PsfMatchConfigDF¶
- class lsst.ip.diffim.PsfMatchConfigDF(*args, **kw)¶
Bases:
PsfMatchConfig
The parameters specific to the delta-function (one basis per-pixel) Psf-matching basis
Attributes Summary
Controlling the Afw background fitting (
SubtractBackgroundConfig
, default<class 'lsst.meas.algorithms.subtractBackground.SubtractBackgroundConfig'>
)Mask planes to ignore when calculating diffim statistics Options: NO_DATA EDGE SAT BAD CR INTRP (
List
, default('NO_DATA', 'EDGE', 'SAT')
)Calculate kernel and background uncertainties for each kernel candidate? This comes from the inverse of the covariance matrix.
Radius for calculation of stats in 'core' of KernelCandidate diffim.
Rejects KernelCandidates yielding bad difference image quality.
Rejects KernelCandidates yielding bad difference image quality.
Type of stencil to approximate central derivative (for centralDifference only) (
int
, default9
)Test for maximum condition number when inverting a kernel matrix.
Use singular values (SVD) or eigen values (EIGENVALUE) to determine condition number (
str
, default'EIGENVALUE'
)Use constant variance weighting in single kernel fitting? In some cases this is better for bright star residuals.
Controlling the detection of sources for kernel building Deprecated: This field is no longer used and will be removed after v27.
Include terms (including kernel cross terms) for background in ip_diffim (
bool
, defaultFalse
)Array showing which order derivatives to penalize (for forwardDifference only) (
List
, default(1, 2)
)Remake KernelCandidate using better variance estimate after first pass? Primarily useful when convolving a single-depth image, otherwise not necessary.
Type of basis set for PSF matching kernel.
Number of rows/columns in the convolution kernel; should be odd-valued.
Multiplier of the largest AL Gaussian basis sigma to get the kernel bbox (pixel) size.
Maximum kernel bbox (pixel) size.
Minimum kernel bbox (pixel) size.
Do sigma clipping on the ensemble of kernel sums (
bool
, defaultTrue
)If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), stop at this value.
If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), start at this value.
Fraction of the default lambda strength (N.R.
If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), step in these increments.
If a scan through lambda is needed (minimizeBiasedRisk, minimizeUnbiasedRisk),
How to choose the value of the regularization strength (
str
, default'absolute'
)Value used for absolute determinations of regularization strength (
float
, default0.2
)Maximum condition number for a well conditioned matrix (
float
, default50000000.0
)Maximum allowed sigma for outliers from kernel sum distribution.
Maximum condition number for a well conditioned spatial matrix (
float
, default10000000000.0
)Maximum number of iterations for rejecting bad KernelCandidates in spatial fitting (
int
, default3
)Maximum number of KernelCandidates in each SpatialCell to use in the spatial fitting.
Number of principal components to use for Pca basis, including the mean kernel if requested.
Value of the penalty for kernel border pixels (
float
, default3.0
)Type of regularization.
Scale kernelSize, alardGaussians by input Fwhm (
bool
, defaultTrue
)Do sigma clipping on each raw kernel candidate (
bool
, defaultTrue
)Size (rows) in pixels of each SpatialCell for spatial modeling (
int
, default128
)Size (columns) in pixels of each SpatialCell for spatial modeling (
int
, default128
)Spatial order of differential background variation (
int
, default1
)Do sigma clipping after building the spatial model (
bool
, defaultTrue
)Spatial order of convolution kernel variation (
int
, default2
)Type of spatial functions for kernel and background (
str
, default'chebyshev1'
)Subtract off the mean feature before doing the Pca (
bool
, defaultTrue
)Use afw background subtraction instead of ip_diffim (
bool
, defaultFalse
)Use Bayesian Information Criterion to select the number of bases going into the kernel (
bool
, defaultFalse
)Use the core of the footprint for the quality statistics, instead of the entire footprint.
Use Pca to reduce the dimensionality of the kernel basis sets.
Use regularization to smooth the delta function kernels (
bool
, defaultTrue
)Config for warping exposures to a common alignment (
WarperConfig
, default<class 'lsst.afw.math._warper.WarperConfig'>
)Methods Summary
compare
(other[, shortcut, rtol, atol, output])Compare this configuration to another
Config
for equality.formatHistory
(name, **kwargs)Format a configuration field's history to a human-readable format.
freeze
()Make this config, and all subconfigs, read-only.
items
()Get configurations as
(field name, field value)
pairs.keys
()Get field names.
load
(filename[, root])Modify this config in place by executing the Python code in a configuration file.
loadFromStream
(stream[, root, filename, ...])Modify this Config in place by executing the Python code in the provided stream.
loadFromString
(code[, root, filename, ...])Modify this Config in place by executing the Python code in the provided string.
names
()Get all the field names in the config, recursively.
save
(filename[, root])Save a Python script to the named file, which, when loaded, reproduces this config.
saveToStream
(outfile[, root, skipImports])Save a configuration file to a stream, which, when loaded, reproduces this config.
saveToString
([skipImports])Return the Python script form of this configuration as an executable string.
Subclass hook for computing defaults.
toDict
()Make a dictionary of field names and their values.
update
(**kw)Update values of fields specified by the keyword arguments.
validate
()Validate the Config, raising an exception if invalid.
values
()Get field values.
Attributes Documentation
- afwBackgroundConfig¶
Controlling the Afw background fitting (
SubtractBackgroundConfig
, default<class 'lsst.meas.algorithms.subtractBackground.SubtractBackgroundConfig'>
)
- badMaskPlanes¶
Mask planes to ignore when calculating diffim statistics Options: NO_DATA EDGE SAT BAD CR INTRP (
List
, default('NO_DATA', 'EDGE', 'SAT')
)
- calculateKernelUncertainty¶
Calculate kernel and background uncertainties for each kernel candidate? This comes from the inverse of the covariance matrix. Warning: regularization can cause problems for this step. (
bool
, defaultFalse
)
- candidateCoreRadius¶
Radius for calculation of stats in ‘core’ of KernelCandidate diffim. Total number of pixels used will be (2*radius)**2. This is used both for ‘core’ diffim quality as well as ranking of KernelCandidates by their total flux in this core (
int
, default3
)
- candidateResidualMeanMax¶
Rejects KernelCandidates yielding bad difference image quality. Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. Represents average over pixels of (image/sqrt(variance)). (
float
, default0.25
)
- candidateResidualStdMax¶
Rejects KernelCandidates yielding bad difference image quality. Used by BuildSingleKernelVisitor, AssessSpatialKernelVisitor. Represents stddev over pixels of (image/sqrt(variance)). (
float
, default1.5
)
- centralRegularizationStencil¶
Type of stencil to approximate central derivative (for centralDifference only) (
int
, default9
)Allowed values:
'5'
5-point stencil including only adjacent-in-x,y elements
'9'
9-point stencil including diagonal elements
'None'
Field is optional
- checkConditionNumber¶
Test for maximum condition number when inverting a kernel matrix. Anything above maxConditionNumber is not used and the candidate is set as BAD. Also used to truncate inverse matrix in estimateBiasedRisk. However, if you are doing any deconvolution you will want to turn this off, or use a large maxConditionNumber (
bool
, defaultFalse
)
- conditionNumberType¶
Use singular values (SVD) or eigen values (EIGENVALUE) to determine condition number (
str
, default'EIGENVALUE'
)Allowed values:
'SVD'
Use singular values
'EIGENVALUE'
Use eigen values (faster)
'None'
Field is optional
- constantVarianceWeighting¶
Use constant variance weighting in single kernel fitting? In some cases this is better for bright star residuals. (
bool
, defaultTrue
)
- detectionConfig¶
Controlling the detection of sources for kernel building Deprecated: This field is no longer used and will be removed after v27. (
DetectionConfig
, default<class 'lsst.ip.diffim.psfMatch.DetectionConfig'>
)
- fitForBackground¶
Include terms (including kernel cross terms) for background in ip_diffim (
bool
, defaultFalse
)
- forwardRegularizationOrders¶
Array showing which order derivatives to penalize (for forwardDifference only) (
List
, default(1, 2)
)
- history¶
Read-only history.
- iterateSingleKernel¶
Remake KernelCandidate using better variance estimate after first pass? Primarily useful when convolving a single-depth image, otherwise not necessary. (
bool
, defaultFalse
)
- kernelBasisSet¶
Type of basis set for PSF matching kernel. (
str
, default'alard-lupton'
)Allowed values:
'alard-lupton'
- Alard-Lupton sum-of-gaussians basis set,
The first term has no spatial variation
The kernel sum is conserved
You may want to turn off ‘usePcaForSpatialKernel’
'delta-function'
- Delta-function kernel basis set,
You may enable the option useRegularization
You should seriously consider usePcaForSpatialKernel, which will also enable kernel sum conservation for the delta function kernels
'None'
Field is optional
- kernelSize¶
Number of rows/columns in the convolution kernel; should be odd-valued. Modified by kernelSizeFwhmScaling if scaleByFwhm = true (
int
, default21
)
- kernelSizeFwhmScaling¶
Multiplier of the largest AL Gaussian basis sigma to get the kernel bbox (pixel) size. (
float
, default6.0
)
- lambdaMax¶
If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), stop at this value. If lambdaStepType = log:linear, suggest 2:100 (
float
, default2.0
)
- lambdaMin¶
If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), start at this value. If lambdaStepType = log:linear, suggest -1:0.1 (
float
, default-1.0
)
- lambdaScaling¶
Fraction of the default lambda strength (N.R. 18.5.8) to use. 1e-4 or 1e-5 (
float
, default0.0001
)
- lambdaStep¶
If scan through lambda needed (minimizeBiasedRisk, minimizeUnbiasedRisk), step in these increments. If lambdaStepType = log:linear, suggest 0.1:0.1 (
float
, default0.1
)
- lambdaStepType¶
- If a scan through lambda is needed (minimizeBiasedRisk, minimizeUnbiasedRisk),
use log or linear steps (
str
, default'log'
)
Allowed values:
'log'
Step in log intervals; e.g. lambdaMin, lambdaMax, lambdaStep = -1.0, 2.0, 0.1
'linear'
Step in linear intervals; e.g. lambdaMin, lambdaMax, lambdaStep = 0.1, 100, 0.1
'None'
Field is optional
- lambdaType¶
How to choose the value of the regularization strength (
str
, default'absolute'
)Allowed values:
'absolute'
Use lambdaValue as the value of regularization strength
'relative'
Use lambdaValue as fraction of the default regularization strength (N.R. 18.5.8)
'minimizeBiasedRisk'
Minimize biased risk estimate
'minimizeUnbiasedRisk'
Minimize unbiased risk estimate
'None'
Field is optional
- maxConditionNumber¶
Maximum condition number for a well conditioned matrix (
float
, default50000000.0
)
- maxKsumSigma¶
Maximum allowed sigma for outliers from kernel sum distribution. Used to reject variable objects from the kernel model (
float
, default3.0
)
- maxSpatialConditionNumber¶
Maximum condition number for a well conditioned spatial matrix (
float
, default10000000000.0
)
- maxSpatialIterations¶
Maximum number of iterations for rejecting bad KernelCandidates in spatial fitting (
int
, default3
)
- nStarPerCell¶
Maximum number of KernelCandidates in each SpatialCell to use in the spatial fitting. Set to -1 to use all candidates in each cell. (
int
, default5
)
- numPrincipalComponents¶
Number of principal components to use for Pca basis, including the mean kernel if requested. (
int
, default5
)
- regularizationType¶
Type of regularization. (
str
, default'centralDifference'
)Allowed values:
'centralDifference'
Penalize second derivative using 2-D stencil of central finite difference
'forwardDifference'
Penalize first, second, third derivatives using forward finite differeces
'None'
Field is optional
- spatialModelType¶
Type of spatial functions for kernel and background (
str
, default'chebyshev1'
)Allowed values:
'chebyshev1'
Chebyshev polynomial of the first kind
'polynomial'
Standard x,y polynomial
'None'
Field is optional
- useBicForKernelBasis¶
Use Bayesian Information Criterion to select the number of bases going into the kernel (
bool
, defaultFalse
)
- useCoreStats¶
Use the core of the footprint for the quality statistics, instead of the entire footprint. WARNING: if there is deconvolution we probably will need to turn this off (
bool
, defaultFalse
)
- usePcaForSpatialKernel¶
Use Pca to reduce the dimensionality of the kernel basis sets. This is particularly useful for delta-function kernels. Functionally, after all Cells have their raw kernels determined, we run a Pca on these Kernels, re-fit the Cells using the eigenKernels and then fit those for spatial variation using the same technique as for Alard-Lupton kernels. If this option is used, the first term will have no spatial variation and the kernel sum will be conserved. (
bool
, defaultFalse
)
- warpingConfig¶
Config for warping exposures to a common alignment (
WarperConfig
, default<class 'lsst.afw.math._warper.WarperConfig'>
)
Methods Documentation
- compare(other, shortcut=True, rtol=1e-08, atol=1e-08, output=None)¶
Compare this configuration to another
Config
for equality.- Parameters:
- other
lsst.pex.config.Config
Other
Config
object to compare against this config.- shortcut
bool
, optional If
True
, return as soon as an inequality is found. Default isTrue
.- rtol
float
, optional Relative tolerance for floating point comparisons.
- atol
float
, optional Absolute tolerance for floating point comparisons.
- outputcallable, optional
A callable that takes a string, used (possibly repeatedly) to report inequalities.
- other
- Returns:
- isEqual
bool
True
when the twolsst.pex.config.Config
instances are equal.False
if there is an inequality.
- isEqual
See also
Notes
Unselected targets of
RegistryField
fields and unselected choices ofConfigChoiceField
fields are not considered by this method.Floating point comparisons are performed by
numpy.allclose
.
- formatHistory(name, **kwargs)¶
Format a configuration field’s history to a human-readable format.
- Parameters:
- name
str
Name of a
Field
in this config.- **kwargs
Keyword arguments passed to
lsst.pex.config.history.format
.
- name
- Returns:
- history
str
A string containing the formatted history.
- history
See also
- freeze()¶
Make this config, and all subconfigs, read-only.
- items()¶
Get configurations as
(field name, field value)
pairs.- Returns:
- items
ItemsView
Iterator of tuples for each configuration. Tuple items are:
Field name.
Field value.
- items
- keys()¶
Get field names.
- Returns:
- names
KeysView
List of
lsst.pex.config.Field
names.
- names
- load(filename, root='config')¶
Modify this config in place by executing the Python code in a configuration file.
- Parameters:
- filename
str
Name of the configuration file. A configuration file is Python module.
- root
str
, optional Name of the variable in file that refers to the config being overridden.
For example, the value of root is
"config"
and the file contains:config.myField = 5
Then this config’s field
myField
is set to5
.
- filename
- loadFromStream(stream, root='config', filename=None, extraLocals=None)¶
Modify this Config in place by executing the Python code in the provided stream.
- Parameters:
- streamfile-like object,
str
,bytes
, orCodeType
Stream containing configuration override code. If this is a code object, it should be compiled with
mode="exec"
.- root
str
, optional Name of the variable in file that refers to the config being overridden.
For example, the value of root is
"config"
and the file contains:config.myField = 5
Then this config’s field
myField
is set to5
.- filename
str
, optional Name of the configuration file, or
None
if unknown or contained in the stream. Used for error reporting.- extraLocals
dict
ofstr
toobject
, optional Any extra variables to include in local scope when loading.
- streamfile-like object,
See also
Notes
For backwards compatibility reasons, this method accepts strings, bytes and code objects as well as file-like objects. New code should use
loadFromString
instead for most of these types.
- loadFromString(code, root='config', filename=None, extraLocals=None)¶
Modify this Config in place by executing the Python code in the provided string.
- Parameters:
- code
str
,bytes
, orCodeType
Stream containing configuration override code.
- root
str
, optional Name of the variable in file that refers to the config being overridden.
For example, the value of root is
"config"
and the file contains:config.myField = 5
Then this config’s field
myField
is set to5
.- filename
str
, optional Name of the configuration file, or
None
if unknown or contained in the stream. Used for error reporting.- extraLocals
dict
ofstr
toobject
, optional Any extra variables to include in local scope when loading.
- code
- Raises:
- ValueError
Raised if a key in extraLocals is the same value as the value of the root argument.
- save(filename, root='config')¶
Save a Python script to the named file, which, when loaded, reproduces this config.
- Parameters:
- filename
str
Desination filename of this configuration.
- root
str
, optional Name to use for the root config variable. The same value must be used when loading (see
lsst.pex.config.Config.load
).
- filename
- saveToStream(outfile, root='config', skipImports=False)¶
Save a configuration file to a stream, which, when loaded, reproduces this config.
- Parameters:
- outfilefile-like object
Destination file object write the config into. Accepts strings not bytes.
- root
str
, optional Name to use for the root config variable. The same value must be used when loading (see
lsst.pex.config.Config.load
).- skipImports
bool
, optional If
True
then do not includeimport
statements in output, this is to support human-oriented output frompipetask
where additional clutter is not useful.
- saveToString(skipImports=False)¶
Return the Python script form of this configuration as an executable string.
- Parameters:
- Returns:
- code
str
A code string readable by
loadFromString
.
- code
- setDefaults()¶
Subclass hook for computing defaults.
Notes
Derived
Config
classes that must compute defaults rather than using theField
instances’s defaults should do so here. To correctly use inherited defaults, implementations ofsetDefaults
must call their base class’ssetDefaults
.
- toDict()¶
Make a dictionary of field names and their values.
See also
Notes
This method uses the
toDict
method of individual fields. Subclasses ofField
may need to implement atoDict
method for this method to work.
- update(**kw)¶
Update values of fields specified by the keyword arguments.
- Parameters:
- **kw
Keywords are configuration field names. Values are configuration field values.
Notes
The
__at
and__label
keyword arguments are special internal keywords. They are used to strip out any internal steps from the history tracebacks of the config. Do not modify these keywords to subvert aConfig
instance’s history.Examples
This is a config with three fields:
>>> from lsst.pex.config import Config, Field >>> class DemoConfig(Config): ... fieldA = Field(doc='Field A', dtype=int, default=42) ... fieldB = Field(doc='Field B', dtype=bool, default=True) ... fieldC = Field(doc='Field C', dtype=str, default='Hello world') ... >>> config = DemoConfig()
These are the default values of each field:
>>> for name, value in config.iteritems(): ... print(f"{name}: {value}") ... fieldA: 42 fieldB: True fieldC: 'Hello world'
Using this method to update
fieldA
andfieldC
:>>> config.update(fieldA=13, fieldC='Updated!')
Now the values of each field are:
>>> for name, value in config.iteritems(): ... print(f"{name}: {value}") ... fieldA: 13 fieldB: True fieldC: 'Updated!'
- validate()¶
Validate the Config, raising an exception if invalid.
- Raises:
- lsst.pex.config.FieldValidationError
Raised if verification fails.
Notes
The base class implementation performs type checks on all fields by calling their
validate
methods.Complex single-field validation can be defined by deriving new Field types. For convenience, some derived
lsst.pex.config.Field
-types (ConfigField
andConfigChoiceField
) are defined inlsst.pex.config
that handle recursing into subconfigs.Inter-field relationships should only be checked in derived
Config
classes after calling this method, and base validation is complete.
- values()¶
Get field values.
- Returns:
- values
ValuesView
Iterator of field values.
- values