def halo_gas(halo):
"""
Generate an image (returns a pixel grid) for the gas in the halo
specified.
"""
baryonic_ids_in_halo_at_z0 = np.unique(
np.concatenate([
baryons_end.gas_ids[baryons_end.gas_halos == halo],
baryons_end.star_ids[baryons_end.star_halos == halo],
]))
_, mask, _ = np.intersect1d(
baryons_ini.gas_ids,
baryonic_ids_in_halo_at_z0,
assume_unique=True,
return_indices=True,
)
c = np.concatenate([
baryons_ini.gas_coordinates[mask],
np.array([[0.0, 0.0, 0.0], [50000.0, 50000.0, 50000.0]]),
])
hsml = np.concatenate([hsml_ini_gas[mask], [0.0, 0.0]])
qv = QuickView(c, hsml=hsml, **kwargs).get_image()
return qv
def lagrangian_region(lr):
"""
Generate an image (returns a pixel grid) for the gas in the lagrangian region
specified.
"""
mask = data.lagrangian_regions == lr
c = np.concatenate([
baryons_ini.gas_coordinates[mask],
np.array([[0.0, 0.0, 0.0], [50000.0, 50000.0, 50000.0]]),
])
hsml = np.concatenate([hsml_ini_gas[mask], [0.0, 0.0]])
qv = QuickView(c, hsml=hsml, **kwargs).get_image()
return qv
def generate_views(data: SimulationData, res=2048) -> Tuple[np.ndarray]:
"""
Generates the views on the data from py-sphviewer.
Returns the overall image for the whole box and then a zoomed region.
"""
qv_all = QuickView(
data.coordinates,
data.masses,
r="infinity",
plot=False,
xsize=res,
ysize=res,
logscale=False,
p=0,
np=48,
)
zoomed_res = (res * 6) // 10
mask = np.logical_and(
np.logical_and(
data.coordinates[0] > (data.boxsize / 2 - 4 - 20),
data.coordinates[0] < (data.boxsize / 2 + 6 - 20),
),
np.logical_and(
data.coordinates[1] > (data.boxsize / 2 - 3.5 - 20),
data.coordinates[1] < (data.boxsize / 2 + 6.5 - 20),
),
)
qv_zoomed = QuickView(
data.coordinates.T[mask].T,
data.masses[mask],
r="infinity",
plot=False,
xsize=zoomed_res,
ysize=zoomed_res,
logscale=False,
np=48,
)
return qv_all.get_image(), qv_zoomed.get_image()
gas_coordinates[:, 0] < largest_center[0] + distance_to_plot,
gas_coordinates[:, 1] > largest_center[1] - distance_to_plot,
gas_coordinates[:, 1] < largest_center[1] + distance_to_plot,
gas_coordinates[:, 2] > largest_center[2] - distance_to_plot,
gas_coordinates[:, 2] < largest_center[2] + distance_to_plot,
])
#%%
gas_coordinates_around_center = gas_coordinates[mask]
#%%
# First we'll set up some plotting commands
kwargs = dict(xsize=2048, ysize=2048, r="infinity", logscale=False, plot=False)
#%%
qv_nomass = QuickView(gas_coordinates_around_center, **kwargs)
#%%
fig, ax = plt.subplots()
ax.axis("off")
ax.imshow(
qv_nomass.get_image(),
norm=LogNorm(),
extent=[
-distance_to_plot, distance_to_plot, -distance_to_plot,
distance_to_plot
],
)
# Plot the virial radius of the cluster on top as a check
circle = plt.Circle((0, 0),
def get_normalized_image(image):
image = (image-np.min(image))/(np.max(image)-np.min(image))
return image
pos1 = np.zeros([5000, 3])
pos2 = np.zeros([10000, 3])
pos1[:, 0:2] = -1+2*np.random.rand(5000, 2)
pos2[:, 0:2] = -1+2*np.random.rand(10000, 2)
r2 = np.sqrt(pos2[:, 0]**2+pos2[:, 1]**2)
k2, = np.where(r2 < 0.5)
pos2 = pos2[k2, :]
qv1 = QuickView(pos1.T, np.ones(len(pos1)),
r='infinity', logscale=False, plot=False,
extent=[-1, 1, -1, 1], x=0, y=0, z=0)
qv2 = QuickView(pos2.T, np.ones(len(pos2)),
r='infinity', logscale=False, plot=False,
extent=[-1, 1, -1, 1], x=0, y=0, z=0)
image1 = cm.gist_heat(get_normalized_image(qv1.get_image()))
image2 = cm.gist_stern(get_normalized_image(qv2.get_image()))
fig = plt.figure(1, figsize=(10, 5))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
blend = Blend(image1, 1.0*image2)
screen = blend.Screen()
overlay = blend.Overlay()
def read_galaxy(self, itype, gn, sgn, centre, load_region_length):
p, t = defineprojection(projection[indices[n]])
data = {}
eagle_data = EagleSnapshot(
'/disks/eagle/L0100N1504/REFERENCE/data/snapshot_027_z000p101/snap_027_z000p101.0.hdf5'
)
centre *= self.h
region = np.array([(centre[0] - 0.5 * load_region_length),
(centre[0] + 0.5 * load_region_length),
(centre[1] - 0.5 * load_region_length),
(centre[1] + 0.5 * load_region_length),
(centre[2] - 0.5 * load_region_length),
(centre[2] + 0.5 * load_region_length)])
eagle_data.select_region(*region)
f = h5py.File(
'/disks/eagle/L0100N1504/REFERENCE/data/snapshot_027_z000p101/snap_027_z000p101.0.hdf5',
'r')
constants = f['Constants']
Mpc = constants.attrs['CM_PER_MPC']
SMass = constants.attrs["SOLAR_MASS"]
for att in ['GroupNumber', 'Coordinates', 'Mass']:
if parttype == 1 and att == 'Mass':
data[att] = np.ones(len(data['GroupNumber'])) * SMass * 9.7e6
continue
tmp = eagle_data.read_dataset(itype, att)
cgs = f['PartType%i/%s' %
(itype, att)].attrs.get('CGSConversionFactor')
aexp = f['PartType%i/%s' %
(itype, att)].attrs.get('aexp-scale-exponent')
hexp = f['PartType%i/%s' %
(itype, att)].attrs.get('h-scale-exponent')
data[att] = np.multiply(tmp,
cgs * self.a**aexp * self.h**hexp,
dtype='f8')
if att == 'Coordinates':
centre2 = np.multiply(centre,
cgs / Mpc * self.a**aexp * self.h**hexp)
galx = np.zeros(len(myData))
galy = np.zeros(len(myData))
galz = np.zeros(len(myData))
j = 1
print myData[1][2], cgs, Mpc, self.a, aexp, self.h, hexp
print np.multiply(myData[1][2],
cgs / Mpc * self.a**aexp * self.h**hexp)
for i in range(
1,
len(myData)): #Only if first galaxy in list is centre
galx[j] = np.multiply(
myData[i][2], cgs / Mpc * self.a**aexp * self.h**hexp *
self.h) - centre2[0]
galy[j] = np.multiply(
myData[i][3], cgs / Mpc * self.a**aexp * self.h**hexp *
self.h) - centre2[1]
galz[j] = np.multiply(
myData[i][4], cgs / Mpc * self.a**aexp * self.h**hexp *
self.h) - centre2[2]
j += 1
print galx, galy, galz
x1, y1, x2, y2, x3, y3, l1, l2, l3, l4, l5, l6 = find_galaxy(
projection[indices[n]], galx, galy, galz)
f.close()
mask = (data['GroupNumber'] == gn)
for att in data.keys():
data[att] = data[att][mask]
for i in range(3):
if i == 0:
stringtitle = parttype_string + " for p=0 and t=0" + " at snapshot 27 with redshift z = 0.1"
x = x1
y = y1
xlabel = l1
ylabel = l2
if i == 1:
p = p + 90
stringtitle = parttype_string + " for p=90 and t=0" + " at snapshot 27 with redshift z = 0.1"
x = x2
y = y2
xlabel = l3
ylabel = l4
if i == 2:
stringtitle = parttype_string + " for p=0 and t=90" + " at snapshot 27 with redshift z = 0.1"
t = t + 90
p = p - 90
x = x3
y = y3
xlabel = l5
ylabel = l6
qv_parallel = QuickView(data['Coordinates'] / Mpc,
data['Mass'] / SMass,
r='infinity',
plot=False,
p=p,
t=t,
x=centre2[0],
y=centre2[1],
z=centre2[2],
max_hsml=max_hsml,
extent=[-0.5, 0.5, -0.5, 0.5])
plt.imshow(qv_parallel.get_image(),
extent=qv_parallel.get_extent(),
cmap='viridis',
origin='lower')
plt.colorbar()
color = ['r', 'k', 'm', 'w']
plt.title(stringtitle)
plt.xlabel(xlabel)
print p, t, x, y
plt.ylabel(ylabel)
plt.xlim(-0.21, 0.21)
plt.ylim(-0.21, 0.21)
#plt.clim(5,)
#plt.scatter(x, y, facecolors = 'none', color = 'r')
marker = ['o', 's', '^', 'D']
for j in range(len(x)):
plt.scatter(x[j],
y[j],
marker=marker[j],
facecolors='none',
color='r',
label=myData[j][5])
plt.legend(bbox_to_anchor=(-0.3, -0.15), loc=3, prop={'size': 8})
plt.show()
#%%
one_image = reduce(darker_colour, images)
#%%
fig, ax = plt.subplots(figsize=(8, 8))
ax.axis("off")
fig.subplots_adjust(0, 0, 1, 1)
ax.imshow(one_image, origin="lower")
fig.savefig("lagrangian_regions_combined_secondset.png", dpi=300)
#%% [markdown]
# We need to make the dark matter for the background!
#%%
qv = QuickView(data.snapshot_end.dark_matter.coordinates, **kwargs)
#%%
plt.imshow(qv.get_image, cmap="bone", norm=LogNorm())
plt.gca().axis("off")
#%% [markdown]
# On top of everyone's favourite dark matter image, we also want to show the
# halo virial radii.
#%%
with open(f"{directory}/lt/halo_catalogue.pickle", "rb") as handle:
halo_catalogue = load(handle)
#%%
halo_masses = (sum([
# y = t['Y'][gas][xmask][ymask]
#
# return x, y
#
# x, y = pos(box[l])
# axs[l, k].scatter(x, y, c='grey', s=0.05, marker='.')
x = np.array(t['X'][gas])
y = np.array(t['Y'][gas])
z = np.array(t['Z'][gas])
pos = np.column_stack((x, y, z))
qv = QuickView(pos,
r='infinity',
plot=False,
x=0,
y=0,
z=0,
extent=[-110, 110, -110, 110])
img = qv.get_image()
extent = qv.get_extent()
im = axs[l, k].imshow(img, extent=extent, cmap='twilight')
#if (k == 2): fig.colorbar(im, ax=axs[l, k], pad=0.1)
axs[l, k].tick_params(left=False,
right=False,
top=False,
bottom=False,
length=2,
tickdir='in',
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_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
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["OMP_NUM_THREADS"] = "1"
print("Comparing with pySPHViewer")
coordinates = zeros((number_of_particles, 3))
coordinates[:, 0] = x
coordinates[:, 1] = y
h = 1.778_002 * h # The kernel_gamma we use.
t = time()
qv = QuickView(
coordinates,
hsml=h,
mass=m,
xsize=res,
ysize=res,
r="infinity",
plot=False,
logscale=False,
).get_image()
dt_pysphviewer = time() - t
print(f"pySPHViewer took {dt_pysphviewer} on the same problem.")
print(f"Note that pySPHViewer is running in single-threaded mode.")
ratio = dt_us / dt_pysphviewer
if ratio < 1.0:
print(f"That makes us {1 / ratio} x faster 😀 ")
else:
print(f"That makes pySphviewer {ratio} x faster 😔")
#This file contains a few lines as example of
#the use of py-sphviewer.
import h5py
from sphviewer.tools import QuickView
halo = h5py.File('dm_halo.h5py', 'r')
pos = halo['Coordinates'].value
qv = QuickView(pos.T, r='infinity', nb=8)
image = (image-np.min(image))/(np.max(image)-np.min(image))
return image
pos1 = np.zeros([5000,3])
pos2 = np.zeros([10000,3])
pos1[:,0:2] = -1+2*np.random.rand(5000,2)
pos2[:,0:2] = -1+2*np.random.rand(10000,2)
r2 = np.sqrt(pos2[:,0]**2+pos2[:,1]**2)
k2, = np.where(r2 < 0.5)
pos2 = pos2[k2,:]
qv1 = QuickView(pos1.T, np.ones(len(pos1)),
r='infinity', logscale=False, plot=False,
extent=[-1,1,-1,1], x=0, y=0, z=0)
qv2 = QuickView(pos2.T, np.ones(len(pos2)),
r='infinity', logscale=False, plot=False,
extent=[-1,1,-1,1], x=0, y=0, z=0)
image1 = cm.gist_heat(get_normalized_image(qv1.get_image()))
image2 = cm.gist_stern(get_normalized_image(qv2.get_image()))
fig = plt.figure(1, figsize=(10,5))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
blend = Blend(image1, 1.0*image2)