# Dark Emulator
[](https://anaconda.org/nishimichi/dark_emulator)
[](https://anaconda.org/nishimichi/dark_emulator)
[](https://anaconda.org/nishimichi/dark_emulator)
[](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
```
Please note that updates on conda are currently halted due to an unresolved issue in conda build. Therefore, we recommend using the version in pip or installing from the source using the following command.
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 `george` (a software package for the Gaussian process) and colossus
```
conda install -c conda-forge george
pip install colossus
```
From version 1.1.0, `dark_emulator` uses FFTLog implementation by [Fang et al (2019); arXiv:1911.11947](https://arxiv.org/abs/1911.11947).
## 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.
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"description": "# Dark Emulator\n[](https://anaconda.org/nishimichi/dark_emulator)\n[](https://anaconda.org/nishimichi/dark_emulator)\n[](https://anaconda.org/nishimichi/dark_emulator)\n[](https://anaconda.org/nishimichi/dark_emulator)\n\nA 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).\n\n## Install\nIn order to install dark emulator package, use pip:\n```\n pip install dark_emulator\n```\nor use conda:\n```\n conda install -c nishimichi dark_emulator\n```\nPlease note that updates on conda are currently halted due to an unresolved issue in conda build. Therefore, we recommend using the version in pip or installing from the source using the following command.\n\nIf the above does not work for you, you may download the source files from this repository and install via\n```\npython -m pip install -e .\n```\nafter moving to the top directory of the source tree.\nIn that case, you need to install `george` (a software package for the Gaussian process) and colossus\n```\nconda install -c conda-forge george\npip install colossus\n```\nFrom version 1.1.0, `dark_emulator` uses FFTLog implementation by [Fang et al (2019); arXiv:1911.11947](https://arxiv.org/abs/1911.11947).\n\n## Usage\nYou can then check how Dark Emulator works by running a tutorial notebook at\n```\ndocs/tutorial.ipynb\ndocs/tutorial-hod.ipynb\n```\nSee also the documentation on [readthedocs](https://dark-emulator.readthedocs.io/en/latest/).\n\n## Code Paper\nThe 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.\n\n",
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