To run an example of kbmod from start to finish you can use the following steps:
1) Setup kbmod with the setup.bash file in the main directory of the repo by: source setup.bash
2) Run example_search.py with the following settings:
image_dir: The directory with the images from a single field
results_dir: The directory where you want to save the results
results_suffix: Suffix to add when saving results files. Setting this as test then files will be saved as "..._test.txt". Five results files would be saved:
lc_test.txt: File with the light curves from results after going through Kalman Filter.results_test.txt: File with the x,y pixel locations, velocity, flux, likelihood, and number of unmasked observations of each result.ps_test.txt: File with the summed postage stamp for each result.times_test.txt: File with the MJD of each observation in the saved lightcurve.filtered_likes_test.txt: The recalculated likelihood values for each results after removing observations with the Kalman Filter.3) Command will be like the following:
python3 example_search.py --image_dir image_dir --results_dir results_dir --results_suffix test --time_file image_times.dat4) Once you have the results you can use the steps in this notebook to create plots with postage stamps and lightcurves side-by-side for a final look by eye.
In [1]:
import os
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
from create_stamps import create_stamps
In [3]:
# Directory where output from kbmod search are located
results_dir = '/home/kbmod-usr/cuda-workspace/HITS/clean_setup/results_dir/'
In [4]:
stamper = create_stamps()
In [5]:
lc_filename = os.path.join(results_dir, 'lc_test.txt')
lc_list = stamper.load_lightcurves(lc_filename)
In [6]:
stamp_filename = os.path.join(results_dir, 'ps_test.txt')
stamps = stamper.load_stamps(stamp_filename)
In [7]:
result_filename = os.path.join(results_dir, 'results_test.txt')
results = stamper.load_results(result_filename)
In [8]:
stamps_fig = stamper.plot_stamps(results, lc_list, stamps, 0.03)
In [ ]: