Quickstart#
Welcome! You’ve found the documentation for cogsworth, good for you! This is an epic crossover episode of a package in which we bring the worlds of rapid population synthesis and galactic dynamics together and see what happens.
You should know that package relies heavily on cosmic and gala and I recommend you check out their respective documentation. As a general rule, anything that you can do with either of those packages, you can also do with cogsworth!
Okay, let’s get into this!
Basic imports#
For most of the examples that I give in this documentation you’re probably going to need the following basic imports. You can always read more about numpy and astropy.units at those respective links.
[1]:
import numpy as np
import astropy.units as u
import pandas as pd
import matplotlib.pyplot as plt
Here are some plotting settings as well to make sure everything looks good.
[2]:
# ensure jupyter actually uses your fancy retina display
%config InlineBackend.figure_format = 'retina'
# make pandas show *every* column
pd.set_option("display.max_columns", None)
# various adjustments to matplotlib settings
plt.rc('font', family='serif')
plt.rcParams['text.usetex'] = False
fs = 24
params = {'figure.figsize': (12, 8),
'legend.fontsize': fs,
'axes.labelsize': fs,
'xtick.labelsize': 0.9 * fs,
'ytick.labelsize': 0.9 * fs,
'axes.linewidth': 1.1,
'xtick.major.size': 7,
'xtick.minor.size': 4,
'ytick.major.size': 7,
'ytick.minor.size': 4}
plt.rcParams.update(params)
Create your first population#
The central class in this package is Population. The class gives you access to the functionality of the entire package with one convenient interface. Let’s start by creating a small population of binaries with the default settings.
[3]:
import cogsworth
p = cogsworth.pop.Population(2000, processes=6)
p
[3]:
<Population - 2000 systems - galactic_potential=MilkyWayPotential, SFH=Wagg2022>
Though this class is initialised, we still need to actually create the population to find its present day state. We do this by running:
[4]:
p.create_population()
Run for 2000 binaries
Ended up with 2601 binaries with m1 > 0 solar masses
[6e-02s] Sample initial binaries
[2.1s] Evolve binaries (run COSMIC)
2619it [00:15, 164.21it/s]
[22.0s] Get orbits (run gala)
Overall: 24.2s
This function has just
Inspect population#
Full evolution#
p now contains information about the stellar evolution of each constituent star of each binary, as well as the path of the binary through the galaxy. Let’s investigate some of those things.
Stellar Evolution#
First we can take a look at the cosmic bpp evolution table and see how the stars evolved over time.
[5]:
p.bpp
[5]:
| tphys | mass_1 | mass_2 | kstar_1 | kstar_2 | sep | porb | ecc | RRLO_1 | RRLO_2 | evol_type | aj_1 | aj_2 | tms_1 | tms_2 | massc_1 | massc_2 | rad_1 | rad_2 | mass0_1 | mass0_2 | lum_1 | lum_2 | teff_1 | teff_2 | radc_1 | radc_2 | menv_1 | menv_2 | renv_1 | renv_2 | omega_spin_1 | omega_spin_2 | B_1 | B_2 | bacc_1 | bacc_2 | tacc_1 | tacc_2 | epoch_1 | epoch_2 | bhspin_1 | bhspin_2 | bin_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 1.312040 | 0.916052 | 1.0 | 1.0 | 389.347168 | 596.500915 | 0.048697 | 0.008480 | 0.006298 | 1.0 | 0.000000 | 0.000000 | 4.431838e+03 | 1.686870e+04 | 0.0 | 0.0 | 1.288946 | 0.812461 | 1.312040 | 0.916052 | 2.314598 | 0.411310 | 6298.992263 | 5151.227494 | 0.0 | 0.0 | 1.000000e-10 | 0.042145 | 1.000000e-10 | 0.230041 | 2956.940137 | 369.300206 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 0 | 2676.059289 | 1.312040 | 0.916052 | 1.0 | 1.0 | 389.347168 | 596.500915 | 0.048697 | 0.010343 | 0.006498 | 10.0 | 2676.059289 | 2676.059289 | 4.431838e+03 | 1.686870e+04 | 0.0 | 0.0 | 1.572105 | 0.838188 | 1.312040 | 0.916052 | 3.259826 | 0.460600 | 6213.373532 | 5217.109481 | 0.0 | 0.0 | 6.596324e-08 | 0.042141 | 8.538053e-03 | 0.237351 | 1987.693052 | 346.978103 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 1 | 0.000000 | 0.347239 | 0.106375 | 0.0 | 0.0 | 103.297516 | 180.660729 | 0.008988 | 0.006699 | 0.004736 | 1.0 | 0.000000 | 0.000000 | 2.769280e+05 | 3.025956e+06 | 0.0 | 0.0 | 0.331618 | 0.137021 | 0.347239 | 0.106375 | 0.018370 | 0.001203 | 3706.618831 | 2916.884470 | 0.0 | 0.0 | 3.472386e-01 | 0.053188 | 3.316178e-01 | 0.089064 | 0.463615 | 0.000106 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 1 | 6537.672436 | 0.347239 | 0.106375 | 0.0 | 0.0 | 103.297516 | 180.660728 | 0.008988 | 0.006734 | 0.004736 | 10.0 | 6537.672436 | 6537.672436 | 2.769280e+05 | 3.025956e+06 | 0.0 | 0.0 | 0.333350 | 0.137021 | 0.347239 | 0.106375 | 0.018535 | 0.001204 | 3705.234013 | 2917.471334 | 0.0 | 0.0 | 3.472386e-01 | 0.053188 | 3.333502e-01 | 0.089064 | 0.458809 | 0.000106 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 |
| 2 | 0.000000 | 0.441613 | 0.305573 | 0.0 | 0.0 | 2997.784375 | 22006.872240 | 0.199693 | 0.000410 | 0.000363 | 1.0 | 0.000000 | 0.000000 | 1.597990e+05 | 3.630782e+05 | 0.0 | 0.0 | 0.404736 | 0.302325 | 0.441613 | 0.305573 | 0.030560 | 0.014580 | 3810.400047 | 3664.134184 | 0.0 | 0.0 | 2.060029e-01 | 0.305573 | 1.908210e-01 | 0.302325 | 2.495535 | 0.186921 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2598 | 6125.119762 | 1.093116 | 0.412978 | 1.0 | 0.0 | 58.579628 | 42.341477 | 0.083616 | 0.051068 | 0.024116 | 10.0 | 6125.119762 | 6125.119762 | 8.102277e+03 | 2.071070e+05 | 0.0 | 0.0 | 1.274613 | 0.386572 | 1.093116 | 0.412978 | 1.839541 | 0.024106 | 5980.780039 | 3674.401118 | 0.0 | 0.0 | 2.167168e-02 | 0.244272 | 3.075838e-01 | 0.214464 | 690.631279 | 1.573507 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2598 |
| 2599 | 0.000000 | 0.500954 | 0.245336 | 0.0 | 0.0 | 123.025609 | 183.067636 | 0.505861 | 0.017062 | 0.013433 | 1.0 | 0.000000 | 0.000000 | 1.169159e+05 | 5.759382e+05 | 0.0 | 0.0 | 0.458292 | 0.260597 | 0.500954 | 0.245336 | 0.042761 | 0.009594 | 3894.587791 | 3554.561531 | 0.0 | 0.0 | 1.494033e-01 | 0.245336 | 1.807503e-01 | 0.260597 | 5.914073 | 0.038864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2599 |
| 2599 | 10359.501366 | 0.500954 | 0.245336 | 0.0 | 0.0 | 123.025603 | 183.067621 | 0.505861 | 0.017394 | 0.013486 | 10.0 | 10359.501366 | 10359.501366 | 1.169159e+05 | 5.759382e+05 | 0.0 | 0.0 | 0.467211 | 0.261616 | 0.500954 | 0.245336 | 0.044360 | 0.009653 | 3892.794291 | 3553.062517 | 0.0 | 0.0 | 1.494033e-01 | 0.245336 | 1.842683e-01 | 0.261616 | 5.693593 | 0.042188 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2599 |
| 2600 | 0.000000 | 0.164596 | 0.150179 | 0.0 | 0.0 | 8.087533 | 4.751127 | 0.587051 | 0.156048 | 0.151860 | 1.0 | 0.000000 | 0.000000 | 1.363662e+06 | 1.638005e+06 | 0.0 | 0.0 | 0.201627 | 0.188167 | 0.164596 | 0.150179 | 0.003536 | 0.002837 | 3148.632794 | 3084.596718 | 0.0 | 0.0 | 8.229777e-02 | 0.075089 | 1.310577e-01 | 0.122309 | 0.002206 | 0.001153 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2600 |
| 2600 | 5515.220958 | 0.164596 | 0.150179 | 0.0 | 0.0 | 8.079928 | 4.744427 | 0.586676 | 0.156172 | 0.151961 | 10.0 | 5515.220958 | 5515.220958 | 1.363662e+06 | 1.638005e+06 | 0.0 | 0.0 | 0.201780 | 0.188286 | 0.164596 | 0.150179 | 0.003541 | 0.002840 | 3148.493586 | 3084.483652 | 0.0 | 0.0 | 8.229777e-02 | 0.075089 | 1.311572e-01 | 0.122386 | 85.968852 | 82.285239 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2600 |
8702 rows × 44 columns
There are of course many things that you can do with this table! But let’s say, maybe you want get any binaries that experience mass transfer
[6]:
# get any binaries that have an evol_type indicating Roche Lobe overflow started
mt_bin_nums = p.bpp[p.bpp["evol_type"] == 3.0]["bin_num"].unique()
Let’s take a look at the evolution of the first binary in this list
[7]:
p.bpp[p.bpp["bin_num"] == mt_bin_nums[0]]
[7]:
| tphys | mass_1 | mass_2 | kstar_1 | kstar_2 | sep | porb | ecc | RRLO_1 | RRLO_2 | evol_type | aj_1 | aj_2 | tms_1 | tms_2 | massc_1 | massc_2 | rad_1 | rad_2 | mass0_1 | mass0_2 | lum_1 | lum_2 | teff_1 | teff_2 | radc_1 | radc_2 | menv_1 | menv_2 | renv_1 | renv_2 | omega_spin_1 | omega_spin_2 | B_1 | B_2 | bacc_1 | bacc_2 | tacc_1 | tacc_2 | epoch_1 | epoch_2 | bhspin_1 | bhspin_2 | bin_num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 0.000000 | 1.104100 | 1.012107 | 1.0 | 1.0 | 213.830561 | 249.113765 | 0.148956 | 0.014566 | 0.013565 | 1.0 | 0.000000 | 0.000000 | 6.223421e+03 | 8.517492e+03 | 0.000000 | 0.000000 | 1.024446 | 0.916834 | 1.104100 | 1.012107 | 1.544484 | 1.028177 | 6385.883991 | 6097.352128 | 0.000000 | 0.000000 | 2.060427e-02 | 3.151838e-02 | 2.324936e-01 | 2.362826e-01 | 1186.661852 | 698.412380 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 3 |
| 3 | 6223.421651 | 1.104100 | 1.012107 | 2.0 | 1.0 | 213.830558 | 249.113760 | 0.148956 | 0.029185 | 0.017194 | 2.0 | 6223.421651 | 6223.421651 | 6.223421e+03 | 8.517492e+03 | 0.129662 | 0.000000 | 2.052605 | 1.162116 | 1.104100 | 1.012107 | 4.354075 | 1.883020 | 5845.759002 | 6300.260851 | 0.125672 | 0.000000 | 1.574637e-01 | 4.019846e-02 | 9.443739e-01 | 3.664868e-01 | 334.458372 | 434.694313 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 3 |
| 3 | 6464.269263 | 1.103697 | 1.012108 | 3.0 | 1.0 | 213.871059 | 249.208232 | 0.148956 | 0.032436 | 0.017430 | 2.0 | 6472.435241 | 6464.260537 | 6.231431e+03 | 8.517480e+03 | 0.164622 | 0.000000 | 2.281499 | 1.178385 | 1.103697 | 1.012108 | 4.177217 | 1.941489 | 5487.584724 | 6304.633316 | 0.115141 | 0.000000 | 4.695386e-01 | 4.122311e-02 | 1.408134e+00 | 3.805931e-01 | 209.992070 | 422.777566 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -8.165978 | 0.008726 | 0.0 | 0.0 | 3 |
| 3 | 6958.269115 | 1.020508 | 1.021260 | 3.0 | 1.0 | 204.192269 | 236.661699 | 0.000000 | 1.000025 | 0.015981 | 3.0 | 6966.435092 | 6731.607201 | 6.231431e+03 | 8.239905e+03 | 0.448510 | 0.000000 | 77.361543 | 1.236691 | 1.103697 | 1.021260 | 1708.429889 | 2.150557 | 4237.954997 | 6313.594537 | 0.075342 | 0.000000 | 5.719982e-01 | 4.287875e-02 | 7.728620e+01 | 4.196191e-01 | 9.696840 | 9.696840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -8.165978 | 226.661914 | 0.0 | 0.0 | 3 |
| 3 | 6958.269115 | 1.020508 | 1.021260 | 3.0 | 1.0 | 204.192269 | 236.661699 | 0.000000 | 1.000025 | 0.015981 | 7.0 | 6966.435092 | 6731.607201 | 6.231431e+03 | 8.239905e+03 | 0.448510 | 0.000000 | 77.361543 | 1.236691 | 1.103697 | 1.021260 | 1708.429889 | 2.150557 | 4237.954997 | 6313.594537 | 0.075342 | 0.000000 | 5.719982e-01 | 4.287875e-02 | 7.728620e+01 | 4.196191e-01 | 9.696840 | 9.696840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -8.165978 | 226.661914 | 0.0 | 0.0 | 3 |
| 3 | 6958.269115 | 0.448510 | 1.021260 | 10.0 | 1.0 | 23.366916 | 10.798159 | 0.000000 | 1.000025 | 0.015981 | 8.0 | 0.000000 | 6731.607201 | 6.231431e+03 | 8.239905e+03 | 0.448510 | 0.000000 | 77.361543 | 1.236691 | 0.448510 | 1.021260 | 1708.429889 | 2.150557 | 4237.954997 | 6313.594537 | 0.075342 | 0.000000 | 5.719982e-01 | 4.287875e-02 | 7.728620e+01 | 4.196191e-01 | 9.696840 | 9.696840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -8.165978 | 226.661914 | 0.0 | 0.0 | 3 |
| 3 | 6958.269115 | 0.448510 | 1.021260 | 10.0 | 1.0 | 23.366916 | 10.798159 | 0.000000 | 0.002077 | 0.117206 | 4.0 | 0.000000 | 6731.607201 | 1.000000e+10 | 8.239905e+03 | 0.448510 | 0.000000 | 0.015068 | 1.236691 | 0.448510 | 1.021260 | 51.831542 | 2.150557 | 126731.845782 | 6313.594537 | 0.015068 | 0.000000 | 1.000000e-10 | 4.287875e-02 | 1.000000e-10 | 4.196191e-01 | 9.696840 | 9.696840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 226.661914 | 0.0 | 0.0 | 3 |
| 3 | 8466.567314 | 0.448509 | 1.021260 | 10.0 | 2.0 | 23.294156 | 10.747763 | 0.000000 | 0.002084 | 0.179402 | 2.0 | 1508.298199 | 8239.905400 | 1.000000e+10 | 8.239905e+03 | 0.448509 | 0.121199 | 0.015068 | 1.887062 | 0.448510 | 1.021260 | 0.000608 | 3.296585 | 7416.918859 | 5687.123570 | 0.015068 | 0.128764 | 1.000000e-10 | 2.098883e-01 | 1.000000e-10 | 9.444792e-01 | 9.696838 | 238.284783 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 226.661914 | 0.0 | 0.0 | 3 |
| 3 | 8785.489392 | 0.448514 | 1.020829 | 10.0 | 3.0 | 23.300466 | 10.753690 | 0.000000 | 0.002083 | 0.203961 | 2.0 | 1827.220278 | 8571.854093 | 1.000000e+10 | 8.252677e+03 | 0.448514 | 0.160205 | 0.015068 | 2.145783 | 0.448510 | 1.020829 | 0.000485 | 3.548319 | 7008.941228 | 5432.282077 | 0.015068 | 0.116311 | 1.000000e-10 | 4.303124e-01 | 1.000000e-10 | 1.319157e+00 | 119.930378 | 144.010001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 213.635300 | 0.0 | 0.0 | 3 |
| 3 | 9301.463232 | 0.448766 | 1.014692 | 10.0 | 3.0 | 21.238223 | 9.376902 | 0.000000 | 0.002281 | 1.000315 | 3.0 | 2343.194117 | 9087.827932 | 1.000000e+10 | 8.252677e+03 | 0.448766 | 0.256860 | 0.015064 | 9.579990 | 0.448510 | 1.020829 | 0.000362 | 60.276522 | 6514.971685 | 5219.442698 | 0.015064 | 0.096882 | 1.000000e-10 | 7.578313e-01 | 1.000000e-10 | 9.483107e+00 | 244.736557 | 244.736557 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 213.635300 | 0.0 | 0.0 | 3 |
| 3 | 9301.463232 | 0.448766 | 1.014692 | 10.0 | 3.0 | 21.238223 | 9.376902 | 0.000000 | 0.002281 | 1.000315 | 7.0 | 2343.194117 | 9087.827932 | 1.000000e+10 | 8.252677e+03 | 0.448766 | 0.256860 | 0.015064 | 9.579990 | 0.448510 | 1.020829 | 0.000362 | 60.276522 | 6514.971685 | 5219.442698 | 0.015064 | 0.096882 | 1.000000e-10 | 7.578313e-01 | 1.000000e-10 | 9.483107e+00 | 244.736557 | 244.736557 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 213.635300 | 0.0 | 0.0 | 3 |
| 3 | 9301.463232 | 0.448766 | 0.256860 | 10.0 | 10.0 | 0.858908 | 0.109826 | 0.000000 | 0.002281 | 1.000315 | 8.0 | 2343.194117 | 0.000000 | 1.000000e+10 | 8.252677e+03 | 0.448766 | 0.256860 | 0.015064 | 9.579990 | 0.448510 | 0.256860 | 0.000362 | 60.276522 | 6514.971685 | 5219.442698 | 0.015064 | 0.096882 | 1.000000e-10 | 7.578313e-01 | 1.000000e-10 | 9.483107e+00 | 244.736557 | 244.736557 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 213.635300 | 0.0 | 0.0 | 3 |
| 3 | 9301.463232 | 0.448766 | 0.256860 | 10.0 | 10.0 | 0.858908 | 0.109826 | 0.000000 | 0.040980 | 0.067994 | 4.0 | 2343.194117 | 0.000000 | 1.000000e+10 | 1.000000e+10 | 0.448766 | 0.256860 | 0.015064 | 0.019376 | 0.448510 | 0.256860 | 0.000362 | 29.683786 | 6514.971685 | 97221.385902 | 0.015064 | 0.019376 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 244.736557 | 244.736557 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 9301.463232 | 0.0 | 0.0 | 3 |
| 3 | 9468.684212 | 0.448766 | 0.256860 | 10.0 | 10.0 | 0.821833 | 0.102792 | 0.000000 | 0.042828 | 0.071061 | 10.0 | 2510.415098 | 167.220980 | 1.000000e+10 | 1.000000e+10 | 0.448766 | 0.256860 | 0.015064 | 0.019376 | 0.448510 | 0.256860 | 0.000334 | 0.004664 | 6383.831312 | 10884.647945 | 0.015064 | 0.019376 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 244.736783 | 21804.799668 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6958.269115 | 9301.463232 | 0.0 | 0.0 | 3 |
We can also put this to use. Let’s see how the initial orbital periods are different for binaries that experience mass transfer.
[8]:
experienced_mt = p.initC["bin_num"].isin(mt_bin_nums)
plt.hist(np.log10(p.initC["porb"][experienced_mt]), bins="fd", density=True, label="Mass transfer")
plt.hist(np.log10(p.initC["porb"][~experienced_mt]), bins="fd", density=True, label="No mass transfer", alpha=0.8)
plt.legend()
plt.xlabel("Log Initial Orbital Period [days]");
As we can see in the plot above, systems that experience mass transfer are biased towards shorter orbital periods. This is expected as these systems are closer and therefore it is easier to initiate mass transfer.
Orbits#
We also have access to the full orbit of each binary in the population in p.orbits. I’m going to cheat and pick a fairly simple one to show from this random population (a binary that didn’t disrupt and is pretty close to us at present day).
[10]:
fig, ax = plt.subplots()
final_rho = np.sum(p.final_pos[:, :2]**2, axis=1)**(0.5)
nice_orbits = p.orbits[(final_rho > 4 * u.kpc) & (final_rho < 5 * u.kpc)]
nice_orbit = np.random.choice(nice_orbits)
nice_orbit.cylindrical.plot(["rho", "z"], axes=ax)
plt.show()
If this part interests you then I recommend that you go and check out the excellent gala docs about orbits and what you can do with them.
Present Day#
In many cases, you may not really care what happened over the history of the galaxy (way to let the past go!). In these cases you can instead only focus on the present day state of each binary.
For example, we could extract any binary that ended up disrupted
[11]:
p.final_bpp[p.disrupted]
[11]:
| tphys | mass_1 | mass_2 | kstar_1 | kstar_2 | sep | porb | ecc | RRLO_1 | RRLO_2 | evol_type | aj_1 | aj_2 | tms_1 | tms_2 | massc_1 | massc_2 | rad_1 | rad_2 | mass0_1 | mass0_2 | lum_1 | lum_2 | teff_1 | teff_2 | radc_1 | radc_2 | menv_1 | menv_2 | renv_1 | renv_2 | omega_spin_1 | omega_spin_2 | B_1 | B_2 | bacc_1 | bacc_2 | tacc_1 | tacc_2 | epoch_1 | epoch_2 | bhspin_1 | bhspin_2 | bin_num | metallicity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14 | 9271.793239 | 1.277584 | 1.045460 | 13.0 | 11.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 9233.432094 | 9170.038423 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 1.045460 | 0.000014 | 0.007542 | 8.290455 | 1.045460 | 2.764296e-10 | 4.142444e-05 | 6318.321503 | 5355.823136 | 0.000014 | 0.007542 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.967924e+07 | 8.006508e-06 | 6.550953e+10 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 38.361144 | 101.754816 | 0.0 | 0.0 | 14 | 0.014156 |
| 114 | 6918.544648 | 1.277584 | 2.186881 | 13.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 6906.134976 | 6899.532570 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 2.186881 | 0.000014 | 0.000014 | 3.335919 | 6.294155 | 4.941294e-10 | 7.096979e-10 | 7305.766198 | 7997.863240 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 6.237181e+05 | 1.526617e+06 | 4.270392e+12 | 1.745880e+12 | 0.0 | 0.0 | 0.0 | 0.0 | 12.409672 | 19.012079 | 0.0 | 0.0 | 114 | 0.022085 |
| 227 | 4624.571393 | 1.277584 | 1.277584 | 13.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 4603.658740 | 4602.126355 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 1.277584 | 0.000014 | 0.000014 | 10.754057 | 10.382978 | 1.111999e-09 | 1.112740e-09 | 8948.118757 | 8949.608375 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 9.784041e+06 | 1.633628e+07 | 5.786279e+11 | 3.465800e+11 | 0.0 | 0.0 | 0.0 | 0.0 | 20.912653 | 22.445039 | 0.0 | 0.0 | 227 | 0.025619 |
| 255 | 6990.020668 | 1.277584 | 3.265258 | 13.0 | 14.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 6976.816004 | 6973.751657 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 3.265258 | 0.000014 | 0.000014 | 3.447006 | 7.980605 | 4.841682e-10 | 1.000000e-10 | 7268.665207 | 4927.514433 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 2.275363e+07 | 2.000000e+08 | 1.128877e+11 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 13.204664 | 16.269011 | 0.0 | 0.0 | 255 | 0.016138 |
| 516 | 9242.457226 | 1.673253 | 6.759477 | 13.0 | 14.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 9231.447162 | 9228.204198 | 1.000000e+10 | 1.000000e+10 | 1.673253 | 6.759477 | 0.000014 | 0.000029 | 4.937222 | 10.850028 | 3.313422e-10 | 1.000000e-10 | 6611.115886 | 3424.760024 | 0.000014 | 0.000029 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.224536e+07 | 2.000000e+08 | 9.292913e+10 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 11.010064 | 14.253027 | 0.0 | 0.0 | 516 | 0.008719 |
| 537 | 2534.761692 | 1.628164 | 4.151160 | 13.0 | 14.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 2524.524013 | 2516.576799 | 1.000000e+10 | 1.000000e+10 | 1.628164 | 4.151160 | 0.000014 | 0.000018 | 4.791675 | 20.157112 | 4.350195e-09 | 1.000000e-10 | 12584.407266 | 4370.206338 | 0.000014 | 0.000018 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.399221e+07 | 2.000000e+08 | 9.504924e+11 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 10.237679 | 18.184892 | 0.0 | 0.0 | 537 | 0.011143 |
| 539 | 2945.427457 | 1.242000 | 1.242000 | 13.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 2898.312481 | 2875.288776 | 1.000000e+10 | 1.000000e+10 | 1.242000 | 1.242000 | 0.000014 | 0.000014 | 7.635984 | 1.409163 | 2.752967e-09 | 2.797232e-09 | 11224.211866 | 11269.060871 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 2.045095e+07 | 2.097634e+06 | 5.400704e+11 | 5.283211e+12 | 0.0 | 0.0 | 0.0 | 0.0 | 47.114977 | 70.138682 | 0.0 | 0.0 | 539 | 0.007549 |
| 763 | 10912.362349 | 1.242000 | 1.210080 | 13.0 | 12.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 10802.590686 | 10802.417098 | 1.000000e+10 | 1.000000e+10 | 1.242000 | 1.210080 | 0.000014 | 0.004817 | 1.327798 | 1.210080 | 1.981690e-10 | 8.091102e-06 | 5813.859053 | 4455.562762 | 0.000014 | 0.004817 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 7.947976e+05 | 1.696254e-05 | 8.422263e+11 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 109.771663 | 109.945251 | 0.0 | 0.0 | 763 | 0.002353 |
| 877 | 2608.091664 | 1.277584 | 2.256181 | 13.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 2589.615674 | 2580.774104 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 2.256181 | 0.000014 | 0.000014 | 2.993657 | 16.400125 | 3.514311e-09 | 5.179533e-09 | 11930.692247 | 13145.534315 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 2.465635e+06 | 7.432766e+06 | 5.061400e+12 | 1.680862e+12 | 0.0 | 0.0 | 0.0 | 0.0 | 18.475991 | 27.317560 | 0.0 | 0.0 | 877 | 0.010151 |
| 988 | 7368.810850 | 1.797485 | 1.277584 | 13.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 7355.670660 | 7346.337629 | 1.000000e+10 | 1.000000e+10 | 1.797485 | 1.277584 | 0.000014 | 0.000014 | 15.228894 | 11.249244 | 5.475341e-10 | 4.366858e-10 | 7495.634143 | 7083.499353 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.029400e+06 | 3.137554e+06 | 2.198962e+12 | 7.221963e+11 | 0.0 | 0.0 | 0.0 | 0.0 | 13.140190 | 22.473221 | 0.0 | 0.0 | 988 | 0.015343 |
| 1202 | 11020.842523 | 1.338653 | 2.174984 | 13.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 10999.401887 | 10989.225379 | 1.000000e+10 | 1.000000e+10 | 1.338653 | 2.174984 | 0.000014 | 0.000014 | 3.875944 | 15.548451 | 2.009831e-10 | 2.787349e-10 | 5834.389948 | 6331.453611 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 5.355453e+05 | 1.483339e+07 | 1.307672e+12 | 4.730052e+10 | 0.0 | 0.0 | 0.0 | 0.0 | 21.440636 | 31.617144 | 0.0 | 0.0 | 1202 | 0.004518 |
| 1213 | 8566.608072 | 1.277584 | 0.816890 | 13.0 | 11.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 8546.469239 | 8285.251177 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 0.816890 | 0.000014 | 0.010117 | 3.487948 | 0.816890 | 3.226542e-10 | 4.251490e-05 | 6567.346546 | 4654.504036 | 0.000014 | 0.010117 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.863971e+06 | 5.694967e-06 | 7.759394e+11 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 20.138834 | 281.356895 | 0.0 | 0.0 | 1213 | 0.016192 |
| 1503 | 4886.691967 | 8.716199 | 1.418384 | 14.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 4881.118867 | 4868.512854 | 1.000000e+10 | 1.000000e+10 | 8.716199 | 1.418384 | 0.000037 | 0.000014 | 12.066534 | 12.893160 | 1.000000e-10 | 1.066447e-09 | 3015.942886 | 8855.037185 | 0.000037 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 2.000000e+08 | 3.920919e+06 | 0.000000e+00 | 1.424520e+12 | 0.0 | 0.0 | 0.0 | 0.0 | 5.573100 | 18.179113 | 0.0 | 0.0 | 1503 | 0.009089 |
| 1617 | 10738.577502 | 1.277584 | 0.858966 | 13.0 | 11.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 10699.230749 | 10238.690282 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 0.858966 | 0.000014 | 0.009639 | 8.352511 | 0.858966 | 2.058760e-10 | 1.148951e-05 | 5869.579965 | 3438.109919 | 0.000014 | 0.009639 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 7.106228e+06 | 5.966283e-06 | 9.988880e+10 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 39.346753 | 499.887220 | 0.0 | 0.0 | 1617 | 0.005590 |
| 1749 | 10712.374183 | 1.242000 | 1.242000 | 13.0 | 13.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 10661.142677 | 10658.503334 | 1.000000e+10 | 1.000000e+10 | 1.242000 | 1.242000 | 0.000014 | 0.000014 | 7.336366 | 1.418485 | 2.034623e-10 | 2.035631e-10 | 5852.300009 | 5853.024561 | 0.000014 | 0.000014 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 6.020050e+07 | 4.994469e+06 | 1.167669e+10 | 1.405403e+11 | 0.0 | 0.0 | 0.0 | 0.0 | 51.231505 | 53.870849 | 0.0 | 0.0 | 1749 | 0.005076 |
| 1887 | 7045.164959 | 1.277584 | 0.937066 | 13.0 | 11.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 7001.867311 | 6853.980378 | 1.000000e+10 | 1.000000e+10 | 1.277584 | 0.937066 | 0.000014 | 0.008764 | 7.942539 | 0.937066 | 4.807099e-10 | 4.032334e-05 | 7255.650627 | 4935.342188 | 0.000014 | 0.008764 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 5.794463e+07 | 6.616747e-06 | 4.396243e+10 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 43.297648 | 191.184581 | 0.0 | 0.0 | 1887 | 0.005753 |
| 2528 | 9475.960797 | 1.295946 | 4.583379 | 13.0 | 14.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 9462.541626 | 9458.335874 | 1.000000e+10 | 1.000000e+10 | 1.295946 | 4.583379 | 0.000014 | 0.000019 | 3.743613 | 23.129337 | 2.657343e-10 | 1.000000e-10 | 6256.298934 | 4159.046693 | 0.000014 | 0.000019 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.535968e+07 | 2.000000e+08 | 7.384291e+10 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 13.419171 | 17.624923 | 0.0 | 0.0 | 2528 | 0.010446 |
| 2588 | 10111.421499 | 1.430918 | 7.039752 | 13.0 | 14.0 | -1.0 | -1.0 | -1.0 | 0.0001 | 0.0001 | 10.0 | 10097.104502 | 10092.406471 | 1.000000e+10 | 1.000000e+10 | 1.430918 | 7.039752 | 0.000014 | 0.000030 | 4.164249 | 22.633148 | 2.494010e-10 | 1.000000e-10 | 6157.864477 | 3355.892117 | 0.000014 | 0.000030 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 1.000000e-10 | 2.474748e+06 | 2.000000e+08 | 3.465346e+11 | 0.000000e+00 | 0.0 | 0.0 | 0.0 | 0.0 | 14.316997 | 19.015028 | 0.0 | 0.0 | 2588 | 0.007267 |
We can also look at where each binary ended up, and colour the disrupted binaries differently.
[13]:
final_coords = p.get_final_mw_skycoord()
[28]:
fig, ax = plt.subplots()
ax.scatter(final_coords[:len(p)][~p.disrupted].icrs.ra, final_coords[:len(p)][~p.disrupted].icrs.dec, s=0.5, c="yellow", label="Bound")
ax.scatter(final_coords[:len(p)][p.disrupted].icrs.ra, final_coords[:len(p)][p.disrupted].icrs.dec, s=15, c="C1")
ax.scatter(final_coords[len(p):].icrs.ra, final_coords[len(p):].icrs.dec, s=15, c="C1", label="Disrupted")
ax.set(xlabel="Right Ascension [deg]", ylabel="Declination [deg]", facecolor="black")
ax.legend(markerscale=6, handletextpad=.0, framealpha=0.5)
plt.show()
And we see that often (but not always) they are found off the galactic plane (due to the same supernova kick that disrupted binary)
Classify population#
The Population can also classify the present-day state of each binary. You can autogenerate these classes by accessing p.classes
[29]:
p.classes
[29]:
| dco | co-1 | co-2 | xrb | walkaway-t-1 | walkaway-t-2 | runaway-t-1 | runaway-t-2 | walkaway-o-1 | walkaway-o-2 | runaway-o-1 | runaway-o-2 | widow-1 | widow-2 | stellar-merger-co-1 | stellar-merger-co-2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 1 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 2 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 3 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 4 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2596 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 2597 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 2598 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 2599 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
| 2600 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False |
2601 rows × 16 columns
This table has a flag for each class and for each binary. We can do some pandas DataFrame manipulation to get some statistics for the different classes
[30]:
p.classes.astype(int).sum()
[30]:
dco 2
co-1 20
co-2 17
xrb 0
walkaway-t-1 0
walkaway-t-2 0
runaway-t-1 0
runaway-t-2 0
walkaway-o-1 0
walkaway-o-2 0
runaway-o-1 0
runaway-o-2 0
widow-1 0
widow-2 0
stellar-merger-co-1 7
stellar-merger-co-2 14
dtype: int64
Inspect specific binaries#
cogsworth has simple plotting routines to understand the evolution of binaries in your population. Here’s an example for a binary that experiences a disruption.
These plots show the galactic orbit of the binary (indicating supernova events and different orbits for the primary and secondary after the disruption) and a cartoon timeline of its evolution.
[46]:
random_num = np.random.choice(p.bin_nums[p.disrupted])
p.plot_orbit(random_num, t_max=50 * u.Myr)
p.plot_cartoon_binary(random_num)
[46]:
(<Figure size 1200x2250 with 1 Axes>, <Axes: >)
Predict observables#
You can also use the Population class to predict what we might actually observe for this population in different filters. This uses dust maps to correct for extinction, applies bolometric corrections and blends the stars if necessary.
Tip
Note that you need some of the optional dependencies to run this last section - this is described more on the main Install page where you can find out how to install the optional dependencies and download the data for the dust maps and Gaia selection function.
[ ]:
p.get_observables(assume_mw_galactocentric=True, filters=["G", "BP", "RP"])
| Av_1 | Av_2 | M_abs_1 | m_app_1 | M_abs_2 | m_app_2 | G_app_1 | G_app_2 | teff_obs | log_g_obs | secondary_brighter | G_abs_1 | G_abs_2 | BP_app_1 | BP_app_2 | BP_abs_1 | BP_abs_2 | RP_app_1 | RP_app_2 | RP_abs_1 | RP_abs_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4.609716 | 6.0 | 3.457010 | 18.793058 | 5.581687 | 20.917734 | 22.167281 | inf | 6213.373532 | 4.163052 | False | 3.217009 | inf | 23.606863 | inf | 3.490352 | inf | 21.052861 | inf | 2.783254 | inf |
| 1 | 4.149601 | 6.0 | 9.070032 | 24.304126 | 12.038693 | 27.272786 | 27.579349 | inf | 3705.234013 | 4.932894 | False | 9.563646 | inf | 29.726378 | inf | 10.527712 | inf | 26.313477 | inf | 8.617683 | inf |
| 2 | 6.000000 | 6.0 | 8.506240 | 23.954165 | 9.322090 | 24.770016 | 27.915992 | inf | 3808.470780 | 4.859531 | False | 8.581067 | inf | 30.600001 | inf | 9.491915 | inf | 26.582832 | inf | 7.662284 | inf |
| 3 | 2.640000 | 6.0 | 13.432190 | 28.688393 | 10.568172 | 25.824374 | 30.248303 | inf | 6383.831312 | 7.734212 | False | 10.956415 | inf | 31.330705 | inf | 10.971066 | inf | 29.253035 | inf | 10.881733 | inf |
| 4 | 6.000000 | 6.0 | 8.896379 | 24.927311 | 11.036545 | 27.067477 | 29.172366 | inf | 3766.276995 | 4.920190 | False | 9.259249 | inf | 31.864843 | inf | 10.180998 | inf | 27.838403 | inf | 8.335461 | inf |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2596 | 6.000000 | 6.0 | 11.840547 | 27.044317 | 11.920082 | 27.123853 | 31.123640 | inf | 2937.380678 | 5.157263 | False | 12.567412 | inf | 35.488541 | inf | 14.742283 | inf | 29.693975 | inf | 11.300885 | inf |
| 2597 | 6.000000 | 6.0 | 9.143538 | 22.104229 | 11.302421 | 24.263112 | 26.416418 | inf | 3632.654232 | 4.928741 | False | 9.696250 | inf | 29.390216 | inf | 10.780614 | inf | 25.056891 | inf | 8.694578 | inf |
| 2598 | 1.716000 | 6.0 | 4.078223 | 17.209767 | 8.784677 | 21.916221 | 18.543946 | inf | 5980.780039 | 4.265977 | False | 3.977241 | inf | 19.213415 | inf | 4.266016 | inf | 17.764923 | inf | 3.524240 | inf |
| 2599 | 2.244000 | 6.0 | 8.122511 | 22.387619 | 9.778345 | 24.043453 | 24.385123 | inf | 3892.794291 | 4.798839 | False | 8.375090 | inf | 25.844305 | inf | 9.251238 | inf | 23.264670 | inf | 7.474368 | inf |
| 2600 | 6.000000 | 6.0 | 10.867271 | 25.279251 | 11.106774 | 25.518754 | 29.287202 | inf | 3148.493586 | 5.044728 | False | 11.395861 | inf | 33.106884 | inf | 13.098007 | inf | 27.878378 | inf | 10.209342 | inf |
2601 rows × 21 columns
[ ]:
cogsworth.plot.plot_cmd(p, "G", "BP", "RP")
(<Figure size 700x1000 with 2 Axes>, <Axes: xlabel='BP - RP', ylabel='G'>)
And more…!#
That’s it for your quickstart introduction to cogsworth but that’s only the beginning of what you can do with it! You can also learn how to use hydrodynamical zoom-in simulations or predict the gravitational-wave signal in LISA for your binaries. I’ve got pages of documentation ready for you to learn all of about it.
If you’re interested in learning more you’ve got a couple of options - you could:
Check out the other tutorials for more step-by-step guides of using
cogsworthBrowse the gallery of examples to see how you could put
cogsworthto useSearch through the user guide if you want to know how a specific class or function works
Hope you enjoy using cogsworth!
Note
This tutorial was generated from a Jupyter notebook that can be found here.