def runStats(runf,
props=[
'star_mass', 'log_Teff', 'c_core_mass', 'total_mass_h1',
'total_mass_he4', 'log_L', 'log_R'
]):
"""Prints out relevant run information such as remnant C core and
final mass.
Args:
runf (str): run folder to look in.
props (list of str): h.header names to print out. default is
['star_mass', 'log_Teff', 'c_core_mass', 'total_mass_h1',
'total_mass_he4', 'log_L', 'log_R'].
"""
h = mr.MesaData(os.path.join(runf, "LOGS/history.data"))
l = mr.MesaLogDir(os.path.join(runf, "LOGS"))
print ("Version: {version_number}\nInitial Mass(Z): "\
"{initial_mass} ({initial_z})".format(**h.header_data))
print("Profiles found: {}".format(len(l.profile_numbers)))
print("Models run: {}".format(h.data('model_number')[-1]))
print("Initial zones: {}".format(max(h.data('num_zones'))))
print("\nSimtime: {:10.9} Gyr\n".format(h.data('star_age')[-1] / 1e9))
print("{:20}{:<20} {:<20}\n".format('Prop', 'Initial', 'Final'))
otp = "{:20}{:<20.5f} {:<20.5f}"
for arg in props:
print(otp.format(arg, h.data(arg)[0], h.data(arg)[-1]))
if 'c_core_mass' in props:
p = l.profile_data()
zone = np.where(p.mass < p.c_core_mass)[0][0]
print ("Final Mass and Radius of the Carbon Core: "\
"{:6.5e} Msun {:6.5e} cm".format(p.mass[zone], p.R[zone]*_Rs))
print("150km match head x_match: {:.6e}".format(p.R[zone] * _Rs /
np.sqrt(2.)))
return len(l.profile_numbers)
def plot_hist(folder, xaxis, yaxis, save=False, name=None):
'''
Basic function to quick plot any parameter from history file
Parameters:
-----------
folder : str
LOGS directory
xaxis : str
parameter from history to plot on xaxis
yaxis : str
parameter from history to plot on yaxis
save : bool
True to save the plot, False to only show. Default is False
name : str
if save = True, name is a string with filename to ouput
'''
path = ms.MesaLogDir(folder)
hist = path.history
x = hist.data(xaxis)
y = hist.data(yaxis)
plt.plot(x, y, 'k.-')
plt.ylabel(yaxis)
plt.xlabel(xaxis)
if save is True:
plt.savefig(name + '.pdf')
plt.savefig(name + '.png')
return 'File save with name ' + name
else:
plt.show()
def getprofile(fname, pnumber):
#print(fname, pnumber)
profiles_indir = mr.MesaLogDir(fname)
pr = profiles_indir.profile_data(profile_number=pnumber)
teff = pr.Teff
r = pr.photosphere_r
l = pr.photosphere_L
m = pr.initial_mass
m_solar = 1.9892 * 10**(33)
r_solar = 6.9598 * 10**(10)
R = r * r_solar
M = m * m_solar
G = 6.67428 * 10**(-8)
g = G * M / R**2
chi_l = (np.abs(np.log10(l) - Log_L_obs) / Log_L_obs_unc)**2
chi_teff = (np.abs(np.log10(teff) - Log_Teff_obs) / Log_Teff_obs_unc)**2
chi_g = (np.abs(np.log10(g) - Log_g_obs) / Log_g_obs_unc)**2
observations = [chi_l, chi_teff, chi_g]
return observations
def getpdata(newdir, pnumber):
l = mr.MesaLogDir(newdir)
p = l.profile_data(profile_number=pnumber)
mass = p.initial_mass
return mass
def mesa_args(direc, profile):
'''read in what mesa data file to use'''
arg1 = direc
arg = arg1.split('_')
hist = "history_" + arg[0] + ".data"
direc = mr.MesaLogDir(log_path=arg1, history_file=hist)
prof = direc.profile_data(int(profile))
# read info about the MESA star
lab_mass = str(round(prof.star_mass, 3))
year = str(round(prof.star_age, 3))
model = str(round(prof.model_number, 3))
mesa_lab = year + " yr, " + lab_mass + " $M_{\\odot}$, " + model
return (prof, mesa_lab, lab_mass)
def getWholeHistory(folders, xkey='log_Teff', ykey='log_L'):
"""returns aggregated history for a pair of
keys from a list of sorted folders.
Defaults to HR variables log_Teff and log_L
"""
xvals, yvals = np.array([]), np.array([])
for f in folders:
h = mr.MesaData(os.path.join(f, "LOGS/history.data"))
l = mr.MesaLogDir(os.path.join(f, "LOGS"))
p = l.profile_data(profile_number=len(l.profile_numbers))
xrow, yrow = getHistProp(p, h, xkey, ykey)
xvals = np.append(xvals, xrow)
yvals = np.append(yvals, yrow)
return xvals, yvals
def getobsparams(fname):
filename = 'temp1_l012.txt'
list_of_lists = []
pdata = []
with open(filename) as f:
for line in f:
inner_list = [elt.strip() for elt in line.split(',')]
list_of_lists.append(inner_list)
for i in range(0, len(list_of_lists)):
profnum = list_of_lists[i][4]
profiles_indir = mr.MesaLogDir(fname)
pr = profiles_indir.profile_data(profile_number=profnum)
teff = pr.Teff
r = pr.photosphere_r
l = pr.photosphere_L
m = pr.initial_mass
m_solar = 1.9892 * 10**(33)
r_solar = 6.9598 * 10**(10)
R = r * r_solar
M = m * m_solar
G = 6.67428 * 10**(-8)
g = G * M / R**2
pdata += [[np.log10(teff), np.log10(g), np.log10(l)]]
np.asarray(pdata)
np.savetxt("temp2_l012.txt", pdata, delimiter=",", newline="\n", fmt="%s")
filename1 = 'temp1_l012.txt'
filename2 = 'temp2_l012.txt'
files = [[filename1, filename2]]
with open('results_l012.txt', 'w') as outfile:
for file in files:
with open(file[0]) as newfile, open(file[1]) as newfile1:
lines = zip(newfile, newfile1)
for line in lines:
outfile.write(line[0].rstrip() + "," + line[1])
return
#getobsparams('/home/janne/Gunter_project/44_tau/output_postms_3ms/LOGS-1.5-0.02-0.7-0.2')
def centralCond(runf, prof_number, show=False, tstamp=True):
"""Plots central rho vs central t found in the profile.
Args:
runf (str): run folder to look in.
prof_number (int): profile to plot.
byM (bool): plot by mass (True) or by radius (False).
show (bool): if True, returns the mpl.figure object.
Returns:
mpl.figure: plot of central rho vs central t.
"""
h = mr.MesaData(os.path.join(runf, "LOGS/history.data"))
l = mr.MesaLogDir(os.path.join(runf, "LOGS"))
profs = l.profile_numbers
if prof_number > len(profs):
print("Profile not found. ({}/{})".format(prof_number, len(profs)))
else:
print("Plotting T_c vs Rho_c. {}/{}.".format(prof_number, len(profs)))
p = l.profile_data(profile_number=prof_number)
rmax = l.profile_data(profile_number=1).photosphere_r
fig = plt.figure(figsize=(7, 5))
layout = (1, 1)
ax1 = plt.subplot2grid(layout, (0, 0),
aspect="auto",
adjustable='box-forced')
plotCenterTRho(p, h, ax1)
plt.tight_layout()
if tstamp:
ax1.annotate("{:0=8.7f}$\cdot 10^9$ yr".format(
float(p.star_age) / 1e9),
xy=(0.70, 0.08),
xycoords='axes fraction',
fontsize=10)
tag = 'trhoc'
if not os.path.exists("{}/png/".format(runf)):
os.mkdir("{}/png/".format(runf, tag))
os.mkdir("{}/png/{}".format(runf, tag))
elif not os.path.exists("{}/png/{}/".format(runf, tag)):
os.mkdir("{}/png/{}".format(runf, tag))
if show:
return fig
else:
plt.savefig("{0}/png/{1}/{1}_{2:05}".format(runf, tag, p.model_number))
plt.close(fig)
print("Wrote: {0}/png/{1}/{1}_{2:05}".format(runf, tag, p.model_number))
def prep_mesa(base): ''' Extract relevant information from MESA model for use with the ModeAnalyzer class below base--location of the mesa model ''' ld=mesa_reader.MesaLogDir(base+'/LOGS') pidx=mesa_reader.MesaProfileIndex(base+'/LOGS/profiles.index') idx_last=pidx.model_numbers[-1] prof=ld.profile_data(model_number=idx_last) prof.R=(prof.R*u.R_sun).cgs prof.mass=(prof.mass*u.M_sun).cgs prof.Rho=prof.Rho*u.g*u.cm**-3. prof.P=prof.P*u.g*u.cm**-1.*u.s**-2. return prof
def plot_prof(folder, xaxis, yaxis, mod_n, save=False, name=None):
'''
Basic function to quick plot any parameter from profiles file
Parameters:
-----------
folder : str
LOGS directory
xaxis : str
parameter from history to plot on xaxis
yaxis : str
parameter from history to plot on yaxis
save : bool
True to save the plot, False to only show. Default is False
name : str
if save = True, name is a string with filename to ouput
'''
path = ms.MesaLogDir(folder)
if mod_n is None:
profiles = path.model_numbers
model = profiles[-1]
# m_ind = -1
else:
model = mod_n
# m_ind = -1 # mod_n
prof = path.profile_data(model)
x = prof.data(xaxis)
y = prof.data(yaxis)
plt.plot(x, y, 'k.-')
plt.ylabel(yaxis)
plt.xlabel(xaxis)
if save is True:
plt.savefig(name + '.pdf')
plt.savefig(name + '.png')
return 'File save with name ' + name
else:
plt.show()
def print_totalm(path):
'''
Print final mass and total mass of h1, he4, c12 and o16
Parameter
--------
path : strings
path for LOGS directory
'''
np.set_printoptions(formatter={'float': '{: 0.5}'.format})
print('Total mass of isotopes (Msun)')
print('mf, h1, he4, c12, o16,')
fol = ms.MesaLogDir(path)
hist = fol.history
mass = hist.data('star_mass')
t_h1 = hist.data('total_mass_h1')
t_he = hist.data('total_mass_he4')
t_c12 = hist.data('total_mass_c12')
t_o16 = hist.data('total_mass_o16')
print(mass[-1], t_h1[-1], t_he[-1], t_c12[-1], t_o16[-1])
def plot_lum(folder, title=' ', save=False, name=None):
'''
Function to plot the Luminosity versus Age to check if occured
cristalization
Parameter
---------
folder : str
path to LOGS folder -> '/path/to/LOGS/'
title : str
title to use on the plot -> 'title'
save : bool
if True, save a .png file of the plot
name : str
name of the .png file if the parameter save is True
Returns
-------
show the plot or save a .png file
'''
path = ms.MesaLogDir(folder)
hist = path.history
lum = hist.data('log_L')
age = hist.data('star_age') # age = hist.data('star_age')
mi = hist.initial_mass
plt.figure(figsize=(10, 8))
plt.plot(age, lum)
plt.suptitle(r'$\log L$ X Age', fontsize=18)
plt.title('Mi = ' + str(mi) + ' ' + title)
plt.xlabel('Age [Years]',
fontsize=16) # plt.xlabel('Age [years]', fontsize=16)
plt.ylabel(r'$\log L$', fontsize=16)
if save is True:
plt.savefig(name + '.pdf')
return 'File save with name ' + name
else:
plt.show()
def hrd(folder, title=' ', save=False, name=None):
'''
Function to plot the Hertzsprung-Russel diagram
Parameter
---------
folder : str
path to LOGS folder -> '/path/to/LOGS/'
title : str
title to use on the plot -> 'title'
save : bool
if True, save a .png file of the plot
name : str
name of the .png file if the parameter save is True
Returns
-------
show the plot or save a .png file
'''
path = ms.MesaLogDir(folder)
hist = path.history
lum = hist.data('log_L')
teff = hist.data('log_Teff')
mi = hist.initial_mass
plt.figure(figsize=(10, 8))
plt.plot(teff, lum)
plt.gca().invert_xaxis()
plt.suptitle('HR Diagram', fontsize=18)
plt.title('Mi = ' + str(mi) + ' ' + title)
plt.xlabel(r'$\log T_{Eff}$', fontsize=16)
plt.ylabel(r'$\log L$', fontsize=16)
if save is True:
plt.savefig(name + '.pdf')
return 'File save with name ' + name
else:
plt.show()
def get_mat_mcenter(path):
'''
Extract the initial and final mass, total star age, log L and Teff in the
last model of evolution, and central masses of h1, he4, c12, n14, o16, ne20
and mg24. Age in years
Parameters
----------
path : string
LOGS directory path
Returns
-------
total star age in years, log Teff, Teff
'''
fol = ms.MesaLogDir(log_path=path)
h = fol.history
age = h.data('star_age')
logT = h.data('log_Teff')
teff = 10**logT
logL = h.data('log_L')
c_h1 = h.data('center_h1')
c_he4 = h.data('center_he4')
c_c12 = h.data('center_c12')
c_n14 = h.data('center_n14')
c_o16 = h.data('center_o16')
c_ne20 = h.data('center_ne20')
mi = h.data('star_mass')
prof_numbers = fol.profile_numbers
prof = fol.profile_data(profile_number=prof_numbers[-1])
c_mg24 = prof.data('mg24')
return np.array([
mi[0], mi[-1], age[-1], logL[-1], teff[-1], c_h1[-1], c_he4[-1],
c_c12[-1], c_n14[-1], c_o16[-1], c_ne20[-1], c_mg24[0]
])
def get_profs(history, dirs):
'''get the indices of profiles during various interesting points in the stars evolution '''
# 2d array
profs = []
# run thru each star we want to plot
for i in range(len(history)):
direc = mr.MesaLogDir(log_path=dirs[i], history_file=history[i])
profs_star = []
# cycle thru all profiles sequentially
for j in range(len(dirs[i].profile_numbers)):
# data for this profile
dat = dirs[i].profile_data(profile_numbers[j])
# preZAMS
nucpercent = 10**(dat.power_nuc_burn / dat.photosphere_L)
if nucpercent > 0.900 and hit_ZAMS == False and history[
i].star_age[j] > 50.0:
print("")
# add this array to our 2D array
profs.append(profs_star)
# plot_mesa_hr.py
# plots the output of mesa onto an hr diagram
import sys
import os
import string
import numpy as np
import pandas as pd
from astropy.io import ascii
from astropy.io import fits
import matplotlib.pyplot as plt
import mesa_reader as mr
# Read MESA Output
log_dir = mr.MesaLogDir('/Users/galaxies-air/Courses/Stars/ps1_mesa/LOGS')
profile = log_dir.profile_data(800)
# Read polytrope files
poly_colnames = ['xi', 'theta', 'd_theta']
n15_file = '/Users/galaxies-air/code/courses/stars/ps2/output/Poly-n1.5.txt'
n15_df = ascii.read(n15_file).to_pandas()
n15_df.columns = poly_colnames
n3_file = '/Users/galaxies-air/code/courses/stars/ps2/output/Poly-n3.0.txt'
n3_df = ascii.read(n3_file).to_pandas()
n3_df.columns = poly_colnames
axisfont = 14
ticksize = 12
ticks = 8
Log_g_obs_unc = 0.1
n = 10
#Three sigma intervals
Log_Teff_ns = n * Log_Teff_obs_unc
Log_L_ns = n * Log_L_obs_unc
Log_g_ns = n * Log_g_obs_unc
Log_Teff_lower = Log_Teff_obs - Log_Teff_ns
Log_Teff_upper = Log_Teff_obs + Log_Teff_ns
Log_L_lower = Log_L_obs - Log_L_ns
Log_L_upper = Log_L_obs + Log_L_ns
Log_g_lower = Log_g_obs - Log_g_ns
Log_g_upper = Log_g_obs + Log_g_ns
dire = mr.MesaLogDir('/home/janne/Gunter_project/44_tau/example_3ms/LOGS-1')
histnos = []
modelnos = []
profnos = []
filtered = []
#filtered1 = []
#filtered2 = []
#filtered3 = []
profarray = []
for root, dirs, files in sorted(
os.walk('/home/janne/Gunter_project/44_tau/example_3ms/LOGS-3')):
for file in files:
if file.startswith('profile') and file.endswith(
'.data'): #and os.path.exists(file)==True:
#print(natsort.natsorted(dirs,reverse=True))
h = mr.MesaData(dirs)
mass = h.initial_mass
index = h.star_age > 2.0e7
noms_Teff = h.log_Teff[index]
noms_L = h.log_L[index]
#plot(h.log_Teff, h.log_L)
#plot(noms_Teff, noms_L, label='%s$M_{\odot}$' %mass)
#plt.gca().invert_xaxis()
if file.endswith('profile1.data'):
pdirs = os.path.join(root)
print(pdirs)
l = mr.MesaLogDir(pdirs)
p = l.profile_data()
Z = p.initial_z
#M = p.initial_mass
plt.plot(Log_Teff_obs, Log_L_obs, 'r*', MarkerSize=10)
plt.rcParams.update({'font.size': 20})
plt.plot(noms_Teff,
noms_L,
'.',
MarkerSize=5,
label='Mass=%s' % mass)
# set axis labels
xlabel(r'$\logT_{eff}$')
ylabel(r'$\logL$')
def snapshot(runf,
prof_number=1,
byM=False,
rmax=0.0,
species=_ap13,
show=False,
core=False):
"""Plots a grid of general plots for the profile: Dens,
Temp, Pres, Abundances(X), and HR.
Args:
runf (str): run folder to look in or file to use.
prof_number (int): profile to plot(skipped if runf is a file).
byM (bool): plot by mass (True) or by radius (False).
rmax (float): radius limit in Rsun/Msun (depending on byM).
species (list): list of named species to plot on the X plot.
show (bool): if True, returns the mpl.figure object.
"""
if os.path.isdir(runf):
folder = True
h = mr.MesaData(os.path.join(runf, "LOGS/history.data"))
l = mr.MesaLogDir(os.path.join(runf, "LOGS"))
profs = l.profile_numbers
if prof_number > len(profs):
print("Profile not found. ({}/{})".format(prof_number, len(profs)))
else:
print("Plotting Abundances. {}/{}.".format(prof_number,
len(profs)))
p = l.profile_data(profile_number=prof_number)
if byM and not rmax:
rmax = p.initial_mass
elif not byM and not rmax:
rmax = l.profile_data(profile_number=1).photosphere_r
else:
folder = False
p = mr.MesaData(os.path.join(runf))
h = None
if byM and not rmax:
rmax = p.initial_mass
elif not byM and not rmax:
rmax = p.photosphere_r
fig = plt.figure(figsize=(15, 9))
# fill the grid with axes
layout = (3, 3)
ax1 = plt.subplot2grid(layout, (0, 0), aspect="auto")
ax2 = plt.subplot2grid(layout, (1, 0),
aspect="auto",
adjustable='box-forced') #, sharex=ax1)
ax3 = plt.subplot2grid(layout, (2, 0),
aspect="auto",
adjustable='box-forced')
ax4 = plt.subplot2grid(layout, (0, 1),
aspect="auto",
adjustable='box-forced',
rowspan=2)
ax5 = plt.subplot2grid(layout, (2, 1),
aspect="auto",
adjustable='box-forced')
plotMainProp(p,
ax1,
prop='dens',
byM=byM,
rmax=rmax,
tstamp=True,
color='black',
core=core)
plotMainProp(p,
ax2,
prop='temp',
byM=byM,
rmax=rmax,
tstamp=False,
color='red',
core=core)
plotMainProp(p,
ax3,
prop='pres',
byM=byM,
rmax=rmax,
tstamp=False,
color='green',
xtitle=True,
core=core)
plotAbundances(p, ax4, species=species, byM=byM, rmax=rmax, core=core)
if h is not None:
plotHR(p, h, ax5)
fig.subplots_adjust(hspace=0.5, wspace=0.4)
tag = 'prof'
if folder:
# build filetree and show or save the figure
if not os.path.exists("{}/png/".format(runf)):
os.mkdir("{}/png/".format(runf, tag))
os.mkdir("{}/png/{}".format(runf, tag))
elif not os.path.exists("{}/png/{}/".format(runf, tag)):
os.mkdir("{}/png/{}".format(runf, tag))
if show:
return fig
else:
plt.savefig("{0}/png/{1}/{1}_{2:05}".format(
runf, tag, p.model_number))
plt.close(fig)
print("Wrote: {0}/png/{1}/{1}_{2:05}".format(runf, tag,
p.model_number))
else:
# don't save, just return the profile to the notebook
return fig
def onedir(fname):
Log_Teff_obs = 3.839
#Log_L_obs = 1.340
Log_g_obs = 3.6
Log_Teff_obs_unc = 0.007
#Log_L_obs_unc = 0.0065
Log_g_obs_unc = 0.1
n = 3
Log_Teff_ns = n*Log_Teff_obs_unc
#Log_L_ns = n*Log_L_obs_unc
Log_g_ns = n*Log_g_obs_unc
Log_Teff_lower = Log_Teff_obs - Log_Teff_ns
Log_Teff_upper = Log_Teff_obs + Log_Teff_ns
#Log_L_lower = Log_L_obs - Log_L_ns
#Log_L_upper = Log_L_obs + Log_L_ns
Log_g_upper = Log_g_obs + Log_g_ns
Log_g_lower = Log_g_obs - Log_g_ns
radial_funda = 6.8980
#radial_first = 8.9606
#l = 10
#radial_unc = l* 0.05
#radial_funda_lower = radial_funda - radial_unc
#radial_funda_upper = radial_funda + radial_unc
#radial_first_upper = radial_first + radial_unc
#radial_first_lower = radial_first - radial_unc
frequencies = []
frequency_dirs = []
frequency_roots = []
temp = []
dires = []
test_result = []
logg_dires = []
diffsndirs = []
minimum = []
for root, dirs, files in sorted(os.walk(fname)):
logdirs = os.path.join(root)
dires += [logdirs]
for file in files:
if file.startswith('history'):
#print(os.path.join(root,file))
direcs = os.path.join(root,file)
rooties = os.path.join(root)
#print(natsort.natsorted(dirs,reverse=True))
h = mr.MesaData(direcs)
mass = h.initial_mass
model = h.model_number
index = h.star_age > 2.0e7
noms_model = model[index]
noms_logg = h.log_g[index]
logg_dir = [noms_logg, noms_model, rooties]
logg_dires.append(logg_dir)
noms_Teff = h.log_Teff[index]
noms_L = h.log_L[index]
test_result = np.zeros((len(noms_model),3))
#rint(test_result)
for i in range(0,len(noms_model)):
test_result[i][0] = noms_model[i]
test_result[i][1] = noms_logg[i]
test_result[i][2] = noms_Teff[i]
plt.plot(noms_Teff, noms_logg, '-',label='M=%s' %mass)
if file.startswith('profile') and file.endswith('.data'):
profiledirs = os.path.join(root,file)
pnum = re.search('profile(.+?).data', profiledirs)
if pnum:
pnums = pnum.group(1)
profiles_indir = mr.MesaLogDir(fname)
pr = profiles_indir.profile_data(profile_number=pnums)
pr_modelnos = pr.model_number
if pr_modelnos == noms_model[0]:
cutoff = pnums
if file.endswith('freqs.dat'):
gyredirs = os.path.join(root,file)
gyreroots = os.path.join(root)
fnum = re.search('profile(.+?)-freqs.dat', gyredirs)
if fnum:
fnums = fnum.group(1)
if int(cutoff) <= int(fnums):
#print(fnums)
#print(gyredirs)
freqs = gyr.readmesa(gyredirs)
frequencies += [freqs]
frequency_dirs += [gyredirs]
frequency_roots += [gyreroots]
allfreqs = [freqs ,gyredirs,gyreroots]
temp.append(allfreqs)
profiles = []
differs = []
temp3 = []
minValue = None
for i in range(0,len(temp)):
if size(temp[i][0]) ==1:
continue
if temp[i][0][0][1] == 1 and temp[i][0][1][1] == 2:
#print('hej')
succesfull_profiles = temp[i][1]
profile_directories = temp[i][2]
difference = np.abs(temp[i][0][0][4]-radial_funda)
#print(difference)
#difference_next = np.abs(temp[i+1][0][0][4]-radial_funda)
currentValue = difference
if minValue == None:
minValue = currentValue
else:
minValue = min(minValue, currentValue)
temp2 = [succesfull_profiles, difference]
diffsndirs.append(temp2)
profiles.append(profile_directories)
differs.append(difference)
minimum = differs.index(minValue)
minimum_profile = diffsndirs[minimum]
gnum = re.search('profile(.+?)-freqs.dat', minimum_profile[0])
if gnum:
gnums = gnum.group(1)
profiledata = mr.MesaLogDir(fname)
p = profiledata.profile_data(profile_number=gnums)
teff = p.Teff
#print(teff)
logteff = np.log10(teff)
lmodel = p.photosphere_L
logl = np.log10(lmodel)
modelno_profile = p.model_number
entry = np.where(logg_dires[0][1]== modelno_profile)
best_logg = logg_dires[0][0][entry]
plt.plot(logteff,best_logg,'k.', MarkerSize = 15)
"""
results_final = []
for root, dirs, files in sorted(os.walk('/home/janne/Gunter_project/44_tau/example_3ms/')):
for dire in dirs:
plt.figure(dire)
dires = os.path.join(root,dire)
#print(dires)
results = petersen_plot(dires)
results_final.append(results)
#print(results)
results
#print(logteff)
#alllogteffs.append(logteffs)
#plt.plot([alllogteffs[i][0],alllogteffs[i][-1]],[working_logg_dires[0], working_logg_dires[-1]],'k--')
"""
# set axis labels
xlabel(r'$\logT_{eff}$')
ylabel(r'$\log(g)$')
legend()
plt.gca().invert_xaxis()
plt.gca().invert_yaxis()
plt.rcParams.update({'font.size': 20})
#PLOT ERRORBOX:
plt.plot([Log_Teff_lower, Log_Teff_upper, Log_Teff_upper, Log_Teff_lower, Log_Teff_lower], [Log_g_lower, Log_g_lower, Log_g_upper, Log_g_upper, Log_g_lower], 'r-.', alpha=0.5, linewidth=3)
return best_logg,logteff
# plot_mesa_hr.py
# plots the output of mesa onto an hr diagram
import sys
import os
import string
import numpy as np
import pandas as pd
from astropy.io import ascii
from astropy.io import fits
import matplotlib.pyplot as plt
import mesa_reader as mr
# Read MESA Output
log_dir_solar = mr.MesaLogDir(
'/Users/galaxies-air/Courses/Stars/ps4/ps4_mesa_z0/LOGS')
log_dir_lowz = mr.MesaLogDir(
'/Users/galaxies-air/Courses/Stars/ps4/ps4_mesa_z-2/LOGS')
profile_solar = log_dir_solar.profile_data(817)
profile_lowz = log_dir_lowz.profile_data(918)
axisfont = 14
ticksize = 12
ticks = 8
titlefont = 24
legendfont = 14
textfont = 16
def plot_mesa(x_solar, y_solar, x_lowz, y_lowz, xlabel, ylabel, savename):
fig, ax = plt.subplots(figsize=(8, 7))
def useful_profiles(dirname):
histnos = []
modelnos = []
profnos = []
filtered = []
#filtered1 = []
#filtered2 = []
#filtered3 = []
profarray = []
dire = mr.MesaLogDir(dirname)
for root, dirs, files in sorted(os.walk(dirname)):
for file in files:
if file.startswith('profile') and file.endswith(
'.data'): #and os.path.exists(file)==True:
#print(root,file)
m = re.search('profile(.+?).data', file)
if m:
modelno = m.group(1)
#print(modelnos)
modelnos += [modelno]
if file.startswith('history'):
dirs = os.path.join(root, file)
#print(dirs)
#print(natsort.natsorted(dirs,reverse=True))
h = mr.MesaData(dirs)
Log_Teff_theo = h.log_Teff
Log_L_theo = h.log_L
Log_g_theo = h.log_g
#mask = Log_Teff_lower < Log_Teff_theo and Log_Teff_upper > Log_Teff_theo
histno = h.model_number
histnos += [histno]
histnos = np.array(histnos)
for i in range(0, len(Log_Teff_theo)):
mask1 = Log_Teff_lower < Log_Teff_theo[
i] and Log_Teff_upper > Log_Teff_theo[
i] and Log_L_lower < Log_L_theo[
i] and Log_L_upper > Log_L_theo[
i] and Log_g_lower < Log_g_theo[
i] and Log_g_upper > Log_g_theo[i]
filtered += [mask1]
filtered = np.array(filtered)
array = histnos[0]
array_filtered = array[filtered]
# for j in filtered:
# if j == True:
profiles = []
for i in modelnos:
#i = int(i)
p = dire.profile_data(profile_number=i)
profno = p.model_number
#print('Trues:')
for number in array_filtered:
#print(number)
#for l in range(1,len(array_filtered)):
if profno == number:
print(i)
#models = 'profile{}-freqs.data'.format(i)
profiles.append(i)
#print(models)
#profnos += [profno]
profiles
return profiles
#!/usr/bin/env python
import mesa_reader as mr
import matplotlib.pyplot as plt
import numpy as np
import sys
# set up some ploting stuff
fig = plt.figure(figsize=(12, 8))
plt.style.use('fast')
pallette = plt.get_cmap('magma')
pallette1 = plt.get_cmap('viridis')
# command line arguments
ddd = str(sys.argv[1])
hhh = str(sys.argv[2])
direc = mr.MesaLogDir(log_path=ddd, history_file=hhh)
profs = []
names = []
for i in range(3, len(sys.argv) - 1):
profs.append(direc.profile_data(int(sys.argv[i])))
names.append("model " + str(sys.argv[i]))
# last argument is name for plots
ttl = sys.argv[-1]
# temp
for i in range(len(profs)):
# set label
year = str(int(profs[i].star_age))
mass = str(round(profs[i].star_mass, 2))
model = str(profs[i].model_number)
def main():
print("In main analysis")
run_dir = "/home/vishal/UMassD/sem2/astro_phy/project/runs"
os.chdir(run_dir)
cmd = 'find shock_* -iname LOGS_part5'
results = os.popen(cmd).read().split('\n')
#print(results)
count = 1
masses = []
Zs = []
all_data = []
fe_core_z1 = [] # z=0.0001
fe_core_z2 = [] # z=0.001
fe_core_z3 = [] # z=0.01
fe_core_z4 = [] # z=0.1
si_core_z1 = [] # z=0.0001
si_core_z2 = [] # z=0.001
si_core_z3 = [] # z=0.01
si_core_z4 = [] # z=0.1
initial_mass_z1 = []
initial_mass_z2 = []
initial_mass_z3 = []
initial_mass_z4 = []
final_rem_masses = []
corrs_mass = []
tot_ran = 0
G = 6.67408e-11
M_sol = 1.98e+30
for paths in results:
if paths == "":
continue
print(paths)
count += 1
#pdb.set_trace()
model_name = paths.split('/')[1]
mass = float(re.findall('M_[0-9.]+',model_name)[0].split('_')[-1])# this is a string
masses.append(mass)
print("mass:"+str(mass))
z = float(re.findall('Z_[0-9.]+',model_name)[0].split('_')[-1])# this is a string
print("z:"+str(z))
Zs.append(z)
# read the history.data file and extract the Fe, Si mass.
#LOG_name = 'LOGS_finish/'
#data_path = paths.split('/')[-3] + "/" + paths.split('/')[-2]
# us the data_path
#pdb.set_trace()
try:
l = mr.MesaLogDir(paths)
pl = l.profile_data()
except:
print("Not able to read data from " + paths)
continue
M_in = pl.data('mass') * M_sol
R_in = pl.data('radius_cm') * 10**-2
vel_esc_sq = 2 * G * (M_in/R_in)
vel_sq = (pl.data('vel_km_per_s') * 10**3)**2
a = vel_sq[-200:-1] < vel_esc_sq[-200:-1]
try:
mass_indx = np.where(a==True)[0][0]
except:
print("issues")
continue
rem_mass = pl.data('mass')[-200:-1][mass_indx] # solar masses
tot_ran += 1
final_rem_masses.append(rem_mass)
corrs_mass.append(mass)
"""
#d = mr.MesaData(data_path + "/LOGS_finish/history.data")
fe_core_mass = d.data('fe_core_mass')[-1]
si_core_mass = d.data('si_core_mass')[-1]
all_data.append((mass,z,fe_core_mass,si_core_mass))
#pdb.set_trace()
if (z == 0.0001):
fe_core_z1.append(fe_core_mass)
si_core_z1.append(si_core_mass)
initial_mass_z1.append(mass)
elif (z==0.001):
fe_core_z2.append(fe_core_mass)
si_core_z2.append(si_core_mass)
initial_mass_z2.append(mass)
elif (z == 0.01):
fe_core_z3.append(fe_core_mass)
si_core_z3.append(si_core_mass)
initial_mass_z3.append(mass)
elif (z==0.1):
fe_core_z4.append(fe_core_mass)
si_core_z4.append(si_core_mass)
initial_mass_z4.append(mass)
"""
pdb.set_trace()
########################################################################
# Plot for Fe core and Si shell mass after the ms_to_ccsn models
pdb.set_trace()
# Plot the results:
plt.figure(1)
plt.subplot(2,2,1)
plt.scatter(initial_mass_z1,fe_core_z1,marker='^')
plt.scatter(initial_mass_z1,si_core_z1,marker='s')
#plt.scatter(initial_mass_z1,np.log10(fe_core_z1))
plt.title('z=0.0001')
#plt.xlabel('Initial Progenitor($M_\odot\$)')
plt.ylabel('Core Mass($M_{\odot}$)')
plt.subplot(2,2,2)
plt.scatter(initial_mass_z2,fe_core_z2,marker='^')
plt.scatter(initial_mass_z2,si_core_z2,marker='s')
#plt.scatter(initial_mass_z2,np.log10(fe_core_z2))
plt.title('z=0.001')
#plt.xlabel('Initial Progenitor($M_\odot\$)')
#plt.ylabel('Fe Core($M_\odot\$)')
plt.subplot(2,2,3)
plt.scatter(initial_mass_z3,fe_core_z3,marker='^')
plt.scatter(initial_mass_z3,si_core_z3,marker='s')
#plt.scatter(initial_mass_z3,np.log10(fe_core_z3))
plt.title('z=0.01')
plt.xlabel('Main Sequence Mass($M_{\odot}$)')
plt.ylabel('Core Mass($M_{\odot}$)')
plt.subplot(2,2,4)
plt.scatter(initial_mass_z4,fe_core_z4,marker='^')
plt.scatter(initial_mass_z4,si_core_z4,marker='s')
#plt.scatter(initial_mass_z4,np.log10(fe_core_z4))
plt.title('z=0.1')
plt.xlabel('Main Sequence Mass($M_{\odot}$)')
#plt.ylabel('Fe Core($M_\odot\$)')
plt.figlegend(['Fe Core' ,'Si Shell'])
plt.savefig('Fe_core_vs_initial_mass_log10.png')
plt.show()
########################################################################
# All the Fe core vs Initial mass model:
pdb.set_trace()
all_fe_core = fe_core_z1 + fe_core_z2 + fe_core_z3 + fe_core_z4
all_si_core = si_core_z1 + si_core_z2 + si_core_z3 + si_core_z4
all_initial_mass = initial_mass_z1 + initial_mass_z2 + initial_mass_z3 + initial_mass_z4
plt.figure(2)
plt.scatter(initial_mass_z1,fe_core_z1,marker='^',color="gray",alpha=0.25)
plt.scatter(initial_mass_z2,fe_core_z2,marker='^',color="gray",alpha=0.5)
plt.scatter(initial_mass_z3,fe_core_z3,marker='^',color="gray",alpha=0.75)
plt.scatter(initial_mass_z4,fe_core_z4,marker='^',color="gray",alpha=1.0)
plt.legend(["0.0001","0.001","0.01","0.1"], title="Metallicity")
plt.xlabel('Main Sequence Mass($M_{\odot}$)')
plt.ylabel('Core Mass($M_{\odot}$)')
plt.show()
###########################################################################
# Pickle the data up:
pdb.set_trace()
all_data = [all_fe_core , all_si_core]
pickle.dump(all_data,open('pickle_data.p','w'))
def species(runf,
prof_number=1,
byM=False,
rmax=0.0,
rmin=0.0,
core=False,
species=_ap13,
thresh=-6,
tstamp=True,
show=False):
"""Plots species found in a specified profile or a file.
Args:
runf (str): run folder to look in or file to use.
prof_number (int): profile to plot(skipped if runf is a file).
byM (bool): plot by mass (True) or by radius (False).
rmin/max (float): radius limits in Rsun/Msun (depending on byM).
core (bool): show c_core_mass in plot.
species (list): list of named species to plot.
thresh (float): sets lower limit of the plot.
tstamp (bool): write sim time on plot.
show (bool): if True, returns the mpl.figure object.
Returns:
mpl.figure: plot of abundances.
"""
if os.path.isdir(runf):
folder = True
l = mr.MesaLogDir(os.path.join(runf, "LOGS"))
profs = l.profile_numbers
if prof_number > len(profs):
print("Profile not found. ({}/{})".format(prof_number, len(profs)))
else:
print("Plotting Abundances. {}/{}.".format(prof_number,
len(profs)))
p = l.profile_data(profile_number=prof_number)
if byM and not rmax:
rmax = p.initial_mass
elif not byM and not rmax:
rmax = l.profile_data(profile_number=1).photosphere_r
else:
folder = False
p = mr.MesaData(runf)
if byM and not rmax:
rmax = p.initial_mass
elif not byM and not rmax:
rmax = p.photosphere_r
fig = plt.figure(figsize=(11, 7))
layout = (1, 1)
ax1 = plt.subplot2grid(layout, (0, 0),
aspect='auto',
adjustable='box-forced')
plotAbundances(p,
ax1,
species=species,
byM=byM,
core=core,
tstamp=tstamp,
rmax=rmax,
rmin=rmin,
xtitle=True,
thresh=thresh)
lgd = ax1.legend(ncol=6,
loc='upper left',
bbox_to_anchor=(1.0, 1.02),
columnspacing=0.3,
labelspacing=0.1,
markerfirst=False,
numpoints=3)
plt.tight_layout(pad=1.0, h_pad=0.0, w_pad=0.5, rect=(0, 0, 0.5, 1))
tag = 'abun'
if folder:
# build filetree and show or save the figure
if not os.path.exists("{}/png/".format(runf)):
os.mkdir("{}/png/".format(runf, tag))
os.mkdir("{}/png/{}".format(runf, tag))
elif not os.path.exists("{}/png/{}/".format(runf, tag)):
os.mkdir("{}/png/{}".format(runf, tag))
if show:
return fig
else:
plt.savefig("{0}/png/{1}/{1}_{2:05}".format(
runf, tag, p.model_number))
plt.close(fig)
print("Wrote: {0}/png/{1}/{1}_{2:05}".format(runf, tag,
p.model_number))
else:
# don't save, just return the profile to the notebook
return fig
if file.startswith('profile') and file.endswith('.data'):
profiledirs = os.path.join(root, file)
profileroots = os.path.join(root)
constraint_noprems = 500
pnum = re.search('profile(.+?).data', profiledirs)
if pnum:
pnums = pnum.group(1)
if int(pnums) >= int(constraint_noprems):
#print(pnums)
profiles_indir = mr.MesaLogDir(
'/home/janne/Gunter_project/44_tau/example_10_masses/LOGS-1.50-0.02-0.7-0.4'
)
pr = profiles_indir.profile_data(profile_number=pnums)
pr_modelnos = pr.model_number
teff = pr.Teff
#logteff = np.log(teff)
r = pr.photosphere_r
m = pr.initial_mass
g = m / r**2
#logg = np.log(g)
profile_numbers.append(pnums)
loggs.append(g)
logteffs.append(teff)
alle = [g, teff, pnums]
def main():
print("In main analysis")
run_dir = "/home/vishal/UMassD/sem2/astro_phy/project/runs"
os.chdir(run_dir)
cmd = 'find set_* -iname final.mod'
results = os.popen(cmd).read().split('\n')
cmd2 = 'find shock_* -iname LOGS_part5'
results2 = os.popen(cmd2).read().split('\n')
#print(results)
count = 1
masses = []
Zs = []
all_data = []
fe_core_z1 = [] # z=0.0001
fe_core_z2 = [] # z=0.001
fe_core_z3 = [] # z=0.01
fe_core_z4 = [] # z=0.1
si_core_z1 = [] # z=0.0001
si_core_z2 = [] # z=0.001
si_core_z3 = [] # z=0.01
si_core_z4 = [] # z=0.1
initial_mass_z1 = []
initial_mass_z2 = []
initial_mass_z3 = []
initial_mass_z4 = []
final_rem_masses_z1 = []
final_rem_masses_z2 = []
final_rem_masses_z3 = []
final_rem_masses_z4 = []
corrs_mass_z1 = []
corrs_mass_z2 = []
corrs_mass_z3 = []
corrs_mass_z4 = []
tot_ran = 0
G = 6.67408e-11
M_sol = 1.98e+30
for paths in results:
if paths == "":
continue
print(paths)
count += 1
model_name = paths.split('/')[-2]
mass = float(re.findall(
'M_[0-9.]+', model_name)[0].split('_')[-1]) # this is a string
masses.append(mass)
print("mass:" + str(mass))
z = float(re.findall('Z_[0-9.]+',
model_name)[0].split('_')[-1]) # this is a string
print("z:" + str(z))
Zs.append(z)
# read the history.data file and extract the Fe, Si mass.
LOG_name = 'LOGS_finish/'
data_path = paths.split('/')[-3] + "/" + paths.split('/')[-2]
#pdb.set_trace()
d = mr.MesaData(data_path + "/LOGS_finish/history.data")
fe_core_mass = d.data('fe_core_mass')[-1]
si_core_mass = d.data('si_core_mass')[-1]
all_data.append((mass, z, fe_core_mass, si_core_mass))
#pdb.set_trace()
if (z == 0.0001):
fe_core_z1.append(fe_core_mass)
si_core_z1.append(si_core_mass)
initial_mass_z1.append(mass)
elif (z == 0.001):
fe_core_z2.append(fe_core_mass)
si_core_z2.append(si_core_mass)
initial_mass_z2.append(mass)
elif (z == 0.01):
fe_core_z3.append(fe_core_mass)
si_core_z3.append(si_core_mass)
initial_mass_z3.append(mass)
elif (z == 0.1):
fe_core_z4.append(fe_core_mass)
si_core_z4.append(si_core_mass)
initial_mass_z4.append(mass)
for paths in results2:
if paths == "":
continue
print(paths)
count += 1
model_name = paths.split('/')[1]
mass = float(re.findall(
'M_[0-9.]+', model_name)[0].split('_')[-1]) # this is a string
print("mass:" + str(mass))
z = float(re.findall('Z_[0-9.]+',
model_name)[0].split('_')[-1]) # this is a string
print("z:" + str(z))
try:
l = mr.MesaLogDir(paths)
pl = l.profile_data()
except:
print("Not able to read data from " + paths)
continue
M_in = pl.data('mass') * M_sol
R_in = pl.data('radius_cm') * 10**-2
vel_esc_sq = 2 * G * (M_in / R_in)
vel_sq = (pl.data('vel_km_per_s') * 10**3)**2
a = vel_sq[-200:-1] < vel_esc_sq[-200:-1]
try:
mass_indx = np.where(a == True)[0][0]
except:
print("issues")
continue
rem_mass = pl.data('mass')[-200:-1][mass_indx] # solar masses
tot_ran += 1
if (z == 0.0001):
final_rem_masses_z1.append(rem_mass)
corrs_mass_z1.append(mass)
elif (z == 0.001):
final_rem_masses_z2.append(rem_mass)
corrs_mass_z2.append(mass)
elif (z == 0.01):
final_rem_masses_z3.append(rem_mass)
corrs_mass_z3.append(mass)
elif (z == 0.1):
final_rem_masses_z4.append(rem_mass)
corrs_mass_z4.append(mass)
#pdb.set_trace()
for indx, m in enumerate(initial_mass_z1):
if m in corrs_mass_z1:
plt.scatter(m,
fe_core_z1[indx],
marker='^',
color='blue',
alpha=0.25)
plt.scatter(m,
si_core_z1[indx],
marker='s',
color='orange',
alpha=0.25)
for indx, m in enumerate(initial_mass_z2):
if m in corrs_mass_z2:
plt.scatter(m,
fe_core_z2[indx],
marker='^',
color='blue',
alpha=0.5)
plt.scatter(m,
si_core_z2[indx],
marker='s',
color='orange',
alpha=0.5)
for indx, m in enumerate(initial_mass_z3):
if m in corrs_mass_z3:
plt.scatter(m,
fe_core_z3[indx],
marker='^',
color='blue',
alpha=0.75)
plt.scatter(m,
si_core_z3[indx],
marker='s',
color='orange',
alpha=0.75)
for indx, m in enumerate(initial_mass_z4):
if m in corrs_mass_z4:
plt.scatter(m,
fe_core_z4[indx],
marker='^',
color='blue',
alpha=1.0)
plt.scatter(m,
si_core_z4[indx],
marker='s',
color='orange',
alpha=1.0)
#plt.legend(['Si Shell'])
#plt.scatter(corrs_mass_z1,final_rem_masses_z1,color='red',alpha=0.25)
plt.scatter(corrs_mass_z2, final_rem_masses_z2, color='red', alpha=0.5)
plt.scatter(corrs_mass_z3, final_rem_masses_z3, color='red', alpha=0.75)
pdb.set_trace()
plt.legend(['Fe Core', 'Si Shell'])
#plt.scatter(corrs_mass_z4,final_rem_masses_z4,color='red',alpha=1.0)
#plt.legend(['Z=0.001','Z=0.01'])
#plt.legend(['Bound mass'])
plt.xlabel('Main Sequence Mass($M_{\odot}$)')
plt.ylabel('Core Mass($M_{\odot}$)')
plt.show()
def mod_abun(path,
h12=None,
h23=None,
name='abun.dat',
output=None,
profile_number=None,
iso1=None,
iso2='he4',
iso3='h1'):
'''
Function to modify abundance profile of one star
Parameters:
-----------
path : str
LOGS folder
h12 : float
mass of iso1 to exchange by iso2 (usually mass of c12 to
exhange by he4)
h23 : float
h12 = mass of iso2 to exchange by iso3 (usually mass of
he4 to exhange by h1)
name : str
name of output file
ouput : str
output directory. If None, output to current folder
profile_number : int
profile number to extract information. If None, program
will search for last profile
iso1 : str
first isotope to exchange mass. Usually c12, default is
None -> in this case exchange only he4 by h1
iso2 : str
second isotope to exchange mass. Usually he4, default is
he4
iso3 : str
last isotope to exchange mass. Usually h1, default is h1
'''
out = out_dir(output)
path = ms.MesaLogDir(path)
hist = path.history
star_m = hist.data('star_mass')
if profile_number is None:
profiles = path.model_numbers
model = profiles[-1]
model_index = -1
else:
model = profile_number
model_index = profile_number - 1
prof = path.profile_data(model)
headers = [
'q', 'dm', 'xq', 'h1', 'he3', 'he4', 'c12', 'c13', 'n13', 'n14', 'n15',
'o16', 'o17', 'o18', 'f19', 'ne20', 'ne22', 'mg24', 'si28'
]
data = np.array([
prof.data('q'),
prof.data('dm'), 1 - prof.data('q'),
prof.data('h1'),
prof.data('he3'),
prof.data('he4'),
prof.data('c12'),
prof.data('c13'),
prof.data('n13'),
prof.data('n14'),
prof.data('n15'),
prof.data('o16'),
prof.data('o17'),
prof.data('o18'),
prof.data('f19'),
prof.data('ne20'),
prof.data('ne22'),
prof.data('mg24'),
prof.data('si28')
])
df = pd.DataFrame(data.T, columns=headers)
if iso1 is None:
print('Substituindo {} por {}'.format(str(iso2), str(iso3)))
m23 = h23 * star_m[model_index]
new_abun = exchange_isotopes(iso2, iso3, m23, df)
# print('massa estrela = {}, valor que multiplicou a massa = {}'.format
# (star_m[model_index], h23))
print('Substituido {} de {} por {}'.format(
np.sum(new_abun[str(iso3)] * df['dm'] * 5.027652086e-34),
str(iso2), str(iso3)))
else:
print('Substituindo {} por {}'.format(str(iso1), str(iso2)))
print('E tambem substituindo {} por {}'.format(str(iso2), str(iso3)))
m12 = h12 * star_m[model_index]
abun12 = exchange_isotopes(iso1, iso2, m12, df)
m23 = h23 * star_m[model_index]
new_abun = exchange_isotopes(iso2, iso3, m23, abun12)
print('Substituido {} de {} por {}'.format(
np.sum(new_abun[str(iso2)] * df['dm'] * 5.027652086e-34),
str(iso1), str(iso2)))
print('Substituido {} de {} por {}'.format(
np.sum(new_abun[str(iso3)] * df['dm'] * 5.027652086e-34),
str(iso2), str(iso3)))
data_clean = new_abun.drop(['q', 'dm'], axis=1)
# Dados de colunas e linhas para o header do arquivo
h1 = data_clean.shape[0]
h2 = data_clean.shape[1] - 1
header = str(h1) + ', ' + str(h2)
# Salvando os dados
np.savetxt(os.path.join(out, str(name)),
data_clean,
fmt='%1.15f',
header=header,
comments='')
def abun_plot(folder,
mod_n=None,
x_lim=12,
title=' ',
save=False,
name=None,
isotope=None,
x_axis='atm'):
'''
Plot the abundance profile at model number using log(1-q) on x-axis
(better to see information on atmosfere)
Parameter
---------
folder : str
path to LOGS folder -> '/path/to/LOGS/'
mod_n : int
model number to calculate the profiles. If None, the last model
will be used
x_lim : int
limit to x-axis on profile plot. Default = 12
title : str
title to use on the plot -> 'title'
save : bool
if True, save a .png file of the plot
name : str
name of the .png file if the parameter save is True
x_axis : str
can be 'atm' or 'nuc'. If 'atm' is chosen, x-axis will be -log(1-q)
(better for atmosfere region). If 'nuc', x-axis will be q (better
for nucleus region). Default is 'atm'
isotope: Not implemented yet
Returns
-------
show the plot or save a .png file
'''
# Talvez implementar um parametro para escolher quais isotopos
# serao plotados
# if isotope == 'CO':
# isos = ['h1', 'he4', 'c12', 'n14', 'o16', 'ne20', 'mg24']
# elif isotope == 'ONe':
# isos = ['h1', 'he3','he4', 'c12', 'n14', 'o16', 'ne20', 'mg24']
path = ms.MesaLogDir(folder)
hist = path.history
# models = hist.data('model_number')
mass = hist.data('star_mass')
mi = hist.initial_mass
teff = hist.data('log_Teff')
if mod_n is None:
profiles = path.model_numbers
model = profiles[-1]
m_ind = -1
else:
model = mod_n
m_ind = -1 # mod_n
prof = path.profile_data(model)
# isos = ['h1', 'he4', 'c12', 'n14', 'o16', 'ne20', 'mg24']
# for i in isos:
if x_axis == 'atm':
x = -prof.data('logxq')
xlabel = r'$\log (1 - q)$'
else:
x = prof.data('q')
x_lim = 1
xlabel = r'$\frac{m}{M}$'
h1 = prof.data('h1')
#he3 = prof.data('he3')
he4 = prof.data('he4')
c12 = prof.data('c12')
#c13 = prof.data('c13')
#n13 = prof.data('n13')
n14 = prof.data('n14')
#n15 = prof.data('n15')
o16 = prof.data('o16')
#o18 = prof.data('o18')
ne20 = prof.data('ne20')
#ne22 = prof.data('ne22')
mg24 = prof.data('mg24')
plt.figure(figsize=(14, 7))
# plt.plot(x, he3, color='c', lw=1.5, label=r'$He3$')
plt.plot(x, he4, color='darkolivegreen', lw=1.5, label=r'$He4$')
plt.plot(x, c12, color='k', lw=1.5, label=r'$C12$')
# plt.plot(x, c13, color='grey', lw=1.5, label=r'$C13$')
# plt.plot(x, n13, color='darkred', lw=1.5, label=r'$N13$')
plt.plot(x, n14, color='r', lw=1.5, label=r'$N14$')
# plt.plot(x, n15, color='salmon', lw=1.5, label=r'$N15$')
plt.plot(x, o16, color='g', lw=1.5, label=r'$O16$')
# plt.plot(x, o18, color='limegreen', lw=1.5, label=r'$O18$')
plt.plot(x, ne20, color='y', lw=1.5, label=r'$Ne20$')
# plt.plot(x, ne22, color='orange', lw=1.5, label=r'$Ne22$')
# plt.plot(x, mg24, color='m', lw=1.5, label=r'$Mg24$')
plt.plot(x, h1, color='b', lw=1.5, label=r'$H1$')
plt.xlim(0, x_lim)
plt.xlabel(xlabel)
plt.ylabel('mass fraction')
plt.suptitle(title)
plt.title('Abundance at model ' + str(model) + r' $M_i = $ ' + str(mi) +
r' $M_f = $ ' + str(mass[m_ind]) + r' $Teff = $ ' +
str(10**teff[m_ind]))
plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
if save is True:
plt.savefig(name + '.pdf')
plt.savefig(name + '.png')
return 'File save with name ' + name
else:
plt.show()
def massconstrain(fname):
Log_Teff_obs = 3.839
Log_g_obs = 3.6
Log_Teff_obs_unc = 0.007
#Log_L_obs_unc = 0.0065
Log_g_obs_unc = 0.1
n = 3
Log_Teff_ns = n * Log_Teff_obs_unc
#Log_L_ns = n*Log_L_obs_unc
Log_g_ns = n * Log_g_obs_unc
Log_Teff_lower = Log_Teff_obs - Log_Teff_ns
Log_Teff_upper = Log_Teff_obs + Log_Teff_ns
#Log_L_lower = Log_L_obs - Log_L_ns
#Log_L_upper = Log_L_obs + Log_L_ns
Log_g_upper = Log_g_obs + Log_g_ns
Log_g_lower = Log_g_obs - Log_g_ns
radial_funda = 6.8980
radial_first = 8.9606
loggs = []
logteffs = []
profile_numbers = []
alle_final = []
#best_gs_radial = []
#best_teffs_radial = []
#best_gs_first = []
#best_teffs_first = []
for root, dirs, files in sorted(os.walk(fname)):
files.sort(key=lambda x: '{0:0>20}'.format(x))
for file in files:
if file.startswith('profile') and file.endswith('.data'):
profiledirs = os.path.join(root, file)
profileroots = os.path.join(root)
constraint_noprems = 450
pnum = re.search('profile(.+?).data', profiledirs)
if pnum:
pnums = pnum.group(1)
if int(pnums) >= int(constraint_noprems):
#print(pnums)
profiles_indir = mr.MesaLogDir(fname)
pr = profiles_indir.profile_data(profile_number=pnums)
pr_modelnos = pr.model_number
teff = pr.Teff
#logteff = np.log(teff)r = pr.photosphere_r
r = pr.photosphere_r
m = pr.initial_mass
m_solar = 1.9892 * 10**(33)
r_solar = 6.9598 * 10**(10)
R = r * r_solar
M = m * m_solar
G = 6.67428 * 10**(-8)
#logg = np.log(g)
g = G * M / R**2
profile_numbers.append(pnums)
loggs.append(g)
logteffs.append(teff)
alle = [g, teff, pnums]
alle_final.append(alle)
plt.plot(np.log10(logteffs), np.log10(loggs), label='mass = %f' % m)
plt.plot([
Log_Teff_lower, Log_Teff_upper, Log_Teff_upper, Log_Teff_lower,
Log_Teff_lower
], [Log_g_lower, Log_g_lower, Log_g_upper, Log_g_upper, Log_g_lower],
'r-.',
alpha=0.5,
linewidth=3)
plt.plot(Log_Teff_obs, Log_g_obs, 'r*')
#ax.set_xlim([3.7,4.05])
#ax.set_ylim([3.2,4.2])
alle = []
differences_radial = []
differences_first = []
minValueRadial = None
minValueFirst = None
diffsndirs_radial = []
diffsndirs_first = []
for root, dirs, files in sorted(os.walk(fname)):
files.sort(key=lambda x: '{0:0>20}'.format(x))
for file in files:
if file.endswith('freqs.dat'):
gyredirs = os.path.join(root, file)
gyreroots = os.path.join(root)
gnum = re.search('profile(.+?)-freqs.dat', gyredirs)
if gnum:
gnums = gnum.group(1)
if int(gnums) >= int(constraint_noprems):
#print(gnums)
freqs = gyr.readmesa(gyredirs)
if freqs[0][1] == 1 and freqs[1][1] == 2:
difference_funda = np.abs(freqs[0][4] - radial_funda)
difference_first = np.abs(freqs[1][4] - radial_first)
#differences.append()
currentValueRadial = difference_funda
if minValueRadial == None:
minValueRadial = currentValueRadial
else:
minValueRadial = min(minValueRadial,
currentValueRadial)
currentValueFirst = difference_first
if minValueFirst == None:
minValueFirst = currentValueFirst
else:
minValueFirst = min(minValueFirst,
currentValueFirst)
differences_radial.append(difference_funda)
differences_first.append(difference_first)
temp_radial = [gyredirs, difference_funda]
temp_first = [gyredirs, difference_first]
diffsndirs_radial.append(temp_radial)
diffsndirs_first.append(temp_first)
minimum_radial = differences_radial.index(minValueRadial)
minimum_profile_radial = diffsndirs_radial[minimum_radial]
minimum_first = differences_first.index(minValueFirst)
minimum_profile_first = diffsndirs_first[minimum_first]
best_g_radial = alle_final[minimum_radial][0]
best_teff_radial = alle_final[minimum_radial][1]
best_g_first = alle_final[minimum_first][0]
best_teff_first = alle_final[minimum_first][1]
allbest_radial = [
best_g_radial, best_teff_radial, minimum_profile_radial[0]
]
allbest_first = [best_g_first, best_teff_first, minimum_profile_first[0]]
plt.plot(np.log10(best_teff_radial),
np.log10(best_g_radial),
'k.',
MarkerSize=15)
plt.plot(np.log10(best_teff_first),
np.log10(best_g_first),
'g.',
MarkerSize=8)
allbest = [allbest_radial, allbest_first]
xlabel(r'$\logT_{eff}$')
ylabel(r'$\log(g)$')
legend()
plt.rcParams.update({'font.size': 15})
#plt.axis((3.8,3.9,3.3,4.0))
plt.gca().invert_xaxis()
plt.gca().invert_yaxis()
return allbest
