def change_module(widget, value):
if value == "History":
frame.set_state_attribute("select_plot", states_mesa[1:], options={"":"mesa_w_data", "HR-Diagram":"hrd", "Plot":"plot", "Kippenhahn":"kippenhahn", "Kippenhahan contour":"kip_cont", "TCRhoC plot":"tcrhoc", "Get data":"get_data"})
frame.set_state_attribute("contain_model_select", states_mesa, visible=False)
frame.set_attributes("contain_model_select", visible=False)
elif value == "Profile":
frame.set_attributes("load_data", disabled=True)
frame.set_state_attribute("select_plot", states_mesa[1:], options={"":"mesa_w_data", "Plot":"plot", "Get data":"get_data"})
mass = float(frame.get_attribute("mass", "value"))
Z = float(frame.get_attribute("Z", "value"))
dir = frame.get_attribute("address_bar", "value")
if manual_data_select:
mdir, mmodel = frame.get_state_data("model_data")
if (mdir != dir) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(dir)
mmodel = pre_data.model
frame.set_state_data("model_data", (dir, mmodel))
else:
mmass, mZ, mmodel = frame.get_state_data("model_data")
if (mmass != mass) or (mZ != Z) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(mass=mass, Z=Z)
mmodel = pre_data.model
frame.set_state_data("model_data", (mass, Z, mmodel))
frame.set_state_attribute("contain_model_select", states_mesa, visible=True)
frame.set_attributes("contain_model_select", visible=True)
frame.set_attributes("model_select", value=str(mmodel[-1]))
frame.set_attributes("load_data", disabled=False)
else:
frame.set_state_attribute("contain_model_select", states_mesa, visible=False)
frame.set_attributes("contain_model_select", visible=False)
def change_module(widget, value):
if value == "History":
frame.set_state_attribute("select_plot",
states_mesa[1:],
options={
"": "mesa_w_data",
"HR-Diagram": "hrd",
"Plot": "plot",
"Kippenhahn": "kippenhahn",
"Kippenhahan contour": "kip_cont",
"TCRhoC plot": "tcrhoc",
"Get data": "get_data"
})
frame.set_state_attribute("contain_model_select",
states_mesa,
visible=False)
frame.set_attributes("contain_model_select", visible=False)
elif value == "Profile":
frame.set_attributes("load_data", disabled=True)
frame.set_state_attribute("select_plot",
states_mesa[1:],
options={
"": "mesa_w_data",
"Plot": "plot",
"Get data": "get_data"
})
mass = float(frame.get_attribute("mass", "value"))
Z = float(frame.get_attribute("Z", "value"))
dir = frame.get_attribute("address_bar", "value")
if manual_data_select:
mdir, mmodel = frame.get_state_data("model_data")
if (mdir != dir) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(dir)
mmodel = pre_data.model
frame.set_state_data("model_data", (dir, mmodel))
else:
mmass, mZ, mmodel = frame.get_state_data("model_data")
if (mmass != mass) or (mZ != Z) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(mass=mass, Z=Z)
mmodel = pre_data.model
frame.set_state_data("model_data", (mass, Z, mmodel))
frame.set_state_attribute("contain_model_select",
states_mesa,
visible=True)
frame.set_attributes("contain_model_select", visible=True)
frame.set_attributes("model_select", value=str(mmodel[-1]))
frame.set_attributes("load_data", disabled=False)
else:
frame.set_state_attribute("contain_model_select",
states_mesa,
visible=False)
frame.set_attributes("contain_model_select", visible=False)
def load(widget):
clear_output()
data = None
mass = float(frame.get_attribute("mass", "value"))
Z = float(frame.get_attribute("Z", "value"))
dir = frame.get_attribute("address_bar", "value")
if frame.get_attribute("model_select", "value") != "":
model = int(frame.get_attribute("model_select", "value"))
else:
model = 1
module = frame.get_attribute("select_module", "value")
if module == "H5 out":
if manual_data_select:
data = mp.se(dir)
else:
data = mp.se(mass=mass, Z=Z)
frame.set_state("nugrid_w_data")
properties = ["mass", "radius", "rho", "temperature"]
frame.set_attributes("xaxis",
options=properties + data.se.isotopes)
frame.set_attributes("yaxis",
options=properties + data.se.isotopes)
elif module == "History":
if manual_data_select:
data = ms.history_data(dir)
else:
data = ms.history_data(mass=mass, Z=Z)
frame.set_state("mesa_w_data")
frame.set_attributes("xaxis", options=sorted(data.cols.keys()))
frame.set_attributes("yaxis", options=sorted(data.cols.keys()))
elif module == "Profile":
if manual_data_select:
data = ms.mesa_profile(dir, num=model)
else:
data = ms.mesa_profile(mass=mass, Z=Z, num=model)
frame.set_state("mesa_w_data")
frame.set_attributes("xaxis", options=sorted(data.cols.keys()))
frame.set_attributes("yaxis", options=sorted(data.cols.keys()))
else:
nugrid_or_mesa = frame.get_attribute("select_nugrid_mesa", 'value')
if nugrid_or_mesa == "NuGrid":
frame.set_state("nugrid")
elif nugrid_or_mesa == "Mesa":
frame.set_state("mesa")
frame.set_state_data("class_instance", data)
frame.set_attributes("select_plot", selected_label="")
def mass_Z_handler(name, value):
if frame.get_attribute("contain_model_select", "visible"):
mass = float(frame.get_attribute("mass", "value"))
Z = float(frame.get_attribute("Z", "value"))
dir = frame.get_attribute("address_bar", "value")
if manual_data_select:
mdir, mmodel = frame.get_state_data("model_data")
if (mdir != dir) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(dir)
frame.set_state_data("model_data", (dir, pre_data.model))
else:
mmass, mZ, mmodel = frame.get_state_data("model_data")
if (mmass != mass) or (mZ != Z) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(mass=mass, Z=Z)
frame.set_state_data("model_data", (mass, Z, pre_data.model))
def load(widget):
clear_output()
data = None
mass = float(frame.get_attribute("mass", "value"))
Z = float(frame.get_attribute("Z", "value"))
dir = frame.get_attribute("address_bar", "value")
if frame.get_attribute("model_select", "value") != "":
model = int(frame.get_attribute("model_select", "value"))
else:
model = 1
module = frame.get_attribute("select_module", "value")
if module == "H5 out":
if manual_data_select:
data = mp.se(dir)
else:
data = mp.se(mass=mass, Z=Z)
frame.set_state("nugrid_w_data")
properties = ["mass", "radius", "rho", "temperature"]
frame.set_attributes("xaxis", options=properties+data.se.isotopes)
frame.set_attributes("yaxis", options=properties+data.se.isotopes)
elif module == "History":
if manual_data_select:
data = ms.history_data(dir)
else:
data = ms.history_data(mass=mass, Z=Z)
frame.set_state("mesa_w_data")
frame.set_attributes("xaxis", options=sorted(data.cols.keys()))
frame.set_attributes("yaxis", options=sorted(data.cols.keys()))
elif module == "Profile":
if manual_data_select:
data = ms.mesa_profile(dir, num=model)
else:
data = ms.mesa_profile(mass=mass, Z=Z, num=model)
frame.set_state("mesa_w_data")
frame.set_attributes("xaxis", options=sorted(data.cols.keys()))
frame.set_attributes("yaxis", options=sorted(data.cols.keys()))
else:
nugrid_or_mesa = frame.get_attribute("select_nugrid_mesa", 'value')
if nugrid_or_mesa == "NuGrid":
frame.set_state("nugrid")
elif nugrid_or_mesa == "Mesa":
frame.set_state("mesa")
frame.set_state_data("class_instance", data)
frame.set_attributes("select_plot", selected_label="")
def mass_Z_handler(name, value):
if frame.get_attribute("contain_model_select", "visible"):
mass = float(frame.get_attribute("mass", "value"))
Z = float(frame.get_attribute("Z", "value"))
dir = frame.get_attribute("address_bar", "value")
if manual_data_select:
mdir, mmodel = frame.get_state_data("model_data")
if (mdir != dir) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(dir)
frame.set_state_data("model_data", (dir, pre_data.model))
else:
mmass, mZ, mmodel = frame.get_state_data("model_data")
if (mmass != mass) or (mZ != Z) or (mmodel == None):
clear_output()
pre_data = ms.mesa_profile(mass=mass, Z=Z)
frame.set_state_data("model_data",
(mass, Z, pre_data.model))
profileN = [100, 1000, 1500, 2000, 2500]
fig = plt.figure(figsize=(8.267, 11.692))
# gridspec inside gridspec
gs = gridspec.GridSpec(5, 4, wspace=0.1, hspace=0.1)
for i in range(4):
h=ms.history_data(model_paths[i])
for j in range(5):
ax = plt.subplot(gs[j,i])
print i,j
p=ms.mesa_profile(model_paths[i],profileN[j],num_type='nearest_model')
mass = p.get('mass') * u.Msun # solar mass
dm = copy.deepcopy(mass)
dm[:-1] = mass[:-1]-mass[1:]
r = p.get('radius') * u.Rsun# solar radius
dr = copy.deepcopy(r)
dr[:-1] = r[:-1]-r[1:]
csound = p.get('csound') * u.cm/u.s #cm/s
t_sc = dr/(csound)
mdot_sc = dm/t_sc
t_ff = math.pi/2. * r**(3./2.) / np.sqrt(2.*const.G*(mass))
mdot_ff = dm/t_ff
plt.semilogy(mass.to('Msun').value,mdot_ff.to('Msun/yr').value,label="$local\ dm/dt_{free\ fall}$",linewidth=2)
plt.semilogy(mass.to('Msun').value,mdot_sc.to('Msun/yr').value, label="$local\ dm/dt_{sound\ crossing}$",linewidth=2)
break
## Find constants a_n, c_n and d_n
a_n = []
c_n = []
d_n = []
for i in range(7):
a_n.append(1/(-3./t[Xi_1[i]] * theta_prime[i][Xi_1[i]]))
c_n.append(9/(4*np.pi)*a_n[-1]**2/t[Xi_1[i]]/(N[i]+1))
d_n.append(c_n[-1]*(4*np.pi)**(4./3)/a_n[-1]**(4./3)/3**(4./3))
## Load MESA data!
pro = ms.mesa_profile('mesa_zams/LOGS',750)
R = pro.get('logR') # log10 R / Rsun
P = pro.get('logP') # log10 P in cgs units of pressure dyne / cm2 = barye
Rho = pro.get('logRho') # log10 Rho in cgs units g /cm3
T = pro.get('logT') # log10 T in cgs units Kelvin
# Plot Pressure Profile
plot = True
if plot == True:
fig,ax = mp.subplots()
p1, = ax.plot(10**(R)/(10**R).max(),10**(P)/(10**P).max(),'b')
ax.grid()
ax.set_xlabel('Radius / Rsun',fontsize=15)
ax.set_ylabel('Pressure / P_c',fontsize=15)
def mesa_profileplot(vars, lognum, type='', prefix='profile'):
#from matplotlib.transforms import offset_copy
"""
python routine to read and plot profile files
vars: a vector of names
var[0] is name of x variable
var[1] is name of first y variable
var[2] is name of second y variable, etc.
"""
clf()
Msun = 1.988e+33
Rsun = 6.955e+10
gconst = 6.67e-8
xvar = vars[0]
a1 = ms.mesa_profile('.', lognum, num_type='log_num', log_prefix=prefix)
Teff0 = a1.header_attr.get('Teff')
Mass = a1.header_attr.get('star_mass')
Mh = a1.header_attr.get('star_mass_h1')
Mhe = a1.header_attr.get('star_mass_he4')
Mh = Mh / Mass
Mhe = Mhe / Mass
rad = a1.get('radius')
radius = rad[0]
logg = np.log10(gconst * Mass * Msun / (Rsun * radius)**2)
lMh = round(np.log10(Mh), 3)
lMhe = round(np.log10(Mhe), 3)
Mass = round(Mass, 4)
Teff = int(round(Teff0, 0))
flab = r'$T_{\rm eff}$' + r'$={0} \, K, \, M_\star/M_\odot={1},\,$'.format(
Teff, Mass)
#flab2=r'$\log \,M_{\rm H}/M_\star=' + '{0}$, '.format(lMh) + '$\, M_{\\rm He}=' + '{0}$'.format(lMhe)
flab2 = r'$\log M_{\rm H}/M_\star\,=' + '{0},\,$ '.format(lMh)
flab3 = r'$\log M_{\rm He}/M_\star=' + '{0}$ '.format(lMhe)
flab = flab + flab2 + flab3
fig = figure(1, frameon=False)
ax = fig.add_subplot(111)
plt.text(0.50,
1.06,
flab,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
size='large')
if xvar == 'phi':
r = a1.get('radius')
N2 = a1.get('brunt_N2')
aN = np.sqrt(np.abs(N2))
ndim = np.size(r)
dr = 0 * r
phi = 0 * r
dr[1:ndim - 1] = r[1:ndim - 1] - r[2:ndim]
dr[0] = 0.
phi[0] = 0.
for i in np.arange(1, ndim):
phi[i] = phi[i - 1] + dr[i] * aN[i] / r[i]
phi = phi / phi[ndim - 1]
x = 1. - phi
else:
x = a1.get(xvar)
n = np.size(vars)
for i in np.arange(1, n):
yvar = vars[i]
y1 = a1.get(yvar)
if type == 'sl':
semilogy(x, y1)
#semilogy(x,y1,'o')
else:
plot(x, y1)
if n == 2:
ylabel(yvar)
else:
leg = vars[1:]
legend(leg, 'best', fancybox=True, shadow=False)
x1, x2 = xlim()
if xvar == 'logxq':
x1 = -18
xlim(0.5, -18)
xlabel(r'$\log (1-M_r/M_\star)$', size='x-large')
elif xvar == 'phi':
xlim(-0.025, 1.025)
xlabel(r'$\Phi$', size='xx-large')
else:
dx = x2 - x1
x1s = x1 - 0.05 * dx
x2s = x2 + 0.05 * dx
xlim(x1s, x2s)
xlabel(xvar)
y1, y2 = ylim()
dy = y2 - y1
y1s = y1 - 0.05 * dy
y2s = y2 + 0.05 * dy
ylim(y1s, y2s)
mod_number_1 = model_number_1[index1]
st_age_1 = star_age_1[index1]
mod_number_2 = model_number_2[index2]
st_age_2 = star_age_2[index2]
st_age = np.concatenate((st_age_1, st_age_2), axis=0)
prof_1 = np.empty(
(len(mod_number_1), 1), dtype=object
) #error return without exception set avoided with dtype=object
for i in range(len(mod_number_1)):
for path, dirs, files in os.walk(
dir1): #os.walk avoids [Errno21] Is a directory
prof_1[i:, ] = ms.mesa_profile(dir1, mod_number_1[i])
prof_2 = np.empty(
(len(mod_number_2), 1), dtype=object
) #error return without exception set avoided with dtype=object
for i in range(len(mod_number_2)):
for path, dirs, files in os.walk(
dir2): #os.walk avoids [Errno21] Is a directory
prof_2[i:, ] = ms.mesa_profile(dir2, mod_number_2[i])
prof = np.concatenate(
(prof_1, prof_2), axis=0
)[:, 0] # Remove "dtype=object" from the final profiles to get the information
# Profile variables. Add as many as desired
idx[i] = (prof_index[i][0][0:])
idxa[i], idxb[i], idxc[i] = idx[i].split(
) # Take the elements from the string
idxa[i] = float(idxa[i]) # Convert them to floating point numbers
index[i] = np.where(model_number == idxa[i])[0][0]
mod_number = model_number[index]
st_age = star_age[index]
prof = np.empty(
(len(mod_number), 1), dtype=object
) #error return without exception set avoided with dtype=object
for i in range(len(mod_number)):
for path, dirs, files in os.walk(
direc): #os.walk avoids [Errno21] Is a directory
prof[i:, ] = ms.mesa_profile(direc, mod_number[i])
prof = prof[:,
0] # Remove "dtype=object" from the final profiles to get the information
# Profile variables. Add as many as desired
logxq = []
radius = []
logT = []
logRho = []
logP = []
lum = []
q = []
ye = []
def mesa_profileplot(vars,lognum,type='',prefix='profile'):
#from matplotlib.transforms import offset_copy
"""
python routine to read and plot profile files
vars: a vector of names
var[0] is name of x variable
var[1] is name of first y variable
var[2] is name of second y variable, etc.
"""
clf()
Msun=1.988e+33
Rsun=6.955e+10
gconst=6.67e-8
xvar=vars[0]
a1=ms.mesa_profile('.',lognum,num_type='log_num',log_prefix=prefix)
Teff0= a1.header_attr.get('Teff')
Mass= a1.header_attr.get('star_mass')
Mh= a1.header_attr.get('star_mass_h1')
Mhe= a1.header_attr.get('star_mass_he4')
Mh=Mh/Mass
Mhe=Mhe/Mass
rad=a1.get('radius')
radius=rad[0]
logg = np.log10(gconst*Mass*Msun/(Rsun*radius)**2)
lMh=round(np.log10(Mh),3)
lMhe=round(np.log10(Mhe),3)
Mass=round(Mass,4)
Teff=int(round(Teff0,0))
flab=r'$T_{\rm eff}$' + r'$={0} \, K, \, M_\star/M_\odot={1},\,$'.format(Teff,Mass)
#flab2=r'$\log \,M_{\rm H}/M_\star=' + '{0}$, '.format(lMh) + '$\, M_{\\rm He}=' + '{0}$'.format(lMhe)
flab2=r'$\log M_{\rm H}/M_\star\,=' + '{0},\,$ '.format(lMh)
flab3=r'$\log M_{\rm He}/M_\star=' + '{0}$ '.format(lMhe)
flab=flab+flab2+flab3
fig=figure(1,frameon=False)
ax = fig.add_subplot(111)
plt.text(0.50, 1.06, flab, horizontalalignment='center', verticalalignment='center',transform = ax.transAxes,size='large')
if xvar == 'phi':
r = a1.get('radius')
N2 = a1.get('brunt_N2')
aN = np.sqrt(np.abs(N2))
ndim=np.size(r)
dr = 0*r
phi = 0*r
dr[1:ndim-1]=r[1:ndim-1]-r[2:ndim]
dr[0]=0.
phi[0]=0.
for i in np.arange(1,ndim):
phi[i]=phi[i-1] + dr[i]*aN[i]/r[i]
phi=phi/phi[ndim-1]
x=1.-phi
else:
x = a1.get(xvar)
n=np.size(vars)
for i in np.arange(1,n):
yvar=vars[i]
y1 = a1.get(yvar)
if type == 'sl':
semilogy(x,y1)
#semilogy(x,y1,'o')
else:
plot(x,y1)
if n==2:
ylabel(yvar)
else:
leg=vars[1:]
legend(leg,'best',fancybox=True,shadow=False)
x1,x2=xlim()
if xvar == 'logxq':
x1=-18
xlim(0.5,-18)
xlabel(r'$\log (1-M_r/M_\star)$',size='x-large')
elif xvar == 'phi':
xlim(-0.025,1.025)
xlabel(r'$\Phi$',size='xx-large')
else:
dx=x2-x1
x1s=x1-0.05*dx
x2s=x2+0.05*dx
xlim(x1s,x2s)
xlabel(xvar)
y1,y2=ylim()
dy=y2-y1
y1s=y1-0.05*dy
y2s=y2+0.05*dy
ylim(y1s,y2s)
'''
Astro 252 Homework 2
Plotting MESA Profiles
10/3/2015
Nicholas Kern
'''
## Import Modules
import numpy as np
import pylab as mp
import mesa as ms
from matplotlib import rc
## Constants
from cgs_units import *
## Load MESA Data
## Load MESA data!
pro = ms.mesa_profile('mesa_zams2/LOGS',100)
R = pro.get('logR') # log10 R / Rsun
P = pro.get('logP') # log10 P in cgs units of pressure dyne / cm2 = barye
Rho = pro.get('logRho') # log10 Rho in cgs units g /cm3
T = pro.get('logT') # log10 T in cgs units Kelvin
