jasmine

JASMINE: Joint Analysis of Simulation for Microlensing INterested Events

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pip installable as jasmine-astro

pip install jasmine-astro

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1. Reading RTModel outputs

ModelResults class

from jasmine import ModelResults
model = ModelResults(file_to_be_read='[your_path]/[Final]Models/LX0000-1.txt')
print(model.model_type, model.model_extensive_name)
print(model.model_parameters)

See notebook analysis/reading_rtmodel_models.ipynb

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2. Generating a Binary lens signal based on one of the 113 RTModel templates

RTModelTemplateForBinaryLightCurve class

from jasmine import RTModelTemplateForBinaryLightCurve
rtmodel_template_two_lenses = RTModelTemplateForBinaryLightCurve(template_line=2,
                                                                     path_to_template=template_path,
                                                                     input_peak_t1=300,
                                                                     input_peak_t2=302)
magnification, times = rtmodel_template_two_lenses_.rtmodel_magnification_using_vbb()

See notebook analysis/generating_lightcurves_from_rtmodel_templates.ipynb

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3. Microlensing Data Challenge Simulations

Splitting master file

1. Make sure you downloaded the master_file.txt and wfirstColumnNumbers.txt from the data challenge folder:
   https://github.com/microlensing-data-challenge/data-challenge-1/tree/master/Answers
2. Run python jasmine/files_organizer/data_challenge_prep.py , changing path as needed. It splits master file and create 4 new files:
   • binary_star.csv
   • bound_planet.csv
   • cataclysmic_variables.csv
   • single_lens.csv

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Using the LightcurveEventDataChallenge class:

from jasmine import LightcurveEventDataChallenge
the_lightcurve = LightcurveEventDataChallenge(2) # Binary star # Call the lightcurve class
vars(the_lightcurve).keys() # See what are the available attributes and subclasses
the_lightcurve.lens # subclass
lightcurve_datapoints = the_lightcurve.lightcurve_data(filter_='W149', folder_path_='../data') # Get the lightcurve datapoints

See notebook analysis/getting_information_about_a_lightcurve.ipynb for details.

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If you opt to not use a class. You can use the functions below: See notebook analysis/reading_the_data_challenge.ipynb for more details.

1. Reading the four master csv files

Call the function you need, and it returns a pandas dataframe:
import jasmine.files_organizer.data_challenge_reader as dcr

• dataframe = dcr.binary_star_master_reader()
• dataframe = dcr.bound_planet_master_reader()
• dataframe = dcr.cataclysmic_variables_master_reader()
• dataframe = dcr.single_lens_master_reader()

Obs: The column you are looking for is: data_challenge_lc_number.

2. Reading the light curve data points files

The function lightcurve_data_reader reads the light curve files and returns a pandas dataframe with BJD, Magnitude, Error and days (days = BJD - 2450000) ':

import jasmine.files_organizer.data_challenge_reader as dcr
lightcurve_df = dcr.lightcurve_data_reader(data_challenge_lc_number_=5, folder_path_='../data')