def get_lick_regions():
""" Read definitions of bands of the Lick indices and return bands. """
table = os.path.join(tables_dir, "BANDS")
return np.loadtxt(table, usecols=(2,3)), np.loadtxt(table, usecols=(4,5)),\
np.loadtxt(table, usecols=(6,7))
if __name__ == "__main__":
wdir = home + "/single2"
os.chdir(wdir)
# plt.switch_backend('macosx')
plt.ioff()
save = True
block = False
specs = speclist()
specs = ["fin1_n3311cen1_s27.fits", "fin1_n3311cen2_s37.fits"]
# Workaround to deal with cases where you have only one object in the file
if isinstance(specs, str):
specs = [specs]
###########################################################################
xlims = [4800, 5850]
w_tem = w_temp(velscale)
red, bands, blue = get_lick_regions()
names = [
r'Hd$_A$', r'Hd$_F$', r'CN$_1$', r'CN$_2$', 'Ca4227', r'G4300',
r'Hg_A', r'Hg_F', r'Fe4383', r'Ca4455', r'Fe4531', r'C4668',
r'H$\beta$', r'Fe5015', r'Mg$_1$', r'Mg$_2', r'Mg $b$', r'Fe5270',
r'Fe5335', r'Fe5406', r'Fe5709', r'Fe5782', r'Na_D', r'TiO_1', r'TiO_2'
]
textsize = 16
Prepare files for alternative pPXF run.
"""
import os
import pyfits as pf
import matplotlib.pyplot as plt
from config import *
from run_ppxf import speclist
if __name__ == "__main__":
spec_dir = os.path.join(home, "single1")
sky_dir = os.path.join(home, "sky/1D")
for spectrum in speclist():
print spectrum
spec = os.path.join(spec_dir, spectrum)
sky = os.path.join(sky_dir, spectrum.replace("fin", "sky"))
outdir = os.path.join(home, "single3")
output = os.path.join(outdir, spectrum)
if not (os.path.exists(spec) and os.path.exists(sky)):
continue
sdata = pf.getdata(spec)
skydata = pf.getdata(sky)
header = pf.getheader(spec, verify="ignore")
sdata += skydata
ax = plt.subplot(111)
ax.plot(sdata, "-k")
ax.plot(sdata - skydata, "-b")
ax.plot(skydata, "-y")
mad = 1.4826 * np.median(np.abs(data - median), axis=0)
line = "{0:26s}".format(spec) + \
"".join(["{0:10s}".format("{0:.5f}".format(x)) for x in mad])
results.append(line)
with open("mc_lick_nsim{0}.txt".format(Nsim), "w") as f:
f.write("\n".join(results))
return
if __name__ == "__main__":
wdir = os.path.join(home, "m6pc")
outdir = os.path.join(wdir, "mc_logs")
if not os.path.exists(outdir):
os.mkdir(outdir)
os.chdir(wdir)
bcorr = BroadCorr(os.path.join(tables_dir, "lickcorr_m.txt"))
specs = speclist()
bands = os.path.join(tables_dir, "BANDS")
lick_types = np.loadtxt(bands, usecols=(8,))
lick_indices = np.genfromtxt(bands, usecols=(0,), dtype=None).tolist()
Nsim = 100
header = table_header()
if True:
pool = mp.Pool()
for i, spec in enumerate(specs):
pool.apply_async(run_mc, args=(spec, i))
# print spec
# run_mc(spec, i)
# break
pool.close()
pool.join()
write_table(specs, Nsim)
"summary.txt")
if not os.path.exists(logfile):
continue
with open(logfile, "r") as f:
header = f.readline()
lines.append(f.readline())
table = os.path.join(working_dir, "populations_{0}.txt".format(modelname))
with open(table, "w") as f:
f.write(header)
f.write("\n".join(lines))
if __name__ == "__main__":
working_dir = os.path.join(home, "single2")
os.chdir(working_dir)
plt.ioff()
specs = speclist()
# specs = ["fin1_n3311cen1_s23.fits", "fin1_n3311cen1_s30.fits"]
db = ""
modelname = "miles" if db == "2" else "thomas"
dirs = [x.replace(".fits", "_db{0}".format(db)) for x in specs]
lims = [[9 + np.log10(1.), 9 + np.log10(15.)], [-2.25, 0.90], [-0.3, 0.5]]
plims = [[np.log10(1.), 1.2], [-2.3, 0.7], [-0.4, 0.6]]
fignums = [4, 7, 8]
pairs = [[0,1], [0,2], [1,2]]
plt.ioff()
pp = PdfPages(os.path.join(working_dir,
"mcmc_results_{0}.pdf".format(modelname)))
plt.figure(1, figsize=(9,6.5))
plt.minorticks_on()
table_summary, table_results = [], []
sndata = dict(np.loadtxt("ppxf_results.dat", usecols=(0,10), dtype=str))
Prepare files for alternative pPXF run.
"""
import os
import pyfits as pf
import matplotlib.pyplot as plt
from config import *
from run_ppxf import speclist
if __name__ == "__main__":
spec_dir = os.path.join(home, "single1")
sky_dir = os.path.join(home, "sky/1D")
for spectrum in speclist():
print spectrum
spec = os.path.join(spec_dir, spectrum)
sky = os.path.join(sky_dir, spectrum.replace("fin", "sky"))
outdir = os.path.join(home, "single3")
output = os.path.join(outdir, spectrum)
if not (os.path.exists(spec) and os.path.exists(sky)):
continue
sdata = pf.getdata(spec)
skydata = pf.getdata(sky)
header = pf.getheader(spec, verify="ignore")
sdata += skydata
ax = plt.subplot(111)
ax.plot(sdata, "-k")
ax.plot(sdata - skydata, "-b")
ax.plot(skydata, "-y")
if not os.path.exists(logfile):
continue
with open(logfile, "r") as f:
header = f.readline()
lines.append(f.readline())
table = os.path.join(working_dir, "populations_{0}.txt".format(modelname))
with open(table, "w") as f:
f.write(header)
f.write("\n".join(lines))
if __name__ == "__main__":
working_dir = os.path.join(home, "single2")
os.chdir(working_dir)
plt.ioff()
specs = speclist()
# specs = ["fin1_n3311cen1_s23.fits", "fin1_n3311cen1_s30.fits"]
db = ""
modelname = "miles" if db == "2" else "thomas"
dirs = [x.replace(".fits", "_db{0}".format(db)) for x in specs]
lims = [[9 + np.log10(1.), 9 + np.log10(15.)], [-2.25, 0.90], [-0.3, 0.5]]
plims = [[np.log10(1.), 1.2], [-2.3, 0.7], [-0.4, 0.6]]
fignums = [4, 7, 8]
pairs = [[0, 1], [0, 2], [1, 2]]
plt.ioff()
pp = PdfPages(
os.path.join(working_dir, "mcmc_results_{0}.pdf".format(modelname)))
plt.figure(1, figsize=(9, 6.5))
plt.minorticks_on()
table_summary, table_results = [], []
sndata = dict(np.loadtxt("ppxf_results.dat", usecols=(0, 10), dtype=str))
