pydolphot

A python wrapper for DOLPHOT.

pydolphot is inspired by dweisz.

Features

Support WFC3, ACS, and WFPC2.

Support handling more than two filters.

Instruction

Preparation

If it's the first time you use the code, please remember to set dol variable to the location of this repository on your computer. Here is an example using bash

export dol="/data/Github/pydolphot"

It uses axel to accelerate the download process. Be sure to install it before running the code. If axel is not found, it will use wget.

Download the data (optional)

Get access for the data from HST archieve and ask for an anonymous download using email (This is only format support by this script). You can use your favorite ways to download the data, just put them under folder raw once you finish the download.

Use the following command to download the data (change the last 5 digits to your request)

python $dol/download.py index [-h] [--user user] [--passwd passwd] index

index is the anonymous number of your request.

All the raw data downloaded will appear in the folder raw and remain unchanged. Feel free to remove them after you have reduced the data.

Run the dolphot

dol.py combine all the procedures into one file

python $dol/dol.py [-ih] [--force]

It will ask you to select one the drz fits file as the template. If no drizzle is required, anyone should work.

If force is enabled, then it will just pick the first drz it finds, which is useful when you want it be automatic. -i, --info would print the basic information of this data, including the PI's name (if available), Proposal ID, filters, and exposure time.

This file will do mask, split, calsky, and dolphot automatically and generate the output files output1, output2, ...

After the previous command finishes, we need to combine the output files into a single file

python $dol/phot.py

It will read the filters from output{chip}.columns and save the result into o.summary.fits in folder final.

In the meantime, it will also make a selection on the signal-to-noise ratio, sharpness, crowdedness, and object type. The corresponding result is saved in o.gst.fits.

If you want to know more about the selection criteria, please refer to dolphot for more information. And if you change the criteria here, remember to make the same change in photfake.py.

Completeness test

Use the following command to generate a fake star list automatically in the name of complete.fits

python $dol/comp.py [-h] [-n fake star number]

It would generate a fake star list span all the magnitude range with a weighted number. The data set is composed of 5 columns: chip, X, Y, and brightness in all the filters. The filter name should end with _VEGA, for example, F475W_VEGA or F814W_VEGA.

Fake star test

python $dol/fake.py [-f fakefile] [-r run] [-c num] [--force] [--con]

The fakefile is set to complete by default --run control the number of fake stars per run. This is designed to control the influence of brightness variance from the fake star.

-c control the number of core used. This code use multiple-core to acceleration the fake star test, which can save a lot of time.

--force if enabled, it would clear up the output folder from the last run and begin a new start

--con if enabled, it would continue the last run, which may be terminated for any cause.

During the fake star test, a folder named after the fake star file will be created and store all the middle files. Be sure not to delete it before you run photfake.py command. Please don't name the fake star file as final.fits, which would leave a lot of trash in folder final.

photfake.py is used to generate the result from fake star tests. The parameters should keep the same as fake.py

python $dol/photfake.py [-f outputfolder] [-r run] [-c num]

--folder is the output folder's name from fake.py

-r which be the same as the parameter in fake.py

-c control the number of core used.

The output files will be saved as f.complete.fits (the middle part is the same as the fake star file name) in folder final. The column flag present whether the fake star is detected or not.

Both fake.py and photfake.py utilize multiple cores to accelerate the calculation. You may want to change the size of the pool depending on the condition of your computer.

Cheatsheet

If you are lazy and don't want to try this code step by step, you can also use the workflow I have built

python $dol/workflow.py index