def plot_maps(inputfile, outputfile, eps=False, exclude=[], sky=False, labelfibers=True, MLWA=True):
if MLWA:
data = np.loadtxt(inputfile, usecols=(12,), unpack=True)
label = "Mean Light Weighted Age [Gyr]"
minval = np.nanmin(data)
maxval = np.nanmax(data)
else:
data = np.loadtxt(inputfile, usecols=(11,), unpack=True)
label = "Mean Mass Weighted Age [Gyr]"
minval = np.nanmin(data) # 7
maxval = np.nanmax(data) # 10
map_ax = GPP.plot_img(
data,
fitsfile="/d/monk/eigenbrot/WIYN/14B-0456/NGC_891.fits",
imrot=67.0,
pa=295.787,
center=[35.6034125, 42.32349444],
clabel=label,
method="cubic",
cmap="gnuplot2",
exclude=exclude,
sky=sky,
minval=minval,
maxval=maxval,
)
fiber_ax = GPP.plot(
data,
fitsfile="/d/monk/eigenbrot/WIYN/14B-0456/NGC_891.fits",
imrot=67.0,
pa=295.787,
center=[35.6034125, 42.32349444],
clabel=label,
cmap="gnuplot2",
labelfibers=labelfibers,
exclude=exclude,
sky=sky,
minval=minval,
maxval=maxval,
)
if eps:
fiber_name = outputfile + "_fibers.eps"
map_name = outputfile + "_map.eps"
fiber_ax.figure.savefig(fiber_name, format="eps")
map_ax.figure.savefig(map_name, format="eps")
else:
pp = PDF(outputfile)
pp.savefig(fiber_ax.figure)
pp.savefig(map_ax.figure)
pp.close()
# plt.close(fiber_ax.figure)
# plt.close(map_ax.figure)
return fiber_ax
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 create_locations(binfile, galcenter=[35.637962,42.347629],
ifucenter=[35.637962,42.347629], reffiber=105,
galpa=293.3, ifupa=295.787, kpc_scale=0.0485):
"""Given a binned data file, creat a list of bin locations, relative to a galactic center.
The function is designed to give physical meaning to the location of each
bin created by :func:`bin`. For each bin found in the FITS header it
computes physical coordinates in both arcsec and kpc. This method was
written with a 2D projection of cylindrical coordinates in mind (r,z), but
the user is free to fully define the location and meaning of the
coordinate system through keyword options.
The output is a text file containig the location of each bin in the
desired coordinate system.
The current defaults are for NGC 891 and WIYN proposal 14B-0456. You
should probably change them.
Parameters
----------
binfile : str
The name of a FITS file produced by :func:`bin`
galcenter : list, optional
A list containing the [RA,DEC] coordinates of the center of the
coordinate system. The units are decimal degrees.
ifucenter : list, optional
A list containing the center of the fiber specified in
**reffiber**. Units are decimal degrees. This is usually the
coordinates of your GradPak pointing.
reffiber : int, optional
The *fiber* number whoes coordinates are given in **ifucenter**. This
is generally the fiber you used to align GradPak during observations.
galpa : float, optional
The position angle of the desired coordinate system. This angle
defines rotation of the horizontal axis relative to the sky.
ifupa : float, optional
The position angle of GradPak. This is defined as an absolute PA,
relative to North, *not* to the defined coordinate system. This is
generally the PA you entered during GradPak observations.
kpc_scale : float, optional
kpc/arcsec of the object in question
Returns
-------
None :
The result is file with the suffix "locations.dat" that contains
information about the location in the user defined coordinates of each
aperture (bin). Coordinates are given in arcsec and kpc. Note that the
reported "size" is the size of the fibers that went into each bin, not
the size of the bin itself. This is admittedly confusing.
"""
hdu = pyfits.open(binfile)[0]
numaps = hdu.data.shape[0]
binhead = hdu.header
patches = GPP.get_binned_patches(binhead)
refpatches = GPP.GradPak_patches()
patches, refpatches = GPP.transform_patches(patches,refpatches=refpatches,
pa=ifupa, center=ifucenter,
reffiber=reffiber)
decrad = galcenter[1]*2*np.pi/360.
parad = galpa*2*np.pi/360.
f = open('{}_locations.dat'.format(binfile.split('.ms.fits')[0]),'w')
f.write("""# Generated on {}
# Inpute file: {}
#
""".format(time.asctime(),binfile))
f.write('# {:4}{:>10}{:>10}{:>10}{:>10}{:>10}\n#\n'.format('Apnum',
'size (")',
'r (")',
'z (")',
'r (kpc)',
'z (kpc)'))
for i, p in enumerate(patches[:,1]):
fibers = binhead['BIN{:03}F'.format(i+1)]
radius = refpatches[int(fibers.split(' ')[0]) - 1][1].get_radius()
ra_diff = 3600*(galcenter[0] - p.center[0])*np.cos(decrad)
dec_diff = 3600*(galcenter[1] - p.center[1])
r_diff = ra_diff*np.cos(parad) - dec_diff*np.sin(parad)
z_diff = -1*(ra_diff*np.sin(parad) + dec_diff*np.cos(parad))
print i+1, p.center, ra_diff, dec_diff, r_diff, z_diff
f.write(str('{:7n}'+5*'{:10.3f}'+'\n').format(i,
radius,
r_diff,
z_diff,
r_diff*kpc_scale,
z_diff*kpc_scale))
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
def all_maps(
output,
col=12,
inputprefix="NGC_891",
inputsuffix="fit.dat",
labelfibers=False,
label="Mean Light Weighted Age [Gyr]",
log=False,
plotbins=False,
minval=None,
maxval=None,
exclude=None,
binned=False,
cmap="gnuplot2",
):
pp = PDF(output)
centers = [
[35.603413, 42.323494],
[35.615154, 42.3426],
[35.636325, 42.37935],
[35.625617, 42.35935],
[35.591642, 42.304247],
[35.648196, 42.401217],
]
ax = None
if exclude is None:
exclude = [[], [], [], [], [], []]
figlist = []
for i in range(6):
print i
try:
inputfile = glob("{}*P{}*{}".format(inputprefix, i + 1, inputsuffix))[0]
except IndexError:
print "Could not find P{} data, skipping".format(i + 1)
continue
print inputfile
if binned:
fitsname = glob("{}*P{}*ms*fits".format(inputprefix, i + 1))[0]
print fitsname
binhead = pyfits.open(fitsname)[0].header
else:
binhead = None
data = np.loadtxt(inputfile, usecols=(col,), unpack=True)
if col == 12:
label = "Mean Light Weighted Age [Gyr]"
minval = 0
maxval = 13 # np.nanmax(data)
elif col == 11:
label = "Mean Mass Weighted Age [Gyr]"
minval = 0 # np.nanmin(data)#7
maxval = 10 # np.nanmax(data)#10
elif col == 13:
label = "$A_V$"
data *= 1.086
minval = 0
maxval = 6
elif col == 19:
data = np.log10(data)
label = r"Log( Metallicity [$Z_{\odot}$] )"
# cmap = mplcolors.ListedColormap(['black','blue','green','yellow','red','white'])
# norm = mplcolors.BoundaryNorm([0,0.005,0.02,0.2,0.4,1,2.5], cmap.N)
minval = -4
maxval = 1
elif col == 65:
data *= 1.086
if log:
data = np.log10(data)
if i == 0:
label = "Log( {} )".format(label)
print "excluding", exclude[i]
ax = GPP.plot(
data,
ax=ax,
binheader=binhead,
plotbins=plotbins,
figsize=(8, 4),
fitsfile="/d/monk/eigenbrot/WIYN/14B-0456/NGC_891.fits",
wcsax=False,
imrot=67, # 64.213,
pa=295.787,
center=centers[i],
clabel=label,
cmap=cmap,
labelfibers=labelfibers,
exclude=exclude[i],
sky=False,
minval=minval,
maxval=maxval,
alpha=1,
)
# Do it again, for the single-pointing figure
sax = GPP.plot(
data,
ax=None,
binheader=binhead,
plotbins=plotbins,
figsize=(8, 4),
fitsfile="/d/monk/eigenbrot/WIYN/14B-0456/NGC_891.fits",
wcsax=False,
imrot=67, # 64.213,
pa=295.787,
center=centers[i],
clabel=label,
cmap=cmap,
labelfibers=labelfibers,
exclude=exclude[i],
sky=False,
minval=minval,
maxval=maxval,
alpha=1,
)
sax.text(
0.5, 0.8, "P{}".format(i + 1), color="r", fontsize=20, transform=sax.transAxes, ha="center", va="center"
)
figlist.append(sax.figure)
ax.set_xlim(160, 755)
ax.set_ylim(350, 600)
header = pyfits.open("/d/monk/eigenbrot/WIYN/14B-0456/NGC_891.fits")[0].header
hw = pywcs.WCS(header)
centpx = hw.wcs_sky2pix([[35.63689, 42.34633]], 0)[0]
xticks = np.arange(-300, 301, 100) / (header["CDELT1"] * 3600.0) + centpx[0]
yticks = np.arange(-100, 201, 100) / (header["CDELT2"] * 3600.0) + centpx[1]
ax.set_xticks(xticks)
ax.set_yticks(yticks)
xlabs = ["{:3.0f}".format((centpx[0] - i) * header["CDELT1"] * 3600.0) for i in ax.get_xticks()]
ylabs = ["{:3.0f}".format((i - centpx[1]) * header["CDELT2"] * 3600.0) for i in ax.get_yticks()]
ax.set_xticklabels(xlabs)
ax.set_yticklabels(ylabs)
ax.set_xlabel("arcsec")
ax.set_ylabel("arcsec")
pp.savefig(ax.figure)
for f in figlist:
pp.savefig(f)
pp.close()
plt.close("all")
return ax
def bin(datafile, errfile, SNR, outputfile, waverange=None, exclude=[],
logfile=None, logfits=None):
"""Bin GradPak fibers along a row until the desired SNR is achieved.
The way fibers are grouped together is somewhat simplistic, but the
underlying mathematics is correct.
The first bin starts at the smallest non-sky, non-excluded fiber (usually
fiber #3) and just keeps adding the next fiber until the desire SNR is
reached. Fibers are not binned across rows, which ensures that each bin is
made up only of fibers of one size, but also limits the amount of binning
that can be done. The major limitation of this method is that the ends of
rows can often be left with a single, low SNR fiber. For example, if a bin
is constructed containing fibers 14-16 and fiber 17 (at the end of the
row) is below the threshold it will not be added to the previous bin and
instead will make up a single bin with low a low SNR. This should be easy
to fix but in practice it doesn't cause that much of an issue so I just
haven't done it.
The spectrum for each bin is an average of all the contributing fibers,
weighted by each individual's SNR^2. The corresponding error bin is a sum
in quadrature, weighted in the same way.
The history of each bin is recorded in the output FITS header using two keywords:
**BINXXXF** The fibers that went into bin XXX
**BINXXXP** The position (in arcsec) of the center of bin XXX. This is
the *unweighted* average of the centers of the contributing fibers.
Parameters
----------
datafile : str
The name of a multispec FITS file that you want to bin
errfile : str
The name of a multispec FITS file containing error vectors corresponding to the datafile
SNR : float
The minimum SNR in each bin
outputfile : str
The base name of the binned spectral and error arrays. The results
will be called OUTPUTFILE.ms.fits and OUTPUTFILE.me.fits.
waverange : list (default: None)
A 2 element list containing the minimum and maximum wavelengths
to consider *for the SNR calculations*. Regardless of this parameter
the final output will span the same wavelength range as the input
files.
exclude : list (default: [])
A list containing fibers to exlcude from binning. Sky fibers are
automatically excluded and fiber numbers start at 1.
logfile : str
Name of a log file that will contain, for each aperture, a
list of the individual fibers and the total S/N.
logfits : str
Name of a FITS file that will contain a separate HDU for each
aperture. Each HDU will contain an array with two dimensions
that records the fiber numbers and associated weights for that
aperture.
Returns
-------
finalf : numpy.ndarray
Array with shape NBINS x NWAVE containing the binned data
finatle : numpy.ndarray
Array with same shape as finalf containing the errors on the binned data
fibdict : dict
Dictionary of questionable usefulness that contains information about
which fibers went into each bin. The keys are ROW_BIN and the entries
are a list of the fibers that went into that bin (e.g., '1_2' for the
second bin in the first row).
"""
hdu = pyfits.open(datafile)[0]
data = hdu.data
err = pyfits.open(errfile)[0].data
if waverange is not None:
wave = (np.arange(data.shape[1]) + hdu.header['CRPIX1'] - 1)\
*hdu.header['CDELT1'] + hdu.header['CRVAL1']
waveidx = np.where((wave >= waverange[0]) & (wave <= waverange[1]))[0]
else:
waveidx = None
data *= 1e17
err *= 1e17
y_values = np.array([c.center[1] for c in GPP.GradPak_patches()[:,1]])
x_values = np.array([c.center[0] for c in GPP.GradPak_patches()[:,1]])
fibnums = np.arange(109) + 1
row_pos = np.unique(y_values)
finalf = np.zeros(data.shape[1])
finale = np.zeros(data.shape[1])
fibdict = {}
binnum = 1
if logfile is not None:
lf = open(logfile,'w')
if logfits is not None:
log_HDUs = [] #This will hold [fiber_list, weight_list] for each aperture
for i in range(row_pos.size):
if row_pos[i] > 80:
continue
idx = np.where(y_values == row_pos[i])[0]
b = 0
n = 0
while fibnums[idx[n]] in exclude:
print 'Skipping fiber {}'.format(fibnums[idx[n]])
n += 1
while n < len(idx):
try:
while fibnums[idx[n]] in exclude:
print 'Skipping fiber {}'.format(fibnums[idx[n]])
n += 1
except IndexError: #The rest of the fibers in the row are excluded
break
fstack = data[idx[n]][None,:]
estack = err[idx[n]][None,:]
tmp = compute_SN(data[idx[n]], err[idx[n]], waveidx)
snstack = np.array([[tmp]])
fibers = [fibnums[idx[n]]]
xpos = [x_values[idx[n]]]
ypos = [y_values[idx[n]]]
while tmp < SNR:
n += 1
print 'fibers: {}, SNR: {}'.format(fibers, tmp)
if n > len(idx) - 1:
print "WARNING, SN threshold not met in row {}, bin {}".\
format(i,b)
break
if fibnums[idx[n]] in exclude:
print 'Skipping fiber {}'.format(fibnums[idx[n]])
continue
fstack = np.vstack((fstack, data[idx[n]]))
estack = np.vstack((estack, err[idx[n]]))
snstack = np.vstack((snstack, compute_SN(data[idx[n]],
err[idx[n]],
waveidx)))
tmpf, tmpe = create_bin(fstack, estack, snstack)
tmp = compute_SN(tmpf, tmpe, waveidx)
fibers.append(fibnums[idx[n]])
xpos.append(x_values[idx[n]])
ypos.append(y_values[idx[n]])
binf, bine = create_bin(fstack, estack, snstack)
binsn = compute_SN(binf, bine, waveidx)
print 'binned aperture {}: {}, SNR: {}'.format(binnum,fibers, binsn)
if logfile is not None:
lf.write('binned aperture {}: {}, SNR: {}\n'.format(binnum,fibers, binsn))
if logfits is not None:
log_HDUs.append(pyfits.ImageHDU(np.vstack([fibers, snstack.T[0]])))
bin_x_pos = np.mean(xpos)
bin_y_pos = np.mean(ypos)
fibstr = [str(i) for i in fibers]
hdu.header.update('BIN{:03}F'.format(binnum),' '.join(fibstr))
hdu.header.update('BIN{:03}P'.format(binnum),' '.\
join([str(bin_x_pos),str(bin_y_pos)]))
finalf = np.vstack((finalf,binf))
finale = np.vstack((finale,bine))
fibdict['{}_{}'.format(i,b)] = fibers
b += 1
n += 1
binnum += 1
finalf = finalf[1:]/1e17
finale = finale[1:]/1e17
pyfits.PrimaryHDU(finalf, hdu.header).\
writeto('{}.ms.fits'.format(outputfile),clobber=True)
pyfits.PrimaryHDU(finale, hdu.header).\
writeto('{}.me.fits'.format(outputfile),clobber=True)
if logfits is not None:
lP = pyfits.PrimaryHDU()
lP.header.update('HDUDIM','APERTURE')
lP.header.update('AXIS1','FIBERS')
lP.header.update('AXIS2','0: FIBER NUMBER 1: WEIGHT')
pyfits.HDUList([lP] + log_HDUs).writeto(logfits, clobber=True)
return finalf, finale, fibdict
def create_locations(binfile, galcenter=[35.637962,42.347629],
ifucenter=[35.637962,42.347629], reffiber=105,
galpa=293.3, ifupa=295.787, kpc_scale=0.0485):
"""Given a binned data file, creat a list of bin locations, relative to a galactic center.
The function is designed to give physical meaning to the location of each
bin created by :func:`bin`. For each bin found in the FITS header it
computes physical coordinates in both arcsec and kpc. This method was
written with a 2D projection of cylindrical coordinates in mind (r,z), but
the user is free to fully define the location and meaning of the
coordinate system through keyword options.
The output is a text file containig the location of each bin in the
desired coordinate system.
The current defaults are for NGC 891 and WIYN proposal 14B-0456. You
should probably change them.
Parameters
----------
binfile : str
The name of a FITS file produced by :func:`bin`
galcenter : list, optional
A list containing the [RA,DEC] coordinates of the center of the
coordinate system. The units are decimal degrees.
ifucenter : list, optional
A list containing the center of the fiber specified in
**reffiber**. Units are decimal degrees. This is usually the
coordinates of your GradPak pointing.
reffiber : int, optional
The *fiber* number whoes coordinates are given in **ifucenter**. This
is generally the fiber you used to align GradPak during observations.
galpa : float, optional
The position angle of the desired coordinate system. This angle
defines rotation of the horizontal axis relative to the sky.
ifupa : float, optional
The position angle of GradPak. This is defined as an absolute PA,
relative to North, *not* to the defined coordinate system. This is
generally the PA you entered during GradPak observations.
kpc_scale : float, optional
kpc/arcsec of the object in question
Returns
-------
None :
The result is file with the suffix "locations.dat" that contains
information about the location in the user defined coordinates of each
aperture (bin). Coordinates are given in arcsec and kpc. Note that the
reported "size" is the size of the fibers that went into each bin, not
the size of the bin itself. This is admittedly confusing.
"""
hdu = pyfits.open(binfile)[0]
numaps = hdu.data.shape[0]
binhead = hdu.header
patches = GPP.get_binned_patches(binhead)
refpatches = GPP.GradPak_patches()
patches, refpatches = GPP.transform_patches(patches,refpatches=refpatches,
pa=ifupa, center=ifucenter,
reffiber=reffiber)
decrad = galcenter[1]*2*np.pi/360.
parad = galpa*2*np.pi/360.
f = open('{}_locations.dat'.format(binfile.split('.ms.fits')[0]),'w')
f.write("""# Generated on {}
# Inpute file: {}
#
""".format(time.asctime(),binfile))
f.write('# {:4}{:>10}{:>10}{:>10}{:>10}{:>10}\n#\n'.format('Apnum',
'size (")',
'r (")',
'z (")',
'r (kpc)',
'z (kpc)'))
for i, p in enumerate(patches[:,1]):
fibers = binhead['BIN{:03}F'.format(i+1)]
radius = refpatches[int(fibers.split(' ')[0]) - 1][1].get_radius()
ra_diff = 3600*(galcenter[0] - p.center[0])*np.cos(decrad)
dec_diff = 3600*(galcenter[1] - p.center[1])
r_diff = ra_diff*np.cos(parad) - dec_diff*np.sin(parad)
z_diff = -1*(ra_diff*np.sin(parad) + dec_diff*np.cos(parad))
print i+1, p.center, ra_diff, dec_diff, r_diff, z_diff
f.write(str('{:7n}'+5*'{:10.3f}'+'\n').format(i,
radius,
r_diff,
z_diff,
r_diff*kpc_scale,
z_diff*kpc_scale))
f.close()
return
