def simulation_example(path, zi=6., name=None):
'''
Sometimes, it's useful to start with a simulation object, and then load your snapshot
'''
import seren3
sim = None # init
# Load our simulation
if (name is not None): # load by name
sim = seren3.load(name)
else:
# Just init from the path
sim = seren3.init(path)
print sim
# Now, lets load the snapshot which is closest to the desired redshift
ioutput = sim.redshift(zi) # output number
snapshot = sim[ioutput]
# There are also interpolators for age -> redshift and vise versa, for our chosen cosmology
age_func = sim.age_func(zmax=100., zmin=0.)
z_func = sim.redshift_func(zmax=100., zmin=0.)
age_of_universe = age_func(snapshot.z)
redshift = z_func(age_of_universe)
print snapshot.z, redshift, age_of_universe
def plot_RT2_panels(**kwargs):
import seren3
sim = seren3.load("RT2_nohm")
ioutputs = [106, 100, 90, 80, 70, 60]
plot_panels(sim, ioutputs, 2, 3, **kwargs)
def _tmp_2():
import seren3
sim_names = ["RT2_nohm", "RT5_nohm"]
sims = [seren3.load(n) for n in sim_names]
ioutputs = [42, 48, 60, 70, 80, 90, 100, 106]
ppaths = ["%s/pickle/" % sim.path for sim in sims]
cols = ["r", "b"]
plot_Mc_var(sims, [ioutputs, ioutputs], ppaths, sim_names, cols, False, **kwargs)
def plot_Mc_z_xHII_by_names(simnames,
labels,
colours,
NN,
fix_alpha=False,
**kwargs):
import seren3
sims = [seren3.load(name) for name in simnames]
ioutputs = [[42, 48, 60, 70, 80, 90, 100, 106] for n in simnames]
pickle_paths = ["%s/pickle/" % sim.path for sim in sims]
ann_paths = [sim.path for sim in sims]
plot_Mc_z_xHII(sims, ioutputs, pickle_paths, ann_paths, labels, \
colours, NN, fix_alpha=False, **kwargs)
# coding: utf-8
# In[39]:
import seren3
iout = 108
# sim = seren3.load("RT2")
sin_sim = seren3.load("BPASS_SIN")
bin_sim = seren3.load("BPASS_BIN")
sin_snap = sin_sim[iout]
bin_snap = bin_sim[iout]
cosmo = sin_snap.cosmo
import matplotlib
import matplotlib.pylab as plt
from seren3.analysis.plots import fit_scatter, fit_median
nbins = 5
# In[60]:
import numpy as np
import pickle
import random
from seren3.scripts.mpi import write_fesc_hid_dict
def load_fesc(snapshot):
import seren3
iout = 108
# sim = seren3.load("RT2")
sin_sim = seren3.load("2Mpc_BPASS_SIN")
bin_sim = seren3.load("2Mpc_BPASS_BIN")
sin_snap = sin_sim[iout]
bin_snap = bin_sim[iout]
cosmo = sin_snap.cosmo
import matplotlib
import matplotlib.pylab as plt
from seren3.analysis.plots import fit_scatter, fit_median
nbins = 5
# In[60]:
import numpy as np
import pickle
import random
from seren3.scripts.mpi import write_fesc_hid_dict
def load_fesc(snapshot):
db = write_fesc_hid_dict.load_db(snapshot.path, snapshot.ioutput)
hids = db.keys()
mvir = np.zeros(len(hids))
fesc = np.zeros(len(hids))
nphotons = np.zeros(len(hids))
def test2(sim_labels, the_mass_bins=[7., 8., 9., 10.], **kwargs):
import numpy as np
import matplotlib.pylab as plt
from seren3.array import SimArray
from pymses.utils import constants as C
import matplotlib.gridspec as gridspec
import seren3
sim1 = seren3.load("RT2_nohm")
sim2 = seren3.load("RAMSES")
snap1 = sim1[106]
snap2 = sim2[93]
info = snap1.info
cols = None
if "cols" not in kwargs:
from seren3.utils import plot_utils
cols = plot_utils.ncols(len(the_mass_bins),
cmap=kwargs.pop("cmap", "Set1"))[::-1]
# cols = plot_utils.ncols(2, cmap=kwargs.pop("cmap", "Set1"))[::-1]
else:
cols = kwargs.pop("cols")
z = (1. / info["aexp"]) - 1.
fig = plt.figure(figsize=(10, 8))
gs = gridspec.GridSpec(4, 9, wspace=0., hspace=0.)
ax1 = fig.add_subplot(gs[1:-1, :4])
ax2 = fig.add_subplot(gs[:1, :4], sharex=ax1)
axs = np.array([ax1, ax2])
nbins = 7
ax = ax1
binned_cdf1 = plot(snap1.path,
snap1.ioutput,
"%s/pickle/" % snap1.path,
"nHI",
ax=None,
nbins=nbins)
binned_cdf2 = plot(snap2.path,
snap2.ioutput,
"%s/pickle/" % snap2.path,
"nHI",
ax=None,
nbins=nbins)
text_pos = (7.2, 0.1)
text = 'z = %1.2f' % z
# ax.text(text_pos[0], text_pos[1], text, color="k", size=18)
# for bcdf, ls in zip([binned_cdf1, binned_cdf2], ["-", "--"]):
# label = "All"
# x,y,std,stderr,n = bcdf["all"]
# e = ax.errorbar(x, y, yerr=std, color="k", label=label,\
# fmt="o", markerfacecolor="w", mec='k', capsize=2, capthick=2, elinewidth=2, linestyle=ls, linewidth=2.)
for bcdf, ls in zip([binned_cdf1, binned_cdf2], ["-", "--"]):
for ibin, c in zip(range(len(the_mass_bins)), cols):
# for ibin, c in zip([0, len(the_mass_bins)-1], cols):
x, y, std, stderr, n = bcdf[ibin]
if ibin == len(the_mass_bins) - 1:
upper = r"$\infty$"
else:
upper = "%i" % the_mass_bins[ibin + 1]
lower = "%i" % the_mass_bins[ibin]
label = "[%s, %s)" % (lower, upper)
if (ls == "--"):
label = None
upper_err = []
lower_err = []
for i in range(len(y)):
ue = 0.
le = 0.
if (y[i] + std[i] > 1):
ue = 1. - y[i]
else:
ue = std[i]
if (y[i] - std[i] < 0):
le = y[i]
else:
le = std[i]
upper_err.append(ue)
lower_err.append(le)
yerr = [lower_err, upper_err]
e = ax.errorbar(x, y, yerr=yerr, color=c, label=label,\
fmt="o", markerfacecolor=c, mec='k', capsize=2, capthick=2, elinewidth=2, linestyle=ls, linewidth=2.)
for sl, ls in zip(sim_labels, ["-", "--"]):
ax.plot([2, 4], [-100, -100],
linewidth=2.,
linestyle=ls,
color="k",
label=sl)
# ax.legend(prop={"size":18}, loc="lower right")
ax.set_ylim(-0.05, 1.2)
ax = ax2
linestyles = ["-", "--", "-.", ":"]
for ibin, c, ls in zip(range(len(the_mass_bins)), cols, linestyles):
# for ibin, c, ls in zip([0, len(the_mass_bins)-1], cols, linestyles):
x1, y1, std1, stderr1, n1 = binned_cdf1[ibin]
x2, y2, std2, stderr2, n2 = binned_cdf2[ibin]
# ydiv = y1/y2
ydiv = y1 - y2
# error_prop = np.abs(ydiv) * np.sqrt( (std1/y1)**2 + (std2/y2)**2 )
error_prop = np.sqrt((std1)**2 + (std2)**2)
print ibin, error_prop
e = ax.errorbar(x1, ydiv, yerr=error_prop, color=c,\
fmt="o", markerfacecolor=c, mec='k', capsize=2, capthick=2, elinewidth=2, linestyle=ls, linewidth=2.)
axs[0].set_xlabel(r"log$_{10}$ n$_{\mathrm{HI}}$ [m$^{-3}$]")
# axs[0].set_xlabel(r"log$_{10}$ T [K]")
axs[0].set_ylabel(r"CDF")
axs[1].set_ylabel(r"$\Delta$ CDF")
plt.setp(ax2.get_xticklabels(), visible=False)
# ax = axs[0]
# leg = ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15),
# fancybox=True, shadow=False, ncol=3, prop={"size":18})
# leg.set_title(r"log$_{10}$(Mvir) [M$_{\odot}$/h]", prop = {'size':'x-large'})
n_star = SimArray(info["n_star"].express(C.H_cc),
"cm**-3").in_units("m**-3")
for axi in axs.flatten():
axi.vlines(np.log10(n_star), -1, 2, color='k', linestyle='-.')
# axi.vlines(np.log10(2e4), -5, 5, color='k', linestyle='-.')
axs[1].set_ylim(-0.2, 1.3)
# axs[1].set_ylim(0.2, -1.3)
##################################################################################
ax1 = fig.add_subplot(gs[1:-1, 5:])
ax2 = fig.add_subplot(gs[:1, 5:], sharex=ax1)
axs = np.array([ax1, ax2])
ax = ax1
binned_cdf1 = plot(snap1.path,
snap1.ioutput,
"%s/pickle/" % snap1.path,
"T2_minus_Tpoly",
ax=None,
nbins=nbins)
binned_cdf2 = plot(snap2.path,
snap2.ioutput,
"%s/pickle/" % snap2.path,
"T2_minus_Tpoly",
ax=None,
nbins=nbins)
text_pos = (7.2, 0.1)
text = 'z = %1.2f' % z
# ax.text(text_pos[0], text_pos[1], text, color="k", size=18)
# for bcdf, ls in zip([binned_cdf1, binned_cdf2], ["-", "--"]):
# label = "All"
# x,y,std,stderr,n = bcdf["all"]
# e = ax.errorbar(x, y, yerr=std, color="k", label=label,\
# fmt="o", markerfacecolor="w", mec='k', capsize=2, capthick=2, elinewidth=2, linestyle=ls, linewidth=2.)
for bcdf, ls in zip([binned_cdf1, binned_cdf2], ["-", "--"]):
for ibin, c in zip(range(len(the_mass_bins)), cols):
# for ibin, c in zip([0, len(the_mass_bins)-1], cols):
x, y, std, stderr, n = bcdf[ibin]
if ibin == len(the_mass_bins) - 1:
upper = r"$\infty$"
else:
upper = "%i" % the_mass_bins[ibin + 1]
lower = "%i" % the_mass_bins[ibin]
label = "[%s, %s)" % (lower, upper)
if (ls == "--"):
label = None
upper_err = []
lower_err = []
for i in range(len(y)):
ue = 0.
le = 0.
if (y[i] + std[i] > 1):
ue = 1. - y[i]
else:
ue = std[i]
if (y[i] - std[i] < 0):
le = y[i]
else:
le = std[i]
upper_err.append(ue)
lower_err.append(le)
yerr = [lower_err, upper_err]
e = ax.errorbar(x, y, yerr=yerr, color=c, label=label,\
fmt="o", markerfacecolor=c, mec='k', capsize=2, capthick=2, elinewidth=2, linestyle=ls, linewidth=2.)
for sl, ls in zip(sim_labels, ["-", "--"]):
ax.plot([2, 4], [-100, -100],
linewidth=2.,
linestyle=ls,
color="k",
label=sl)
# ax.legend(prop={"size":18}, loc="lower right")
ax.set_ylim(-0.05, 1.2)
ax = ax2
linestyles = ["-", "--", "-.", ":"]
for ibin, c, ls in zip(range(len(the_mass_bins)), cols, linestyles):
# for ibin, c, ls in zip([0, len(the_mass_bins)-1], cols, linestyles):
x1, y1, std1, stderr1, n1 = binned_cdf1[ibin]
x2, y2, std2, stderr2, n2 = binned_cdf2[ibin]
# ydiv = y1/y2
ydiv = y1 - y2
# error_prop = np.abs(ydiv) * np.sqrt( (std1/y1)**2 + (std2/y2)**2 )
error_prop = np.sqrt((std1)**2 + (std2)**2)
print ibin, error_prop
e = ax.errorbar(x1, ydiv, yerr=error_prop, color=c,\
fmt="o", markerfacecolor=c, mec='k', capsize=2, capthick=2, elinewidth=2, linestyle=ls, linewidth=2.)
# axs[0].set_xlabel(r"log$_{10}$ n$_{\mathrm{HI}}$ [m$^{-3}$]")
axs[0].set_xlabel(r"log$_{10}$ T - T$_{\mathrm{J}}$ [K/$\mu$]")
# axs[0].set_ylabel(r"CDF")
# axs[1].set_ylabel(r"$\Delta$ CDF")
plt.setp(ax2.get_xticklabels(), visible=False)
ax = axs[0]
leg = ax.legend(loc='upper center',
bbox_to_anchor=(-0.15, -0.2),
fancybox=True,
shadow=False,
ncol=3,
prop={"size": 18})
leg.set_title(r"log$_{10}$(Mvir) [M$_{\odot}$/h] : z = %1.2f" % snap1.z,
prop={'size': 'x-large'})
n_star = SimArray(info["n_star"].express(C.H_cc),
"cm**-3").in_units("m**-3")
for axi in axs.flatten():
# axi.vlines(np.log10(n_star), -1, 2, color='k', linestyle='-.')
axi.vlines(np.log10(2e4), -5, 5, color='k', linestyle='-.')
# axs[1].set_ylim(-0.2, 1.3)
axs[1].set_ylim(0.2, -1.3)
# plt.yscale("log")
# plt.tight_layout()
plt.savefig("/home/ds381/rt2_hd_cdf_nHI_T_z6_15.pdf", format="pdf")
plt.show()
def plot_fb_panels(sim_name,
ioutputs,
sim_label,
weight="mw",
weight_label="M",
**kwargs):
import numpy as np
import seren3
import matplotlib.pylab as plt
from seren3.analysis.plots import fit_scatter
fig, axes = plt.subplots(nrows=4,
ncols=len(ioutputs),
sharex=True,
sharey=True,
figsize=(10, 10))
fig.subplots_adjust(hspace=0.05)
fig.subplots_adjust(wspace=0.05)
sim = seren3.load(sim_name)
for i in range(len(ioutputs)):
# ioutput = sim.redshift(zi)
ioutput = ioutputs[i]
snapshot = sim[ioutput]
pickle_path = "%s/pickle/" % snapshot.path
axs = axes[:, i]
axs[0].set_title("z = %1.2f" % (snapshot.z))
log_mvir, fb, ftidal, xHII, T, T_U, pid = neural_net2.load_training_arrays(
snapshot, pickle_path=pickle_path, weight=weight)
cosmo = snapshot.cosmo
cosmic_mean_b = cosmo["omega_b_0"] / cosmo["omega_M_0"]
y_min = 0.
y_max = (fb / cosmic_mean_b).max()
fb_cosmic_mean = fb / cosmic_mean_b
mvir = 10**log_mvir
log_xHI = np.log10(1. - xHII)
log_ftidal = np.log10(ftidal)
bc, mean, std, sterr = fit_scatter(log_mvir,
fb_cosmic_mean,
nbins=10,
ret_sterr=True)
def _plot(mvir, fb, carr, ax, **kwargs):
ax.errorbar(10**bc,
mean,
yerr=std,
linewidth=3.,
color="k",
linestyle="--")
return ax.scatter(mvir, fb, c=carr, **kwargs)
labels = [r"log$_{10} \langle F_{\mathrm{tidal}} \rangle_{t_{\mathrm{dyn}}}$",\
r"$\langle x_{\mathrm{HII}} \rangle_{\mathrm{%s}}$" % weight_label,\
# r"log$_{10} \langle x_{\mathrm{HII}} \rangle_{\mathrm{%s}}$" % weight_label,\
r"log$_{10} \langle T \rangle_{\mathrm{%s}}$/$T_{\mathrm{vir}}$" % weight_label ,\
r"log$_{10}$ T/|U|"]
count = 0
cbar = None
for ax, carr, lab in zip(axs.flatten(),
[np.log10(ftidal), xHII, T, T_U], labels):
ax.errorbar(10**bc,
mean,
yerr=std,
linewidth=1.5,
color="k",
linestyle="--")
plt.xlim(5e6, 1.5e10)
sp = None
if (count == 0):
sp = ax.scatter(mvir,
fb_cosmic_mean,
c=carr,
vmin=-1,
vmax=4,
**kwargs)
if (count == 2):
sp = ax.scatter(mvir,
fb_cosmic_mean,
c=carr,
vmin=-0.5,
vmax=1.5,
**kwargs)
else:
sp = ax.scatter(mvir, fb_cosmic_mean, c=carr, **kwargs)
if (count == 1):
cbar = fig.colorbar(sp, ax=ax, ticks=[1., 0.75, 0.5, 0.25, 0.])
else:
cbar = fig.colorbar(sp, ax=ax)
if (i == len(ioutputs) - 1):
cbar.set_label(lab)
# ax.set_xlabel(r"log$_{10}$ M$_{\mathrm{vir}}$ [M$_{\odot}$/h]")
if (i == 0):
ax.set_ylabel(
r"f$_{\mathrm{b}}$[$\Omega_{\mathrm{b}}$/$\Omega_{\mathrm{M}}$]"
)
ax.set_xscale("log")
count += 1
ax.set_ylim(y_min, y_max)
axs[-1].set_xlabel(r"M$_{\mathrm{vir}}$ [M$_{\odot}$/h]")
for ax in axes.flatten():
ax.set_xlim(5e6, 1.5e10)
fig.tight_layout()