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 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
