/
make_pepper_movie.py
394 lines (389 loc) · 17.9 KB
/
make_pepper_movie.py
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# Create movies with commands like
# python make_pepper_movie.py -o test.mpg --base=movies/pepperx5/pepperRvR --pepper --noaxes --timescdm=5.
# python make_pepper_movie.py -o test.mpg --base=movies/pepperx5/pepperRvR --pepper --noaxes --timescdm=5. --movie
import os, os.path
import copy
import pickle
import numpy
import tqdm
import subprocess
import statsmodels.api as sm
import matplotlib
matplotlib.use('Agg')
from galpy.util import bovy_plot, bovy_conversion, multi
from matplotlib import pyplot
import seaborn as sns
import simulate_streampepper
from streampepperdf import streampepperdf
from optparse import OptionParser
from gd1_util import R0, V0
def get_options():
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
# make frames or combine into movie?
parser.add_option("--movie",action="store_true",
dest="movie",default=False,
help="If set, combine frames into a movie")
# Output
parser.add_option("-o",dest='outputfilename',default=None,
help="Name of the file that will hold the movie")
parser.add_option("--base",dest='basefilename',default=None,
help="Basename of the frame files (incl. directory)")
parser.add_option("--skip",action="store_true",
dest="skip",default=False,
help="If set, skip existing frames")
# Simulation options
parser.add_option("-n",dest='nparticles',default=10000,type='int',
help="Number of particles to sample for each tail")
parser.add_option("--nsnap",dest='nsnap',default=1024,type='int',
help="Number of snapshots to produce (must match a gd1pappernsnap.pkl file and gd1peppernsnap_trailing file)")
# Single impact?
parser.add_option("--single",action="store_true",
dest="single",default=False,
help="If set, perform a single, large impact")
parser.add_option("--singletimpact",
dest='singletimpact',default=4.,type='float',
help="Time of impact (in Gyr) for the single impact case")
parser.add_option("--singlemimpact",
dest='singlemimpact',default=10.,type='float',
help="Mass of impact (in 10^7 msun) for the single impact case")
# Full range
parser.add_option("--pepper",action="store_true",
dest="pepper",default=False,
help="If set, sample from all possible impacts")
parser.add_option("--timescdm",dest='timescdm',default=1.,type='float',
help="Use a rate that is timescdm times the CDM prediction")
parser.add_option("--Mmin",dest='Mmin',default=5.,type='float',
help="log10() of the minimum mass in Msun")
parser.add_option("--Mmax",dest='Mmax',default=9.,type='float',
help="log10() of the max mass in Msun")
# Ploting options
parser.add_option("--noaxes",action="store_true",
dest="noaxes",default=False,
help="If set, don't plot axes")
parser.add_option("--lowess",action="store_true",
dest="lowess",default=False,
help="If set, add a trendline to each tail")
return parser
def create_frames(options,args):
# First reload the model
with open('gd1pepper%isampling.pkl' % options.nsnap,'rb') as savefile:
sdf_pepper_leading= pickle.load(savefile)
with open('gd1pepper%isampling_trailing.pkl' % options.nsnap,'rb') as savefile:
sdf_pepper_trailing= pickle.load(savefile)
# Output times
timpacts= sdf_pepper_leading._uniq_timpact
# Sample unperturbed aAt
numpy.random.seed(1)
Oml,anglel,dtl= super(streampepperdf,sdf_pepper_leading)._sample_aAt(\
options.nparticles)
Omt,anglet,dtt= super(streampepperdf,sdf_pepper_trailing)._sample_aAt(\
options.nparticles)
# Setup progenitor
prog= sdf_pepper_leading._progenitor().flip()
prog.integrate(numpy.linspace(0.,9./bovy_conversion.time_in_Gyr(V0,R0),
10001),sdf_pepper_leading._pot)
prog.flip()
# Setup impacts
if options.single:
# Hit the leading arm and the trailing arm 1 Gyr later
m= options.singlemimpact/bovy_conversion.mass_in_1010msol(V0,R0)/1000.
t= timpacts[\
numpy.argmin(\
numpy.fabs(\
numpy.array(timpacts)\
-options.singletimpact\
/bovy_conversion.time_in_Gyr(V0,R0)))]
sdf_pepper_leading.set_impacts(\
impactb=[0.5*simulate_streampepper.rs(options.singlemimpact*10.**7.)],
subhalovel=numpy.array([[-25.,155.,30.]])/V0,
impact_angle=[0.2],
timpact=[t],
GM=[m],rs=[simulate_streampepper.rs(options.singlemimpact*10.**7.)])
# Trailing
m= options.singlemimpact/bovy_conversion.mass_in_1010msol(V0,R0)/1000.
t= timpacts[\
numpy.argmin(\
numpy.fabs(\
numpy.array(timpacts)\
-(options.singletimpact+1.)\
/bovy_conversion.time_in_Gyr(V0,R0)))]
sdf_pepper_trailing.set_impacts(\
impactb=[1.*simulate_streampepper.rs(options.singlemimpact*10.**7.)],
subhalovel=numpy.array([[-25.,155.,30.]])/V0,
impact_angle=[-0.3],
timpact=[t],
GM=[m],rs=[simulate_streampepper.rs(options.singlemimpact*10.**7.)])
elif options.pepper:
# Sampling functions
massrange=[options.Mmin,options.Mmax]
plummer= False
Xrs= 5.
nsubhalo= simulate_streampepper.nsubhalo
rs= simulate_streampepper.rs
dNencdm= simulate_streampepper.dNencdm
sample_GM= lambda: (10.**((-0.5)*massrange[0])\
+(10.**((-0.5)*massrange[1])\
-10.**((-0.5)*massrange[0]))\
*numpy.random.uniform())**(1./(-0.5))\
/bovy_conversion.mass_in_msol(V0,R0)
rate_range= numpy.arange(massrange[0]+0.5,massrange[1]+0.5,1)
rate= numpy.sum([dNencdm(sdf_pepper_leading,10.**r,Xrs=Xrs,
plummer=plummer)
for r in rate_range])
rate= options.timescdm*rate
sample_rs= lambda x: rs(x*bovy_conversion.mass_in_1010msol(V0,R0)*10.**10.,
plummer=plummer)
# Pepper both
sdf_pepper_leading.simulate(rate=rate,sample_GM=sample_GM,
sample_rs=sample_rs,Xrs=Xrs)
print numpy.amax(sdf_pepper_leading._GM)*bovy_conversion.mass_in_1010msol(V0,R0)
sdf_pepper_trailing.simulate(rate=rate,sample_GM=sample_GM,
sample_rs=sample_rs,Xrs=Xrs)
print numpy.amax(sdf_pepper_trailing._GM)*bovy_conversion.mass_in_1010msol(V0,R0)
else:
# Hit both with zero
sdf_pepper_leading.set_impacts(\
impactb=[0.],
subhalovel=numpy.array([[-25.,155.,30.]])/V0,
impact_angle=[0.2],
timpact=[timpacts[0]],
GM=[0.],rs=[1.])
sdf_pepper_trailing.set_impacts(\
impactb=[0.],
subhalovel=numpy.array([[-25.,155.,30.]])/V0,
impact_angle=[-0.2],
timpact=[timpacts[0]],
GM=[0.],rs=[1.])
# Now make all frames
dum= multi.parallel_map(
(lambda x: _plot_one_frame(x,options,prog,timpacts,
sdf_pepper_leading,sdf_pepper_trailing,
Oml,Omt,anglel,anglet,dtl,dtt)),
range(len(timpacts)),
numcores=numpy.amin([len(timpacts),30]))
return None
def _plot_one_frame(ii,options,prog,timpacts,
sdf_pepper_leading,sdf_pepper_trailing,
Oml,Omt,anglel,anglet,dtl,dtt):
bovy_plot.bovy_print(fig_height=3.,fig_width=7.)
xlabel= r'$X_{\mathrm{orb}}\,(\mathrm{kpc})$'
ylabel= r'$Y_{\mathrm{orb}}\,(\mathrm{kpc})$'
xrange= [-12.,12.]
yrange= [-1.75,.3]
TL= _projection_orbplane(prog)
if options.skip and os.path.exists(options.basefilename+'_%s.png'\
% str(ii).zfill(5)):
return None
timpact= timpacts[-ii-1]
overplot= False
for sdf_pepper,Om,angle,dt \
in zip([sdf_pepper_leading,sdf_pepper_trailing],
[Oml,Omt],[anglel,anglet],[dtl,dtt]):
tOm= copy.deepcopy(Om)
tangle= copy.deepcopy(angle)
tdt= copy.deepcopy(dt)
if timpact > sdf_pepper._timpact[-1]:
# No impact yet
tangle-= tOm*timpact
else:
tangle-= tOm*timpact
# Apply all kicks relevant for this impact
# (copied from streampepperdf)
dangle_at_impact= angle\
-numpy.tile(sdf_pepper._progenitor_angle.T,
(options.nparticles,1)).T\
-(tOm-numpy.tile(sdf_pepper._progenitor_Omega.T,
(options.nparticles,1)).T)\
*sdf_pepper._timpact[-1]
dangle_par_at_impact=\
numpy.dot(dangle_at_impact.T,
sdf_pepper._dsigomeanProgDirection)\
*sdf_pepper._sgapdfs[-1]._gap_sigMeanSign
dOpar= numpy.dot((tOm-numpy.tile(sdf_pepper._progenitor_Omega.T,
(options.nparticles,1)).T).T,
sdf_pepper._dsigomeanProgDirection)\
*sdf_pepper._sgapdfs[-1]._gap_sigMeanSign
relevant_timpact= sdf_pepper._timpact[\
sdf_pepper._timpact > timpact]
for kk,ti in enumerate(relevant_timpact[::-1]):
# Calculate and apply kicks (points not yet released have
# zero kick)
dOr= sdf_pepper._sgapdfs[-kk-1]._kick_interpdOr(dangle_par_at_impact)
dOp= sdf_pepper._sgapdfs[-kk-1]._kick_interpdOp(dangle_par_at_impact)
dOz= sdf_pepper._sgapdfs[-kk-1]._kick_interpdOz(dangle_par_at_impact)
tOm[0,:]+= dOr
tOm[1,:]+= dOp
tOm[2,:]+= dOz
if kk < len(relevant_timpact)-1:
run_to_timpact= relevant_timpact[::-1][kk+1]
else:
run_to_timpact= timpact
tangle[0,:]+=\
sdf_pepper._sgapdfs[-kk-1]._kick_interpdar(dangle_par_at_impact)\
+dOr*(ti-timpact)
tangle[1,:]+=\
sdf_pepper._sgapdfs[-kk-1]._kick_interpdap(dangle_par_at_impact)\
+dOp*(ti-timpact)
tangle[2,:]+=\
sdf_pepper._sgapdfs[-kk-1]._kick_interpdaz(dangle_par_at_impact)\
+dOz*(ti-timpact)
# Update parallel evolution
dOpar+=\
sdf_pepper._sgapdfs[-kk-1]._kick_interpdOpar(dangle_par_at_impact)
dangle_par_at_impact+= dOpar*(ti-run_to_timpact)
# Convert to RvR coordinates for this time
coorddf= copy.deepcopy(sdf_pepper._sgapdfs_coordtransform[timpact])
coorddf._interpolate_stream_track_kick_aA()
coorddf._interpolatedObsTrack= coorddf._kick_interpolatedObsTrack
coorddf._ObsTrack= coorddf._gap_ObsTrack
coorddf._interpolatedObsTrackXY= coorddf._kick_interpolatedObsTrackXY
coorddf._ObsTrackXY= coorddf._gap_ObsTrackXY
coorddf._allinvjacsTrack= coorddf._gap_allinvjacsTrack
coorddf._interpolatedObsTrackAA= coorddf._kick_interpolatedObsTrackAA
coorddf._ObsTrackAA= coorddf._gap_ObsTrackAA
coorddf._nTrackChunks= coorddf._nTrackChunksImpact
coorddf._thetasTrack= coorddf._gap_thetasTrack
coorddf._interpolatedThetasTrack= coorddf._kick_interpolatedThetasTrack
coorddf._progenitor_angle-= coorddf._progenitor_Omega*timpact
coorddf._progenitor_angle= coorddf._progenitor_angle % (2.*numpy.pi)
tangle= tangle % (2.*numpy.pi)
RvR= coorddf._approxaAInv(tOm[0,:],tOm[1,:],tOm[2,:],
tangle[0,:],tangle[1,:],tangle[2,:],
interp=True)
cindx= numpy.array([coorddf._find_closest_trackpointaA(tOm[0,ll],tOm[1,ll],tOm[2,ll],
tangle[0,ll],tangle[1,ll],tangle[2,ll],
interp=True)
for ll in range(len(tOm[0]))],dtype='int')
# Progenitor and its orbit at the current time
cprog= prog(timpact)
cprog.integrate(numpy.linspace(0.,3.,101),sdf_pepper._pot)
cprogf= cprog.flip()
cprogf.integrate(numpy.linspace(0.,3.,101),sdf_pepper._pot)
# compute the orbit and rotate everything such that the derivative
# of the orbit points along X
tvec= numpy.empty((3,2))
tvec[0,0]= cprog.x(numpy.linspace(0.,3.,101)[1])
tvec[1,0]= cprog.y(numpy.linspace(0.,3.,101)[1])
tvec[2,0]= cprog.z(numpy.linspace(0.,3.,101)[1])
tvec[0,1]= cprogf.x(numpy.linspace(0.,3.,101)[1])
tvec[1,1]= cprogf.y(numpy.linspace(0.,3.,101)[1])
tvec[2,1]= cprogf.z(numpy.linspace(0.,3.,101)[1])
tx= numpy.dot(TL,tvec)[0]
ty= numpy.dot(TL,tvec)[1]
dx= tx[1]-tx[0]
dy= ty[1]-ty[0]
mag= numpy.sqrt(dx**2.+dy**2.)
dx/= mag
dy/= mag
rot= numpy.array([[dx,dy],[-dy,dx]])
# Plot
indx= tdt > timpact
# Rotate to 'orbital plane'
tvec= numpy.empty((3,options.nparticles))
tvec[0,:]= RvR[0]*numpy.cos(RvR[5])
tvec[1,:]= RvR[0]*numpy.sin(RvR[5])
tvec[2,:]= RvR[3]
tx= numpy.dot(TL,tvec)[0]
ty= numpy.dot(TL,tvec)[1]
tpx= numpy.dot(TL,[cprog.x(),cprog.y(),cprog.z()])[0]
tpy= numpy.dot(TL,[cprog.x(),cprog.y(),cprog.z()])[1]
plotx= numpy.dot(rot,numpy.array([(tx[indx]-tpx)*R0,
(ty[indx]-tpy)*R0]))[0]
ploty= numpy.dot(rot,numpy.array([(tx[indx]-tpx)*R0,
(ty[indx]-tpy)*R0]))[1]
txrange=\
[xrange[0]\
*(9.-timpact*bovy_conversion.time_in_Gyr(V0,R0))/9.-1.,
xrange[1]\
*(9.-timpact*bovy_conversion.time_in_Gyr(V0,R0))/9.+1.]
bovy_plot.bovy_plot(plotx,
ploty,
'.',ms=2.,
color=sns.color_palette("colorblind")[3],
alpha=0.2/(options.nparticles/10000.),
xlabel=xlabel,
ylabel=ylabel,
xrange=txrange,
yrange=yrange,
zorder=4,
overplot=overplot)
# Add trendline
if options.lowess:
lowess = sm.nonparametric.lowess
z= lowess(ploty,plotx,frac=0.02/(options.nparticles/10000.))
bovy_plot.bovy_plot(z[::100*(options.nparticles//10000),0],
z[::100*(options.nparticles//10000),1],
color=sns.color_palette('colorblind')[2],
lw=1.5,zorder=2,overplot=True)
overplot= True
# Plot progenitor orbit
for tp in [cprog,cprogf]:
tvec= numpy.empty((3,101))
tvec[0]= tp.x(numpy.linspace(0.,3.,101))
tvec[1]= tp.y(numpy.linspace(0.,3.,101))
tvec[2]= tp.z(numpy.linspace(0.,3.,101))
tx= numpy.dot(TL,tvec)[0]
ty= numpy.dot(TL,tvec)[1]
plotx= numpy.dot(rot,numpy.array([(tx-tpx)*R0,
(ty-tpy)*R0]))[0]
ploty= numpy.dot(rot,numpy.array([(tx-tpx)*R0,
(ty-tpy)*R0]))[1]
bovy_plot.bovy_plot(plotx,ploty,
color=sns.color_palette('colorblind')[0],
lw=1.25,zorder=0,
overplot=True)
if options.noaxes:
pyplot.subplots_adjust(bottom=0.02,left=0.02,right=.98,top=.98)
else:
pyplot.subplots_adjust(bottom=0.175,left=0.11,right=0.965,top=0.95)
if options.noaxes:
pyplot.axis('off')
bovy_plot.bovy_end_print(options.basefilename+'_%s.png'\
% str(ii).zfill(5))
return None
def _projection_orbplane(prog):
L= prog.L()[0]
Lx= L[0]
Ly= L[1]
Lz= L[2]
L= numpy.sqrt(Lx**2.+Ly**2.+Lz**2.)
Lx/= L
Ly/= L
Lz/= L
Txz= numpy.zeros((3,3))
Tz= numpy.zeros((3,3))
Txz[0,0]= Lx/numpy.sqrt(Lx**2.+Ly**2.)
Txz[1,1]= Lx/numpy.sqrt(Lx**2.+Ly**2.)
Txz[1,0]= Ly/numpy.sqrt(Lx**2.+Ly**2.)
Txz[0,1]= -Ly/numpy.sqrt(Lx**2.+Ly**2.)
Txz[2,2]= 1.
Tz[0,0]= Lz
Tz[1,1]= 1.
Tz[2,2]= Lz
Tz[2,0]= -numpy.sqrt(Lx**2.+Ly**2.)
Tz[0,2]= numpy.sqrt(Lx**2.+Ly**2.)
TL= numpy.dot(Tz,Txz)
return TL
def create_movie(options,args):
framerate= 25
bitrate= 1000000
try:
subprocess.check_call(['ffmpeg',
'-i',
options.basefilename+'_%05d.png',
'-y',
'-framerate',str(framerate),
'-r',str(framerate),
'-b', str(bitrate),
options.outputfilename])
except subprocess.CalledProcessError:
print "'ffmpeg' failed"
return None
if __name__ == '__main__':
parser= get_options()
options,args= parser.parse_args()
if not options.movie:
create_frames(options,args)
else:
create_movie(options,args)