def test_coords():
# test Li Jing's coord transforms
#ra,dec,l,b,j0,k0,w10,w20,lambda,beta
arcsec = 1.0 / 3600.0
data = np.loadtxt("/home/newbym/Desktop/Mgiant_wise_sgr.csv",
delimiter=",",
skiprows=1)
ra, dec = data[:, 0], data[:, 1]
l, b = data[:, 2], data[:, 3]
lam, bet = data[:, 8], data[:, 9]
badl, badb, badlam, badbet, badlam2, badbet2 = [], [], [], [], [], []
for i in range(data.shape[0]):
new_l, new_b = ac.EqTolb(ra[i], dec[i])
if np.abs(new_l - l[i]) > arcsec: badl.append(new_l - l[i])
if np.abs(new_b - b[i]) > arcsec: badb.append(new_b - b[i])
new_lam, new_bet = ac.lb2sgr(l[i], b[i], 30.)[3:5]
if np.abs(new_lam - lam[i]) > arcsec: badlam.append(lam[i])
if np.abs(new_bet - bet[i]) > arcsec: badbet.append(bet[i])
new_lam2, new_bet2 = ac.lb2sgr(new_l, new_b, 30.)[3:5]
if np.abs(new_lam2 - lam[i]) > arcsec: badlam2.append(lam[i])
if np.abs(new_bet2 - bet[i]) > arcsec: badbet2.append(bet[i])
print data.shape[0]
print np.mean(badl), np.mean(badb)
print np.mean(badlam), np.mean(badbet), np.std(badbet)
print np.mean(badlam2), np.mean(badbet2), np.std(badbet2)
print "Done"
def sgr_rv_skyplot():
data = np.loadtxt("/home/newbym2/Dropbox/Research/sgrLetter/sgr_spec_all.csv", delimiter=",")
#dered_g,dered_r,l,b,ELODIERVFINAL,ELODIERVFINALERR,plate,p_ra,p_dec
g0, r0 = data[:,0], data[:,1]
l, b = data[:,2], data[:,3]
rv, rv_err = data[:,4], data[:,5]
plates = data[:,6]
p_ra, p_dec = data[:,7], data[:,8]
d = ac.getr(g0)
# Transform to vgsr from Yanny+ 2009
vgsr = ac.rv_to_vgsr(rv,l,b)
X,Y,Z, lsgr, bsgr, r_sgr = ac.lb2sgr(l, b, d)
pl,pb = ac.EqTolb(p_ra, p_dec)
pd = 20.0*sc.ones(len(pl))
pX,pY,pZ, plsgr, pbsgr, pr_sgr = ac.lb2sgr(pl, pb, pd)
mean, stdev, nsig = -118.233333, 30.222222, 1.0
#plates = plate_center_utils()
locs = []
for i in range(len(data[:,0])):
if g0[i] < 20.0: continue
if g0[i] > 23.0: continue
#if lsgr[i] > 230.0: continue
locs.append([lsgr[i], bsgr[i], vgsr[i], plsgr[i], pbsgr[i] ])
locs = np.array(locs)
sgr = []
for i in range(len(locs[:,0])):
if locs[i,2] < (mean - stdev*nsig): continue
if locs[i,2] > (mean + stdev*nsig): continue
sgr.append([locs[i,0], locs[i,1]])
sgr = np.array(sgr)
plt.figure()
plt.scatter(locs[:,0], locs[:,1], c="k", s=1)
plt.scatter(sgr[:,0], sgr[:,1], c='r', s=10)
plt.scatter(locs[:,3], locs[:,4], c='b', s=10)
plt.plot([190.0, 250.0, 250.0, 190.0],[10.0, 10.0, -10.0, -10.0], "b-")
plt.plot([190.0, 320.0], [0.0, 0.0], "b--")
plt.text(195.0, 33.0, "$\mu=$-118.2, $\sigma=$30.2\n{0} stars in {1}$\sigma$".format(len(sgr), nsig), fontsize=16)
plt.xlim(190.0, 320.0)
#plt.ylim(-75.0, 40.0)
plt.ylim(40.0, -75.0)
plt.xlabel(r"$\Lambda$", fontsize=16)
plt.ylabel(r"$B$", fontsize=16)
plt.show()
"""
hist, edges = np.histogram(sgr[:,1], bins=23, range=(-75.0, 40.0))
plt.bar(edges[:-1], hist, width=5.0, color="grey")
plt.xlabel("B", fontsize=16)
plt.text(-50.0, 25.0, "Stars within "+str(nsig)+r"$\sigma$ of mean $v_{\rm gsr}$; $\Lambda<230$", fontsize=12)
plt.show()"""
plt.close()
def sgr_rv_cut():
data = np.loadtxt("/home/newbym2/Dropbox/Research/sgrLetter/sgr_spec_all.csv", delimiter=",")
#dered_g,dered_r,l,b,ELODIERVFINAL,ELODIERVFINALERR,plate,p_ra,p_dec
g0, r0 = data[:,0], data[:,1]
l, b = data[:,2], data[:,3]
rv, rv_err = data[:,4], data[:,5]
plates = data[:,6]
p_ra, p_dec = data[:,7], data[:,8]
d = ac.getr(g0)
# Transform to vgsr from Yanny+ 2009
vgsr = ac.rv_to_vgsr(rv,l,b)
X,Y,Z, lsgr, bsgr, r_sgr = ac.lb2sgr(l, b, d)
mean, stdev, nsig = -118.233333, 30.222222, 2.0
keep = []
for i in range(len(data[:,0])):
if abs(bsgr[i]) < 10.0:
if g0[i] < 20.0: continue
if g0[i] > 23.0: continue
if vgsr[i] < (mean - stdev*nsig): continue
if vgsr[i] > (mean + stdev*nsig): continue
keep.append([(g0[i]-r0[i]), g0[i]], p_ra[i], p_dec[i])
keep = np.array(keep)
plt.figure()
plt.scatter(keep[:,0], keep[:,1], s=1)
lims = plt.ylim()
plt.ylim(lims[1], lims[0])
plt.ylabel(r"$g_0$")
plt.xlabel(r"$(g-r)_0$")
plt.show()
plt.close()
def bhb_tools(infile="/home/newbym2/Desktop/sdssN-stars/sgr_bhbs_all.csv",
out=[],
gmin=15.0,
gmax=22.5,
suffix="All"):
""" bhb stuff """
data = np.loadtxt(infile, delimiter=",", skiprows=1)
ff = 1
if "c-c" in out:
ug0 = data[:, 2] - data[:, 3]
gr0 = data[:, 3] - data[:, 4]
plt.figure(ff)
ff += 1
plt.scatter(gr0, ug0, c="k", s=2)
plt.xlabel(r"$(g-r)_0$")
plt.ylabel(r"$(u-g)_0$")
plt.xlim(-0.5, 1.5)
plt.ylim(3.0, 0.0)
if "c-mag" in out:
#rr = ac.getr(data[:,3], 0.7) ## BHB M #Not working right?
gr0 = data[:, 3] - data[:, 4]
cmag = pp.HistMaker(gr0, data[:, 3], 0.01, 0.1)
cmag.yflip = 1
cmag.scale = 'sqrt'
cmag.varea = (0.0, 2000.0)
cmag.plot()
plt.show()
if "sgrplot" in out:
# use only primaries
sgr = []
for i in range(data.shape[0]):
#if ac.SDSS_primary(data[i,0],data[i,1],wedge,fmt="lb",low=9,high=27)==0: continue
if data[i, 3] > gmax: continue
if data[i, 3] < gmin: continue
sgr.append([data[i, 0], data[i, 1]])
sgr = np.array(sgr)
lam, bet = (ac.lb2sgr(sgr[:, 0], sgr[:, 1], 30.0))[3:5]
hist = pp.HistMaker(lam,
bet,
1.0,
1.0,
xarea=(20.0, 320.0),
yarea=(-70.0, 40.0))
hist.xflip = 1
hist.yflip = 1
hist.varea = (0.0, 6.0)
hist.savehist("sgr_bhbs.csv", fmt='%.1f')
#hist.ticks= [[20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0],[-40.0, -20.0, 0.0, 20.0]]
# mask the data
if "mask" in out:
mask = np.loadtxt("Rhist_sgr.csv", delimiter=",")
for i in range(len(mask[:, 0])):
for j in range(len(mask[0, :])):
if mask[i, j] == 0.0: hist.H[i, j] = 0.0
hist.savehist("bhb" + suffix + "_hist.csv", fmt='%.1f')
pp.PlotHist(hist,
imfile="BHB_" + suffix + ".png",
cbarO='horizontal',
cax=[0.1, 0.75, 0.8, 0.02])
def FTO_skyplot(path="/home/mnewby/Desktop/", gmin=16.0, gmax=22.5,
multistep=None):
"""Make a skyplot with updated MSTO stars from SDSS """
#files = glob.glob(path+"stars*")
#files.append(path+"South/stars-79-new.txt")
#files.append(path+"South/stars-82-new.txt")
#files.append(path+"South/stars-86-new.txt")
#files = glob.glob(path+"FTO_All*")
#files.append(path+"FTO_south_dirty.csv")
#files = glob.glob(path+"*dirty*")
#files = [path+"MSTO_North_plus20.csv", path+"MSTO_South_minus20.csv"]
files = [path+"blue_all.csv"]
out = []
for f in files:
#data = np.loadtxt(f, skiprows=1)
data = np.loadtxt(f, delimiter=",", skiprows=1)
print "Loaded:", f
for i in range(data.shape[0]):
#if data[i,2] < ac.getr(16.0): continue
#if data[i,2] > ac.getr(22.5): continue
gmag = data[i,3] # for FTO data
#gmag = data[i,2] #for MSTO data
if gmag < gmin: continue
if gmag > gmax: continue
#Distance is ambiguous for blue stars
#lam, bet = (ac.lb2sgr(data[i,0], data[i,1], ac.getr(data[i,3]) ) )[3:5]
lam, bet = (ac.lb2sgr(data[i,0], data[i,1], 10000.0) )[3:5]
out.append([lam, bet, gmag])
print "Transformed coordinates:", f
out = np.array(out)
if multistep != None:
#runs = np.arange(10.0, 50.0, 5.0) #for distance
runs = np.arange(gmin, gmax, multistep)
runs = [20.0+0.25, 20.66+0.25, 21.33+0.25, 22.0+0.25, 23.0+0.25] #Belokurov 2006 + g-r=0.25
for run in runs:
print "Starting run", run
gslice = []
for i in range(out.shape[0]):
if out[i,2] < run: continue
if out[i,2] >= (run+multistep): continue
gslice.append(out[i,:])
gslice = np.array(gslice)
hist = pp.HistMaker(gslice[:,0], gslice[:,1], 0.5, 0.5, yarea=(-70.0, 40.0), xarea=(20.0, 320.0) )
hist.savehist(outfile="FTO_"+str(run)+"_hist.csv", fmt='%.1f')
hist.yflip = 1
hist.xflip = 1
hist.ticks = (None, [-60, -40.0, -20.0, 0.0, 20.0, 40.0])
hist.varea = (0.0, 12.0)
pp.PlotHist(hist, imfile="FTO_"+str(run)+".png", cbarO='horizontal')
else:
hist = pp.HistMaker(out[:,0], out[:,1], 1.0, 1.0, yarea=(-70.0, 40.0), xarea=(20.0, 320.0) )
hist.savehist(outfile="BLUE_all_hist.csv", fmt='%.1f')
hist.yflip=1
hist.xflip=1
hist.labels=(r"$\Lambda$","B")
hist.ticks = (None, [-40.0, -20.0, 0.0, 20.0, 40.0, 60.0, 80.0])
hist.varea=(0.0, 30.0)
pp.PlotHist(hist, imfile="BLUE_all.png", cbarO='horizontal')
def lambet_profiles(path="/home/newbym2/Desktop/sdssN-stars/", glb="*bhbs_all*",
lj=0, bj=1, gj=2, lbins=2.5, bbins=0.5, suffix="_bhbs", coords="sgr"):
""" remixed from plot_profiles in utilities1 """
files = glob.glob(path+glb)
print files
data=[]
g_cut_low, g_cut_high = 16.0, 22.5
pb = pr.Progressbar(steps=len(files), prefix="Loading Stars:", suffix=None,
symbol="#", active="=", brackets="[]", percent=True, size=40)
for f in files:
count=0
stripedata = np.loadtxt(f, delimiter=",", skiprows=1)
for i in range(stripedata.shape[0]):
if stripedata[i,gj] < g_cut_low: continue
if stripedata[i,gj] > g_cut_high: continue
#if ac.SDSS_primary(temp[0],temp[1],wedge,fmt="lb",low=9,high=27)==0: continue
data.append([stripedata[i,lj], stripedata[i,bj], stripedata[i,gj] ] )
pb.updatebar(float(files.index(f)+1)/float(len(files)) )
data = np.array(data)
if coords == "sgr":
count, nStars, pb2 = 0, float(len(data[:,0])), pr.Progressbar(steps=100,
prefix="Changing Coordinates:", suffix=None, symbol="#", active="=",
brackets="[]", percent=True, size=40)
for i in range(data.shape[0]):
count += 1
data[i,0], data[i,1] = ac.lb2sgr(data[i,0], data[i,1], 50.0 )[3:5]
#data[i,2] = data[i,2]
if count % 1000 == 0: pb2.updatebar(float(count)/nStars)
elif coords == "lbr": pass
#data[i,0], data[i,1] = ac.lb2GC(data[i,0], data[i,1], 15)
#data[i,0], data[i,1] = ac.lbToEq(data[i,0], data[i,1])
else: print "!!! Coordinate system not recognized !!!"; sys.exit(2)
pb2.endbar()
# loop over Lambda slices and plot Beta histograms
pb3 = pr.Progressbar(steps=len(files), prefix="Making Slices:", suffix=None,
symbol="#", active="=", brackets="[]", percent=True, size=40)
#Ls = (40.0, 130.0, lbins)
#Ls = (200.0, 300.0, lbins)
Ls = (201.25, 301.25, lbins) #for wedges offset by half a wedge
Lsteps = int((Ls[1]-Ls[0])/Ls[2])
for i in range(Lsteps):
Lname = str(Ls[0]+(Ls[2]*i) )[:6]+suffix
new = []
for j in range(len(data[:,0])):
if data[j,0] < Ls[0]+(Ls[2]*i): continue
if data[j,0] > Ls[0]+(Ls[2]*(i+1)): continue
new.append([data[j,0], data[j,1], data[j,2]])
if len(new) < 1: continue
new = np.array(new)
#np.savetxt("stars-lambda-"+("00"+str(i))[-2:]+".txt", new, fmt='%.6f')
pb3.updatebar(float(i)/float(Lsteps))
Lhist, Ledges = np.histogram(new[:,1], int(100/bbins), (-60.0, 40.0))
#output to file
outstuff = sc.array(zip(Ledges+(bbins/2.0), Lhist))
np.savetxt(Lname+".out", outstuff, fmt='%.2f')
pb3.endbar()
print "Ended Successfully"
def mag_plots(gmin=16.0, gmax=22.5):
path = "/home/mnewby/Desktop/"
#path="/home/newbym/Desktop/FTO-stars/"
#files = [path+"MSTO_North_plus20.csv", path+"MSTO_South_minus20.csv"]
#files = [path+"MSTO_North.csv", path+"MSTO_South.csv"]
files = [path + "blue_all.csv"]
out = []
for f in files:
data = np.loadtxt(f, delimiter=",", skiprows=1)
print "Loaded:", f
pb = pr.Progressbar(steps=data.shape[0],
prefix="Loading Stars:",
suffix=None,
symbol="#",
active="=",
brackets="[]",
percent=True,
size=40)
for i in range(data.shape[0]):
#if data[i,2] < ac.getr(16.0): continue
#if data[i,2] > ac.getr(22.5): continue
gmag = data[i, 3] # for BHB data
#gmag = data[i,2] #for MSTO data
if gmag < gmin: continue
if gmag > gmax: continue
lam, bet = (ac.lb2sgr(data[i, 0], data[i, 1], 30.0))[3:5]
if lam > 170.0:
#if bet > 10.0: continue
#if bet < 2.5: continue
if bet > -2.5: continue
if bet < -10.0: continue
else:
#if bet < -2.5: continue
#if bet > 5.0: continue
if bet > -5.0: continue
if bet < -12.5: continue
out.append([lam, bet, gmag])
if i % 10000 == 0: pb.updatebar(float(i) / float(data.shape[0]))
pb.endbar()
print "Transformed coordinates:", f
out = np.array(out)
hist = pp.HistMaker(out[:, 0],
out[:, 2],
0.5,
0.1,
yarea=(16.0, 22.5),
xarea=(0.0, 360.0))
hist.savehist(outfile="blue_faint_faint_hist.csv", fmt='%.1f')
hist.yflip = 0
hist.xflip = 1
hist.labels = (r"$\Lambda$", r"$g_0$")
hist.ticks = (sc.arange(20.0, 321.0, 20.0), sc.arange(16.0, 22.6, 0.5))
#hist.ticklabels = (None, ["16.0", "16.5", "17.0", "17.5", "18.0", "18.5", "19.0", "19.5", "20.0",
# "20.5", "21.0", "21.5", "22.0", "22.5"])
hist.varea = (0.0, 30.0)
pp.PlotHist(hist, imfile="blue_faint_faint.png", cbarO='horizontal')
pp.PlotHist(hist, imfile="blue_faint_faint.ps", cbarO='horizontal')
def sgr_plot(folder="./sep_lbr"):
files = glob.glob(folder + "/*.txt")
plt.figure(1)
for file in files:
data = fi.read_data(file)
Xs, Ys, Zs, lam, beta, r = coor.lb2sgr(data[:, 0], data[:, 1], data[:,
2])
plt.scatter(lam, beta, s=1, c="black")
plt.show()
plt.close('all')
def make_single_stream_plot(filename):
data = np.loadtxt(filename)
for i in range(len(data[:,0])):
#data[i,0], data[i,1] = ac.lbToEq(data[i,0], data[i,1])
#if ac.SDSS_primary(temp[0],temp[1],wedge,fmt="lb",low=9,high=27)==0: continue
data[i,0], data[i,1] = (ac.lb2sgr(data[i,0], data[i,1], 10.0))[3:5]
lam, bet = new_shift_sgr(data[i,0], data[i,1])
data2.append([lam, bet, data[i,2]])
# Not done yet!!!
return -1
def process_spec():
pp = np.loadtxt("/home/newbym2/Dropbox/Research/sgrLetter/plate_stars.csv", delimiter=",")
pl,pb = ac.EqTolb(pp[:,1], pp[:,2])
pd = 20.0
pX,pY,pZ, plsgr, pbsgr, pr_sgr = ac.lb2sgr(pl, pb, pd)
norm = pp[:,3] + pp[:,4]
out = []
for i in range(len(pp[:,0])):
out.append([plsgr[i], pbsgr[i], pp[i,4]/norm[i], norm[i]])
np.savetxt("/home/newbym2/Dropbox/Research/sgrLetter/plate_data.csv", np.array(out), delimiter=",")
def make_sim_stream_plot(filein="stream_50shift.txt", RGB=0, imfile=None):
""" Makes the plot for the simulated streams
/home/newbym2/Dropbox/Research/sgrnorth_paper/sgr_separated_stars_MRT.txt"""
folder = "/home/newbym2/Dropbox/Research/sgrLetter/"
filename=folder + filein
#file2="/home/newbym2/Dropbox/Research/sgrnorth_paper/sgr_separated_stars_MRT.txt"
#file2="streamgen_sgr_sim.txt"
#file2="streamgen_sgrfidprim.txt"
file2 = folder+"streamgen_sfp2.txt"
data = np.loadtxt(filename)
data2 = []
datasgr = np.loadtxt(file2)
for i in range(len(data[:,0])):
#data[i,0], data[i,1] = ac.lbToEq(data[i,0], data[i,1])
#if ac.SDSS_primary(temp[0],temp[1],wedge,fmt="lb",low=9,high=27)==0: continue
data[i,0], data[i,1], data[i,2] = (ac.lb2sgr(data[i,0], data[i,1], data[i,2]))[3:]
lam, bet = new_shift_sgr(data[i,0], data[i,1]) #move 2nd stream to new position
data2.append([lam, bet, ac.getg(data[i,2], M=4.2)])
data2 = sc.array(data2)
for i in range(len(datasgr[:,0])):
datasgr[i,0], datasgr[i,1] = (ac.lb2sgr(datasgr[i,0], datasgr[i,1], 10.0))[3:5]
datasgr[i,2] = ac.getg(data[i,2], M=4.2)
"""
data2 = np.loadtxt(file2)
for i in range(len(data2[:,0])):
#data2[i,0], data2[i,1] = ac.lbToEq(data2[i,0], data2[i,1])
data2[i,0], data2[i,1] = (ac.lb2sgr(data2[i,0], data2[i,1], 10.0))[3:5]
"""
if RGB==1:
data3 = np.concatenate((data2,datasgr), axis=0)
#data3 = np.concatenate((data2,data), axis=0)
RGB_plot(data3, imfile=imfile, mask_data="Bhist_sgr.csv", muddle=0)
else:
sky = pp.HistMaker(np.concatenate([data[:,0],data2[:,0]]), np.concatenate([data[:,1],data2[:,1]]),
xsize=0.5, ysize=0.5, xarea=(120.0, 250.0), yarea=(-10.0, 50.0))
sky.varea = (0.0,200.0)
sky.H = sky.H + np.random.normal(60.0, 15.0, sky.H.shape)
#for i in range(sky.H.shape[0]):
# if i < 14: sky.H[i,:] = sky.H[i,:]*0.0; continue
# sky.H[i,:] = sky.H[i,:] + 45.0 - (0.5/1.0)*i
pp.PlotHist(sky, "streamgen_bifExtra_radec.png")
sky.savehist("streamgen_bifExtra.csv")
print "Ended Successfully"
def sky_map(infile="/home/newbym2/Desktop/sdssN-stars/sdssN_sky.csv"):
"""Map a variable in Lambda, Beta """
data = np.loadtxt(infile, delimiter=",", skiprows=1)
l, b = data[:,0], data[:,1]
g_ext, r_ext = data[:,4], data[:,5]
g_sky, r_sky = data[:,6], data[:,7]
print np.ma.min(g_ext), np.ma.max(g_ext)
lam, bet = ac.lb2sgr(l, b, 30.0)[3:5]
plt.figure()
sc = plt.scatter(lam, bet, c=r_ext, cmap=pp.spectral_wb, edgecolors='none',
s=3, alpha=0.3, vmin=0, vmax=0.4)
plt.colorbar(sc)
plt.xlim(230.0, 255.0); plt.ylim(15.0, -10.0)
plt.show()
def make_total_plot(path="/home/newbym2/Desktop/starfiles", RGB=0, imfile=None,
rcut=None, vrange=None, outfile=None):
""" Makes a 2D histogram """
files = glob.glob(path+"/stars*")
print files
data=[]
pb = pr.Progressbar(steps=len(files), prefix="Loading Stars:", suffix=None,
symbol="#", active="=", brackets="[]", percent=True, size=40)
for f in files:
wedge = int(f.split("-")[1].split(".")[0])
count=0
stripedata = open(f, "r")
for line in stripedata:
count = count + 1
if count==1: continue
if line.strip()=="": continue
temp = line.split()
for i in range(len(temp)): temp[i] = float(temp[i])
if rcut != None:
if temp[2] < rcut[0]: continue
if temp[2] > rcut[1]: continue
if ac.SDSS_primary(temp[0],temp[1],wedge,fmt="lb",low=9,high=27)==0: continue
data.append(temp)
stripedata.close()
pb.updatebar(float(files.index(f)+1)/float(len(files)) )
data = np.array(data)
count, nStars, pb2 = 0, float(len(data[:,0])), pr.Progressbar(steps=100,
prefix="Changing Coordinates:", suffix=None, symbol="#", active="=",
brackets="[]", percent=True, size=40)
for i in range(len(data[:,0])):
count = count + 1
#data[i,0], data[i,1] = ac.lb2GC(data[i,0], data[i,1], 15)
#data[i,0], data[i,1] = ac.lbToEq(data[i,0], data[i,1])
data[i,0], data[i,1] = (ac.lb2sgr(data[i,0], data[i,1], 10.0))[3:5]
data[i,2] = ac.getg(data[i,2], 4.2)
if count % 100 == 0: pb2.updatebar(float(count)/nStars)
pb2.endbar()
if RGB==1: RGB_plot(data, imfile=imfile)
else:
allsky = pp.HistMaker(data[:,0], data[:,1], xsize=0.5, ysize=0.5,
#xarea=(200.0, 300.0), yarea=(-40.0, 30.0))
xarea=(230.0, 255.0), yarea=(-10.0, 15.0))
#allsky.scale = 'sqrt'
allsky.cmap="bw"
allsky.yflip = 1
if vrange != None: allsky.varea = (vrange[0], vrange[1])
if outfile==None: pp.PlotHist(allsky, "sgrall_GC.png")
else: pp.PlotHist(allsky, outfile)
#allsky.savehist("SDSSnorthGC.csv")
print "Ended Successfully"
def test_coords():
# test Li Jing's coord transforms
#ra,dec,l,b,j0,k0,w10,w20,lambda,beta
arcsec = 1.0 / 3600.0
data = np.loadtxt("/home/newbym/Desktop/Mgiant_wise_sgr.csv", delimiter=",", skiprows=1)
ra, dec = data[:,0], data[:,1]
l, b = data[:,2], data[:,3]
lam, bet = data[:,8], data[:,9]
badl, badb, badlam, badbet, badlam2, badbet2 = [], [], [], [], [], []
for i in range(data.shape[0]):
new_l, new_b = ac.EqTolb(ra[i], dec[i])
if np.abs(new_l - l[i]) > arcsec: badl.append(new_l - l[i])
if np.abs(new_b - b[i]) > arcsec: badb.append(new_b - b[i])
new_lam, new_bet = ac.lb2sgr(l[i],b[i], 30.)[3:5]
if np.abs(new_lam - lam[i]) > arcsec: badlam.append(lam[i])
if np.abs(new_bet - bet[i]) > arcsec: badbet.append(bet[i])
new_lam2, new_bet2 = ac.lb2sgr(new_l,new_b, 30.)[3:5]
if np.abs(new_lam2 - lam[i]) > arcsec: badlam2.append(lam[i])
if np.abs(new_bet2 - bet[i]) > arcsec: badbet2.append(bet[i])
print data.shape[0]
print np.mean(badl), np.mean(badb)
print np.mean(badlam), np.mean(badbet), np.std(badbet)
print np.mean(badlam2), np.mean(badbet2), np.std(badbet2)
print "Done"
def lam_wedges():
sys.path.insert(0, '../milkyway-tools')
import sdss_visualizers as sdss
path = "/home/newbym2/Desktop/lam_starfiles/"
files = glob.glob(path+"/stars*")
for f in files:
# get lambdas from naming scheme
wedge = float(f[-6:-4])
name = "lam-"+str(200.0 + (wedge*2.5) )[:6]
stars = np.loadtxt(f)
print "Loaded ", f
for i in range(stars.shape[0]):
stars[i,0], stars[i,1] = (ac.lb2sgr(stars[i,0], stars[i,1], 30.0))[3:5]
sdss.plot_stripe_mur(stars, wedge, outname=name, mag=1, scale=1, color=1,
mu_lim=None, r_lim=(0.0, 50.0), vm=10.0, nu_flatten=0, bar=1, raw_coords=1)
print "Finished file {0} of {1}:".format(files.index(f)+1, len(files) )
print "### - Done"
def mag_plots(gmin=16.0, gmax=22.5):
path = "/home/mnewby/Desktop/"
#path="/home/newbym/Desktop/FTO-stars/"
#files = [path+"MSTO_North_plus20.csv", path+"MSTO_South_minus20.csv"]
#files = [path+"MSTO_North.csv", path+"MSTO_South.csv"]
files = [path+"blue_all.csv"]
out = []
for f in files:
data = np.loadtxt(f, delimiter=",", skiprows=1)
print "Loaded:", f
pb = pr.Progressbar(steps=data.shape[0], prefix="Loading Stars:", suffix=None,
symbol="#", active="=", brackets="[]", percent=True, size=40)
for i in range(data.shape[0]):
#if data[i,2] < ac.getr(16.0): continue
#if data[i,2] > ac.getr(22.5): continue
gmag = data[i,3] # for BHB data
#gmag = data[i,2] #for MSTO data
if gmag < gmin: continue
if gmag > gmax: continue
lam, bet = (ac.lb2sgr(data[i,0], data[i,1], 30.0))[3:5]
if lam > 170.0:
#if bet > 10.0: continue
#if bet < 2.5: continue
if bet > -2.5: continue
if bet < -10.0: continue
else:
#if bet < -2.5: continue
#if bet > 5.0: continue
if bet > -5.0: continue
if bet < -12.5: continue
out.append([lam, bet, gmag])
if i % 10000 == 0: pb.updatebar(float(i)/float(data.shape[0]) )
pb.endbar()
print "Transformed coordinates:", f
out = np.array(out)
hist = pp.HistMaker(out[:,0], out[:,2], 0.5, 0.1, yarea=(16.0, 22.5), xarea=(0.0, 360.0) )
hist.savehist(outfile="blue_faint_faint_hist.csv", fmt='%.1f')
hist.yflip=0
hist.xflip=1
hist.labels=(r"$\Lambda$",r"$g_0$")
hist.ticks = (sc.arange(20.0, 321.0, 20.0), sc.arange(16.0, 22.6, 0.5) )
#hist.ticklabels = (None, ["16.0", "16.5", "17.0", "17.5", "18.0", "18.5", "19.0", "19.5", "20.0",
# "20.5", "21.0", "21.5", "22.0", "22.5"])
hist.varea=(0.0, 30.0)
pp.PlotHist(hist, imfile="blue_faint_faint.png", cbarO='horizontal')
pp.PlotHist(hist, imfile="blue_faint_faint.ps", cbarO='horizontal')
def bhb_tools(infile="/home/newbym2/Desktop/sdssN-stars/sgr_bhbs_all.csv",
out=[], gmin=15.0, gmax=22.5, suffix="All"):
""" bhb stuff """
data = np.loadtxt(infile, delimiter=",", skiprows=1)
ff = 1
if "c-c" in out:
ug0 = data[:,2] - data[:,3]
gr0 = data[:,3] - data[:,4]
plt.figure(ff); ff+=1
plt.scatter(gr0, ug0, c="k", s=2)
plt.xlabel(r"$(g-r)_0$"); plt.ylabel(r"$(u-g)_0$")
plt.xlim(-0.5, 1.5); plt.ylim(3.0, 0.0)
if "c-mag" in out:
#rr = ac.getr(data[:,3], 0.7) ## BHB M #Not working right?
gr0 = data[:,3] - data[:,4]
cmag = pp.HistMaker(gr0,data[:,3], 0.01, 0.1)
cmag.yflip=1
cmag.scale='sqrt'
cmag.varea=(0.0, 2000.0)
cmag.plot()
plt.show()
if "sgrplot" in out:
# use only primaries
sgr = []
for i in range(data.shape[0]):
#if ac.SDSS_primary(data[i,0],data[i,1],wedge,fmt="lb",low=9,high=27)==0: continue
if data[i,3] > gmax: continue
if data[i,3] < gmin: continue
sgr.append([data[i,0], data[i,1]])
sgr = np.array(sgr)
lam, bet = (ac.lb2sgr(sgr[:,0], sgr[:,1], 30.0))[3:5]
hist = pp.HistMaker(lam, bet, 1.0, 1.0, xarea=(20.0, 320.0), yarea=(-70.0, 40.0))
hist.xflip=1
hist.yflip=1
hist.varea=(0.0, 6.0)
hist.savehist("sgr_bhbs.csv", fmt='%.1f')
#hist.ticks= [[20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0],[-40.0, -20.0, 0.0, 20.0]]
# mask the data
if "mask" in out:
mask = np.loadtxt("Rhist_sgr.csv", delimiter=",")
for i in range(len(mask[:,0])):
for j in range(len(mask[0,:])):
if mask[i,j] == 0.0: hist.H[i,j] = 0.0
hist.savehist("bhb"+suffix+"_hist.csv", fmt='%.1f')
pp.PlotHist(hist, imfile="BHB_"+suffix+".png", cbarO='horizontal', cax=[0.1, 0.75, 0.8, 0.02])
def sgr_rv_fits():
data = np.loadtxt("/home/newbym2/Dropbox/Research/sgrLetter/sgr_spec.csv", delimiter=",")
#dered_g,dered_r,l,b,ELODIERVFINAL,ELODIERVFINALERR
g0, r0 = data[:,0], data[:,1]
l, b = data[:,2], data[:,3]
rv, rv_err = data[:,4], data[:,5]
d = ac.getr(g0)
# Transform to vgsr from Yanny+ 2009
vgsr = ac.rv_to_vgsr(rv,l,b)
X,Y,Z, lsgr, bsgr, r_sgr = ac.lb2sgr(l, b, d)
for w in [0.5, 1.0, 2.5, 5.0, 7.5, 10.0, 15.0, 20.0, 25.0, 30.0, 50.0]:
keep = []
for i in range(len(data[:,0])):
if abs(bsgr[i]) < w:
if g0[i] < 20.0: continue
if g0[i] > 23.0: continue
keep.append(vgsr[i])
hist, edges = np.histogram(np.array(keep), bins=60, range=(-300.0, 300.0))
y, x = hist, edges[:-1]
e = func.poisson_errors(y)
fitter = fit.ToFit(x,y,e)
fitter.function=func.double_gaussian_one_fixed
fitter.update_params([3.0, -120.0, 30.0, 2.0, 0.0, 120.0])
fitter.step = [1.0, 10.0, 1.0, 1.0, 0.0, 0.0]
fitter.param_names = ["amp", "mu", "sigma", "amp", "mu", "sigma"]
path1 = fit.gradient_descent(fitter, its=10000, line_search=0)
path2 = fit.MCMC(fitter)
new_params=fitter.params
xx = sc.arange(-300.0, 300.0, 1.0)
yy = func.double_gaussian_one_fixed(xx, new_params)
y1 = func.gaussian_function(xx, new_params[:3])
y2 = func.gaussian_function(xx, new_params[3:])
fig = plt.figure()
plt.bar(edges[:-1], hist, width=10.0, color="white")
plt.plot(xx,yy, "k-")
plt.plot(xx,y1, "k--")
plt.plot(xx,y2, "k--")
plt.title("cut width = "+str(w))
plt.xlabel(r"$v_{\rm gsr}$", fontsize=16)
plt.ylabel(r"Counts")
#plt.ylim(0.0, 60.0)
#plt.show()
plt.savefig("/home/newbym2/Dropbox/Research/sgrLetter/sgr_spec/r_cut_relative"+str(w)+".png")
plt.close()
def split_by_plate():
# dered_g,dered_r,l,b,ELODIERVFINAL,ELODIERVFINALERR,plate,p_ra,p_dec
data = np.loadtxt("/home/newbym2/Dropbox/Research/sgrLetter/sgr_spec_all.csv", delimiter=",")
mean, stdev, nsig = -118.233333, 30.222222, 1.0
plates, pos = [], []
for i in range(len(data[:,0])):
if plates.count(data[i,6]) > 0: continue
plates.append(data[i,6])
pos.append([data[i,7], data[i,8]])
out = []
for i, plate in enumerate(plates):
# get stars for plate
stars = []
for j in range(len(data[:,0])):
if data[j,6] == plate: stars.append(data[j,:])
if stars == []: continue
# apply cuts
keep = []
for star in stars:
if star[0] < 20.0: continue
if star[0] > 20.5: continue #using stricter cut
X,Y,Z, lsgr, bsgr, r_sgr = ac.lb2sgr(star[2], star[3], ac.getr(star[0]))
if lsgr < 190.0: continue
if lsgr > 250.0: continue
if abs(bsgr) > 10.0: continue
vgsr = ac.rv_to_vgsr(star[4],star[2],star[3])
keep.append([lsgr, bsgr, vgsr])
if keep == []: continue
# vgsr selection
yes, no = 0, 0
for k in keep:
if k[2] < (mean - stdev*nsig): no += 1; continue
if k[2] > (mean + stdev*nsig): no += 1; continue
yes += 1
print "Plate {0}: In {1} of Sgr versus out: {2}, {3}".format(
plate, nsig, yes, no)
# compile plate info positions
out.append([plate, pos[i][0], pos[i][1], yes, no])
savename = "/home/newbym2/Dropbox/Research/sgrLetter/plate_stars.csv"
np.savetxt(savename, sc.array(out), delimiter=",")
def crotus_cut(path="/home/newbym2/Desktop/starfiles", cdata=None):
if cdata == None:
files = glob.glob(path+"/stars*")
data=[]
r_cut_low, r_cut_high = ac.getr(16.0), ac.getr(23.5) #30.0, 45.0
pb = pr.Progressbar(steps=len(files), prefix="Loading Stars:", suffix=None,
symbol="#", active="=", brackets="[]", percent=True, size=40)
for f in files:
wedge = int(f.split("-")[1].split(".")[0])
sdata = np.loadtxt(f, skiprows=1)
for i in range(sdata.shape[0]):
if (sdata[i,2] < r_cut_low) or (sdata[i,2] > r_cut_high): continue
if ac.SDSS_primary(sdata[i,0],sdata[i,1],wedge,fmt="lb",low=9,high=27)==0: continue
lam, bet = (ac.lb2sgr(sdata[i,0], sdata[i,1], 10.0))[3:5]
if (lam <230.0) or (lam > 255.0): continue
data.append([sdata[i,0], sdata[i,1], sdata[i,2], lam, bet])
pb.updatebar(float(files.index(f)+1)/float(len(files)) )
pb.endbar()
data = np.array(data)
np.savetxt("crotus_data.txt", data, fmt='%.6f')
else:
data = []
rdata = np.loadtxt(cdata)
rlim0 = 28.5 #ac.getr(22.5)
rlim1 = 45.0
for i in range(rdata.shape[0]):
if rdata[i,2] < rlim0: continue
if rdata[i,2] > rlim1: continue
data.append(rdata[i,:])
data = np.array(data)
#Now do analysis
sky = pp.HistMaker(data[:,3], data[:,4], xsize=0.25, ysize=0.25,
xarea=(230.0, 255.0), yarea=(-10.0, 15.0))
sky.varea = (0.0, 60.0)
sky.cmap="color"
#sky.scale = 'sqrt'
sky.yflip = 1
pp.PlotHist(sky, "crotus_cut.png")
#sky.savehist("streamgen_bifExtra.csv")
print "### - Done"
def sky_map(infile="/home/newbym2/Desktop/sdssN-stars/sdssN_sky.csv"):
"""Map a variable in Lambda, Beta """
data = np.loadtxt(infile, delimiter=",", skiprows=1)
l, b = data[:, 0], data[:, 1]
g_ext, r_ext = data[:, 4], data[:, 5]
g_sky, r_sky = data[:, 6], data[:, 7]
print np.ma.min(g_ext), np.ma.max(g_ext)
lam, bet = ac.lb2sgr(l, b, 30.0)[3:5]
plt.figure()
sc = plt.scatter(lam,
bet,
c=r_ext,
cmap=pp.spectral_wb,
edgecolors='none',
s=3,
alpha=0.3,
vmin=0,
vmax=0.4)
plt.colorbar(sc)
plt.xlim(230.0, 255.0)
plt.ylim(15.0, -10.0)
plt.show()
def sky_map(infile="/home/newbym2/Desktop/sdssN-stars/sgr_sky_data.csv", dumb=1):
"""Map a variable in Lambda, Beta """
if dumb == 1:
data = np.loadtxt(infile, delimiter=",", skiprows=1)
l, b = data[:,0], data[:,1]
g_ext, r_ext = data[:,4], data[:,5]
g_sky, r_sky = data[:,6], data[:,7]
print np.ma.min(g_ext), np.ma.max(g_ext)
lam, bet = ac.lb2sgr(l, b, 30.0)[3:5]
plt.figure()
sc = plt.scatter(lam, bet, c=g_ext, cmap=pp.spectral_wb, edgecolors='none',
s=3, alpha=0.3, vmin=0, vmax=0.4)
plt.colorbar(sc)
plt.xlim(320, 20.0); plt.ylim(40.0, -70.0)
else:
path="/home/newbym2/Desktop/FTO-stars/"
files = glob.glob(path+"FTO_All*")
files.append(path+"FTO_south_dirty.csv")
x_range, y_range = (20, 320), (-70, 40)
x_step, y_step = 0.5, 0.5
x_bins, y_bins = (x_range[1]-x_range[0])/x_step, (y_range[1]-y_range[0])/y_step
try:
grid = np.loadtxt("reddening_out.txt")
except IOError:
grid = np.zeros((y_bins+1, x_bins+1), float)
for f in files:
data = np.loadtxt(f, delimiter=",", skiprows=1)
pb = pr.Progressbar(steps=data.shape[0], prefix="Loading {0}".format(f.split("/")[-1]),
suffix=None, symbol="#", active="=", brackets="[]", percent=True, size=40)
for i in range(data.shape[0]):
lam, bet = (ac.lb2sgr(data[i,0], data[i,1], 30.0))[3:5]
gridx = (lam - x_range[0])/x_step
gridy = (bet - y_range[0])/y_step
if (gridx < 0) or (gridy < 0): continue
if (gridx > x_bins+1) or (gridy > y_bins +1): continue
if grid[gridy, gridx] < data[i,13]: grid[gridy, gridx] = data[i,13]
if i % 1000 == 0: pb.updatebar(float(i)/float(data.shape[0]) )
pb.endbar()
np.savetxt("reddening_out.txt", grid, fmt="%.2f")
""" plot stuff """
grid = grid[:,::-1]
grid = grid[::-1,:]
fig = plt.figure(dpi=120)
ax1 = fig.add_subplot(111)
im = ax1.imshow(grid, cmap=pp.spectral_wb, norm=None, aspect=None, interpolation='nearest',
alpha=None, origin='lower', extent=None, vmin=0.0, vmax=2.0)
cbar = plt.colorbar(mappable=im, aspect=30, orientation="horizontal")
xpos = np.arange(0.0, 601.0, 20.0)
xlab = []
for i in range(len(xpos)):
if i % 3 == 0:
xx = (xpos[i]*x_step) + x_range[0]
xlab.append(str(xx).split(".")[0] )
else: xlab.append("")
plt.xticks(xpos, xlab[::-1])
ypos = np.arange(0.0, 221.0, 20.0)
ylab = []
for i in range(len(ypos)):
if i % 2 == 0:
yy = (ypos[i]*y_step) + y_range[0]
ylab.append(str(yy).split(".")[0] )
else: ylab.append("")
plt.yticks(ypos, ylab[::-1])
plt.show()
def FTO_skyplot(path="/home/mnewby/Desktop/",
gmin=16.0,
gmax=22.5,
multistep=None):
"""Make a skyplot with updated MSTO stars from SDSS """
#files = glob.glob(path+"stars*")
#files.append(path+"South/stars-79-new.txt")
#files.append(path+"South/stars-82-new.txt")
#files.append(path+"South/stars-86-new.txt")
#files = glob.glob(path+"FTO_All*")
#files.append(path+"FTO_south_dirty.csv")
#files = glob.glob(path+"*dirty*")
#files = [path+"MSTO_North_plus20.csv", path+"MSTO_South_minus20.csv"]
files = [path + "blue_all.csv"]
out = []
for f in files:
#data = np.loadtxt(f, skiprows=1)
data = np.loadtxt(f, delimiter=",", skiprows=1)
print "Loaded:", f
for i in range(data.shape[0]):
#if data[i,2] < ac.getr(16.0): continue
#if data[i,2] > ac.getr(22.5): continue
gmag = data[i, 3] # for FTO data
#gmag = data[i,2] #for MSTO data
if gmag < gmin: continue
if gmag > gmax: continue
#Distance is ambiguous for blue stars
#lam, bet = (ac.lb2sgr(data[i,0], data[i,1], ac.getr(data[i,3]) ) )[3:5]
lam, bet = (ac.lb2sgr(data[i, 0], data[i, 1], 10000.0))[3:5]
out.append([lam, bet, gmag])
print "Transformed coordinates:", f
out = np.array(out)
if multistep != None:
#runs = np.arange(10.0, 50.0, 5.0) #for distance
runs = np.arange(gmin, gmax, multistep)
runs = [
20.0 + 0.25, 20.66 + 0.25, 21.33 + 0.25, 22.0 + 0.25, 23.0 + 0.25
] #Belokurov 2006 + g-r=0.25
for run in runs:
print "Starting run", run
gslice = []
for i in range(out.shape[0]):
if out[i, 2] < run: continue
if out[i, 2] >= (run + multistep): continue
gslice.append(out[i, :])
gslice = np.array(gslice)
hist = pp.HistMaker(gslice[:, 0],
gslice[:, 1],
0.5,
0.5,
yarea=(-70.0, 40.0),
xarea=(20.0, 320.0))
hist.savehist(outfile="FTO_" + str(run) + "_hist.csv", fmt='%.1f')
hist.yflip = 1
hist.xflip = 1
hist.ticks = (None, [-60, -40.0, -20.0, 0.0, 20.0, 40.0])
hist.varea = (0.0, 12.0)
pp.PlotHist(hist,
imfile="FTO_" + str(run) + ".png",
cbarO='horizontal')
else:
hist = pp.HistMaker(out[:, 0],
out[:, 1],
1.0,
1.0,
yarea=(-70.0, 40.0),
xarea=(20.0, 320.0))
hist.savehist(outfile="BLUE_all_hist.csv", fmt='%.1f')
hist.yflip = 1
hist.xflip = 1
hist.labels = (r"$\Lambda$", "B")
hist.ticks = (None, [-40.0, -20.0, 0.0, 20.0, 40.0, 60.0, 80.0])
hist.varea = (0.0, 30.0)
pp.PlotHist(hist, imfile="BLUE_all.png", cbarO='horizontal')
def lambet_profiles(path="/home/newbym2/Desktop/sdssN-stars/",
glb="*bhbs_all*",
lj=0,
bj=1,
gj=2,
lbins=2.5,
bbins=0.5,
suffix="_bhbs",
coords="sgr"):
""" remixed from plot_profiles in utilities1 """
files = glob.glob(path + glb)
print files
data = []
g_cut_low, g_cut_high = 16.0, 22.5
pb = pr.Progressbar(steps=len(files),
prefix="Loading Stars:",
suffix=None,
symbol="#",
active="=",
brackets="[]",
percent=True,
size=40)
for f in files:
count = 0
stripedata = np.loadtxt(f, delimiter=",", skiprows=1)
for i in range(stripedata.shape[0]):
if stripedata[i, gj] < g_cut_low: continue
if stripedata[i, gj] > g_cut_high: continue
#if ac.SDSS_primary(temp[0],temp[1],wedge,fmt="lb",low=9,high=27)==0: continue
data.append(
[stripedata[i, lj], stripedata[i, bj], stripedata[i, gj]])
pb.updatebar(float(files.index(f) + 1) / float(len(files)))
data = np.array(data)
if coords == "sgr":
count, nStars, pb2 = 0, float(len(data[:, 0])), pr.Progressbar(
steps=100,
prefix="Changing Coordinates:",
suffix=None,
symbol="#",
active="=",
brackets="[]",
percent=True,
size=40)
for i in range(data.shape[0]):
count += 1
data[i, 0], data[i, 1] = ac.lb2sgr(data[i, 0], data[i, 1],
50.0)[3:5]
#data[i,2] = data[i,2]
if count % 1000 == 0: pb2.updatebar(float(count) / nStars)
elif coords == "lbr":
pass
#data[i,0], data[i,1] = ac.lb2GC(data[i,0], data[i,1], 15)
#data[i,0], data[i,1] = ac.lbToEq(data[i,0], data[i,1])
else:
print "!!! Coordinate system not recognized !!!"
sys.exit(2)
pb2.endbar()
# loop over Lambda slices and plot Beta histograms
pb3 = pr.Progressbar(steps=len(files),
prefix="Making Slices:",
suffix=None,
symbol="#",
active="=",
brackets="[]",
percent=True,
size=40)
#Ls = (40.0, 130.0, lbins)
#Ls = (200.0, 300.0, lbins)
Ls = (201.25, 301.25, lbins) #for wedges offset by half a wedge
Lsteps = int((Ls[1] - Ls[0]) / Ls[2])
for i in range(Lsteps):
Lname = str(Ls[0] + (Ls[2] * i))[:6] + suffix
new = []
for j in range(len(data[:, 0])):
if data[j, 0] < Ls[0] + (Ls[2] * i): continue
if data[j, 0] > Ls[0] + (Ls[2] * (i + 1)): continue
new.append([data[j, 0], data[j, 1], data[j, 2]])
if len(new) < 1: continue
new = np.array(new)
#np.savetxt("stars-lambda-"+("00"+str(i))[-2:]+".txt", new, fmt='%.6f')
pb3.updatebar(float(i) / float(Lsteps))
Lhist, Ledges = np.histogram(new[:, 1], int(100 / bbins),
(-60.0, 40.0))
#output to file
outstuff = sc.array(zip(Ledges + (bbins / 2.0), Lhist))
np.savetxt(Lname + ".out", outstuff, fmt='%.2f')
pb3.endbar()
print "Ended Successfully"
def sky_map(infile="/home/newbym2/Desktop/sdssN-stars/sgr_sky_data.csv",
dumb=1):
"""Map a variable in Lambda, Beta """
if dumb == 1:
data = np.loadtxt(infile, delimiter=",", skiprows=1)
l, b = data[:, 0], data[:, 1]
g_ext, r_ext = data[:, 4], data[:, 5]
g_sky, r_sky = data[:, 6], data[:, 7]
print np.ma.min(g_ext), np.ma.max(g_ext)
lam, bet = ac.lb2sgr(l, b, 30.0)[3:5]
plt.figure()
sc = plt.scatter(lam,
bet,
c=g_ext,
cmap=pp.spectral_wb,
edgecolors='none',
s=3,
alpha=0.3,
vmin=0,
vmax=0.4)
plt.colorbar(sc)
plt.xlim(320, 20.0)
plt.ylim(40.0, -70.0)
else:
path = "/home/newbym2/Desktop/FTO-stars/"
files = glob.glob(path + "FTO_All*")
files.append(path + "FTO_south_dirty.csv")
x_range, y_range = (20, 320), (-70, 40)
x_step, y_step = 0.5, 0.5
x_bins, y_bins = (x_range[1] - x_range[0]) / x_step, (
y_range[1] - y_range[0]) / y_step
try:
grid = np.loadtxt("reddening_out.txt")
except IOError:
grid = np.zeros((y_bins + 1, x_bins + 1), float)
for f in files:
data = np.loadtxt(f, delimiter=",", skiprows=1)
pb = pr.Progressbar(steps=data.shape[0],
prefix="Loading {0}".format(
f.split("/")[-1]),
suffix=None,
symbol="#",
active="=",
brackets="[]",
percent=True,
size=40)
for i in range(data.shape[0]):
lam, bet = (ac.lb2sgr(data[i, 0], data[i, 1], 30.0))[3:5]
gridx = (lam - x_range[0]) / x_step
gridy = (bet - y_range[0]) / y_step
if (gridx < 0) or (gridy < 0): continue
if (gridx > x_bins + 1) or (gridy > y_bins + 1): continue
if grid[gridy, gridx] < data[i, 13]:
grid[gridy, gridx] = data[i, 13]
if i % 1000 == 0:
pb.updatebar(float(i) / float(data.shape[0]))
pb.endbar()
np.savetxt("reddening_out.txt", grid, fmt="%.2f")
""" plot stuff """
grid = grid[:, ::-1]
grid = grid[::-1, :]
fig = plt.figure(dpi=120)
ax1 = fig.add_subplot(111)
im = ax1.imshow(grid,
cmap=pp.spectral_wb,
norm=None,
aspect=None,
interpolation='nearest',
alpha=None,
origin='lower',
extent=None,
vmin=0.0,
vmax=2.0)
cbar = plt.colorbar(mappable=im, aspect=30, orientation="horizontal")
xpos = np.arange(0.0, 601.0, 20.0)
xlab = []
for i in range(len(xpos)):
if i % 3 == 0:
xx = (xpos[i] * x_step) + x_range[0]
xlab.append(str(xx).split(".")[0])
else:
xlab.append("")
plt.xticks(xpos, xlab[::-1])
ypos = np.arange(0.0, 221.0, 20.0)
ylab = []
for i in range(len(ypos)):
if i % 2 == 0:
yy = (ypos[i] * y_step) + y_range[0]
ylab.append(str(yy).split(".")[0])
else:
ylab.append("")
plt.yticks(ypos, ylab[::-1])
plt.show()
def plot_profiles(path="/home/newbym2/Desktop/starfiles", savewedge=False, suffix=""):
files = glob.glob(path+"/stars*")
print files
suffix = "_rcut_IV"
data=[]
r_cut_low, r_cut_high = 40.0, ac.getr(22.5) #ac.getr(16.0), ac.getr(22.5) #20.0 OR 30.0, 45.0
pb = pr.Progressbar(steps=len(files), prefix="Loading Stars:", suffix=None,
symbol="#", active="=", brackets="[]", percent=True, size=40)
for f in files:
wedge = int(f.split("-")[1].split(".")[0])
count=0
stripedata = open(f, "r")
for line in stripedata:
count = count + 1
if count==1: continue
if line.strip()=="": continue
temp = line.split()
for i in range(len(temp)): temp[i] = float(temp[i])
if (temp[2] < r_cut_low) or (temp[2] > r_cut_high): continue
if ac.SDSS_primary(temp[0],temp[1],wedge,fmt="lb",low=9,high=27)==0: continue
data.append(temp)
stripedata.close()
pb.updatebar(float(files.index(f)+1)/float(len(files)) )
datar = np.array(data)
data = np.zeros(datar.shape, float)
count, nStars, pb2 = 0, float(len(data[:,0])), pr.Progressbar(steps=100,
prefix="Changing Coordinates:", suffix=None, symbol="#", active="=",
brackets="[]", percent=True, size=40)
for i in range(len(data[:,0])):
count = count + 1
#data[i,0], data[i,1] = ac.lb2GC(data[i,0], data[i,1], 15)
#data[i,0], data[i,1] = ac.lbToEq(data[i,0], data[i,1])
data[i,0], data[i,1] = (ac.lb2sgr(datar[i,0], datar[i,1], 30.0))[3:5]
data[i,2] = ac.getg(datar[i,2], 4.2)
if count % 100 == 0: pb2.updatebar(float(count)/nStars)
pb2.endbar()
# loop over Lambda slices and plot Beta histograms
pb3 = pr.Progressbar(steps=len(files), prefix="Making Slices:", suffix=None,
symbol="#", active="=", brackets="[]", percent=True, size=40)
Ls = (200.0, 300.0, 2.5)
Lsteps = int((Ls[1]-Ls[0])/Ls[2])
for i in range(Lsteps):
Lname = str(Ls[0]+(Ls[2]*i) )[:6]+suffix
new = []
for j in range(len(data[:,0])):
if data[j,0] < Ls[0]+(Ls[2]*i): continue
if data[j,0] > Ls[0]+(Ls[2]*(i+1)): continue
if savewedge:
new.append([datar[j,0], datar[j,1], data[j,2]])
continue
new.append(data[j,1])
new = np.array(new)
if savewedge:
np.savetxt("stars-lambda-"+("00"+str(i))[-2:]+".txt", new, fmt='%.6f')
else:
Lhist, Ledges = np.histogram(new, 140, (-30.0, 40.0))
#output to file
outstuff = sc.array(zip(Ledges+0.25, Lhist))
np.savetxt(Lname+".out", outstuff)
#plot
plt.figure(1)
plt.bar(Ledges[:-1], Lhist, 0.5)
plt.title(Lname)
plt.xlabel("B")
plt.savefig(Lname+".png")
plt.close('all')
pb3.updatebar(float(i)/float(Lsteps))
print "Ended Successfully"
