def load_sightline_scatter_data(sim, ray_id, output=3195):
fn = '../../data/unanalyzed_spectra/ray_%s_%i_%i.h5' % (sim, output,
ray_id)
plot_data = h5.File(fn, 'r')['grid']
l = YTArray(plot_data['l'], 'cm')
l = np.array(l.in_units('kpc'))
temperature = np.array(plot_data['temperature'])
density = np.array(plot_data['density'])
metallicity = np.array(
plot_data['metallicity']
) * 77.22007722007721 # converting from code units to zsun
vx = np.array(plot_data['relative_velocity_x']) / 1e5 # converting to km/s
vy = np.array(plot_data['relative_velocity_y']) / 1e5 # converting to km/s
vz = np.array(plot_data['relative_velocity_z']) / 1e5 # converting to km/s
vlos = np.array(plot_data['velocity_los']) / 1e5
dl = np.array(plot_data['dl'])
# O VI and H I column densities
oden = np.array(plot_data['O_p5_number_density'])
ocol = dl * np.array(oden)
sicol = dl * np.array(plot_data['Si_p2_number_density'])
hcol = dl * np.array(plot_data['H_p0_number_density'])
return l, temperature, density, metallicity, vlos, ocol, sicol
plt.savefig('Coal_cumulative2.png')
# plt.figure()
# plt.loglog(Pbins.in_units('day'),Coalbins.in_units('yr'),ls='-',color='k')
# plt.xlabel(r'$\mathrm{Orbital\,Period\,(days)}$')
# plt.ylabel(r'$\mathrm{coalescence\,time\,(years)}$')
#
# plt.figure()
# plt.loglog(Abins.in_units('AU'),Coalbins.in_units('yr'),ls='-',color='k')
# plt.xlabel(r'$\mathrm{Semimajor\,axis\,(AU)}$')
# plt.ylabel(r'$\mathrm{coalescence\,time\,(years)}$')
plt.figure()
ax = plt.gca()
plt.loglog(Pbins.in_units('day'),Coalbins.in_units('yr'),ls='-',color='k')
plt.xlabel(r'$\mathrm{Orbital\,Period\,(days)}$')
plt.ylabel(r'$\mathrm{coalescence\,time\,(years)}$')
ax.set_xlim(Pbins.in_units('day').min(),Pbins.in_units('day').max())
# ax.set_ylim(Coalbins.in_units('yr').min(),Coalbins.in_units('yr').max())
ax.set_ylim(1e9,1e12)
xtickloc = [2,3,4,5,8,10,20]
ax.set_xticks(xtickloc)
ax.set_xticklabels(xtickloc)
ax2 = plt.twiny()
ax2.set_xscale('log')
ax2.set_xlim(ax.get_xlim())
tickloc = Pbins[::len(Pbins)/5]
def plot_vel(filament,ds,dataset,fil=-1,maskarray=False):
#Routine to plot velocity of particles along a filament
#Set gravitational constant
G = YTQuantity(6.67408E-11,'m**3/(kg * s**2)')
#Gather velocity and density values from disk, done in parallel to speed computation
#We first gather a list of the profiles on the disk, then reshape this into a list of [density,other] profiles
#This is then iterated over in parallel to load the correct data
filelist = sorted(os.listdir(''.join(['/shome/mackie/data/',dataset,'/profiles'])))
profnumbers = len(filelist)/2
files = [ [filelist[i],filelist[i+profnumbers]] for i in range(profnumbers)]
del filelist,profnumbers
storage = {}
for stor,file_in_dir in ytpar.parallel_objects(files,storage=storage):
#Determines correct index to give to segment profiles
filnum = int(file_in_dir[0][7:10])
segnum = int(file_in_dir[0][13:16])
#Calc total density for each segment
densprof = yt.load(''.join(['/shome/mackie/data/',dataset,'/profiles/',file_in_dir[0]]))
dm = densprof.data['dark_matter_density'].in_units('g/cm**3')
dens = densprof.data['density'].in_units('g/cm**3')
totaldens = dm + dens
del densprof,dm,dens
#Get velocity profiles
velprof = yt.load(''.join(['/shome/mackie/data/',dataset,'/profiles/',file_in_dir[1]]))
vel = velprof.data['cylindrical_radial_velocity'].in_units('km/s')
stor.result = (vel,totaldens)
stor.result_id = (filnum,segnum)
#Restruct dict into np array of correct structure.
vel_profs = [ [] for i in range(len(filament))]
densprofs = [ [] for i in range(len(filament))]
x = yt.load(''.join(['/shome/mackie/data/',dataset,'/profiles/',file_in_dir[0]])).data['x']
xarr = [[] for i in range(len(filament))]
for key,values in sorted(storage.items()):
filnum,segnum = key
vel,dens = values
xarr[filnum].append(x.in_units('Mpc'))
vel_profs[filnum].append(vel.in_units('km/s'))
densprofs[filnum].append(dens)
for i in range(len(xarr)):
xarr[i] = YTArray(np.array(xarr[i]),'Mpc')
vel_profs[i] = YTArray(np.array(vel_profs[i]),'km/s')
vel_profs = YTArray(np.array(vel_profs),'km/s')
xarr = YTArray(np.array(xarr),'Mpc')
del storage
#Turn into np arrays for QoL
densprofs = np.array(densprofs)
#Gather x bins from disk
#Determine masses and thus escape velocities
mass = [get_masses(densprofs[i],x,filament[i],ds,accumulate=True) for i in range(len(filament))]
mass = np.array(mass)
mass = YTArray(mass,'g')
print mass[1][1]
del densprofs
print xarr[1][1]
vel_ratio = ( ( (2*G* mass) / xarr) ** (1.0/2.0))
vel_ratio = vel_ratio.in_units('km/s')
if yt.is_root():
print mass[1][1]
print xarr[1][1]
print vel_ratio[1][1]
#vel_ratio is **approx** escape vel, used to ratio later
#Generate ratio of velocity to escape velocity
vel_profs = (vel_profs.in_units('km/s')/vel_ratio.in_units('km/s'))
del vel_ratio
if fil > -1:
length_plot = plot_vel_fil(vel_profs[fil].v,gen_dists(filament[fil],ds),x)
else:
length_plot = None
if maskarray:
print"Masking Data"
vel_profs = np.ma.masked(vel_profs, mask = ~maskarray,fill_value=np.nan)
#Flatten vel profs, ought to bemore elegant solution
vel_prof_flatten = []
for fil in vel_profs:
for seg in fil:
vel_prof_flatten.append(seg)
vel_profs = np.array(vel_prof_flatten)
del vel_prof_flatten
plot = probmap.prob_heat_map(vel_profs,'radial_velocity',x=x)
return plot,length_plot
