def appendcatalogs(catalog1, catalog2, catalogOUT):
"""
The task appends catalogs using only the catalog1's header. Catalog1(withheader)+catalog2(woheader)
The final (composed) catalog is saved as catalogOUT.
NEEDLESS TO SAY BOTH CATALOGS HAVE TO HAVE THE SAME FORMAT (ROWS&COLUMNS) !!!
-----
"""
print 'Reading file1: ', catalog1
data1 = C.loaddata(catalog1) # Loading the whole catalog1 content.
head1 = C.loadheader(catalog1) # Loading the original header1.
print 'Reading file2: ', catalog2
data2 = C.loaddata(catalog2) # Loading the whole catalog2 content.
head2 = C.loadheader(catalog2) # Loading the original header2.
outcat = catalogOUT
print outcat
try:
nf1 = N.shape(data1)[0]
nc1 = N.shape(data1)[1]
except:
nf1 = 1
nc1 = N.shape(data1)[0]
try:
nf2 = N.shape(data2)[0]
nc2 = N.shape(data2)[1]
except:
nf2 = 1
nc2 = N.shape(data2)[0]
print 'Dimensions catalogue_1: ncols: %i, nraws: %i' % (nf1, nc1)
print 'Dimensions catalogue_2: ncols: %i, nraws: %i' % (nf2, nc2)
if nc1 == nc2:
nf = nf1 + nf2
nc = nc1
newdata = N.zeros((nf, nc), float)
for ii in range(nf1):
if nf1 < 2: newdata[ii, :] = data1[:]
else: newdata[ii, :] = data1[ii, :]
for ii in range(nf2):
if nf2 < 2: newdata[ii + nf1, :] = data2[:]
else: newdata[ii + nf1, :] = data2[ii, :]
C.savedata(newdata, outcat, dir="",
header=head1) # Saving and creating the new catalog.
else:
print 'Different number of rows between catalogs. Impossible to append catalogs !!'
def minimum_photouncert(catalog, columns):
"""
"""
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
mm = A.get_magnitudes(catalog, columns)
em = A.get_errmagnitudes(catalog, columns)
nl = len(mm[:,
0]) # nl is the number of detections inside every single band.
nf = len(mm[0, :]) # nf is the number of bands inside the catalog.
errmag = N.zeros((nl, nf),
float) # Where the new photo errors will be saved.
for jj in range(nf):
for ii in range(nl):
if em[ii, jj] < 0.01: errmag[ii, jj] = 0.03
elif em[ii, jj] > 1.0: errmag[ii, jj] = 1.0
else: errmag[ii, jj] = em[ii, jj]
# New values of mags error overwrites now the original data.
vars, evars, posref, zpe, zpo = A.get_usefulcolumns(columns)
data[:, evars] = errmag[:, N.arange(nf)]
finalcatalog = catalog[:-3] + 'ecor.cat'
C.savedata(data, finalcatalog, dir="",
header=head) # Saving & creating a new catalog.
def replace_photo_uncert(catalog, columns):
"""
"""
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
mm = A.get_magnitudes(catalog, columns)
em = A.get_errmagnitudes(catalog, columns)
filters = B.get_filter_list(columns)
nl = len(mm[:,
0]) # nl is the number of detections inside every single band.
nf = len(mm[0, :]) # nf is the number of bands inside the catalog.
errmag = U.zeros((nl, nf),
float) # Where the new photo errors will be saved.
for jj in range(nf):
maglim = B.get_limitingmagnitude(mm[:, jj], em[:, jj], 1., 0.25)
print 'Limiting Magnitude for filter %s: %.3f' % (filters[jj], maglim)
for ii in range(nl):
if mm[ii, jj] == -99.:
errmag[ii, jj] = 0.00
elif mm[ii, jj] == 99.:
errmag[ii, jj] = maglim
else:
errmag[ii, jj] = em[ii, jj]
# New values of mags error overwrites now the original data.
vars, evars, posref, zpe, zpo = get_usefulcolumns(columns)
data[:, evars] = errmag[:, U.arange(nf)]
finalcatalog = catalog[:-3] + 'upp.cat'
C.savedata(data, finalcatalog, dir="",
header=head) # Saving & creating a new catalog.
def replace_kerttu_errmags(catalog, columns, finalcatalog):
"""
import alhambra_kerttu_fixerrmags as AFM
catalog = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu.cat'
columns = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu.columns'
finalcatalog = '/Users/albertomolino/doctorado/articulos/ALHAMBRA/kerttu/test_photoz/kerttu3.cat'
AFM.replace_kerttu_errmag(catalog,columns,finalcatalog)
------
"""
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
mm = A.get_magnitudes(catalog, columns)
em = A.get_errmagnitudes(catalog, columns)
filters = B.get_filter_list(columns)
nl = len(mm[:,
0]) # nl is the number of detections inside every single band.
nf = len(mm[0, :]) # nf is the number of bands inside the catalog.
errmag = U.zeros((nl, nf),
float) # Where the new photo errors will be saved.
for jj in range(nf):
for ii in range(nl):
if mm[ii, jj] == -99.: errmag[ii, jj] = 0.00
else: errmag[ii, jj] = em[ii, jj]
# New values of mags error overwrites now the original data.
vars, evars, posref, zpe, zpo = A.get_usefulcolumns(columns)
data[:, evars] = errmag[:, U.arange(nf)]
C.savedata(data, finalcatalog, dir="",
header=head) # Saving & creating a new catalog.
def compress_bpz_catalogues(catalogue, sample, outname):
"""
It selects a subsample of sources from
an input catalogue.
:param catalogue:
:return:
"""
head1 = C.loadheader(catalogue)
data1 = C.loaddata(catalogue)
C.savedata(data1[sample, :], outname, dir="", header=head1)
def replacing_nans_catalogs(catalog, newname):
"""
vars = []
evars = []
data = C.loaddata(catalog)
mags = data[:,vars]
emags = data[:,evars]
"""
vars = N.array([
15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 63, 66,
69, 72, 75, 78, 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111
])
evars = vars[:] + 1
s2n_vars = vars[:] + 2
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
mm = data[:, vars]
em = data[:, evars]
s2n = data[:, s2n_vars]
nl = len(mm[:,
0]) # nl is the number of detections inside every single band.
nf = len(mm[0, :]) # nf is the number of bands inside the catalog.
newmag = U.zeros((nl, nf),
float) # Where the new photo errors will be saved.
errmag = U.zeros((nl, nf),
float) # Where the new photo errors will be saved.
new_s2n = U.zeros((nl, nf), float)
for jj in range(len(vars)):
for ii in range(nl):
if abs(mm[ii, jj]) > 60.:
newmag[ii, jj] = -99.0
errmag[ii, jj] = 0.00
new_s2n[ii, jj] = -1
elif s2n[ii, jj] < 0.00001:
new_s2n[ii, jj] = 0.
else:
newmag[ii, jj] = mm[ii, jj]
errmag[ii, jj] = em[ii, jj]
new_s2n[ii, jj] = s2n[ii, jj]
# New values of mags error overwrites now the original data.
data[:, vars] = newmag[:, U.arange(nf)]
data[:, evars] = errmag[:, U.arange(nf)]
data[:, s2n_vars] = new_s2n[:, U.arange(nf)]
C.savedata(data, newname, dir="",
header=head) # Saving & creating a new catalog.
def run():
cs = U.get_str(lista_fluxcomp, 0)
nc = len(cs)
cat = U.get_str(lista_catalogs, 0)
ncat = len(cat)
print 'Number of catalogues to convert: ', nc
cols = U.get_str(lista_columns, 0)
ncols = len(cols)
print 'nc,ncat,ncols: ', nc, ncat, ncols
for ii in range(nc):
head = coeio.loadheader(cs[ii])
nh = len(head)
print 'Number of variables: ', nh
# pausa = raw_input('paused')
nf = nh - 5
body = coeio.loaddata(cs[ii])
ng = len(body)
# pausa = raw_input('paused')
field = ((cat[ii].split('/')[-1]).split('.')[1])[2]
pointing = ((cat[ii].split('/')[-1]).split('.')[1])[5]
ccd = ((cat[ii].split('/')[-1]).split('.')[1])[8]
print 'Reading Field: %s, Pointing: %s, CCD: %s' % (field, pointing,
ccd)
# pausa = raw_input('paused')
filename = final_root + cat[ii].split('/')[-1][:-3] + 'flux_comparison'
print 'filename', filename
filename2 = final_root + cat[ii].split('/')[-1][:-3] + 'columns'
A.copyfile(cols[ii], filename2)
# pausa = raw_input('paused')
outfile = open(filename, 'w')
for ii in range(len(head)):
outfile.write('%s \n' % (head[ii]))
for ss in range(ng):
ids = body[ss, 0]
# print 'IDs',ids
ids814 = convert814ids(ids, int(field), int(pointing), int(ccd))
for jj in range(nh):
if jj == 0: outfile.write('%s ' % (ids814))
if jj == 1: outfile.write('%.2f ' % (body[ss, 1]))
if jj == 2: outfile.write('%.2f ' % (body[ss, 2]))
if jj == 3: outfile.write('%.2f ' % (body[ss, 3]))
if jj == 4: outfile.write('%.2f ' % (body[ss, 4]))
if jj > 4:
# print body[ss,jj]
# pausa = raw_input('paused')
outfile.write('%s ' % (body[ss, jj]))
outfile.write('\n')
outfile.close()
def runZPcal_catalogue(reference, frame, final):
"""
----
filter_ref_cat,alig_frame_cat,alig_cal_frame_cat
"""
plots = 1
data2 = C.loaddata(frame) # Loading the whole catalog2 content.
head2 = C.loadheader(frame) # Loading the original header2.
pos_mags = 12 # ([12,20,21,22])
mag_r = U.get_data(reference, 12)
mag_f = U.get_data(frame, 12)
# good_sample = U.greater_equal(mag_r,16.) * U.less_equal(mag_r,21.5)
good_sample = U.greater_equal(mag_r, 16.) * U.less_equal(mag_r, 19.)
mag_r2, mag_f2 = U.multicompress(good_sample, (mag_r, mag_f))
offset = U.mean_robust(mag_f2 - mag_r2)
if plots:
plt.figure(11, figsize=(12, 9), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
plt.plot(mag_r, (mag_f - mag_r - offset), 'ko', ms=10, alpha=0.1)
plt.xlim(16, 25)
plt.ylim(-5, 5.)
plt.xlabel('AB', size=25)
plt.ylabel('Mf-Mr', size=25)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.legend(['Offset: %.4f' % (offset)], loc='upper right', numpoints=1)
plt.title(A.getfilename(frame), size=15)
plt.grid()
figurename = final[:-3] + 'png'
print 'figurename: ', figurename
plt.savefig(figurename, dpi=100)
plt.close()
# Here it saves the offset in an ASCII file
fileout = open(final[:-3] + 'txt', 'w')
linea = '%s %.5f \n' % (final, offset)
fileout.write(linea)
fileout.close()
# The offset is only applied to m!=99. magnitudes.
new_mags = U.where(abs(mag_f) < 99, mag_f - offset, mag_f)
data2[:, pos_mags] = new_mags
C.savedata(data2, final, dir="", header=head2)
print ' '
def flagging_dobledetections_mergecolumns(catalog):
"""
This serves to append an extra column (each to both inputted catalogs)
indicating either a detection was repeated and with the lowest S/N
of the two.
Sources flagged as 1 are those detections to be excluded when combining
both catalogs into a single one.
--------
import alhambra_overlap as alhov
cat2 = '/Volumes/amb22/catalogos/reduction_v4e/f02/f02p01_colorproext_2_ISO.cat'
alhov.flagging_dobledetections_mergecolumns(cat2)
"""
data = coeio.loaddata(catalog) # Loading the whole catalog content.
head = coeio.loadheader(catalog) # Loading the original header.
nc = len(data.T) # Number columns
dim = len(data[:,0]) # Number elements
print 'nc,dim',nc,dim
var1 = head[-3].split()[-1]
var2 = head[-2].split()[-1]
if var1 == var2:
print 'Duplicated columns. Merging information...'
uno = data[:,72]
dos = data[:,73]
tres = uno+dos
newdata = U.zeros((dim,nc-1),float)
for ii in range(nc-1):
for jj in range(dim):
if ii == nc-1:
print 'pepe'
newdata[jj,ii] = tres[jj]
else:
newdata[jj,ii] = data[jj,ii]
head2 = head[:-1]
head2[-1]='#'
outcat = catalog[:-4]+'.mergedcolumns.cat'
coeio.savedata(newdata,outcat, dir="",header=head2) # Saving and creating the new catalog.
# Renaming files
ap.renamefile(catalog,catalog+'.oldold.cat')
if not os.path.exists(catalog): ap.renamefile(outcat,catalog)
def remove_detections_bysegmmaps(field, pointing, ccd):
"""
It uses the segmentation-maps to remove fake detections
when masking out saturated stars.
----
import alhambra_fakedets as AF
AF.remove_detections_bysegmmaps(2,1,1)
"""
root = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field)
root2images = '/Volumes/amb22/imagenes/f0%i/' % (field)
catalog = root + 'f0%ip0%i_colorproext_%i_ISO.irmsF814W.free.cat' % (
field, pointing, ccd)
ids, x, y, area = U.get_data(catalog, (0, 3, 4, 5))
dim = len(ids)
valor = U.zeros(dim)
ima1 = root2images + 'f0%ip0%i_F814W_%i.swp.seg.fits' % (field, pointing,
ccd)
ima2 = root2images + 'f0%ip0%i_F814W_%i.swp.segnomask.fits' % (
field, pointing, ccd)
segm1 = pyfits.open(ima1)[0].data
segm2 = pyfits.open(ima2)[0].data
for ii in range(dim):
xo = x[ii]
yo = y[ii]
dimx = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[1]
dimy = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[0]
perc[ii] = (datos[yo - size:yo + size, xo - size:xo + size].sum() /
(dimx * dimy * 1.))
# Defining the sample to be keep.
good = U.greater(valor, 0)
idr = U.compress(good, ids)
dim2 = len(idr)
print 'Dimensions: Original: %i, Final: %i, Excluded: %i detections. ' % (
dim, dim2, dim - dim2)
finalcat = root + 'f0%ip0%i_colorproext_%i_ISO.irmsF814W.free.cat' % (
field, pointing, ccd)
data1 = coeio.loaddata(catalog) # Loading the whole catalog content.
head = coeio.loadheader(catalog)
data2 = data1[good, :]
coeio.savedata(data2, finalcat, dir="",
header=head) # Saving & creating a new catalog.
def remove_fakeabsorptions_F814W(field, pointing, ccd):
"""
Using the rmsweight images, it gets rid of
detections with imrs_F814W < 0.5.
-------------------------------------------
import alhambra_fakedets as AF
AF.remove_fakeabsorptions_F814W(2,1,1)
"""
root = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field)
catalog = root + 'f0%ip0%i_colorproext_%i_ISO.cat' % (field, pointing, ccd)
ids, x, y, area = U.get_data(catalog, (0, 3, 4, 5))
dim = len(ids)
perc = U.zeros(dim)
# Opening F814W Weight image
ima = alh.alhambra_invrmsimagelist(field, pointing, ccd)[-1]
datos = pyfits.open(ima)[0].data
for ii in range(dim):
if area[ii] > 1:
size = int(round(U.sqrt(area[ii]) / 2.))
xo = x[ii]
yo = y[ii]
dimx = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[1]
dimy = U.shape(datos[yo - size:yo + size, xo - size:xo + size])[0]
perc[ii] = (datos[yo - size:yo + size, xo - size:xo + size].sum() /
(dimx * dimy * 1.))
# Defining the sample to be keep.
good = U.greater(perc, 0.5)
idr = U.compress(good, ids)
dim2 = len(idr)
print 'Dimensions: Original: %i, Final: %i, Excluded: %i detections. ' % (
dim, dim2, dim - dim2)
finalcat = root + 'f0%ip0%i_colorproext_%i_ISO.irmsF814W.free.cat' % (
field, pointing, ccd)
data1 = coeio.loaddata(catalog) # Loading the whole catalog content.
head = coeio.loadheader(catalog)
data2 = data1[good, :]
coeio.savedata(data2, finalcat, dir="",
header=head) # Saving & creating a new catalog.
def get_alhambra_GOLD(field,pointing,ccd):
"""
import alhambragold as alhgold
alhgold.get_alhambra_GOLD(2,1,1)
"""
root_catalogs = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/'%(field)
root_gold = '/Volumes/amb22/catalogos/reduction_v4f/GOLD/'
catalog = root_catalogs+'alhambra.F0%iP0%iC0%i.ColorProBPZ.cat' %(field,pointing,ccd)
if os.path.exists(catalog):
data1 = coeio.loaddata(catalog) # Loading the whole catalog content.
head1 = coeio.loadheader(catalog) # Loading the original header.
nc1 = len(data1.T)
dim1 = len(data1[:,0])
nh = len(head1)
# Final catalog.
catout = root_gold+'alhambra.gold.F0%iP0%iC0%i.ColorProBPZ.cat' %(field,pointing,ccd)
outfile = open(catout,'w')
# Reducing the length of the catalogs according to input ids
ids = U.get_str(catalog,0)
mo = U.get_data(catalog,65)
cond1 = U.less(mo,23.000)
data2 = data1[cond1,:]
nraws = U.shape(data2)[0]
ncols = U.shape(data2)[1]
# Setting the IDs to its final values (including F814W+field+pointing+ccd)
finalids = alh.getalhambrafinalids(field,pointing,ccd,'ISO')
finalids2 = U.compress(cond1,finalids)
# Restoring header...
for ii in range(nh):
outfile.write('%s \n'%(head1[ii]))
formato = '%s %i %i %i %.4f %.4f %.3f %.3f %i %.2f %.2f %.4f %.3f %.3f %.1f %.2f %.3f %.2f %i '
formato += '%.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f '
formato += '%.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f '
formato += '%.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f '
formato += '%.3f %.3f %.3f '
formato += '%i %i %.3f %i %.2f %i '
formato += '%.3f %.3f %.3f %.3f %.3f %.3f %.3f '
formato += '%.3f %.3f %.3f %.3f '
formato += '%.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f '
formato += '%.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f %.3f '
formato += '%.3f %.3f %.3f %.3f %i %i '
form = formato.split()
# Here it defines the format to be used.
for jj in range(nraws):
for ss in range(ncols):
goodform = ''
goodform = form[ss]+' '
if ss == 0:
outfile.write(goodform%(int(finalids2[jj])))
else:
outfile.write(goodform%(data2[jj,ss]))
outfile.write(' \n')
outfile.close()
import coeio as C import useful as U root_to_cat = '/Users/albertomolino/doctorado/articulos/SPLUS/' root_to_cat += 'calibration/sample4simulations/data/20180329/NGSL_convolved/' #catalog = root_to_cat + 'SPLUS_SDSS_filters_4Laura.list_sample_stars_' #catalog += 'SDSSDR10_aa_chi2_red_min.eq.0.03_20102017.cat' catalog = root_to_cat + 'sample_NGSL_convolved_29032018.dat' new_catalog = catalog[:-3]+'extended.cat' # display information? verbose = 0 # Reading the catalogue. datos = C.loaddata(catalog) head = C.loadheader(catalog) # Reading catalog dimensions. n_stars_ori = N.shape(datos)[0] n_cols = N.shape(datos)[1] n_filters = 12 #20 ##################################### # Magnitude range we want to expand. m_min = 12 #################################### m_max = 21 #################################### delta_m = 0.01 #################################### base_m = N.arange(m_min,m_max+delta_m,delta_m) n_m_ele = len(base_m) # Number of stars per magnitude-bin. n_stars_final = 200 ####################################
def check_variable_candidate(alhambraid):
"""
It replaces failed magnitudes in the ALHAMBRA catalogues (artificial absorptions,
non-observed sources assigned as non-detected with upper limits) by m=-99,em=99
It might decrease the amount of low Odds at bright magnitudes.
----
import alhambra_photools as A
A.replacing_fakeabsorptions(2,1,2)
A.check_sample(image,catalog,posID,posX,posY,posMAG)
A.alhambra_id_finder(ra1,dec1)
idd = int(id[pos])
A.alhambra_colorstamp_byID(id)
f,p,c,ids = A.alhambra_id_finder(37.4992,1.2482)
"""
field = int(str(alhambraid)[3])
pointing = int(str(alhambraid)[4])
ccd = int(str(alhambraid)[5])
numero = str(alhambraid)[-5:]
print numero
for ii in range(2):
if numero[0] == '0': numero = numero[1:]
print numero
root2cats = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field)
catalog = root2cats + 'originals/f0%ip0%i_colorproext_%i_ISO.cat' % (
field, pointing, ccd)
cols1 = root2cats + 'f0%ip0%i_%i_tot_ISO_eB10.columns' % (field, pointing,
ccd)
cols2 = root2cats + 'f0%ip0%i_colorproext_%i_ISO_phz_eB10.columns' % (
field, pointing, ccd)
if os.path.exists(cols1): columns = cols1
else: columns = cols2
filters = B.get_filter_list(columns)
print filters
data = C.loaddata(catalog) # Loading the whole catalog content.
head = C.loadheader(catalog) # Loading the original header.
m = A.get_magnitudes(catalog, columns)
# em = get_errmagnitudes(catalog,columns)
root2 = '/Volumes/amb22/catalogos/reduction_v4e/'
fluxc1 = root2 + 'f0%i/f0%ip0%i_colorproext_%i_ISO_phz_eB11.flux_comparison' % (
field, field, pointing, ccd)
fluxc2 = root2 + 'f0%i/f0%ip0%i_colorproext_%i_ISO.flux_comparison' % (
field, field, pointing, ccd)
if os.path.exists(fluxc1): fluxc = fluxc1
else: fluxc = fluxc2
ido, ftt, foo, efoo, zb, tb, mm = P.get_usefulfluxcomparison(
columns, fluxc)
pos = A.get_position(ido, int(numero))
print ido[pos], mm[pos]
plt.figure(1, figsize=(10, 7), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
# P.plot1sedfitting(foo[:,jj],efoo[:,jj],ftt[:,jj],zb[jj],tb[jj],root_bpz_sed+'eB11.list',filters)
plt.plot(U.arange(20) + 1, foo[0:20, pos], 'k-', alpha=0.4, lw=6)
plt.plot(U.arange(20) + 1, foo[0:20, pos], 'ko', alpha=0.4, ms=12)
plt.errorbar(U.arange(20) + 1,
foo[0:20, pos], (efoo[0:20, pos] / 1.),
fmt="ko",
alpha=0.4,
ms=10)
minf = (foo[0:20, pos].min()) * 1.1
maxf = (foo[0:20, pos].max()) * 1.1
maxef = (efoo[0:20, pos].max()) * 1.1
# plt.ylim(minf-maxef,maxf+maxef)
plt.xlim(0, 21)
plt.xlabel('Filter', size=25)
plt.ylabel('Flux', size=25)
plt.legend(['Magnitude: %.2f' % (m[pos][-1])],
loc='upper right',
numpoints=1,
fontsize=20)
plt.title(alhambraid, size=25)
plt.grid()
plt.show()
namefig = '/Users/albertomolino/doctorado/photo/variability/analysis/vocheck.ID%s.png' % (
alhambraid)
plt.savefig(namefig, dpi=125)
outcat = '/Users/albertomolino/doctorado/photo/variability/analysis/vocheck.ID%s.cat' % (
alhambraid)
A.select_rows_bylist_pro(catalog, ido[pos], outcat)
print ' '
mastercat = root2cat + 'S82_master_gals.cat'
"""
# This loop was needed for the original version
# where the FIELDS were strings rather than integers.
tiles_name = U.get_str(mastercat,0)
nt = len(tiles_name)
tiles = N.zeros(nt)
for ss in range(nt):
tiles[ss] = int(tiles_name[ss].split('-')[-1])
"""
tiles = U.get_data(mastercat, 0) # use get_str with original cat!
single_tiles = N.unique(tiles).astype(int)
n_single_tiles = len(single_tiles)
# Reading entire catalogue.
header_mastercat = C.loadheader(mastercat)
data_mastercat = C.loaddata(mastercat)
for ii in range(n_single_tiles):
tile_cat = root2cat + 'tile%i_S82.cat' % (single_tiles[ii])
good = N.equal(tiles, single_tiles[ii])
n_gals = len(data_mastercat[good, 1])
if not os.path.exists(tile_cat):
"""
fwhm = data_mastercat[good,9]
magr = data_mastercat[good,82] # Petro
seeing,stars = sct.get_seeing_from_data_pro(fwhm,magr)
fwhm_norm = fwhm /(1.*seeing)
data_mastercat[good,9] = fwhm_norm
data_mastercat[good,0] = N.ones(n_gals) * int(single_tiles[ii])
"""
def replace_fakeabsorptions_pro(field, pointing, ccd):
"""
Updated version to get rid of detections with imrs_* < 0.5
setting their magnitudes to m=-99,em=0.0.
Additionally,it removes detections with imrs_F814W == 0.0
----------------------------------------------------------------------------
It replaces failed magnitudes in the ALHAMBRA catalogues (artificial absorptions,
non-observed sources assigned as non-detected with upper limits) by m=-99,em=99
It might decrease the amount of low Odds at bright magnitudes.
----
import alhambra_photools as A
A.replace_fakeabsorptions_pro(2,1,2)
"""
plots = 1
root = '/Volumes/amb22/catalogos/reduction_v4f/f0%i/' % (field)
catalog = root + 'f0%ip0%i_colorproext_%i_ISO.irmsF814W.free.cat' % (
field, pointing, ccd)
catweight = root + 'f0%ip0%i_ColorProBPZ_%i_ISO.rmsweights.dat' % (
field, pointing, ccd)
dataweight = coeio.loaddata(catweight)
# print U.shape(dataweight)
# ids,x,y,area = U.get_data(catalog,(0,3,4,5))
cols1 = root + 'f0%ip0%i_%i_tot_ISO_eB10.columns' % (field, pointing, ccd)
cols2 = root + 'f0%ip0%i_colorproext_%i_ISO_phz_eB10.columns' % (
field, pointing, ccd)
if os.path.exists(cols1): columns = cols1
else: columns = cols2
data = coeio.loaddata(catalog) # Loading the whole catalog content.
head = coeio.loadheader(catalog) # Loading the original header.
vars, evars, posref, zpe, zpo = alh.get_usefulcolumns(columns)
# print 'dim vars', len(vars)
mags = data[:, vars]
nl = U.shape(data)[
0] # nl is the number of detections inside every single band.
nf = len(vars) # nf is the number of bands inside the catalog.
# print 'nl,nf: ',nl,nf
kk = 0
for jj in range(nl):
filtoff = 0
for ii in range(nf):
pos_mag = vars[ii]
pos_emag = evars[ii]
if dataweight[jj, ii] < 0.5:
# print data[jj,pos_mag],pos_mag,pos_emag
data[jj, pos_mag] = -99.0000
data[jj, pos_emag] = 0.0000
data[jj, 67] -= 1
kk += 1
filtoff += 1
# print data[jj,0]
# print data[jj,pos_mag]
# if filtoff > 0:
# print '%i excluded for detection %i: '%(filtoff,data[jj,0])
# # pausa = raw_input('paused')
print 'Replaced %i magnitudes. ' % (kk)
# New values of mags error overwrites now the original data.
finalcatalog = root + 'f0%ip0%i_colorproext_%i_ISO.test.cat' % (
field, pointing, ccd)
coeio.savedata(data, finalcatalog, dir="",
header=head) # Saving & creating a new catalog.
import coeio as C import useful as U root_to_cat = '/Users/albertomolino/doctorado/articulos/SPLUS/' root_to_cat += 'calibration/sample4simulations/data/20180329/NGSL_convolved/' catalog = root_to_cat + 'sample_final_models_convolved_04042018.cat' catalog_sdss_colors = root_to_cat + 'IveReal_GR_normed.cat' models = U.get_str(root_to_cat + 'ngsl_pick_wd.list', 0) new_catalog = catalog[:-3] + 'extended_gr.cat' # display information? verbose = 0 # Reading the general catalogue. datos_main = C.loaddata(catalog) head_main = C.loadheader(catalog) # gr synthetic models #gr_models = datos_main[:,6]-datos_main[:,8] #SPLUS gr_models = datos_main[:, 16] - datos_main[:, 17] #SDSS # Reading the SDSS_color catalog. sdss_gr, density_gr = U.get_data(catalog_sdss_colors, (0, 1)) n_color_bins = len(sdss_gr) - 1 # Reading catalog dimensions. n_stars_ori = N.shape(datos_main)[0] n_cols = N.shape(datos_main)[1] n_filters = n_cols - 1 #12 #20 ##################################### # Magnitude range we want to expand.
root2bpz = '/Users/albertomolino/codigos/bpz-1.99.2/'
#spectra = 'COSMOSeB11new_recal'
spectra = 'eB11'
# J-PLUS related data
mainroot = '/Users/albertomolino/Postdoc/T80S_Pipeline/Commisioning/S82/Dec2017/'
root2cats = mainroot + 'splus_cats_NGSL/JPLUS_specz_sample_March2018/'
jplus_spz_cat = root2cats + 'jplus2sdssdr12_z05.cat'
jplus_spz_columns = root2cats + 'jplus2sdssdr12_z05.columns'
tiles = U.get_data(jplus_spz_cat, 1)
single_tiles = N.unique(tiles).astype(int)
n_single_tiles = len(single_tiles)
# Reading entire catalogue.
header_jplus = C.loadheader(jplus_spz_cat)
data_jplus = C.loaddata(jplus_spz_cat)
#C.savedata(data1[sample,:],outname,dir="",header=head1)
#n_single_tiles = 1
for ii in range(n_single_tiles):
tile_jplus_spz_upl_cat = root2cats + 'tile_%i_jplus2sdssdr12_z05.ecor.upp.cat' % (
single_tiles[ii])
good = N.equal(tiles, single_tiles[ii])
n_gals = len(data_jplus[good, 0])
if n_gals > 50:
if not os.path.exists(tile_jplus_spz_upl_cat):
tile_jplus_spz_cat = root2cats + 'tile_%i_jplus2sdssdr12_z05.cat' % (
single_tiles[ii])
# Individual (tile) catalogue.