def main():
"""
Produces the figure and saves it as a PDF.
"""
plt.clf()
axes = setup_axes()
plot_oscillatory(axes,
vice.output("../../simulations/SFRoscil_amp0p3_per2"),
"crimson")
plot_oscillatory(axes,
vice.output("../../simulations/SFEoscil_amp0p2_per2"),
"deepskyblue")
plot_pdf(axes[-1], vice.output("../../simulations/SFRoscil_amp0p3_per2"),
"crimson")
plot_pdf(axes[-1], vice.output("../../simulations/SFEoscil_amp0p2_per2"),
"deepskyblue")
axes[2].text(0.14,
150,
r"%g $\leq$ [Fe/H] $\leq$ %g" % (_FEH_MIN_, _FEH_MAX_),
fontsize=25)
legend(axes[1])
plt.tight_layout()
visuals.yticklabel_formatter(axes[2])
plt.savefig(sys.argv[1])
plt.clf()
def plot_output_3axes(axes, name, color, element, second_linestyle='-'):
"""
Plots the star formation rate and infall rate on axes[0], [X/Fe]-[Fe/H]
tracks on axes[1], and the dN/d[X/Fe] MDF on axes[2].
Parameters
==========
axes :: list
A 3-element list of matplotlib subplots
name :: str
The name of the VICE output to plot
color :: str
The name of the color to pass to matplotlib's named colors mapping
element :: str
The symbol for the element X
second_linestyle :: str [default :: '-']
The linestyle to use in the middle and right-hand panels
"""
out = vice.output(name)
axes[0].plot(out.history["time"], out.history["sfr"], c=colors()[color])
axes[0].plot(out.history["time"],
out.history["ifr"],
c=colors()[color],
linestyle='--')
axes[1].plot(out.history["[Fe/H]"],
out.history["[%s/Fe]" % (element)],
c=colors()[color],
linestyle=second_linestyle)
axes[2].plot(list(
map(lambda x, y: (x + y) / 2., out.mdf["bin_edge_left"],
out.mdf["bin_edge_right"])),
out.mdf["dN/d[%s/Fe]" % (element)],
c=colors()[color],
linestyle=second_linestyle)
def main(output, comparison, stem, colormap="winter", N=10000):
r"""
Produce a 15-panel figure showing a scatter plot of randomly sampled
stellar populations in [O/Fe]-[Fe/H] space color-coded by birth radius for
different present-day galactic regions.
Parameters
----------
output : ``str``
The relative or absolute path to the VICE output whose predicted
[O/Fe]-[Fe/H] distribution is to be visualized.
comparison : ``str``
The relative or absolute path to the VICE output whose predicted
gas-phase track for the solar circle (R = 8 kpc) will be plotted as a
reference in all panels.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
colormap : ``str`` [default : "winter"]
The colormap to use in color-coding stellar populations by their
birth radius.
N : ``int`` [default : 10,000]
The number of stellar populations to sample in each galactic region.
"""
axes = setup_axes()
dummy = axes[-1]
axes = axes[:-1]
outputs = [vice.output(_) for _ in [output, comparison]]
for i in outputs:
i.stars["abszfinal"] = [
abs(_) for _ in zheights(i.name)[:i.stars.size[0]]
]
for i in range(len(axes)):
for j in range(len(axes[i])):
sc = scatterplot_subsample(axes[i][j],
filter_multioutput_stars(
outputs[0].stars,
int(RADII[j] / ZONE_WIDTH),
int(RADII[j + 1] / ZONE_WIDTH) - 1,
HEIGHTS[-2 - i],
HEIGHTS[-1 - i],
min_mass=0),
colormap=colormap,
N=N)
plot_solar_annulus_track(axes[i][j], outputs[1])
cbar_ax = plt.gcf().add_axes([0.92, 0.05, 0.02, 0.95])
cbar = plt.colorbar(sc, cax=cbar_ax, pad=0.0, orientation="vertical")
cbar.set_label(r"$R_\text{gal}$ of birth [kpc]", labelpad=10)
cbar.set_ticks(range(2, 16, 2))
plt.tight_layout()
plt.subplots_adjust(hspace=0, wspace=0, bottom=0.08, left=0.06, right=0.93)
cbar_ax.set_position([
axes[-1][-1].get_position().x1, axes[-1][-1].get_position().y0, 0.025,
axes[0][-1].get_position().y1 - axes[-1][-1].get_position().y0
])
plt.savefig("%s.png" % (stem))
plt.savefig("%s.pdf" % (stem))
plt.close()
def main(output1, output2, stem, times = [2, 4, 6, 8, 10, END_TIME]):
r"""
Produce a two-panel figure comparing the gas-phase [O/Fe]-[Fe/H] tracks
of two models in the left-hand panel and a comparison of their SN Ia rates
in the right-hand panel.
Parameters
----------
output1 : ``str``
The relative or absolute path to the first of the two VICE outputs.
This one will be illustrated using solid lines.
output2 : ``str``
The relative or absolute path to the second of the two VICE outputs.
This one will be illustrated using dotted lines.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
times : ``list`` [elements of type ``float``]
[default : [2, 4, 6, 8, 10, 13.2]]
The times in Gyr at which to mark the positions of the [O/Fe]-[Fe/H]
tracks of the two models.
"""
axes = setup_axis()
out1 = vice.output(output1)
out2 = vice.output(output2)
plot_tracks(axes[0], out1, label = True)
plot_tracks(axes[0], out2, linestyle = ':')
plot_ticks(axes[0], out1, times, marker = markers()['x'])
plot_ticks(axes[0], out2, times, marker = markers()["point"])
plot_ia_rate_proxies(axes[1], out1)
plot_ia_rate_proxies(axes[1], out2, linestyle = ':')
leg = axes[0].legend(loc = mpl_loc("lower left"), ncol = 1, frameon = False,
bbox_to_anchor = (0.01, 0.01), handlelength = 0, fontsize = 20)
for i in range(len(RADII)):
leg.get_texts()[i].set_color(COLORS[i])
leg.legendHandles[i].set_visible(False)
plt.tight_layout()
plt.savefig("%s.png" % (stem))
plt.savefig("%s.pdf" % (stem))
def main(static, insideout, lateburst, outerburst, stem):
r"""
Plot the surface densities of star formation, infall, and gas as functions
of time for the four SFH models described in Johnson et al. (2021).
Parameters
----------
static : ``str``
The relative path to the VICE output with a constant SFH.
insideout : ``str``
The relative path to the VICE output with an inside-out SFH.
lateburst : ``str``
The relative path to the VICE output with a late-burst SFH.
outerburst : ``str``
The relative path to the VICE output with an outer-burst SFH.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
"""
axes = setup_axes()
plot_evolution([row[0] for row in axes], vice.output(static), label=True)
plot_evolution([row[1] for row in axes], vice.output(insideout))
plot_evolution([row[2] for row in axes], vice.output(lateburst))
plot_evolution([row[3] for row in axes], vice.output(outerburst))
leg = axes[1][0].legend(loc=mpl_loc("upper center"),
ncol=4,
frameon=False,
bbox_to_anchor=(0.5, 0.99),
handlelength=0,
columnspacing=0.8,
fontsize=20)
for i in range(len(RADII)):
leg.get_texts()[i].set_color(COLORS[i])
leg.legendHandles[i].set_visible(False)
plt.tight_layout()
plt.subplots_adjust(hspace=0, wspace=0, left=0.08)
plt.savefig("%s.png" % (stem))
plt.savefig("%s.pdf" % (stem))
def plot_track(ax, name, ref, color, linestyle): """ Plots [O/X]-[X/H] tracks on a matplotlib subplot Args: ===== ax: The name of the subplot name: The name of the VICE output ref: The symbol for the reference element color: The color of the line linestyle: The linestyle to use """ out = vice.output(name) ax.plot(out.history["[%s/h]" % (ref)], out.history["[o/%s]" % (ref)], c = visuals.colors()[color], linestyle = linestyle)
def plot_vice_comparison(ax, name):
"""
Plots the [Mg/Fe]-[Fe/H] track of a given VICE model on the subplot.
Parameters
==========
ax :: matplotlib subplot
The axis to plot on
name :: str
The relative path to the VICE output
"""
out = vice.output(name)
ax.plot(out.history["[fe/h]"],
out.history["[mg/fe]"],
c=visuals.colors()["black"],
linestyle='--')
def plot_track(ax, name, color): """ Plots a single [Sr/O]-[O/H] track Parameters ========== ax :: subplot The matplotlib axis object to plot on name :: str The name of the VICE output color :: str The name of the color to plot in """ output = vice.output(name) ax.plot(output.history["[O/H]"], output.history["[Sr/O]"], c = visuals.colors()[color])
def plot_ifr(ax, name, color): """ Plots the gas inflow rate on a given matplotlib subplot given the name of the VICE output Parameters ========== ax :: subplot The matplotlib axis to plot the inflow rate on name :: str The name of the VICE output color :: str The name of the color to plot in """ out = vice.output(name) ax.plot(out.history["time"], out.history["ifr"], linestyle = '--', c = visuals.colors()[color])
def plot_inset(inset, name, color, **kwargs):
"""
Plots [O/Fe]-[Fe/H] tracks on a set of inset axes
Parameters
==========
inset :: subplot
The matplotlib axis object to plot the tracks on
name :: str
The name of the VICE output
color :: str
The color to plot the output in
kwargs :: varying types
Other keyword arguments to pass to inset.plot
"""
out = vice.output(name)
inset.plot(out.history["[Fe/H]"],
out.history["[O/Fe]"],
c=colors()[color],
**kwargs)
def plot_reference(axes):
"""
Plots the [O/Fe]-[Fe/H] tracks and the stellar [O/Fe] distribution on the
proper set of axes
Parameters
==========
axes :: list
The list of matplotlib axis objects to plot on
"""
out = vice.output("../../simulations/default")
axes[-2].plot(out.history["[Fe/H]"],
out.history["[O/Fe]"],
c=colors()["black"],
linestyle=':')
bins = list(
map(lambda x, y: (x + y) / 2., out.mdf["bin_edge_left"],
out.mdf["bin_edge_right"]))
axes[-1].plot(bins,
out.mdf["dN/d[O/Fe]"],
c=colors()["black"],
linestyle=':')
def plot_output(axes, name, color):
"""
Plots a VICE output on the output figure
Parameters
==========
axes :: 1-D list
The list of matplotlib axis objects to plot on
name :: str
The name of the VICE output
color :: str
The name of the color to plot in
"""
out = vice.output(name)
axes[0].plot(out.history["[Fe/H]"],
out.history["[Sr/Fe]"],
c=visuals.colors()[color])
bin_centers = list(
map(lambda x, y: (x + y) / 2., out.mdf["bin_edge_left"],
out.mdf["bin_edge_right"]))
axes[1].plot(bin_centers,
out.mdf["dn/d[sr/fe]"],
c=visuals.colors()[color])
def plot_output_3axes(axes, name, color):
"""
Overrides the visuals.plot_output_3axes function to omit time = 0 for the
infall rate. VICE does not know the infall rate at time = 0 when ran in
star formation mode.
"""
out = vice.output(name)
axes[0].plot(out.history["time"][1:],
out.history["ifr"][1:],
c=visuals.colors()[color],
linestyle='--')
axes[0].plot(out.history["time"],
out.history["sfr"],
c=visuals.colors()[color],
linestyle='-')
axes[1].plot(out.history["[Fe/H]"],
out.history["[O/Fe]"],
c=visuals.colors()[color])
axes[2].plot(list(
map(lambda x, y: (x + y) / 2., out.mdf["bin_edge_left"],
out.mdf["bin_edge_right"])),
out.mdf["dn/d[O/Fe]"],
c=visuals.colors()[color])
def main():
"""
Runs the tests on the singlezone object, the output object, and the
mirror function.
"""
warnings.filterwarnings("ignore")
print("=================================================================")
print("TESTING: vice.singlezone")
print(" vice.output")
print(" vice.mirror")
out = open("test_sz_output_mirror.out", 'w')
_MODES_ = ["ifr", "sfr", "gas"]
_IMF_ = ["kroupa", "salpeter"]
_ETA_ = [2.5, lambda t: 2.5 * math.exp(-t / 4.0)]
_ZIN_ = [
0, 1.0e-8, lambda t: 1.0e-8 * (t / 10.0), {
"o": lambda t: 0.0057 * (t / 10.0),
"fe": 0.0013
}
]
_RECYCLING_ = ["continuous", 0.4]
_RIA_ = ["plaw", "exp", lambda t: t**-1.5]
_TAU_STAR_ = [2.0, lambda t: 2.0 + t / 10.0]
_SCHMIDT_ = [False, True]
_AGB_MODEL_ = ["cristallo11", "karakas10"]
a = 0
b = 2304
keys = ["success", "failure"]
singlezone_tracker = dict(zip(keys, [0, 0]))
mirror_tracker = dict(zip(keys, [0, 0]))
output_tracker = dict(zip(keys, [0, 0]))
history_tracker = dict(zip(keys, [0, 0]))
mdf_tracker = dict(zip(keys, [0, 0]))
for i in _MODES_:
for j in _IMF_:
for k in _ETA_:
for l in _ZIN_:
for m in _RECYCLING_:
for n in _RIA_:
for o in _TAU_STAR_:
for p in _SCHMIDT_:
for q in _AGB_MODEL_:
metadata = dict(
elements=["fe", "o", "c"],
mode=i,
IMF=j,
eta=k,
Zin=l,
recycling=m,
RIa=n,
tau_star=o,
schmidt=p,
agb_model=q,
dt=0.05)
try:
vice.singlezone(**metadata).run(
_OUTTIMES_, overwrite=True)
singlezone_tracker["success"] += 1
except:
singlezone_tracker["failure"] += 1
try:
foo = vice.history("onezonemodel")
assert isinstance(
foo, vice.dataframe)
history_tracker["success"] += 1
except:
history_tracker["failure"] += 1
try:
foo = vice.mdf("onezonemodel")
assert isinstance(
foo, vice.dataframe)
mdf_tracker["success"] += 1
except:
mdf_tracker["failure"] += 1
success = True
try:
foo = vice.output("onezonemodel")
assert isinstance(foo, vice.output)
assert isinstance(
foo.history, vice.dataframe)
assert isinstance(
foo.mdf, vice.dataframe)
assert isinstance(
foo.ccsne_yields,
vice.dataframe)
assert isinstance(
foo.sneia_yields,
vice.dataframe)
assert isinstance(
foo.elements, tuple)
output_tracker["success"] += 1
try:
mir = vice.mirror(foo)
assert isinstance(
mir, vice.singlezone)
mirror_tracker["success"] += 1
except:
mirror_tracker["failure"] += 1
except:
output_tracker["failure"] += 1
a += 1
sys.stdout.write("Progress: %.1f%%\r" %
(a / b * 100))
sys.stdout.flush()
#######
message = """\
vice.singlezone :: %d successes :: %d failures
vice.history :: %d successes :: %d failures
vice.mdf :: %d successes :: %d failures
vice.output :: %d successes :: %d failures
vice.mirror :: %d successes :: %d failures \
""" % (singlezone_tracker["success"], singlezone_tracker["failure"],
history_tracker["success"], history_tracker["failure"],
mdf_tracker["success"], mdf_tracker["failure"],
output_tracker["success"], output_tracker["failure"],
mirror_tracker["success"], mirror_tracker["failure"])
print(message)
out.write(message)
out.close()
def main(output, stem):
r"""
Produce a figure comparing the model-predicted age-[O/H] and age-[Fe/H]
relations in the solar annulus as predicted by VICE in comparison to the
relations reported by Feuillet et al. (2018, 2019) [1]_ [2]_.
Parameters
----------
output : ``str``
The relative or absolute path to the VICE output containing the
model predicted data.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
.. [1] Feuillet et al. (2018), MNRAS, 477, 2326
.. [2] Feuillet et al. (2019), MNRAS, 489, 1742
"""
ax1, ax2 = setup_axes()
output = vice.output(output)
output.stars["abszfinal"] = [
abs(_) for _ in zheights(output.name)[:output.stars.size[0]]
]
min_radius = 7
max_radius = 9
min_z = 0
max_z = 0.5
# majority of plotting and calculations done by these functions in
# galactic_regions.py
plot_amr(ax1, "O", "H", output, min_radius, max_radius, min_z, max_z)
sc = plot_amr(ax2, "Fe", "H", output, min_radius, max_radius, min_z, max_z)
median_ages(ax1,
"O",
"H",
output,
min_radius,
max_radius,
min_z,
max_z,
zorder=2)
median_ages(ax2,
"Fe",
"H",
output,
min_radius,
max_radius,
min_z,
max_z,
label=True,
zorder=2)
feuillet2019_amr(ax1,
"O",
"H",
min_radius,
max_radius,
min_z,
max_z,
zorder=2)
feuillet2019_amr(ax2,
"Fe",
"H",
min_radius,
max_radius,
min_z,
max_z,
label=True,
zorder=2)
feuillet2018_amr(ax1, 'o')
feuillet2018_amr(ax2, 'fe', label=True)
# legend and colorbar formatting
kwargs = {
"ncol": 1,
"frameon": False,
"fontsize": 20,
"loc": mpl_loc("lower left"),
"bbox_to_anchor": (0.01, 0.01)
}
handles, labels = ax2.get_legend_handles_labels()
handles = [handles[1], handles[2], handles[0]]
labels = [labels[1], labels[2], labels[0]]
ax2.legend(handles, labels, **kwargs)
cbar_ax = plt.gcf().add_axes([0.92, 0.05, 0.02, 0.95])
cbar = plt.colorbar(sc, cax=cbar_ax, pad=0.0, orientation="vertical")
cbar.set_label(r"$R_\text{gal}$ of birth [kpc]", labelpad=10)
cbar.set_ticks(range(2, 16, 2))
plt.tight_layout()
plt.subplots_adjust(hspace=0, right=0.8)
cbar_ax.set_position([
ax2.get_position().x1,
ax2.get_position().y0, 0.05,
ax1.get_position().y1 - ax2.get_position().y0
])
plt.savefig("%s.pdf" % (stem))
plt.savefig("%s.png" % (stem))
plt.close()
def main(element, outputs, stem, radial_bins = [3, 5, 7, 9, 11, 13, 15],
z_bins = [0.0, 0.5, 1.0, 2.0],
labels = ["Inside-Out", "Late-Burst", "Outer-Burst"],
# apogee = False,
colors = ["black", "red", "gold", "green", "blue", "darkviolet"]):
r"""
For some element X, plot the metallicity distribution functions (MDFs) in
[X/H] in different galactic regions as predicted by a given VICE model,
with or without a comparison to the APOGEE data.
Parameters
----------
element : ``str``
The element X to plot the distributions in [X/H] for. Must be included
in the model output.
outputs : ``list`` [elements of type ``str``]
A list of relative or absolute or absolute paths to the VICE outputs to
predictions of.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
radial_bins : ``list`` [default : [3, 5, 7, 9, 11, 13, 15]]
The bin-edges in galactocentric radius in kpc in which to compute the
MDFs. Must be in ascending order.
z_bins : ``list`` [default : [0.0, 0.5, 1.0, 2.0]]
The bin-edges in height above/below the disk midplane in kpc in which
to compute the MDFs. Must be in ascending order. Sign is neglected.
labels : ``list`` [elements of type ``str``]
[default : ["Inside-Out", "Late-Burst", "Outer-Burst"]]
A descriptor to attach to each of the ``outputs`` to distinguish which
panel belongs to which model visually.
apogee : ``bool`` [default : False]
Whether or not to plot the APOGEE data for comparison in another column
of panels.
colors : ``list`` [elements of type ``str``]
[default : ["black", "red", "gold", "green", "blue", "darkviolet"]]
The colors to use for each bin in radius. Must contain at least one
the number of elements in ``radial_bins`` minus 1.
The output figure will show each model as a column of panels, with a row
for each bin in |z|, and bins in radius shown color-coded on the same panel.
Points will be plotted at the top of the bottom row of panels corresopnding
to what the mode abundance would be if it followed the Johnson et al.
(2021) adopted form exactly.
.. note:: The distributions will be normalized such that the area over
their extent is equal to one.
"""
apogee = True # used to be keyword argument
# finish setting up the subplots
axes = setup_axes(element, ncols = len(outputs) + int(apogee),
nrows = len(z_bins) - 1)
if apogee: labels.append("APOGEE DR16")
for i in range(len(axes[0])): axes[0][i].set_title(labels[i], fontsize = 25)
z_bins = list(sorted(z_bins))[::-1] # reverse ordering -> high z at top
for i in range(len(axes)): axes[i][-1].set_ylabel(
r"$\left|z\right|$ = %g - %g" % (z_bins[i + 1], z_bins[i]))
# read in the model-predicted data, calculate the plot distributions for
# all models in all regions
outputs = [vice.output(_) for _ in outputs]
for i in outputs: i.stars["abszfinal"] = [abs(_) for _ in zheights(
i.name)[:i.stars.size[0]]]
for i in range(len(axes[-1])):
for j in range(len(radial_bins) - 1):
axes[-1][i].scatter(target_mode_abundance(radial_bins[j]),
{"o": 4.25, "fe": 3.4}[element.lower()],
c = named_colors()[colors[j]],
marker = markers()["point"], s = 40, zorder = 10)
for i in range(len(z_bins) - 1):
for j in range(len(outputs)):
for k in range(len(radial_bins) - 1):
plot_predicted_mdf(axes[i][j], element, outputs[j].stars,
radial_bins[k], radial_bins[k + 1], z_bins[i + 1],
z_bins[i], colors[k])
# label = k in [2 * i, 2 * i + 1] and not j)
if apogee:
for k in range(len(radial_bins) - 1):
plot_apogee_distributions(axes[i][-1], element, radial_bins[k],
radial_bins[k + 1], z_bins[i + 1], z_bins[i], colors[k],
label = not i)
else: pass
# plot the legends and save the figure
# legend_kwargs = {
# "loc": mpl_loc("upper left"),
# "ncol": 1,
# "fontsize": 20,
# "frameon": False,
# "bbox_to_anchor": (0.01, 0.99),
# "handlelength": 0
# }
# for i in range(len(z_bins) - 1):
# leg = axes[i][0].legend(**legend_kwargs)
# for j in range(len(leg.legendHandles)):
# leg.get_texts()[j].set_color(colors[len(leg.legendHandles) * i + j])
# leg.legendHandles[j].set_visible(False)
legend_kwargs = {
"loc": mpl_loc("upper right"),
"ncol": 1,
"fontsize": 18,
"frameon": False,
"bbox_to_anchor": (0.99, 0.99),
"handlelength": 0
}
axes[0][1].legend(**legend_kwargs)
for i in range(len(radial_bins) - 1):
leg.get_texts()[i].set_color(colors[i])
leg.legendHandles[i].set_visible(False)
plt.tight_layout()
plt.subplots_adjust(wspace = 0, hspace = 0, bottom = 0.1, left = 0.08)
plt.savefig("%s.png" % (stem))
plt.savefig("%s.pdf" % (stem))
plt.close()
def main(upperleft, upperright, lowerleft, lowerright, stem,
min_rgal = 7, max_rgal = 9, min_absz = 0, max_absz = 0.5,
names = [["Constant SFR", "Inside-Out"], ["Late-Burst", "Outer-Burst"]]):
r"""
Plot a 2x2 panel figure showing comparing the model-predicted age-[O/Fe]
relations to the Feuillet et al. (2019) [1]_ measurements in a given
galactic region.
Parameters
----------
upperleft : ``str``
The relative or absolute path to the VICE output whose predicted
age-[O/Fe] relation is to go in the upper-left panel.
upperright: ``str``
The relative or absolute path to the VICE output whose predicted
age-[O/Fe] relation is to go in the upper-right panel.
lowerleft : ``str``
The relative or absolute path to the VICE output whose predicted
age-[O/Fe] relation is to go in the lower-left panel.
lowerright : ``str``
The relative or absolute path to the VICE output whose predicted
age-[O/Fe] relation is to go in the lower-right panel.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
min_rgal : ``float`` [default : 7.0]
Minimum galactocentric radius in kpc.
max_rgal : ``float`` [default : 9.0]
Maximum galactocentric radius in kpc.
min_absz : ``float`` [default : 0.0]
Minimum height above/below the disk midplane |z| in kpc.
max_absz : ``float`` [default : 0.5]
Maximum height above/below the disk midplane |z| in kpc.
names : 2x2 element ``list`` [elements of type ``str``]
[default : [["Constant SFR", "Inside-Out"], ["Late-Burst",
"Outer-Burst"]]]
Short descriptors for each model to denote which is which on each
panel.
.. [1] Feuillet et al. (2019), MNRAS, 489, 1742
"""
global MIN_RGAL
global MAX_RGAL
global MIN_ABSZ
global MAX_ABSZ
global ZONE_MIN
global ZONE_MAX
MIN_RGAL = min_rgal
MAX_RGAL = max_rgal
MIN_ABSZ = min_absz
MAX_ABSZ = max_absz
ZONE_MIN = int(MIN_RGAL / ZONE_WIDTH)
ZONE_MAX = int((MAX_RGAL - ZONE_WIDTH) / ZONE_WIDTH)
axes = setup_axes()
outputs = [
[vice.output(upperleft), vice.output(upperright)],
[vice.output(lowerleft), vice.output(lowerright)]
]
for i in range(2):
for j in range(2):
axes[i][j].text(1, 0.4, names[i][j], fontsize = 18)
feuillet2019_amr(axes[i][j], "O", "Fe", min_rgal, max_rgal,
min_absz, max_absz, label = not i and not j)
outputs[i][j].stars["abszfinal"] = [abs(k) for k in zheights(
outputs[i][j].name)[:outputs[i][j].stars.size[0]]]
sc = plot_relation(axes[i][j], outputs[i][j],
label = i == 0 and j == 0)
axes[0][0].legend(loc = mpl_loc("upper left"), ncol = 1, frameon = False,
bbox_to_anchor = (0.01, 0.87), handletextpad = 0.4, fontsize = 18)
cbar_ax = plt.gcf().add_axes([0.92, 0.05, 0.02, 0.95])
cbar = plt.colorbar(sc, cax = cbar_ax, pad = 0.0, orientation = "vertical")
cbar.set_label(r"$R_\text{gal}$ of birth [kpc]", labelpad = 10)
cbar.set_ticks(range(2, 16, 2))
plt.tight_layout()
plt.subplots_adjust(hspace = 0, wspace = 0, left = 0.15, bottom = 0.1,
right = 0.85)
cbar_ax.set_position([
axes[-1][-1].get_position().x1,
axes[-1][-1].get_position().y0,
0.05,
axes[0][-1].get_position().y1 - axes[-1][-1].get_position().y0
])
plt.savefig("%s.pdf" % (stem))
plt.savefig("%s.png" % (stem))
def main(element, outputs, stem, radii = [[5, 7], [7, 9], [9, 11], [11, 13]],
feuillet2019 = True,
labels = ["Constant SFR", "Inside-Out", "Late-Burst", "Outer-Burst"],
**subplots_adjust_kwargs):
r"""
Produce a plot comparing the age-[X/H] relation predicted by a set of
models in various galactic regions.
Parameters
----------
element : ``str``
The element X in age-[X/H] relation.
outputs : array-like [elements of type ``str``]
The relative or absolute paths to the VICE outputs whose predicted
age-[X/H] relations are to be visualized.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
radii : ``list`` [elements are two-component ``list``s storing ``float``s]
[default : [[5, 7], [7, 9], [9, 11], [11, 13]]]
The ranges in radius defining the galactic regions to visualize the
age-[X/H] relation within.
feuillet2019 : ``bool`` [default : True]
Whether or not to include the measurements of Feuillet et al. (2019)
[1]_ as a comparison.
labels : ``list`` [elements of type ``str``]
Short descriptors distinguishing which model is which in the
visualization. Models will be plotted in the order in which they appear
in the ``outputs`` array.
subplots_adjust_kwargs : varying types
Additional keywords to pass to ``pyplot.subplots_adjust``.
.. [1] Feuillet et al. (2019), MNRAS, 489, 1742
"""
axes = setup_axes(element, nrows = len(outputs), radii = radii,
labels = labels)
outputs = [vice.output(_) for _ in outputs]
for i in outputs: i.stars["abszfinal"] = [abs(_) for _ in zheights(
i.name)[:i.stars.size[0]]]
for i in range(len(axes)):
for j in range(len(axes[i])):
# majority of the plotting and calculations done by these
# functions in galactic_regions.py
print([i, j])
sc = plot_amr(axes[i][j], element, 'h', outputs[i], radii[j][0],
radii[j][1], 0.0, 0.5)
median_ages(axes[i][j], element, 'h', outputs[i], radii[j][0],
radii[j][1], 0.0, 0.5, label = not i and not j and feuillet2019)
if feuillet2019: feuillet2019_amr(axes[i][j], element, 'h',
radii[j][0], radii[j][1], 0.0, 0.5,
label = not i and not j)
# legend, colorbar, and subplots formatting
if feuillet2019:
legend_kwargs = {
"ncol": 1,
"frameon": False,
"fontsize": 20,
"loc": mpl_loc("lower left"),
"bbox_to_anchor": (0.01, 0.01)
}
axes[0][0].legend(**legend_kwargs)
else: pass
cbar_ax = plt.gcf().add_axes([0.92, 0.05, 0.02, 0.95])
cbar = plt.colorbar(sc, cax = cbar_ax, orientation = "vertical")
cbar.set_label(r"$R_\text{gal}$ of birth [kpc]", labelpad = 10)
cbar.set_ticks(range(2, 16, 2))
plt.tight_layout()
default_adjustments = {
"hspace": 0,
"wspace": 0,
"left": 0.09,
"right": 0.91
}
for i in subplots_adjust_kwargs.keys():
default_adjustments[i] = subplots_adjust_kwargs[i]
plt.subplots_adjust(**default_adjustments)
cbar_ax.set_position([
axes[-1][-1].get_position().x1,
axes[-1][-1].get_position().y0,
0.025,
axes[0][-1].get_position().y1 - axes[-1][-1].get_position().y0
])
plt.savefig("%s.png" % (stem))
plt.savefig("%s.pdf" % (stem))
def main(element_x, element_y, output, stem):
r"""
Illustrate the age-[X/Y] relation in 12 galactic regions as predicted by a
given VICE model in comparison to the measurements of Feuillet et al.
(2019) [1]_.
Parameters
----------
element_x : ``str``
The element X in age-[X/Y] relation.
element_y : ``str``
The element Y in age-[X/Y] relation.
output : ``str``
The relative or absolute path to the VICE output whose predicted
age-[X/Y] relation is to be visualized here.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
.. [1] Feuillet et al. (2019), MNRAS, 489, 1742
"""
axes = setup_axes(element_x, element_y)[:-1]
output = vice.output(output)
output.stars["abszfinal"] = [
abs(i) for i in zheights(output.name)[:output.stars.size[0]]
]
for i in range(len(axes)):
for j in range(len(axes[i])):
print([i, j])
sc = plot_amr(axes[i][j], element_x, element_y, output, RADII[j],
RADII[j + 1], HEIGHTS[i + 1], HEIGHTS[i])
if i == 2 and j == 1:
xvals, yvals = median_ages(axes[i][j], element_x, element_y,
output, RADII[j], RADII[j + 1],
HEIGHTS[i + 1], HEIGHTS[i])
else:
median_ages(axes[i][j],
element_x,
element_y,
output,
RADII[j],
RADII[j + 1],
HEIGHTS[i + 1],
HEIGHTS[i],
label=not i and not j)
feuillet2019_amr(axes[i][j],
element_x,
element_y,
RADII[j],
RADII[j + 1],
HEIGHTS[i + 1],
HEIGHTS[i],
label=i == 0 and j == 0)
for i in range(len(axes)):
for j in range(len(axes[i])):
axes[i][j].plot(xvals, yvals, c=named_colors()["black"])
legend_kwargs = {"ncol": 1, "frameon": False, "fontsize": 20}
if element_y.lower() == 'h':
legend_kwargs["loc"] = mpl_loc("lower left")
legend_kwargs["bbox_to_anchor"] = (0.01, 0.01)
else:
legend_kwargs["loc"] = mpl_loc("upper left")
legend_kwargs["bbox_to_anchor"] = (0.01, 0.99)
axes[0][0].legend(**legend_kwargs)
cbar_ax = plt.gcf().add_axes([0.92, 0.05, 0.02, 0.95])
cbar = plt.colorbar(sc, cax=cbar_ax, pad=0.0, orientation="vertical")
cbar.set_label(r"$R_\text{gal}$ of birth [kpc]", labelpad=10)
cbar.set_ticks(range(2, 16, 2))
plt.tight_layout()
plt.subplots_adjust(hspace=0, wspace=0, bottom=0.08, right=0.91, left=0.09)
cbar_ax.set_position([
axes[-1][-1].get_position().x1, axes[-1][-1].get_position().y0, 0.025,
axes[0][-1].get_position().y1 - axes[-1][-1].get_position().y0
])
plt.savefig("%s.png" % (stem))
plt.savefig("%s.pdf" % (stem))
plt.close()
def main(outputs,
stem,
labels=["Constant SFR", "Inside-Out", "Late-Burst", "Outer-Burst"],
times=[2, 4, 6, 8, 10, END_TIME],
colors=["red", "gold", "green", "blue", "darkviolet", "black"],
ylim=[0.5, 20],
yticks=[1, 10]):
r"""
Produce a plot of the SFE timescale :math:`\tau_\star` as a function of
galactocentric radius at various timestamps for a set of VICE models.
Parameters
----------
outputs : ``list`` [elements of type ``str``]
The relative or absolute paths to the VICE outputs whose predicted SFE
timescales are to be visualized here. They will be plotted in the
order they appear in this list.
stem : ``str``
The relative or absolute path to the output image, with no extension.
This function will save the figure in both PDF and PNG formats.
labels : array-like [elements of type ``str``] or ``None``
[default : ["Constant SFR", "Inside-Out", "Late-Burst", "Outer-Burst"]]
Short descriptors for each of the models to be plotted. Must contain
at least as many elements as ``outputs``. If ``None``, no labels will
be included.
times : array-like [elements of type ``float``]
[default : [2, 4, 6, 8, 10, 13.2]]
The time-stamps at which to plot the SFE timescale as a function of
radius.
colors : array-like [elements of type ``str``]
[default : ["red", "gold", "green", "blue", "darkviolet", "black"]]
The colors in which to plot the SFE timescales. Must contain at least
as many elements as ``outputs``.
ylim : array-like [default : [0.5, 20]]
y-axis limits for :math:`\tau_\star` in Gyr. Note that this function
will use a log-scaled y-axis.
yticks : array-like [default : [1, 10]]
The values to place major ticks on the y-axis at.
"""
axes = setup_axes(len(outputs))
for i in range(len(axes)):
axes[i].set_yscale("log")
axes[i].set_ylim(ylim)
axes[i].set_yticks(yticks)
yticklabel_formatter(axes[i])
if labels is not None: axes[i].set_title(labels[i], fontsize=25)
outputs = [vice.output(_) for _ in outputs]
for i in range(len(outputs)):
plot_sfe(axes[i], outputs[i], times, colors, label=not i)
leg = axes[0].legend(
loc=mpl_loc("upper left"),
ncol=1,
frameon=False,
bbox_to_anchor=(0.01, 0.99),
handlelength=0,
# fontsize = 20, labelspacing = 0.2)
fontsize=20)
for i in range(len(times)):
leg.get_texts()[i].set_color(colors[i])
leg.legendHandles[i].set_visible(False)
plt.tight_layout()
plt.subplots_adjust(wspace=0, bottom=0.2)
plt.savefig("%s.png" % (stem))
plt.savefig("%s.pdf" % (stem))
