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'''
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This script updates the content of a fits table, adding new columns
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and/or rows (i.e. objects) to it, by considering as input a user-defined ASCII table.
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The new columns (rows) defined in the ascii file are appended at the end
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(bottom) of the fits table.
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IMPORTANT: The 1st line of the ASCII table must contain the names
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of the columns, and must be UNCOMMENTED!
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NOTE: Ra and DEC must be in **decimal degrees**, both in FITS and
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ASCII tables.
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The syntax is:
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$ python edit_FITS.py <table>.fits <ascii_file>
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@author: Alessandro NASTASI for IAS - IDOC
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@date: 21/05/2015
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'''
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__author__ = "Alessandro Nastasi"
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__credits__ = ["Alessandro Nastasi"]
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__license__ = "GPL"
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__version__ = "1.0"
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__date__ = "21/05/2015"
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import numpy as np
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import os, sys, re, time
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import string
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import asciidata
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import pyfits
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from datetime import date
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import astCoords
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class bcolors:
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HEADER = '\033[95m'
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OKBLUE = '\033[94m'
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OKGREEN = '\033[92m'
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WARNING = '\033[93m'
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FAIL = '\033[91m'
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ENDC = '\033[0m'
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_FIELDS_DICTIONARY = {
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'INDEX': { 'format': 'I', 'unit': 'None' },
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'COORD_SOURCE': { 'format': '5A', 'unit': 'None' },
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'x':{ 'format': 'E', 'unit': 'None' },
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'y':{ 'format': 'E', 'unit': 'None' },
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'z':{ 'format': 'E', 'unit': 'None' },
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'ACT_INDEX': { 'format': 'I', 'unit': 'None' },
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'INDEX_ACT': { 'format': 'I', 'unit': 'None' },
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'CATALOG': { 'format': '7A', 'unit': 'None' },
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'SNR': { 'format': 'E', 'unit': 'None' },
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'ERR_REDSHIFT': { 'format': 'E', 'unit': 'None' },
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'M500': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
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'ERR_M500': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
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'YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
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'ERR_YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
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'THETA': { 'format': 'E', 'unit': 'arcmin' },
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'ACT_CATALOG': { 'format': '7A', 'unit': 'None' },
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'ACT_NAME': { 'format': '18A', 'unit': 'None' },
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'ACT_GLON': { 'format': 'E', 'unit': 'degrees' },
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'ACT_GLAT': { 'format': 'E', 'unit': 'degrees' },
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'ACT_RA': { 'format': 'E', 'unit': 'degrees' },
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'ACT_DEC': { 'format': 'E', 'unit': 'degrees' },
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'ACT_SNR': { 'format': 'E', 'unit': 'None' },
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'ACT_REDSHIFT': { 'format': 'E', 'unit': 'None' },
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'ACT_ERR_REDSHIFT': { 'format': 'E', 'unit': 'None' },
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'ACT_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
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'ACT_REDSHIFT_REF': { 'format': '19A', 'unit': 'None' },
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'ACT_M500': { 'format': 'E', 'unit': '10^14 h^-1 solar mass' },
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'ACT_ERR_M500': { 'format': 'E', 'unit': '10^14 h^-1 solar mass' },
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'ACT_YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
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'ACT_ERR_YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
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'ACT_THETA': { 'format': 'E', 'unit': 'arcmin' },
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'ACT_PAPER': { 'format': '56A', 'unit': 'None' },
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'INDEX_AMI': { 'format': 'I', 'unit': 'None' },
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'AMI_INDEX': { 'format': 'I', 'unit': 'None' },
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'AMI_NAME': { 'format': '18A', 'unit': 'None' },
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'AMI_RA': { 'format': 'E', 'unit': 'Degrees' },
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'AMI_DEC': { 'format': 'E', 'unit': 'Degrees' },
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'AMI_GLON': { 'format': 'E', 'unit': 'Degrees' },
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'AMI_GLAT': { 'format': 'E', 'unit': 'Degrees' },
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'AMI_REDSHIFT': { 'format': 'E', 'unit': 'None' },
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'AMI_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
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'AMI_REDSHIFT_REF': { 'format': '36A', 'unit': 'None' },
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'AMI_ALT_NAME': { 'format': '60A', 'unit': 'None' },
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'INDEX_CARMA': { 'format': 'I', 'unit': 'None' },
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'CARMA_INDEX': { 'format': 'I', 'unit': 'None' },
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'CARMA_NAME': { 'format': '18A', 'unit': 'None' },
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'CARMA_RA': { 'format': 'E', 'unit': 'Degrees' },
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'CARMA_DEC': { 'format': 'E', 'unit': 'Degrees' },
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'CARMA_GLON': { 'format': 'E', 'unit': 'Degrees' },
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'CARMA_GLAT': { 'format': 'E', 'unit': 'Degrees' },
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'CARMA_REDSHIFT': { 'format': 'E', 'unit': 'None' },
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'CARMA_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
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'CARMA_REDSHIFT_REF': { 'format': '36A', 'unit': 'None' },
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'CARMA_M500': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
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'CARMA_ERR_M500': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
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'PSZ1_INDEX': { 'format': 'I', 'unit': 'None' },
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'INDEX_PSZ1': { 'format': 'I', 'unit': 'None' },
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'NAME': { 'format': '18A', 'unit': 'None' },
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'GLON': { 'format': 'D', 'unit': 'degrees' },
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'GLAT': { 'format': 'D', 'unit': 'degrees' },
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'RA': { 'format': 'D', 'unit': 'degrees' },
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'DEC': { 'format': 'D', 'unit': 'degrees' },
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'RA_MCXC': { 'format': 'E', 'unit': 'degrees' },
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'DEC_MCXC': { 'format': 'E', 'unit': 'degrees' },
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'REDSHIFT': { 'format': 'E', 'unit': 'None' },
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'REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
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'REDSHIFT_SOURCE': { 'format': 'I', 'unit': 'None' },
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'REDSHIFT_REF': { 'format': '36A', 'unit': 'None' },
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'ALT_NAME': { 'format': '66A', 'unit': 'None' },
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'YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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'ERRP_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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'ERRM_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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'M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
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'ERRP_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
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'ERRM_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
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'S_X': { 'format': 'E', 'unit': 'erg/s/cm2' },
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'ERR_S_X': { 'format': 'E', 'unit': 'erg/s/cm2' },
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'Y_PSX_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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'SN_PSX': { 'format': 'E', 'unit': 'None' },
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'PIPELINE': { 'format': 'I', 'unit': 'None' },
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'PIPE_DET': { 'format': 'I', 'unit': 'None' },
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'PCCS': { 'format': 'L', 'unit': 'None' },
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'VALIDATION': { 'format': 'I', 'unit': 'None' },
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'ID_EXT': { 'format': '25A', 'unit': 'None' },
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'POS_ERR': { 'format': 'E', 'unit': 'arcmin' },
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'COSMO': { 'format': 'L', 'unit': 'None' },
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'COMMENT': { 'format': 'L', 'unit': 'None' },
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'QN': { 'format': 'E', 'unit': 'None' },
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'PSZ1_NAME': { 'format': '18A', 'unit': 'None' },
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'PSZ1_GLON': { 'format': 'D', 'unit': 'degrees' },
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'PSZ1_GLAT': { 'format': 'D', 'unit': 'degrees' },
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'PSZ1_RA': { 'format': 'D', 'unit': 'degrees' },
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'PSZ1_DEC': { 'format': 'D', 'unit': 'degrees' },
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'PSZ1_RA_MCXC': { 'format': 'E', 'unit': 'degrees' },
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'PSZ1_DEC_MCXC': { 'format': 'E', 'unit': 'degrees' },
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'PSZ1_REDSHIFT': { 'format': 'E', 'unit': 'None' },
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'PSZ1_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
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'PSZ1_REDSHIFT_SOURCE': { 'format': 'I', 'unit': 'None' },
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'PSZ1_REDSHIFT_REF': { 'format': '36A', 'unit': 'None' },
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'PSZ1_ALT_NAME': { 'format': '66A', 'unit': 'None' },
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'PSZ1_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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'PSZ1_ERRP_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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'PSZ1_ERRM_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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'PSZ1_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
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'PSZ1_ERRP_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
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'PSZ1_ERRM_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
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'PSZ1_S_X': { 'format': 'E', 'unit': 'erg/s/cm2' },
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'PSZ1_ERR_S_X': { 'format': 'E', 'unit': 'erg/s/cm2' },
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'PSZ1_Y_PSX_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
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217
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'PSZ1_SN_PSX': { 'format': 'E', 'unit': 'None' },
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218
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'PSZ1_PIPELINE': { 'format': 'I', 'unit': 'None' },
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219
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'PSZ1_PIPE_DET': { 'format': 'I', 'unit': 'None' },
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220
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'PSZ1_PCCS': { 'format': 'L', 'unit': 'None' },
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221
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'PSZ1_VALIDATION': { 'format': 'I', 'unit': 'None' },
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222
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'PSZ1_ID_EXT': { 'format': '25A', 'unit': 'None' },
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223
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'PSZ1_POS_ERR': { 'format': 'E', 'unit': 'arcmin' },
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224
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'PSZ1_SNR': { 'format': 'E', 'unit': 'None' },
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225
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'PSZ1_COSMO': { 'format': 'L', 'unit': 'None' },
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226
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'PSZ1_COMMENT': { 'format': 'L', 'unit': 'None' },
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227
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'PSZ1_QN': { 'format': 'E', 'unit': 'None' },
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228
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229
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230
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'PSZ2_INDEX': { 'format': 'I', 'unit': 'None' },
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231
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'INDEX_PSZ2': { 'format': 'I', 'unit': 'None' },
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232
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233
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234
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235
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236
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237
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238
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239
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240
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241
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'PCCS2': { 'format': 'L', 'unit': 'None' },
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242
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'PSZ': { 'format': 'I', 'unit': 'None' },
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243
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'IR_FLAG': { 'format': 'I', 'unit': 'None' },
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244
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'Q_NEURAL': { 'format': 'E', 'unit': 'None' },
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245
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'Y5R500': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
|
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246
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'Y5R500_ERR': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
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247
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'PSZ2_VALIDATION': { 'format': 'I', 'unit': 'None' },
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248
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'REDSHIFT_ID': { 'format': '25A', 'unit': 'None' },
|
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249
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250
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'MSZ': { 'format': 'E', 'unit': '10^14 Msol' },
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251
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'MSZ_ERR_UP': { 'format': 'E', 'unit': '10^14 Msol' },
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252
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'MSZ_ERR_LOW': { 'format': 'E', 'unit': '10^14 Msol' },
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253
|
'MCXC': { 'format': '25A', 'unit': 'None' },
|
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254
|
'REDMAPPER': { 'format': '25A', 'unit': 'None' },
|
|
255
|
'ACT': { 'format': '25A', 'unit': 'None' },
|
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256
|
'SPT': { 'format': '25A', 'unit': 'None' },
|
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257
|
'WISE_SIGNF': { 'format': 'E', 'unit': 'None' },
|
|
258
|
'WISE_FLAG': { 'format': 'I', 'unit': 'None' },
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259
|
'AMI_EVIDENCE': { 'format': 'E', 'unit': 'None' },
|
|
260
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261
|
'PSZ2_COMMENT': { 'format': '128A', 'unit': 'None' },
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262
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263
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'PSZ2_NAME': { 'format': '18A', 'unit': 'None' },
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264
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'PSZ2_GLON': { 'format': 'D', 'unit': 'degrees' },
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265
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'PSZ2_GLAT': { 'format': 'D', 'unit': 'degrees' },
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266
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'PSZ2_RA': { 'format': 'D', 'unit': 'degrees' },
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267
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'PSZ2_DEC': { 'format': 'D', 'unit': 'degrees' },
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268
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'PSZ2_POS_ERR': { 'format': 'E', 'unit': 'arcmin' },
|
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269
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'PSZ2_SNR': { 'format': 'E', 'unit': 'None' },
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270
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'PSZ2_PIPELINE': { 'format': 'I', 'unit': 'None' },
|
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271
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'PSZ2_PIPE_DET': { 'format': 'I', 'unit': 'None' },
|
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272
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'PSZ2_PCCS2': { 'format': 'L', 'unit': 'None' },
|
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273
|
'PSZ2_PSZ': { 'format': 'I', 'unit': 'None' },
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274
|
'PSZ2_IR_FLAG': { 'format': 'I', 'unit': 'None' },
|
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275
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'PSZ2_Q_NEURAL': { 'format': 'E', 'unit': 'None' },
|
|
276
|
'PSZ2_Y5R500': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
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277
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'PSZ2_Y5R500_ERR': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
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278
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279
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'PSZ2_REDSHIFT_ID': { 'format': '25A', 'unit': 'None' },
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280
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'PSZ2_REDSHIFT': { 'format': 'E', 'unit': 'None' },
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281
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'PSZ2_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
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282
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'PSZ2_MSZ': { 'format': 'E', 'unit': '10^14 Msol' },
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283
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'PSZ2_MSZ_ERR_UP': { 'format': 'E', 'unit': '10^14 Msol' },
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284
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'PSZ2_MSZ_ERR_LOW': { 'format': 'E', 'unit': '10^14 Msol' },
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285
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'PSZ2_MCXC': { 'format': '25A', 'unit': 'None' },
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286
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'PSZ2_REDMAPPER': { 'format': '25A', 'unit': 'None' },
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287
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'PSZ2_ACT': { 'format': '25A', 'unit': 'None' },
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288
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'PSZ2_SPT': { 'format': '25A', 'unit': 'None' },
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289
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'PSZ2_WISE_SIGNF': { 'format': 'E', 'unit': 'None' },
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290
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'PSZ2_WISE_FLAG': { 'format': 'I', 'unit': 'None' },
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291
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'PSZ2_AMI_EVIDENCE': { 'format': 'E', 'unit': 'None' },
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292
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'PSZ2_COSMO': { 'format': 'L', 'unit': 'None' },
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293
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294
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295
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296
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'PLCK_INDEX': { 'format': 'I', 'unit': 'None' },
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|
297
|
'INDEX_PLCK': { 'format': 'I', 'unit': 'None' },
|
|
298
|
|
|
299
|
|
|
300
|
|
|
301
|
|
|
302
|
|
|
303
|
|
|
304
|
|
|
305
|
|
|
306
|
|
|
307
|
|
|
308
|
|
|
309
|
|
|
310
|
|
|
311
|
|
|
312
|
|
|
313
|
|
|
314
|
|
|
315
|
|
|
316
|
|
|
317
|
|
|
318
|
|
|
319
|
|
|
320
|
|
|
321
|
|
|
322
|
|
|
323
|
|
|
324
|
|
|
325
|
|
|
326
|
|
|
327
|
|
|
328
|
|
|
329
|
|
|
330
|
|
|
331
|
|
|
332
|
'PLCK_NAME': { 'format': '18A', 'unit': 'None' },
|
|
333
|
'PLCK_GLON': { 'format': 'D', 'unit': 'degrees' },
|
|
334
|
'PLCK_GLAT': { 'format': 'D', 'unit': 'degrees' },
|
|
335
|
'PLCK_RA': { 'format': 'D', 'unit': 'degrees' },
|
|
336
|
'PLCK_DEC': { 'format': 'D', 'unit': 'degrees' },
|
|
337
|
'PLCK_RA_MCXC': { 'format': 'E', 'unit': 'degrees' },
|
|
338
|
'PLCK_DEC_MCXC': { 'format': 'E', 'unit': 'degrees' },
|
|
339
|
'PLCK_REDSHIFT': { 'format': 'E', 'unit': 'None' },
|
|
340
|
'PLCK_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
|
|
341
|
'PLCK_REDSHIFT_SOURCE': { 'format': 'I', 'unit': 'None' },
|
|
342
|
'PLCK_REDSHIFT_REF': { 'format': '36A', 'unit': 'None' },
|
|
343
|
'PLCK_ALT_NAME': { 'format': '66A', 'unit': 'None' },
|
|
344
|
'PLCK_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
|
|
345
|
'PLCK_ERRP_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
|
|
346
|
'PLCK_ERRM_YZ_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
|
|
347
|
'PLCK_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
|
|
348
|
'PLCK_ERRP_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
|
|
349
|
'PLCK_ERRM_M_YZ_500': { 'format': 'E', 'unit': '10^14 solar mass' },
|
|
350
|
'PLCK_S_X': { 'format': 'E', 'unit': 'erg/s/cm2' },
|
|
351
|
'PLCK_ERR_S_X': { 'format': 'E', 'unit': 'erg/s/cm2' },
|
|
352
|
'PLCK_Y_PSX_500': { 'format': 'E', 'unit': '10^-4 arcmin squared' },
|
|
353
|
'PLCK_SN_PSX': { 'format': 'E', 'unit': 'None' },
|
|
354
|
'PLCK_PIPELINE': { 'format': 'I', 'unit': 'None' },
|
|
355
|
'PLCK_PIPE_DET': { 'format': 'I', 'unit': 'None' },
|
|
356
|
'PLCK_PCCS': { 'format': 'L', 'unit': 'None' },
|
|
357
|
'PLCK_VALIDATION': { 'format': 'I', 'unit': 'None' },
|
|
358
|
'PLCK_ID_EXT': { 'format': '25A', 'unit': 'None' },
|
|
359
|
'PLCK_POS_ERR': { 'format': 'E', 'unit': 'arcmin' },
|
|
360
|
'PLCK_SNR': { 'format': 'E', 'unit': 'None' },
|
|
361
|
'PLCK_COSMO': { 'format': 'L', 'unit': 'None' },
|
|
362
|
'PLCK_COMMENT': { 'format': 'L', 'unit': 'None' },
|
|
363
|
'PLCK_QN': { 'format': 'E', 'unit': 'None' },
|
|
364
|
|
|
365
|
|
|
366
|
'SPT_INDEX': { 'format': 'I', 'unit': 'None' },
|
|
367
|
'INDEX_SPT': { 'format': 'I', 'unit': 'None' },
|
|
368
|
|
|
369
|
|
|
370
|
|
|
371
|
|
|
372
|
|
|
373
|
|
|
374
|
|
|
375
|
|
|
376
|
|
|
377
|
|
|
378
|
'REDSHIFT_LIMIT': { 'format': 'E', 'unit': 'None' },
|
|
379
|
|
|
380
|
'M500_fidCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
381
|
'ERR_M500_fidCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
382
|
'M500_PlanckCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
383
|
'ERR_M500_PlanckCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
384
|
'YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
|
|
385
|
'ERR_YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
|
|
386
|
|
|
387
|
'LX': { 'format': 'E', 'unit': '10^44 erg/s' },
|
|
388
|
'YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
|
|
389
|
'ERR_YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
|
|
390
|
|
|
391
|
'PAPER': { 'format': '59A', 'unit': 'None' },
|
|
392
|
'XRAY': { 'format': 'L', 'unit': 'None' },
|
|
393
|
'STRONG_LENS': { 'format': 'L', 'unit': 'None' },
|
|
394
|
|
|
395
|
'SPT_CATALOG': { 'format': '7A', 'unit': 'None' },
|
|
396
|
'SPT_NAME': { 'format': '16A', 'unit': 'None' },
|
|
397
|
'SPT_GLON': { 'format': 'E', 'unit': 'degrees' },
|
|
398
|
'SPT_GLAT': { 'format': 'E', 'unit': 'degrees' },
|
|
399
|
'SPT_RA': { 'format': 'E', 'unit': 'degrees' },
|
|
400
|
'SPT_DEC': { 'format': 'E', 'unit': 'degrees' },
|
|
401
|
'SPT_SNR': { 'format': 'E', 'unit': 'None' },
|
|
402
|
'SPT_REDSHIFT': { 'format': 'E', 'unit': 'None' },
|
|
403
|
'SPT_ERR_REDSHIFT': { 'format': 'E', 'unit': 'None' },
|
|
404
|
'SPT_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
|
|
405
|
'SPT_REDSHIFT_REF': { 'format': '19A', 'unit': 'None' },
|
|
406
|
|
|
407
|
'SPT_REDSHIFT_LIMIT': { 'format': 'E', 'unit': 'None' },
|
|
408
|
'SPT_XRAY': { 'format': 'L', 'unit': 'None' },
|
|
409
|
'SPT_STRONG_LENS': { 'format': 'L', 'unit': 'None' },
|
|
410
|
|
|
411
|
'SPT_M500_fidCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
412
|
'SPT_ERR_M500_fidCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
413
|
'SPT_M500_PlanckCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
414
|
'SPT_ERR_M500_PlanckCosmo': { 'format': 'E', 'unit': '10^14 h70^-1 solar mass' },
|
|
415
|
|
|
416
|
'SPT_LX': { 'format': 'E', 'unit': '10^44 erg/s' },
|
|
417
|
'SPT_YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
|
|
418
|
'SPT_ERR_YSZ': { 'format': 'E', 'unit': '10^-6 arcmin squared' },
|
|
419
|
'SPT_THETA': { 'format': 'E', 'unit': 'arcmin' },
|
|
420
|
'SPT_PAPER': { 'format': '59A', 'unit': 'None' }
|
|
421
|
|
|
422
|
}
|
|
423
|
|
|
424
|
|
|
425
|
|
|
426
|
|
|
427
|
name_mass_key = ['M500']
|
|
428
|
name_errMass_key = ['ERR_M500']
|
|
429
|
|
|
430
|
name_ra_key = 'RA'
|
|
431
|
name_dec_key = 'DEC'
|
|
432
|
name_coordinates_keys = ['RA_MCXC', 'DEC_MCXC', name_ra_key, name_dec_key]
|
|
433
|
|
|
434
|
name_Name_key = 'NAME'
|
|
435
|
name_index_key = 'INDEX'
|
|
436
|
name_catalog_key = 'CATALOG'
|
|
437
|
name_redshift_key = 'REDSHIFT'
|
|
438
|
name_zLimit_key = 'REDSHIFT_LIMIT'
|
|
439
|
name_zErr_key = 'ERR_REDSHIFT'
|
|
440
|
name_zType_key = 'REDSHIFT_TYPE'
|
|
441
|
name_zRef_key = 'REDSHIFT_REF'
|
|
442
|
name_altName_key = 'ALT_NAME'
|
|
443
|
name_paper_key = 'PAPER'
|
|
444
|
|
|
445
|
|
|
446
|
_UNDEF_VALUES_ = {
|
|
447
|
'FLOAT' : {np.nan},
|
|
448
|
'INT' : {-1},
|
|
449
|
'STRING' : {'NULL'},
|
|
450
|
name_zType_key : {'undef'},
|
|
451
|
'PIPELINE' : {0},
|
|
452
|
'PIPE_DET' : {0}
|
|
453
|
}
|
|
454
|
|
|
455
|
def remove_duplicated_names(string):
|
|
456
|
'''
|
|
457
|
This function removes duplicated names of a string, assuming they are separated by ';'
|
|
458
|
In addition, it takes out 'NULL', 'NaN', 'False' from the final, composite string.
|
|
459
|
It is used for the creation of ALT_NAME field.
|
|
460
|
'''
|
|
461
|
string = string.replace('; ',';')
|
|
462
|
tmp = [item for item in string.split(';') if item.upper() not in ["-", "NULL", "NAN", "NONE", "FALSE"] and len(item)>0 ]
|
|
463
|
|
|
464
|
tmp_uniq = []
|
|
465
|
set_tmp = set()
|
|
466
|
for item in tmp:
|
|
467
|
if item not in set_tmp:
|
|
468
|
tmp_uniq.append(item)
|
|
469
|
set_tmp.add(item)
|
|
470
|
|
|
471
|
|
|
472
|
if len(tmp)==0: new_string = '-'
|
|
473
|
else: new_string = "; ".join(tmp_uniq)
|
|
474
|
return new_string
|
|
475
|
|
|
476
|
def set_undef_values(fits_data):
|
|
477
|
'''
|
|
478
|
Set the proper 'undef' values according to the format/name of the field
|
|
479
|
'''
|
|
480
|
print "\n\t>> Checking/setting undefined values for the different fields ..."
|
|
481
|
for i, name in enumerate(fits_data.names):
|
|
482
|
sys.stdout.write('\t%i/%i > %s (format %s) : Done \r' % (i+1, len(fits_data.names), name, fits_data.formats[i]))
|
|
483
|
sys.stdout.flush()
|
|
484
|
for j in range(fits_data.size):
|
|
485
|
if name == name_index_key:
|
|
486
|
if fits_data[name_Name_key][j] <= 0: fits_data[j][i] = -1
|
|
487
|
elif name == name_redshift_key and fits_data[name][j] == -1.0:
|
|
488
|
fits_data[j][i] = np.nan
|
|
489
|
elif name.find(name_zType_key) >= 0 and str(fits_data[name][j]) == 'Null':
|
|
490
|
fits_data[j][i] = "undef"
|
|
491
|
elif fits_data.formats[i] in 'EDJ':
|
|
492
|
if str(fits_data[j][i]) in ['-1.6375e+30','-1.63750e+30', '-1.6375E+30', '-1.63750E+30', 'None', 'NULL']:
|
|
493
|
fits_data[j][i] = np.nan
|
|
494
|
elif fits_data.formats[i].find('A') >= 0:
|
|
495
|
fits_data[j][i] = remove_duplicated_names(fits_data[j][i])
|
|
496
|
if str(fits_data[j][i]).upper() in ["", "0.0", "NULL", "NAN", "NONE", "FALSE"] or str(fits_data[j][i]) == 'False':
|
|
497
|
fits_data[j][i] = "-"
|
|
498
|
elif name in ['PIPELINE','PIPE_DET']:
|
|
499
|
if fits_data[j][i] <= 0: fits_data[j][i] = 0
|
|
500
|
print '\n'
|
|
501
|
return fits_data
|
|
502
|
|
|
503
|
def recreate_reformatted_column(hdulist, field_name, new_format, new_vector):
|
|
504
|
'''
|
|
505
|
Update the length (format) of a 'STRING' (format = 'xA') FIELD.
|
|
506
|
The only way, though, is to re-create the column with the new format.
|
|
507
|
It is used during the creation of NAME, ALT_NAME or REDSHIFT_REF.
|
|
508
|
'''
|
|
509
|
name_vec = []
|
|
510
|
format_vec = []
|
|
511
|
unit_vec = []
|
|
512
|
|
|
513
|
fits_keywds = hdulist.data.names
|
|
514
|
coldefs = pyfits.ColDefs(hdulist.columns)
|
|
515
|
|
|
516
|
|
|
517
|
for j in range(fits_keywds.index(field_name)+1, len(fits_keywds)):
|
|
518
|
name_vec.append(coldefs.names[j])
|
|
519
|
format_vec.append(coldefs.formats[j])
|
|
520
|
unit_vec.append(coldefs.units[j])
|
|
521
|
|
|
522
|
|
|
523
|
tmp = 0
|
|
524
|
for j in range(fits_keywds.index(field_name)+1, len(fits_keywds)):
|
|
525
|
coldefs.del_col(name_vec[tmp])
|
|
526
|
tmp+=1
|
|
527
|
|
|
528
|
|
|
529
|
coldefs.del_col(field_name)
|
|
530
|
|
|
531
|
|
|
532
|
col_tmp = pyfits.Column(name = field_name, format = new_format, unit = 'None', array = new_vector)
|
|
533
|
coldefs.add_col(col_tmp)
|
|
534
|
hdulist.columns = coldefs
|
|
535
|
|
|
536
|
|
|
537
|
tmp = 0
|
|
538
|
data_vec_tmp = []
|
|
539
|
for j in range(fits_keywds.index(field_name)+1, len(fits_keywds)):
|
|
540
|
data_vec_tmp = hdulist.data[name_vec[tmp]]
|
|
541
|
col_tmp = pyfits.Column(name = name_vec[tmp], format = format_vec[tmp], unit = unit_vec[tmp], array = data_vec_tmp)
|
|
542
|
coldefs.add_col(col_tmp)
|
|
543
|
tmp +=1
|
|
544
|
data_vec_tmp = []
|
|
545
|
|
|
546
|
hdulist = pyfits.new_table(coldefs)
|
|
547
|
return hdulist
|
|
548
|
|
|
549
|
'''
|
|
550
|
*** >> START << ***
|
|
551
|
'''
|
|
552
|
|
|
553
|
if (len(sys.argv) > 1):
|
|
554
|
fits_file = sys.argv[1]
|
|
555
|
ascii_file = sys.argv[2]
|
|
556
|
else:
|
|
557
|
print bcolors.WARNING + "\n\tSintax:\t$ python edit_FITS.py <fits_file> <ascii_file>\n" + bcolors.ENDC
|
|
558
|
os._exit(0)
|
|
559
|
|
|
560
|
|
|
561
|
file_report_name = 'summary_updates.tab'
|
|
562
|
file_report = open(file_report_name, 'w')
|
|
563
|
|
|
564
|
question = bcolors.OKBLUE+ "[Q]" + bcolors.ENDC
|
|
565
|
info = bcolors.WARNING+ "[I]" + bcolors.ENDC
|
|
566
|
error = bcolors.FAIL+ "[ERR]" + bcolors.ENDC
|
|
567
|
|
|
568
|
|
|
569
|
delim=raw_input("\n%s Please enter the column delimiter of the ASCII table (default is ','):\t" % question)
|
|
570
|
if not delim:
|
|
571
|
|
|
572
|
ascii_table=asciidata.open(ascii_file, 'r', delimiter=',')
|
|
573
|
else:
|
|
574
|
ascii_table=asciidata.open(ascii_file, 'r', delimiter=delim)
|
|
575
|
|
|
576
|
Ncol_ascii = ascii_table.ncols
|
|
577
|
Nrows_ascii = (ascii_table.nrows) - 1
|
|
578
|
|
|
579
|
print "\n\t\t **** ASCII table details ****"
|
|
580
|
print "\t\t Number of columns: %s" % (Ncol_ascii)
|
|
581
|
print "\t\t Number of rows: %s" % (Nrows_ascii)
|
|
582
|
print "\t\t **** **** **** **** **** ****"
|
|
583
|
|
|
584
|
ascii_keywds=[]
|
|
585
|
keys_form_unit = {}
|
|
586
|
|
|
587
|
for i in range(ascii_table.ncols):
|
|
588
|
tmpKey = str(ascii_table[i][0]).strip()
|
|
589
|
ascii_keywds.append(tmpKey)
|
|
590
|
if tmpKey in _FIELDS_DICTIONARY:
|
|
591
|
keys_form_unit[tmpKey] = {}
|
|
592
|
keys_form_unit[tmpKey]['TFORM'] = _FIELDS_DICTIONARY[tmpKey]['format']
|
|
593
|
keys_form_unit[tmpKey]['TUNIT'] = _FIELDS_DICTIONARY[tmpKey]['unit']
|
|
594
|
|
|
595
|
|
|
596
|
hdulist = pyfits.open(fits_file)
|
|
597
|
fits_header = hdulist[1].header
|
|
598
|
fits_data = hdulist[1].data
|
|
599
|
|
|
600
|
|
|
601
|
Ncol_fits = int(fits_header['TFIELDS'])
|
|
602
|
|
|
603
|
|
|
604
|
Nrows_fits = fits_header['NAXIS2']
|
|
605
|
|
|
606
|
print "\n\t\t **** FITS table details ****"
|
|
607
|
print "\t\t Number of columns: %s" % (Ncol_fits)
|
|
608
|
print "\t\t Number of rows: %s" % (Nrows_fits)
|
|
609
|
print "\t\t **** *** *** *** *** *** ***"
|
|
610
|
|
|
611
|
|
|
612
|
fits_keywds=[]
|
|
613
|
original_fits_keywds = []
|
|
614
|
|
|
615
|
for i in range(Ncol_fits):
|
|
616
|
original_fits_keywds.append(fits_data.names[i])
|
|
617
|
fits_keywds.append(fits_data.names[i])
|
|
618
|
|
|
619
|
|
|
620
|
common_keywds=[]
|
|
621
|
commonKeywds_index=[]
|
|
622
|
keywds_to_update=[]
|
|
623
|
for j in range(Ncol_fits):
|
|
624
|
if fits_keywds[j] in ascii_keywds:
|
|
625
|
common_keywds.append(fits_keywds[j])
|
|
626
|
commonKeywds_index.append(j+1)
|
|
627
|
|
|
628
|
|
|
629
|
keywds_to_update.append(fits_keywds[j])
|
|
630
|
|
|
631
|
print "\n\t%s The following keyword(s) will be updated in the FITS table: " % info , keywds_to_update
|
|
632
|
|
|
633
|
|
|
634
|
keywds_to_add=[item for item in ascii_keywds if item not in fits_keywds]
|
|
635
|
|
|
636
|
print "\n\t%s The following new keyword(s) will be added to the FITS table: " % info , keywds_to_add
|
|
637
|
|
|
638
|
|
|
639
|
|
|
640
|
for i in range(len(keywds_to_add)):
|
|
641
|
if keywds_to_add[i] not in _FIELDS_DICTIONARY:
|
|
642
|
keys_form_unit[keywds_to_add[i]] = {}
|
|
643
|
message = "\n%s Please enter the format (\'TFORM\') of the new field \"%s\" (e.g.: 5A, E, L, ...): " % (question, keywds_to_add[i])
|
|
644
|
keys_form_unit[keywds_to_add[i]]['TFORM'] = raw_input(message)
|
|
645
|
message = "\n%s Please enter the unit (\'TUNIT\') of the new field \"%s\" (e.g.: None, arcmin, ...): " % (question, keywds_to_add[i])
|
|
646
|
keys_form_unit[keywds_to_add[i]]['TUNIT'] = raw_input(message)
|
|
647
|
else:
|
|
648
|
keys_form_unit[keywds_to_add[i]] = {}
|
|
649
|
keys_form_unit[keywds_to_add[i]]['TFORM'] = _FIELDS_DICTIONARY[keywds_to_add[i]]['format']
|
|
650
|
keys_form_unit[keywds_to_add[i]]['TUNIT'] = _FIELDS_DICTIONARY[keywds_to_add[i]]['unit']
|
|
651
|
|
|
652
|
|
|
653
|
fits_keywds.append(keywds_to_add[i])
|
|
654
|
|
|
655
|
'''
|
|
656
|
*** Add the NEW COLUMNS to FITS table ***
|
|
657
|
'''
|
|
658
|
|
|
659
|
|
|
660
|
a_tmp = []
|
|
661
|
|
|
662
|
coldefs = pyfits.ColDefs(hdulist[1].columns)
|
|
663
|
columns = []
|
|
664
|
|
|
665
|
for keys in keywds_to_add:
|
|
666
|
if keys_form_unit[keys]['TFORM'] == 'E' or keys_form_unit[keys]['TFORM'] == 'D':
|
|
667
|
a_tmp = [-1.6375E+30] * Nrows_fits
|
|
668
|
elif keys_form_unit[keys]['TFORM'] == 'I':
|
|
669
|
a_tmp = [-1] * Nrows_fits
|
|
670
|
elif keys_form_unit[keys]['TFORM'] == 'L':
|
|
671
|
a_tmp = [False] * Nrows_fits
|
|
672
|
elif keys_form_unit[keys]['TFORM'].find('A') >= 0:
|
|
673
|
a_tmp = ['Null'] * Nrows_fits
|
|
674
|
|
|
675
|
while True:
|
|
676
|
|
|
677
|
try:
|
|
678
|
col_tmp = pyfits.Column(name=keys, format=keys_form_unit[keys]['TFORM'], unit=keys_form_unit[keys]['TUNIT'], array=a_tmp)
|
|
679
|
columns.append(col_tmp)
|
|
680
|
break
|
|
681
|
except ValueError:
|
|
682
|
print bcolors.FAIL+ "\n\t\t*** FORMAT INCONSISTENT WITH DATA ***" + bcolors.ENDC
|
|
683
|
keys_form_unit[keys]['TFORM'] = raw_input("\n%s Please, enter again the format (\'TFORM\') of the new field \"%s\": " % (question, keys))
|
|
684
|
|
|
685
|
'''
|
|
686
|
*** 1st data UPDATE: new fields added as new columns ***
|
|
687
|
'''
|
|
688
|
|
|
689
|
|
|
690
|
for i in columns: coldefs.add_col(i)
|
|
691
|
hdulist = pyfits.new_table(coldefs)
|
|
692
|
|
|
693
|
|
|
694
|
|
|
695
|
|
|
696
|
fits_data = hdulist.data
|
|
697
|
|
|
698
|
'''
|
|
699
|
*** Object identification via POSITION matching, NAME or INDEX ***
|
|
700
|
'''
|
|
701
|
match_option = False
|
|
702
|
match_radius = 300.0
|
|
703
|
|
|
704
|
name_index_fits = ''
|
|
705
|
name_index_ascii = ''
|
|
706
|
|
|
707
|
print '\n%s Which method do you want to use for the object matching: by POSITION (1) by NAME (2) or by INDEX (3)?' % question
|
|
708
|
while match_option == False:
|
|
709
|
message = "\n\t-> Please enter 1, 2 or 3: "
|
|
710
|
method = raw_input(message)
|
|
711
|
if method == '1':
|
|
712
|
|
|
713
|
if name_ra_key not in fits_data.names or name_dec_key not in fits_data.names or name_ra_key not in ascii_keywds or name_dec_key not in ascii_keywds:
|
|
714
|
print bcolors.FAIL+ "\n\t>> NO %s and %s found in FITS and ASCII tables: POSITION matching not possible <<" % (name_ra_key, name_dec_key) + bcolors.ENDC
|
|
715
|
else:
|
|
716
|
match_option = method
|
|
717
|
match_radius = float(raw_input('\n\t%s Please enter the match radius (in arcsec): ' % question))
|
|
718
|
elif method == '2' : match_option = method
|
|
719
|
elif method == '3' :
|
|
720
|
check_name_index_fits = False
|
|
721
|
while check_name_index_fits == False:
|
|
722
|
name_index_fits = raw_input('\n\t-> Please enter the column name of the INDEX in the FITS file: ')
|
|
723
|
if name_index_fits not in fits_keywds:
|
|
724
|
print bcolors.FAIL+ "\n\t*** '%s' NOT in FITS Keywords ***" % name_index_fits+ bcolors.ENDC
|
|
725
|
else:
|
|
726
|
check_name_index_fits = True
|
|
727
|
index_fits = np.array( fits_data[name_index_fits] )
|
|
728
|
|
|
729
|
check_name_index_ascii = False
|
|
730
|
while check_name_index_ascii == False:
|
|
731
|
name_index_ascii = raw_input('\n\t-> Please enter the column name of the INDEX in the ASCII file: ')
|
|
732
|
if name_index_ascii not in ascii_keywds:
|
|
733
|
print bcolors.FAIL+ "\n\t*** '%s' NOT in ASCII Keywords ***" % name_index_ascii+ bcolors.ENDC
|
|
734
|
else:
|
|
735
|
check_name_index_ascii = True
|
|
736
|
index_ascii = [ (ascii_table[k][j]) for k in range(ascii_table.ncols) if ascii_table[k][0] == name_index_ascii for j in range(1,ascii_table.nrows) ]
|
|
737
|
|
|
738
|
match_option = method
|
|
739
|
|
|
740
|
else: print bcolors.FAIL+ "\n\t*** Wrong option ***"+ bcolors.ENDC
|
|
741
|
|
|
742
|
name_fits = np.array(fits_data[name_Name_key])
|
|
743
|
ra_fits = np.array(fits_data[ name_ra_key ])
|
|
744
|
dec_fits = np.array(fits_data[ name_dec_key ])
|
|
745
|
|
|
746
|
name_ascii = []
|
|
747
|
ra_ascii = []
|
|
748
|
dec_ascii = []
|
|
749
|
|
|
750
|
for k in range(ascii_table.ncols):
|
|
751
|
if ascii_keywds[k]==name_Name_key:
|
|
752
|
for j in range(ascii_table.nrows -1): name_ascii.append((ascii_table[k][j+1]).strip())
|
|
753
|
if ascii_keywds[k]==name_ra_key:
|
|
754
|
for j in range(ascii_table.nrows -1): ra_ascii.append(float(ascii_table[k][j+1]))
|
|
755
|
elif ascii_keywds[k]==name_dec_key:
|
|
756
|
for j in range(ascii_table.nrows -1): dec_ascii.append(float(ascii_table[k][j+1]))
|
|
757
|
|
|
758
|
dist_asec = []
|
|
759
|
|
|
760
|
|
|
761
|
rowAscii_match = []
|
|
762
|
rowFits_match = []
|
|
763
|
|
|
764
|
|
|
765
|
rowAscii_new = []
|
|
766
|
|
|
767
|
method_dict = {
|
|
768
|
'1' : 'POSITION (dist < %.1f")' % match_radius,
|
|
769
|
'2' : 'NAME',
|
|
770
|
'3' : 'INDEX'
|
|
771
|
}
|
|
772
|
|
|
773
|
print "\n\t>> Matching ASCII/FITS tables by %s ...\n" % method_dict[method]
|
|
774
|
|
|
775
|
num_tot_matches = 0
|
|
776
|
for j in range(Nrows_fits):
|
|
777
|
num_multiple_matches = 0
|
|
778
|
id_matches = []
|
|
779
|
ra_dec_matches = []
|
|
780
|
|
|
781
|
if match_option == '1':
|
|
782
|
tmp_idxs_matches = []
|
|
783
|
tmp_dist_matches = []
|
|
784
|
|
|
785
|
for i in range(Nrows_ascii):
|
|
786
|
dist_tmp = 3600. * astCoords.calcAngSepDeg(float(ra_fits[j]), float(dec_fits[j]), ra_ascii[i], dec_ascii[i])
|
|
787
|
if dist_tmp <= match_radius:
|
|
788
|
tmp_idxs_matches.append(i)
|
|
789
|
tmp_dist_matches.append(round(dist_tmp,1))
|
|
790
|
num_tot_matches += 1
|
|
791
|
num_multiple_matches += 1
|
|
792
|
|
|
793
|
idx_match = 0
|
|
794
|
if len( tmp_idxs_matches ) > 1:
|
|
795
|
print bcolors.WARNING+ "\n\t! WARNING ! %i objects found within %.1f arcsec from %s \n" % ( len(tmp_idxs_matches), match_radius, name_fits[j]) + bcolors.ENDC
|
|
796
|
for idx in range( len(tmp_idxs_matches) ): print '\t%i: %s (dist = %s")' % ( (idx+1, name_ascii[ tmp_idxs_matches[idx]], tmp_dist_matches[idx] ) )
|
|
797
|
tmp_check = False
|
|
798
|
while tmp_check == False:
|
|
799
|
tmp_entry = int(raw_input('\t-> Please enter the number of the matching object: '))
|
|
800
|
if tmp_entry in range(1, len(tmp_idxs_matches)+1 ):
|
|
801
|
tmp_check = True
|
|
802
|
idx_match = tmp_idxs_matches[ tmp_entry - 1 ]
|
|
803
|
else:
|
|
804
|
print bcolors.FAIL+ "\n\t*** Wrong option ***\n"+ bcolors.ENDC
|
|
805
|
|
|
806
|
id_matches.append((name_ascii[idx_match]).strip())
|
|
807
|
ra_dec_matches.append(ra_ascii[idx_match])
|
|
808
|
ra_dec_matches.append(dec_ascii[idx_match])
|
|
809
|
|
|
810
|
|
|
811
|
rowAscii_match.append(idx_match)
|
|
812
|
rowFits_match.append(j)
|
|
813
|
|
|
814
|
elif len( tmp_idxs_matches ) == 1:
|
|
815
|
idx_match = tmp_idxs_matches[0]
|
|
816
|
|
|
817
|
id_matches.append((name_ascii[idx_match]).strip())
|
|
818
|
ra_dec_matches.append(ra_ascii[idx_match])
|
|
819
|
ra_dec_matches.append(dec_ascii[idx_match])
|
|
820
|
|
|
821
|
|
|
822
|
rowAscii_match.append(idx_match)
|
|
823
|
rowFits_match.append(j)
|
|
824
|
|
|
825
|
elif match_option == '2':
|
|
826
|
for i in range(Nrows_ascii):
|
|
827
|
if (name_fits[j]).strip() == (name_ascii[i]).strip():
|
|
828
|
num_multiple_matches += 1
|
|
829
|
num_tot_matches += 1
|
|
830
|
if num_multiple_matches > 1:
|
|
831
|
|
|
832
|
print '%s Found %i objects with the same name : %s\nAborted.\n' % (error, num_multiple_matches, name_fits[j]); os._exit(0)
|
|
833
|
|
|
834
|
|
|
835
|
rowAscii_match.append(i)
|
|
836
|
rowFits_match.append(j)
|
|
837
|
|
|
838
|
elif match_option == '3':
|
|
839
|
for i in range(Nrows_ascii):
|
|
840
|
if int(index_fits[j]) == int(index_ascii[i]) and (int(index_fits[j]) >= 0 and int(index_ascii[i]) >= 0):
|
|
841
|
num_tot_matches += 1
|
|
842
|
|
|
843
|
|
|
844
|
rowAscii_match.append(i)
|
|
845
|
rowFits_match.append(j)
|
|
846
|
break
|
|
847
|
|
|
848
|
for i in range(Nrows_ascii):
|
|
849
|
if i not in rowAscii_match: rowAscii_new.append(i)
|
|
850
|
|
|
851
|
print "\n\t%s Found %s matching clusters between FITS/ASCII table to be UPDATED in the FITS table" % (info, len(rowAscii_match))
|
|
852
|
|
|
853
|
print "\n\t%s Found %s NEW clusters in the ASCII table to be ADDED to the FITS table" % (info, len(rowAscii_new))
|
|
854
|
|
|
855
|
|
|
856
|
idx_name = fits_keywds.index(name_Name_key)
|
|
857
|
clName_fits=[]
|
|
858
|
|
|
859
|
for k in range(Nrows_fits):
|
|
860
|
clName_fits.append(fits_data[k][idx_name])
|
|
861
|
|
|
862
|
common_clNames=[]
|
|
863
|
new_clNames=[]
|
|
864
|
|
|
865
|
for i, idx in enumerate(rowAscii_match):
|
|
866
|
common_clNames.append(clName_fits[rowFits_match[i]])
|
|
867
|
|
|
868
|
for idx in rowAscii_new:
|
|
869
|
idx_name = ascii_keywds.index(name_Name_key)
|
|
870
|
new_clNames.append( ascii_table[idx_name][idx+1] )
|
|
871
|
|
|
872
|
|
|
873
|
h_factor = 1.0
|
|
874
|
tmp_check = False
|
|
875
|
|
|
876
|
mass_in_ascii = set(name_mass_key) & set(ascii_keywds)
|
|
877
|
if mass_in_ascii:
|
|
878
|
print "\n%s Concerning %s, do you want to:\n\t1) Convert from h70^-1 -> h100^-1\n\t2) Convert from h100^-1 -> h70^-1\n\t3) Keep the original values of the ASCII table" % (question, mass_in_ascii.pop())
|
|
879
|
while tmp_check == False:
|
|
880
|
message = "\n\t-> Please enter 1, 2 or 3: "
|
|
881
|
h_opt = raw_input(message)
|
|
882
|
if h_opt == '1': h_factor = 0.7; tmp_check = True
|
|
883
|
elif h_opt == '2': h_factor = 1./0.7; tmp_check = True
|
|
884
|
elif h_opt == '3': h_factor = 1.; tmp_check = True
|
|
885
|
else: print bcolors.FAIL+ "\n\t*** Wrong option ***"+ bcolors.ENDC
|
|
886
|
|
|
887
|
newRow_num = Nrows_fits + len(rowAscii_new)
|
|
888
|
|
|
889
|
'''
|
|
890
|
*** 2nd data UPDATE: add the new clusters as new (initially empty) rows ***
|
|
891
|
'''
|
|
892
|
hdulist = pyfits.new_table(hdulist, nrows=newRow_num)
|
|
893
|
|
|
894
|
|
|
895
|
if name_catalog_key in fits_keywds and name_catalog_key not in ascii_keywds and len(rowAscii_new) > 0:
|
|
896
|
new_catalog = raw_input("\n%s Please enter the value of %s for the new cluster(s): " % (question, name_catalog_key))
|
|
897
|
|
|
898
|
|
|
899
|
'''
|
|
900
|
*** Update the PAPER column ***
|
|
901
|
'''
|
|
902
|
paper_flag = False
|
|
903
|
updated_paper_vec = []
|
|
904
|
max_length_paper = 0
|
|
905
|
cnt = 0
|
|
906
|
|
|
907
|
|
|
908
|
if name_paper_key in ascii_keywds and name_paper_key not in fits_keywds and len(rowAscii_new) == 0:
|
|
909
|
for j in range(Nrows_fits):
|
|
910
|
|
|
911
|
|
|
912
|
if j in rowFits_match:
|
|
913
|
paper_tmp = ascii_table[ascii_keywds.index(name_paper_key)][rowAscii_match[cnt]+1]
|
|
914
|
cnt += 1
|
|
915
|
else:
|
|
916
|
paper_tmp = "Null"
|
|
917
|
|
|
918
|
paper_tmp = remove_duplicated_names(paper_tmp)
|
|
919
|
updated_paper_vec.append(paper_tmp)
|
|
920
|
if len(paper_tmp) > max_length_paper: max_length_paper = len(paper_tmp)
|
|
921
|
|
|
922
|
|
|
923
|
|
|
924
|
elif name_paper_key in fits_keywds:
|
|
925
|
new_paper_vec = []
|
|
926
|
col_paper_fits = fits_keywds.index(name_paper_key)
|
|
927
|
|
|
928
|
if name_paper_key in ascii_keywds:
|
|
929
|
paper_flag = True
|
|
930
|
for i in range(Nrows_ascii):
|
|
931
|
new_paper_vec.append( ascii_table[ascii_keywds.index(name_paper_key)][i+1].strip() )
|
|
932
|
else:
|
|
933
|
|
|
934
|
if len(new_clNames)>0:
|
|
935
|
tmp_new_paper = raw_input("\n%s Please insert the new reference to add: " % question)
|
|
936
|
new_paper_vec=[tmp_new_paper for x in range( Nrows_ascii ) ]
|
|
937
|
paper_flag = True
|
|
938
|
else:
|
|
939
|
new_paper_vec=['' for x in range( Nrows_ascii ) ]
|
|
940
|
|
|
941
|
|
|
942
|
for j in range(Nrows_fits):
|
|
943
|
paper_old = (fits_data[j][col_paper_fits]).strip()
|
|
944
|
if j in rowFits_match:
|
|
945
|
if paper_old == "Null":
|
|
946
|
paper_tmp = new_paper_vec[ rowAscii_match[cnt] ]
|
|
947
|
cnt+=1
|
|
948
|
else:
|
|
949
|
paper_tmp = paper_old+"; "+new_paper_vec[ rowAscii_match[cnt] ]
|
|
950
|
cnt += 1
|
|
951
|
else:
|
|
952
|
paper_tmp = paper_old
|
|
953
|
|
|
954
|
paper_tmp = remove_duplicated_names(paper_tmp)
|
|
955
|
updated_paper_vec.append(paper_tmp)
|
|
956
|
if len(paper_tmp) > max_length_paper: max_length_paper = len(paper_tmp)
|
|
957
|
|
|
958
|
|
|
959
|
if name_paper_key in fits_keywds and paper_flag:
|
|
960
|
hdulist.columns.del_col(name_paper_key)
|
|
961
|
|
|
962
|
|
|
963
|
col_tmp = pyfits.Column(name=name_paper_key, format=str(max_length_paper)+'A', unit = 'None', array=updated_paper_vec)
|
|
964
|
paper_flag = True
|
|
965
|
|
|
966
|
if paper_flag:
|
|
967
|
coldefs = pyfits.ColDefs(hdulist.columns)
|
|
968
|
coldefs.add_col(col_tmp)
|
|
969
|
hdulist = pyfits.new_table(coldefs)
|
|
970
|
|
|
971
|
|
|
972
|
|
|
973
|
len_ALT_NAME = []
|
|
974
|
new_altName_vec = []
|
|
975
|
old_altName_vec = []
|
|
976
|
new_altName = ""
|
|
977
|
cnt = 0
|
|
978
|
|
|
979
|
altName_flag = False
|
|
980
|
name_in_altName = False
|
|
981
|
replace_altName = False
|
|
982
|
|
|
983
|
|
|
984
|
if len(common_clNames) > 0:
|
|
985
|
|
|
986
|
if name_altName_key not in ascii_keywds and name_altName_key in fits_keywds:
|
|
987
|
if name_Name_key in fits_keywds and name_Name_key in ascii_keywds:
|
|
988
|
answer_check = False
|
|
989
|
tmp = raw_input("\n\t%s Do you want to add the old clusters' %s listed in FITS table to %s? [y/n]: " % (question, name_Name_key, name_altName_key) )
|
|
990
|
while answer_check == False:
|
|
991
|
if tmp in 'yesYES1' and tmp != '':
|
|
992
|
name_in_altName = True
|
|
993
|
answer_check = True
|
|
994
|
elif tmp in 'nN' and tmp != '': answer_check = True
|
|
995
|
else: tmp = raw_input(bcolors.FAIL+ "\n\t\t*** Please enter a valid answer ***" + bcolors.ENDC + ' [y/n] : ')
|
|
996
|
|
|
997
|
col_altName_fits = fits_keywds.index( name_altName_key )
|
|
998
|
|
|
999
|
for j in range(Nrows_fits):
|
|
1000
|
|
|
1001
|
oldVal_fits = (fits_data[j][col_altName_fits]).strip()
|
|
1002
|
old_altName_vec.append(oldVal_fits)
|
|
1003
|
if j in rowFits_match:
|
|
1004
|
|
|
1005
|
|
|
1006
|
if name_in_altName:
|
|
1007
|
altName_flag = True
|
|
1008
|
|
|
1009
|
name_fits = np.array(fits_data[name_Name_key])
|
|
1010
|
new_altName = oldVal_fits+"; "+name_fits[j]
|
|
1011
|
|
|
1012
|
else: new_altName = oldVal_fits
|
|
1013
|
|
|
1014
|
new_altName = remove_duplicated_names(new_altName)
|
|
1015
|
new_altName_vec.append(new_altName)
|
|
1016
|
len_ALT_NAME.append(len(new_altName))
|
|
1017
|
|
|
1018
|
cnt += 1
|
|
1019
|
|
|
1020
|
else:
|
|
1021
|
new_altName = remove_duplicated_names(oldVal_fits)
|
|
1022
|
new_altName_vec.append(oldVal_fits)
|
|
1023
|
|
|
1024
|
elif name_altName_key in ascii_keywds and name_altName_key in fits_keywds:
|
|
1025
|
|
|
1026
|
answer_check = False
|
|
1027
|
tmp = raw_input("\n\t%s %s is both in ASCII/FITS tables. Do you want the ASCII values to REPLACE or to be APPENDED to the FITS ones? [r/a]: " % (question, name_altName_key) )
|
|
1028
|
while answer_check == False:
|
|
1029
|
if tmp in 'rR' and tmp != '':
|
|
1030
|
replace_altName = True
|
|
1031
|
answer_check = True
|
|
1032
|
elif tmp in 'aA' and tmp != '': answer_check = True
|
|
1033
|
else: tmp = raw_input(bcolors.FAIL+ "\n\t\t*** Please enter a valid answer ***" + bcolors.ENDC + ' [r/a] : ')
|
|
1034
|
|
|
1035
|
if name_Name_key in fits_keywds and name_Name_key in ascii_keywds:
|
|
1036
|
answer_check = False
|
|
1037
|
tmp = raw_input("\n\t%s Do you want to add the old clusters' %s listed in FITS table to %s? [y/n]: " % (question, name_Name_key, name_altName_key) )
|
|
1038
|
while answer_check == False:
|
|
1039
|
if tmp in 'yesYES1' and tmp != '':
|
|
1040
|
name_in_altName = True
|
|
1041
|
answer_check = True
|
|
1042
|
elif tmp in 'nN' and tmp != '': answer_check = True
|
|
1043
|
else: tmp = raw_input(bcolors.FAIL+ "\n\t\t*** Please enter a valid answer ***" + bcolors.ENDC + ' [y/n] : ')
|
|
1044
|
|
|
1045
|
col_altName_ascii = ascii_keywds.index( name_altName_key )
|
|
1046
|
|
|
1047
|
if replace_altName:
|
|
1048
|
altName_flag = True
|
|
1049
|
print "\n\t%s %s replaced" % (info, name_altName_key)
|
|
1050
|
|
|
1051
|
if name_in_altName:
|
|
1052
|
names_fits = fits_data[name_Name_key].strip()
|
|
1053
|
for j in range(Nrows_fits):
|
|
1054
|
oldVal_fits = (fits_data[ name_altName_key ][j]).strip()
|
|
1055
|
old_altName_vec.append(oldVal_fits)
|
|
1056
|
if j in rowFits_match:
|
|
1057
|
new_altName = ascii_table[col_altName_ascii][rowAscii_match[cnt]+1]+"; "+name_fits[j]
|
|
1058
|
cnt+=1
|
|
1059
|
else: new_altName = oldVal_fits
|
|
1060
|
|
|
1061
|
new_altName = remove_duplicated_names(new_altName)
|
|
1062
|
new_altName_vec.append(new_altName)
|
|
1063
|
else:
|
|
1064
|
for j in range(Nrows_fits):
|
|
1065
|
oldVal_fits = (fits_data[ name_altName_key ][j]).strip()
|
|
1066
|
old_altName_vec.append(oldVal_fits)
|
|
1067
|
if j in rowFits_match:
|
|
1068
|
new_altName = ascii_table[col_altName_ascii][rowAscii_match[cnt]+1]
|
|
1069
|
cnt+=1
|
|
1070
|
else: new_altName = oldVal_fits
|
|
1071
|
|
|
1072
|
new_altName = remove_duplicated_names(new_altName)
|
|
1073
|
new_altName_vec.append(new_altName)
|
|
1074
|
|
|
1075
|
elif not replace_altName:
|
|
1076
|
if name_in_altName:
|
|
1077
|
altName_flag = True
|
|
1078
|
print '\n\t%s %s appended & %s added' % (info, name_altName_key, name_Name_key)
|
|
1079
|
names_fits = fits_data[name_Name_key].strip()
|
|
1080
|
for j in range(Nrows_fits):
|
|
1081
|
oldVal_fits = (fits_data[ name_altName_key ][j]).strip()
|
|
1082
|
old_altName_vec.append(oldVal_fits)
|
|
1083
|
if j in rowFits_match:
|
|
1084
|
new_altName = "; ".join([ oldVal_fits, ascii_table[col_altName_ascii][rowAscii_match[cnt]+1], name_fits[j] ])
|
|
1085
|
cnt+=1
|
|
1086
|
else:
|
|
1087
|
new_altName = oldVal_fits
|
|
1088
|
|
|
1089
|
new_altName = remove_duplicated_names(new_altName)
|
|
1090
|
new_altName_vec.append(new_altName)
|
|
1091
|
else:
|
|
1092
|
for j in range(Nrows_fits):
|
|
1093
|
oldVal_fits = (fits_data[ name_altName_key ][j]).strip()
|
|
1094
|
old_altName_vec.append(oldVal_fits)
|
|
1095
|
if j in rowFits_match:
|
|
1096
|
if oldVal_fits in [np.nan, "NULL", "NaN", "False"]: new_altName = ascii_table[col_altName_ascii][rowAscii_match[cnt]+1]
|
|
1097
|
else:
|
|
1098
|
new_altName = "%s; %s" % (oldVal_fits, ascii_table[col_altName_ascii][rowAscii_match[cnt]+1])
|
|
1099
|
cnt+=1
|
|
1100
|
else: new_altName = oldVal_fits
|
|
1101
|
|
|
1102
|
new_altName = remove_duplicated_names(new_altName)
|
|
1103
|
new_altName_vec.append(new_altName)
|
|
1104
|
|
|
1105
|
|
|
1106
|
maxLength_altName_fits = max([len(item) for item in fits_data[ name_altName_key ]])
|
|
1107
|
maxLength_altName_ascii = max([len(item) for item in ascii_table[col_altName_ascii]])
|
|
1108
|
|
|
1109
|
maxLength_altName_new = max([len(item) for item in new_altName_vec])
|
|
1110
|
|
|
1111
|
len_ALT_NAME = [maxLength_altName_fits, maxLength_altName_ascii, maxLength_altName_new]
|
|
1112
|
|
|
1113
|
'''
|
|
1114
|
*** Update the length of ALT_NAME. The only way, though, is to re-create the column with the new format ***
|
|
1115
|
'''
|
|
1116
|
if altName_flag:
|
|
1117
|
|
|
1118
|
|
|
1119
|
name_vec = []
|
|
1120
|
format_vec = []
|
|
1121
|
unit_vec = []
|
|
1122
|
|
|
1123
|
|
|
1124
|
for j in range(fits_keywds.index(name_altName_key)+1, len(fits_keywds)):
|
|
1125
|
name_vec.append(coldefs.names[j])
|
|
1126
|
format_vec.append(coldefs.formats[j])
|
|
1127
|
unit_vec.append(coldefs.units[j])
|
|
1128
|
|
|
1129
|
|
|
1130
|
tmp = 0
|
|
1131
|
for j in range(fits_keywds.index(name_altName_key)+1, len(fits_keywds)):
|
|
1132
|
coldefs.del_col(name_vec[tmp])
|
|
1133
|
tmp+=1
|
|
1134
|
|
|
1135
|
|
|
1136
|
coldefs.del_col(name_altName_key)
|
|
1137
|
|
|
1138
|
|
|
1139
|
col_tmp = pyfits.Column(name=name_altName_key, format=str(max(len_ALT_NAME))+'A', unit = 'None', array=new_altName_vec)
|
|
1140
|
coldefs.add_col(col_tmp)
|
|
1141
|
hdulist.columns = coldefs
|
|
1142
|
|
|
1143
|
|
|
1144
|
tmp = 0
|
|
1145
|
data_vec_tmp = []
|
|
1146
|
|
|
1147
|
for j in range(fits_keywds.index(name_altName_key)+1, len(fits_keywds)):
|
|
1148
|
data_vec_tmp = hdulist.data[name_vec[tmp]]
|
|
1149
|
col_tmp = pyfits.Column(name=name_vec[tmp], format=format_vec[tmp], unit = unit_vec[tmp], array=data_vec_tmp)
|
|
1150
|
coldefs.add_col(col_tmp)
|
|
1151
|
tmp +=1
|
|
1152
|
data_vec_tmp = []
|
|
1153
|
|
|
1154
|
hdulist = pyfits.new_table(coldefs)
|
|
1155
|
|
|
1156
|
'''
|
|
1157
|
*** Write summary report for matching/new clusters ***
|
|
1158
|
|
|
1159
|
and also
|
|
1160
|
|
|
1161
|
*** 3rd data UPDATE: the columns of new clusters are filled in with the correct values ***
|
|
1162
|
'''
|
|
1163
|
|
|
1164
|
|
|
1165
|
if len(rowAscii_match) > 0:
|
|
1166
|
length_new_field = []
|
|
1167
|
index_ascii_field_vec = []
|
|
1168
|
|
|
1169
|
tmp_lenght = ''
|
|
1170
|
for fields in ascii_keywds:
|
|
1171
|
index_ascii_field = ascii_keywds.index(fields)
|
|
1172
|
index_ascii_field_vec.append(index_ascii_field)
|
|
1173
|
index_fits_field = coldefs.names.index(fields)
|
|
1174
|
if coldefs.formats[index_fits_field].find('A') >= 0:
|
|
1175
|
tmp_lenght = coldefs.formats[index_fits_field].split('A')[0]
|
|
1176
|
elif coldefs.formats[index_fits_field].find('E') >= 0 or coldefs.formats[index_fits_field].find('D') >= 0:
|
|
1177
|
tmp_lenght = '15'
|
|
1178
|
elif coldefs.formats[index_fits_field].find('I') >= 0:
|
|
1179
|
max_len_int = max(len(str(elem).strip()) for elem in ascii_table[index_ascii_field])
|
|
1180
|
tmp_lenght = str(max_len_int + 3)
|
|
1181
|
elif coldefs.formats[index_fits_field].find('L') >= 0:
|
|
1182
|
tmp_lenght = '6'
|
|
1183
|
|
|
1184
|
length_new_field.append( max( int(tmp_lenght), len(fields)+3 ) )
|
|
1185
|
|
|
1186
|
file_report.write("\n# >>>> CLUSTERS PROPERTIES ** UPDATED ** IN THE FITS TABLE <<<<\n\n")
|
|
1187
|
to_write = ""
|
|
1188
|
|
|
1189
|
|
|
1190
|
for tmp, fields in enumerate(ascii_keywds):
|
|
1191
|
|
|
1192
|
max_len_new = length_new_field[tmp]
|
|
1193
|
|
|
1194
|
if fields in fits_keywds:
|
|
1195
|
max_len_old = max(len(str(elem).strip()) for elem in fits_data[fields])
|
|
1196
|
else:
|
|
1197
|
max_len_old = max_len_new
|
|
1198
|
|
|
1199
|
if fields in [name_Name_key, name_zRef_key]:
|
|
1200
|
|
|
1201
|
|
|
1202
|
|
|
1203
|
maxLength_fits = max([len(item) for item in fits_data[fields]])
|
|
1204
|
maxLength_ascii = max([len(item) for item in ascii_table[index_ascii_field_vec[tmp]] ])
|
|
1205
|
|
|
1206
|
|
|
1207
|
if maxLength_ascii > maxLength_fits:
|
|
1208
|
print '\n\t%s New %ss are longer than ones in fits: recreating the column with larger size (%sA -> %sA)' % (info, fields, maxLength_fits, maxLength_ascii)
|
|
1209
|
|
|
1210
|
new_format = '%sA' % maxLength_ascii
|
|
1211
|
|
|
1212
|
hdulist = recreate_reformatted_column(hdulist, fields, new_format, hdulist.data[fields] )
|
|
1213
|
|
|
1214
|
|
|
1215
|
elif fields == name_altName_key and altName_flag:
|
|
1216
|
max_len_old = max([len(item) for item in old_altName_vec])
|
|
1217
|
max_len_new = max([len(item) for item in new_altName_vec])
|
|
1218
|
|
|
1219
|
|
|
1220
|
elif fields == name_paper_key and paper_flag:
|
|
1221
|
max_len_new = max_length_paper
|
|
1222
|
|
|
1223
|
label_tot_length = str(int(max_len_old) + int(max_len_new) +3)
|
|
1224
|
formatting = '{0:^%ss}' % (label_tot_length)
|
|
1225
|
|
|
1226
|
to_write += formatting.format( fields )
|
|
1227
|
|
|
1228
|
file_report.write(to_write+"\n")
|
|
1229
|
|
|
1230
|
|
|
1231
|
for r, idx in enumerate(rowAscii_match):
|
|
1232
|
|
|
1233
|
|
|
1234
|
clRow_fits = rowFits_match[r]
|
|
1235
|
clRow_ascii = idx
|
|
1236
|
|
|
1237
|
to_write = "\n"
|
|
1238
|
|
|
1239
|
for tmp, fields in enumerate(ascii_keywds):
|
|
1240
|
|
|
1241
|
kwCol_fits = hdulist.data.names.index(fields)
|
|
1242
|
kwCol_ascii = ascii_keywds.index(fields)
|
|
1243
|
|
|
1244
|
oldVal_fits = hdulist.data[clRow_fits][kwCol_fits]
|
|
1245
|
newVal_ascii = ascii_table[kwCol_ascii][clRow_ascii+1]
|
|
1246
|
|
|
1247
|
|
|
1248
|
|
|
1249
|
|
|
1250
|
if str(newVal_ascii).strip() in ['', '-', '-1.6375E+30', '-1.6375e+30']:
|
|
1251
|
|
|
1252
|
if keys_form_unit[fields]['TFORM'].find('A') >=0 : newVal_ascii = '-'
|
|
1253
|
|
|
1254
|
else : newVal_ascii = -1.6375e+30
|
|
1255
|
|
|
1256
|
if (fields in name_mass_key or fields in name_errMass_key) and newVal_ascii != -1.6375e+30:
|
|
1257
|
newVal_ascii = h_factor * float(newVal_ascii)
|
|
1258
|
|
|
1259
|
max_len_new = length_new_field[tmp]
|
|
1260
|
|
|
1261
|
if fields in fits_keywds:
|
|
1262
|
max_len_old = max(len(str(elem).strip()) for elem in fits_data[fields])
|
|
1263
|
else:
|
|
1264
|
max_len_old = max_len_new
|
|
1265
|
|
|
1266
|
|
|
1267
|
if fields == name_altName_key and altName_flag:
|
|
1268
|
max_len_old = max([len(item) for item in old_altName_vec])
|
|
1269
|
max_len_new = max([len(item) for item in new_altName_vec])
|
|
1270
|
|
|
1271
|
oldVal_fits = old_altName_vec[clRow_fits]
|
|
1272
|
newVal_ascii = new_altName_vec[clRow_fits]
|
|
1273
|
|
|
1274
|
elif fields == name_paper_key and paper_flag:
|
|
1275
|
max_len_new = max_length_paper
|
|
1276
|
|
|
1277
|
oldVal_fits = fits_data[name_paper_key][clRow_fits]
|
|
1278
|
newVal_ascii = new_paper_vec[clRow_ascii]
|
|
1279
|
|
|
1280
|
formatting = ' {0:>%ss} | {1:<%ss} ' % (max_len_old, max_len_new)
|
|
1281
|
to_write += formatting.format(str(oldVal_fits), str(newVal_ascii))
|
|
1282
|
|
|
1283
|
|
|
1284
|
if keys_form_unit[fields]['TFORM'] == 'L':
|
|
1285
|
if str(newVal_ascii).upper() in ["TRUE", "YES", "1.0"]: hdulist.data[clRow_fits][kwCol_fits] = True
|
|
1286
|
elif str(newVal_ascii).upper() in ["FALSE", "NO", "0.0", "", "NONE", "NULL", "[]", "{}"]: hdulist.data[clRow_fits][kwCol_fits] = False
|
|
1287
|
else:
|
|
1288
|
try:
|
|
1289
|
hdulist.data[clRow_fits][kwCol_fits] = newVal_ascii
|
|
1290
|
except:
|
|
1291
|
if str(newVal_ascii) == 'nan': hdulist.data[clRow_fits][kwCol_fits] = np.nan
|
|
1292
|
|
|
1293
|
file_report.write(to_write)
|
|
1294
|
|
|
1295
|
|
|
1296
|
|
|
1297
|
fits_keywds=[]
|
|
1298
|
length_label_vec = []
|
|
1299
|
Ncol_fits=int(hdulist.header['TFIELDS'])
|
|
1300
|
for i in range(Ncol_fits):
|
|
1301
|
fits_keywds.append(hdulist.data.names[i])
|
|
1302
|
|
|
1303
|
if len(rowAscii_new) > 0:
|
|
1304
|
file_report.write("\n\n# >>>> NEW CLUSTERS ** ADDED ** TO THE FITS TABLE <<<<\n\n")
|
|
1305
|
to_write = ""
|
|
1306
|
tmp = 0
|
|
1307
|
for fields in fits_keywds:
|
|
1308
|
|
|
1309
|
format_tmp = coldefs.formats[tmp]
|
|
1310
|
tmp_length = ''
|
|
1311
|
|
|
1312
|
if format_tmp.find('A') >= 0:
|
|
1313
|
tmp_length = format_tmp.split('A')[0]
|
|
1314
|
elif format_tmp.find('E') >= 0 or format_tmp.find('D') >= 0:
|
|
1315
|
tmp_length = '15'
|
|
1316
|
elif format_tmp.find('I') >= 0:
|
|
1317
|
index_fits_field = fits_keywds.index(fields)
|
|
1318
|
max_len_int = len(str(fits_data[-1][index_fits_field]))
|
|
1319
|
tmp_length = str(max_len_int+3)
|
|
1320
|
elif format_tmp.find('L') >= 0:
|
|
1321
|
tmp_length = '6'
|
|
1322
|
|
|
1323
|
length_label_vec.append( max( int(tmp_length), len(fields)+3 ) )
|
|
1324
|
|
|
1325
|
formatting = '{0:^%ss}' % (length_label_vec[-1])
|
|
1326
|
|
|
1327
|
to_write +=formatting.format( fields )
|
|
1328
|
tmp +=1
|
|
1329
|
|
|
1330
|
file_report.write(to_write+"\n")
|
|
1331
|
|
|
1332
|
j = 0
|
|
1333
|
|
|
1334
|
for name in new_clNames:
|
|
1335
|
|
|
1336
|
to_write = "\n"
|
|
1337
|
for k, field in enumerate(fits_keywds):
|
|
1338
|
index_field = coldefs.names.index(field)
|
|
1339
|
format_field = coldefs.formats[index_field]
|
|
1340
|
|
|
1341
|
kwCol_fits = fits_keywds.index(field)
|
|
1342
|
oldVal_fits = hdulist.data[Nrows_fits+j][kwCol_fits]
|
|
1343
|
|
|
1344
|
if field in ascii_keywds:
|
|
1345
|
kwCol_ascii = ascii_keywds.index(field)
|
|
1346
|
|
|
1347
|
newVal_ascii = ascii_table[kwCol_ascii][rowAscii_new[j]+1]
|
|
1348
|
|
|
1349
|
if format_field.find('A') >= 0 and (newVal_ascii.strip()).upper() in ['', '-', "NULL", "NAN", "NONE", "FALSE"]: newVal_ascii = '-'
|
|
1350
|
elif str(newVal_ascii).strip() in ['-1.6375E+30', '-1.6375e+30']: newVal_ascii = -1.6375e+30
|
|
1351
|
if (fields in name_mass_key or fields in name_errMass_key) and newVal_ascii != -1.6375e+30:
|
|
1352
|
newVal_ascii = h_factor * float(newVal_ascii)
|
|
1353
|
else:
|
|
1354
|
oldVal_fits = hdulist.data[Nrows_fits+j][kwCol_fits]
|
|
1355
|
|
|
1356
|
if field == name_index_key:
|
|
1357
|
|
|
1358
|
newVal_ascii = 1 + hdulist.data[Nrows_fits+j-1][kwCol_fits]
|
|
1359
|
|
|
1360
|
elif field == name_catalog_key:
|
|
1361
|
newVal_ascii = str(new_catalog)
|
|
1362
|
|
|
1363
|
|
|
1364
|
elif field == 'GLON':
|
|
1365
|
if len(ra_ascii) > 0 and len(dec_ascii)>0: newVal_ascii = round(astCoords.convertCoords('J2000', 'GALACTIC', ra_ascii[rowAscii_new[j]], dec_ascii[rowAscii_new[j]], 2000)[0], 5)
|
|
1366
|
elif field == 'GLAT':
|
|
1367
|
if len(ra_ascii) > 0 and len(dec_ascii)>0: newVal_ascii = round(astCoords.convertCoords('J2000', 'GALACTIC', ra_ascii[rowAscii_new[j]], dec_ascii[rowAscii_new[j]], 2000)[1], 5)
|
|
1368
|
|
|
1369
|
elif field == name_zErr_key:
|
|
1370
|
newVal_ascii = np.nan
|
|
1371
|
elif field == name_zLimit_key:
|
|
1372
|
newVal_ascii = np.nan
|
|
1373
|
elif field == name_paper_key:
|
|
1374
|
newVal_ascii = tmp_new_paper
|
|
1375
|
elif format_field == 'L':
|
|
1376
|
newVal_ascii = False
|
|
1377
|
elif field in name_coordinates_keys:
|
|
1378
|
newVal_ascii = np.nan
|
|
1379
|
elif format_field == 'I':
|
|
1380
|
newVal_ascii = -1
|
|
1381
|
elif format_field == 'E':
|
|
1382
|
newVal_ascii = -1.6375E+30
|
|
1383
|
elif format_field.find("A") >= 0:
|
|
1384
|
newVal_ascii = 'Null'
|
|
1385
|
|
|
1386
|
|
|
1387
|
if format_field == 'L':
|
|
1388
|
if str(newVal_ascii).upper() in ["TRUE", "YES", "1.0"]: hdulist.data[Nrows_fits+j][kwCol_fits] = True
|
|
1389
|
elif str(newVal_ascii).upper() in ["FALSE", "NO", "0.0", "", "NONE", "NULL", "[]", "{}"]: hdulist.data[Nrows_fits+j][kwCol_fits] = False
|
|
1390
|
else:
|
|
1391
|
try:
|
|
1392
|
hdulist.data[Nrows_fits+j][kwCol_fits] = newVal_ascii
|
|
1393
|
except:
|
|
1394
|
if str(newVal_ascii) == 'nan': hdulist.data[Nrows_fits][kwCol_fits] = np.nan
|
|
1395
|
else: print '%s A problem occurred for cluster Name = %s : field = %s , value = %s \nAborted.\n' % (error, name, field, newVal_ascii); os._exit(0)
|
|
1396
|
|
|
1397
|
formatting = '{0:^%ss}' % (length_label_vec[k])
|
|
1398
|
to_write += formatting.format( str(newVal_ascii) )
|
|
1399
|
j += 1
|
|
1400
|
file_report.write(to_write)
|
|
1401
|
|
|
1402
|
|
|
1403
|
'''
|
|
1404
|
*** 4th data UPDATE: update the fits HEADER with the Version number and the creation date ***
|
|
1405
|
'''
|
|
1406
|
|
|
1407
|
hdulist.header.add_comment("", before="TTYPE1")
|
|
1408
|
version = raw_input("\n%s Please enter the Version number of the new table: " % question)
|
|
1409
|
|
|
1410
|
version_check = False
|
|
1411
|
while version_check == False:
|
|
1412
|
try:
|
|
1413
|
if float(version): version_check = True
|
|
1414
|
except ValueError:
|
|
1415
|
print bcolors.FAIL+ "\n\t\t*** Version number not valid ***" + bcolors.ENDC
|
|
1416
|
version = raw_input("\n\t-> Please enter a valid version number: ")
|
|
1417
|
|
|
1418
|
hdulist.header.add_comment("*** Version " +str(version)+" ***", before="TTYPE1")
|
|
1419
|
|
|
1420
|
today = date.today().strftime("%A %d. %B %Y")
|
|
1421
|
comment = "*** Compiled at IDOC/IAS on %s ***" % (today)
|
|
1422
|
hdulist.header.add_comment(comment, before="TTYPE1")
|
|
1423
|
|
|
1424
|
hdulist.header.add_comment("", before="TTYPE1")
|
|
1425
|
extname = raw_input("\n%s Please enter the name of the new FITS table (without extension): " % question)
|
|
1426
|
hdulist.header.update('EXTNAME', extname, before='TTYPE1')
|
|
1427
|
|
|
1428
|
|
|
1429
|
hdulist.writeto('new_table.fits')
|
|
1430
|
|
|
1431
|
file_report.close()
|
|
1432
|
|
|
1433
|
|
|
1434
|
hdulist = pyfits.open('new_table.fits')
|
|
1435
|
fits_header = hdulist[1].header
|
|
1436
|
fits_data = hdulist[1].data
|
|
1437
|
|
|
1438
|
command = "rm new_table.fits"
|
|
1439
|
os.system(command)
|
|
1440
|
|
|
1441
|
Ncol_fits = int(fits_header['TFIELDS'])
|
|
1442
|
Nrows_fits = fits_header['NAXIS2']
|
|
1443
|
|
|
1444
|
|
|
1445
|
_UNDEF_VALUES_ = {
|
|
1446
|
'FLOAT' : {np.nan},
|
|
1447
|
'INT' : {-1},
|
|
1448
|
'STRING' : {'NULL'},
|
|
1449
|
name_zType_key : {'undef'},
|
|
1450
|
'PIPELINE' : {0},
|
|
1451
|
'PIPE_DET' : {0}
|
|
1452
|
}
|
|
1453
|
|
|
1454
|
'''
|
|
1455
|
*** 5th data UPDATE: set the proper 'undef' values for the different fields ***
|
|
1456
|
'''
|
|
1457
|
|
|
1458
|
hdulist[1].data = set_undef_values(fits_data)
|
|
1459
|
|
|
1460
|
file_output = extname+'.fits'
|
|
1461
|
print "\n\t>> New updated file:" + bcolors.OKGREEN + " %s " % (file_output) + bcolors.ENDC
|
|
1462
|
print "\t>> Details of the applied updates are reported in:" + bcolors.OKGREEN + " %s " % (file_report_name) + bcolors.ENDC + "\n"
|
|
1463
|
hdulist.writeto(file_output)
|