def run(regionfile,
imagefile,
args={},
preset='',
shape=(100, 100),
tablefile="objectsTb",
stampsfile="pstamp_"):
"""
Runs the pipeline for arc postage stamps creation and segmentation (in this order)
For each point inside (DS9) 'regionfile' take a window of size 'shape' around it.
Sextractor config parameters can be passed using 'args'. So far, catalog output
parameters are declared through the global variable 'PARAMS'.
Use 'preset' for optimized settings. See sltools.image.sextractor for more info.
Input:
- regionfile str : DS9 region file
- imagefile str : FITS filename
- args {'key',value} : Sextractor config params
- preset str : See sltools.image.sextractor
- shape (int,int) : Object cutouts shape (dx,dy)
- tablefile str : FITS table output filename
- stampsfile str : Output file rootnames
Output:
Write to 'tablefile' FITS table the segmented objects properties ('PARAMS')
---
"""
rootname = imagefile[:-5]
# Object's centroid will be placed over postamp's central point
x_i = shape[0] / 2
y_i = shape[1] / 2
x_size, y_size = shape
tbList = []
flList = []
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile)
X = asarray(D_in['x'])
Y = asarray(D_in['y'])
centroids = XY = zip(X, Y)
R = asarray(D_in['size'])
logging.debug("Centroids: %s", XY)
if len(XY) == 0:
logging.warning("No objects in given Centroids list. Finishing.")
return
img, hdr = pyfits.getdata(imagefile, header=True)
# For each "centroid":
#
for i in xrange(len(XY)):
logging.info("Process %s, point: %s", i, XY[i])
# Take a snapshot
#
obj, hdr = imcp.cutout(img,
hdr,
xo=X[i],
yo=Y[i],
x_size=x_size,
y_size=y_size)
file_i = rootname + "_pstamp_" + str(X[i]) + "_" + str(Y[i]) + ".fits"
pyfits.writeto(file_i, obj, hdr, clobber=True)
logging.info("Snapshot %s created", file_i)
flList.append(file_i)
# Run Sextractor over newly created sanpshot..
#
Dsex = SE.run_segobj(file_i, PARAMS, preset) ### SEXTRACTOR CALL
segimg = pyfits.getdata(Dsex['SEGMENTATION'])
cathdu = pyfits.open(Dsex['CATALOG'])[1]
logging.info("Segmentation done.")
logging.info("Files %s,%s,%s created", Dsex['SEGMENTATION'],
Dsex['OBJECTS'], Dsex['CATALOG'])
objID = segobjs.centroid2id(segimg, (x_i, y_i))
# The following block searchs for the nearest neighbour and check if the IDs are
# in accordance. If the given centroid is not part of an object (i.e, ID==0),
# the nearest object is taken in place; and if the IDs do not match and non-zero
# valued just an warning is printed to "debug" level.
#
centroids_sex = zip(cathdu.data.field('X_IMAGE'),
cathdu.data.field('Y_IMAGE'))
[logging.debug("SE Centroids: %s", _c) for _c in centroids_sex]
nrst_indx_dist = tbmtch.nearest_neighbour([centroids[i]],
centroids_sex)
logging.debug("Nearest point index|distance: %s (length:%s)",
nrst_indx_dist, len(nrst_indx_dist))
_indx, _dist = nrst_indx_dist[0]
nrst_obj = cathdu.data[_indx]
_id = nrst_obj.field('NUMBER')
if objID == 0:
objID = _id
logging.warning(
"Object ID is 0 for Centroid:%s. Using object (ID) %s, instead.",
centroids[i], _id)
if objID != _id:
logging.debug(
"Oops, two different objects were found matching point %s, objIDs %s and %s",
centroids[i], objID, _id)
logging.info("ObjectID: %s being readout", objID)
tbList.append(fts.select_entries(cathdu, 'NUMBER', objID))
arc_i = rootname + "_nrstArc_" + str(X[i]) + "_" + str(Y[i]) + ".fits"
arc = imcp.select(segimg, obj, objID)
pyfits.writeto(arc_i, arc, hdr, clobber=True)
# Create output table
new_tbhdu = tbList[0]
for i in xrange(1, len(tbList)):
new_tbhdu = fts.extend_tbHDU(new_tbhdu, tbList[i])
tb_flList = fts.dict_to_tbHDU({'filename': flList})
new_tbhdu = fts.merge_tbHDU(new_tbhdu, tb_flList)
tablefile = rootname + "_arcscat.fits"
new_tbhdu.writeto(tablefile, clobber=True)
return
def run(regionfile,
imagefile,
args={},
preset='HST_Arcs',
shape=(100,
100)): # , tablefile="objectsTb"):# ,stampsfile="pstamp_"):
"""
Runs the pipeline for postamps creation and segmented
run( regionfile, imagefile [,...] )
For each point listed inside DS9 'regionfile', considered object
centroids in 'imagefile', a snapshot and object segmentation are
done; shape and rootname (suffixed by "objID", fits extension)
are passed through 'shape' and 'stampsfile', resp.
Sextractor config parameters can be passed using 'args'. So far,
catalog output parameters are hardcoded.
Use 'preset' for optimized settings. See sltools.image.sextractor
for more info.
Input:
- regionfile : str
DS9 region file
- imagefile : str
FITS filename
- args : {'option':'value',}
Sextractor config params
- preset : str
See sltools.image.sextractor
- shape : (int,int)
Object cutouts shape (pixels,pixels)
- tablefile : str
FITS table output filename
- stampsfile : str
Output file rootnames
Output:
- tbhdu : pyfits.BinTableHDU
Output (fits) table with objects' computed parameters
---
"""
# Sextractor Params:
PARAMS = [
'NUMBER', 'ELONGATION', 'ELLIPTICITY', 'ISOAREA_IMAGE', 'A_IMAGE',
'B_IMAGE', 'THETA_IMAGE', 'X_IMAGE', 'Y_IMAGE', 'X2_IMAGE', 'Y2_IMAGE',
'XY_IMAGE'
]
# Object's centroid will be placed over postamp's central point
x_i = shape[0] / 2
y_i = shape[1] / 2
_tbList = []
_flList = []
_imList = []
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile)
X = asarray(D_in['x'])
Y = asarray(D_in['y'])
centroids = zip(X, Y)
####################################################################
# Segment the whole image at once
# Needs:
# X , Y , image(FITS) filename and Sextractor arguments/inputs
selobjimg_W, selobjhdu_W = whole_image(X, Y, imagefile, PARAMS, preset)
selobjhdu_W.writeto('Whole_image.fit', clobber=True)
pyfits.writeto('Whole_image.fits', selobjimg_W, clobber=True)
####################################################################
img, hdr = pyfits.getdata(imagefile, header=True)
logging.debug("Centroids: %s", centroids)
rootname = re.sub(".fits", "", imagefile)
# For each centroid, do:
#
i = -1
xy = centroids[:]
for o_o in centroids:
i += 1
logging.info("Process %s, point: %s", i, o_o)
# Take a snapshot
#
_obj, _hdr = imcp.snapshot(img, hdr, centroid=o_o, shape=shape)
file_i = rootname + "_ps" + str(i) + ".fits"
pyfits.writeto(file_i, _obj, _hdr, clobber=True)
del _obj, _hdr
logging.info("Poststamp %s created", file_i)
# Run Sextractor over newly created sanpshot..
#
_Dsex = sextractor.run_segobj(file_i, PARAMS, preset=preset)
### SEXTRACTOR CALL
segimg = pyfits.getdata(_Dsex['SEGMENTATION'])
objimg = pyfits.getdata(_Dsex['OBJECTS'])
cathdu = pyfits.open(_Dsex['CATALOG'])[1]
objID = segimg[y_i, x_i]
if not objID:
lixo = xy.remove(o_o)
continue
logging.info("ObjectID: %s being readout", objID)
_tbList.append(fts.select_entries(cathdu, 'NUMBER', objID))
_flList.append(file_i)
_imList.append(imcp.copy_objects(objimg, segimg, [objID]))
# Initialize output table and image..
#
selobjhdu_S = _tbList[0]
selobjimg_S = np.zeros(img.shape, dtype=img.dtype)
selobjimg_S = combine.add_images(selobjimg_S,
_imList[0],
x=xy[0][0],
y=xy[0][1])
#
# and do the same for each object
#
for i in xrange(1, len(_tbList)):
selobjhdu_S = fts.extend_tbHDU(selobjhdu_S, _tbList[i])
selobjimg_S = combine.add_images(selobjimg_S,
_imList[i],
x=xy[i][0],
y=xy[i][1])
# Write down the FITS catalog..
#
tb_flList = fts.dict_to_tbHDU({'filename': _flList})
selobjhdu_S = fts.merge_tbHDU(selobjhdu_S, tb_flList)
# And the FITS image, composed by the well segmented objects..
#
selobjhdu_S.writeto('Stamps_compose.fit', clobber=True)
pyfits.writeto('Stamps_compose.fits', selobjimg_S, clobber=True)
return
def run(regionfile, imagefile, args={}, preset='', shape=(100,100), tablefile="objectsTb",stampsfile="pstamp_"):
"""
Runs the pipeline for arc postage stamps creation and segmentation (in this order)
For each point inside (DS9) 'regionfile' take a window of size 'shape' around it.
Sextractor config parameters can be passed using 'args'. So far, catalog output
parameters are declared through the global variable 'PARAMS'.
Use 'preset' for optimized settings. See sltools.image.sextractor for more info.
Input:
- regionfile str : DS9 region file
- imagefile str : FITS filename
- args {'key',value} : Sextractor config params
- preset str : See sltools.image.sextractor
- shape (int,int) : Object cutouts shape (dx,dy)
- tablefile str : FITS table output filename
- stampsfile str : Output file rootnames
Output:
Write to 'tablefile' FITS table the segmented objects properties ('PARAMS')
---
"""
rootname = imagefile[:-5]
# Object's centroid will be placed over postamp's central point
x_i = shape[0]/2
y_i = shape[1]/2
x_size,y_size = shape
tbList = []
flList = []
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile)
X = asarray(D_in['x'])
Y = asarray(D_in['y'])
centroids = XY = zip(X,Y)
R = asarray(D_in['size'])
logging.debug("Centroids: %s",XY)
if len(XY) == 0:
logging.warning("No objects in given Centroids list. Finishing.")
return
img,hdr = pyfits.getdata(imagefile,header=True)
# For each "centroid":
#
for i in xrange(len(XY)):
logging.info("Process %s, point: %s",i,XY[i])
# Take a snapshot
#
obj,hdr = imcp.cutout(img,hdr,xo=X[i],yo=Y[i],x_size=x_size,y_size=y_size)
file_i = rootname+"_pstamp_"+str(X[i])+"_"+str(Y[i])+".fits"
pyfits.writeto(file_i,obj,hdr,clobber=True)
logging.info("Snapshot %s created",file_i)
flList.append(file_i)
# Run Sextractor over newly created sanpshot..
#
Dsex = SE.run_segobj(file_i, PARAMS,preset) ### SEXTRACTOR CALL
segimg = pyfits.getdata(Dsex['SEGMENTATION'])
cathdu = pyfits.open(Dsex['CATALOG'])[1]
logging.info("Segmentation done.")
logging.info("Files %s,%s,%s created",Dsex['SEGMENTATION'],Dsex['OBJECTS'],Dsex['CATALOG'])
objID = segobjs.centroid2id(segimg,(x_i,y_i))
# The following block searchs for the nearest neighbour and check if the IDs are
# in accordance. If the given centroid is not part of an object (i.e, ID==0),
# the nearest object is taken in place; and if the IDs do not match and non-zero
# valued just an warning is printed to "debug" level.
#
centroids_sex = zip(cathdu.data.field('X_IMAGE'),cathdu.data.field('Y_IMAGE'))
[ logging.debug("SE Centroids: %s",_c) for _c in centroids_sex ]
nrst_indx_dist = tbmtch.nearest_neighbour([centroids[i]],centroids_sex)
logging.debug("Nearest point index|distance: %s (length:%s)",nrst_indx_dist,len(nrst_indx_dist))
_indx,_dist = nrst_indx_dist[0]
nrst_obj = cathdu.data[_indx]
_id = nrst_obj.field('NUMBER')
if objID == 0:
objID = _id
logging.warning("Object ID is 0 for Centroid:%s. Using object (ID) %s, instead.",centroids[i],_id)
if objID != _id:
logging.debug("Oops, two different objects were found matching point %s, objIDs %s and %s",centroids[i],objID,_id)
logging.info("ObjectID: %s being readout",objID)
tbList.append(fts.select_entries(cathdu,'NUMBER',objID))
arc_i = rootname+"_nrstArc_"+str(X[i])+"_"+str(Y[i])+".fits"
arc = imcp.select(segimg,obj,objID)
pyfits.writeto(arc_i,arc,hdr,clobber=True)
# Create output table
new_tbhdu = tbList[0]
for i in xrange(1,len(tbList)):
new_tbhdu = fts.extend_tbHDU(new_tbhdu,tbList[i])
tb_flList = fts.dict_to_tbHDU({'filename':flList})
new_tbhdu = fts.merge_tbHDU(new_tbhdu,tb_flList)
tablefile = rootname+"_arcscat.fits"
new_tbhdu.writeto(tablefile,clobber=True)
return
def run(regionfile, imagefile, args={}, preset='HST_Arcs', shape=(100,100)):# , tablefile="objectsTb"):# ,stampsfile="pstamp_"):
"""
Runs the pipeline for postamps creation and segmented
run( regionfile, imagefile [,...] )
For each point listed inside DS9 'regionfile', considered object
centroids in 'imagefile', a snapshot and object segmentation are
done; shape and rootname (suffixed by "objID", fits extension)
are passed through 'shape' and 'stampsfile', resp.
Sextractor config parameters can be passed using 'args'. So far,
catalog output parameters are hardcoded.
Use 'preset' for optimized settings. See sltools.image.sextractor
for more info.
Input:
- regionfile : str
DS9 region file
- imagefile : str
FITS filename
- args : {'option':'value',}
Sextractor config params
- preset : str
See sltools.image.sextractor
- shape : (int,int)
Object cutouts shape (pixels,pixels)
- tablefile : str
FITS table output filename
- stampsfile : str
Output file rootnames
Output:
- tbhdu : pyfits.BinTableHDU
Output (fits) table with objects' computed parameters
---
"""
# Sextractor Params:
PARAMS=['NUMBER','ELONGATION','ELLIPTICITY','ISOAREA_IMAGE','A_IMAGE','B_IMAGE','THETA_IMAGE','X_IMAGE','Y_IMAGE','X2_IMAGE','Y2_IMAGE','XY_IMAGE']
# Object's centroid will be placed over postamp's central point
x_i = shape[0]/2;
y_i = shape[1]/2;
_tbList = [];
_flList = [];
_imList = [];
# Read DS9 regionfile and input imagefile..
#
D_in = asc.read_ds9cat(regionfile);
X = asarray(D_in['x']);
Y = asarray(D_in['y']);
centroids = zip(X,Y);
####################################################################
# Segment the whole image at once
# Needs:
# X , Y , image(FITS) filename and Sextractor arguments/inputs
selobjimg_W,selobjhdu_W = whole_image(X,Y,imagefile,PARAMS,preset);
selobjhdu_W.writeto('Whole_image.fit',clobber=True);
pyfits.writeto('Whole_image.fits',selobjimg_W,clobber=True);
####################################################################
img,hdr = pyfits.getdata(imagefile,header=True);
logging.debug("Centroids: %s",centroids);
rootname = re.sub(".fits","",imagefile);
# For each centroid, do:
#
i=-1;
xy = centroids[:];
for o_o in centroids:
i+=1;
logging.info("Process %s, point: %s",i,o_o);
# Take a snapshot
#
_obj,_hdr = imcp.snapshot( img, hdr, centroid=o_o, shape=shape );
file_i = rootname+"_ps"+str(i)+".fits";
pyfits.writeto(file_i,_obj,_hdr,clobber=True);
del _obj,_hdr;
logging.info("Poststamp %s created",file_i);
# Run Sextractor over newly created sanpshot..
#
_Dsex = sextractor.run_segobj(file_i, PARAMS,preset=preset); ### SEXTRACTOR CALL
segimg = pyfits.getdata(_Dsex['SEGMENTATION']);
objimg = pyfits.getdata(_Dsex['OBJECTS']);
cathdu = pyfits.open(_Dsex['CATALOG'])[1];
objID = segimg[y_i,x_i];
if not objID:
lixo = xy.remove(o_o);
continue;
logging.info("ObjectID: %s being readout",objID);
_tbList.append(fts.select_entries(cathdu,'NUMBER',objID));
_flList.append(file_i);
_imList.append(imcp.copy_objects(objimg,segimg,[objID]));
# Initialize output table and image..
#
selobjhdu_S = _tbList[0];
selobjimg_S = np.zeros(img.shape,dtype=img.dtype);
selobjimg_S = combine.add_images(selobjimg_S,_imList[0],x=xy[0][0],y=xy[0][1]);
#
# and do the same for each object
#
for i in xrange(1,len(_tbList)):
selobjhdu_S = fts.extend_tbHDU(selobjhdu_S,_tbList[i]);
selobjimg_S = combine.add_images(selobjimg_S,_imList[i],x=xy[i][0],y=xy[i][1]);
# Write down the FITS catalog..
#
tb_flList = fts.dict_to_tbHDU({'filename':_flList});
selobjhdu_S = fts.merge_tbHDU(selobjhdu_S,tb_flList);
# And the FITS image, composed by the well segmented objects..
#
selobjhdu_S.writeto('Stamps_compose.fit',clobber=True);
pyfits.writeto('Stamps_compose.fits',selobjimg_S,clobber=True)
return;