def generate_temperature_plot(rundir, distance, scale, sigma=None):
gadgetparms = parse_gadget_parms(rundir + "snaps/gadget2.par")
TimeBetSnapshot = gadgetparms['TimeBetSnapshot']
print "Distance matrix:\n", distance
# bestsnap = numpy.intersect1d(numpy.where(distance > 650), numpy.where(distance < 750))[0]
bestsnap = numpy.nanargmin(numpy.abs(distance - 701))
print "Bestsnap, distance =", bestsnap, distance[bestsnap]
# snaprange = len(distance)
snaprange = bestsnap + 3 if bestsnap + 3 < len(distance) else bestsnap
for snap in range(snaprange):
plot_temperature_along_merger_axis(rundir, snap, distance[snap], scale,
TimeBetSnapshot, sigma)
def make_all_hists(timestamp):
rundir = "../runs/{0}/".format(timestamp)
if not (os.path.isdir(rundir + "out") or os.path.exists(rundir + "out")):
os.mkdir(rundir + "out")
gadgetparms = parse_gadget_parms(rundir + "snaps/gadget2.par")
TimeBetSnapshot = gadgetparms["TimeBetSnapshot"]
snaps = sorted(glob.glob(rundir + "snaps/snapshot_*"),
key=os.path.getmtime)
for snap in snaps:
# e.g. "../runs/20160727T1112/snaps/snapshot_051"
snapnr = snap.split('/')[-1].split('_')[-1] # gets the 051
if snapnr == "000" or snapnr == "033":
plot_hist_gasvx(rundir, snapnr, TimeBetSnapshot)
def plot_all(rundir):
# For time counter
gadgetparms = parse_gadget_parms(rundir+"snaps/gadget2.par")
TimeBetSnapshot = gadgetparms['TimeBetSnapshot']
toyclusterparms = [file for file in os.listdir(rundir+"ICs") if "par" in file][0]
toyclusterparms = parse_toycluster_parms(toyclusterparms)
particles = toyclusterparms['Ntotal']
if particles > 2e6:
print "Strap in, this could take a while :-) ..."
files = sorted(glob.glob(rundir+"rotation2/*.fits.fz"), key=os.path.getmtime)
with fits.open(files[0]) as f:
header = f[0].header
data = f[0].data
# Set colourtable based on P-Smac module/flag (in fits header)
for line in repr(header).split("\n"):
if "Effect_Module" in line:
module = line.strip().split("=")[-1].strip().split("/")[0]
if "Effect_Flag" in line:
flag = line.strip().split("=")[-1].strip().split("/")[0]
if "XYSize" in line: # Could also be obtained from gadgetparms
scale = line.strip().split("=")[-1].strip().split("/")[0]
if "Description" in line: # For tile title: physical property
name = line.strip().split("/")[-1].strip()
# To see entire header, including comments starting with "/"
# print line
cmap = helix_tables(int(module.strip()), int(flag.strip()))
scale = "[{0:.1f} Mpc]^2".format(float(scale)/1000)
xlen = header['NAXIS1']
ylen = header['NAXIS2']
# files = [file for file in files if "xray_180_" in file]
for filename in files:
make_plot(filename, cmap, scale, xlen, ylen, name)
def __init__(self, arguments, discard_firstbins=True):
self.timestamp = arguments.simulationID[0]
self.observed = ObservedCluster(
arguments.clustername) if arguments.clustername else None
if arguments.clustername and arguments.clustername == "cygA" and discard_firstbins:
print "WARNING: Discarding first three CygA bins.\n"
self.observed.radius = self.observed.radius[3:]
self.observed.binsize = self.observed.binsize[3:]
self.observed.density = self.observed.density[3:]
self.observed.density_std = self.observed.density_std[3:]
self.observed.number_density = self.observed.number_density[3:]
self.observed.number_density_std = self.observed.number_density_std[
3:]
if arguments.IC_only:
# TODO: there is something different in the Toycluster rho
# than in the Gadget output :(
self.IC_only = True
self.icdir = "../runs/{0}/ICs/".format(self.timestamp)
self.snapdir = "../runs/{0}/ICs/".format(self.timestamp)
self.snaps = ["../runs/{0}/ICs/IC_single_0".format(self.timestamp)]
else:
self.IC_only = False
self.icdir = "../runs/{0}/ICs/".format(self.timestamp)
self.snapdir = "../runs/{0}/snaps/".format(self.timestamp)
self.snaps = sorted(glob.glob(
"../runs/{0}/snaps/snapshot_*".format(self.timestamp)),
key=os.path.getmtime)
print self.snaps, "\n"
self.outdir = "../runs/{0}/out/".format(self.timestamp)
if not (os.path.isdir(self.outdir) or os.path.exists(self.outdir)):
os.mkdir(self.outdir)
if self.IC_only:
self.TimeBetSnapshot = 0
else:
# For time counter
gadgetparms = parse_gadget_parms(self.snapdir + "gadget2.par")
self.TimeBetSnapshot = gadgetparms['TimeBetSnapshot']
def generate_distance_plots(rundir):
# For time counter
gadgetparms = parse_gadget_parms(rundir + "snaps/gadget2.par")
TimeBetSnapshot = gadgetparms['TimeBetSnapshot']
# toyclusterparms = parse_toycluster_parms(rundir+"ICs/ic_both_hybrid.par")
# particles = toyclusterparms['Ntotal']
projection = "z"
projection = "_projection-{0}".format(projection)
option = "xray-surface-brightness"
xraycube = rundir + "analysis/" + option + projection + ".fits.fz"
print "Simulation surface brightness:", xraycube
with fits.open(xraycube) as f:
header = f[0].header
data = f[0].data
# Set colourtable based on P-Smac module/flag (in fits header)
for line in repr(header).split("\n"):
if "Effect_Module" in line:
module = line.strip().split("=")[-1].strip().split("/")[0]
if "Effect_Flag" in line:
flag = line.strip().split("=")[-1].strip().split("/")[0]
if "XYSize" in line: # Could also be obtained from gadgetparms
scale = line.strip().split("=")[-1].strip().split("/")[0]
cmap = helix_tables(int(module.strip()), int(flag.strip()))
number_of_snapshots = header['NAXIS3']
distance = numpy.zeros(number_of_snapshots)
x = numpy.zeros(number_of_snapshots, dtype=object)
y = numpy.zeros(number_of_snapshots, dtype=object)
for n in range(number_of_snapshots):
distance[n], x[n], y[n] = plot_snapshot(rundir, data[n], n, scale,
cmap, TimeBetSnapshot)
return distance, x, y, scale
def find_snapshot(rundir):
""" Find the most likely snapshot number based on the X-ray observation.
From the X-ray observation we find the two peaks of emission are separated
by 700 seconds of arc. """
# For time counter
gadgetparms = parse_gadget_parms(rundir + "snaps/gadget2.par")
TimeBetSnapshot = gadgetparms['TimeBetSnapshot']
# toyclusterparms = parse_toycluster_parms(rundir+"ICs/ic_both_hybrid.par")
# particles = toyclusterparms['Ntotal']
projection = "z"
projection = "_projection-{0}".format(projection)
option = "xray-surface-brightness"
xraycube = rundir + "analysis/" + option + projection + ".fits.fz"
print xraycube
with fits.open(xraycube) as f:
header = f[0].header
data = f[0].data
# Set colourtable based on P-Smac module/flag (in fits header)
for line in repr(header).split("\n"):
if "Effect_Module" in line:
module = line.strip().split("=")[-1].strip().split("/")[0]
if "Effect_Flag" in line:
flag = line.strip().split("=")[-1].strip().split("/")[0]
if "XYSize" in line: # Could also be obtained from gadgetparms
scale = line.strip().split("=")[-1].strip().split("/")[0]
cmap = helix_tables(module.strip(), flag.strip())
# To see entire header, including comments starting with "/"
# print line
print "Effect_Module", module
print "Effect_Flag", flag
number_of_snapshots = header['NAXIS3']
xlen = int(header['NAXIS1'])
ylen = int(header['NAXIS2'])
pixelscale = float(scale) / int(xlen)
print "# snapshots =", number_of_snapshots
print "BoxSize =", scale
print "xlen =", xlen
print "ylen =", ylen
print "kpc/pixel =", pixelscale
# https://stackoverflow.com/questions/9111711
neighborhood_size = 2**(-5) * xlen
threshold = 0
fig = pyplot.figure(figsize=(12, 9))
fig.gca().set_xlim(0, 0.75 * xlen)
fig.gca().set_ylim(0, 0.75 * ylen)
im = pyplot.imshow(numpy.log(data[0][0.125 * xlen:0.875 * xlen,
0.125 * ylen:0.875 * ylen]),
cmap=cmap)
hank = fig.gca().plot(0, 0, "ro")
scale_text = "[{0:.1f} Mpc]^2".format(float(scale) / 1000)
pad = 4
pyplot.text(xlen - pad,
pad,
scale_text,
color="white",
size=42,
horizontalalignment="right",
verticalalignment="bottom")
def init():
print "hank"
im.set_data(numpy.zeros((xlen, ylen)))
global hank
hank = fig.gca().plot(0, 0, "ro")
for n in range(len(hank)):
hank.pop(n).remove()
return [im]
def animate(i):
global hank
for n in range(len(hank)):
hank.pop(n).remove()
snapshot = numpy.log(data[i])
#borderless_snapshot = snapshot[]
snapshot = snapshot[0.125 * xlen:0.875 * xlen,
0.125 * ylen:0.875 * ylen]
data_max = scipy.ndimage.filters.maximum_filter(
snapshot, neighborhood_size)
maxima = (snapshot == data_max)
data_min = scipy.ndimage.filters.minimum_filter(
snapshot, neighborhood_size)
diff = ((data_max - data_min) > threshold)
maxima[diff == 0] = 0
labeled, num_objects = scipy.ndimage.label(maxima)
slices = scipy.ndimage.find_objects(labeled)
x, y = [], []
for dy, dx in slices:
x_center = (dx.start + dx.stop - 1) / 2
x.append(x_center)
y_center = (dy.start + dy.stop - 1) / 2
y.append(y_center)
print "Snapshot:", i
for a, b in zip(x, y):
print " (x, y) = ({0:3d}, {1:3d})".format(a, b)
if len(x) == 2:
distance = numpy.sqrt(p2(x[0] - x[1]) + p2(y[0] - y[1]))
print " distance = {0:<5.2f}".format(distance)
print " distance = {0:<5.2f} kpc".format(distance * pixelscale)
im.set_data(snapshot)
if i < number_of_snapshots - 1:
hank = fig.gca().plot(x, y, "ro")
return [im]
anim = animation.FuncAnimation(fig,
animate,
init_func=init,
frames=number_of_snapshots,
interval=500,
repeat_delay=5000)
pyplot.show()
return
pad = 4 # number of pixels padding for text placement (of tile titles)
for n in range(number_of_snapshots):
# Set up four-panel plot, stitched together
pyplot.figure(figsize=(16, 16))
pyplot.style.use(["dark_background"])
gs1 = gridspec.GridSpec(2, 2)
#gs1 = gridspec.GridSpec(1, 1)
gs1.update(wspace=0, hspace=0) # remove spacing between axes.
for i in range(4): # gridspec indexes start at 0
#for i in range(1): # gridspec indexes start at 0
ax = pyplot.subplot(gs1[i])
pyplot.axis('on')
ax.set_xticklabels([])
ax.set_yticklabels([])
# NB: breaks when using pyplot.show(), save only!
ax.set_aspect('equal')
# Plot every panel. TODO: change way log/sqrt scheme is chosen
# Now all properties look good in log stretch, but DM density
# seems to look better using sqrt scaling!
if "dm" not in chosen[i]:
ax.imshow(numpy.log(data[n]), cmap=cmap[i])
else:
ax.imshow(numpy.sqrt(data[n]), cmap=cmap[i])
# Tile text: name of physical property
pyplot.text(pad if i % 2 == 0 else xlen - pad,
pad,
names[i],
color="white",
size=42,
horizontalalignment="left" if i % 2 == 0 else "right",
verticalalignment="top")
# pyplot.text(pad if i%2==0 else xlen-pad, ylen-pad,
# names[i], color="white", size=42,
# horizontalalignment="left" if i%2==0 else "right",
# verticalalignment="bottom")
# Image scale (lives in lower right corner of tile 3)
pyplot.text(xlen - pad,
ylen - pad,
scale,
color="white",
size=42,
horizontalalignment="right",
verticalalignment="bottom")
# pyplot.suptitle("T = {0:05.2f} Myr".format(0.05*n),
pyplot.suptitle("T = {0:04.2f} Gyr".format(TimeBetSnapshot * n),
color="white",
size=52,
y=0.95)
pyplot.tight_layout()
pyplot.savefig(rundir +
"out/snapshot{0}_{1:03d}.png".format(projection, n))
pyplot.close()
print_progressbar(n, number_of_snapshots)
def make_video(rundir, chosen, projection):
""" Make a video of four physical properties: the chosen options """
if not (os.path.isdir(rundir + "out") or os.path.exists(rundir + "out")):
os.mkdir(rundir + "out")
projection = "_projection-{0}".format(projection)
# For time counter
gadgetparms = parse_gadget_parms(rundir + "snaps/gadget2.par")
TimeBetSnapshot = gadgetparms['TimeBetSnapshot']
toyclusterparms = parse_toycluster_parms(rundir + "ICs/ic_both_hybrid.par")
particles = toyclusterparms['Ntotal']
if particles > 2e6:
print "Strap in, this could take a while :-) ..."
headers = []
data = []
cmap = []
names = []
for option in chosen:
with fits.open(rundir + "analysis/" + option + projection +
".fits.fz") as f:
headers.append(f[0].header)
data.append(f[0].data)
# Set colourtable based on P-Smac module/flag (in fits header)
for line in repr(f[0].header).split("\n"):
if "Effect_Module" in line:
module = line.strip().split("=")[-1].strip().split("/")[0]
if "Effect_Flag" in line:
flag = line.strip().split("=")[-1].strip().split("/")[0]
if "XYSize" in line: # Could also be obtained from gadgetparms
scale = line.strip().split("=")[-1].strip().split("/")[0]
if "Description" in line: # For tile title: physical property
names.append(line.strip().split("/")[-1])
cmap.append(helix_tables(module.strip(), flag.strip()))
# To see entire header, including comments starting with "/"
# print line
scale = "[{0:.1f} Mpc]^2".format(float(scale) / 1000)
number_of_snapshots = headers[0]['NAXIS3']
xlen = headers[0]['NAXIS1']
ylen = headers[0]['NAXIS2']
pad = 4 # number of pixels padding for text placement (of tile titles)
for n in range(number_of_snapshots):
# Set up four-panel plot, stitched together
pyplot.figure(figsize=(16, 16))
pyplot.style.use(["dark_background"])
gs1 = gridspec.GridSpec(2, 2)
#gs1 = gridspec.GridSpec(1, 1)
gs1.update(wspace=0, hspace=0) # remove spacing between axes.
for i in range(4): # gridspec indexes start at 0
#for i in range(1): # gridspec indexes start at 0
ax = pyplot.subplot(gs1[i])
pyplot.axis('on')
ax.set_xticklabels([])
ax.set_yticklabels([])
# NB: breaks when using pyplot.show(), save only!
ax.set_aspect('equal')
# Plot every panel. TODO: change way log/sqrt scheme is chosen
# Now all properties look good in log stretch, but DM density
# seems to look better using sqrt scaling!
if "dm" not in chosen[i]:
ax.imshow(numpy.log(data[i][n]), cmap=cmap[i])
else:
ax.imshow(numpy.sqrt(data[i][n]), cmap=cmap[i])
# Tile text: name of physical property
pyplot.text(pad if i % 2 == 0 else xlen - pad,
pad,
names[i],
color="white",
size=42,
horizontalalignment="left" if i % 2 == 0 else "right",
verticalalignment="top")
# pyplot.text(pad if i%2==0 else xlen-pad, ylen-pad,
# names[i], color="white", size=42,
# horizontalalignment="left" if i%2==0 else "right",
# verticalalignment="bottom")
# Image scale (lives in lower right corner of tile 3)
pyplot.text(xlen - pad,
ylen - pad,
scale,
color="white",
size=42,
horizontalalignment="right",
verticalalignment="bottom")
# pyplot.suptitle("T = {0:05.2f} Myr".format(0.05*n),
pyplot.suptitle("T = {0:04.2f} Gyr".format(TimeBetSnapshot * n),
color="white",
size=52,
y=0.95)
pyplot.tight_layout()
pyplot.savefig(rundir +
"out/snapshot{0}_{1:03d}.png".format(projection, n))
pyplot.close()
print_progressbar(n, number_of_snapshots)