# Type III Radio Burst Automatic Recognition Algorithm
The automatic recognition of the Type III radio burst using Hough Transform.
## Installation
### Python
install from pypi
```bash
pip install type3detect
```
install from git
```bash
git clone https://github.com/peijin94/type3detect.git
cd src/python
python pip install .
```
### Matlab:
```bash
git clone https://github.com/peijin94/type3detect.git
cd src/matlab
```
## Example
Read fits file and detect type III radio bursts in dynamic spectrum.
```python
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
from type3detect import detectRadioburst as drb
from type3detect import radioTools as rt
fname = './LOFAR_20220701_070000_LBA_OUTER_S0.fits'
# preprocess
(dyspec,t_fits,f_fits,hdu) = drb.read_fits(fname) # read LOFAR dynspec fits
(dyspec,f_fits) = drb.cut_low(dyspec,f_fits,f_low_cut_val=30) # remove below freq (RFI)
(data_fits_new,data_fits_new_smooth) = drb.preproc(dyspec,gauss_sigma=1.5)
# binarization
bmap = drb.binarization(data_fits_new,N_order=6,peak_r=1.002)
# detect verticle features
lines = drb.hough_detect(bmap,dyspec,threshold=40,line_gap=10,line_length=30,
theta=np.linspace(np.pi/2-np.pi/8,np.pi/2-1/180*np.pi,300))
line_sets = drb.line_grouping(lines)
# get electron beam information from radio bursts
(v_beam, f_range_burst, t_range_burst, model_curve_set,
t_set_arr_set,f_set_arr_set,t_model_arr,f_model_arr
)= drb.get_info_from_linegroup(line_sets,t_fits,f_fits)
# detailed demo in ./type3detect_demo.ipynb
```
Example: Implementation with for LOFAR dynamic spectrum.
Demo Notebook: [type3detect_demo.ipynb](https://github.com/peijin94/type3detect/blob/master/type3detect_demo.ipynb)
### Binarization
Use several ways to transform the flux intensity data into binary. ([LocalMax(matlab)](src/matlab/get_local_max_map.m)) ([binarization(Python)](src/python/src/detectRadioBurst.py))
### Active-Contour method for Backbone
The Active Contour Method for the backbone of the radio burst (**ACBone**)
Take the result of the Hough transform as a initail position and iteratively move the line to find the backbone position. The demo code : ([ACBone-Matlab](src/matlab/active_contour.m), [ACBone-Python](src/python/type3detect/ACBone.py))
Eventually, we can obtain the centerline of a Type III radio burst.
## Citation
Make sure to cite the paper if you use the idea or code in this repo: [A type III radio burst automatic analysis system and statistic results for a half solar cycle with Nançay Decameter Array data](https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/201833260#R16) Peijin Zhang. A&A 2018.10
bibtex:
```
@article{zhang2018type,
title={A type III radio burst automatic analysis system and statistic results for a half solar cycle with Nan{\c{c}}ay Decameter Array data},
author={Zhang, PJ and Wang, Chuan Bing and Ye, Lin},
journal={Astronomy \& Astrophysics},
volume={618},
pages={A165},
year={2018},
publisher={EDP Sciences}
}
```
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"description": "# Type III Radio Burst Automatic Recognition Algorithm\n\nThe automatic recognition of the Type III radio burst using Hough Transform.\n\n## Installation\n\n### Python\n\ninstall from pypi\n```bash\npip install type3detect\n```\n\ninstall from git\n```bash\ngit clone https://github.com/peijin94/type3detect.git\ncd src/python\npython pip install .\n```\n\n### Matlab:\n```bash\ngit clone https://github.com/peijin94/type3detect.git\ncd src/matlab\n```\n\n## Example \n\nRead fits file and detect type III radio bursts in dynamic spectrum.\n\n```python\nimport matplotlib.dates as mdates\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom type3detect import detectRadioburst as drb\nfrom type3detect import radioTools as rt\n\nfname = './LOFAR_20220701_070000_LBA_OUTER_S0.fits'\n\n# preprocess\n(dyspec,t_fits,f_fits,hdu) = drb.read_fits(fname) # read LOFAR dynspec fits\n(dyspec,f_fits) = drb.cut_low(dyspec,f_fits,f_low_cut_val=30) # remove below freq (RFI)\n(data_fits_new,data_fits_new_smooth) = drb.preproc(dyspec,gauss_sigma=1.5)\n\n# binarization\nbmap = drb.binarization(data_fits_new,N_order=6,peak_r=1.002)\n\n# detect verticle features\nlines = drb.hough_detect(bmap,dyspec,threshold=40,line_gap=10,line_length=30,\n theta=np.linspace(np.pi/2-np.pi/8,np.pi/2-1/180*np.pi,300))\nline_sets = drb.line_grouping(lines)\n\n# get electron beam information from radio bursts\n(v_beam, f_range_burst, t_range_burst, model_curve_set,\n t_set_arr_set,f_set_arr_set,t_model_arr,f_model_arr\n )= drb.get_info_from_linegroup(line_sets,t_fits,f_fits)\n\n# detailed demo in ./type3detect_demo.ipynb\n```\n\nExample: Implementation with for LOFAR dynamic spectrum.\n\nDemo Notebook: [type3detect_demo.ipynb](https://github.com/peijin94/type3detect/blob/master/type3detect_demo.ipynb)\n\n\n\n\n### Binarization\n\nUse several ways to transform the flux intensity data into binary. ([LocalMax(matlab)](src/matlab/get_local_max_map.m)) ([binarization(Python)](src/python/src/detectRadioBurst.py))\n\n\n### Active-Contour method for Backbone\n\nThe Active Contour Method for the backbone of the radio burst (**ACBone**)\n\nTake the result of the Hough transform as a initail position and iteratively move the line to find the backbone position. The demo code : ([ACBone-Matlab](src/matlab/active_contour.m), [ACBone-Python](src/python/type3detect/ACBone.py))\n\n\n\nEventually, we can obtain the centerline of a Type III radio burst.\n\n\n## Citation\n\nMake sure to cite the paper if you use the idea or code in this repo: [A type III radio burst automatic analysis system and statistic results for a half solar cycle with Nan\u00e7ay Decameter Array data](https://www.aanda.org/component/article?access=doi&doi=10.1051/0004-6361/201833260#R16) Peijin Zhang. A&A 2018.10\n\nbibtex:\n```\n@article{zhang2018type,\n title={A type III radio burst automatic analysis system and statistic results for a half solar cycle with Nan{\\c{c}}ay Decameter Array data},\n author={Zhang, PJ and Wang, Chuan Bing and Ye, Lin},\n journal={Astronomy \\& Astrophysics},\n volume={618},\n pages={A165},\n year={2018},\n publisher={EDP Sciences}\n}\n```\n",
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