| Name | reddemcee JSON |
| Version |
0.6.3
JSON |
| download |
| home_page | None |
| Summary | An Adaptative Parallel Tempering wrapper for emcee 3 for personal use |
| upload_time | 2024-05-24 19:23:11 |
| maintainer | None |
| docs_url | None |
| author | ReddTea |
| requires_python | >=3.6 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Reddemcee
An Adaptative Parallel Tempering wrapper for emcee 3 for personal use, which
someone in the community might find useful on it's own.
# Overview
Reddemcee is simply a wrapper for the excellent MCMC implementation [emcee](https://arxiv.org/abs/1202.3665),
that contains an adaptative parallel tempering version of the sampler, according to [Vousden et al. implementation](https://arxiv.org/abs/1501.05823).
It's coded in such a way that minimal differences in input are required, and it's
fully compatible with emcee (v. 3.1.3).
# Dependencies
This code makes use of:
- Numpy
- pandas
- tqdm (https://pypi.python.org/pypi/tqdm)
- emcee (https://github.com/dfm/emcee)
Most of them come with conda, if some are missing they can be easily installed with pip.
# Installation
In the console type in your work folder
```sh
pip install reddemcee
```
# Usage
Please refer to the test file in the tests folder.
```python
import numpy as np
import reddemcee
def log_like(x, ivar):
return -0.5 * np.sum(ivar * x ** 2)
def log_prior(x):
return 0.0
ndim, nwalkers = 5, 100
ntemps = 5
ivar = 1. / np.random.rand(ndim)
p0 = list(np.random.randn(10, nwalkers, ndim))
sampler = reddemcee.PTSampler(nwalkers,
ndim,
log_like,
log_prior,
ntemps=ntemps,
adaptative=True,
logl_args=[ivar],
)
sampler.run_mcmc(p0, 100, 2) # starting pos, nsweeps, nsteps
```
# Additional Options
ntemps
betas
pool
adaptative
config_adaptation_halflife rn: adaptations reduced by half at this time
config_adaptation_rate rn: smaller, faster
moves
backend
## Stored
ratios
betas_history
betas_history_bool
ratios_history
## Funcs
thermodynamic_integration(self,
coef=3,
sampler_dict = {'flat':False,
'discard':10})
get_Z(discard=1, coef=3, largo=100)
get_attr(x)
get_func(x)
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