MERIDA: MOA9yr Exploration and Research Interface for Dataset Analysis
Installing from github (should be most updated available version):
git clone https://github.com/stelais/merida.git
pip install -r requirements.txt
Installing as a pip package:
pip install merida
- In
visualization_tool.py
:- Change the
lightcurve_name
variable to the name of the light curve you want to visualize. - Define where your data is
data_path ='data/positive'
- Change the
After done that, you just have to run the following command in the terminal:
bokeh serve --show visualization_tool.py
- In
lightcurve_downloader.py
:- Change the
lightcurve_name
variable to the name of the light curve you want to download. - You can change the path by adding the variable
path_to_save_ ='[the_path_you_want_]/'
- Change the
After done that, you just have to run the following command in the terminal:
python lightcurve_downloader.py
- This will take ~ 55 days... For now, I recommend breaking this function in 15 pieces and run parallel, e.g. piece #2:
metadata = Metadata() n = 2 df_total = metadata.dataframe df_temp = df_total[df_total['ROW_NUM'] > (n-1)*160604] df = df_temp[df_temp['ROW_NUM'] <= n*160604]
- In
all_lightcurves_downloader.py
:- You can change the path by changing the variable
path_to_save_ ='[the_path_you_want_]/'
- You can also change the extension
lightcurve_extension_='.[extension]'
. Onlyfeather
andCSV
supported for now.
- You can change the path by changing the variable
After done that, you just have to run the following command in the terminal:
python all_lightcurves_downloader.py
[Currently]
- All lightcurves from MOA 9 year data set from NEXSci archive should work. If it doesn't work, you can let me know.
- Script to download ALL lightcurves from MOA 9 year data set from NEXSci archive.
- Script to read METADATA.
[Future]
- Identify any data for MOA 9 yeardata set from NEXSci archive based on RA and DEC.
[Virtual tool in progress...] https://merida.onrender.com/visualization_tool
-
Only the light curve gb10-R-5-6-130249 as an example (it load this data from this Github repository)
-
Takes about one minute to load it
visualization_tool.py