Classes | |
| class | PrettyMosaicConfig |
| class | PrettyMosaicConnections |
| class | PrettyMosaicTask |
| class | PrettyPictureBackgroundFixerConnections |
| class | PrettyPictureConnections |
| class | PrettyPictureStarFixerConfig |
| class | PrettyPictureStarFixerConnections |
| class | PrettyPictureStarFixerTask |
Functions | |
| Struct | run (self, Mapping[str, Exposure] images) |
| None | runQuantum (self, QuantumContext butlerQC, InputQuantizedConnection inputRefs, OutputQuantizedConnection outputRefs) |
| dict[str, Exposure] | makeInputsFromRefs (self, Iterable[DatasetRef] refs, Butler|QuantumContext butler) |
| dict[str, DeferredDatasetHandle] | makeInputsFromArrays (self, **kwargs) |
| dict[int, DeferredDatasetHandle] | makeInputsFromExposures (self, **kwargs) |
| fixBackground (self, image, detection_mask=None) | |
| run (self, Exposure inputCoadd) | |
Variables | |
| doWrite | |
| r | |
| g | |
| b | |
| array : `numpy.array` | |
| mean : `float` | |
| sigma : `float` | |
| tuple | lower_pos = (array - mu) < sigma |
| sigma_ratio = min_sig / sigma | |
| arr : `numyp.ndarray` | |
| maxLikely = np.median(image, axis=None) | |
| mask = image < maxLikely | |
| tuple | initial_std = (image[mask] - maxLikely).std() |
| mu_hat | |
| sigma_hat | |
| threshhold = mu_hat + pos_sigma_mult * sigma_hat | |
| tuple | image_mask = (image < threshhold) * (image > (mu_hat - 5 * sigma_hat)) |
Function Documentation
◆ fixBackground()
| lsst.pipe.tasks.prettyPictureMaker._task.fixBackground | ( | self, | |
| image, | |||
| detection_mask = None ) |
Estimate and subtract the background from an image.
This function estimates the background level in an image using a median-based
approach combined with Gaussian fitting and radial basis function interpolation.
It aims to provide a more accurate background estimation than a simple median
filter, especially in images with varying background levels.
Parameters
----------
image : `numpy.ndarray`
The input image as a NumPy array.
Returns
-------
numpy.ndarray
An array representing the estimated background level across the image.
◆ makeInputsFromArrays()
| dict[str, DeferredDatasetHandle] lsst.pipe.tasks.prettyPictureMaker._task.makeInputsFromArrays | ( | self, | |
| ** | kwargs ) |
Make valid inputs for the run method from numpy arrays.
Parameters
----------
kwargs : `numpy.ndarray`
This is standard python kwargs where the left side of the equals
is the data band, and the right side is the corresponding `numpy.ndarray`
array.
Returns
-------
sortedImages : `dict` of `str` to \
`~lsst.daf.butler.DeferredDatasetHandle`
A dictionary of `~lsst.daf.butlger.DeferredDatasetHandle`\ s keyed
by the band they correspond to.
◆ makeInputsFromExposures()
| dict[int, DeferredDatasetHandle] lsst.pipe.tasks.prettyPictureMaker._task.makeInputsFromExposures | ( | self, | |
| ** | kwargs ) |
Make valid inputs for the run method from `Exposure` objects.
Parameters
----------
kwargs : `Exposure`
This is standard python kwargs where the left side of the equals
is the data band, and the right side is the corresponding
`Exposure`.
Returns
-------
sortedImages : `dict` of `int` to \
`~lsst.daf.butler.DeferredDatasetHandle`
A dictionary of `~lsst.daf.butler.DeferredDatasetHandle`\ s keyed
by the band they correspond to.
◆ makeInputsFromRefs()
| dict[str, Exposure] lsst.pipe.tasks.prettyPictureMaker._task.makeInputsFromRefs | ( | self, | |
| Iterable[DatasetRef] | refs, | ||
| Butler | QuantumContext | butler ) |
Make valid inputs for the run method from butler references.
Parameters
----------
refs : `Iterable` of `DatasetRef`
Some `Iterable` container of `Butler` `DatasetRef`\ s
butler : `Butler` or `QuantumContext`
This is the object that fetches the input data.
Returns
-------
sortedImages : `dict` of `str` to `Exposure`
A dictionary of `Exposure`\ s keyed by the band they
correspond to.
◆ run() [1/2]
| lsst.pipe.tasks.prettyPictureMaker._task.run | ( | self, | |
| Exposure | inputCoadd ) |
Estimate a background for an input Exposure and remove it.
Parameters
----------
inputCoadd : `Exposure`
The exposure the background will be removed from.
Returns
-------
result : `Struct`
A `Struct` that contains the exposure with the background removed.
This `Struct` will have an attribute named ``outputCoadd``.
◆ run() [2/2]
| Struct lsst.pipe.tasks.prettyPictureMaker._task.run | ( | self, | |
| Mapping[str, Exposure] | images ) |
Turns the input arguments in arguments into an RGB array.
Parameters
----------
images : `Mapping` of `str` to `Exposure`
A mapping of input images and the band they correspond to.
Returns
-------
result : `Struct`
A struct with the corresponding RGB image, and mask used in
RGB image construction. The struct will have the attributes
outputRGBImage and outputRGBMask. Each of the outputs will
be a `NDarray` object.
Notes
-----
Construction of input images are made easier by use of the
makeInputsFrom* methods.
◆ runQuantum()
| None lsst.pipe.tasks.prettyPictureMaker._task.runQuantum | ( | self, | |
| QuantumContext | butlerQC, | ||
| InputQuantizedConnection | inputRefs, | ||
| OutputQuantizedConnection | outputRefs ) |
Variable Documentation
◆ arr
| lsst.pipe.tasks.prettyPictureMaker._task.arr : `numyp.ndarray` |
◆ array
| lsst.pipe.tasks.prettyPictureMaker._task.array : `numpy.array` |
r = Field[float](doc="The amount of red contained in this channel")
g = Field[float](doc="The amount of green contained in this channel")
b = Field[float](doc="The amount of blue contained in this channel")
class PrettyPictureConfig(PipelineTaskConfig, pipelineConnections=PrettyPictureConnections):
channelConfig = ConfigDictField(
doc="A dictionary that maps band names to their rgb channel configurations",
keytype=str,
itemtype=ChannelRGBConfig,
default={},
)
cieWhitePoint = ListField[float](
doc="The white point of the input arrays in ciexz coordinates", maxLength=2, default=[0.28, 0.28]
)
arrayType = ChoiceField[str](
doc="The dataset type for the output image array",
default="uint8",
allowed={
"uint8": "Use 8 bit arrays, 255 max",
"uint16": "Use 16 bit arrays, 65535 max",
"half": "Use 16 bit float arrays, 1 max",
"float": "Use 32 bit float arrays, 1 max",
},
)
recenterNoise = Field[float](
doc="Recenter the noise away from zero. Supplied value is in units of sigma",
optional=True,
default=None,
)
noiseSearchThreshold = Field[float](
doc=(
"Flux threshold below which most flux will be considered noise, used to estimate noise properties"
),
default=10,
)
doPsfDeconvolve = Field[bool](
doc="Use the PSF in a Richardson-Lucy deconvolution on the luminance channel.", default=False
)
doPSFDeconcovlve = Field[bool](
doc="Use the PSF in a Richardson-Lucy deconvolution on the luminance channel.",
default=False,
deprecated="This field will be removed in v32. Use doPsfDeconvolve instead.",
optional=True,
)
doRemapGamut = Field[bool](
doc="Apply a color correction to unrepresentable colors; if False, clip them.", default=True
)
doExposureBrackets = Field[bool](
doc="Apply exposure bracketing to aid in dynamic range compression", default=True
)
doLocalContrast = Field[bool](doc="Apply local contrast optimizations to luminance.", default=True)
imageRemappingConfig = ConfigurableActionField[BoundsRemapper](
doc="Action controlling normalization process"
)
luminanceConfig = ConfigurableActionField[LumCompressor](
doc="Action controlling luminance scaling when making an RGB image"
)
localContrastConfig = ConfigurableActionField[LocalContrastEnhancer](
doc="Action controlling the local contrast correction in RGB image production"
)
colorConfig = ConfigurableActionField[ColorScaler](
doc="Action to control the color scaling process in RGB image production"
)
exposureBracketerConfig = ConfigurableActionField[ExposureBracketer](
doc=(
"Exposure scaling action used in creating multiple exposures with different scalings which will "
"then be fused into a final image"
),
)
gamutMapperConfig = ConfigurableActionField[GamutFixer](
doc="Action to fix pixels which lay outside RGB color gamut"
)
exposureBrackets = ListField[float](
doc=(
"Exposure scaling factors used in creating multiple exposures with different scalings which will "
"then be fused into a final image"
),
optional=True,
default=[1.25, 1, 0.75],
deprecated=(
"This field will stop working in v31 and be removed in v32, "
"please set exposureBracketerConfig.exposureBrackets"
),
)
gamutMethod = ChoiceField[str](
doc="If doRemapGamut is True this determines the method",
default="inpaint",
allowed={
"mapping": "Use a mapping function",
"inpaint": "Use surrounding pixels to determine likely value",
},
deprecated="This field will stop working in v31 and be removed in v32, please set gamutMapperConfig",
)
def setDefaults(self):
self.channelConfig["i"] = ChannelRGBConfig(r=1, g=0, b=0)
self.channelConfig["r"] = ChannelRGBConfig(r=0, g=1, b=0)
self.channelConfig["g"] = ChannelRGBConfig(r=0, g=0, b=1)
return super().setDefaults()
def _handle_deprecated(self):
# check if gamutMethod is set
if len(self._history["gamutMethod"]) > 1:
# This has been set in config, update it in the new location
self.gamutMapperConfig.gamutMethod = self.gamutMethod
if len(self._history["exposureBrackets"]) > 1:
self.exposureBracketerConfig.exposureBrackets = self.exposureBrackets
if self.exposureBrackets is None:
self.doExposureBrackets = False
if len(self.localContrastConfig._history["doLocalContrast"]) > 1:
self.doLocalContrast = self.localContrastConfig.doLocalContrast
# Handle doPsfDeconcovlve typo fix
if len(self._history["doPSFDeconcovlve"]) > 1:
self.doPsfDeconvolve = self.doPSFDeconcovlve
def freeze(self):
# ensure this is not already frozen
if self._frozen is not True:
self._handle_deprecated()
super().freeze()
class PrettyPictureTask(PipelineTask):
_DefaultName = "prettyPicture" ConfigClass = PrettyPictureConfig config: ConfigClass def _find_normal_stats(self, array):
◆ b
◆ doWrite
◆ g
◆ image_mask
| tuple lsst.pipe.tasks.prettyPictureMaker._task.image_mask = (image < threshhold) * (image > (mu_hat - 5 * sigma_hat)) |
◆ initial_std
◆ lower_pos
# Extract negative values efficiently
values_noise = array[array < self.config.noiseSearchThreshold]
# find the mode
center = mode(np.round(values_noise, 2)).mode
# extract the negative values
values_neg = array[array < center]
# Return infinity if no negative values found
if values_neg.size == 0:
return 0, np.inf
try:
# Fit half-normal distribution to absolute negative values
mu, sigma = halfnorm.fit(np.abs(values_neg))
except (ValueError, RuntimeError):
# Handle fitting failures (e.g., constant data, optimization issues)
return 0, np.inf
return center, sigma
def _match_sigmas_and_recenter(self, *arrays, factor=1):
◆ mask
| lsst.pipe.tasks.prettyPictureMaker._task.mask = image < maxLikely |
◆ maxLikely
| lsst.pipe.tasks.prettyPictureMaker._task.maxLikely = np.median(image, axis=None) |
◆ mean
◆ mu_hat
◆ r
◆ sigma
◆ sigma_hat
◆ sigma_ratio
| lsst.pipe.tasks.prettyPictureMaker._task.sigma_ratio = min_sig / sigma |
◆ threshhold
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