def v_hist():
""""""
t = Table.read("../data/deimos.fits")
print(t.keys())
N = len(t)
ra = np.zeros(N)*u.deg
dec = np.zeros(N)*u.deg
for j in range(N):
ra[j] = Angle(t['RA'][j] + ' hours')
dec[j] = Angle(t['DEC'][j] + 'degrees')
#
fname = '../data/TriS-1.out'
ra_s, dec_s, i, priority, inmask = np.loadtxt(fname, usecols=(1,2,4,6,8), unpack=True, dtype='|S11, |S11, <f4, <f4, <i4', skiprows=4)
Nm = np.size(ra_s)
ra_m = np.zeros(Nm)*u.deg
dec_m = np.zeros(Nm)*u.deg
for j in range(Nm):
ra_m[j] = Angle(ra_s[j] + ' hours')
dec_m[j] = Angle(dec_s[j] + 'degrees')
# match catalogs
ref = ICRS(ra_m, dec_m, unit=(u.degree, u.degree))
cat = ICRS(ra, dec, unit=(u.degree, u.degree))
id1, d21, d31 = cat.match_to_catalog_sky(ref)
matches = d21<0.5*u.arcsec
t_priority = priority[id1]
i_m = i[id1]
indices = (t['ZQUALITY']>-1)
t['Z'] *= 300000
add_npcolumn(t, name="priority", vec=t_priority)
#print(t_priority, np.size(t_priority))
plt.close()
plt.figure()
plt.subplot(221)
plt.hist(t['Z'], bins=np.arange(-400,200,10), histtype='step', color='k')
plt.hist(t['Z'][indices], bins=np.arange(-400,200,10), histtype='step', color='b')
plt.hist(t['Z'][t_priority>=500], bins=np.arange(-400,200,10), histtype='step', color='r')
plt.minorticks_on()
plt.subplot(222)
plt.plot(i_m[indices], t['Z'][indices], 'ko')
print(t['Z'][t_priority>=500], i_m[t_priority>=500])
print(np.max(i_m[indices]))
def sdss_IAU_id_to_ra_dec(sdssids, matchtocatalog=None):
"""
Converts IAU SDSS identifiers (e.g.,"J151503.37+421253.9") to their RA/Decs
Returns an ICRS object if `matchtocatalog` is None, otherwise
`catalogidx, skysep`
"""
import re
from astropy.coordinates import ICRS, Latitude, Longitude
rex = re.compile(r'J(\d{2})(\d{2})(\d{2}(?:\.\d{1,2})?)'
r'([+-])(\d{2})(\d{2})(\d{2}(?:\.\d)?)')
if isinstance(sdssids, six.string_types):
sdssids = [sdssids]
ras = []
decs = []
for idi in sdssids:
res = rex.match(idi)
if res is None:
raise ValueError('Could not match ' + idi)
res = res.groups()
rah, ram, rasc = res[:3]
desgn, ded, dem, des = res[-4:]
ras.append(':'.join((rah, ram, rasc)))
decs.append(desgn + ':'.join((ded, dem, des)))
coords = ICRS(ra=Longitude(ras, u.hourangle), dec=Latitude(decs, u.degree))
if matchtocatalog:
if not hasattr(matchtocatalog, 'match_to_catalog_sky'):
matchtocatalog = ICRS(ra=np.array(matchtocatalog['ra'])*u.deg,
dec=np.array(matchtocatalog['dec'])*u.deg)
idx, sep, sep3d = coords.match_to_catalog_sky(matchtocatalog)
return idx, sep.to(u.arcsec)
else:
return coords
def sdss_IAU_id_to_ra_dec(sdssids, matchtocatalog=None):
"""
Converts IAU SDSS identifiers (e.g.,"J151503.37+421253.9") to their RA/Decs
Returns an ICRS object if `matchtocatalog` is None, otherwise
`catalogidx, skysep`
"""
import re
from astropy.coordinates import ICRS, Latitude, Longitude
rex = re.compile(r'J(\d{2})(\d{2})(\d{2}(?:\.\d{1,2})?)'
r'([+-])(\d{2})(\d{2})(\d{2}(?:\.\d)?)')
if isinstance(sdssids, six.string_types):
sdssids = [sdssids]
ras = []
decs = []
for idi in sdssids:
res = rex.match(idi)
if res is None:
raise ValueError('Could not match ' + idi)
res = res.groups()
rah, ram, rasc = res[:3]
desgn, ded, dem, des = res[-4:]
ras.append(':'.join((rah, ram, rasc)))
decs.append(desgn + ':'.join((ded, dem, des)))
coords = ICRS(ra=Longitude(ras, u.hourangle), dec=Latitude(decs, u.degree))
if matchtocatalog:
if not hasattr(matchtocatalog, 'match_to_catalog_sky'):
matchtocatalog = ICRS(ra=np.array(matchtocatalog['ra']) * u.deg,
dec=np.array(matchtocatalog['dec']) * u.deg)
idx, sep, sep3d = coords.match_to_catalog_sky(matchtocatalog)
return idx, sep.to(u.arcsec)
else:
return coords
def match_objects(folder_labels):
print("----------------------------------------")
print("Running Module 2: Match Objects")
# stores all "_" filter txt files (eventually will group by similar exposure times in event there are more than one J,Ks files
j_files = np.hstack(np.array(glob.glob('Data/{}/*-J.txt'.format(folder))) for folder in folder_labels)
k_files = np.hstack(np.array(glob.glob('Data/{}/*-Ks.txt'.format(folder))) for folder in folder_labels)
print j_files
print k_files
j_filter_data = np.empty([0,5])
ra1 = np.empty([0,1])
ra2 = np.empty([0,1])
k_filter_data = np.empty([0,5])
dec1 = np.empty([0,1])
dec2 = np.empty([0,1])
print '\nPlease wait while all data is loaded from files...'
# appends data from all J filter files in folder if multiple
for j in j_files:
# generate numpy array from txt files
new_data1 = np.genfromtxt(j, unpack = False)
j_filter_data = np.vstack([j_filter_data, new_data1])
print 'Data from J filter file', j, 'loaded...'
print j_filter_data.shape
ra1 = j_filter_data[:,3]
dec1 = j_filter_data[:,4]
print 'J Filter shape:', ra1.shape, dec1.shape
# appends data from all Ks filter files in folder if multiple
for k in k_files:
new_data2 = np.genfromtxt(k, unpack = False)
k_filter_data = np.vstack([k_filter_data, new_data2])
print 'Data from Ks filter file', k, 'loaded...'
print k_filter_data.shape
ra2 = k_filter_data[:,3]
dec2 = k_filter_data[:,4]
print 'Ks Filter shape:', ra2.shape, dec2.shape
print 'Data from all files loaded.'
print '\nPlease wait while objects are matched across the J and Ks Filter...'
# j filter ra/dec info to be compared
# in units of degrees
obj_1 = ICRS(ra1, dec1, unit=(u.degree, u.degree))
# k filter ra/dec info to be compared
# in units of degrees
obj_2 = ICRS(ra2, dec2, unit=(u.degree, u.degree))
# This function will match all objects from obj_1 (j-filter) with an object in obj_2 (ks-filter)
# By its nature, match_to_catalog_sky compares object data against catalog of sky, which would yield a closest match for every object
# In our application, we compare two different sets of object data to each other, which should not guarantee a match
# This means that we will get as many matches as there are objects in the smaller object data set (in this case, the j-filter will always have less objects)
# This could be problematic since a "closest" match will be made relative to the available objects in obj_2 (ks-filter)
# So a match does not necessarily constitute a match for us
# To remedy this, I have run the matched objects through another test to compare the sum of differences against a set tolerance
# This will ensure that all matches are within a controlled and tolerable closeness
idx, d2d, d3d = obj_1.match_to_catalog_sky(obj_2) # match j band to k band since there are more objects in k
# gets data matches from k-filter data based on matched indexes returned by match_to_catalog_sky
matches = k_filter_data[idx,:]
# appends all data columns for matched objects in a [:,10] array
# j-filter
# 0 -> Classification
# 1 -> Aper_flux_3
# 2 -> Aper_flux_3_err
# 3 -> RA
# 4 -> DEC
# k-filter
# 5 -> Classification
# 6 -> Aper_flux_3
# 7 -> Aper_flux_3_err
# 8 -> RA
# 9 -> DEC
filter_matches = np.column_stack([j_filter_data, matches])
# CHANGE THE TOLERANCE TO SEE WHAT WORKS BEST
tolerance = 0.0001
# compares sum of differences between RA/DEC values to set tolerance to ensure quality matches
# see long comment above to see reasoning
within_tol_index = ((abs(filter_matches[:,3]-filter_matches[:,8]) < tolerance) & (abs(filter_matches[:,4]-filter_matches[:,9]) < tolerance)).nonzero()
stellar_obj_matches = (np.squeeze((filter_matches[within_tol_index,:]), axis=0))
print '\nNumber of matches found', stellar_obj_matches.shape
np.savetxt('Resources/J_Ks-matches.txt', stellar_obj_matches, fmt=['%.1f', '%.10f', '%.10f', '%.10f', '%.10f','%.1f', '%.10f', '%.10f', '%.10f', '%.10f'], delimiter=' ')
def match_Stars(fits_file, save_location='./', nobsfile='obsout', standard_stars_file='standard_stars.dat', out_nobsfile='final_obsout', header_line=5, SN_coord=None, dist_treshold = 0.0004, sel_3D=True ):
'''- fits_file is the full path of the reference fits file.
- save_location is the directory in which the output will be saved.
- nobsfile is the full path of the nobsfile.
- standard_stars_file is the full path of the standard stars file.
- phot_band is the photometric reference band.
- index_band is the indec of the band in the standard_stars_file.
- offset is an integer which changes the nobsfile line that is used to name the star. e.g, if g is the reference but the band order in
nobsfile is u then g the offset should be'1'
- header_line is the line number of the header of the standard_stars_file
- dist_treshold the match is discarded if the stars are farther apart than this value (degrees)
The output is four images showing the matched stars plus 'final_obsout', which is the nobsfile with the star names
in place.'''
import pyfits
hdulist = pyfits.open(fits_file)
prihdr = hdulist[0].header
phot_band = prihdr['filter1']
index_band = phot_band.lower()
print phot_band
if SN_coord==None:
SN_c = ICRS(prihdr['RA']+prihdr['DEC'], unit = (u.hourangle, u.degree))
SN_coord = [SN_c.ra.degree, SN_c.dec.degree]
obsout_file = nobsfile
gri_file = standard_stars_file
if os.path.isfile(fits_file) == True:
w = WCS(fits_file)
##
obsout = genfromtxt(obsout_file,dtype=None, missing_values='INDEF')
gri = genfromtxt(gri_file,dtype=None, missing_values='INDEF', skip_header=header_line-1,names=True)
##
def marker_size_(in_mag):
return 20*(nanmax(in_mag)-in_mag)/(nanmax(in_mag)-nanmin(in_mag))
def select_phot_(in_v,phot_out,phot_list):
out_v = copy(in_v)
for i in range(size(out_v)):
if phot_list[i]!=phot_out:
out_v[i]=nan
return out_v
##
figure(figsize=(8,8))
# all_pix2world is from http://docs.astropy.org/en/stable/wcs/
lon, lat =w.all_pix2world(obsout['f4'], obsout['f5'], 0)
lon = select_phot_( lon , phot_out=phot_band, phot_list=obsout['f1'])
lat = select_phot_( lat , phot_out=phot_band, phot_list=obsout['f1'])
scatter(lon,lat,marker='o',s=marker_size_(obsout['f6']) )
scatter(gri['RA'],gri['dec'],color='r',marker='o',s=marker_size_(gri[index_band]) )
plot(SN_coord[0],SN_coord[1],'+g')
gca().invert_yaxis()
savefig(save_location+'out1.pdf')
#
# match_to_catalog_sky is from https://www.sites.google.com/site/mrpaulhancock/blog/theage-oldproblemofcross-matchingastronomicalsources
indx1 = [i for i in range(size(lon)) if not isnan(lon*lat)[i]]
cat1_ra = lon[indx1]
cat1_dec = lat[indx1]
cat2_ra = gri['RA']
cat2_dec = gri['dec']
cat3_ra = [SN_coord[0]]
cat3_dec = [SN_coord[1]]
cat1 = ICRS(cat1_ra,cat1_dec,unit = (u.degree,u.degree))
cat2 = ICRS(cat2_ra,cat2_dec,unit = (u.degree,u.degree))
cat3 = ICRS(cat3_ra,cat3_dec,unit = (u.degree,u.degree))
index_list,dist2d,dist3d = cat2.match_to_catalog_sky(cat1)
index_list_SN,dist2d_3,dist3d_3 = cat3.match_to_catalog_sky(cat1)
indexes_gri = []
indexes_obsout = []
distances=[]
for i in range(size(gri)):
j = indx1[index_list[i]]
cos_dec = cos(lat[j]*pi/(180.))
dist_ = ( sqrt(sum(array([(lon[j] - gri['RA'][i])*cos_dec , lat[j] - gri['dec'][i]])**2)) )
if dist_ < dist_treshold:
indexes_gri.append(i)
indexes_obsout.append(j)
distances.append(dist_)
if sel_3D:
valuesA_=[]
valuesB_=[]
for i in indexes_gri:
j = indx1[index_list[i]]
valuesA_.append(obsout['f6'][j])
valuesB_.append(gri[index_band][i])
valuesA_=array(valuesA_);valuesB_=array(valuesB_)
sigma_res = median(abs(valuesA_- valuesB_ - median(valuesA_ - valuesB_)))
res_ = abs(valuesA_- valuesB_ - median(valuesA_ - valuesB_))
indexes_gri_new = []
indexes_obsout = []
distances=[]
ii=0
for i in indexes_gri:
j = indx1[index_list[i]]
cos_dec = cos(lat[j]*pi/(180.))
dist_ = ( sqrt(sum(array([(lon[j] - gri['RA'][i])*cos_dec , lat[j] - gri['dec'][i]])**2)) )
if dist_ < dist_treshold:
if not sel_3D or res_[ii] < 4.*sigma_res:
indexes_gri_new.append(i)
indexes_obsout.append(j)
distances.append(dist_)
ii+=1
indexes_gri = indexes_gri_new
indexes_gri_new = []
indexes_obsout = []
distances=[]
ii=0
for i in indexes_gri:
j = indx1[index_list[i]]
cos_dec = cos(lat[j]*pi/(180.))
dist_ = ( sqrt(sum(array([(lon[j] - gri['RA'][i])*cos_dec , lat[j] - gri['dec'][i]])**2)) )
if dist_ < dist_treshold:
if not sel_3D or res_[ii] < 3.*sigma_res:
indexes_gri_new.append(i)
indexes_obsout.append(j)
distances.append(dist_)
ii+=1
indexes_gri = indexes_gri_new
indexes_obsout_SN = nan
j = indx1[index_list_SN[0]]
cos_dec = cos(lat[j]*pi/(180.))
dist_ = ( sqrt(sum(array([(lon[j] - SN_coord[0])*cos_dec , lat[j] - SN_coord[1]])**2)) )
if dist_ < dist_treshold:
#indexes_gri.append(i)
indexes_obsout_SN = j
figure()
hist(distances,1000,label='catalog stars')
plot([dist_ ,dist_],[0,1],'g', linewidth=10, label='SN')
plot([dist_treshold,dist_treshold],[0,3],':k', label='dist_treshold')
xlim((0,0.002))
xlabel('distance (degree)')
ylabel('N stars')
legend()
savefig(save_location+'out2.pdf')
#
figure(figsize=(8,8))
offset = list(obsout['f1']).index(phot_band)
for i in indexes_gri:
j = indx1[index_list[i]]
obsout[j-offset][0]=gri[i][0]
scatter(lon[j],lat[j],marker='o',s=marker_size_(obsout['f6'])[j] )
scatter(gri['RA'][i],gri['dec'][i],color='r',marker='o',s=marker_size_(gri[index_band])[i] )
plot(SN_coord[0],SN_coord[1],'+g')
plot([lon[j],gri['RA'][i]],[lat[j],gri['dec'][i]])
if not isnan(indexes_obsout_SN):
j=indexes_obsout_SN
obsout[j-offset][0] = 'SN'
scatter(lon[j],lat[j],marker='o',color='g',s=marker_size_(obsout['f6'])[j] )
gca().invert_yaxis()
savefig(save_location+'out3.pdf')
#
figure()
for i in indexes_gri:
j = indx1[index_list[i]]
plot( obsout['f6'][j],gri[index_band][i],'bo')
savefig(save_location+'out4.pdf')
## Save final_obsout
f=open(save_location+out_nobsfile,'w+')
for j in range(len(obsout)):
for k in range(len(obsout[j])):
if k <len(obsout[j])-1:
f.write(str(obsout[j][k]).replace('nan','INDEF').replace('False','INDEF'))
for h in range(13-len(str(obsout[j][k]).replace('nan','INDEF').replace('False','INDEF'))):
f.write(' ')
else:
f.write(str(obsout[j][k]).replace('nan','INDEF').replace('False','INDEF')+'\n')
f.close()
else:
print fits_file + 'not found'
return
def v_hist():
""""""
t = Table.read("../data/deimos.fits")
print(t.keys())
N = len(t)
ra = np.zeros(N) * u.deg
dec = np.zeros(N) * u.deg
for j in range(N):
ra[j] = Angle(t['RA'][j] + ' hours')
dec[j] = Angle(t['DEC'][j] + 'degrees')
#
fname = '../data/TriS-1.out'
ra_s, dec_s, i, priority, inmask = np.loadtxt(
fname,
usecols=(1, 2, 4, 6, 8),
unpack=True,
dtype='|S11, |S11, <f4, <f4, <i4',
skiprows=4)
Nm = np.size(ra_s)
ra_m = np.zeros(Nm) * u.deg
dec_m = np.zeros(Nm) * u.deg
for j in range(Nm):
ra_m[j] = Angle(ra_s[j] + ' hours')
dec_m[j] = Angle(dec_s[j] + 'degrees')
# match catalogs
ref = ICRS(ra_m, dec_m, unit=(u.degree, u.degree))
cat = ICRS(ra, dec, unit=(u.degree, u.degree))
id1, d21, d31 = cat.match_to_catalog_sky(ref)
matches = d21 < 0.5 * u.arcsec
t_priority = priority[id1]
i_m = i[id1]
indices = (t['ZQUALITY'] > -1)
t['Z'] *= 300000
add_npcolumn(t, name="priority", vec=t_priority)
#print(t_priority, np.size(t_priority))
plt.close()
plt.figure()
plt.subplot(221)
plt.hist(t['Z'], bins=np.arange(-400, 200, 10), histtype='step', color='k')
plt.hist(t['Z'][indices],
bins=np.arange(-400, 200, 10),
histtype='step',
color='b')
plt.hist(t['Z'][t_priority >= 500],
bins=np.arange(-400, 200, 10),
histtype='step',
color='r')
plt.minorticks_on()
plt.subplot(222)
plt.plot(i_m[indices], t['Z'][indices], 'ko')
print(t['Z'][t_priority >= 500], i_m[t_priority >= 500])
print(np.max(i_m[indices]))
