def main():
iso25D = 40 / 0.396
#band = Settings.setBand()
# dataDir = '/media/46F4A27FF4A2713B_/work2/data'
fitsdir = Settings.getConstants().dataDir+'SDSS'+Settings.getConstants().band
# fitsDir = '../data/SDSS/'
# dataDir = '../data'
band = Settings.getConstants().band
#missing = np.genfromtxt('u_wrong_skies.csv', delimiter = ',', dtype = object)[:, 0]
#print missing
#missing = np.genfromtxt('wrong_tsfield.csv', delimiter = ',', dtype = int)
#missing = np.genfromtxt("susp_z.csv", dtype = int, delimiter = "\n")
#print missing
#for x, i in enumerate(missing):
for i in range(Settings.getConstants().lim_lo, Settings.getConstants().lim_hi):
#print i, lim_lo, lim_hi, setBand()
i = int(i) - 1
try:
print Settings.getSkyFitValues(str(i + 1)).sky, Settings.getSkyFitValues(str(i + 1)).isoA
print 'filename', GalaxyParameters.getSDSSUrl(i)
print 'filledFilename', GalaxyParameters.getFilledUrl(i, band)
print i, 'i'
output = Photometry.calculateGrowthCurve(i)
#utils.writeOut(output, band+'_flux'+str(Settings.getConstants().lim_lo)+'.csv')
except IOError as err:
print 'err', err
output = [str(i+1), 'File not found', err]
utils.writeOut(output, band+'_fluxErrors.csv')
pass
def calculateGrowthCurve(i):
CALIFA_ID = str(i+1)
dbDir = '../db/'
imgDir = 'img/'+Settings.getConstants().band+'/'
center = Photometry.getCenter(i)
distances = Photometry.createDistanceArray(i)
pa = db.dbUtils.getFromDB('pa', Settings.getConstants().dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0]
ba = db.dbUtils.getFromDB('ba', Settings.getConstants().dbDir+'CALIFA.sqlite', 'bestBA', ' where califa_id = '+ CALIFA_ID)[0]
#print CALIFA_ID, pa
#pa = -1*(90 - pa)
# --------------------------------------- starting ellipse GC photometry
print 'ELLIPTICAL APERTURE'
try:
#returns np.mean(y), fluxSlope, np.mean(yM), fluxSlopeM, fluxData[-1, 0]
sky, slope, skyM, slopeM, isoA = Photometry.fitSky(center, distances, pa, ba, CALIFA_ID)
except IndexError:
sky = 'nan'
slope = 'nan'
isoA = 'nan'
print 'aaaa'
out = (CALIFA_ID, sky, slope, skyM, slopeM, isoA)
utils.writeOut(out, "gc2_"+Settings.getConstants().band+"_sky.csv")
def checkOutput(dataDir, ID, run, rerun, camcol, runstr, band, field_str, fitsDir, filledDir): outputFilename = dataDir+'/'+filledDir+'fpC-'+runstr+'-'+band+camcol+'-'+field_str+'.fits' print outputFilename try: f = open(outputFilename) #print 'musu', ID except IOError as e: out = [ID] utils.writeOut(out, 'missing_'+setBand()+'.csv') pass
def calculateGrowthCurve(i):
CALIFA_ID = str(i+1)
band = Settings.getConstants().band
dbDir = '../db/'
imgDir = 'img/2/'+Settings.getConstants().band+'/'
center = Photometry.getCenter(i)
distances = Photometry.createDistanceArray(i)
e = Photometry.findClosestEdge(distances, center)
pa = db.dbUtils.getFromDB('pa', Settings.getConstants().dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0]
ba = db.dbUtils.getFromDB('ba', Settings.getConstants().dbDir+'CALIFA.sqlite', 'bestBA', ' where califa_id = '+ CALIFA_ID)[0]
utils.writeOut([CALIFA_ID, e], 'closest_edge.csv')
def measure(galaxyList):
band = Settings.getConstants().band
for i in galaxyList:
i = int(i) - 1
try:
print 'filename', GalaxyParameters.getSDSSUrl(i)
print 'filledFilename', GalaxyParameters.getFilledUrl(i, band)
Photometry.calculateGrowthCurve(i)
except IOError as err:
print 'err', err
output = [str(i+1), 'File not found', err]
utils.writeOut(output, "ellipseErrors_new.csv")
pass
def measureFluxInOtherBands(galaxyList):
band = Settings.getConstants().band
out = []
for i in galaxyList:
i = int(i) - 1
try:
print 'filledFilename', GalaxyParameters.getFilledUrl(i, band)
mag, sky = getFluxUnderMask(i)
out.append((i+1, mag, sky))
except IOError as err:
print 'err', err
output = [str(i+1), 'File not found', err]
utils.writeOut(output, "ellipseErrors.csv")
pass
np.savetxt("gc_ellmask_"+band+".csv", out, fmt="%s, %f, %f")
def doFitting(galaxyList):
for i in galaxyList:
i = int(i) - 1
band = Settings.getConstants().band
try:
print 'filename', GalaxyParameters.getSDSSUrl(i)
print 'filledFilename', GalaxyParameters.getFilledUrl(i, band)
print i, 'i'
output = Photometry.calculateGrowthCurve(i)
#utils.writeOut(output, band+'_log'+str(Settings.getConstants().lim_lo)+'.csv')
except IOError as err:
print 'err', err
output = [str(i+1), 'File not found', err]
utils.writeOut(output, band+'_skyFitErrors.csv')
pass
def calculateGrowthCurve(name, ba, pa):
imgName = 'vimos/sdss/SDSS/'+name+"/galaxy_"+Settings.getConstants().band+'_large.fits'
dataFile = pyfits.open(imgName)
img = dataFile[0].data
try:
mask = pyfits.open('vimos/sdss/SDSS/'+name+"/mask_large.fits")[0].data
print mask.shape, img.shape
if mask.shape != img.shape:
print mask.shape, img.shape, 'ALERT'
pass
#mask = mask/255
except IOError:
mask = np.zeros(img.shape)
center = Photometry.getCenter(img)
distances = Astrometry.makeDistanceArray(img, center)
# --------------------------------------- starting ellipse GC photometry
print 'ELLIPTICAL APERTURE'
try:
#returns np.mean(y), fluxSlope, np.mean(yM), fluxSlopeM, fluxData[-1, 0]
sky, slope, skyM, slopeM, isoA = Photometry.fitSky(center, distances, pa, ba, img, mask, name)
except IndexError:
print 'aaa'
sky = 'nan'
slope = 'nan'
isoA = 'nan'
print 'aaaa'
out = (name, sky, slope, skyM, slopeM, isoA)
utils.writeOut(out, "vimos_sky_new_"+Settings.getConstants().band+"_ell.csv")
print 'Circular APERTURE --------------------------'
try:
#returns np.mean(y), fluxSlope, np.mean(yM), fluxSlopeM, fluxData[-1, 0]
sky, slope, skyM, slopeM, isoA = Photometry.fitSky(center, distances, 0, 1, img, mask, name)
except IndexError:
print 'aaa'
sky = 'nan'
slope = 'nan'
isoA = 'nan'
print 'aaaa'
out = (name, sky, slope, skyM, slopeM, isoA)
utils.writeOut(out, "vimos_sky_new_"+Settings.getConstants().band+"_circ.csv")
def main():
iso25D = 40 / 0.396
#dataDir = '/media/46F4A27FF4A2713B_/work2/data/'
dataDir = '../data'
band = setBand()
#outputFile = dataDir+'/growthCurvePhotometry.csv'
listFile = dataDir+'/SDSS_photo_match.csv'
fitsDir = dataDir+'/SDSS/'
#filledDir = 'filled_'+band+'/'
filledDir = 'filled2/'
imgDir = 'img/'+band
simpleFile = dataDir+'/CALIFA_mother_simple.csv'
maskFile = dataDir+'/maskFilenames.csv'
dataFile = 'list.txt'
for ID in range(1, 940):
print 'id,', int(ID)
ra = GalaxyParameters.SDSS(listFile, int(ID)).ra
dec = GalaxyParameters.SDSS(listFile, int(ID)).dec
run = GalaxyParameters.SDSS(listFile, int(ID)).run
rerun = GalaxyParameters.SDSS(listFile, int(ID)).rerun
camcol = GalaxyParameters.SDSS(listFile, int(ID)).camcol
field = GalaxyParameters.SDSS(listFile, int(ID)).field
runstr = GalaxyParameters.SDSS(listFile, int(ID)).runstr
field_str = sdss.field2string(field)
print 'getting header info...'
rFile = fitsDir+'r/fpC-'+runstr+'-r'+camcol+'-'+field_str+'.fit.gz'
WCS=astWCS.WCS(rFile)
pa = utils.convert(db.dbUtils.getFromDB('pa', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+str(ID)))[0][0]
pa_align = utils.convert(db.dbUtils.getFromDB('pa_align', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+str(ID)))[0][0]
ang = WCS.getRotationDeg()
print ang
pa_align2 = (360 - ang) - 90 + pa
#if pa_align2 >= 180:
# pa_align2 = pa_align2 - 180
# if pa_align2 <= 0:
# pa_align2 = pa_align2 + 180
out = int(ID), pa, pa_align2
print out
utils.writeOut(out, "align_pa.csv")
def solve(filename):
pizzas, teams = readIn(filename)
num_pizzas = len(pizzas)
# compute how many number of deliveries
# 2a+3b+4c<=M
candidates = getDeliveries(num_pizzas, teams)
# for each a,b,c, diliver pizza
# calculate 2D table
max_score = 0
best_delivers = []
# O(n*m*4)
for candidate in candidates:
picked_pizza = []
delivers = [[] for _ in range(len(candidate))]
score = []
for i, team_size in enumerate(candidate):
for _ in range(team_size):
value_table = computeTable(pizzas, picked_pizza, delivers[i])
if len(value_table) > 1:
max_val = 0
for (key, val) in value_table.items():
if val > max_val:
max_val = val
max_val_pizza = key
picked_pizza.append(max_val_pizza)
delivers[i].append(max_val_pizza)
else:
picked_pizza.append(list(value_table.keys())[0])
delivers[i].append(list(value_table.keys())[0])
unique_ingre, _ = compare(pizzas, delivers[i])
score.append(unique_ingre)
score = np.sum(np.array(score)**2)
if score > max_score:
max_score = score
best_delivers = delivers
writeOut(filename, best_delivers)
indices = [10, 43, 104, 146, 156, 176, 194, 251, 265, 272, 302, 342, 418, 429, 431, 448, 525, 533, 566, 577, 638, 659, 721, 787, 878, 902, 938, 939] for i, x in enumerate(indices): indices[i] = x - 1 data[indices, 1] = 1 #flag: 2 denotes off-center galaxies: center = [246, 428, 679] data[center, 2] = 2 print data[center].shape, data.shape #flag: 4 marks galaxies that are closer to the edge of the frame than 2 HLSMA (in r band): #select g.califa_id, (0.396*c.closest_edge)/g.elhlr from gc2_r as g, closest_edge as c where (c.closest_edge)/g.elhlr < 2 and g.califa_id = c.califa_id indices = [126, 244, 258, 260, 273, 304, 417, 434, 545, 696, 741, 763, 809, 841, 880] for i, x in enumerate(indices): indices[i] = x - 1 data[indices, 3] = 4 #flag: 8 denotes galaxies that have suspicious/unreliable measurements (heavily masked) indices = [10, 22, 46, 53, 63, 86, 135, 154, 156, 161, 162, 175, 185, 223, 233, 245, 248, 255, 266, 267, 271, 355, 390, 426, 444, 457, 460, 491, 541, 577, 592, 617, 670, 680, 684, 699, 710, 714, 781, 784, 785, 801, 806, 819, 824, 827, 852, 858, 870, 874, 880, 881, 882, 883, 888, 893, 897, 908, 911, 924, 928, 932, 938, 939] for i, x in enumerate(indices): indices[i] = x - 1 data[indices, 4] = 8 for i in range(0, 939): data[i, 5] = np.sum(data[i, 1:5]) out = data[i, :] print 'did you delete the file?' utils.writeOut(out, 'gc2_flags.txt')
logger.info(' Finish building Haar-like features ... >')
##################
#####
##################
logger.info('< F : {0} , D : {1} , Ftarget : {2} >'.format(
constants.F, constants.D, constants.Ftarget))
logger.info('< Start training cascade classifer ... >')
cascade = trainCascade.train(constants.F, constants.D, constants.Ftarget,
faces, non_faces, features)
logger.info('< Finish training cascade classifer ... >')
##################
#####
##################
logger.info('< Exporting final model ... >')
run_name = strftime("%Y%m%d-%H%M%S", time.localtime()).replace('-', '')
filename = '../result/' + run_name + '/cascade.txt'
utils.writeOut(cascade, filename)
logger.info('< Finish exporting final model ... >')
##################
#####
##################
end = time.time()
m, s = divmod((end - start), 60)
h, m = divmod(m, 60)
logger.info(
'< Elasped {0} hour(s) : {1} minute(s) : {2} second(s) >'.format(
h, m, s))
old_time = intersection.TL[street_id]
new_time = [
min(timestamp, old_time[0]),
max(timestamp, old_time[1]),
]
if not intersection.isConflict(
new_time[0]) and not intersection.isConflict(
new_time[1]):
intersection.TL[street_id] = new_time
else:
if not intersection.isConflict(timestamp):
intersection.TL[street_id] = [timestamp, timestamp + 1]
else:
intersection = Intersection(intersection_id)
intersection.TL[street_id] = [timestamp, timestamp + 1]
result[intersection_id] = intersection
return result
if __name__ == "__main__":
directory = "quali/"
for file in os.listdir(directory):
basic_info, street_list, car_list = readIn(directory + file)
sim_time = basic_info[0]
bonus = basic_info[-1]
result = solve(sim_time, bonus, street_list, car_list)
writeOut("output/" + file[0], result)
# -*- coding: utf-8 -*-
import numpy as np
import db
import utils
dbDir = '../db/'
data = np.empty((939, 3), dtype = object)
#califa_id = utils.convert(db.dbUtils.getFromDB('CALIFA_ID', dbDir+'CALIFA.sqlite', 'gc_r'))
ra = utils.convert(db.dbUtils.getFromDB('ra', dbDir+'CALIFA.sqlite', 'mothersample'))
dec = utils.convert(db.dbUtils.getFromDB('dec', dbDir+'CALIFA.sqlite', 'mothersample'))
print ra.shape
for i in range(0, 939):
data[i, 0] = str(ra[i, 0])+'d'
data[i, 1] = str(dec[i, 0])+'d'
data[i, 2] = 0.05
print i, data[i]
utils.writeOut(data[i, 0:3], "ned_query_coords.csv", ";")
#np.savetxt("ned_query_coords.csv", data, delimiter = ';')
def main():
iso25D = 40 / 0.396
# dataDir = '/media/46F4A27FF4A2713B_/work2/data/'
dataDir = "../data"
band = setBand()
outputFile = dataDir + "/growthCurvePhotometry.csv"
listFile = dataDir + "/SDSS_photo_match.csv"
fitsDir = dataDir + "/SDSS/"
filledDir = "filled_" + band + "/"
imgDir = "img/" + band
simpleFile = dataDir + "/CALIFA_mother_simple.csv"
maskFile = dataDir + "/maskFilenames.csv"
dataFile = "list.txt"
csvReader = csv.reader(open(dataFile, "rU"), delimiter=",")
"""
for row in csvReader:
ID = int(string.strip(row[0]))
ra = string.strip(row[1])
dec = string.strip(row[2])
run = string.strip(row[3])
rerun = string.strip(row[4])
camcol = string.strip(row[5])
field = string.strip(row[6])
runstr = sdss.run2string(run)
field_str = sdss.field2string(field)
outputFilename = dataDir+'/'+filledDir+'fpC-'+runstr+'-'+band+camcol+'-'+field_str+'.fits'
try:
f = open(GalaxyParameters.getSDSSUrl(listFile, dataDir, ID-1))
print 'it is here', ID - 1
pass
except IOError as e:
print e, ID - 1
os.system('wget -P '+fitsDir+band+' http://das.sdss.org/imaging/'+run+'/'+rerun+'/corr/'+camcol+'/fpC-'+runstr+'-'+band+camcol+'-'+field_str+'.fit.gz')
pass
try:
f = open(fitsDir+'r/fpC-'+runstr+'-r'+camcol+'-'+field_str+'.fit.gz')
print 'it is here', 'r', ID - 1
pass
except IOError as e:
print e, 'r', ID - 1
os.system('wget -P '+fitsDir+'r/ http://das.sdss.org/imaging/'+run+'/'+rerun+'/corr/'+camcol+'/fpC-'+runstr+'-r'+camcol+'-'+field_str+'.fit.gz')
pass
#checking output file
try:
f = open(outputFilename)
print 'musu', ID
except IOError as e:
utils.writeOut(str(ID))
pass
"""
missing = np.genfromtxt("missing.csv")
for row in csvReader:
# print '********************************', row[0]
ID = string.strip(row[0])
# CHECK THE DELIMITERS!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
if (ID in x for x in missing):
print ID, "did you check the delimiters?"
if int(ID) >= 250:
ra = string.strip(row[1])
dec = string.strip(row[2])
run = string.strip(row[3])
rerun = string.strip(row[4])
camcol = string.strip(row[5])
field = string.strip(row[6])
runstr = sdss.run2string(run)
field_str = sdss.field2string(field)
print ID
inFile = fitsDir + band + "/fpC-" + runstr + "-" + band + camcol + "-" + field_str + ".fit.gz"
gz = gzip.open(inFile)
imgFile = pyfits.open(gz)
img = imgFile[0].data
head = imgFile[0].header
print "getting header info..."
rFile = fitsDir + "r/fpC-" + runstr + "-r" + camcol + "-" + field_str + ".fit.gz"
rgz = gzip.open(rFile)
imgFiler = pyfits.open(rgz)
try:
maskFile = pyfits.open(GalaxyParameters.getMaskUrl(listFile, dataDir, simpleFile, int(ID) - 1))
mask = maskFile[0].data
print mask.shape
WCSr = astWCS.WCS(rFile)
WCS = astWCS.WCS(inFile)
band_center = WCS.wcs2pix(
WCS.getCentreWCSCoords()[0], WCS.getCentreWCSCoords()[1]
) #'other band image center coords in r image coordinate system'
r_center = WCS.wcs2pix(
WCSr.getCentreWCSCoords()[0], WCSr.getCentreWCSCoords()[1]
) #'r center coords in r image coordinate system'
shift = [band_center[0] - r_center[0], band_center[1] - r_center[1]]
print type(shift)
# note the swap!
shift = [ceil(shift[1]), ceil(shift[0])]
print shift, img.shape
img = sdss.getShiftedImage(img, shift)
mask = sdss.getShiftedImage(mask, [-1 * shift[0], -1 * shift[1]])
outputFilename = (
dataDir + "/" + filledDir + "fpC-" + runstr + "-" + band + camcol + "-" + field_str + ".fits"
)
print outputFilename
info = (ID, shift, outputFilename)
utils.writeOut(info)
Interpolation.callInpaint(img, mask, outputFilename, ID)
utils.writeOut(info)
except IOError as e:
utils.writeOut((ID, e))
pass
else:
print "passing", ID
pass
def main():
iso25D = 40 / 0.396
#dataDir = '/media/46F4A27FF4A2713B_/work2/data/'
dataDir = '../data'
band = setBand()
outputFile = dataDir+'/growthCurvePhotometry.csv'
listFile = dataDir+'/SDSS_photo_match.csv'
fitsDir = dataDir+'/SDSS/'
#filledDir = 'filled_'+band+'/'
filledDir = 'filled2/'
imgDir = 'img/'+band
simpleFile = dataDir+'/CALIFA_mother_simple.csv'
maskFile = dataDir+'/maskFilenames.csv'
dataFile = 'list.txt'
csvReader = csv.reader(open(dataFile, "rU"), delimiter=',')
for row in csvReader:
ID = int(string.strip(row[0]))
ra = string.strip(row[1])
dec = string.strip(row[2])
run = string.strip(row[3])
rerun = string.strip(row[4])
camcol = string.strip(row[5])
field = string.strip(row[6])
runstr = sdss.run2string(run)
field_str = sdss.field2string(field)
#checkInput(listFile, dataDir, ID, run, rerun, camcol, runstr, band, field_str, fitsDir)
#checkOutput(dataDir, ID, run, rerun, camcol, runstr, band, field_str, fitsDir, filledDir)
#missing = np.genfromtxt('missing_'+setBand()+'.csv')
#print missing, 'missing'
#ID = missing
#csvReader = csv.reader(open(dataFile, "rU"), delimiter=',')
#for row in csvReader:
# ID = string.strip(row[0])
#CHECK THE DELIMITERS!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# if (int(ID) in x for x in missing):
# print ID, 'did you check the delimiters?'
#for i, ID in enumerate(missing):
if ID == 577:
print 'id,', int(ID) - 1
ra = GalaxyParameters.SDSS(listFile, int(ID) - 1).ra
dec = GalaxyParameters.SDSS(listFile, int(ID) - 1).dec
run = GalaxyParameters.SDSS(listFile, int(ID) - 1).run
rerun = GalaxyParameters.SDSS(listFile, int(ID) - 1).rerun
camcol = GalaxyParameters.SDSS(listFile, int(ID) - 1).camcol
field = GalaxyParameters.SDSS(listFile, int(ID) - 1).field
runstr = GalaxyParameters.SDSS(listFile, int(ID) - 1).runstr
field_str = sdss.field2string(field)
print ID
inFile = fitsDir+band+'/fpC-'+runstr+'-'+band+camcol+'-'+field_str+'.fit.gz'
print inFile, 'input'
gz = gzip.open(inFile)
imgFile = pyfits.open(gz)
img = imgFile[0].data
head = imgFile[0].header
print 'getting header info...'
rFile = fitsDir+'r/fpC-'+runstr+'-r'+camcol+'-'+field_str+'.fit.gz'
rgz = gzip.open(rFile)
imgFiler = pyfits.open(rgz)
try:
maskFile = pyfits.open(GalaxyParameters.getMaskUrl(listFile, dataDir, simpleFile, int(ID)-1))
mask = maskFile[0].data
print mask.shape
WCSr=astWCS.WCS(rFile)
WCS=astWCS.WCS(inFile)
band_center = WCS.wcs2pix(WCS.getCentreWCSCoords()[0], WCS.getCentreWCSCoords()[1]) #'other band image center coords in r image coordinate system'
r_center = WCS.wcs2pix(WCSr.getCentreWCSCoords()[0], WCSr.getCentreWCSCoords()[1]) #'r center coords in r image coordinate system'
shift = [band_center[0] - r_center[0], band_center[1] - r_center[1]]
print shift, 'shift'
#print type(shift)
#note the swap!
shift = [ceil(shift[1]), ceil(shift[0])]
print shift, img.shape
img = sdss.getShiftedImage(img, shift)
mask = sdss.getShiftedImage(mask, [-1*shift[0], -1*shift[1]])
outputFilename = dataDir+'/'+filledDir+'fpC-'+runstr+'-'+band+camcol+'-'+field_str+'.fits'
print outputFilename
info = (ID, shift, outputFilename)
Interpolation.callInpaint(img, mask, outputFilename, int(ID - 1))
utils.writeOut(info, band+'_interp_log.csv')
except IOError as e:
utils.writeOut((ID, e), band+'_fill_errors.csv')
pass
def calculateGrowthCurve(listFile, dataDir, i):
CALIFA_ID = str(i+1)
inputImage = Photometry.getInputFile(listFile, dataDir, i)
dbDir = '../db/'
imgDir = 'img/'+setBand()+'/'
center = Photometry.getCenter(listFile, i, dataDir)
distances = Photometry.createDistanceArray(listFile, i, dataDir)
#hdu = pyfits.PrimaryHDU(distances)
#hdu.writeto('distances.fits')
sky = inputImage[np.where(distances > int(round(Photometry.iso25D)))]
skyMean = np.mean(sky)
skySD = np.std(sky)
#i+1 in the next line reflects the fact that CALIFA id's start with 1
pa = db.dbUtils.getFromDB('PA', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0] #parsing tuples
ba = db.dbUtils.getFromDB('ba', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0]#parsing tuples
#ba = 1
#r_mag = db.dbUtils.getFromDB('r_mag', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0]#parsing tuples
#r_e = db.dbUtils.getFromDB('re', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+ CALIFA_ID)[0][0]#parsing tuples
#lucy_re = db.dbUtils.getFromDB('re', dbDir+'CALIFA.sqlite', 'lucie', ' where id = '+ CALIFA_ID)[0][0]#parsing tuples
#l_SkyMean = db.dbUtils.getFromDB('sky', dbDir+'CALIFA.sqlite', 'lucie', ' where id = '+ CALIFA_ID)[0][0] - 1000#parsing tuples
# print 'ba', ba
#fluxData = Photometry.buildGrowthCurve(inputImage, center, distances, skyMean, pa, ba)
#isoA = fluxData.shape[0]
# --------------------------------------- starting GC photometry in circular annuli
print 'CIRCULAR APERTURE'
#circFlux, circFluxData = Photometry.circularFlux(inputImage, center, distances, skyMean)
circFlux, circFluxData, gc_sky = Photometry.buildGrowthCurve(inputImage, center, distances, skyMean, pa, 1, str(i+1))
circRadius = circFluxData.shape[0]
#print circRadius, 'circle radius'
#otherFlux = circFluxData[-1, 1]
#print 'fluxes: mask:', circFlux, 'sum', otherFlux
try:
circHLR = circFluxData[np.where(np.floor(circFlux/circFluxData[:,1]) == 1)][0][0] - 1 #Floor() -1 -- last element where the ratio is 2
except IndexError as e:
circHLR = str(e)
circMag = Photometry.calculateFlux(circFlux, listFile, i)
# --------------------------------------- starting ellipse GC photometry
print 'ELLIPTICAL APERTURE'
totalFlux, fluxData, gc_sky = Photometry.buildGrowthCurve(inputImage, center, distances, skyMean, pa, ba, CALIFA_ID, e=True)
otherFlux = fluxData[fluxData.shape[0]-1, 6]
elMajAxis = fluxData.shape[0]
#print totalFlux - otherFlux, 'flux diff'
#print 't', totalFlux, 'o', otherFlux
diff = [CALIFA_ID, totalFlux - otherFlux]
utils.writeOut(diff, 'fluxdiff.txt')
elMag = Photometry.calculateFlux(totalFlux, listFile, i)
try:
elHLR = fluxData[np.where(np.floor(totalFlux/fluxData[:, 1]) == 1)][0][0] - 1 #Floor() -1 -- last element where the ratio is 2
print elHLR
except IndexError as e:
print 'err'
elHLR = e
plotGrowthCurve.plotGrowthCurve(fluxData, CALIFA_ID)
# --------------------- writing output jpg file with both outermost annuli
outputImage = inputImage
circpix = ellipse.draw_ellipse(inputImage.shape, center[0], center[1], pa, circRadius, 1)
elPix = ellipse.draw_ellipse(inputImage.shape, center[0], center[1], pa, elMajAxis, ba)
outputImage[circpix] = 0
outputImage[elPix] = 0
outputImage, cdf = imtools.histeq(outputImage)
#scipy.misc.imsave('img/output/'+CALIFA_ID+'.jpg', outputImage)
scipy.misc.imsave(imgDir+'snapshots/'+CALIFA_ID+'_gc.jpg', outputImage)
#hdu = pyfits.PrimaryHDU(outputImage)
#outputName = 'CALIFA'+CALIFA_ID+'.fits'
#hdu.writeto(outputName)
# ------------------------------------- formatting output row
output = [CALIFA_ID, elMag, elHLR, circMag, circHLR, np.mean(sky), gc_sky]
print output
#print skyMean, oldSky, 'sky'
return output
