BUG: RAMSES star_age and particle_birth_time are swapped

[lconaboy]

Bug report

Bug summary

Looking at the yt docs (https://yt-project.org/doc/examining/loading_data.html#ramses-data), ('star', particle_birth_time') should give me the formation time relative to the big bang and ('star', 'star_age') should give me the number of years a star particle's been around. Instead, it seems the other way around -- I'm getting formation times of 0 Myr and ages equal to the output's current time (see example below)., where I'd expect the smallest birth time to be ~171 Myr and the smallest age to be ~0 Myr.

The ages from ramses/utils/f90/getstarlist.f90 match particle_birth_time as currently calculated in yt, mostly to within +/- 1 Myr, with some tail. Indeed, the return (tsim - t) * t_scale line in convert_ramses_ages is how ages are calculated in getstarlist.f90 (see line 336 in https://bitbucket.org/rteyssie/ramses/src/master/utils/f90/getstarlist.f90).

So, in yt particle_birth_time = formation_time = time_simu - t should actually be star_age , and the function star_age returns data.ds.current_time - formation_time = time_tot + time_simu - time_simu + t = time_tot + t = particle_birth_time. These time_simu and time_tot parameters are calculated from the friedman function from ramses, which is reproduced in yt. t is the birth time of the star. I think the fix then is something like: convert_rames_ages in frontends/ramses/io.py instead of returning

(tsim - t) * t_scale

should return

data.ds.current_time - ((tsim - t) * t_scale)

or similar.

Code for reproduction
(Sorry, haven't had time to test with the yt data files, can do if you need me to)

import yt
import numpy as np
from yt.data_objects.particle_filters import add_particle_filter

print('yt version:', yt.__version__)

bbox = [[0.4, 0.4, 0.4], [0.6, 0.6, 0.6]]
ds = yt.load('./output_00031', bbox=bbox)
c = ds.arr([50., 50., 50.], 'Mpccm/h')
r = ds.arr(5., 'Mpccm/h')

# Get a sphere
s = ds.sphere(c, r)

# Print current time
print(f'ds.current_time: {ds.current_time.in_units("Myr"):.6f}')
print(f'ds.cosmology.t_from_z(ds.current_redshift): {ds.cosmology.t_from_z(ds.current_redshift).in_units("Myr"):.6f}')

# Check they all have the right family value
assert((s['star', 'particle_family'] == 2).all())

# Try native particle_birth_time from yt-determined stars
t = s['star', 'particle_birth_time'].in_units('Myr')
a = s['star', 'star_age'].in_units('Myr')
print('-- using star family')
print(f'(birth) min: {t.min():.3f}, min nonzero: {t[t>0.].min():.3f}, max: {t.max():.3f}')
print(f'(age)   min: {a.min():.3f}, min nonzero: {a[a>0.].min():.3f}, max: {a.max():.3f}')
print(f'(b+a)   allclose(a+t, ds.current_time): {np.allclose(np.array(a+t), np.array(ds.current_time.in_units("Myr")))}')


# Try explicitly filtering particles a la docs
def filtered_stars(pfilter, data):
    filter = data['io', 'conformal_birth_time'] != 0
    return filter

add_particle_filter('filtered_stars',
                    function=filtered_stars,
                    filtered_type='io',
                    requires=['conformal_birth_time'] )
ds.add_particle_filter('filtered_stars')

# Get a sphere
s = ds.sphere(c, r)

t = s['filtered_stars', 'particle_birth_time'].in_units('Myr')
a = s['filtered_stars', 'star_age'].in_units('Myr')
print('-- using particle filter')
print(f'(birth) min: {t.min():.3f}, min nonzero: {t[t>0.].min():.3f}, max: {t.max():.3f}')
print(f'(age)   min: {a.min():.3f}, min nonzero: {a[a>0.].min():.3f}, max: {a.max():.3f}')
print(f'(b+a)   allclose(a+t, ds.current_time): {np.allclose(np.array(a+t), np.array(ds.current_time.in_units("Myr")))}')

Actual outcome

yt version: 4.0.2
yt : [INFO     ] 2022-03-24 17:21:00,117 Parameters: current_time              = 4.223387956490154
yt : [INFO     ] 2022-03-24 17:21:00,118 Parameters: domain_dimensions         = [256 256 256]
yt : [INFO     ] 2022-03-24 17:21:00,119 Parameters: domain_left_edge          = [0. 0. 0.]
yt : [INFO     ] 2022-03-24 17:21:00,120 Parameters: domain_right_edge         = [1. 1. 1.]
yt : [INFO     ] 2022-03-24 17:21:00,120 Parameters: cosmological_simulation   = 1
yt : [INFO     ] 2022-03-24 17:21:00,120 Parameters: current_redshift          = 11.690083646079087
yt : [INFO     ] 2022-03-24 17:21:00,120 Parameters: omega_lambda              = 0.685999989509583
yt : [INFO     ] 2022-03-24 17:21:00,120 Parameters: omega_matter              = 0.314000010490417
yt : [INFO     ] 2022-03-24 17:21:00,120 Parameters: omega_radiation           = 0.0
yt : [INFO     ] 2022-03-24 17:21:00,121 Parameters: hubble_constant           = 0.673000030517578
yt : [INFO     ] 2022-03-24 17:22:03,834 Adding particle_type: DM
yt : [INFO     ] 2022-03-24 17:22:03,849 Adding particle_type: star
yt : [INFO     ] 2022-03-24 17:22:03,864 Adding particle_type: cloud
yt : [INFO     ] 2022-03-24 17:22:03,880 Adding particle_type: dust
yt : [INFO     ] 2022-03-24 17:22:03,896 Adding particle_type: star_tracer
yt : [INFO     ] 2022-03-24 17:22:03,912 Adding particle_type: cloud_tracer
yt : [INFO     ] 2022-03-24 17:22:03,928 Adding particle_type: dust_tracer
yt : [INFO     ] 2022-03-24 17:22:03,944 Adding particle_type: gas_tracer
ds.current_time: 381.033802 Myr
ds.cosmology.t_from_z(ds.current_redshift): 382.298844 Myr
-- using star family
(birth) min: 0.000 Myr, min nonzero: 0.002 Myr, max: 210.223 Myr
(age)   min: 170.811 Myr, min nonzero: 170.811 Myr, max: 381.034 Myr
(b+a)   allclose(a+t, ds.current_time): True
-- using particle filter
(birth) min: 0.000 Myr, min nonzero: 0.002 Myr, max: 210.223 Myr
(age)   min: 170.811 Myr, min nonzero: 170.811 Myr, max: 381.034 Myr
(b+a)   allclose(a+t, ds.current_time): True

Expected outcome
Would expect birth times >~ 171 Myr (from looking at the simulation output logs) and stellar ages from 0 (youngest) to 381 - 171 = 210 Myr (current time - oldest).

Version Information

• Operating System: Ubuntu 18.04
• Python Version: 3.8.5
• yt version: 4.0.2
• Other Libraries (if applicable): numpy

python from anaconda distribution, yt installed through pip.

[welcome]

Hi, and welcome to yt! Thanks for opening your first issue. We have an issue template that helps us to gather relevant information to help diagnosing and fixing the issue.

[matthewturk]

Thanks for opening this, @lconaboy ! @cphyc , do you think you can take this on?

[cphyc]

Hi @lconaboy , could you tell me if #3864 is indeed working as you expect?