AGNfitter
========
**A Bayesian MCMC approach to fitting Spectral Energy Distributions of AGN and galaxies**
Welcome to AGNfitter!
AGNfitter is a Python algorithm implementing a fully Bayesian MCMC method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGN) and galaxies from the sub-mm to the UV.
Through this method, you will be able to robustly disentangle the physical processes responsible for the emission of your sources.
You only need a catalog of photometric data (wavelengths, fluxes and errors), take a few decisions (if you wish), and you are ready to go (see Example).
AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star forming regions. A detailed presentation, test and discussion of AGNfitter can be found in `https://arxiv.org/abs/1606.05648#`
Requirements
-------------
* Numpy version 1.6 or later
* Matplotlib version 1.4.0 or later
* Scipy
* Astropy version 1.2 or later (pip install --no-deps astropy)
* acor (pip install acor)
Installation
----------------
Installation can be done by cloning this Github repository.
After installation, let's do a quick test:
**1)** In `example/SETTINGS_AGNfitter.py`, go to `def CATALOG_settings()` and change
cat['path'] ='/Users/USER/AGNfitter/'
to your AGNfitter path. These test settings point to the example catalog contained in `data/catalog_example.txt`.
**2)** In the terminal, go to your AGNfitter path and start
./RUN_AGNfitter_multi.py example/SETTINGS_AGNfitter.py
You should have a nice example in your `cat['path']/OUTPUT` folder.
###
Either make sure that the root AGNfitter directory is on your `PATH` or specify the full path to `RUN_AGNfitter_multi.py`.
###
Quick start
------------
**TASK 0 (optional):** If you wish to have a working path other than the AGNfitter code path, please change
cat['workingpath'] = cat['path']
to your costumized working path.
**TASK 1:** In your working path, configure your settings creating a file `my_SETTINGS_AGNfitter.py`.
This file should be created based on the example in `example/SETTINGS_AGNfitter.py` (copy+paste).
To get AGNfitter running this is the ONLY file you need to modify.
*TASK 1a:* Specify your catalog's format in:
def CATALOG_settings()
cat['path'] ='/Users/USER/AGNfitter/'
cat['filename'] = 'data/catalog_example.txt
cat['filetype'] = 'ASCII' ## catalog file type: 'ASCII' or 'FITS'.
cat['name'] = 0#'ID' ## If ASCII: Column index (int) of source IDs
cat...
*TASK 1b:* To construct the dictionary please go to
def FILTERS_settings():
filters['dict_zarray'] = np.arange(zmin, zmax, zinterval)
Here you can specify the redshift ranges or a redshift array you need for you catalog.
The DICT_default only includes z=[0.283, 1.58] for the test.
Please, consider this process takes around 0.1 minute per redshift element.
This process might be lengthy but you only have to do it once.
You can use the default combination of photometric bands by leaving
filters['Bandset'] = 'BANDSET_default'.
Otherwise, if you like, you can specify the photometric bands included in your catalog by setting
def FILTERS_settings():
...
filters['Bandset'] = 'BANDSET_settings'
filters['SPIRE500']= False
filters['SPIRE350']= True
and assigning 'True' to the keys corresponding to the photometric bands in your catalog.
**TASK 2:** Run AGNfitter with
./RUN_AGNfitter_multi.py my_SETTINGS_AGNfitter.py
This will run AGNfitter in series. In general there are a few more runtime options (see below).
Done!
Documentation
----------------
A careful documentation will be soon available. In the meantime, some notes are available in the [Wiki](https://github.com/GabrielaCR/AGNfitter/wiki) as response to questions asked by users.
Citation and License
----------------
Please cite `Calistro Rivera et al. (2016)`_ if this code has achieved its purpose and contributed to your
research.
The BibTeX entry for the paper is:
@ARTICLE{2016arXiv160605648C,
author = {{Calistro Rivera}, G. and {Lusso}, E. and {Hennawi}, J.~F. and {Hogg}, D.~W.},
title = "{AGNfitter: A Bayesian MCMC approach to fitting spectral energy distributions of AGN}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1606.05648},
keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2016,
month = jun,
adsurl = {http://adsabs.harvard.edu/abs/2016arXiv160605648C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
AGNfitter is an open-source software made available under the MIT License. See
the LICENSE file for details.
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AGNfitter:MCMC方法为AGN和星系提供的SED拟合代码
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AGNfitter 贝叶斯MCMC方法拟合AGN和星系的光谱能量分布 欢迎来到AGNfitter! AGNfitter是一种Python算法,实现了完全贝叶斯MCMC方法,以拟合从亚毫米到UV的活性银河核(AGN)和星系的光谱能量分布(SED)。 通过这种方法,您将能够稳固地弄清负责排放源的物理过程。 您只需要一个光度数据目录(波长,通量和误差),做出一些决定(如果需要),就可以开始使用了(请参见示例)。 AGNfitter利用大量的理论,经验和半经验模型库来同时表征核星系和宿主星系的发射。 该模型由四个物理排放分量组成:吸积盘,AGN加热尘埃圆环,恒星群体和恒星形成区域中的冷尘。 可以在https://arxiv.org/abs/1606.05648#找到有关AGNfitter的详细演示,测试和讨论。 要求 Numpy版本1.6或更高版本 Matplotlib版本1.4.0或更高
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AGNfitter-master.zip (81个子文件)
AGNfitter-master
error.log 0B
functions
MCMC_AGNfitter.py 4KB
DICTIONARIES_AGNfitter.py 25KB
PLOTandWRITE_AGNfitter.py 23KB
PARAMETERSPACE_AGNfitter.py 9KB
__init__.py 221B
MODEL_AGNfitter.py 10KB
CONSTRUCT_modelobjects.py 5KB
DATA_AGNfitter.py 13KB
triangle.py 11KB
models
STARBURST
dalehelou_charyelbaz_v1.pickle 2.39MB
dalehelou_charyelbaz_files.npz 2.39MB
FILTERS
WISE
NRSR-W4.txt 82KB
NRSR-W1.txt 13KB
NRSR-W3.txt 82KB
NRSR-W2.txt 18KB
SPITZER
mips24.res 4KB
irac_ch4.res 13KB
irac_ch2.res 13KB
irac_ch3.res 12KB
irac_ch1.res 16KB
mips70.res 4KB
mips160.res 13KB
2MASS
H_2mass.res 8KB
J_2mass.res 244KB
Ks_2mass.res 9KB
GALEX
galex2500.res 736B
galex1500.res 448B
HERSCHEL
SPIRE_500mu.txt 16KB
SPIRE_350mu.txt 16KB
SPIRE_250mu.txt 16KB
PACS_160mu.txt 121KB
PACS_100mu.txt 48KB
OTHERS
wircam_Ks.res 22KB
wircam_H.res 21KB
WFCAM_Y.res 6KB
J_wfcam.res 6KB
flamingos_Ks.res 30KB
CHFT
r_megaprime_sagem.res 10KB
i_megaprime_sagem.res 11KB
z_megaprime_sagem.res 11KB
g_megaprime_sagem.res 10KB
u_megaprime_sagem.res 6KB
FILTERS.tar.gz 143KB
SDSS
u_SDSS.res 376B
r_SDSS.res 462B
z_SDSS.res 552B
i_SDSS.res 441B
g_SDSS.res 441B
VISTA
Y_uv.res 6KB
K_uv.res 13KB
H_uv.res 11KB
J_uv.res 7KB
SUBARU
z_subaru.res 5KB
r_subaru.res 5KB
i_subaru.res 5KB
suprime_FDCCD_z.res 5KB
g_subaru.res 5KB
V_subaru.res 5KB
B_subaru.res 5KB
GALAXY
bc03_275templates.pickle 2.58MB
bc03_275templates_files.pickle 2.58MB
BBB
richards_files.npz 7KB
richards.pickle 7KB
TORUS
silva_v1.pickle 29KB
silva_v1_files.npz 29KB
example
run_agnfitter.qsub 892B
SETTINGS_AGNfitter.py 8KB
emcee
ensemble.py 17KB
ptsampler.py 14KB
utils.py 7KB
__init__.py 934B
sampler.py 4KB
tests.py 8KB
mh.py 4KB
OUTPUT
test_file.py 9B
data
catalog_example.txt 2KB
LICENSE 1KB
.gitignore 674B
README.md 5KB
RUN_AGNfitter_multi.py 8KB
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