def Plot_HRD(root_dir='.', show_legend=False):
'''
Creates HRD for with every system found in the specified directory
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
show_legend : Bool
If True, display the legend on the HRD
'''
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
# print(subdir)
try:
ms.history_data(subdir).hrd(label=str(subdir[0:4]),
colour='k')
except:
pass
plt.title("HRD")
if show_legend:
plt.legend()
plt.show()
def Plot_PMdotM(root_dir="."):
'''
Plots the relative mass loss per binary cycle
Parameters
----------
root_dir : string
path to the directory containing the history.data file to be used
Returns
-------
Plots :
1. Relative mass loss of the system with each step
'''
m1 = ms.history_data(root_dir)
xval = m1.get("model_number")
yval = mesa_calc.Calc_PMdotM(root_dir)
plt.plot(xval, yval)
plt.xlabel("model number")
plt.ylabel("PMdot/M")
plt.show()
def Find_Value(root_dir=".", value=None):
'''
Finds the initial and final value of a user given property. If the
system is broken return "broken".
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
'''
value_matrix = [["directory", "initial", "final"]]
# value = raw_input("what value? ")
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
try:
m1 = ms.history_data(subdir)
x_i = m1.get(value)[0]
x_f = m1.get(value)[-1]
except:
x_i = "broken"
x_f = "broken"
# preface = subdir + " : "
x_i_string = "initial " + str(value) + " : " + str(x_i)
x_f_string = "final " + str(value) + " : " + str(x_f)
# print(preface)
# print(x_i_string)
# print(x_f_string)
value_matrix.append([subdir, x_i_string, x_f_string])
return value_matrix
def Calc_PMdotM(root_dir="."):
'''
Calculates the relative mass loss per cycle of a binary system
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
Returns
-------
pmdotm : array_like
relative amount of mass lost per binary period
'''
m1 = ms.history_data(root_dir)
i = 0
m1m = m1.get("star_1_mass")
m1lgmdot = m1.get("lg_mstar_dot_1")
m1p = m1.get("period_days")
pmdotm = []
for i in xrange(len(m1m)):
pmdotm.append((10**(m1lgmdot[i])) / (m1m[i]) * (m1p[i] / 365))
return pmdotm
def Find_Prob(root_dir="."): # Check weird files
'''
Determine if the files were corrupted in any way by checking if the
results iterate correctly
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
'''
Weirdness = []
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
print(subdir)
try:
m1 = ms.history_data(subdir)
model = m1.get('model_number')
for row in len(1, xrange(model)):
if model[row - 1] > model[row]:
Weirdness.append(subdir)
break
print(subdir)
except:
pass
return Weirdness
def Plot_EvenSpacing(root_dir, separation):
'''
Creates a HRD with data points "evenly" spaced along the curve
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
separation : int
Distance between subsequent datapoints
Returns
-------
Plots
A HRD with the entirel data set with the evenly spaced points overlayed
on top
'''
m1 = ms.history_data(root_dir)
L = m1.get("log_L")
Teff = m1.get("log_Teff")
newL = []
newT = []
point_index = val_find.Find_EvenSpacing(Teff, separation)
print(point_index)
for i in point_index:
newL.append(L[i])
newT.append(Teff[i])
m1.hrd()
plt.plot(newT, newL, 'o')
def Plot_Bifurcation(root_dir='.', show_legend=False):
'''
Creates period vs mass plot for with every system found in
the specified directory
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
show_legend : Bool
If True, display the legend on the HRD
'''
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
# print(subdir)
try:
m1 = ms.history_data(subdir)
plt.plot(m1.get('star_1_mass'), m1.get('period_days'))
except:
pass
if show_legend:
plt.legend()
plt.show()
def Find_Bifurcation(root_dir="."):
mass = []
bifur_period = []
ordered_period_list = []
convergence = []
mass_dirs = os.walk(root_dir).next()[1]
mass_dirs.sort()
for mass_dir in mass_dirs:
bp_found = False
print(mass_dir)
mass.append(mass_dir)
os.chdir(mass_dir)
period_dirs = []
sub_dirs = filter(os.path.isdir, os.listdir(os.getcwd()))
for directory in sub_dirs:
split_vals = directory.split('_')
period_val = split_vals[1][0:-1]
period_dirs.append(float(period_val))
sub_dirs = np.array(sub_dirs[0:len(period_dirs)])
period_dirs = np.array(period_dirs)
period_inds = period_dirs.argsort()
sorted_dirs = sub_dirs[period_inds]
ordered_period_list.append(sorted_dirs)
sub_converge = []
os.chdir("../")
# print(sorted_dirs)
for subdir in sorted_dirs:
try:
file_str = mass_dir + "/" + subdir + "/LOGS"
m1 = ms.history_data(file_str)
period = m1.get('period_days')
if period[-1] > period[0]:
sub_converge.append(False)
else:
sub_converge.append(True)
except:
sub_converge.append(True)
convergence.append(sub_converge)
i = 0
while not bp_found:
if sub_converge[i] != sub_converge[i + 1]:
print(sorted_dirs[i], sorted_dirs[i + 1])
bifur_period.append((sorted_dirs[i], sorted_dirs[i + 1]))
bp_found = True
else:
i += 1
return mass, ordered_period_list, convergence, bifur_period
def Find_Ongoing_RLOF(root_dir="."):
'''
Determine if the system was still in RLOF when it stopped running
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
'''
RLOF_list = []
rerun = []
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
try:
m1 = ms.history_data(subdir)
rel_RLOF = m1.get('lg_mtransfer_rate')[-1]
if rel_RLOF > -12:
RLOF_list.append(subdir[0:-5])
except:
pass
RLOF_list.sort()
for RLOF_dirs in RLOF_list:
file_to_open = RLOF_dirs + "/run_log"
runlog = open(file_to_open, 'r')
runlog.seek(0)
data = runlog.readlines()
if ('max_age' in data[-7]) or ('dt_is_zero' in data[-3]):
# print(RLOF_dirs + " needs to continue")
found = False
i = 0
while not found:
if 'last photo' not in data[i]:
i = i - 1
else:
lp_index = i + 1
lp_find = False
p_index = 0
while not lp_find:
if data[lp_index][p_index] != '/':
p_index = p_index - 1
else:
p_index = p_index + 1
lp_find = True
found = True
lp = data[lp_index][p_index:-1]
dnf_string = RLOF_dirs + " last photo: " + str(lp)
rerun.append(dnf_string)
runlog.close()
return rerun, RLOF_list
def Find_Outburst(root_dir=".", outburst_tol=0.1, number_tol=0, time_tol=1e6,
plot=False):
outburst_list = []
outnum_list = []
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
# print(subdir)
try:
outburst_num = 0
m1 = ms.history_data(subdir)
m1mt1 = 10**m1.get('lg_mstar_dot_1')[0:-1]
m1mt2 = 10**m1.get('lg_mstar_dot_1')[1:]
delta_MT = abs(m1mt1 - m1mt2)
percent_delta = (delta_MT / m1mt1)
m1_age = m1.get('star_age')
# model = m1.get('model_number')
delta_list = np.where(percent_delta >= outburst_tol)[0]
for k, g in groupby(enumerate(delta_list),
lambda (i, x): i - x):
group = map(itemgetter(1), g)
if len(group) > 2:
delta_list = np.setdiff1d(delta_list, group)
i = 0
tries = 10
while i <= len(delta_list) - 2 and tries > 0 \
and len(delta_list) > 1:
if abs(m1_age[delta_list[i]] -
m1_age[delta_list[i + 1]]) <= time_tol:
# print(abs(m1_age[i] - m1_age[i + 1]))
# print('restart')
delta_list = np.delete(delta_list, i)
i = 0
tries -= 1
else:
tries = 10
i += 1
if plot:
plt.clf()
plt.plot(m1_age[1:],
np.log10(percent_delta))
if len(delta_list) >= 2:
plt.plot(m1_age[1:][delta_list],
np.log10(percent_delta)[delta_list],
'or')
outburst_num = len(delta_list) // 2
print(subdir, outburst_num)
if outburst_num > number_tol:
outburst_list.append(subdir)
outnum_list.append(outburst_num)
except:
print("Convergence issues")
def Plot_CoreMass(root_dir="."):
'''
Plots the masses of the core for the following elements:
Hydrogen (H)
Helium (He)
Carbon (C)
Oxygen (0)
Silicon (Si)
Iron (Fe)
Parameters
----------
root_dir : string
path to the directory containing the history.data file to be used
Returns
-------
Plots :
1. The core masses of the various elements as the star evoloves
'''
m1 = ms.history_data(root_dir)
age = m1.get('star_age')
total_mass = m1.get('star_mass')
conv_core = m1.get('mass_conv_core')
he_core = m1.get('he_core_mass')
c_core = m1.get('c_core_mass')
o_core = m1.get('o_core_mass')
si_core = m1.get('si_core_mass')
fe_core = m1.get('fe_core_mass')
plt.plot(age, total_mass, label='total mass')
plt.plot(age, conv_core, label='Convective core')
plt.plot(age, he_core, label='He core')
plt.plot(age, c_core, label='C core')
plt.plot(age, o_core, label='O core')
plt.plot(age, si_core, label='Si core')
plt.plot(age, fe_core, label='Fe core')
plt.xlabel("Time")
plt.ylabel("M_sun")
plt.legend(loc=3)
try:
output_string = mesa_calc.Calc_MassVals(root_dir)
plt.figtext(0.15, 0.75, output_string)
except:
pass
plt.show()
def Plot_Gen_Quick(root_dir="."):
i = 0
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
try:
i = i + 1
plt.figure(i)
plt.clf()
m1 = ms.history_data(subdir)
plt.plot(m1.get('star_age'), m1.get('lg_mstar_dot_1'))
plt.title(subdir)
plt.savefig(root_dir + str(i))
except:
pass
def Calc_MassVals(root_dir="."):
'''
Calculates the relative mass of each element in the core
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
Returns-
-------
output_string : string
string of the masses of the various elements near the core
'''
m1 = ms.history_data(root_dir)
age = m1.get('star_age')
# conv_core = m1.get('mass_conv_core')
he_core = m1.get('he_core_mass')
c_core = m1.get('c_core_mass')
o_core = m1.get('o_core_mass')
si_core = m1.get('si_core_mass')
fe_core = m1.get('fe_core_mass')
h1_frac = m1.get('center_h1')
zero_point = np.where(h1_frac == 0)[0][0]
zero_time = age[zero_point]
he_mass = he_core[zero_point]
c_mass = c_core[zero_point]
o_mass = o_core[zero_point]
si_mass = si_core[zero_point]
fe_mass = fe_core[zero_point]
output_string = ("Time:" + str(zero_time) + ' yrs\n' +
"He Mass:" + str(he_mass) + ' M_sun\n' +
"C Mass:" + str(c_mass) + '\n' +
"O Mass:" + str(o_mass) + '\n' +
"Si Mass:" + str(si_mass) + '\n' +
"Fe Mass:" + str(fe_mass)
)
return output_string
def Plot_CoreFrac(root_dir="."):
'''
Plots the mass fractions of the core for the following elements:
Hydrogen (H)
Helium (He)
Carbon (C)
Oxygen (0)
Parameters
----------
root_dir : string
path to the directory containing the history.data file to be used
Returns
-------
Plots :
1. The core mass fractions of the various elements as the star evoloves
'''
m1 = ms.history_data(root_dir)
age = m1.get('star_age')
h1_frac = m1.get('center_h1')
he4_frac = m1.get('center_he4')
c12_frac = m1.get('center_c12')
o16_frac = m1.get('center_o16')
plt.plot(age, h1_frac, label='H Fraction')
plt.plot(age, he4_frac, label='He Fraction')
plt.plot(age, c12_frac, label='C Fraction')
plt.plot(age, o16_frac, label='O Fraction')
plt.xlabel("Time")
plt.legend(loc=3)
try:
output_string = mesa_calc.Calc_MassVals(root_dir)
plt.figtext(0.15, 0.75, output_string)
except:
pass
plt.show()
def Plot_Gen_Init_Mesh():
"""
Creates a plot showing a mesh of the initial systems after one
timestep. MESA data output doesn't include the 0th step so
the mesh is technically not an "initial" mesh.
"""
for subdir, dirs, files in os.walk("."):
for file_name in files:
if file_name == "history.data":
try:
m1 = ms.history_data(subdir)
plt.plot(m1.get('star_1_mass')[0],
np.log10(m1.get('period_days')[0]), 'k+')
except:
pass
plt.title("Initial Parameter Mesh")
plt.savefig("init_mesh")
def Plot_Gen(*args):
'''
Creates seperate plots for each argument given
Parameters
----------
args : string
Paths to the directories containing the history.log files to be plotted
Returns
-------
Plots :
One plot per argument with x and y variables defined by the user
'''
history_list = []
for dirs in args:
history_list.append(ms.history_data(dirs))
for colname in sorted(history_list[0].cols):
print(colname)
xaxis = raw_input("Enter x axis variable: ")
lg_mtransfer_rate = raw_input("Enter y axis variables: ")
for vals in history_list:
xvariable = vals.get(xaxis)
yvariable = vals.get(lg_mtransfer_rate)
varlabel = raw_input("label: ")
plt.plot(xvariable, yvariable, label=varlabel)
userxlabel = raw_input("Enter x label: ")
userylabel = raw_input("Enter y label: ")
usertitle = raw_input("Enter title: ")
plt.xlabel(userxlabel)
plt.ylabel(userylabel)
plt.title(usertitle)
plt.legend()
plt.show()
def Find_End_Result(root_dir="."):
'''
Determine the final result of the star. This can be one of the following
results:
1. Merger
2. Star has a convective core
3. Star does not have a convective core
4. Star has a convective core but angular momentum issues
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
'''
no_conv_core = ["no convective cores : "]
conv_core = ["convective cores : "]
conv_core_bad_J = ["bad angular momentum and convective : "]
merged = ["mergers : "]
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
print(subdir)
try:
m1 = ms.history_data(subdir)
sep = m1.get('binary_separation')
if True in (sep <= 0.1):
merged.append(subdir)
else:
if m1.get("mass_conv_core")[-1] == 0:
no_conv_core.append(subdir)
else:
conv_core.append(subdir)
except:
pass
for new_dir in conv_core[1:]:
conv_core_bad_J.append(Find_Bad_J(new_dir[:-5]))
return merged, conv_core, no_conv_core, conv_core_bad_J
def Plot_Gen_HRD_wPeriod():
m1 = ms.history_data("default/3.202R_2.19d_Def/LOGS")
m2 = ms.history_data("div_10/3.202R_2.19d_Def/LOGS")
m3 = ms.history_data("div_100/3.202R_2.19d_Def/LOGS")
m4 = ms.history_data("default/3.202R_2.19d_Mod/LOGS")
m5 = ms.history_data("div_10/3.202R_2.19d_Mod/LOGS")
m6 = ms.history_data("div_100/3.202R_2.19d_Mod/LOGS")
fig1, ax1 = plt.subplots(1, figsize=(10, 7.5))
ax1.plot(m3.get('star_age'), m3.get('lg_mstar_dot_1'), lw=0.1)
ax1.plot(m2.get('star_age'), m2.get('lg_mstar_dot_1'), lw=0.5)
ax1.plot(m1.get('star_age'), m1.get('lg_mstar_dot_1'), lw=1.0)
ax1.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax1.set_xlim(4E7, 5E7)
ax1.set_ylim(-14, -6)
ax1.set_ylabel(r'Mass Trasnfer Rate log$(\dot{M_\odot})$', fontsize=20)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.grid()
ax1.set_title("Default Magnetic Braking", fontsize=26)
xtext1 = ax1.xaxis.get_offset_text()
xtext1.set_size(20)
ytext1 = ax1.yaxis.get_offset_text()
ytext1.set_size(20)
fig1.tight_layout()
# plt.show()
fig1.savefig("2a")
fig2, ax2 = plt.subplots(1, figsize=(10, 7.5))
ax2.plot(m6.get('star_age'), m6.get('lg_mstar_dot_1'), lw=0.1)
ax2.plot(m5.get('star_age'), m5.get('lg_mstar_dot_1'), lw=0.5)
ax2.plot(m4.get('star_age'), m4.get('lg_mstar_dot_1'), lw=1.0)
ax2.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax2.set_xlim(4E7, 5E7)
ax2.set_ylim(-14, -6)
ax2.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.grid()
ax2.set_title("Modified Magnetic Braking", fontsize=26)
xtext2 = ax2.xaxis.get_offset_text()
xtext2.set_size(20)
ytext2 = ax2.yaxis.get_offset_text()
ytext2.set_size(20)
fig2.tight_layout()
# plt.show()
fig2.savefig("HRD_wPer")
def Plot_Gen_Rel_Overflow():
m1 = ms.history_data("default/3.202R_2.19d_Def/LOGS")
m2 = ms.history_data("div_10/3.202R_2.19d_Def/LOGS")
m3 = ms.history_data("div_100/3.202R_2.19d_Def/LOGS")
m4 = ms.history_data("default/3.202R_2.19d_Mod/LOGS")
m5 = ms.history_data("div_10/3.202R_2.19d_Mod/LOGS")
m6 = ms.history_data("div_100/3.202R_2.19d_Mod/LOGS")
fig1, ax1 = plt.subplots(1, figsize=(10, 7.5))
ax1.plot(m1.get('star_age'), m1.get('rl_relative_overflow_1'))
ax1.plot(m2.get('star_age'), m2.get('rl_relative_overflow_1'))
ax1.plot(m3.get('star_age'), m3.get('rl_relative_overflow_1'))
ax1.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax1.set_xlim(0, 4E8)
ax1.set_ylabel(r'Relative Roche Lobe Overflow', fontsize=20)
ax1.set_ylim(-0.5, 0.5)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.grid()
ax1.set_title("Default Magnetic Braking", fontsize=26)
xtext1 = ax1.xaxis.get_offset_text()
xtext1.set_size(20)
ytext1 = ax1.yaxis.get_offset_text()
ytext1.set_size(20)
fig1.tight_layout()
# plt.show()
fig1.savefig("RO_a")
fig2, ax2 = plt.subplots(1, figsize=(10, 7.5))
ax2.plot(m4.get('star_age'), m4.get('rl_relative_overflow_1'))
ax2.plot(m5.get('star_age'), m5.get('rl_relative_overflow_1'))
ax2.plot(m6.get('star_age'), m6.get('rl_relative_overflow_1'))
ax2.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax2.set_xlim(0, 1.5E8)
ax2.set_ylabel(r'Relative Roche Lobe Overflow', fontsize=20)
ax2.set_ylim(-0.5, 0.5)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.grid()
ax2.set_title("Default Magnetic Braking", fontsize=26)
xtext2 = ax2.xaxis.get_offset_text()
xtext2.set_size(20)
ytext2 = ax2.yaxis.get_offset_text()
ytext2.set_size(20)
fig2.tight_layout()
# plt.show()
fig2.savefig("RO_b")
def Plot_RLOF(root_dir="."):
'''
Goes into the inputted directory and plots the relative roche lobe overflow
from the donor star.
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
Returns
-------
Plots :
1. The relative roche lobe overflow of the desired system with the
various properties of the system at this time.
'''
m1 = ms.history_data(root_dir)
yval = m1.get("rl_relative_overflow_1")
xaxis = raw_input("Enter x axis variable: ")
xval = m1.get(xaxis)
plt.plot(xval, yval)
plt.ylabel("rl_relative_overflow_1")
plt.xlabel(xaxis)
plt.ylim(0, max(yval))
try:
output_string = val_find.Find_RLOF_Prop(root_dir)
plt.figtext(0.15, 0.75, output_string)
except:
pass
plt.show()
def Find_RLOF_Check(root_dir="."):
'''
Loops through the directories to determine of a system goes into RLOF.
If it does then list the time it goes into RLOF and if it is still in
that state
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
'''
rlof_cont = ["rlof ongoing"]
no_rlof = ["no rlof"]
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
# print(subdir)
try:
m1 = ms.history_data(subdir)
except:
print("Convergence issues")
preface = subdir + " : "
print(preface)
try:
m1rlof = m1.get('rl_relative_overflow_1')
rlof_index = np.where(m1rlof > 0)[0]
# rlof_start = []
rlof_ends = []
i = 0
while ((rlof_index[i + 1] - rlof_index[i]) < 100)\
and (i < len(rlof_index) - 2):
rlof_ends.append(rlof_index[i])
i = i + 1
if len(rlof_ends) == 0:
rlof_ends = [rlof_index[-1]]
rlof_start = rlof_index[1]
rlof_end = rlof_ends[-1]
# print("RLOF Success")
# # initial star age
t_i = m1.get("star_age")[rlof_start]
# evolution time
dt = m1.get("star_age")[rlof_end] - t_i
if m1rlof[rlof_end] == m1rlof[-1]:
rlof_cont.append(subdir + " " + str(dt))
# print("RLOF still ongoing")
except:
pass
# print("no RLOF")
# print("RLOF still ongoing: ")
# for line in rlof_cont:
# print(line)
# print("")
# print("No RLOF: ")
# for line in no_rlof:
# print(line)
return rlof_cont, no_rlof
def Find_RLOF_Prop(root_dir="."):
'''
Determine the value of various properties at the beginning of RLOF
and at the end of RLOF.
NOTE: The values representing the "end" of RLOF may not be correct,
The star can easily stop transferring mass and restart multiple
times, therefore the "end" value represents the last value in
the simulations that had a relative RLOF greater than 0
Values given:
Initial time of RLOF
Final donor mass after RLOF
Final companion mass after RLOF
Initial radius at the start of RLOF of donor
Final radius after RLOF of donor
Final period after RLOF
Total time or RLOF
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
'''
value_matrix = [["directory",
"rlof start",
"final star 1 mass",
"final star 2 mass",
"initial radius",
"final radius",
"initial period",
"final period",
"evolution time"]]
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
m1 = ms.history_data(subdir)
preface = subdir + " : "
print(preface)
try:
m1rlof = m1.get('rl_relative_overflow_1')
rlof_index = np.where(m1rlof > 0)[0]
# rlof_start = []
rlof_ends = []
i = 0
while ((rlof_index[i + 1] - rlof_index[i]) < 100)\
and (i < len(rlof_index) - 2):
rlof_ends.append(rlof_index[i])
i = i + 1
if len(rlof_ends) == 0:
rlof_ends = [rlof_index[-1]]
rlof_start = rlof_index[1]
rlof_end = rlof_ends[-1]
# # initial star age
t_i = m1.get("star_age")[rlof_start]
# final masses
M1_f = m1.get("star_1_mass")[rlof_end]
M2_f = m1.get("star_2_mass")[rlof_end]
# final period
P_f = m1.get("period_days")[rlof_end]
P_i = m1.get("period_days")[rlof_start]
# evolution time
dt = m1.get("star_age")[rlof_end] - t_i
# radius
r_i = 10**m1.get("log_R")[rlof_start]
r_f = 10**m1.get("log_R")[rlof_end]
# t_i_string = "RLOF starts at: " + str(t_i)
# M1_f_string = "final star 1 mass: " + str(M1_f)
# M2_f_string = "final star 2 mass: " + str(M2_f)
# r_i_string = "initial radius: " + str(r_i)
# r_f_string = "final radius: " + str(r_f)
# P_i_string = "initial period: " + str(P_i)
# P_f_string = "final period: " + str(P_f)
# dt_string = "RLOF time: " + str(dt)
# print(t_i_string)
# print(M1_f_string)
# print(M2_f_string)
# print(r_i_string)
# print(r_f_string)
# print(P_i_string)
# print(P_f_string)
# print(dt_string)
value_matrix.append([subdir, t_i, M1_f, M2_f,
r_i, r_f, P_i, P_f, dt])
except:
print("no RLOF")
return value_matrix
def Plot_Gen_Mdot_Compare():
mi_1 = ms.history_data("mod_6/LOGS")
mi_2 = ms.history_data("mod_8/LOGS")
# mi_3 = ms.history_data("mod_9/LOGS")
mi_4 = ms.history_data("mod_11/LOGS")
# mi_5 = ms.history_data("mod_10/LOGS")
mi_6 = ms.history_data("mod_12/LOGS")
me_1 = ms.history_data("mod_7/LOGS")
me_2 = ms.history_data("mod_13/LOGS")
me_3 = ms.history_data("mod_14/LOGS")
# m_str = "/home/kvan/mesa_work_dirs/8677_outburst_check/def_tol_test/LOGS"
# m_old = ms.history_data(m_str)
mi_1_label = "Implicit MT, No Max dt, Def Tolerances"
mi_2_label = "Implicit MT, 1E6 Max dt, Def Tolerances"
# mi_3_label = "Implicit MT, 1E6 Max dt, Strict Tol, Def Norms"
mi_4_label = "Implicit MT, 1E6 Max dt, Strict Tolerances"
# mi_5_label = "Implicit MT, 1E5 Max dt, Strict Tol/Norms"
mi_6_label = "Implicit MT, No Max dt, Strict Tolerances"
me_1_label = "Explicit MT, No Max dt, Def Tolerances"
me_2_label = "Explicit MT, 1E6 Max dt, Def Tolerances"
me_3_label = "Explicit MT, 1E6 Max dt, Strict Tolerances"
# m_old_label = "Previous Results"
fig1, ax1 = plt.subplots(1, figsize=(24, 13.5))
ax1.plot(mi_1.get('star_age'), mi_1.get('lg_mstar_dot_1'),
label=mi_1_label)
ax1.plot(mi_2.get('star_age'), mi_2.get('lg_mstar_dot_1') - 0.5,
label=mi_2_label)
# ax1.plot(mi_3.get('star_age'), mi_3.get('lg_mstar_dot_1') - 1.0,
# label=mi_3_label)
ax1.plot(mi_4.get('star_age'), mi_4.get('lg_mstar_dot_1') - 1.0,
label=mi_4_label)
# ax1.plot(mi_5.get('star_age'), mi_5.get('lg_mstar_dot_1') - 2.0,
# label=mi_5_label)
ax1.plot(mi_6.get('star_age'), mi_6.get('lg_mstar_dot_1') - 1.5,
label=mi_6_label)
ax1.plot(me_1.get('star_age'), me_1.get('lg_mstar_dot_1') - 2.0,
label=me_1_label)
ax1.plot(me_2.get('star_age'), me_2.get('lg_mstar_dot_1') - 2.5,
label=me_2_label)
ax1.plot(me_3.get('star_age'), me_3.get('lg_mstar_dot_1') - 3.0,
label=me_3_label)
# ax1.plot(m_old.get('star_age'), m_old.get('lg_mstar_dot_1') - 4.5,
# label=m_old_label)
ax1.set_xlabel(r'years', fontsize=20)
ax1.set_ylabel(r'Mass Loss Rate log($\dot{M_\odot}$)', fontsize=20)
xtext1 = ax1.xaxis.get_offset_text()
xtext1.set_size(20)
ytext1 = ax1.yaxis.get_offset_text()
ytext1.set_size(20)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.grid()
ax1.legend(loc=3)
fig1.tight_layout()
# plt.show()
fig1.savefig("Mdot_Compare")
def Plot_Gen_Simp_Mdot():
m1 = ms.history_data("def_mdot/3.202R_2.19d_Def/LOGS")
m2 = ms.history_data("implicit_test/3.202R_2.19d_Def/LOGS")
m3 = ms.history_data("def_mdot/3.202R_2.19d_Mod/LOGS")
m4 = ms.history_data("implicit_test/3.202R_2.19d_Mod/LOGS")
fig1, ax1 = plt.subplots(1, figsize=(10, 7.5))
ax1.plot(m1.get('star_age'), m1.get('lg_mstar_dot_1'))
ax1.plot(m2.get('star_age'), m2.get('lg_mstar_dot_1') - 0.5)
ax1.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax1.set_xlim(0, 4E8)
ax1.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.grid()
ax1.set_title("Default Magnetic Braking", fontsize=26)
xtext1 = ax1.xaxis.get_offset_text()
xtext1.set_size(20)
ytext1 = ax1.yaxis.get_offset_text()
ytext1.set_size(20)
fig1.tight_layout()
# plt.show()
fig1.savefig("Mdota")
fig2, ax2 = plt.subplots(1, figsize=(10, 7.5))
ax2.plot(m3.get('star_age'), m3.get('lg_mstar_dot_1'))
ax2.plot(m4.get('star_age'), m4.get('lg_mstar_dot_1') - 0.5)
ax2.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax2.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.grid()
ax2.set_title("Modified Magnetic Braking", fontsize=26)
xtext2 = ax2.xaxis.get_offset_text()
xtext2.set_size(20)
ytext2 = ax2.yaxis.get_offset_text()
ytext2.set_size(20)
fig2.tight_layout()
# plt.show()
fig2.savefig("Mdotb")
fig3, ax3 = plt.subplots(1, figsize=(10, 7.5))
ax3.plot(m1.get('period_days'), m1.get('lg_mstar_dot_1'))
ax3.plot(m2.get('period_days'), m2.get('lg_mstar_dot_1') - 0.5)
ax3.set_xlabel(r'Period (Days)', fontsize=20)
ax3.set_xscale('log')
ax3.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax3.tick_params(axis='both', which='major', labelsize=20)
ax3.grid()
ax3.set_title("Default Magnetic Braking", fontsize=26)
fig3.tight_layout()
# plt.show()
fig3.savefig("Mdotc")
fig4, ax4 = plt.subplots(1, figsize=(10, 7.5))
ax4.plot(m3.get('period_days'), m3.get('lg_mstar_dot_1'))
ax4.plot(m4.get('period_days'), m4.get('lg_mstar_dot_1') - 0.5)
ax4.set_xlabel(r'Period (Days)', fontsize=20)
ax4.set_xscale('log')
ax4.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax4.tick_params(axis='both', which='major', labelsize=20)
ax4.grid()
ax4.set_title("Modified Magnetic Braking", fontsize=26)
fig4.tight_layout()
# plt.show()
fig4.savefig("Mdotd")
def Plot_Gen_Mdot_Tol_MB():
m01 = ms.history_data("def_mdot/3.202R_2.19d_Def/LOGS")
m02 = ms.history_data("tol_converg_test/3.202R_2.19d_Def/LOGS")
m03 = ms.history_data("tol_converg_test2/3.202R_2.19d_Def/LOGS")
m04 = ms.history_data("fr_test/3.202R_2.19d_Def/LOGS")
m05 = ms.history_data("cur_mdot_0_test/3.202R_2.19d_Def/LOGS")
m06 = ms.history_data("def_mdot/3.202R_2.19d_Mod/LOGS")
m07 = ms.history_data("tol_converg_test/3.202R_2.19d_Mod/LOGS")
m08 = ms.history_data("tol_converg_test2/3.202R_2.19d_Mod/LOGS")
m09 = ms.history_data("fr_test/3.202R_2.19d_Mod/LOGS")
m10 = ms.history_data("cur_mdot_0_test/3.202R_2.19d_Mod/LOGS")
fig1, ax1 = plt.subplots(1, figsize=(10, 7.5))
ax1.plot(m01.get('star_age'), m01.get('lg_mstar_dot_1'),
label='Default Tolerance')
ax1.plot(m02.get('star_age'), m02.get('lg_mstar_dot_1') - 0.5,
label=r'$\frac{1}{10}$ Tolerance')
ax1.plot(m03.get('star_age'), m03.get('lg_mstar_dot_1') - 1.0,
label=r'$\frac{1}{100}$ Tolerance')
ax1.plot(m04.get('star_age'), m04.get('lg_mstar_dot_1') - 1.5,
label=r'$\frac{1}{10}$ $\frac{R-R_l}{R}$')
ax1.plot(m05.get('star_age'), m05.get('lg_mstar_dot_1') - 2.0,
label=r'Independent $\dot{M}$ Calculation')
ax1.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax1.set_xlim(0, 4E8)
ax1.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax1.set_ylim(-18, -6)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.grid()
ax1.legend(loc=4)
ax1.set_title("Default Magnetic Braking", fontsize=26)
xtext1 = ax1.xaxis.get_offset_text()
xtext1.set_size(20)
ytext1 = ax1.yaxis.get_offset_text()
ytext1.set_size(20)
fig1.tight_layout()
# plt.show()
fig1.savefig("Mdot_Age_Def")
fig2, ax2 = plt.subplots(1, figsize=(10, 7.5))
ax2.plot(m01.get('period_days'), m01.get('lg_mstar_dot_1'))
ax2.plot(m02.get('period_days'), m02.get('lg_mstar_dot_1') - 0.5)
ax2.plot(m03.get('period_days'), m03.get('lg_mstar_dot_1') - 1.0)
ax2.plot(m04.get('period_days'), m04.get('lg_mstar_dot_1') - 1.5)
ax2.plot(m05.get('period_days'), m05.get('lg_mstar_dot_1') - 2.0)
ax2.set_xlabel(r'Period (Days)', fontsize=20)
ax2.set_xscale('log')
ax2.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax2.set_ylim(-18, -6)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.grid()
ax2.set_title("Default Magnetic Braking", fontsize=26)
fig2.tight_layout()
# plt.show()
fig2.savefig("Mdot_Per_Def")
fig3, ax3 = plt.subplots(1, figsize=(10, 7.5))
ax3.plot(m06.get('star_age'), m06.get('lg_mstar_dot_1'))
ax3.plot(m07.get('star_age'), m07.get('lg_mstar_dot_1') - 0.5)
ax3.plot(m08.get('star_age'), m08.get('lg_mstar_dot_1') - 1.0)
ax3.plot(m09.get('star_age'), m09.get('lg_mstar_dot_1') - 1.5)
ax3.plot(m10.get('star_age'), m10.get('lg_mstar_dot_1') - 2.0)
ax3.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax3.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax3.set_ylim(-18, -6)
ax3.tick_params(axis='both', which='major', labelsize=20)
ax3.grid()
ax3.set_title("Modified Magnetic Braking", fontsize=26)
xtext3 = ax3.xaxis.get_offset_text()
xtext3.set_size(20)
ytext3 = ax3.yaxis.get_offset_text()
ytext3.set_size(20)
fig3.tight_layout()
# plt.show()
fig3.savefig("Mdot_Age_Mod")
fig4, ax4 = plt.subplots(1, figsize=(10, 7.5))
ax4.plot(m06.get('period_days'), m06.get('lg_mstar_dot_1'))
ax4.plot(m07.get('period_days'), m07.get('lg_mstar_dot_1') - 0.5)
ax4.plot(m08.get('period_days'), m08.get('lg_mstar_dot_1') - 1.0)
ax4.plot(m09.get('period_days'), m09.get('lg_mstar_dot_1') - 1.5)
ax4.plot(m10.get('period_days'), m10.get('lg_mstar_dot_1') - 2.0)
ax4.set_xlabel(r'Period (Days)', fontsize=20)
ax4.set_xscale('log')
ax4.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax4.set_ylim(-18, -6)
ax4.tick_params(axis='both', which='major', labelsize=20)
ax4.grid()
ax4.set_title("Modified Magnetic Braking", fontsize=26)
fig4.tight_layout()
# plt.show()
fig4.savefig("Mdot_Per_Mod")
def Plot_Gen_MDot_Per_Age():
m1 = ms.history_data("def_mdot/3.202R_2.19d_Def/LOGS")
m2 = ms.history_data("def_mdot_10/3.202R_2.19d_Def/LOGS")
m3 = ms.history_data("def_mdot_100/3.202R_2.19d_Def/LOGS")
m4 = ms.history_data("def_mdot/3.202R_2.19d_Mod/LOGS")
m5 = ms.history_data("def_mdot_10/3.202R_2.19d_Mod/LOGS")
m6 = ms.history_data("def_mdot_100/3.202R_2.19d_Mod/LOGS")
fig1, ax1 = plt.subplots(1, figsize=(10, 7.5))
ax1.plot(m1.get('star_age'), m1.get('lg_mstar_dot_1'),
label='Default MB, 1E5 Timestep')
ax1.plot(m2.get('star_age'), m2.get('lg_mstar_dot_1'),
label='1E4 Timestep')
ax1.plot(m3.get('star_age'), m3.get('lg_mstar_dot_1'),
label='1E3 Timestep')
ax1.plot(m4.get('star_age'), m4.get('lg_mstar_dot_1'),
label='Modified MB, 1E5 Timestep')
ax1.plot(m5.get('star_age'), m5.get('lg_mstar_dot_1'),
label='1E4 Timestep')
ax1.plot(m6.get('star_age'), m6.get('lg_mstar_dot_1'),
label='1E3 Timestep')
ax1.set_xlabel(r'Time Since Binary Formation (years)', fontsize=20)
ax1.set_xlim(0, 4E8)
ax1.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax1.tick_params(axis='both', which='major', labelsize=20)
ax1.grid()
ax1.legend(loc=4)
xtext1 = ax1.xaxis.get_offset_text()
xtext1.set_size(20)
ytext1 = ax1.yaxis.get_offset_text()
ytext1.set_size(20)
fig1.tight_layout()
# plt.show()
fig1.savefig("Mdot_Age")
fig2, ax2 = plt.subplots(1, figsize=(10, 7.5))
ax2.plot(m1.get('period_days')[0], m1.get('lg_mstar_dot_1')[0], 'k*',
ms=15)
ax2.plot(m1.get('period_days'), m1.get('lg_mstar_dot_1'))
ax2.plot(m2.get('period_days'), m2.get('lg_mstar_dot_1'))
ax2.plot(m3.get('period_days'), m3.get('lg_mstar_dot_1'))
ax2.plot(m4.get('period_days'), m4.get('lg_mstar_dot_1'))
ax2.plot(m5.get('period_days'), m5.get('lg_mstar_dot_1'))
ax2.plot(m6.get('period_days'), m6.get('lg_mstar_dot_1'))
ax2.set_xlabel(r'Period (Days)', fontsize=20)
ax2.set_xscale('log')
ax2.set_ylabel(r'Mass Loss Rate log$(\dot{M_\odot})$', fontsize=20)
ax2.tick_params(axis='both', which='major', labelsize=20)
ax2.grid()
ax2.legend(loc=3)
xtext2 = ax1.xaxis.get_offset_text()
xtext2.set_size(20)
ytext2 = ax1.yaxis.get_offset_text()
ytext2.set_size(20)
fig2.tight_layout()
# plt.show()
fig2.savefig("Mdot_Per")
def Plot_HRD_Detailed(root_dir="."):
'''
Should be in the directory containing all the single star evolutions.
Creates an HRD diagram with mass values listed next to the evolution
curves. Adds data points to selected points where we begin binary
evolution, in this case we have it at the periods of 1 day, 2 days
10 days and 100 days.
Parameters
----------
root_dir : string
Path to the directory containing various single star evolutions
used in later to create binaries
Returns
-------
A detailed HRD plot containing all single star evolutions with points
showing select periods for binary formation
Figure 1 in paper
'''
d1_plotted = False
d2_plotted = False
d10_plotted = False
d100_plotted = False
mass_dirs = next(os.walk(root_dir))[1]
mass_dirs.sort()
per_100_list = []
per_10_list = []
per_2_list = []
per_1_list = []
for mass in mass_dirs:
rad_dirs = next(os.walk(mass))[1]
rad_dirs.sort()
for i in xrange(len(rad_dirs)):
# Cut the 'R' character off the directory to sort radii
rad_dirs[i] = float(rad_dirs[i][0:-1])
rad_dirs.sort()
per_100_str = mass + '/' + str(format(rad_dirs[-1], '.3f')) + 'R/LOGS'
per_100_list.append(per_100_str)
per_10_str = mass + '/' + str(format(rad_dirs[28], '.3f')) + 'R/LOGS'
per_10_list.append(per_10_str)
per_2_str = mass + '/' + str(format(rad_dirs[14], '.3f')) + 'R/LOGS'
per_2_list.append(per_2_str)
per_1_str = mass + '/' + str(format(rad_dirs[8], '.3f')) + 'R/LOGS'
per_1_list.append(per_1_str)
plt.figure(1, figsize=(24, 13.5))
plt.clf()
for index in xrange(len(per_100_list)):
model_100 = ms.history_data(per_100_list[index])
model_10 = ms.history_data(per_10_list[index])
model_2 = ms.history_data(per_2_list[index])
model_1 = ms.history_data(per_1_list[index])
model_100.hrd(colour='k', s2ms=True)
h1 = model_100.get('center_h1')
idx = np.where(h1[0] - h1 >= 3.e-3)[0][0]
plt.text(model_100.get('log_Teff')[idx] + 0.01,
model_100.get('log_L')[idx] - 0.1,
str(model_100.get('star_mass')[0]) + r'$\dot{M_\odot}$')
if not d1_plotted:
d1_plotted = True
plt.plot(model_1.get('log_Teff')[-1],
model_1.get('log_L')[-1],
'r*', ms=8, label="P=1 d")
plt.plot(model_1.get('log_Teff')[-1],
model_1.get('log_L')[-1],
'r*', ms=12)
if not d2_plotted:
d2_plotted = True
plt.plot(model_2.get('log_Teff')[-1],
model_2.get('log_L')[-1],
'bo', ms=8, label="P=2 d")
plt.plot(model_2.get('log_Teff')[-1],
model_2.get('log_L')[-1],
'bo', ms=10)
if not d10_plotted:
d10_plotted = True
plt.plot(model_10.get('log_Teff')[-1],
model_10.get('log_L')[-1],
'cs', ms=8, label="P=10 d")
plt.plot(model_10.get('log_Teff')[-1],
model_10.get('log_L')[-1],
'cs', ms=8)
if not d100_plotted:
d100_plotted = True
plt.plot(model_100.get('log_Teff')[-1],
model_100.get('log_L')[-1],
'g^', ms=8, label="P=100 d")
plt.plot(model_100.get('log_Teff')[-1],
model_100.get('log_L')[-1],
'g^', ms=10)
plt.xlabel(r'log$T_{eff}$', fontsize=26)
plt.ylabel(r'log$L$', fontsize=26)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.legend(loc=2, numpoints=1)
plt.grid()
plt.savefig('Detailed_HRD')
def Plot_Outburst_Props(file_path="filtered_data.npy", tol1=0.05, tol2=10,
mk_mdot=True, mk_rstar=False, mk_rlobe=False,
mk_rho=False, mk_delta_rho=False, mk_t_dyn=False,
mk_sep=False, mk_mstar=False, mk_mratio=False):
'''
Uses a saved numpy file and randomly chooses an outburst to analyze
Parameters
----------
file_path : string
Path and name to the numpy file to be loaded
tol1 : float
The tolerance for change in mass transfer rate between data points to
be considered important to the analysis
tol2 : float
The number of subsequent data points that are ignored after determing
a point to be important to the analysis
mk_mdot : boolean
Determine if the function should create the mass transfer rate plot
mk_rstar : boolean
Determine if the function should create the stellar radius plot
mk_rlobe : boolean
Determine if the function should create the Roche lobe radius plot
mk_rho : boolean
Determine if the funection should create the density profile plot
mk_delta_rho : boolean
Determine if the funection should create a plot showing the difference
in density profiles
mk_t_dyn : boolean
Determine if the funection should create a plot comparing dynamic time
and the period of the system
mk_sep : boolean
Determine if the funection should create a plot showing the evolution
of binary separation between stars
mk_mstar : boolean
Determine if the function should create a plot showing the mass
evolution of the companion star
mk_mratio : boolean
Determineif the funection should create a plot showing the evolution
of the mass ratio
Returns
-------
Plots :
1. Donor star mass transfer rate as a function of time
2. Donor star radius as a function of time
3. Donor star roche lobe as a function of time
4. Density profile of donor star before and at outburst peak
5. Change in density profile of donor star
6. A comparison between period and dynamic timescale
7. Separation over the course of an outburst
8. Donor mass over the course of an outburst
9. The mass ratio of the system over the course of an outburst
'''
try:
filtered_results = np.load(file_path)
start_index = np.where(filtered_results[:, 3] < -7)[0][0]
filtered_results = filtered_results[start_index:]
filtered_model = filtered_results[:, 0]
filtered_period = filtered_results[:, 1]
filtered_sep = filtered_results[:, 2]
filtered_mtransfer = filtered_results[:, 3]
filtered_mass1 = filtered_results[:, 4]
filtered_mass2 = filtered_results[:, 5]
filtered_dt = filtered_results[:, 6]
filtered_index = filtered_results[:, 7]
filtered_times = filtered_results[:, 8]
# Other important properties not used
filtered_age = filtered_results[:, 9]
filtered_radius = filtered_results[:, 10]
filtered_rl1 = filtered_results[:, 11]
filtered_rl2 = filtered_results[:, 12]
filtered_mdot1 = filtered_results[:, 13]
filtered_wind1 = filtered_results[:, 14]
except:
filtered_results = ms.history_data("./LOGS")
MT = filtered_results.get('lg_mtransfer_rate')
start_index = np.where(MT < -3)[0][0]
filtered_results = filtered_results[start_index:]
filtered_model = filtered_results.get('model_number')
filtered_period = filtered_results.get('period_days')
filtered_sep = filtered_results.get('binary_separation')
filtered_mtransfer = filtered_results.get('lg_mtransfer_rate')
filtered_mass1 = filtered_results.get('star_1_mass')
filtered_mass2 = filtered_results.get('star_2_mass')
filtered_age = filtered_results.get('star_age')
filtered_radius = 10**filtered_results.get('log_R')
filtered_rl1 = filtered_results.get('rl_relative_overflow_1')
quiescent_index = [0]
for i in xrange(1, len(filtered_mtransfer) - 1):
if filtered_mtransfer[i + 1] > -7:
quiescent_index.append(i)
plt.plot(filtered_age[i], filtered_mtransfer[i], 'or')
if (abs(filtered_mtransfer[i + 1] - filtered_mtransfer[i]) > tol1):
i += 1
else:
# print(i - quiescent_index[-1])
if abs(i - quiescent_index[-1]) > tol2:
if filtered_mtransfer[i] < filtered_mtransfer[i + 1]:
if (filtered_mtransfer[quiescent_index[-1]:i] > -8).any():
quiescent_index.append(i)
plt.plot(filtered_age[i], filtered_mtransfer[i], 'ok')
else:
i += 1
else:
i += 1
else:
i += 1
temp_ind = np.random.randint(len(quiescent_index), size=1)[0]
if filtered_mtransfer[quiescent_index[temp_ind] + 1] > -7:
# print("ind1 = temp_ind")
ind1 = quiescent_index[temp_ind]
ind2 = quiescent_index[temp_ind + 1]
else:
# print("ind2 = temp_ind")
ind1 = quiescent_index[temp_ind - 1]
ind2 = quiescent_index[temp_ind]
quiescent_model = filtered_model[ind1]
outburst_model = filtered_model[ind1 + 1]
if mk_rho or mk_delta_rho:
q_profile = ms.mesa_profile('LOGS', quiescent_model)
o_profile = ms.mesa_profile('LOGS', outburst_model)
else:
print("quiescent_model = " + str(quiescent_model))
print("outburst_model = " + str(outburst_model))
if mk_mdot:
print("Creating mass transfer outburst plot")
vert_line = np.linspace(-6, -12, 100)
xpos1 = np.linspace(filtered_age[ind1], filtered_age[ind1], 100)
xpos2 = np.linspace(filtered_age[ind2], filtered_age[ind2], 100)
plt.figure(1, figsize=(24, 13.5))
plt.clf()
plt.plot(filtered_age[ind1:ind2], filtered_mtransfer[ind1:ind2], 'o')
plt.plot(filtered_age, filtered_mtransfer, '-', lw=0.5)
plt.plot(xpos1, vert_line, 'k')
plt.plot(xpos2, vert_line, 'k')
# plt.xlim(filtered_age[ind1-10], filtered_age[ind2+10])
plt.xlabel(r'Time Since Binary Formation (years)', fontsize=20)
plt.ylabel(r'Mass Transfer Rate log$(\dot{M_\odot})$', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title('Mass Transfer of the Binary System During Outburst',
fontsize=26)
plt.grid()
figname = 'outburst_mdot'
plt.savefig(figname)
# plt.show()
if mk_rstar:
print("Creating radius outburst plot")
plt.figure(2, figsize=(24, 13.5))
plt.clf()
plt.plot(filtered_age[ind1:ind2], filtered_radius[ind1:ind2], 'o')
plt.plot(filtered_age[ind1:ind2], filtered_radius[ind1:ind2], '-',
lw=0.5)
plt.xlabel(r'Time Since Binary Formation (years)', fontsize=20)
plt.ylabel(r'Star 1 Radius $(R_\odot)$', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title('Radius of Star 1 During Outburst', fontsize=26)
plt.grid()
figname = 'outburst_radius'
plt.savefig(figname)
# plt.show()
if mk_rlobe:
print("Creating roche lobe radius outburst plot")
plt.figure(3, figsize=(24, 13.5))
plt.clf()
plt.plot(filtered_age[ind1:ind2], filtered_rl1[ind1:ind2], 'o')
plt.plot(filtered_age[ind1:ind2], filtered_rl1[ind1:ind2], '-', lw=0.5)
# plt.plot(filtered_age[ind1:ind2], filtered_rl2[ind1:ind2], '.',
# label='Accretor Roche Lobe')
# plt.plot(filtered_age[ind1:ind2], filtered_rl2[ind1:ind2], '-',
# lw=0.5)
plt.xlabel(r'Time Since Binary Formation (years)', fontsize=20)
plt.ylabel(r'Roche Lobe Radius $(R_\odot)$', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title('Roche Lobe Radius During Outburst', fontsize=26)
plt.grid()
# plt.legend()
figname = 'outburst_roche_lobe_radius'
plt.savefig(figname)
# plt.show()
if mk_rho:
print("Creating density profile")
plt.figure(4, figsize=(24, 13.5))
plt.clf()
# q_profile.density_profile(ixaxis='radius', ifig=4, label="Quiescent")
# o_profile.density_profile(ixaxis='radius', ifig=4, label="Outburst")
q_r = q_profile.get("logR")
o_r = o_profile.get("logR")
# q_m = q_profile.get("mass")
# o_m = o_profile.get("mass")
q_rho = q_profile.get("logRho")
o_rho = o_profile.get("logRho")
plt.plot(o_r, o_rho, label="Outburst")
plt.plot(q_r, q_rho, label="Quiescent")
plt.xlabel(r'Radius log$_{10}(R/R_\odot)$', fontsize=20)
plt.ylabel(r'Density log$_{10}(\rho/g cm^{-3})$', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title("Density Profile Before and During Outburst")
plt.grid()
plt.legend()
figname = 'density_profile'
plt.savefig(figname)
# plt.show()
if mk_delta_rho:
print("Creating difference in density profile")
plt.figure(5, figsize=(24, 13.5))
plt.clf()
if len(q_rho) > len(o_rho):
q_rho = q_rho[0:len(o_rho)]
q_r = q_r[0:len(o_rho)]
elif len(q_rho) < len(o_rho):
o_rho = o_rho[0:len(q_rho)]
plt.plot(q_r, o_rho - q_rho)
plt.xlabel(r'Radius log$_{10}(R/R_\odot)$', fontsize=20)
plt.ylabel(r'Difference in density log$_{10}(\rho/g cm^{-3})$',
fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title("Change in Density Profile Before and During Outburst")
plt.grid()
plt.legend()
figname = 'delta_density_profile'
plt.savefig(figname)
# plt.show()
if mk_t_dyn:
print("Comparing period and dynamical timescale")
plt.figure(6, figsize=(24, 13.5))
plt.clf()
# Calculating dynamic timescale
G = 3.5E8
# G in units of R_sun, M_sun, yr
r3 = filtered_radius ** 3
t_dyn = np.sqrt(r3 / (2 * G * filtered_mass1))
plt.plot(filtered_age, np.log10(t_dyn),
label='Dynamic timescale')
# Need to convert period days to years
plt.plot(filtered_age, np.log10(filtered_period * 0.00274),
label='Period')
plt.xlabel(r'Time Since Binary Formation (years)', fontsize=20)
plt.ylabel(r'Time log$$(years)', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title("Dynamic Timescale and Period of the System")
plt.grid()
plt.legend()
figname = 'period_timescale_comparison'
plt.savefig(figname)
# plt.show()
if mk_sep:
print("Creating separation plot")
plt.figure(7, figsize=(24, 13.5))
plt.clf()
plt.plot(filtered_age[ind1:ind2], filtered_sep[ind1:ind2], 'o')
plt.plot(filtered_age[ind1:ind2], filtered_sep[ind1:ind2], '-', lw=0.5)
plt.xlabel(r'Time Since Binary Formation (years)', fontsize=20)
plt.ylabel(r'Binary Separation $(R_\odot)$', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title('Binary Separation During Outburst', fontsize=26)
plt.grid()
figname = 'outburst_sep'
plt.savefig(figname)
# plt.show()
if mk_mstar:
print("Creating donor mass plot")
plt.figure(8, figsize=(24, 13.5))
plt.clf()
plt.plot(filtered_age[ind1:ind2], filtered_mass1[ind1:ind2], 'o')
plt.plot(filtered_age[ind1:ind2], filtered_mass1[ind1:ind2], '-',
lw=0.5)
plt.xlabel(r'Time Since Binary Formation (years)', fontsize=20)
plt.ylabel(r'Donor Mass $(\dot{M_\odot})$', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title('Donor Mass During Outburst', fontsize=26)
plt.grid()
figname = 'outburst_mass'
plt.savefig(figname)
# plt.show()
if mk_mratio:
print("Creating mass ratio plot")
plt.figure(9, figsize=(24, 13.5))
plt.clf()
q = filtered_mass1[ind1:ind2] / filtered_mass2[ind1:ind2]
plt.plot(filtered_age[ind1:ind2], q, 'o')
plt.plot(filtered_age[ind1:ind2], q, '-', lw=0.5)
plt.xlabel(r'Time Since Binary Formation (years)', fontsize=20)
plt.ylabel(r'Mass Ratio', fontsize=20)
plt.tick_params(axis='both', which='major', labelsize=20)
plt.title('Mass Ratio During Outburst', fontsize=26)
plt.grid()
figname = 'outburst_mass_ratio'
plt.savefig(figname)
def Find_F_Properties(root_dir="."):
'''
Find the initial and final values of the following properties:
Initial and final helium core fraction
Final donor mass
Final companion mass
Initial radius of donor
Final radius of donor
Final orbital period
Final model number
Can easily add more properties as necessary
Parameters
----------
root_dir : string
Path to the directory containing the history.data file to be used
'''
value_matrix = [["directory",
"initial he core fraction",
"final star 1 mass",
"final star 2 mass",
"initial radius",
"final radius",
"initial period",
"final period",
"evolution time",
"final model number"]]
for subdir, dirs, files in os.walk(root_dir):
for file_name in files:
if file_name == "history.data":
m1 = ms.history_data(subdir)
# want various values
# initial center he core frac
X_i = m1.get("center_he4")[0]
# # initial star age
t_i = m1.get("star_age")[0]
# final masses
M1_f = m1.get("star_1_mass")[-1]
M2_f = m1.get("star_2_mass")[-1]
# final period
P_i = m1.get("period_days")[0]
P_f = m1.get("period_days")[-1]
# evolution time
dt = m1.get("star_age")[-1] - t_i
# radius
r_i = 10**m1.get("log_R")[0]
r_f = 10**m1.get("log_R")[-1]
# model number
model = m1.get("model_number")[-1]
# preface = subdir + " : "
# X_i_string = "initial he core frac: " + str(X_i)
# M1_f_string = "final star 1 mass: " + str(M1_f)
# M2_f_string = "final star 2 mass: " + str(M2_f)
# r_i_string = "initial radius: " + str(r_i)
# r_f_string = "final radius: " + str(r_f)
# P_i_string = "initial period: " + str(P_i)
# P_f_string = "final period: " + str(P_f)
# dt_string = "evolution time: " + str(dt)
# model_string = "final model number: " + str(model)
# print(preface)
# print(X_i_string)
# print(M1_f_string)
# print(M2_f_string)
# print(r_i_string)
# print(r_f_string)
# print(P_i_string)
# print(P_f_string)
# print(dt_string)
# print(model_string)
value_matrix.append([subdir, X_i, M1_f, M2_f,
r_i, r_f, P_i, P_f, dt, model])
return value_matrix
def Plot_Quick(dir1, dir2):
m1 = ms.history_data(dir1)
m2 = ms.history_data(dir2)
m_age1 = m1.get("star_age")
m_age2 = m2.get("star_age")
log_R1 = m1.get('log_R')
log_R2 = m2.get('log_R')
log_W1 = m1.get('lg_wind_mdot_1')
log_W2 = m2.get('lg_wind_mdot_1')
lum1 = m1.get('luminosity')
lum2 = m2.get('luminosity')
mass1 = m1.get('star_1_mass')
mass2 = m2.get('star_1_mass')
mdot1 = m1.get('lg_mstar_dot_1')
mdot2 = m2.get('lg_mstar_dot_1')
he_core1 = m1.get('he_core_mass')
he_core2 = m2.get('he_core_mass')
# Age and Radius
plt.figure(1)
plt.clf()
plt.plot(m_age1, log_R1, 'or', markeredgecolor='r', label="Default MB")
plt.plot(m_age2, log_R2, '.b', markeredgecolor='b', label="Modified MB")
plt.xlim(0, max(m_age2))
plt.ylim(min(min(log_R1), min(log_R2)),
max(max(log_R1), max(log_R2)))
plt.legend()
plt.xlabel("Star Age")
plt.ylabel("Log_R (R_sun)")
plt.title("Radius")
plt.savefig("Radius")
# Age and wind
plt.figure(2)
plt.clf()
plt.plot(m_age1, log_W1, 'or', markeredgecolor='r', label="Default MB")
plt.plot(m_age2, log_W2, '.b', markeredgecolor='b', label="Modified MB")
plt.xlim(0, max(m_age2))
plt.ylim(min(min(log_W1), min(log_W2)),
max(max(log_W1), max(log_W2)))
plt.legend()
plt.xlabel("Star Age")
plt.ylabel("Log_W (MSun)")
plt.title("Stellar Wind")
plt.savefig("Wind")
# Age and luminosity
plt.figure(3)
plt.clf()
plt.plot(m_age1, lum1, 'or', markeredgecolor='r', label="Default MB")
plt.plot(m_age2, lum2, '.b', markeredgecolor='b', label="Modified MB")
plt.xlim(0, max(m_age2))
plt.ylim(min(min(lum1), min(lum2)),
max(max(lum1), max(lum2)))
plt.legend()
plt.xlabel("Star Age")
plt.ylabel("Luminosity (L_sun)")
plt.title("Luminosity")
plt.savefig("Lum")
# Age and mass loss
plt.figure(4)
plt.clf()
plt.plot(m_age1, mdot1, 'or', markeredgecolor='r', label="Default MB")
plt.plot(m_age2, mdot2, '.b', markeredgecolor='b', label="Modified MB")
plt.xlim(0, max(m_age2))
plt.ylim(min(min(mdot1), min(mdot2)),
max(max(mdot1), max(mdot2)))
plt.legend()
plt.xlabel("Star Age")
plt.ylabel("Log_Mdot (Msun)")
plt.title("Mass Loss")
plt.savefig("Mdot")
# Age and convective envelope
plt.figure(5)
plt.clf()
plt.plot(m_age1, mass1 - he_core1, 'or',
markeredgecolor='r', label="Default MB")
plt.plot(m_age2, mass2 - he_core2, '.b',
markeredgecolor='b', label="Modified MB")
plt.xlim(0, max(m_age2))
plt.ylim(min(min(mass1 - he_core1), min(mass2 - he_core2)) * 0.9,
max(max(mass1 - he_core1), max(mass2 - he_core2)) * 1.1)
plt.legend()
plt.xlabel("Star Age")
plt.ylabel("Convective Envelope Mass")
plt.title("Convective Envelope")
plt.savefig("Conv")
# Age and Mass
plt.figure(6)
plt.clf()
plt.plot(m_age1, mass1, 'or', markeredgecolor='r', label="Default MB")
plt.plot(m_age2, mass2, '.b', markeredgecolor='b', label="Modified MB")
plt.xlim(0, max(m_age2))
plt.ylim(min(min(mass1), min(mass2)) * 0.9,
max(max(mass1), max(mass2)) * 1.1)
plt.legend()
plt.xlabel("Star Age")
plt.ylabel("Star Mass(Msun)")
plt.title("Mass")
plt.savefig("Mass")
