ZTF Forced Photometry service utilities

ztfutils.py

import math
import re
from typing import Literal
import numpy as np
import pandas as pd
import requests
import multiprocessing
from multiprocessing.pool import ThreadPool
import os

'''
# ZTF Forced photometry utilities
'''

__all__ = ['request_fp', 'download_lightcurves', 'filter_lightcurve', 'read_lc', 'get_airmass', 'color_correction']

_JulianDay = float | int
ZTF_AUTH = ('ztffps', 'dontgocrazy!')

# Function to request forced photometry from ZTF
def request_fp(ztf_user : str, ztf_passkey : str, coordinates : np.ndarray,
					start_epoch : _JulianDay | None = None, end_epoch : _JulianDay | None = None) -> None:
	'''
	## Perform a request to ZTF Forced Photometry service (ZFPS)
	### Parameters:
	* `ztf_user`, `ztf_passkey` (str): ZTF Forced photometry service authentication email and userpass
	* `coordinates` (ndarray): 2D array containing right ascension and declination in degrees
	* `start_epoch` (JulianDay): Start epoch for the requested light curves. If None then ZTF will compute it from the start of the survey (JD= 2458194.5)
	* `end_epoch` (JulianDay): End epoch for the requested light curves. If None, ZTF will take the recentmost epoch from the current day.
	
	## Further instructions:
	https://irsa.ipac.caltech.edu/data/ZTF/docs/ztf_zfps_userguide.pdf 
	'''
	url = 'https://ztfweb.ipac.caltech.edu/cgi-bin/batchfp.py/submit'
	
	assert bool(start_epoch) == bool(end_epoch), 'Both or None start and end epochs should be specified.'
	if start_epoch:
		assert start_epoch < end_epoch, 'start_epoch should always be smaller than end_epoch'
	
	if len(coordinates) >= 1500:
		raise ValueError('Max request batch size is 1500 elements')
	
	print('Attempting to send a request to ztfweb.ipac.caltech.edu ...')
	parameters = {
				'ra': str(coordinates[:, 0].tolist()),
				'dec': str(coordinates[:, 1].tolist()),
				'email': ztf_user,
				'userpass': ztf_passkey
				}
	if start_epoch:
		parameters['jdstart'] = str(start_epoch)
		parameters['jdend'] = str(end_epoch)
	
	response = requests.post(url, auth= ZTF_AUTH, data = parameters)
	if response.status_code == 200:
		print('Done. Check your email tomorrow 👍')
	
	print(f'Process exited with code {response.status_code}')
	
_ZTFBand = Literal['ZTF_g', 'ZTF_r', 'ZTF_i']
def filter_lightcurve(lc : pd.DataFrame, *, max_seeing : float = 4,
							 band : _ZTFBand = 'ZTF_g', strict_calibration : bool = True) -> pd.DataFrame:
	'''Performs a quality filtering process as described in: https://irsa.ipac.caltech.edu/data/ZTF/docs/ztf_zfps_userguide.pdf '''
	filter = (
				(lc['infobitssci'] <= (0 if not strict_calibration else 33554432)) &					# Bad weather conditions
				 (lc['sciinpseeing'] < max_seeing) &							# Max seeing of 4 arcsec
				 (lc['filter'] == band) &							# Only use g-band filter
				 (lc['scisigpix'] <= 25) &
				#  (lc['mag_tot'] < 20.5) &
				 (lc['diffmaglim'] > 20.5) &
				 (lc['ccdid'] == lc['ccdid'].mode()[0]) &
				 (lc['forcediffimflux'] > -9999)
				 )
	return lc[filter]


def download_lightcurves(ztf_user : str, ztf_passkey : str, download_dir : str, 
								 threads : int = multiprocessing.cpu_count() - 1) -> None:
	'''
	## Download forced photometry processed lightcurves from ZTF
	Requires to have sent a request to ZTF prior to download.
	### Parameters:
	* `ztf_user`, `ztf_passkey` (str): ZTF Forced photometry service authentication email and userpass
	* `download_dir` (str): Output directory where the lightcurves will be saved. In case that the directory doesnt exist, a new one will be created.
	* `threads` (int): Number of CPU cores to accelerate the download process, defaults to the number of cores installed in the system minus one.
	The lightcurves will be saved in `download_dir` without further processing in an ASCII txt format.
	'''
	
	response_url = 'https://ztfweb.ipac.caltech.edu/cgi-bin/getBatchForcedPhotometryRequests.cgi'
	ztf_url = 'https://ztfweb.ipac.caltech.edu'
	
	if not os.path.isdir(download_dir):
		os.mkdir(download_dir)
	
	response = requests.get(response_url ,auth= ZTF_AUTH,
								params= {
									'email': ztf_user,
									'userpass': ztf_passkey,
									'option': 'All recent jobs',
									'action': 'Query Database'})
	if response.status_code != 200:
		raise ConnectionError(f'Something went wrong, status code: {response.status_code}')
	response_table = pd.read_html(response.text)[0]

	session = requests.Session()
	pool = ThreadPool(threads)

	def do_download(row : pd.Series):
		response = session.get(ztf_url + row['lightcurve'], auth= ZTF_AUTH)
		if response.status_code != 200:
			print(f'ERROR fetching lightcurve {row["reqId"]}, status code: {response.status_code}')
			return
		
		with open(download_dir + f'{row["ra"]}_{row["dec"]}__fplc{row["reqId"]}.txt', 'w') as out:
			out.write(response.text)
		print(f'Sucessfully downloaded light curve {row["reqId"]} at {out.name}')
	pool.map(do_download, response_table.iloc)
	pool.close()
	pool.join()
	session.close()

	print('Done.')

FPCOORD_PATTERN = re.compile(r'([\d|\.]*)_?([\d|\.|\-]*)__.*')
def read_lc(path : str, include_coords= True, **read_kws)-> pd.DataFrame | tuple[pd.DataFrame, tuple[float, float]]:
	'''
	## Reads a forced photometry lightcurve into a DataFrame.
	By default, the output lightcurves from ZTF cannot be parsed as .csv files by pandas, this function setups many parameters of the read_csv function to read such lightcurves, including chaning the names for the columns and computing useful columns.
	### Parameters:
	* `path` (str): The input path to the lightcurve ASCII file (should end in .txt)
	* `include_coords` (bool): Extract and include the RA, DEC coordinates from the file name?. Note that this only applies if the lighturves were downloaded using `download_lightcurves`  
	* `read_kws` (Any): Additional parameters passed to pandas.read_csv 
	Note that passing `sep` , `comment`, `index_col`, `skipinitialspace` in `read_kws` will raise an error since they are already supplied by this function.
	'''

	lc = pd.read_csv(path, sep= ' ', comment= '#', index_col= 0, skipinitialspace= True, **read_kws)\
				.rename(columns= lambda col: col.replace(',', ''))
	
	
	# Useful columns
	lc['mjd'] = 				lc['jd'] - 2400000.5
	lc['nearestrefflux'] = 	10 ** ( 0.4 * (lc['zpdiff'] - lc['nearestrefmag']) )
	lc['flux_tot'] = 			lc['forcediffimflux'] + lc['nearestrefflux']
	lc['mag_tot'] = 			lc['zpdiff'] - 2.5 * np.log10(lc['flux_tot'])

	lc['magerr'] = 				(2.5 / math.log(10)) * (lc['forcediffimfluxunc'] / lc['flux_tot'])
	lc['snr_pixnoise_mag'] = 	lc['forcediffimsnr'] / lc['scisigpix']
	lc['snr_pixnoise_flux'] = 	(lc['forcediffimflux'] / lc['scisigpix'])
	lc['snt'] = 					lc['forcediffimflux']/lc['forcediffimfluxunc']
	
	if include_coords:
		matches = FPCOORD_PATTERN.match(os.path.basename(path))
		assert len(groups:=matches.groups()) == 2
		ra, dec = float(groups[0]), float(groups[1])
		return lc, (ra, dec)
	return lc

def get_airmass(lc : pd.DataFrame, coords : tuple[float, float]):
	from astropy.coordinates.builtin_frames import AltAz
	from astropy.coordinates.earth import EarthLocation
	from astropy.coordinates.sky_coordinate import SkyCoord
	from astropy.time.core import Time
	import astropy.units as u
	
	ra, dec = coords
	palomar_location = EarthLocation(lat= 33.35805555555555 	 * u.deg,
												lon= -116.86194444444445 * u.deg,
												height = 1712 * u.m)
	skycoord = SkyCoord(ra, dec, unit='deg').transform_to(
					AltAz(
						obstime= Time(lc['jd'], format='jd'), 
						location=palomar_location
						))
	return skycoord.secz

def color_correction(lc : pd.DataFrame, gps1 : float, rps1 : float, gps1_err : float = 0, rps1_err : float = 0,
							clr_name = 'clrcoeff', clru_name= 'clrcoeffunc', mag_name= 'mag_tot', magerr_name = 'magerr'):
	lc['mag_corr'] = lc[mag_name] + (gps1 - rps1) * lc[clr_name]

	# Not verified
	lc['mag_corr_err'] = np.sqrt( lc[magerr_name]**2 + ((gps1 - rps1) * lc[clru_name])**2 ) + ( (gps1_err - rps1_err) * lc[clr_name] )**2
	return lc

ZTF Forced Photometry service utilities

This script is made to simplify the process of requesting, downloading and processing light curves from the Zwitcky Transient Facility Forced Photometry Service (ZFPS).
It contains several functions with detailed documentation for each one of the aforementioned cases:

• Request forced photmetry with request_fp
• Download the light curves from ZTF servers with download_lightcurves
• Filter the outliers and badly calibrated points out of the light curve with filter_lightcurve
• Reads the downloaded light curves using pandas with read_lc

For further details, see:
https://irsa.ipac.caltech.edu/data/ZTF/docs/ztf_zfps_userguide.pdf

Description of the functions

Perform a request to ZTF Forced Photometry service (ZFPS)

request_fp

Parameters:

• ztf_user, ztf_passkey (str): ZTF Forced photometry service authentication email and userpass
• coordinates (ndarray): 2D array containing right ascension and declination in degrees
• start_epoch (JulianDay): Start epoch for the requested light curves. If None then ZTF will compute it from the start of the survey (JD= 2458194.5)
• end_epoch (JulianDay): End epoch for the requested light curves. If None, ZTF will take the recentmost epoch from the current day.

Download forced photometry processed lightcurves from ZTF

download_lightcurves
Requires to have sent a request to ZTF prior to download.

Parameters:

• ztf_user, ztf_passkey (str): ZTF Forced photometry service authentication email and userpass
• download_dir (str): Output directory where the lightcurves will be saved. In case that the directory doesnt exist, a new one will be created.
• threads (int): Number of CPU cores to accelerate the download process, defaults to the number of cores installed in the system minus one.

The lightcurves will be saved in download_dir without further processing in an ASCII txt format.

Filter light curve data

filter_lightcurve
Performs a quality filtering process as described in: https://irsa.ipac.caltech.edu/data/ZTF/docs/ztf_zfps_userguide.pdf (Section 6.1)

Parameters:

• lc (DataFrame): The input light curve to operate.
• max_seeing (arcseconds): Maximum width of the PSF allowed during the observation. Defaults to 4".
• band (Literal['ZTF_g', 'ZTF_r', 'ZTF_i']): The ZTF band to filter the data from the light curve file. Defaults to ZTF_g
• strict_calibration (bool): Whether to discard all bad data points (True) or just poorly calibrated/contaminated ones(False). Defaults to True.

Read a forced photometry lightcurve into a DataFrame.

read_lc
By default, the output lightcurves from ZTF cannot be parsed as .csv files by pandas, this function setups many parameters of the read_csv function to read such lightcurves, including chaning the names for the columns.

Parameters:

• path (str): The input path to the lightcurve ASCII file (should end in .txt)
• read_kws (Any): Additional parameters passed to pandas.read_csv

Note that passing sep , comment, index_col, skipinitialspace in read_kws will raise an error since they are already supplied by this function.

Note that this script was tested with python 3.11, other versions (specially the ones that dont have annotations yet) may break.

Usage

from ztfutils import *

coordinates = candidates[['ra', 'dec']]
request_fp('[email protected]', '1234', coordinates.to_numpy(), start_epoch= 2458194.5, end_epoch= 2460370.5)