Metadata-Version: 2.1
Name: mrf
Version: 1.0.2
Summary: Multi-Resolution Filtering (MRF) is a method for isolating faint, extended emission in low-resolution images.
Home-page: https://github.com/AstroJacobLi/mrf
Author: Jiaxuan Li, Pieter van Dokkum
Author-email: jiaxuan_li@pku.edu.cn, pieter.vandokkum@yale.edu
License: MIT
Description: # MRF: Multi-Resolution Filtering
        Multi-Resolution Filtering: a method for isolating faint, extended emission in [Dragonfly](http://dragonflytelescope.org) data and other low resolution images.
        
        <p align="center">
          <img src="https://github.com/AstroJacobLi/mrf/blob/master/df-logo.png" width="40%">
        </p>
        
        Documentation
        -------------
        Please read the documentation and tutorial at https://mrfiltering.readthedocs.io/en/latest/.
        
        
        Applications
        ------------
        - Subtract compact objects from low-resolution images (such as Dragonfly) to reveal low surface brightness features.
        - Download corresponding high resolution image (HSC, CFHT) of given Dragonfly image.
        - Characterize and subtract stellar halos in Dragonfly image.
        
        Examples
        ------------
        This example shows the tidal feature of NGC 5907, described in [van Dokkum et al. (2019)](https://ui.adsabs.harvard.edu/abs/2019arXiv190611260V/abstract). The images presented there just used this algorithm. Full resolution Dragonfly images and MRF results can be found [here](https://www.pietervandokkum.com/ngc5907). Check [this notebook](https://github.com/AstroJacobLi/mrf/blob/master/examples/mrfTask-n5907.ipynb) for more details in how to do MRF using this Python package! :rocket: 
        
        ![MRF on NGC 5907](https://github.com/AstroJacobLi/mrf/raw/master/examples/n5907-demo.png)
        
        This example shows how powerful MRF is in extracting low surface brightness features. The ultra-diffuse galaxy M101-DF3 is revealed by MRF after subtracting compact objects and bright star halos according to [van Dokkum et al. (in prep)](https://www.pietervandokkum.com). Check [this notebook](https://github.com/AstroJacobLi/mrf/blob/master/examples/mrfTask-m101df3.ipynb) for more details.
        
        ![MRF on M101-DF3](https://github.com/AstroJacobLi/mrf/raw/master/examples/m101-df3-demo.png)
        
        You can also use [this script](https://github.com/AstroJacobLi/mrf/blob/master/examples/mrf-task.py) to run the MRF task. Take NGC 5907 as an example:
        
        ```bash
        python mrf-task.py n5907_df_g.fits ngc5907_cfht_g.fits ngc5907_cfht_r.fits ngc5907-task.yaml --galcat='gal_cat_n5907.txt' --output='n5907_g'
        ```
        
        Installation
        ------------
        
        ```bash
        mkdir <install dir>
        cd <install dir>
        git clone git@github.com:AstroJacobLi/mrf.git
        cd mrf
        python setup.py install
        ```
        
        If you don't have `git` configured, you can also download the `zip` file directly from https://github.com/AstroJacobLi/mrf/archive/master.zip, then unzip it and install in the same way. 
        
        To test if `mrf` is installed successfully, import `mrf` in Python:
        
        ```python
        import mrf, os
        print(os.path.isfile(os.path.join(mrf.__path__[0], 'iraf/macosx/x_images.e')))
        ```
        `True` means you have installed `mrf` successfully! Bravo!
        
        `Python>=3` is needed, but you can try whether `mrf` still works under `python2`. Check out the dependence of `mrf` depends from `requirements.txt`.
        
        Acknowledgement
        ---------------
        Many scripts and snippets are from [`kungpao`](https://github.com/dr-guangtou/kungpao) (written by [Song Huang](http://dr-guangtou.github.io) and [Jiaxuan Li](http://astrojacobli.github.io)). [Johnny Greco](http://johnnygreco.github.io) kindly shared his idea of the code structure. [Roberto Abraham](http://www.astro.utoronto.ca/~abraham/Web/Welcome.html) found the first few bugs of this package and provided useful solutions. Here we appreciate their help!
        
        Citation
        -------
        If you use this code, please reference the `doi` below, and make sure to cite the dependencies as listed in [requirements](https://github.com/AstroJacobLi/mrf/blob/master/requirements.txt). 
        
        `mrf` is a free software made available under MIT License. For details see the LICENSE file. 
        
        Copyright 2019 [Jiaxuan Li](http://astrojacobli.github.io) and [Pieter van Dokkum](http://pietervandokkum.com). 
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=2.7
Description-Content-Type: text/markdown
