def verify_info(config):
"""Verify that the info file we made is consistent with what Matt's script builds.
"""
print 'Verifying info catalog...'
rel = fitsio.read(config['release_info'])
print 'Full catalog has %d rows'%len(rel)
v18 = fitsio.read(os.path.join('/Users/Mike/Astro/des/SV/v18',config['flatcats_info']))
print 'v18 catalog has %d rows'%len(v18)
q = np.where(rel['SVA1_FLAG'] <= 3)[0]
print 'mask has %d rows'%len(q)
assert len(q) == len(v18)
for col in ['COADD_OBJECTS_ID', 'RA', 'DEC',
'MAG_AUTO_G', 'MAG_AUTO_R', 'MAG_AUTO_I', 'MAG_AUTO_Z',
'PHOTOZ_BIN', 'MEAN_PHOTOZ']:
print 'Test rel[%r] == v18[%r]'%(col,col.lower())
assert np.all(rel[col][q] == v18[col.lower()])
# v18 used 4 for im3shape, ngmix flags rather than 1.
v18['im3shape_flags'] /= 4
v18['ngmix_flags'] /= 4
for col1, col2 in [ ('IM3SHAPE_FLAG', 'im3shape_flags'),
('NGMIX_FLAG', 'ngmix_flags'),
('SVA1_FLAG', 'sva1_gold_flags') ]:
print 'Test rel[%r] == v18[%r]'%(col1,col2)
assert np.all(rel[col1][q] == v18[col2])
print 'info file passed verification tests'
return q
def __init__(self, fn, time0, gp=True, skip=0, invert=False):
self.gp = gp
data, hdr = fitsio.read(fn, header=True)
aps = fitsio.read(fn, 2)
self.texp = (hdr["INT_TIME"] * hdr["NUM_FRM"]) / 86400.0
# Choose the photometry with the smallest variance.
var = aps["cdpp6"]
var[var < 0.0] = np.inf
i = np.argmin(var)
# Load the data.
self.skip = int(skip)
self.time = data["time"] - time0
self.flux = data["flux"][:, i]
if invert:
mu = np.median(self.flux[np.isfinite(self.flux)])
self.flux = 2 * mu - self.flux
self.quality = np.array(data["quality"], dtype=int)
q = data["quality"]
q = ((q == 0) | (q == 16384).astype(bool))
self.m = (np.isfinite(self.time) &
np.isfinite(self.flux) &
(np.arange(len(self.time)) > int(skip)) &
q)
def read_means(s2n_min, args):
fname = get_fname(s2n_min, args)
print('reading:',fname)
means = fitsio.read(fname)
means_nocorr = fitsio.read(fname, ext='nocorr')
return means, means_nocorr
def add_depth_tag(decals, brick, outdir, overwrite=False):
outfn = os.path.join(outdir, 'tractor', brick[:3], 'tractor-%s.fits' % brick)
if os.path.exists(outfn) and not overwrite:
print 'Exists:', outfn
return
fn = decals.find_file('tractor', brick=brick)
if not os.path.exists(fn):
print 'Does not exist:', fn
return
T = fits_table(fn, lower=False)
primhdr = fitsio.read_header(fn)
hdr = fitsio.read_header(fn, ext=1)
print 'Read', len(T), 'from', fn
T.decam_depth = np.zeros((len(T), len(decals.allbands)), np.float32)
T.decam_galdepth = np.zeros((len(T), len(decals.allbands)), np.float32)
bands = 'grz'
ibands = [decals.index_of_band(b) for b in bands]
ix = np.clip(np.round(T.bx).astype(int), 0, 3599)
iy = np.clip(np.round(T.by).astype(int), 0, 3599)
for iband,band in zip(ibands, bands):
fn = decals.find_file('depth', brick=brick, band=band)
if os.path.exists(fn):
print 'Reading', fn
img = fitsio.read(fn)
T.decam_depth[:,iband] = img[iy, ix]
fn = decals.find_file('galdepth', brick=brick, band=band)
if os.path.exists(fn):
print 'Reading', fn
img = fitsio.read(fn)
T.decam_galdepth[:,iband] = img[iy, ix]
outfn = os.path.join(outdir, 'tractor', brick[:3], 'tractor-%s.fits' % brick)
trymakedirs(outfn, dir=True)
for s in [
'Data product of the DECam Legacy Survey (DECaLS)',
'Full documentation at http://legacysurvey.org',
]:
primhdr.add_record(dict(name='COMMENT', value=s, comment=s))
# print 'Header:', hdr
# T.writeto(outfn, header=hdr, primheader=primhdr)
# Yuck, all this to get the units right
tmpfn = outfn + '.tmp'
fits = fitsio.FITS(tmpfn, 'rw', clobber=True)
fits.write(None, header=primhdr)
cols = T.get_columns()
units = []
for i in range(1, len(cols)+1):
u = hdr.get('TUNIT%i' % i, '')
units.append(u)
# decam_depth units
fluxiv = '1/nanomaggy^2'
units[-2] = fluxiv
units[-1] = fluxiv
fits.write([T.get(c) for c in cols], names=cols, header=hdr, units=units)
fits.close()
os.rename(tmpfn, outfn)
print 'Wrote', outfn
def chisq_fig(good, img, g_det, g_detiv, r_det, r_detiv, wcs):
g_det1 = fitsio.read('1d/detmap-g.fits')
g_detiv1 = fitsio.read('1d/detiv-g.fits')
r_det1 = fitsio.read('1d/detmap-r.fits')
r_detiv1 = fitsio.read('1d/detiv-r.fits')
g_sn1 = g_det1 * np.sqrt(g_detiv1)
r_sn1 = r_det1 * np.sqrt(r_detiv1)
goodpix1 = np.logical_and(g_detiv1 > 0.5 * np.median(g_detiv1),
r_detiv1 > 0.5 * np.median(r_detiv1))
g_sn = g_det * np.sqrt(g_detiv)
r_sn = r_det * np.sqrt(r_detiv)
red_sed = [1., 2.5]
# Detect on the single image
#c3x,c3y = detect_sources(np.hypot(g_sn1, r_sn1), 3.)
c3x,c3y = detect_sources(np.hypot(g_sn1, r_sn1), 4.5)
keep = goodpix1[c3y, c3x]
c3x = c3x[keep]
c3y = c3y[keep]
# Compute the S/N required for g-only or r-only to trigger the
# "red" SED detector
dm=[np.array([[1,0]]), np.array([[0,1]])]
div=[np.ones(2), np.ones(2)]
sn = sedsn(dm, div, red_sed)
sng = sn[0,0]
snr = sn[0,1]
plt.figure(figsize=(6,4))
plt.subplots_adjust(right=0.95, top=0.98)
from matplotlib.patches import Circle
plt.clf()
# Annotate points as "true" or "false" based on deeper data.
real = (np.hypot(g_sn[c3y,c3x], r_sn[c3y,c3x]) > 10.)
fake = np.logical_not(real)
#plt.plot(g_sn1[c3y,c3x][fake], r_sn1[c3y,c3x][fake], '.', color='0.5', alpha=0.2, label='False Peaks')
plt.plot(g_sn1[c3y,c3x][fake], r_sn1[c3y,c3x][fake], 'x', color='0.5', alpha=0.5, label='False Detections')
plt.plot(g_sn1[c3y,c3x][real], r_sn1[c3y,c3x][real], '.', color='k', alpha=0.5, label='Real Detections')
a = np.linspace(0, 2.*np.pi, 100)
plt.plot(5.*np.sin(a), 5.*np.cos(a), 'b-', label='Chi-squared detection')
# r
plt.axhline(5., color='r', linestyle=':', label='r-band only detection')
# red
m=-sng/snr
b=5./snr
xx = np.array([-20,40])
plt.plot(xx, b+m*xx, 'm-', mew=2, linestyle='--', label="``Red'' SED-matched detection")
plt.legend(loc='upper right', framealpha=1.0)
plt.axis('square')
plt.axis([-10,40,-10,20])
plt.xlabel('g band S/N')
plt.ylabel('r band S/N')
plt.axhline(0, color='k', alpha=0.25)
plt.axvline(0, color='k', alpha=0.25)
plt.savefig('sed-matched.pdf')
def test_redmagic_fitter(self):
np.random.seed(12345)
file_path = 'data_for_tests/redmagic_test'
# Read in the red-sequence parametrization
# Read in the input data for comparison (no afterburner)
calstr = fitsio.read(os.path.join(file_path, 'rcal_str_preab.fit'), ext=1, lower=True)
# Read in the input data for testing (no afterburner)
calstr2 = fitsio.read(os.path.join(file_path, 'rcal_str2.fit'), ext=1, lower=True)
# Make a zred structure for mstar...
config = Configuration(os.path.join('data_for_tests', 'testconfig.yaml'))
zredstr = RedSequenceColorPar(None, config=config)
# Set up the fitter...
#randomn = np.random.normal(size=calstr2['z'][0, :].size)
# Old IDL code did not sample for the selection, I think this was wrong
randomn = np.zeros(calstr2['z'][0, :].size)
rmfitter = RedmagicParameterFitter(calstr['nodes'][0, :], calstr['corrnodes'][0, :],
calstr2['z'][0, :], calstr2['z_err'][0, :],
calstr2['chisq'][0, :], calstr2['mstar'][0, :],
calstr2['zcal'][0, :], calstr2['zcal_e'][0, :],
calstr2['refmag'][0, :], randomn,
calstr2['zmax'][0, :],
calstr['etamin'][0], calstr['n0'][0],
calstr2['volume'][0, :], calstr2['zrange'][0, :],
calstr2['zbinsize'][0],
zredstr, maxchi=20.0,
ab_use=calstr2['afterburner_use'][0, :])
# These match the IDL values
testing.assert_almost_equal(rmfitter(calstr['cmax'][0, :]), 1.9331937798956758)
p0_cval = np.zeros(calstr['nodes'][0, :].size) + 2.0
testing.assert_almost_equal(rmfitter(p0_cval), 317.4524284321642)
cvals = rmfitter.fit(p0_cval)
# This does not match the IDL output, because this is doing a lot
# better job minimizing the function, at least in this test.
# I hope this is just because of the size of the testbed, which is
# really way too small for something like this.
testing.assert_almost_equal(cvals, np.array([2.61657263, 2.20376531, 1.00663991]))
# Now we have to check the fitting with the afterburner
biasvals = np.zeros(rmfitter._corrnodes.size)
eratiovals = np.ones(rmfitter._corrnodes.size)
biasvals, eratiovals = rmfitter.fit_bias_eratio(cvals, biasvals, eratiovals)
cvals = rmfitter.fit(cvals, biaspars=biasvals, eratiopars=eratiovals, afterburner=True)
testing.assert_almost_equal(cvals, np.array([3.39002141, 1.74421087, 0.93541002]))
testing.assert_almost_equal(biasvals, np.array([0.00896487, -0.02456343, 0.02006761]))
testing.assert_almost_equal(eratiovals, np.array([1.49999937, 1.01673233, 0.65657318]))
def read_all_data(config):
default_columns = ['RA', 'DEC', 'Z', 'MAG_I']
photometric_cat = fitsio.read(config['data_p'],
cols=default_columns)
spectroscopic_cat = fitsio.read(config['data_s'],
cols=default_columns)
return photometric_cat, spectroscopic_cat
def _readK2SFF(self, name, campaign, path, processing):
fileNameMAST = self._getCanonicalFileName(name, campaign, 'mast')
fileNameMASTFits = ''.join([fileNameMAST[0:-3], 'fits'])
filePathMASTFits = os.path.join(path, fileNameMASTFits)
MASTInFile = fitsio.read(filePathMASTFits)
self._numCadences = MASTInFile.shape[0]
startT = -1.0
lineNum = 0
while startT == -1.0:
startTCand = MASTInFile[lineNum][0]
startTCandNext = MASTInFile[lineNum + 1][0]
if not np.isnan(startTCand) and not np.isnan(startTCandNext):
startT = float(startTCand)
dt = float(startTCandNext) - float(startTCand)
else:
lineNum += 1
self.startT = startT
self._dt = dt # Increment between epochs.
self.cadence = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.t = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.x = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.y = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.yerr = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.mask = np.require(np.zeros(self.numCadences), requirements=[
'F', 'A', 'W', 'O', 'E']) # Numpy array of mask values.
for i in xrange(self.numCadences):
dataLine = MASTInFile[i]
self.cadence[i] = int(dataLine[2])
self.yerr[i] = math.sqrt(sys.float_info[0])
if dataLine[9] == 0:
self.t[i] = float(dataLine[0]) - self.startT
else:
if not np.isnan(dataLine[0]):
self.t[i] = float(dataLine[0]) - self.startT
else:
self.t[i] = self.t[i - 1] + self.dt
fileName = self._getCanonicalFileName(name, campaign, 'k2sff')
fileNameFits = ''.join([fileName[0:-3], 'fits'])
filePathFits = os.path.join(path, fileNameFits)
dataInFile = fitsio.read(filePathFits)
for i in xrange(dataInFile.shape[0]):
dataLine = dataInFile[i]
cadNum = int(dataLine[5])
index = np.where(self.cadence == cadNum)[0][0]
self.y[index] = float(dataLine[2])
self.mask[index] = 1.0
valSum = 0.0
countSum = 0.0
for i in xrange(self.numCadences - 1):
valSum += self.mask[i + 1]*self.mask[i]*math.pow((self.y[i + 1] - self.y[i]), 2.0)
countSum += self.mask[i + 1]*self.mask[i]
noise = math.sqrt(valSum/countSum)
for i in xrange(self.numCadences):
if self.mask[i] == 1.0:
self.yerr[i] = noise
def stage1(ps=None, T=None, detmap=None, detnoise=None,
band=None, coadd_id=None, basefn=None, psfnorm=None, cowcs=None,
psfsig=None, coadd=None, hot=None, sig1=None, **kwargs):
T.pix = np.array([None] * len(T))
T.ox = np.array([None] * len(T))
T.oy = np.array([None] * len(T))
T.sig1 = np.zeros(len(T))
for i,wise in enumerate(T):
intfn = get_l1b_file(wisedir, wise.scan_id, wise.frame_num, band)
print 'intfn:', intfn
maskfn = intfn.replace('-int-', '-msk-')
if mask_gz:
maskfn = maskfn + '.gz'
print 'maskfn', maskfn
fn = os.path.basename(intfn).replace('-int', '')
comaskfn = os.path.join('%smask' % basefn,
'unwise-mask-' + coadd_id + '-' + fn + '.gz')
print 'comaskfn', comaskfn
if not (os.path.exists(intfn) and os.path.exists(maskfn)):
print 'file not found; skipping'
continue
#print 'Reading...'
img = fitsio.read(intfn)
mask = fitsio.read(maskfn)
comask = fitsio.read(comaskfn)
#print 'Filtering...'
img -= wise.sky1
img[mask > 0] = 0.
img[comask > 0] = 0.
zpscale = 1. / zeropointToScale(wise.zeropoint)
img *= zpscale
detmapi = gaussian_filter(img, psfsig, mode='constant')
detmapi /= psfnorm**2
#print 'Detmap range', img.min(), img.max()
#print 'Resampling...'
wcs = Tan(intfn)
try:
Yo,Xo,Yi,Xi,nil = resample_with_wcs(cowcs, wcs, [], None)
except OverlapError:
print 'No overlap; skipping'
continue
T.pix[i] = detmapi[Yi,Xi]
T.ox [i] = Xo
T.oy [i] = Yo
T.sig1[i] = np.sqrt(1./wise.weight)
print 'Saved', len(T.pix[i]), 'pixels'
return dict(T=T)
def _readK2VARCAT(self, name, campaign, path, processing):
fileNameMAST = self._getCanonicalFileName(name, campaign, 'mast')
fileNameMASTFits = ''.join([fileNameMAST[0:-3], 'fits'])
filePathMASTFits = os.path.join(path, fileNameMASTFits)
MASTInFile = fitsio.read(filePathMASTFits)
self._numCadences = MASTInFile.shape[0]
startT = -1.0
lineNum = 0
while startT == -1.0:
startTCand = MASTInFile[lineNum][0]
startTCandNext = MASTInFile[lineNum + 1][0]
if not np.isnan(startTCand) and not np.isnan(startTCandNext):
startT = float(startTCand)
dt = float(startTCandNext) - float(startTCand)
else:
lineNum += 1
self.startT = startT
self._dt = dt # Increment between epochs.
self.cadence = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.t = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.x = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.y = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.yerr = np.require(np.zeros(self.numCadences), requirements=['F', 'A', 'W', 'O', 'E'])
self.mask = np.require(np.zeros(self.numCadences), requirements=[
'F', 'A', 'W', 'O', 'E']) # Numpy array of mask values.
for i in xrange(self.numCadences):
dataLine = MASTInFile[i]
self.cadence[i] = int(dataLine[2])
self.yerr[i] = math.sqrt(sys.float_info[0])
if dataLine[9] == 0:
self.t[i] = float(dataLine[0]) - self.startT
else:
if not np.isnan(dataLine[0]):
self.t[i] = float(dataLine[0]) - self.startT
else:
self.t[i] = self.t[i - 1] + self.dt
fileName = self._getCanonicalFileName(name, campaign, 'k2varcat')
fileNameFits = ''.join([fileName[0:-3], 'fits'])
filePathFits = os.path.join(path, fileNameFits)
try:
dataInFile = fitsio.read(filePathFits)
except IOError as Err:
pass
else:
for i in xrange(dataInFile.shape[0]):
dataLine = dataInFile[i]
time = float(dataLine[0]) - self.startT
if not np.isnan(time):
index = np.where(self.t == time)[0][0]
self.y[index] = float(dataLine[3])
self.yerr[index] = float(dataLine[4])
self.mask[index] = 1.0
else:
pass
def test_pickle():
"""Test the reading a file written with python 2 pickling is readable with python 2 or 3.
"""
if __name__ == '__main__':
logger = piff.config.setup_logger(verbose=2)
else:
logger = piff.config.setup_logger(log_file='output/test_pickle.log')
# First, this is the output file written by the above test_single function on python 2.
# Shoudl be trivially readable by python 2, but make sure it is also readable by python 3.
psf = piff.read('input/test_single_py27.piff', logger=logger)
wcs1 = galsim.TanWCS(
galsim.AffineTransform(0.26, 0.05, -0.08, -0.24, galsim.PositionD(1024,1024)),
galsim.CelestialCoord(-5 * galsim.arcmin, -25 * galsim.degrees)
)
wcs2 = galsim.TanWCS(
galsim.AffineTransform(0.25, -0.02, 0.01, 0.24, galsim.PositionD(1024,1024)),
galsim.CelestialCoord(5 * galsim.arcmin, -25 * galsim.degrees)
)
data1 = fitsio.read('input/test_single_cat1.fits')
data2 = fitsio.read('input/test_single_cat2.fits')
field_center = galsim.CelestialCoord(0 * galsim.degrees, -25 * galsim.degrees)
for chipnum, data, wcs in [(1,data1,wcs1), (2,data2,wcs2)]:
for k in range(len(data)):
x = data['x'][k]
y = data['y'][k]
e1 = data['e1'][k]
e2 = data['e2'][k]
s = data['s'][k]
#print('k,x,y = ',k,x,y)
#print(' true s,e1,e2 = ',s,e1,e2)
image_pos = galsim.PositionD(x,y)
star = piff.Star.makeTarget(x=x, y=y, wcs=wcs, stamp_size=48, pointing=field_center,
chipnum=chipnum)
star = psf.drawStar(star)
#print(' fitted s,e1,e2 = ',star.fit.params)
np.testing.assert_almost_equal(star.fit.params, [s,e1,e2], decimal=6)
# This is a DES Y3 PSF file that Matt Becker reported hadn't been readable with python 3.
# The problem was it had been written with python 2's pickle, which isn't directly
# compatible with python 3. The code has been fixed to make it readable. This unit
# test is just to ensure that it remains so.
# However, it only works if pixmappy is installed, so if not, just bail out.
try:
import pixmappy
except ImportError:
return
fname = os.path.join('input', 'D00240560_r_c01_r2362p01_piff.fits')
psf = piff.PSF.read(fname, logger=logger)
image = psf.draw(x=103.3, y=592.0)
def __init__(self, galaxy, scale, band):
self.topdir = os.path.join(os.getenv('HIZEA_DATA'),'hst','aplus','cutouts')
self.imfile = os.path.join(self.topdir,'{}-{}-{}.fits'.format(galaxy,scale,band))
self.ivarfile = os.path.join(self.topdir,'{}-{}-{}-ivar.fits'.format(galaxy,scale,band))
self.psffile = os.path.join(self.topdir,'{}-{}-{}.psf'.format(galaxy,scale,band))
print('Reading image {}'.format(self.imfile))
self.image = fitsio.read(self.imfile, ext=0)
self.header = fitsio.read_header(self.imfile)
print('Reading image {}'.format(self.ivarfile))
self.ivar = fitsio.read(self.ivarfile, ext=0)
def loadLabels(self,filename=None):
if filename is None: filename = self.labelfile
data = fitsio.read(filename)
distances = fitsio.read(filename,ext='DISTANCE_MODULUS')['DISTANCE_MODULUS']
if not (self.pixels == data['PIXEL']).all():
raise Exception("Pixels do not match")
if not (self.distances == distances).all():
raise Exception("Distance moduli do not match.")
self.labels = data['LABEL'].astype(int)
self.nlabels = self.labels.max()
if self.nlabels != (len(np.unique(self.labels)) - 1):
raise Exception("Incorrect number of labels found.")
return self.labels, self.nlabels
def verify_ngmix(config, q, ng):
"""Verify that the ngmix file we made is consistent with what Matt's script builds.
"""
print 'Verifying ngmix catalog...'
rel = fitsio.read(config['release_ngmix'])
print 'Full catalog has %d rows'%len(rel)
v18 = fitsio.read(os.path.join('/Users/Mike/Astro/des/SV/v18',config['flatcats_ngmix']))
print 'v18 catalog has %d rows'%len(v18)
print 'mask has %d rows'%len(q)
assert len(q) == len(v18)
relq = rel[q]
for col1, col2 in [ ('COADD_OBJECTS_ID', 'coadd_objects_id'),
('E_1', 'exp_e_1'),
('E_2', 'exp_e_2'),
('SENS_AVG', 'exp_e_sens_avg'),
('W', 'exp_w'),
('E_COV_1_1', 'exp_e_cov_1_1'),
('E_COV_1_2', 'exp_e_cov_1_2'),
('E_COV_2_1', 'exp_e_cov_2_1'),
('E_COV_2_2', 'exp_e_cov_2_2'),
('MEAN_PSF_E1', 'psfrec_e_1'),
('MEAN_PSF_E2', 'psfrec_e_2'),
('STAMP_SIZE', 'box_size') ]:
print 'Test rel[%r] == v18[%r]'%(col1,col2)
assert np.all(relq[col1] == v18[col2])
for col1, col2 in [ ('SNR_W', 'exp_s2n_w'),
('SNR_R', 'exp_s2n_r'),
('FLUX_I', 'exp_flux_i'),
('MAG_I', 'exp_mag_i'),
('T', 'exp_T'),
('T_ERR', 'exp_T_err'),
('T_R', 'exp_T_r'),
('SNR_T', 'exp_T_s2n'),
('SNR_T_R', 'exp_T_s2n_r'),
('LOG10_SB_I', 'exp_log10sb_i'),
('MEAN_PSF_T', 'psfrec_T'),
('SENS_1', 'exp_e_sens_1'),
('SENS_2', 'exp_e_sens_2'),
('ARATE', 'exp_arate'),
('MASK_FRAC', 'mask_frac') ]:
print 'Test rel[%r] ~= v18[%r]'%(col1,col2)
assert np.all(np.abs(relq[col1] - v18[col2]) <= 1.e-7 * np.abs(v18[col2]))
assert np.all(relq['ERROR_FLAG'] == (v18['flags'] | v18['exp_flags']))
print 'ngmix file passed verification tests'
def verify_im3shape(config, q, im):
"""Verify that the im3shape file we made is consistent with what Matt's script builds.
"""
print 'Verifying im3shape catalog...'
rel = fitsio.read(config['release_im3shape'])
print 'Full catalog has %d rows'%len(rel)
v18 = fitsio.read(os.path.join('/Users/Mike/Astro/des/SV/v18',config['flatcats_im3shape']))
print 'v18 catalog has %d rows'%len(v18)
print 'mask has %d rows'%len(q)
assert len(q) == len(v18)
relq = rel[q][im]
v18 = v18[im]
for col1, col2 in [ ('COADD_OBJECTS_ID', 'coadd_objects_id'),
('E_1', 'e1'),
('E_2', 'e2'),
('NBC_M', 'nbc_m'),
('NBC_C1', 'nbc_c1'),
('NBC_C2', 'nbc_c2'),
('W', 'w'),
('ERROR_FLAG', 'error_flag'),
('INFO_FLAG', 'info_flag'),
('MEAN_PSF_E1', 'mean_psf_e1_sky'),
('MEAN_PSF_E2', 'mean_psf_e2_sky'),
('STAMP_SIZE', 'stamp_size'),
('N_EXPOSURE', 'n_exposure') ]:
print 'Test rel[%r] == v18[%r]'%(col1,col2)
assert np.all(relq[col1] == v18[col2])
for col1, col2 in [ ('SNR_W', 'snr'),
('SNR_R', 'round_snr'),
('RADIUS', 'radius'),
('MEAN_RGPP_RP', 'mean_rgpp_rp'),
('MEAN_PSF_FWHM', 'mean_psf_fwhm'),
('RA_SHIFT', 'ra_as'),
('DEC_SHIFT', 'dec_as'),
('CHI2', 'chi2_pixel'),
('LIKELIHOOD', 'likelihood') ]:
print 'Test rel[%r] ~= v18[%r]'%(col1,col2)
assert np.all(np.abs(relq[col1] - v18[col2]) <= 1.e-7 * np.abs(v18[col2]))
assert np.all(relq['IS_BULGE'] == (v18['bulge_flux'] > 0.))
assert np.all(relq['IS_BULGE'] == (v18['disc_flux'] == 0.))
flux1 = relq['FLUX_R']
flux2 = v18['mean_flux'] * (v18['bulge_flux'] + v18['disc_flux'])
assert np.all(np.abs(flux1 - flux2) <= 1.e-7 * np.abs(flux2))
print 'im3shape file passed verification tests'
def do_file_sums(self, fname):
"""
get sums for a single file
"""
sums=self.get_sums_struct()
print("processing:",fname)
try:
data=fitsio.read(fname)
except IOError as err:
print(str(err))
return None
if 'shear_index' not in data.dtype.names:
data=self._add_shear_index(data)
else:
w,=where(data['shear_index']==-1)
if w.size > 0:
data['shear_index'][w]=0
data=self._preselect(data)
sums=self.do_sums1(data)
return sums
def write_rgb():
#g,r,z = [fitsio.read('detmap-%s.fits' % band) for band in 'grz']
g,r,z = [fitsio.read('coadd-%s.fits' % band) for band in 'grz']
plt.figure(figsize=(10,10))
plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
plt.clf()
for (im1,cc),scale in zip([(g,'b'),(r,'g'),(z,'r')],
[2.0, 1.2, 0.4]):
im = im1 * scale
im = im[im != 0]
plt.hist(im.ravel(), histtype='step', color=cc,
range=[np.percentile(im, p) for p in (1,98)], bins=50)
ps.savefig()
#rgb = get_rgb_image(g,r,z, alpha=0.8, m=0.02)
#rgb = get_rgb_image(g,r,z, alpha=16., m=0.005, m2=0.002,
#rgb = get_rgb_image(g,r,z, alpha=32., m=0.01, m2=0.002,
rgb = get_rgb_image(g,r,z, alpha=8., m=0.0, m2=0.0,
scale_g = 2.,
scale_r = 1.1,
scale_z = 0.5,
Q = 10)
#for im in g,r,z:
# mn,mx = [np.percentile(im, p) for p in [20,99]]
# print 'mn,mx:', mn,mx
plt.clf()
plt.imshow(rgb, interpolation='nearest', origin='lower')
ps.savefig()
fitsio.write('rgb.fits', rgb)
def _consumeRead(self, path, cmd, header=None):
# /home/data/wincharis/H2RG-C17206-ASIC-104/UpTheRamp/20160712210126/H2RG_R01_M01_N01.fits
dirName, fileName = os.path.split(path)
cmd.diag('text="checking %s"' % (fileName))
match = re.match('^H2RG_R0*(\d+)_M0*(\d+)_N0*(\d+)\.fits', fileName)
if match is None:
cmd.warn("failed to split up filename: %s" % (file))
return
rampN, groupN, readN = [int(m) for m in match.group(1,2,3)]
cmd.diag('text="new read %d %d %d"' % (rampN, groupN, readN))
if readN == 1:
if header is not None:
cmd.diag('text="getting header"')
subaruHdr = header
else:
subaruHdr = pyfits.Header()
cards = [dict(name='IDLPATH', value=dirName)]
for c in subaruHdr.cards:
cards.append(dict(name=c.keyword, value=c.value, comment=c.comment))
phdu = fitsio.FITSHDR(cards)
fitsio.write(self.outfile, None, header=phdu, clobber=True)
cmd.diag('text="new file %s"' % (self.outfile))
inData, inHdr = fitsio.read(path, header=True)
stackFile = fitsio.FITS(self.outfile, mode='rw')
stackFile.write(inData, header=inHdr)
stackFile[-1].write_checksum()
stackFile.close()
cmd.inform('readN=%d,%d,%d,%s' % (rampN,groupN,readN,self.outfile))
def load_data():
import glob;
columns_list = [ config['columns'][name] for name in config['columns'].keys() ]
filelist_results = glob.glob('%s/DES*.gz'%config['results_dir'])
list_data = []
logger.info('found %d files' % len(filelist_results))
for file_results in filelist_results[args.first:(args.first+args.num)]:
res=fitsio.read(file_results)
warnings.warn('%s'%str(res.dtype.names))
res=res[columns_list]
res.dtype.names=config['columns'].keys()
if config['method'] == 'im3shape':
warnings.warn('using 1 as m1 and m2')
res['m1'] = 1
res['m2'] = 1
res_cut = apply_cuts(res)
logger.info('%s size=%d/%d' % (file_results,len(res_cut),len(res)) )
list_data.append(res_cut)
res = np.concatenate(list_data)
logger.info('total data size=%d' % (len(res)))
return res
def readFITSMappable(self, mappable, fieldmap):
"""
For the file held in mappable, read in the fields defined in
fieldmap. File must be in FITS format.
"""
mapunit = {}
ft = mappable.dtype
fname = mappable.name
for f in fieldmap.keys():
fields = []
for val in fieldmap[ft].values():
if hasattr(val, '__iter__'):
fields.extend(val)
else:
fields.extend([val])
data = fitsio.read(fname, columns=fields)
for mapkey in fieldmap[ft].keys():
mapunit[mapkey] = data[fieldmap[ft][mapkey]]
if hasattr(fieldmap[ft][mapkey], '__iter__'):
dt = mapunit[mapkey].dtype[0]
ne = len(mapunit[mapkey])
nf = len(fieldmap[ft][mapkey])
mapunit[mapkey] = mapunit[mapkey].view(dt).reshape((ne,nf))
return mapunit
def read_map2(filename,field=0,dtype=np.float64,nest=False,hdu=1,h=False,verbose=True,memmap=False):
hdr=fitsio.read_header(filename,ext=hdu)
fullsky = False
try:
if (hdr['OBJECT'].strip() == 'PARTIAL') :
# partial sky format
fullsky=False
else:
fullsky=True
except:
# if no OBJECT in header, assume full sky
fullsky=True
if fullsky:
m=hp.read_map(filename,field=field,dtype=dtype,nest=nest,hdu=hdu,h=h,verbose=verbose,memmap=memmap)
else:
# partial sky
st=fitsio.read(filename,ext=1)
nside=hdr['NSIDE']
m=np.zeros(12*nside*nside,dtype=dtype) + hp.UNSEEN
if ((hdr['ORDERING'].strip() == 'NESTED') and (not nest)) :
# change from nest to ring...
m[hp.nest2ring(nside,st['PIXEL'])] = st['SIGNAL']
elif ((hdr['ORDERING'].strip() == 'RING') and (nest)):
# change from ring to nest...
m[hp.ring2nest(nside,st['PIXEL'])] = st['SIGNAL']
else :
# straight up
m[st['PIXEL']] = st['SIGNAL']
return m
def _load_data(self):
import fitsio
import desdb
print("loading catalogs")
df=desdb.DESFiles()
alldata=[]
cols=['flags','alphawin_j2000','deltawin_j2000','spread_model','mag_auto']
nr=len(self.run_data)
for i,rdata in enumerate(self.run_data):
coadd_run=rdata['run']
tilename=rdata['tilename']
dd = {'coadd_run':coadd_run,
'tilename':tilename,
'band':self.select_band}
f=df.url(type='coadd_cat',
coadd_run=coadd_run,
tilename=tilename,
band=self.select_band)
print(" %d/%d %s" % (i+1,nr,f))
cat=fitsio.read(f,columns=cols,lower=True)
dd['data'] = cat
alldata.append(dd)
self.alldata=alldata
def mkgalmapY3ac(res,zr,gz='.gz',md='',fore='',wm='',syscut=''): gl = [] for i in range(0,12*res*res): gl.append(0) #f = fitsio.read(dir+'dr1_lss_red_'+zr+'_v0_redux.fits.gz',ext=1) f = fitsio.read(dir+'test'+zr+mask+'.fits'+gz,ext=1) ngt = 0 w = 1. zem = 0 fw = '' if fore == 'fore': fw = '_fore' #if fore == 'auto': # fw = '_auto' if md == 'nodepth': md = '_none' #else: # md = '_'+md for i in range(0,len(f)): ra,dec = f[i]['RA'],f[i]['DEC'] #if f[i]['v0'+md+fw] == 1.: #if wm != '': # w = float(ln[4]) #if z > zmin and z < zmax: th,phi = radec2thphi(ra,dec) p = hp.ang2pix(res,th,phi,nest=True) gl[p] += w ngt += w print len(gl),ngt return gl
def old_read_ccd_data(expcat, work, expnum):
all_data = []
ccdnums = []
for k in range(len(expcat)):
ccdnum = expcat[k]['ccdnum']
if expcat[k]['flag'] != 0:
#print('Skipping ccd %d because it is blacklisted: '%ccdnum, expcat[k]['flag'])
continue
cat_file = os.path.join(work, str(expnum), "psf_cat_%d_%d.fits"%(expnum,ccdnum))
#print('cat_file = ',cat_file)
try:
data = fitsio.read(cat_file)
except (OSError, IOError):
print('Unable to open cat_file %s. Skipping this file.'%cat_file)
continue
all_data.append(data)
ccdnums.extend([ccdnum] * len(data))
#print('all_data = ',all_data)
all_data = np.concatenate(all_data)
ccdnums = np.array(ccdnums, dtype=int)
#print('all_data => ',all_data)
#print('ccdnums = ',ccdnums)
return all_data, ccdnums
def read_prior(**keys):
"""
parameters
----------
run: string, keyword
String representing the run, e.g. sfit-noisefree-c01
partype: string, keyword
Something extra to identify this
ext: string, keyword, optional
Extension for file, default 'fits'
"""
import fitsio
from ngmix.gmix import GMixND
fname=get_prior_file(**keys)
print("reading:",fname)
data = fitsio.read(fname)
weights=data['weights']
means=data['means']
covars=data['covars']
if len(means.shape) == 1:
means = means.reshape( (means.size, 1) )
prior = GMixND(weights,
means,
covars)
return prior
def aspcapStar(loc_id,apogee_id,ext=1,dr=None,header=True,
aspcapWavegrid=False):
"""
NAME:
aspcapStar
PURPOSE:
Read an aspcapStar file for a given star
INPUT:
loc_id - location ID (field for 1m targets)
apogee_id - APOGEE ID of the star
ext= (1) extension to load
header= (True) if True, also return the header
dr= return the path corresponding to this data release (general default)
aspcapWavegrid= (False) if True, output the spectrum on the ASPCAP
wavelength grid
OUTPUT:
aspcapStar file or (aspcapStar file, header)
HISTORY:
2014-11-25 - Written - Bovy (IAS)
"""
filePath= path.aspcapStarPath(loc_id,apogee_id,dr=dr)
if not os.path.exists(filePath):
download.aspcapStar(loc_id,apogee_id,dr=dr)
data= fitsio.read(filePath,ext,header=header)
return data
def _load_psf_map(self, **kw):
"""
we fake the coadd psf
"""
extra_data=kw.get('extra_data',{})
map_file=extra_data.get('psf_map',None)
if map_file is None:
raise RuntimeError("for Y3 you must send a map file")
data=fitsio.read(map_file)
psf_map={}
for i in xrange(data.size):
if i==0:
ii=i+1
else:
ii=i
fname=data['im_filename'][i].strip()
psf_path = data['psf_local_path'][ii].strip()
keep = fname.split('_')[0:0+3]
key = '-'.join(keep )
psf_map[key] = psf_path
self._psf_map=psf_map
def read_my_lackner_sersicn_fits(n_gauss):
"""
read file holding my gaussian fits to lackner sersicn distribution
"""
import fitsio
fname=get_my_lackner_sersicn_fits(n_gauss)
return fitsio.read(fname)
def get_phoenix_spectra(teff,logg,z):
ftemplate = 'lte{teff:05d}-{logg:4.2f}{z:+3.1f}.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits'
PHOENIX_DIR = spiderman.rcParams['PHOENIX_DIR']
filename = os.path.join(PHOENIX_DIR,ftemplate.format(teff=teff,logg=logg,z=z))
# changing to si, W / m^3 / str
if use_fitsio:
flux,h = fitsio.read(filename, ext=0, header=True)
else:
flux,h = fits.getdata(filename, ext=0, header=True)
flux = flux*1e-7*1e6/(np.pi)
crval = h['CRVAL1']
cdelt = h['CDELT1']
ctype = h['CTYPE1']
if ctype == 'AWAV-LOG':
wvl = (np.exp(crval + cdelt*np.arange(0,len(flux))))*1e-10
else:
print('ctype is not log! It is {}'.format(ctype))
return wvl, flux
def filter(brickname, filename, region):
if not intersect(sweep, region):
return None, None
try:
objects = fitsio.read(filename, 1, upper=True)
chunkheader = fitsio.read_header(filename, 0, upper=True)
except:
if ns.ignore_errors:
print('IO error on %s' % filename)
return None, None
else:
raise
mask = objects['BRICK_PRIMARY'] != 0
objects = objects[mask]
mask = objects['RA'] >= ra1
mask &= objects['RA'] < ra2
mask &= objects['DEC'] >= dec1
mask &= objects['DEC'] < dec2
objects = objects[mask]
chunk = np.empty(len(objects), dtype=SWEEP_DTYPE)
for colname in chunk.dtype.names:
if colname not in objects.dtype.names:
# skip missing columns
continue
try:
chunk[colname][...] = objects[colname][...]
except ValueError:
print('failed on column `%s`' % colname)
raise
chunkheader = dict([(key, chunkheader[key]) for key in chunkheader.keys()])
return chunk, chunkheader
def make_bright_star_mask_in_hp(nside, pixnum, verbose=True, gaiaepoch=2015.5,
maglim=12., matchrad=1., maskepoch=2023.0):
"""Make a bright star mask in a HEALPixel using Tycho, Gaia and URAT.
Parameters
----------
nside : :class:`int`
(NESTED) HEALPixel nside.
pixnum : :class:`int`
A single HEALPixel number.
verbose : :class:`bool`
If ``True`` then log informational messages.
Returns
-------
:class:`recarray`
The bright star mask in the form of `maskdatamodel.dtype`.
Notes
-----
- Runs in a a minute or so for a typical nside=4 pixel.
- See :func:`~desitarget.brightmask.make_bright_star_mask` for
descriptions of the output mask and the other input parameters.
"""
# ADM start the clock.
t0 = time()
# ADM read in the Tycho files.
tychofns = find_tycho_files_hp(nside, pixnum, neighbors=False)
tychoobjs = []
for fn in tychofns:
tychoobjs.append(fitsio.read(fn, ext='TYCHOHPX'))
tychoobjs = np.concatenate(tychoobjs)
# ADM create the Tycho reference magnitude, which is VT then HP
# ADM then BT in order of preference.
tychomag = tychoobjs["MAG_VT"].copy()
tychomag[tychomag == 0] = tychoobjs["MAG_HP"][tychomag == 0]
tychomag[tychomag == 0] = tychoobjs["MAG_BT"][tychomag == 0]
# ADM discard any Tycho objects below the input magnitude limit
# ADM and outside of the HEALPixels of interest.
theta, phi = np.radians(90-tychoobjs["DEC"]), np.radians(tychoobjs["RA"])
tychohpx = hp.ang2pix(nside, theta, phi, nest=True)
ii = (tychohpx == pixnum) & (tychomag < maglim)
tychomag, tychoobjs = tychomag[ii], tychoobjs[ii]
if verbose:
log.info('Read {} (mag < {}) Tycho objects (pix={})...t={:.1f} mins'.
format(np.sum(ii), maglim, pixnum, (time()-t0)/60))
# ADM read in the associated Gaia files. Also grab
# ADM neighboring pixels to prevent edge effects.
gaiafns = find_gaia_files(tychoobjs, neighbors=True)
gaiaobjs = []
cols = 'SOURCE_ID', 'RA', 'DEC', 'PHOT_G_MEAN_MAG', 'PMRA', 'PMDEC'
for fn in gaiafns:
if os.path.exists(fn):
gaiaobjs.append(fitsio.read(fn, ext='GAIAHPX', columns=cols))
gaiaobjs = np.concatenate(gaiaobjs)
gaiaobjs = rfn.rename_fields(gaiaobjs, {"SOURCE_ID": "REF_ID"})
# ADM limit Gaia objects to 3 magnitudes fainter than the passed
# ADM limit. This leaves some (!) leeway when matching to Tycho.
gaiaobjs = gaiaobjs[gaiaobjs['PHOT_G_MEAN_MAG'] < maglim + 3]
if verbose:
log.info('Read {} (G < {}) Gaia sources (pix={})...t={:.1f} mins'.format(
len(gaiaobjs), maglim+3, pixnum, (time()-t0)/60))
# ADM substitute URAT where Gaia proper motions don't exist.
ii = ((np.isnan(gaiaobjs["PMRA"]) | (gaiaobjs["PMRA"] == 0)) &
(np.isnan(gaiaobjs["PMDEC"]) | (gaiaobjs["PMDEC"] == 0)))
if verbose:
log.info('Add URAT for {} Gaia objs with no PMs (pix={})...t={:.1f} mins'
.format(np.sum(ii), pixnum, (time()-t0)/60))
urat = add_urat_pms(gaiaobjs[ii], numproc=1)
if verbose:
log.info('Found an additional {} URAT objects (pix={})...t={:.1f} mins'
.format(np.sum(urat["URAT_ID"] != -1), pixnum, (time()-t0)/60))
for col in "PMRA", "PMDEC":
gaiaobjs[col][ii] = urat[col]
# ADM need to track the URATID to track which objects have
# ADM substituted proper motions.
uratid = np.zeros_like(gaiaobjs["REF_ID"])-1
uratid[ii] = urat["URAT_ID"]
# ADM match to remove Tycho objects already in Gaia. Prefer the more
# ADM accurate Gaia proper motions. Note, however, that Tycho epochs
# ADM can differ from the mean (1991.5) by as as much as 0.86 years,
# ADM so a star with a proper motion as large as Barnard's Star
# ADM (10.3 arcsec) can be off by a significant margin (~10").
margin = 10.
ra, dec = rewind_coords(gaiaobjs["RA"], gaiaobjs["DEC"],
gaiaobjs["PMRA"], gaiaobjs["PMDEC"],
epochnow=gaiaepoch)
# ADM match Gaia to Tycho with a suitable margin.
if verbose:
log.info('Match Gaia to Tycho with margin={}" (pix={})...t={:.1f} mins'
.format(margin, pixnum, (time()-t0)/60))
igaia, itycho = radec_match_to([ra, dec],
[tychoobjs["RA"], tychoobjs["DEC"]],
sep=margin, radec=True)
if verbose:
log.info('{} matches. Refining at 1" (pix={})...t={:.1f} mins'.format(
len(itycho), pixnum, (time()-t0)/60))
# ADM match Gaia to Tycho at the more exact reference epoch.
epoch_ra = tychoobjs[itycho]["EPOCH_RA"]
epoch_dec = tychoobjs[itycho]["EPOCH_DEC"]
# ADM some of the Tycho epochs aren't populated.
epoch_ra[epoch_ra == 0], epoch_dec[epoch_dec == 0] = 1991.5, 1991.5
ra, dec = rewind_coords(gaiaobjs["RA"][igaia], gaiaobjs["DEC"][igaia],
gaiaobjs["PMRA"][igaia], gaiaobjs["PMDEC"][igaia],
epochnow=gaiaepoch,
epochpast=epoch_ra, epochpastdec=epoch_dec)
# ADM catch the corner case where there are no initial matches.
if ra.size > 0:
_, refined = radec_match_to([ra, dec], [tychoobjs["RA"][itycho],
tychoobjs["DEC"][itycho]], radec=True)
else:
refined = np.array([], dtype='int')
# ADM retain Tycho objects that DON'T match Gaia.
keep = np.ones(len(tychoobjs), dtype='bool')
keep[itycho[refined]] = False
tychokeep, tychomag = tychoobjs[keep], tychomag[keep]
if verbose:
log.info('Kept {} Tychos with no Gaia match (pix={})...t={:.1f} mins'
.format(len(tychokeep), pixnum, (time()-t0)/60))
# ADM now we're done matching to Gaia, limit Gaia to the passed
# ADM magnitude limit and to the HEALPixel boundary of interest.
theta, phi = np.radians(90-gaiaobjs["DEC"]), np.radians(gaiaobjs["RA"])
gaiahpx = hp.ang2pix(nside, theta, phi, nest=True)
ii = (gaiahpx == pixnum) & (gaiaobjs['PHOT_G_MEAN_MAG'] < maglim)
gaiakeep, uratid = gaiaobjs[ii], uratid[ii]
if verbose:
log.info('Mask also comprises {} Gaia sources (pix={})...t={:.1f} mins'
.format(len(gaiakeep), pixnum, (time()-t0)/60))
# ADM move the coordinates forwards to the input mask epoch.
epoch_ra, epoch_dec = tychokeep["EPOCH_RA"], tychokeep["EPOCH_DEC"]
# ADM some of the Tycho epochs aren't populated.
epoch_ra[epoch_ra == 0], epoch_dec[epoch_dec == 0] = 1991.5, 1991.5
ra, dec = rewind_coords(
tychokeep["RA"], tychokeep["DEC"], tychokeep["PM_RA"], tychokeep["PM_DEC"],
epochnow=epoch_ra, epochnowdec=epoch_dec, epochpast=maskepoch)
tychokeep["RA"], tychokeep["DEC"] = ra, dec
ra, dec = rewind_coords(
gaiakeep["RA"], gaiakeep["DEC"], gaiakeep["PMRA"], gaiakeep["PMDEC"],
epochnow=gaiaepoch, epochpast=maskepoch)
gaiakeep["RA"], gaiakeep["DEC"] = ra, dec
# ADM finally, format according to the mask data model...
gaiamask = np.zeros(len(gaiakeep), dtype=maskdatamodel.dtype)
tychomask = np.zeros(len(tychokeep), dtype=maskdatamodel.dtype)
for col in "RA", "DEC":
gaiamask[col] = gaiakeep[col]
gaiamask["PM"+col] = gaiakeep["PM"+col]
tychomask[col] = tychokeep[col]
tychomask["PM"+col] = tychokeep["PM_"+col]
gaiamask["REF_ID"] = gaiakeep["REF_ID"]
# ADM take care to rigorously convert to int64 for Tycho.
tychomask["REF_ID"] = tychokeep["TYC1"].astype('int64')*int(1e6) + \
tychokeep["TYC2"].astype('int64')*10 + tychokeep["TYC3"]
gaiamask["REF_CAT"], tychomask["REF_CAT"] = 'G2', 'T2'
gaiamask["REF_MAG"] = gaiakeep['PHOT_G_MEAN_MAG']
tychomask["REF_MAG"] = tychomag
gaiamask["URAT_ID"], tychomask["URAT_ID"] = uratid, -1
gaiamask["TYPE"], tychomask["TYPE"] = 'PSF', 'PSF'
mask = np.concatenate([gaiamask, tychomask])
# ADM ...and add the mask radii.
mask["IN_RADIUS"], mask["NEAR_RADIUS"] = radii(mask["REF_MAG"])
if verbose:
log.info("Done making mask...(pix={})...t={:.1f} mins".format(
pixnum, (time()-t0)/60.))
return mask
def test_direct():
# If the catalogs are small enough, we can do a direct calculation to see if comes out right.
# This should exactly match the treecorr result if brute=True.
ngal = 100
s = 10.
rng = np.random.RandomState(8675309)
x = rng.normal(0,s, (ngal,) )
y = rng.normal(0,s, (ngal,) )
w = rng.random_sample(ngal)
kap = rng.normal(0,3, (ngal,) )
cat = treecorr.Catalog(x=x, y=y, w=w, k=kap)
min_sep = 1.
bin_size = 0.2
nrbins = 10
nubins = 5
nvbins = 5
kkk = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins, brute=True)
kkk.process(cat, num_threads=2)
true_ntri = np.zeros((nrbins, nubins, 2*nvbins), dtype=int)
true_weight = np.zeros((nrbins, nubins, 2*nvbins), dtype=float)
true_zeta = np.zeros((nrbins, nubins, 2*nvbins), dtype=float)
for i in range(ngal):
for j in range(i+1,ngal):
for k in range(j+1,ngal):
d12 = np.sqrt((x[i]-x[j])**2 + (y[i]-y[j])**2)
d23 = np.sqrt((x[j]-x[k])**2 + (y[j]-y[k])**2)
d31 = np.sqrt((x[k]-x[i])**2 + (y[k]-y[i])**2)
d3, d2, d1 = sorted([d12, d23, d31])
rindex = np.floor(np.log(d2/min_sep) / bin_size).astype(int)
if rindex < 0 or rindex >= nrbins: continue
if [d1, d2, d3] == [d23, d31, d12]: ii,jj,kk = i,j,k
elif [d1, d2, d3] == [d23, d12, d31]: ii,jj,kk = i,k,j
elif [d1, d2, d3] == [d31, d12, d23]: ii,jj,kk = j,k,i
elif [d1, d2, d3] == [d31, d23, d12]: ii,jj,kk = j,i,k
elif [d1, d2, d3] == [d12, d23, d31]: ii,jj,kk = k,i,j
elif [d1, d2, d3] == [d12, d31, d23]: ii,jj,kk = k,j,i
else: assert False
# Now use ii, jj, kk rather than i,j,k, to get the indices
# that correspond to the points in the right order.
u = d3/d2
v = (d1-d2)/d3
if (x[jj]-x[ii])*(y[kk]-y[ii]) < (x[kk]-x[ii])*(y[jj]-y[ii]):
v = -v
uindex = np.floor(u / bin_size).astype(int)
assert 0 <= uindex < nubins
vindex = np.floor((v+1) / bin_size).astype(int)
assert 0 <= vindex < 2*nvbins
www = w[i] * w[j] * w[k]
zeta = www * kap[i] * kap[j] * kap[k]
true_ntri[rindex,uindex,vindex] += 1
true_weight[rindex,uindex,vindex] += www
true_zeta[rindex,uindex,vindex] += zeta
pos = true_weight > 0
true_zeta[pos] /= true_weight[pos]
np.testing.assert_array_equal(kkk.ntri, true_ntri)
np.testing.assert_allclose(kkk.weight, true_weight, rtol=1.e-5, atol=1.e-8)
np.testing.assert_allclose(kkk.zeta, true_zeta, rtol=1.e-5, atol=1.e-8)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check that running via the corr3 script works correctly.
config = treecorr.config.read_config('configs/kkk_direct.yaml')
cat.write(config['file_name'])
treecorr.corr3(config)
data = fitsio.read(config['kkk_file_name'])
np.testing.assert_allclose(data['r_nom'], kkk.rnom.flatten())
np.testing.assert_allclose(data['u_nom'], kkk.u.flatten())
np.testing.assert_allclose(data['v_nom'], kkk.v.flatten())
np.testing.assert_allclose(data['ntri'], kkk.ntri.flatten())
np.testing.assert_allclose(data['weight'], kkk.weight.flatten())
np.testing.assert_allclose(data['zeta'], kkk.zeta.flatten(), rtol=1.e-3)
# Also check the "cross" calculation. (Real cross doesn't work, but this should.)
kkk = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins, brute=True)
kkk.process(cat, cat, cat, num_threads=2)
np.testing.assert_array_equal(kkk.ntri, true_ntri)
np.testing.assert_allclose(kkk.weight, true_weight, rtol=1.e-5, atol=1.e-8)
np.testing.assert_allclose(kkk.zeta, true_zeta, rtol=1.e-5, atol=1.e-8)
config['file_name2'] = config['file_name']
config['file_name3'] = config['file_name']
treecorr.corr3(config)
data = fitsio.read(config['kkk_file_name'])
np.testing.assert_allclose(data['r_nom'], kkk.rnom.flatten())
np.testing.assert_allclose(data['u_nom'], kkk.u.flatten())
np.testing.assert_allclose(data['v_nom'], kkk.v.flatten())
np.testing.assert_allclose(data['ntri'], kkk.ntri.flatten())
np.testing.assert_allclose(data['weight'], kkk.weight.flatten())
np.testing.assert_allclose(data['zeta'], kkk.zeta.flatten(), rtol=1.e-3)
# Repeat with binslop = 0
# And don't do any top-level recursion so we actually test not going to the leaves.
kkk = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
bin_slop=0, max_top=0)
kkk.process(cat)
np.testing.assert_array_equal(kkk.ntri, true_ntri)
np.testing.assert_allclose(kkk.weight, true_weight, rtol=1.e-5, atol=1.e-8)
np.testing.assert_allclose(kkk.zeta, true_zeta, rtol=1.e-5, atol=1.e-8)
# Check a few basic operations with a GGCorrelation object.
do_pickle(kkk)
kkk2 = kkk.copy()
kkk2 += kkk
np.testing.assert_allclose(kkk2.ntri, 2*kkk.ntri)
np.testing.assert_allclose(kkk2.weight, 2*kkk.weight)
np.testing.assert_allclose(kkk2.meand1, 2*kkk.meand1)
np.testing.assert_allclose(kkk2.meand2, 2*kkk.meand2)
np.testing.assert_allclose(kkk2.meand3, 2*kkk.meand3)
np.testing.assert_allclose(kkk2.meanlogd1, 2*kkk.meanlogd1)
np.testing.assert_allclose(kkk2.meanlogd2, 2*kkk.meanlogd2)
np.testing.assert_allclose(kkk2.meanlogd3, 2*kkk.meanlogd3)
np.testing.assert_allclose(kkk2.meanu, 2*kkk.meanu)
np.testing.assert_allclose(kkk2.meanv, 2*kkk.meanv)
np.testing.assert_allclose(kkk2.zeta, 2*kkk.zeta)
kkk2.clear()
kkk2 += kkk
np.testing.assert_allclose(kkk2.ntri, kkk.ntri)
np.testing.assert_allclose(kkk2.weight, kkk.weight)
np.testing.assert_allclose(kkk2.meand1, kkk.meand1)
np.testing.assert_allclose(kkk2.meand2, kkk.meand2)
np.testing.assert_allclose(kkk2.meand3, kkk.meand3)
np.testing.assert_allclose(kkk2.meanlogd1, kkk.meanlogd1)
np.testing.assert_allclose(kkk2.meanlogd2, kkk.meanlogd2)
np.testing.assert_allclose(kkk2.meanlogd3, kkk.meanlogd3)
np.testing.assert_allclose(kkk2.meanu, kkk.meanu)
np.testing.assert_allclose(kkk2.meanv, kkk.meanv)
np.testing.assert_allclose(kkk2.zeta, kkk.zeta)
ascii_name = 'output/kkk_ascii.txt'
kkk.write(ascii_name, precision=16)
kkk3 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins)
kkk3.read(ascii_name)
np.testing.assert_allclose(kkk3.ntri, kkk.ntri)
np.testing.assert_allclose(kkk3.weight, kkk.weight)
np.testing.assert_allclose(kkk3.meand1, kkk.meand1)
np.testing.assert_allclose(kkk3.meand2, kkk.meand2)
np.testing.assert_allclose(kkk3.meand3, kkk.meand3)
np.testing.assert_allclose(kkk3.meanlogd1, kkk.meanlogd1)
np.testing.assert_allclose(kkk3.meanlogd2, kkk.meanlogd2)
np.testing.assert_allclose(kkk3.meanlogd3, kkk.meanlogd3)
np.testing.assert_allclose(kkk3.meanu, kkk.meanu)
np.testing.assert_allclose(kkk3.meanv, kkk.meanv)
np.testing.assert_allclose(kkk3.zeta, kkk.zeta)
fits_name = 'output/kkk_fits.fits'
kkk.write(fits_name)
kkk4 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins)
kkk4.read(fits_name)
np.testing.assert_allclose(kkk4.ntri, kkk.ntri)
np.testing.assert_allclose(kkk4.weight, kkk.weight)
np.testing.assert_allclose(kkk4.meand1, kkk.meand1)
np.testing.assert_allclose(kkk4.meand2, kkk.meand2)
np.testing.assert_allclose(kkk4.meand3, kkk.meand3)
np.testing.assert_allclose(kkk4.meanlogd1, kkk.meanlogd1)
np.testing.assert_allclose(kkk4.meanlogd2, kkk.meanlogd2)
np.testing.assert_allclose(kkk4.meanlogd3, kkk.meanlogd3)
np.testing.assert_allclose(kkk4.meanu, kkk.meanu)
np.testing.assert_allclose(kkk4.meanv, kkk.meanv)
np.testing.assert_allclose(kkk4.zeta, kkk.zeta)
with assert_raises(TypeError):
kkk2 += config
kkk5 = treecorr.KKKCorrelation(min_sep=min_sep/2, bin_size=bin_size, nbins=nrbins)
with assert_raises(ValueError):
kkk2 += kkk5
kkk6 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size/2, nbins=nrbins)
with assert_raises(ValueError):
kkk2 += kkk6
kkk7 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins*2)
with assert_raises(ValueError):
kkk2 += kkk7
kkk8 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
min_u=0.1)
with assert_raises(ValueError):
kkk2 += kkk8
kkk0 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
max_u=0.1)
with assert_raises(ValueError):
kkk2 += kkk0
kkk10 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
nubins=nrbins*2)
with assert_raises(ValueError):
kkk2 += kkk10
kkk11 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
min_v=0.1)
with assert_raises(ValueError):
kkk2 += kkk11
kkk12 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
max_v=0.1)
with assert_raises(ValueError):
kkk2 += kkk12
kkk13 = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
nvbins=nrbins*2)
with assert_raises(ValueError):
kkk2 += kkk13
# Currently not implemented to only have cat2 or cat3
with assert_raises(NotImplementedError):
kkk.process(cat, cat2=cat)
with assert_raises(NotImplementedError):
kkk.process(cat, cat3=cat)
with assert_raises(NotImplementedError):
kkk.process_cross21(cat, cat)
def __init__(self, galf, ranf, sysmapf):
self.galm = hp.read_map(galf, verbose=False)
self.ranm = hp.read_map(ranf, verbose=False)
self.sysm = ft.read(sysmapf, lower=True)
self.cols = self.sysm.dtype.names
print('attributes : {}'.format(self.cols))
def gaia_gfas_from_sweep(filename, maglim=18.):
"""Create a set of GFAs for one sweep file.
Parameters
----------
filename: :class:`str`
A string corresponding to the full path to a sweep file name.
maglim : :class:`float`, optional, defaults to 18
Magnitude limit for GFAs in Gaia G-band.
Returns
-------
:class:`~numpy.ndarray`
GFA objects from Gaia, formatted according to `desitarget.gfa.gfadatamodel`.
"""
# ADM read in the objects.
objects = fitsio.read(filename)
# ADM As a mild speed up, only consider sweeps objects brighter than 3 mags
# ADM fainter than the passed Gaia magnitude limit. Note that Gaia G-band
# ADM approximates SDSS r-band.
ii = ((objects["FLUX_G"] > 10**((22.5 - (maglim + 3)) / 2.5)) |
(objects["FLUX_R"] > 10**((22.5 - (maglim + 3)) / 2.5)) |
(objects["FLUX_Z"] > 10**((22.5 - (maglim + 3)) / 2.5)))
objects = objects[ii]
nobjs = len(objects)
# ADM only retain objects with Gaia matches.
# ADM It's fine to propagate an empty array if there are no matches
# ADM The sweeps use 0 for objects with no REF_ID.
objects = objects[objects["REF_ID"] > 0]
# ADM determine a TARGETID for any objects on a brick.
targetid = encode_targetid(objid=objects['OBJID'],
brickid=objects['BRICKID'],
release=objects['RELEASE'])
# ADM format everything according to the data model.
gfas = np.zeros(len(objects), dtype=gfadatamodel.dtype)
# ADM make sure all columns initially have "ridiculous" numbers.
gfas[...] = -99.
gfas["REF_CAT"] = ""
gfas["REF_EPOCH"] = 2015.5
# ADM remove the TARGETID, BRICK_OBJID, REF_CAT, REF_EPOCH columns
# ADM and populate them later as they require special treatment.
cols = list(gfadatamodel.dtype.names)
for col in [
"TARGETID", "BRICK_OBJID", "REF_CAT", "REF_EPOCH", "URAT_ID",
"URAT_SEP"
]:
cols.remove(col)
for col in cols:
gfas[col] = objects[col]
# ADM populate the TARGETID column.
gfas["TARGETID"] = targetid
# ADM populate the BRICK_OBJID column.
gfas["BRICK_OBJID"] = objects["OBJID"]
# ADM REF_CAT and REF_EPOCH didn't exist before DR8.
for refcol in ["REF_CAT", "REF_EPOCH"]:
if refcol in objects.dtype.names:
gfas[refcol] = objects[refcol]
# ADM cut the GFAs by a hard limit on magnitude.
ii = gfas['GAIA_PHOT_G_MEAN_MAG'] < maglim
gfas = gfas[ii]
# ADM remove any sources based on LSLGA (retain Tycho/T2 sources).
# ADM the try/except/decode catches both bytes and unicode strings.
try:
ii = np.array(rc.decode()[0] == "L" for rc in gfas["REF_CAT"])
except AttributeError:
ii = np.array([i[0] == "L" for rc in gfas["REF_CAT"]])
gfas = gfas[~ii]
return gfas
def inittab(qsocatpath, outtab):
'''
Populate table with information from QSO catalogue,
make empty tables for BAL information to be added later.
Parameters
----------
qsocatpath : fits file
QSO catalogue to be read.
outtab : fits file
where to write resulting catalogue to.
Returns
-------
colnames : list
card names relating to BALs
'''
#Open input catalogue fits file to get num objects in catalogue.
cathdu = fits.open(qsocatpath, lazy_load_hdus=False)
NROWS = len(cathdu[1].data)
# Check if strings have been turned into character arrays, and if so read the catalog with fitsio instead
# This is necessary for fuji and guadalupe
if cathdu[1].data['SURVEY'].dtype == 'O':
cathdu[1].data = fitsio.read(qsocatpath)
# PCA Fit Coefficients and chisq result
pca_array = np.zeros([NROWS, bc.NPCA], dtype=float) # PCA Fit Coefficients
col0 = fits.Column(name='PCA_COEFFS', format='5E', array=pca_array)
pca_chi2 = np.zeros([NROWS], dtype=float) # PCA reduced chi2
col1 = fits.Column(name='PCA_CHI2', format='E', array=pca_chi2)
# Collection of empty arrays to set up rest of the BAL catalog
# PM Note: Might change to -1. to indicate empty field
zfloat_col = np.zeros([NROWS], dtype=float)
zint_col = np.zeros([NROWS], dtype=float)
zneg_col = np.array([-99] * NROWS, dtype=float) # For BAL_PROB
zbyte_col = np.ones(NROWS, dtype=np.ubyte) # For bit masking in BALMASK
zfloat_bicol = np.zeros(
[NROWS, bc.NBI],
dtype=float) # Second dimension is max number of BI troughs
zfloat_aicol = np.zeros(
[NROWS, bc.NAI],
dtype=float) # Second dimension is max number of AI troughs
# This will come from CNN Classifier
# All set to -99 to indicate that data from BALcats not been read yet
# Will be set to -1 once updated
col2 = fits.Column(name='BAL_PROB', format='E', array=zneg_col)
# These quantities are from trough fit
col3 = fits.Column(name='BI_CIV', format='E', array=zfloat_col)
col4 = fits.Column(name='ERR_BI_CIV', format='E', array=zfloat_col)
col5 = fits.Column(name='NCIV_2000', format='J', array=zint_col)
col6 = fits.Column(name='VMIN_CIV_2000', format='5E', array=zfloat_bicol)
col7 = fits.Column(name='VMAX_CIV_2000', format='5E', array=zfloat_bicol)
col8 = fits.Column(name='POSMIN_CIV_2000', format='5E', array=zfloat_bicol)
col9 = fits.Column(name='FMIN_CIV_2000', format='5E', array=zfloat_bicol)
col10 = fits.Column(name='AI_CIV', format='E', array=zfloat_col)
col11 = fits.Column(name='ERR_AI_CIV', format='E', array=zfloat_col)
col12 = fits.Column(name='NCIV_450', format='J', array=zint_col)
col13 = fits.Column(name='VMIN_CIV_450', format='17E', array=zfloat_aicol)
col14 = fits.Column(name='VMAX_CIV_450', format='17E', array=zfloat_aicol)
col15 = fits.Column(name='POSMIN_CIV_450',
format='17E',
array=zfloat_aicol)
col16 = fits.Column(name='FMIN_CIV_450', format='17E', array=zfloat_aicol)
col17 = fits.Column(name='BI_SIIV', format='E', array=zfloat_col)
col18 = fits.Column(name='ERR_BI_SIIV', format='E', array=zfloat_col)
col19 = fits.Column(name='NSIIV_2000', format='J', array=zint_col)
col20 = fits.Column(name='VMIN_SIIV_2000', format='5E', array=zfloat_bicol)
col21 = fits.Column(name='VMAX_SIIV_2000', format='5E', array=zfloat_bicol)
col22 = fits.Column(name='POSMIN_SIIV_2000',
format='5E',
array=zfloat_bicol)
col23 = fits.Column(name='FMIN_SIIV_2000', format='5E', array=zfloat_bicol)
col24 = fits.Column(name='AI_SIIV', format='E', array=zfloat_col)
col25 = fits.Column(name='ERR_AI_SIIV', format='E', array=zfloat_col)
col26 = fits.Column(name='NSIIV_450', format='J', array=zint_col)
col27 = fits.Column(name='VMIN_SIIV_450', format='17E', array=zfloat_aicol)
col28 = fits.Column(name='VMAX_SIIV_450', format='17E', array=zfloat_aicol)
col29 = fits.Column(name='POSMIN_SIIV_450',
format='17E',
array=zfloat_aicol)
col30 = fits.Column(name='FMIN_SIIV_450', format='17E', array=zfloat_aicol)
# Seperate column not populated in runbalfinder which serves as a bitmask
col31 = fits.Column(
name='BALMASK', format='B',
array=zbyte_col) # default is '1' (not found in baltable)
#Columns relating to BAL information from runbalfinder
BALcols = fits.ColDefs([
col0, col1, col2, col3, col4, col5, col6, col7, col8, col9, col10,
col11, col12, col13, col14, col15, col16, col17, col18, col19, col20,
col21, col22, col23, col24, col25, col26, col27, col28, col29, col30
])
#Columns already contained in QSO catalogue
catcols = cathdu[1].columns
totcols = catcols + BALcols + col31
#List of card names in BinHDU relating to BALs
#Will beiterated through in initcreate.popqsotab()
balcolnames = BALcols.info('name', False)['name']
#Create a new BinHDU with all columns
newbhdu = fits.BinTableHDU.from_columns(totcols)
#Update BinHDU of cathdu with newbhdu
cathdu[1] = newbhdu
cathdu[1].header['EXTNAME'] = 'ZCATALOG'
try:
cathdu.writeto(outtab)
except OSError:
print("File ", outtab, " already exists.")
return balcolnames
print("Empty BAL columns added to input QSO catalogue at ",
str(qsocatpath), " and written to ", str(outtab), ".")
return balcolnames
def test_direct():
# If the catalogs are small enough, we can do a direct calculation to see if comes out right.
# This should exactly match the treecorr result if bin_slop=0.
ngal = 200
s = 10.
np.random.seed(8675309)
x1 = np.random.normal(0,s, (ngal,) )
y1 = np.random.normal(0,s, (ngal,) )
w1 = np.random.random(ngal)
k1 = np.random.normal(5,1, (ngal,) )
x2 = np.random.normal(0,s, (ngal,) )
y2 = np.random.normal(0,s, (ngal,) )
w2 = np.random.random(ngal)
g12 = np.random.normal(0,0.2, (ngal,) )
g22 = np.random.normal(0,0.2, (ngal,) )
cat1 = treecorr.Catalog(x=x1, y=y1, w=w1, k=k1)
cat2 = treecorr.Catalog(x=x2, y=y2, w=w2, g1=g12, g2=g22)
min_sep = 1.
max_sep = 50.
nbins = 50
bin_size = np.log(max_sep/min_sep) / nbins
kg = treecorr.KGCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, bin_slop=0.)
kg.process(cat1, cat2)
true_npairs = np.zeros(nbins, dtype=int)
true_weight = np.zeros(nbins, dtype=float)
true_xi = np.zeros(nbins, dtype=complex)
for i in range(ngal):
# It's hard to do all the pairs at once with numpy operations (although maybe possible).
# But we can at least do all the pairs for each entry in cat1 at once with arrays.
rsq = (x1[i]-x2)**2 + (y1[i]-y2)**2
r = np.sqrt(rsq)
logr = np.log(r)
expmialpha = ((x1[i]-x2) - 1j*(y1[i]-y2)) / r
ww = w1[i] * w2
xi = -ww * k1[i] * (g12 + 1j*g22) * expmialpha**2
index = np.floor(np.log(r/min_sep) / bin_size).astype(int)
mask = (index >= 0) & (index < nbins)
np.add.at(true_npairs, index[mask], 1)
np.add.at(true_weight, index[mask], ww[mask])
np.add.at(true_xi, index[mask], xi[mask])
true_xi /= true_weight
print('true_npairs = ',true_npairs)
print('diff = ',kg.npairs - true_npairs)
np.testing.assert_array_equal(kg.npairs, true_npairs)
print('true_weight = ',true_weight)
print('diff = ',kg.weight - true_weight)
np.testing.assert_allclose(kg.weight, true_weight, rtol=1.e-5, atol=1.e-8)
print('true_xi = ',true_xi)
print('kg.xi = ',kg.xi)
print('kg.xi_im = ',kg.xi_im)
np.testing.assert_allclose(kg.xi, true_xi.real, rtol=1.e-4, atol=1.e-8)
np.testing.assert_allclose(kg.xi_im, true_xi.imag, rtol=1.e-4, atol=1.e-8)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check that running via the corr2 script works correctly.
config = treecorr.config.read_config('configs/kg_direct.yaml')
cat1.write(config['file_name'])
cat2.write(config['file_name2'])
treecorr.corr2(config)
data = fitsio.read(config['kg_file_name'])
np.testing.assert_allclose(data['R_nom'], kg.rnom)
np.testing.assert_allclose(data['npairs'], kg.npairs)
np.testing.assert_allclose(data['weight'], kg.weight)
np.testing.assert_allclose(data['kgamT'], kg.xi, rtol=1.e-3)
np.testing.assert_allclose(data['kgamX'], kg.xi_im, rtol=1.e-3)
# Repeat with binslop not precisely 0, since the code flow is different for bin_slop == 0.
# And don't do any top-level recursion so we actually test not going to the leaves.
kg = treecorr.KGCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, bin_slop=1.e-16,
max_top=0)
kg.process(cat1, cat2)
np.testing.assert_array_equal(kg.npairs, true_npairs)
np.testing.assert_allclose(kg.weight, true_weight, rtol=1.e-5, atol=1.e-8)
np.testing.assert_allclose(kg.xi, true_xi.real, rtol=1.e-3, atol=1.e-3)
np.testing.assert_allclose(kg.xi_im, true_xi.imag, rtol=1.e-3, atol=1.e-3)
# Check a few basic operations with a KGCorrelation object.
do_pickle(kg)
kg2 = kg.copy()
kg2 += kg
np.testing.assert_allclose(kg2.npairs, 2*kg.npairs)
np.testing.assert_allclose(kg2.weight, 2*kg.weight)
np.testing.assert_allclose(kg2.meanr, 2*kg.meanr)
np.testing.assert_allclose(kg2.meanlogr, 2*kg.meanlogr)
np.testing.assert_allclose(kg2.xi, 2*kg.xi)
np.testing.assert_allclose(kg2.xi_im, 2*kg.xi_im)
kg2.clear()
kg2 += kg
np.testing.assert_allclose(kg2.npairs, kg.npairs)
np.testing.assert_allclose(kg2.weight, kg.weight)
np.testing.assert_allclose(kg2.meanr, kg.meanr)
np.testing.assert_allclose(kg2.meanlogr, kg.meanlogr)
np.testing.assert_allclose(kg2.xi, kg.xi)
np.testing.assert_allclose(kg2.xi_im, kg.xi_im)
ascii_name = 'output/kg_ascii.txt'
kg.write(ascii_name, precision=16)
kg3 = treecorr.KGCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins)
kg3.read(ascii_name)
np.testing.assert_allclose(kg3.npairs, kg.npairs)
np.testing.assert_allclose(kg3.weight, kg.weight)
np.testing.assert_allclose(kg3.meanr, kg.meanr)
np.testing.assert_allclose(kg3.meanlogr, kg.meanlogr)
np.testing.assert_allclose(kg3.xi, kg.xi)
np.testing.assert_allclose(kg3.xi_im, kg.xi_im)
fits_name = 'output/kg_fits.fits'
kg.write(fits_name)
kg4 = treecorr.KGCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins)
kg4.read(fits_name)
np.testing.assert_allclose(kg4.npairs, kg.npairs)
np.testing.assert_allclose(kg4.weight, kg.weight)
np.testing.assert_allclose(kg4.meanr, kg.meanr)
np.testing.assert_allclose(kg4.meanlogr, kg.meanlogr)
np.testing.assert_allclose(kg4.xi, kg.xi)
np.testing.assert_allclose(kg4.xi_im, kg.xi_im)
def test_direct_spherical():
# Repeat in spherical coords
ngal = 100
s = 10.
np.random.seed(8675309)
x1 = np.random.normal(0,s, (ngal,) )
y1 = np.random.normal(0,s, (ngal,) ) + 200 # Put everything at large y, so small angle on sky
z1 = np.random.normal(0,s, (ngal,) )
w1 = np.random.random(ngal)
k1 = np.random.normal(5,1, (ngal,) )
x2 = np.random.normal(0,s, (ngal,) )
y2 = np.random.normal(0,s, (ngal,) ) + 200
z2 = np.random.normal(0,s, (ngal,) )
w2 = np.random.random(ngal)
g12 = np.random.normal(0,0.2, (ngal,) )
g22 = np.random.normal(0,0.2, (ngal,) )
ra1, dec1 = coord.CelestialCoord.xyz_to_radec(x1,y1,z1)
ra2, dec2 = coord.CelestialCoord.xyz_to_radec(x2,y2,z2)
cat1 = treecorr.Catalog(ra=ra1, dec=dec1, ra_units='rad', dec_units='rad', w=w1, k=k1)
cat2 = treecorr.Catalog(ra=ra2, dec=dec2, ra_units='rad', dec_units='rad', w=w2, g1=g12, g2=g22)
min_sep = 1.
max_sep = 10.
nbins = 50
bin_size = np.log(max_sep/min_sep) / nbins
kg = treecorr.KGCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
sep_units='deg', bin_slop=0.)
kg.process(cat1, cat2)
r1 = np.sqrt(x1**2 + y1**2 + z1**2)
r2 = np.sqrt(x2**2 + y2**2 + z2**2)
x1 /= r1; y1 /= r1; z1 /= r1
x2 /= r2; y2 /= r2; z2 /= r2
north_pole = coord.CelestialCoord(0*coord.radians, 90*coord.degrees)
true_npairs = np.zeros(nbins, dtype=int)
true_weight = np.zeros(nbins, dtype=float)
true_xi = np.zeros(nbins, dtype=complex)
c1 = [coord.CelestialCoord(r*coord.radians, d*coord.radians) for (r,d) in zip(ra1, dec1)]
c2 = [coord.CelestialCoord(r*coord.radians, d*coord.radians) for (r,d) in zip(ra2, dec2)]
for i in range(ngal):
for j in range(ngal):
rsq = (x1[i]-x2[j])**2 + (y1[i]-y2[j])**2 + (z1[i]-z2[j])**2
r = np.sqrt(rsq)
r *= coord.radians / coord.degrees
logr = np.log(r)
index = np.floor(np.log(r/min_sep) / bin_size).astype(int)
if index < 0 or index >= nbins:
continue
# Rotate shears to coordinates where line connecting is horizontal.
# Original orientation is where north is up.
theta2 = 90*coord.degrees - c2[j].angleBetween(c1[i], north_pole)
expm2theta2 = np.cos(2*theta2) - 1j * np.sin(2*theta2)
g2 = g12[j] + 1j * g22[j]
g2 *= expm2theta2
ww = w1[i] * w2[j]
xi = -ww * k1[i] * g2
true_npairs[index] += 1
true_weight[index] += ww
true_xi[index] += xi
true_xi /= true_weight
print('true_npairs = ',true_npairs)
print('diff = ',kg.npairs - true_npairs)
np.testing.assert_array_equal(kg.npairs, true_npairs)
print('true_weight = ',true_weight)
print('diff = ',kg.weight - true_weight)
np.testing.assert_allclose(kg.weight, true_weight, rtol=1.e-5, atol=1.e-8)
print('true_xi = ',true_xi)
print('kg.xi = ',kg.xi)
np.testing.assert_allclose(kg.xi, true_xi.real, rtol=1.e-4, atol=1.e-8)
np.testing.assert_allclose(kg.xi_im, true_xi.imag, rtol=1.e-4, atol=1.e-8)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check that running via the corr2 script works correctly.
config = treecorr.config.read_config('configs/kg_direct_spherical.yaml')
cat1.write(config['file_name'])
cat2.write(config['file_name2'])
treecorr.corr2(config)
data = fitsio.read(config['kg_file_name'])
np.testing.assert_allclose(data['R_nom'], kg.rnom)
np.testing.assert_allclose(data['npairs'], kg.npairs)
np.testing.assert_allclose(data['weight'], kg.weight)
np.testing.assert_allclose(data['kgamT'], kg.xi, rtol=1.e-3)
np.testing.assert_allclose(data['kgamX'], kg.xi_im, rtol=1.e-3)
# Repeat with binslop not precisely 0, since the code flow is different for bin_slop == 0.
# And don't do any top-level recursion so we actually test not going to the leaves.
kg = treecorr.KGCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
sep_units='deg', bin_slop=1.e-16, max_top=0)
kg.process(cat1, cat2)
np.testing.assert_array_equal(kg.npairs, true_npairs)
np.testing.assert_allclose(kg.weight, true_weight, rtol=1.e-5, atol=1.e-8)
np.testing.assert_allclose(kg.xi, true_xi.real, rtol=1.e-3, atol=1.e-3)
np.testing.assert_allclose(kg.xi_im, true_xi.imag, rtol=1.e-3, atol=1.e-3)
def run(self, log, terminal, cl_params, mapping_pipelines):
log.doMessage(
'INFO',
'\n{t.underline}Start imaging.{t.normal}'.format(t=terminal))
# ------------------------------------------------- identify imaging scripts
MapStruct = namedtuple("MapStruct", "nchans, window, start, end")
maps = {}
for mp in mapping_pipelines:
nchans = int(
mp.mp_object.row_list.get(mp.start, mp.feed, mp.window,
mp.pol)['NCHANS'])
maps[MapStruct(nchans, mp.window, mp.start, mp.end)] = set()
for mp in mapping_pipelines:
nchans = int(
mp.mp_object.row_list.get(mp.start, mp.feed, mp.window,
mp.pol)['NCHANS'])
maps[MapStruct(nchans, mp.window, mp.start, mp.end)].add(mp.feed)
log.doMessage('DBG', 'maps', maps)
for thismap in maps:
log.doMessage(
'INFO', 'Imaging window {win} '
'for map scans {start}-{stop}'.format(win=thismap.window,
start=thismap.start,
stop=thismap.end))
scanrange = str(thismap.start) + '_' + str(thismap.end)
imfiles = glob.glob('*' + scanrange + '*window' +
str(thismap.window) + '*pol*' + '.fits')
if not imfiles:
# no files found
log.doMessage('ERR', 'No calibrated files found.')
continue
# filter file list to only include those with a feed calibrated for use in this map
feeds = map(str, sorted(maps[thismap]))
ff = fitsio.FITS(imfiles[0])
nchans = int([
xxx['tdim'] for xxx in ff[1].get_info()['colinfo']
if xxx['name'] == 'DATA'
][0][0])
ff.close()
if cl_params.channels:
channels = str(cl_params.channels)
elif nchans:
chan_min = int(nchans * .02) # start at 2% of nchan
chan_max = int(nchans * .98) # end at 98% of nchans
channels = str(chan_min) + ':' + str(chan_max)
infiles = ' '.join(imfiles)
if cl_params.keeptempfiles:
keeptempfiles = '1'
else:
keeptempfiles = '0'
# get the source name and restfrequency from an input file
tabledata = fitsio.read(imfiles[0])
source = tabledata['OBJECT'][0].strip()
restfreq = tabledata['RESTFREQ'][0]
del tabledata
freq = "_%.0f_MHz" % (restfreq * 1e-6)
output_basename = source + '_' + scanrange + freq
if cl_params.average <= 1:
average = 1
else:
average = cl_params.average
if cl_params.clobber:
clobber = ' --clobber'
else:
clobber = ''
self.grid(log, channels, str(average), output_basename,
str(cl_params.verbose), clobber, infiles)
def mask_targets(targs, inmaskdir, nside=2, pixlist=None):
"""Add bits for if objects occupy masks, and SAFE (BADSKY) locations.
Parameters
----------
targs : :class:`str` or `~numpy.ndarray`
An array of targets/skies etc. created by, e.g.,
:func:`desitarget.cuts.select_targets()` OR the filename of a
file that contains such a set of targets/skies, etc.
inmaskdir : :class:`str`, optional
An input bright star mask file or HEALPixel-split directory as
made by :func:`desitarget.brightmask.make_bright_star_mask()`
nside : :class:`int`, optional, defaults to 2
The nside at which the targets were generated. If the mask is
a HEALPixel-split directory, then this helps to perform more
efficient masking as only the subset of masks that are in
pixels containing `targs` at this `nside` will be considered
(together with neighboring pixels to account for edge effects).
pixlist : :class:`list` or `int`, optional
A set of HEALPixels corresponding to the `targs`. Only the subset
of masks in HEALPixels in `pixlist` at `nside` will be considered
(together with neighboring pixels to account for edge effects).
If ``None``, then the pixels touched by `targs` is derived from
from `targs` itself.
Returns
-------
:class:`~numpy.ndarray`
Input targets with the `DESI_TARGET` column updated to reflect
the `BRIGHT_OBJECT` bits and SAFE (`BADSKY`) sky locations added
around the perimeter of the mask.
Notes
-----
- `Tech Note 2346`_ details SAFE (BADSKY) locations.
"""
t0 = time()
# ADM Check if targs is a file name or the structure itself.
if isinstance(targs, str):
if not os.path.exists(targs):
raise ValueError("{} doesn't exist".format(targs))
targs = fitsio.read(targs)
# ADM determine which pixels are occupied by targets.
if pixlist is None:
theta, phi = np.radians(90-targs["DEC"]), np.radians(targs["RA"])
pixlist = list(set(hp.ang2pix(nside, theta, phi, nest=True)))
else:
# ADM in case an integer was passed.
pixlist = np.atleast_1d(pixlist)
log.info("Masking using masks in {} at nside={} in HEALPixels={}".format(
inmaskdir, nside, pixlist))
pixlist = add_hp_neighbors(nside, pixlist)
# ADM read in the (potentially HEALPixel-split) mask.
sourcemask = io.read_targets_in_hp(inmaskdir, nside, pixlist)
ntargs = len(targs)
log.info('Total number of masks {}'.format(len(sourcemask)))
log.info('Total number of targets {}...t={:.1f}s'.format(ntargs, time()-t0))
# ADM update the bits depending on whether targets are in a mask.
# ADM also grab masks that contain or are near a target.
dt, mx = set_target_bits(targs, sourcemask, return_masks=True)
targs["DESI_TARGET"] = dt
inmasks, nearmasks = mx
# ADM generate SAFE locations for masks that contain a target.
safes = get_safe_targets(targs, sourcemask[inmasks])
# ADM update the bits for the safe locations depending on whether
# ADM they're in a mask.
safes["DESI_TARGET"] = set_target_bits(safes, sourcemask)
# ADM combine the targets and safe locations.
done = np.concatenate([targs, safes])
log.info('Generated {} SAFE (BADSKY) locations...t={:.1f}s'.format(
len(done)-ntargs, time()-t0))
# ADM remove any SAFE locations that are in bright masks (because they aren't really safe).
ii = (((done["DESI_TARGET"] & desi_mask.BAD_SKY) == 0) |
((done["DESI_TARGET"] & desi_mask.IN_BRIGHT_OBJECT) == 0))
done = done[ii]
log.info("...of these, {} SAFE (BADSKY) locations aren't in masks...t={:.1f}s"
.format(len(done)-ntargs, time()-t0))
log.info('Finishing up...t={:.1f}s'.format(time()-t0))
return done
def main():
global plate
global mjd
global fiber
global ra
global dec
global thing_id
# ================= PARSER =================
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--cat',
type=str,
default=None,
help='full path to the QUASAR catalogue FITS file, required')
parser.add_argument(
'--nobs',
type=int,
default=None,
help=
'whith this option, the program will show only objects that were observed a specified number of times'
)
parser.add_argument('--id',
type=int,
default=None,
help='the unique identifier of the object')
parser.add_argument('--ra',
type=float,
default=None,
help='target right ascension')
parser.add_argument('--dec',
type=float,
default=None,
help='target declination')
parser.add_argument('--rad',
type=float,
default=None,
help='search radius, must specify --ra and --dec')
parser.add_argument('-v', action='store_true', help='verbose')
parser.add_argument(
'--index',
type=int,
default=None,
nargs='+',
help='position of the object in the catalog (row number)')
parser.add_argument(
'--multi',
action='store_true',
help='identify duplicate QSOs by comparing unique object identifiers')
args = parser.parse_args()
if args.cat:
catalog_path = args.cat
columns = ['PLATE', 'MJD', 'FIBERID', 'RA', 'DEC', 'THING_ID']
catdata, cathead = fitsio.read(catalog_path,
ext=1,
header=True,
columns=columns)
else:
parser.error(
'Must specify the path to the QSO catalog using --cat option.')
# CODE TO REDUCE THE spAll-DR12.fits file to contain only neccessary data
#reduced_cat = fitsio.FITS('/Volumes/Transcend/Isotropy/spectra/spAll-DR12-reduced.fits','rw')
#hdict = {'PLATE', 'MJD', 'FIBERID', 'RA', 'DEC','THING_ID'}
#nrows = catdata.size
#reduced_cat.write(catdata)
#reduced_cat.close()
#sys.exit()
# ================= READ DATA FROM CATALOG =================
s = catdata.size
plate = np.zeros(s, dtype='int')
mjd = np.zeros(s, dtype='int')
fiber = np.zeros(s, dtype='int')
ra = np.zeros(s, dtype='float')
dec = np.zeros(s, dtype='float')
thing_id = np.zeros(s, dtype='int')
for i in xrange(s):
plate[i] = catdata[i][0]
mjd[i] = catdata[i][1]
fiber[i] = catdata[i][2]
ra[i] = catdata[i][3]
dec[i] = catdata[i][4]
thing_id[i] = catdata[i][5]
# ================= INDEX tag =================
if args.index:
index = args.index
print 'PRINTING ALL OBSERVATIONS ON THE OBJECT IN ROW', index
for i in index:
if args.v:
print_info_verbose(i)
else:
print_info_short(i)
# ================= MULTI tag =================
if args.multi:
# the search criterion is the unique 'thing_id' identifier
# return the positions of objects with the same thing_id value
uniq_src = np.unique(thing_id)
s = uniq_src.size
if s == thing_id.size:
print 'All QSOs in the catalog are unique, quitting program'
sys.exit()
# index = np.zeros(np.size(thing_id),dtype=('i8,i6'))
k = 0
X = []
for i in uniq_src:
ind = np.nonzero(thing_id == i)[0]
#index[count] = (i,np.nonzero(thing_id==i)[0])
entry = (i, ind.size, ind)
if args.nobs:
if ind.size == args.nobs:
X.append(entry)
print k, X[k]
k = k + 1
else:
X.append(entry)
print k, X[k]
k = k + 1
# ================= ID tag =================
if args.id:
pos = np.nonzero(thing_id == args.id)[0]
print 'PRINTING DATA ON ALL OBSERVATIONS OF OBJECT ', args.id
print '======================================================'
for i in pos:
if args.v:
print_info_verbose(i)
else:
print_info_short(i)
# ================= COORDINATES tag =================
if (args.ra and args.dec and not args.rad):
print 'Radius not given'
elif (not args.ra and args.dec and args.rad):
print 'RA not given'
elif (args.ra and not args.dec and args.rad):
print 'DEC not given'
elif (args.ra and args.dec and args.rad):
ra0 = args.ra
dec0 = args.dec
rad = args.rad
coord_0 = SkyCoord(ra0, dec0, unit='deg')
dist_list = []
coord_array = SkyCoord(ra, dec, unit='deg')
dist_array = coord_0.separation(coord_array)
src_list = []
for src in xrange(catdata.size):
inside_radius = dist_array[src].is_within_bounds(
0 * u.deg, rad * u.arcsec)
if inside_radius:
src_list.append(src)
print src
print src_list
def process(inpath, args, pool=None, pool_timeout=300):
"""Process a single GFA exposure.
"""
global GFA
if not inpath.exists():
logging.error('Non-existant path: {0}'.format(inpath))
return
if args.dry_run:
print(f'{inpath}')
return
# Is this a skycam exposure?
if inpath.name.startswith('sky'):
process_sky(inpath, args.outpath)
return
# Is this a guiding exposure?
guiding = inpath.name.startswith('guide')
# Lookup the NIGHT, EXPID, EXPTIME from the primary header.
hdr_ext = 'GUIDER' if guiding else 'GFA'
hdr = fitsio.read_header(str(inpath), ext=hdr_ext)
for k in 'NIGHT', 'EXPID', 'EXPTIME':
if k not in hdr:
logging.info('Skipping exposure with missing {0}: {1}'.format(
k, inpath))
return
cameras_key = 'ACQCAM' if guiding else 'IMAGECAM'
cameras = hdr.get(cameras_key)
if cameras is None:
logging.info('Skipping exposure with missing {0}/{1}: {2}'.format(
hdr_ext, cameras_key, inpath))
return
if cameras == 'GUIDE0,FOCUS1,GUIDE2,GUIDE3,FOCUS4,GUIDE5,FOCUS6,GUIDE7,GUIDE8,FOCUS':
# The full lists of 10 cameras exceeds the 71-char max length in the FITS standard.
# https://heasarc.gsfc.nasa.gov/docs/software/fitsio/c/c_user/node20.html
logging.warning('Patching {0}/{1} keyword value.'.format(
hdr_ext, cameras_key))
cameras = 'GUIDE0,FOCUS1,GUIDE2,GUIDE3,FOCUS4,GUIDE5,FOCUS6,GUIDE7,GUIDE8,FOCUS9'
if cameras == 'G0,G2,G3,G5,G7,G8,F1,F4,F6,F9':
# Data after the Nov-2020 restart needs this for GFA FITS files.
cameras = 'GUIDE0,FOCUS1,GUIDE2,GUIDE3,FOCUS4,GUIDE5,FOCUS6,GUIDE7,GUIDE8,FOCUS9'
night = str(hdr['NIGHT'])
expid = '{0:08d}'.format(hdr['EXPID'])
if hdr['EXPTIME'] == 0:
logging.info('Skipping zero EXPTIME: {0}/{1}'.format(night, expid))
return
stars_expected = {}
if guiding and args.guide_stars:
assert GMM is not None, 'GMM not initialized.'
PlateMaker, GuiderExpected = None, None
try:
GuiderExpected, _, _ = load_guider_centroids(inpath.parent, expid)
except (ValueError, KeyError):
logging.warning('Guider centroids json file not readable.')
try:
PlateMaker = fitsio.read(str(inpath), ext='PMGSTARS')
except IOError as e:
logging.warning('PMGSTARS extension not found.')
if PlateMaker is not None:
# Use PlateMaker (row, col) for expected positions of each guide star.
for camera in np.unique(PlateMaker['GFA_LOC']):
stars = PlateMaker[PlateMaker['GFA_LOC'] == camera]
stars_expected[camera] = np.array(
(stars['COL'], stars['ROW'], stars['MAG'])).T
elif GuiderExpected is not None:
# Fall back to guider centroids. I assume the JSON files uses the same coordinate
# convention as PlateMaker since it copies the PlateMaker values when both are present.
for camera in GuiderExpected:
nstars = len(GuiderExpected[camera])
if nstars > 0:
stars_expected[camera] = np.array(
(GuiderExpected[camera][:, 0],
GuiderExpected[camera][:, 1], np.zeros(nstars))).T
else:
logging.warning(f'No guide stars available for {night}/{expid}.')
# Prepare the output path.
outpath = args.outpath / night / expid
outpath.mkdir(parents=True, exist_ok=True)
# Are there already existing outputs?
fitspath = outpath / 'gfaetc_{0}.fits'.format(expid)
if fitspath.exists() and not args.overwrite:
logging.info('Will not overwrite outputs in {0}'.format(outpath))
return
# Process each camera in the input.
logging.info('Processing {0} from {1}'.format(cameras, inpath))
results = {}
framepath = outpath if args.save_frames else None
only_cameras = [] if args.only_cameras is None else args.only_cameras.split(
',')
for camera in cameras.split(','):
if camera not in GFA.gfa_names:
logging.warning(
'Ignoring invalid camera name in header: "{0}".'.format(
camera))
continue
if len(only_cameras) > 0 and camera not in only_cameras:
logging.info('Skipping {0} because only-cameras is "{1}".'.format(
camera, args.only_cameras))
continue
if guiding and camera.startswith('FOCUS'):
# Guiding exposures do not record FOCUS data.
continue
# Fetch this camera's CCD temperatures and exposure times.
if guiding:
info = fitsio.read(str(inpath),
ext=camera + 'T',
columns=('MJD-OBS', 'EXPTIME', 'GCCDTEMP'))
# stars might be None if --guide-stars arg not present or no guide stars available.
stars = stars_expected.get(camera, None)
else:
info = fitsio.read_header(str(inpath), ext=camera)
stars = None
mjdobs = info['MJD-OBS']
exptime = info['EXPTIME']
ccdtemp = info['GCCDTEMP']
process_args = (inpath, night, expid, guiding, camera, mjdobs, exptime,
ccdtemp, framepath, stars, args.max_dither,
args.num_dither)
if pool is None:
result = process_one(*process_args)
if result is None:
logging.error('Error processing HDU {0}'.format(camera))
else:
results[camera] = result
else:
results[camera] = pool.apply_async(process_one, process_args)
if pool:
# Collect the pooled results.
for camera in results:
try:
result = results[camera].get(timeout=pool_timeout)
if result is None:
logging.error(
'Error pool processing HDU {0}'.format(camera))
else:
results[camera] = result
except TimeoutError:
logging.error(
'Timeout waiting for {0} pool result'.format(camera))
del results[camera]
# Save the output FITS file.
with fitsio.FITS(str(fitspath), 'rw', clobber=True) as hdus:
meta = {
k: hdr.get(k)
for k in ('NIGHT', 'EXPID', 'MJD-OBS', 'EXPTIME', 'PROGRAM',
'HEXPOS', 'TRUSTEMP', 'ADC1PHI', 'ADC2PHI', 'MOUNTHA',
'MOUNTAZ', 'MOUNTEL', 'MOUNTDEC')
}
hdus.write(np.zeros(1), header=meta)
for camera in results:
# Retrieve the result of processing the initial image and any subsequent guide frames.
initial, frames = results[camera]
if camera.startswith('GUIDE'):
hdus.write(np.stack(initial).astype(np.float32),
extname=camera)
else:
L, R = initial
if L is not None:
hdus.write(np.stack(L).astype(np.float32),
extname=camera + 'L')
if R is not None:
hdus.write(np.stack(R).astype(np.float32),
extname=camera + 'R')
if frames is not None:
(Dsum, WDsum, Msum, fit_params, gmm_params) = frames
hdus.write(np.stack((Dsum, WDsum, Msum)).astype(np.float32),
extname=camera + 'G')
hdus.write(fit_params.astype(np.float32), extname=camera + 'P')
hdus.write(gmm_params.astype(np.float32), extname=camera + 'M')
try:
# Produce a summary plot of the delivered image quality measured from the first image.
fig = plot_image_quality(
{camera: result[0]
for camera, result in results.items()}, meta)
# Save the summary plot.
figpath = outpath / 'gfadiq_{0}.png'.format(expid)
plt.savefig(figpath)
plt.close(fig)
logging.info('Wrote {0}'.format(figpath))
except Exception as e:
logging.warning('Failed to create image quality plot.')
print('Number of pixels below the 2-3rd of average population:{}'.format(np.sum(low)))
wl = np.where(low)[0]
weights = np.ones(len(np.unique(pix)))
weights[wl] = np.round(histpix[p][wl]/avgpop, 2)
# print(len(weights),len(low),len(histpix),weights)
upix = np.unique(pix)
return pix,upix, weights
###########################################################
nside = 8
path = '/utahsystem/July2020/'
data = 'eBOSS_LRG_NGC_pip_v7_2_new.dat.fits'
random = 'eBOSS_LRG_NGC_pip_v7_2_ran_withS.fits'
d = fitsio.read(path+data)
dra = d['RA']
ddec = d['DEC']
r = fitsio.read(path+random)
rra = r['RA']
rdec = r['DEC']
pix, uniqpix, weights = get_pixnum(rra,rdec,nside)
picut, pimax = 60, 60
###################################
chunks = glob(path+'/RR_NGC_LRG_z0.6_z1.0_downsampled/*.fits')
def _cached_catalog_read(fname):
return fitsio.read(fname)
save_dir = os.path.join(os.getcwd(), cfg['output']['save_dir'])
if not os.path.exists(save_dir):
os.mkdir(save_dir)
log_dir = os.path.join(os.getcwd(), cfg['output']['save_dir'], cfg['output']['log_dir'])
if not os.path.exists(log_dir):
os.mkdir(log_dir)
try:
sig_cut = float(sys.argv[1])
except:
sig_cut = 5.5
print('Plotting hotspots with sig > {}'.format(sig_cut))
candidate_list = fits.read(candidate_list)
try: # simple
candidate_list = candidate_list[candidate_list['SIG'] > sig_cut]
except: # ugali
candidate_list = candidate_list[candidate_list['TS'] > 25]
# for PS1
#candidate_list = candidate_list[candidate_list['DEC'] > -15]
print('{} candidates found...').format(len(candidate_list))
############################################################
#for candidate in [candidate_list[:10]]:
for candidate in candidate_list:
try: # simple
def run(self):
outdir=mkdir(self.config['output']['simdir'])
logdir=mkdir(join(outdir,'log'))
# Actually copy config instead of re-writing
shutil.copy(self.config.filename,outdir)
configfile = join(outdir,os.path.basename(self.config.filename))
if 'simulate' in self.opts.run:
logger.info("Running 'simulate'...")
if self.opts.num is None: self.opts.num = self.config['simulator']['njobs']
for i in range(self.opts.num):
outfile=join(outdir,self.config['output']['simfile']%i)
base = splitext(os.path.basename(outfile))[0]
logfile=join(logdir,base+'.log')
jobname=base
script = self.config['simulator']['script']
cmd='%s %s %s --seed %i'%(script,configfile,outfile,i)
#cmd='%s %s %s'%(script,self.opts.config,outfile)
self.batch.submit(cmd,jobname,logfile)
time.sleep(0.1)
if 'analyze' in self.opts.run:
logger.info("Running 'analyze'...")
dirname = self.config['simulate']['dirname']
catfiles = sorted(glob.glob(join(dirname,self.config['simulate']['catfile'])))
popfile = join(dirname,self.config['simulate']['popfile'])
batch = self.config['simulate']['batch']
for i,catfile in enumerate(catfiles):
basename = os.path.basename(catfile)
outfile = join(outdir,basename)
base = splitext(os.path.basename(outfile))[0]
logfile=join(logdir,base+'.log')
jobname=base
if exists(outfile) and not self.opts.force:
msg = "Found %s;"%outfile
if exists(logfile) and len(self.batch.bfail(logfile)):
msg += " failed."
logger.info(msg)
else:
msg += " skipping..."
logger.info(msg)
continue
script = self.config['simulate']['script']
cmd='%s %s -m 0 --rerun -p %s -c %s -o %s'%(script,configfile,popfile,catfile,outfile)
self.batch.max_jobs = batch.get('max_jobs',200)
opts = batch.get(self.opts.queue,dict())
self.batch.submit(cmd,jobname,logfile,**opts)
time.sleep(0.1)
if 'sensitivity' in self.opts.run:
logger.info("Running 'sensitivity'...")
if 'merge' in self.opts.run:
logger.info("Running 'merge'...")
filenames=join(outdir,self.config['simulate']['catfile'])
infiles=sorted(glob.glob(filenames))
print("Reading %i files..."%len(infiles))
data = np.concatenate([fitsio.read(f,ext=1) for f in infiles])
hdr = fitsio.read_header(infiles[0],ext=1)
outfile = "./merged_sims.fits"
logger.info("Writing %s..."%outfile)
fitsio.write(outfile,data,header=hdr,clobber=True)
if 'plot' in self.opts.run:
logger.info("Running 'plot'...")
import ugali.utils.plotting
import pylab as plt
plotdir = mkdir(self.config['output']['plotdir'])
data = fitsio.read(join(outdir,"merged_sims.fits"))
data = data[~np.isnan(data['ts'])]
bigfig,bigax = plt.subplots()
for dist in np.unique(data['fit_distance']):
logger.info(' Plotting distance: %s'%dist)
ts = data['ts'][data['fit_distance'] == dist]
ugali.utils.plotting.drawChernoff(bigax,ts,bands='none',color='gray')
fig,ax = plt.subplots(1,2,figsize=(10,5))
ugali.utils.plotting.drawChernoff(ax[0],ts,bands='none',pdf=True)
ugali.utils.plotting.drawChernoff(ax[1],ts)
fig.suptitle(r'Chernoff ($\mu = %g$)'%dist)
ax[0].annotate(r"$N=%i$"%len(ts), xy=(0.15,0.85), xycoords='axes fraction',
bbox={'boxstyle':"round",'fc':'1'})
basename = 'chernoff_u%g.png'%dist
outfile = os.path.join(plotdir,basename)
plt.savefig(outfile)
bigfig.suptitle('Chernoff!')
basename = 'chernoff_all.png'
outfile = os.path.join(plotdir,basename)
plt.savefig(outfile)
#idx=np.random.randint(len(data['ts'])-1,size=400)
#idx=slice(400)
#ugali.utils.plotting.plotChernoff(data['ts'][idx])
#ugali.utils.plotting.plotChernoff(data['fit_ts'])
plt.ion()
"""
-c <couchbase config> - default environment COUCHBASE_BACKEND_CFG
-i - reinitialize the object id counter so that the next object will be given oject id = 1
"""
opts, args = getopt.getopt(sys.argv[1:], "n:h?c:i")
opts = dict(opts)
if '-h' in opts or '-?' in opts or len(args) != 3:
print Usage
sys.exit(1)
init_oid = "-i" in opts
config = opts.get("-c")
group_size = int(opts.get("-n", 100000))
bucket, dataset, path = args
data = fitsio.read(path)
print "%d objects in the input file %s" % (len(data), path)
backend = CouchBaseBackend(bucket)
if init_oid:
counter_key = "%s:@bliss_next_object_id" % (dataset, )
try:
backend.delete([counter_key]) # remove if exists
except:
pass
backend.counter(counter_key, initial=1)
print "Counter bliss_next_object_id initialized to 1"
add_objects(backend, data, dataset, group_size)
def test_kkk():
# Use kappa(r) = A exp(-r^2/2s^2)
#
# The Fourier transform is: kappa~(k) = 2 pi A s^2 exp(-s^2 k^2/2) / L^2
#
# B(k1,k2) = <k~(k1) k~(k2) k~(-k1-k2)>
# = (2 pi A (s/L)^2)^3 exp(-s^2 (|k1|^2 + |k2|^2 - k1.k2))
# = (2 pi A (s/L)^2)^3 exp(-s^2 (|k1|^2 + |k2|^2 + |k3|^2)/2)
#
# zeta(r1,r2) = (1/2pi)^4 int(d^2k1 int(d^2k2 exp(ik1.x1) exp(ik2.x2) B(k1,k2) ))
# = 2/3 pi A^3 (s/L)^2 exp(-(x1^2 + y1^2 + x2^2 + y2^2 - x1x2 - y1y2)/3s^2)
# = 2/3 pi A^3 (s/L)^2 exp(-(d1^2 + d2^2 + d3^2)/6s^2)
A = 0.05
s = 10.
if __name__ == '__main__':
ngal = 200000
L = 30. * s # Not infinity, so this introduces some error. Our integrals were to infinity.
tol_factor = 1
else:
# Looser tests from nosetests that don't take so long to run.
ngal = 10000
L = 20. * s
tol_factor = 5
rng = np.random.RandomState(8675309)
x = (rng.random_sample(ngal)-0.5) * L
y = (rng.random_sample(ngal)-0.5) * L
r2 = (x**2 + y**2)/s**2
kappa = A * np.exp(-r2/2.)
min_sep = 11.
max_sep = 15.
nbins = 3
min_u = 0.7
max_u = 1.0
nubins = 3
min_v = 0.1
max_v = 0.3
nvbins = 2
cat = treecorr.Catalog(x=x, y=y, k=kappa, x_units='arcmin', y_units='arcmin')
kkk = treecorr.KKKCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins,
sep_units='arcmin', verbose=1)
kkk.process(cat)
# log(<d>) != <logd>, but it should be close:
print('meanlogd1 - log(meand1) = ',kkk.meanlogd1 - np.log(kkk.meand1))
print('meanlogd2 - log(meand2) = ',kkk.meanlogd2 - np.log(kkk.meand2))
print('meanlogd3 - log(meand3) = ',kkk.meanlogd3 - np.log(kkk.meand3))
print('meand3 / meand2 = ',kkk.meand3 / kkk.meand2)
print('meanu = ',kkk.meanu)
print('max diff = ',np.max(np.abs(kkk.meand3/kkk.meand2 -kkk.meanu)))
print('max rel diff = ',np.max(np.abs((kkk.meand3/kkk.meand2 -kkk.meanu)/kkk.meanu)))
print('(meand1 - meand2)/meand3 = ',(kkk.meand1-kkk.meand2) / kkk.meand3)
print('meanv = ',kkk.meanv)
print('max diff = ',np.max(np.abs((kkk.meand1-kkk.meand2)/kkk.meand3 -np.abs(kkk.meanv))))
print('max rel diff = ',np.max(np.abs(((kkk.meand1-kkk.meand2)/kkk.meand3-np.abs(kkk.meanv))
/ kkk.meanv)))
np.testing.assert_allclose(kkk.meanlogd1, np.log(kkk.meand1), rtol=1.e-3)
np.testing.assert_allclose(kkk.meanlogd2, np.log(kkk.meand2), rtol=1.e-3)
np.testing.assert_allclose(kkk.meanlogd3, np.log(kkk.meand3), rtol=1.e-3)
np.testing.assert_allclose(kkk.meand3/kkk.meand2, kkk.meanu, rtol=1.e-5 * tol_factor)
np.testing.assert_allclose(np.abs(kkk.meand1-kkk.meand2)/kkk.meand3, np.abs(kkk.meanv),
rtol=1.e-5 * tol_factor, atol=1.e-5 * tol_factor)
np.testing.assert_allclose(kkk.meanlogd3-kkk.meanlogd2, np.log(kkk.meanu),
atol=1.e-3 * tol_factor)
np.testing.assert_allclose(np.log(np.abs(kkk.meand1-kkk.meand2))-kkk.meanlogd3,
np.log(np.abs(kkk.meanv)), atol=2.e-3 * tol_factor)
d1 = kkk.meand1
d2 = kkk.meand2
d3 = kkk.meand3
#print('rnom = ',np.exp(kkk.logr))
#print('unom = ',kkk.u)
#print('vnom = ',kkk.v)
#print('d1 = ',d1)
#print('d2 = ',d2)
#print('d3 = ',d3)
# The L^2 term in the denominator of true_zeta is the area over which the integral is done.
# Since the centers of the triangles don't go to the edge of the box, we approximate the
# correct area by subtracting off 2d2 from L, which should give a slightly better estimate
# of the correct area to use here.
L = L - 2.*d2
true_zeta = (2.*np.pi/3) * A**3 * (s/L)**2 * np.exp(-(d1**2+d2**2+d3**2)/(6.*s**2))
#print('ntri = ',kkk.ntri)
print('zeta = ',kkk.zeta)
print('true_zeta = ',true_zeta)
#print('ratio = ',kkk.zeta / true_zeta)
#print('diff = ',kkk.zeta - true_zeta)
print('max rel diff = ',np.max(np.abs((kkk.zeta - true_zeta)/true_zeta)))
np.testing.assert_allclose(kkk.zeta, true_zeta, rtol=0.1 * tol_factor)
np.testing.assert_allclose(np.log(np.abs(kkk.zeta)), np.log(np.abs(true_zeta)),
atol=0.1 * tol_factor)
# Check that we get the same result using the corr3 functin:
cat.write(os.path.join('data','kkk_data.dat'))
config = treecorr.config.read_config('configs/kkk.yaml')
config['verbose'] = 0
treecorr.corr3(config)
corr3_output = np.genfromtxt(os.path.join('output','kkk.out'), names=True, skip_header=1)
np.testing.assert_almost_equal(corr3_output['zeta'], kkk.zeta.flatten())
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check the fits write option
out_file_name = os.path.join('output','kkk_out.fits')
kkk.write(out_file_name)
data = fitsio.read(out_file_name)
np.testing.assert_almost_equal(data['r_nom'], np.exp(kkk.logr).flatten())
np.testing.assert_almost_equal(data['u_nom'], kkk.u.flatten())
np.testing.assert_almost_equal(data['v_nom'], kkk.v.flatten())
np.testing.assert_almost_equal(data['meand1'], kkk.meand1.flatten())
np.testing.assert_almost_equal(data['meanlogd1'], kkk.meanlogd1.flatten())
np.testing.assert_almost_equal(data['meand2'], kkk.meand2.flatten())
np.testing.assert_almost_equal(data['meanlogd2'], kkk.meanlogd2.flatten())
np.testing.assert_almost_equal(data['meand3'], kkk.meand3.flatten())
np.testing.assert_almost_equal(data['meanlogd3'], kkk.meanlogd3.flatten())
np.testing.assert_almost_equal(data['meanu'], kkk.meanu.flatten())
np.testing.assert_almost_equal(data['meanv'], kkk.meanv.flatten())
np.testing.assert_almost_equal(data['zeta'], kkk.zeta.flatten())
np.testing.assert_almost_equal(data['sigma_zeta'], np.sqrt(kkk.varzeta.flatten()))
np.testing.assert_almost_equal(data['weight'], kkk.weight.flatten())
np.testing.assert_almost_equal(data['ntri'], kkk.ntri.flatten())
# Check the read function
# Note: These don't need the flatten. The read function should reshape them to the right shape.
kkk2 = treecorr.KKKCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
min_u=min_u, max_u=max_u, min_v=min_v, max_v=max_v,
nubins=nubins, nvbins=nvbins,
sep_units='arcmin', verbose=1)
kkk2.read(out_file_name)
np.testing.assert_almost_equal(kkk2.logr, kkk.logr)
np.testing.assert_almost_equal(kkk2.u, kkk.u)
np.testing.assert_almost_equal(kkk2.v, kkk.v)
np.testing.assert_almost_equal(kkk2.meand1, kkk.meand1)
np.testing.assert_almost_equal(kkk2.meanlogd1, kkk.meanlogd1)
np.testing.assert_almost_equal(kkk2.meand2, kkk.meand2)
np.testing.assert_almost_equal(kkk2.meanlogd2, kkk.meanlogd2)
np.testing.assert_almost_equal(kkk2.meand3, kkk.meand3)
np.testing.assert_almost_equal(kkk2.meanlogd3, kkk.meanlogd3)
np.testing.assert_almost_equal(kkk2.meanu, kkk.meanu)
np.testing.assert_almost_equal(kkk2.meanv, kkk.meanv)
np.testing.assert_almost_equal(kkk2.zeta, kkk.zeta)
np.testing.assert_almost_equal(kkk2.varzeta, kkk.varzeta)
np.testing.assert_almost_equal(kkk2.weight, kkk.weight)
np.testing.assert_almost_equal(kkk2.ntri, kkk.ntri)
assert kkk2.coords == kkk.coords
assert kkk2.metric == kkk.metric
assert kkk2.sep_units == kkk.sep_units
assert kkk2.bin_type == kkk.bin_type
def calculate(self, infile, field=1, simple=False):
logger.info("Calculating magnitude limit from %s" % infile)
#manglefile = self.config['mangle']['infile_%i'%field]
#footfile = self.config['data']['footprint']
#try:
# footprint = fitsio.read(footfile)['I'].ravel()
#except:
# logger.warn("Couldn't open %s; will try again."%footfile)
# footprint = footfile
mag_column = self.config['catalog']['mag_%i_field' % field]
magerr_column = self.config['catalog']['mag_err_%i_field' % field]
# For simple maglims
release = self.config['data']['release'].lower()
band = self.config['catalog']['mag_%i_band' % field]
pixel_pix_name = 'PIX%i' % self.nside_pixel
# If the data already has a healpix pixel assignment then use it
# Otherwise recalculate...
try:
data = fitsio.read(infile, columns=[pixel_pix_name])
except ValueError as e:
logger.info(str(e))
columns = [
self.config['catalog']['lon_field'],
self.config['catalog']['lat_field']
]
data = fitsio.read(infile, columns=columns)[columns]
pix = ang2pix(self.nside_pixel, data[columns[0]], data[columns[1]])
data = recfuncs.rec_append_fields(data, pixel_pix_name, pix)
#mask_pixels = np.arange( hp.nside2npix(self.nside_mask), dtype='int')
mask_maglims = np.zeros(hp.nside2npix(self.nside_mask))
out_pixels = np.zeros(0, dtype='int')
out_maglims = np.zeros(0)
# Find the objects in each pixel
pixel_pix = data[pixel_pix_name]
mask_pix = ugali.utils.skymap.superpixel(pixel_pix, self.nside_pixel,
self.nside_mask)
count = Counter(mask_pix)
pixels = sorted(count.keys())
pix_digi = np.digitize(mask_pix, pixels).argsort()
idx = 0
min_num = 500
signal_to_noise = 10.
magerr_lim = 1 / signal_to_noise
for pix in pixels:
# Calculate the magnitude limit in each pixel
num = count[pix]
objs = data[pix_digi[idx:idx + num]]
idx += num
if simple:
# Set constant magnitude limits
logger.debug("Simple magnitude limit for %s" % infile)
mask_maglims[pix] = MAGLIMS[release][band]
elif num < min_num:
logger.info('Found <%i objects in pixel %i' % (min_num, pix))
mask_maglims[pix] = 0
else:
mag = objs[mag_column]
magerr = objs[magerr_column]
# Estimate the magnitude limit as suggested by:
# https://deswiki.cosmology.illinois.edu/confluence/display/DO/SVA1+Release+Document
# (https://desweb.cosmology.illinois.edu/confluence/display/Operations/SVA1+Doc)
maglim = np.median(mag[(magerr > 0.9 * magerr_lim)
& (magerr < 1.1 * magerr_lim)])
# Alternative method to estimate the magnitude limit by fitting median
#mag_min, mag_max = mag.min(),mag.max()
#mag_bins = np.arange(mag_min,mag_max,0.1) #0.1086?
#x,y = ugali.utils.binning.binnedMedian(mag,magerr,mag_bins)
#x,y = x[~np.isnan(y)],y[~np.isnan(y)]
#magerr_med = interp1d(x,y)
#mag0 = np.median(x)
#maglim = brentq(lambda a: magerr_med(a)-magerr_lim,x.min(),x.max(),disp=False)
# Median from just objects near magerr cut
mask_maglims[pix] = maglim
logger.debug("%i (n=%i): maglim=%g" %
(pix, num, mask_maglims[pix]))
subpix = ugali.utils.skymap.subpixel(pix, self.nside_mask,
self.nside_pixel)
maglims = np.zeros(len(subpix)) + mask_maglims[pix]
out_pixels = np.append(out_pixels, subpix)
out_maglims = np.append(out_maglims, maglims)
# Remove empty pixels
logger.info("Removing empty pixels")
idx = np.nonzero(out_maglims > 0)[0]
out_pixels = out_pixels[idx]
out_maglims = out_maglims[idx]
# Remove pixels outside the footprint
if self.footfile:
logger.info("Checking footprint against %s" % self.footfile)
lon, lat = pix2ang(self.nside_pixel, out_pixels)
if self.config['coords']['coordsys'] == 'gal':
ra, dec = gal2cel(lon, lat)
else:
ra, dec = lon, lat
footprint = inFootprint(self.footprint, ra, dec)
idx = np.nonzero(footprint)[0]
out_pixels = out_pixels[idx]
out_maglims = out_maglims[idx]
logger.info("MAGLIM = %.3f +/- %.3f" %
(np.mean(out_maglims), np.std(out_maglims)))
return out_pixels, out_maglims
def main(config, outpath, magflag, clobber=False):
files = config['Runtime']['outpath']
files = glob(files)
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
files = files[rank::size]
cc = config['Cosmology']
nb_config = config['NBody']
cosmo = Cosmology(**cc)
domain = Domain(cosmo, **nb_config.pop('Domain'))
domain.decomp(comm, comm.rank, comm.size)
d = domain.dummyDomain()
nbody = NBody(cosmo, d, **nb_config)
model = ADDGALSModel(nbody, **config['GalaxyModel']['ADDGALSModel'])
filters = config['GalaxyModel']['ADDGALSModel']['colorModelConfig'][
'filters']
train = fitsio.read(config['GalaxyModel']['ADDGALSModel']
['colorModelConfig']['trainingSetFile'])
nk = len(filters)
for f in files:
pixnum = f.split('.')[-2]
fname = '{}-{}.{}.fits'.format(outpath, magflag, pixnum)
if os.path.exists(fname) & (not clobber):
continue
g = fitsio.read(f,
columns=[
'SEDID', 'Z', 'MAG_R_EVOL', 'MU', 'Z_COS', 'VX',
'VY', 'VZ', 'PX', 'PY', 'PZ'
])
ngal = len(g)
pos = np.zeros((ngal, 3))
vel = np.zeros((ngal, 3))
pos[:, 0] = g['PX']
pos[:, 1] = g['PY']
pos[:, 2] = g['PZ']
vel[:, 0] = g['VX']
vel[:, 1] = g['VY']
vel[:, 2] = g['VZ']
v_r = np.sum(pos * vel, axis=1) / np.sqrt(np.sum(pos**2, axis=1))
z_rsd = g['Z_COS'] + v_r * (1 + g['Z_COS']) / 299792.458
del pos, vel, v_r
m_r = np.copy(g['MAG_R_EVOL'])
mu = np.copy(g['MU'])
coeffs = train['COEFFS'][g['SEDID']]
del g
mags = np.zeros(ngal,
dtype=np.dtype([('TMAG', (np.float, nk)),
('AMAG', (np.float, nk)),
('LMAG', (np.float, nk)),
('OMAG', (np.float, nk)),
('OMAGERR', (np.float, nk)),
('FLUX', (np.float, nk)),
('IVAR', (np.float, nk)),
('Z', np.float)]))
mags['Z'] = z_rsd
z_a = copy(mags['Z'])
z_a[z_a < 1e-6] = 1e-6
mags['TMAG'], mags['AMAG'] = model.colorModel.computeMagnitudes(
m_r, z_rsd, coeffs, filters)
for i in range(len(filters)):
mags['LMAG'][:, i] = mags['TMAG'][:, i] - 2.5 * np.log10(mu)
fitsio.write(fname, mags, clobber=clobber)
def read_SV_fits(self):
import fitsio
subset_photoz_bin = False
if subset_photoz_bin:
# Subset to a single photo-z bin
photoz_bin = 0
Dataset.__init__(self, desfilename,
"DESData_colordiff_bin" + str(photoz_bin), '')
data = fitsio.read(self.filename)
# Mask out the bad objects
SVA1_FLAG_mask = (data['SVA1_FLAG'] == 0)
NGMIX_FLAG_mask = (data['NGMIX_FLAG'] == 0)
PHOTOZ_FLAG_mask = (data['PHOTOZ_BIN'] > -1)
data = data[SVA1_FLAG_mask & NGMIX_FLAG_mask & PHOTOZ_FLAG_mask]
# Read in the desired columns.
# from SVA_GOLD
# WLINFO filtered by Umaa to omit objects with
# SVA1_FLAG != 0
# NGMIX_FLAG != 0
# PHOTOZ_BIN != -1
# We want R, G-R, R-I, I-Z
self.data = data['MAG_AUTO_R']
self.features = ['MAG_AUTO_R']
# G-R
self.data = np.vstack(
[self.data, data['MAG_AUTO_G'] - data['MAG_AUTO_R']])
self.features += ['G-R']
# R-I
self.data = np.vstack(
[self.data, data['MAG_AUTO_R'] - data['MAG_AUTO_I']])
self.features += ['R-I']
# I-Z
self.data = np.vstack(
[self.data, data['MAG_AUTO_I'] - data['MAG_AUTO_Z']])
self.features += ['I-Z']
# MEAN_PHOTOZ
self.data = np.vstack([self.data, data['MEAN_PHOTOZ']])
self.features += ['MEAN_PHOTOZ']
# PHOTOZ_BIN
self.data = np.vstack([self.data, data['PHOTOZ_BIN']])
self.features += ['PHOTOZ_BIN']
# Data is d x n
print self.data.shape
# Scale some features as needed
for f in self.features:
if 'MAG_AUTO' in f: # subtract the min
minval = np.min(self.data[self.features.index(f), :])
self.data[self.features.index(f), :] -= minval
print 'Subtracting %f from %s.' % (minval, f)
newf = f + '-sub%.2f' % minval
self.features[self.features.index(f)] = newf
f = newf
print '%s range: ' % f,
print self.data[self.features.index(f), :].min(),
print self.data[self.features.index(f), :].max()
self.labels = ['%d_%.6f_%.6f' % (id,ra,dec) for (id,ra,dec) in \
zip(data['COADD_OBJECTS_ID'],
data['RA'],
data['DEC'])]
self.xvals = np.arange(self.data.shape[0]).reshape(-1, 1)
self.features = np.array(self.features)
if subset_photoz_bin:
# Subset to a single photo-z bin
keep = (self.data[np.where(self.features == 'PHOTOZ_BIN')[0][0],:] == \
photoz_bin)
self.data = self.data[:, keep]
# Still annoys me that you can't index a list with a list
self.labels = [self.labels[k] for k in np.where(keep)[0]]
# Remove the MEAN_PHOTOZ and PHOTOZ_BIN features
print('Removing PHOTOZ features.')
features_keep = ((self.features != 'PHOTOZ_BIN') &
(self.features != 'MEAN_PHOTOZ'))
self.data = self.data[features_keep, :]
self.features = self.features[features_keep]
self.xvals = np.arange(self.data.shape[0]).reshape(-1, 1)
def test_kg():
# Use gamma_t(r) = gamma0 exp(-r^2/2r0^2) around a bunch of foreground lenses.
# i.e. gamma(r) = -gamma0 exp(-r^2/2r0^2) (x+iy)^2/r^2
# For each lens, we divide this by a random kappa value assigned to that lens, so
# the final kg output shoudl be just gamma_t.
nlens = 1000
nsource = 30000
r0 = 10.
L = 50. * r0
gamma0 = 0.05
np.random.seed(8675309)
xl = (np.random.random_sample(nlens)-0.5) * L
yl = (np.random.random_sample(nlens)-0.5) * L
kl = np.random.normal(0.23, 0.05, (nlens,) )
xs = (np.random.random_sample(nsource)-0.5) * L
ys = (np.random.random_sample(nsource)-0.5) * L
g1 = np.zeros( (nsource,) )
g2 = np.zeros( (nsource,) )
for x,y,k in zip(xl,yl,kl):
dx = xs-x
dy = ys-y
r2 = dx**2 + dy**2
gammat = gamma0 * np.exp(-0.5*r2/r0**2) / k
g1 += -gammat * (dx**2-dy**2)/r2
g2 += -gammat * (2.*dx*dy)/r2
lens_cat = treecorr.Catalog(x=xl, y=yl, k=kl, x_units='arcmin', y_units='arcmin')
source_cat = treecorr.Catalog(x=xs, y=ys, g1=g1, g2=g2, x_units='arcmin', y_units='arcmin')
kg = treecorr.KGCorrelation(bin_size=0.1, min_sep=1., max_sep=20., sep_units='arcmin',
verbose=1)
kg.process(lens_cat, source_cat)
r = kg.meanr
true_gt = gamma0 * np.exp(-0.5*r**2/r0**2)
print('kg.xi = ',kg.xi)
print('kg.xi_im = ',kg.xi_im)
print('true_gammat = ',true_gt)
print('ratio = ',kg.xi / true_gt)
print('diff = ',kg.xi - true_gt)
print('max diff = ',max(abs(kg.xi - true_gt)))
np.testing.assert_allclose(kg.xi, true_gt, rtol=0.1)
np.testing.assert_allclose(kg.xi_im, 0., atol=1.e-2)
# Check that we get the same result using the corr2 function:
lens_cat.write(os.path.join('data','kg_lens.dat'))
source_cat.write(os.path.join('data','kg_source.dat'))
config = treecorr.read_config('configs/kg.yaml')
config['verbose'] = 0
config['precision'] = 8
treecorr.corr2(config)
corr2_output = np.genfromtxt(os.path.join('output','kg.out'), names=True, skip_header=1)
print('kg.xi = ',kg.xi)
print('from corr2 output = ',corr2_output['kgamT'])
print('ratio = ',corr2_output['kgamT']/kg.xi)
print('diff = ',corr2_output['kgamT']-kg.xi)
np.testing.assert_allclose(corr2_output['kgamT'], kg.xi, rtol=1.e-3)
print('xi_im from corr2 output = ',corr2_output['kgamX'])
np.testing.assert_allclose(corr2_output['kgamX'], 0., atol=1.e-2)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check the fits write option
out_file_name1 = os.path.join('output','kg_out1.fits')
kg.write(out_file_name1)
data = fitsio.read(out_file_name1)
np.testing.assert_almost_equal(data['R_nom'], np.exp(kg.logr))
np.testing.assert_almost_equal(data['meanR'], kg.meanr)
np.testing.assert_almost_equal(data['meanlogR'], kg.meanlogr)
np.testing.assert_almost_equal(data['kgamT'], kg.xi)
np.testing.assert_almost_equal(data['kgamX'], kg.xi_im)
np.testing.assert_almost_equal(data['sigma'], np.sqrt(kg.varxi))
np.testing.assert_almost_equal(data['weight'], kg.weight)
np.testing.assert_almost_equal(data['npairs'], kg.npairs)
# Check the read function
kg2 = treecorr.KGCorrelation(bin_size=0.1, min_sep=1., max_sep=20., sep_units='arcmin')
kg2.read(out_file_name1)
np.testing.assert_almost_equal(kg2.logr, kg.logr)
np.testing.assert_almost_equal(kg2.meanr, kg.meanr)
np.testing.assert_almost_equal(kg2.meanlogr, kg.meanlogr)
np.testing.assert_almost_equal(kg2.xi, kg.xi)
np.testing.assert_almost_equal(kg2.xi_im, kg.xi_im)
np.testing.assert_almost_equal(kg2.varxi, kg.varxi)
np.testing.assert_almost_equal(kg2.weight, kg.weight)
np.testing.assert_almost_equal(kg2.npairs, kg.npairs)
assert kg2.coords == kg.coords
assert kg2.metric == kg.metric
assert kg2.sep_units == kg.sep_units
assert kg2.bin_type == kg.bin_type
def test_direct_spherical():
# Repeat in spherical coords
ngal = 50
s = 10.
rng = np.random.RandomState(8675309)
x = rng.normal(0,s, (ngal,) )
y = rng.normal(0,s, (ngal,) ) + 200 # Put everything at large y, so small angle on sky
z = rng.normal(0,s, (ngal,) )
w = rng.random_sample(ngal)
kap = rng.normal(0,3, (ngal,) )
w = np.ones_like(w)
ra, dec = coord.CelestialCoord.xyz_to_radec(x,y,z)
cat = treecorr.Catalog(ra=ra, dec=dec, ra_units='rad', dec_units='rad', w=w, k=kap)
min_sep = 1.
bin_size = 0.2
nrbins = 10
nubins = 5
nvbins = 5
kkk = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
sep_units='deg', brute=True)
kkk.process(cat)
r = np.sqrt(x**2 + y**2 + z**2)
x /= r; y /= r; z /= r
true_ntri = np.zeros((nrbins, nubins, 2*nvbins), dtype=int)
true_weight = np.zeros((nrbins, nubins, 2*nvbins), dtype=float)
true_zeta = np.zeros((nrbins, nubins, 2*nvbins), dtype=float)
rad_min_sep = min_sep * coord.degrees / coord.radians
for i in range(ngal):
for j in range(i+1,ngal):
for k in range(j+1,ngal):
d12 = np.sqrt((x[i]-x[j])**2 + (y[i]-y[j])**2 + (z[i]-z[j])**2)
d23 = np.sqrt((x[j]-x[k])**2 + (y[j]-y[k])**2 + (z[j]-z[k])**2)
d31 = np.sqrt((x[k]-x[i])**2 + (y[k]-y[i])**2 + (z[k]-z[i])**2)
d3, d2, d1 = sorted([d12, d23, d31])
rindex = np.floor(np.log(d2/rad_min_sep) / bin_size).astype(int)
if rindex < 0 or rindex >= nrbins: continue
if [d1, d2, d3] == [d23, d31, d12]: ii,jj,kk = i,j,k
elif [d1, d2, d3] == [d23, d12, d31]: ii,jj,kk = i,k,j
elif [d1, d2, d3] == [d31, d12, d23]: ii,jj,kk = j,k,i
elif [d1, d2, d3] == [d31, d23, d12]: ii,jj,kk = j,i,k
elif [d1, d2, d3] == [d12, d23, d31]: ii,jj,kk = k,i,j
elif [d1, d2, d3] == [d12, d31, d23]: ii,jj,kk = k,j,i
else: assert False
# Now use ii, jj, kk rather than i,j,k, to get the indices
# that correspond to the points in the right order.
u = d3/d2
v = (d1-d2)/d3
if ( ((x[jj]-x[ii])*(y[kk]-y[ii]) - (x[kk]-x[ii])*(y[jj]-y[ii])) * z[ii] +
((y[jj]-y[ii])*(z[kk]-z[ii]) - (y[kk]-y[ii])*(z[jj]-z[ii])) * x[ii] +
((z[jj]-z[ii])*(x[kk]-x[ii]) - (z[kk]-z[ii])*(x[jj]-x[ii])) * y[ii] ) > 0:
v = -v
uindex = np.floor(u / bin_size).astype(int)
assert 0 <= uindex < nubins
vindex = np.floor((v+1) / bin_size).astype(int)
assert 0 <= vindex < 2*nvbins
www = w[i] * w[j] * w[k]
zeta = www * kap[i] * kap[j] * kap[k]
true_ntri[rindex,uindex,vindex] += 1
true_weight[rindex,uindex,vindex] += www
true_zeta[rindex,uindex,vindex] += zeta
pos = true_weight > 0
true_zeta[pos] /= true_weight[pos]
np.testing.assert_array_equal(kkk.ntri, true_ntri)
np.testing.assert_allclose(kkk.weight, true_weight, rtol=1.e-5, atol=1.e-8)
np.testing.assert_allclose(kkk.zeta, true_zeta, rtol=1.e-4, atol=1.e-6)
try:
import fitsio
except ImportError:
print('Skipping FITS tests, since fitsio is not installed')
return
# Check that running via the corr3 script works correctly.
config = treecorr.config.read_config('configs/kkk_direct_spherical.yaml')
cat.write(config['file_name'])
treecorr.corr3(config)
data = fitsio.read(config['kkk_file_name'])
np.testing.assert_allclose(data['r_nom'], kkk.rnom.flatten())
np.testing.assert_allclose(data['u_nom'], kkk.u.flatten())
np.testing.assert_allclose(data['v_nom'], kkk.v.flatten())
np.testing.assert_allclose(data['ntri'], kkk.ntri.flatten())
np.testing.assert_allclose(data['weight'], kkk.weight.flatten())
np.testing.assert_allclose(data['zeta'], kkk.zeta.flatten(), rtol=1.e-3)
# Repeat with binslop = 0
# And don't do any top-level recursion so we actually test not going to the leaves.
kkk = treecorr.KKKCorrelation(min_sep=min_sep, bin_size=bin_size, nbins=nrbins,
sep_units='deg', bin_slop=0, max_top=0)
kkk.process(cat)
np.testing.assert_array_equal(kkk.ntri, true_ntri)
np.testing.assert_allclose(kkk.weight, true_weight, rtol=1.e-5, atol=1.e-8)
np.testing.assert_allclose(kkk.zeta, true_zeta, rtol=1.e-4, atol=1.e-6)
pixfn = '/global/cfs/cdirs/desi/target/catalogs/dr9m/0.42.0/pixweight/main/resolve/dark/pixweight-dark.fits'
hdr = fits.getheader(pixfn,1)
nside,nest = hdr['HPXNSIDE'],hdr['HPXNEST']
print(nside,nest)
R_G=3.214 # http://legacysurvey.org/dr8/catalogs/#galactic-extinction-coefficients
R_R=2.165
R_Z=1.211
dr = '9'
print('test')
if dr == '9':
#this will be needed no matter the sample, might want more
rall = fitsio.read('/global/cfs/cdirs/desi/target/catalogs/dr9m/0.42.0/randoms/resolve/randoms-randomized-1.fits')
print(len(rall))
sdir = '/project/projectdirs/desi/users/ajross/dr9/'
def mask(dd,mb=[1,5,6,7,11,12,13]):
keep = (dd['NOBS_G']>0) & (dd['NOBS_R']>0) & (dd['NOBS_Z']>0)
print(len(dd[keep]))
keepelg = keep
for bit in mb:
keepelg &= ((dd['MASKBITS'] & 2**bit)==0)
print(len(dd[keepelg]))
dd = dd[keepelg]
return dd
rall = mask(rall)
def densvsinput_pix(type,parl,wsel,reg=None,ff='targetDR9m42.fits',xlab='',vmin=None,vmax=None,ebvcut=None,edscut=None,sn2cut=None,fpsfcut=None,gfluxcut=None,rfluxcut=None,gbcut=None,nbin=10,weights=None,titl=''):
#input custom map/mask
ft = fitsio.read(sdir+type+ff)
print(len(ft))
rl = rall
if reg:
wr = rall['PHOTSYS'] == reg
rl = rl[wr]
wd = ft['PHOTSYS'] == reg
ft = ft[wd]
if gfluxcut:
wg = ft['FLUX_G']/ft['MW_TRANSMISSION_G'] > gfluxcut
print(len(ft))
ft = ft[wg]
print(len(ft))
if rfluxcut:
wg = ft['FLUX_R']/ft['MW_TRANSMISSION_R'] > rfluxcut
ft = ft[wg]
rth,rphi = radec2thphi(rl['RA'],rl['DEC'])
rpix = hp.ang2pix(nside,rth,rphi,nest=nest)
dth,dphi = radec2thphi(ft['RA'],ft['DEC'])
dpix = hp.ang2pix(nside,dth,dphi,nest=nest)
pixlr = np.zeros(12*nside*nside)
pixlg = np.zeros(12*nside*nside)
if weights is None:
weights = np.ones(len(pixlr))
for pix in rpix:
pixlr[pix] += 1.
print('randoms done')
for i in range(0,len(dpix)):
pix = dpix[i]
pixlg[pix] += 1.
wp = wsel
wp &= (pixlr > 0) & (weights*0 == 0)
parv = fitsio.read(pixfn)
ebv = parv['EBV']
sn2tf = 10.**(-0.4*R_G*ebv*2.)*parv['PSFDEPTH_G'] + 10.**(-0.4*R_R*ebv*2.)*parv['PSFDEPTH_R'] + 10.**(-0.4*R_Z*ebv*2.)*parv['PSFDEPTH_Z']
print(len(parv[wp]))
if sn2cut:
wp &= (sn2tf > sn2cut)
if fpsfcut:
wpsf = ft['MORPHTYPE'] == 'PSF'
pixlgp = np.zeros(12*nside*nside)
dpixp = dpix[wpsf]
for i in range(0,len(dpixp)):
pix = dpixp[i]
pixlgp[pix] += 1.
fpsf = pixlgp/pixlg
wp &= (fpsf < fpsfcut)
if ebvcut:
wp &= (parv['EBV'] < ebvcut)
if edscut:
eds = parv['EBV']/parv['STARDENS']
wp &= (eds < edscut)
parv = parl
wp &= parv !=0
wp &= parv*0 == 0
print(len(parv[wp]))
if vmin is None:
vmin = np.min(parv[wp])
if vmax is None:
vmax = np.max(parv[wp])
parv = parv[wp]
rh,bn = np.histogram(parv,bins=nbin,range=(vmin,vmax),weights=pixlr[wp])
dh,db = np.histogram(parv,bins=bn,weights=pixlg[wp]*weights[wp])
norm = sum(rh)/sum(dh)
sv = dh/rh*norm
ep = np.sqrt(dh)/rh*norm
bc = []
for i in range(0,len(bn)-1):
bc.append((bn[i]+bn[i+1])/2.)
plt.errorbar(bc,sv-1.,ep,fmt='ko')
plt.hist(parv,bins=nbin,range=(vmin,vmax),weights=pixlr[wp]*0.2*np.ones(len(pixlr[wp]))/np.max(rh))
plt.ylim(-.3,.3)
plt.xlabel(xlab)
plt.ylabel('Ngal/<Ngal> - 1')
plt.title(type+' in '+reg + ' footprint, using pixelized map'+titl)
plt.show()
wv = (parv>=vmin) & (parv <=vmax)
frac = sum(pixlr[wp][~wv])/sum(pixlr[wp])
print('fraction of randoms not included in plot: '+str(frac))
return bc,sv,ep
def test_meanify():
if __name__ == '__main__':
rtol = 1.e-1
atol = 2.e-2
bin_spacing = 30 # arcsec
else:
rtol = 1.e-1
atol = 3.e-2
bin_spacing = 150 # arcsec
psf_file = 'test_mean_*.piff'
average_file = 'average.fits'
psfs_list = sorted(glob.glob(os.path.join('output', 'test_mean_*.piff')))
config0 = {
'output' : {
'file_name' : psfs_list,
},
'hyper' : {
'file_name' : 'output/'+average_file,
}}
config1 = {
'output' : {
'file_name' : psf_file,
'dir': 'output',
},
'hyper' : {
'file_name' : average_file,
'dir': 'output',
'bin_spacing' : bin_spacing,
'statistic' : 'mean',
'params_fitted': [0, 2]
}}
config2 = {
'output' : {
'file_name' : psf_file,
'dir': 'output',
},
'hyper' : {
'file_name' : average_file,
'dir': 'output',
'bin_spacing' : bin_spacing,
'statistic' : 'median',
}}
for config in [config0, config1, config2]:
piff.meanify(config)
## test if found initial average
average = fitsio.read(os.path.join('output',average_file))
params0 = make_average(coord=average['COORDS0'][0] / 0.26, gp=False)
keys = ['hlr', 'g1', 'g2']
for i,key in enumerate(keys):
if config == config1 and i == 1:
np.testing.assert_allclose(np.zeros(len(average['PARAMS0'][0][:,i])),
average['PARAMS0'][0][:,i], rtol=0, atol=0)
else:
np.testing.assert_allclose(params0[key], average['PARAMS0'][0][:,i],
rtol=rtol, atol=atol)
## gaussian process testing of meanify
np.random.seed(68)
x = np.random.uniform(0, 2048, size=1000)
y = np.random.uniform(0, 2048, size=1000)
coord = np.array([x,y]).T
average = make_average(coord=coord)
stars = params_to_stars(average, noise=0.0, rng=None)
stars_training = stars[:900]
stars_validation = stars[900:]
fit_hyp = [False, True]
for fit in fit_hyp:
gp = piff.GPInterp2pcf(kernel="0.009 * RBF(300.*0.26)",
optimize=fit_hyp, white_noise=1e-5, average_fits='output/average.fits')
gp.initialize(stars_training)
gp.solve(stars_training)
stars_interp = gp.interpolateList(stars_validation)
params_interp = np.array([s.fit.params for s in stars_interp])
params_validation = np.array([s.fit.params for s in stars_validation])
params_training = np.array([s.fit.params for s in stars_training])
np.testing.assert_allclose(params_interp, params_validation, rtol=rtol, atol=atol)
def densvsimpar_pix(type,par,reg=None,ff='targetDR9m42.fits',vmin=None,vmax=None,ebvcut=None,edscut=None,sn2cut=None,fpsfcut=None,gfluxcut=None,rfluxcut=None,gbcut=None,nbin=10,weights=None,titl=''):
ft = fitsio.read(sdir+type+ff)
print(len(ft))
rl = rall
if reg:
wr = rall['PHOTSYS'] == reg
rl = rl[wr]
wd = ft['PHOTSYS'] == reg
ft = ft[wd]
if gfluxcut:
wg = ft['FLUX_G']/ft['MW_TRANSMISSION_G'] > gfluxcut
print(len(ft))
ft = ft[wg]
print(len(ft))
if rfluxcut:
wg = ft['FLUX_R']/ft['MW_TRANSMISSION_R'] > rfluxcut
ft = ft[wg]
rth,rphi = radec2thphi(rl['RA'],rl['DEC'])
rpix = hp.ang2pix(nside,rth,rphi,nest=nest)
dth,dphi = radec2thphi(ft['RA'],ft['DEC'])
dpix = hp.ang2pix(nside,dth,dphi,nest=nest)
pixlr = np.zeros(12*nside*nside)
pixlg = np.zeros(12*nside*nside)
if par.split('-')[0] == 'VAR' or par.split('-')[0] == 'STDPER':
pixlp = np.zeros(12*nside*nside)
pixlv = np.zeros(12*nside*nside)
if weights is None:
weights = np.ones(len(pixlr))
for pix in rpix:
pixlr[pix] += 1.
print('randoms done')
for i in range(0,len(dpix)):
pix = dpix[i]
pixlg[pix] += 1.
if par.split('-')[0] == 'VAR' or par.split('-')[0] == 'STDPER':
pixlp[pix] += ft[i][par.split('-')[1]]
pixlv[pix] += ft[i][par.split('-')[1]]**2.
wp = (pixlr > 0) & (weights*0 == 0)
parv = fitsio.read(pixfn)
ebv = parv['EBV']
sn2tf = 10.**(-0.4*R_G*ebv*2.)*parv['PSFDEPTH_G'] + 10.**(-0.4*R_R*ebv*2.)*parv['PSFDEPTH_R'] + 10.**(-0.4*R_Z*ebv*2.)*parv['PSFDEPTH_Z']
print(len(parv[wp]))
if sn2cut:
wp &= (sn2tf > sn2cut)
if fpsfcut:
wpsf = ft['MORPHTYPE'] == 'PSF'
pixlgp = np.zeros(12*nside*nside)
dpixp = dpix[wpsf]
for i in range(0,len(dpixp)):
pix = dpixp[i]
pixlgp[pix] += 1.
fpsf = pixlgp/pixlg
wp &= (fpsf < fpsfcut)
if ebvcut:
wp &= (parv['EBV'] < ebvcut)
if edscut:
eds = parv['EBV']/parv['STARDENS']
wp &= (eds < edscut)
if gbcut is not None:
print('applying background cut of '+str(gbcut))
rf = fitsio.read('/global/u2/r/rongpu/share/desi/sky_residual_dr9_partial/sky_residual_dr9_north_256.fits')
gb = np.zeros(12*nside*nside)
for i in range(0,len(rf)):
px = rf['hp_idx'][i]
gb[px] = rf['g_blobsky'][i]
gb = hp.reorder(gb,r2n=True)
wp &= (gb != 0)
wp &= (gb < gbcut)
print(len(parv[wp]))
if len(par.split('-')) > 1:
if par.split('-')[0] == 'VAR':
parv = pixlv[wp]/pixlg[wp]-(pixlp[wp]/pixlg[wp])**2.
elif par.split('-')[0] == 'STDPER':
var = pixlv[wp]/pixlg[wp]-(pixlp[wp]/pixlg[wp])**2.
parv = var**.5/(pixlp[wp]/pixlg[wp])
elif par.split('-')[1] == 'X':
parv = parv[wp][par.split('-')[0]]*parv[wp][par.split('-')[2]]
elif par.split('-')[1] == 'DIV':
parv = parv[wp][par.split('-')[0]]/parv[wp][par.split('-')[2]]
elif par == 'PSFTOT':
parv = (parv[wp]['PSFSIZE_G'])*(parv[wp]['PSFSIZE_R'])*(parv[wp]['PSFSIZE_Z'])
elif par == 'SN2TOT_FLAT':
ebv = parv[wp]['EBV']
parv = 10.**(-0.4*R_G*ebv*2.)*parv[wp]['PSFDEPTH_G'] + 10.**(-0.4*R_R*ebv*2.)*parv[wp]['PSFDEPTH_R'] + 10.**(-0.4*R_Z*ebv*2.)*parv[wp]['PSFDEPTH_Z']
elif par == 'SN2TOT_G':
ebv = parv[wp]['EBV']
parv = 10.**(-0.4*R_G*ebv*2.)*parv[wp]['PSFDEPTH_G']
elif par == 'fracPSF':
wpsf = ft['MORPHTYPE'] == 'PSF'
pixlgp = np.zeros(12*nside*nside)
dpixp = dpix[wpsf]
for i in range(0,len(dpixp)):
pix = dpixp[i]
pixlgp[pix] += 1.
parv = pixlgp[wp]/pixlg[wp]
else:
parv = parv[wp][par]
wo = parv*0 == 0
if vmin is None:
vmin = np.min(parv[wo])
if vmax is None:
vmax = np.max(parv[wo])
rh,bn = np.histogram(parv,bins=nbin,range=(vmin,vmax),weights=pixlr[wp])
dh,db = np.histogram(parv,bins=bn,weights=pixlg[wp]*weights[wp])
norm = sum(rh)/sum(dh)
sv = dh/rh*norm
ep = np.sqrt(dh)/rh*norm
bc = []
for i in range(0,len(bn)-1):
bc.append((bn[i]+bn[i+1])/2.)
plt.errorbar(bc,sv-1.,ep,fmt='ko')
plt.hist(parv,bins=nbin,range=(vmin,vmax),weights=pixlr[wp]*0.2*np.ones(len(pixlr[wp]))/np.max(rh))
plt.ylim(-.3,.3)
plt.xlabel(par)
plt.ylabel('Ngal/<Ngal> - 1')
plt.title(type+' in '+reg + ' footprint, using pixelized map'+titl)
plt.show()
wv = (parv>=vmin) & (parv <=vmax)
frac = sum(pixlr[wp][~wv])/sum(pixlr[wp])
print('fraction of randoms not included in plot: '+str(frac))
return bc,sv,ep
def main():
ns = parse_args()
if ns.ignore_errors:
print("Warning: *** Will ignore broken tractor catalog files ***")
print(" *** Disable -I for final data product. ***")
bricks = list_bricks(ns)
tree, nobj, morecols = read_external(ns.external, ns)
# get the data type of the match
brickname, path = bricks[0]
peek = fitsio.read(path, 1, upper=True)
matched_catalog = sharedmem.empty(nobj, dtype=peek.dtype)
matched_catalog['OBJID'] = -1
matched_distance = sharedmem.empty(nobj, dtype='f4')
# convert to radian
tol = ns.tolerance / (60. * 60.) * (np.pi / 180)
matched_distance[:] = tol
nprocessed = np.zeros((), dtype='i8')
nmatched = np.zeros((), dtype='i8')
ntotal = np.zeros((), dtype='i8')
t0 = time()
with sharedmem.MapReduce(np=ns.numproc) as pool:
def work(brickname, path):
try:
objects = fitsio.read(path, 1, upper=True)
except:
if ns.ignore_errors:
print ("IO Error on %s" %path)
return None, None, None
else:
raise
pos = radec2pos(objects['RA'], objects['DEC'])
d, i = tree.query(pos, 1)
assert (objects['OBJID'] != -1).all()
with pool.critical:
mask = d < matched_distance[i]
mask &= objects['BRICK_PRIMARY']
i = i[mask]
matched_catalog[i] = objects[mask]
matched_distance[i] = d[mask]
matched = mask.sum()
return brickname, matched, len(objects)
def reduce(brickname, matched, total):
if brickname is None:
return
nprocessed[...] += 1
nmatched[...] += matched
ntotal[...] += total
if ns.verbose:
if nprocessed % 1000 == 0:
print("Processed %d files, %g / second, matched %d / %d objects."
% (nprocessed, nprocessed / (time() - t0), nmatched, ntotal)
)
pool.map(work, bricks, star=True, reduce=reduce)
nrealmatched = (matched_catalog['OBJID'] != -1).sum()
if ns.verbose:
print("Processed %d files, %g / second, matched %d / %d objects into %d slots."
% (nprocessed, nprocessed / (time() - t0),
nmatched, ntotal,
nrealmatched)
)
try:
os.makedirs(os.path.dirname(ns.dest))
except OSError:
pass
header = {}
header['NMATCHED'] = nrealmatched
header['NCOLLISION'] = nmatched - nrealmatched
header['TOL_ARCSEC'] = ns.tolerance
# Optionally add the new columns
if len(morecols) > 0:
newdtype = matched_catalog.dtype.descr
for coldata, col in zip( morecols, ns.copycols ):
newdtype = newdtype + [(col, coldata.dtype)]
newdtype = np.dtype(newdtype)
_matched_catalog = np.empty(matched_catalog.shape, dtype=newdtype)
for field in matched_catalog.dtype.fields:
_matched_catalog[field] = matched_catalog[field]
for coldata, col in zip( morecols, ns.copycols ):
_matched_catalog[col] = coldata
matched_catalog = _matched_catalog.copy()
del _matched_catalog
for format in ns.format:
save_file(ns.dest, matched_catalog, header, format)
def plotvshp_compmc(type,sys,rng,mcl=None,ws=None,reg=None,ff='targetDR9m42.fits',gdzm=0,ebvm=100,title='',effac=1.,mingd=0,maxgd=1.e6,minpsfg=0,maxpsfg=100,south=True):
ft = fitsio.read(sdir+type+ff)
print(len(ft))
rl = rall
if reg:
wr = rall['PHOTSYS'] == reg
rl = rl[wr]
wd = ft['PHOTSYS'] == reg
ft = ft[wd]
rth,rphi = radec2thphi(rl['RA'],rl['DEC'])
rpix = hp.ang2pix(nside,rth,rphi,nest=nest)
dth,dphi = radec2thphi(ft['RA'],ft['DEC'])
dpix = hp.ang2pix(nside,dth,dphi,nest=nest)
r1 = np.zeros(12*nside*nside)
d1= np.zeros(12*nside*nside)
for pix in rpix:
r1[pix] += 1.
print('randoms done')
for pix in dpix:
d1[pix] += 1.
hpq = fitsio.read(pixfn)
#hpq = parv[par]
w = r1 > 0
print(len(hpq[w]))
w &= hpq['GALDEPTH_Z'] > gdzm
w &= hpq['GALDEPTH_G'] > mingd
w &= hpq['GALDEPTH_G'] < maxgd
w &= hpq['EBV'] < ebvm
w &= hpq['PSFSIZE_G'] > minpsfg
w &= hpq['PSFSIZE_G'] < maxpsfg
if ws is not None:
w &= ws*0 == 0
if mcl is not None:
w &= mcl*0 == 0
w &= mcl > 0
print(len(hpq[w]))
#w
if sys != 'gdc' and sys != 'rdc' and sys != 'zdc' and sys != 'dg' and sys != 'dr' and sys != 'dz' and sys != 'dgr' and sys != 'drz' and sys != 'dgz':
sm = hpq[w][sys]
xlab = sys
else:
if sys == 'gdc':
print('g band depth, extinction corrected')
sm = hpq[w]['GALDEPTH_G']*10.**(-0.4*R_G*hpq[w]['EBV'])
xlab = 'g band depth, extinction corrected'
if sys == 'rdc':
sm = hpq[w]['GALDEPTH_R']*10.**(-0.4*R_R*hpq[w]['EBV'])
xlab = 'r band depth, extinction corrected'
if sys == 'zdc':
sm = hpq[w]['GALDEPTH_Z']*10.**(-0.4*R_Z*hpq[w]['EBV'])
xlab = 'z band depth, extinction corrected'
if sys == 'dg':
sm = dg[w]
xlab = 'g band PS1 residual'
if sys == 'dr':
sm = dr[w]
xlab = 'r band PS1 residual'
if sys == 'dz':
sm = dz[w]
xlab = 'z band PS1 residual'
if sys == 'dgr':
sm = dg[w]-dr[w]
xlab = 'g-r band PS1 residual'
if sys == 'drz':
sm = dr[w]-dz[w]
xlab = 'r-z band PS1 residual'
if sys == 'dgz':
sm = dg[w]-dz[w]
xlab = 'g-z band PS1 residual'
ds = np.ones(len(d1))
print(len(ds),len(d1),len(w),len(sm))
hdnoc = np.histogram(sm,weights=d1[w],range=rng)
#print(hd1)
hr1 = np.histogram(sm,weights=r1[w],bins=hdnoc[1],range=rng)
xl = []
for i in range(0,len(hr1[0])):
xl.append((hr1[1][i]+hr1[1][i+1])/2.)
plt.errorbar(xl,hdnoc[0]/hr1[0]/(sum(d1[w])/sum(r1[w])),np.sqrt(hdnoc[0])/hr1[0]/(sum(d1[w])/sum(r1[w])),fmt='ko',label='raw')
if ws is not None:
ds = ws
hd1 = np.histogram(sm,weights=d1[w]*ds[w],bins=hdnoc[1],range=rng)
plt.plot(xl,hd1[0]/hr1[0]/(sum(d1[w]*ds[w])/sum(r1[w])),'b-',label='+ EBV weights')
#hd1 = np.histogram(sm,weights=d1[w]*ds[w],bins=hdnoc[1],range=rng)
#plt.plot(xl,hd1[0]/hr1[0]/(sum(d1[w]*ds[w])/sum(r1[w])),'k--',label='with stellar density weights')
if mcl is not None:
dmcse = mcl**effac
hd1 = np.histogram(sm,weights=d1[w]/dmcse[w],bins=hdnoc[1],range=rng)
plt.plot(xl,hd1[0]/hr1[0]/(sum(d1[w]/dmcse[w])/sum(r1[w])),'r-',label='+MC weights')
if ws is not None and mcl is not None:
hd1 = np.histogram(sm,weights=d1[w]*ds[w]/dmcse[w],bins=hdnoc[1],range=rng)
plt.plot(xl,hd1[0]/hr1[0]/(sum(d1[w]*ds[w]/dmcse[w])/sum(r1[w])),'-',color='purple',label='+MC weights + EBV weights')
#dmcs = mcls**effac
#hd1 = np.histogram(sm,weights=d1[w]*ds[w]/dmcs[w],bins=hdnoc[1],range=rng)
#plt.plot(xl,hd1[0]/hr1[0]/(sum(d1[w]*ds[w]/dmcs[w])/sum(r1[w])),'b-',label='+MC; sed w ext sigma')
#dmco = mclo**effac
#hd1 = np.histogram(sm,weights=d1[w]*ds[w]/dmco[w],bins=hdnoc[1],range=rng)
#plt.plot(xl,hd1[0]/hr1[0]/(sum(d1[w]*ds[w]/dmco[w])/sum(r1[w])),'-',color='purple',label='old MC')
#plt.title(str(mp)+reg)
plt.plot(xl,np.ones(len(xl)),'k:',label='null')
plt.legend()#(['raw','with stellar density weights','+sed ext MC','just sed MC','old MC','null']))
plt.ylabel('relative density')
plt.xlabel(xlab)
plt.ylim(0.7,1.3)
plt.title(title)
plt.show()
def process_field(results, errors, run, camcol, filter, field, params_bright,
params_dim, params_removestars):
"""Calls the correct order of actions needed to detect trails per frame.
Writes "results.txt" and "errors.txt".
Order of operations:
1. Check if .fits file exists
a. If it doesn't see a compressed .bz2 version exists. If it does
b. uncompress it to $FITS_DUMP env. var location. If the env. var.
was not set, decompress to the fits_dump folder in the package
2. Remove known object from the image by drawing squares over them
3. Try detecting a bright trail (very fast). If successful write results
and stop.
4. if bright detection is not made, try detecting a dim trail. If found
write results and stop, otherwise just stop.
5. clean-up (remove unpacked fits, close files, dump silenced errors)
Parameters
----------
results : file
a file object, a stream or any such counterpart to which results
will be written
errors : file
a file object, stream, or any such counterpart to which errors will be
written
run : int
run designation
camcol : int
camcol designation, 1 to 6
filter : str
filter designation, one of ugriz
params_bright : dict
dictionary containing execution parameters required by process_bright
params_dim : dict
dictionary containing execution parameters required by process_dim
params_removestars : dict
dictionary containing execution parameters required by remove_stars
"""
removefits = False
try:
origfitspath = files.filename('frame',
run=run,
camcol=camcol,
field=field,
filter=filter)
# downright sadness that fitsio doesn't support bz2 compressed fits'
# if there is no fits, but only fits.bz2 you have to open, decompress,
# save, and reopen with fitsio. We also don't want to delete existing
# unpacked fits files so we set the removefits flag to True only when
# *we* created a file by unpacking
if not os.path.exists(origfitspath):
bzpath = origfitspath + ".bz2"
if not os.path.exists(bzpath):
errmsg = ("File {0} or its bz2 compressed version not found. "
"Are you sure they exist?")
raise FileNotFoundError(errmsg.format(origfitspath))
with open(bzpath, "rb") as compressedfits:
fitsdata = bz2.decompress(compressedfits.read())
# see if user uncompressed fits dumping location is set
try:
fitsdmp = os.environ["FITS_DUMP"]
except KeyError:
# if not default to the fits_dump dir in the package
modloc = os.path.split(__file__)[0]
fitsdmp = os.path.join(modloc, "fits_dump/")
fitspath = os.path.join(fitsdmp,
os.path.split(origfitspath)[-1])
# save the uncompressed fits
with open(fitspath, "wb") as decompressed:
decompressed.write(fitsdata)
# setting the flag here, after we have certainly written and
# closed the file succesfully helps escape any errors later on
# in case we try to remove unexsiting file
removefits = True
else:
fitspath = origfitspath
img = fitsio.read(fitspath)
h = fitsio.read_header(fitspath)
printit = (f"{run} {camcol} {filter} {field} {h['TAI']} {h['CRPIX1']} "
"{h['CRPIX2']} {h['CRVAL1']} {h['CRVAL2']} {h['CD1_1']} "
"{h['CD1_2']} {h['CD2_1']} {h['CD2_2']} ")
img = remove_stars(img, run, camcol, filter, field,
**params_removestars)
# WARNING mirror the image vertically
# it seems CV2 and FITSIO set different pix coords
img = cv2.flip(img, 0)
detection, res = process_field_bright(img, **params_bright)
if detection:
results.write(printit +
f"{res['x1']} {res['y1']} {res['x2']} {res['y2']}\n")
else:
detection, res = process_field_dim(img, **params_dim)
if detection:
results.write(
printit +
f"{res['x1']} {res['y1']} {res['x2']} {res['y2']}\n")
except Exception as e:
if params_bright["debug"] or params_dim["debug"]:
traceback.print_exc(limit=3)
errors.write(f"{run} {camcol} {filter} {field}\n")
traceback.print_exc(limit=3, file=errors)
errors.write(str(e) + "\n\n")
pass
finally:
if removefits:
os.remove(fitspath)
data_path = glob.glob(os.path.join(o.input_path, 'lsst_e_*.fits.gz'))
parent_path = os.path.dirname(o.input_path)
instcat_path = glob.glob(os.path.join(parent_path, 'instCat/phosim*.txt'))[0]
phosim_pars = pd.read_table(instcat_path, index_col=0, header=None, sep=' ').T
seeing = float(phosim_pars['seeing'].values[0])
bandpass = bandpass_all[int(phosim_pars['filter'].values[0])]
psf_fwhm = []
mean_bkg = []
median_bkg = []
bkg_noise = []
for i, dp in enumerate(data_path):
if i % 10 == 0:
print('Analyzed %d of %d images' % (i, len(data_path)))
data, h = fitsio.read(dp, ext=0, header=True)
psf_fwhm.append(get_total_seeing(h))
aux1, aux2, aux3 = compute_bkg(data)
mean_bkg.append(aux1)
median_bkg.append(aux2)
bkg_noise.append(aux3)
psf_fwhm = np.array(psf_fwhm)
mean_bkg = np.array(mean_bkg)
median_bkg = np.array(median_bkg)
bkg_noise = np.array(bkg_noise)
if imsim_installed:
mean_bkg_imsim, median_bkg_imsim, bkg_noise_imsim = compare_with_imsim(
phosim_pars)
else:
