https://github.com/FRBs/PreFRBLE
PrEFRBLE: Probability Estimates for Fast Radio Burst to obtain model Likelihood Estimates
https://github.com/FRBs/PreFRBLE
Last synced: 2 months ago
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PrEFRBLE: Probability Estimates for Fast Radio Burst to obtain model Likelihood Estimates
- Host: GitHub
- URL: https://github.com/FRBs/PreFRBLE
- Owner: FRBs
- License: other
- Created: 2019-02-17T22:20:43.000Z (over 6 years ago)
- Default Branch: LClass
- Last Pushed: 2020-10-16T10:10:17.000Z (over 4 years ago)
- Last Synced: 2024-11-02T05:32:43.589Z (7 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 213 MB
- Stars: 6
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
Awesome Lists containing this project
- frbsoft - GitHub
README
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---------------------------------------------PrEFRBLE-----------------------------------------
"Probability Estimates for FRBs to obtain model Likelihood Estimates"
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PrEFRBLE is a python package build to compare observations of Fast Radio Bursts (FRBs) to theoretical predictions. To this end, it predicts the contribution from the different regions along the line of sight to measures observed with FRBs. The results for different contributors are combined to predict the measurement in different scenarios for the full LoS. Finally, observations are probed against thse predictions to obtain the model likelihood.
Brought to you by:
Stefan Hackstein
[](https://doi.org/10.5281/zenodo.3862636)
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REFERENCES
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If using PrEFRBLE in a journal article, please remember to cite these references:
[Hackstein et al. 2019](https://doi.org/10.1093/mnras/stz2033)
[Hackstein et al. 2020](https://doi.org/10.1093/mnras/staa2572)
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INSTALLATION
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If you want to install PreFRBLE in an existing environment, simply run
pip install $PreFRBLE_DIR/PreFRBLE
where you loaded the git repository to $PreFRBLE_DIR.
Don't forget to adjust the directory in PreFRBLE/PreFRBLE/file_system.py before installation
However, it is advised to use a virtual environment to not interfer with your other programs. This can be done automatically by running
bash setup_PreFRBLE.sh
Type "y" when asked to create new environment for the first time.
When the setup ends without problems, you can activate the environment with
source .activate_PreFRBLE
Then you should be able to import the PreFRBLE package in python
python
import PreFRBLE
If you make changes to the source code in PreFRBLE/PreFRBLE, these can be applied by running the setup again (type anything, bot not "y", when asked about new environment)
General usage is explained in the jupyter notebooks found in notebooks/. Start the jupyter notebook server with
jupyter notebook
Navigate to the notebooks folder and start with
basic_usage.ipynb
To use results of www.frbcat.org, download the csv (include RM and scattering) and place in the main folder as frbcat.csv
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MODELS
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We present details on how to obtain predictions from the individual models in notebooks/model/
Except for the IGM model, which required too complex code for jupyter notebook and had to be performed on a computation cluster. This code can be found in IGM/.
List of models that are currently included:
Source:
- magnetar, uniform / stellar wind environment (Piro & Gaensler 2018)
Host Galaxy:
- ensemble of axisymmetric galaxies (Rodrigues et al. 2016 & 2018)
- MW-like spiral galaxy, (NE2001 & JF12) -- no scattering
- starburst dwarf galaxy, (Heesen et al. 2011) -- no scattering
Intervening Galaxies:
- ensemble of axisymmetric galaxies (Rodrigues et al. 2016 & 2018)
Inter Galactic Medium:
- constrained MHD models with primordial / astrophysical origin of intergalactic magnetic fields, (Hackstein et al. 2019, Vazza et al. 2018)
Milky Way:
- NE2001 & JF12 (Cordes & Lazio 2002, Jansson & Farrar 2012) -- no scattering
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TODO
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-- PlotLikelihood: correct density flag
-- change PreFRBLE to PrEFRBLE everywhere