Notebook

DOLPHOT_LC¶

Dolphot-LC is an automated Hubble Space Telescope (HST) data pipeline based on the popular Dolphot analysis package. This package allows for the creation of lightcurves and difference images from HST data.

This Jupyter Notebook allows the user to run their own fits images through the pipeline and generate results. Requires a coadded template image and science images that are already aligned to template.

Before running this notebook, ensure the following steps are done.

Follow the dolphot_lc installation steps listed here - https://dolphot-lc.readthedocs.io/en/latest/install.html

Activate your python=3.7, dolphot astroconda environment.

conda activate /env name/

To use install this conda environment in Jupyter Notebook, run the command:

 python -m ipykernel install --user --name=/env name/

Now change the kernel version of this notebook to your conda environment.

In [ ]:

import dolphot_lc as dlc
import time
import os

start = time.time()
sexpath = 'source-extractor'

List working directories and add your fits files to the correct locations.

Use the example folder as the base directory. Within this directory create the folders "Images", "image_backup", and "ref". Within image_backup folder create the folders "ims", "ref", and "template". Within Images folder create the folders "ims" and "template".

Place fits image files in /example/Images/, /example/Images/ims/, and /example/image_backup/ims/.

Make sure your coadded image is named "coadd_CLASH_sci.fits". Place this file in /example/ref/, and /example/image_backup/ref/.

Make sure your coadded image is named "coadd_tweak_F110W_sci.fits". Place this file in /example/Images/, /example/Images/template, and /example/image_backup/template/.

Note that the same file is used as the template and registration image.

In [ ]:

imroot = os.getcwd()                                # example folder / base directory

orig_im_loc = f'{imroot}/image_backup/ims'          # image
orig_temp_loc = f'{imroot}/image_backup/template'   # template
im_loc = f'{imroot}/Images'                         # path to working directory
ref_image = f'{imroot}/ref/coadd_CLASH_sci.fits'    # registration image

dolphot_path = '/Users/rwhite/dolphot_lc/example/dolphot2.0/bin'

List the RA and DEC coordinates of the object in sexigesimal and degree units:

In [ ]:

sn_ra_me, sn_dec_me = '13:47:31.8180', '-11:45:51.914'

objCoords = {'S1': [206.8833, -11.7644],
             'S2': [206.8833, -11.7644],
             'S3': [206.8833, -11.7644],
             'S4': [206.8833, -11.7644],
             'SX': [206.8833, -11.7644]
             }

Input dolphot parameters:

For help with this step, identify the HST instrument used to aquire your images and visit DOLPHOT user's guide for recommended settings. http://americano.dolphinsim.com/dolphot/dolphotWFC3.pdf

In [ ]:

dolphot_params = {
    'UseWCS': 1,
    'raper': 5,
    'rchi': 1.5,
    'rsky0': 8,
    'rsky1': 3,
    'rpsf': 15,
    'WFC3IRpsfType': 1,      # wide field camera 3 IR
    }

Run dolphot process, nothing to change here.

In [ ]:

a = dlc.prep_directory(orig_im_loc, orig_temp_loc, im_loc, ref_image,
                       dolphot_path, imroot, sn_ra_me, sn_dec_me, sexpath,
                       dolphot_params)

dlc.prep_files_for_dolphot('/dolphot_prepped',
                           r_in=15,
                           r_out=35,
                           step=4,
                           sig_low=2.25,
                           sig_high=2.00,
                           dlc_param=a)  #Processes images through masking/spliting/calcsky programs in Dolphot

dlc.dolphot_simultaneous(a)         #Creates Dolphot parameter file & runs on processed images


dlc.blot_back(r_in=15,              #Inner radius of sky annulus
              r_out=35,             #Outer radius of sky annulus
              step=4,               #How often is the sky value is sampled in pixels
              sig_low=2.25,         #Low sigma under which samples will be rejected
              sig_high=2.00,        #High sigma above which samples will be rejected
              dlc_param=a)          #Parameter object from prep_directory function
        #Blots coadded template image to distorted science images & creates difference image

In [ ]:

dlc.dolphot_force(apermag=False, force_same_mag=True,  psfphot=1,
                  objCoords=objCoords, dlc_param=a)             #Runs Dolphot on difference images

end = time.time()

a = (end - start)/60
m = 0
while a > 1:
    a = a - 1
    m = m + 1
print(f'Time: {m}:{str(int(60*a)).zfill(2)}')

Aftering running DOLPHOT forced photometry, the results are placed in the /example/diffs/ folder to be analyzed.

Analyze the Results¶

Refer to the analyze_data.py file in order to garner results. Add the following code to this file.

Declare the type of object to be analyzed and pixel coordinates of the object.

objs = [['SN', 567.9625526342381, 176.49369319498047]]

Finally, run:

imroot = os.getcwd()                                # example folder / base directory
imdir_prepped = f'{imroot}/diffs/'
a = dolphot_output( imdir_prepped + 'singlestar')
for objname,x,y in objs:
    a.info_single_object(x, y, objname, 'orig', settings=False, verbose=False)