Python datacatalog_glob示例

示例#1

文件： producer_prnu_offline.py 项目： zguangyu/harnessed-jobs

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()
lambda_files = siteUtils.datacatalog_glob('*_lambda_flat_*.fits',
                                          testtype='LAMBDA',
                                          imgtype='FLAT',
                                          description='Lambda files:')
mask_files = eotestUtils.glob_mask_files()
gains = eotestUtils.getSensorGains(jobname='fe55_offline')
# @todo Set correction image when it becomes available.
correction_image = None

task = sensorTest.PrnuTask()
task.run(sensor_id, lambda_files, mask_files, gains, correction_image)

示例#2

文件： producer_read_noise_offline.py 项目： lsst-camera-dh/harnessed-jobs

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()

# Use Fe55 exposures and the overscan region instead of the bias
# frames since the vendor data are not guaranteed to have the same
# gains for the bias frames.
bias_files = siteUtils.datacatalog_glob('*_fe55_fe55_*.fits',
                                        testtype="FE55",
                                        imgtype="FE55",
                                        description='Bias files (using overscan):')

gains = eotestUtils.getSensorGains(jobname='fe55_offline')
system_noise_files = siteUtils.dependency_glob('noise_*.fits', 
                                               jobname=siteUtils.getProcessName('system_noise'))
if not system_noise_files:
    system_noise_files = None
mask_files = eotestUtils.glob_mask_files()

task = sensorTest.ReadNoiseTask()
task.run(sensor_id, bias_files, gains,
         system_noise_files=system_noise_files, mask_files=mask_files,
         use_overscan=True)

示例#3

文件： producer_dark_current_offline.py 项目： lsst-camera-dh/harnessed-jobs

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()
dark_files = siteUtils.datacatalog_glob('*_dark_dark_*.fits',
                                        testtype='DARK', 
                                        imgtype='DARK', 
                                        description='Dark files:')
mask_files = eotestUtils.glob_mask_files()
gains = eotestUtils.getSensorGains(jobname='fe55_offline')

task = sensorTest.DarkCurrentTask()
task.run(sensor_id, dark_files, mask_files, gains)

示例#4

文件： producer_fe55_offline.py 项目： lsst-camera-dh/harnessed-jobs

#!/usr/bin/env python
import os
import lsst.eotest.sensor as sensorTest
import siteUtils

sensor_id = siteUtils.getUnitId()
fe55_files = siteUtils.datacatalog_glob('*_fe55_fe55_*.fits',
                                        testtype="FE55",
                                        imgtype="FE55",
                                        description='Fe55 files:')

# Roll-off defects mask needs an input file to get the vendor
# geometry and will be used for all analyses.
rolloff_mask_file = '%s_rolloff_defects_mask.fits' % sensor_id
sensorTest.rolloff_mask(fe55_files[0], rolloff_mask_file)

task = sensorTest.Fe55Task()
task.run(sensor_id, fe55_files, (rolloff_mask_file,), accuracy_req=0.01)

示例#5

文件： producer_flat_pairs_offline.py 项目： lsst-camera-dh/harnessed-jobs

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()

ccd_vendor = siteUtils.getCcdVendor()

flat_files = siteUtils.datacatalog_glob('*_flat*flat?_*.fits',
                                        testtype='FLAT',
                                        imgtype='FLAT',
                                        description='Flat files:')
mask_files = eotestUtils.glob_mask_files()
gains = eotestUtils.getSensorGains(jobname='fe55_offline')

task = sensorTest.FlatPairTask()
task.run(sensor_id, flat_files, mask_files, gains)

if ccd_vendor == 'ITL':
    #
    # Perform linearity analysis using special dataset from ITL
    try:
        flat_files = siteUtils.datacatalog_glob('*_linearity_flat*.fits',
                                                testtype='LINEARITY',
                                                imgtype='FLAT',
                                                description='ITL linearity files:')
        if flat_files:
            task = sensorTest.LinearityTask()
            task.run(sensor_id, flat_files, mask_files, gains)
    except:

示例#6

文件： producer_fe55_offline.py 项目： zguangyu/harnessed-jobs

#!/usr/bin/env python
import os
import lsst.eotest.sensor as sensorTest
import siteUtils

sensor_id = siteUtils.getUnitId()
fe55_files = siteUtils.datacatalog_glob('*_fe55_fe55_*.fits',
                                        testtype="FE55",
                                        imgtype="FE55",
                                        description='Fe55 files:')

# Roll-off defects mask needs an input file to get the vendor
# geometry and will be used for all analyses.
rolloff_mask_file = '%s_rolloff_defects_mask.fits' % sensor_id
sensorTest.rolloff_mask(fe55_files[0], rolloff_mask_file)

task = sensorTest.Fe55Task()
task.run(sensor_id, fe55_files, (rolloff_mask_file, ), accuracy_req=0.01)

示例#7

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()
sflat_files = siteUtils.datacatalog_glob('*_sflat_500_flat_H*.fits',
                                         testtype='SFLAT_500',
                                         imgtype='FLAT',
                                         description='Superflat files:')
mask_files = eotestUtils.glob_mask_files()

task = sensorTest.DarkPixelsTask()
task.run(sensor_id, sflat_files, mask_files)

示例#8

文件： producer_cte_offline.py 项目： lsst-camera-dh/harnessed-jobs

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()

gains = eotestUtils.getSensorGains(jobname='fe55_offline')
sflat_high_files = siteUtils.datacatalog_glob('*_sflat_500_flat_H*.fits',
                                              testtype='SFLAT_500',
                                              imgtype='FLAT',
                                              description='Superflat high files:')
task = sensorTest.CteTask()
task.run(sensor_id, sflat_high_files, flux_level='high', gains=gains)

sflat_low_files = siteUtils.datacatalog_glob('*_sflat_500_flat_L*.fits',
                                             testtype='SFLAT_500',
                                             imgtype='FLAT',
                                             description='Superflat low files:')
task = sensorTest.CteTask()
task.run(sensor_id, sflat_low_files, flux_level='low', gains=gains)

示例#9

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()

# Use Fe55 exposures and the overscan region instead of the bias
# frames since the vendor data are not guaranteed to have the same
# gains for the bias frames.
bias_files = siteUtils.datacatalog_glob(
    '*_fe55_fe55_*.fits',
    testtype="FE55",
    imgtype="FE55",
    description='Bias files (using overscan):')

gains = eotestUtils.getSensorGains(jobname='fe55_offline')
system_noise_files = siteUtils.dependency_glob(
    'noise_*.fits', jobname=siteUtils.getProcessName('system_noise'))
if not system_noise_files:
    system_noise_files = None
mask_files = eotestUtils.glob_mask_files()

task = sensorTest.ReadNoiseTask()
task.run(sensor_id,
         bias_files,
         gains,
         system_noise_files=system_noise_files,
         mask_files=mask_files,
         use_overscan=True)

示例#10

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()

ccd_vendor = siteUtils.getCcdVendor()

flat_files = siteUtils.datacatalog_glob('*_flat*flat?_*.fits',
                                        testtype='FLAT',
                                        imgtype='FLAT',
                                        description='Flat files:')
mask_files = eotestUtils.glob_mask_files()
gains = eotestUtils.getSensorGains(jobname='fe55_offline')

task = sensorTest.FlatPairTask()
task.run(sensor_id, flat_files, mask_files, gains)

if ccd_vendor == 'ITL':
    #
    # Perform linearity analysis using special dataset from ITL
    try:
        flat_files = siteUtils.datacatalog_glob(
            '*_linearity_flat*.fits',
            testtype='LINEARITY',
            imgtype='FLAT',
            description='ITL linearity files:')
        if flat_files:
            task = sensorTest.LinearityTask()
            task.run(sensor_id, flat_files, mask_files, gains)

示例#11

文件： producer_dark_current_offline.py 项目： zguangyu/harnessed-jobs

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()
dark_files = siteUtils.datacatalog_glob('*_dark_dark_*.fits',
                                        testtype='DARK',
                                        imgtype='DARK',
                                        description='Dark files:')
mask_files = eotestUtils.glob_mask_files()
gains = eotestUtils.getSensorGains(jobname='fe55_offline')

task = sensorTest.DarkCurrentTask()
task.run(sensor_id, dark_files, mask_files, gains)

示例#12

文件： producer_ptc_offline.py 项目： zguangyu/harnessed-jobs

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()
flat_files = siteUtils.datacatalog_glob('*_flat*flat?_*.fits',
                                        testtype='FLAT',
                                        imgtype='FLAT',
                                        description='Flat files:')
mask_files = eotestUtils.glob_mask_files()
gains = eotestUtils.getSensorGains(jobname='fe55_offline')

task = sensorTest.PtcTask()
task.run(sensor_id, flat_files, mask_files, gains)

示例#13

#!/usr/bin/env python
import lsst.eotest.sensor as sensorTest
import siteUtils
import eotestUtils

sensor_id = siteUtils.getUnitId()
trap_file = siteUtils.datacatalog_glob('*_trap_ppump_*.fits',
                                       testtype='TRAP',
                                       imgtype='PPUMP',
                                       description='Trap file:')[0]
mask_files = eotestUtils.glob_mask_files()
gains = eotestUtils.getSensorGains(jobname='fe55_offline')

task = sensorTest.TrapTask()
task.run(sensor_id, trap_file, mask_files, gains)