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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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'PSZ1_SN_PSX': { 'format': 'E', 'unit': 'None' },
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'PSZ1_PIPELINE': { 'format': 'I', 'unit': 'None' },
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'PSZ1_PIPE_DET': { 'format': 'I', 'unit': 'None' },
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'PSZ1_PCCS': { 'format': 'L', 'unit': 'None' },
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'PSZ1_VALIDATION': { 'format': 'I', 'unit': 'None' },
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'PSZ1_ID_EXT': { 'format': '25A', 'unit': 'None' },
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'PSZ1_POS_ERR': { 'format': 'E', 'unit': 'arcmin' },
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'PSZ1_SNR': { 'format': 'E', 'unit': 'None' },
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'PSZ1_COSMO': { 'format': 'L', 'unit': 'None' },
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'PSZ1_COMMENT': { 'format': 'L', 'unit': 'None' },
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'PSZ1_QN': { 'format': 'E', 'unit': 'None' },
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'PSZ2_INDEX': { 'format': 'I', 'unit': 'None' },
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'INDEX_PSZ2': { 'format': 'I', 'unit': 'None' },
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'PCCS2': { 'format': 'L', 'unit': 'None' },
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'PSZ': { 'format': 'I', 'unit': 'None' },
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'IR_FLAG': { 'format': 'I', 'unit': 'None' },
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'Q_NEURAL': { 'format': 'E', 'unit': 'None' },
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'Y5R500': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
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'Y5R500_ERR': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
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'PSZ2_VALIDATION': { 'format': 'I', 'unit': 'None' },
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'REDSHIFT_ID': { 'format': '25A', 'unit': 'None' },
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'MSZ': { 'format': 'E', 'unit': '10^14 Msol' },
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'MSZ_ERR_UP': { 'format': 'E', 'unit': '10^14 Msol' },
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'MSZ_ERR_LOW': { 'format': 'E', 'unit': '10^14 Msol' },
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'MCXC': { 'format': '25A', 'unit': 'None' },
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'REDMAPPER': { 'format': '25A', 'unit': 'None' },
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'ACT': { 'format': '25A', 'unit': 'None' },
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'SPT': { 'format': '25A', 'unit': 'None' },
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'WISE_SIGNF': { 'format': 'E', 'unit': 'None' },
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'WISE_FLAG': { 'format': 'I', 'unit': 'None' },
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'AMI_EVIDENCE': { 'format': 'E', 'unit': 'None' },
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'PSZ2_COMMENT': { 'format': '128A', 'unit': 'None' },
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'PSZ2_NAME': { 'format': '18A', 'unit': 'None' },
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'PSZ2_GLON': { 'format': 'D', 'unit': 'degrees' },
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'PSZ2_GLAT': { 'format': 'D', 'unit': 'degrees' },
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'PSZ2_RA': { 'format': 'D', 'unit': 'degrees' },
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'PSZ2_DEC': { 'format': 'D', 'unit': 'degrees' },
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'PSZ2_POS_ERR': { 'format': 'E', 'unit': 'arcmin' },
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'PSZ2_SNR': { 'format': 'E', 'unit': 'None' },
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'PSZ2_PIPELINE': { 'format': 'I', 'unit': 'None' },
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'PSZ2_PIPE_DET': { 'format': 'I', 'unit': 'None' },
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'PSZ2_PCCS2': { 'format': 'L', 'unit': 'None' },
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'PSZ2_PSZ': { 'format': 'I', 'unit': 'None' },
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'PSZ2_IR_FLAG': { 'format': 'I', 'unit': 'None' },
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'PSZ2_Q_NEURAL': { 'format': 'E', 'unit': 'None' },
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'PSZ2_Y5R500': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
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'PSZ2_Y5R500_ERR': { 'format': 'E', 'unit': '10^-3 arcmin^2' },
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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' },
|
| 281 |
|
|
'PSZ2_REDSHIFT_TYPE': { 'format': '5A', 'unit': 'None' },
|
| 282 |
|
|
'PSZ2_MSZ': { 'format': 'E', 'unit': '10^14 Msol' },
|
| 283 |
|
|
'PSZ2_MSZ_ERR_UP': { 'format': 'E', 'unit': '10^14 Msol' },
|
| 284 |
|
|
'PSZ2_MSZ_ERR_LOW': { 'format': 'E', 'unit': '10^14 Msol' },
|
| 285 |
|
|
'PSZ2_MCXC': { 'format': '25A', 'unit': 'None' },
|
| 286 |
|
|
'PSZ2_REDMAPPER': { 'format': '25A', 'unit': 'None' },
|
| 287 |
|
|
'PSZ2_ACT': { 'format': '25A', 'unit': 'None' },
|
| 288 |
|
|
'PSZ2_SPT': { 'format': '25A', 'unit': 'None' },
|
| 289 |
|
|
'PSZ2_WISE_SIGNF': { 'format': 'E', 'unit': 'None' },
|
| 290 |
|
|
'PSZ2_WISE_FLAG': { 'format': 'I', 'unit': 'None' },
|
| 291 |
|
|
'PSZ2_AMI_EVIDENCE': { 'format': 'E', 'unit': 'None' },
|
| 292 |
|
|
'PSZ2_COSMO': { 'format': 'L', 'unit': 'None' },
|
| 293 |
|
|
|
| 294 |
|
|
|
| 295 |
|
|
|
| 296 |
|
|
'PLCK_INDEX': { 'format': 'I', 'unit': 'None' },
|
| 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) |