def multi_den(dir_name, base_name, nproc, size):
ntot, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, \
'0020', nproc)
rc('font', size=15)
fig = plt.figure()
fig.set_figwidth(9)
fig.set_figheight(8)
fig.subplots_adjust(left = 0.10, right = 0.88, top = 0.95, bottom = 0.07, \
wspace = 0.0, hspace = 0.0)
plt.figtext(0.5, 0.97, 'Multi density', ha='center', size='large')
majorLocator = ticker.MultipleLocator(10)
minorLocator = ticker.MultipleLocator(5)
nullFormatter = ticker.NullFormatter()
################### DEN 1 ####################################
ax = fig.add_subplot(221)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
# cb.set_label('Density [cm$^{-3}$]')
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### DEN 2 ####################################
ax = fig.add_subplot(222)
# arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
# cb.set_label('Density [cm$^{-3}$]')
plt.xlim(-size, size)
plt.ylim(-size, size)
################### DEN 3 ####################################
ax = fig.add_subplot(223)
# arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
# cb.set_label('Density [cm$^{-3}$]')
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### DEN 4 ####################################
ax = fig.add_subplot(224)
# arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
mapable = plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size)
plt.ylim(-size, size)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
cbaxes = fig.add_axes([0.89, 0.07, 0.02, 0.88])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Density [cm$^{-3}$]')
########################## save ######################################
figure_name = 'MultiDen-xz-' + dir_name + '.png'
plt.savefig(figure_name)
import sys
if len(sys.argv) < 6:
print 'hist: not enough parameter!'
print '\tUsage: histp.py dir_name base_name snap_num nproc radius'
print '\tExample: histp.py hd-1 1E3 0001 1 20.0'
sys.exit(1)
dir_name = sys.argv[1]
base_name = sys.argv[2]
snap_num = sys.argv[3]
nproc = int(sys.argv[4])
radius = float(sys.argv[5])
ntot = 0
ntot, time, arr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num,
nproc)
r = arr['pos'][:, 0] * arr['pos'][:, 0] + arr['pos'][:, 1] * arr['pos'][:, 1] \
+ arr['pos'][:, 2] * arr['pos'][:, 2]
r = np.sqrt(r)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ml = 1E4
mh = 1E7
logml = log10(ml)
logmh = log10(mh)
mass = arr['mass'][idc]
def multi_den(dir_name, base_name, nproc, size):
ntot, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, \
'0020', nproc)
rc('font', size = 15)
fig = plt.figure()
fig.set_figwidth(9)
fig.set_figheight(8)
fig.subplots_adjust(left = 0.10, right = 0.88, top = 0.95, bottom = 0.07, \
wspace = 0.0, hspace = 0.0)
plt.figtext(0.5, 0.97, 'Multi density', ha = 'center', size = 'large')
majorLocator = ticker.MultipleLocator(10)
minorLocator = ticker.MultipleLocator(5)
nullFormatter = ticker.NullFormatter()
################### DEN 1 ####################################
ax = fig.add_subplot(221)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
# cb.set_label('Density [cm$^{-3}$]')
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### DEN 2 ####################################
ax = fig.add_subplot(222)
# arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
# cb.set_label('Density [cm$^{-3}$]')
plt.xlim(-size, size)
plt.ylim(-size, size)
################### DEN 3 ####################################
ax = fig.add_subplot(223)
# arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
# cb.set_label('Density [cm$^{-3}$]')
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### DEN 4 ####################################
ax = fig.add_subplot(224)
# arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
den = arr['den'][:]
idw = np.where(arr['type'] == mp.TYPE_WARM)
idc = np.where(arr['type'] == mp.TYPE_COLD)
ids = np.where(arr['type'] == mp.TYPE_STAR_PARAL)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
mapable = plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-7, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size)
plt.ylim(-size, size)
# cb = plt.colorbar(ticks = ticker.LogLocator(base = 10.0))
cbaxes = fig.add_axes([0.89, 0.07, 0.02, 0.88])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Density [cm$^{-3}$]')
########################## save ######################################
figure_name = 'MultiDen-xz-' + dir_name + '.png'
plt.savefig(figure_name)
def multi_cold_mass(dir_name, base_name, nproc, size, snap_num):
title = dir_name
# plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
majorLocator = ticker.MultipleLocator(20)
minorLocator = ticker.MultipleLocator(4)
nullFormatter = ticker.NullFormatter()
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
vmin = 1.0E4
vmax = 2.0E7
rc('font', size=18)
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(9)
fig.subplots_adjust(left = 0.10, right = 0.85, top = 0.95, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
############################### snap 0 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[0], nproc)
ax = fig.add_subplot(221)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 1 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[1], nproc)
ax = fig.add_subplot(222)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 2 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[2], nproc)
ax = fig.add_subplot(223)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################ snap 3 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[3], nproc)
ax = fig.add_subplot(224)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.87, 0.10, 0.03, 0.85])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Cold cloud mass [M$_\odot$]')
######################## label ######################################
label = 't = 0.5 Gyr'
plt.figtext(0.28, 0.9, label, ha='center', size=20)
label = 't = 1 Gyr'
plt.figtext(0.68, 0.9, label, ha='center', size=20)
label = 't = 2 Gyr'
plt.figtext(0.28, 0.47, label, ha='center', size=20)
label = 't = 3 Gyr'
plt.figtext(0.68, 0.47, label, ha='center', size=20)
########################## save ######################################
figure_name = 'McPanel-xz-' + dir_name + '.png'
plt.savefig(figure_name)
######################################################################
############################## X - Y #################################
######################################################################
rc('font', size=18)
plt.clf()
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(9)
fig.subplots_adjust(left = 0.10, right = 0.85, top = 0.95, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
############################### snap 0 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[0], nproc)
ax = fig.add_subplot(221)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 1 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[1], nproc)
ax = fig.add_subplot(222)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 2 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[2], nproc)
ax = fig.add_subplot(223)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################ snap 3 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name,
snap_num[3], nproc)
ax = fig.add_subplot(224)
arr = np.sort(rarr, order='mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.87, 0.10, 0.03, 0.85])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Cold cloud mass [M$_\odot$]')
######################## label ######################################
label = 't = 0.5 Gyr'
plt.figtext(0.28, 0.9, label, ha='center', size=20)
label = 't = 1 Gyr'
plt.figtext(0.68, 0.9, label, ha='center', size=20)
label = 't = 2 Gyr'
plt.figtext(0.28, 0.47, label, ha='center', size=20)
label = 't = 3 Gyr'
plt.figtext(0.68, 0.47, label, ha='center', size=20)
########################## save ######################################
figure_name = 'McPanel-xy-' + dir_name + '.png'
plt.savefig(figure_name)
def multi_den(snap_num, size):
dir = [
'81-hd-3Hs2', '91-hd-3Hs2', '101-hd-3Hs2', '80-hd-3Hs2', '90-hd-3Hs2',
'100-hd-3Hs2'
]
proc_num = [2, 8, 8, 2, 8, 8]
snap_name = '%04d' % snap_num
title = 't = %d Myr' % (10 * snap_num)
base_name = 'dwarf'
rc('font', size=15)
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(6.5)
fig.subplots_adjust(left = 0.10, right = 0.88, top = 0.92, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
# plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
majorLocator = ticker.MultipleLocator(10)
minorLocator = ticker.MultipleLocator(5)
nullFormatter = ticker.NullFormatter()
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
################### hdc-81 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[0], base_name,
snap_name, proc_num[0])
ax = fig.add_subplot(231)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-91 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[1], base_name,
snap_name, proc_num[1])
ax = fig.add_subplot(232)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-101 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[2], base_name,
snap_name, proc_num[2])
ax = fig.add_subplot(233)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-80 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[3], base_name,
snap_name, proc_num[3])
ax = fig.add_subplot(234)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-90 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[4], base_name,
snap_name, proc_num[4])
ax = fig.add_subplot(235)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-100 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[5], base_name,
snap_name, proc_num[5])
ax = fig.add_subplot(236)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.89, 0.10, 0.02, 0.82])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Density [cm$^{-3}$]')
######################## label ######################################
label = '$2\\times10^8 \mathsf{M}_\odot$'
plt.figtext(0.23, 0.87, label, ha='center', size='large')
label = '$2\\times10^9 \mathsf{M}_\odot$'
plt.figtext(0.49, 0.87, label, ha='center', size='large')
label = '$2\\times10^{10} \mathsf{M}_\odot$'
plt.figtext(0.75, 0.87, label, ha='center', size='large')
########################## save ######################################
figure_name = 'DPanel-xz-' + snap_name + '.png'
plt.savefig(figure_name)
############################ X - Y ###################################
snap_name = '%04d' % snap_num
title = 't = %d Myr' % (10 * snap_num)
base_name = 'dwarf'
rc('font', size=15)
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(6.5)
fig.subplots_adjust(left = 0.10, right = 0.88, top = 0.92, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
# plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
majorLocator = ticker.MultipleLocator(10)
minorLocator = ticker.MultipleLocator(5)
nullFormatter = ticker.NullFormatter()
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
################### hdc-81 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[0], base_name,
snap_name, proc_num[0])
ax = fig.add_subplot(231)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-91 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[1], base_name,
snap_name, proc_num[1])
ax = fig.add_subplot(232)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-101 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[2], base_name,
snap_name, proc_num[2])
ax = fig.add_subplot(233)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-80 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[3], base_name,
snap_name, proc_num[3])
ax = fig.add_subplot(234)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-90 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[4], base_name,
snap_name, proc_num[1])
ax = fig.add_subplot(235)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-100 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[5], base_name,
snap_name, proc_num[2])
ax = fig.add_subplot(236)
arr = np.sort(rarr, order='den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.89, 0.10, 0.02, 0.82])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Density [cm$^{-3}$]')
######################## label ######################################
label = '$2\\times10^8 \mathsf{M}_\odot$'
plt.figtext(0.23, 0.87, label, ha='center', size='large')
label = '$2\\times10^9 \mathsf{M}_\odot$'
plt.figtext(0.49, 0.87, label, ha='center', size='large')
label = '$2\\times10^{10} \mathsf{M}_\odot$'
plt.figtext(0.75, 0.87, label, ha='center', size='large')
########################## save ######################################
figure_name = 'DPanel-xy-' + snap_name + '.png'
plt.savefig(figure_name)
def multi_den(dir_name, base_name, nproc, size, snap_num):
title = dir_name
rc('font', size = 18)
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(9)
fig.subplots_adjust(left = 0.10, right = 0.85, top = 0.95, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
# plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
majorLocator = ticker.MultipleLocator(2)
minorLocator = ticker.MultipleLocator(1)
nullFormatter = ticker.NullFormatter()
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
############################### snap 0 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[0], nproc)
ax = fig.add_subplot(221)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 1 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[1], nproc)
ax = fig.add_subplot(222)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 2 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[2], nproc)
ax = fig.add_subplot(223)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################ snap 3 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[3], nproc)
ax = fig.add_subplot(224)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.87, 0.10, 0.03, 0.85])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Density [cm$^{-3}$]')
######################## label ######################################
label = 't = 0.1 Gyr'
plt.figtext(0.28, 0.9, label, ha = 'center', size = 20)
label = 't = 0.5 Gyr'
plt.figtext(0.68, 0.9, label, ha = 'center', size = 20)
label = 't = 1.0 Gyr'
plt.figtext(0.28, 0.47, label, ha = 'center', size = 20)
label = 't = 1.5 Gyr'
plt.figtext(0.68, 0.47, label, ha = 'center', size = 20)
########################## save ######################################
figure_name = 'DPanel-xz-' + dir_name + '.png'
plt.savefig(figure_name)
#######################################################################
########################### X - Y ####################################
#######################################################################
rc('font', size = 18)
plt.clf()
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(9)
fig.subplots_adjust(left = 0.10, right = 0.85, top = 0.95, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
# plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
############################### snap 0 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[0], nproc)
ax = fig.add_subplot(221)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 1 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[1], nproc)
ax = fig.add_subplot(222)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################# snap 2 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[2], nproc)
ax = fig.add_subplot(223)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################################ snap 3 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, snap_num[3], nproc)
ax = fig.add_subplot(224)
arr = np.sort(rarr, order = 'den')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
den = arr['den'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idw], y[idw], s = 1, c = den[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e-6, vmax = 10, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.87, 0.10, 0.03, 0.85])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Density [cm$^{-3}$]')
######################## label ######################################
label = 't = 0.1 Gyr'
plt.figtext(0.28, 0.9, label, ha = 'center', size = 20)
label = 't = 0.5 Gyr'
plt.figtext(0.68, 0.9, label, ha = 'center', size = 20)
label = 't = 1.0 Gyr'
plt.figtext(0.28, 0.47, label, ha = 'center', size = 20)
label = 't = 1.5 Gyr'
plt.figtext(0.68, 0.47, label, ha = 'center', size = 20)
########################## save ######################################
figure_name = 'DPanel-xy-' + dir_name + '.png'
plt.savefig(figure_name)
sys.exit(1)
dir_name = sys.argv[1]
base_name = sys.argv[2]
nproc = int(sys.argv[3])
size = float(sys.argv[4])
start = int(sys.argv[5])
end = int(sys.argv[6])
#for i in range(start, end + 1):
i = start
while i <= end:
snap_num = '%04d' % i
print '##################### snap ', snap_num, ' ######################'
ntot = 0
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir_name, base_name, \
snap_num, nproc)
title = 't = %4d Myr' % int(time + 0.1)
mp_snap.gen_snap_star_dm(rarr, dir_name, base_name, snap_num, size, \
title_name = title)
print ' '
i += 10
######################### end of main function ###########################
def multi_Mc(snap_num, size):
dir = ['81-hd-3Hs1', '91-hd-3Hs1', '101-hd-3Hs1', '80-hd-3Hs1', '90-hd-3Hs1', '100-hd-3Hs1']
proc_num = [2, 8, 8, 2, 8, 8]
vmin = 1.0E3
vmax = 1.0E7
snap_name = '%04d' % snap_num
title = 't = %d Myr' % (10 * snap_num)
base_name = 'dwarf'
rc('font', size = 15)
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(6.5)
fig.subplots_adjust(left = 0.10, right = 0.88, top = 0.92, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
# plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
majorLocator = ticker.MultipleLocator(5)
minorLocator = ticker.MultipleLocator(2.5)
nullFormatter = ticker.NullFormatter()
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
################### hdc-81 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[0], base_name, snap_name, proc_num[0])
ax = fig.add_subplot(231)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-91 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[1], base_name, snap_name, proc_num[1])
ax = fig.add_subplot(232)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-101 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[2], base_name, snap_name, proc_num[2])
ax = fig.add_subplot(233)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-80 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[3], base_name, snap_name, proc_num[3])
ax = fig.add_subplot(234)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-90 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[4], base_name, snap_name, proc_num[1])
ax = fig.add_subplot(235)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-100 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[5], base_name, snap_name, proc_num[2])
ax = fig.add_subplot(236)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idc], z[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.89, 0.10, 0.02, 0.82])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Cloud mass [M$_\odot$]')
######################## label ######################################
label = '$2\\times10^8 \mathsf{M}_\odot$'
plt.figtext(0.23, 0.87, label, ha = 'center', size = 'large')
label = '$2\\times10^9 \mathsf{M}_\odot$'
plt.figtext(0.49, 0.87, label, ha = 'center', size = 'large')
label = '$2\\times10^{10} \mathsf{M}_\odot$'
plt.figtext(0.75, 0.87, label, ha = 'center', size = 'large')
########################## save ######################################
figure_name = 'McPanel-xz-' + snap_name + '.png'
plt.savefig(figure_name)
######################### X - Y ######################################
print "\n############## X - Y ###############\n"
rc('font', size = 15)
plt.clf()
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(6.5)
fig.subplots_adjust(left = 0.10, right = 0.88, top = 0.92, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
# plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
################### hdc-81 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[0], base_name, snap_name, proc_num[0])
ax = fig.add_subplot(231)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-91 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[1], base_name, snap_name, proc_num[1])
ax = fig.add_subplot(232)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-101 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[2], base_name, snap_name, proc_num[2])
ax = fig.add_subplot(233)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-80 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[3], base_name, snap_name, proc_num[3])
ax = fig.add_subplot(234)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-90 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[4], base_name, snap_name, proc_num[1])
ax = fig.add_subplot(235)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-100 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, dir[5], base_name, snap_name, proc_num[2])
ax = fig.add_subplot(236)
arr = np.sort(rarr, order = 'mass')
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
mass = arr['mass'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Y [kpc]')
plt.scatter(x[idc], y[idc], s = 1, c = mass[idc], marker = 'o', \
cmap = cmap, norm = norm, vmin = vmin, vmax = vmax, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.89, 0.10, 0.02, 0.82])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Cloud mass [M$_\odot$]')
######################## label ######################################
label = '$2\\times10^8 \mathsf{M}_\odot$'
plt.figtext(0.23, 0.87, label, ha = 'center', size = 'large')
label = '$2\\times10^9 \mathsf{M}_\odot$'
plt.figtext(0.49, 0.87, label, ha = 'center', size = 'large')
label = '$2\\times10^{10} \mathsf{M}_\odot$'
plt.figtext(0.75, 0.87, label, ha = 'center', size = 'large')
########################## save ######################################
figure_name = 'McPanel-xy-' + snap_name + '.png'
plt.savefig(figure_name)
def multi_hist(snap_num):
dir = ['81-hd-3Hs2', '80-hd-3Hs2', '91-hd-3Hs2', '90-hd-3Hs2', \
'101-hd-3Hs2', '100-hd-3Hs2']
proc_num = [2, 8, 8]
ml = 1.0E3
mh = 1.0E7
logml = log10(ml)
logmh = log10(mh)
snap_name = '%04d' % snap_num
title = 't = %d Myr' % (10 * snap_num)
base_name = 'dwarf'
rc('font', size = 15)
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(10)
fig.subplots_adjust(left = 0.12, right = 0.95, top = 0.95, bottom = 0.08, \
wspace = 0.0, hspace = 0.0)
plt.figtext(0.53, 0.96, title, ha = 'center', size = 'large')
# majorLocator = ticker.MultipleLocator(5)
# minorLocator = ticker.MultipleLocator(2.5)
nullFormatter = ticker.NullFormatter()
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
nbin = 15
ymin = 1.1E-7
ymax = 1.0E1
################################# 81 #################################
ntot, time, arr = mp.read_snap_paral(mp.prefix, dir[0], base_name, snap_name, proc_num[0])
xm, count = mp.calc_hist(arr, ml, mh, nbin)
ax = fig.add_subplot(321)
plt.xlabel('M$_\mathsf{COLD}$ [M$_\odot$]')
plt.ylabel('dN / d(log M$_\mathsf{COLD}$) / M$_\mathsf{COLD}$')
plt.xscale('log')
plt.yscale('log')
plt.plot(xm, count, drawstyle = 'steps-mid')
plt.xlim(ml, mh)
plt.ylim(ymin, ymax)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
################################# 80 #################################
ntot, time, arr = mp.read_snap_paral(mp.prefix, dir[1], base_name, snap_name, proc_num[0])
xm, count = mp.calc_hist(arr, ml, mh, nbin)
ax = fig.add_subplot(322)
plt.xlabel('M$_\mathsf{COLD}$ [M$_\odot$]')
# plt.ylabel('dN / d(log M$_\mathsf{COLD}$) / M$_\mathsf{COLD}$')
plt.xscale('log')
plt.yscale('log')
plt.plot(xm, count, drawstyle = 'steps-mid')
plt.xlim(ml * 1.01, mh)
plt.ylim(ymin, ymax)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
ymin = 2.0E-7
ymax = 9.0E1
################################# 91 #################################
ntot, time, arr = mp.read_snap_paral(mp.prefix, dir[2], base_name, snap_name, proc_num[1])
xm, count = mp.calc_hist(arr, ml, mh, nbin)
ax = fig.add_subplot(323)
plt.xlabel('M$_\mathsf{COLD}$ [M$_\odot$]')
plt.ylabel('dN / d(log M$_\mathsf{COLD}$) / M$_\mathsf{COLD}$')
plt.xscale('log')
plt.yscale('log')
plt.plot(xm, count, drawstyle = 'steps-mid')
plt.xlim(ml, mh)
plt.ylim(ymin, ymax)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
################################# 90 #################################
ntot, time, arr = mp.read_snap_paral(mp.prefix, dir[3], base_name, snap_name, proc_num[1])
xm, count = mp.calc_hist(arr, ml, mh, nbin)
ax = fig.add_subplot(324)
plt.xlabel('M$_\mathsf{COLD}$ [M$_\odot$]')
# plt.ylabel('dN / d(log M$_\mathsf{COLD}$) / M$_\mathsf{COLD}$')
plt.xscale('log')
plt.yscale('log')
plt.plot(xm, count, drawstyle = 'steps-mid')
plt.xlim(ml * 1.01, mh)
plt.ylim(ymin, ymax)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
ymin = 5.0E-6
ymax = 9.0E1
################################# 101 #################################
ntot, time, arr = mp.read_snap_paral(mp.prefix, dir[4], base_name, snap_name, 8)
xm, count = mp.calc_hist(arr, ml, mh, nbin)
ax = fig.add_subplot(325)
plt.xlabel('M$_\mathsf{COLD}$ [M$_\odot$]')
plt.ylabel('dN / d(log M$_\mathsf{COLD}$) / M$_\mathsf{COLD}$')
plt.xscale('log')
plt.yscale('log')
plt.plot(xm, count, drawstyle = 'steps-mid')
plt.xlim(ml, mh)
plt.ylim(ymin, ymax)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
################################# 100 #################################
ntot, time, arr = mp.read_snap_paral(mp.prefix, dir[5], base_name, snap_name, 8)
xm, count = mp.calc_hist(arr, ml, mh, nbin)
ax = fig.add_subplot(326)
plt.xlabel('M$_\mathsf{COLD}$ [M$_\odot$]')
# plt.ylabel('dN / d(log M$_\mathsf{COLD}$) / M$_\mathsf{COLD}$')
plt.xscale('log')
plt.yscale('log')
plt.plot(xm, count, drawstyle = 'steps-mid')
plt.xlim(ml * 1.01, mh)
plt.ylim(ymin, ymax)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
figure_name = 'histPanel-' + snap_name + '.png'
plt.savefig(figure_name)
figure_name = 'histPanel-' + snap_name + '.eps'
plt.savefig(figure_name)
def multi_T(snap_num, size):
snap_name = '%04d' % snap_num
title = 't = %d Myr' % (10 * snap_num)
base_name = 'dwarf'
rc('font', size = 15)
fig = plt.figure()
fig.set_figwidth(10)
fig.set_figheight(6.5)
fig.subplots_adjust(left = 0.10, right = 0.88, top = 0.92, bottom = 0.10, \
wspace = 0.0, hspace = 0.0)
plt.figtext(0.5, 0.94, title, ha = 'center', size = 'large')
majorLocator = ticker.MultipleLocator(20)
minorLocator = ticker.MultipleLocator(4)
nullFormatter = ticker.NullFormatter()
cmap = mpl.cm.jet
norm = mpl.colors.LogNorm()
################### hdc-81 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, '81-SG-4', base_name, snap_name, 1)
ax = fig.add_subplot(231)
arr = np.sort(rarr, order = 'T')
# arr = arr[::-1]
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
T = arr['T'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = T[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e3, vmax = 1e7, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-91 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, '91-SG', base_name, snap_name, 1)
ax = fig.add_subplot(232)
arr = np.sort(rarr, order = 'T')
# arr = arr[::-1]
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
T = arr['T'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = T[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e3, vmax = 1e7, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-101 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, '101-SG', base_name, snap_name, 8)
ax = fig.add_subplot(233)
arr = np.sort(rarr, order = 'T')
# arr = arr[::-1]
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
T = arr['T'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
# plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = T[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e3, vmax = 1e7, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-80 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, '80-4', base_name, snap_name, 1)
ax = fig.add_subplot(234)
arr = np.sort(rarr, order = 'T')
# arr = arr[::-1]
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
T = arr['T'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = T[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e3, vmax = 1e7, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
# ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-90 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, '90', base_name, snap_name, 1)
ax = fig.add_subplot(235)
arr = np.sort(rarr, order = 'T')
# arr = arr[::-1]
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
T = arr['T'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = T[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e3, vmax = 1e7, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
################### hdc-100 ####################################
ntot, time, rarr = mp.read_snap_paral(mp.prefix, '100', base_name, snap_name, 8)
ax = fig.add_subplot(236)
arr = np.sort(rarr, order = 'T')
# arr = arr[::-1]
x = arr['pos'][:, 0]
y = arr['pos'][:, 1]
z = arr['pos'][:, 2]
type = arr['type'][:]
r = np.sqrt(x * x + y * y + z * z)
T = arr['T'][:]
boolarr = np.logical_and(type == mp.TYPE_WARM, r < 50.0)
# boolarr = np.logical_and(den > 5.0E-5, boolarr)
idw = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_COLD, r < 50.0)
idc = np.where(boolarr)
boolarr = np.logical_and(type == mp.TYPE_STAR_PARAL, r < 50.0)
ids = np.where(boolarr)
width, height = rcParams['figure.figsize']
sz = min(width, height)
plt.xlabel('X [kpc]')
# plt.ylabel('Z [kpc]')
plt.scatter(x[idw], z[idw], s = 1, c = T[idw], marker = 'o', \
cmap = cmap, norm = norm, vmin = 1e3, vmax = 1e7, edgecolors = 'none')
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_minor_locator(minorLocator)
# ax.xaxis.set_major_formatter(nullFormatter)
ax.yaxis.set_major_formatter(nullFormatter)
plt.xlim(-size, size * 0.99)
plt.ylim(-size, size)
######################### color bar ##################################
# [xcoord, ycoord, width, height]
cbaxes = fig.add_axes([0.89, 0.10, 0.02, 0.82])
cb = plt.colorbar(orientation='vertical', \
ticks = ticker.LogLocator(base = 10.0), cax = cbaxes)
cb.set_label('Temperature [K]')
######################## label ######################################
label = '$2\\times10^8 \mathsf{M}_\odot$'
plt.figtext(0.23, 0.87, label, ha = 'center', size = 'large')
label = '$2\\times10^9 \mathsf{M}_\odot$'
plt.figtext(0.49, 0.87, label, ha = 'center', size = 'large')
label = '$2\\times10^{10} \mathsf{M}_\odot$'
plt.figtext(0.75, 0.87, label, ha = 'center', size = 'large')
########################## save ######################################
figure_name = 'TPanel-xz-' + snap_name + '.png'
plt.savefig(figure_name)
