emcee | Python ensemble sampling toolkit for affine-invariant MCMC | Time Series Database library
kandi X-RAY | emcee Summary
kandi X-RAY | emcee Summary
The Python ensemble sampling toolkit for affine-invariant MCMC
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Top functions reviewed by kandi - BETA
- Generate an emcee
- Generate a random walker
- Compute the log - probability of the given coordinates
- Run MCCM
- Calculate the autocorrelation time of the chain
- Estimate the integrated time of a time series
- Calculate the index of the auto window
- Save a single step
- Check the state
- Grow the chain
- Check that blobs are consistent
- Reset the chain
- Open the backend
- Propose an ensemble
- Updates the state of an existing state
- Find the given meta string
- Return whether the walkers depend on the given coordinates
- Calculate the updated vector
- Mark a function as deprecated
- Return the random state of the file
- Grow the chain
- Return the last state of the sampler
- Plot gaussian scaling
- Read a value from the sampler
- Propose a new position
- Get the shape of the walker
emcee Key Features
emcee Examples and Code Snippets
Lines of Code : 67License : Permissive (MIT)
Copy>>> import jax
>>> import emcee_jax
>>>
>>> def log_prob(theta, a1=100.0, a2=20.0):
... x1, x2 = theta
... return -(a1 * (x2 - x1**2)**2 + (1 - x1)**2) / a2
...
>>> num_walkers, num_steps = 100, 10 from tensorBNN.layer import DenseLayer
from tensorBNN.network import network
from tensorBNN.activationFunctions import Relu
from tensorBNN.likelihood import GaussianLikelihood
import warnings
warnings.filterwarnings("ignore", category=DeprecationWar
from cobaya.yaml import yaml_load
from cobaya.model import get_model
info_yaml = """
debug: True
likelihood:
soliket.LensingLiteLikelihood:
sim_number: 1
stop_at_error: True
params:
# Sampled
logA:
prior:
min: 2.6
ma emcee.__version__
pip install -U emcee
conda install -c conda-forge emcee
not 0.68 < od0 < 0.70 and 60 < H0 < 80
>> False
not (0.68 < od0 < 0.70 and 60 < H0 < 80)
>> True
from emcee import moves
emcee_moves = [('KDEMove', 0.5), ('DEMove', 0.5)]
mv = [(getattr(moves, method)(), val) for method, val in emcee_moves]
plt.plot(new_x, result.eval(omega_eff=new_x/1000., thetas=thetas))
absl-py==0.7.1
alabaster==0.7.11
altgraph==0.16.1
anaconda-client==1.7.2
anaconda-navigator==1.9.7
anaconda-project==0.8.2
appdirs==1.4.3
apyori==1.1.1
asn1crypto==0.24.0
astor==0.7.1
astroid==2.0.4
astropy==3.0.4
atomicwrites==1.2.1
attrsimport time
import numpy as np
from emcee import PTSampler
import corner
import matplotlib.pyplot as plt
import scipy.optimize as op
t1 = time.time()
np.random.seed(6) # To reproduce results
# Choose the "true" parameters.
m_true = -0.95def lnprior(theta):
a, b, c = theta
#flat priors on b, c
if not 1.0 < b < 2.0 and c > 0:
return -np.inf
#gaussian prior on a and c
mu = 0.5
sigma = 0.1
### your prior is gaussian * (1/c), take nCommunity Discussions
Trending Discussions on emcee
QUESTION
I am parallelizing emcee using multiprocessing module as stated in the emcee document. However, htop shows that the program keeps using a limited number of cores (26-27). The computer I am running my code on has 80 available cores and I would like to make use of all of them to speed up the code.
Can someone please help me with this? I did not find any solution in the document.
...ANSWER
Answered 2021-Sep-20 at 13:17In case someone runs into the same issue, I just found out that emcee will use a number of cores equal to half the number of walkers. In my case I had 50 walkers and hence, 25 CPU cores were being used.
QUESTION
Using a Jupyter Notebook running with the python2 kernel, I tried to import emcee and I got an error message:
ANSWER
Answered 2021-Jul-24 at 06:40Here the issue is not from your side, this issue is mainly from there side. They,there refers to emcee.
Explination:
They are basically using annotation here where you are getting error, and you are using python2 to import this library, which don't support annotation.
Here's a bit of information about annotation(Some of the stackoverflow question).
primes: List[int] = []andstats: Dict[str, int] = {}. Everything between:and the=is a type hint, soprimesis indeed defined asList[int], and initially set to an empty list (andstatsis an empty dictionary initially, defined asDict[str, int]).
It's a function annotation; function arguments and the return value can be tagged with arbitrary Python expressions. Python itself ignores the annotation (other than saving it), but third-party tools can make use of them.
You can't use the new (3.5+) annotation syntax directly in 2.7, but if you have python 3.4+ you can install mypy and run it on your 2.7 code with a different syntax. See PEP 484.
Now if jupyter notebook is not using python3 then the solution is create virtual environment and try running there. You may add your issue in new issue they will help but if you know what you are doing then you may change that code too. If your edited code works for both python2 and 3 then you may pull request.
QUESTION
I am having trouble accessing the values generated from emcee using the get_chain() method. My code is provided below:
...ANSWER
Answered 2020-Jul-02 at 16:57As you suspect, this is probably due to having installed an old version of emcee. You can check the version installed with
QUESTION
I am trying to implement lmfit into my fitting routines, and I am having issues defining the errors. I premise that I read previous questions regarding the topic on this platform, and I also went through the docs, but some of my doubts are still there.
Below is a complete and minimal example of what I am trying to achieve.
...ANSWER
Answered 2020-Jun-02 at 12:06What you say in your edits is correct: You want to use weights=1./err to properly weight the residual of data and model by the uncertainties in the data, err.
You probably want to use the same in your call to model.fit(..., method='emcee') too.
I should say that the use of emcee in lmfit is rather confusing and gives the unfortunate impression that it is doing a fit. This is simply not true, as emcee (and, really MCMC as a method) can not really do a fit in the sense of "systematically refine parameter values in order to find an improved solution". What it is doing is exploring parameter space in the vicinity of the input parameter values (that happen to be the solution from the Nelder method).
This exploration may find (more like "stumble upon" than "seek") an improved solution and the results will reflect the exploration it does.
QUESTION
In Python 2.7 I used to be able to use the following code (I'm using the emcee package):
...ANSWER
Answered 2020-Feb-26 at 20:17As Pete said, your example works for python 3.6.8. However, another way of doing what you want is:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install emcee
You can use emcee like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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