axs[i].set_ylim(0,50)
axs[i].set_xlabel(lab[i], fontsize = 14)
axs[i].text(-1.4, 45, "$\mu={0:5.2f}$".format(avr_pho[i]), fontsize = 14, color = "r")
axs[i].text(-1.4, 39, "$\mu={0:5.2f}$".format(avr_sed[i]), fontsize = 14)
#axs[i].text(-1.4, 46, "$\mu={0:5.2f}$".format(avr_sed[i]), fontsize = 14)
#axs[i].text(-1.4, 40, "$\sigma={0:5.2f}$".format(std_sed[i]), fontsize = 14)
fig.text(0.05, 0.55, r"$\bf Frequency$", fontsize = 14, rotation = "vertical")
#axs[i].set_yticks([])
if i == 2 : axs[i].set_xticks(axs[i].get_xticks()[:- 1])
if i == 1 or i == 3 : axs[i].tick_params(labelleft = False, labelright = True)
if i == 2 :
axs[i].hist(res_sed[i], 10, ec = "#7F7F7F", fc = "#7F7F7F")
axs[i].hist(res_pho[i], 10, ec = "k", fc = "none", ls = "dotted", histtype = "step")
else :
axs[i].hist(res_sed[i], rt.nbins(res_pho[i], range_ = ran)[0], ec = "#7F7F7F", fc = "#7F7F7F")
axs[i].hist(res_pho[i], rt.nbins(res_pho[i], range_ = ran)[0], ec = "k", fc = "none", ls = "dotted", histtype = "step")
axs[i].axvline(avr_pho[i], ls = "--", color = "r", lw = 2, label = r"${\rm photo\,SED}$")
axs[i].axvline(avr_sed[i], ls = "--", color = "k", lw = 2, label = r"${\rm full\,SED}$")
#axs[i].text(-1.49, 1.0, r"$68\%\,{\rm range}\,{\rm ratio}$", fontsize = 12)
#if i == 0 : axs[i].legend(loc = "upper left", frameon = False, fontsize = 12)
#plt.tight_layout()
fig.subplots_adjust(wspace = 0.0)
plt.savefig("../../plots/photometric_fit/residual_hists_sed.pdf", bbox_inches = "tight")
#plt.savefig("../../plots/photometric_fit/residual_hists.png", bbox_inches = "tight")
#plt.show()
lcolor_x = (u_lib - g_lib)[dmask]
lcolor_y = (g_lib - r_lib)[dmask]
#lcolor_y = (r_lib - i_lib)[dmask]
scolor_x = (u_sam - g_sam)
scolor_y = (g_sam - r_sam)
#scolor_y = (r_sam - i_sam)
# define plot properties ---------------------------------------------------------------------------
fig, axs = plt.subplots(2, 2, sharex="col", sharey="row", figsize=(10, 9))
# construct 0.1 models per bin contour of SSAG -----------------------------------------------------
nx, xi, xf, bsx = nbins(lcolor_x)
ny, yi, yf, bsy = nbins(lcolor_y)
print("bin sizes:")
print("xbins : {0:5.3f}".format(bsx))
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)
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)
H, xe, ye = np.histogram2d(ocolor_x, ocolor_y, bins = (nx, ny), normed = True)
H = gaussian_filter(H, sigma = 2)
lab = [r"$M/M_\textrm{SSAG}$", r"$\left<\log(t)\right>_M$", r"$\left<\log(Z/Z_\odot)\right>_M$", r"$A_V$"]
plt.figure(figsize = (9, 8))
axs = [plt.subplot2grid((16, 17), (0, 0), rowspan = 8, colspan = 8),
plt.subplot2grid((16, 17), (0, 8), rowspan = 8, colspan = 8),
plt.subplot2grid((16, 17), (8, 0), rowspan = 8, colspan = 8),
plt.subplot2grid((16, 17), (8, 8), rowspan = 8, colspan = 8)]
axc = plt.subplot2grid((16, 17), (0, 16), rowspan = 16, colspan = 1)
lcolor_x = (u_lib - g_lib)[dmask]
lcolor_y = (g_lib - r_lib)[dmask]
#lcolor_y = (r_lib - i_lib)[dmask]
scolor_x = (u_sam - g_sam)
scolor_y = (g_sam - r_sam)
#scolor_y = (r_sam - i_sam)
# define plot properties ---------------------------------------------------------------------------
fig, axs = plt.subplots(2, 2, sharex = "col", sharey = "row", figsize = (10,9))
# construct 0.1 models per bin contour of SSAG -----------------------------------------------------
nx, xi, xf, bsx = nbins(lcolor_x)
ny, yi, yf, bsy = nbins(lcolor_y)
print("bin sizes:")
print("xbins : {0:5.3f}".format(bsx))
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)
ran = axs[i].set_xlim(-1.5, +1.5)
axs[i].set_ylim(0, 35)
axs[i].set_xlabel(lab[i], fontsize=14)
axs[i].text(-1.4, 32, "$\mu={0:5.2f}$".format(avr[i]), fontsize=14)
axs[i].text(-1.4, 27, "$\sigma={0:5.2f}$".format(std[i]), fontsize=14)
fig.text(0.05, 0.55, r"$\bf Frequency$", fontsize=14, rotation="vertical")
#axs[i].set_yticks([])
if i == 2: axs[i].set_xticks(axs[i].get_xticks()[:-1])
if i == 1 or i == 3: axs[i].tick_params(labelleft=False, labelright=True)
if i == 2: axs[i].hist(res[i], 10, ec="#7F7F7F", fc="#7F7F7F")
else:
axs[i].hist(res[i],
rt.nbins(res[i], range_=ran)[0],
ec="#7F7F7F",
fc="#7F7F7F")
axs[i].axvline(avr[i], ls="--", color="k", lw=2, label=r"${\bf mean}$")
#axs[i].axvline(med[i], ls = "--", color = "r", lw = 1, label = r"${\bf median}$")
axs[i].axvline(avr[i] - std[i],
ls="-.",
color="k",
lw=2,
label=r"${\bf std.\,deviation}$")
axs[i].axvline(avr[i] + std[i], ls="-.", color="k", lw=2)
#if i == 0 : axs[i].legend(loc = "upper left", frameon = False, fontsize = 12)
#plt.tight_layout()
fig.subplots_adjust(wspace=0.0)