def petersen_plot(fname, minValueFirst):
#directory = '/home/janne/Gunter_project/44_tau/example_3ms/LOGS-1'
list_number = [
os.path.join(fname, file) for file in sorted(os.listdir(fname))
if file.endswith('-freqs.dat')
]
constraint_noprems = 480
periods = []
ratios = []
minima = []
alle = []
for file in list_number:
#file.sort(key=lambda x: '{0:0>20}'.format(x))
pnum = re.search('profile(.+?)-freqs', file)
if pnum:
pnums = pnum.group(1)
if int(pnums) >= int(constraint_noprems):
#print(pnums)
freqs = gyr.readmesa(file)
if size(freqs) == 1:
continue
if not freqs[0][1] == 1 and freqs[1][1] == 2:
continue
allfrequencies = [freqs[0][4] / freqs[1][4], file, pnums]
rat = freqs[0][4] / freqs[1][4]
currentValueFirst = np.abs(rat - r)
if minValueFirst == None:
minValueFirst = currentValueFirst
else:
minValueFirst = min(minValueFirst, currentValueFirst)
minima += [minValueFirst, pnums]
ratios += [freqs[0][4] / freqs[1][4]]
periods += [1. / freqs[0][4]]
alle.append(allfrequencies)
plt.plot(np.log10(1. / radial_funda), observed_ratio, 'k*')
#legend()
plt.plot(np.log10(periods), ratios, 'r-')
#plt.gca().invert_xaxis()
# plt.gca().invert_yaxis()
ylabel(r'$\Pi_1/\Pi_0$')
xlabel(r'$log\Pi_0$')
plt.rcParams.update({'font.size': 15})
return alle, minValueFirst, ratios, periods
def getfundfreqs(fname):
constraint_noprems = 400
fnumbers = []
alle = []
#fname = '/home/janne/Gunter_project/44_tau/output_postms_3ms/LOGS-1.5-0.02-0.7-0.2/'
for root, dirs, files in sorted(os.walk(fname)):
files.sort(key=lambda x: '{0:0>20}'.format(x))
for file in files:
if not file.endswith('freqs.dat'): #and os.path.exists(file)==True:
continue
fdires = os.path.join(root,file)
fdirec = os.path.join(root)
finaldir = fdirec.strip("/")
fdata = gar.readmesa(fdires)
dire = finaldir.split('-')
mass = float(dire[1])
z = float(dire[2])
y = float(dire[3])
mlt = float(dire[4])
#ove = [float(dire[5])]
if fdata[0][1] == 1 and fdata[1][1] == 2:
fnum = re.search('profile(.+?)-freqs.dat', fdires)
if fnum:
fnums = fnum.group(1)
if int(fnums) >= int(constraint_noprems):
fnumbers += [fnums]
#STRUCTURE: radial fundamental, radial first, mass, z, y, mixing-length, profile number
alle += [[fdata[0][4], fdata[1][4], mass, z, y, mlt, fnums]]
np.asarray(alle)
np.savetxt("test1.txt", alle, delimiter=",", newline = "\n", fmt="%s")
#np.savetxt('test1.txt', alle, fmt='%10d' )
return
#getfundfreqs('/home/janne/Gunter_project/44_tau/output_postms_3ms/LOGS-1.5-0.02-0.7-0.2/')
def allfreqs(fname):
alldata = []
alle = []
print(fname)
for root, dirs, files in sorted(os.walk(fname)):
for file in files:
if not file.endswith(
'freqs.dat'): #and os.path.exists(file)==True:
continue
dires = os.path.join(root, file)
#data = gar.readmesa(list_number[file])
data = gar.readmesa(dires)
#print(data)
if data[0][1] == 1 and data[1][1] == 2:
alle += [data, dires]
#print(alldata)
#dataset += []
#print(file, len(dataset))
alldata += [alle]
return alldata
def get_freqs(fname):
alle = []
fnumbers = []
allobs = []
for root, dirs, files in sorted(os.walk(fname)):
files.sort(key=lambda x: '{0:0>20}'.format(x))
for file in files:
if not file.endswith(
'freqs.dat'): #and os.path.exists(file)==True:
continue
fdires = os.path.join(root, file)
fdata = gar.readmesa(fdires)
if fdata[0][1] == 1 and fdata[1][1] == 2:
fnum = re.search('profile(.+?)-freqs.dat', fdires)
if fnum:
fnums = fnum.group(1)
if int(fnums) >= int(constraint_noprems):
chi_radial = (np.abs(radial_funda - fdata[0][4]) /
radial_funda_unc)**2
chi_first = (np.abs(radial_first - fdata[1][4]) /
radial_first_unc)**2
fnumbers += [fnums]
alle += [[chi_radial, chi_first, fnums]]
for i in fnumbers:
#print(i)
obs = getprofile(fname, i)
allobs += [obs]
return alle, allobs
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 2 15:21:06 2018
@author: janne
data.dtype.names
"""
import gyre_output_read as gar
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import seaborn as sns
from pylab import *
data, header = gar.readmesa(
'/home/janne/Gunter_project/gunther_project/44_tau/44tau_nad_output.txt')
harm_degree = data['l']
radial_order = data['n_pg']
acou_num = data['n_p']
grav_num = data['n_g']
#re_omega = data['Re(omega)']
#im_omega = data['Im(omega)']
#re_omega_int = data['Re(omega_int)']
#m_omega_int = data['Im(omega_int)']
re_freq = data[
'Refreq'] # these are the frequencies that will be compared to Lenz and observations.
im_freq = data['Imfreq'] #imaginary part of frequencies are not observable.
cd_to_mhz = 11.58
freq_obs = [
def chis(fname, profname):
finalarray = []
bm_array2 = []
models = 'profile{}-freqs.dat'.format(profname)
for root, dirs, files in sorted(os.walk(fname)):
for file in files:
if not file.startswith(models): #and os.path.exists(file)==True:
continue
names = []
dires = os.path.join(root, file)
data = gar.readmesa(dires)
harm_degree = data['l']
radial_order = data['n_pg']
re_freq_theo = data[
'Refreq'] # these are the frequencies that will be compared to Lenz and observations.
#im_freq = data['Imfreq'] #imaginary part of frequencies are not observable.
#print(re_freq_theo)
re_freq_obs = [6.8980, 8.9607, 11.20, 13.48]
re_freq_obs_unc = [
2.762223525 * 10**(-7), 8.454940424 * 10**(-7), 1 * 10**(-7),
1 * 10**(-7)
]
#print(len(np.atleast_1d(re_freq_theo)))
#re_freq_obs_unc = np.ones(len(re_freq_obs)) * 0.1
remaining_obs = re_freq_obs
#remaining_obs_unc = freq_obs_uncertainties
remaining_theo = np.atleast_1d(re_freq_theo)
remaining_ells = np.atleast_1d(harm_degree)
remaining_radial = np.atleast_1d(radial_order)
best_matching = []
#names = []
for ii in range(
min(len(np.atleast_1d(re_freq_theo)), len(re_freq_obs))
): #default: freq_obs, but depends if freq_obs is longer than re_freq
best = np.inf
bestjj = -1
bestkk = -1
#print(len(remaining_obs))
for jj in range(len(np.atleast_1d(remaining_theo))):
for kk in range(len(remaining_obs)):
val = (remaining_theo[jj] -
remaining_obs[kk])**2 / 4 #put uncertainty here
if (val < best):
best = val
bestjj = jj
bestkk = kk
# print(best, bestjj, bestkk)
best_matching += [[
remaining_ells[bestjj], remaining_radial[bestjj],
remaining_theo[bestjj], remaining_obs[bestkk]
]]
remaining_theo = np.delete(remaining_theo, bestjj)
remaining_ells = np.delete(remaining_ells, bestjj)
remaining_obs = np.delete(remaining_obs, bestkk)
#remaining_obs_unc = np.delete(remaining_obs_unc, bestkk)
remaining_radial = np.delete(remaining_radial, bestjj)
bm_array = np.asarray(best_matching)
bm_array2 += [bm_array]
finalarray += [[bm_array, file]]
#print(finalarray)
#print(bm_array2)
temp = []
for i in range(len(bm_array2)):
if len(bm_array2[i]) >= 2:
#farr += finalarray.pop(i)
temp.append(finalarray[i])
#else:
#print(finalarray[i][1])
finalarray = temp
chi2 = np.zeros(len(finalarray))
modelnos = np.zeros(len(finalarray))
#print(len(finalarray))
#number = []
for i, (array, modelno) in enumerate(finalarray):
array = np.asarray(array)
if len(array) == 4:
m = re.search('profile(.+?)-freqs.dat', modelno)
if m:
modelnos[i] = m.group(1)
#print(modelnos)
delta = array[:, 2] - array[:, 3]
chi2[i] = np.sum((delta**2) / (np.asarray(re_freq_obs_unc)**2))
chi2[i] /= len(delta)
#test_result.append(modelnos + chi2)
test_result = np.zeros((len(chi2), 2))
#rint(test_result)
for i in range(0, len(modelnos)):
test_result[i][0] = modelnos[i]
test_result[i][1] = chi2[i]
print(modelnos)
print(chi2)
#print(test_result)
#print(results)
return test_result, finalarray
#c = '/home/janne/Gunter_project/44_tau/example_zs/LOGS-2.0-0.022-0.29-1.5'
#a = '/home/janne/Gunter_project/gunther_project/LOGS_44_tau_testrun/'
#b = '/home/janne/Gunter_project/44_tau/example_3ms/LOGS-1'
#
#results = chis(b, '14')
file) for file in os.listdir(
'/home/janne/Gunter_project/gunther_project/LOGS_44_tau_testrun/')
if file.endswith('.dat')
]
finalarray = []
names = []
bm_array2 = []
for file in range(len(list_number)):
if file == 4 or file == 6:
continue
#if os.path.exists(file)==False:
# continue
#print(file)
data = gar.readmesa(list_number[file])
harm_degree = data['l']
radial_order = data['n_pg']
#acou_num = data['n_p']
#grav_num = data['n_g']
#re_omega = data['Re(omega)']
#im_omega = data['Im(omega)']
#re_omega_int = data['Re(omega_int)']
#m_omega_int = data['Im(omega_int)']
re_freq_theo = data[
'Refreq'] # these are the frequencies that will be compared to Lenz and observations.
#im_freq = data['Imfreq'] #imaginary part of frequencies are not observable.
#print(re_freq_theo)
def to_get_chi2(fname):
filename = 'results_l012.txt'
final_list = []
with open(filename) as f:
for line in f:
inner_list = [elt.strip() for elt in line.split(',')]
final_list.append(inner_list)
a = np.asarray(final_list)
profnums = a[:, 4]
finalarray = []
bm_array2 = []
#fname = '/home/janne/Gunter_project/44_tau/output_postms_3ms/LOGS-1.5-0.02-0.7-0.2'
for h in profnums:
direc = fname + '/profile' + h + '-freqs.dat'
#dires = os.path.join(root,file)
data = gar.readmesa(direc)
harm_degree = data['l']
radial_order = data['n_pg']
re_freq_theo = data[
'Refreq'] # these are the frequencies that will be compared to Lenz and observations.
#im_freq = data['Imfreq'] #imaginary part of frequencies are not observable.
#print(re_freq_theo)
re_freq_obs = [
6.8980, 7.0060, 9.1175, 11.5196, 8.9606, 9.5613, 7.3034, 6.7953,
9.5801, 6.3391, 8.6393, 11.2919
]
re_freq_obs_unc = np.ones(len(re_freq_obs)) * 10**(-7)
#print(len(np.atleast_1d(re_freq_theo)))
#re_freq_obs_unc = np.ones(len(re_freq_obs)) * 0.1
remaining_obs = re_freq_obs
#remaining_obs_unc = freq_obs_uncertainties
remaining_theo = np.atleast_1d(re_freq_theo)
remaining_ells = np.atleast_1d(harm_degree)
remaining_radial = np.atleast_1d(radial_order)
best_matching = []
#names = []
for ii in range(
min(len(np.atleast_1d(re_freq_theo)), len(re_freq_obs))
): #default: freq_obs, but depends if freq_obs is longer than re_freq
best = np.inf
bestjj = -1
bestkk = -1
#print(len(remaining_obs))
for jj in range(len(np.atleast_1d(remaining_theo))):
for kk in range(len(remaining_obs)):
val = (remaining_theo[jj] -
remaining_obs[kk])**2 / 4 #put uncertainty here
if (val < best):
best = val
bestjj = jj
bestkk = kk
# print(best, bestjj, bestkk)
best_matching += [[
remaining_ells[bestjj], remaining_radial[bestjj],
remaining_theo[bestjj], remaining_obs[bestkk]
]]
remaining_theo = np.delete(remaining_theo, bestjj)
remaining_ells = np.delete(remaining_ells, bestjj)
remaining_obs = np.delete(remaining_obs, bestkk)
#remaining_obs_unc = np.delete(remaining_obs_unc, bestkk)
remaining_radial = np.delete(remaining_radial, bestjj)
#diff = np.asarray(diff)
#final = np.append(bm_array,diff)
bm_array = np.asarray(best_matching)
bm_array2 += [bm_array]
finalarray += [[bm_array, h]]
chi2sum = {}
for i in range(len(finalarray)):
for k in range(len(finalarray[i][0])):
pname = finalarray[i][1]
if pname not in chi2sum.keys():
chi2sum[pname] = 0
chi2sum[pname] = chi2sum[pname] + (
np.abs(finalarray[i][0][k][2] - finalarray[i][0][k][3]) / 1 *
10**(-7))**2
kolonne = chi2sum.values()
output = np.column_stack((final_list, list(kolonne)))
np.savetxt("results_l012.txt",
output,
delimiter=",",
newline="\n",
fmt="%s")
return kolonne
#kolonne = to_get_chi2('/home/janne/Gunter_project/44_tau/output_postms_3ms/LOGS-1.5-0.02-0.7-0.2', 'test3.txt')
dire = '/home/janne/Gunter_project/44_tau/example_3ms/LOGS-3'
l = mr.MesaLogDir('/home/janne/Gunter_project/44_tau/example_3ms/LOGS-3')
time_age = []
fund_freqs = []
modelno = []
for root, dirs, files in sorted(os.walk(dire)):
files.sort(key=lambda x: '{0:0>20}'.format(x))
#natsorted(files, key=lambda y: y.lower())
for file in files:
if file.endswith('freqs.dat'): #and os.path.exists(file)==True:
dires = os.path.join(root, file)
#print(dires)
#print(dires)
data = gar.readmesa(dires)
re_freq_theo = data['Refreq']
radial_order = data['n_pg']
if radial_order[0] == 1:
re_freq_fund = re_freq_theo[1]
#print(re_freq_fund)
m = re.search('profile(.+?)-freqs.dat', file)
if m:
modelnos = m.group(1)
#print(modelno)
'/home/janne/Gunter_project/44_tau/example_10_masses/LOGS-1.50-0.02-0.7-0.4'
)):
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()
currentValue = difference_funda
if minValue == None:
minValue = currentValue
else:
minValue = min(minValue, currentValue)
differences.append(difference_funda)
temp2 = [gyredirs, difference_funda]
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
def chis(fname):
finalarray = []
bm_array2 = []
for root, dirs, files in sorted(os.walk(fname)):
for file in files:
if not file.endswith(
'freqs.dat'): #and os.path.exists(file)==True:
continue
#print(file)
names = []
# continue
dires = os.path.join(root, file)
#data = gar.readmesa(list_number[file])
data = gar.readmesa(dires)
#print(dires, data)
#print(dires)
harm_degree = data['l']
radial_order = data['n_pg']
#print(type(harm_degree))
#acou_num = data['n_p']
#grav_num = data['n_g']
#re_omega = data['Re(omega)']
#im_omega = data['Im(omega)']
#re_omega_int = data['Re(omega_int)']
#m_omega_int = data['Im(omega_int)']
re_freq_theo = data[
'Refreq'] # these are the frequencies that will be compared to Lenz and observations.
#im_freq = data['Imfreq'] #imaginary part of frequencies are not observable.
#print(re_freq_theo)
re_freq_obs = [6.8980, 8.9607]
re_freq_obs_unc = [2.762223525 * 10**(-7), 8.454940424 * 10**(-7)]
#print(len(np.atleast_1d(re_freq_theo)))
#re_freq_obs_unc = np.ones(len(re_freq_obs)) * 0.1
remaining_obs = re_freq_obs
#remaining_obs_unc = freq_obs_uncertainties
remaining_theo = np.atleast_1d(re_freq_theo)
remaining_ells = np.atleast_1d(harm_degree)
remaining_radial = np.atleast_1d(radial_order)
best_matching = []
#names = []
for ii in range(
min(len(np.atleast_1d(re_freq_theo)), len(re_freq_obs))
): #default: freq_obs, but depends if freq_obs is longer than re_freq
best = np.inf
bestjj = -1
bestkk = -1
#print(len(remaining_obs))
for jj in range(len(np.atleast_1d(remaining_theo))):
for kk in range(len(remaining_obs)):
val = (remaining_theo[jj] -
remaining_obs[kk])**2 / 4 #put uncertainty here
if (val < best):
best = val
bestjj = jj
bestkk = kk
# print(best, bestjj, bestkk)
best_matching += [[
remaining_ells[bestjj], remaining_radial[bestjj],
remaining_theo[bestjj], remaining_obs[bestkk]
]]
remaining_theo = np.delete(remaining_theo, bestjj)
remaining_ells = np.delete(remaining_ells, bestjj)
remaining_obs = np.delete(remaining_obs, bestkk)
#remaining_obs_unc = np.delete(remaining_obs_unc, bestkk)
remaining_radial = np.delete(remaining_radial, bestjj)
#diff = np.asarray(diff)
#final = np.append(bm_array,diff)
bm_array = np.asarray(best_matching)
bm_array2 += [bm_array]
finalarray += [[bm_array, file]]
#print(file)
#print(finalarray)
#print(bm_array2)
#print(len(bm_array2[26])) = 1
#farr = []
temp = []
for i in range(len(bm_array2)):
if len(bm_array2[i]) >= 2:
#farr += finalarray.pop(i)
temp.append(finalarray[i])
#else:
#print(finalarray[i][1])
finalarray = temp
chi2 = np.zeros(len(finalarray))
modelnos = np.zeros(len(finalarray))
#print(len(finalarray))
#number = []
for i, (array, modelno) in enumerate(finalarray):
array = np.asarray(array)
if len(array) == 4:
m = re.search('profile(.+?)-freqs.dat', modelno)
if m:
modelnos[i] = m.group(1)
#print(modelnos)
delta = array[:, 2] - array[:, 3]
chi2[i] = np.sum((delta**2) / (np.asarray(re_freq_obs_unc)**2))
chi2[i] /= len(delta)
#test_result.append(modelnos + chi2)
test_result = np.zeros((len(chi2), 2))
#rint(test_result)
for i in range(0, len(modelnos)):
test_result[i][0] = modelnos[i]
test_result[i][1] = chi2[i]
#, chi2[i]]
#test_results += [test_result]
#print(len(modelnos))
#print(modelnos)
#print(chi2)
plt.figure()
plt.plot(modelnos, np.log(chi2), '*', linestyle='None')
plt.xlabel('profile/model')
plt.ylabel('log(chi2)')
#print(test_result)
#print(results)
return test_results
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
