def plot_heights(inputfile, outputfile, title=""):
MMWA, MLWA, SNR = np.loadtxt(inputfile, usecols=(11, 12, 14), unpack=True)
pp = PDF(outputfile)
ax = GPP.plot_rows(MMWA, weights=SNR, ylabel="Age [Gyr]", label="MMWA", kpc_scale=0.0485)
GPP.plot_rows(MLWA, weights=SNR, label="MLWA", ax=ax, kpc_scale=0.0485)
ax.legend(loc=0, scatterpoints=1, numpoints=1, frameon=False)
ax.set_xlim(-0.5, 3)
ax.set_title(title)
pp.savefig(ax.figure)
pp.close()
plt.close(ax.figure)
return
def all_heights(output, inputprefix='NGC_891', err=True, binned=True):
datname = output.split('.pdf')[0]+'.dat'
f = open(datname,'w')
f.write('#{:5}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}\n#\n'.\
format('','height [kpc]','age [Gyr]',
'age eom','age stderr',
'AV','AV eom','AV stderr',
'Z/Z_sol','Z eom', 'Z stderr'))
pp = PDF(output)
symblist = ["o","^","v","s","*","x"]
colorlist = ['b','c','g','y','m','r']
kpc_scale = 0.0485
plist = [6,3,4,2,1,5]
zcorrlist = [-0.01, -0.113, -0.225, -0.07, -0.142, -0.004]
rlist = [-10.0, -6.4, -2.3, 0.8, 4.4, 8.1]
ageax = None
AVax = None
Zax = None
for i in range(6):
inputfile = glob('{}*P{}*allz2*.dat'.format(inputprefix,plist[i]))[0]
print inputfile
if binned:
fitsname = glob('{}*P{}*.ms*fits'.format(inputprefix,plist[i]))[0]
print fitsname
binhead = pyfits.open(fitsname)[0].header
else:
binhead = False
MLWA, MLWZ, TAUV, SNR = np.loadtxt(inputfile, usecols=(62,64,66,67),
unpack=True)
MLWZ = np.log10(MLWZ)
ageax, tmpz, tmpage, tmperr, tmpstd = GPP.plot_rows(MLWA,
binheader=binhead,
weights=SNR,
ax=ageax,
label='{}'.\
format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
zcorr=zcorrlist[i],
err=err,
marker=symblist[i],
linestyle='',
color=colorlist[i])
AVax, _, tmpAV, tmpAVerr, tmpAVstd = GPP.plot_rows(TAUV*1.086,
binheader = binhead,
weights=SNR,
ax=AVax,
label='{}'.\
format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
zcorr=zcorrlist[i],
err=err,
marker=symblist[i],
linestyle='',
color=colorlist[i])
Zax, _, tmpZ, tmpZerr, tmpZstd = GPP.plot_rows(MLWZ,
binheader = binhead,
weights=SNR,
ax=Zax,
label='{}'.\
format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
zcorr=zcorrlist[i],
err=err,
marker=symblist[i],
linestyle='',
color=colorlist[i])
f.write('\n# P{} '.format(plist[i])+92*'#'+'\n# r ~ {} kpc\n'.\
format(rlist[i]))
for i in range(tmpz.size):
f.write('{:6}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}\n'.\
format('',tmpz[i],tmpage[i],tmperr[i],tmpstd[i],
tmpAV[i],tmpAVerr[i],tmpAVstd[i],
tmpZ[i],tmpZerr[i],tmpZstd[i]))
try:
z = np.vstack((z,tmpz))
age = np.vstack((age,tmpage))
err = np.vstack((err,tmperr))
std = np.vstack((std,tmpstd))
AV = np.vstack((AV,tmpAV))
AVstd = np.vstack((AVstd,tmpAVstd))
AVerr = np.vstack((AVerr,tmpAVerr))
Z = np.vstack((Z,tmpZ))
Zerr = np.vstack((Zerr,tmpZerr))
Zstd = np.vstack((Zstd,tmpZstd))
except UnboundLocalError:
z = tmpz
age = tmpage
err = tmperr
std = tmpstd
AV = tmpAV
AVstd = tmpAVstd
AVerr = tmpAVerr
Z = tmpZ
Zerr = tmpZerr
Zstd = tmpZstd
bigz = np.nanmean(z,axis=0)
bigage = np.nanmean(age,axis=0)
bigerr = np.sqrt(
np.nansum(err*(age - bigage)**2,axis=0)/
((err.shape[0] - 1.)/(err.shape[0]) * np.nansum(err,axis=0)))
bigAV = np.nanmean(AV,axis=0)
bigAVerr = np.sqrt(
np.nansum(AVerr*(AV - bigAV)**2,axis=0)/
((AVerr.shape[0] - 1.)/(AVerr.shape[0]) * np.nansum(AVerr,axis=0)))
bigZ = np.nanmean(Z,axis=0)
bigZerr = np.sqrt(
np.nansum(Zerr*(Z - bigZ)**2,axis=0)/
((Zerr.shape[0] - 1.)/(Zerr.shape[0]) * np.nansum(Zerr,axis=0)))
with open('{}_means.dat'.format(datname.split('.dat')[0]),'w') as fm:
fm.write(str('#{:>9}'+'{:>10}'*6+'\n').format('height',
'MLWA',
'MLWA err',
'Av',
'Av err',
'Z',
'Z err'))
for i in range(bigz.size):
fm.write('{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}\n'.\
format(bigz[i],bigage[i],bigerr[i],bigAV[i],bigAVerr[i],bigZ[i],bigZerr[i]))
ageax.plot(bigz, bigage)
ageax.fill_between(bigz,bigage-bigerr,bigage+bigerr,alpha=0.1)
ageax.legend(loc=1,title='radius [kpc]',scatterpoints=1,numpoints=1,frameon=False)
ageax.set_xlim(-0.6,2.6)
ageax.set_ylim(0,13)
ageax.set_ylabel('Light-weighted age [Gyr]')
ageax.set_title('Generated on {}'.format(time.asctime()))
AVax.plot(bigz,bigAV)
AVax.fill_between(bigz,bigAV-bigAVerr,bigAV+bigAVerr,alpha=0.1)
AVax.legend(loc=1,title='radius [kpc]',scatterpoints=1,numpoints=1,frameon=False)
AVax.set_xlim(-0.6,2.6)
AVax.set_ylim(0,6)
AVax.set_ylabel(r'$A_V$')
Zax.plot(bigz,bigZ)
Zax.fill_between(bigz, bigZ-bigZerr, bigZ+bigZerr,alpha=0.1)
Zax.legend(loc=1,title='radius [kpc]',scatterpoints=1,
numpoints=1,frameon=False)
Zax.set_xlim(-0.6,2.6)
# Zax.set_ylim(-1.5,3.0)
Zax.set_ylabel(r'Log( MLWZ [$Z_{\odot}$] )')
pp.savefig(ageax.figure)
pp.savefig(AVax.figure)
pp.savefig(Zax.figure)
plt.close(ageax.figure)
plt.close(AVax.figure)
plt.close(Zax.figure)
pp.close()
f.close()
return
def all_heights(output, inputprefix="NGC_891", err=True, binned=False, reg=True):
datname = output.split(".pdf")[0] + ".dat"
f = open(datname, "w")
f.write(
"#{:5}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}{:>13}\n#\n".format(
"",
"height [kpc]",
"age [Gyr]",
"age eom",
"age stderr",
"AV",
"AV eom",
"AV stderr",
"Z/Z_sol",
"Z eom",
"Z stderr",
)
)
pp = PDF(output)
symblist = ["o", "^", "v", "s", "*", "x"]
colorlist = ["b", "c", "g", "y", "m", "r"]
kpc_scale = 0.0485
plist = [6, 3, 4, 2, 1, 5]
rlist = [-10.0, -6.4, -2.3, 0.8, 4.4, 8.1]
# rlist = [4.4,0.8,-6.4,-2.3,8.1,-10.0]
ax = None
AVax = None
metalax = None
if reg:
psi0ax = None
tausfax = None
invtausfax = None
tformax = None
massax = None
for i in range(6):
inputfile = glob("{}*P{}*fit.dat".format(inputprefix, plist[i]))[0]
print inputfile
if binned:
fitsname = glob("{}*P{}*ms*fits".format(inputprefix, plist[i]))[0]
print fitsname
binhead = pyfits.open(fitsname)[0].header
else:
binhead = False
if reg:
MMWA, MLWA, TAUV, SNR, Z, PSI0, TAUSF, TFORM = np.loadtxt(
inputfile, usecols=(11, 12, 13, 14, 17, 18, 19, 20), unpack=True
)
mdata = np.loadtxt(inputfile)
MASS = np.sum(mdata[:, 1:11], axis=1)
else:
MMWA, MLWA, TAUV, SNR, Z = np.loadtxt(inputfile, usecols=(11, 12, 13, 14, 19), unpack=True)
Z = np.log10(Z)
ax, tmpz, tmpage, tmperr, tmpstd = GPP.plot_rows(
MLWA,
binheader=binhead,
weights=SNR,
ax=ax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
AVax, _, tmpAV, tmpAVerr, tmpAVstd = GPP.plot_rows(
TAUV * 1.086,
binheader=binhead,
weights=SNR,
ax=AVax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
metalax, _, tmpmetal, tmpmetalerr, tmpmetalstd = GPP.plot_rows(
Z,
binheader=binhead,
weights=SNR,
ax=metalax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
if reg:
psi0ax, _, tmppsi0, tmppsi0err, tmppsi0std = GPP.plot_rows(
np.log10(PSI0),
binheader=binhead,
weights=SNR,
ax=psi0ax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
tausfax, _, tmptausf, tmptausferr, tmptausfstd = GPP.plot_rows(
TAUSF,
binheader=binhead,
weights=SNR,
ax=tausfax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
invtausfax, _, tmpinvtausf, tmpinvtausferr, tmpinvtausfstd = GPP.plot_rows(
1.0 / TAUSF,
binheader=binhead,
weights=SNR,
ax=invtausfax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
tformax, _, tmptform, tmptformerr, tmptformstd = GPP.plot_rows(
TFORM,
binheader=binhead,
weights=SNR,
ax=tformax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
massax, _, tmpmass, tmpmasserr, tmpmassstd = GPP.plot_rows(
np.log10(MASS),
binheader=binhead,
weights=SNR,
ax=massax,
label="{}".format(rlist[i]),
fullout=True,
kpc_scale=kpc_scale,
err=err,
marker=symblist[i],
linestyle="",
color=colorlist[i],
)
f.write("\n# P{} ".format(i + 1) + 92 * "#" + "\n# r ~ {} kpc\n".format(rlist[i]))
for i in range(tmpz.size):
f.write(
"{:6}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}{:13.3f}\n".format(
"",
tmpz[i],
tmpage[i],
tmperr[i],
tmpstd[i],
tmpAV[i],
tmpAVerr[i],
tmpAVstd[i],
tmpmetal[i],
tmpmetalerr[i],
tmpmetalstd[i],
)
)
try:
z = np.vstack((z, tmpz))
age = np.vstack((age, tmpage))
err = np.vstack((err, tmperr))
std = np.vstack((std, tmpstd))
AV = np.vstack((AV, tmpAV))
AVstd = np.vstack((AVstd, tmpAVstd))
AVerr = np.vstack((AVerr, tmpAVerr))
metal = np.vstack((metal, tmpmetal))
metalerr = np.vstack((metalerr, tmpmetalerr))
metalstd = np.vstack((metalstd, tmpmetalstd))
if reg:
psi0 = np.vstack((psi0, tmppsi0))
psi0err = np.vstack((psi0err, tmppsi0err))
psi0std = np.vstack((psi0std, tmppsi0std))
tausf = np.vstack((tausf, tmptausf))
tausferr = np.vstack((tausferr, tmptausferr))
tausfstd = np.vstack((tausfstd, tmptausfstd))
invtausf = np.vstack((invtausf, tmpinvtausf))
invtausferr = np.vstack((invtausferr, tmpinvtausferr))
invtausfstd = np.vstack((invtausfstd, tmpinvtausfstd))
tform = np.vstack((tform, tmptform))
tformerr = np.vstack((tformerr, tmptformerr))
tformstd = np.vstack((tformstd, tmptformstd))
mass = np.vstack((mass, tmpmass))
masserr = np.vstack((masserr, tmpmasserr))
massstd = np.vstack((massstd, tmpmassstd))
except UnboundLocalError:
z = tmpz
age = tmpage
err = tmperr
std = tmpstd
AV = tmpAV
AVstd = tmpAVstd
AVerr = tmpAVerr
metal = tmpmetal
metalerr = tmpmetalerr
metalstd = tmpmetalstd
if reg:
psi0 = tmppsi0
psi0err = tmppsi0err
psi0std = tmppsi0std
tausf = tmptausf
tausferr = tmptausferr
tausfstd = tmptausfstd
invtausf = tmpinvtausf
invtausferr = tmpinvtausferr
invtausfstd = tmpinvtausfstd
tform = tmptform
tformerr = tmptformerr
tformstd = tmptformstd
mass = tmpmass
masserr = tmpmasserr
massstd = tmpmassstd
bigz = np.nanmean(z, axis=0)
bigage = np.nanmean(age, axis=0)
bigerr = np.sqrt(
np.nansum(err * (age - bigage) ** 2, axis=0) / ((err.shape[0] - 1.0) / (err.shape[0]) * np.nansum(err, axis=0))
)
bigAV = np.nanmean(AV, axis=0)
bigAVerr = np.sqrt(
np.nansum(AVerr * (AV - bigAV) ** 2, axis=0)
/ ((AVerr.shape[0] - 1.0) / (AVerr.shape[0]) * np.nansum(AVerr, axis=0))
)
bigmetal = np.nanmean(metal, axis=0)
bigmetalerr = np.sqrt(
np.nansum(metalerr * (metal - bigmetal) ** 2, axis=0)
/ ((metalerr.shape[0] - 1.0) / (metalerr.shape[0]) * np.nansum(metalerr, axis=0))
)
if reg:
bigpsi0 = np.nanmean(psi0, axis=0)
bigpsi0err = np.sqrt(
np.abs(
np.nansum(psi0err * (psi0 - bigpsi0) ** 2, axis=0)
/ ((psi0err.shape[0] - 1.0) / (psi0err.shape[0]) * np.nansum(psi0err, axis=0))
)
)
bigtausf = np.nanmean(tausf, axis=0)
bigtausferr = np.sqrt(
np.nansum(tausferr * (tausf - bigtausf) ** 2, axis=0)
/ ((tausferr.shape[0] - 1.0) / (tausferr.shape[0]) * np.nansum(tausferr, axis=0))
)
biginvtausf = np.nanmean(invtausf, axis=0)
biginvtausferr = np.sqrt(
np.nansum(invtausferr * (invtausf - biginvtausf) ** 2, axis=0)
/ ((invtausferr.shape[0] - 1.0) / (invtausferr.shape[0]) * np.nansum(invtausferr, axis=0))
)
bigtform = np.nanmean(tform, axis=0)
bigtformerr = np.sqrt(
np.nansum(tformerr * (tform - bigtform) ** 2, axis=0)
/ ((tformerr.shape[0] - 1.0) / (tformerr.shape[0]) * np.nansum(tformerr, axis=0))
)
bigmass = np.nanmean(mass, axis=0)
bigmasserr = np.sqrt(
np.nansum(masserr * (mass - bigmass) ** 2, axis=0)
/ ((masserr.shape[0] - 1.0) / (masserr.shape[0]) * np.nansum(masserr, axis=0))
)
with open("{}_means.dat".format(datname.split(".dat")[0]), "w") as fm:
fm.write(str("#{:>9}" + "{:>10}" * 6 + "\n").format("height", "MLWA", "MLWA err", "Av", "Av err", "Z", "Z err"))
for i in range(bigz.size):
fm.write(
"{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}{:10.4f}\n".format(
bigz[i], bigage[i], bigerr[i], bigAV[i], bigAVerr[i], bigmetal[i], bigmetalerr[i]
)
)
ax.plot(bigz, bigage)
ax.fill_between(bigz, bigage - bigerr, bigage + bigerr, alpha=0.1)
ax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
ax.set_xlim(-0.1, 2.6)
ax.set_ylim(-2, 10)
ax.set_ylabel("Light-weighted age [Gyr]")
ax.set_title("Generated on {}".format(time.asctime()))
AVax.plot(bigz, bigAV)
AVax.fill_between(bigz, bigAV - bigAVerr, bigAV + bigAVerr, alpha=0.1)
AVax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
AVax.set_xlim(-0.1, 2.6)
AVax.set_ylim(0, 6)
AVax.set_ylabel(r"$A_V$")
# plotz = np.linspace(0,2.5,20)
# for t in range(len(rlist)):
# modelAv = mA.A_vec(np.abs(rlist[t]),plotz,zd=1.0,tau0=0.85,hd=7.68)
# AVax.plot(plotz,modelAv,color=colorlist[t])
metalax.plot(bigz, bigmetal)
metalax.fill_between(bigz, bigmetal - bigmetalerr, bigmetal + bigmetalerr, alpha=0.1)
metalax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
metalax.set_xlim(-0.1, 2.6)
# metalax.set_ylim(-1.5,3.0)
metalax.set_ylabel(r"Log( $Z/Z_{\odot}$ )")
if reg:
psi0ax.plot(bigz, bigpsi0)
psi0ax.fill_between(bigz, bigpsi0 - bigpsi0err, bigpsi0 + bigpsi0err, alpha=0.1)
psi0ax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
psi0ax.set_xlim(-0.1, 2.6)
psi0ax.set_ylabel(r"Log($\psi_0$)")
tausfax.plot(bigz, bigtausf)
tausfax.fill_between(bigz, bigtausf - bigtausferr, bigtausf + bigtausferr, alpha=0.1)
tausfax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
tausfax.set_ylim(-1, 5)
tausfax.set_xlim(-0.1, 2.6)
tausfax.set_ylabel(r"$\tau_{\mathrm{sf}}$")
invtausfax.plot(bigz, biginvtausf)
invtausfax.fill_between(bigz, biginvtausf - biginvtausferr, biginvtausf + biginvtausferr, alpha=0.1)
invtausfax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
invtausfax.set_ylim(-1, 5)
invtausfax.set_xlim(-0.1, 2.6)
invtausfax.set_ylabel(r"$1/\tau_{\mathrm{sf}}$")
tformax.plot(bigz, bigtform)
tformax.fill_between(bigz, bigtform - bigtformerr, bigtform + bigtformerr, alpha=0.1)
tformax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
tformax.set_xlim(-0.1, 2.6)
tformax.set_ylabel(r"$t_{\mathrm{form}}$")
massax.plot(bigz, bigmass)
massax.fill_between(bigz, bigmass - bigmasserr, bigmass + bigmasserr, alpha=0.1)
massax.legend(loc=1, title="radius [kpc]", scatterpoints=1, numpoints=1, frameon=False)
massax.set_xlim(-0.1, 2.6)
massax.set_ylabel(r"Log($M_{\mathrm{total}}$)")
pp.savefig(ax.figure)
pp.savefig(AVax.figure)
pp.savefig(metalax.figure)
plt.close(ax.figure)
plt.close(AVax.figure)
plt.close(metalax.figure)
if reg:
pp.savefig(psi0ax.figure)
pp.savefig(tausfax.figure)
pp.savefig(tformax.figure)
pp.savefig(invtausfax.figure)
pp.savefig(massax.figure)
plt.close(psi0ax.figure)
plt.close(tausfax.figure)
plt.close(invtausfax.figure)
plt.close(tformax.figure)
plt.close(massax.figure)
pp.close()
f.close()
return
