Photometry_5.py

#!/usr/bin/env python
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# xxxxxxxxxxxxxxxxxxxxxxxx-------------------PHOTOMETRY OF OBJECT FRAMES-----------------xxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #

# ------------------------------------------------------------------------------------------------------------------- #
# Import Required Libraries
# ------------------------------------------------------------------------------------------------------------------- #
import os
import re
import sys
import glob
import math
import ephem
import datetime
import numpy as np
import pandas as pd
import easygui as eg
from pyraf import iraf
from astropy.io import fits
from astropy.coordinates import Angle
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Observatory Site Details
# ------------------------------------------------------------------------------------------------------------------- #
OBS_NAME = 'Indian Astronomical Observatory, Hanle'
OBS_LONG = '78:57:51'
OBS_LAT = '32:46:46'
OBS_ALT = 4486
OBS_TIMEZONE = +5.5
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Telescope CCD Specifications
# ------------------------------------------------------------------------------------------------------------------- #
read_noise = 4.87
ccd_gain = 1.22
data_max = 55000
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Extinction Coefficients (In Magnitudes) For Hanle In Different Photometric Bands
# ------------------------------------------------------------------------------------------------------------------- #
eeta = {'7BesU': 0.36, '6BesB': 0.21, '5BesV': 0.12, '4BesR': 0.09, '3BesI': 0.05}
eeta_err = {'7BesU': 0.07, '6BesB': 0.04, '5BesV': 0.04, '4BesR': 0.04, '3BesI': 0.03}
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Image Header Keywords
# ------------------------------------------------------------------------------------------------------------------- #
RA_keyword = 'RA'
DEC_keyword = 'DEC'
UT_keyword = 'UT'
DATE_keyword = 'DATE-OBS'
FILTER_keyword = 'FILTER'
AIRMASS_keyword = 'AIRMASS'
EXPTIME_keyword = 'EXPTIME'
TIMESTART_keyword = 'TM_START'
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Object Details
# ------------------------------------------------------------------------------------------------------------------- #
OBJECT_NAME = 'ASASSN-14dq'
OBJECT_RA = '21:57:59.9'
OBJECT_DEC = '+24:16:08.1'
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Load Required IRAF Packages
# ------------------------------------------------------------------------------------------------------------------- #
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.ptools(_doprint=0)
iraf.ccdred.instrument = 'ccddb$kpno/camera.dat'
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Functions For Handling Files & Lists
# ------------------------------------------------------------------------------------------------------------------- #

def remove_file(file_name):
    """
    Removes the file 'file_name' in the constituent directory.
    Args:
         file_name  : Name of the file to be removed from the current directory
    Returns:
        None
    """
    try:
        os.remove(file_name)
    except OSError:
        pass


def remove_similar_files(common_text):
    """
    Removes similar files based on the string 'common_text'.
    Args:
        common_text : String containing partial name of the files to be deleted
    Returns:
        None
    """
    for residual_file in glob.glob(common_text):
        remove_file(residual_file)


def group_similar_files(text_list, common_text, exceptions=''):
    """
    Groups similar files based on the string 'common_text'. Writes the similar files
    onto the list 'text_list' (only if this string is not empty) and appends the similar
    files to a list 'python_list'.
    Args:
        text_list   : Name of the output text file with names grouped based on the 'common_text'
        common_text : String containing partial name of the files to be grouped
        exceptions  : String containing the partial name of the files that need to be excluded
    Returns:
        list_files  : Python list containing the names of the grouped files
    """
    list_files = glob.glob(common_text)
    if exceptions != '':
        list_exception = exceptions.split(',')
        for file_name in glob.glob(common_text):
            for text in list_exception:
                test = re.search(text, file_name)
                if test:
                    try:
                        list_files.remove(file_name)
                    except ValueError:
                        pass

    list_files.sort()
    if len(text_list) != 0:
        with open(text_list, 'w') as f:
            for file_name in list_files:
                f.write(file_name + '\n')

    return list_files


def text_list_to_python_list(text_list):
    """
    Returns data in the file 'text_list' as a python_list.
    Args:
        text_list   : Input file containing filenames
    Returns:
        python_list : List of all the elements in the file 'text_list'
    Raises:
        ERROR : File 'text_list 'Not Found
    """
    if os.path.isfile(text_list):
        with open(text_list, 'r+') as f:
            python_list = f.read().split()
            return python_list
    else:
        print("ERROR : File '{0}' Not Found".format(text_list))
        sys.exit(1)


def list_statistics(list_values):
    """
    Returns the statistics of the list of elements in the input 'list_values'.
    Args:
        list_values : Input list of elements
    Returns:
        value_mean  : Mean of the list of elements
        value_median: Median of the list of elements
        value_std   : Standard Deviation of the list of elements
    """
    value_mean = np.mean(list_values)
    value_median = np.median(list_values)
    value_std = np.std(list_values)

    return value_mean, value_median, value_std


def reject(list_values, iterations=2):
    """
    Rejects outliers from the input 'list_values'.
    Args:
        list_values : Input list of elements
        iterations  : No. of iterations of rejection to be run on the input list
    Returns:
        list_reject : Output list after rejecting outliers from the input 'list_values'
    """
    list_reject = filter(lambda x: x != 'INDEF', list_values)
    list_reject = map(float, list_reject)
    list_reject.sort()

    for _ in range(0, iterations):
        if len(list_values) > 2:
            value_mean, value_median, value_std = list_statistics(list_reject)

            if abs(list_reject[0] - value_median) < abs(list_reject[-1] - value_median):
                remove_index = -1
            else:
                remove_index = 0

            if abs(list_reject[remove_index] - value_median) > value_std:
                list_reject.pop(remove_index)

    return list_reject


def python_list_to_text_list(python_list, text_list):
    """
    Put the data from the input 'python_list' to a file 'text_list' line-wise.
    Args:
        python_list : Python_list from which data has to be read
        text_list   : Name of the text file onto which data has to be appended
    Returns:
        None
    """
    with open(text_list, 'w') as f:
        for element in python_list:
            f.write(str(element) + '\n')


def zip_list(list_lists):
    """
    Combines all the lists in a list to a single list element-wise.
    Args:
        list_lists  : List of all the lists which needs to be zipped
    Returns:
        new_list    : List with the combined elements
    """
    new_list = []
    for index in range(0, len(list_lists[0])):
        value = ''
        for val in range(0, len(list_lists)):
            value += list_lists[val][index]
        new_list.append(str(value))

    return new_list


def display_text(text_to_display):
    """
    Displays text mentioned in the string 'text_to_display'
    Args:
        text_to_display : Text to be displayed
    Returns:
        None
    """
    print("\n" + "# " + "-" * (12 + len(text_to_display)) + " #")
    print("# " + "-" * 5 + " " + str(text_to_display) + " " + "-" * 5 + " #")
    print("# " + "-" * (12 + len(text_to_display)) + " #" + "\n")

# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Functions For Tasks In IRAF
# ------------------------------------------------------------------------------------------------------------------- #

def imexam_fwhm(text_list, coord_file, log_imexam='log_imexam'):
    """
    Examines the images in the list 'python_list' at the coordinates mentioned in the file "stars.coo"
    and logs the output onto the file "log_imexam".
    Args:
        text_list    : Text list containing names of FITS files
        coord_file   : Text file listing the coordinates of selected stars in the field
        log_imexam   : Name of the text list to record log of IMEXAM
    Returns:
        None
    """
    remove_file(log_imexam)
    list_files = text_list_to_python_list(text_list)

    task = iraf.images.tv.imexam
    task.unlearn()

    for file_name in list_files:
        task.logfile = log_imexam       	        # Log File To Record Output Of The Commands
        task.keeplog = 'yes'                        # Log Output Results?
        task.defkey = 'a'                           # Default Key For Cursor x-y Input List
        task.imagecur = coord_file	                # Image Display Cursor Input
        task.use_display = 'no'                     # Use The Image Display?

        task(input=file_name, frame=1)


def data_pars(fwhm_value, data_max):
    """
    Edits the data dependent parameters(DATAPARS) required by the DAOPHOT tasks.
    Args:
        fwhm_value  : Mean FWHM value for the image file
        data_max    : Maximum good pixel value
    Returns:
        None
    """
    task = iraf.noao.digiphot.daophot.datapars
    task.unlearn()

    task.scale = 1.0                                # Scale Of The Image In Arcseconds Per Pixel
    task.fwhmpsf = float(fwhm_value)                # FWHM Of The PSF In Scale Units
    task.emission = 'yes'                           # All Features Are Considered To Be Emission Features
    task.datamin = 'INDEF'                          # Minimum Good Pixel Value
    task.datamax = data_max                         # Maximum Good Pixel Value
    task.noise = 'poisson'                          # Noise Model Used To Estimate Uncertainties In APPHOT Magnitudes
    task.sigma = 'INDEF'                            # Standard Deviation Of The Sky Pixels
    task.readnoise = read_noise                     # Readout Noise Of The CCD In Electrons
    task.epadu = ccd_gain                           # Gain Of The CCD In Electrons Per ADU
    task.exposure = EXPTIME_keyword                 # Exposure Time Keyword In Image Header
    task.airmass = AIRMASS_keyword                  # Airmass Keyword In Image Header
    task.filter = FILTER_keyword                    # Filter Keyword In Image Header
    task.obstime = UT_keyword                       # UT Keyword In Image Header


def center_pars():
    """
    Edits the centering algorthm parameters(CENTERPARS) required by the DAOPHOT tasks.
    Returns:
        None
    """
    task = iraf.noao.digiphot.daophot.centerpars
    task.unlearn()

    task.calgorithm = 'centroid'                    # Centering Algorithm
    task.cbox = 5                                   # Centering Box Width In Scale Units
    task.cthreshold = 0                             # Centering Threshold In Sigma Above Background


def fitsky_pars(fwhm_value):
    """
    Edits the sky fitting algorithm parameters(FITSKYPARS) requred by the DAOPHOT tasks.
    Args:
        fwhm_value  : Mean FWHM value for the image file
    Returns:
        None
    """
    task = iraf.noao.digiphot.daophot.fitskypars
    task.unlearn()

    task.unlearn()
    task.salgorithm = 'mode'                        # Sky Fitting Algorithm
    task.annulus = 5 * float(fwhm_value)            # Inner Radius Of Sky Annulus In Scale Units
    task.dannulus = 3                               # Width Of Sky Annulus In Scale Units


def phot_pars(aperture_values):
    """
    Edits the photometry parameters(PHOTPARS) required by the DAOPHOT tasks.
    Args:
        aperture_values : Mean FWHM value for the image file
    Returns:
        None
    """
    task = iraf.noao.digiphot.daophot.photpars
    task.unlearn()

    task.weighting = 'constant'                     # Photometric Weighting Scheme
    task.aperture = aperture_values                 # List Of Aperture Radii In Scale Units
    task.zmag = 25                                  # Zero Point Of Magnitude Scale


def dao_pars(fwhm_value, psf_radius):
    """
    Edits the DAOPHOT fitting parameters(DAOPARS) for the DAOPHOT tasks which compute the PSF.
    Args:
        fwhm_value  : Mean FWHM value for the image file
        psf_radius  : Radius for which PSF model is defined - (psf_radius * fwhm_value)
    Returns:
        None
    """
    psf_aperture = float(psf_radius) * float(fwhm_value)

    task = iraf.noao.digiphot.daophot.daopars
    task.unlearn()

    task.function = 'penny2'                        # Functional Form Of Analytic Component Of PSF Model
    task.varorder = 2                               # Order Of Variability Of The PSF Model
    task.nclean = 2                                 # Adtl. Iterations PSF Task Performs To Compute PSF Lookup-Tables
    task.fitsky = 'yes'                             # Computes New Sky Values For The Stars In The Input List
    task.matchrad = float(fwhm_value)               # Tolerance In Scale Units For Matching Stellar Centroids
    task.psfrad = 2 * psf_aperture                  # Radius Of The PSF Circle In Scale Units For The PSF Model
    task.fitrad = float(fwhm_value)                 # Fitting Radius In Scale Units
    task.sannulus = 5 * float(fwhm_value)           # Inner Radius Of Sky Annulus In Scale Units
    task.wsannulus = 3                              # Width Of The Sky Annulus In Scale Units


def phot(file_name, coord_file):
    """
    Performs PHOT task on the file 'file_name. Selects candidate stars from coordinate file 'coord_file'.
    Args:
        file_name    : FITS file on which aperture photometry is to be performed
        coord_file   : Name of the coordinate file containing candidate star
    Returns:
        None
    """
    task = iraf.noao.digiphot.daophot.phot
    task.unlearn()

    task.interactive = 'no'                         # Interactive Mode?
    task.radplot = 'no'                             # Plot The Radial Profiles?
    task.verbose = 'no'                             # Print Messages About Progress Of The Task?
    task.verify = 'no'                              # Verify Critical Parameters?
    task.update = 'no'                              # Update Critical Parameters(If Verify Is Yes)?

    task(image=file_name, coords=coord_file, output='default')


def pstselect(file_name, magfile_name, fwhm_value, data_max, psf_radius):
    """
    Selects candidate PSF stars from a photometry file(Performs PSTSELECT task) for the image file.
    Args:
        file_name   : Name of the image file
        magfile_name: Name of the mag file generated from PHOT task
        fwhm_value  : Mean FWHM value for the image file
        data_max    : Maximum good pixel value
        psf_radius  : Radius for which PSF model is defined = (psf_radius * fwhm_value)
    Returns:
        None
    """
    data_pars(fwhm_value, data_max)
    dao_pars(fwhm_value, psf_radius)

    task = iraf.noao.digiphot.daophot.pstselect
    task.unlearn()

    task.interactive = 'no'                         # Select PSF stars interactively?
    task.verbose = 'no'                             # Print Messages About Progress Of The Task?
    task.verify = 'no'                              # Verify Critical PSTSELECT Parameters?
    task.update = 'no'                              # Update Critical PSTSELECT Parameters(If Verify Is Yes)?

    task(image=file_name, photfile=magfile_name, pstfile='default', maxnpsf=25)


def psf(file_name, magfile_name, pstfile_name, fwhm_value, data_max, psf_radius):
    """
    Builds the PSF(Performs PSF task) for the image file. Builds the PSF for only the stars mentioned in the PST file.
    Args:
        file_name   : Name of the image file
        magfile_name: Name of the mag file generated from PHOT task for the image file
        pstfile_name: Name of the pst file generated from PSTSELECT task for the image file
        fwhm_value  : Mean FWHM value for the image file
        data_max    : Maximum good pixel value
        psf_radius  : Radius for which PSF model is defined -> (psf_radius * fwhm_value)
    Returns:
        None
    """
    data_pars(fwhm_value, data_max)
    dao_pars(fwhm_value, psf_radius)

    task = iraf.noao.digiphot.daophot.psf
    task.unlearn()

    task.matchbyid = 'yes'                          # Match PSF Star List To Photometry By ID Number?
    task.interactive = 'no'                         # Compute PSF Interactively?
    task.showplots = 'no'                           # Show Plots Of PSF Stars?
    task.verbose = 'no'                             # Print Messages About The Progress Of The Task?
    task.verify = 'no'                              # Verify Critical PSF Parameters?
    task.update = 'no'                              # Update Critical PSF Parameters (If Verify Is Yes)?

    task(image=file_name, photfile=magfile_name, pstfile=pstfile_name, psfimage='default',
         opstfile='default', groupfile='default')


def allstar(file_name, magfile_name, psffile_name, fwhm_value, data_max, psf_radius):
    """
    Groups and fits PSF to multiple stars simultaneously(Performs ALLSTAR task) in the image file.
    Args:
        file_name    : Name of the image file
        magfile_name : Name of the mag file generated from PHOT task for the image file
        psffile_name : Name of the psf file generated from PSF task for the image file
        fwhm_value   : Mean FWHM value for the image file
        data_max     : Maximum good pixel value
        psf_radius   : Radius for which PSF model is defined - (psf_radius * fwhm_value)
    Returns:
        None
    """
    data_pars(fwhm_value, data_max)
    dao_pars(fwhm_value, psf_radius)

    task = iraf.noao.digiphot.daophot.allstar
    task.unlearn()

    task.verbose = 'no'                             # Print Messages About Progress Of The Task?
    task.verify = 'no'                              # Verify Critical ALLSTAR Parameters?
    task.update = 'no'                              # Update Critical ALLSTAR Parameters(If Verify Is Yes)?

    task(image=file_name, photfile=magfile_name, psfimage=psffile_name, allstarfile='default',
         rejfile='default', subimage='default')


def txdump(common_text, output_file):
    """
    Performs TXDUMP task on the MAG or ALS files generated by photometry tasks. This extracts
    useful data from magnitude files.
    Args:
        common_text : Partial name of the MAG or ALS files from which data is to be extracted
        output_file : Output file where data from the list of input files is to be written
    Returns:
        None
    """
    if re.search('mag', common_text):
        fields = "ID, IMAGE, IFILTER, XCENTER, YCENTER, MSKY, XAIRMASS, RAPERT, MAG, MERR"
    else:
        fields = "ID, IMAGE, IFILTER, XCENTER, YCENTER, MSKY, XAIRMASS, PSFRAD, MAG, MERR"

    task = iraf.noao.digiphot.ptools.txdump
    task.unlearn()

    file_temp = 'temp_dump'
    group_similar_files(file_temp, common_text=common_text)
    task(textfile='@' + file_temp, fields=fields, expr='yes', Stdout=output_file)
    remove_file(file_temp)

# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Functions For Accessing & Manipulating Text File Data
# ------------------------------------------------------------------------------------------------------------------- #

def read_column(file_name, col_index, title_rows=0):
    """
    Extracts the specified column as a list from a text file.
    Args:
        file_name   : Text file from which the specified column has to be extracted
        col_index   : Index of the column to be extracted
        title_rows  : No. of rows used for title description
    Returns:
        data_column : List of all the elements extracted from the column
    """
    file_df = pd.read_csv(filepath_or_buffer=file_name, sep='\s+', header=None, skiprows=title_rows, engine='python')
    data_column = file_df.iloc[:, col_index].tolist()

    return data_column


def read_file(file_name, title_rows=0):
    """
    Extracts the file data as a list of columns from a text file.
    Args:
        file_name   : Text file from which file data has to be extracted
        title_rows  : No. of rows used for title description
    Returns:
        data_file   : List of all columns extracted from the text file
    """
    file_df = pd.read_csv(filepath_or_buffer=file_name, sep='\s+', header=None, skiprows=title_rows, engine='python')
    data_file = [file_df.iloc[:, index].tolist() for index in range(0, file_df.shape[1])]

    return data_file


def read_magfile(file_name, col_nos, fmt='{:>8}', title_rows=0):
    """
    Extracts the mag file data as a list of columns specified by 'col_nos' from a text file and formats
    the list according to the format specified in 'fmt'.
    Args:
        file_name   : Text file from which file data has to be extracted
        col_nos     : Indexes of columns to be read from the file ('7:9' or '7,8,9')
        fmt         : Format of storing data in the list
        title_rows  : No. of rows used for title description
    Returns:
        data_file   : List of all columns extracted from the text file
    """
    file_df = pd.read_csv(filepath_or_buffer=file_name, sep='\s+', header=None, skiprows=title_rows, engine='python')
    rows, columns = file_df.shape

    if re.search(':', col_nos):
        col_indexes = range(int(col_nos.split(':')[0]), int(col_nos.split(':')[-1]), 1)
    elif re.search(',', col_nos):
        col_indexes = col_nos.split(',')
    else:
        print("Invalid Format Of Entering Column Numbers")
        sys.exit(1)

    data_file = [file_df.iloc[:, index].tolist() for index in col_indexes]

    for col_index in range(0, len(col_indexes)):
        for row_index in range(0, rows):
            try:
                float(data_file[col_index][row_index])
            except ValueError:
                new_fmt = fmt[0:3] + 's}'
                data_file[col_index][row_index] = new_fmt.format(str(data_file[col_index][row_index]))
            else:
                data_file[col_index][row_index] = fmt.format(float(data_file[col_index][row_index]))

    return data_file

# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Functions For Performing Photometry
# ------------------------------------------------------------------------------------------------------------------- #

def calculate_fwhm(textlist_files, coord_file='stars.coo', log_imexam='log_imexam'):
    """
    Calculates the Mean FWHM of all the files in the list 'list_files'. It determines the FWHM using
    IMEXAMINE task on the stars mentioned in the file "stars.coo".
    Args:
        textlist_files  : Text list containing names of FITS files whose FWHM is to be determined
        coord_file      : Text file listing the coordinates of selected stars in the field
        log_imexam      : Name of the text list to record log of IMEXAM
    Returns:
        list_mean_fwhm  : Python list containing Mean FWHM of all the FITS files
    """
    imexam_fwhm(textlist_files, coord_file=coord_file, log_imexam=log_imexam)
    coord_df = pd.read_csv(coord_file, sep='\s+', comment='#', header=None)
    data_df = pd.read_csv(log_imexam, sep='\s+', comment='#', header=None)
    count = coord_df.shape[0]
    rows, columns = data_df.shape
    col_moff = columns - 2

    list_fwhm = [data_df.iloc[0 + count * i: (i + 1) * count, col_moff].tolist() for i in range(0, rows / count)]

    list_mean_fwhm = []
    for fwhm_values in list_fwhm:
        list_fwhm = [value for value in fwhm_values if value != 'INDEF']
        mean = float(np.mean(a=reject(list_fwhm)))
        list_mean_fwhm.append(round(mean, 1))

    return list_mean_fwhm


def calculate_airmass(textlist_files):
    """
    Calculates AIRMASS for the list of all FITS files and appends respective details in the headers.
    Args:
        textlist_files : List of all FITS files whose headers have to be edited
    Returns:
        None
    """
    list_files = text_list_to_python_list(textlist_files)

    for file_name in list_files:
        hdulist = fits.open(file_name, mode='update')
        file_header = hdulist[0].header
        date_obs = file_header[DATE_keyword]
        time_start = file_header[TIMESTART_keyword]

        if RA_keyword in file_header.keys():
            object_ra = file_header[RA_keyword]
        else:
            object_ra = OBJECT_RA

        if DEC_keyword in file_header.keys():
            object_dec = file_header[DEC_keyword]
        else:
            object_dec = OBJECT_DEC

        time_utc = str(datetime.timedelta(seconds=int(time_start)))
        datetime_utc = date_obs + ' ' + time_utc
        jd = ephem.julian_date(datetime_utc)

        telescope = ephem.Observer()
        telescope.lon = OBS_LONG
        telescope.lat = OBS_LAT
        telescope.elevation = OBS_ALT
        telescope.pressure = 0
        telescope.epoch = ephem.J2000
        telescope.date = datetime_utc

        obj_pos = ephem.FixedBody()
        obj_pos._ra = object_ra
        obj_pos._dec = object_dec
        obj_pos._epoch = ephem.J2000
        obj_pos.compute(telescope)

        time_sidereal = telescope.sidereal_time()
        object_alt = Angle(str(obj_pos.alt) + ' degrees').degree
        airmass = 1 / math.cos(math.radians(90 - object_alt))

        list_keywords = ['OBSERVAT', 'OBS_LAT', 'OBS_LONG', 'OBS_ALT', 'TIMEZONE', 'DATE_OBS', 'UT', 'JD', 'ST', 'RA',
                         'DEC', 'ALT', 'AZ', 'AIRMASS']

        dict_header = {'OBSERVAT': OBS_NAME, 'OBS_LAT': OBS_LAT, 'OBS_LONG': OBS_LONG, 'OBS_ALT': OBS_ALT,
                       'TIMEZONE': OBS_TIMEZONE, 'DATE_OBS': date_obs, 'UT': time_utc, 'JD': jd, 'ST': time_sidereal,
                       'RA': object_ra, 'DEC': object_dec, 'ALT': obj_pos.alt, 'AZ': obj_pos.az, 'AIRMASS': airmass}

        for keyword in list_keywords:
            if keyword in file_header.keys():
                file_header.remove(keyword, remove_all=True)
            file_header.append(card=(keyword, dict_header[keyword]))

        hdulist.flush()
        hdulist.close()


def extract_val(aper_string, fwhm):
    """
    Calculates apertures to be calculated in terms of 'Pixels' from a string supplying apertures
    in terms of FWHM value of the image.
    Args:
        aper_string : String specifing apertures in terms of FWHM of the image
        fwhm        : FWHM of the image to which photometry is being done
    Returns:
        aper_values : String containing apertures to be used for photometry
    """
    if re.search(':', aper_string):
        list_aper = aper_string.split(':')
        if len(list_aper) == 2:
            list_aper = np.arange(float(list_aper[0]), 1 + float(list_aper[1]), 1)
        elif len(aper_string.split(':')) == 3:
            list_aper = np.arange(float(list_aper[0]), float(list_aper[2]) + float(list_aper[1]), float(list_aper[2]))
    else:
        list_aper = aper_string.split(',')

    aper_values = ''
    for value in list_aper:
        aper_values += str(float(value) * float(fwhm)) + ','

    return aper_values[:-1]


def aper_phot(textlist_files, textlist_fwhm, coord_file, phot_radius='1', data_max='INDEF'):
    """
    Performs aperture photometry (PHOT task) on the files in the list 'textlist_files'. Selects candidate
    stars from the coordinate file 'coord_file'.
    Args:
        textlist_files  : List of all FITS files on which aperture photometry is to be performed
        textlist_fwhm   : List of Mean FWHM values of all the FITS files
        coord_file      : Name of the coordinate file containing candidate star
        phot_radius     : String containing the apertures at which photometry is to be done("1,4")
        data_max        : Maximum good pixel value
    Returns:
        None
    """
    list_files = text_list_to_python_list(textlist_files)
    list_fwhm = text_list_to_python_list(textlist_fwhm)

    for index, file_name in enumerate(list_files):
        aperture_values = extract_val(str(phot_radius), list_fwhm[index])
        data_pars(list_fwhm[index], data_max)
        center_pars()
        fitsky_pars(list_fwhm[index])
        phot_pars(aperture_values)
        phot(file_name=file_name, coord_file=coord_file)

    display_text("Aperture Photometry Is Completed For Aperture Values (x FWHM): {0}".format(phot_radius))


def psf_phot(textlist_files, textlist_fwhm, mag_suffix='.mag.3', mag_apply='.mag.2', psf_radius='1', data_max='INDEF'):
    """
    Performs PSF Photometry on the text list 'textlist_files'.
    Args:
        textlist_files  : List of all FITS files on which PSF photometry is to be performed
        textlist_fwhm   : List of Mean FWHM values of all the FITS files
        mag_suffix      : Suffix of the mag files from PHOT task to be used for selecting candidate stars
        mag_apply       : Suffix of the mag files from PHOT task on which PSF photometry is to be performed stars
        psf_radius      : PSF fit radius in units of FWHM
        data_max        : Maximum good pixel value
    Returns:
        None
    """
    global run_count

    list_files = text_list_to_python_list(textlist_files)
    list_fwhm = text_list_to_python_list(textlist_fwhm)

    for index, file_name in enumerate(list_files):
        file_mag = file_name + mag_suffix
        file_mag2 = file_name + mag_apply

        pstselect(file_name, file_mag, list_fwhm[index], data_max, psf_radius)

        file_pst = file_name + '.pst.' + str(run_count * 2 - 1)
        psf(file_name, file_mag, file_pst, list_fwhm[index], data_max, psf_radius)

        file_psf = file_name + '.psf.' + str(run_count) + '.fits'
        allstar(file_name, file_mag2, file_psf, list_fwhm[index], data_max, psf_radius)

    run_count += 1
    display_text("PSF Photometry Is Completed For PSF Radius = {0} * FWHM".format(psf_radius))


def tabular_mag(input_file, output_file):
    """
    Takes the output from a MAG or ALS file and computes a tabular magnitude file.
    Args:
        input_file  : Input MAG or ALS file
        output_file : Name of the output file onto which the tabular magnitudes are to be written
    Returns:
        None
    """
    data_file = read_file(input_file)
    [star_id, img_name, ifilter, xcenter, ycenter, sky_counts, airmass] = data_file[0:7]

    col_data = 7
    star_count = max(map(int, star_id))
    columns = len(data_file)
    rows = len(data_file[0])
    apertures = (columns - col_data) / 3

    list_col = []
    for index in range(0, 3):
        list_col.append(str(col_data + index * apertures) + ':' + str(col_data + (index + 1) * apertures))

    list_aper = zip_list(read_magfile(input_file, col_nos=list_col[0], fmt='{:8.2f}'))
    list_mag = zip_list(read_magfile(input_file, col_nos=list_col[1], fmt='{:10.4f}'))
    list_err = zip_list(read_magfile(input_file, col_nos=list_col[2], fmt='{:8.3f}'))

    aper_names = ''
    mag_names = ''
    err_names = ''
    for value in range(1, apertures + 1):
        aper_names += '{:8s}'.format('AP_' + str(value))
        mag_names += '{:10s}'.format('MAG_' + str(value))
        err_names += '{:8s}'.format('ERR_' + str(value))

    with open(str(output_file), 'w') as fout:
        fout.write("{0:>4s}{1:>10s}{2:>11s}{3:>13s}  {4}{5}{6}\n\n".format
                   ('ID', 'XCENTER', 'YCENTER', 'SKY_COUNTS', aper_names, mag_names, err_names))

        for index in range(0, rows):
            if index % star_count == 0:
                fout.write("# IMAGE #{0} - {1}, FILTER - {2}, AIRMASS - {3}\n".format
                           ((index / star_count) + 1, img_name[index], ifilter[index], airmass[index]))

            fout.write("{0:>3.0f}{1:>11.3f}{2:>11.3f}{3:>11.4f}{4}{5} {6}\n".format
                       (float(star_id[index]), float(xcenter[index]), float(ycenter[index]), float(sky_counts[index]),
                        list_aper[index], list_mag[index], list_err[index]))

            if index % star_count == star_count - 1:
                fout.write('\n')

# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Manual Setup - GUI Code
# ------------------------------------------------------------------------------------------------------------------- #
# remove_resfile = eg.boolbox(msg='Remove Residual Files From Previous Run Of This Script?',
#                             title='Remove Residual Files', choices=['Yes', 'No'])
# ctext = eg.enterbox(msg='Enter The Common Text Of Files On Which Photometry Is To Be Done?',
#                     title='Photometry Using IRAF', default='ca_*' + OBJECT_NAME + '*.fits')
# phot_index = eg.indexbox(msg='Perform Which Type Of Photometry?', title='Aperture and/or PSF Photometry',
#                          choices=['Aperture Photometry Only', 'Aperture & PSF Photometry'])
# coord_file = eg.enterbox(msg='File Name With Coordinates Of Field Stars:', title='Field Star Coordinates',
#                          default='stars.coo')
# coord_file2 = eg.enterbox(msg='File Name With Coordinates Of Field Stars And SN:', title='Field Star Coordinates',
#                           default='stars.coo')
# aperture_values = eg.enterbox(msg='Enter The Apertures At Which Photometry Is To Be Done:',
#                               title='1st Set Of Apertures', default='1')
# aperture_values2 = eg.enterbox(msg='Enter The 2nd Set Of Apertures At Which Photometry Is To Be Done:',
#                                title='2nd Set Of Apertures', default='1')
remove_resfile = True
ctext = 'ca_*' + OBJECT_NAME + '*.fits'
phot_index = 1
coord_file = 'stars.coo'
coord_file2 = 'stars_sn.coo'
aperture_values = '1'
aperture_values2 = '1,4'
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Remove Residual Files From Previous Run Of Photometry Tasks (PHOT, PSTSELECT, PSF, ALLSTAR)
# ------------------------------------------------------------------------------------------------------------------- #
if remove_resfile:
    for text in ['tmp*', '*pst.*', '*.psf.*', '*psg.*', '*.als.*', '*arj.*', '*.sub.*', '*.mag.*', 'list_*', 'log*']:
        remove_similar_files(common_text=text)
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Groups FITS Files On Which Photometry Is To Be Performed
# ------------------------------------------------------------------------------------------------------------------- #
text_list = 'list_files'
textlist_fwhm = 'list_fwhm'
list_files = group_similar_files(text_list, common_text=ctext, exceptions='psf,sub')

if len(list_files) == 0:
    print("No Files Available For Performing Photometry : Common_Text = '{0}'".format(ctext))
    sys.exit(1)
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Determines The FWHM Of The Stars In The Field (stars.coo)
# ------------------------------------------------------------------------------------------------------------------- #
list_fwhm = calculate_fwhm(textlist_files=text_list, coord_file=coord_file)
python_list_to_text_list(python_list=list_fwhm, text_list=textlist_fwhm)
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Calculates AIRMASS & Appends Respective Details To The Headers
# ------------------------------------------------------------------------------------------------------------------- #
calculate_airmass(textlist_files=text_list)
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Performs Photometry On Images
# ------------------------------------------------------------------------------------------------------------------- #
run_count = 1
aper_phot(text_list, textlist_fwhm, coord_file2, phot_radius=aperture_values2, data_max='INDEF')
aper_phot(text_list, textlist_fwhm, coord_file2, phot_radius=aperture_values, data_max='INDEF')
aper_phot(text_list, textlist_fwhm, coord_file, phot_radius=aperture_values, data_max='INDEF')
aper_phot(text_list, textlist_fwhm, coord_file, phot_radius=aperture_values2, data_max='INDEF')

if phot_index == 1:
    for aperture in extract_val(aperture_values, 1).split(','):
        psf_phot(text_list, textlist_fwhm, psf_radius=aperture, data_max='INDEF')
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Groups Mag Files From PHOT Task Into A Separate List And Makes A List Of Epochs Of Observation
# ------------------------------------------------------------------------------------------------------------------- #
mag_suffix = 4
list_mag = group_similar_files('', '*.mag.' + str(mag_suffix))
list_dates = set([file_name[3:13] for file_name in list_mag])
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Groups MAG Files Date-Wise Into A File 'list_date_mag4', Applies TXDUMP Task To Obtain 'output_date_mag4'
# Computes Tabular Magnitude Files From MAG Files Generated Through Aperture Photometry
# ------------------------------------------------------------------------------------------------------------------- #
for date in list_dates:
    txdump('*' + date + '*.mag.' + str(mag_suffix), output_file='output_' + date + '_mag' + str(mag_suffix))
    tabular_mag(input_file='output_' + date + '_mag' + str(mag_suffix), output_file='OUTPUT_tabular_' + date)

display_text("Tabular Magnitudes Have Been Computed For MAG Files")
# ------------------------------------------------------------------------------------------------------------------- #


# ------------------------------------------------------------------------------------------------------------------- #
# Computes Tabular Magnitude Files From ALS Files Generated Through PSF Photometry
# ------------------------------------------------------------------------------------------------------------------- #
als_suffix = 1
for date in list_dates:
    txdump('*' + date + '*.als.' + str(als_suffix), output_file='output_' + date + '_als' + str(als_suffix))
    tabular_mag('output_' + date + '_als' + str(als_suffix), output_file='OUTPUT_tabularpsf_' + date)

display_text("Tabular Magnitudes Have Been Computed For ALS Files")
# ------------------------------------------------------------------------------------------------------------------- #