print("ybins : {0:5.3f}".format(bsy))
H, xe, ye = np.histogram2d(lcolor_x, lcolor_y, bins = (nx, ny), normed = True)
H = gaussian_filter(H, sigma = 2)
# plot contour and residual of sample model galaxies -----------------------------------------------
my_cm = mpl.colors.ListedColormap(np.loadtxt("colormap.dat") / 255)
for i, ax in enumerate(np.ravel(axs)) :
ax.set_xlim(0.5, 2.5)
ax.set_ylim(0.0, 1.25)
ax.contour(xe[:- 1], ye[:- 1], H.T, levels = [0.1], colors = ["#1A1A1A"], lw = 2.5)
sizes = std[:, i] * 100
if i == 0 or i == 3 : colors = err(phy_sam[:, i], med[:, i])
else : colors = err(phy_sam[:, i], med[:, i], relative = False)
xg = np.linspace(0.5, 2.5, 100)
yg = np.linspace(0.0, 1.25, 100)
zg = griddata(scolor_x, scolor_y, std[:, i], xg, yg, interp = "linear")
#cont = ax.contourf(xg, yg, zg, vmin = -1.0, vmax = +1.0, levels = np.linspace(-1., +1., 9), cmap = my_cm)
cont = ax.contour(xg, yg, zg, 3)
plt.colorbar(cont)
#if i == 0 :
#sc = ax.scatter(scolor_x, scolor_y, lw = 0, c = colors, s = sizes, vmin = -1.0, vmax = +1.0, cmap = my_cm)
#cb = plt.colorbar(sc)
#cb.set_label("residual")
#cb.set_ticks(np.linspace(-1., +1., 9))
print("ybins : {0:5.3f}".format(bsy))
H, xe, ye = np.histogram2d(lcolor_x, lcolor_y, bins=(nx, ny), normed=True)
H = gaussian_filter(H, sigma=2)
# plot contour and residual of sample model galaxies -----------------------------------------------
my_cm = mpl.colors.ListedColormap(np.loadtxt("colormap.dat") / 255)
for i, ax in enumerate(np.ravel(axs)):
ax.set_xlim(0.5, 2.5)
ax.set_ylim(0.0, 1.25)
ax.contour(xe[:-1], ye[:-1], H.T, levels=[0.1], colors=["#1A1A1A"], lw=2.5)
sizes = std[:, i] * 100
if i == 0 or i == 3: colors = err(phy_sam[:, i], med[:, i])
else: colors = err(phy_sam[:, i], med[:, i], relative=False)
xg = np.linspace(0.5, 2.5, 100)
yg = np.linspace(0.0, 1.25, 100)
zg = griddata(scolor_x, scolor_y, std[:, i], xg, yg, interp="linear")
#cont = ax.contourf(xg, yg, zg, vmin = -1.0, vmax = +1.0, levels = np.linspace(-1., +1., 9), cmap = my_cm)
cont = ax.contour(xg, yg, zg, 3)
plt.colorbar(cont)
#if i == 0 :
#sc = ax.scatter(scolor_x, scolor_y, lw = 0, c = colors, s = sizes, vmin = -1.0, vmax = +1.0, cmap = my_cm)
#cb = plt.colorbar(sc)
#cb.set_label("residual")
#cb.set_ticks(np.linspace(-1., +1., 9))
import thesis_redux_tools as rt
from operator import __getslice__
#table = np.loadtxt("../../outputs/photometric_fit/remote_set3/phot_fit_SDSS.physical")
#table = np.loadtxt("../../outputs/photometric_fit/phot_fit_SDSS_dust_fitting_monoZ_dust_fitting_off.physical")
table = np.loadtxt(
"../../outputs/photometric_fit/remote_set3/photofit_SDSS.physical")
names = np.loadtxt("../../inputs/SFHs_set3/set3_list.log", dtype=np.str)
names = map(__getslice__, names, [0] * names.size, [12] * names.size)
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 = [
"mass residuals", "mass weighted age residuals",
"flux weighted age residuals", "metallicity residuals",
"dust extinction residuals"
]
for j in xrange(len(res)):
fig, axs = plt.subplots(5, 13, sharex=True, sharey=True, figsize=(20, 15))
plt.xlim(-1.5, +1.5)
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)
lcolor_x = (u_lib - g_lib)[((V - pV) < 1.2) & (mu < 1.)]
lcolor_y = (g_lib - r_lib)[((V - pV) < 1.2) & (mu < 1.)]
zmask = redshift < 0.03
ocolor_x = (u_obs - g_obs)[zmask]
ocolor_y = (g_obs - r_obs)[zmask]
ug = np.repeat(u - g, 100)
gr = np.repeat(g - r, 100)
nx, xi, xf, bsx = rt.nbins(ocolor_x)
ny, yi, yf, bsy = rt.nbins(ocolor_y)
from matplotlib import cm
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"]
osed = np.loadtxt("../../outputs/spectroscopic_fit/table_din.v3.log")[order_sed]
sdss = dl.load_data("SDSS")
jpas = dl.load_data("JPAS")
nams = ["M_lib", "M_mod", "log_t_M_lib", "log_t_M_mod", "log_t_L_lib", "log_t_L_mod", "Z_M_lib", "Z_M_mod", "Av_lib", "Av_mod"]
sdss.physical[nams[6]] = np.log10(sdss.physical[nams[6]])
res_sed = [rt.err(sdss.physical[n], osed[:, i]) if i == 0 else rt.err(sdss.physical[n], osed[:, i], False) for i, n in enumerate(nams[::2])]
sdss.physical[nams[6]] = 10 ** sdss.physical[nams[6]]
fig, axs = plt.subplots(2, 3, sharex = True, figsize = (10, 6))
axins = inset_axes(axs[0, 0], width = "50%", height = "4%", loc = 2)
for i, j in product(xrange(axs.shape[0]), xrange(axs.shape[1])) :
if j == 0 :
if i == 0 :
axs[i, j].set_title("SDSS")
axs[i, j].set_ylim(-1.5, 3.5)
axs[i, j].set_ylabel(r"$\Delta\,M/M_\textrm{SSAG}$", fontsize = 15)
sc = axs[i, j].scatter(sdss.residuals["log_t_M"], sdss.residuals["M"], lw = 0, s = 7, c = sdss.residuals["Av"], vmin = sdss.residuals["Av"].min(), vmax = sdss.residuals["Av"].max())
x, y = rt.binner(sdss.residuals["log_t_M"], sdss.residuals["M"], "mean", 10)
axs[i, j].plot(x, y, "--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]:
import numpy as np
import scipy.stats.mstats as st
import pyfits as pyf
import thesis_redux_tools as rt
from operator import __getslice__
#table = np.loadtxt("../../outputs/photometric_fit/remote_set3/phot_fit_SDSS.physical")
#table = np.loadtxt("../../outputs/photometric_fit/phot_fit_SDSS_dust_fitting_monoZ_dust_fitting_off.physical")
table = np.loadtxt("../../outputs/photometric_fit/remote_set3/photofit_SDSS.physical")
names = np.loadtxt("../../inputs/SFHs_set3/set3_list.log", dtype = np.str)
names = map(__getslice__, names, [0] * names.size, [12] * names.size)
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 = ["mass residuals", "mass weighted age residuals", "flux weighted age residuals", "metallicity residuals", "dust extinction residuals"]
for j in xrange(len(res)) :
fig, axs = plt.subplots(5, 13, sharex = True, sharey = True, figsize = (20, 15))
plt.xlim(-1.5, +1.5)
plt.ylim(0, 40)
axs = np.ravel(axs)
for i in xrange(65) :
data = res[j][i * 100:(i + 1) * 100]
median = np.median(data)
p16 = st.scoreatpercentile(data, 16.0)
p84 = st.scoreatpercentile(data, 84.0)
]
osed = np.loadtxt(
"../../outputs/spectroscopic_fit/table_din.v3.log")[order_sed]
sdss = dl.load_data("SDSS")
jpas = dl.load_data("JPAS")
nams = [
"M_lib", "M_mod", "log_t_M_lib", "log_t_M_mod", "log_t_L_lib",
"log_t_L_mod", "Z_M_lib", "Z_M_mod", "Av_lib", "Av_mod"
]
sdss.physical[nams[6]] = np.log10(sdss.physical[nams[6]])
res_sed = [
rt.err(sdss.physical[n], osed[:, i]) if i == 0 else rt.err(
sdss.physical[n], osed[:, i], False) for i, n in enumerate(nams[::2])
]
sdss.physical[nams[6]] = 10**sdss.physical[nams[6]]
fig, axs = plt.subplots(2, 3, sharex=True, figsize=(10, 6))
axins = inset_axes(axs[0, 0], width="50%", height="4%", loc=2)
for i, j in product(xrange(axs.shape[0]), xrange(axs.shape[1])):
if j == 0:
if i == 0:
axs[i, j].set_title("SDSS")
axs[i, j].set_ylim(-1.5, 3.5)
axs[i, j].set_ylabel(r"$\Delta\,M/M_\textrm{SSAG}$", fontsize=15)
sc = axs[i, j].scatter(sdss.residuals["log_t_M"],
#!/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"\%"))