Metadata-Version: 2.1
Name: dark-emulator
Version: 1.0.22
Summary: dark emulator package
Home-page: https://dark-emulator.readthedocs.io
Author: Takahiro Nishimichi, Hironao Miyatake
Author-email: dark_emulator@ipmu.jp
License: UNKNOWN
Description: # Dark Emulator
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        [![Anaconda-Server Badge](https://anaconda.org/nishimichi/dark_emulator/badges/latest_release_date.svg)](https://anaconda.org/nishimichi/dark_emulator)
        [![Anaconda-Server Badge](https://anaconda.org/nishimichi/dark_emulator/badges/license.svg)](https://anaconda.org/nishimichi/dark_emulator)
        [![Anaconda-Server Badge](https://anaconda.org/nishimichi/dark_emulator/badges/downloads.svg)](https://anaconda.org/nishimichi/dark_emulator)
        
        A repository for a cosmology tool `dark_emulator` to emulate halo clustering statistics. The code is developed based on Dark Quest simulation suite (https://darkquestcosmology.github.io/). The current version supports the halo mass function and two point correlation function (both halo-halo and halo-matter cross).
        
        ## Install
        In order to install dark emulator package, use pip:
        ```
           pip install dark_emulator
        ```
        or use conda:
        ```
           conda install -c nishimichi dark_emulator
        ```
        If the above does not work for you, you may download the source files from this repository and install via
        ```
        python -m pip install -e .
        ```
        after moving to the top directory of the source tree.
        In that case, you need to install `pyfftlog`, `george` (a software package for the Gaussian process) and colossus
        ```
        conda install -c conda-forge george
        conda install -c conda-forge pyfftlog
        pip install colossus
        ```
        
        ## Usage
        You can then check how Dark Emulator works by running a tutorial notebook at
        ```
        docs/tutorial.ipynb
        docs/tutorial-hod.ipynb
        ```
        See also the documentation on [readthedocs](https://dark-emulator.readthedocs.io/en/latest/).
        
        ## Code Paper
        The main reference for our halo emulation strategy is: "Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering", by T. Nishimichi et al., [ApJ 884, 29 (2019)](https://iopscience.iop.org/article/10.3847/1538-4357/ab3719/meta), [arXiv:1811.09504](https://arxiv.org/abs/1811.09504). Please also refer to the paper "Cosmological inference from emulator based halo model I: Validation tests with HSC and SDSS mock catalogs", by H. Miyatake et al.,  [arXiv:2101.00113](https://arxiv.org/abs/2101.00113) for the implementation and performance of the halo-galaxy connection routines.
        
        
Keywords: cosmology,large scale structure,halo,gaussian process,machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
