RadVel

https://radvel.readthedocs.io/en/latest/
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RadVel is a Python package for modeling Keplerian orbits and radial velocity time series data to...
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### JSON setup file configuration

Source: https://radvel.readthedocs.io/en/latest/_sources/api_service.rst.txt

A JSON document defining the RadVel setup parameters, replacing the legacy Python setup module.

```json
{
  "starname": "epic203771098",
  "nplanets": 2,
  "instnames": ["j"],
  "fitting_basis": "per tc secosw sesinw k",
  "bjd0": 2454833.0,
  "planet_letters": {"1": "b", "2": "c"},
  "params": {
    "per1": {"value": 20.885258, "vary": false},
    "tc1":  {"value": 2072.79438, "vary": false},
    "secosw1": {"value": 0.0, "vary": false},
    "sesinw1": {"value": 0.1, "vary": false},
    "k1":   {"value": 10.0},
    "per2": {"value": 42.363011, "vary": false},
    "tc2":  {"value": 2082.62516, "vary": false},
    "secosw2": {"value": 0.0, "vary": false},
    "sesinw2": {"value": 0.1, "vary": false},
    "k2":   {"value": 10.0},
    "dvdt": {"value": 0.0},
    "curv": {"value": 0.0},
    "gamma_j": {"value": 1.0, "vary": false, "linear": true},
    "jit_j":   {"value": 2.6}
  },
  "data": {"kind": "dataset_ref", "dataset": "epic203771098.csv"},
  "priors": [
    {"type": "eccentricity", "num_planets": 2},
    {"type": "positivek",    "num_planets": 2},
    {"type": "jeffreys",     "param": "k1", "minval": 0.01, "maxval": 1000},
    {"type": "jeffreys",     "param": "k2", "minval": 0.01, "maxval": 1000},
    {"type": "hardbounds",   "param": "jit_j", "minval": 0.0, "maxval": 15.0},
    {"type": "gaussian",     "param": "dvdt", "mu": 0.0, "sigma": 1.0},
    {"type": "gaussian",     "param": "curv", "mu": 0.0, "sigma": 0.1}
  ],
  "stellar": {"mstar": 1.12, "mstar_err": 0.05}
}
```

--------------------------------

### Initialize and use GeneralRVModel

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/model.html

Example showing how to initialize a GeneralRVModel with custom parameters and a forward model function.

```python
>>> import radvel
#  In this example, we'll assume a function called 'my_rv_function' that
#  computes RV values has been defined elsewhere. We'll assume that 
#  'my_rv_function' depends on planets' usual RV parameters
#  contained in radvel.Parameters as well as some additional
#  parameter, 'my_param'.
>>> params = radvel.Parameters(2)
>>> params['my_param'] = rv.Parameter(my_param_value,vary=True)
>>> rvmodel = radvel.GeneralRVModel(myparams,my_rv_function)
>>> rv = rvmodel(10)
```

--------------------------------

### Posterior Output Example

Source: https://radvel.readthedocs.io/en/latest/tutorials/K2-24_Fitting%2BMCMC.html

Example output showing parameter values, variation status, and applied priors.

```text
parameter                     value      vary
per1                        20.8853      False
tc1                         2072.79      False
secosw1                        0.01      False
sesinw1                        0.01      False
logk1                        1.5503       True
per2                         42.363      False
tc2                         2082.63      False
secosw2                        0.01      False
sesinw2                        0.01      False
logk2                       1.36832       True
dvdt                     -0.0295124       True
curv                     0.00183616       True
gamma                        -4.013      False
jit                         2.08458       True
tp1                         2070.18
e1                           0.0002
w1                         0.785398
k1                          4.71289
tp2                         2077.33
e2                           0.0002
w2                         0.785398
k2                          3.92875

Priors
------
Gaussian prior on jit, mu=1.0986122886681096, sigma=0.5
Gaussian prior on logk2, mu=1.6094379124341003, sigma=10
Gaussian prior on logk1, mu=1.6094379124341003, sigma=10
Gaussian prior on gamma, mu=0, sigma=10
```

--------------------------------

### Likelihood Module Imports and Setup

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/likelihood.html

Initializes the module by importing necessary libraries and configuring a custom warning formatter.

```python
from __future__ import annotations

import warnings

import numpy as np
from numpy.typing import ArrayLike
from scipy.linalg import cho_factor, cho_solve

import radvel.model
from radvel import gp
from radvel.model import GeneralRVModel, RVModel

_has_celerite = gp._try_celerite()
if _has_celerite:
    import celerite


def custom_formatwarning(msg: str, *args: object, **kwargs: object) -> str:
    # ignore everything except the message
    return str(msg) + '\n'


warnings.formatwarning = custom_formatwarning
```

--------------------------------

### Create a Parameters object

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/model.html

Example of initializing a Parameters object for a 2-planet system with a custom mapping from planet numbers to letters.

```python
>>> import radvel
# create a Parameters object for a 2-planet system with
# custom planet number to letter mapping
>>> params = radvel.Parameters(2, planet_letters={1:'d', 2:'e'})
```

--------------------------------

### Start the radvel API service

Source: https://radvel.readthedocs.io/en/latest/_sources/ui_guide.rst.txt

Run the Docker container to start the API service, which includes the UI at /ui.

```console
$ docker run --rm -p 8000:8000 -v "$PWD/.runs:/data" \
    ghcr.io/california-planet-search/radvel-api:latest
$ open http://localhost:8000/ui
```

--------------------------------

### Initialize MCMC Run Parameters

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/mcmc.html

Sets up timing, burn-in status, and convergence tracking variables before starting the MCMC loop.

```python
        num_run = int(np.round(nrun / (checkinterval -1)))
        statevars.totsteps = nrun*statevars.nwalkers*statevars.ensembles
        statevars.mixcount = 0
        statevars.ismixed = 0
        if proceed and statevars.preburned != 0:
            statevars.burn_complete = True
            statevars.nburn = statevars.preburned
        else:
            statevars.burn_complete = False
            statevars.nburn = 0
        statevars.ncomplete = statevars.nburn
        statevars.pcomplete = 0
        statevars.rate = 0
        statevars.ar = 0
        statevars.minAfactor = -1
        statevars.maxArchange = np.inf
        statevars.mintz = -1
        statevars.maxgr = np.inf
        statevars.t0 = time.time()
```

--------------------------------

### Quickstart RadVel service with Docker

Source: https://radvel.readthedocs.io/en/latest/_sources/api_service.rst.txt

Run the RadVel API service using a named Docker volume for data persistence and access the UI at localhost:8000.

```console
$ docker run --rm -p 8000:8000 \
    -v rv-data:/data \
    ghcr.io/california-planet-search/radvel-api:latest
$ open http://localhost:8000/ui
```

--------------------------------

### _namespace_from_dict function

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/utils.html

Builds a SimpleNamespace mirroring a setup-file's globals from a dict. Shared between initialize_posterior_from_dict and the API setup shim.

```python
def _namespace_from_dict(config):
    """Build a SimpleNamespace mirroring a setup-file's globals from a dict.

    Shared between :func:`initialize_posterior_from_dict` and the API setup
    shim that the HTTP service writes into a run directory so the legacy
    :func:`initialize_posterior` loader sees the same namespace.
    """
    P = types.SimpleNamespace()
    P.starname = config.get('starname', 'unnamed')
    P.nplanets = int(config['nplanets'])
    P.instnames = list(config['instnames'])
    P.fitting_basis = config['fitting_basis']
    P.bjd0 = float(config.get('bjd0', 0.0))

    planet_letters = config.get('planet_letters')
    if planet_letters is not None:
        P.planet_letters = {int(k): v for k, v in planet_letters.items()}

    P.data = _data_to_dataframe(config['data']).reset_index(drop=True)
    # The CLI setup-file convention requires a 'tel' column. Inline-rows
    # payloads include it explicitly, but built-in CSV fixtures don't —
    # default to the single instrument when there's only one defined.
    if 'tel' not in P.data.columns and len(P.instnames) == 1:
        P.data['tel'] = P.instnames[0]
```

--------------------------------

### Run radvel fit on example configuration

Source: https://radvel.readthedocs.io/en/latest/_sources/quickstartcli.rst.txt

Performs a maximum-likelihood fit using the HD164922.py example configuration file. Results are placed by default in a directory named after the configuration file (without .py), or you can specify an output directory with the -o flag.

```bash
$ radvel fit -s /path/to/radvel/example_planets/HD164922.py
```

--------------------------------

### MultiNest execution and result extraction

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/nested_sampling.html

Example of running the MultiNest sampler and extracting posterior samples and evidence statistics.

```python
ndim = len(post.name_vary_params())

pymultinest.run(loglike, prior_transform, ndim, **run_kwargs)

a = pymultinest.Analyzer(
    outputfiles_basename=run_kwargs["outputfiles_basename"], n_params=ndim
)

results = {}
results["samples"] = a.get_equal_weighted_posterior()[:, :-1]
results["lnZ"] = a.get_stats()["global evidence"]
results["lnZerr"] = a.get_stats()["global evidence error"]

if tmp:
    shutil.rmtree(output_dir)

return results
```

--------------------------------

### POST /runs/{run_id}/mcmc

Source: https://radvel.readthedocs.io/en/latest/api_service.html

Starts an asynchronous MCMC job.

```APIDOC
## POST /runs/{run_id}/mcmc

### Description
Starts an asynchronous MCMC job and returns a job_id.

### Method
POST

### Endpoint
/runs/{run_id}/mcmc

### Parameters
#### Path Parameters
- **run_id** (string) - Required - The ID of the run.

#### Request Body
- **nsteps** (integer) - Optional - Number of steps.
- **nwalkers** (integer) - Optional - Number of walkers.
- **ensembles** (integer) - Optional - Number of ensembles.
```

--------------------------------

### MultiNest Output Log

Source: https://radvel.readthedocs.io/en/latest/tutorials/toi141_fitting_nested_sampling.html

Example output from MultiNest v3.10 showing convergence warnings and final log-evidence calculation.

```text
 *****************************************************
 MultiNest v3.10
 Copyright Farhan Feroz & Mike Hobson
 Release Jul 2015

 no. of live points =  300
 dimensionality =   14
 *****************************************************

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.

 MultiNest Warning!
 Parameter            6  of mode            1  is converging towards the edge of the prior.
  analysing data from tmpdir/out.txt ln(ev)=  -688.18227660867854      +/-  0.38357873738457438
 Total Likelihood Evaluations:       110915
 Sampling finished. Exiting MultiNest
```

--------------------------------

### radvel.report module imports and setup

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/report.html

Imports required modules, sets up the Jinja2 environment, and defines the print basis and units dictionary used for parameter labels.

```python
from __future__ import annotations

from types import ModuleType
import numpy as np
import pandas as pd

import subprocess
import os
import tempfile
import shutil
from operator import itemgetter
from jinja2 import Environment, PackageLoader, select_autoescape

import radvel
from radvel.posterior import Posterior

env = Environment(loader=PackageLoader('radvel', 'templates'))
print_basis = 'per tc tp e w k'
units = {
    'per': 'days',
    'tp': 'JD',
    'tc': 'JD',
    'e': '',
    'w': 'radians',
    'k': 'm s$^{-1}$',
    'logk': r'$\ln{(\rm m\ s^{-1})}$',
    'secosw': '',
    'sesinw': '',
    'gamma': 'm s$-1$',
    'jitter': 'm s$-1$',
    'logjit': r'$\ln{(\rm m\ s^{-1})}$',
    'jit': r'$\rm m\ s^{-1}$',
    'dvdt': 'm s$^{-1}$ d$^{-1}$',
    'curv': 'm s$^{-1}$ d$^{-2}$',
    'gp_amp': 'm s$-1$',
    'gp_explength': 'days',
    'gp_per': 'days',
    'gp_perlength': '',
    # 'mpsini': '$M_\earth$',
    # 'rp': '$R_\earth$',
    # 'rhop': 'g cm$^{-3}$',
}
```

--------------------------------

### radvel derive

Source: https://radvel.readthedocs.io/en/latest/_sources/quickstartcli.rst.txt

Combines RV time-series properties with host star parameters from the setup file to derive physical parameters for the planetary system. See the epic203771098.py example for including stellar parameters.

```bash
$ radvel derive -s /path/to/radvel/example_planets/HD164922.py
```

--------------------------------

### Install radvel in development mode

Source: https://radvel.readthedocs.io/en/latest/_sources/quickstartcli.rst.txt

Development installation for contributing to radvel. Clones the repository, installs problematic dependencies via conda (recommended for macOS), and installs radvel in editable mode.

```bash
$ git clone https://github.com/California-Planet-Search/radvel.git
$ cd radvel

# Install problematic dependencies via conda (recommended for macOS)
$ conda install pytables h5py

# Install radvel in development mode
$ pip install -e .
```

--------------------------------

### Initialize Ensemble Samplers

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/mcmc.html

Sets up the emcee EnsembleSampler objects and initial positions for each ensemble.

```python
        statevars.samplers = []
        statevars.samples = []
        statevars.initial_positions = []
        for e in range(ensembles):
            pi = post.get_vary_params()
            p0 = np.vstack([pi]*statevars.nwalkers)
            p0 += [np.random.rand(statevars.ndim)*pscales for i in range(statevars.nwalkers)]
            if not proceed:
                statevars.initial_positions.append(p0)
            else:
                statevars.initial_positions.append(statevars.prechains[e][-1, :, :])
            statevars.samplers.append(emcee.EnsembleSampler(statevars.nwalkers, statevars.ndim, post.logprob_array))
```

--------------------------------

### Install radvel on macOS with conda dependencies

Source: https://radvel.readthedocs.io/en/latest/_sources/quickstartcli.rst.txt

Alternative installation for macOS users encountering compilation issues, especially with newer Xcode versions. Installs problematic dependencies via conda before installing radvel.

```bash
$ conda install pytables h5py
$ pip install radvel
```

--------------------------------

### Install radvel with pip

Source: https://radvel.readthedocs.io/en/latest/_sources/quickstartcli.rst.txt

Installs radvel via pip. Recommended after setting up a scientific Python environment such as Anaconda or Miniconda.

```bash
$ pip install radvel
```

--------------------------------

### Import necessary libraries

Source: https://radvel.readthedocs.io/en/latest/tutorials/K2-24_Fitting%2BMCMC.html

Initializes the environment with required scientific and plotting libraries.

```python
%matplotlib inline

import os

import matplotlib
import numpy as np
import pylab as pl
import pandas as pd
from scipy import optimize

import corner

import radvel
import radvel.likelihood
from radvel.plot import orbit_plots, mcmc_plots

matplotlib.rcParams['font.size'] = 14
```

--------------------------------

### radvel.utils.initialize_posterior

Source: https://radvel.readthedocs.io/en/latest/utils.html

Initializes a Posterior object from a Python setup file. It parses the setup file and initializes the RVModel, Likelihood, Posterior, and priors.

```APIDOC
## radvel.utils.initialize_posterior

### Description
Initializes a Posterior object from a Python setup file. It parses the setup file and initializes the RVModel, Likelihood, Posterior, and priors.

### Method
Function call

### Endpoint
`radvel.utils.initialize_posterior(config_file: str, decorr: bool = False) -> tuple[ModuleType, Posterior]`

### Parameters
- **config_file** (string) - Required - Path to config file
- **decorr** (bool) - Optional - Decorrelate RVs against columns defined in the decorr_vars list

### Returns
- (object representation of config file, radvel.Posterior object) - tuple

### Return Type
tuple
```

--------------------------------

### Build and Optimize Three-Planet Model

Source: https://radvel.readthedocs.io/en/latest/tutorials/toi141_fitting_nested_sampling.html

Initializes a three-planet model and performs optimization using the Nelder-Mead method.

```python
post_three = build_model(3)

res = optimize.minimize(
    post_three.neglogprob_array, post_three.get_vary_params(), method='Nelder-Mead',
    options=dict(maxiter=200, maxfev=100000, xatol=1e-8)
)
post_three.set_vary_params(res.x)
print(post_three)
```

--------------------------------

### GET /jobs/{id}

Source: https://radvel.readthedocs.io/en/latest/api_service.html

Polls the status of an asynchronous job.

```APIDOC
## GET /jobs/{id}

### Description
Retrieves the current state and telemetry of an asynchronous job.

### Method
GET

### Endpoint
/jobs/{id}

### Parameters
#### Path Parameters
- **id** (string) - Required - The ID of the job.
```

--------------------------------

### Build Two-Planet Model

Source: https://radvel.readthedocs.io/en/latest/tutorials/toi141_fitting_nested_sampling.html

Initializes the two-planet model by passing 2 to the build_model function and prints the parameter configuration.

```python
post_two = build_model(2)
print(post_two)
```

--------------------------------

### Run UltraNest Sampler

Source: https://radvel.readthedocs.io/en/latest/tutorials/k2_24_demo_all_samplers.html

Executes the UltraNest sampler and prints the execution time.

```python
ultranest_start = time.time()
# Ultranest does not accept False for "resume"
ultranest_results = rns.run(post, sampler="ultranest", output_dir="ultranest_demo", proceed=PROCEED, overwrite=OVERWRITE)
ultranest_time = time.time() - ultranest_start
print(f"Running ultranest took {ultranest_time / 60:.2f} min")
```

```text
[ultranest] Sampling 400 live points from prior ...

[ultranest] Explored until L=-8e+01  8 [-82.8705..-82.8701]*| it/evals=10099/269614 eff=3.7513% N=400   0     00  0
[ultranest] Likelihood function evaluations: 269614
[ultranest] Writing samples and results to disk ...
[ultranest] Writing samples and results to disk ... done
[ultranest]   logZ = -103.4 +- 0.1932
[ultranest] Effective samples strategy satisfied (ESS = 2740.8, need >400)
[ultranest] Posterior uncertainty strategy is satisfied (KL: 0.46+-0.07 nat, need <0.50 nat)
[ultranest] Evidency uncertainty strategy is satisfied (dlogz=0.19, need <0.5)
[ultranest]   logZ error budget: single: 0.21 bs:0.19 tail:0.01 total:0.19 required:<0.50
[ultranest] done iterating.
Running ultranest took 4.17 min
```

--------------------------------

### MCMC Execution Output

Source: https://radvel.readthedocs.io/en/latest/tutorials/K2-24_Fitting%2BMCMC.html

Example console output during the MCMC sampling process.

```text
200000/4000000 (5.0%) steps complete; Running 21445.43 steps/s; Mean acceptance rate = 55.4%; Min Auto Factor =  75; Max Auto Relative-Change =   inf; Min Tz = 18327.9; Max G-R = 1.002
Discarding burn-in now that the chains are marginally well-mixed

460000/4000000 (11.5%) steps complete; Running 17740.35 steps/s; Mean acceptance rate = 55.0%; Min Auto Factor =  90; Max Auto Relative-Change = 0.0245; Min Tz = 16833.8; Max G-R = 1.002
Chains are well-mixed after 460000 steps! MCMC completed in 23.4 seconds
```

--------------------------------

### GET /runs/{run_id}/files/{filename}

Source: https://radvel.readthedocs.io/en/latest/api_service.html

Downloads a file associated with a run.

```APIDOC
## GET /runs/{run_id}/files/{filename}

### Description
Downloads a specific file generated by the run.

### Method
GET

### Endpoint
/runs/{run_id}/files/{filename}

### Parameters
#### Path Parameters
- **run_id** (string) - Required - The ID of the run.
- **filename** (string) - Required - The name of the file to download.
```

--------------------------------

### Initialize radvel Parameters

Source: https://radvel.readthedocs.io/en/latest/tutorials/GaussianProcess-tutorial.html

Create a Parameters object for a single-planet system.

```python
nplanets=1
params = radvel.Parameters(nplanets,basis='per tc secosw sesinw k')
```

--------------------------------

### Run Dynesty Static Sampler

Source: https://radvel.readthedocs.io/en/latest/tutorials/k2_24_demo_all_samplers.html

Executes the static Dynesty sampler and prints the execution time.

```python
dynesty_start = time.time()
dynesty_results = rns.run(post, sampler="dynesty-static", output_dir="dynesty_demo", proceed=PROCEED, overwrite=OVERWRITE)
dynesty_time = time.time() - dynesty_start
print(f"Running dynesty took {dynesty_time / 60:.2f} min")
```

```text
iter: 10799 | +500 | bound: 1410 | nc: 1 | ncall: 1075955 | eff(%):  1.051 | loglstar:   -inf < -82.470 <    inf | logz: -103.429 +/-    nan | dlogz:  0.001 >  0.509

Running dynesty took 15.31 min
```

--------------------------------

### Import necessary libraries

Source: https://radvel.readthedocs.io/en/latest/tutorials/GaussianProcess-tutorial.html

Perform preliminary imports for data handling, radvel modeling, and optimization.

```python
import numpy as np
import pandas as pd
import os
import radvel
import radvel.likelihood
from radvel.plot import orbit_plots, mcmc_plots
from scipy import optimize

%matplotlib inline
```

--------------------------------

### Compare Model Evidence

Source: https://radvel.readthedocs.io/en/latest/tutorials/toi141_fitting_nested_sampling.html

Print the log-evidence values for one, two, and three-planet models to determine the best fit.

```python
print(f"Evidence one planet: {results_single['lnZ']:.2f} +/- {results_single['lnZerr']:.2f}")
print(f"Evidence two planet: {results_two['lnZ']:.2f} +/- {results_two['lnZerr']:.2f}")
print(f"Evidence three planet: {results_three['lnZ']:.2f} +/- {results_three['lnZerr']:.2f}")
```

--------------------------------

### Run nested sampling with PyMultiNest (_run_multinest)

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/nested_sampling.html

Configures and runs the PyMultiNest sampler. It sets outputfiles_basename to <output_dir>/out and resume to proceed. Raises ValueError if 'outputfiles_basename' or 'resume' are in run_kwargs. Creates the output directory if needed. Returns a dictionary with samples, lnZ, and lnZerr.

```python
import pymultinest

run_kwargs = run_kwargs or {}

# By default, assume we want a temporary output dir
tmp = True

if output_dir is None:
    output_dir = "tmpdir"
else:
    # if an actual outupt dir was specified, it is not temporary
    tmp = False

if "outputfiles_basename" in run_kwargs:
    raise ValueError(
        "outputfiles_basename not supported for multinest. Use radvel's output_dir instead."
    )
run_kwargs["outputfiles_basename"] = os.path.join(output_dir, "out")

if "resume" in run_kwargs:
    raise ValueError(
        "'resume' not supported for multinest. Use radvel's 'proceed' instead."
    )
run_kwargs["resume"] = proceed

os.makedirs(output_dir, exist_ok=tmp or overwrite or proceed)
```

--------------------------------

### Define a UserDefinedPrior

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/prior.html

Example of how to define a custom prior using the UserDefinedPrior class by providing a function that returns the log-value of a PDF.

```python
>>> def myPriorFunc(inp_list):
...     if inp_list[0] > 0. and inp_list[0] < 1.:
...         return 0.
...     else:
...         return -np.inf
>>> myTexString = 'Uniform Prior on $\sqrt{e}$'
>>> myPrior = radvel.prior.UserDefinedPrior(['se'], myPriorFunc, myTexString)
```

--------------------------------

### Plot initial model

Source: https://radvel.readthedocs.io/en/latest/tutorials/K2-24_Fitting%2BMCMC.html

Visualizes the initial model fit before optimization.

```python
pl.figure()
plot_results(like)
```

```text
<Figure size 640x480 with 0 Axes>
```

--------------------------------

### POST /runs

Source: https://radvel.readthedocs.io/en/latest/api_service.html

Creates a new run.

```APIDOC
## POST /runs

### Description
Creates a new run and returns the run_id.

### Method
POST

### Endpoint
/runs
```

--------------------------------

### initialize_posterior function

Source: https://radvel.readthedocs.io/en/latest/_modules/radvel/utils.html

Initializes a Posterior object from a Python setup file. Parses the file and returns a tuple of the config module and Posterior object.

```python
[docs]
def initialize_posterior(config_file: str, decorr: bool = False) -> tuple[ModuleType, radvel.posterior.Posterior]:
    """Initialize Posterior object from a Python setup file.

    Parse a setup file and initialize the RVModel, Likelihood, Posterior and priors.

    Args:
        config_file (string): path to config file
        decorr (bool): (optional) decorrelate RVs against columns defined in the decorr_vars list

    Returns:
        tuple: (object representation of config file, radvel.Posterior object)
    """

    system_name = os.path.basename(config_file).split('.')[0]
    P = load_module_from_file(system_name, os.path.abspath(config_file))
    return _finalise_posterior(P, decorr=decorr)
```

--------------------------------

### Run MultiNest Sampler

Source: https://radvel.readthedocs.io/en/latest/tutorials/k2_24_demo_all_samplers.html

Executes the MultiNest sampler and prints the execution time.

```python
multinest_start = time.time()
multinest_results = rns.run(post, sampler="multinest", output_dir="multinest_demo", proceed=PROCEED, overwrite=OVERWRITE)
multinest_time = time.time() - multinest_start
print(f"Running multinest took {multinest_time / 60:.2f} min")
```

```text
 *****************************************************
 MultiNest v3.10
 Copyright Farhan Feroz & Mike Hobson
 Release Jul 2015

 no. of live points =  400
 dimensionality =    5
 *****************************************************
  analysing data from multinest_demo/out.txt ln(ev)=  -102.44719426706034      +/-  0.20587852822593408
 Total Likelihood Evaluations:        21919
 Sampling finished. Exiting MultiNest

Running multinest took 0.32 min
```

--------------------------------

### Create and activate conda virtual environment

Source: https://radvel.readthedocs.io/en/latest/_sources/OSX-multiprocessing.rst.txt

Optional step to create and activate a conda virtual environment for best results before rebuilding numpy.

```bash
$ conda create --name radvel_virtualenv --python=3.6 # create a virtual environment for best results
$ source activate radvel_virtualenv # activate the newly minted virtual environment
```
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