KBMod Analysis Demo

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.
time_file: Location of a file with each row containing (visit_num, visit_mjd) for observations in dataset.

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.dat

4) 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 [20]:

    
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%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/fake_results/'
im_dir = '/home/kbmod-usr/cuda-workspace/HITS/clean_setup/test_images/'



In [4]:

    
image_list = sorted(os.listdir(im_dir))
image_list = [os.path.join(im_dir, im_name) for im_name in image_list]



In [5]:

    
stamper = create_stamps()



In [6]:

    
times_filename = os.path.join(results_dir, 'times_test.txt')
times_list = stamper.load_times(times_filename)



In [7]:

    
lc_filename = os.path.join(results_dir, 'lc_test.txt')
lc_list = stamper.load_lightcurves(lc_filename)



In [8]:

    
stamp_filename = os.path.join(results_dir, 'ps_test.txt')
stamps = stamper.load_stamps(stamp_filename)



In [9]:

    
result_filename = os.path.join(results_dir, 'results_test.txt')
results = stamper.load_results(result_filename)



In [10]:

    
keep_idx = stamper.stamp_filter(stamps, 0.03)









    



Center filtering keeps 25 out of 42 stamps.



In [11]:

    
stamps_fig = stamper.plot_stamps(results, lc_list, stamps, 0.03)









    



Center filtering keeps 25 out of 42 stamps.



In [15]:

    
fake_df = pd.read_csv('/home/kbmod-usr/cuda-workspace/HITS/clean_setup/fake_results/results_fakes.txt', delimiter=' ', 
            header=None, names=['x', 'y', 'vx', 'vy', 'flux', 'mag_val'])



In [16]:

    
res_df = pd.DataFrame(results).iloc[keep_idx]



In [17]:

    
mag_results = []
for res_idx in keep_idx:
    mag_results.append(stamper.calc_mag(image_list, lc_list[res_idx], times_list[res_idx]))



In [25]:

    
fig = plt.figure(figsize=(12,12))
plt.hist(fake_df['mag_val'], bins=np.arange(20, 26), histtype='step', lw=6)
plt.hist(mag_results, bins=np.arange(20,26), histtype='step', lw=6)
plt.xlabel('magnitude')
plt.ylabel('counts')









    Out[25]:





Text(0,0.5,'counts')



In [ ]: