Python Functions¶
All of these can be copy-pasted into notebooks or python scripts.
The packages that are necessary for the function are also provided.
Cutout coadd¶
In the future, the RSP will have an image cutout service and this function will not be necessary. A demonstration of its use can be found in tutorial notebook 03a, Image Display and Manipulation.
import lsst.geom as geom
from lsst.daf.butler import Butler
butler = Butler('dp02', collections='2.2i/runs/DP0.2')
def cutout_coadd(butler, ra, dec, band='r', datasetType='deepCoadd',
skymap=None, cutoutSideLength=51, **kwargs):
"""
Produce a cutout from a coadd at the given ra, dec position.
Adapted from DC2 tutorial notebook by Michael Wood-Vasey.
Parameters
----------
butler: lsst.daf.persistence.Butler
Helper object providing access to a data repository
ra: float
Right ascension of the center of the cutout, in degrees
dec: float
Declination of the center of the cutout, in degrees
band: string
Filter of the image to load
datasetType: string ['deepCoadd']
Which type of coadd to load. Doesn't support 'calexp'
skymap: lsst.afw.skyMap.SkyMap [optional]
Pass in to avoid the Butler read. Useful if you have lots of them.
cutoutSideLength: float [optional]
Size of the cutout region in pixels.
Returns
-------
MaskedImage
"""
radec = geom.SpherePoint(ra, dec, geom.degrees)
cutoutSize = geom.ExtentI(cutoutSideLength, cutoutSideLength)
if skymap is None:
skymap = butler.get("skyMap")
tractInfo = skymap.findTract(radec)
patchInfo = tractInfo.findPatch(radec)
xy = geom.PointI(tractInfo.getWcs().skyToPixel(radec))
bbox = geom.BoxI(xy - cutoutSize // 2, cutoutSize)
patch = tractInfo.getSequentialPatchIndex(patchInfo)
coaddId = {'tract': tractInfo.getId(), 'patch': patch, 'band': band}
parameters = {'bbox': bbox}
cutout_image = butler.get(datasetType, parameters=parameters,
dataId=coaddId)
return cutout_image
Cutout calexp¶
In the future, the RSP will have an image cutout service and this function will not be necessary. A demonstration of its use can be found in tutorial notebook 03a, Image Display and Manipulation.
import lsst.geom as geom
from lsst.daf.butler import Butler
butler = Butler('dp02', collections='2.2i/runs/DP0.2')
def cutout_calexp(butler, ra, dec, visit, detector,
cutoutSideLength=51, **kwargs):
"""
Produce a cutout from a calexp at the given ra, dec position.
Adapted from cutout_coadd which was adapted from a DC2 tutorial
notebook by Michael Wood-Vasey.
Parameters
----------
butler: lsst.daf.persistence.Butler
Helper object providing access to a data repository
ra: float
Right ascension of the center of the cutout, in degrees
dec: float
Declination of the center of the cutout, in degrees
visit: int
Visit id of the calexp's visit
detector: int
Detector for the calexp
cutoutSideLength: float [optional]
Size of the cutout region in pixels.
Returns
-------
MaskedImage
"""
dataId = {'visit': visit, 'detector': detector}
radec = geom.SpherePoint(ra, dec, geom.degrees)
cutoutSize = geom.ExtentI(cutoutSideLength, cutoutSideLength)
calexp_wcs = butler.get('calexp.wcs', **dataId)
xy = geom.PointI(calexp_wcs.skyToPixel(radec))
bbox = geom.BoxI(xy - cutoutSize // 2, cutoutSize)
parameters = {'bbox': bbox}
cutout_image = butler.get('calexp', parameters=parameters, **dataId)
return cutout_image
Remove figure¶
Removing large figures from a notebook when creating many data-rich figures can help to avoid problems. A demonstration of this function’s use can be found in tutorial notebook 03a, Image Display and Manipulation.
import matplotlib.pyplot as plt
import gc
def remove_figure(fig):
"""
Remove a figure to reduce memory footprint.
Parameters
----------
fig: matplotlib.figure.Figure
Figure to be removed.
Returns
-------
None
"""
# get the axes and clear their images
for ax in fig.get_axes():
for im in ax.get_images():
im.remove()
fig.clf() # clear the figure
plt.close(fig) # close the figure
gc.collect() # call the garbage collector