import forkmap #import multiprocessing # if forkmap fails... import star from readandreplace_fct import * import src.lib.utils as fn from MCS_interface import MCS_interface #### rewriteconfig = True nofitnum = False redofromscratch = True #### if rewriteconfig == True: psfstars = star.readmancat(psfstarcat) nbrpsf = len(psfstars) starscouplelist = repr([(int(s.x), int(s.y)) for s in psfstars]) config_template = justread(os.path.join(configdir, "template_pyMCS_psf_config.py")) # Select images to treat db = KirbyBase() if thisisatest : print "This is a test run." images = db.select(imgdb, ['gogogo', 'treatme', 'testlist',psfkeyflag], [True, True, True, True], returnType='dict', sortFields=['setname', 'mjd']) elif update: print "This is an update." images = db.select(imgdb, ['gogogo', 'treatme', 'updating', psfkeyflag], [True, True, True, True], returnType='dict', sortFields=['setname', 'mjd']) askquestions=False else :
seeing = "Seeing : %4.2f" % image['seeing']
ell = "Ellipticity : %4.2f" % image['ell']
skylevel = "Sky level : %4.2f" % image['skylevel']
stddev = "Preali sky stddev : %4.2f" % image['prealistddev']
airmass = "Airmass : %4.2f" % image['airmass']
az = "Azimuth : %6.2f" % image['az']
infolist = ["", date, seeing, ell, skylevel, stddev, airmass, az]
f2nimg.writeinfo(infolist)
# try to add new stuff: plot sextracted objects.
print "WARNING: if you want to display these images, you need to change your default.param output parameters to: NUMBER / X_IMAGE / Y_IMAGE only you can use the imgplot.param as the PARAMETERS_NAME of your default_see_template.sex file"
starcat = os.path.join(alidir, image["imgname"] + '.cat')
import star
extstars = star.readmancat(starcat)
for star in extstars:
f2nimg.drawcircle(star.x, star.y, r=10, colour='white', label=None)
#pngname = "%04d.png" % (i+1)
pngname = image['imgname'] + ".png"
pngpath = os.path.join(pngdir, pngname)
f2nimg.tonet(pngpath)
orderlink = os.path.join(
pngdir, "%05i.png" %
(i + 1)) # a link to get the images sorted for the movies etc.
os.symlink(pngpath, orderlink)
if i == 0:
print "Here is the first image:"
import headerstuff import star import numpy as np import matplotlib.pyplot as plt import matplotlib.dates print "You want to analyze the deconvolution %s" %deckey print "Deconvolved object : %s" % decobjname if plotnormfieldname == None: print "I will use the normalization coeffs used for the deconvolution." else: print "Using %s for the normalization." % (plotnormfieldname) deckeynormused = plotnormfieldname ptsources = star.readmancat(ptsrccat) print "Number of point sources : %i" % len(ptsources) print "Names of sources : %s" % ", ".join([s.name for s in ptsources]) db = KirbyBase() images = db.select(imgdb, [deckeyfilenum], ['\d\d*'], returnType='dict', useRegExp=True, sortFields=['mjd']) print "%i images" % len(images) fieldnames = db.getFieldNames(imgdb) plt.figure(figsize=(15,15)) mhjds = np.array([image["mhjd"] for image in images]) for s in ptsources:
# We look for the ds9 region files, read them, and mask corresponding regions in the original images !!.
# Useful only with a small number of psf stars, very polluted (i.e. space images such HST images)
execfile("../config.py")
from kirbybase import KirbyBase, KBError
from variousfct import *
import cosmics # used to read and write the fits files
import ds9reg
import glob
import numpy as np
import star
import sys
psfstars = star.readmancat(psfstarcat)
# We read the region files
for i, s in enumerate(psfstars):
s.filenumber = (i + 1)
possiblemaskfilepath = os.path.join(configdir,
"%s_mask_%s.reg" % (psfkey, s.name))
if os.path.exists(possiblemaskfilepath):
s.reg = ds9reg.regions(
128,
128) # hardcoded for now # THIS IS BAD MALTE, THIS IS BAD !!!!!
s.reg.readds9(possiblemaskfilepath, verbose=False)
s.reg.buildmask(verbose=False)
print "You masked %i pixels of star %s." % (np.sum(s.reg.mask), s.name)
else:
print "No mask file for star %s." % (s.name)
print "I made an alias to this reference image here :"
print linkpath
print "Saturation level (in e-) of ref image : %.2f" % (
refimage["saturlevel"] * refimage["gain"])
print "Now I will need some alignment stars (write them into configdir/alistars.cat)."
proquest(askquestions)
# Load the reference sextractor catalog
refsexcat = os.path.join(alidir, refimage['imgname'] + ".cat")
refautostars = star.readsexcat(refsexcat, maxflag=16, posflux=True)
refautostars = star.sortstarlistbyflux(refautostars)
refscalingfactor = refimage['scalingfactor']
# read and identify the manual reference catalog
refmanstars = star.readmancat(
alistarscat) # So these are the "manual" star coordinates
id = star.listidentify(
refmanstars,
refautostars,
tolerance=identtolerance,
onlysingle=True,
verbose=True) # We find the corresponding precise sextractor coordinates
if len(id["nomatchnames"]) != 0:
print "Warning : the following stars could not be identified in the sextractor catalog :"
print "\n".join(id["nomatchnames"])
print "I will go on, disregarding these stars."
proquest(askquestions)
preciserefmanstars = star.sortstarlistbyflux(id["match"])
maxalistars = len(refmanstars)
execfile("../config.py")
from kirbybase import KirbyBase, KBError
from variousfct import *
from readandreplace_fct import *
import star
import numpy as np
in2filepath = os.path.join(decdir, "in2.txt")
in2file = open(in2filepath)
in2filelines = in2file.readlines()
in2file.close()
print "Reading from :"
print in2filepath
ptsrcs = star.readmancat(ptsrccat)
nbptsrcs = len(ptsrcs)
#print in2filelines
# quick and dirty filtering of the trash ...
goodlines = []
for line in in2filelines:
if line[0] == "-" or line[0] == "|":
continue
goodlines.append(line)
# we translate all this into a tiny db :
ptsrcdb = []
for (i, ptsrc) in enumerate(ptsrcs):
import star
import numpy as np
import matplotlib.pyplot as plt
db = KirbyBase()
images = db.select(imgdb, ['gogogo', 'treatme'], [True, True],
returnType='dict')
nbrofimages = len(images)
print "I respect treatme, and selected only %i images" % (nbrofimages)
# Read the manual star catalog :
photomstarscatpath = os.path.join(configdir, "photomstars.cat")
photomstars = star.readmancat(photomstarscatpath)
print "Photom stars :"
#print "\n".join(["%s\t%.2f\t%.2f" % (s.name, s.x, s.y) for s in photomstars])
for s in photomstars:
plt.figure(figsize=(12, 8))
peakadus = np.array(
[image["%s_%s_peakadu" % (sexphotomname, s.name)] for image in images])
n, bins, patches = plt.hist(peakadus,
1000,
range=(0, 67000),
histtype='stepfilled',
facecolor='grey')
"""
Little helper that creates the psfstars.cat files from the alistar catalog and the psfname
"""
execfile("../config.py")
from variousfct import *
import star
import os, sys
# Read the manual star catalog :
alistarscatpath = os.path.join(configdir, "alistars.cat")
alistars = star.readmancat(alistarscatpath)
psfnamestars = [
e for e in psfname
] # we assume psfname is only the list of stars, named with only 1 letter (no aa or other funny stuff)
mystars = [s for s in alistars if s.name in psfnamestars]
print "I will write individual coordinates catalogs for the following stars:"
print[star.name for star in mystars]
proquest(askquestions)
# the stars
if os.path.isfile(os.path.join(configdir, "psf_%s.cat" % psfname)):
print "The psf catalog already exists ! I stop here."
sys.exit()
else:
file = open(os.path.join(configdir, "psf_%s.cat" % psfname), 'w')
for star in mystars:
file.write(star.name + '\t' + str(star.x) + '\t' + str(star.y) + '\t' +
import star
import numpy as np
db = KirbyBase()
images = db.select(imgdb,
["gogogo", "treatme", "decfilenum_" + selectiondeckey],
[True, True, '\d\d*'],
returnType='dict',
useRegExp=True)
print "I will export the deconvolution %s" % selectiondeckey
print "This corresponds to ", len(images), "images."
# We extend these general fields by the flux measurements for the selected deconvolution.
ptsrc = star.readmancat(os.path.join(configdir,
selectiondeckey + "_ptsrc.cat"))
print "Sources : " + ", ".join([src.name for src in ptsrc])
proquest(askquestions)
generalcolumns = [{
"name": fieldname,
"data": [image[fieldname] for image in images]
} for fieldname in generalfields]
generalcolumns.append({
"name":
"decnormcoeff",
"data": [image["decnorm_" + selectiondeckey] for image in images]
})
# This field called "decnormcoeff" is the actual normalization coefficients used for your deconvolution.
startat = 0
# you can put here an "iteration" number from which you want to restart, if you had to stop the scirpt etc.
redofromscratch = True
######################
lookbackkey = deckey # This could be hardcoded to any other string.
lookbackdir = os.path.join(workdir, "lookback_" + deckey)
# Greeting ...
print "So you want to build lookback pages for", deckey, "?"
# We read in the point sources that should be available
ptsrc = [src.name for src in star.readmancat(ptsrccat)]
print "Sources : ", ptsrc
variousfct.proquest(askquestions)
if os.path.isdir(lookbackdir) and redofromscratch:
print "The lookbackdir exists."
print "I would delete existing stuff."
variousfct.proquest(askquestions)
shutil.rmtree(lookbackdir)
if not os.path.isdir(lookbackdir):
os.mkdir(lookbackdir)
db = KirbyBase()
# Instead of 3, I simply move back or change wisely the config files previously backuped:
execfile(os.path.join(configdir, 'deconv_config_update.py'))
allimages = db.select(imgdb, [deckeyfilenum], ['\d\d*'],
returnType='dict',
useRegExp=True,
sortFields=[deckeyfilenum])
refimage = [
image for image in allimages if image['imgname'] == refimgname
][0]
allimages.insert(0, refimage.copy())
images[0][deckeyfilenum] = mcsname(1)
nbimg = len(allimages)
ptsrc = star.readmancat(ptsrccat)
nbptsrc = len(ptsrc)
# alter in.txt
in_backuped = open(
os.path.join(workdir, 'updating_' + os.path.basename(inpath)),
'r').readlines()
new_in_backuped = open(inpath, 'w')
toreplace = []
for ind, line in enumerate(in_backuped):
if line[0] not in ['|', '-'] and in_backuped[ind - 1][0] == '-':
indstart = ind
for i in np.arange(len(in_backuped))[ind:]:
if in_backuped[i][0] == '-':
else:
images = db.select(imgdb, ['gogogo', 'treatme'], [True, True],
returnType='dict')
nbrofimages = len(images)
# we prepare the database
if "nbrcoeffstars" not in db.getFieldNames(imgdb):
print "I will add some fields to the database."
proquest(askquestions)
db.addFields(imgdb, [
'nbrcoeffstars:int', 'maxcoeffstars:int', 'medcoeff:float',
'sigcoeff:float', 'spancoeff:float'
])
# we read the handwritten star catalog
normstars = star.readmancat(normstarscat)
for s in normstars:
print s.name
print "Checking reference image ..."
refsexcat = os.path.join(alidir, refimgname + ".alicat")
refcatstars = star.readsexcat(refsexcat,
propfields=[
"FLUX_APER", "FLUX_APER1", "FLUX_APER2",
"FLUX_APER3", "FLUX_APER4"
])
id = star.listidentify(normstars, refcatstars, tolerance=identtolerance)
refidentstars = id["match"]
# now refidentstars contains the same stars as normstars, but with sex fluxes.
'''
print "="*25
