def run_ds9(filename_A, filename_B, radius=1):
"""
Input:
- filename_A : DS9 region file
- filename_B : DS9 region file
Output:
- tbhdu
---
"""
# Open two tables, for now, ds9 region files:
#
DA_in = ascii_data.read_ds9cat(filename_A);
DB_in = ascii_data.read_ds9cat(filename_B);
# Translate them to table HDU (FITS tables Header D Unit)
tbhdu_check = fits_data.dict_to_tbHDU(DA_in,tbname=filename_A);
tbhdu_truth = fits_data.dict_to_tbHDU(DB_in,tbname=filename_B);
return run(tbhdu_check,tbhdu_truth,radius);
def run_ds9(filename_A, filename_B, radius=1):
"""
Input:
- filename_A : DS9 region file
- filename_B : DS9 region file
Output:
- tbhdu
---
"""
# Open two tables, for now, ds9 region files:
#
DA_in = ascii_data.read_ds9cat(filename_A);
DB_in = ascii_data.read_ds9cat(filename_B);
# Translate them to table HDU (FITS tables Header D Unit)
tbhdu_check = fits_data.dict_to_tbHDU(DA_in,tbname=filename_A);
tbhdu_truth = fits_data.dict_to_tbHDU(DB_in,tbname=filename_B);
return run(tbhdu_check,tbhdu_truth,radius);
def process_output(imgfile, catfile, clean=False):
from scipy import ndimage
import numpy as np
# ----------
# Catalog
catalog_file = catfile
os.system("sed -e 's/^[ ]*//' %s | tr -s ' ' > %s" %
('data/' + catfile, catalog_file))
columns = [
'X', 'Y', 'ecc', 'lambda1', 'lambda2', 'A', 'B', 'theta', 'size'
]
Dcatin = ascii_data.dict_from_csv(catalog_file,
columns,
header_lines=10,
delimiter=' ')
# Lenzen outputs the 'Y' positions inverted. Lets fix that:
hdr = pyfits.getheader('data/' + imgfile)
y_size = hdr['naxis1']
x_size = hdr['naxis2']
Y_inv = Dcatin['Y']
finalimg = re.sub("_out_img.fits", "_out_result.fits", imgfile)
if len(Y_inv):
Dcatin['Y'] = [str(y_size - int(i)) for i in Y_inv]
Catin = fits_data.dict_to_tbHDU(Dcatin)
# Select entries to output arc data
Catout = fits_data.sample_entries(Catin, ecc=0.7)
Catout.name = "Lenzen_arcsfound"
finalcat = catalog_file[:-4] + '.fits'
Catout.writeto(finalcat, clobber=True)
# Image
img = ndimage.imread('data/result.ppm')
comb = np.maximum(img[..., 0], img[..., 1])
diff = img[..., 2] - comb
pyfits.writeto(finalimg, diff[::-1], clobber=True)
else:
blank_array = np.zeros((y_size, x_size))
pyfits.writeto(finalimg, blank_array, clobber=True)
if clean:
os.system('rm -rf temp/ %s %s' % (imgfile, catfile))
return
def process_output(imgfile,catfile,clean=False):
from scipy import ndimage
import numpy as np
# ----------
# Catalog
catalog_file = catfile
os.system("sed -e 's/^[ ]*//' %s | tr -s ' ' > %s" % ('data/'+catfile,catalog_file))
columns = ['X','Y','ecc','lambda1','lambda2','A','B','theta','size']
Dcatin = ascii_data.dict_from_csv(catalog_file,columns,header_lines=10,delimiter=' ')
# Lenzen outputs the 'Y' positions inverted. Lets fix that:
hdr = pyfits.getheader('data/'+imgfile)
y_size = hdr['naxis1']
x_size = hdr['naxis2']
Y_inv = Dcatin['Y']
finalimg = re.sub("_out_img.fits","_out_result.fits",imgfile)
if len(Y_inv):
Dcatin['Y'] = [ str(y_size-int(i)) for i in Y_inv ]
Catin = fits_data.dict_to_tbHDU(Dcatin)
# Select entries to output arc data
Catout = fits_data.sample_entries(Catin,ecc=0.7)
Catout.name = "Lenzen_arcsfound"
finalcat = catalog_file[:-4]+'.fits'
Catout.writeto(finalcat,clobber=True)
# Image
img = ndimage.imread('data/result.ppm')
comb = np.maximum(img[...,0],img[...,1])
diff = img[...,2] - comb
pyfits.writeto(finalimg,diff[::-1],clobber=True)
else:
blank_array = np.zeros((y_size,x_size))
pyfits.writeto(finalimg,blank_array,clobber=True)
if clean:
os.system('rm -rf temp/ %s %s' % (imgfile,catfile));
return
imgfile = args[0]
# Run Horesh's arcfinder
finalcat = run(imgfile, inputfile)
FITSout = True
# To finish the process, convert the ASCII table into FITS if asked for.
if FITSout:
# finalfit = finalcat[:-4]+".fit";
finalfit = 'Catalog_arcs_horesh.out.fit'
cat = numpy.loadtxt(finalcat).T
if cat.ndim == 1:
cat.shape = (len(cat), 1)
dcat = {
'ID': cat[0],
'X': cat[1],
'Y': cat[2],
'STmag': cat[3],
'Length': cat[4],
'Width': cat[5],
'L/W': cat[6],
'Area': cat[7]
}
tbhdu = fits_data.dict_to_tbHDU(dcat, tbname="Horesh_arcsfound")
tbhdu.writeto(finalfit, clobber=True)
# os.system('rm -f %s' % (finalcat));
sys.exit(0)
# Read DS9 regionfile..
#
D_in = asc.read_ds9cat(regionfile)
objimg = pyfits.getdata(imagename, header=False)
if segimg:
segimg = pyfits.getdata(segimg)
D_in['filename'] = imagename
D_out = run(D_in, objimg, segimg, shape=shape, objsfile=objsfile, hdr=None)
if cattype.upper() == 'FITS':
outname = catfile + re.sub(".reg", ".fit", regionfile)
tbhdu = fts.dict_to_tbHDU(D_out, D_out['filename'] + "_stamps",
'filename', 'x', 'y', 'color', 'segmented',
'id', 'objfile')
try:
tbhdu.writeto(outname)
except IOError:
os.remove(outname)
tbhdu.writeto(outname)
else:
outname = catfile + re.sub(".reg", ".csv", regionfile)
asc.dict_to_csv(D_out,
filename=outname,
fieldnames=[
'filename', 'x', 'y', 'color', 'segmented', 'id',
'objfile'
])
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(tbhdu_check, tbhdu_truth, radius=1):
"""
Check if positions in tables match within a given radius
Required fieldnames inside tbhdu's:
'X' and 'Y' or 'X_IMAGE' and 'Y_IMAGE'
Input:
- tbhdu_check tbHDU : Being-checked Table (FITS)
- tbhdu_truth tbHDU : Truth Table (FITS)
- radius float : Distance parameters for objects position matching
Output:
- new_tbhdu tbHDU : New table with matching information (fields):
'X' = [int] x position of input table
'Y' = [int] y position of input table
'XY_MATCH' = [bool] was X,Y object matched within 'radius'?
'X_NEAR' = [int] x position of nearest object
'Y_NEAR' = [int] y position of nearest object
* obs: tbHDU = pyfits.BinTableHDU
---
"""
Ntotal_truth = tbhdu_truth.header['naxis2']
Ntotal_check = tbhdu_check.header['naxis2']
logging.debug("Input params (tbC,tbT,radius): %s,%s,%s" % (tbhdu_check.name,tbhdu_truth.name,radius))
logging.debug("Check table length: %d",Ntotal_check)
logging.debug("Truth table length: %d",Ntotal_truth)
tbname_check = tbhdu_check.name
tbname_truth = tbhdu_truth.name
tbname_out = tbname_check+"_matched_objs"
try:
x_A = tbhdu_check.data.field('X')
y_A = tbhdu_check.data.field('Y')
except:
x_A = tbhdu_check.data.field('X_IMAGE')
y_A = tbhdu_check.data.field('Y_IMAGE')
try:
x_B = tbhdu_truth.data.field('X')
y_B = tbhdu_truth.data.field('Y')
except:
x_B = tbhdu_truth.data.field('X_IMAGE')
y_B = tbhdu_truth.data.field('Y_IMAGE')
xy_check = zip(x_A,y_A)
xy_truth = zip(x_B,y_B)
dict_Matchout = match_positions(xy_check,xy_truth,radius)
# Ntrue = int(tbhdu_check_wneib.header['ntrue']);
# Nfalse = int(tbhdu_check_wneib.header['nfalse']);
new_tbhdu = fits_data.dict_to_tbHDU(dict_Matchout,tbname_out)
new_tbhdu.header.update('hierarch truthtbname',tbname_truth,"Name of Truth table used")
new_tbhdu.header.update('hierarch ntottruth',Ntotal_truth,"Total number of arcs in Truth table")
new_tbhdu.header.update('ntrue',Ntrue,"Number of found objs in Check table")
new_tbhdu.header.update('nfalse',Nfalse,"Number of not-found objs in Check table")
Ntrue = np.where(new_tbhdu.data.field('XY_MATCH') == True).size
Nfalse = np.where(new_tbhdu.data.field('XY_MATCH') == False).size
Ntotal = Ntrue + Nfalse
logging.debug("Matched table columns/data: %s",new_tbhdu.data)
# Completeza:
Comp = float(Ntrue)/Ntotal
logging.info("Completeness (N_true/N_total): %s" % (Comp))
# Contaminacao:
Cont = float(Nfalse)/Ntotal
logging.info("Contamination (N_false/N_total): %s" % (Cont))
return new_tbhdu
def run(tbhdu_check, tbhdu_truth, radius=1):
"""
Check if positions in tables match within a given radius
Required fieldnames inside tbhdu's:
'X' and 'Y' or 'X_IMAGE' and 'Y_IMAGE'
Input:
- tbhdu_check tbHDU : Being-checked Table (FITS)
- tbhdu_truth tbHDU : Truth Table (FITS)
- radius float : Distance parameters for objects position matching
Output:
- new_tbhdu tbHDU : New table with matching information (fields):
'X' = [int] x position of input table
'Y' = [int] y position of input table
'XY_MATCH' = [bool] was X,Y object matched within 'radius'?
'X_NEAR' = [int] x position of nearest object
'Y_NEAR' = [int] y position of nearest object
* obs: tbHDU = pyfits.BinTableHDU
---
"""
Ntotal_truth = tbhdu_truth.header['naxis2']
Ntotal_check = tbhdu_check.header['naxis2']
logging.debug("Input params (tbC,tbT,radius): %s,%s,%s" % (tbhdu_check.name,tbhdu_truth.name,radius))
logging.debug("Check table length: %d",Ntotal_check)
logging.debug("Truth table length: %d",Ntotal_truth)
tbname_check = tbhdu_check.name
tbname_truth = tbhdu_truth.name
tbname_out = tbname_check+"_matched_objs"
try:
x_A = tbhdu_check.data.field('X')
y_A = tbhdu_check.data.field('Y')
except:
x_A = tbhdu_check.data.field('X_IMAGE')
y_A = tbhdu_check.data.field('Y_IMAGE')
try:
x_B = tbhdu_truth.data.field('X')
y_B = tbhdu_truth.data.field('Y')
except:
x_B = tbhdu_truth.data.field('X_IMAGE')
y_B = tbhdu_truth.data.field('Y_IMAGE')
xy_check = zip(x_A,y_A)
xy_truth = zip(x_B,y_B)
dict_Matchout = match_positions(xy_check,xy_truth,radius)
# Ntrue = int(tbhdu_check_wneib.header['ntrue']);
# Nfalse = int(tbhdu_check_wneib.header['nfalse']);
new_tbhdu = fits_data.dict_to_tbHDU(dict_Matchout,tbname_out)
new_tbhdu.header.update('hierarch truthtbname',tbname_truth,"Name of Truth table used")
new_tbhdu.header.update('hierarch ntottruth',Ntotal_truth,"Total number of arcs in Truth table")
new_tbhdu.header.update('ntrue',Ntrue,"Number of found objs in Check table")
new_tbhdu.header.update('nfalse',Nfalse,"Number of not-found objs in Check table")
Ntrue = np.where(new_tbhdu.data.field('XY_MATCH') == True).size
Nfalse = np.where(new_tbhdu.data.field('XY_MATCH') == False).size
Ntotal = Ntrue + Nfalse
logging.debug("Matched table columns/data: %s",new_tbhdu.data)
# Completeza:
Comp = float(Ntrue)/Ntotal
logging.info("Completeness (N_true/N_total): %s" % (Comp))
# Contaminacao:
Cont = float(Nfalse)/Ntotal
logging.info("Contamination (N_false/N_total): %s" % (Cont))
return new_tbhdu
dest='input', default=None,
help="Horesh' arcfinder input parameters file");
(opts,args) = parser.parse_args();
if len(args) == 0:
parser.error("No input images were given.");
inputfile = opts.input;
imgfile = args[0];
# Run Horesh's arcfinder
finalcat = run(imgfile,inputfile)
FITSout = True;
# To finish the process, convert the ASCII table into FITS if asked for.
if FITSout:
# finalfit = finalcat[:-4]+".fit";
finalfit = 'Catalog_arcs_horesh.out.fit';
cat = numpy.loadtxt(finalcat).T;
if cat.ndim == 1:
cat.shape = (len(cat),1);
dcat = {'ID':cat[0], 'X':cat[1], 'Y':cat[2], 'STmag':cat[3], 'Length':cat[4], 'Width':cat[5], 'L/W':cat[6], 'Area':cat[7]};
tbhdu = fits_data.dict_to_tbHDU(dcat,tbname="Horesh_arcsfound");
tbhdu.writeto(finalfit,clobber=True);
# os.system('rm -f %s' % (finalcat));
sys.exit(0);
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='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;
