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توضیحات

Bayesian Radial Velcoity Zero-Point Correction

ویژگی	مقدار
سیستم عامل	-
نام فایل	brav0-0.2.0
نام	brav0
نسخه کتابخانه	0.2.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Thomas Vandal
ایمیل نویسنده	thomasvandal@hotmail.com
آدرس صفحه اصلی	https://github.com/vandalt/brav0
آدرس اینترنتی	https://pypi.org/project/brav0/
مجوز	MIT License

# brav0 _brav0_ (**B**ayesian **Ra**dial **V**elocity **0**-point correction) is a tool to correct zero-point variations in radial velocity (RV) timeseries. This means _brav0_ takes several datasets (usually from the same instrument) and models variations that are common to all datasets. ## Installation _brav0_ can be installed with pip: `python -m pip install brav0`. To use the development version of _brav0_, clone the repository and install it: ```shell git clone https://github.com/vandalt/brav0.git cd brav0 python -m pip install -U -e ".[dev]" ``` _Note: In both cases, as of release 0.1, the development pace will probably be relatively fast for a while so users should update often, either by pulling from the upstream Github repository or by upgrading with `python -m pip install -U brav0`. ## Using _brav0_ _brav0_ is accessible as a command line script or as a Python library. The script requires a configuration file. There are example config files as well as a notebook using the API in the `examples` directory. ### brav0 CLI The CLI is the main way to use _brav0_. It does not (yet) provide an command to run everything at once. The main ZP correction steps are instead separated in various commands. First, we run `source` to load all the input individual data and merge it in a single pandas dataframe. ``` brav0 source config.yml ``` This produces a `raw.csv` file in the output directory, indexed by original file name. Then, we can preprocess the data by doing a series cleanups and by re-formatting the dataframe (e.g. index with object names). ``` brav0 preprocess config.yml ``` This produces the `processed.csv` file the `raw_plots` directory with timeseries and periodogram plots before PP, and the `pp_plots` directory with plots after PP. Once the data is ready, we can remove known planets. Currently, the only way to do this in `brav0` is to use the [NASA explanet archive](https://exoplanetarchive.ipac.caltech.edu/) to remove known planets. It performs Monte-Carlo error propagation and removes "non-controversial" planets only (as defined by the archive). ``` brav0 remove-planets config.yml ``` The resulting dataset is stored in `no_planets.csv` with corresponding plots in `no_planet`. After removing known planets, we can fit the Zero-point model joinlty to all data. The config file specifies if we do MCMC, MAP optimization, or just use a fixed model (recommended only when all parameters have deterministic values). Here is an example where we fit a GP with a Matern 3/2 kernel: ``` brav0 model config.yml Matern32 ``` This produces the model curve and the optimization or sampling results in a directory with the model name (or other subdirectory when using the `-o` option). Finally, we can generate summary information and plots about a given ZP model: ``` brav0 summary config.yml /path/to/model/dir ``` This will save plots in the model directory. ## Why brav0 ? Fitting RV zero-points can be done with relatively simple tools. _brav0_ was originally written to explore the use of Gaussian processes to model RV zero-points. When fitting a GP along with parameters for each standard (calibration) star, the number of parameter can be high, such that sampling the posterior distribution efficiently is challenging. _brav0_ uses PyMC3 to perform gradient-based inference (other backends are not excluded, contributions are welcome!). By using `exoplanet` and `celerite2`, _brav0_ enables efficient inference to derive a zero-point correction error estimates.

نیازمندی

مقدار	نام
-	numpy
-	scipy
-	pandas
-	astropy
-	matplotlib
-	requests
-	radvel
-	astroquery
-	h5py
-	pymc3
-	arviz
-	xarray
-	pymc3-ext
-	aesara-theano-fallback
-	exoplanet
-	celerite2
-	python-box
-	orbits
-	tqdm
-	ipython
-	jupytext
-	black
-	isort
-	flake8
-	ipdb
-	pytest

نحوه نصب

نصب پکیج whl brav0-0.2.0:

pip install brav0-0.2.0.whl

نصب پکیج tar.gz brav0-0.2.0:

pip install brav0-0.2.0.tar.gz