class WorkflowConfig(pexConfig.Config):
# name of this workflow
shortName = pexConfig.Field("name of this workflow", str)
# platform configuration file
platform = pexConfig.ConfigField("platform configuration file",
plat.PlatformConfig)
# plugin type
configurationType = pexConfig.Field("plugin type", str)
# plugin class name
configurationClass = pexConfig.Field("orca plugin class", str)
# configuration
configuration = pexConfig.ConfigChoiceField("configuration", typemap)
# this usually isn't used, but is here because the design calls for this
# possibility.
# database name
database = pexConfig.ConfigChoiceField(
"database", fake.FakeTypeMap(data.DatabaseConfig))
# task
task = pexConfig.ConfigChoiceField("task",
fake.FakeTypeMap(task.TaskConfig))
# monitor configuration
monitor = pexConfig.ConfigField("monitor configuration", mon.MonitorConfig)
class Complex(pexConfig.Config):
c = pexConfig.ConfigField("an inner config", InnerConfig)
r = pexConfig.ConfigChoiceField("a registry field",
typemap=GLOBAL_REGISTRY,
default="AAA",
optional=False)
p = pexConfig.ConfigChoiceField("another registry",
typemap=GLOBAL_REGISTRY,
default="BBB",
optional=True)
class Config3(pexConfig.Config):
a = pexConfig.ConfigChoiceField(doc="single non-optional",
typemap=TYPEMAP,
default="AAA", multi=False, optional=False)
b = pexConfig.ConfigChoiceField(doc="single optional",
typemap=TYPEMAP,
default="AAA", multi=False, optional=True)
c = pexConfig.ConfigChoiceField(doc="multi non-optional",
typemap=TYPEMAP,
default=["AAA"], multi=True, optional=False)
d = pexConfig.ConfigChoiceField(doc="multi optional",
typemap=TYPEMAP,
default=["AAA"], multi=True, optional=True)
class ImagePsfMatchConfig(pexConfig.Config):
"""Configuration for image-to-image Psf matching.
"""
kernel = pexConfig.ConfigChoiceField(
doc="kernel type",
typemap=dict(AL=PsfMatchConfigAL, DF=PsfMatchConfigDF),
default="AL",
)
selectDetection = pexConfig.ConfigurableField(
target=SourceDetectionTask,
doc="Initial detections used to feed stars to kernel fitting",
)
selectMeasurement = pexConfig.ConfigurableField(
target=SingleFrameMeasurementTask,
doc="Initial measurements used to feed stars to kernel fitting",
)
def setDefaults(self):
# High sigma detections only
self.selectDetection.reEstimateBackground = False
self.selectDetection.thresholdValue = 10.0
# Minimal set of measurments for star selection
self.selectMeasurement.algorithms.names.clear()
self.selectMeasurement.algorithms.names = ('base_SdssCentroid',
'base_PsfFlux',
'base_PixelFlags',
'base_SdssShape',
'base_GaussianFlux',
'base_SkyCoord')
self.selectMeasurement.slots.modelFlux = None
self.selectMeasurement.slots.apFlux = None
self.selectMeasurement.slots.calibFlux = None
class SnapPsfMatchConfig(ImagePsfMatchConfig):
kernel = pexConfig.ConfigChoiceField(
doc="kernel type",
typemap=dict(AL=SnapPsfMatchConfigAL, DF=SnapPsfMatchConfigDF),
default="AL",
)
doWarping = pexConfig.Field(dtype=bool,
doc="Warp the snaps?",
default=False)
def setDefaults(self):
ImagePsfMatchConfig.setDefaults(self)
# No spatial variation in model
self.kernel.active.spatialKernelOrder = 0
# Don't fit for differential background
self.kernel.active.fitForBackground = False
# Small kernel size
self.kernel.active.kernelSize = 7
# With zero spatial order don't worry about spatial clipping
self.kernel.active.spatialKernelClipping = False
class ProductionConfig(pexConfig.Config):
# production configuration
production = pexConfig.ConfigField("production configuration", Production)
# database configuration
database = pexConfig.ConfigChoiceField("database information",
fake.FakeTypeMap(db.DatabaseConfig))
# workflow configuration
workflow = pexConfig.ConfigChoiceField(
"workflow", fake.FakeTypeMap(work.WorkflowConfig))
# config check
configCheckCare = pexConfig.Field("config check care", int, default=-1)
# class that handles production configuration
configurationClass = pexConfig.Field("configuration class", str)
class DatabaseConfig(pexConfig.Config):
# database name
name = pexConfig.Field("database name", str)
# database system configuration
system = pexConfig.ConfigField("database system info", DatabaseSystem)
# class used to configure database
configurationClass = pexConfig.Field("database configuration class", str)
# type of database configuration
configuration = pexConfig.ConfigChoiceField("configuration", dbTypemap)
class TaskConfig(pexConfig.Config):
# script directory
scriptDir = pexConfig.Field("script directory", str)
# pre script (run before any jobs)
preScript = pexConfig.ConfigField("pre script", ScriptConfig)
# pre job script (run before each job)
preJob = pexConfig.ConfigField("pre job", JobTemplateConfig)
# post job script (run after each job)
postJob = pexConfig.ConfigField("post job", JobTemplateConfig)
# worker job configuration
workerJob = pexConfig.ConfigField("worker job", JobTemplateConfig)
# DAG generator script to use to create DAG submission file
generator = pexConfig.ConfigChoiceField("generator", typemap)
class ModelPsfMatchConfig(pexConfig.Config):
"""!Configuration for model-to-model Psf matching"""
kernel = pexConfig.ConfigChoiceField(
doc="kernel type",
typemap=dict(AL=PsfMatchConfigAL, ),
default="AL",
)
doAutoPadPsf = pexConfig.Field(
dtype=bool,
doc=
("If too small, automatically pad the science Psf? "
"Pad to smallest dimensions appropriate for the matching kernel dimensions, "
"as specified by autoPadPsfTo. If false, pad by the padPsfBy config."
),
default=True,
)
autoPadPsfTo = pexConfig.RangeField(
dtype=float,
doc=
("Minimum Science Psf dimensions as a fraction of matching kernel dimensions. "
"If the dimensions of the Psf to be matched are less than the "
"matching kernel dimensions * autoPadPsfTo, pad Science Psf to this size. "
"Ignored if doAutoPadPsf=False."),
default=1.4,
min=1.0,
max=2.0)
padPsfBy = pexConfig.Field(
dtype=int,
doc=
"Pixels (even) to pad Science Psf by before matching. Ignored if doAutoPadPsf=True",
default=0,
)
def setDefaults(self):
# No sigma clipping
self.kernel.active.singleKernelClipping = False
self.kernel.active.kernelSumClipping = False
self.kernel.active.spatialKernelClipping = False
self.kernel.active.checkConditionNumber = False
# Variance is ill defined
self.kernel.active.constantVarianceWeighting = True
# Do not change specified kernel size
self.kernel.active.scaleByFwhm = False
class ModelPsfMatchConfig(pexConfig.Config):
"""!Configuration for model-to-model Psf matching"""
kernel = pexConfig.ConfigChoiceField(
doc="kernel type",
typemap=dict(AL=PsfMatchConfigAL, ),
default="AL",
)
def setDefaults(self):
# No sigma clipping
self.kernel.active.singleKernelClipping = False
self.kernel.active.kernelSumClipping = False
self.kernel.active.spatialKernelClipping = False
self.kernel.active.checkConditionNumber = False
# Variance is ill defined
self.kernel.active.constantVarianceWeighting = True
# Psfs are typically small; reduce the kernel size
self.kernel.active.kernelSizeMin = 11
self.kernel.active.kernelSize = 11
class Config2(pexConf.Config):
c = pexConf.ConfigField(dtype=Config1, doc="holder for Config1")
b = pexConf.ConfigChoiceField(typemap=typemap, doc="choice holder for Config1")
class Config2(pexConf.Config):
r = pexConf.ConfigChoiceField("Config2.r", {"c1": Config1}, default="c1")
class Config3(pexConf.Config):
r = pexConf.ConfigChoiceField("Config3.r", {
"c1": Config1,
"c2": Config2
},
default="c1")
class CondorInfoConfig(pexConfig.Config):
"""A pex_config file describing the platform specific information about
remote user logins.
"""
platform = pexConfig.ConfigChoiceField("platform info",
FakeTypeMap(UserConfig))
class AuthDatabaseConfig(pexConfig.Config):
# authorization configuration
authInfo = pexConfig.ConfigChoiceField("auth info", fake.FakeTypeMap(AuthInfoConfig))