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
def do_kick_drift_integrals(index):
global a, a_dump, drift_fac, kick_fac, t, Δt
# Update the scale factor and the cosmic time. This also
# tabulates a(t), needed for the kick and drift integrals.
a_next = expand(a, t, 0.5*Δt)
t_next = t + 0.5*Δt
if a_next >= a_dump:
# Dump time reached. A smaller time step than
# 0.5*Δt is needed to hit a_dump exactly.
a_next = a_dump
t_next = cosmic_time(a_dump, a, t, t_next)
expand(a, t, t_next - t)
a = a_next
t = t_next
# Do the kick and drift integrals
# ∫_t^(t + Δt/2)dt/a and ∫_t^(t + Δt/2)dt/a**2.
kick_fac[index] = scalefactor_integral(-1)
drift_fac[index] = scalefactor_integral(-2)
# As this non-compiled code should work regardless of whether
# the main CO𝘕CEPT code is compiled or not, we need to flood
# this name space with names from commons explicitly, as
# 'from commons import *' does not import C level variables.
commons_flood()
# Initiate the cosmic time and the scale factor,
# and do the call to CLASS if enable_class is True.
initiate_time()
# Array of scale factor values at which to compute the cosmic time
N_points = 50
scale_factors = logspace(log10(a_begin), log10(1), N_points)
# Compute the cosmic time for each value of the scale factor
cosmic_times = [cosmic_time(a) for a in scale_factors]
# Dependent on the mode, save the computed cosmic times
compiled = not user_params['_pure_python']
mode = f'class={enable_class}_compiled={compiled}'
np.savetxt(f'{this_dir}/t_{mode}.dat', cosmic_times)
# If all four data files exist, plot and analyze these
data_filenames = glob(f'{this_dir}/*.dat')
if sum(bool(re.search(f'^{this_dir}/t_class=(True|False)_compiled=(True|False)\.dat$' , fname))
for fname in data_filenames) == 4:
masterprint('Analyzing {} data ...'.format(this_test))
# Load in the data
all_times = {}
for filename in data_filenames:
if re.search('class=True', filename):
# As this non-compiled code should work regardless of whether
# the main CO𝘕CEPT code is compiled or not, we need to flood
# this name space with names from commons explicitly, as
# 'from commons import *' does not import C level variables.
commons_flood()
# Initiate the cosmic time and the scale factor,
# and do the call to CLASS if enable_class_background is True.
initiate_time()
# Array of scale factor values at which to compute the cosmic time
N_points = 50
scale_factors = logspace(log10(a_begin), log10(1), N_points)
# Compute the cosmic time for each value of the scale factor
cosmic_times = [cosmic_time(a) for a in scale_factors]
# Dependent on the mode, save the computed cosmic times
compiled = not user_params['_pure_python']
mode = f'class={enable_class_background}_compiled={compiled}'
np.savetxt(f'{this_dir}/t_{mode}.dat', cosmic_times)
# If all four data files exist, plot and analyze these
data_filenames = glob(f'{this_dir}/*.dat')
if sum([bool(re.search(f'^{this_dir}/t_class=(True|False)_compiled=(True|False)\.dat$' , fname))
for fname in data_filenames]) == 4:
masterprint('Analyzing {} data ...'.format(this_test))
# Load in the data
all_times = {}
for filename in data_filenames:
if re.search('class=True', filename):
