def re_eu_ratios(n):
"""
Plot various isotope and element ratios of Re and Eu.
Make three subplots:
- Re/Eu
- Re-isos/Eu-151
- Re-isos/Eu-153
"""
Eu_isos = ["Eu-151", "Eu-153"]
Re_isos = ["Re-187", "Re-185"]
fractions = ["Re/Eu"] \
+ [re+'/'+Eu_isos[0] for re in Re_isos] \
+ [re+'/'+Eu_isos[1] for re in Re_isos]
spectro_notation = ['[' + frac + ']' for frac in fractions]
#make instance of 'Omega'
inst = om.omega(galaxy="milky_way",
special_timesteps=n,
ns_merger_on=True,
bhns_merger_on=True)
plotter = vs.visualize(inst,
"test_omega",
num_yaxes=3,
age=0,
loa_spectro_abu=spectro_notation)
for frac, index in zip(fractions, [0, 1, 1, 2, 2]):
plotter.add_time_relabu_singleomega(frac, index_yaxis=index)
limit = [-3, 0]
plotter.zoom([False, limit, limit])
plotter.finalize(save="test_rnscp_fraction_n%d" % n,
title="Re/Eu - fractions",
show=True)
return None
def test_simple(n):
"""
Just plot a simple standard MW-galaxy to make sure the
plotting syntax is up-to-date
"""
inst = om.omega(galaxy="milky_way",
special_timesteps=n,
ns_merger_on=True,
bhns_merger_on=True)
plotter = vs.visualize(inst, "test_omega", num_yaxes=3, age=0)
Eu_isos = ["Eu-151", "Eu-153"]
Re_isos = ["Re-187", "Re-185"]
Os_isos = [
"Os-184", "Os-186", "Os-187", "Os-188", "Os-189", "Os-190", "Os-192"
]
for i, iso_list in enumerate([Eu_isos, Re_isos, Os_isos]):
for iso in iso_list:
plotter.add_time_relabu_singleomega(iso,
index_yaxis=i,
ylabel="log abundance")
plotter.finalize(save="test_rncp_isos_n%d.png" % n,
show=True,
title="R-process isotopes of 'Omega'")
return None
def __initialisation(self):
'''
Read the merger tree and declare and fill arrays.
'''
# Run an OMEGA simulation in order to copy basic arrays
#print ('Should add the r-process tables, extra_yields_table, etc...')
self.o_ini = omega.omega(table=self.table, pop3_table=self.pop3_table,\
special_timesteps=2, cte_sfr=0.0, mgal=1e10,\
print_off=self.print_off)
# Calculate the number of redshifts
self.nb_redshifts = len(self.redshifts)
# Declare the galaxy instance array
self.galaxy_inst = [0]*self.nb_redshifts
for i_i in range(0,self.nb_redshifts):
self.galaxy_inst[i_i] = [0]*len(self.br_m_halo[i_i])
# Declare the extra initial baryonic mass added to galaxies
self.dm_bar_added_iso = [0.0]*self.nb_redshifts
for i_i in range(0,self.nb_redshifts):
self.dm_bar_added_iso[i_i] = np.zeros(\
(len(self.br_m_halo[i_i]), self.o_ini.nb_isotopes))
# Get the final redshift
self.redshift_f = min(self.redshifts)
# Get the primordial composition (mass fraction)
iniabu_table = 'yield_tables/iniabu/iniab_bb_walker91.txt'
ytables_bb = ry.read_yield_sn1a_tables( \
os.path.join(nupy_path, iniabu_table), self.o_ini.history.isotopes)
self.prim_x_frac = np.array(ytables_bb.get(quantity='Yields'))
del ytables_bb
# Define the information of whether branches are sub-halo or not
if len(self.br_is_sub) > 0:
self.is_sub_info = True
else:
self.is_sub_info = False
def run_omega(self):
from NuPyCEE import omega as o
o1 = o.omega()
###################################
### Inflow must match Matteucci ###
###################################
inflow_matteucci = (0.3,1.5) #[M_sol/(Gyr pc^2)]
inflow = 10.0 #[M_sol/yr]
################################
### compile sets of 'Omega's ###
""" Vary parameters and find the best fit """
################################
if False: #test current parameters to make sure plotting works
inst = om.omega(special_timesteps=n,
#dt=1.0e+3,
imf_type=imf_type,
sfh_file=sfh_file_relpath,
mgal=mgal,
m_DM_0=m_DM_0, stellar_mass_0=stellar_mass_0,
sn1a_on=False,
in_out_control=True,
inflow_rate=10.0, mass_loading=mass_loading,
ns_merger_on=True, f_merger=f_merger)
#use visualize to compare 'Omega' and 'Eris'
#compare spectroscopic data
Harry_Plotter = vs.visualize(inst, "simple_test",
num_yaxes=3)
Harry_Plotter.add_time_relabu('[O/H]', index_yaxis=0)
Harry_Plotter.add_time_relabu('[Fe/H]', index_yaxis=1)
Harry_Plotter.add_time_relabu('[Eu/H]', index_yaxis=2)
Harry_Plotter.zoom([[-4,1],[-4,1],[-4,1]])
Harry_Plotter.finalize(title="Spectroscopic plotting test in bestfitfile", save="test_spectro")
#compare SN/KN-rates
Harry_Plotter = vs.visualize(inst, "simple_test",
while len(test_array_bool) < num_tests:
test_array_bool.append(False)
num_steps = 350
###########################################
### Tests various ways of inserting SFR ###
###########################################
#relative path to sfh-file from 'NuPyCEE'-directory
sfh_file_dir = "../reproduce_shen/"
sfh_file_relpath1 = sfh_file_dir+"time_sfr_Shen_2015.txt"
sfh_file_relpath2 = sfh_file_dir+"timegal5e8_sfr_Shen_2015.txt"
if test_all or test_array_bool[0]:
print "First test, vanilla 'Omega'"
om.omega(special_timesteps=num_steps)
if test_all or test_array_bool[1]:
print "Second test, sfh-file"
sfh_file_omega = om.omega(special_timesteps=num_steps,
sfh_file=sfh_file_relpath1)
#plot sfr
plotting_object = vs.visualize(sfh_file_omega,
"Omega(sfh-file)",
age=0)
plotting_object.add_time_sfr()
#plotting_object.add_tbirth()
plotting_object.finalize(show=True)
if test_all or test_array_bool[2]:
def precompute_ssps(main_table="K10K06",
sn1a_table="I99",
pop3_table="HW10",
**kwargs):
"""
Pre-calculate the ejecta of simple stellar populations.
You can ignore the warning.
Returns SSPs_in, kwargs (kwargs has to be passed to GAMMA run: see caga.generate_kwargs)
agb/massive stars (main_table):
K10K06 (default): Karakas10/Kobayashi06, no hypernovae
nuK06: nugrid/Kobayashi06
nuN13: nugrid/Nomoto13
K10nu: Karakas10/nugrid
nugrid (OMEGA default): nugrid_MESAonly_fryer12delay
sn1a (sn1a_table):
I99 (default): Iwamoto et al. 1999, W7
pop3 (pop3_table):
HW10 (default): Heger+Woosley 2010
N13: Nomoto+2013
All other kwargs are passed to omega (e.g. ns_merger_on)
"""
## Get file names of yield tables based on shortened keys, making things more consistent
main_dict = {
"K10K06": "agb_and_massive_stars_K10_K06_0.0HNe.txt",
"nuK06": "agb_and_massive_stars_nugrid_K06.txt",
"nuN13": "agb_and_massive_stars_nugrid_N13.txt",
"K10nu": "agb_and_massive_stars_nugrid_K10.txt",
"nugrid": "agb_and_massive_stars_nugrid_MESAonly_fryer12delay.txt",
"C15K06": "agb_and_massive_stars_FRUITY_K06.txt",
}
main_table = "yield_tables/" + main_dict[main_table]
sn1a_dict = {
"I99": "sn1a_i99_W7.txt",
}
sn1a_table = "yield_tables/" + sn1a_dict[sn1a_table]
pop3_dict = {
"HW10": "popIII_heger10.txt",
"N13": "popIII_N13.txt",
}
pop3_table = "yield_tables/" + pop3_dict[pop3_table]
table_kwargs = {
"table": main_table,
"sn1a_table": sn1a_table,
"pop3_table": pop3_table
}
kwargs.update(table_kwargs)
o_for_SSPs = omega.omega(special_timesteps=2,
pre_calculate_SSPs=True,
**kwargs)
# Copy the SSPs array
SSPs_in = [
o_for_SSPs.ej_SSP, o_for_SSPs.ej_SSP_coef, o_for_SSPs.dt_ssp,
o_for_SSPs.t_ssp
]
# Return both the SSPs array and the kwargs used to compute this, for passing into GAMMA
return SSPs_in, kwargs
import NuPyCEE.omega as om
import visualize as vs
########################################################
### Run 'Omega' calculation with best-fit parameters ###
""" Best-fit parameters still in progress """
########################################################
num_steps = 140
#get SFR from Shen(2015)/'Eris'
sfh_file_dir = "../reproduce_shen/"
sfh_file_relpath = sfh_file_dir + "time_sfr_Shen_2015.txt"
### make instances of 'Omega' and 'visualize' ###
rncp_omega_obj = om.omega(special_timesteps=num_steps,
sfh_file=sfh_file_relpath,
imf_type="kroupa93",
ns_merger_on=True,
f_merger=0.01,
nb_nsm_per_m=0.5)
rncp_plot_obj = vs.visualize(rncp_omega_obj, r"$\Omega$", num_yaxes=3, age=0)
#subplot [Eu/H] for all data
rncp_plot_obj.add_time_relabu("[Eu/H]", 0)
#subplot [Eu/H], [Re/H], [Os/H] for 'Omega' data
rncp_plot_obj.add_time_relabu_singleomega("[Eu/H]", 1)
rncp_plot_obj.add_time_relabu_singleomega("[Os/H]", 1)
rncp_plot_obj.add_time_relabu_singleomega("[Re/H]", 1)
#subplot [Eu/H], [Re-187/H], [Os-187/H] for 'Omega' data
rncp_plot_obj.add_time_relabu_singleomega("[Eu/H]", 2)
rncp_plot_obj.add_time_relabu_singleomega("[Os-187/H]", 2)
rncp_plot_obj.add_time_relabu_singleomega("[Re-187/H]", 2)
#show
rncp_plot_obj.finalize(save="rncp_1.png",
def __initialisation(self):
'''
Read the merger tree and declare and fill arrays.
'''
# Run an OMEGA simulation in order to copy basic arrays
self.o_ini = omega.omega(table=self.kwargs["table"], \
pop3_table=self.kwargs["pop3_table"],\
special_timesteps=2, cte_sfr=0.0, \
mgal=1e10, print_off=True)
# Add NuPyCEE kwargs arguments to speed-up the initialization of branches
self.kwargs["input_yields"] = True
self.kwargs["ytables_in"] = self.o_ini.ytables
self.kwargs["isotopes_in"] = self.o_ini.history.isotopes
self.kwargs["ytables_1a_in"] = self.o_ini.ytables_1a
self.kwargs["inter_Z_points"] = self.o_ini.inter_Z_points
self.kwargs["nb_inter_Z_points"] = self.o_ini.nb_inter_Z_points
self.kwargs["y_coef_M"] = self.o_ini.y_coef_M
self.kwargs["y_coef_M_ej"] = self.o_ini.y_coef_M_ej
self.kwargs["y_coef_Z_aM"] = self.o_ini.y_coef_Z_aM
self.kwargs["y_coef_Z_bM"] = self.o_ini.y_coef_Z_bM
self.kwargs["y_coef_Z_bM_ej"] = self.o_ini.y_coef_Z_bM_ej
self.kwargs["tau_coef_M"] = self.o_ini.tau_coef_M
self.kwargs["tau_coef_M_inv"] = self.o_ini.tau_coef_M_inv
self.kwargs["tau_coef_Z_aM"] = self.o_ini.tau_coef_Z_aM
self.kwargs["tau_coef_Z_bM"] = self.o_ini.tau_coef_Z_bM
self.kwargs["tau_coef_Z_aM_inv"] = self.o_ini.tau_coef_Z_aM_inv
self.kwargs["tau_coef_Z_bM_inv"] = self.o_ini.tau_coef_Z_bM_inv
self.kwargs["y_coef_M_pop3"] = self.o_ini.y_coef_M_pop3
self.kwargs["y_coef_M_ej_pop3"] = self.o_ini.y_coef_M_ej_pop3
self.kwargs["tau_coef_M_pop3"] = self.o_ini.tau_coef_M_pop3
self.kwargs["tau_coef_M_pop3_inv"] = self.o_ini.tau_coef_M_pop3_inv
self.kwargs[
"inter_lifetime_points_pop3"] = self.o_ini.inter_lifetime_points_pop3
self.kwargs[
"inter_lifetime_points_pop3_tree"] = self.o_ini.inter_lifetime_points_pop3_tree
self.kwargs[
"nb_inter_lifetime_points_pop3"] = self.o_ini.nb_inter_lifetime_points_pop3
self.kwargs["inter_lifetime_points"] = self.o_ini.inter_lifetime_points
self.kwargs[
"inter_lifetime_points_tree"] = self.o_ini.inter_lifetime_points_tree
self.kwargs[
"nb_inter_lifetime_points"] = self.o_ini.nb_inter_lifetime_points
self.kwargs[
"nb_inter_M_points_pop3"] = self.o_ini.nb_inter_M_points_pop3
self.kwargs[
"inter_M_points_pop3_tree"] = self.o_ini.inter_M_points_pop3_tree
self.kwargs["nb_inter_M_points"] = self.o_ini.nb_inter_M_points
self.kwargs["inter_M_points"] = self.o_ini.inter_M_points
self.kwargs["y_coef_Z_aM_ej"] = self.o_ini.y_coef_Z_aM_ej
# Calculate the number of redshifts
self.nb_redshifts = len(self.redshifts)
# Declare the galaxy instance array
self.galaxy_inst = [0] * self.nb_redshifts
for i_i in range(0, self.nb_redshifts):
self.galaxy_inst[i_i] = [0] * len(self.br_m_halo[i_i])
# Declare the extra initial baryonic mass added to galaxies
self.dm_bar_added_iso = [0.0] * self.nb_redshifts
for i_i in range(0, self.nb_redshifts):
self.dm_bar_added_iso[i_i] = np.zeros(\
(len(self.br_m_halo[i_i]), self.o_ini.nb_isotopes))
# Set the final redshift
self.kwargs["redshift_f"] = min(self.redshifts)
# Get the primordial composition (mass fraction)
iniabu_table = 'yield_tables/iniabu/iniab_bb_walker91.txt'
ytables_bb = ry.read_yields_Z( \
os.path.join(nupy_path, iniabu_table), self.o_ini.history.isotopes)
self.prim_x_frac = np.array(ytables_bb.get(Z=0.0, quantity='Yields'))
del ytables_bb
# Define the information of whether branches are sub-halo or not
if len(self.br_is_sub) > 0:
self.is_sub_info = True
else:
self.is_sub_info = False
