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flake8 and comment length
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@MorganSchmitz
MorganSchmitz committed Oct 8, 2020
1 parent 7768d09 commit 7dadcb9
Showing 1 changed file with 46 additions and 31 deletions.
77 changes: 46 additions & 31 deletions python/lsst/ip/diffim/dcrModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,11 +71,13 @@ def fromImage(cls, maskedImage, dcrNumSubfilters, filterInfo=None, effectiveWave
maskedImage : `lsst.afw.image.MaskedImage`
Input coadded image to divide equally between the subfilters.
dcrNumSubfilters : `int`
Number of sub-filters used to model chromatic effects within a band.
Number of sub-filters used to model chromatic effects within a
band.
filterInfo : `lsst.afw.image.Filter`, optional
The filter definition, set in the current instruments' obs package.
Note: this object will be changed in DM-21333.
Required for any calculation of DCR, including making matched templates.
Required for any calculation of DCR, including making matched
templates.
effectiveWavelength : `float`
The effective wavelengths of the current filter.
bandwidth : `float`
Expand Down Expand Up @@ -132,11 +134,13 @@ def fromDataRef(cls, dataRef, effectiveWavelength, bandwidth, datasetType="dcrCo
datasetType : `str`, optional
Name of the DcrModel in the registry {"dcrCoadd", "dcrCoadd_sub"}
numSubfilters : `int`
Number of sub-filters used to model chromatic effects within a band.
Number of sub-filters used to model chromatic effects within a
band.
**kwargs
Additional keyword arguments to pass to look up the model in the data registry.
Common keywords and their types include: ``tract``:`str`, ``patch``:`str`,
``bbox``:`lsst.afw.geom.Box2I`
Additional keyword arguments to pass to look up the model in the
data registry.
Common keywords and their types include: ``tract``:`str`,
``patch``:`str`, ``bbox``:`lsst.afw.geom.Box2I`
Returns
-------
Expand Down Expand Up @@ -173,7 +177,8 @@ def fromQuantum(cls, availableCoaddRefs, effectiveWavelength, bandwidth):
Parameters
----------
availableCoaddRefs : `dict` of `int` : `lsst.daf.butler.DeferredDatasetHandle`
availableCoaddRefs : `dict` of
`int` : `lsst.daf.butler.DeferredDatasetHandle`
Dictionary of spatially relevant retrieved coadd patches,
indexed by their sequential patch number.
effectiveWavelength : `float`
Expand Down Expand Up @@ -335,7 +340,8 @@ def variance(self):
return self._variance

def getReferenceImage(self, bbox=None):
"""Calculate a reference image from the average of the subfilter images.
"""Calculate a reference image from the average of the subfilter
images.
Parameters
----------
Expand Down Expand Up @@ -398,7 +404,8 @@ def buildMatchedTemplate(self, exposure=None, order=3,
Calculate DCR for two evenly-spaced wavelengths in each subfilter,
instead of at the midpoint. Default: True
splitThreshold : `float`, optional
Minimum DCR difference within a subfilter required to use ``splitSubfilters``
Minimum DCR difference within a subfilter required to use
``splitSubfilters``
amplifyModel : `float`, optional
Multiplication factor to amplify differences between model planes.
Used to speed convergence of iterative forward modeling.
Expand All @@ -411,9 +418,10 @@ def buildMatchedTemplate(self, exposure=None, order=3,
Raises
------
ValueError
If neither ``exposure`` or all of ``visitInfo``, ``bbox``, and ``wcs`` are set.
If neither ``exposure`` or all of ``visitInfo``, ``bbox``, and
``wcs`` are set.
"""
if self.effectiveWavelength is None or self.bandwidth is None::
if self.effectiveWavelength is None or self.bandwidth is None:
raise ValueError("'effectiveWavelength' and 'bandwidth' must be set for the DcrModel in order "
"to calculate DCR.")
if exposure is not None:
Expand Down Expand Up @@ -551,11 +559,13 @@ def regularizeModelFreq(self, modelImages, bbox, statsCtrl, regularizationFactor
Notes
-----
This implementation of frequency regularization restricts each subfilter
image to be a smoothly-varying function times a reference image.
This implementation of frequency regularization restricts each
subfilter image to be a smoothly-varying function times a reference
image.
"""
# ``regularizationFactor`` is the maximum change between subfilter images, so the maximum difference
# between one subfilter image and the average will be the square root of that.
# ``regularizationFactor`` is the maximum change between subfilter
# images, so the maximum difference between one subfilter image and the
# average will be the square root of that.
maxDiff = np.sqrt(regularizationFactor)
noiseLevel = self.calculateNoiseCutoff(modelImages[0], statsCtrl, bufferSize=5, mask=mask, bbox=bbox)
referenceImage = self.getReferenceImage(bbox)
Expand All @@ -566,11 +576,13 @@ def regularizeModelFreq(self, modelImages, bbox, statsCtrl, regularizationFactor
referenceImage[badPixels] = 0.
filterWidth = regularizationWidth
fwhm = 2.*filterWidth
# The noise should be lower in the smoothed image by sqrt(Nsmooth) ~ fwhm pixels
# The noise should be lower in the smoothed image by
# sqrt(Nsmooth) ~ fwhm pixels
noiseLevel /= fwhm
smoothRef = ndimage.filters.gaussian_filter(referenceImage, filterWidth, mode='constant')
# Add a three sigma offset to both the reference and model to prevent dividing by zero.
# Note that this will also slightly suppress faint variations in color.
# Add a three sigma offset to both the reference and model to prevent
# dividing by zero. Note that this will also slightly suppress faint
# variations in color.
smoothRef += 3.*noiseLevel

lowThreshold = smoothRef/maxDiff
Expand Down Expand Up @@ -630,12 +642,13 @@ def applyImageThresholds(self, image, highThreshold=None, lowThreshold=None, reg
"""Restrict image values to be between upper and lower limits.
This method flags all pixels in an image that are outside of the given
threshold values. The threshold values are taken from a reference image,
so noisy pixels are likely to get flagged. In order to exclude those
noisy pixels, the array of flags is eroded and dilated, which removes
isolated pixels outside of the thresholds from the list of pixels to be
modified. Pixels that remain flagged after this operation have their
values set to the appropriate upper or lower threshold value.
threshold values. The threshold values are taken from a reference
image, so noisy pixels are likely to get flagged. In order to exclude
those noisy pixels, the array of flags is eroded and dilated, which
removes isolated pixels outside of the thresholds from the list of
pixels to be modified. Pixels that remain flagged after this operation
have their values set to the appropriate upper or lower threshold
value.
Parameters
----------
Expand All @@ -649,9 +662,9 @@ def applyImageThresholds(self, image, highThreshold=None, lowThreshold=None, reg
regularizationWidth : `int`, optional
Minimum radius of a region to include in regularization, in pixels.
"""
# Generate the structure for binary erosion and dilation, which is used to remove noise-like pixels.
# Groups of pixels with a radius smaller than ``regularizationWidth``
# will be excluded from regularization.
# Generate the structure for binary erosion and dilation, which is used
# to remove noise-like pixels. Groups of pixels with a radius smaller
# than ``regularizationWidth`` will be excluded from regularization.
filterStructure = ndimage.iterate_structure(ndimage.generate_binary_structure(2, 1),
regularizationWidth)
if highThreshold is not None:
Expand Down Expand Up @@ -689,13 +702,14 @@ def applyDcr(image, dcr, useInverse=False, splitSubfilters=False, splitThreshold
Calculate DCR for two evenly-spaced wavelengths in each subfilter,
instead of at the midpoint. Default: False
splitThreshold : `float`, optional
Minimum DCR difference within a subfilter required to use ``splitSubfilters``
Minimum DCR difference within a subfilter required to use
``splitSubfilters``
doPrefilter : `bool`, optional
Spline filter the image before shifting, if set. Filtering is required,
so only set to False if the image is already filtered.
Filtering takes ~20% of the time of shifting, so if `applyDcr` will be
called repeatedly on the same image it is more efficient to precalculate
the filter.
called repeatedly on the same image it is more efficient to
precalculate the filter.
order : `int`, optional
The order of the spline interpolation, default is 3.
Expand Down Expand Up @@ -762,7 +776,8 @@ def calculateDcr(visitInfo, wcs, effectiveWavelength, bandwidth, dcrNumSubfilter
dcrShift = []
weight = [0.75, 0.25]
for wl0, wl1 in wavelengthGenerator(effectiveWavelength, bandwidth, dcrNumSubfilters):
# Note that diffRefractAmp can be negative, since it's relative to the midpoint of the full band
# Note that diffRefractAmp can be negative, since it's relative to the
# midpoint of the full band
diffRefractAmp0 = differentialRefraction(wavelength=wl0, wavelengthRef=effectiveWavelength,
elevation=visitInfo.getBoresightAzAlt().getLatitude(),
observatory=visitInfo.getObservatory(),
Expand Down

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