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repo_functions.py
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repo_functions.py
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import os
import re
import numpy as np
import matplotlib.pyplot as plt
#import data_dirlib.patches as patch
import time as t
import more_itertools as mit
import h5py
import csv
import datetime
#from datetime import datetime
import ast
from nugridpy import utils as u
from nugridpy import nugridse as mp
### functions to load data (models and solar abundances):
# function to load Sieverding+2018
def get_profiles(fname,isotopes=["mg24"],presn=True,decayed=False):
""" extracts mass fraction profiles from hdf5 data file.
keywork argument isotopes=["al26"] specifies which isotopes to return.
Return value is a dictionary containing a "pre-sn" and post-sn" key and the mass fractions for each isotope as well as the mass coordinates.
"""
# Open HDF5 file
data_file=h5py.File(fname)
results=dict()
#initialize output dicts
results["pre-sn"]=dict()
results["post-sn"]=dict()
types=["post-sn"]
if presn:
types.append("pre-sn")
for typ in types:
data=data_file[typ]
mr=data["mass_coordinates_sun"]
results[typ]["mr"]=mr
# need to decode binary isotope names to get strings
isos=[ name.decode() for name in data["isotopes"] ]
#find index jiso for isotope and get zonal mass fractions
#for iso in isotopes:
# jiso=isos.index(iso)
# if decayed and typ=="post-sn":
# results[typ][iso]=data["mass_fractions_decayed"][:,jiso]
# else:
# results[typ][iso]=data["mass_fractions"][:,jiso]
for iso in isotopes:
try:
jiso=isos.index(iso)
if decayed and typ=="post-sn":
results[typ][iso]=data["mass_fractions_decayed"][:,jiso]
else:
results[typ][iso]=data["mass_fractions"][:,jiso]
except ValueError:
print('Sie18, missing isotope set to zero: ',iso); results[typ][iso] = np.zeros(len(mr))
return(results)
# function to read Lawson data
def load_lawson22(file_name, num_species):
print(file_name)
#mass_lines = %sx findstr "mass enclosed" {filename}
# getting mass enclosed and num particle
# mass_lines = !grep 'mass enclosed' {file_name}
mass_lines = []
with open(file_name, "rt") as f:
for ln, line in enumerate(f):
if 'mass enclosed' in line:
mass_lines.append(line)
mass = [float(row.split()[3]) for row in mass_lines]
numpart = [int(row.split()[0][1:]) for row in mass_lines]
number_of_parts = len(numpart) # number of particles (it may change from model to model)
print('# particles = ',number_of_parts)
# open and read abundances for all trajectories
a,x,z,iso = [],[],[],[]
with open(file_name, "rt") as f:
i = 0
while i < number_of_parts:
f.readline(); f.readline();
j = 0
a_i,x_i,z_i,iso_i = [],[],[],[]
while j < num_species:
line = f.readline().split()
a_i.append(int(line[0]))
z_i.append(int(line[1]))
x_i.append(float(line[2]))
iso_i.append(f"{get_el_from_z(line[1])}-{line[0]}")
j += 1
a.append(a_i); z.append(z_i); x.append(x_i); iso.append(iso_i)
i += 1
return (mass, numpart, number_of_parts, a, z, x, iso)
# load solar data
def load_solar(file_name_solar='iniab1.4E-02As09.ppn'):
'''
Function loading the solar abundances that we need to get all the ratios. More than one solar is needed, for
different models. :( If not specified a default file is loaded.
Parameter :
file_name : name of the solar abundance file (NuGrid format)
Output (what SIMPLE needs):
s_iso_new : name of isotopes (this will not change for different solar files)
s_abu : solar abundances
'''
f=open(file_name_solar,'r')
solar_data = f.readlines()
f.close()
s_iso = []; z_iso = []; abu_s = []
for i in solar_data:
s_iso.append(i[3:9].strip())
z_iso.append(int(i[0:3]))
abu_s.append(float(i[10:]))
s_iso = np.array(s_iso); abu_s = np.array(abu_s)
a_iso = [i[2:].strip() for i in s_iso]; a_iso[0] = '1'; a_iso = [int(i) for i in a_iso]
s_iso[0] = 'h 1' # correction for H1 special name
# here below some simple way to build the isotope name with the same structure of the models.
iso_new_s = [i[:2].strip().capitalize()+'-'+i[2:].strip() for i in s_iso]
return (abu_s, iso_new_s)
#######################################
# utils functions: rearrange data, make plots, etc
# function to calculate all ratios and slopes from abundance profiles
def give_ratios(abu, iso_up, iso_down, ref_mu_and_epsilon, ref_slope, method = 'dilution'):
""" function: returns delta*1000, epsilon*1e4 (or mu*1e6), with and without mass correction, and slopes
Inputs are: abu: stellar abundances
iso_abu: isotopes names consistent with stellar abundance arrays
abu_solar: solar abundances
iso_solar: isotopes names consistent with the solar abundance array
iso_up: isotopes I want to calculate the delta for
iso_down: normalization isotope for delta
ref_mu_and_epsilon: list of reference isotope used to normalize to get epsilon and mu
ref_slope: reference isotope used to normalize epsilon*1e4 (ds or dsm) to get the slopes
method: dummy variable not used here, introduced for give_ratios_gm"""
rho = [abu[e_label.index(iso_up[i])]/\
abu[e_label.index(iso_down[i])]/\
(s_abu[s_iso_new.index(iso_up[i].replace("*", ""))]/\
s_abu[s_iso_new.index(iso_down[i].replace("*", ""))])-1.\
for i in range(len(iso_up))]
ind_ = iso_up.index(ref_mu_and_epsilon[0])
# ds_maria = ds without applying mass correction (factor)
ds_maria = np.array(rho) / np.array(rho[ind_])
# mass correction factor, derived using natural logs of atomic masses
factor = [np.log(iso_masses[name_iso_masses.index(iso_up[i].replace("*", ""))]/\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))]) /\
np.log(iso_masses[name_iso_masses.index(ref_mu_and_epsilon[i].replace("*", ""))]/\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))])\
for i in range(len(iso_up))] ; factor = np.array(factor)
# ds is epsilon (/10000) or mu (/1e6), based on kinetic fractionation law - Steele+ 2012
# Steele+ 2012 only applied for 1 element. Generalize?
# or, do we need to generalize here, if we plot e,g., mu(Ni) vs mu(Fe)
ds = [np.array(ds_maria[i]) - factor[i] for i in range(len(iso_up))]
# and now the slopes...
ind_ = iso_up.index(ref_slope)
# do we need to generalize here, and allow for ds_element1 and ds_element2...
# corr_factor_mix = ...
slope = np.array(ds) / np.array(ds[ind_]) # * corr_factor_mix
# done
return(rho, slope)
# function to calculate all ratios and slopes from abundance profiles
# originally developed by "Georgy Makhatadze" <[email protected]>
def give_ratios_gm(abu, e_label,iso_masses,name_iso_masses,iso_up, iso_down, iso_norm, iso_slope,\
iso_chem = None, chem_factor = None, solar_ref_for_ratios = 'iniab1.4E-02As09.ppn',\
abu_convert = True, s_abu_convert = True, approximation_method = 'dilution',\
precision = 0.01, starting_dilution_factor = 1, dilution_step = 0.1,\
iteration_method = 'precision', largest_offset = 0.0001):
"""Function: returns isotope ratios in rho-notation and slopes for internally normalised data
Inputs are:
abu: stellar abundances
iso_abu: isotopes names consistent with stellar abundance arrays
abu_solar: solar abundances
iso_solar: isotopes names consistent with the solar abundance array
std_ratio: isotope ratios in standards, denominator should have ratio of 1
std_iso: isotope names for standards
iso_up: numerator isotopes
iso_down: denominator isotopes
iso_norm: normalising isotopes
iso_slope: abscissa isotope used to get the slopes, must be one of iso_up
iso_chem: isotope used to scale all elements relative to each other, needs to have its chem_factor = 1
MP: man... this is bad.. what if Si28 is not in e-label?
chem_factor: chemical fractionation factors,
each stellar abundance in iso_up is multiplied by number from this array, must be relative to iso_chem
abu_convert: should we convert abundances from mass to number units
s_abu_convert: same but for solar
approximation_method: slope ind_slope method, can be:
dilution - artificial dilute sample, similar to Simon+09/ApJ, Makhatadze+23/GCA & maybe more refs
linear - linearisation from Dauphas+04/EPSL
better_linear - improved linearisation from Dauphas+14/EPSL & Lugaro+23/EPJA, similar to Steele+12/ApJ
std_linear - better_linear, but uses isotope ratios of certified standards instead of solar abundances,
still uses solar abundances for the coefficient between elements
precision: maximum allowed difference from previous iteration in relative units for the dilution method
starting_dilution_factor: starting dilution_factor
dilution_step: how much the factor goes down each iteration
iteration_method: how dilution_factor is chosen
largest_offset - by setting the largest allowed mass-independent offset from the solar,
similar to Ek's notebook for Lugaro+23/EPJA
precision - by iterating until slopes stop changing
dot_product - WIP, ask Andrés & Georgy
"""
# getting default values for some arguments
if iso_chem == None: iso_chem = iso_up[0]
if chem_factor == None: chem_factor = np.array([1.]*len(iso_up))
s_abu, s_iso_new = load_solar(file_name_solar = solar_ref_for_ratios)
#print(solar_ref_for_ratios,s_abu[s_iso_new.index('O-16')])
# my understanding is that you always want to convert.... why do we need this then?
# conversion for stellar and solar abundances is happening separately now
if abu_convert:
# abundances converted from mass to number unit
abu_num = [abu[e_label.index(iso_up[i])]/\
iso_masses[name_iso_masses.index(iso_up[i].replace("*",""))]\
for i in range(len(iso_up))]
abu_num = np.array(abu_num)
# abundances for iso_chem are fixed and calculated here now
abu_num_chem = np.array(abu[e_label.index(iso_chem)]/\
iso_masses[name_iso_masses.index(iso_chem.replace("*",""))])
elif not abu_convert:
abu_num = [abu[e_label.index(iso_up[i])] for i in range(len(iso_up))]
abu_num_chem = np.array(abu[e_label.index(iso_chem)])
else:
print('invalid abu_convert')
if s_abu_convert:
# solar abundances converted from mass to number unit
s_abu_num = [s_abu[s_iso_new.index(iso_up[i].replace("*", ""))]/\
iso_masses[name_iso_masses.index(iso_up[i].replace("*",""))]\
for i in range(len(iso_up))]
s_abu_num = np.array(s_abu_num)
s_abu_num_chem = s_abu[s_iso_new.index(iso_chem.replace("*",""))]/\
iso_masses[name_iso_masses.index(iso_chem.replace("*",""))]
elif not s_abu_convert:
s_abu_num = [s_abu[s_iso_new.index(iso_up[i].replace("*", ""))]\
for i in range(len(iso_up))]
s_abu_num = np.array(s_abu_num)
s_abu_num_chem = s_abu[s_iso_new.index(iso_chem.replace("*",""))]
else:
print('invalid s_abu_convert')
# rho for the stellar source, as defined in Dauphas+04
if approximation_method == 'std_linear':
# as deviation from the standard
# here rho is already masked on iso_up
rho = [abu_num[i]/\
abu_num[iso_up.index(iso_down[i])]/\
(std_ratio[std_iso.index(iso_up[i].replace("*", ""))]/\
std_ratio[std_iso.index(iso_down[i].replace("*", ""))])-1.\
for i in range(len(iso_up))]
rho = np.array(rho)
else:
# as deviation from the solar
rho = [abu_num[i]/\
abu_num[iso_up.index(iso_down[i])]/\
(s_abu_num[i]/\
s_abu_num[iso_up.index(iso_down[i])])-1.\
for i in range(len(iso_up))]
rho = np.array(rho)
# index for slope isotope
ind_slope = iso_up.index(iso_slope)
if approximation_method != 'dilution':
# rho for normalising ratios, as defined in Dauphas+04
if approximation_method == 'std_linear':
# as deviation from the standard
rho_norm = [abu_num[iso_up.index(iso_norm[i])]/\
abu_num[iso_up.index(iso_down[i])]/\
(std_ratio[std_iso.index(iso_norm[i].replace("*", ""))]/\
std_ratio[std_iso.index(iso_down[i].replace("*", ""))])-1.\
for i in range(len(iso_norm))]
rho_norm = np.array(rho_norm)
else:
# as deviation from the solar
rho_norm = [abu_num[iso_up.index(iso_norm[i])]/\
abu_num[iso_up.index(iso_down[i])]/\
(s_abu_num[iso_up.index(iso_norm[i])]/\
s_abu_num[iso_up.index(iso_down[i])])-1.\
for i in range(len(iso_norm))]
rho_norm = np.array(rho_norm)
# the only difference between linear and better_linear is how atomic masses are treated
if approximation_method == 'linear':
# linear
mass_diff_coef = [(iso_masses[name_iso_masses.index(iso_up[i].replace("*", ""))]-\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))])/\
(iso_masses[name_iso_masses.index(iso_norm[i].replace("*", ""))]-\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))])\
for i in range(len(iso_up))]
else:
# logarithmic
mass_diff_coef = [np.log(iso_masses[name_iso_masses.index(iso_up[i].replace("*", ""))]/\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))])/\
np.log(iso_masses[name_iso_masses.index(iso_norm[i].replace("*", ""))]/\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))])\
for i in range(len(iso_up))]
mass_diff_coef = np.array(mass_diff_coef)
# coefficient that takes care of different elements plotted together
diff_ele_coef = [abu_num[iso_up.index(iso_down[i])]/\
s_abu_num[iso_up.index(iso_down[i])]\
for i in range(len(iso_down))]
diff_ele_coef = np.array(diff_ele_coef)
# linearised mass-independent rho-values with the applied element coefficient
rho_mind = [(rho[i] - rho_norm[i]*mass_diff_coef[i])*diff_ele_coef[i]\
for i in range(len(iso_up))]
rho_mind = np.array(rho_mind)
rho_mind_slope = np.array(rho_mind[ind_slope])
# those numbers do not have any meaning outside of slope calculation
# slope on plots where iso_up is ordinate and iso_norm is abscissa
slope = np.array(rho_mind/rho_mind_slope)
elif approximation_method == 'dilution':
# starting dilution_factor
dilution_factor = starting_dilution_factor
# counter for the iterations
counter = 0
# everything below is in a fake diluted sample
while True:
counter = counter + 1
print('step', counter, 'dilution_factor =', dilution_factor)
if counter > 42:
print('reached', counter, 'iterations, time to stop')
break
# you are dead !! dilution_factor constrained by dp
# from the way it is coded. dilution_factor < 1e-15
# ratios (over iso_chem) in a mixture
ratio_chem_dilute = [(abu_num[i] * dilution_factor * chem_factor[i] +\
s_abu_num[i] * (1.-dilution_factor))/\
(abu_num_chem * dilution_factor +\
s_abu_num_chem * (1.-dilution_factor))\
for i in range(len(iso_up))]
ratio_chem_dilute = np.array(ratio_chem_dilute)
# mixing math fixed
# the equations taken from Rb-Sr lecture notes by Kostitsyn
# https://wiki.web.ru/wiki/%D0%93%D0%B5%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B9_%D1%84%D0%B0%D0%BA%D1%83%D0%BB%D1%8C%D1%82%D0%B5%D1%82_%D0%9C%D0%93%D0%A3:%D0%93%D0%B5%D0%BE%D1%85%D0%B8%D0%BC%D0%B8%D1%8F_%D0%B8%D0%B7%D0%BE%D1%82%D0%BE%D0%BF%D0%BE%D0%B2_%D0%B8_%D0%B3%D0%B5%D0%BE%D1%85%D1%80%D0%BE%D0%BD%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D1%8F
# isotope ratios in the dilute mixture
ratio_up = [ratio_chem_dilute[i]/\
ratio_chem_dilute[iso_up.index(iso_down[i])]\
for i in range(len(iso_up))]
ratio_up = np.array(ratio_up)
ratio_norm = [ratio_chem_dilute[iso_up.index(iso_norm[i])]/\
ratio_chem_dilute[iso_up.index(iso_down[i])]\
for i in range(len(iso_norm))]
ratio_norm = np.array(ratio_norm)
# internally normalised mass-independent ratios for the mixtures
ratio_mind = [ratio_up[i]\
*((s_abu_num[iso_up.index(iso_norm[i])]/\
s_abu_num[iso_up.index(iso_down[i])])/\
ratio_norm[i])**\
(np.log(iso_masses[name_iso_masses.index(iso_up[i].replace("*", ""))]/\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))])/\
np.log(iso_masses[name_iso_masses.index(iso_norm[i].replace("*", ""))]/\
iso_masses[name_iso_masses.index(iso_down[i].replace("*", ""))]))\
for i in range(len(iso_up))]
ratio_mind = np.array(ratio_mind)
ratio_mind_slope = np.array(ratio_mind[ind_slope])
# mass-independent rho-values for the mixtures
rho_mind = [ratio_mind[i]/\
(s_abu_num[iso_up.index(iso_up[i])]/\
s_abu_num[iso_up.index(iso_down[i])])-1.\
for i in range(len(iso_up))]
rho_mind = np.array(rho_mind)
rho_mind_slope = np.array(rho_mind[ind_slope])
# slope on plots where iso_up is ordinate and iso_norm is abscissa
slope = np.array(rho_mind/rho_mind_slope + 1e-200)
# forces no division by zero
if iteration_method == 'precision':
if counter == 1:
slope_prev = np.array(slope)
dilution_factor = dilution_factor*dilution_step
else:
# calculates differences between the current iteration and previous
slope_diff = np.abs(np.arctan(slope) - np.arctan(slope_prev))
# add inverse slope if too big ?
# checks if difference is within the specified precision
precision_check = False
for i in range(len(slope_diff)):
# yep... this would not work with e.g., AGBs. 1 mass coordinate, the surface.
# So, no len() but float here.
for j in range(len(slope_diff[i])):
if slope_diff[i][j] > precision:
precision_check = True
if precision_check:
slope_prev = np.array(slope)
dilution_factor = dilution_factor*dilution_step
if dilution_factor < 1e-12:
print('dilution_factor too small, max slope_diff =', np.max(slope_diff),\
'\ntook', counter, 'iterations, final dilution_factor',\
dilution_factor*dilution_step)
break
else:
print('took', counter, 'iterations, final dilution_factor', dilution_factor)
break
elif iteration_method == 'dot_product':
if counter == 1:
rho_mind_prev = np.array(rho_mind)
rho_mind_slope_prev = np.array(rho_mind_slope)
dilution_factor = dilution_factor*dilution_step
else:
precision_check = False
for i in range(len(rho_mind)):
for j in range(len(rho_mind[i])):
dot_product = rho_mind[i][j]*rho_mind_prev[i][j] +\
rho_mind_slope[j]*rho_mind_slope_prev[j]
max_dot_product = ((rho_mind[i][j]**2.+rho_mind_slope[j]**2.)**(-2.))*\
((rho_mind_prev[i][j]**2.+rho_mind_slope_prev[j]**2.)**(-2.))
if (np.abs(dot_product) < 0.9 * max_dot_product) and (max_dot_product != 0):
precision_check = True
if precision_check:
rho_mind_prev = np.array(rho_mind)
rho_mind_slope_prev = np.array(rho_mind_slope)
dilution_factor = dilution_factor*dilution_step
if dilution_factor < 1e-100:
print('dilution_factor too small\ntook', counter,\
'iterations, final dilution_factor',\
dilution_factor/dilution_step)
break
else:
print('took', counter, 'iterations, final dilution_factor', dilution_factor)
break
elif iteration_method == 'largest_offset':
if counter == 1:
dilution_factor = largest_offset / np.max(np.abs(rho_mind))
elif np.max(np.abs(rho_mind)) > largest_offset:
dilution_factor = dilution_factor * largest_offset / np.max(np.abs(rho_mind))
else:
print('took', counter, 'iterations, final dilution_factor', dilution_factor)
break
else:
print('invalid iteration_method')
else:
print('invalid approximation_method')
# done
return(rho, slope, rho_mind_slope)
def func_species_deck(string_set,abundance,iso_list_spec,iso_list_master):
"""Documentation:
# function will return isotope abundance if present in the list, or zero to be added
string_set: identifier of the origin set
abundance: abundances to be found
iso_list_spec: list of isotopes present in the specific model
iso_list_master: this should be iso_list, defined by the user """
dum = []
for i in iso_list_master:
try:
dum.append(abundance[iso_list_spec.index(i)])
except ValueError:
dum.append(0.)
print(string_set,', missing isotope set to zero: ',i)
return(dum)
#!Evelyn new initialization to make Lawson et al. faster
global start_time
z_names = ['Neut', 'H', 'He', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne', 'Na', 'Mg', 'Al',
'Si', 'P', 'S', 'Cl', 'Ar', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co',
'Ni', 'Cu', 'Zn', 'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr', 'Rb', 'Sr', 'Y', 'Zr', 'Nb',
'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd', 'In', 'Sn', 'Sb', 'Te', 'I', 'Xe', 'Cs',
'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm',
'Yb', 'Lu', 'Hf', 'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb', 'Bi',
'Po', 'At', 'Rn', 'Fr', 'Ra', 'Ac', 'Th', 'Pa', 'U']
def print_time(message,start_time):
print(f"{message} - {(t.time()-start_time):.2f} s")
def get_el_from_z(z):
'''
Very simple Vfunction that gives the atomic number AS A STRING when given the element symbol.
Uses predefined a dictionnary.
Parameter :
z : string or number
For the other way, see get_z_from_el
'''
z = int(z)
return (z_names[z])
# here there is the main loading function
# function loading all the data from the models
def load_stellar_data(data_dir,check_data_file,reqmass,iso_list,e_label):
if check_data_file == False:
#############################
# loading Ritter+18 model
#############################
fol2mod = data_dir+'R18/'
# load instances of models
# 15Msun
pt_15 = mp.se(fol2mod,'M15.0Z2.0e-02.Ma.0020601.out.h5',rewrite=True)
cyc_15 = pt_15.se.cycles[-1]
#pt_15.se.get('temperature')
t9_cyc_15 = pt_15.se.get(cyc_15,'temperature')
mass_15 = pt_15.se.get(cyc_15,'mass')
# 20Msun
pt_20 = mp.se(fol2mod,'M20.0Z2.0e-02.Ma.0021101.out.h5',rewrite=True)
cyc_20 = pt_20.se.cycles[-1]
#pt_20.se.get('temperature')
t9_cyc_20 = pt_20.se.get(cyc_20,'temperature')
mass_20 = pt_20.se.get(cyc_20,'mass')
# 25Msun
pt_25 = mp.se(fol2mod,'M25.0Z2.0e-02.Ma.0023601.out.h5',rewrite=True)
cyc_25 = pt_25.se.cycles[-1]
#pt_25.se.get('temperature')
t9_cyc_25 = pt_25.se.get(cyc_25,'temperature')
mass_25 = pt_25.se.get(cyc_25,'mass')
#################################################################################
# loading AGB models
# test case, M=3Msun, Z=0.03, Battino et al., getting only the last surf file
################################################################################
pt_3 = mp.se(data_dir+'agb_surf_m3z2m3/','96101.surf.h5',rewrite=True)
### ### ###
# loading Pignatari+16 model
fol2mod = data_dir+'P16/'
# load instances of models
# 15Msun
P16_15 = mp.se(fol2mod,'M15.0',rewrite=True)
cyc_P16_15 = P16_15.se.cycles[-1]
#pt_15.se.get('temperature')
t9_cyc_P16_15 = P16_15.se.get(cyc_P16_15,'temperature')
mass_P16_15 = P16_15.se.get(cyc_P16_15,'mass')
# 20Msun
P16_20 = mp.se(fol2mod,'M20.0',rewrite=True)
cyc_P16_20 = P16_20.se.cycles[-1]
#pt_20.se.get('temperature')
t9_cyc_P16_20 = P16_20.se.get(cyc_P16_20,'temperature')
mass_P16_20 = P16_20.se.get(cyc_P16_20,'mass')
# 25Msun
P16_25 = mp.se(fol2mod,'M25.0',rewrite=True)
cyc_P16_25 = P16_25.se.cycles[-1]
#pt_25.se.get('temperature')
t9_cyc_P16_25 = P16_25.se.get(cyc_P16_25,'temperature')
mass_P16_25 = P16_25.se.get(cyc_P16_25,'mass')
### ### ###
################################################
# Loading Lawson+22
#################################################
dir_law = data_dir+'LAW22/'
models_list = ['M15s_run15f1_216M1.3bgl_mp.txt','M20s_run20f1_300M1.56jl_mp.txt','M25s_run25f1_280M1.83rrl_mp.txt']
num_species = 5209
start_time = t.time()
numpart_all = []; massinc_all = []
anum_all = []; znum_all = []; x_all = []
iso_name_all = []; num_of_part_all = []
for i in models_list:
mass, numpart, number_of_parts, a, z, x, iso = load_lawson22(dir_law+i,num_species)
massinc_all.append(mass); num_of_part_all.append(number_of_parts); numpart_all.append(numpart)
anum_all.append(a); znum_all.append(z); x_all.append(x); iso_name_all.append(iso)
print_time("done with Lawson",start_time)
### ### ###
##########################################
# dir where Sieverdin models are located
dir_sie = data_dir+'SIE18/'
file_sie_all = ["s15_data.hdf5","s20_data.hdf5","s25_data.hdf5"]
########################################################################
### ### ###
#########################################
# Rauscher 2002
#############################
dir_rau = data_dir+'R02/'
#models_rau = ['s15a28c.expl_yield']
models_rau = ['s15a28c.expl_yield','s20a28n.expl_yield','s25a28d.expl_yield']
start_time = t.time()
rau_mass = []; rau_isos = []; rau_x = []
for i in range(len(models_rau)):
filename = dir_rau+models_rau[i]
print(filename)
f = open(filename,'r')
head = f.readline(); isos_dum = head.split()[5:] # getting isotopes, not first header names
dum_a = [re.findall('\d+', ik)[0] for ik in isos_dum] # getting the A from isotope name
dum_el = [re.sub(r'[0-9]+', '', ik) for ik in isos_dum] # getting the element name from the isotope name
dum_new_iso = [dum_el[ik].capitalize()+'-'+dum_a[ik] for ik in range(len(isos_dum))]
rau_isos.append(dum_new_iso) # isotope name that we can use around, just neutron name is different, but not care
#
data = f.readlines()[:-2] # getting the all data, excepting the last two lines
# done reading, just closing the file now
#
dum = [float(ii.split()[1])/1.989e+33 for ii in data]; rau_mass.append(dum) # converting in Msun too.
x_dum = []
data = [row.split()[3:] for row in data]
#
x_dum = np.transpose(data); x_dum = np.asfarray(x_dum,float)
print_time("x transpose and done",start_time)
rau_x.append(x_dum)
### ### ###
########################
# data from LC18
##########################
dir_lc18 = data_dir+'LC18/'
models_lc18 = ['015a000.dif_iso_nod','020a000.dif_iso_nod','025a000.dif_iso_nod']
skip_heavy_ = 43 # usedd to skip final ye and spooky abundances (see below)
start_time = t.time()
lc18_mass_1 = []; lc18_isos = []; lc18_x = []
for i in range(len(models_lc18)):
filename = dir_lc18+models_lc18[i]
print(filename)
f = open(filename,'r')
# getting isotopes, not first header names, and final ye and spooky abundances (group of isolated isotopes,
# probably sorted with artificial reactions handling mass conservation or sink particles approach)
head = f.readline(); isos_dum = head.split()[4:-skip_heavy_]
# correcting names to get H1 (and the crazy P and A)
isos_dum[0]=isos_dum[0]+'1'; isos_dum[1]=isos_dum[1]+'1'; isos_dum[6]=isos_dum[6]+'1'
dum_a = [re.findall('\d+', ik)[0] for ik in isos_dum] # getting the A from isotope name
dum_el = [re.sub(r'[0-9]+', '', ik) for ik in isos_dum] # getting the element name from the isotope name
dum_new_iso = [dum_el[ik].capitalize()+'-'+dum_a[ik] for ik in range(len(isos_dum))]
lc18_isos.append(dum_new_iso) # isotope name that we can use around, just neutron name is different, but not care
#
data = f.readlines()[:-1] # getting the all data, excepting the last fake line (bounch of zeros)
# done reading, just closing the file now
#
dum = [float(ii.split()[0]) for ii in data]; lc18_mass_1.append(dum) # converting in Msun too.
x_dum = []
data = [row.split()[4:-skip_heavy_] for row in data]
#
x_dum = np.transpose(data); x_dum = np.asfarray(x_dum,float)
print_time("x transpose and done",start_time)
lc18_x.append(x_dum)
### ### ###
####################################
# alright. The previous cells just need to be run once. Once you got instances for all the models,
# you are good to go with the analysis.
# what of the three models I want for each flavour?
# this is the deck for the models
if reqmass not in [15, 20, 25]:
print("Error. You can choose only 15, 20, 25 solar mass")
exit()
if reqmass == 15:
ind_ = 0
elif reqmass == 20:
ind_ = 1
elif reqmass == 25:
ind_ = 2
if check_data_file == False:
models = {
15: {
"r18_exp": pt_15,
"r18_cyc": cyc_15,
"r18_t9_cyc": t9_cyc_15,
"r18_mass": mass_15,
"p16_exp": P16_15,
"p16_cyc": cyc_P16_15,
"p16_t9_cyc": t9_cyc_P16_15,
"p16_mass": mass_P16_15
},
20: {
"r18_exp": pt_20,
"r18_cyc": cyc_20,
"r18_t9_cyc": t9_cyc_20,
"r18_mass": mass_20,
"p16_exp": P16_20,
"p16_cyc": cyc_P16_20,
"p16_t9_cyc": t9_cyc_P16_20,
"p16_mass": mass_P16_20
},
25: {
"r18_exp": pt_25,
"r18_cyc": cyc_25,
"r18_t9_cyc": t9_cyc_25,
"r18_mass": mass_25,
"p16_exp": P16_25,
"p16_cyc": cyc_P16_25,
"p16_t9_cyc": t9_cyc_P16_25,
"p16_mass": mass_P16_25
}
}
# Ritter+18
r18_exp = models[reqmass]["r18_exp"]
r18_cyc = models[reqmass]["r18_cyc"]
r18_t9_cyc = models[reqmass]["r18_t9_cyc"]
r18_mass = models[reqmass]["r18_mass"]
# Pignatari+16
p16_exp = models[reqmass]["p16_exp"]
p16_cyc = models[reqmass]["p16_cyc"]
p16_t9_cyc = models[reqmass]["p16_t9_cyc"]
p16_mass = models[reqmass]["p16_mass"]
# lawson+22 # 0=15Msun; 1=20Msun; 2=25Msun
numpart = numpart_all[ind_]; la22_mass = massinc_all[ind_]
anum = anum_all[ind_]; znum = znum_all[ind_]; x = x_all[ind_]
iso_name = iso_name_all[ind_]; num_of_part = num_of_part_all[ind_]
# sieverdin et al # 0=15Msun; 1=20Msun; 2=25Msun
file_sie = file_sie_all[ind_]
# Rauscher+02 # 0=15Msun; 1=20Msun; 2=25Msun
rau02_mass = rau_mass[ind_]; rau02_abund_1 = rau_x[ind_]
rau_isos_1 = rau_isos[ind_]
# Limongi & Chieffi 2018 # 0=15Msun; 1=20Msun; 2=25Msun
lc18_mass = lc18_mass_1[ind_]; lc18_x_1 = lc18_x[ind_]
lc18_isos_1 = lc18_isos[ind_]
################################
# Battino et al. -test case AGB
###############################
sparsity_surf = 100
pt_agb = pt_3
time_ev = pt_3.se.ages[0::sparsity_surf]
print(len(time_ev))
### ### ###
##############################################################################################
# getting mass and abundances for single isotopes, or single isotopes + radiogenic "by hand",
# or elements by adding isotopes
###############################################################################################
# ritter+18
cyc_ = r18_cyc
r18_mass = r18_exp.se.get(cyc_,'mass')
# not loading the full arrays all the time, just once, and transposing around
dum_ab = np.transpose(r18_exp.se.get(cyc_,'iso_massf')); dum_iso = r18_exp.se.isotopes
r18_abund = []
for i in iso_list:
#iso_abund = 0.; tmp = [r18_exp.se.get(cyc_,'iso_massf',ii) for ii in i]
iso_abund = 0.; tmp = func_species_deck('rit18',dum_ab,dum_iso,i)
iso_abund = [np.sum(tmp,axis = 0)][0]
r18_abund.append(iso_abund)
r18_abund = np.array(r18_abund)
# pignatari+16
cyc_ = p16_cyc
p16_mass = p16_exp.se.get(cyc_,'mass')
# not loading the full arrays all the time, just once, and transposing around
dum_ab = np.transpose(p16_exp.se.get(cyc_,'iso_massf')); dum_iso = p16_exp.se.isotopes
p16_abund = []
for i in iso_list:
#iso_abund = 0.; tmp = [r18_exp.se.get(cyc_,'iso_massf',ii) for ii in i]
iso_abund = 0.; tmp = func_species_deck('p16',dum_ab,dum_iso,i)
iso_abund = [np.sum(tmp,axis = 0)][0]
p16_abund.append(iso_abund)
p16_abund = np.array(p16_abund)
# Lawson+22
la22_abund = []
for j in iso_list:
tmp = []
for jj in j:
dum = [x[i][iso_name[i].index(jj)] for i in range(num_of_part)]
tmp.append(dum)
iso_abund = [np.sum(tmp,axis = 0)][0]
la22_abund.append(iso_abund)
la22_abund = np.array(la22_abund)
# Sieverdin+
flat = [x for sublist in iso_list for x in sublist]
iso_list_sie = [s.replace("-", "").lower() for s in flat]
#print(iso_list_sie)
results=get_profiles(dir_sie+file_sie,isotopes=iso_list_sie,decayed=False)
sie_abund = []
for i in iso_list:
iso_abund = 0.; tmp = [results["post-sn"][iso.replace("-", "").lower()] for iso in i]
iso_abund = [np.sum(tmp,axis = 0)][0]
sie_abund.append(iso_abund)
sie_abund = np.array(sie_abund)
mass_sie = results["post-sn"]["mr"] # mass
# Rauscher+2002
rau02_abund = []
for j in iso_list:
#tmp = [rau02_abund_1[rau_isos_1.index(jj)] for jj in j]
tmp = func_species_deck('rau02',rau02_abund_1,rau_isos_1,j)
#print(tmp)
rau_iso_abund = [np.sum(tmp,axis = 0)][0]
rau02_abund.append(rau_iso_abund)
rau02_abund = np.array(rau02_abund)
# LC+2018
lc18_abund = []
for j in iso_list:
#tmp = [lc18_x_1[lc18_isos_1.index(jj)] for jj in j]
tmp = func_species_deck('lc18',lc18_x_1,lc18_isos_1,j)
#print(tmp)
lc18_iso_abund = [np.sum(tmp,axis = 0)][0]
lc18_abund.append(lc18_iso_abund)
lc18_abund = np.array(lc18_abund)
### ### ###
###############################
# AGB surf test
# not loading the full arrays all the time, just once, and transposing around
#dum_ab = np.transpose(pt_3.se.get('iso_massf')[0::sparsity_surf]); dum_iso = pt_3.se.isotopes
#dum_ab = [pt_3.se.get(int(dum_cyc),'iso_massf') for dum_cyc in pt_3.se.cycles[0::sparsity_surf]]
#
# oh no... issue in data written in surf files, some as arrays some as list(array(...)). Talk with Umberto, check
# mppnp revision (an oldy??) and dealing with allocatable arrays. So, just grabbing the last one, bah...
#dum_ab = pt_3.se.get('iso_massf')[-1]
ccc = pt_agb.se.cycles[-1] ; dum_ab = pt_agb.se.get(ccc,'iso_massf')
dum_ab = np.array(dum_ab)
# ! *** !
# this is done to create from the single surf file a 2d array. This is temporary, since in give_ratio_gm there is a check
# assuming that there is more than 1 mass coordinate given in dilution/precision setup
dum_ab = np.stack((dum_ab,dum_ab), axis=0)
#
dum_iso = pt_agb.se.isotopes
agb_abund = []
for i in iso_list:
#iso_abund = 0.; tmp = [pt_3.se.get(cyc_,'iso_massf',ii) for ii in i]
iso_abund = 0.; tmp = func_species_deck('bat20',np.transpose(dum_ab),dum_iso,i)
iso_abund = [np.sum(tmp,axis = 0)][0]
agb_abund.append(iso_abund)
agb_abund = np.array(agb_abund)
### ### ###
#################################################################################
# preparation for light mode version: write all abundances in an external file:
# notice the crazy array of lists to list... this is not needed, just to check to be able to write and read
# multiple times for testing, writing lists as strings and read arrays from strings in the cell below.
data_pot= open('selected_abundances_data.txt','w')
data_pot.write(datetime.date.today().strftime('%Y-%m-%d %H:%M:%S')+' file with selected species - light mode Marco'+'\n')
# list of species to check and labels
data_pot.write(str(iso_list)+' \n') # isotope list
data_pot.write(str(np.array(list(e_label)).tolist())+' \n') # list for labels in plots
# rau02
data_pot.write(str(list(rau02_mass))+' \n') # list of Rau02 mass coordinates
data_pot.write(str(np.array(list(rau02_abund)).tolist())+' \n') # list of Rau02 abundances for given mass coordinates
# rit18
data_pot.write(str(np.array(list(r18_mass)).tolist())+' \n') # list of Rit18 mass coordinates
data_pot.write(str(np.array(list(r18_abund)).tolist())+' \n') # list of Rit18 abundances for given mass coordinates
# let's add some pain here... the temperature array to check the masscut in the processed ejecta
data_pot.write(str(np.array(list(r18_t9_cyc)).tolist())+' \n')
# pgn16
data_pot.write(str(np.array(list(p16_mass)).tolist())+' \n') # list of Pgn16 mass coordinates
data_pot.write(str(np.array(list(p16_abund)).tolist())+' \n') # list of Pgn16 abundances for given mass coordinates
# let's add some pain here... the temperature coordinate to check the processed ejecta
data_pot.write(str(np.array(list(p16_t9_cyc)).tolist())+' \n')
# law22
data_pot.write(str(np.array(list(la22_mass)).tolist())+' \n') # list of Law22 mass coordinates
data_pot.write(str(np.array(list(la22_abund)).tolist())+' \n') # list of Law22 abundances for given mass coordinates
# sie18
data_pot.write(str(np.array(list(mass_sie)).tolist())+' \n') # list of Sie18 mass coordinates
data_pot.write(str(np.array(list(sie_abund)).tolist())+' \n') # list of Sie18 abundances for given mass coordinates
# LC18
data_pot.write(str(np.array(list(lc18_mass)).tolist())+' \n') # list of LC18 mass coordinates
data_pot.write(str(np.array(list(lc18_abund)).tolist())+' \n') # list of LC18 abundances for given mass coordinates
# bat20
data_pot.write(str(np.array(list(agb_abund)).tolist())+' \n') # list of Bat20 abundances for the surface (final step)
data_pot.close()
print('All models loaded')
return()
#def func_el_corr(whatever, abu, ref_up, ref_down, mode=0,file_fractionation=None,what_in_file=None):
# """Documentation:
# # mode = 0: c= (ref_EL1_el2/ref_el1_EL2)_sun/(ref_EL1_el2/ref_el1_EL2)_sun = 1. Same without correction factor;
# # mode = 1: c= (ref_EL1_el2/ref_el1_EL2)_star*/(ref_EL1_el2/ref_el1_EL2)_sun
# # mode = 2: c= (ref_EL1_el2/ref_el1_EL2)_file/(ref_EL1_el2/ref_el1_EL2)_sun
# # function will return whatever * correction factor
#
# whatever: whatever is read, delta, epsilon, mu, slope...
# abu: abundances from the models used to calculate the whatever loaded
# ref_up: ref element 1 in normalization
# ref_down: ref element 2 in normalization
# mode: see above
# file_fractionation: file from wich it is read the element fractionation in the sample
# what_in_file: specify what correction is needed, given the case of interest """
#
# if mode == 0:
#
# whatever_corrected = whatever
#
# elif mode == 1:
#
# c = abu[e_label.index(ref_up)]/abu[e_label.index(ref_down)]/\
# (s_abu[s_iso_new.index(ref_up.replace("*", ""))]/\
# s_abu[s_iso_new.index(ref_down.replace("*", ""))])
# whatever_corrected = whatever * np.array(c)
#
# elif mode == 2:
#
# # open file and read the fractionation you want from what_in_file
# f_ = open(file_fractionation, 'r')
# header = f_.readline()
# if header.split()[0] != ref_up.rpartition('-')[0]:
# print('Element 1 in '+file_fractionation+' does not match '+ref_up+'! Stop!')
# if header.split()[1] != ref_down.rpartition('-')[0]:
# print('Element 2 in '+file_fractionation+' does not match '+ref_down+'! Stop!')
# for i in f_.readlines():
# if i.split()[0] == what_in_file:
# c = float(i.split()[1])
# break
# f_.close() # done with the file
# c = c/(s_abu[s_iso_new.index(ref_up.replace("*", ""))]/s_abu[s_iso_new.index(ref_down.replace("*", ""))])
# whatever_corrected = whatever * np.array(c)
#
# return(whatever_corrected)
#
# utility function to generate automatically CCSN ejecta zone structure in abundance plots (e.g., mass fractions vs mass coordinates). see e.g., Meyer+ 1995
def structure(all_models, reqmass, rmodel, y2, file_name_structure='file_name', write_structure_data=False):
mass = all_models[rmodel][reqmass]['masscoord']
he4 = all_models[rmodel][reqmass]['abu']['He-4']
c12 = all_models[rmodel][reqmass]['abu']['C-12']
ne20 = all_models[rmodel][reqmass]['abu']['Ne-20']
o16 = all_models[rmodel][reqmass]['abu']['O-16']
si28 = all_models[rmodel][reqmass]['abu']['Si-28']
n14 = all_models[rmodel][reqmass]['abu']['N-14']
ni56 = all_models[rmodel][reqmass]['abu']['Ni-56']
masscut = mass[0]
massmax = mass[-1]
print("m_cut: "+str(masscut))
print("massmax: "+str(massmax))
si_layer_log = False ; ni_layer_log = False # default, switched to true if they are found
# definition of borders
ih = np.where((he4 > 0.5))[0][-1]
print("Lower boundary of the H shell: "+str(mass[ih]))
ihe1 = np.where((n14 > o16) & (n14 > c12) & (n14 > 1.e-3))[0][0]