def plot_ts_pub(ts,
xlabel=r'\textit{time} (day)',
ylabel=r'\textit(num of attacks)',
legend='',
title='',
col='blue',
lstyle='-',
lw=2,
marker='',
outfile=None):
# rcParams['figure.figsize'] = 15, 6
rcParams.update(params)
plt.plot(ts,
color=col,
label=legend,
linestyle=lstyle,
linewidth=lw,
marker=marker)
plt.xlabel(xlabel, fontsize=20)
plt.ylabel(ylabel, fontsize=20)
plt.legend(loc="upper left")
# plt.title(title)
if outfile is not None:
plt.savefig(outfile, format="pdf")
return plt
def plot_momentum(output_path, name_list):
# Plot parameters
params = {
"font.size": 12,
"font.family": "Times",
"text.usetex": True,
"figure.figsize": (5, 4),
"figure.subplot.left": 0.15,
"figure.subplot.right": 0.95,
"figure.subplot.bottom": 0.18,
"figure.subplot.top": 0.9,
"lines.markersize": 2,
"lines.linewidth": 2.0,
}
rcParams.update(params)
parttype_list = [0, 4]
for parttype in parttype_list:
plt.figure()
ax = plt.subplot(1, 1, 1)
if parttype == 4:
ax.set_title("Stellar component")
ylabel = "$j_{\mathrm{stars}}$ [kpc km/s]"
if parttype == 0:
ax.set_title("HI+H2 gas")
ylabel = "$j_{\mathrm{gas}}$ [kpc km/s]"
plt.grid("True")
for i, name in enumerate(name_list):
data = np.loadtxt(f"{output_path}/momentum_parttype_%i_" %
(parttype) + name + ".txt")
# In case of no objects len(shape) = 1
if len(np.shape(data)) > 1:
plt.plot(data[:, 0],
data[:, 1],
"o",
color=color[i],
label=name)
plt.xlabel("Stellar Mass [M$_{\odot}$]")
plt.ylabel(ylabel)
plt.yscale("log")
plt.xscale("log")
plt.xlim(1e6, 1e12)
plt.ylim(1e-1, 1e4)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.legend(labelspacing=0.2,
handlelength=2,
handletextpad=0.4,
frameon=False)
plt.savefig(f"{output_path}/momentum_parttype_%i.png" % parttype,
dpi=200)
plt.close()
def plot_simplex(points, names=('C1', 'C2'), proba_triplet=None):
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.pylab import rcParams
def _project(points):
from math import sqrt, sin, cos, pi
p1, p2, p3 = points.T / sqrt(3)
x = (p2 - p1) * cos(pi / 6) + 0.5
y = p3 - (p1 + p2) * sin(pi / 6) + 1 / (2 * sqrt(3))
return np.vstack((x, y)).T
vert0 = _project(
np.array([[0.3333, 0.3333, 0.3333], [0.5, 0.5, 0], [0.5, 0, 0.5],
[0, 0.5, 0.5]]))
fig = plt.figure()
fig.set_size_inches(8, 7)
nl, ne, nr = np.max(points, axis=0)
for i, n in enumerate((nl, ne, nr)):
if n < 0.001:
print("p{} is too small, switching to 2d plot".format(names[::-1] +
["rope"]))
coords = sorted(set(range(3)) - i)
return plot2d(points[:, coords], labels[coords])
# triangle
fig.gca().add_line(
Line2D([0, 0.5, 1.0, 0], [0, np.sqrt(3) / 2, 0, 0], color='black'))
# decision lines
for i in (1, 2, 3):
fig.gca().add_line(
Line2D([vert0[0, 0], vert0[i, 0]], [vert0[0, 1], vert0[i, 1]],
color='black'))
# vertex labels
rcParams.update({'font.size': 16})
fig.gca().text(-0.08,
-0.08,
'p({} ({}))'.format(names[0], proba_triplet[0]),
color='black')
fig.gca().text(0.44, np.sqrt(3) / 2 + 0.05, 'p(rope)', color='black')
fig.gca().text(0.650,
-0.08,
'p({} ({}))'.format(names[1], proba_triplet[2]),
color='black')
# project and draw points
tripts = _project(points[:, [0, 2, 1]])
plt.hexbin(tripts[:, 0], tripts[:, 1], mincnt=1, cmap=plt.cm.Greens_r)
# Leave some padding around the triangle for vertex labels
fig.gca().set_xlim(-0.2, 1.2)
fig.gca().set_ylim(-0.2, 1.2)
fig.gca().axis('off')
return fig
def test_plot():
params = {
'axes.labelsize': 8,
'text.fontsize': 8,
'legend.fontsize': 10,
'xtick.labelsize': 10,
'ytick.labelsize': 10,
'text.usetex': False,
'figure.figsize': [6.5, 4.0]
}
rcParams.update(params)
def plot_simplex(points, names=('C1', 'C2')):
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.pylab import rcParams
def _project(points):
from math import sqrt, sin, cos, pi
p1, p2, p3 = points.T / sqrt(3)
x = (p2 - p1) * cos(pi / 6) + 0.5
y = p3 - (p1 + p2) * sin(pi / 6) + 1 / (2 * sqrt(3))
return np.vstack((x, y)).T
vert0 = _project(np.array(
[[0.3333, 0.3333, 0.3333], [0.5, 0.5, 0], [0.5, 0, 0.5], [0, 0.5, 0.5]]))
fig = plt.figure()
fig.set_size_inches(8, 7)
nl, ne, nr = np.max(points, axis=0)
for i, n in enumerate((nl, ne, nr)):
if n < 0.001:
print("p{} is too small, switching to 2d plot".format(names[::-1] + ["rope"]))
coords = sorted(set(range(3)) - i)
return plot2d(points[:, coords], labels[coords])
# triangle
fig.gca().add_line(
Line2D([0, 0.5, 1.0, 0],
[0, np.sqrt(3) / 2, 0, 0], color='orange'))
# decision lines
for i in (1, 2, 3):
fig.gca().add_line(
Line2D([vert0[0, 0], vert0[i, 0]],
[vert0[0, 1], vert0[i, 1]], color='orange'))
# vertex labels
rcParams.update({'font.size': 16})
fig.gca().text(-0.08, -0.08, 'p({})'.format(names[0]), color='orange')
fig.gca().text(0.44, np.sqrt(3) / 2 + 0.05, 'p(rope)', color='orange')
fig.gca().text(1.00, -0.08, 'p({})'.format(names[1]), color='orange')
# project and draw points
tripts = _project(points[:, [0, 2, 1]])
plt.hexbin(tripts[:, 0], tripts[:, 1], mincnt=1, cmap=plt.cm.Blues_r)
# Leave some padding around the triangle for vertex labels
fig.gca().set_xlim(-0.2, 1.2)
fig.gca().set_ylim(-0.2, 1.2)
fig.gca().axis('off')
return fig
def matplot_settings():
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 4,4
pgf_with_xelatex = {
'text.usetex': True,
'text.latex.unicode': True,
'pgf.rcfonts': False,
'font.family': 'sans-serif',
"pgf.texsystem": "xelatex",
"pgf.preamble": [
r"\usepackage{amssymb}",
r"\usepackage{amsmath}",
r"\usepackage{fontspec}",
r"\setmainfont{Gentium Book Basic}",
r"\setsansfont{Open Sans Light}",
r"\usepackage{unicode-math}",
r"\setmathfont{TeX Gyre Termes Math}"
]
}
rcParams.update(pgf_with_xelatex)
def plot_surface_densities(sigma_SFR, sigma_gas, sigma_H2, stellar_mass,
MorphologyPlotsInWeb, output_path):
# Get the default KS relation for correct IMF
def KS(sigma_g, n, A):
return A * sigma_g**n
Sigma_g = np.logspace(-2, 3, 1000)
Sigma_star = KS(Sigma_g, 1.4, 1.515e-4)
# Plot parameters
params = {
"font.size": 12,
"font.family": "Times",
"text.usetex": True,
"figure.figsize": (5.5, 4),
"figure.subplot.left": 0.15,
"figure.subplot.right": 0.85,
"figure.subplot.bottom": 0.18,
"figure.subplot.top": 0.9,
"lines.markersize": 4,
"lines.linewidth": 2.0,
}
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.grid("True")
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
"--",
color="grey",
label=r"1.51e-4 $\times$ $\Sigma_{g}^{1.4}$",
)
plt.scatter(
sigma_H2,
sigma_SFR,
c=stellar_mass,
alpha=0.9,
s=25,
vmin=6,
vmax=10,
cmap="CMRmap_r",
edgecolors="none",
zorder=2,
)
plt.xlabel("log $\\Sigma_{H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$")
plt.ylabel(
"log $\\Sigma_{\\rm SFR}$ $[{\\rm M_\\odot \\cdot yr^{-1} \\cdot kpc^{-2}}]$"
)
plt.xlim(-1.0, 3.0)
plt.ylim(-6.0, 0.0)
plt.legend()
cbar_ax = fig.add_axes([0.87, 0.18, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[6, 7, 8, 9, 10, 11, 12], cax=cbar_ax)
cb.set_label(label="$\log_{10}$ M$_{*}$/M$_{\odot}$", labelpad=0.5)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(f"{output_path}/surface_density_gas.png", dpi=200)
plt.close()
title = "$\\Sigma_{H_2}$ vs $\\Sigma_{\\rm SFR}$"
caption = "Integrated surface densities of H2 gas and star-forming gas for each individual galaxy. "
caption += "Quantities are calculated summing up all gas (and SFR) within the galaxies' stellar half mass radius."
filename = "surface_density_gas.png"
id = abs(hash("surface_density_gas"))
MorphologyPlotsInWeb.load_plots(title, caption, filename, id)
#######
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.grid("True")
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
"--",
color="grey",
label=r"1.51e-4 $\times$ $\Sigma_{g}^{1.4}$",
)
plt.scatter(
sigma_gas,
sigma_SFR,
c=stellar_mass,
alpha=0.9,
s=25,
vmin=6,
vmax=10,
cmap="CMRmap_r",
edgecolors="none",
zorder=2,
)
plt.xlabel(
"log $\\Sigma_{HI}+ \\Sigma_{H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$")
plt.ylabel(
"log $\\Sigma_{\\rm SFR}$ $[{\\rm M_\\odot \\cdot yr^{-1} \\cdot kpc^{-2}}]$"
)
plt.xlim(-1.0, 3.0)
plt.ylim(-6.0, 0.0)
plt.legend()
cbar_ax = fig.add_axes([0.87, 0.18, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[6, 7, 8, 9, 10, 11, 12], cax=cbar_ax)
cb.set_label(label="$\log_{10}$ M$_{*}$/M$_{\odot}$", labelpad=0.5)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(f"{output_path}/surface_density_H2.png", dpi=200)
plt.close()
caption = "Integrated surface densities of H2+HI gas and star-forming gas for each individual galaxy. "
caption += "Quantities are calculated summing up all gas (and SFR) within the galaxies' stellar half mass radius."
filename = "surface_density_H2.png"
id = abs(hash("surface_density_H2"))
MorphologyPlotsInWeb.load_plots(title, caption, filename, id)
def survival_plots(SAVE_PATH, KM_FILE_NAME, rules, lifelinesModels):
# generate pdf with survival model (all rules) and each rule (vs cmp and pop)
print('> generating plots')
def get_RuleString(rule_id, rule_dict):
return '{}: '.format(rule_id) + ' and '.join([
'<{}={}>'.format(str(attr),
str(val).replace('.0', ''))
for (attr, val) in rule_dict.items()
])
pdf = matplotlib.backends.backend_pdf.PdfPages(SAVE_PATH) # .pdf file
# Final rule-model figure
rcParams.update({'font.size': 14})
rcParams['figure.figsize'] = 15, 10
plt.figure(num='ruleModel')
ax = plt.subplot(111)
kmModels = pd.read_csv(KM_FILE_NAME, header=0)
x = kmModels.times.values
columns = list(kmModels.columns)
columns.remove('times')
for column in columns:
if column == "population":
ax.plot(x,
kmModels[column],
color='k',
label='{}'.format(column),
linestyle=':')
else:
ax.plot(x, kmModels[column], label='{}'.format(column))
plt.title('Rule-Set (subgroups) Survival Models')
plt.tight_layout(pad=1.25)
plt.xlabel('Time')
plt.xlim(0, x[-1] + 3)
plt.xticks([])
plt.ylabel('Survival probability')
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.legend(frameon=False, bbox_to_anchor=(1.01, 1), loc=2)
pdf.savefig(plt.figure(num='ruleModel'), bbox_inches='tight')
plt.close(fig='ruleModel')
# Individual rules
n_rows = math.ceil(len(rules) / 2)
rcParams['figure.figsize'] = 15, 3 * n_rows
rcParams['axes.titlesize'] = 11
#plt.figure(num='indiv_rules')
fig, axes = plt.subplots(nrows=n_rows,
ncols=2,
sharex=True,
sharey=True,
num='indiv_rules')
ax_id = 0
for rule_id in rules.keys():
ax = axes.flatten()[ax_id]
ax_id += 1
lifelinesModels['population'].plot(ax=ax, ci_show=False, c='k', ls=':')
lifelinesModels[rule_id]['sg'].plot(ax=ax,
ci_show=False,
c='firebrick')
lifelinesModels[rule_id]['cmp'].plot(ax=ax,
ci_show=False,
c='tab:blue')
ax.set_title(get_RuleString(rule_id, rules[rule_id]), loc='left')
ax.set_xlabel('Time')
ax.set_ylabel('Survival probability')
ax.set_ylim([0, 1])
ax.set_xlim([0, x[-1] + 3])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.legend(frameon=False, bbox_to_anchor=(1.01, 1), loc=2)
plt.tight_layout(pad=1.25)
plt.xticks([])
for i in range(ax_id, 2 * n_rows):
fig.delaxes(axes.flatten()[i])
pdf.savefig(plt.figure(num='indiv_rules'), bbox_inches='tight')
plt.close(fig='indiv_rules')
pdf.close()
return
def plot_galaxy_parts(partsDATA, parttype, ang_momentum, halo_data, index,
output_path, simulation_name):
# Plot ranges
r_limit = 5 * halo_data.half_mass_radius_star[index]
r_img = 30.0
if r_limit < r_img:
r_img = r_limit
xmin = -r_img
ymin = -r_img
xmax = r_img
ymax = r_img
pos_parts = partsDATA[:, 0:3].copy()
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="z")
edge_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="y")
pos_face_on = np.matmul(face_on_rotation_matrix, pos_parts.T)
pos_face_on = pos_face_on.T
pos_edge_on = np.matmul(edge_on_rotation_matrix, pos_parts.T)
pos_edge_on = pos_edge_on.T
if parttype == 0:
density = np.log10(partsDATA[:, 11])
if parttype == 4:
density = np.log10(partsDATA[:, 8])
# Sort particles for better viewing
arg_sort = np.argsort(density)
density = density[arg_sort]
pos_face_on = pos_face_on[arg_sort, :]
pos_edge_on = pos_edge_on[arg_sort, :]
denmin = np.min(density)
denmax = np.max(density)
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 2, 1)
if parttype == 4:
title = "Stellar component"
if parttype == 0:
title = "Gas component"
ax.set_title(title)
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("y [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.scatter(
pos_face_on[:, 0],
pos_face_on[:, 1],
c=density,
alpha=1,
s=10,
vmin=denmin,
vmax=denmax,
cmap="magma",
edgecolors="none",
)
ax.autoscale(False)
ax = plt.subplot(1, 2, 2)
if parttype == 4:
kappa = halo_data.kappa_co[index]
mass = halo_data.log10_stellar_mass[index]
ac = halo_data.axis_ca[index]
cb = halo_data.axis_cb[index]
ba = halo_data.axis_ba[index]
radius = halo_data.half_mass_radius_star[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{*}/M_{\odot} = $%0.2f" % (mass)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
title += "\n Stellar half mass radius %0.2f kpc" % (radius)
if parttype == 0:
kappa = halo_data.gas_kappa_co[index]
mass = halo_data.log10_gas_mass[index]
ac = halo_data.gas_axis_ca[index]
cb = halo_data.gas_axis_cb[index]
ba = halo_data.gas_axis_ba[index]
radius = halo_data.half_mass_radius_star[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{gas}/M_{\odot} = $%0.2f" % (mass)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
title += "\n Stellar half mass radius %0.2f kpc" % (radius)
ax.set_title(title)
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("z [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.scatter(
pos_edge_on[:, 0],
pos_edge_on[:, 1],
c=density,
alpha=1,
s=10,
vmin=denmin,
vmax=denmax,
cmap="magma",
edgecolors="none",
)
ax.autoscale(False)
cbar_ax = fig.add_axes([0.86, 0.22, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[4, 6, 8, 10], cax=cbar_ax)
cb.set_label(label=r"$\log_{10}$ $\rho$ [M$_{\odot}$/kpc$^{3}$]",
labelpad=0.5)
if parttype == 4:
outfile = (f"{output_path}/galaxy_sparts_%i_" % (index) +
simulation_name + ".png")
if parttype == 0:
outfile = f"{output_path}/galaxy_parts_%i_" % (
index) + simulation_name + ".png"
fig.savefig(outfile, dpi=150)
plt.close("all")
def render_luminosity_map(
parts_data,
luminosity,
filtname,
ang_momentum,
halo_data,
index,
output_path,
simulation_name,
):
pos_parts = parts_data[:, 0:3].copy()
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum)
edge_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="y")
pos_face_on = np.matmul(face_on_rotation_matrix, pos_parts.T)
pos_face_on = pos_face_on.T
pos_edge_on = np.matmul(edge_on_rotation_matrix, pos_parts.T)
pos_edge_on = pos_edge_on.T
hsml_parts = parts_data[:, 7]
r_limit = 5 * halo_data.half_mass_radius_star[index]
r_img = 30.0
if r_limit < r_img:
r_img = r_limit
xmin = -r_img
ymin = -r_img
xmax = r_img
ymax = r_img
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 2, 1)
###### plot one side ########################
qv = QuickView(
pos_face_on,
mass=luminosity,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=0,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("y [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img, vmin=img.max() - 2.3)
ax.imshow(img, cmap="magma", extent=ext, vmin=img.max() - 2.3)
ax.autoscale(False)
###### plot another side ########################
ax = plt.subplot(1, 2, 2)
qv = QuickView(
pos_edge_on,
mass=luminosity,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=90,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("z [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img, vmin=img.max() - 2.3)
ims = ax.imshow(img, cmap="magma", vmin=img.max() - 2.3, extent=ext)
ax.autoscale(False)
cbar_ax = fig.add_axes([0.86, 0.22, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ims, ticks=[4, 6, 8, 10, 12], cax=cbar_ax)
cb.set_label(label=r"$\log_{10}$ $\Sigma$ [Jy/kpc$^{2}$]", labelpad=0.5)
outfile = (f"{output_path}/galaxy_%s_map_%i_" % (filtname, index) +
simulation_name + ".png")
fig.savefig(outfile, dpi=150)
plt.close("all")
return
def plot_integrated_surface_densities(sigma_SFR, sigma_gas, sigma_H2,
stellar_mass, output_path,
simulation_name):
# Plot parameters
params = {
"font.size": 12,
"font.family": "Times",
"text.usetex": True,
"figure.figsize": (5.5, 4),
"figure.subplot.left": 0.15,
"figure.subplot.right": 0.85,
"figure.subplot.bottom": 0.18,
"figure.subplot.top": 0.9,
"lines.markersize": 4,
"lines.linewidth": 2.0,
}
# Get the default KS relation for correct IMF
def KS(sigma_g, n, A):
return A * sigma_g**n
Sigma_g = np.logspace(-2, 3, 1000)
Sigma_star = KS(Sigma_g, 1.4, 1.515e-4)
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.grid("True")
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
"--",
color="grey",
label=r"1.51e-4 $\times$ $\Sigma_{\rm g}^{1.4}$",
)
plt.scatter(
sigma_H2,
sigma_SFR,
c=stellar_mass,
alpha=0.9,
s=25,
vmin=6,
vmax=10,
cmap="CMRmap_r",
edgecolors="none",
zorder=2,
)
plt.xlabel("log $\\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$")
plt.ylabel(
"log $\\Sigma_{\\rm SFR}$ $[{\\rm M_\\odot \\cdot yr^{-1} \\cdot kpc^{-2}}]$"
)
plt.xlim(-1.0, 4.0)
plt.ylim(-6.0, 1.0)
plt.legend()
cbar_ax = fig.add_axes([0.87, 0.18, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[6, 7, 8, 9, 10, 11, 12], cax=cbar_ax)
cb.set_label(label="$\log_{10}$ M$_{*}$/M$_{\odot}$", labelpad=0.5)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(f"{output_path}/surface_density_gas_" + simulation_name +
".png",
dpi=200)
plt.close()
#######
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.grid("True")
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
"--",
color="grey",
label=r"1.51e-4 $\times$ $\Sigma_{\rm g}^{1.4}$",
)
plt.scatter(
sigma_gas,
sigma_SFR,
c=stellar_mass,
alpha=0.9,
s=25,
vmin=6,
vmax=10,
cmap="CMRmap_r",
edgecolors="none",
zorder=2,
)
plt.xlabel(
"log $\\Sigma_{\\rm HI}+ \\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$"
)
plt.ylabel(
"log $\\Sigma_{\\rm SFR}$ $[{\\rm M_\\odot \\cdot yr^{-1} \\cdot kpc^{-2}}]$"
)
plt.xlim(-1.0, 4.0)
plt.ylim(-6.0, 1.0)
plt.legend()
cbar_ax = fig.add_axes([0.87, 0.18, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[6, 7, 8, 9, 10, 11, 12], cax=cbar_ax)
cb.set_label(label="$\log_{10}$ M$_{*}$/M$_{\odot}$", labelpad=0.5)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(f"{output_path}/surface_density_H2_" + simulation_name +
".png",
dpi=200)
plt.close()
def plot_combined_surface_densities(combined_data, output_path,
simulation_name):
# Get the default KS relation for correct IMF
def KS(sigma_g, n, A):
return A * sigma_g**n
# Plot parameters
params = {
"font.size": 12,
"font.family": "Times",
"text.usetex": True,
"figure.figsize": (5.5, 4),
"figure.subplot.left": 0.15,
"figure.subplot.right": 0.85,
"figure.subplot.bottom": 0.18,
"figure.subplot.top": 0.9,
"lines.markersize": 2,
"lines.linewidth": 2.0,
}
rcParams.update(params)
sigma_SFR = combined_data.SFR_surface_density
metals = combined_data.gas_metallicity
metals[metals == 0] = 1e-6
metals = np.log10(np.array(metals, dtype="float"))
arg_sort = np.argsort(metals)
metals = metals[arg_sort[::-1]]
sigma_SFR = sigma_SFR[arg_sort[::-1]]
for plot in ["gas", "H2"]:
# star formation rate surgface density vs surface density
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.grid("True")
Sigma_g = np.logspace(-2, 3, 1000)
Sigma_star = KS(Sigma_g, 1.4, 1.515e-4)
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
"--",
color="grey",
label=r"1.51e-4 $\times$ $\Sigma_{\rm g}^{1.4}$",
)
Sigma_g = np.logspace(-1, 4, 1000)
Sigma_star = KS(Sigma_g, 1.06, 2.511e-4)
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
lw=1,
color="green",
label=r"2.51e-4 $\times$ $\Sigma_{\rm g}^{1.06}$ (Pessa+ 2021)",
linestyle="-",
)
if plot == "gas":
sigma_gas = combined_data.neutral_gas_surface_density
t_gas = combined_data.depletion_time_neutral_gas
else:
sigma_gas = combined_data.molecular_gas_surface_density
t_gas = combined_data.depletion_time_molecular_gas
sigma_gas = sigma_gas[arg_sort[::-1]]
t_gas = t_gas[arg_sort[::-1]]
plt.scatter(
sigma_gas,
sigma_SFR,
c=metals,
alpha=0.9,
s=10,
vmin=-3,
vmax=1,
cmap="CMRmap_r",
edgecolors="none",
zorder=2,
)
select = np.where((metals > -1.2) & (metals < -0.8))[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_SFR[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="crimson",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=-1",
)
select = np.where((metals > -0.2) & (metals < 0.2))[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_SFR[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="mediumpurple",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=0",
)
select = np.where(metals > 0.6)[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_SFR[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="lightblue",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=1",
)
x, y, y_down, y_up = median_relations(sigma_gas, sigma_SFR)
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(x, y, "-", lw=1.5, color="grey", label="All")
select = np.where(sigma_SFR > -5.5)[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_SFR[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(x, y, "-", lw=1.5, color="black", label="star-forming")
if plot == "gas":
plt.xlabel(
"log $\\Sigma_{\\rm HI} + \\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$"
)
else:
plt.xlabel(
"log $\\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$")
plt.ylabel(
"log $\\Sigma_{\\rm SFR}$ $[{\\rm M_\\odot \\cdot yr^{-1} \\cdot kpc^{-2}}]$"
)
plt.xlim(-1.0, 4.0)
plt.ylim(-6.0, 1.0)
plt.legend(
loc=[0.0, 0.5],
labelspacing=0.2,
handlelength=1,
handletextpad=0.2,
frameon=False,
)
cbar_ax = fig.add_axes([0.87, 0.18, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[-3, -2, -1, 0, 1], cax=cbar_ax)
cb.set_label(label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$", labelpad=0.5)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(
f"{output_path}/combined_surface_density_{plot}_" +
simulation_name + ".png",
dpi=200,
)
plt.close()
# Depletion time vs surface density
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.grid("True")
Sigma_g = np.logspace(-1, 4, 1000)
Sigma_star = KS(Sigma_g, 1.4, 1.515e-4)
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_g) - np.log10(Sigma_star) + 6.0,
color="red",
label="KS law (Kennicutt 98)",
linestyle="--",
)
plt.scatter(
sigma_gas,
t_gas,
c=metals,
alpha=0.9,
s=10,
vmin=-3,
vmax=1,
cmap="CMRmap_r",
edgecolors="none",
zorder=2,
)
select = np.where((metals > -1.2) & (metals < -0.8))[0]
x, y, y_down, y_up = median_relations(sigma_gas[select], t_gas[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="crimson",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=-1",
)
select = np.where((metals > -0.2) & (metals < 0.2))[0]
x, y, y_down, y_up = median_relations(sigma_gas[select], t_gas[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="mediumpurple",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=0",
)
select = np.where(metals > 0.6)[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_SFR[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="lightblue",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=1",
)
x, y, y_down, y_up = median_relations(sigma_gas, t_gas)
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(x, y, "-", lw=1.5, color="grey", label="All")
select = np.where(sigma_SFR > -5.5)[0]
x, y, y_down, y_up = median_relations(sigma_gas[select], t_gas[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(x, y, "-", lw=1.5, color="black", label="star-forming")
if plot == "gas":
plt.xlabel(
"log $\\Sigma_{\\rm HI} + \\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$"
)
plt.ylabel(
"log $\\rm t_{gas} = (\\Sigma_{\\rm HI} + \\Sigma_{\\rm H_2})/ \\Sigma_{\\rm SFR}$ $[{\\rm yr }]$"
)
else:
plt.xlabel(
"log $\\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$")
plt.ylabel(
"log $\\rm t_{H_2} = \\Sigma_{\\rm H_2} / \\Sigma_{\\rm SFR}$ $[{\\rm yr }]$"
)
plt.xlim(-1, 4.0)
plt.ylim(7, 12)
plt.legend(
loc=[0.0, 0.5],
labelspacing=0.2,
handlelength=1,
handletextpad=0.2,
frameon=False,
)
cbar_ax = fig.add_axes([0.87, 0.18, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[-3, -2, -1, 0, 1], cax=cbar_ax)
cb.set_label(label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$", labelpad=0.5)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(
f"{output_path}/depletion_time_combined_surface_density_{plot}_" +
simulation_name + ".png",
dpi=200,
)
plt.close()
#######
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
plt.grid("True")
sigma_gas = combined_data.neutral_gas_surface_density
sigma_ratio = combined_data.H2_to_neutral_surface_density_ratio
sigma_gas = sigma_gas[arg_sort[::-1]]
sigma_ratio = sigma_ratio[arg_sort[::-1]]
plt.scatter(
sigma_gas,
sigma_ratio,
c=metals,
alpha=0.9,
s=5,
vmin=-3,
vmax=1,
cmap="CMRmap_r",
edgecolors="none",
zorder=2,
label="method:grid",
)
select = np.where((metals > -1.2) & (metals < -0.8))[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_ratio[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="crimson",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=-1",
)
select = np.where((metals > -0.2) & (metals < 0.2))[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_ratio[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="mediumpurple",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=0",
)
select = np.where(metals > 0.6)[0]
x, y, y_down, y_up = median_relations(sigma_gas[select],
sigma_ratio[select])
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(
x,
y,
"-",
lw=1.5,
color="lightblue",
label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$=1",
)
x, y, y_down, y_up = median_relations(sigma_gas, sigma_ratio)
plt.plot(x, y, "-", lw=2, color="white")
plt.plot(x, y, "-", lw=1.5, color="grey", label="All")
sigma_gas = combined_data.radii_neutral_gas_surface_density
sigma_ratio = combined_data.radii_H2_to_neutral_surface_density_ratio
plt.plot(
sigma_gas,
sigma_ratio,
"o",
ms=4,
alpha=0.5,
color="tab:blue",
label="method:annuli",
)
plt.xlabel(
"log $\\Sigma_{\\rm HI} + \\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$"
)
plt.ylabel(
r"log $\Sigma_{\mathrm{H2}} / (\Sigma_{\mathrm{HI}}+\Sigma_{\mathrm{H2}})$"
)
plt.xlim(-1.0, 4.0)
plt.ylim(-8.0, 0.5)
plt.legend(
loc=[0.65, 0.0],
labelspacing=0.2,
handlelength=1,
handletextpad=0.2,
frameon=False,
)
cbar_ax = fig.add_axes([0.87, 0.18, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[-3, -2, -1, 0, 1], cax=cbar_ax)
cb.set_label(label="log Z$_{\mathrm{gas}}$/Z$_{\odot}$", labelpad=0.5)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(
f"{output_path}/combined_surface_density_ratios_" + simulation_name +
".png",
dpi=200,
)
plt.close()
def surface_ratios_plots(output_path, index, name_list):
# Load data from Schruba +2021
SchrubaData = np.loadtxt("./plotter/obs_data/Schruba2011_data.txt",
usecols=(4, 5, 6))
nonan = np.logical_and(
np.isnan(SchrubaData[:, 0]) == False,
np.isnan(SchrubaData[:, 1]) == False)
Schruba_H1 = SchrubaData[nonan, 0] # HI surface density [Msol / pc-2]
Schruba_H2 = SchrubaData[nonan, 1] # H2 surface density [Msol / pc-2]
flags = SchrubaData[nonan, 2]
select_flags = np.logical_or(flags == 1,
flags == 0) # Disregarding upper limits
Schruba_H1 = Schruba_H1[select_flags]
Schruba_H2 = Schruba_H2[select_flags]
x_Schruba = np.log10(Schruba_H1 + Schruba_H2)
y_Schruba = np.log10(Schruba_H2 / (Schruba_H1 + Schruba_H2))
rcParams.update(params)
methods = ["grid", "radii"]
for method in methods:
plt.figure()
ax = plt.subplot(1, 1, 1)
# Krumholz 2009 lines
Sigma_neutral = np.arange(-1, 3, 0.2)
RH2 = 1.0 / Krumholz_eq39(10**Sigma_neutral, 0.5)
FH2 = np.log10(1.0 / (1.0 + RH2))
plt.plot(Sigma_neutral,
FH2,
"--",
color="darkred",
label="Krumholz+ (2009): f = 0.5")
RH2 = 1.0 / Krumholz_eq39(10**Sigma_neutral, 0.1)
FH2 = np.log10(1.0 / (1.0 + RH2))
plt.plot(Sigma_neutral,
FH2,
":",
color="tab:red",
label="Krumholz+ (2009): f = 0.1")
plt.plot(x_Schruba,
y_Schruba,
"o",
color="darkred",
label="Schruba+ (2011)")
color = ["tab:blue", "tab:orange"]
for i, name in enumerate(name_list):
if method == "grid":
data = np.loadtxt(f"{output_path}/Surface_density_ratio_" +
method + "_%i_" % (index) + name + ".txt")
Sigma_gas = data[:, 0]
Sigma_ratio = data[:, 1]
(
Median_Sigma_gas,
Median_Sigma_ratio,
Sigma_ratio_err_down,
Sigma_ratio_err_up,
) = median_relations(Sigma_gas, Sigma_ratio)
plt.plot(Sigma_gas,
Sigma_ratio,
"o",
color=color[i],
alpha=0.6)
plt.plot(Median_Sigma_gas,
Median_Sigma_ratio,
"-",
lw=2,
color="white")
plt.plot(
Median_Sigma_gas,
Median_Sigma_ratio,
"-",
lw=1.2,
color=color[i],
label=name,
)
plt.fill_between(
Median_Sigma_gas,
Sigma_ratio_err_down,
Sigma_ratio_err_up,
alpha=0.2,
color=color[i],
)
if method == "radii":
data1 = np.loadtxt(
f"{output_path}/Surface_density_ratio_radii_250pc_%i_" %
(index) + name + ".txt")
data2 = np.loadtxt(
f"{output_path}/Surface_density_ratio_radii_800pc_%i_" %
(index) + name + ".txt")
plt.plot(data1[:, 0],
data1[:, 1],
"o",
ms=4,
color=color[i],
alpha=0.2)
plt.plot(data2[:, 0],
data2[:, 1],
"o",
ms=4,
color=color[i],
label=name)
plt.xlim(-1.0, 4.0)
plt.ylim(-8.0, 0.5)
plt.ylabel(
r"log $\Sigma_{\mathrm{H2}} / (\Sigma_{\mathrm{HI}}+\Sigma_{\mathrm{H2}})$"
)
plt.xlabel(
r"log $\Sigma_{\mathrm{HI}}+\Sigma_{\mathrm{H2}}$ [M$_{\odot}$ pc$^{-2}$]"
)
plt.legend(
loc="lower right",
labelspacing=0.2,
handlelength=2,
handletextpad=0.4,
frameon=False,
)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.savefig(
f"{output_path}/Surface_density_ratio_" + method + "_%i.png" %
(index),
dpi=200,
)
plt.close()
def KS_relation_plots(output_path, index, name_list):
# read the observational data for the KS relations
observational_data = read_obs_data("./plotter/obs_data")
# Get the default KS relation for correct IMF
def KS(sigma_g, n, A):
return A * sigma_g**n
rcParams.update(params)
methods = ["grid", "radii"]
for method in methods:
for mode in range(2):
plt.figure()
ax = plt.subplot(1, 1, 1)
Sigma_g = np.logspace(-1, 4, 1000)
Sigma_star = KS(Sigma_g, 1.4, 1.515e-4)
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
color="red",
label=r"1.51e-4 $\times$ $\Sigma_{g}^{1.4}$",
linestyle="--",
)
Sigma_g = np.logspace(-1, 4, 1000)
Sigma_star = KS(Sigma_g, 1.06, 2.511e-4)
plt.plot(
np.log10(Sigma_g),
np.log10(Sigma_star),
lw=1,
color="green",
label=r"2.51e-4 $\times$ $\Sigma_{g}^{1.06}$ (Pessa+ 2021)",
linestyle="-",
)
# load the observational data
if mode == 0:
for ind, observation in enumerate(observational_data):
if observation.gas_surface_density is not None:
if observation.description == "Bigiel et al. (2008) inner":
data = observation.bin_data_KS(
np.arange(-1, 3, 0.25), 0.4)
plt.errorbar(
data[0],
data[1],
yerr=[data[2], data[3]],
fmt="v",
ms=6,
label=observation.description,
color="darkred",
)
elif observation.description == "Bigiel et al. (2010) outer":
data2 = observation.bin_data_KS(
np.arange(-1, 3, 0.25), 0.4)
plt.errorbar(
data2[0],
data2[1],
yerr=[data2[2], data2[3]],
fmt="o",
ms=6,
label=observation.description,
color="darkred",
)
plt.xlabel(
"log $\\Sigma_{\\rm HI}+ \\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$"
)
elif mode == 1:
for ind, observation in enumerate(observational_data):
if observation.gas_surface_density is not None:
if observation.description == "Bigiel et al. (2008) inner":
data = observation.bin_data_KS_molecular(
np.arange(-1, 3, 0.25), 0.4)
plt.errorbar(
data[0],
data[1],
yerr=[data[2], data[3]],
fmt="<",
ms=6,
label=observation.description,
color="darkred",
)
plt.xlabel(
"log $\\Sigma_{\\rm H_2}$ $[{\\rm M_\\odot\\cdot pc^{-2}}]$"
)
color = ["tab:blue", "tab:orange"]
for i, name in enumerate(name_list):
if mode == 0:
data = np.loadtxt(f"{output_path}/KS_relation_best_" +
method + "_%i_" % (index) + name +
".txt")
elif mode == 1:
data = np.loadtxt(f"{output_path}/KS_molecular_relation_" +
method + "_%i_" % (index) + name +
".txt")
surface_density = data[:, 0]
SFR_surface_density = data[:, 1]
# Get median lines
(
median_surface_density,
median_SFR_surface_density,
SFR_surface_density_err_down,
SFR_surface_density_err_up,
) = median_relations(surface_density, SFR_surface_density)
plt.plot(surface_density,
SFR_surface_density,
"o",
color=color[i])
plt.plot(
median_surface_density,
median_SFR_surface_density,
"-",
lw=2,
color="white",
)
plt.plot(
median_surface_density,
median_SFR_surface_density,
"-",
lw=1.2,
color=color[i],
label=name,
)
plt.fill_between(
median_surface_density,
SFR_surface_density_err_down,
SFR_surface_density_err_up,
alpha=0.2,
color=color[i],
)
plt.legend(
loc="upper left",
labelspacing=0.2,
handlelength=1,
handletextpad=0.2,
frameon=False,
)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.ylabel(
"log $\\Sigma_{\\rm SFR}$ $[{\\rm M_\\odot \\cdot yr^{-1} \\cdot kpc^{-2}}]$"
)
plt.xlim(-1.0, 4.0)
plt.ylim(-6.5, 1.0)
if mode == 0:
plt.savefig(
f"{output_path}/KS_relation_best_" + method + "_%i.png" %
(index),
dpi=200,
)
plt.close()
elif mode == 1:
plt.savefig(
f"{output_path}/KS_molecular_relation_" + method +
"_%i.png" % (index),
dpi=200,
)
plt.close()
def plot_axis_ratios(output_path, name_list):
# Plot parameters
params = {
"font.size": 12,
"font.family": "Times",
"text.usetex": True,
"figure.figsize": (11, 3.5),
"figure.subplot.left": 0.05,
"figure.subplot.right": 0.95,
"figure.subplot.bottom": 0.15,
"figure.subplot.top": 0.9,
"figure.subplot.wspace": 0.3,
"figure.subplot.hspace": 0.3,
"lines.markersize": 2,
"lines.linewidth": 2.0,
}
rcParams.update(params)
for title, parttype in zip(["HI+H2 gas", "Stellar component"], [0, 4]):
########
plt.figure()
ax = plt.subplot(1, 3, 1)
ax.set_title(title)
plt.grid("True")
for i, name in enumerate(name_list):
data = np.loadtxt(f"{output_path}/Axis_ratios_parttype_%i_" %
(parttype) + name + ".txt")
# In case of no objects len(shape) = 1
if len(np.shape(data)) > 1:
plt.plot(data[:, 0],
data[:, 1],
"o",
color=color[i],
label=name)
plt.xscale("log")
plt.xlabel("Stellar Mass [M$_{\odot}$]")
plt.ylabel("c/a")
plt.xlim(1e6, 1e12)
plt.ylim(0.0, 1.0)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
########
ax = plt.subplot(1, 3, 2)
plt.grid("True")
for i, name in enumerate(name_list):
data = np.loadtxt(f"{output_path}/Axis_ratios_parttype_%i_" %
(parttype) + name + ".txt")
# In case of no objects len(shape) = 1
if len(np.shape(data)) > 1:
plt.plot(data[:, 0],
data[:, 2],
"o",
color=color[i],
label=name)
plt.xscale("log")
plt.xlabel("Stellar Mass [M$_{\odot}$]")
plt.ylabel("c/b")
plt.xlim(1e6, 1e12)
plt.ylim(0.0, 1.0)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
########
ax = plt.subplot(1, 3, 3)
plt.grid("True")
for i, name in enumerate(name_list):
data = np.loadtxt(f"{output_path}/Axis_ratios_parttype_%i_" %
(parttype) + name + ".txt")
# In case of no objects len(shape) = 1
if len(np.shape(data)) > 1:
plt.plot(data[:, 0],
data[:, 3],
"o",
color=color[i],
label=name)
plt.xscale("log")
plt.xlabel("Stellar Mass [M$_{\odot}$]")
plt.ylabel("b/a")
plt.xlim(1e6, 1e12)
plt.ylim(0.0, 1.0)
ax.tick_params(direction="in", axis="both", which="both", pad=4.5)
plt.legend(labelspacing=0.2,
handlelength=2,
handletextpad=0.4,
frameon=False)
plt.savefig(f"{output_path}/Axis_ratios_parttype_%i.png" % parttype,
dpi=200)
plt.close()
def plot_galaxy(parts_data, parttype, ang_momentum, halo_data, index,
output_path, simulation_name):
# partsDATA contains particles data and is structured as follow
# [ (:3)Position[Mpc]: (0)X | (1)Y | (2)Z ]
if parttype == 4:
cmap = plt.cm.magma
if parttype == 0:
cmap = plt.cm.viridis
pos_parts = parts_data[:, 0:3].copy()
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="z")
edge_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="y")
pos_face_on = np.matmul(face_on_rotation_matrix, pos_parts.T)
pos_face_on = pos_face_on.T
pos_edge_on = np.matmul(edge_on_rotation_matrix, pos_parts.T)
pos_edge_on = pos_edge_on.T
hsml_parts = parts_data[:, 7]
mass = parts_data[:, 3]
r_limit = 5 * halo_data.half_mass_radius_star[index]
r_img = 30.0
if r_limit < r_img:
r_img = r_limit
xmin = -r_img
ymin = -r_img
xmax = r_img
ymax = r_img
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 2, 1)
if parttype == 4:
title = "Stellar component"
if parttype == 0:
title = "HI+H2 gas"
ax.set_title(title)
###### plot one side ########################
qv = QuickView(
pos_face_on,
mass=mass,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=0,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("y [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img)
ax.imshow(img, cmap=cmap, extent=ext)
ax.autoscale(False)
###### plot another side ########################
ax = plt.subplot(1, 2, 2)
if parttype == 4:
kappa = halo_data.kappa_co[index]
mass_galaxy = halo_data.log10_stellar_mass[index]
ac = halo_data.axis_ca[index]
cb = halo_data.axis_cb[index]
ba = halo_data.axis_ba[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{*}/M_{\odot} = $%0.2f" % (mass_galaxy)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
if parttype == 0:
kappa = halo_data.gas_kappa_co[index]
mass_galaxy = halo_data.log10_gas_mass[index]
ac = halo_data.gas_axis_ca[index]
cb = halo_data.gas_axis_cb[index]
ba = halo_data.gas_axis_ba[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{gas}/M_{\odot} = $%0.2f" % (mass_galaxy)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
ax.set_title(title)
qv = QuickView(
pos_edge_on,
mass=mass,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=0,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("z [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img)
ims = ax.imshow(img, cmap=cmap, extent=ext)
ax.autoscale(False)
cbar_ax = fig.add_axes([0.86, 0.22, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ims, ticks=[4, 6, 8, 10, 12], cax=cbar_ax)
cb.set_label(label=r"$\log_{10}$ $\Sigma$ [M$_{\odot}$/kpc$^{2}$]",
labelpad=0.5)
if parttype == 0:
outfile = f"{output_path}/galaxy_gas_%i_" % (
index) + simulation_name + ".png"
if parttype == 4:
outfile = f"{output_path}/galaxy_stars_%i_" % (
index) + simulation_name + ".png"
fig.savefig(outfile, dpi=150)
plt.close("all")
return
import numpy as np
import pandas as pd
# Matplotlib for visualizing graphs
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
# Sklearn for creating a dataset
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
# Set parameters for plotting
params = {
'axes.titlesize': 'xx-large', # Set title size
'axes.labelsize': 'x-large', # Set label size
'figure.figsize': (8, 6) # Set a figure Size
}
rcParams.update(params)
# Sample size
#M = 200
# No. of input features
#n = 1
# Learning Rate
#l_r = 0.05
# Number of iterations for updates
#epochs = 51
#X, y = make_regression(n_samples=M, n_features=n, n_informative=n, n_targets=1, random_state=42, noise=10)
def plot_graph(X, y):
# Plot the original set of datapoints
_ = plt.scatter(X, y, alpha=0.8)
from parameters import *
from plot import *
from sample import *
import sys, os, glob
from tqdm import tqdm
tqdm.monitor_interval = 0
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
rcParams.update({'figure.autolayout': True})
rcParams['figure.figsize'] = (15, 8)
plt.style.use('ggplot')
import seaborn as sns
sns.set()
EVENTS = [
'Goals', 'Attempts', 'Corners', 'Fouls', 'Yellow cards',
'Second yellow cards', 'Red cards', 'Substitutions', 'Free kicks',
'Offsides', 'Hand balls', 'Penaltys'
]
NB_SAMPLES = 10
model = load_latest_model()
