Source code for tkp.utility.containers
"""
Container classes for the TKP pipeline.
These provide convenient means of marshalling the various types of data --
lightcurves, detections, sources, etc -- that the pipeline must handle.
"""
import logging
logger = logging.getLogger(__name__)
[docs]class ObjectContainer(list):
"""A container class for objects.
What sort of objects? Well, anything that has a position and we
want to keep lists of, really. So detections (ie, an individual
source measurement on an image), sources (ie all the detections of
a given object in a given image stack) and lightcurves (ie, all
the sources associated with a given object through time).
You probably don't want to use this on it's own: see ExtractionResults,
TargetList or source for more useful derived classes.
"""
def closest_to(self, pix_x, pix_y):
distance, target = False, False
logger.debug("Beginning a search for objects near %.1f, %.1f: ",
pix_x, pix_y)
logger.debug("%s contains %d objects", str(self), len(self))
for obj in self:
tmpdist = (pix_x - obj.x)**2 + (pix_y - obj.y)**2
logger.debug("Object at %f, %f", obj.x, obj.y)
logger.debug("Has distance %f", tmpdist)
if not distance:
distance = tmpdist
target = obj
else:
if tmpdist < distance:
target = obj
distance = tmpdist
logger.debug("Best distance is now %f", distance)
logger.debug("From object %s", str(target))
if not distance:
return (target, distance)
else:
return (target, distance**0.5)
def __setslice__(self, section, items):
"""
Not implemented.
"""
raise NotImplementedError
def __iadd__(self, y):
"""
Not implemented.
"""
raise NotImplementedError
def __imul__(self, y):
"""
Not implemented.
"""
raise NotImplementedError
def __mul__(self, y):
"""
Not implemented.
"""
raise NotImplementedError
def __rmul__(self, y):
"""
Not implemented.
"""
raise NotImplementedError
def __str__(self):
return 'Container: ' + str(len(self)) + ' object(s).'
