def do_plot(self, subcmd, opts, arg):
"""
Create a set of images
${cmd_usage}
${cmd_option_list}
"""
pf = _fix_pf(arg)
center = opts.center
if opts.center == (-1, -1, -1):
mylog.info("No center fed in; seeking.")
v, center = pf.h.find_max("Density")
center = na.array(center)
pc = raven.PlotCollection(pf, center=center)
if opts.axis == 4:
axes = range(3)
else:
axes = [opts.axis]
for ax in axes:
mylog.info("Adding plot for axis %i", ax)
if opts.projection:
pc.add_projection(opts.field,
ax,
weight_field=opts.weight,
center=center)
else:
pc.add_slice(opts.field, ax, center=center)
if opts.grids: pc.plots[-1].modify["grids"]()
pc.set_width(opts.width, opts.unit)
pc.set_cmap(opts.cmap)
if opts.zlim: pc.set_zlim(*opts.zlim)
if not os.path.isdir(opts.output): os.makedirs(opts.output)
pc.save(os.path.join(opts.output, "%s" % (pf)))
def run_func(pf, *args, **kwargs):
PlotTypes.Initialize()
pf1 = _fix_pf(pf)
if "center" not in kwargs:
kwargs['center'] = pf1.h.find_max("Density")[1]
p = func(pf1, *args, **kwargs)
# This next bit is to ensure we have a strong reference
# until the plot object is deleted
if pf1 is not pf: p["pf"] = pf1
return p
def do_hop(self, subcmd, opts, arg):
"""
Run HOP on one or more datasets
${cmd_option_list}
"""
pf = _fix_pf(arg)
kwargs = {'dm_only': opts.dm_only}
if opts.threshold is not None: kwargs['threshold'] = opts.threshold
hop_list = HaloFinder(pf, **kwargs)
if opts.output is None: fn = "%s.hop" % pf
else: fn = opts.output
hop_list.write_out(fn)
def do_stats(self, subcmd, opts, arg):
"""
Print stats and maximum density for one or more datasets
${cmd_option_list}
"""
pf = _fix_pf(arg)
pf.h.print_stats()
v, c = pf.h.find_max("Density")
print "Maximum density: %0.5e at %s" % (v, c)
if opts.output is not None:
t = pf["InitialTime"] * pf['years']
open(opts.output,
"a").write("%s (%0.5e years): %0.5e at %s\n" % (pf, t, v, c))
def do_zoomin(self, subcmd, opts, arg):
"""
Create a set of zoomin frames
${cmd_option_list}
"""
pf = _fix_pf(arg)
min_width = opts.min_width * pf.h.get_smallest_dx()
if opts.axis == 4:
axes = range(3)
else:
axes = [opts.axis]
pc = PlotCollection(pf)
for ax in axes:
if opts.projection:
p = pc.add_projection(opts.field, ax, weight_field=opts.weight)
else:
p = pc.add_slice(opts.field, ax)
if opts.unit_boxes: p.modify["units"](factor=8)
if opts.text is not None:
p.modify["text"]((0.02, 0.05),
opts.text.replace(r"\n", "\n"),
text_args=dict(size="medium", color="w"))
pc.set_width(opts.max_width, '1')
# Check the output directory
if not os.path.isdir(opts.output):
os.mkdir(opts.output)
# Figure out our zoom factor
# Recall that factor^nframes = min_width / max_width
# so factor = (log(min/max)/log(nframes))
mylog.info("min_width: %0.3e max_width: %0.3e nframes: %0.3e",
min_width, opts.max_width, opts.nframes)
factor = 10**(math.log10(min_width / opts.max_width) / opts.nframes)
mylog.info("Zoom factor: %0.3e", factor)
w = 1.0
for i in range(opts.nframes):
mylog.info("Setting width to %0.3e", w)
mylog.info("Saving frame %06i", i)
pc.set_width(w, "1")
if opts.zlim:
pc.set_zlim(*opts.zlim)
if opts.dex:
pc.set_zlim('min', None, opts.dex)
pc.set_cmap(opts.cmap)
pc.save(os.path.join(opts.output, "%s_frame%06i" % (pf, i)))
w = factor**i
def do_amira(self, subcmd, opts, start, stop):
"""
Export multiple data sets in amira format
${cmd_usage}
${cmd_option_list}
"""
from yt.extensions.HierarchySubset import ExtractedHierarchy
import h5py
first = int(start)
last = int(stop)
# Set up our global metadata
afile = h5py.File(opts.output, "w")
md = afile.create_group("/globalMetaData")
md.attrs['datatype'] = 0
md.attrs['staggering'] = 1
md.attrs['fieldtype'] = 1
md.attrs['minTimeStep'] = first
md.attrs['maxTimeStep'] = last
times = []
# Get our staggering correct based on skip
timesteps = na.arange(first, last + 1, opts.skip, dtype='int32')
time_offset = None
t2 = []
offset = None
if opts.recenter:
tpf = _fix_pf("%s%04i" % (opts.basename, timesteps[-1]))
offset = tpf.h.find_max("Density")[1]
del tpf
for n in timesteps:
# Try super hard to get the right parameter file
pf = _fix_pf("%s%04i" % (opts.basename, n))
hh = pf.h
times.append(pf["InitialTime"] * pf["years"])
eh = ExtractedHierarchy(pf,
opts.min_level,
max_level=opts.max_level,
offset=offset,
always_copy=opts.always_copy)
eh.export_output(afile, n, opts.field)
t2.append(pf["InitialTime"])
# This should be the same
md.attrs['rootDelta'] = (pf["unitary"] /
pf["TopGridDimensions"]).astype('float64')
md.attrs['minTime'] = times[0]
md.attrs['maxTime'] = times[-1]
md.attrs['numTimeSteps'] = len(timesteps)
# I think we just want one value here
rel_times = na.array(
times, dtype='float64') - int(opts.subtract_time) * times[0]
md.create_dataset("sorted_times", data=na.array(rel_times))
md.create_dataset("sorted_timesteps", data=timesteps)
afile.close()