#Reading out mixing regions and data, and plotting independently
kipp_args = mkipp.Kipp_Args()
fig = plt.figure()
axis = plt.gca()
profile_paths = mesa_data.get_profile_paths(["LOGS"])
#if data is distributed among several history.data files, you can provide them
history_paths = ["LOGS/history.data"]
#read profile data
#kipp_data.get_xyz_data returns an object containing
# xyz_data.xlims : limits of data in x coordinate
# xyz_data.X : 2D array of xaxis values of profile data
# xyz_data.Y : 2D array of xaxis values of profile data
# xyz_data.Z : 2D array of xaxis values of profile data
# the last three can be used as inputs for matplotlib contour or contourf
xyz_data = kipp_data.get_xyz_data(profile_paths, kipp_args)
#read mixing regions
#kipp_data.get_mixing_zones returns an object containing
# mixing_zones.zones : matplotlib Path objects for each mixing zone.
# These can be plotted using add_patch(...)
# mixing_zones.mix_types : Integer array containing the type of each zone
# mixing_zones.x_coords : x coordinates for points at the surface
# mixing_zones.y_coords : y coordinates for points at the surface
# mixing_zones.histories : mesa_data history files to access additional data
# the last three can be used as inputs for matplotlib contour or contourf
mixing_zones = kipp_data.get_mixing_zones(history_paths, kipp_args, xlims = xyz_data.xlims)
# just plot convection, overshooting and semiconvection
for i,zone in enumerate(mixing_zones.zones):
color = ""
#Convective mixing
if mixing_zones.mix_types[i] == 1: #convection
#Reading out mixing regions and data, and plotting independently
kipp_args = mkipp.Kipp_Args()
fig = plt.figure()
axis = plt.gca()
profile_paths = mesa_data.get_profile_paths(["LOGS"])
#if data is distributed among several history.data files, you can provide them
history_paths = ["LOGS/history.data"]
#read profile data
#kipp_data.get_xyz_data returns an object containing
# xyz_data.xlims : limits of data in x coordinate
# xyz_data.X : 2D array of xaxis values of profile data
# xyz_data.Y : 2D array of xaxis values of profile data
# xyz_data.Z : 2D array of xaxis values of profile data
# the last three can be used as inputs for matplotlib contour or contourf
xyz_data = kipp_data.get_xyz_data(profile_paths, kipp_args.xaxis_divide, kipp_args)
#read mixing regions
#kipp_data.get_mixing_zones returns an object containing
# mixing_zones.zones : matplotlib Path objects for each mixing zone.
# These can be plotted using add_patch(...)
# mixing_zones.mix_types : Integer array containing the type of each zone
# mixing_zones.x_coords : x coordinates for points at the surface
# mixing_zones.y_coords : y coordinates for points at the surface
# mixing_zones.histories : mesa_data history files to access additional data
# the last three can be used as inputs for matplotlib contour or contourf
mixing_zones = kipp_data.get_mixing_zones(history_paths, kipp_args.xaxis_divide, xyz_data.xlims, kipp_args)
# just plot convection, overshooting and semiconvection
for i,zone in enumerate(mixing_zones.zones):
color = ""
#Convective mixing
if mixing_zones.mix_types[i] == 1: #convection
#Reading out mixing regions and data, and plotting independently
kipp_args = mkipp.Kipp_Args()
fig = plt.figure()
axis = plt.gca()
profile_paths = mesa_data.get_profile_paths(["LOGS"])
#if data is distributed among several history.data files, you can provide them
history_paths = ["LOGS/history.data"]
#read profile data
#kipp_data.get_xyz_data returns an object containing
# xyz_data.xlims : limits of data in x coordinate
# xyz_data.X : 2D array of xaxis values of profile data
# xyz_data.Y : 2D array of xaxis values of profile data
# xyz_data.Z : 2D array of xaxis values of profile data
# the last three can be used as inputs for matplotlib contour or contourf
xyz_data = kipp_data.get_xyz_data(profile_paths, kipp_args.xaxis_divide,
kipp_args)
#read mixing regions
#kipp_data.get_mixing_zones returns an object containing
# mixing_zones.zones : matplotlib Path objects for each mixing zone.
# These can be plotted using add_patch(...)
# mixing_zones.mix_types : Integer array containing the type of each zone
# mixing_zones.x_coords : x coordinates for points at the surface
# mixing_zones.y_coords : y coordinates for points at the surface
# mixing_zones.histories : mesa_data history files to access additional data
# the last three can be used as inputs for matplotlib contour or contourf
mixing_zones = kipp_data.get_mixing_zones(history_paths,
kipp_args.xaxis_divide,
xyz_data.xlims, kipp_args)
# just plot convection, overshooting and semiconvection
for i, zone in enumerate(mixing_zones.zones):
color = ""
#Reading out mixing regions and data, and plotting independently
kipp_args = mkipp.Kipp_Args()
fig = plt.figure()
axis = plt.gca()
profile_paths = mesa_data.get_profile_paths(["LOGS"])
#if data is distributed among several history.data files, you can provide them
history_paths = ["LOGS/history.data"]
#read profile data
#kipp_data.get_xyz_data returns an object containing
# xyz_data.xlims : limits of data in x coordinate
# xyz_data.X : 2D array of xaxis values of profile data
# xyz_data.Y : 2D array of xaxis values of profile data
# xyz_data.Z : 2D array of xaxis values of profile data
# the last three can be used as inputs for matplotlib contour or contourf
xyz_data = kipp_data.get_xyz_data(profile_paths, kipp_args)
#read mixing regions
#kipp_data.get_mixing_zones returns an object containing
# mixing_zones.zones : matplotlib Path objects for each mixing zone.
# These can be plotted using add_patch(...)
# mixing_zones.mix_types : Integer array containing the type of each zone
# mixing_zones.x_coords : x coordinates for points at the surface
# mixing_zones.y_coords : y coordinates for points at the surface
# mixing_zones.histories : mesa_data history files to access additional data
# the last three can be used as inputs for matplotlib contour or contourf
mixing_zones = kipp_data.get_mixing_zones(history_paths, kipp_args, xlims = xyz_data.xlims)
# just plot convection, overshooting and semiconvection
for i,zone in enumerate(mixing_zones.zones):
color = ""
#Convective mixing
if mixing_zones.mix_types[i] == 1: #convection
bar.set_label("Helium abundance")
axis.set_xlabel("Time (Myr)")
axis.set_ylabel("Mass (solar masses)")
plt.savefig("Kippenhahn2.png")
#Reading out mixing regions and data, and plotting independently
kipp_args = mkipp.Kipp_Args()
fig = plt.figure()
axis = plt.gca()
profile_names = ["LOGS/profile"+str(int(i))+".data" \
for i in np.loadtxt("LOGS/profiles.index", skiprows = 1, usecols = (2,))]
#if data is distributed among several history.data files, you can provide them
history_names = ["LOGS/history.data"]
#read profile data
min_x_coord, max_x_coord, X, Y, Z = \
kipp_data.get_xyz_data(profile_names, kipp_args.xaxis_divide, kipp_args)
#read mixing regions
zones, mix_types, x_coords, y_coords, histories = kipp_data.get_mixing_zones(\
history_names, kipp_args.xaxis_divide, min_x_coord, max_x_coord, kipp_args)
# just plot convection, overshooting and semiconvection
for i,zone in enumerate(zones):
color = ""
#Convective mixing
if mix_types[i] == 1: #convection
color = '#332288'
#Overshooting
elif mix_types[i] == 3: #overshooting
color = '#117733'
#Semiconvective mixing
elif mix_types[i] == 4: #semiconvection
color = '#CC6677'