def test_star():
os.chdir(os.path.join(home, "data/star"))
specs = ['HD102070_spectra.fits', "HD102070_noflux.fits"]
a = np.zeros((2,25))
cs = ["k", "r"]
fs = []
for i, spec in enumerate(specs):
star = pf.getdata(spec)
w = wavelength_array(spec)
# plt.plot(w, star/np.nanmedian(star), "-{0}".format(cs[i]))
# check_ppxf(spec, velscale) # Velocity of stars is zero
flux = lector.broad2lick(w, star, 2.1, vel=0.)
sn = snr(flux)
noise = np.ones_like(flux) * np.median(flux) / sn
lick, lickerrs = lector.lector(w, flux, noise, bands,
vel = 0., cols=(0,8,2,3,4,5,6,7))
a[i] = lick
fs.append(interp1d(w, star/np.nanmedian(star), bounds_error=False, fill_value=0))
# plt.show()
# plt.clf()
w = np.linspace(4850, 5780, 1000)
p = np.poly1d(np.polyfit(w, fs[0](w) / fs[1](w), 20))
# plt.plot(w, fs[0](w) / fs[1](w), "-k" )
# plt.plot(w, p(w), "-r")
# plt.show()
cols = ["Index ", "Calib", "Raw", "Sch07", "Delta", "%", "offset"]
cols = ["{0:10s}".format(x) for x in cols]
sch = data_schiavon07()
model_table = os.path.join(tables_dir, \
"models_thomas_2010_metal_extrapolated.dat")
lims, ranges = get_model_lims(model_table, factor=0)
lims = np.diff(lims).T[0]
print "Results for the test on standard star HD 102070"
print "".join(cols)
for j,index in enumerate(indices):
if j < 12 or j>20:
continue
print "{0:6s}{1:10.2f}{2:10.2f}{6:10.2f}{3:10.2f}{4:10.2f}{5:10.2f}".format(index, \
a[0][j], a[1][j], (a[0][j] - a[1][j]),
(a[0][j] - a[1][j])/ a[0][j], offset[j], sch[j])
return w,p
for cols in lims:
v = [x[cols[0]-1:cols[1]-1] for x in table]
print v
if __name__ == "__main__":
spectype = "single2"
table = os.path.join(home, spectype, "results.tab")
spec = np.genfromtxt(table, dtype=None, usecols=(0,))
ids = [x.split("n3311")[-1].replace(".fits", "").replace("_", " ") for x \
in spec]
sns = np.loadtxt(table, usecols=(14,))
rs, pas = np.loadtxt(table, usecols=(3,4)).T
# idx = np.where(sns > sn_cut)[0]
cols = np.array([39,41,47,49,51,53,55])
model_table = os.path.join(tables_dir, "models_thomas_2010.dat")
lims, ranges = get_model_lims(model_table)
idx = np.array([12,13,16,17,18,19])
cols2 = np.array([69,72,75])
lims = lims[idx]
data = np.loadtxt(table, usecols=cols)
errs = np.loadtxt(table, usecols=cols+1)
pop = np.loadtxt(table, usecols=cols2)
popm = pop - np.loadtxt(table, usecols=cols2 + 1)
popp = np.loadtxt(table, usecols=cols2 + 2) - pop
pstring = pm_string(pop, popm, popp)
for i in range(len(data)):
for j in range(len(lims)):
if data[i,j] < lims[j,0] or data[i,j] > lims[j,1]:
data[i,j] = np.nan
results = []
cds_table = []
else:
idx3311 = np.where(r <= 10 ** (r_tran))[0]
idxhalo = np.where(r > 10 ** (r_tran))[0]
r3311 = r[idx3311]
rhalo = r[idxhalo]
lick3311 = lick[:, idx3311]
lickhalo = lick[:, idxhalo]
errs1_3311 = lickerr[:, idx3311]
errs_halo = lickerr[:, idxhalo]
#########################################################################
# First figure, simple indices
app = "_pa" if restrict_pa else ""
mkfig1 = True
gray = "0.75"
##########################################################################
lims, ranges = get_model_lims(os.path.join(tables_dir, "models_thomas_2010_metal_extrapolated.dat"))
idx = np.array([12, 13, 16, 17, 18, 19, 20])
lims = lims[idx]
# Setting the colormap properties for the scatter plots
cmap = brewer2mpl.get_map("Blues", "sequential", 9).mpl_colormap
cmap = nc.cmap_discretize(cmap, 3)
color = cm.get_cmap(cmap)
norm = Normalize(vmin=0, vmax=45)
if mkfig1:
plt.figure(1, figsize=(6, 14))
gs = gridspec.GridSpec(7, 1)
gs.update(left=0.15, right=0.95, bottom=0.1, top=0.94, wspace=0.1, hspace=0.09)
tex = []
for j, ll in enumerate(lick):
# print indices[j], ranges[j], ssp.fn(9.,0.12,.4)[ii[j]]
if j == 0:
else:
idx3311 = np.where(r <= 10**(r_tran))[0]
idxhalo = np.where(r > 10**(r_tran))[0]
r3311 = r[idx3311]
rhalo = r[idxhalo]
lick3311 = lick[:, idx3311]
lickhalo = lick[:, idxhalo]
errs1_3311 = lickerr[:, idx3311]
errs_halo = lickerr[:, idxhalo]
#########################################################################
# First figure, simple indices
app = "_pa" if restrict_pa else ""
mkfig1 = True
gray = "0.75"
##########################################################################
lims, ranges = get_model_lims(
os.path.join(tables_dir, "models_thomas_2010_metal_extrapolated.dat"))
idx = np.array([12, 13, 16, 17, 18, 19, 20])
lims = lims[idx]
# Setting the colormap properties for the scatter plots
cmap = brewer2mpl.get_map('Blues', 'sequential', 9).mpl_colormap
cmap = nc.cmap_discretize(cmap, 3)
color = cm.get_cmap(cmap)
norm = Normalize(vmin=0, vmax=45)
if mkfig1:
plt.figure(1, figsize=(6, 14))
gs = gridspec.GridSpec(7, 1)
gs.update(left=0.15,
right=0.95,
bottom=0.1,
top=0.94,
wspace=0.1,
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from config import *
from mcmc_model import get_model_lims
if __name__ == "__main__":
os.chdir(os.path.join(home, "single2"))
data1 = np.loadtxt("populations.txt",
usecols=(1, 2, 3, 5, 6, 7, 9, 10, 11))
data2 = np.loadtxt("populations_miles.txt",
usecols=(1, 2, 3, 5, 6, 7, 9, 10, 11))
lims, ranges = get_model_lims(os.path.join(tables_dir, "MILESII.txt"))
ranges[0] = [9.8, 10.2]
ranges[1] = [-1.5, 0.6]
err_cut = np.array([0.2, 0.7, 0.22])
fig = plt.figure(1, figsize=(14, 4.5))
labels = [r"$\log$ Age (yr)", r"[Z/H]", r"[$\alpha$/Fe]"]
plt.subplots_adjust(left=0.065,
right=0.98,
bottom=0.13,
top=0.97,
wspace=0.25)
for i, j in enumerate([0, 3, 6]):
idx1 = np.intersect1d(
np.where(data1[:, j] >= ranges[i, 0])[0],
np.where(data1[:, j] <= ranges[i, 1])[0])
merr1 = 0.5 * np.abs((data1[:, j + 1] - data1[:, j + 2]))
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from config import *
from mcmc_model import get_model_lims
if __name__ == "__main__":
os.chdir(os.path.join(home, "single2"))
data1 = np.loadtxt("populations.txt", usecols=(1,2,3,5,6,7,9,10,11))
data2 = np.loadtxt("populations_miles.txt", usecols=(1,2,3,5,6,7,9,10,11))
lims, ranges = get_model_lims(os.path.join(tables_dir, "MILESII.txt"))
ranges[0] = [9.8,10.2]
ranges[1] = [-1.5, 0.6]
err_cut = np.array([0.2, 0.7, 0.22])
fig = plt.figure(1, figsize=(14, 4.5))
labels = [r"$\log$ Age (yr)", r"[Z/H]", r"[$\alpha$/Fe]"]
plt.subplots_adjust(left=0.065, right=0.98, bottom=0.13, top=0.97,
wspace=0.25)
for i,j in enumerate([0,3,6]):
idx1 = np.intersect1d(np.where(data1[:,j] >= ranges[i,0])[0],
np.where(data1[:,j] <= ranges[i,1])[0])
merr1 = 0.5 * np.abs((data1[:,j+1] - data1[:,j+2]))
merr2 = 0.5 * np.abs((data2[:,j+1] - data2[:,j+2]))
merr = np.maximum(merr1, merr2)
idx2 = np.where(merr <= err_cut[i])[0]
idx = np.intersect1d(idx1, idx2)
if j < 12 or j>20:
continue
print indices[j],
# print aflux[j],
# print araw[j],
# print lims[j]
print "{0:10.2f}".format(np.median(aflux[j])),
print "{0:10.2f}".format(np.median(araw[j])),
print "{0:10.2f}".format(np.median((aflux[j] - araw[j])/(aflux[j])))
# print "{0:6s}{1:10.2f}{2:10.2f}{6:10.2f}{3:10.2f}{4:10.2f}{5:10.2f}".format(index, \
# a[0][j], a[1][j], (a[0][j] - a[1][j]),
# (a[0][j] - a[1][j])/ a[0][j], offset[j], sch[j])
if __name__ == "__main__":
bands = os.path.join(tables_dir, "BANDS")
offset = np.loadtxt(os.path.join(tables_dir,"LICK_OFFSETS.dat"),
usecols=(1,)).T
indices = np.loadtxt(bands, usecols=(0,), dtype=str)
model_table = os.path.join(tables_dir, \
"models_thomas_2010_metal_extrapolated.dat")
lims, ranges = get_model_lims(model_table, factor=0)
w,p = test_star()
test_galaxy(w,p)
for cols in lims:
v = [x[cols[0]-1:cols[1]-1] for x in table]
print v
if __name__ == "__main__":
spectype = "single2"
table = os.path.join(home, spectype, "results.tab")
spec = np.genfromtxt(table, dtype=None, usecols=(0,))
ids = [x.split("n3311")[-1].replace(".fits", "").replace("_", " ") for x \
in spec]
sns = np.loadtxt(table, usecols=(14,))
rs, pas = np.loadtxt(table, usecols=(3,4)).T
# idx = np.where(sns > sn_cut)[0]
cols = np.array([39,41,47,49,51,53,55])
model_table = os.path.join(tables_dir, "models_thomas_2010.dat")
lims, ranges = get_model_lims(model_table)
idx = np.array([12,13,16,17,18,19])
cols2 = np.array([69,72,75])
lims = lims[idx]
data = np.loadtxt(table, usecols=cols)
errs = np.loadtxt(table, usecols=cols+1)
pop = np.loadtxt(table, usecols=cols2)
popm = pop - np.loadtxt(table, usecols=cols2 + 1)
popp = np.loadtxt(table, usecols=cols2 + 2) - pop
pstring = pm_string(pop, popm, popp)
for i in range(len(data)):
for j in range(len(lims)):
if data[i,j] < lims[j,0] or data[i,j] > lims[j,1]:
data[i,j] = np.nan
results = []
cds_table = []