def timeloop():
global a, a_dump, drift_fac, i_dump, kick_fac, t, Δt
# Do nothing if no dump times exist
if len(a_dumps) == 0:
return
# Get the output filename patterns
output_filenames = prepare_output_times()
# Load initial conditions
particles = load_particles(IC_file)
# The number of time steps before Δt is updated
Δt_update_freq = 10
# Initial cosmic time t, where a(t) = a_begin
a = a_begin
t = cosmic_time(a)
# The time step size should be a
# small fraction of the age of the universe.
Δt = Δt_factor*t
# Arrays containing the drift and kick factors ∫_t^(t + Δt/2)dt/a
# and ∫_t^(t + Δt/2)dt/a**2. The two elements in each variable are
# the first and second half of the factor for the entire time step.
drift_fac = zeros(2, dtype=C2np['double'])
kick_fac = zeros(2, dtype=C2np['double'])
# Scalefactor at next dump and a corresponding index
i_dump = 0
a_dump = a_dumps[i_dump]
# Possible output at a == a_begin
dump(particles, output_filenames)
# The main time loop
masterprint('Begin main time loop')
timestep = -1
while i_dump < len(a_dumps):
timestep += 1
# Print out message at beginning of each time step
masterprint(terminal.bold('\nTime step {}'.format(timestep))
+ '{:<14} {}' .format('\nScale factor:', significant_figures(a, 4,
fmt='Unicode'))
+ '{:<14} {} Gyr'.format('\nCosmic time:', significant_figures(t/units.Gyr, 4,
fmt='Unicode'))
)
# Kick (first time is only half a kick, as kick_fac[1] == 0)
do_kick_drift_integrals(0)
particles.kick(kick_fac[0] + kick_fac[1])
if dump(particles, output_filenames, 'drift'):
continue
# Update Δt every Δt_update_freq time step
if not (timestep % Δt_update_freq):
# Let the positions catch up to the momenta
particles.drift(drift_fac[0])
Δt = Δt_factor*t
# Reset the second kick factor,
# making the next operation a half kick.
kick_fac[1] = 0
continue
# Drift
do_kick_drift_integrals(1)
particles.drift(drift_fac[0] + drift_fac[1])
if dump(particles, output_filenames, 'kick'):
continue
# This file has to be run in pure Python mode! # Include the concept_dir in the searched paths and get directory of this file import sys, os sys.path.append(os.environ['concept_dir']) this_dir = os.path.dirname(os.path.realpath(__file__)) # Imports from the CO𝘕CEPT code from commons import * from snapshot import load_particles # Read in the snapshot particles = load_particles(this_dir + '/snapshot') N = particles.N posx = particles.posx posy = particles.posy posz = particles.posz # Volume and linear size of cube the volume of a sphere with radius R_tophat V = 4*π/3*R_tophat**3 L = V**(1/3) # The number of complete L*L*L cubes within the box N_cubes_lin = int(boxsize//L) N_cubes = N_cubes_lin**3 # The number of particles in each of these cubes, if the snapshot is completely homogeneous N_in_cubes_homo = N*V/boxsize**3 # Count how many particles lie within each of the L*L*L cubes
# Include the concept_dir in the searched paths and get directory of this file
import sys, os
sys.path.append(os.environ['concept_dir'])
this_dir = os.path.dirname(os.path.realpath(__file__))
# Imports from the CO𝘕CEPT code
from commons import *
from snapshot import load_particles
# Determine the number of snapshots from the outputlist file
N_snapshots = 1
# Read in data from the CO𝘕CEPT snapshots
particles_cython = []
for i in (1, 2, 4):
particles_cython.append(load_particles(this_dir + '/output/snapshot_cython_' + str(i), compare_params=False))
particles_python = []
for i in (1, 2, 4):
particles_python.append(load_particles(this_dir + '/output/snapshot_python_' + str(i), compare_params=False))
# Using the particle order of the 0'th snapshot as the standard, find the corresponding
# ID's in the snapshots and order these particles accoringly.
N = particles_python[0].N
D2 = zeros(N)
ID = zeros(N, dtype='int')
for i in range(N_snapshots):
for j in range(3):
x = particles_cython[j].posx
y = particles_cython[j].posy
z = particles_cython[j].posz
x_procs = particles_python[j].posx
import sys, os
sys.path.append(os.environ['concept_dir'])
this_dir = os.path.dirname(os.path.realpath(__file__))
# Imports from the CO𝘕CEPT code
from commons import *
from snapshot import load_particles
# Determine the number of snapshots from the outputlist file
N_snapshots = np.loadtxt(this_dir + '/outputlist').size
# Read in data from the CO𝘕CEPT snapshots
particles = []
for i in range(N_snapshots):
fname = 'snapshot_a={:.2f}'.format(np.loadtxt(this_dir + '/outputlist')[i])
particles.append([load_particles(this_dir + '/output_' + str(j) + '/' + fname, compare_params=False) for j in (1, 2, 4, 8)])
# Using the particle order of the 0'th snapshot as the standard, find the corresponding
# ID's in the snapshots and order these particles accoringly.
N = particles[0][0].N
D2 = zeros(N)
ID = zeros(N, dtype='int')
for i in range(N_snapshots):
x = particles[i][0].posx
y = particles[i][0].posy
z = particles[i][0].posz
for j in (1, 2, 3):
x_procs = particles[i][j].posx
y_procs = particles[i][j].posy
z_procs = particles[i][j].posz
for l in range(N):
