def plot_radius_density(data_dir, halo_id):
xfield = 'radius'
yfield = 'density'
filename = os.path.join(data_dir, "%s_%06d.h5" % (yfield, halo_id))
fontsize = 14
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
top_buffer=0.14,
bottom_buffer=0.13,
left_buffer=0.16,
right_buffer=0.19)
my_axes = my_fig[0]
my_axes.set_xscale('log')
my_axes.set_yscale('log')
my_cax = my_fig.add_cax(my_axes,
"right",
buffer=0.02,
length=0.95,
width=0.04)
plot_phase(filename,
'cell_mass',
'Msun',
my_axes,
my_cax=my_cax,
cmap=yt_colormaps['dusk'])
my_cax.yaxis.set_label_text("M [M$_{\\odot}$]")
xlim = (2e-6, 2e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
ylim = (1e-25, 1e-10)
ymajor = np.logspace(-25, -10, 6)
yminor = np.logspace(-25, -10, 16)
ylabel = "$\\rho$ [g/cm$^{-3}$]"
mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
draw_major_grid(my_axes, 'y', ymajor)
my_axes.yaxis.set_label_text(ylabel)
output_filename = "figures/%s_%s.pdf" % \
(xfield, yfield)
pyplot.savefig(output_filename)
def plot_density_H2_fraction(data_dir, halo_id):
xfield = 'density'
yfield = 'H2_fraction'
filename = os.path.join(data_dir, "%s_%06d.h5" % (yfield, halo_id))
fontsize = 14
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
top_buffer=0.03,
bottom_buffer=0.13,
left_buffer=0.14,
right_buffer=0.19)
my_axes = my_fig[0]
xscale = 'log'
yscale = 'log'
field = 'cell_mass'
units = 'Msun'
cmap = yt_colormaps['dusk']
clabel = "M [M$_{\\odot}$]"
xlim = (1e-3, 1e13)
xmajor = np.logspace(-3, 12, 6)
xminor = np.logspace(-3, 13, 17)
xlabel = "n [cm$^{-3}$]"
ylim = (1e-8, 1)
ymajor = np.logspace(-8, 0, 9)
yminor = None
ylabel = "f$_{\\rm H_{2}}$"
output_filename = "figures/%s_%s.pdf" % \
(xfield, yfield)
make_phase_plot(my_fig, my_axes, filename, field, units, cmap, clabel,
xlim, xmajor, xminor, xscale, xlabel, ylim, ymajor, yminor,
yscale, ylabel, output_filename)
def get_distribution(vals, n=101):
if vals.size == 0:
return np.zeros(n)
id_sort = np.argsort(vals)
dist = vals[id_sort[np.clip(
np.round(np.linspace(0, 1, n) * id_sort.size).astype(int), 0,
id_sort.size - 1)]]
return dist
if __name__ == "__main__":
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
left_buffer=0.17,
right_buffer=0.04,
bottom_buffer=0.13,
top_buffer=0.11,
vertical_buffer=0)
# palette = sns.color_palette(palette="colorblind")
palette = \
[(0.0, 0.4470588235294118, 0.6980392156862745),
(0.0, 0.6196078431372549, 0.45098039215686275),
(0.8352941176470589, 0.3686274509803922, 0.0),
(0.8, 0.4745098039215686, 0.6549019607843137),
(0.9411764705882353, 0.8941176470588236, 0.25882352941176473),
(0.33725490196078434, 0.7058823529411765, 0.9137254901960784)]
cmap = yt_colormaps["arbre"]
def plot_velocity_profiles(data_dir, file_prefix):
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
top_buffer=0.14,
bottom_buffer=0.12,
left_buffer=0.09,
right_buffer=0.02)
# http://www.colourlovers.com/palette/1329926/graphic_artist.
# colors = ["#B00029", "#90B004", "#19849C", "#851370", "#544D4D", "black"]
colors = ["red", "green", "blue", "purple", "#544D4D", "black"]
my_axes = my_fig[0]
my_axes.set_xscale('log')
fields = [
"velocity_magnitude", "tangential_velocity_magnitude",
"velocity_spherical_radius", "sound_speed"
]
for i, field in enumerate(fields):
filename = os.path.join(
data_dir, f"{file_prefix}_2D_profile_radius_{field}_None.h5")
plot_profile_distribution(my_axes,
filename,
'cell_mass',
x_units="pc",
y_units='km/s',
alpha_scale=0.7,
pkwargs=dict(color=colors[i], linewidth=1))
fn = os.path.join(data_dir,
f"{file_prefix}_1D_profile_radius_cell_mass.h5")
pds = yt.load(fn)
pradius = pds.profile.x.to("pc")
vsigma = pds.profile.standard_deviation['data',
'velocity_magnitude'].to("km/s")
my_axes.plot(pradius[vsigma > 0],
vsigma[vsigma > 0],
alpha=0.9,
linewidth=1,
color=colors[4],
zorder=998)
field = "matter_mass"
fn = os.path.join(data_dir, f"{file_prefix}_1D_profile_radius_None.h5")
mds = yt.load(fn)
radius = mds.profile.x.to("pc")
mass = mds.profile[field]
dfil = mass > 0
v_sp = np.sqrt(G * mass[dfil].cumsum() / radius[dfil]).to("km/s")
my_axes.plot(radius[dfil],
v_sp,
alpha=0.9,
linewidth=1,
color=colors[5],
zorder=997)
ylim = (-5, 13)
ymajor = np.arange(-5, 16, 5.)
yminor = np.arange(-5, 15, 1.)
my_axes.yaxis.set_label_text("v [km / s]")
my_axes.yaxis.labelpad = -3
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
xlim = (1e-1, 2e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
labels = [
"|v|", "v$_{\\rm tan}$", "v$_{\\rm rad}$", "c$_{\\rm s}$", "$\\sigma$",
"v$_{\\rm c}$"
]
dist = [True] * 4 + [False] * 2
plot_items = list(zip(colors, labels, dist))
plot_profile_distribution_legend(my_axes, plot_items, alpha_scale=0.7)
pyplot.savefig("figures/velocity_profiles.pdf")
image = my_axes.scatter(px,
py,
c=color,
s=s,
norm=mynorm,
cmap=cmap,
edgecolor="black",
linewidth=0.5)
return image
if __name__ == "__main__":
my_fig = GridFigure(2,
1,
figsize=(5, 8),
square=True,
left_buffer=0.02,
right_buffer=0.2,
vertical_buffer=0.02)
cbar_buffer = 0.02
cbar_length = 0.95
cbar_width = 0.05
data_dir = "normal_BG1/halo_2170858"
# data_dir = "normal_BG1/halo_2171203"
dsfn = "RedshiftOutput0077"
# data_dir = "Rarepeak_LWB/halo_1367222/"
# data_dir = "Rarepeak_LWB/halo_1372918"
# dsfn = "RedshiftOutput0041"
proj_axis = "x"
for i, my_axes in enumerate(my_fig):
from matplotlib import pyplot, ticker
import numpy as np
# import seaborn as sns
from scipy import stats
import yt
from grid_figure import GridFigure
if __name__ == "__main__":
my_fig = GridFigure(3, 1, figsize=(4.5, 7),
left_buffer=0.22, right_buffer=0.02,
bottom_buffer=0.09, top_buffer=0.02,
vertical_buffer=0)
# palette = sns.color_palette(palette="colorblind")
palette = \
[(0.0, 0.4470588235294118, 0.6980392156862745),
(0.0, 0.6196078431372549, 0.45098039215686275),
(0.8352941176470589, 0.3686274509803922, 0.0),
(0.8, 0.4745098039215686, 0.6549019607843137),
(0.9411764705882353, 0.8941176470588236, 0.25882352941176473),
(0.33725490196078434, 0.7058823529411765, 0.9137254901960784)]
labels = ["rare peak", "normal", "void"]
ds_list = [
yt.load("Rarepeak_LWB/black_hole_growth_stats.h5"),
yt.load("normal_BG1/black_hole_growth_stats.h5"),
yt.load("void_BG1/black_hole_growth_stats.h5")
]
r_growth = [ds.data["relative_growth"] - 1 for ds in ds_list]
def plot_timescale_profiles(data_dir, file_prefix):
my_fig = GridFigure(1,
1,
figsize=(6, 4.5),
top_buffer=0.13,
bottom_buffer=0.12,
left_buffer=0.14,
right_buffer=0.02,
horizontal_buffer=0.05,
vertical_buffer=0)
my_axes = my_fig[0]
my_axes.set_xscale('log')
my_axes.set_yscale('log')
xlim = (5e-3, 3e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
fields = [
"sound_crossing_time", "total_dynamical_time", "cooling_time",
"vortical_time"
]
units = ["yr", f"{np.sqrt(2)}*yr", "yr", f"{1/(2*np.pi)}*yr"]
labels = ["sound-crossing", "free-fall", "cooling", "mixing"]
colors = ["orange", "red", "green", "blue"]
filename = os.path.join(data_dir, "DD0295_1D_profile_radius_cell_mass.h5")
ds = yt.load(filename)
x_data = ds.profile.x.to("pc")
used = ds.profile.used
for field, unit, label, color in zip(fields, units, labels, colors):
if field == "sound_crossing_time":
cs = ds.profile["data", "sound_speed"]
vt = ds.profile.standard_deviation["data", "velocity_magnitude"]
v = np.sqrt(cs**2 + vt**2)
y_data = (2 * x_data / v).to(unit)
else:
y_data = ds.profile["data", field].to(unit)
my_axes.plot(x_data[used],
y_data[used],
color=color,
alpha=0.7,
linewidth=1.5,
label=label)
ylim = (1e4, 1e8)
ymajor = np.logspace(2, 10, 5)
yminor = np.logspace(1, 9, 5)
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
my_axes.yaxis.set_label_text("t [yr]")
my_axes.legend()
# my_axes.yaxis.labelpad = 4
pyplot.savefig("figures/timescale_profiles.pdf")
fn_head = '%s' % fn_head
# Get the list of ion_fields from the first file available
ion_fields = [
'density', 'metal_density', 'temperature', 'H_number_density',
'C_p1_number_density', 'C_p2_number_density', 'Ne_p7_number_density',
'O_p5_number_density', 'Si_p3_number_density'
]
fig = GridFigure(3,
3,
top_buffer=0.01,
bottom_buffer=0.08,
left_buffer=0.12,
right_buffer=0.02,
vertical_buffer=0.08,
horizontal_buffer=0.08,
figsize=(12, 8))
# Step through each ion and make plots of azimuthal angle vs N
for i, field in enumerate(ion_fields):
for c, (k, v) in enumerate(profiles_dict.items()):
n_files = len(v)
cdens_arr = np.array([])
a_arr = np.array([])
r_arr = np.array([])
for j in range(n_files):
f = h5.File(v[j], 'r')
a_arr = np.concatenate((a_arr, f['phi'].value))
import numpy as np
# import seaborn as sns
from scipy import stats
import yt
from yt.utilities.cosmology import Cosmology
from grid_figure import GridFigure
from yt.extensions.p3bh import *
if __name__ == "__main__":
my_fig = GridFigure(2,
2,
figsize=(7, 5.5),
left_buffer=0.12,
right_buffer=0.04,
bottom_buffer=0.12,
top_buffer=0.02,
vertical_buffer=0.12,
horizontal_buffer=0.16)
# palette = sns.color_palette(palette="colorblind")
palette = \
[(0.0, 0.4470588235294118, 0.6980392156862745),
(0.0, 0.6196078431372549, 0.45098039215686275),
(0.8352941176470589, 0.3686274509803922, 0.0),
(0.8, 0.4745098039215686, 0.6549019607843137),
(0.9411764705882353, 0.8941176470588236, 0.25882352941176473),
(0.33725490196078434, 0.7058823529411765, 0.9137254901960784)]
labels = ["rare peak", "normal", "void"]
log('Generating Plot 1')
p1 = yt.OffAxisProjectionPlot(ds, E1, ('gas', 'H_number_density'), center=c,
width=width, data_source=box, north_vector=L, weight_field=None)
log('Generating Plot 2')
p2 = yt.OffAxisProjectionPlot(ds, E1, ('gas', 'Mg_p1_number_density'), center=c,
width=width, data_source=box, north_vector=L, weight_field=None)
log('Generating Plot 3')
p3 = yt.OffAxisProjectionPlot(ds, E1, ('gas', 'O_p5_number_density'), center=c,
width=width, data_source=box, north_vector=L, weight_field=None)
log('Generating Plot 4')
p4 = yt.OffAxisProjectionPlot(ds, E1, ('gas', 'Si_p3_number_density'), center=c,
width=width, data_source=box, north_vector=L, weight_field=None)
print("Stitching together")
fig = GridFigure(2, 2, top_buffer=0.01, bottom_buffer=0.01, left_buffer=0.01, right_buffer=0.13, vertical_buffer=0.01, horizontal_buffer=0.01, figsize=(9,8))
# Actually plot in the different axes
plot1 = fig[0].imshow(p1.frb['H_number_density'], norm=LogNorm())
# clim1 = plot1.get_clim()
plot1.set_cmap('thermal')
plot2 = fig[1].imshow(p2.frb['Mg_p1_number_density'], norm=LogNorm())
# plot2.set_clim(clim)
plot2.set_cmap('thermal')
plot3 = fig[2].imshow(p3.frb['O_p5_number_density'], norm=LogNorm())
# clim = plot3.get_clim()
plot3.set_cmap('thermal')
plot4 = fig[3].imshow(p4.frb['Si_p3_number_density'], norm=LogNorm())
# plot4.set_clim(clim)
plot4.set_cmap('thermal')
my_axes.axhline(y=ymaj,
color="black",
alpha=0.2,
linestyle=":",
zorder=1)
return my_cax
if __name__ == "__main__":
fontsize = 16
cmap = yt_colormaps["algae"]
my_fig = GridFigure(3,
1,
figsize=(6, 9),
left_buffer=0.18,
right_buffer=0.16,
bottom_buffer=0.08,
top_buffer=0.02,
vertical_buffer=0)
data_dirs = ["Rarepeak_LWB", "normal_BG1", "void_BG1"]
titles = ["rare peak", "normal", "void"]
for i, my_axes in enumerate(my_fig):
my_cax = plot_profile(my_fig, my_axes, data_dirs[i], titles[i])
if i == len(my_fig) - 1:
my_axes.xaxis.set_label_text(
"Bondi-Hoyle to Eddington ratio [1 / M$_{\\odot}$]",
fontsize=fontsize)
else:
if grid:
for my_y in major:
my_axes.axhline(y=my_y,
color="black",
linestyle=":",
linewidth=1.0,
alpha=0.2,
zorder=-100)
if __name__ == "__main__":
my_fig = GridFigure(2,
2,
figsize=(8, 6),
left_buffer=0.13,
right_buffer=0.03,
bottom_buffer=0.10,
top_buffer=0.09,
vertical_buffer=0,
horizontal_buffer=0.13)
# palette = sns.color_palette(palette="colorblind")
palette = \
[(0.0, 0.4470588235294118, 0.6980392156862745),
(0.0, 0.6196078431372549, 0.45098039215686275),
(0.8352941176470589, 0.3686274509803922, 0.0),
(0.8, 0.4745098039215686, 0.6549019607843137),
(0.9411764705882353, 0.8941176470588236, 0.25882352941176473),
(0.33725490196078434, 0.7058823529411765, 0.9137254901960784)]
cmap = yt_colormaps["arbre"]
ticklabel.set_fontsize(lfontsize)
if __name__ == "__main__":
halo_dir = "halo_2170858"
# halo_dir = "halo_2171203"
ds = yt.load(os.path.join(halo_dir, "bh_clump_distance_edge_1e-03.h5"))
ds_i = yt.load(
os.path.join(halo_dir, "bh_clump_distance_edge_1e-03_inner_0.25.h5"))
fontsize = 12
my_fig = GridFigure(1,
1,
top_buffer=0.12,
bottom_buffer=0.13,
left_buffer=0.14,
right_buffer=0.04,
horizontal_buffer=0.05,
vertical_buffer=0.1,
figsize=(6, 4.5))
my_axes = my_fig[0]
my_axes.set_yscale("log")
plot_distribution(my_axes,
ds.data["time"].to("Myr"),
ds.data["distance_distribution"].to("pc"),
step=10)
plot_distribution(my_axes,
ds_i.data["time"].to("Myr"),
ds_i.data["distance_distribution"].to("pc"),
step=3,
show_dist=False,
PlotWindow objects (e.g., slices, projections)
"""
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from grid_figure import GridFigure
from matplotlib.colors import LogNorm
import yt
# Create GridFigure object
fig = GridFigure(1,
1,
top_buffer=0.02,
bottom_buffer=0.15,
left_buffer=0.18,
right_buffer=0.01,
vertical_buffer=0.01,
horizontal_buffer=0.005,
figsize=(4, 4))
# Now treat fig like a list of NxN elements. Each element is a MPL axis object
ax = fig[0]
# Generate your desired projection or slice or whatever
ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
proj = yt.ProjectionPlot(ds, 'x', 'density')
# Use imshow with your axis object to show the FixedResolutionBuffer
# of the projection image data; Render in Log space.
plot = ax.imshow(proj.frb['density'].v, norm=LogNorm())
def plot_timescale_profiles(data_dir, halo_id):
my_fig = GridFigure([3, 1],
1,
figsize=(6, 4.5),
top_buffer=0.13,
bottom_buffer=0.12,
left_buffer=0.14,
right_buffer=0.02,
horizontal_buffer=0.05,
vertical_buffer=0)
colors = ["red", "green", "blue"]
for i, my_axes in enumerate(my_fig):
my_axes.set_xscale('log')
my_axes.set_yscale('log')
xlim = (2e-6, 3e2)
tx = twin_unit_axes(my_axes, xlim, "r", "pc", top_units="AU")
if i == 0:
fields = ["dynamical_time", "cooling_time", "vortical_time"]
units = ["%f*yr" % np.sqrt(2), "yr", "%f*yr" % (1 / (2 * np.pi))]
for i, field in enumerate(fields):
filename = os.path.join(data_dir,
"%s_%06d.h5" % (field, halo_id))
plot_profile_distribution(my_axes,
filename,
'cell_mass',
x_units="pc",
y_units=units[i],
alpha_scale=0.7,
pkwargs=dict(color=colors[i],
linewidth=1))
my_axes.xaxis.set_visible(False)
ylim = (10, 1e10)
ymajor = np.logspace(2, 10, 5)
yminor = np.logspace(1, 9, 5)
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
my_axes.yaxis.set_label_text("t [yr]")
# my_axes.yaxis.labelpad = 4
labels = ("free-fall", "cooling", "mixing")
dist = [True] * 3
plot_items = list(zip(colors, labels, dist))
plot_profile_distribution_legend(my_axes,
plot_items,
alpha_scale=0.7,
lheight=0.27,
label_rotation=315)
if i == 1:
fields = ["cooling_dynamical_ratio", "vortical_dynamical_ratio"]
units = [str(1 / np.sqrt(2)), str(1 / (2 * np.pi * np.sqrt(2)))]
for i, field in enumerate(fields):
filename = os.path.join(data_dir,
"%s_%06d.h5" % (field, halo_id))
plot_profile_distribution(my_axes,
filename,
'cell_mass',
x_units="pc",
y_units=units[i],
alpha_scale=0.7,
pkwargs=dict(color=colors[i + 1],
linewidth=1))
ds = yt.load(filename)
x_data = ds.profile.x.to("pc")
z_data = ds.profile['cell_mass'].transpose()
z_sum = z_data.sum(axis=0)
rfil = z_sum > 0
my_axes.plot(x_data[rfil],
np.ones(x_data[rfil].size),
alpha=0.9,
color=colors[0],
linewidth=1,
zorder=1)
tx.xaxis.set_visible(False)
ylim = (0.01, 100)
ymajor = np.logspace(-1, 1, 2)
yminor = np.logspace(-2, 2, 5)
draw_major_grid(my_axes,
'y',
ymajor,
color='black',
linestyle='-',
linewidth=1,
alpha=0.2)
ty = mirror_yticks(my_axes, ylim, ymajor, yminor=yminor)
my_axes.yaxis.set_label_text("t / t$_{\\rm ff}$")
my_axes.yaxis.labelpad = 0
pyplot.savefig("figures/timescale_profile_distributions.pdf")