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

---

butler Commands help

! butler --help

Usage: butler [OPTIONS] COMMAND [ARGS]...

Options:
  --log-level LEVEL|COMPONENT=LEVEL ...
                                  The logging level. Supported levels are
                                  [CRITICAL|ERROR|WARNING|INFO|DEBUG]
  --long-log                      Make log messages appear in long format.
  -h, --help                      Show this message and exit.

Commands:
  certify-calibrations        Certify calibrations in a repository.
  config-dump                 Dump butler config to stdout.
  config-validate             Validate the configuration files.
  convert                     Convert a gen2 repo to gen3.
  create                      Create an empty Gen3 Butler repository.
  define-visits               Define visits from exposures.
  import                      Import data into a butler repository.
  ingest-raws                 Ingest raw frames.
  make-discrete-skymap        Define a discrete skymap from calibrated
                              exposures.
  prune-collection            Remove a collection and possibly prune...
  query-collections           Search for collections.
  query-data-ids              List the data IDs in a repository.
  query-dataset-types         Get the dataset types in a repository.
  query-datasets              List the datasets in a repository.
  register-instrument         Add an instrument to the repository
  remove-dataset-type         Remove a dataset type definition from a...
  write-curated-calibrations  Add an instrument's curated calibrations.

Creating a generation 3 butler repository

---

! butler create --help

Usage: butler create [OPTIONS] REPO

  Create an empty Gen3 Butler repository.

  REPO is the URI or path to the new repository. Will be created if it does
  not exist.

Options:
  --seed-config TEXT       Path to an existing YAML config file to apply (on
                           top of defaults).
  --dimension-config TEXT  Path to an existing YAML config file with dimension
                           configuration.
  --standalone             Include all defaults in the config file in the
                           repo, insulating the repo from changes in package
                           defaults.
  --override               Allow values in the supplied config to override all
                           repo settings.
  -f, --outfile TEXT       Name of output file to receive repository
                           configuration. Default is to write butler.yaml into
                           the specified repo.
  -@, --options-file TEXT  URI to YAML file containing overrides of command
                           line options. The YAML should be organized as a
                           hierarchy with subcommand names at the top level
                           options for that subcommand below.
  -h, --help               Show this message and exit.

---

• 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

---

! butler register-instrument --help

Usage: butler register-instrument [OPTIONS] REPO INSTRUMENT ...

  Add an instrument to the data repository.

  REPO is the URI or path to the gen3 repository. Will be created if it does
  not already exist

  The fully-qualified name of an Instrument subclass.

Options:
  -h, --help  Show this message and exit.

---

• 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
    
    

• Note that for butler subcommands once an instrument is registered you can refer to that instrument by the short name
  (e.g. » butler write-curated-calibrations s3://bucket_name HSC )

---

Ingest raw frames into from a directory into the butler registry

---

! butler ingest-raws --help

Usage: butler ingest-raws [OPTIONS] REPO LOCATIONS ...

  Ingest raw frames into from a directory into the butler registry

  REPO is the URI or path to the gen3 repository. Will be created if it does
  not already exist

  LOCATIONS specifies files to ingest and/or locations to search for files.

Options:
  --regex TEXT                    Regex string used to find files in
                                  directories listed in LOCATIONS. Searches
                                  for fits files by default.
  -c, --config TEXT=TEXT ...      Config override, as a key-value pair.
  -C, --config-file FILE          Path to a pex config override to be included
                                  after the Instrument config overrides are
                                  applied.
  --output-run TEXT               The name of the run datasets should be
                                  output to.
  -t, --transfer [auto|link|symlink|hardlink|copy|move|relsymlink]
                                  The external data transfer mode.
  -j, --processes INTEGER RANGE   Number of processes to use.
  --ingest-task TEXT              The fully qualified class name of the ingest
                                  task to use.
  -@, --options-file TEXT         URI to YAML file containing overrides of
                                  command line options. The YAML should be
                                  organized as a hierarchy with subcommand
                                  names at the top level options for that
                                  subcommand below.
  -h, --help                      Show this message and exit.

---

• 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

! butler convert --help

Usage: butler convert [OPTIONS] REPO

  Convert a Butler gen 2 repository into a gen 3 repository.

  REPO is the URI or path to the gen3 repository. Will be created if it does
  not already exist

Options:
  --gen2root TEXT                 Root path of the gen 2 repo to be converted.
                                  [required]
  --skymap-name TEXT              Name of the new gen3 skymap (e.g.
                                  'discrete/ci_hsc').
  --skymap-config TEXT            Path to skymap config file defining the new
                                  gen3 skymap.
  --calibs TEXT                   Path to the gen 2 calibration repo. It can
                                  be absolute or relative to gen2root.
  --reruns TEXT ...               List of gen 2 reruns to convert.
  -t, --transfer [auto|link|symlink|hardlink|copy|move|relsymlink]
                                  Mode to use to transfer files into the new
                                  repository.
  -j, --processes INTEGER RANGE   Number of processes to use.
  -C, --config-file TEXT          Path to a `ConvertRepoConfig` override to be
                                  included after the Instrument config
                                  overrides are applied.
  -@, --options-file TEXT         URI to YAML file containing overrides of
                                  command line options. The YAML should be
                                  organized as a hierarchy with subcommand
                                  names at the top level options for that
                                  subcommand below.
  -h, --help                      Show this message and exit.

---

• 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

---

! butler import --help

Usage: butler import [OPTIONS] REPO DIRECTORY

  Import data into a butler repository.

  REPO is the URI or path to the new repository. Will be created if it does
  not exist.

  DIRECTORY is the folder containing dataset files.

Options:
  -t, --transfer [auto|link|symlink|hardlink|copy|move|relsymlink]
                                  The external data transfer mode.
  --export-file FILENAME          Name for the file that contains database
                                  information associated with the exported
                                  datasets.  If this is not an absolute path,
                                  does not exist in the current working
                                  directory, and --dir is provided, it is
                                  assumed to be in that directory.  Defaults
                                  to "export.yaml".
  -s, --skip-dimensions TEXT ...  Dimensions that should be skipped during
                                  import
  -@, --options-file TEXT         URI to YAML file containing overrides of
                                  command line options. The YAML should be
                                  organized as a hierarchy with subcommand
                                  names at the top level options for that
                                  subcommand below.
  -h, --help                      Show this message and exit.

---

• 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 "import" 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 Butler
import lsst.afw.display as afwDisplay
import pylab as plt
import os,glob
import lsst.geom as geom

---

Accessing the butler repository

butler = Butler.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 = Butler.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_calexp in datasetRefs:
    print(ref_calexp.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_calexp = datasetRefs[16]
print(ref_calexp.dataId)

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

DatasetRef is a combination of dataset type and dataId and can refer to an explicit dataset in a specific run (if ref.dataId is defined)

print(ref_calexp)
print(ref_calexp.dataId)
print(ref_calexp.datasetType)

calexp@{band: r, instrument: HSC, detector: 6, physical_filter: HSC-R, visit_system: 0, visit: 903346}, sc=ExposureF] (id=1647)
{band: r, instrument: HSC, detector: 6, physical_filter: HSC-R, visit_system: 0, visit: 903346}
DatasetType('calexp', {band, instrument, detector, physical_filter, visit_system, visit}, ExposureF)

---

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)
  
• getDirect(ref: 'DatasetRef', *, parameters: 'Optional[Dict[str, Any]]' = None,) : Retrieve a stored dataset.Unlike Butler.get, this method allows datasets outside the Butler'scollection to be read as long as the DatasetRef that identifies them can be obtained separately.
  
• get( datasetRefOrType: 'Union[DatasetRef, DatasetType, str]', dataId: 'Optional[DataId]' = None, *, parameters: 'Optional[Dict[str, Any]]' = None, collections: 'Any' = None,) : Retrieve a stored dataset.

# To get the image,we pass the dataId
calexp = butler.getDirect(ref_calexp)

#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')

(-0.5, 2047.5, -0.5, 4175.5)

How to create a table using the data

src = butler.get("src",dataId=ref_calexp.dataId)
src = src.copy(True)
src.asAstropy()

Table length=1397

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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775968331164288373	5.6015511554439374	-0.006574142448364473	775968331164287966	False	False	False	False	0	False	nan	nan	nan	False	False	False	False	False	False	False	93	4148	3366483	1	2	False	92.16733676953965	4148.172200336253	False	False	False	False	92.98282463437299	92.98282463437299	4148.472999258422	4148.472999258422	0.046280753	0.046280753	0.0396056	0.0396056	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	0.0	0.0	4480.181687074007	4480.181687074007	0.0	4480.622147262241	4480.622147262241	4.409927824848226	3.739095026317329	-1.0178358201435613	4.409927824848226	3.739095026317329	-1.0178358201435613	4.406896090846493	3.745349784047676	-1.018118227334851	4.406896090846493	3.745349784047676	-1.018118227334851	False	False	False	205.00653647533093	-63.195312740815965	False	False	0.9070564053924074	False	4.428018358180668	3.7588893409791204	-1.0159196754910798	0.18443994	0.1238309	0.15656877	92.9896521691794	4148.473014363811	4529.819106721551	94.34012615830277	4.485267571044155	3.859196356204598	-0.8441167853627614	-8.700044	1.9960501	-7.385359	False	False	False	False	False	False	0.01461038581865713	0.01461038581865713	-0.005309539386718806	-0.005309539386718806	4.495679870195096	4.495679870195096	3.8655301196881817	3.8655301196881817	-0.8481480387883067	-0.8481480387883067	False	False	False	False	False	False	False	False	nan	nan	nan	nan	True	False	False	False	True	92.99686978997968	92.99686978997968	4148.472504061268	4148.472504061268	4.437320297906425	4.437320297906425	3.762932765953329	3.762932765953329	-1.0178800872352187	-1.0178800872352187	False	False	False	False	False	False	False	False	False	False	False	False	92.97567688365987	92.97567688365987	4148.480370457786	4148.480370457786	4.140165706154972	4.140165706154972	3.880421188003176	3.880421188003176	-0.4213135264408258	-0.4213135264408258	False	False	False	False	False	False	False	False	4516.441	4516.441	3035.856201171875	44.4947509765625	False	False	False	4119.82275390625	62.02000045776367	False	False	False	4676.728515625	79.04793548583984	False	False	False	5063.0361328125	127.44880676269531	False	False	True	5317.8623046875	5317.8623046875	5317.8623046875	182.5872802734375	182.5872802734375	182.5872802734375	False	False	False	False	False	False	True	True	True	5575.517226554453	278.0477576503051	False	False	5967.2873758897185	430.3187277260812	False	False	nan	nan	True	True	nan	nan	True	True	nan	nan	True	True	5280.2624226357575	5280.2624226357575	5280.2624226357575	77.65380744063684	77.65380744063684	77.65380744063684	False	False	False	0.05316537075329741	7.116663404021189	False	False	False	False	0.30400361311863466	0.0	False	1.0670816684654828e-08	7.922989032938049e-07	7.596290870202657e-07	1.1669725354790424e-08	False	False	False	True	False	False	True	False	False	False	False	False	5377.68989110548	5377.68989110548	72.98461559713645	72.98461559713645	71.640495	71.640495	False	False	False	False	False	False	True	nan	True	5327.868373773032	120.09274771884449	3.317277	11.935975	2.5296898	False	False	False	False	False	False	False	False	False	False	True	1.1647564051526267	1.1647564051526267	1.1647564051526267	0.0	0.0	0.0	False	False	False	0.9796555015855863	0.9796555015855863	0.0	0.0	False	False	1.0526134741484383	0.0	False	0.0	False	262	False	False	False

More plotting

# 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 = Butler.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}