#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats.mstats as st
import thesis_redux_tools as rt
name_pho = np.loadtxt("../../inputs/SFHs_set3/set3_list.log", usecols = (0,), dtype = "|S")
name_sed = np.loadtxt("../../outputs/spectroscopic_fit/names.log", dtype = "|S")
order_sed = [j for i in xrange(120) for j in xrange(12000) if name_pho[i] == name_sed[j][:12]+".fits.gz"]
tform, mass, burstm, burstm, hdelta, b4000 = np.loadtxt("../../inputs/set3_catalog.txt", usecols = (5, 16, 17, 18, 44, 45), unpack = True)
table = np.loadtxt("../../outputs/photometric_fit/remote_set3/photofit_SDSS.physical")
table[:, 2:6] = 10 ** table[:, 2:6]
table = rt.binned_stat(table, 100)
ave_sed = np.loadtxt("../../outputs/spectroscopic_fit/table_din.v3.log")[order_sed]
ave_sed[:, 1:3] = 10 ** ave_sed[:, 1:3]
ave_sed = rt.binned_stat(ave_sed, bin_size = 100)
#res_pho = [rt.err(table[:, i], table[:, i + 1]) if i in [0] else rt.err(table[:, i], table[:, i + 1], False) for i in xrange(0, 10, 2)]
#res_sed = [rt.err(table[:, ::2][:, i], ave_sed[:, i]) if i in [0] else rt.err(table[:, ::2][:, i], ave_sed[:, i], False) for i in xrange(5)]
# --------------------------------------------------------------------------------------------------
lm = np.array([0, 8])
plt.figure(figsize = (6.5, 6))
plt.plot(lm, lm, "--k")
tks, tls = [], []
r_sam.append(f[0].header["rmag"])
i_sam.append(f[0].header["imag"])
z_sam.append(f[0].header["zmag"])
phy_sam.append([f[0].header["mass"], f[0].header["mwla"], f[0].header["rfwla"], f[0].header["z"], f[0].header["v"] - f[0].header["pv"]])
u_sam = np.array(u_sam)
g_sam = np.array(g_sam)
r_sam = np.array(r_sam)
i_sam = np.array(i_sam)
z_sam = np.array(z_sam)
phy_sam = np.array(phy_sam)
phy_mod = np.loadtxt("../../outputs/photometric_fit/remote_set1/phot_fit.models", usecols = range(5, 10))
med = binned_stat(phy_mod, 100, stat = "median")
std = binned_stat(phy_mod, 100, stat = "stdev")
# read SFH library data ----------------------------------------------------------------------------
sfhs_cat = "../../inputs/run07_sfh_catalog.txt"
u_lib, g_lib, r_lib, i_lib, z_lib, V, pV, mu = np.genfromtxt(sfhs_cat, missing = '""', usecols = range(39, 44) + [38, 25, 14], unpack = True)
# define mask --------------------------------------------------------------------------------------
dmask = (V - pV < 2.0) & (mu <= 1.0)
# MAKE PLOT ========================================================================================
# define color-color plane -------------------------------------------------------------------------
f[0].header["mass"], f[0].header["mwla"], f[0].header["rfwla"],
f[0].header["z"], f[0].header["v"] - f[0].header["pv"]
])
u_sam = np.array(u_sam)
g_sam = np.array(g_sam)
r_sam = np.array(r_sam)
i_sam = np.array(i_sam)
z_sam = np.array(z_sam)
phy_sam = np.array(phy_sam)
phy_mod = np.loadtxt(
"../../outputs/photometric_fit/remote_set1/phot_fit.models",
usecols=range(5, 10))
med = binned_stat(phy_mod, 100, stat="median")
std = binned_stat(phy_mod, 100, stat="stdev")
# read SFH library data ----------------------------------------------------------------------------
sfhs_cat = "../../inputs/run07_sfh_catalog.txt"
u_lib, g_lib, r_lib, i_lib, z_lib, V, pV, mu = np.genfromtxt(
sfhs_cat, missing='""', usecols=range(39, 44) + [38, 25, 14], unpack=True)
# define mask --------------------------------------------------------------------------------------
dmask = (V - pV < 2.0) & (mu <= 1.0)
# MAKE PLOT ========================================================================================
# define color-color plane -------------------------------------------------------------------------
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from scipy.ndimage.filters import gaussian_filter
from matplotlib import rc, cm
from matplotlib.colors import ListedColormap as LC
import sys
sdss_cat = "../../../degree_thesis/data/photometry/total_photometry.txt"
u_obs, g_obs, r_obs, i_obs, z_obs, redshift = np.loadtxt(sdss_cat, usecols = range(1, 6) + [11], unpack = True)
name_sed = np.loadtxt("../../outputs/spectroscopic_fit/names.log", dtype = "|S")
name_pho = np.loadtxt("../../inputs/SFHs_set3/set3_list.log", usecols = (0,), dtype = "|S")
order_sed = [j for i in xrange(120) for j in xrange(12000) if name_pho[i] == name_sed[j][:12]+".fits.gz"]
table = np.loadtxt("../../outputs/photometric_fit/remote_set3/photofit_SDSS.physical")
ave_sed = rt.binned_stat(np.loadtxt("../../outputs/spectroscopic_fit/table_din.v4.log")[order_sed], bin_size = 1)
chis = ave_sed[:, 5:]
ave_sed = ave_sed[:, :5]
sfhs_cat = "../../inputs/run09_sfh_catalog.txt"
u_lib, g_lib, r_lib, i_lib, z_lib, tm, tf, tform, Z, V, pV, mass, mass1gyr, A, mu = np.genfromtxt(sfhs_cat,
missing = '""', usecols = range(39, 44) + [33, 35, 5, 12, 38, 25, 16, 18, 19, 14], unpack = True)
u, g, r, i, z = np.genfromtxt("../../inputs/set3_catalog.txt", usecols = range(39, 39 + 5), unpack = True, missing = '""')
table[:, 6:8] = np.log10(table[:, 6:8])
res_sed = [rt.err(table[:, ::2][:, i], ave_sed[:, i]) if i in [0] else rt.err(table[:, ::2][:, i], ave_sed[:, i], False) for i in xrange(5)]
res_sed.pop(2)
i.append(f[0].header["imag"])
z.append(f[0].header["zmag"])
sfr.append(f[0].header["mass1gyr"] / f[0].header["mass"])
u = np.repeat(u, 100)
g = np.repeat(g, 100)
r = np.repeat(r, 100)
i = np.repeat(i, 100)
z = np.repeat(z, 100)
sfr = np.array(sfr)
ur = (u - r)[table[:, 6] > 0.4]
table = rt.binned_stat(table, 100, "median")
ur = rt.binned_stat(ur, 100, "median")
table[:, 6:8] = np.log10(table[:, 6:8])
res = [
rt.err(table[:,
i], table[:, i +
1]) if i == 0 else rt.err(table[:,
i], table[:, i +
1], False)
for i in xrange(0, 10, 2)
]
lab = [
r"$\Delta\,M/M_\textrm{SSAG}$", r"$\Delta\,\left<\log(t)\right>_M$",
r"$\Delta\,\left<\log(t)\right>_{L_r}$",
r"$\Delta\,\left<\log(Z/Z_\odot)\right>_M$", r"$\Delta\,A_V$"
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats.mstats as st
import thesis_redux_tools as rt
table = np.loadtxt(
"../../outputs/photometric_fit/photoz/photofit_JPASz3p00.physical")
table = rt.binned_stat(table, 50)
table[:, 2:6] = 10**table[:, 2:6]
res_pho = [
rt.err(table[:, i], table[:, i + 1]) if i in [0] else rt.err(
table[:, i], table[:, i + 1], False) for i in xrange(0, 10, 2)
]
#print table[np.argmax(res_pho[0]), 0], np.max(res_pho[0])
#print table[np.argmax(res_pho[1]), 2], np.max(res_pho[1])
mask = table[:, 6] > 0.4
# --------------------------------------------------------------------------------------------------
lm = np.array([0, 1])
plt.figure(figsize=(8, 7))
plt.plot(lm, lm, "--k")
tks, tls = [], []
for per in [10, 30, 50]:
dtype="|S")
name_sed = np.loadtxt("../../outputs/spectroscopic_fit/names.log", dtype="|S")
order_sed = [
j for i in xrange(120) for j in xrange(12000)
if name_pho[i] == name_sed[j][:12] + ".fits.gz"
]
tform, mass, burstm, burstm, hdelta, b4000 = np.loadtxt(
"../../inputs/set3_catalog.txt",
usecols=(5, 16, 17, 18, 44, 45),
unpack=True)
table = np.loadtxt(
"../../outputs/photometric_fit/remote_set3/photofit_SDSS.physical")
table[:, 2:6] = 10**table[:, 2:6]
table = rt.binned_stat(table, 100)
ave_sed = np.loadtxt(
"../../outputs/spectroscopic_fit/table_din.v3.log")[order_sed]
ave_sed[:, 1:3] = 10**ave_sed[:, 1:3]
ave_sed = rt.binned_stat(ave_sed, bin_size=100)
#res_pho = [rt.err(table[:, i], table[:, i + 1]) if i in [0] else rt.err(table[:, i], table[:, i + 1], False) for i in xrange(0, 10, 2)]
#res_sed = [rt.err(table[:, ::2][:, i], ave_sed[:, i]) if i in [0] else rt.err(table[:, ::2][:, i], ave_sed[:, i], False) for i in xrange(5)]
# --------------------------------------------------------------------------------------------------
lm = np.array([0, 8])
plt.figure(figsize=(6.5, 6))
plt.plot(lm, lm, "--k")
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats.mstats as st
import thesis_redux_tools as rt
table = np.loadtxt("../../outputs/photometric_fit/photoz/photofit_JPASz3p00.physical")
table = rt.binned_stat(table, 50)
table[:, 2:6] = 10 ** table[:, 2:6]
res_pho = [rt.err(table[:, i], table[:, i + 1]) if i in [0] else rt.err(table[:, i], table[:, i + 1], False) for i in xrange(0, 10, 2)]
#print table[np.argmax(res_pho[0]), 0], np.max(res_pho[0])
#print table[np.argmax(res_pho[1]), 2], np.max(res_pho[1])
mask = table[:, 6] > 0.4
# --------------------------------------------------------------------------------------------------
lm = np.array([0, 1])
plt.figure(figsize = (8, 7))
plt.plot(lm, lm, "--k")
tks, tls = [], []
for per in [10, 30, 50] :
m1, m2 = rt.err_slope(per)
tks.append(m2 * lm[1])
tls.append((str(per) + r"\%"))