Create models of rotating and non-rotating planets by solving the differential
equations for hydrostatic equilibrium, and create initial conditions for e.g.
smoothed particle hydrodynamics (SPH) simulations by placing particles that
precisely match the planet's profiles.
See the `tutorial.ipynb` notebook for an introductory tutorial and examples
(https://github.com/srbonilla/WoMa), with additional documentation in this
file, below. Please see the class and function docstrings for full details.
Presented in Ruiz-Bonilla et al. ([2020, MNRAS 500:3](https://academic.oup.com/mnras/article/500/3/2861/6007797)).
Includes SEAGen (https://github.com/jkeger/seagen;
Kegerreis et al. [2019, MNRAS 487:4](https://doi.org/10.1093/mnras/stz1606))
with modifications for spinning planets.
Sergio Ruiz-Bonilla: sergio.ruiz-bonilla@durham.ac.uk
Jacob Kegerreis: jacob.kegerreis@durham.ac.uk
Tom Sandnes: thomas.d.sandnes@durham.ac.uk
If you find any bugs, potential improvements, or features worth adding, then
please let us know!
Files
-----
+ `tutorial.ipynb` Jupyter notebook tutorial and examples.
+ `woma/` Code directory.
+ `main.py` The main program classes and functions.
+ `data/` Data for equation of state (EoS) tables.
+ `eos/` EoS and temperature-density relations.
+ `spherical_funcs/` Functions for spherical profiles.
+ `spin_funcs/` Functions for spinning profiles.
+ `misc/` Miscellaneous functions and constants.
+ `README.md` This file. General info and documentation.
+ `LICENSE.txt` GNU general public license v3+.
+ `setup.py`, `setup.cfg`, `MANIFEST.in` PyPI package files.
Installation and requirements
-----------------------------
+ Install the package with `pip install woma`, or downloaded directly from https://pypi.org/project/woma/
+ Python 3 (tested with 3.6.9)
+ seagen>=1.5, numpy, numba>=0.50.1, h5py
Documentation
=============
See the `tutorial.ipynb` notebook for a tutorial and examples.
This documentation summarises the different primary options available.
Full documentation is provided in the class and function docstrings,
particularly for the various utilities (e.g. equations of state functions)
that are not the focus of the primary initial-conditions topics
in the tutorial and here.
Most functions take an optional `verbosity` argument
that controls the amount of printed information.
Set `0` for no printing, `1` for standard output, or `2` for extra details.
### Contents
1. Spherical profiles
2. Spinning profiles
3. Particle placement
4. (Bonus) Impact initial conditions
Notation etc.
-------------
+ Formatted with [black](https://github.com/psf/black).
+ Arrays are explicitly labelled with a prefix `A1_`, or `An_` for an
`n`-dimensional array.
+ `numba` provides great speed increases, but places some odd requirements and
limitations that lead us to some awkward or ugly but functional approaches...
## 1. Spherical profiles
All profiles require the temperature and either the pressure or density at the
surface: `T_s` and `P_s` or `rho_s`,
plus the material and temperature-density relation for each layer (see below):
`A1_mat_layer` and `A1_T_rho_type`.
For a fixed-entropy relation, only the surface density is needed.
The optional parameter `num_prof` sets the number of profile integration steps.
Default 1000.
### Equations of state (EoS)
It is important to check that the EoS you use are appropriate
for your application.
So far, we have implemented several Tillotson, ANEOS, SESAME,
and Hubbard & MacFarlane (1980) materials, with more on the way.
Custom materials in SESAME-style tables can also be provided.
Materials are set for each layer with `A1_mat_layer`
using the following material names,
which are converted internally into material IDs,
set by a base type ID (multiplied by 100) plus a minor type:
+ Tillotson (Melosh 1989; Benz & Asphaug 1999): `1`
+ Iron: `Til_iron` : `100`
+ Granite: `Til_granite` : `101`
+ Water: `Til_water` : `102`
+ Basalt: `Til_basalt` : `103`
+ Hubbard & MacFarlane (1980): `2`
+ Hydrogen-helium atmosphere: `HM80_HHe` : `200`
+ Ice H20-CH4-NH3 mix: `HM80_ice` : `201`
+ Rock SiO2-MgO-FeS-FeO mix: `HM80_rock` : `202`
+ SESAME (Lyon & Johnson 1992) and others in similar formats: `3`
+ Iron (2140): `SESAME_iron` : `300`
+ Basalt (7530): `SESAME_basalt` : `301`
+ Water (7154): `SESAME_water` : `302`
+ Senft & Stewart (2008) water: `SS08_water` : `303`
+ Haldemann, J. et al. (2020) AQUA: `AQUA` : `304`
+ Chabrier, G. et al. (2019) Hydrogen: `CMS19_H` : `305`
+ Chabrier, G. et al. (2019) Helium: `CMS19_He` : `306`
+ Chabrier & Debras (2021) H/He mixture Y=0.245 (Jupiter): `CD21_HHe` : `307`
+ ANEOS (in SESAME-style tables): `4`
+ Forsterite (Stewart et al. 2019): `ANEOS_forsterite` : `400`
+ Iron (Stewart, zenodo.org/record/3866507): `ANEOS_iron` : `401`
+ Fe85Si15 (Stewart, zenodo.org/record/3866550): `ANEOS_Fe85Si15` : `402`
+ Custom (in SESAME-style tables): ``9``
+ User-provided custom material(s): ``900``, ``901``, ..., ``904``
These are defined in `woma/misc/glob_vars.py`,
including the file paths for custom tables.
### Temperature-density relations
These relations are set for each layer with `A1_T_rho_type`:
+ `"adiabatic"`: Adiabatic, only available for some EoS.
+ `"power=a"` where `a` is a float: A power law T ~ rho^`a`.
So e.g. `"power=0"` for isothermal.
+ `"entropy=s"` where `s` is a float: A fixed specific entropy (J K^-1 kg^-1).
Similar to adiabatic, but uses this entropy directly instead of deriving it
from the temperature and density or pressure.
### Profile generation
There are several options for which additional parameters are set and which
unknowns are found, depending on the number of layers in the planet.
Most of these functions are simple iterative bisection searches over the unknown
parameter(s) to find a valid planet in hydrostatic equilibrium that satisfies
the set attribute values.
The additional function arguments like `R_max` set things like the upper bound
for an iteration so usually do not need to be precise.
Optional arguments for these functions (in addition to the `verbosity`) set:
+ `tol`: The tolerance for finding unknown parameters as a fractional
difference between two consecutive iterations. Default usually 0.001,
depending on the method.
+ `num_attempt`: The maximum number of iteration attempts if the tolerance has
still not been reached. Default usually 40, depending on the method.
If the outer radii or masses of some but not all layers are required as inputs,
then the unknown elements in the input arrays can be left as `None`, e.g.:
`A1_R_layer = [3.1415, None]` or `A1_M_layer = [None, 1.23, 4.56]`.
#### 1 layer
+ `gen_prof_L1_find_R_given_M()`, requires:
+ Total mass: `self.M`
+ Maximum radius: `R_max`
+ `gen_prof_L1_find_M_given_R()`, requires:
+ Total radius: `self.R`
+ Maximum mass: `M_max`
#### 2 layers
+ `gen_prof_L2_find_R1_given_M_R()`, requires:
+ Total radius: `self.R`
+ Total mass: `self.M`
+ `gen_prof_L2_find_M_given_R_R1()`, requires:
+ Total radius: `self.R`
+ Layer 1 outer radius: `self.A1_R_layer[0]`
+ Maximum mass: `M_max`
+ `gen_prof_L2_find_R_given_M_R1()`, requires:
+ Total mass: `self.M`
+ Layer 1 outer radius: `self.A1_R_layer[0]`
+ Maximum radius: `R_max`
+ `gen_prof_L2_find_R_R1_given_M1_M2()`, requires:
+ Layer 1 and 2 masses: `self.A1_M_layer`
+ Minimum and maximum radii: `R_min`, `R_max`
#### 3 layers
+ `gen_prof_L3_find_M_given_R_R1_R2()`, requires:
+ Layer 1, 2, and 3 outer radii: `self.A1_R_layer`
+ `gen_prof_L3_find_R1_given_M_R_R2()`, requires:
+ Total mass: `self.M`
+ Total radius: `self.R`
+ Layer 2 outer radius: `self.A1_R_layer[1]`
+ `gen_prof_L3_find_R2_given_M_R_R1()`, requires:
+ Total mass: `self.M`
+ Total radius: `self.R`
+ Layer 1 outer radius: `self.A1_R_layer[0]`
+ `gen_prof_L3_find_R_given_M_R1_R2()`, requires:
+ Total mass: `self.M`
+ Layer 1 and 2 outer radii: `self.A1_R_layer[0]`, `[1]`
+ `gen_prof_L3_find_R1_R2_given_M_R_I()`, requires:
+ Total mass: `self.M`
#### Adding layers
+ `gen_prof_given_inner_prof()`:
After generating an initial planet, a new layer can be added on top by
integrating outwards. Requires:
+ Name of the material in the new layer: `mat`
+ Temperature-density relation in the new layer: `T_rho_type`
+ Minimum density at which the new layer will stop: `rho_min`
+ Minimum pressure at which the new layer will stop: `P_min`
#### Additional parameters
See the `main.py` and other docstrings for full details.
The class's `num_prof` parameter sets the number of radial profile steps,
while the profile generating functions take arguments like `tol`
and/or `num_attempt` that control the convergence criterion and
maximum number of iterations to do to find the unknown parameters.
## 2. Spinning profiles
See `tutorial.ipynb` for the main usage:
```python
spherical_planet = woma.Planet( . . . )
spin_planet = woma.SpinPlanet(
planet = spherical_planet,
period = 24, # hours
)
```
The output attributes available from the `spin_planet` object
are documented in the `SpinPlanet` class docstring in `woma/main.py`.
The primary outputs are the arrays of properties of the nested spheroids,
including their equatorial and polar radii (semi-major and semi-minor axes),
`A1_R` and `A1_Z`,
and for example their masses, densities, pressures, and temperatures,
`A1_m`, `A1_rho`, `A1_P`, and `A1_T`.
Additional parameters are similar to those in the spherical case mentioned
above. See the docstrings for full details.
## 3. Particle placement
See `tutorial.ipynb` for the main usage:
```python
planet = woma.Planet( . . . )
# or
planet = woma.SpinPlanet( . . . )
N_particles = 1e7
N_ngb = 48 # Optional number of neighbours for approximate SPH smoothing lengths
particles = woma.ParticleSet(planet, N_particles, N_ngb=N_ngb)
```
The output attributes available from the `particles` object
are documented in the `ParticlePlanet` class docstring in `woma/main.py`.
Particles can be saved to a SWIFT-style HDF5 file with the `save()` method.
See its docstring and `save_particle_data()` in `woma/misc/io.py` for details.
The specific entropies of the particles can be added to the object using the
`set_material_entropies()` or `calculate_entropies()` methods.
## 4. Impact initial conditions
One of the motivations for WoMa's development was to create initial conditions
for the modelling of planetary giant impacts. (Check out the open-source SWIFT
code at www.swiftsim.com and see e.g. http://icc.dur.ac.uk/giant_impacts/ for
more info about these applications.)
Therefore, we include here some simple utilities for setting up the initial
conditions for an impact scenario between two planets, as described in Appendix
A of [Kegerreis et al. (2020), ApJ 897:161](iopscience.iop.org/article/10.3847/1538-4357/ab9810).
See the `tutorial.ipynb` notebook for an example and the docstrings of
`impact_pos_vel_b_v_c_r()` and `impact_pos_vel_b_v_c_t()` in
`woma/misc/utils.py` for the full documentation.
Raw data
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"author_email": "sergio.ruiz-bonilla@durham.ac.uk",
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"description": "![WoMa](http://astro.dur.ac.uk/~cklv53/woma_logo.png \"WoMa\")\n\nCreate models of rotating and non-rotating planets by solving the differential\nequations for hydrostatic equilibrium, and create initial conditions for e.g.\nsmoothed particle hydrodynamics (SPH) simulations by placing particles that\nprecisely match the planet's profiles.\n\nSee the `tutorial.ipynb` notebook for an introductory tutorial and examples\n(https://github.com/srbonilla/WoMa), with additional documentation in this\nfile, below. Please see the class and function docstrings for full details.\n\nPresented in Ruiz-Bonilla et al. ([2020, MNRAS 500:3](https://academic.oup.com/mnras/article/500/3/2861/6007797)).\n\nIncludes SEAGen (https://github.com/jkeger/seagen;\nKegerreis et al. [2019, MNRAS 487:4](https://doi.org/10.1093/mnras/stz1606))\nwith modifications for spinning planets.\n\nSergio Ruiz-Bonilla: sergio.ruiz-bonilla@durham.ac.uk \nJacob Kegerreis: jacob.kegerreis@durham.ac.uk \nTom Sandnes: thomas.d.sandnes@durham.ac.uk\n\nIf you find any bugs, potential improvements, or features worth adding, then\nplease let us know!\n\n\nFiles\n-----\n+ `tutorial.ipynb` Jupyter notebook tutorial and examples.\n+ `woma/` Code directory.\n + `main.py` The main program classes and functions.\n + `data/` Data for equation of state (EoS) tables.\n + `eos/` EoS and temperature-density relations.\n + `spherical_funcs/` Functions for spherical profiles.\n + `spin_funcs/` Functions for spinning profiles.\n + `misc/` Miscellaneous functions and constants.\n+ `README.md` This file. General info and documentation.\n+ `LICENSE.txt` GNU general public license v3+.\n+ `setup.py`, `setup.cfg`, `MANIFEST.in` PyPI package files.\n\n\nInstallation and requirements\n-----------------------------\n+ Install the package with `pip install woma`, or downloaded directly from https://pypi.org/project/woma/\n+ Python 3 (tested with 3.6.9)\n\t + seagen>=1.5, numpy, numba>=0.50.1, h5py\n\n\nDocumentation\n=============\nSee the `tutorial.ipynb` notebook for a tutorial and examples.\nThis documentation summarises the different primary options available.\n\nFull documentation is provided in the class and function docstrings,\nparticularly for the various utilities (e.g. equations of state functions)\nthat are not the focus of the primary initial-conditions topics\nin the tutorial and here.\n\nMost functions take an optional `verbosity` argument\nthat controls the amount of printed information.\nSet `0` for no printing, `1` for standard output, or `2` for extra details.\n\n### Contents\n1. Spherical profiles \n2. Spinning profiles \n3. Particle placement\n4. (Bonus) Impact initial conditions\n\n\nNotation etc.\n-------------\n+ Formatted with [black](https://github.com/psf/black).\n+ Arrays are explicitly labelled with a prefix `A1_`, or `An_` for an\n`n`-dimensional array.\n+ `numba` provides great speed increases, but places some odd requirements and\nlimitations that lead us to some awkward or ugly but functional approaches...\n\n\n\n\n\n\n## 1. Spherical profiles\nAll profiles require the temperature and either the pressure or density at the\nsurface: `T_s` and `P_s` or `rho_s`,\nplus the material and temperature-density relation for each layer (see below):\n`A1_mat_layer` and `A1_T_rho_type`.\nFor a fixed-entropy relation, only the surface density is needed.\n\nThe optional parameter `num_prof` sets the number of profile integration steps.\nDefault 1000.\n\n\n### Equations of state (EoS)\nIt is important to check that the EoS you use are appropriate\nfor your application.\n\nSo far, we have implemented several Tillotson, ANEOS, SESAME,\nand Hubbard & MacFarlane (1980) materials, with more on the way.\nCustom materials in SESAME-style tables can also be provided.\n\nMaterials are set for each layer with `A1_mat_layer`\nusing the following material names,\nwhich are converted internally into material IDs,\nset by a base type ID (multiplied by 100) plus a minor type:\n\n+ Tillotson (Melosh 1989; Benz & Asphaug 1999): `1`\n + Iron: `Til_iron` : `100`\n + Granite: `Til_granite` : `101`\n + Water: `Til_water` : `102`\n + Basalt: `Til_basalt` : `103`\n+ Hubbard & MacFarlane (1980): `2`\n + Hydrogen-helium atmosphere: `HM80_HHe` : `200`\n + Ice H20-CH4-NH3 mix: `HM80_ice` : `201`\n + Rock SiO2-MgO-FeS-FeO mix: `HM80_rock` : `202`\n+ SESAME (Lyon & Johnson 1992) and others in similar formats: `3`\n + Iron (2140): `SESAME_iron` : `300`\n + Basalt (7530): `SESAME_basalt` : `301`\n + Water (7154): `SESAME_water` : `302`\n + Senft & Stewart (2008) water: `SS08_water` : `303`\n + Haldemann, J. et al. (2020) AQUA: `AQUA` : `304`\n + Chabrier, G. et al. (2019) Hydrogen: `CMS19_H` : `305`\n + Chabrier, G. et al. (2019) Helium: `CMS19_He` : `306`\n + Chabrier & Debras (2021) H/He mixture Y=0.245 (Jupiter): `CD21_HHe` : `307`\n+ ANEOS (in SESAME-style tables): `4`\n + Forsterite (Stewart et al. 2019): `ANEOS_forsterite` : `400`\n + Iron (Stewart, zenodo.org/record/3866507): `ANEOS_iron` : `401`\n + Fe85Si15 (Stewart, zenodo.org/record/3866550): `ANEOS_Fe85Si15` : `402`\n+ Custom (in SESAME-style tables): ``9``\n + User-provided custom material(s): ``900``, ``901``, ..., ``904``\n\nThese are defined in `woma/misc/glob_vars.py`,\nincluding the file paths for custom tables.\n\n\n### Temperature-density relations\nThese relations are set for each layer with `A1_T_rho_type`:\n\n+ `\"adiabatic\"`: Adiabatic, only available for some EoS.\n+ `\"power=a\"` where `a` is a float: A power law T ~ rho^`a`.\n So e.g. `\"power=0\"` for isothermal.\n+ `\"entropy=s\"` where `s` is a float: A fixed specific entropy (J K^-1 kg^-1).\n Similar to adiabatic, but uses this entropy directly instead of deriving it\n from the temperature and density or pressure.\n\n\n### Profile generation\nThere are several options for which additional parameters are set and which\nunknowns are found, depending on the number of layers in the planet.\n\nMost of these functions are simple iterative bisection searches over the unknown\nparameter(s) to find a valid planet in hydrostatic equilibrium that satisfies\nthe set attribute values.\n\nThe additional function arguments like `R_max` set things like the upper bound\nfor an iteration so usually do not need to be precise.\n\nOptional arguments for these functions (in addition to the `verbosity`) set:\n+ `tol`: The tolerance for finding unknown parameters as a fractional\n difference between two consecutive iterations. Default usually 0.001,\n depending on the method.\n+ `num_attempt`: The maximum number of iteration attempts if the tolerance has\n still not been reached. Default usually 40, depending on the method.\n\nIf the outer radii or masses of some but not all layers are required as inputs,\nthen the unknown elements in the input arrays can be left as `None`, e.g.:\n`A1_R_layer = [3.1415, None]` or `A1_M_layer = [None, 1.23, 4.56]`.\n\n#### 1 layer\n+ `gen_prof_L1_find_R_given_M()`, requires:\n + Total mass: `self.M`\n + Maximum radius: `R_max`\n+ `gen_prof_L1_find_M_given_R()`, requires:\n + Total radius: `self.R`\n + Maximum mass: `M_max`\n\n#### 2 layers\n+ `gen_prof_L2_find_R1_given_M_R()`, requires:\n + Total radius: `self.R`\n + Total mass: `self.M`\n+ `gen_prof_L2_find_M_given_R_R1()`, requires:\n + Total radius: `self.R`\n + Layer 1 outer radius: `self.A1_R_layer[0]`\n + Maximum mass: `M_max`\n+ `gen_prof_L2_find_R_given_M_R1()`, requires:\n + Total mass: `self.M`\n + Layer 1 outer radius: `self.A1_R_layer[0]`\n + Maximum radius: `R_max`\n+ `gen_prof_L2_find_R_R1_given_M1_M2()`, requires:\n + Layer 1 and 2 masses: `self.A1_M_layer`\n + Minimum and maximum radii: `R_min`, `R_max`\n\n#### 3 layers\n+ `gen_prof_L3_find_M_given_R_R1_R2()`, requires:\n + Layer 1, 2, and 3 outer radii: `self.A1_R_layer`\n+ `gen_prof_L3_find_R1_given_M_R_R2()`, requires:\n + Total mass: `self.M`\n + Total radius: `self.R`\n + Layer 2 outer radius: `self.A1_R_layer[1]`\n+ `gen_prof_L3_find_R2_given_M_R_R1()`, requires:\n + Total mass: `self.M`\n + Total radius: `self.R`\n + Layer 1 outer radius: `self.A1_R_layer[0]`\n+ `gen_prof_L3_find_R_given_M_R1_R2()`, requires:\n + Total mass: `self.M`\n + Layer 1 and 2 outer radii: `self.A1_R_layer[0]`, `[1]`\n+ `gen_prof_L3_find_R1_R2_given_M_R_I()`, requires:\n + Total mass: `self.M`\n\n#### Adding layers\n+ `gen_prof_given_inner_prof()`:\n After generating an initial planet, a new layer can be added on top by\n integrating outwards. Requires:\n + Name of the material in the new layer: `mat`\n + Temperature-density relation in the new layer: `T_rho_type`\n + Minimum density at which the new layer will stop: `rho_min`\n + Minimum pressure at which the new layer will stop: `P_min`\n\n#### Additional parameters\nSee the `main.py` and other docstrings for full details.\n\nThe class's `num_prof` parameter sets the number of radial profile steps,\nwhile the profile generating functions take arguments like `tol`\nand/or `num_attempt` that control the convergence criterion and\nmaximum number of iterations to do to find the unknown parameters.\n\n\n\n\n## 2. Spinning profiles \nSee `tutorial.ipynb` for the main usage:\n```python\nspherical_planet = woma.Planet( . . . )\n\nspin_planet = woma.SpinPlanet(\n planet = spherical_planet,\n period = 24, # hours\n)\n```\n\nThe output attributes available from the `spin_planet` object\nare documented in the `SpinPlanet` class docstring in `woma/main.py`.\n\nThe primary outputs are the arrays of properties of the nested spheroids,\nincluding their equatorial and polar radii (semi-major and semi-minor axes),\n`A1_R` and `A1_Z`,\nand for example their masses, densities, pressures, and temperatures,\n`A1_m`, `A1_rho`, `A1_P`, and `A1_T`.\n\nAdditional parameters are similar to those in the spherical case mentioned\nabove. See the docstrings for full details.\n\n\n\n## 3. Particle placement\nSee `tutorial.ipynb` for the main usage:\n```python\nplanet = woma.Planet( . . . )\n# or\nplanet = woma.SpinPlanet( . . . )\n\nN_particles = 1e7\nN_ngb = 48 # Optional number of neighbours for approximate SPH smoothing lengths\n\nparticles = woma.ParticleSet(planet, N_particles, N_ngb=N_ngb)\n```\n\nThe output attributes available from the `particles` object\nare documented in the `ParticlePlanet` class docstring in `woma/main.py`.\n\nParticles can be saved to a SWIFT-style HDF5 file with the `save()` method.\nSee its docstring and `save_particle_data()` in `woma/misc/io.py` for details.\n\nThe specific entropies of the particles can be added to the object using the\n`set_material_entropies()` or `calculate_entropies()` methods.\n\n\n\n## 4. Impact initial conditions\nOne of the motivations for WoMa's development was to create initial conditions\nfor the modelling of planetary giant impacts. (Check out the open-source SWIFT\ncode at www.swiftsim.com and see e.g. http://icc.dur.ac.uk/giant_impacts/ for\nmore info about these applications.)\n\nTherefore, we include here some simple utilities for setting up the initial\nconditions for an impact scenario between two planets, as described in Appendix\nA of [Kegerreis et al. (2020), ApJ 897:161](iopscience.iop.org/article/10.3847/1538-4357/ab9810).\n\nSee the `tutorial.ipynb` notebook for an example and the docstrings of\n`impact_pos_vel_b_v_c_r()` and `impact_pos_vel_b_v_c_t()` in\n`woma/misc/utils.py` for the full documentation.\n\n\n",
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