Quick Start¶
Access stellar model grid data¶
[1]:
from isochrones.mist import MISTIsochroneGrid
grid = MISTIsochroneGrid()
print(len(grid.df))
grid.df.head() # Just the first few rows
1494453
[1]:
| eep | age | feh | mass | initial_mass | radius | density | logTeff | Teff | logg | logL | Mbol | delta_nu | nu_max | phase | dm_deep | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| log10_isochrone_age_yr | feh | EEP | ||||||||||||||||
| 5.0 | -4.0 | 35 | 35 | 5.0 | -3.978406 | 0.100000 | 0.100000 | 1.106082 | 0.104184 | 3.617011 | 4140.105252 | 3.350571 | -0.489734 | 5.964335 | 37.987066 | 299.346079 | -1.0 | 0.002885 |
| 36 | 36 | 5.0 | -3.978406 | 0.102885 | 0.102885 | 1.122675 | 0.102507 | 3.618039 | 4149.909661 | 3.347798 | -0.472691 | 5.921728 | 37.739176 | 298.570836 | -1.0 | 0.003573 | ||
| 37 | 37 | 5.0 | -3.978406 | 0.107147 | 0.107147 | 1.147702 | 0.099921 | 3.619556 | 4164.436984 | 3.343658 | -0.447471 | 5.858678 | 37.345115 | 297.180748 | -1.0 | 0.004247 | ||
| 38 | 38 | 5.0 | -3.978406 | 0.111379 | 0.111379 | 1.173015 | 0.097287 | 3.621062 | 4178.903372 | 3.339612 | -0.422498 | 5.796244 | 36.923615 | 295.526946 | -1.0 | 0.004217 | ||
| 39 | 39 | 5.0 | -3.978406 | 0.115581 | 0.115581 | 1.198615 | 0.094627 | 3.622555 | 4193.289262 | 3.335660 | -0.397776 | 5.734440 | 36.473151 | 293.589960 | -1.0 | 0.004189 |
[2]:
from isochrones.mist import MISTEvolutionTrackGrid
grid_tracks = MISTEvolutionTrackGrid()
print(len(grid_tracks.df))
grid_tracks.df.head()
3619652
[2]:
| nu_max | logg | eep | initial_mass | radius | logTeff | mass | density | Mbol | phase | feh | Teff | logL | delta_nu | interpolated | star_age | age | dt_deep | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| initial_feh | initial_mass | EEP | ||||||||||||||||||
| -4.0 | 0.1 | 1 | 143.524548 | 3.033277 | 1.0 | 0.1 | 1.593804 | 3.620834 | 0.1 | 0.034823 | 5.132871 | -1.0 | -3.978406 | 4176.707371 | -0.157148 | 21.776686 | False | 13343.289397 | 4.125263 | 0.026168 |
| 2 | 145.419039 | 3.038935 | 2.0 | 0.1 | 1.583455 | 3.620769 | 0.1 | 0.035510 | 5.147664 | -1.0 | -3.978406 | 4176.085183 | -0.163066 | 21.993078 | False | 14171.978264 | 4.151430 | 0.026121 | ||
| 3 | 147.409881 | 3.044805 | 3.0 | 0.1 | 1.572790 | 3.620702 | 0.1 | 0.036237 | 5.163015 | -1.0 | -3.978406 | 4175.435381 | -0.169206 | 22.219791 | False | 15048.910447 | 4.177505 | 0.026016 | ||
| 4 | 149.499346 | 3.050886 | 4.0 | 0.1 | 1.561817 | 3.620631 | 0.1 | 0.037006 | 5.178922 | -1.0 | -3.978406 | 4174.757681 | -0.175569 | 22.457004 | False | 15975.827275 | 4.203463 | 0.025996 | ||
| 5 | 151.703570 | 3.057203 | 5.0 | 0.1 | 1.550499 | 3.620558 | 0.1 | 0.037823 | 5.195452 | -1.0 | -3.978406 | 4174.049081 | -0.182181 | 22.706349 | False | 16962.744747 | 4.229496 | 0.025996 |
Interpolate stellar properites¶
[3]:
from isochrones import get_ichrone
mist = get_ichrone('mist')
eep = mist.get_eep(1.01, 9.76, 0.03, accurate=True)
mist.interp_value([eep, 9.76, 0.03], ['Teff', 'logg', 'radius', 'density'])
[3]:
array([5.86016011e+03, 4.36634798e+00, 1.09151255e+00, 1.09589730e+00])
[4]:
mist.interp_mag([eep, 9.76, 0.03, 200, 0.1], bands=['G', 'BP', 'RP'])
[4]:
(5860.16011294621,
4.366347981387894,
-0.005536922088842331,
array([10.99261956, 11.3150264 , 10.50313434]))
Generate synthetic properties of stars¶
[5]:
from isochrones import get_ichrone
tracks = get_ichrone('mist', tracks=True)
mass, age, feh = (1.03, 9.72, -0.11)
tracks.generate(mass, age, feh, return_dict=True) # "accurate=True" makes more accurate, but slower
[5]:
{'nu_max': 2275.6902092679834,
'logg': 4.315208279229787,
'eep': 394.24,
'initial_mass': 1.03,
'radius': 1.1692076259176427,
'logTeff': 3.785191265391399,
'mass': 1.0297274169057322,
'density': 0.9097687776092286,
'Mbol': 4.162373757546131,
'phase': 0.0,
'feh': -0.19095007384845408,
'Teff': 6100.263434973235,
'logL': 0.23105049698154745,
'delta_nu': 114.32933695055772,
'interpolated': 0.0,
'star_age': 5302578707.515498,
'age': 9.722480201790624,
'dt_deep': 0.0036558739980003118,
'J': 3.2044197352759696,
'H': 2.91756110497181,
'K': 2.890399473719951,
'G': 4.085847599912897,
'BP': 4.349405878788243,
'RP': 3.6587316339856084,
'W1': 2.8807983122840044,
'W2': 2.885550073210391,
'W3': 2.8685709557487264,
'TESS': 3.653543903981804,
'Kepler': 4.004222279916473}
[6]:
from isochrones.priors import ChabrierPrior
import numpy as np
# Simulate a 1000-star cluster at 8kpc
N = 1000
masses = ChabrierPrior().sample(N)
feh = -1.8
age = np.log10(6e9) # 6 Gyr
distance = 8000. # 8 kpc
AV = 0.15
# By default this will return a dataframe
%timeit tracks.generate(masses, age, feh, distance=distance, AV=AV)
df = tracks.generate(masses, age, feh, distance=distance, AV=AV)
The slowest run took 158.58 times longer than the fastest. This could mean that an intermediate result is being cached.
1 loop, best of 3: 9.04 ms per loop
[7]:
df = df.dropna()
print(len(df)) # about half of the original simulated stars are nans
df.head()
503
[7]:
| nu_max | logg | eep | initial_mass | radius | logTeff | mass | density | Mbol | phase | ... | H | K | G | BP | RP | W1 | W2 | W3 | TESS | Kepler | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10804.874097 | 4.914275 | 303.258462 | 0.418821 | 0.374324 | 3.631195 | 0.418811 | 11.354937 | 8.178493 | 0.0 | ... | 20.662206 | 20.501401 | 23.037457 | 23.718192 | 22.251047 | 20.363681 | 20.324516 | 20.219805 | 22.229761 | 22.950946 |
| 1 | 21841.644652 | 5.197122 | 252.271094 | 0.150592 | 0.161974 | 3.583987 | 0.150591 | 50.030150 | 10.467041 | 0.0 | ... | 22.738821 | 22.531319 | 25.488818 | 26.383334 | 24.589471 | 22.380110 | 22.316618 | 22.177299 | 24.559047 | 25.416412 |
| 7 | 2838.154305 | 4.435801 | 384.922283 | 0.849837 | 0.924219 | 3.833683 | 0.849572 | 1.517288 | 4.187702 | 0.0 | ... | 17.866850 | 17.848031 | 18.902509 | 19.108502 | 18.534769 | 17.834259 | 17.825916 | 17.803109 | 18.528245 | 18.837217 |
| 8 | 180.963558 | 3.194705 | 490.813513 | 0.968456 | 4.116643 | 3.742612 | 0.967435 | 0.019564 | 1.854663 | 2.0 | ... | 14.901613 | 14.851377 | 16.530374 | 16.884757 | 15.996079 | 14.816213 | 14.800818 | 14.770045 | 15.985842 | 16.451727 |
| 9 | 1931.725171 | 4.282014 | 416.535309 | 0.911882 | 1.142684 | 3.860309 | 0.911438 | 0.861278 | 3.460707 | 0.0 | ... | 17.332797 | 17.316050 | 18.172883 | 18.341059 | 17.864912 | 17.304300 | 17.297109 | 17.275839 | 17.857626 | 18.111831 |
5 rows × 29 columns
[8]:
import holoviews as hv
hv.extension('bokeh')
import hvplot.pandas
df['BP-RP'] = df.BP - df.RP
df.hvplot.scatter('BP-RP', 'G', hover_cols=['mass', 'radius', 'Teff', 'logg', 'eep']).options(invert_yaxis=True, width=600)
[8]:
Fit physical parameters of a star to observed data¶
[9]:
from isochrones import get_ichrone, SingleStarModel
mist = get_ichrone('mist', bands=['BP', 'RP'])
params = {'Teff': (5700, 100), 'logg': (4.5, 0.1), 'feh': (0.0, 0.15),
'BP': (10.42, 0.01), 'RP': (9.54, 0.01),
'parallax': (10, 0.5)} # mas
mod = SingleStarModel(mist, **params)
mod.fit()
INFO:root:MultiNest basename: ./chains/mist-single-
[10]:
%matplotlib inline
mod.corner_physical();
Check out the numerical sampling results:
[11]:
mod.samples.describe()
[11]:
| eep | age | feh | distance | AV | lnprob | |
|---|---|---|---|---|---|---|
| count | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 |
| mean | 337.710149 | 9.509309 | -0.020312 | 101.801691 | 0.136494 | -39.022183 |
| std | 9.624071 | 0.149170 | 0.078899 | 3.989609 | 0.069615 | 1.311924 |
| min | 304.868138 | 9.043279 | -0.300996 | 88.634174 | 0.000291 | -48.789323 |
| 25% | 330.856377 | 9.400976 | -0.070581 | 99.008511 | 0.085589 | -39.612822 |
| 50% | 339.214747 | 9.526285 | -0.022509 | 101.582447 | 0.129668 | -38.744828 |
| 75% | 345.473042 | 9.630834 | 0.033488 | 104.310959 | 0.183091 | -38.076162 |
| max | 364.435350 | 9.802944 | 0.243018 | 118.737714 | 0.466537 | -37.088602 |
And the derived parameters at those samples:
[12]:
mod.derived_samples.describe()
[12]:
| eep | age | feh | mass | initial_mass | radius | density | logTeff | Teff | logg | ... | Mbol | delta_nu | nu_max | phase | dm_deep | BP_mag | RP_mag | parallax | distance | AV | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | ... | 4643.000000 | 4643.000000 | 4643.000000 | 4643.0 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 | 4643.000000 |
| mean | 337.710149 | 9.509309 | -0.018942 | 0.958078 | 0.958169 | 0.921082 | 1.738516 | 3.755075 | 5690.649562 | 4.491137 | ... | 4.982881 | 157.087407 | 3538.322417 | 0.0 | 0.008306 | 10.420067 | 9.539072 | 9.837970 | 101.801691 | 0.136494 |
| std | 9.624071 | 0.149170 | 0.086400 | 0.030418 | 0.030410 | 0.031022 | 0.137653 | 0.005747 | 75.536605 | 0.020924 | ... | 0.107383 | 6.095095 | 177.717410 | 0.0 | 0.000654 | 0.009287 | 0.009087 | 0.382167 | 3.989609 | 0.069615 |
| min | 304.868138 | 9.043279 | -0.317669 | 0.876002 | 0.876104 | 0.828304 | 1.209516 | 3.740466 | 5501.680997 | 4.396153 | ... | 4.568552 | 131.705546 | 2801.309165 | 0.0 | 0.002593 | 10.386946 | 9.505070 | 8.421924 | 88.634174 | 0.000291 |
| 25% | 330.856377 | 9.400976 | -0.076201 | 0.936277 | 0.936370 | 0.899485 | 1.643165 | 3.750771 | 5634.112958 | 4.476522 | ... | 4.913786 | 152.933682 | 3412.744754 | 0.0 | 0.007966 | 10.413741 | 9.533035 | 9.586720 | 99.008511 | 0.085589 |
| 50% | 339.214747 | 9.526285 | -0.022622 | 0.956896 | 0.956970 | 0.919331 | 1.740170 | 3.754596 | 5683.909988 | 4.492272 | ... | 4.985717 | 157.283081 | 3545.198779 | 0.0 | 0.008247 | 10.420135 | 9.539162 | 9.844220 | 101.582447 | 0.129668 |
| 75% | 345.473042 | 9.630834 | 0.039230 | 0.979404 | 0.979464 | 0.940155 | 1.832595 | 3.758868 | 5740.000379 | 4.506173 | ... | 5.061043 | 161.294116 | 3664.317241 | 0.0 | 0.008619 | 10.426205 | 9.545132 | 10.100142 | 104.310959 | 0.183091 |
| max | 364.435350 | 9.802944 | 0.267804 | 1.083840 | 1.083927 | 1.058840 | 2.217147 | 3.782650 | 6062.846454 | 4.553723 | ... | 5.303972 | 177.056425 | 4130.359858 | 0.0 | 0.011076 | 10.453965 | 9.574724 | 11.282330 | 118.737714 | 0.466537 |
8 rows × 21 columns
Eyeball your posterior predictive with:
[13]:
mod.corner_observed();
Fit a binary star model¶
[14]:
from isochrones import BinaryStarModel
mod2 = BinaryStarModel(mist, **params)
[15]:
mod2.fit()
mod2.corner_physical();
INFO:root:MultiNest basename: ./chains/mist-binary-
[16]:
mod2.derived_samples.head()
[16]:
| eep_0 | eep_1 | age | feh | distance | AV | lnprob | eep_0 | feh_0 | mass_0 | ... | Mbol_1 | delta_nu_1 | nu_max_1 | phase_1 | dm_deep_1 | BP_mag_1 | RP_mag_1 | BP_mag | RP_mag | parallax | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 359.086356 | 249.801089 | 9.821476 | -0.073397 | 98.727349 | 0.178347 | -54.808864 | 359.086356 | -0.104045 | 0.902009 | ... | 10.367668 | 615.092182 | 16669.820345 | 0.0 | 0.006549 | 17.795078 | 15.260853 | 10.463678 | 9.547117 | 10.128906 |
| 1 | 398.209362 | 283.317080 | 10.067148 | -0.280222 | 107.778948 | 0.088950 | -54.414634 | 398.209362 | -0.372728 | 0.826538 | ... | 9.308760 | 463.937102 | 12708.865085 | 0.0 | 0.001280 | 16.173813 | 14.156854 | 10.414967 | 9.566663 | 9.278250 |
| 2 | 389.035743 | 301.852188 | 9.971050 | -0.207856 | 106.526665 | 0.166244 | -54.189376 | 389.035743 | -0.278190 | 0.869004 | ... | 9.014070 | 414.876808 | 11597.777825 | 0.0 | 0.002549 | 15.919824 | 13.882693 | 10.391647 | 9.518239 | 9.387321 |
| 3 | 397.668498 | 266.911796 | 9.915806 | -0.109190 | 120.644797 | 0.249099 | -54.100849 | 397.668498 | -0.176003 | 0.936127 | ... | 9.592630 | 486.986772 | 13422.770521 | 0.0 | 0.006254 | 17.143644 | 14.860496 | 10.432474 | 9.553840 | 8.288795 |
| 4 | 370.982797 | 256.436383 | 9.816281 | -0.205884 | 103.041704 | 0.385523 | -53.934142 | 370.982797 | -0.260589 | 0.914513 | ... | 9.905946 | 562.841780 | 15244.528561 | 0.0 | 0.007484 | 17.169701 | 14.890180 | 10.439209 | 9.511726 | 9.704808 |
5 rows × 44 columns
[17]:
mod2.corner_observed();
