[14] | 1 | #!/bin/env python |
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| 2 | # coding: utf-8 |
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| 3 | # Un programme pour exploiter les donnÃ©es des mires |
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| 4 | # -- FrÃ©dÃ©ric Meynadier |
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| 5 | |
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| 6 | import string |
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| 7 | from numpy import * |
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| 8 | |
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| 9 | from functions import * |
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| 10 | |
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| 11 | from optparse import OptionParser |
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| 12 | parser = OptionParser() |
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| 13 | parser.add_option("-c","--catalog",dest="catalogfile", |
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| 14 | help="Name of the catalog file") |
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| 15 | parser.add_option("-o","--outputfits",dest="outputfits", |
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| 16 | default="output.fits", |
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| 17 | help="Name of the output fits file") |
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| 18 | |
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| 19 | |
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| 20 | (options, args) = parser.parse_args () |
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| 21 | |
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| 22 | # Donner les coordonnÃ©es approximatives des 4 repÃšres |
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| 23 | |
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| 24 | rep = zeros((4,2)) |
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| 25 | i = 0 |
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| 26 | for line in open('reperes.txt'): |
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| 27 | if ((line[0] != "#") and (line != '\n')): |
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| 28 | spl = string.split(line) |
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| 29 | rep[i][0] = float(spl[0]) |
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| 30 | rep[i][1] = float(spl[1]) |
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| 31 | i+=1 |
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| 32 | |
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| 33 | # Lecture du fichier catalogue |
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| 34 | fields = ["FLUX_MAX", |
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| 35 | "X_IMAGE", |
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| 36 | "Y_IMAGE", |
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| 37 | "THETA_IMAGE", |
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| 38 | "ELONGATION", |
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| 39 | "ELLIPTICITY", |
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| 40 | "FWHM_IMAGE", |
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| 41 | "ERRX2_IMAGE", |
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| 42 | "ERRY2_IMAGE"] |
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| 43 | |
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| 44 | catalog = [] |
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| 45 | for line in open(options.catalogfile): |
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| 46 | if ((line[0] != "#") and (line != '\n')): |
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| 47 | tmp = {} |
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| 48 | spl = string.split(line) |
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| 49 | for i in range(len(fields)): |
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| 50 | tmp[fields[i]] = float(spl[i]) |
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| 51 | catalog.append(tmp) |
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| 52 | |
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| 53 | |
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| 54 | # Recherche de la position des repÃšres selon catalogue - attention aux doublons |
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| 55 | real_rep = zeros((4,2)) |
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| 56 | for i in range(4): |
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| 57 | detections = 0 |
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| 58 | for source in catalog: |
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| 59 | pos_source = array([source['X_IMAGE'],source['Y_IMAGE']]) |
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| 60 | if ((modulus(rep[i,:] - pos_source)) < 5): |
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| 61 | detections +=1 |
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| 62 | real_rep[i,:] = pos_source |
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| 63 | if (detections !=1): |
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| 64 | print "Attention ! ambiguÃ¯tÃ© pour repÃšre ",i,\ |
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| 65 | ", nb de dÃ©tections :",detections |
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| 66 | |
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| 67 | |
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| 68 | # DÃ©termination du centre : barycentre des 4 repÃšres |
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| 69 | center = array([sum(real_rep[:,0])/4,sum(real_rep[:,1])/4]) |
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| 70 | |
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| 71 | # Vecteurs unitaires de rÃ©fÃ©rence |
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| 72 | vec_i = ((real_rep[0,:] - real_rep[1,:])/20 \ |
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| 73 | + (real_rep[3,:] - real_rep[2,:])/20) / 2 |
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| 74 | vec_j = ((real_rep[1,:] - real_rep[2,:])/20 \ |
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| 75 | + (real_rep[0,:] - real_rep[3,:])/20) / 2 |
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| 76 | |
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| 77 | # DÃ©tection des noeuds de grille les plus proches de chaque source |
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| 78 | vec_i_2 = dot(vec_i,vec_i) |
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| 79 | vec_j_2 = dot(vec_j,vec_j) |
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| 80 | for source in catalog: |
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| 81 | pos_source = array([source['X_IMAGE'],source['Y_IMAGE']])-center |
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| 82 | # dans le rÃ©fÃ©rentiel grille (en projetant sur les vecteurs unitaires) : |
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| 83 | pos_source_grid = array([dot(pos_source,vec_i)/vec_i_2, |
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| 84 | dot(pos_source,vec_j)/vec_j_2]) |
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| 85 | # on arrondit au noeud le plus proche |
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| 86 | knot = pos_source_grid.round() |
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| 87 | # et on calcule la distance entre les deux |
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| 88 | r = modulus(pos_source_grid - knot) |
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| 89 | # enregistrement des rÃ©sultats |
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| 90 | source['I_GRID'] = knot[0] |
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| 91 | source['J_GRID'] = knot[1] |
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| 92 | source['KNOT_DIST'] = r |
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| 93 | #if (modulus(knot) <1): |
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| 94 | # print source['I_GRID'], source['J_GRID'], source['KNOT_DIST'], pos_source_grid, pos_source,source['X_IMAGE'], source['Y_IMAGE'] |
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| 95 | |
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| 96 | # Chasse aux doublons |
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| 97 | # tableau 81*81 contenant : |
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| 98 | # champ 0 : nb de sources rattachÃ©e au noeud |
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| 99 | # champ 1 : distance actuelle entre le noeud et la source |
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| 100 | # champ 2 : index courant de la source dans catalog |
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| 101 | # champ 3 : distance moyenne aux voisins (calculÃ© plus tard) |
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| 102 | lookup_sources = zeros((81,81,4)) |
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| 103 | for si in range(len(catalog)): |
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| 104 | source = catalog[si] |
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| 105 | i = source['I_GRID']+40 |
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| 106 | j = source['J_GRID']+40 |
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| 107 | if ((lookup_sources[i,j,0] < 1) |
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| 108 | or (lookup_sources[i,j,1] > source['KNOT_DIST'])): |
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| 109 | lookup_sources[i,j,2] = si |
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| 110 | lookup_sources[i,j,1] = source['KNOT_DIST'] |
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| 111 | lookup_sources[i,j,0] += 1 |
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| 112 | |
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| 113 | # On Ã©limine le noeud (1,1), trop prÃšs du "P" |
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| 114 | lookup_sources[41,41,0] = 0 |
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| 115 | |
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| 116 | # Calcul de la distance moyenne entre noeuds voisins |
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| 117 | |
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| 118 | for i in range(1,80): |
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| 119 | for j in range(1,80): |
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| 120 | if (lookup_sources[i,j,0] > 0): |
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| 121 | knot_idx = int(lookup_sources[i,j,2]) |
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| 122 | catalog[knot_idx]['MEAN_DIST_TO_NGBRS'] = 0 |
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| 123 | nb_voisin = 0 |
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| 124 | distance = 0 |
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| 125 | pos_source = array([catalog[knot_idx]['X_IMAGE'], |
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| 126 | catalog[knot_idx]['Y_IMAGE']]) |
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| 127 | for shifts in [[0,1],[1,0],[-1,0],[0,-1]]: |
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| 128 | i_voisin = i + shifts[0] |
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| 129 | j_voisin = j + shifts[1] |
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| 130 | if (lookup_sources[i_voisin,j_voisin,0] > 0): |
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| 131 | neigh_idx = int(lookup_sources[i_voisin,j_voisin,2]) |
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| 132 | nb_voisin +=1 |
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| 133 | pos_voisin = array([catalog[neigh_idx]['X_IMAGE'], |
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| 134 | catalog[neigh_idx]['Y_IMAGE']]) |
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| 135 | distance += modulus(pos_source-pos_voisin) |
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| 136 | if (nb_voisin !=0): |
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| 137 | catalog[knot_idx]['MEAN_DIST_TO_NGBRS'] = distance / nb_voisin |
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| 138 | lookup_sources[i,j,3] = distance / nb_voisin |
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| 139 | |
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| 140 | cleaned_catalog = [] |
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| 141 | for i in range(81): |
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| 142 | for j in range(81): |
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| 143 | if (lookup_sources[i,j,0] > 0): |
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| 144 | cleaned_catalog.append(catalog[int(lookup_sources[i,j,2])]) |
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| 145 | |
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| 146 | # Sortie du catalogue nettoyÃ© des doublons |
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| 147 | |
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| 148 | of = open('clean.cat','w') |
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| 149 | |
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| 150 | fields = ["FLUX_MAX", |
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| 151 | "X_IMAGE", |
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| 152 | "Y_IMAGE", |
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| 153 | "THETA_IMAGE", |
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| 154 | "ELONGATION", |
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| 155 | "ELLIPTICITY", |
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| 156 | "FWHM_IMAGE", |
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| 157 | "ERRX2_IMAGE", |
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| 158 | "ERRY2_IMAGE", |
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| 159 | "I_GRID", |
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| 160 | "J_GRID", |
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| 161 | "KNOT_DIST", |
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| 162 | "MEAN_DIST_TO_NGBRS"] |
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| 163 | |
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| 164 | fmts = ["%10.3f ", |
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| 165 | "%8.3f ", |
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| 166 | "%8.3f ", |
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| 167 | "%8.3f ", |
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| 168 | "%8.3f ", |
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| 169 | "%8.3f ", |
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| 170 | "%8.3f ", |
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| 171 | "%8.5f ", |
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| 172 | "%8.5f ", |
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| 173 | "%5d ", |
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| 174 | "%5d ", |
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| 175 | "%5.3f ", |
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| 176 | "%8.3f"] |
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| 177 | |
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| 178 | for source in cleaned_catalog: |
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| 179 | outs = "" |
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| 180 | for i in range(len(fields)): |
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| 181 | outs += fmts[i] % source[fields[i]] |
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| 182 | outs += "\n" |
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| 183 | of.write(outs) |
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| 184 | |
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| 185 | of.close() |
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| 186 | |
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| 187 | # GÃ©nÃ©ration d'un fichier fits contenant les rÃ©sultats |
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| 188 | |
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| 189 | |
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| 190 | import pyfits |
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| 191 | |
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| 192 | #a = zeros((2048,2048)) |
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| 193 | # |
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| 194 | # patches width in pixel: |
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| 195 | ## width = int(modulus(vec_i)) |
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| 196 | |
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| 197 | ## for i in range(len(cleaned_catalog)): |
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| 198 | ## print i |
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| 199 | ## source = cleaned_catalog[i] |
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| 200 | ## pos_source = array([int(source['X_IMAGE']),int(source['Y_IMAGE'])]) |
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| 201 | ## pos_mean_dist = source['MEAN_DIST_TO_NGBRS'] |
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| 202 | ## for rel_x in range(-width,width): |
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| 203 | ## for rel_y in range(-width,width): |
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| 204 | ## rel_vec = array([rel_x,rel_y]) |
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| 205 | ## # Forme du patch : carrÃ© |
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| 206 | ## if ( (dot(rel_vec,vec_i) < vec_i_2/2) and |
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| 207 | ## (dot(rel_vec,vec_j) < vec_j_2/2)): |
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| 208 | ## (x,y) = pos_source+rel_vec |
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| 209 | ## if ( x>0 and x<2048 and y>0 and y<2048): |
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| 210 | ## a[x,y] = pos_mean_dist |
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| 211 | |
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| 212 | |
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| 213 | |
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| 214 | a = lookup_sources[:,:,3] |
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| 215 | |
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| 216 | hdu = pyfits.PrimaryHDU(a) |
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| 217 | hdulist = pyfits.HDUList([hdu]) |
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| 218 | |
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| 219 | hdu.writeto(options.outputfits,clobber=True) |
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| 220 | |
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| 221 | # Facteur d'Ã©chelle moyen |
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| 222 | |
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| 223 | tmp = [] |
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| 224 | for i in range(81): |
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| 225 | for j in range(81): |
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| 226 | if (lookup_sources[i,j,0] > 0): |
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| 227 | tmp.append(lookup_sources[i,j,3]) |
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| 228 | |
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| 229 | print "Moyenne des mesures : ",sum(tmp)/len(tmp) |
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| 230 | |
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| 231 | # Module des vecteurs unitaires |
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| 232 | |
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| 233 | print "Module vec_i :",modulus(vec_i) |
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| 234 | print "Module vec_j :",modulus(vec_j) |
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