| 1 | #!/bin/env python |
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| 2 | # coding: utf-8 |
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| 3 | # Un programme pour exploiter les données des mires |
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| 4 | # -- Frédéric 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 datetime import * |
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| 12 | |
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| 13 | |
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| 14 | from optparse import OptionParser |
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| 15 | parser = OptionParser() |
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| 16 | parser.add_option("-c","--catalog",dest="catalogfile", |
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| 17 | help="Name of the catalog file") |
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| 18 | parser.add_option("-r","--reference",dest="reference", |
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| 19 | help="Name of the file with reference coordinates") |
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| 20 | parser.add_option("-o","--outputfits",dest="outputfits", |
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| 21 | default="output.fits", |
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| 22 | help="Name of the output fits file") |
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| 23 | parser.add_option("-m","--measurement",dest="measurement", |
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| 24 | default="output.mes", |
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| 25 | help="Name of the output measurement file") |
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| 26 | parser.add_option("--ring",dest="ring", |
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| 27 | default="output.ring", |
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| 28 | help="Name of the output measurement file") |
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| 29 | parser.add_option("-s","--square",dest="square", |
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| 30 | default="output.square", |
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| 31 | help="Name of the output measurement file") |
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| 32 | parser.add_option("-t","--timing",dest="timing", |
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| 33 | help="Name of the input timing file") |
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| 34 | |
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| 35 | |
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| 36 | (options, args) = parser.parse_args () |
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| 37 | |
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| 38 | # Recherche de la date dans un fichier des timings |
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| 39 | # (généré par recup_date, soit : |
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| 40 | # ls -l $1| awk '{print$8 "\t" $7 "\t" $6}' | sort -k2 | sed -e 's/*//' |
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| 41 | # où $1 est le répertoire contenant les .dat non modifiés.) |
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| 42 | # format attendu : nom_fichier phase_cycle date_isoformat |
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| 43 | |
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| 44 | for line in file(options.timing): |
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| 45 | line_s = string.split(line) |
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| 46 | if (len(line_s) > 2): |
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| 47 | nom = line_s[0] [:-4] |
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| 48 | if (nom == options.catalogfile[:-6]): |
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| 49 | dt = datetime.strptime(line_s[2], |
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| 50 | "%Y-%m-%dT%H:%M:%S") |
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| 51 | phase = float(line_s[1]) |
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| 52 | |
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| 53 | # Donner les coordonnées approximatives des 4 repÚres |
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| 54 | |
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| 55 | rep = zeros((4,2)) |
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| 56 | i = 0 |
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| 57 | for line in open(options.reference): |
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| 58 | if ((line[0] != "#") and (line != '\n')): |
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| 59 | spl = string.split(line) |
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| 60 | rep[i][0] = float(spl[0]) |
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| 61 | rep[i][1] = float(spl[1]) |
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| 62 | i+=1 |
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| 63 | |
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| 64 | # Lecture du fichier catalogue |
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| 65 | fields = ["FLUX_MAX", |
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| 66 | "X_IMAGE", |
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| 67 | "Y_IMAGE", |
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| 68 | "THETA_IMAGE", |
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| 69 | "ELONGATION", |
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| 70 | "ELLIPTICITY", |
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| 71 | "FWHM_IMAGE", |
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| 72 | "ERRX2_IMAGE", |
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| 73 | "ERRY2_IMAGE"] |
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| 74 | |
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| 75 | catalog = [] |
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| 76 | for line in open(options.catalogfile): |
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| 77 | if ((line[0] != "#") and (line != '\n')): |
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| 78 | tmp = {} |
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| 79 | spl = string.split(line) |
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| 80 | for i in range(len(fields)): |
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| 81 | tmp[fields[i]] = float(spl[i]) |
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| 82 | tmp['MEAN_DIST_TO_NGBRS']=0 |
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| 83 | catalog.append(tmp) |
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| 84 | |
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| 85 | |
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| 86 | mode_degrade = 0 |
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| 87 | # Recherche de la position des repÚres selon catalogue - attention aux doublons |
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| 88 | real_rep = zeros((4,2)) |
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| 89 | for i in range(4): |
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| 90 | detections = 0 |
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| 91 | for source in catalog: |
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| 92 | pos_source = array([source['X_IMAGE'],source['Y_IMAGE']]) |
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| 93 | max_dist = 5 # pixels |
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| 94 | if ((modulus(rep[i,:] - pos_source)) < max_dist): |
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| 95 | max_dist = modulus(rep[i,:] - pos_source) |
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| 96 | detections +=1 |
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| 97 | real_rep[i,:] = pos_source |
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| 98 | if (detections > 1): |
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| 99 | print "Attention ! ambiguïté pour repÚre ",i,\ |
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| 100 | ", nb de détections :",detections |
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| 101 | |
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| 102 | if (detections ==0): |
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| 103 | print "Erreur ! Pas de détection pour repÚre ",i |
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| 104 | real_rep[i,:]=rep[i,:] |
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| 105 | mode_degrade = 1 |
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| 106 | |
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| 107 | if (mode_degrade==1): |
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| 108 | print "Passage en mode dégradé, sorties réduites" |
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| 109 | exit(2) |
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| 110 | |
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| 111 | # Détermination du centre : barycentre des 4 repÚres |
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| 112 | center = array([sum(real_rep[:,0])/4,sum(real_rep[:,1])/4]) |
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| 113 | |
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| 114 | # Vecteurs unitaires de référence |
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| 115 | vec_i = ((real_rep[0,:] - real_rep[1,:])/20 \ |
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| 116 | + (real_rep[3,:] - real_rep[2,:])/20) / 2 |
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| 117 | vec_j = ((real_rep[1,:] - real_rep[2,:])/20 \ |
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| 118 | + (real_rep[0,:] - real_rep[3,:])/20) / 2 |
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| 119 | |
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| 120 | # Détection des noeuds de grille les plus proches de chaque source |
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| 121 | vec_i_2 = dot(vec_i,vec_i) |
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| 122 | vec_j_2 = dot(vec_j,vec_j) |
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| 123 | for source in catalog: |
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| 124 | pos_source = array([source['X_IMAGE'],source['Y_IMAGE']])-center |
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| 125 | # dans le référentiel grille (en projetant sur les vecteurs unitaires) : |
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| 126 | pos_source_grid = array([dot(pos_source,vec_i)/vec_i_2, |
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| 127 | dot(pos_source,vec_j)/vec_j_2]) |
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| 128 | # on arrondit au noeud le plus proche |
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| 129 | knot = pos_source_grid.round() |
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| 130 | # et on calcule la distance entre les deux |
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| 131 | r = modulus(pos_source_grid - knot) |
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| 132 | # enregistrement des résultats |
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| 133 | source['I_GRID'] = knot[0] |
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| 134 | source['J_GRID'] = knot[1] |
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| 135 | source['KNOT_DIST'] = r |
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| 136 | #if (modulus(knot) <1): |
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| 137 | # 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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| 138 | |
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| 139 | # Chasse aux doublons |
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| 140 | # tableau 81*81 contenant : |
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| 141 | # champ 0 : nb de sources rattachée au noeud |
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| 142 | # champ 1 : distance actuelle entre le noeud et la source |
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| 143 | # champ 2 : index courant de la source dans catalog |
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| 144 | # champ 3 : distance moyenne aux voisins (calculé plus tard) |
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| 145 | lookup_sources = zeros((81,81,4)) |
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| 146 | for si in range(len(catalog)): |
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| 147 | source = catalog[si] |
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| 148 | i = source['I_GRID']+40 |
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| 149 | j = source['J_GRID']+40 |
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| 150 | if ((lookup_sources[i,j,0] < 1) |
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| 151 | or (lookup_sources[i,j,1] > source['KNOT_DIST'])): |
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| 152 | lookup_sources[i,j,2] = si |
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| 153 | lookup_sources[i,j,1] = source['KNOT_DIST'] |
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| 154 | lookup_sources[i,j,0] += 1 |
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| 155 | |
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| 156 | # On élimine le noeud (1,1), trop prÚs du "P" |
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| 157 | lookup_sources[41,41,0] = 0 |
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| 158 | |
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| 159 | # Calcul de la distance moyenne entre noeuds voisins |
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| 160 | |
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| 161 | fen = 2 |
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| 162 | |
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| 163 | shift_list = [] |
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| 164 | |
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| 165 | for i in range(-fen,fen+1): |
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| 166 | for j in range(-fen,fen+1): |
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| 167 | if (i!=0 or j!=0): |
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| 168 | shift_list.append([i,j]) |
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| 169 | |
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| 170 | for i in range(fen,81-fen): |
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| 171 | for j in range(fen,81-fen): |
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| 172 | if (lookup_sources[i,j,0] > 0): |
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| 173 | knot_idx = int(lookup_sources[i,j,2]) |
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| 174 | catalog[knot_idx]['MEAN_DIST_TO_NGBRS'] = 0 |
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| 175 | nb_voisin = 0 |
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| 176 | distance = 0 |
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| 177 | weights = 0 |
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| 178 | pos_source = array([catalog[knot_idx]['X_IMAGE'], |
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| 179 | catalog[knot_idx]['Y_IMAGE']]) |
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| 180 | for shifts in shift_list : |
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| 181 | i_voisin = i + shifts[0] |
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| 182 | j_voisin = j + shifts[1] |
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| 183 | if (lookup_sources[i_voisin,j_voisin,0] > 0): |
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| 184 | neigh_idx = int(lookup_sources[i_voisin,j_voisin,2]) |
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| 185 | nb_voisin +=1 |
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| 186 | pos_voisin = array([catalog[neigh_idx]['X_IMAGE'], |
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| 187 | catalog[neigh_idx]['Y_IMAGE']]) |
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| 188 | distance += modulus(pos_source-pos_voisin) |
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| 189 | weights += modulus(shifts) |
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| 190 | if (nb_voisin > 0): |
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| 191 | catalog[knot_idx]['MEAN_DIST_TO_NGBRS'] = float(distance)/weights |
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| 192 | lookup_sources[i,j,3] = (float(distance)/weights) |
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| 193 | |
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| 194 | cleaned_catalog = [] |
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| 195 | for i in range(81): |
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| 196 | for j in range(81): |
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| 197 | if (lookup_sources[i,j,0] > 0): |
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| 198 | cleaned_catalog.append(catalog[int(lookup_sources[i,j,2])]) |
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| 199 | |
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| 200 | # Sortie du catalogue nettoyé des doublons |
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| 201 | |
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| 202 | of = open('clean.cat','w') |
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| 203 | |
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| 204 | fields = ["FLUX_MAX", |
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| 205 | "X_IMAGE", |
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| 206 | "Y_IMAGE", |
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| 207 | "THETA_IMAGE", |
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| 208 | "ELONGATION", |
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| 209 | "ELLIPTICITY", |
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| 210 | "FWHM_IMAGE", |
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| 211 | "ERRX2_IMAGE", |
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| 212 | "ERRY2_IMAGE", |
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| 213 | "I_GRID", |
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| 214 | "J_GRID", |
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| 215 | "KNOT_DIST", |
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| 216 | "MEAN_DIST_TO_NGBRS"] |
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| 217 | |
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| 218 | fmts = ["%10.3f ", |
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| 219 | "%8.3f ", |
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| 220 | "%8.3f ", |
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| 221 | "%8.3f ", |
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| 222 | "%8.3f ", |
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| 223 | "%8.3f ", |
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| 224 | "%8.3f ", |
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| 225 | "%8.5f ", |
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| 226 | "%8.5f ", |
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| 227 | "%5d ", |
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| 228 | "%5d ", |
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| 229 | "%5.3f ", |
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| 230 | "%8.3f"] |
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| 231 | |
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| 232 | for source in cleaned_catalog: |
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| 233 | outs = "" |
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| 234 | for i in range(len(fields)): |
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| 235 | outs += fmts[i] % source[fields[i]] |
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| 236 | outs += "\n" |
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| 237 | of.write(outs) |
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| 238 | |
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| 239 | of.close() |
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| 240 | |
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| 241 | # Génération d'un fichier fits contenant les résultats |
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| 242 | |
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| 243 | |
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| 244 | import pyfits |
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| 245 | |
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| 246 | #a = zeros((2048,2048)) |
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| 247 | # |
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| 248 | # patches width in pixel: |
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| 249 | ## width = int(modulus(vec_i)) |
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| 250 | |
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| 251 | ## for i in range(len(cleaned_catalog)): |
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| 252 | ## print i |
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| 253 | ## source = cleaned_catalog[i] |
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| 254 | ## pos_source = array([int(source['X_IMAGE']),int(source['Y_IMAGE'])]) |
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| 255 | ## pos_mean_dist = source['MEAN_DIST_TO_NGBRS'] |
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| 256 | ## for rel_x in range(-width,width): |
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| 257 | ## for rel_y in range(-width,width): |
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| 258 | ## rel_vec = array([rel_x,rel_y]) |
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| 259 | ## # Forme du patch : carré |
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| 260 | ## if ( (dot(rel_vec,vec_i) < vec_i_2/2) and |
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| 261 | ## (dot(rel_vec,vec_j) < vec_j_2/2)): |
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| 262 | ## (x,y) = pos_source+rel_vec |
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| 263 | ## if ( x>0 and x<2048 and y>0 and y<2048): |
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| 264 | ## a[x,y] = pos_mean_dist |
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| 265 | |
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| 266 | |
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| 267 | |
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| 268 | a = lookup_sources[:,:,3] |
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| 269 | |
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| 270 | for i in range(81): |
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| 271 | for j in range(81): |
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| 272 | if (a[i,j] > 0): |
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| 273 | a[i,j] = 60.0/a[i,j] |
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| 274 | else : |
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| 275 | a[i,j] = 1.06 |
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| 276 | |
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| 277 | # Interpolation de la case du "P" |
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| 278 | |
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| 279 | a[41,41] = (a[42,41] + a[40,41] + a[41,42] + a[41,40]) /4. |
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| 280 | |
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| 281 | hdu = pyfits.PrimaryHDU(a) |
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| 282 | hdulist = pyfits.HDUList([hdu]) |
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| 283 | |
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| 284 | hdu.writeto(options.outputfits,clobber=True) |
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| 285 | |
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| 286 | |
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| 287 | # Facteur d'échelle moyen |
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| 288 | |
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| 289 | tmp = [] |
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| 290 | for i in range(81): |
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| 291 | for j in range(81): |
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| 292 | if (lookup_sources[i,j,0] == 1): |
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| 293 | tmp.append(lookup_sources[i,j,3]) |
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| 294 | |
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| 295 | |
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| 296 | meas = open(options.measurement,'w') |
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| 297 | |
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| 298 | meas.write("# Moyenne des mesures / Module vec_i / Module vec_j / deltaT (min) / date\n") |
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| 299 | # transfo pixels / arcminutes -> mas / pixel |
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| 300 | moyenne_mesures = 60 * (1. / (sum(tmp)/len(tmp))) |
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| 301 | module_veci = 60 * (1. / modulus(vec_i)) |
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| 302 | module_vecj = 60 * (1. / modulus(vec_j)) |
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| 303 | |
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| 304 | outs = "%10.7f %10.7f %10.7f %7.3f %s\n" % (moyenne_mesures, |
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| 305 | module_veci, |
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| 306 | module_vecj, |
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| 307 | phase, |
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| 308 | dt.isoformat()) |
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| 309 | meas.write(outs) |
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| 310 | |
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| 311 | meas.close() |
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| 312 | |
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| 313 | ## Facteur d'échelle par rapport au centre du champ sur une couronne |
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| 314 | ## représentative du diamÚtre solaire |
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| 315 | |
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| 316 | # Quel noeud est (à peu prÚs) au centre ? |
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| 317 | |
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| 318 | |
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| 319 | dist_mini = 2048 |
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| 320 | i_centre = 0 |
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| 321 | for i in range(len(cleaned_catalog)): |
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| 322 | source = cleaned_catalog[i] |
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| 323 | dist = modulus([source["X_IMAGE"] - 1024, source["Y_IMAGE"] - 1024]) |
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| 324 | if (dist < dist_mini): |
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| 325 | dist_mini = dist |
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| 326 | i_centre = i |
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| 327 | |
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| 328 | #print "Source au centre : ",cleaned_catalog[i_centre] |
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| 329 | |
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| 330 | # On parcourt une couronne de noeuds et on calcule le facteur d'échelle pour chacun |
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| 331 | |
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| 332 | rmin = 14 # en inter-noeud |
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| 333 | rmax = 18 # en inter-noeud |
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| 334 | |
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| 335 | source_centrale = cleaned_catalog[i_centre] |
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| 336 | |
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| 337 | couronne=[] |
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| 338 | |
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| 339 | for source in cleaned_catalog: |
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| 340 | tmp = {} |
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| 341 | dist_knots = modulus([source["I_GRID"] - source_centrale["I_GRID"], |
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| 342 | source["J_GRID"] - source_centrale["J_GRID"]]) |
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| 343 | x_rel = source["X_IMAGE"] - source_centrale["X_IMAGE"] |
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| 344 | y_rel = source["Y_IMAGE"] - source_centrale["Y_IMAGE"] |
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| 345 | dist_pix = modulus([x_rel,y_rel]) |
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| 346 | if ((dist_knots >= rmin) and (dist_knots <= rmax)): |
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| 347 | tmp['ScFact'] = dist_knots * 60. / dist_pix # arcsec / pix |
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| 348 | tmp['r'] = dist_pix |
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| 349 | tmp['theta'] = math.atan(y_rel/x_rel) |
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| 350 | if (x_rel < 0): |
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| 351 | tmp['theta']+=math.pi |
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| 352 | # Offset de pi pour se mettre en convention "astro" |
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| 353 | # theta = 0 -> P droit quand nord vers le haut |
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| 354 | tmp['theta']+=math.pi |
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| 355 | while (tmp['theta'] < 0): |
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| 356 | tmp['theta'] += 2*math.pi |
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| 357 | while (tmp['theta'] >= 2*math.pi): |
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| 358 | tmp['theta'] -= 2*math.pi |
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| 359 | # On n'écrit pas les valeurs aberrantes |
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| 360 | if ((tmp['ScFact'] > 1.05) and (tmp['ScFact'] < 1.07) or 1): |
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| 361 | couronne.append(tmp) |
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| 362 | |
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| 363 | output_couronne = file(options.ring,'w') |
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| 364 | |
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| 365 | outs = "# phase: %f date: %s\n" % (phase, |
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| 366 | dt.isoformat()) |
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| 367 | output_couronne.write(outs) |
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| 368 | |
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| 369 | for source in couronne: |
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| 370 | outs = "%f %f %10.7f\n" % (source['r'], |
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| 371 | math.degrees(source['theta']), |
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| 372 | source['ScFact']) |
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| 373 | output_couronne.write(outs) |
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| 374 | |
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| 375 | |
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| 376 | |
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| 377 | output_couronne.close() |
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| 378 | |
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| 379 | # On parcourt un carré de surface équivalente à la couronne définie ci dessus |
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| 380 | # Le facteur d'échelle est calculé par rapport au point opposé du carré |
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| 381 | # soit un cÎté de 28 noeuds |
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| 382 | |
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| 383 | |
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| 384 | # Le centre a été trouvé pour la couronne |
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| 385 | i_cen = cleaned_catalog[i_centre]["I_GRID"] |
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| 386 | j_cen = cleaned_catalog[i_centre]["J_GRID"] |
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| 387 | |
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| 388 | # On se refait un "lookup" simplifié |
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| 389 | |
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| 390 | synthese = zeros((81,81,2)) |
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| 391 | |
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| 392 | for source in cleaned_catalog: |
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| 393 | synthese[source['I_GRID'],source['J_GRID'],0] = source['X_IMAGE'] |
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| 394 | synthese[source['I_GRID'],source['J_GRID'],1] = source['Y_IMAGE'] |
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| 395 | |
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| 396 | valeurs = [] |
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| 397 | for i in range(-14,15): |
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| 398 | vecteur = synthese[i_cen+14,j_cen+i,:] - synthese[i_cen-14,j_cen+i,:] |
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| 399 | valeurs.append(modulus(vecteur)) |
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| 400 | |
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| 401 | valeurs_orth = [] |
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| 402 | for i in range(-14,15): |
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| 403 | vecteur = synthese[i_cen+i,j_cen+14,:] - synthese[i_cen+i,j_cen-14,:] |
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| 404 | valeurs_orth.append(modulus(vecteur)) |
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| 405 | |
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| 406 | for i in range(len(valeurs)): |
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| 407 | if valeurs[i] > 0: |
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| 408 | valeurs[i] = 28*60./valeurs[i] |
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| 409 | |
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| 410 | for i in range(len(valeurs_orth)): |
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| 411 | if valeurs_orth[i] > 0: |
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| 412 | valeurs_orth[i] = 28*60./valeurs_orth[i] |
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| 413 | |
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| 414 | |
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| 415 | # on vire les valeurs aberrantes |
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| 416 | def f(x): |
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| 417 | return ((x < 1.07) and (x > 1.05)) |
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| 418 | |
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| 419 | valeurs = filter(f, valeurs) |
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| 420 | valeurs_orth = filter(f, valeurs_orth) |
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| 421 | |
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| 422 | output_square = open(options.square,'w') |
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| 423 | outs = "# moyennes: %f / %f phase: %f date: %s\n" % (mean(valeurs), |
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| 424 | mean(valeurs_orth), |
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| 425 | phase, |
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| 426 | dt.isoformat()) |
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| 427 | output_square.write(outs) |
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| 428 | |
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| 429 | for valeur in valeurs: |
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| 430 | outs = "%f\n" % (valeur) |
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| 431 | output_square.write(outs) |
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| 432 | |
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| 433 | output_square.write('orth \n') |
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| 434 | |
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| 435 | for valeur in valeurs_orth: |
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| 436 | outs = "%f\n" % (valeur) |
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| 437 | output_square.write(outs) |
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| 438 | |
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| 439 | output_square.close() |
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| 440 | |
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