Metadata-Version: 2.1
Name: gaiadr3_zeropoint
Version: 0.0.2
Summary: eDR3 zero point functions from Lindegren et al. 2020 implemented in Python.
Home-page: https://gitlab.com/icc-ub/gaiadr3_zeropoint
Author: Pau Ramos
Author-email: p.ramos@unistra.fr
License: LGPLv3+
Description: # gaiadr3_zeropoint
        
        This Python package contains the necessary tools to query the value of the parallax zero-point for Gaia EDR3  and
         Gaia DR3. Based on the functions described in Lindegren et al. 2020, the code returns the estimated parallax zero
         -point given the ecliptic latitude, magnitude and colour of any Gaia (E)DR3 source.
        
        ## Documentation
        
        All classes and methods/functions are documented so use the python help() function to find out more. 
        
        
        ## Installation
        
        This is a Python3 package (*issues may arise if executed with Python2*).
        
        The required dependencies are:
        * [numpy](https://numpy.org/)
        * [pandas](https://pandas.pydata.org/) (only if you want to use the wrapper provided with the code)
        
        
        To install the package:
        
        ### From source (recommended)
        1. Clone the Github repository or download the source files
        2. cd to the directory
        3. Run `python setup.py install` or `python setup install --user` for installation in your own home directory
        
        ### With pip
        ```
        pip install gaiadr3-zeropoint
        ```
        
        
        ## Basic usage
        
        Once the package is installed, you can import it in Python:
        
        ```
        from zero_point import zpt
        ```
        
        Then, first load the coefficient tables by calling the `load_tables()` function. 
        
        Once the tables are loaded, the
         parallax zero-point can be queried as:
        
        ```
        zpt.get_zpt(phot_g_mean_mag, nu_eff_used_in_astrometry, pseudocolour, ecl_lat, astrometric_params_solved)
        ```
        
        This function accepts both single values as well as iterables, and returns a float (or array of such) corresponding to the zero-point of the source(s) with those parameters.
        
        **NOTE**: for 5-p solutions (ra-dec-parallax-pmra-pmdec), the field `astrometric_params_solved` equals 31 and the
         `pseudocolour` variable can take any arbitrary values (even *None*). On the other hand, for 6-p solutions (ra-dec
         -parallax-pmra-pmdec-pseudocolour), the field `astrometric_params_solved` equals 95 and the
          `nu_eff_used_in_astrometry` variable can take any arbitrary values (even *None*).
        
        Finally, if you have a pandas DataFrame (DF) of sources with the columns `phot_g_mean_mag, nu_eff_used_in_astrometry, pseudocolour, ecl_lat, astrometric_params_solved`, you can simply use the pandas wrapper ```zpt_wrapper```:
        
        ``` 
        zero_point = DF.apply(zpt_wrapper,axis=1) 
        ```
        
        
        ## Attribution
        
        If you make use of this package for your research, please acknowledge the following papers: Lindegren+20.
        
        ## Help
        
        If you encounter any problem with the software, please make use of the GitLub Issues page. Otherwise, contact p.ramos@unistra.fr.
        
        Copyright: Pau Ramos, University of Barcelona
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
