Metadata-Version: 2.1
Name: fiplcr
Version: 1.0
Summary: Relative FIP bias diagnostics using linear combinations of spectral lines
Home-page: https://git.ias.u-psud.fr/nzambran/fiplcr
Author: Natalia Zambrana Prado
Author-email: natalia.zambrana-prado@ias.u-psud.fr
License: UNKNOWN
Description: # Relative FIP bias diagnostics using linear combinations of spectral lines
        
        The `fiplcr` Python package allows to perform FIP bias maps measurements on the 
        solar corona from UV intensity maps. It calculates an optimal linear combination
        of the spectral lines in order to obtain an accurate FIP bias map.   
        The proof of concept for this module is published in 
        [Zambrana Prado & Buchlin, 2019](<https://doi.org/10.1051/0004-6361/201834735> "Measuring relative abundances in the solar corona with optimised linear combinations of spectral lines"). 
        In order to apply the Linear Combination Ratio (LCR) method one must follow 
        4 steps:  
        * Selection of the spectral lines  
        * Computation of the contribution functions
        * Determination of the optimal linear combinations
        * Determination of the relative FIP bias from the observations
        
        The 1st step must be done on a case by case basis taking into account the 
        lines available in your observation. We suggest you follow the criteria stated 
        in Sec. 3.3.1 of Zambrana Prado & Buchlin, 2019.  
        The 2nd step is done by the `specline` module of the `fiplcr` module.  
        The 3rd step corresponds to the `linear_combination` module.  
        Finally the 4th step can be done using the `fip_map` function.
        
        Do not forget to define all required variables in the `config.py` file in order 
        to perform all calculations.
        
        ## Installation
        
        `fiplcr` uses [ChiantiPy](https://github.com/chianti-atomic/ChiantiPy) and the
        Chianti database.
        Before installing `fiplcr`, follow the [ChiantiPy installation
        instructions](https://github.com/chianti-atomic/ChiantiPy#installation).
        In particular, make sure to set the `$XUVTOP` environment variable in your
        `.bashrc`.
        
        You can then install `fiplcr` by running the following commands in your
        terminal:
        
        ```python
        git clone https://git.ias.u-psud.fr/nzambran/fiplcr.git
        pip install fiplcr/
        ```
        
        (If you have limited permission, you can install it locally with `pip install
        fiplcr/ --user`.)
        
        The `fiplcr` module is now installed on your system.
        You can safely remove the `fiplcr/` repository that was created.
        
        
        ## Quick start examples
        
        ### Exploring linear combinations and comparing them to a simple 2-line ratio
        
        In order to check if your linear combinations are suited for relative FIP bias
        determination, you can compare their performance to that of a simple two-line 
        ratio.  
        This can be done following the same method presented in Sec. 4 of
        [Zambrana Prado & Buchlin, 2019](<https://doi.org/10.1051/0004-6361/201834735> "Measuring relative abundances in the solar corona with optimised linear combinations of spectral lines").
        
        
        Using a DEM cube (stored in examples/em_example/em_example.npz) we will synthetize 
        radiance maps for the selected spectral lines one wishes to test out. The test 
        case consists in using uniform abundances to compute these radiance maps. The 
        test is considered successful for a given FIP bias determination method if the 
        output relative FIP bias mapis consistent with the input elemental abundance 
        maps, both in uniformity and in value.
        
        Putting yourself in the directory `examples/em_example` you can run the em_test.py 
        file and you will retrieve two LinearComb objects: 
        * The variable `ll`, containing the lines you wish to test out with their 
        corresponding synthetic radiance maps and the obtained relative FIP bias map.
        * The variable `ll_2_lines`, containing the lines for the simple two-line ratio
        with their corresponding synthetic radiance maps and the obtained relative 
        FIP bias map.
        
        The test has four main steps, detailed below:
        1.  We derive a DEM cube from the AIA observation. This is for the sole purpose 
        of producing synthetic radiances, for which we  have  control  over  all  parameters,  
        while  the  DEMs  are representative of different real solar regions.
        2.  Using CHIANTI for the contribution functions and the derived  DEMs,  we  calculate  
        the  synthetic  radiances.  We  assume different uniform abundances for different 
        elements.
        3.  We determine the optimal linear combination coefficients for the LCR method, and 
        the coefficients for the two-line ratio method we are comparing it to.
        4.  We use these coefficients to retrieve the FIP bias in each pixel. If the selected 
        lines are suitable for FIP bias determination, the retrieved FIP bias map should be 
        uniform.
        
        
        ## FULL DESCRIPTION OF EACH MODULE
        
        *Notes:*
        
        If you have trouble using matplotlib and ChiantiPy and you get this kind of 
        error:
        "RuntimeError: LaTeX was not able to process the following string:"
        you will need to install an additional package by running
        
        `sudo apt install dvipng texlive-latex-extra texlive-fonts-recommended`
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.5
Description-Content-Type: text/markdown
