def transFLCfilt(targname,dir='./',matchtol=1):
"""
Replace F606W and F814W (and 606 and 814) throughout this if using
different filters
"""
x = os.listdir(dir)
for ii in x:
if ii.endswith('cut_F606W.dat'):
f606w_file = ii
elif ii.endswith('cut_F814W.dat'):
f814w_file = ii
f606wN = np.genfromtxt(dir+f606w_file,names=True)
f606w = np.genfromtxt(dir+f606w_file)
f814wN = np.genfromtxt(dir+f814w_file,names=True)
f814w = np.genfromtxt(dir+f814w_file)
col606 = np.array(f606wN.dtype.names)
# col814 = np.array(f814wN.dtype.names) # names will be the same between
# filters
# Getting indices of columns
# Will be the same for both filters
x = np.int(np.where(col606=='xDRC1')[0])
y = np.int(np.where(col606=='yDRC1')[0])
idCol = len(col606)
newCol = np.zeros((len(f606w),1),dtype=int)
newCol[:,0] = np.arange(0,len(f606w),1)
f606w_id = np.hstack((f606w,newCol))
newCol = np.zeros((len(f814w),1),dtype=int)
newCol[:,0] = np.arange(0,len(f814w),1)
f814w_id = np.hstack((f814w,newCol))
# Sorting to get the 50 brightest stars
v50 = np.argsort(f606wN['mean'])[:50] # using v abbreviation because
# F606W is a broad V-band
f606w_50 = f606w_id[v50]
i50 = np.argsort(f814wN['mean'])[:50] # using i abbrev. b/c F814W is
# broad I-band
f814w_50 = f814w_id[i50]
master_in = f606w_50[:,[idCol,x,y]] # selecting the columns I need
idV,xv,yv= 0,1,2 # associating short vars with indices
cat = f814w_50 # this has ALL of the columns from F814W catalog
nF_out = True
while nF_out:
master, matchids = matchlistID(master_in,cat,matchtol,xv,yv,x,y,idCol)
if len(master)>=int(6): # because it's a 6D transformation
nF_out = False
print('Minimum Number Reached:{0:d}'.format(len(master)),targname)
else:
print('Need More Stars')
master_in = f606w_50[:,[idCol,x,y]] # resetting, just in case
matchtol += 1
master = np.hstack((master,matchids))
idV, xv, yv, idI = 0, 1, 2, 3, 4 # index columns of master
newCols = np.zeros((len(master),2))
idxCol = master[:,idI]
idxI = np.asarray(idxCol,int)
regI = f814w[idxI]
newCols[:,0] = regI[:,x]
newCols[:,1] = regI[:,y]
tempArr = np.hstack((master,newCols))
idV, xv, yv, idI, xi, yi = 0, 1, 2, 3, 4, 5
# Linear Transform Part
match_arr = np.zeros((len(tempArr),2))
match_arr[:,0] = tempArr[:,xi] # the x in F814W that is going to F606W
match_arr[:,1] = tempArr[:,yi] # the y in F814W that is going to F606W
master_arr = np.zeros((len(tempArr),2))
master_arr[:,0] = tempArr[:,xv] # the x ref F606W
master_arr[:,1] = tempArr[:,yv] # the y ref F606W
weights = np.ones(len(master_arr))
all_arr = np.zeros((len(f814w),2))
all_arr[:,0] = f814w_id[:,x]
all_arr[:,1] = f814w_id[:,y]
print('Transforming ',targname)
new_match, new_all = test_linear(match_arr[:,0],match_arr[:,1],
master_arr[:,0],master_arr[:,1],weights,
weights, all_arr[:,0],all_arr[:,1])
outArr = np.hstack((f814w,new_all))
s0 = ' '
header = s0.join(col606)
header += ' x_f606wTrans y_f606wTrans'
outName = dir + 'flcFiltTrans_' + targname
np.savetxt(outName + '.dat',outArr,header=header)
makePlot(targname,match_arr[:,0],match_arr[:,1],master_arr[:,0],
master_arr[:,1],new_match[:,0],new_match[:,1],
label_1='Original in F814W',label_2='Original in F606W',
label_3='New in F814W 2 F606W',outname=outName+'_matchCheck')
return None
def linTransSEpu(targname,
refFile='None.dat',
allFile='None.dat',
saveDir='./'):
# Going from photutils to source extractor; although it shouldn't
# really matter which direction I go as long as the eventual
# matching code knows what I'm doing here
# Loading the reference file and putting the names in an array;
# the "col" randomly refers to column.
# The -N suffix means that array was loaded with Names,
# and is therefore not has a form (n,)
mFileN = np.genfromtxt(refFile, names=True)
mFile = np.genfromtxt(refFile)
colMs = np.array(mFileN.dtype.names)
# Loading the file with source positions to be transformed;
# calling this the all file because it has all the points,
# not just the few reference points
aFileN = np.genfromtxt(allFile, names=True)
aFile = np.genfromtxt(allFile)
colAs = np.array(aFileN.dtype.names)
# Putting the photutils positions into the match array
# to be used as references
match_arr = np.zeros((len(mFile), 2))
xtrans = np.int(np.where(colMs == 'xPU')[0])
ytrans = np.int(np.where(colMs == 'yPU')[0])
match_arr[:, 0] = mFile[:, xtrans]
match_arr[:, 1] = mFile[:, ytrans]
# Putting the source extractor positions into the master array
master_arr = np.zeros((len(mFile), 2))
x1 = np.int(np.where(colMs == 'xSE')[0])
y1 = np.int(np.where(colMs == 'ySE')[0])
master_arr[:, 0] = mFile[:, x1]
master_arr[:, 1] = mFile[:, y1]
# Weights are needed for the 6D code, but we've
# never really taken them into account
weights = np.zeros((len(master_arr)))
weights.fill(1.0)
# Filling the array that will have the values that need
# to be transformed; in this case, they are going from PU to SE;
# variable names with _bt refer to "before tranform"
all_arr = np.zeros((len(aFile), 2))
x_bt = np.int(np.where(colAs == 'xcenter_f606w')[0])
y_bt = np.int(np.where(colAs == 'ycenter_f606w')[0])
all_arr[:, 0] = aFile[:, x_bt]
all_arr[:, 1] = aFile[:, y_bt]
s0 = ' '
header = s0.join(colAs) # "pulling" the header from the original file
header += ' xSEf606w ySEf606w' # adding new column names; make sure
# there's a SPACE at the beginning, so the names don't run together
outName = saveDir + 'puWtrans'
# Call to the 6D code. More info in that script on the inputs;
# the above portion of code put together the necessary arrays;
# new_match is the positions of the original reference stars
# transformed into the new frame
# new_all is the list of all the transformed positions
new_match, new_all = test_linear(match_arr[:, 0], match_arr[:, 1],
master_arr[:, 0], master_arr[:,
1], weights,
weights, all_arr[:, 0], all_arr[:, 1])
# Tacking the columns onto the original array
outArr = np.hstack((aFile, new_all))
np.savetxt(outName + '.dat', outArr, header=header)
# Making a plot of the transformed reference stars to see
# if anything went wrong
makePlot(targname,
match_arr[:, 0],
match_arr[:, 1],
new_match[:, 0],
new_match[:, 1],
master_arr[:, 0],
master_arr[:, 1],
label_1='Original in PU',
label_2='New in PU 2 SE',
label_3='Original in SE',
outname=outName)
return None
def transFLCdrc(targname, flcFile, drcFile, dir='./', matchtol=1):
flcN = np.genfromtxt(dir + flcFile, names=True)
flc = np.genfromtxt(dir + flcFile)
drcN = np.genfromtxt(dir + drcFile, names=True)
drc = np.genfromtxt(dir + drcFile)
colFs = np.array(flcN.dtype.names)
colDs = np.array(drcN.dtype.names)
xF = np.int(np.where(colFs == 'xDRC1_f606w')[0])
yF = np.int(np.where(colFs == 'yDRC1_f606w')[0])
xD = np.int(np.where(colDs == 'xcenter_f606w')[0])
yD = np.int(np.where(colDs == 'ycenter_f606w')[0])
idColF = len(colFs)
newCol = np.zeros((len(flc), 1), dtype=int)
newCol[:, 0] = np.arange(0, len(flc), 1)
flc_id = np.hstack((flc, newCol))
idColD = len(colDs)
newCol = np.zeros((len(drc), 1), dtype=int)
newCol[:, 0] = np.arange(0, len(drc), 1)
drc_id = np.hstack((drc, newCol))
f50 = np.argsort(flcN['mean_f606w'])[:50]
flc_50 = flc_id[f50]
d50 = np.argsort(drcN['magr_f606w'])[:50]
drc_50 = drc_id[d50]
master_in = drc_50[:, [idColD, xD, yD]]
idD, xd, yd = 0, 1, 2
cat = flc_50
nF_out = True
while nF_out:
master, matchids = matchlistID(master_in, cat, matchtol, xd, yd, xF,
yF, idColF)
if len(master) >= int(6): # because it's a 6D transformation
nF_out = False
print('Minimum Number Reached:{0:d}'.format(len(master)), targname)
else:
print('Need More Stars')
master_in = drc_50[:, [idColD, xD, yD]] # resetting, just in case
matchtol += 1
master = np.hstack((master, matchids))
idD, xd, yd, idF = 0, 1, 2, 3
newCols = np.zeros((len(master), 2))
idxCol = master[:, idF]
idxF = np.asarray(idxCol, int)
regF = flc[idxF]
newCols[:, 0] = regF[:, xF]
newCols[:, 1] = regF[:, yF]
tempArr = np.hstack((master, newCols))
idD, xd, yd, idF, xf, yf = 0, 1, 2, 3, 4, 5
# Linear Transform Part
match_arr = np.zeros((len(tempArr), 2))
match_arr[:, 0] = tempArr[:, xf] # the x in FLC that is going to DRC
match_arr[:, 1] = tempArr[:, yf] # the y in FLC that is going to DRC
master_arr = np.zeros((len(tempArr), 2))
master_arr[:, 0] = tempArr[:, xd] # the x ref DRC
master_arr[:, 1] = tempArr[:, yd] # the y ref DRC
weights = np.ones(len(master_arr))
all_arr = np.zeros((len(flc), 2))
all_arr[:, 0] = flc_id[:, xF]
all_arr[:, 1] = flc_id[:, yF]
print('Transforming ', targname)
new_match, new_all = test_linear(match_arr[:, 0], match_arr[:, 1],
master_arr[:, 0], master_arr[:,
1], weights,
weights, all_arr[:, 0], all_arr[:, 1])
outArr = np.hstack((flc, new_all))
s0 = ' '
header = s0.join(colFs)
header += ' x_DRCtrans y_DRCtrans'
outName = dir + 'flcDRCtrans_' + targname
np.savetxt(outName + '.dat', outArr, header=header)
makePlot(targname,
match_arr[:, 0],
match_arr[:, 1],
master_arr[:, 0],
master_arr[:, 1],
new_match[:, 0],
new_match[:, 1],
label_1='Original in FLC',
label_2='Original in DRC',
label_3='New in FLC 2 DRC',
outname=outName + '_matchCheck')
return None
def drcFiltLinTrans(targname, dir='./', suffix='_pu.dat'):
# Will need to replace filter names with appropriate
# ones for your use.
x = os.listdir(dir)
for ii in x:
if ii.endswith('_F606W' + suffix):
f606w_file = ii
elif ii.endswith('_F814W' + suffix):
f814w_file = ii
# Going from F814W to F606W
refN = np.genfromtxt(dir + 'drcFiltRef_' + targname + '.dat', names=True)
ref = np.genfromtxt(dir + 'drcFiltRef_' + targname + '.dat')
colRs = np.array(refN.dtype.names)
allN = np.genfromtxt(dir + f814w_file, names=True)
all = np.genfromtxt(dir + f814w_file)
colAs = np.array(allN.dtype.names)
# Putting the F814W positions into the match array
# to be used as references.
match_arr = np.zeros((len(ref), 2))
xi2v = np.int(np.where(colRs == 'x_f814w')[0])
yi2v = np.int(np.where(colRs == 'y_f814w')[0])
match_arr[:, 0] = ref[:, xi2v]
match_arr[:, 1] = ref[:, yi2v]
# Putting the F606W positions into the master array
master_arr = np.zeros((len(ref), 2))
xv = np.int(np.where(colRs == 'x_f606w')[0])
yv = np.int(np.where(colRs == 'y_f606w')[0])
master_arr[:, 0] = ref[:, xv]
master_arr[:, 1] = ref[:, yv]
# Weights required for the 6D transformation function
# We haven't assigned useful values to them.
weights = np.ones(len(master_arr))
# Creating an array that will hold the original and transformed values
all_arr = np.zeros((len(all), 2))
# bt stands for before transform
x_bt = np.int(np.where(colAs == 'xcenter')[0])
y_bt = np.int(np.where(colAs == 'ycenter')[0])
all_arr[:, 0] = all[:, x_bt]
all_arr[:, 1] = all[:, y_bt]
outName = dir + 'drcFiltTrans_' + targname
print('Transforming ', targname)
# This takes the xy positions from the points you have matched
# to the master, the xy positions of the corresponding points in
# the master, weights, and the xy points in the match_arr frame
# that will be transformed into the master_arr frame.
new_match, new_all = test_linear(match_arr[:, 0], match_arr[:, 1],
master_arr[:, 0], master_arr[:,
1], weights,
weights, all_arr[:, 0], all_arr[:, 1])
outArr = np.hstack((all, new_all)) # attaching the transformed positions
# to the original (F814W) catalog
s0 = ' '
header = s0.join(colAs)
header += ' x_f606wTrans y_f606wTrans'
np.savetxt(outName + '.dat', outArr, header=header)
makePlot(targname,
match_arr[:, 0],
match_arr[:, 1],
master_arr[:, 0],
master_arr[:, 1],
new_match[:, 0],
new_match[:, 1],
label_1='Original in F814W',
label_2='Original in F606W',
label_3='New in F814W 2 F606W',
outname=outName + '_matchCheck')
return None