def py_read_atlas(*args):
return _py_atlas.py_read_atlas(*args)
def read_atlas(filename, id, trim=False):
"""
Module:
atlas
Name:
read_atlas
Purpose:
Read atlas images and metadata from SDSS fpAtlas files.
Calling Sequence:
from sdsspy import atlas
atlas_dict=atlas.read(filename, id, trim=False)
Note: Typically you will use the sdsspy.files.read('fpatlas', run, camcol, ...)
to read these files.
Inputs:
filename:
the path to an fpAtlas file.
id:
The id of the object in the field.
Optional Inputs:
trim=:
If true, the atlas images will be trimmed to the region with actual
data. For example if an object was deblended from a large complex
region, most of the area in the altas image is just set to the
default background value. This region will be trimmed.
Outputs:
A dictonary. The dictionary has the following keys
'images':
A list with images from each bandpass. 0->u, 1->g, 2->r, 3->i, 4->z
The images are 2-d numpy arrays of type uint16.
'row0', 'col0':
The offset of the lower left hand corner into the original
frame. The center in the atlas image is then [rowc-row0,
colc-col0]. This entry is a list, one for each bandpass.
'drow','dcol':
The difference in center relative to the cononical bandpass,
the r-band.
'rmin','rmax','cmin','cmax':
The bounding box of the atlas image; rmax and cmax are the
exact values stored in the atlas image, and are thus *inclusive*.
atlas_dict['images'][:].shape == (1+rmax-rmin, 1+cmax-cmin)
'run','rerun','camcol','field','id':
The sdss file information.
'filename':
The atlas image file name.
'SOFT_BIAS':
The value of the background in the image.
Exceptions:
IOError is raised is the file cannot be read.
NoAtlasImageError is raised if the requested object does not
have an atlas image. This is a benign error which you
will most likely want to catch.
"""
id=int(id)
try:
imdict = _py_atlas.py_read_atlas(filename, id)
except RuntimeError as e:
# The RuntimeError happens when an object has no atlas image, this
# is no big deal. Re-raise as NoAtlasImageError
raise NoAtlasImageError(e)
if trim:
for band in xrange(5):
im = imdict['images'][band]
# there is no way that I know of to get the exact region.
#
# This is not exact, since even noisy spots could end up zero, but
# at least we know we would be in the noise anyway
w1,w2 = numpy.where(im != imdict['SOFT_BIAS'])
if w1.size > 0:
minrow = w1.min()
maxrow = w1.max()
mincol = w2.min()
maxcol = w2.max()
if ((minrow > 0)
or (maxrow < (im.shape[0]-1))
or (mincol > 0)
or (maxcol < (im.shape[1]-1))):
im = im[minrow:maxrow+1, mincol:maxcol+1]
imdict['images'][band] = im
return imdict
def py_read_atlas(*args): return _py_atlas.py_read_atlas(*args)
