merida

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
cd merida
pip install ./

Installing as a pip package:

pip install merida

To visualize a light curve from the MOA 9 year dataset:

• 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'

After done that, you just have to run the following command in the terminal:

bokeh serve --show visualization_tool.py

To locally download a single MOA9yr lightcurve from the NExSci archive:

• 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_]/'

After done that, you just have to run the following command in the terminal:

python lightcurve_downloader.py

To locally download ALL MOA9yr lightcurves from the NExSci archive [can be improved]:

• 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]'. Only feather and CSV supported for now.
  • Single threaded, this will take ~55 days. The script parallelizes this across 15 processes, and this is adjustable.

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