_flag_badCentroid_notAtMaximum	ext_photometryKron_KronFlux_flag_badInitialCentroid_notAtMaximum	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_notAtMaximum	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_notAtMaximum	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_notAtMaximum	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_notAtMaximum	slot_Centroid_flag_notAtMaximum	base_SdssCentroid_flag_resetToPeak	base_CircularApertureFlux_flag_badCentroid_resetToPeak	base_GaussianFlux_flag_badCentroid_resetToPeak	base_LocalBackground_flag_badCentroid_resetToPeak	base_NaiveCentroid_flag_badInitialCentroid_resetToPeak	base_PsfFlux_flag_badCentroid_resetToPeak	base_SdssShape_flag_badCentroid_resetToPeak	base_Variance_flag_badCentroid_resetToPeak	ext_photometryKron_KronFlux_flag_badInitialCentroid_resetToPeak	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_resetToPeak	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_resetToPeak	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_resetToPeak	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_resetToPeak	slot_Centroid_flag_resetToPeak	base_SdssCentroid_flag_badError	base_CircularApertureFlux_flag_badCentroid_badError	base_GaussianFlux_flag_badCentroid_badError	base_LocalBackground_flag_badCentroid_badError	base_NaiveCentroid_flag_badInitialCentroid_badError	base_PsfFlux_flag_badCentroid_badError	base_SdssShape_flag_badCentroid_badError	base_Variance_flag_badCentroid_badError	ext_photometryKron_KronFlux_flag_badInitialCentroid_badError	ext_shapeHSM_HsmPsfMoments_flag_badCentroid_badError	ext_shapeHSM_HsmShapeRegauss_flag_badCentroid_badError	ext_shapeHSM_HsmSourceMomentsRound_flag_badCentroid_badError	ext_shapeHSM_HsmSourceMoments_flag_badCentroid_badError	slot_Centroid_flag_badError	base_Blendedness_old	base_Blendedness_raw	base_Blendedness_raw_child_instFlux	base_Blendedness_raw_parent_instFlux	base_Blendedness_abs	base_Blendedness_abs_child_instFlux	base_Blendedness_abs_parent_instFlux	base_Blendedness_raw_child_xx	base_Blendedness_raw_child_yy	base_Blendedness_raw_child_xy	base_Blendedness_raw_parent_xx	base_Blendedness_raw_parent_yy	base_Blendedness_raw_parent_xy	base_Blendedness_abs_child_xx	base_Blendedness_abs_child_yy	base_Blendedness_abs_child_xy	base_Blendedness_abs_parent_xx	base_Blendedness_abs_parent_yy	base_Blendedness_abs_parent_xy	base_Blendedness_flag	base_Blendedness_flag_noCentroid	base_Blendedness_flag_noShape	base_FPPosition_x	base_FPPosition_y	base_FPPosition_flag	base_FPPosition_missingDetector_flag	base_Jacobian_value	base_Jacobian_flag	base_SdssShape_xx	base_SdssShape_yy	base_SdssShape_xy	base_SdssShape_xxErr	base_SdssShape_yyErr	base_SdssShape_xyErr	base_SdssShape_x	base_SdssShape_y	base_SdssShape_instFlux	base_SdssShape_instFluxErr	base_SdssShape_psf_xx	base_SdssShape_psf_yy	base_SdssShape_psf_xy	base_SdssShape_instFlux_xx_Cov	base_SdssShape_instFlux_yy_Cov	base_SdssShape_instFlux_xy_Cov	base_SdssShape_flag	base_SdssShape_flag_unweightedBad	base_SdssShape_flag_unweighted	base_SdssShape_flag_shift	base_SdssShape_flag_maxIter	base_SdssShape_flag_psf	ext_shapeHSM_HsmPsfMoments_x	slot_PsfShape_x	ext_shapeHSM_HsmPsfMoments_y	slot_PsfShape_y	ext_shapeHSM_HsmPsfMoments_xx	slot_PsfShape_xx	ext_shapeHSM_HsmPsfMoments_yy	slot_PsfShape_yy	ext_shapeHSM_HsmPsfMoments_xy	slot_PsfShape_xy	ext_shapeHSM_HsmPsfMoments_flag	slot_PsfShape_flag	ext_shapeHSM_HsmPsfMoments_flag_no_pixels	slot_PsfShape_flag_no_pixels	ext_shapeHSM_HsmPsfMoments_flag_not_contained	slot_PsfShape_flag_not_contained	ext_shapeHSM_HsmPsfMoments_flag_parent_source	slot_PsfShape_flag_parent_source	ext_shapeHSM_HsmShapeRegauss_e1	ext_shapeHSM_HsmShapeRegauss_e2	ext_shapeHSM_HsmShapeRegauss_sigma	ext_shapeHSM_HsmShapeRegauss_resolution	ext_shapeHSM_HsmShapeRegauss_flag	ext_shapeHSM_HsmShapeRegauss_flag_no_pixels	ext_shapeHSM_HsmShapeRegauss_flag_not_contained	ext_shapeHSM_HsmShapeRegauss_flag_parent_source	ext_shapeHSM_HsmShapeRegauss_flag_galsim	ext_shapeHSM_HsmSourceMoments_x	slot_Shape_x	ext_shapeHSM_HsmSourceMoments_y	slot_Shape_y	ext_shapeHSM_HsmSourceMoments_xx	slot_Shape_xx	ext_shapeHSM_HsmSourceMoments_yy	slot_Shape_yy	ext_shapeHSM_HsmSourceMoments_xy	slot_Shape_xy	ext_shapeHSM_HsmSourceMoments_flag	base_GaussianFlux_flag_badShape	slot_Shape_flag	ext_shapeHSM_HsmSourceMoments_flag_no_pixels	base_GaussianFlux_flag_badShape_no_pixels	slot_Shape_flag_no_pixels	ext_shapeHSM_HsmSourceMoments_flag_not_contained	base_GaussianFlux_flag_badShape_not_contained	slot_Shape_flag_not_contained	ext_shapeHSM_HsmSourceMoments_flag_parent_source	base_GaussianFlux_flag_badShape_parent_source	slot_Shape_flag_parent_source	ext_shapeHSM_HsmSourceMomentsRound_x	slot_ShapeRound_x	ext_shapeHSM_HsmSourceMomentsRound_y	slot_ShapeRound_y	ext_shapeHSM_HsmSourceMomentsRound_xx	slot_ShapeRound_xx	ext_shapeHSM_HsmSourceMomentsRound_yy	slot_ShapeRound_yy	ext_shapeHSM_HsmSourceMomentsRound_xy	slot_ShapeRound_xy	ext_shapeHSM_HsmSourceMomentsRound_flag	slot_ShapeRound_flag	ext_shapeHSM_HsmSourceMomentsRound_flag_no_pixels	slot_ShapeRound_flag_no_pixels	ext_shapeHSM_HsmSourceMomentsRound_flag_not_contained	slot_ShapeRound_flag_not_contained	ext_shapeHSM_HsmSourceMomentsRound_flag_parent_source	slot_ShapeRound_flag_parent_source	ext_shapeHSM_HsmSourceMomentsRound_Flux	slot_ShapeRound_Flux	base_CircularApertureFlux_3_0_instFlux	base_CircularApertureFlux_3_0_instFluxErr	base_CircularApertureFlux_3_0_flag	base_CircularApertureFlux_3_0_flag_apertureTruncated	base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_4_5_instFlux	base_CircularApertureFlux_4_5_instFluxErr	base_CircularApertureFlux_4_5_flag	base_CircularApertureFlux_4_5_flag_apertureTruncated	base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated	base_CircularApertureFlux_6_0_instFlux	base_CircularApertureFlux_6_0_instFluxErr	base_CircularApertureFlux_6_0_flag	base_CircularApertureFlux_6_0_flag_apertureTruncated	base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_9_0_instFlux	base_CircularApertureFlux_9_0_instFluxErr	base_CircularApertureFlux_9_0_flag	base_CircularApertureFlux_9_0_flag_apertureTruncated	base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_12_0_instFlux	slot_ApFlux_instFlux	slot_CalibFlux_instFlux	base_CircularApertureFlux_12_0_instFluxErr	slot_ApFlux_instFluxErr	slot_CalibFlux_instFluxErr	base_CircularApertureFlux_12_0_flag	slot_ApFlux_flag	slot_CalibFlux_flag	base_CircularApertureFlux_12_0_flag_apertureTruncated	slot_ApFlux_flag_apertureTruncated	slot_CalibFlux_flag_apertureTruncated	base_CircularApertureFlux_12_0_flag_sincCoeffsTruncated	slot_ApFlux_flag_sincCoeffsTruncated	slot_CalibFlux_flag_sincCoeffsTruncated	base_CircularApertureFlux_17_0_instFlux	base_CircularApertureFlux_17_0_instFluxErr	base_CircularApertureFlux_17_0_flag	base_CircularApertureFlux_17_0_flag_apertureTruncated	base_CircularApertureFlux_25_0_instFlux	base_CircularApertureFlux_25_0_instFluxErr	base_CircularApertureFlux_25_0_flag	base_CircularApertureFlux_25_0_flag_apertureTruncated	base_CircularApertureFlux_35_0_instFlux	base_CircularApertureFlux_35_0_instFluxErr	base_CircularApertureFlux_35_0_flag	base_CircularApertureFlux_35_0_flag_apertureTruncated	base_CircularApertureFlux_50_0_instFlux	base_CircularApertureFlux_50_0_instFluxErr	base_CircularApertureFlux_50_0_flag	base_CircularApertureFlux_50_0_flag_apertureTruncated	base_CircularApertureFlux_70_0_instFlux	base_CircularApertureFlux_70_0_instFluxErr	base_CircularApertureFlux_70_0_flag	base_CircularApertureFlux_70_0_flag_apertureTruncated	base_GaussianFlux_instFlux	slot_GaussianFlux_instFlux	slot_ModelFlux_instFlux	base_GaussianFlux_instFluxErr	slot_GaussianFlux_instFluxErr	slot_ModelFlux_instFluxErr	base_GaussianFlux_flag	slot_GaussianFlux_flag	slot_ModelFlux_flag	base_LocalBackground_instFlux	base_LocalBackground_instFluxErr	base_LocalBackground_flag	base_LocalBackground_flag_noGoodPixels	base_LocalBackground_flag_noPsf	base_LocalPhotoCalib_flag	base_LocalPhotoCalib	base_LocalPhotoCalibErr	base_LocalWcs_flag	base_LocalWcs_CDMatrix_1_1	base_LocalWcs_CDMatrix_1_2	base_LocalWcs_CDMatrix_2_1	base_LocalWcs_CDMatrix_2_2	base_PixelFlags_flag	base_PixelFlags_flag_offimage	base_PixelFlags_flag_edge	base_PixelFlags_flag_interpolated	base_PixelFlags_flag_saturated	base_PixelFlags_flag_cr	base_PixelFlags_flag_bad	base_PixelFlags_flag_suspect	base_PixelFlags_flag_interpolatedCenter	base_PixelFlags_flag_saturatedCenter	base_PixelFlags_flag_crCenter	base_PixelFlags_flag_suspectCenter	base_PsfFlux_instFlux	slot_PsfFlux_instFlux	base_PsfFlux_instFluxErr	slot_PsfFlux_instFluxErr	base_PsfFlux_area	slot_PsfFlux_area	base_PsfFlux_flag	slot_PsfFlux_flag	base_PsfFlux_flag_noGoodPixels	slot_PsfFlux_flag_noGoodPixels	base_PsfFlux_flag_edge	slot_PsfFlux_flag_edge	base_Variance_flag	base_Variance_value	base_Variance_flag_emptyFootprint	ext_photometryKron_KronFlux_instFlux	ext_photometryKron_KronFlux_instFluxErr	ext_photometryKron_KronFlux_radius	ext_photometryKron_KronFlux_radius_for_radius	ext_photometryKron_KronFlux_psf_radius	ext_photometryKron_KronFlux_flag	ext_photometryKron_KronFlux_flag_edge	ext_photometryKron_KronFlux_flag_bad_shape_no_psf	ext_photometryKron_KronFlux_flag_no_minimum_radius	ext_photometryKron_KronFlux_flag_no_fallback_radius	ext_photometryKron_KronFlux_flag_bad_radius	ext_photometryKron_KronFlux_flag_used_minimum_radius	ext_photometryKron_KronFlux_flag_used_psf_radius	ext_photometryKron_KronFlux_flag_small_radius	ext_photometryKron_KronFlux_flag_bad_shape	detect_isPrimary	base_GaussianFlux_apCorr	slot_GaussianFlux_apCorr	slot_ModelFlux_apCorr	base_GaussianFlux_apCorrErr	slot_GaussianFlux_apCorrErr	slot_ModelFlux_apCorrErr	base_GaussianFlux_flag_apCorr	slot_GaussianFlux_flag_apCorr	slot_ModelFlux_flag_apCorr	base_PsfFlux_apCorr	slot_PsfFlux_apCorr	base_PsfFlux_apCorrErr	slot_PsfFlux_apCorrErr	base_PsfFlux_flag_apCorr	slot_PsfFlux_flag_apCorr	ext_photometryKron_KronFlux_apCorr	ext_photometryKron_KronFlux_apCorrErr	ext_photometryKron_KronFlux_flag_apCorr	base_ClassificationExtendedness_value	base_ClassificationExtendedness_flag	base_FootprintArea_value	calib_astrometry_used	calib_photometry_used	calib_photometry_reserved
	rad	rad								pix	pix	ct								pix	pix					pix	pix					pix	pix	pix	pix	pix	pix	pix	pix																																																																																																					ct	ct		ct	ct	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2	pix2				mm	mm					pix2	pix2	pix2	pix2	pix2	pix2	pix	pix	ct	ct	pix2	pix2	pix2	ct pix2	ct pix2	ct pix2							pix	pix	pix	pix	pix2	pix2	pix2	pix2	pix2	pix2																		pix	pix	pix	pix	pix2	pix2	pix2	pix2	pix2	pix2													pix	pix	pix	pix	pix2	pix2	pix2	pix2	pix2	pix2											ct	ct				ct	ct				ct	ct				ct	ct				ct	ct	ct	ct	ct	ct										ct	ct			ct	ct			ct	ct			ct	ct			ct	ct			ct	ct	ct	ct	ct	ct				ct	ct																								ct	ct	ct	ct	pix	pix										ct	ct																																			pix
int64	float64	float64	int64	bool	bool	bool	bool	int32	bool	float64	float64	float64	bool	bool	bool	bool	bool	bool	bool	int32	int32	int32	int32	int32	bool	float64	float64	bool	bool	bool	bool	float64	float64	float64	float64	float32	float32	float32	float32	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	bool	bool	bool	float64	float64	bool	bool	float64	bool	float64	float64	float64	float32	float32	float32	float64	float64	float64	float64	float64	float64	float64	float32	float32	float32	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	bool	bool	bool	bool	bool	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	bool	bool	bool	bool	bool	bool	bool	bool	float32	float32	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	float64	float64	float64	float64	bool	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	float64	float64	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	bool	float64	float64	bool	float64	float64	float64	float64	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	float32	float32	bool	bool	bool	bool	bool	bool	bool	float64	bool	float64	float64	float32	float32	float32	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	float64	float64	bool	bool	bool	float64	float64	float64	float64	bool	bool	float64	float64	bool	float64	bool	int32	bool	bool	bool
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775964912370320714	5.5998737555943965	-0.0035355138488289643	775964912370320286	True	True	True	False	0	False	nan	nan	nan	False	False	False	False	False	False	False	1689	4124	1970733	1	3	False	1688.140694703875	4124.033335686149	False	False	False	False	1688.8459787890838	1688.8459787890838	4124.097498857612	4124.097498857612	0.0026189785	0.0026189785	0.0031561572	0.0031561572	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	2.5675521293814984e-07	0.0	552490.9249176382	552490.9249176382	0.0	552490.9249176382	552490.9249176382	4.305932595180299	5.236014008011367	-0.4107778905807167	4.305932595180299	5.236014008011367	-0.4107778905807167	4.305932595180299	5.236014008011367	-0.4107778905807167	4.305932595180299	5.236014008011367	-0.4107778905807167	False	False	False	149.23210041425364	-197.10028747515966	False	False	0.8720328886035255	False	4.327129442209048	5.266378957702336	-0.4101673741936964	0.012689978	0.009935716	0.015444472	1688.8480162076073	4124.092326528614	559186.0775003433	819.949905582273	4.29352592696033	5.2146085198084355	-0.4214313419732974	-5.202573	0.49315038	-6.3318467	False	False	False	False	False	False	0.002605141929400309	0.002605141929400309	-0.0093769429518057	-0.0093769429518057	4.299733330191634	4.299733330191634	5.221381339630677	5.221381339630677	-0.4236027822779901	-0.4236027822779901	False	False	False	False	False	False	False	False	-0.05602747201919556	-0.2580086886882782	0.026548966765403748	0.007113875821232796	False	False	False	False	False	1688.850250592201	1688.850250592201	4124.086808167655	4124.086808167655	4.333835887807614	4.333835887807614	5.273603532682114	5.273603532682114	-0.4126163009732463	-0.4126163009732463	False	False	False	False	False	False	False	False	False	False	False	False	1688.8485128160967	1688.8485128160967	4124.094660527488	4124.094660527488	4.557644299847839	4.557644299847839	4.984696779529898	4.984696779529898	-0.1843990161341591	-0.1843990161341591	False	False	False	False	False	False	False	False	558520.2	558520.2	341340.21875	305.6878967285156	False	False	False	482246.8125	369.4720153808594	False	False	False	559643.1875	402.4106750488281	False	False	False	627073.4375	434.46759033203125	False	False	False	654141.0625	654141.0625	654141.0625	454.8647766113281	454.8647766113281	454.8647766113281	False	False	False	False	False	False	False	False	False	673571.4439994097	486.6609950771544	False	False	686055.8792726025	544.4999962098233	False	False	689985.0276625454	641.5640702783513	False	False	691757.5449161865	865.3801871175555	False	False	nan	nan	True	True	656608.8950341464	656608.8950341464	656608.8950341464	659.8663088732993	659.8663088732993	659.8663088732993	False	False	False	8.469491045264652	11.047967565495929	False	False	False	False	0.30400361311863466	0.0	False	2.6580962402182112e-08	7.534838203497391e-07	7.687121422781525e-07	2.6864887182396045e-08	False	False	False	False	False	False	False	False	False	False	False	False	653617.7553429513	653617.7553429513	541.2452008613989	541.2452008613989	85.380135	85.380135	False	False	False	False	False	False	True	nan	True	654895.2073140406	453.5195564635654	3.5947807	13.094364	2.7208476	False	False	False	False	False	False	False	False	False	False	True	1.1733846077886847	1.1733846077886847	1.1733846077886847	0.0	0.0	0.0	False	False	False	0.9717071224012532	0.9717071224012532	0.0	0.0	False	False	1.044213921621263	0.0	False	0.0	False	2229	False	True	False
775964912370320715	5.599858813587556	-0.0035072697494242674	775964912370320286	False	False	False	False	0	True	1726.0	4103.0	6106.393318163321	False	False	False	False	False	False	False	1726	4103	1970732	1	3	False	1726.036466336072	4103.013860822068	False	False	False	False	1726.139687543526	1726.139687543526	4103.019338728704	4103.019338728704	0.04488962	0.04488962	0.05561554	0.05561554	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	0.0025560667772447834	0.0	5044.638512149788	5044.638512149788	0.0	5043.090342570293	5043.090342570293	4.187059367753122	5.379917204184247	-0.6083560457924025	4.187059367753122	5.379917204184247	-0.6083560457924025	4.1842770502053765	5.369324785825467	-0.6040294326871068	4.1842770502053765	5.369324785825467	-0.6040294326871068	False	False	False	148.67271299850296	-196.78408284648853	False	False	0.8727258470169602	False	4.202478573573412	5.421396870896008	-0.6187755416630194	0.18462457	0.14951897	0.23817445	1726.1357813222521	4103.018770402909	5106.640124322032	112.17323780183874	4.286685483128388	5.201845513901703	-0.4273904132478954	-10.354968	1.5246719	-13.3584	False	False	False	False	False	False	0.002598654693903794	0.002598654693903794	-0.009310679607721415	-0.009310679607721415	4.293064276333002	4.293064276333002	5.208885280364502	5.208885280364502	-0.42929309338732835	-0.42929309338732835	False	False	False	False	False	False	False	False	0.6449207663536072	0.8399807214736938	1.6980828046798706	0.011844919063150883	False	False	False	False	False	1726.1321424717703	1726.1321424717703	4103.018146477574	4103.018146477574	4.2067666691379255	4.2067666691379255	5.433767087618907	5.433767087618907	-0.6203550019139473	-0.6203550019139473	False	False	False	False	False	False	False	False	False	False	False	False	1726.1362202630817	1726.1362202630817	4103.0224089877465	4103.0224089877465	4.47740039365641	4.47740039365641	5.002316314778459	5.002316314778459	-0.2827965066205732	-0.2827965066205732	False	False	False	False	False	False	False	False	5083.237	5083.237	3111.69287109375	47.55425262451172	False	False	False	4394.33740234375	66.57504272460938	False	False	False	5094.8857421875	84.90966796875	False	False	False	5816.193359375	121.56934356689453	False	False	False	6345.90869140625	6345.90869140625	6345.90869140625	159.0196533203125	159.0196533203125	159.0196533203125	False	False	False	False	False	False	False	False	False	7129.066031252965	222.981160484262	False	False	7745.932728126521	326.21335898245076	False	False	8746.818426266189	460.0032577357827	False	False	10123.618597663815	776.8511441558409	False	False	9948.634543217595	1076.8614381047992	False	False	5999.857419993655	5999.857419993655	5999.857419993655	93.19230560228202	93.19230560228202	93.19230560228202	False	False	False	0.6481740819685481	7.4556154506371835	False	False	False	False	0.30400361311863466	0.0	False	2.6410035218014155e-08	7.537701954281284e-07	7.690178478858438e-07	2.6693187473054507e-08	False	False	False	False	False	False	False	False	False	False	False	False	5983.380904932444	5983.380904932444	83.1043850122789	83.1043850122789	85.35766	85.35766	False	False	False	False	False	False	True	nan	True	6440.508553661948	143.65464390252754	4.111105	13.063819	2.7179196	False	False	False	False	False	False	False	False	False	False	True	1.173953694602489	1.173953694602489	1.173953694602489	0.0	0.0	0.0	False	False	False	0.971636582451612	0.971636582451612	0.0	0.0	False	False	1.0443844923628771	0.0	False	0.0	False	819	False	False	False
775964912370320716	5.599844023964057	-0.0035403047628254506	775964912370320286	False	False	False	False	0	True	1684.0	4085.0	333.8752054548914	False	False	False	False	False	False	False	1684	4085	1970711	1	3	False	1684.095866575194	4084.8704009195435	False	False	False	False	1684.0	1684.0	4085.0	4085.0	nan	nan	nan	nan	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	True	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	0.002811008454638632	0.0613530606792696	1953.6383053951647	2081.334550357083	0.036042195937278754	3479.8187102902857	3609.92845913395	84.0200185884442	96.89122853909176	65.53902609079044	94.24996813186962	121.36820427740474	78.9685453104199	141.02224831964006	93.31777550775077	73.77028971897035	142.2150929724864	107.5795127774583	79.62795651134432	True	True	True	149.30482388197197	-196.51382918090874	False	False	0.8725316751270707	False	4.55431440999723	7.525018636287575	-0.68026018935189	nan	nan	nan	1683.9594745853612	4084.9221037840275	nan	nan	4.290368733801489	5.2007579561176085	-0.4271629457274954	nan	nan	nan	True	False	True	False	False	False	0.0026620769765043246	0.0026620769765043246	-0.009345459362608185	-0.009345459362608185	4.296792995047386	4.296792995047386	5.20783117540376	5.20783117540376	-0.4290739129619149	-0.4290739129619149	True	True	False	False	False	False	False	False	0.22787323594093323	0.6798088550567627	0.19511669874191284	0.927221417427063	False	False	False	False	False	1688.589788950165	1688.589788950165	4092.023480049352	4092.023480049352	81.4514760234975	81.4514760234975	53.88077305876533	53.88077305876533	41.53658418958087	41.53658418958087	True	True	True	False	False	False	False	False	False	False	False	False	1686.1132911759646	1686.1132911759646	4091.4317164319	4091.4317164319	74.79300011323573	74.79300011323573	54.76711362942379	54.76711362942379	17.84473950829366	17.84473950829366	True	True	False	False	False	False	False	False	2246.1943	2246.1943	228.74282836914062	38.15634536743164	True	False	False	405.3672790527344	57.52893829345703	True	False	False	470.6403503417969	76.75798034667969	True	False	False	871.2539672851562	115.53478240966797	True	False	False	1437.998046875	1437.998046875	1437.998046875	154.40757751464844	154.40757751464844	154.40757751464844	True	True	True	False	False	False	False	False	False	2080.9667558323126	219.23180371311975	True	False	3040.854558281296	324.08610534332576	True	False	3558.063286766105	468.72889157388363	True	False	4216.248118895733	778.8314769757308	True	False	6568.345740433478	1003.8242085370575	True	False	2321.380845241067	2321.380845241067	2321.380845241067	219.02527962710474	219.02527962710474	219.02527962710474	False	False	False	0.8067578082799903	7.492819166252571	True	False	False	False	0.30400361311863466	0.0	False	2.649245460750531e-08	7.538190684380652e-07	7.688029655004341e-07	2.677733320378086e-08	False	False	False	False	False	False	False	False	False	False	False	False	485.51837284371175	485.51837284371175	66.38022447000998	66.38022447000998	85.342445	85.342445	True	True	False	False	False	False	False	52.75528335571289	False	2201.9893053286282	245.11636450507123	7.2573996	12.626203	2.7183745	True	False	False	False	False	False	False	False	False	True	True	1.17360158345504	1.17360158345504	1.17360158345504	0.0	0.0	0.0	False	False	False	0.9715269038959469	0.9715269038959469	0.0	0.0	False	False	1.044315692637809	0.0	False	nan	True	586	False	False	False

More plotting

calexp = butler.get('calexp', dataId=ref.dataId)

# And plot!
afwDisplay.setDefaultBackend('matplotlib') 
fig = plt.figure(figsize=(10,8))
afw_display = afwDisplay.Display(1)
afw_display.scale('asinh', 'zscale')
afw_display.mtv(calexp)
plt.gca().axis('off')

with afw_display.Buffering():
    for s in src:
        afw_display.dot('+', s.getX(), s.getY(), ctype=afwDisplay.RED)
        afw_display.dot('o', s.getX(), s.getY(), size=20, ctype='orange')

---

Getting the URIs

• Getting the URI of the raw data
  
• getURI(datasetRefOrType, …): Return the URI to the Dataset
  
• getURIs(datasetRefOrType, …): Returns the URIs associated with the dataset
  
• getURIs is the “proper” interface for retrieving URIs to a single dataset because butler supports composite disassembly. This means that you can configure your datastore such that on butler.put() it splits the dataset into its component parts. This means that for an Exposure it would write the image, variance, mask, wcs etc into separate files. The motivation for this is that you can then do butler.get("calexp.wcs",...) and for S3 that will be much much more efficient when disassembled since it will only download the small WCS file and not the entire file so that it can read a small part of it. In general composite disassembly is not the default but you can make it so by putting the relevant line in your seed yaml in the datastore section.
  
• getURI is there for the simple case and will break for you as soon as disassembly is turned on. raws are never disassembled so that’s always safe. If you have disassembled the getURIs dict will be filled in with keys like wcs mapping to a URI. getURIs returns the same answer as getURI in its first return value.

Raw_URI=[]
Raw_URIs=[]
for ref in butler.registry.queryDatasets("calexp", collections=['shared/HSC/rerun/processCcdOutputs']):#, where="detector = 22"):
    uri = butler.getURI(ref)
    Raw_URI.append(uri)
    print("{}\n".format(uri))
   
    
#for ref in butler.registry.queryDatasets("raw", collections=['HSC/raw/all']):#, where="detector = 22"):
    #uri = butler.getURIs(ref)
    #print("{}\n".format(uri))
          
#print("{}\n".format(Raw_URI))
#print("{}\n".format(Raw_URIs))

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/904014/calexp_i_HSC-I_904014_1_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/904014/calexp_i_HSC-I_904014_12_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/904010/calexp_i_HSC-I_904010_10_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903990/calexp_i_HSC-I_903990_25_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903988/calexp_i_HSC-I_903988_23_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903990/calexp_i_HSC-I_903990_18_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/904014/calexp_i_HSC-I_904014_6_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903986/calexp_i_HSC-I_903986_100_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903986/calexp_i_HSC-I_903986_16_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903988/calexp_i_HSC-I_903988_24_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/904010/calexp_i_HSC-I_904010_4_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903986/calexp_i_HSC-I_903986_23_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903988/calexp_i_HSC-I_903988_17_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903988/calexp_i_HSC-I_903988_16_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/904010/calexp_i_HSC-I_904010_100_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/i/HSC-I/903986/calexp_i_HSC-I_903986_22_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903346/calexp_r_HSC-R_903346_6_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903342/calexp_r_HSC-R_903342_4_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903344/calexp_r_HSC-R_903344_11_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903346/calexp_r_HSC-R_903346_12_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903344/calexp_r_HSC-R_903344_5_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903338/calexp_r_HSC-R_903338_18_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903336/calexp_r_HSC-R_903336_24_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903346/calexp_r_HSC-R_903346_1_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903336/calexp_r_HSC-R_903336_17_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903334/calexp_r_HSC-R_903334_23_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903342/calexp_r_HSC-R_903342_100_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903334/calexp_r_HSC-R_903334_100_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903338/calexp_r_HSC-R_903338_25_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903342/calexp_r_HSC-R_903342_10_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903334/calexp_r_HSC-R_903334_22_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903344/calexp_r_HSC-R_903344_0_HSC_shared_HSC_rerun_processCcdOutputs.fits

s3://joshuakitenge-DATA-ed/shared/HSC/rerun/processCcdOutputs/calexp/r/HSC-R/903334/calexp_r_HSC-R_903334_16_HSC_shared_HSC_rerun_processCcdOutputs.fits

---

Exporting

• useful link https://github.com/lsst/testdata_jointcal/blob/master/scripts/export_gen3_hsc.py
• Exporting the whole directory
  
• To do this you'll need to know the Top level directory structure of all of the collections
  
• If you only want to download a specific dataset you can do that as well by just keeping the specfic dataset in the butler export function
  

with butler.export(filename = "exports.yaml") as export:
    # Raw files 
    export.saveDatasets(butler.registry.queryDatasets("raw", collections=['HSC/raw/all']))
    
    # defects files
    
    for collection in ('HSC/calib/curated/1970-01-01T00:00:00', 'HSC/calib/curated/2013-01-31T00:00:00',
                            'HSC/calib/curated/2014-04-03T00:00:00', 'HSC/calib/curated/2014-06-01T00:00:00',
                            'HSC/calib/curated/2015-11-06T00:00:00', 'HSC/calib/curated/2016-04-01T00:00:00',
                            'HSC/calib/curated/2016-11-22T00:00:00','HSC/calib/curated/2016-12-23T00:00:00'):
        export.saveDatasets(registry.queryDatasets(datasetType='defects',collections=collection))
     
    # Calibrations
     
    for datasetTypeName in ('camera','bfKernal', 'transmission_optics', 'transmission_sensor',
                            'transmission_filter', 'transmission_atmosphere'):
        export.saveDatasets(butler.registry.queryDatasets(datasetTypeName, collections=['HSC/calib/unbounded']))    
    
    # Reference catalog
    
    export.saveDatasets(registry.queryDatasets(datasetType='ps1_pv3_3pi_20170110',collections=['refcats']))
    
    # Gen 2 calibrations 
    
    export.saveDatasets(registry.queryDatasets(datasetType='sky',collections=['HSC/calib/gen2/2014-11-12']))
    export.saveDatasets(registry.queryDatasets(datasetType='sky',collections=['HSC/calib/gen2/2014-07-14']))
    for datasetTypeName in ('dark', 'bias', 'flat'):
        export.saveDatasets(butler.registry.queryDatasets(datasetTypeName, collections=['HSC/calib/gen2/2013-11-03']))
    export.saveDatasets(registry.queryDatasets(datasetType='flat',collections=['HSC/calib/gen2/2013-06-17']))
    
    # Coadditions
    
    for datasetTypeName in ('deepCoadd', 'deepCoadd_directWarp', 'deepCoadd_nImage','deepCoadd_psfMatchedWarp',
                            'deepCoadd_skyMap','deep_compareWarpAssembleCoadd_metadata','deep_makeCoaddTempExp_config',
                            'deep_makeCoaddTempExp_metadata','deep_safeClipAssembleCoadd_config','packages'):
        export.saveDatasets(butler.registry.queryDatasets(datasetTypeName, collections=['shared/HSC/rerun/coadd']))
    
    # Forced coaddition photometry
    
    for datasetTypeName in ('deepCoadd_forced_config', 'deblendCoaddSources_metadata', 'deepCoadd_forced_src',
                            'deepCoadd_forced_src_schema','deepCoadd_skyMap','deepCoadd_skyMap','packages'):
        export.saveDatasets(butler.registry.queryDatasets(datasetTypeName, collections=['shared/HSC/rerun/coaddForcedPhot']))
    
    # Coaddition photometry 
    
    for datasetTypeName in ('deblendCoaddSources_config', 'deepCoadd_calexp', 'deepCoadd_deblendedModel_schema',
                            'deepCoadd_deblendedFlux_schema','deepCoadd_det_schema','deepCoadd_meas_schema',
                            'deepCoadd_peak_schema','deepCoadd_ref','deepCoadd_mergeDet_schema','deepCoadd_ref_schema',
                            'deblendCoaddSources_metadata','deepCoadd_measMatchFull','deepCoadd_skyMap','deepCoadd_mergeDet',
                            'detectCoaddSources_config','deepCoadd_measMatch','deepCoadd_calexp_background',
                            'deepCoadd_meas','deepCoadd_det','measureCoaddSources_config','deepCoadd_deblendedFlux',
                            'detectCoaddSources_metadata','mergeCoaddDetections_config','mergeCoaddMeasurements_config',
                            'packages','measureCoaddSources_metadata'):
        export.saveDatasets(butler.registry.queryDatasets(datasetTypeName, collections=['shared/HSC/rerun/coaddPhot']))
    
    # Process CCD outputs
    
    
    for datasetTypeName in ('icSrc_schema', 'packages', 'srcMatchFull',
                            'srcMatch','processCcd_config','src_schema',
                            'processCcd_metadata','calexpBackground','src','icSrc',
                            'calexp'):
        export.saveDatasets(butler.registry.queryDatasets(datasetTypeName, collections=['shared/HSC/rerun/processCcdOutputs']))
    

---

Importing

• Using either a preexisting GEN 3 butler repository or a a new GEN 3 butler repository
  
• Set up the but client for the new repository and make sure that "writeable=True" is there
  
• As I'm transfer the data from one repository to another I'll set ""transfer="auto" otherwise transfer has Options:auto, link , symlink , hardlink , copy , move , relsymlink and direct

butler_im = dafButler.Butler("s3://joshkite-data-up-test-ed",writeable=True)
butler_im.import_(directory="/home/vrs42921/lsst_stack/DATA_gen3",filename="exports.yaml",transfer="auto")

Check if the files transfered

butler_im_test = dafButler.Butler("s3://joshkite-data-up-test-ed")
reg =butler_im_test.registry
for c in reg.queryCollections():
    print(c)

HSC/calib/curated/1970-01-01T00:00:00
HSC/calib/curated/2013-01-31T00:00:00
HSC/calib/curated/2014-04-03T00:00:00
HSC/calib/curated/2014-06-01T00:00:00
HSC/calib/curated/2015-11-06T00:00:00
HSC/calib/curated/2016-04-01T00:00:00
HSC/calib/curated/2016-11-22T00:00:00
HSC/calib/curated/2016-12-23T00:00:00
HSC/calib/gen2/2013-06-17
HSC/calib/gen2/2013-11-03
HSC/calib/gen2/2014-07-14
HSC/calib/gen2/2014-11-12
HSC/calib/unbounded
HSC/raw/all
refcats
shared/HSC/rerun/coadd
shared/HSC/rerun/coaddForcedPhot
shared/HSC/rerun/coaddPhot
shared/HSC/rerun/processCcdOutputs

datasetRefs_test = list(reg.queryDatasets(datasetType='raw',collections=['HSC/raw/all']))
for ref2 in datasetRefs_test:
    print(ref2.dataId)

{band: i, instrument: HSC, detector: 100, physical_filter: HSC-I, exposure: 903986}
{band: i, instrument: HSC, detector: 16, physical_filter: HSC-I, exposure: 903986}
{band: i, instrument: HSC, detector: 22, physical_filter: HSC-I, exposure: 903986}
{band: i, instrument: HSC, detector: 23, physical_filter: HSC-I, exposure: 903986}
{band: i, instrument: HSC, detector: 16, physical_filter: HSC-I, exposure: 903988}
{band: i, instrument: HSC, detector: 17, physical_filter: HSC-I, exposure: 903988}
{band: i, instrument: HSC, detector: 23, physical_filter: HSC-I, exposure: 903988}
{band: i, instrument: HSC, detector: 24, physical_filter: HSC-I, exposure: 903988}
{band: i, instrument: HSC, detector: 18, physical_filter: HSC-I, exposure: 903990}
{band: i, instrument: HSC, detector: 25, physical_filter: HSC-I, exposure: 903990}
{band: i, instrument: HSC, detector: 100, physical_filter: HSC-I, exposure: 904010}
{band: i, instrument: HSC, detector: 10, physical_filter: HSC-I, exposure: 904010}
{band: i, instrument: HSC, detector: 4, physical_filter: HSC-I, exposure: 904010}
{band: i, instrument: HSC, detector: 12, physical_filter: HSC-I, exposure: 904014}
{band: i, instrument: HSC, detector: 1, physical_filter: HSC-I, exposure: 904014}
{band: i, instrument: HSC, detector: 6, physical_filter: HSC-I, exposure: 904014}
{band: r, instrument: HSC, detector: 100, physical_filter: HSC-R, exposure: 903334}
{band: r, instrument: HSC, detector: 16, physical_filter: HSC-R, exposure: 903334}
{band: r, instrument: HSC, detector: 22, physical_filter: HSC-R, exposure: 903334}
{band: r, instrument: HSC, detector: 23, physical_filter: HSC-R, exposure: 903334}
{band: r, instrument: HSC, detector: 17, physical_filter: HSC-R, exposure: 903336}
{band: r, instrument: HSC, detector: 24, physical_filter: HSC-R, exposure: 903336}
{band: r, instrument: HSC, detector: 18, physical_filter: HSC-R, exposure: 903338}
{band: r, instrument: HSC, detector: 25, physical_filter: HSC-R, exposure: 903338}
{band: r, instrument: HSC, detector: 100, physical_filter: HSC-R, exposure: 903342}
{band: r, instrument: HSC, detector: 10, physical_filter: HSC-R, exposure: 903342}
{band: r, instrument: HSC, detector: 4, physical_filter: HSC-R, exposure: 903342}
{band: r, instrument: HSC, detector: 0, physical_filter: HSC-R, exposure: 903344}
{band: r, instrument: HSC, detector: 11, physical_filter: HSC-R, exposure: 903344}
{band: r, instrument: HSC, detector: 5, physical_filter: HSC-R, exposure: 903344}
{band: r, instrument: HSC, detector: 12, physical_filter: HSC-R, exposure: 903346}
{band: r, instrument: HSC, detector: 1, physical_filter: HSC-R, exposure: 903346}
{band: r, instrument: HSC, detector: 6, physical_filter: HSC-R, exposure: 903346}