SCARVS: Statistical tools for Combined Analysis of Radial Velocity Signals

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Contents

• Introduction
• Installation
• Setup
• Usage

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Introduction

Insert text here

Installation

Step 1: Clone the repository

git clone [email protected]:njcuk9999/scarvs.git

Step 2: Install python 3.10

Create a conda or python environment

e.g.

conda create --name scarvs-env python=3.10
conda activate scarvs-env

Step 3: Install scarvs

cd {SCARVS_ROOT}
pip install -U -e .

Note on can also use venv (instead of conda)

Note {SCARVS_ROOT} is the path to the cloned github repository (i.e. /path/to/scarvs)

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Setup

First install scarvs. Once you've done this activate the environment you installed scarvs in. (e.g. conda activate scarvs-env)

To setup SCARVS, you need to run the following command:

scarvs_setup {yaml_file}

where yaml_file is the yaml file you wish to create (if left blank you will be asked for one).

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Usage

Command line

To run SCARVS, you need activate the environemnt you installed scarvs in. (e.g. conda activate scarvs-env)

Then you need to run the following command:

scarvs_run {yaml_file}

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Inside Python/Notebooks

To run SCARVS inside python, you need to import the scarvs module:

from scarvs.recipes import scarvs_run

# define the path to your yaml file
yaml_file = '/path/to/yaml_file.yaml'

# run scarvs
scarvs_run.main(yaml_file)

Overriding parameters

If you wish to override the yaml file, you can do so by passing in a dictionary as follows:

from scarvs.core import startup
from scarvs.core import general

# define the path to your yaml file
yaml_file = '/path/to/yaml_file.yaml'
# get parameters
params = startup.get_params(yaml_file)

# Define data path
params['DATA_DIR'] = '/path/to/data'

# Define plot path
params['PLOT_DIR'] = '/path/to/plots'

# --------------------------------------------------------------
# Then use the following to run scarvs
# ---------------------------------------------------------------
    # run science functions
    for science_func in params['SCIFUNCS']:
        # check whether we should run this function then run it
        if general.check_run(params, science_func, 'SCIENCE'):
            # log the we are running a function
            general.log_run(params, science_func, 'SCIENCE')
            # run the science function
            params['SCIFUNCS'][science_func](params)
    # run plotting functions
    for plot_func in params['PLOTFUNCS']:
        # check whether we should run this function then run it
        if general.check_run(params, plot_func, 'PLOTTING'):
            # log the we are running a function
            general.log_run(params, plot_func, 'PLOTTING')
            # run the plotting function
            params['PLOTFUNCS'][plot_func](params)

Running invidual function

To run an individual function, you can call the dictionary and run the function

from scarvs.core import startup

# define the path to your yaml file
yaml_file = '/path/to/yaml_file.yaml'
# get parameters
params = startup.get_params(yaml_file)

# run the science "TEST" function
params['SCIFUNCS']['TEST'](params)

# run the plotting "TEST" function
params['PLOTFUNCS']['TEST'](params)

A note book and yaml example are provided in the scarvs/docs directory.

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