source: mire/process.py @ 16

#!/bin/env python
# coding: utf-8
# Un programme pour exploiter les données des mires
# -- Frédéric Meynadier

import string
from numpy import *

from functions import *

from optparse import OptionParser
parser = OptionParser()
parser.add_option("-c","--catalog",dest="catalogfile",
                  help="Name of the catalog file")
parser.add_option("-r","--reference",dest="reference",
                  help="Name of the file with reference coordinates")
parser.add_option("-o","--outputfits",dest="outputfits",
                  default="output.fits",
                  help="Name of the output fits file")


(options, args) = parser.parse_args ()

# Donner les coordonnées approximatives des 4 repÚres

rep = zeros((4,2))
i = 0
for line in open(options.reference):
    if ((line[0] != "#") and (line != '\n')):
        spl = string.split(line)
        rep[i][0] = float(spl[0])
        rep[i][1] = float(spl[1])
        i+=1

# Lecture du fichier catalogue
fields = ["FLUX_MAX",
          "X_IMAGE",
          "Y_IMAGE",
          "THETA_IMAGE",
          "ELONGATION",
          "ELLIPTICITY",
          "FWHM_IMAGE",
          "ERRX2_IMAGE",
          "ERRY2_IMAGE"]

catalog = []
for line in open(options.catalogfile):
    if ((line[0] != "#") and (line != '\n')):
        tmp = {}
        spl = string.split(line)
        for i in range(len(fields)):
            tmp[fields[i]] = float(spl[i])
        catalog.append(tmp)


# Recherche de la position des repÚres selon catalogue - attention aux doublons
real_rep = zeros((4,2))
for i in range(4):
    detections = 0
    for source in catalog:
        pos_source = array([source['X_IMAGE'],source['Y_IMAGE']])
        if ((modulus(rep[i,:] - pos_source)) < 5):
            detections +=1
            real_rep[i,:] = pos_source
    if (detections !=1):
        print "Attention ! ambiguïté pour repÚre ",i,\
              ", nb de détections :",detections
        

# Détermination du centre : barycentre des 4 repÚres
center = array([sum(real_rep[:,0])/4,sum(real_rep[:,1])/4])

# Vecteurs unitaires de référence
vec_i = ((real_rep[0,:] - real_rep[1,:])/20 \
         + (real_rep[3,:] - real_rep[2,:])/20) / 2 
vec_j = ((real_rep[1,:] - real_rep[2,:])/20 \
         + (real_rep[0,:] - real_rep[3,:])/20) / 2 

# Détection des noeuds de grille les plus proches de chaque source
vec_i_2 = dot(vec_i,vec_i)
vec_j_2 = dot(vec_j,vec_j)
for source in catalog:
    pos_source = array([source['X_IMAGE'],source['Y_IMAGE']])-center
    # dans le référentiel grille (en projetant sur les vecteurs unitaires) :
    pos_source_grid = array([dot(pos_source,vec_i)/vec_i_2,
                             dot(pos_source,vec_j)/vec_j_2])
    # on arrondit au noeud le plus proche
    knot = pos_source_grid.round()
    # et on calcule la distance entre les deux
    r = modulus(pos_source_grid - knot)
    # enregistrement des résultats
    source['I_GRID'] = knot[0]
    source['J_GRID'] = knot[1]
    source['KNOT_DIST'] = r
    #if (modulus(knot) <1):
    #    print source['I_GRID'], source['J_GRID'], source['KNOT_DIST'], pos_source_grid, pos_source,source['X_IMAGE'], source['Y_IMAGE']

# Chasse aux doublons
# tableau 81*81 contenant :
# champ 0 : nb de sources rattachée au noeud
# champ 1 : distance actuelle entre le noeud et la source
# champ 2 : index courant de la source dans catalog
# champ 3 : distance moyenne aux voisins (calculé plus tard)
lookup_sources = zeros((81,81,4))
for si in range(len(catalog)):
    source = catalog[si]
    i = source['I_GRID']+40
    j = source['J_GRID']+40
    if ((lookup_sources[i,j,0] < 1)
        or (lookup_sources[i,j,1] > source['KNOT_DIST'])):
        lookup_sources[i,j,2] = si 
        lookup_sources[i,j,1] = source['KNOT_DIST']
    lookup_sources[i,j,0] += 1

# On élimine le noeud (1,1), trop prÚs du "P"
lookup_sources[41,41,0] = 0

# Calcul de la distance moyenne entre noeuds voisins

for i in range(1,80):
    for j in range(1,80):
        if (lookup_sources[i,j,0] > 0):
            knot_idx = int(lookup_sources[i,j,2])
            catalog[knot_idx]['MEAN_DIST_TO_NGBRS'] = 0
            nb_voisin = 0
            distance = 0
            pos_source = array([catalog[knot_idx]['X_IMAGE'],
                                catalog[knot_idx]['Y_IMAGE']])
            for shifts in [[0,1],[1,0],[-1,0],[0,-1]]:
                i_voisin = i + shifts[0]
                j_voisin = j + shifts[1]
                if (lookup_sources[i_voisin,j_voisin,0] > 0):
                    neigh_idx = int(lookup_sources[i_voisin,j_voisin,2])
                    nb_voisin +=1
                    pos_voisin = array([catalog[neigh_idx]['X_IMAGE'],
                                        catalog[neigh_idx]['Y_IMAGE']])
                    distance += modulus(pos_source-pos_voisin)
            if (nb_voisin !=0):
                catalog[knot_idx]['MEAN_DIST_TO_NGBRS'] = distance / nb_voisin
                lookup_sources[i,j,3] = distance / nb_voisin
        
cleaned_catalog = []
for i in range(81):
    for j in range(81):
        if (lookup_sources[i,j,0] > 0):
            cleaned_catalog.append(catalog[int(lookup_sources[i,j,2])])

# Sortie du catalogue nettoyé des doublons

of = open('clean.cat','w')

fields = ["FLUX_MAX",
          "X_IMAGE",
          "Y_IMAGE",
          "THETA_IMAGE",
          "ELONGATION",
          "ELLIPTICITY",
          "FWHM_IMAGE",
          "ERRX2_IMAGE",
          "ERRY2_IMAGE",
          "I_GRID",
          "J_GRID",
          "KNOT_DIST",
          "MEAN_DIST_TO_NGBRS"]

fmts = ["%10.3f ",
        "%8.3f ",
        "%8.3f ",
        "%8.3f ",
        "%8.3f ",
        "%8.3f ",
        "%8.3f ",
        "%8.5f ",
        "%8.5f ",
        "%5d ",
        "%5d ",
        "%5.3f ",
        "%8.3f"]

for source in cleaned_catalog:
    outs = ""
    for i in range(len(fields)):
        outs += fmts[i] % source[fields[i]]
    outs += "\n"
    of.write(outs)

of.close()

# Génération d'un fichier fits contenant les résultats


import pyfits

#a = zeros((2048,2048))
#
# patches width in pixel:
## width = int(modulus(vec_i))

## for i in range(len(cleaned_catalog)):
##     print i
##     source = cleaned_catalog[i]
##     pos_source = array([int(source['X_IMAGE']),int(source['Y_IMAGE'])])
##     pos_mean_dist = source['MEAN_DIST_TO_NGBRS']
##     for rel_x in range(-width,width):
##         for rel_y in range(-width,width):
##             rel_vec = array([rel_x,rel_y])
##             # Forme du patch : carré
##             if ( (dot(rel_vec,vec_i) < vec_i_2/2) and
##                  (dot(rel_vec,vec_j) < vec_j_2/2)):
##                 (x,y) = pos_source+rel_vec
##                 if ( x>0 and x<2048 and y>0 and y<2048):
##                     a[x,y] = pos_mean_dist



a = lookup_sources[:,:,3]
            
hdu = pyfits.PrimaryHDU(a)
hdulist = pyfits.HDUList([hdu])

hdu.writeto(options.outputfits,clobber=True)

# Facteur d'échelle moyen

tmp = []
for i in range(81):
    for j in range(81):
        if (lookup_sources[i,j,0] > 0):
            tmp.append(lookup_sources[i,j,3])

print "#Moyenne des mesures /  Module vec_i /  Module vec_j",

print sum(tmp)/len(tmp),modulus(vec_i),modulus(vec_j)