néb diff Petite dentelle hors saison en H-Alpha au 350D et 200/800 et mixage facile...

[Dominik]

Hello !

Je sais que ma photo est hors saison puisqu'il s'agit de la petite dentelle du Cygne dont j'ai fait les acquisitions au moins d'août 2015 ... mais je n'avais pas encore eu le temps de la traiter (si, si, c'est possible !).

De plus c'est la première photo sur laquelle j'ai vraiment voulu m'appliquer à faire des acquisitions avec le filtre EOS-Clip H-Alpha 6nm.

Bref, voilà le résultat :

[IMG]http://www.arnaudom.fr/Img/ngc6960005wa.jpg[/IMG]

Mon matos :
- Newton GSO 200/800 + Monture EQ6 Pro + Canon EOS 350D + Paracorr Televue + Filtre H-Alpha 6nm
- Autoguidage : chercheur 8x50 + bague + Orion Starshoot Autoguider

Aqcuisitions :
15 poses de 8 minutes + 5 poses de 10 minutes + 50 offset + 6 dark + 15 flat.

Comme j'ai fait les poses sur 3 nuits consécutives (en période de pleine lune et dans mon jardin, sous les lampadaires municipaux !...), j'ai un peu mélangé des poses de 8 et de 10 minutes, mes dark, mes flat (accessoirement je me demande si les flats servent à quelque chose vu toute la lumière que bouffe le filtre !!) donc je ne suis pas sûr de la rigueur de mon prétraitement...

L'image est l'extraction de la couche rouge sur laquelle j'ai fait un retrait de gradient, une augmentation des détails avec HDR et une réduction de bruit avec ACDNR sous Pixinsight.

Quelques mois auparavant j'avais traité une image de cette même dentelle dont les acquisitions ont été faite en septembre 2015 et au même endroit, mais cette fois avec le fitre EOS-Clip UHC :

[IMG]http://www.arnaudom.fr/Img/ngc6960004wa.jpg[/IMG]

Ici ce fut plus simple à faire, il a "suffi" de 28 poses de 5 minutes.

Bref, du coup je me suis dit qu'il ne me restait plus qu'à faire comme les pros, à savoir mixer ma couche Ha avec ma photo RVB. J'ai passé un peu de temps à surfer sur le net pour essayer de savoir comment m'y prendre avec Pixinsight mais sans trop de succès, j'ai essayé le script AIP Ha-RVB mais j'obtiens des résultats bizarres, j'imagine que je n'ai pas vraiment compris comment m'en servir.

Du coup en surfant sur les sites de nos amis québécois de l'ACAIQ [URL="http://www.astrosurf.com/d_bergeron/astronomie/Bibliotheque/Acaiq%202014/Acaiq%202014.htm#Martin%20Magnan"]http://www.astrosurf.com/d_bergeron/astronomie/Bibliotheque/Acaiq%202014/Acaiq%202014.htm#Martin%20Magnan[/URL]
j'ai trouvé une vidéo qui donne une méthode relativement simple pour mixer la couche rouge du H-Alpha avec la photo couleur (à 35 minutes sur la quatrième vidéo consacrée à Pixinsight). En deux trois essais j'ai obtenu un résultat sympa, alors voilà ...

Evidemment il faut d'abord que les deux images RVB et Ha soient alignées (process StarAlignment dans Pix).

J'ai nommé rvb_centree et ha_centree mes deux images alignées.

Ensuite un petit coup de courbe sur l'image Ha pour, d'une part, abaisser le niveau du fond du ciel en-dessous du niveau de l'image RVB (sinon l'image finale a son fond de ciel qui rougit...) et d'autre part booster la nébuleuse (bref, on augmente le constraste de l'image Ha).

Ensuite, il ne reste plus qu'à appliquer un coup de process PixelMath avec les paramètres suivants :

[IMG]http://www.arnaudom.fr/Img/pix392.jpg[/IMG]
[IMG]http://www.webastro.net/forum/data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2QAAAKyCAIAAAA1mooZAAAgAElEQVR4nOzdfZQcZ2Hn++fAOQnZADfLTZb1zctN1mdPXklilcNZJsmSLOSFZDc2xJaoFgROAmMwVkxCgLYdVxeYkM1ItsfoAll7xtVGsdAskWQjeeyx1lNqWZ4FYjwmGl8bxqQZeWwi+QY5sZTdM+HQ9496e56q56munqmu7un5/s7nnMxU11v3+KBfnqeqWmx/12EAAABASwz8DAAAADC0KIsAAAAwSsriW975+TdedrxXb3nn5wf+HgAAANAnSVl842XHL/qR8//xN5/75V977pde/+wv/uLp17327177c09ZP/mVn7v4b376hx/+8X97/5t+5gu/9e9bv/3jJ/7LT5z4nZ868WOvevaNlx0f+HsAAABAnyhl8dd+6znv8498+uDJvd7Dk3sfuvnPj368vt95z6c+WPv4rv/8wXf84o7b33ntfe/4wwfeec3/eNeu4+/Z9eafOUFZBAAAGGFKWfyd337ur48+cvuRRz712ZN77zh+2y1zu91DN/3R1A1/cPMHrrzx3W941w2XvWPufR889q5rjr9n18lrdr3lZymLAPrvjmc7nU6n0/nSHcOxn+6+9KXgSItfGvynBwAbo5TFN/72c837Hrn9yCOfnjn5yTtbn/jEg7f82T0f/1Cz8Z7bPlz76DW/tev9b9p57/v++Ph7dp3ctesL7991xc9RFgGU4BOLHU3iptWfspgc9JtPflB7MuuvepRFAKNDKYuvf9Nz/+3eR/7bwZN/effDn7rD33vb3K1/dvi/fvgu9+pPXPf2m679L++/5jdqn3331Sev2fWF9+969E92bb+EsgigBFE/+6fD7uHt7zq83X3yWV2N2yhTWYyPKx+6cNX74H3/lN4JZRHAZhD8D1XXhUpZ/I9vfO7T+082jv7VG5648heW3/CGR3/3w5+67c8/dOdHrp68/u0feef73vHzMz859oV/v/34z31mcsfjH961YxtlEUAJ0mVRanJlThkby2Ln2fseCtaJmh9lEcAmoJuU6WgrYP7mOUu2p8riL79+9Ya/vvNHz/7kK87/64ue/7+/7x+//1X/8G/+86Ht173/Y5d/8opXPfOqV/zDv/qx577/1ede/u/+/lV3Tr/ZvpSyCKAE2bIYl7Yv3aGUvOzcsWbWWB4dlNbMKYvROg8d/qa0MNphdpZcXy6TrZKyKK3w7CcG/TkDGDH6nphpe0X2sN3QFLenyuIvve4bv/I3b/5X//zy9x24afLjhz/0/+zZ9ujrXnHulT/09I+8/Fsv/7GHf+iaD/zOIzd88MjE23/gH7/3zYs/ccXr5imLADbOPLL4T4fddMmL69ez9z0UvxQXu/DVdJV89hPvyi2LwcJUy9SOC0Y7yfRF3chiuFrUQRloBNA3xQtidsP8lqmUxdddunzpY69/9ZkfvNk9sPtP9/3Fh6frN3781c9cJDriZf/4sh3veNPeP/iDrzT+ZOmGXa97+od/ZflHf/M/PUBZBLBxxmsWg3aVvsFFHf+TqmG8YTJ5LW+rL4vPHg7aXjQK+Ozis8rR06KjRwctMg2tdFYAKFtO1dv45kpZfO3PLv3ysd94xQvf94c3Nz7+R59yPrD7l+fe8IpvveIHvvEDL/vHl73m2I8d+otrnrjxDx+87fd++Fuv3P6Vn7J/8ThlEcDGae+G1he+cJNk6E5pYHc8q9lRt7L4CWVA8Z8O3/FktixqppspiwCGQ+p/nErfXCmL2378y+++4f3/19M/9Mpvfd8P/t2PvOrv/89Xnn3l6z/zS+/Z/nu/csu2i57/P37kH/71zzzzb1/9wit/8Ll/82eftC977QJlEcDGZaehFV3KorRV/kN2TGVRHqr85pMfTI1rxsdKXddIWQQwBNL/f2yPfVHexLS5UhZ/9kcf+cCbP/QH73+3dcT6mYd++rUHL91+3WVX/8bbdr3pd6euec/hT49fNvcrv7z0q7/5/152w2fcWz64+z/89BcoiwA2rsey2H0aOv9yw0xZVK+DLDgJTlkEMGjapli8L2ZX1m6ulMWfuGju919fG3/DO977a7//3l97x/gb3vqeN/5u461vO/gnux6/6YNHrr/ukzf95T2f/x9H/YcP3fPQ/tsP/qdffJKyCGDjeiqLylN1TDe4yIOL7pPP5k9DvytumfLP0T6jX8M7WrLPgMwZ+KQsAhhi2lrZpSy+4ace/st3/+F//b2rbtz+jo/u/P1Pv2/X5+t/snD9+59oXPs3H/nAJ97vzszcv/DlhdZXFo6eOHnwvz/467/6d5RFABtXvCxmH5TT/dE5cUvLKYuy7PCkfCnkN5/9kjqyuF3zdEbKIoDRoZTFy36qdfyq9z589dULu977pT+++rEPvW/p+mueuvGaZfeae//o6skP/sVfHzx2/xcX5h5bOHr85OG/fvA33tCmLAIAAIwwpSy++aePP3L1e7/4h+/98h9f/ZUPXb103dVP3fi+p91r2h+75vMfeO8t1350/x2H7znqH3nwxL2fn//rfUd+/VeepiwCAACMMKUs/rtXrf7ua45f+XPHt//88bduO25brZ2/0Hrba1tv/w+ty6wTr/uZ//mGX/rqb/76ypve9MybfuP0r//q13/oonOURQAAgBGWlMW3vPPzb7zseK/e8s7PD/w9AAAAoE9Evw8AAACAzYuyCAAAACOx/V2H2+0VAAAAIIuyCAAAACPKIgAAAIwoiwAAADCiLAIAAMCIsggAAAAjyiIAAACMKIsAAAAwyiuLJ1sPAAAAYOugLAIAAMCIsggAAAAjyiIAAACMNlQWD0ze95La0dhLa/dde++A38+extGX1u7bM+iPFQAAYDRstCy+tHH/wN8DAAAAipCH+VJMm1AWAQAAtpCemuLJfpTFA5P3SRPB91+762iwzp7G0ddM3h/PXL901+wBaZ3Lm/dfXktmkJPV5KntZmZhZklwFPlkklny5FTDI167q8AhAAAARkvxpniyPyOL91+7K2xsBybvi0vhnsbRl9TiJpeUyHD9XUk5k7c62Yyq572zr0k64uy19+qWqGXRVFvDn6V+GR5Ot0MAAIDRU7Apniz5Bpe44d07+5rafXta918ujc/taRxVmmXSzJJyebL1wEl1q2gUMN7nA6k9pO5lkcpiaj/y+uoR4+W6HQIAAIykIk3xZP+uWQzGEeVXUxPEUpmL6mCwvHnfSzLz6Jc3w9Win8M9ZJZIR2nel7kt2nBEtbamdggAADAyhugGl2DQUW6Hxcti3rNv7p19TU19OI66ZGNl0XAIAACAUTH4G1xOtqL5XLWEpaehkzKXW900UtPWypKkLGb3o05D5x4xewgAAIARMdQ3uEQV7f7L1ZtdUlPJyo0pjdkDrQdONu+Ltw3Xzy5Rxy/T+9llOGJcFnU7BAAAGD2DucEl6ILJzcWtB+RSGNS44KJA3YNslD0HzTIgjRR2W5L76Bz5Hhp9WdTtEAAAYCRVcYNLTzLXLAIAAGDYURYBAABgRFkEAACAUXVlEQAAAJtOb2URAAAAWxxlEQAAAEaURQAAABhRFgEAAGBEWQQAAIARZREAAABGlEUAAAAYURYBAABgRFms2p8f6QAAAJSrf9WFsli1gf/HBAAARk//qgtlsWrBX5QQQgghpJRQFkcNZZGQ9UUIMehTIISQYQxlcdRQFglZXyiLhBCiDWVx1FAWCVlfKIuEEKINZXHUVF4WZ8eFGJ81vBBkbHK52Ca9HzlKD7sr6eiVHGugn2HpH9QwffK61ymLhBCiTZGyuLT0xBXdYtqWsqjn2UKJ3dSs5tWE1fDbK75jKSt4NSGE5bSGoSzmVsV0wem6UcnnUMrqG8rGjjXgz3Cky6JuDcoiIYRo07UsFmmKlMV1lcWk/zVtTflrOZawvZV2qiz6DUuEy4egLJr/Sa6wGIx0WRzgZzjiZTG7CmWREEK0yS+LqabYbq+YfqUsbqQsrviOFQwiJuv4DUvUvPjVcGVtrexaFmfHhRgbGwunMyfHU/Oay5Nj2rnI5ckxZWRrdjw9Har7F1nZW2oKVXpN3SzYkXHS1XSG5rPIyQaOFR4p3jZ4eXlyTNmJ9HuXY5kyHJ/hhnaoy7B98uk3LSiLhBCiS05ZzDbFQP6vKZTFwmVRnYmWl0Q/txxLbYp+wxKW4xcri5PLYd0bnw3/XQ7+nZwdl/4dlZanf9fMiea1tN5eC/5RD5dlS2rOGXY5lOHo6zxW2LrjRbOTk8uddGeRfwuOFf2WPliRMx3gZ7ihHZrOeag++dS7piwSQog2prKonX3OrsbIYhllUdP5mra0JCiLqX7Za1kcn+3I/wbnXmsovxBsMqlrA+lxndz95L+mNtH87pHZeh1lcZ3Hist2KvInoXwq6Yad+5F1O3qX10r/DDe0Q9NKQ/XJpxZSFgkhRBtTWSxybSLXLG6sLMZJTUC3k1tbkrIohMj0Qq2uZTH4x1H6tzQ756mZAtT9S11yWUxVg/T4pvkMN3bNYi/HMt5xknwU6oeysWI74M9wQzsscM6D/+Qpi4QQUiSUxcGVRekyxNQNK56tTDcHI4u+Y4noKsYcPZbFzPycaXpT8091NWWx+xmWVxa7Hct8pOizSH0mQ1IW1/cZbmiHBc558J88ZZEQQoqEaeiBl8XgUThyC2zaaimMr1/0bNG1L/ZYFvWzzumLxSaXtXOG5V6zaKgGXc+wxLLY7Vg5Rwo+2tlUf656GrrUz3BDOyxwzoP/5LlmkRBCioQbXIagLKp3ruTe7NJyLN209frLovqPcjiIGP8uv6q7RyDnn/ByR8WMZ5hdo2s2cKy8NxXNomZuF0/dZqGrZIYCOeDPcEM7LHDOg/7kMzulLBJCiDY8OmcYymIwuBhckpg8XlFXFleCaevw13Xd4JK5ZjH8lzn8tzX5BzT4Nzg9ytj92TnmV6Qjpa5Ly590NJ1hnORytwLDdhs4Vu4YZnAS6gkEGyT71Jxej2Wxws9wgzvs+n6G4ZNX9klZJIQQbXgo93CUxXjI0G9YmYHD9Fij37BEtHKhstjHLE+O9TIFTNJJT5eSqpLtn5RFQgjRhq/7Gy6pW1vWYXi+G5oUCJ/egKL74CmLhBCiTZGyuBGUxZ4oj1dcn8rLIiEjEsoiIYRoQ1kcNZRF/XV9yuV9pE/Z3J88ZZEQQrShLI4ayiIhhBBCSgxlcdQEf1EAAIAS9a+6UBarNvD/mAAAwOjpX3WhLAIAAMCIsggAAAAjyiIAAACMKIsAAAAwoixWzSWEEEIIKTv9qy6Uxaq5rjvo5zERQgghZKRCWRwplEVCCCGElBvK4kihLBJCCCGk3FAWRwplkRBCCCHlhrI4UvLL4uz42ORyZf9pEUIIIWQUQlkcKT2Vxdlxoc/4bN//uyOEEELIJknXsri09MQV3WLalrJYEq+mlDm7aVrTWBblYpjtgsuTY4JBR0IIIYRokl8WizRFymJftRxLCFHzkiVN22r4hvX1ZXF2XIixyeVoZHF2PF0XKYuEEEIIMSSnLKaaYru9YvrVtAfK4kb5jiXM1TBLWxbjKmi8ZpGySAghhBBDTGUx2xQD+b+mUBY3qGkLYXuGV/2GJSzHVxYay6IYn6UsEkIIIaT3aMuidvY5uxoji33m1dQJaFXhsihdsUhZJIQQQkhv0ZbFItcmcs1i/6ll0Xes6BYVY4PMvRs6ucmFaxYJIYQQUjCUxSGmHVn0G9Y6y2I4DR3PSiehLBJCCCHEEKahh5huonnjZbEzO05ZJIQQQkjBcIPLUPPszFMVey+Ly5NjQTcMymL0IB11DcoiIYQQQnTh0TlDrmkLoTw9Jy6LPdzgon5ZS7YWUhYJIYQQYggP5d4EPFv9Ahcvbo1Fy2IQvhuaEEIIIb2Gr/sbKZRFQgghhJSbrmVxIyiLVcsvi4QQQgghvYayOFIoi4QQQggpN5TFkUJZJIQQQki5oSyOFMoiIYQQQsoNZXGkuIQQQgghZad/1YWyCAAAACPKIgAAAIwoiwAAADCiLAIAAMCIsli1QV//SgghhJARTP+qC2Wxai6PziGEEEJIqaEsjhTKIiGEEELKDWVxpFAWCSGEEFJuKIsjhbJICCGEkHJDWRwp+WVxdnxscrmy/7QIIYQQMgqhLI6Unsri7LjQZ3y27//dEUIIIWSTpGtZXFp64opuMW1LWSyB71hykbOcVs7KxrIoF8NsF1yeHBMMOhJCCCFEk/yyWKQpUhb7y3csYTfDX/2GJYTtGVfWl8XZcSHGJpejkcXZ8XRdpCwSQgghxJCcsphqiu32iulX0x4oiyVQymJ7xbOF/GuKtizGVdB4zSJlkRBCCCGGmMpitikG8n9NoSyWIFsWw5lov2EJy/GVlY1lUYzPUhYJIYQQ0nu0ZVE7+5xdjZHFKihl0asJUfOCnwuXRemKRcoiIYQQQnqLtiwWuTaRaxYrLItSci5YbHe5Gzq5yYVrFgkhhBBSMJTFYadOQ7ccq+drFuME09DxrHQSyiIhhBBCDGEaetilrlnUzj7HCj1ncXacskgIIYSQguEGl2GnK4vRZYsZphtcgm4YlMXoQTrqGpRFQgghhOjCo3OGnXEauocbXNQva8nWQsoiIYQQQgzhodzDLnWDi/qA7qJlMQjfDU0IIYSQXsPX/Y0UyiIhhBBCyk3XsrgRlMWq5ZdFQgghhJBeQ1kcKZRFQgghhJQbyuJIoSwSQgghpNxQFkcKZZEQQggh5YayOFJcQgghhJCy07/qQlkEAACAEWURAAAARpRFAAAAGFEWAQAAYERZrNqgr38lhBBCyAimf9WFslg1l0fnEEIIIaTUUBZHCmWREEIIIeWGsjhSKIuEEEIIKTeUxZFCWSSEEEJIuaEsjhTKIiGEEELKDWVxpFAWCSGEEFJuupbFpaUnrugW07aUxbK0HEsIq+F3W5OySAghhJByk18WizRFymL/+Q3LsixhOX6XNSmLhBBCCCk3OWUx1RTb7RXTr6Y9UBbL4dnCclqeLYTdzF+TskgIIYSQcmMqi9mmGMj/NYWyWIqmHYwp+g1L1Lx4ud/IjjVSFgkhhBBSbrRlUTv7nF2NkcVKeLXoasWWYwnbi5ZTFgkhhBDS/2jLYpFrE7lmsSLBHHTws+9Y+TPRlEVCCCGElBvK4nBLDR/qRhNllEVCCCGElBumoYea71gik3igMYuySAghhJByww0uw6zlWOlq6DtWzgMXKYuEEEIIKTc8OmeIaSed/YYlhO1xgwshhBBCqggP5R5enq391paWYwlhNymLhBBCCKkgfN3fSKEsEkIIIaTcdC2LG0FZrBplkRBCCCHlhrI4UiiLhBBCCCk3lMWRQlkkhBBCSLmhLI4UyiIhhBBCyg1lcaS4hBBCCCFlp3/VhbIIAAAAI8oiAAAAjCiLAAAAMKIsAgAAwIiyWLVBX/9KCCGEkBFM/6oLZbFqLo/OIYQQQkipoSyOFMoiIYQQQsoNZXGkUBYJIYQQUm4oiyOFskgIIYSQckNZHCmURUIIIYSUG8riSKEsEkIIIaTcdC2LS0tPXNEtpm03cVn0bJHEbpayQ8tp9XsryiIhhBBCyk1+WSzSFEevLLYcSymIntPwKYuEEEII2ZLJKYuppthur5h+Ne1hc5ZFv2EJy/FL3i1lkRBCCCGbMaaymG2KgfxfUzZnWWw3bSFsT9Pb5OFG37GE1fDDPtd0rGjK2gvqZpCap9Y+P17PUkYrk+XqrLdSFr1atIbl+PpSS1kkhBBCSLnRlkXt7HN2tREdWYyqW3pIz6tJDa/lWGGh9OyovcWdL1xNmc4OLoKM9qm85DuWVCvTW4WbyNXQa1AWCSGEEFJNtGWxyLWJI3zN4kpUzoRaGZt2XM78hhXVO3XEUR2VlPplamBSqnrSbtuanUtlMRmnNKEsEkIIIaTcUBZzBTO/mXE+37E0g3+pQpkpi+pQZbSmVxPpFpjsRNqqFYxaZufHKYuEEEII6V+Yhu5Gmf8Nyl8yB11hWYxPRmTWpywSQgghpF/hBpeu5Jnlpi0sx1NmhIuXRWUaOu6I2UsPtdPQiZZjGW+RpiwSQgghpNzw6JwMv2Gl7npOVUPLMt6wnF8WldKZbOXZInWDi/xS+LNXi7aNxjW5wYUQQggh/Q8P5c5qyt/ekp7zzVS0XqahkyfspL4VRn50jjxqqN7goq5AWSSEEEJI/8PX/fVIeYDO0KEsEkIIIaTcdC2LGzF6ZTHvesFhQFkkhBBCSLmhLBYVXHSYmj4eNpRFQgghhJQbyuJIoSwSQgghpNxQFkcKZZEQQggh5YayOFJcQgghhJCy07/qQlkEAACAEWURAAAARpRFAAAAGFEWAQAAYERZBAAAgBFlEQAAAEaURQAAABhRFqs28KclAQAAFEdZrJpb4BtcKIsAAGBIUBartoGy2HIsYXvqQq8mrIZf+bvwbCFEzdvA5pbT6vFwUuxmeh2vpl3Bdyx5sf6g0WfoO5ayZ69m3GTIbPDPAQBADspi1UajLG7QespiUuOattLhWo6VqkpNO/pMlP7nNyyR+QClT7XAygAAbDmUxapRFtsbLYsrvmMJuQ6aP4HUYGFqP+12UArDoimtnOqjAABsXZTFqvWxLCZTsZbjhytI87DJQoXfSGZqTa0rs2ep7QVn1YqOoxwlmT62Gp7U21JlsetJasqi1Opyxv+yZTFVAeUVop9bjtWlKWZP2HcsaXSz5VjhCQdHTNZPPuHgc2vaIhkW1X8O2T9r3p8jtR/5c8v7SwEAYEJZrFq/yqLfsJIy0UgajFIlNZe1+U4tKg1JxVHo9pwqi3JriY8o/xz2G11ZLHKSSllUzke/vvTupLKoWblpp4q13dSMPmb3qTnhpGLKKwR1Wfmswp23HEtYVk05ena32Q+/y5/DWFtz/lIAAOSgLFatn2UxrwnpN28b9ibT7DldFpNhrWTl1NGVwidtXugklRtc5DNU+580opYdqxMiOwapvt9o5fwhN/MJh+9dWSFdPZOqlxq/NOw2++F3+XOkP3ZpfdNfCgCAPJTFqvVtGjqcXkxWSN0grG1L7ZV2qlFpRpsye9ZMQ0cvxWUo00XkQb5k82InKVUudd5ZO7KovwxxJTt0qpm9tZvqyFxG7gmHpTZv4jsuc+rnZtxt9sPP/XPkjZ4a/lJ9+I8cADBKKItV22BZTF9LlxoLDC9ArHnt7lO07fZK0L2U2U/T1KS85yJlMXN0c1nsPr6ljM/Jm2gbj7EsptZv2oYzzHsSTe4J++FEb/I36qUsmj8H9cPP+3NQFgEAZaMsVm0jD+VOVx/t7b1xpyzSBtTxv/w7i+W2WmxkscA0dLHKor5NpTQXvsG520vKkpZjGS7pyznhYOfqCunTU69x7KW6Zf9fBd2fI7sfdRqasggA6BVlsWob+gaX1MP/vJp8h2y0POkE6ghZy7G11yNGewgGq7INybDnLmUx1bfMN7h0P0l95ZIHCEVmeLXrNLRmTl9duWkL/Z0uhhPOu8ElOpD8RJ70Ceh3m/3wu/w5Mvuxuv6lAADIQ1ms2ka/G1p+0o38j720PDUNKnTLY8pTXYw3uKT3UKAsrsRX1wU1UT8NXewkM8OH6ZG/1Fe8xOeTusFFuaU68051w5D68cXsCas3Fyel0LOF5TTlz0F+C6m2qvkcsh9+lz9H+l2nnl5OWQQA9IqyiIr0+iDu0TiZoXrXAACsA2URlSh2I0tVMs+X6RvKIgBgs6Msol/UqeHhaYpVfwiURQDApkZZBAAAgBFlEQAAAEaURQAAABhRFgEAAGBEWQQAAIARZREAAABGlMWqFfwGF1JZBv6fBAAAw4yyWDXXdf8XGZpQFgEAyEdZrJrruv9MhiaURQAA8lEWq+a67nkyNKEsAgCQj7JYNdd1/4kMTSiLAADkoyxWzXXdF8jQhLIIAEA+ymLVXNf9FhmaUBYBAMhHWaya67r/QIYmlEUAAPJRFqvmuu7zZGhCWQQAIB9lsWqu657JzRdu+gWh5Bdu+kL+Fn3PZ98uhHj7Z8vYVcF394WbfiE+YIlHz4ayCABAPspi1VzX/WZuFj5yqXjb3fnrbN4UfHfG1e5+m7j0IwvlnQ9lEQCAfJTFqrmu+2xuTrqXirfty19n86bguzOutu9t4lL3ZHnnQ1kEACAfZbFqruuu5uZh91Kxc59mYbI0+W3fTnGp+/DD7qXhpO6l7sPKbvbtFCLeMFlNWm81WEVemFkSHEU9mSjJqQZHjF+Ut+jy7rInsk9aLT56soJ03NyTUd6+NpRFAADyURar5rru6dyccC2x8y7tYss9ofx0+vRdO4UQ8W8nXEtE255wLWFZO8NX1K1O37UzXE/Zl+ue0C05ffquncmm8jGyR4zP5a6dyWkVeHfq+sG7ilaTj67+0uVk5LdvCmURAIB8lMWqua77jdwcdyzlDhDLOZ68UPO+4dWSRd/wakLUPHXj4NXkp2hF6bdoX/H//UbmFTnSxqn9yOurR9Ttx/zu0ruV31f6DRt+yTsZcyiLAADkoyxWzXXdv8uNf+M2Yd+pfelOWwgh5FfvtMW2G311lWCBuptwSyX2neFq6i6zS6Sj3GmL9MkZjujfuC11ajnvLrNUXqC8R/mX4idjDmURAIB8lMWqua779dzM37hN2NPGl4TYduN8vGTaVn6VFqi7mbaFaafxfuUV1CXJUTT7MRxx/sZtqVPLeXeZ3cqrKe9R/qX4yZhDWQQAIB9lsWqu6y7n5tgN28Rbp4wvHLthm9h2w7Fw2dRbhbJy8ru6G3Ur02HVNZIlU2+NXtKv1cMR9e8us7L8vpKjp34pfjLmUBYBAMhHWaya67pfzc2D118idtyhXXzJ9Q8qP331q3fsEELEq9+xQ8SvpHdzxw5pxa8+eP2O6x/86le/eseOaFG0fnbJV796x454t5n9XGI4ovxK93ennHr2XSU7umOHdPTCJ2MOZREAgHyUxaq5rvtkbuauuyR1ceH225+8fbsQl1w3F66S/IN9yKoAACAASURBVHb7dnHJdbcnW2y/Xd5N8lu8WZRwZ9LBjEvCo8w9Ke86s076iHPXXaJslPfu0q9sv13el3r0aDXNZuaTMYeyCABAPspi1VzXfaK8/OWV4pL6bIk73GqhLAIAkI+yWDXXdZfKy6evFD//4dkSd7jVQlkEACAfZbFqruv+bXn51BXi5z90tMQdbrVQFgEAyEdZrJrruo+ToQllEQCAfJTFqrmu+xgZmlAWAQDIR1msmuu6j5KhCWURAIB8lMWqua77N2RoQlkEACAfZbFqLhmyDPw/CQAAhhllEQAAAEaURQAAABhRFgEAAGBEWQQAAIARZREAAABGlEUAAAAYURYBAABgRFkEAACAUV5ZPNl6AAAAAFsHZREAAABGlEUAAAAYURYBAABgtKGyeGDyvpfUjspeM3l/Kae1p3H0pbX79gzZrgAAAEbGS2pHi6y20bL40obcDu+/vLbeZta876W7Zg+U8uZL3BUAAMAoikf6uq5Zbll84GTr/mt3Hc0sLICyCAAAUInUzHD+yqWXxQdONu+TBxfjqeqX1u679t5kHXnhnkZyusEO9zTiGe37r9119PLm/dfuyuzk3tnXxFtF7TB3V8r5xCt0OQoAAMAISTXFrn2xD2Xx3tnXRGXrwKQ0yBeXSGmFk83Z6AdlODBVFuP2tqeR9MIDk3GlU4czjbt64MCkXGTlrYxHAQAAGBnappjfF/tQFlv3Xx62rviHcPm1u45e3gzLYvq6xtyymAwNardNbW7e1eWpIcNkb8WOAgAAMCoGcoPLAydb0sBhM32v9EtqRy9vPhA0s+jnSG5ZTNaURyVTc8pdy6I6Px7sPO61OUcBAAAYMQO8wUXqappyJrl39jXyrdM9l8X7LzcVRMoiAACA2WBvcLn/8njIsHvlkiZ/ey2L6kxx+uJI7a6y56NOQ1MWAQDAyBvoDS7BYKG0RH0g9v3XNsIRx6iWSRVNHfYrWBbDPhccN3snTXpXmfPZVWiyGwAAYDQM4AYX+RjaSWf5WTbSCF/2G1+ix9boH50T7VC91Tq5WlEZTTTtKn3OqaNTFgEAAFI2VBYBAAAw2iiLAAAAMKIsAgAAwIiyCAAAAKPeyiIAAAC2OMoiAAAAjCiLAAAAMKIsAgAAwIiyCAAAACPKIgAAAIwoiwAAADCiLAIAAMCIsggAAAAjyiIAAACMKIsAAAAwoixWzS2WgZ8nAABAm7JYPdd1O91CWQQAAEOCsli1EsqiVxNy7ObA3xQAABhVlMWq5ZTFD3zgA69+9auffPJJc1lsOZYQouYlS5q21fAH8l68mqjg0Bs/SjXnCQDAiKIsVs1UFq+55hohxHd913d97WtfM5VF37GGqPdQFgEA2AIoi1VzXfef//mfb7311uXl5aAmfuc733nve98rhPju7/7uo0ePmqehm7YQtpe3c9+xoslpy/GDhS3HErbXil6Jl+ev37RFNITpN+KV4tbl2Zp5cN3ecs5QnkBv2vImfsMSNU93FM8WltNKdpK0wKJ7AAAAPaEsVs113YceekgI8f3f//2PP/74d77znXe/+91CiJe97GUPPPBA3jWLXk2dgE5Txh2TlYOaGBYpz04KVs76llWLi5fv1OQemVQudcTOsLfMGSbL5b3pq172KEH5s5zWuvcAAAB6Qlmsmuu6a2trl19+uRDiVa961Vve8hYhxPd8z/ccO3asyw0uagOThuiChWpbCgcIwx+idiW3qGLrZ89BboSmgb1kb7LUOuaTyS+L8gCh37DCDSmLAAD0BWWxasE1i//yL/9y5ZVXBl3ve7/3e+fn57vfDa0drotbUeoW6WDe1VtJ97a4XRVcv73STs0da8uicW/55x83vB7Kolpke94DAADoCWWxavENLt/+9rdrtdrLX/7yEydOFHp0TjKKlloYl0XtJHVOWSywfrtp5xTEbvPOCsoiAACbEGWxavLd0N/+9reffPLJ4s9ZTE/CtqVWpK2S7RVjWexhfXXuW1sWjXtLnWqxaWi5VuZPQydrFt0DAADoCWWxaht7KLc6ztdWypxnC+X2ETtYzVAWe18/vC3aMJpo2JsivU5ycWTqVhWhVL30UeLTa9rGm12MewAAAD2hLFZt498NrTwORr00UH5JblH68ldkffmCRavhK6N00eN4oqE+3d7S5MsfMxPK0f06ynCmchTPFpbTTPahjLMW2gMAAOgJZRGbSeaaRQAA0F+URWwmlEUAACpGWcRmQlkEAKBilEUAAAAYURYBAABgRFkEAACAEWURAAAARpRFAAAAGFEWAQAAYERZBAAAgBFlEQAAAEaURQAAABhRFqvmFsvAzxMAAKBNWaye67qdbqEsAgCAIUFZrNoGy6JniyR2U1pY89Z7Shv7wuWWYwnb6/vn5jtW/H77twkAAEihLFZtA2Wx5VhCbj+e0/DLOKWey6JXE1Z8aMoiAACjjLJYtfWXRb9hCcvxyz8lyiIAADChLFZtAyOLTVvoa5nU9oLq1nKsYKJaKZfJFLbV8KQilSqLfrSx0HXTzDy46YjB8qYtkily/Z79RrxU6qB5J5xzkjmbAACAdaAsVm0j1ywGDSk7Cpgqi3F/8uykfsk/t71a6pLHeJ++Y6mr6S6FzIws6o7YcixhWbW4yZn27Ds1uV8qF2IaTti0q5xNAADA+lAWq7bRu6GjcTi5MqbKYvKS37DCItW0syNwmrKYWs0wxZwpi7ojqst73nPOCZt2lfceAQDA+lAWq1bOo3OMQ4NqCYsvc0w6XMjXTkMHu1VTpCxqjphanrtnaU45GhrMOWHTrnLfIwAAWB/KYtVKe86idL9L97KYmVA2l8UCj+BZZ1nU7rlpC3XuOPg554RzJscpiwAAlI2yWLXyHsqd3O9SbGSxwDR0wRuu11EWTXtWhwOTixFzTjhvV0xDAwBQMspi1Tb06Jz07cDJjR1dymJw70iBG1zU53u3HFv3KEdlDK9YWTTtWW54weWYxe7I0Z4kN7gAAFA6ymLVNvDd0E1buU4vGZArUBZXotuWg0v8mvpp6OjX5AJCabkk2lXy6JzsETW3sGj3nFywaDV8zX3WmhM2n2TeJgAAYB0oi1vUxr7iDwAAbBWUxS2p4I0sAABgy6MsbhXKN6/QFAEAQDGURQAAABhRFgEAAGBEWQQAAIARZREAAABGlEUAAAAYURYBAABgRFkEAACAEWURAAAARpRFAAAAGFEWq+YWy8DPEwAAoE1ZrJ7rup1uoSwCAIAhQVms2gbKYsuxhO2pC72asBr+Rs+q5VjRt0Zn9uY7lhCW4xs392xhOS3TS9V+D7XuIyr1NCp/RwAADBhlsWrDVxZbjiWE3dS/6jcsy7LWWxbLPtVC7yVbFgEAwLpRFqs2bGXRdyxjUwyP2LQpiwAAbFWUxar1sSx6tXguOe52vjTBrCt8ee0q6pGFymJyIKkRxj3Ss0UStZt6trykaYvkfOQiK70Ref24zgazw/LbCebWa55SZ4MV4ml35X0lJ2k1PEOHlpuxZwvLacbnZXsrbb8R/SZNVScL07P8OUfU/+F0f2IAAPqKsli1fpVFv5FMFnuN4AffsdQqmbnYLtyqaWcriN+wwvW7l0UhlDYWlx5l0NE0spjtu9Lmwfv1HUs6efkQLccSllWLTi/5iDw7aWapshi/zdQ6ptNIvVm5LMa7CrtduAflQ/Ad5fTkd2c6ov4Pp/sTAwDQb5TFqvWzLKa6YKrk6Tb3G5awLCvcUOpk8soFyqLcq6ROU6gsquvbTsMKV4uPmzmB5M22HEueBA9PW+lhmbKYrG8uxOk3pdlV3phogWacc0TDH07zJwYAoO8oi1Xr2zR0OLWq9JVMtGUx20vUCxnl7tKU5pPD4pK5ZjFZv1BZTN5X0xY1L948Xl8zJhofIvWZBAONVmr9zDR05u1nepjpUs7MNHQrc0qaN6vMoRvKfXJE4x8u8ycGAKD/KItV22BZTN9Kkmpg4eVxNa9tmHdO0ZVFy9kvd5uk5KQOHdlwWYx6kh+OKQazz75jhduupyymB+26lMXMIcori01bqNPNhgaslkXzH07+EwMA0H+Uxapt5KHc2Qajmy2NOmW6CGppylZm4KrHaWip6xQsi0FN9OR2aDeSM8m+EXUaOnv+qWdDFhtZXM80dPeyqI4gJhcj5hyx+x9O9/82AADQH5TFqm3oG1z8hpWeaI7veKjJdwEnN3nI94XYmq4m3zui3kcSK3SDi3SBo6FO5Q2YNW359prw4Y7Jyuk3YhnKn/Sr/F66l8Vghrf3G1yKlUXpfaVugjHf2aP5w+n+xAAA9BtlsWob/W5o+Tks6ZuXNfPF8jNrTGNR0hV12jJX5NE5yRNk5I6l1qnogTWGEiaNO2qeEy5f9pe9o0X7a9xiC5RF6fSEEHazxGsWlYcKKcOreUfU/OEMf2IAAPqKsghoFH3S+GY+IgAARVAWgYwi9wZt9iMCAFAMZRFYaae+Y6aS3lb9EQEAWAfKIgAAAIwoiwAAADCiLAIAAMCIsggAAAAjyiIAAACMKIsAAAAwoiwCAADAiLIIAAAAI8oiAAAAjCiLVXOLZeDnCQAA0KYsVs913U63UBYBAMCQoCxWbSNlUf02YSHsZl9P1bOF5bQG+FkN/AQAAABlsWobLYtJQWzaor9dauBdTTkBryashj/oPx8AAFsNZbFq5ZXFFd+x+tqfKIsAAICyWLWSy2L0q+9Y0eS05fjRJl4tu1BaU9jeSru90vYbySKpkKXKov4QiZZjCdtr2kIIUfN6OKumLb/qN6xo8/gElPn3Pk++AwAAGWWxaqWVRb9hRR1LGWL0amFXk1Zoe41kzaiKtf2G4wULa1FXazlWcgi5LOoPoWg5lrCsmtJKC51V97IY7oGRRQAAKkdZrFppN7gkzUktW+EIn9K62sma0WiiidTJpK5mOISybcux5JHIns6KsggAwJCiLFatpJFFqfYls7rSVK23Eg4Tyu1QPyK40lbnpjVl0XgImdogi58VZREAgCFGWaxaadPQcfMzV8B2O74eMWfNpi2PU2pHFvMPEcqWxWJnRVkEAGCIURarVt4NLtG0r3wVoF7umuq8sHyhYdLVuh9iJV0Wi59VqixKLZOyCADAwFEWq1bi3dBtrxbcUOzZQhrGazl2w2+vtL1aVN2SGqesGdzgIre6YMBPc82i4RCK9IWMhc9KvqtGuZk6XRa7j24CAICSURartpHvhk6XxaBmWQ1fvfdFGg5Ul8Q7UR9nk1ywaDV8/Q0uqQ21z1/U3PVS+Kya0Yo1zzQNHV3syKNzAACoEmURAAAARpRFAAAAGFEWAQAAYERZBAAAgBFlEQAAAEaURQAAABhRFgEAAGBEWQQAAIARZREAAABGlMWqFfwGF1JBBv4fAwAAw4+yWDXXdU+RIQhlEQCAIiiLVaMsDkkoiwAAFEFZrBplcUhCWQQAoAjKYtUoi0MSyiIAAEVQFqtGWRySUBYBACiij2Vxfqpen/IH+/bmp+oTBxcrPujiwd05b5yyOCShLAIAUESfy+K+pc755wf49uan6nvmzqyde67Kgy4e3F2/63HTG9eWxbnp6Gkut35ugeV9Wy6HsggAQBGUxfL1WhYXPndr1GkWPner607PsbwfyymLAACswwDK4uLB3fU40XStOmftT9XrU/PS+tFL0ra7Dy4GKywenKhPzftT9Xq9Pj2fOYc9c2eend0TbjRxaLHbmbTb/lSy85X24qGJcLfBgRYPTqROIDr/6BDzcVlcPDShrtbWlMW5abnPLHzu1vA3lpe7PB3KIgAARVRdFhcP7q7Xp1vn1jqdTqdzZv62en3fQru90p6frt8yfyZYeX66Xq/XDyydPxvuZ/qLa53zzy8e3F2fOHQq2HbpQL2+/4urK0GHu3nywNxTz5/vdDqdC2dT51CvT84urZ5bCw+3/4ur+WfS9qfqN8+dWTu3utJuB2XxM493LpxtBzXxtrmnnju31lk6UK/fduzp1egoE4eeOt+JTqwulcVbjp1Zkz+BdFlMt5mo67C83OWZUBYBACii4rLoT9V33/P0WlLpFu+9uX5gKexVt84/+8Jqe2V+qj51ZO6224Lu6E/Vdx9Z6Zw/G2279sJqe6XdXjy4p35gKexwNx87s7Z2Iax32XPodMKZ6MV7b67fNr/yfN6Z5JbFm4+d6XTWzq2utBcP7akfWFp7YTU6w+jEVua9ev1A9MZXX1jr5JbFuWn1urqo7LC83OWZUBYBACii2rI4P12vN7/cWZNanT9Vv+XYmQtn24sHd9f3L62dW/Wn6tML55f21W859uwLq/PT9YkjK521c63peiZ3Pd45f3bx4ET9rsc72gsTg2lo6RyCwwV7M51JXllMDhSOGl44u3hoQt3V4sHdORdrFimLt35ugeUlL8+EsggAQBHVl8W/WlJmipNmtnhwT33/0vmnDk1MHFnpXFiYqu9bWnv6nt0TR1Y6ay+shtumE5fFYM46ew7qDS7R4Vo5Z9KlLIYHiqaYz7Wm6+EK4a56u8Fl2EbgRnV5JpRFAACKqLYsBnPNZ6SKJg/LLR7affOxpbmbJ+55ei1oh/vm5/bUp7+4tnbuOc22oS5lsb5vKSmL8dDmqZwzUcvifNwFDWUxs6v5qXpvZVF7jR3Ly12eCWURAIAiqr7BZX66Ht2YshL0vIl7nl4Lh+WCO5pvOfJ01A733Hpb/TNfXAv7WXrbqeDW5m5lsV4/8OXgHPypen3iyEpwnaL5TBYPTtTrBx4/txqfUjAGaSiLqyvz08nNLuHdOcEbL3o3tDQUptzVy/ISl6dDWQQAoIg+l0U50WNrnpq7LVl2cOncWnK54fxUvX7b/Jnw+r/Fg7vr9f2Pr0kzvAv76sq2519Y7VYW98wtzccHnFo430kOZz6TE3eFi6fnF4/cUj+QXxbb7cUjtySP4Fk8cmt4g0u3u6GDTjM3HT87WrnAjuXlLqcsAgCwDv39buiz59ULDKP7hVfDB9Z0Op30jSlnzyertdvPnVvrpMYm5X1G2z53bk1fFoP11849nxxQ3Zv5TJ6PjnPh7OoLUWFVD7T6wlpyX8tz8iFWz60VuRs6rjFz8TeOqKNgLC93uRzKIgAARfS3LCIr+w0uZCChLAIAUARlsWqUxSEJZREAgCIoi1WjLA5JKIsAABRBWayaS4YmA/+PAQCA4UdZBAAAgBFlEQAAAEaURQAAABhRFgEAAGBEWazaoG/qIEkG/h8DAADDj7JYNZdH5wxHKIsAABRBWawaZXFIQlkEAKAIymLVKItDEsoiAABFUBarRlkcklAWAQAogrJYNcrikISyCABAEX0qi4sHJ+pT8+rC+en6xKHFat/e/FR94uBixZ/p4sHd9Snf9CplcUhCWQQAoIg+lsW7Hu+cPystnJ+u75k7s/bCaoVvb36qvmfuzNq556r8TBcP7q7f9Xjn/PPaV7VlcW46eprLrZ9bYHnflsuhLAIAUARlsXy9lsWFz90adZqFz93qutNzLO/HcsoiAADrMJiyGMwOLx7cXQ8iTU/nvNQOeliY3QcXk2NNzftT9Xq9Pj2vnklQFp+d3dNtb3Vp4tifSna+0l48NBHuNjjQ4sGJ1AmEB4oPMR+XxcVDE+pqbU1ZnJuW+8zC524Nf2N5ucvToSwCAFDEwMpivV6fnF1aPbfW6ZyZv61e3//FYMQx56XFg7vrE4dOnVvrdDqdpQP1+v4vrobHunnywNxTz5/vdDqdC2elM+myt/p0K9hb8NK+hXZ7pd32p+o3z51ZO7e60m4HZfEzj3cunG0HNfG2uaeeO7fWWTpQr9927OnV6CgTh54634lOrC6VxVuOnVmTP5x0WUy3majrsLzc5ZlQFgEAKGJwZXHfUqfTCSeIF++9uX7b/Mrz7byX/Kn67nueXuuEE9mLB/fUDyyFHe7mY2fW1i6E9U7SfW9xuVy89+b6gaXzz+eXxZuPnel01s6trrQXD+2pH1hae2G17U/Vdx9Z6UQntjLv1esHlsJp6NUX1jq5ZXFuWr2uLio7LC93eSaURQAAihhYWdwzd0a6qs+fqt9y7MzauVXzS63peiZ3Pd45fzY8lvbCxNy9Nb/cWZP6ZfDShbO5ZTE5UDhqeOHs4qEJdVeLB3fX9y0VvWZRV25u/dwCy0tengllEQCAIvpYFm8+dia/LEr1Lulnxpda0/X6Xy110onLonKsSLe9yXPW8Tl0KYvhgaIp5nOt6Xq4Qrir3m5wGbYRuFFdngllEQCAIvr1UO7Fg3vq+5fk0b75qXrd+3JHmoZOXp1PxvmML506NFG/df6Mckliu72iH8WUD1pwb8kAoVoW5+MuaCiLmV3NT9V7K4vaa+xYXu7yTCiLAAAU0bdvcFk8tLteP/DlqDDNT9eDC/vORxcmJq/6U/X6xJGV4PLBvJem69FNLSvt9uLBqeDW5m5lseDeJuoT9zy9Ft/IcuDxc6vhJtEYpKEsrq7MTyc3u7Tnp+v1ejgNXfRuaGkoTLmrl+UlLk+HsggAQBH9/Lq/U7OTyeWFuw8+daETXdg3P1XfM7c0f1v82JqF89E9KDkvtdsrC/uSPU4cXDp3/oXVbmUxZ29Pzd2m7G0tfunEXeHi6fnFI7fUD+SXxXZ78cgtySN4Fo/cGt7g0u1u6KDTzE3Hz45WLrBjebnLKYsAAKxDn78bevWFteTywuQWkOhSwufPxS9Hk7Y5LwXOnpf3GHS7586t6ctisH7O3laTF1K3yDwfHefC2dUX1sJLEtUDrb6wlryp5+RDrJ5bK3I3dFxj5uJvHFFHwVhe7nI5lEUAAIroc1k0yPlilYF850qVst/gQgYSyiIAAEVQFqtGWRySUBYBAChiMGWxHc4O6xthzksjgLI4JKEsAgBQxMDK4pblkqHJwP9jAABg+FEWAQAAYERZBAAAgBFlEQAAAEaURQAAABhRFgEAAGBEWQQAAIARZREAAABGlMWBefTRx+6+e//evXtvuulje/fuvfvu/Y8++tjAzwoAAEBGWRyAr3+9ffjwPbt3715YWDh9+vSFCxdOnz69sLCwe/fuw4fv+frX2wM/QwAAgABlcQAOH77H87wLFy501Fy4cMHzvMOH7zFt6NlCid1c9zl4trCc1sA/iv7xbCFEzRv0aQAAsNlRFqv26KOPTUxMvPjiix1dXnzxxYmJCdN8tGfLBbFpi/UXvv6WRa8mrIZfwedZ2YEAANiqKItVu/vu/QsLC9qmGGRhYeHuu/drt1XL4orvWOuuSpRFAABQBGWxanv37j19+nROWTx9+vTevXu122rKYvSr71jR5LTl+Mn6ltNKXpJ6lVIW/Ua8cap7SbsVtpddmBxLOcnURLl+/y3HErbXtEUyX5xsazU86d1pD6o5kHoa8Rv0bGE5zXgHtiefkjRVbf4cejoxAABGCWWxajfd9LHs1YqpKxdvuulj2m2Vsug3rKidKEOMXi3VvaLO1HKsZHO5S/lOLWo5yjq+YyVdym84Xt6xFOqAn2H/LccSllWTq626Z/VMtAc1jyymyqKQP6ukC6ber/5zWM+JAQAwKiiLVdvoyKJm6KtpK2NawYhdtL485Cb1S+M0dFK/mrY0mtjudizDTnJeajmWfA6pPcsnbz5o8bKoXuuZnLNpD8rn0PuJAQAwKiiLVSvpmkWp8QRjXWrisqg2wqTcpF6Sp5vDkqQdJzMfK72aeTpbLovJtn7DUg+XTLLnHLRwWTS20vQgaOY813diAACMCspi1Uq7Gzouc+bZz2JlsWkLdZq1S1ksMNOqNDDD/lNlMbNntZMZDlpmWSz6ORQ6MQAARgVlcQA29JzFZDo1msaVJpdz109fzhj2J3XkLLkIT7tb87EUcocz7V8zsmiY7c05aIllsfDnUOjEAAAYFZTFAVj3N7joyp/l+KkHULccO+xPwTWOUSFTnsuolsWo8QS3A1vy5ukbXEzHUshDbsb9p67wazmW8T4S40GLjaoWLovG8+z5xAAAGBWUxYFZx3dDp8ui1GPke19Mj4yRt1XvhpaeraOO1Um7VZ7Ikz2WqhXu0m6a95+9HaQln6r8YCDzQZUDpT6rXq9ZNH8O6zgxAABGBGVxlG3q7/Qb2pMf2hMDAKAfKIujbBPXmqG9d2RoTwwAgP6gLI6yzVUWladIDlMhG9oTAwCgApRFAAAAGFEWAQAAYERZBAAAgBFlEQAAAEaURQAAABhRFgEAAGBEWQQAAIARZREAAABGlEUAAAAYURYH5tFHH7v77v179+696aaP7d279+679z/66GMDPysAAAAZZXEAvv719uHD9+zevXthYeH06dMXLlw4ffr0wsLC7t27Dx++5+tfbw/8DAEAAAKUxQE4fPgez/MuXLjQUXPhwgXP8w4fvid/c9+xRv6rij17ZN8aAACbC2Wxao8++tjExMSLL77Y0eXFF1+cmJgwz0e3HEttUV7N9so4Ma8mrIY/6A8HAAAMG8pi1e6+e//CwkJQDZ955pmrrrrqoosuuuqqq5555plg4cLCwt1379du6ztWvyodZREAAOhQFqu2d+/e06dPB73wqquuiqeTr7rqqmDh6dOn9+7dq9u2aQthGEdsOZawvaYtTUxLs9WW40dr+o1kDjtqh54tTWvbzXbO5ukjtqK1lHWy23p2sufUG/EdS3op5NnCclrSz814l7YnvwtpkFX31tqpN2g1PPVw3d4mAABbHWWxajfd9LH4asWLLroorjcXXXRRfOXiTTd9TLOtVzNfxtdyLGFZtbjuKGOQ0oa+E6/TciypwKkji6bNU0dUumC0vn5bef9eTW6lnq1pwKmyGB8o7HbhrpS3YHpr8rmlDl3gbQIAsNVRFqu2/pHFrmXRaUW/Nm1lnCwYBdTtUK5KSVkssrl6RL9hhedm2NZvWFKztJ2GFR4utX4oXRYNo5LG2fNkeXr/0t6KfUoAAGxtlMWqrf+axW5lUalQmSjTZuiQCQAAH/VJREFUvtnp2uzIn2Fz/RHjLmjcNl6/aYuaFxc1Q9vLTEO3opfUhpcdEE29taTFSusEZbHQ2wQAYKujLFZt/XdDS4NzGdmyqK2VTTunIPY2IZtTFvXbhi3ND8cUg9ln37GkIpjovSwa3lrmfNSyyLwzAABdUBYHYL3PWVSvMsy8pKluqdXUYbb0FXvxz3mttNsRc7b1G5bV8OJ26NWE3TDN/PZcFk1vLXM+yTR0obcJAMBWR1kcgA18g4s6ftaOn7OYvt5Ofah1y7EzzSm4d9gwmqjfXGGsp+Ztg5u1pROwLMswtreusqh9ay3HMt7gUuBtAgCw1VEWB2a93w3dki/MkytRaohOfiBOXLaSq/qshq9c8BftVilS6c1Tp2EayzRtq9yYnDdQup5rFru/NSGE3Uw9qafb2wQAYKujLGJrUasnAADogrKIrYSbWgAA6BFlESNO+X4amiIAAD2iLAIAAMCIsggAAAAjyiIAAACMKIsAAAAwoiwCAADAiLIIAAAAI8pi1VxCCCGEkLLTv+pCWaya67odQgghhJDyQlkcKZRFQgghhJQbyuJIoSwSQgghpNxQFkcKZZEQQggh5YayOFIoi4QQQggpN5TFkUJZJIQQQki5oSyOFMoiIYQQQspN17K4tPTEFd1i2nYzlUXfsUQQq+EP8Zn4jiXspmlbU1lcnhwT47OphbPjYmxyOf+/j9lxoSTayey40O4wWLg8OSZv1PUohBBCCBna5JfFIk1xRMpi2MMG3RS7nskAymKy4ex43Pw0bTFZpBxueXJMUywJIYQQsjmSUxZTTbHdXjH9atoDZXHEymJneXLM2BalBanDaYchCSGEELIpYiqL2aYYyP81ZROXRc8WltOMJ4Rtb6XtN6Lfal68VbIwPWvs2clyT214yUSzsBy/e1nU78pvWJnN118W5ZljqUJqyqJhJlr+NVsWmYkmhBBCNmm0ZVE7+5xdbZRHFj07aXJhtwtfajmWiJuf79SiuqYs92ypO3o1IeRNLPUlqXqazkS7q1LL4vLkePLjmOHaRGlcUfua/hfGFQkhhJDNHG1ZLHJt4ohfs+jZQhoLbNrB4GLc2LRzxMnypq3WOGlvqZdajiXtWXMmObvSyCuLuugH/KQWqdzgkl5bWk9to6nDURUJIYSQzRvKorEsWk5LKotSY1PLojSnHA0B+g1LHS9MLjQMhgbV5JXFnF31Wha7XbOoNDy5LIYb6m9pCVZM7V9THSmMhBBCyOYM09AbKYtNW6hzxMHPmclltSxq5p2NZ5KzqzLL4uy4PHCYeiFnQjlcM7173e9ctUgIIYRsynCDywbKojrspw4HGuaOdRca5p1Jzq5KLIvZkUBtWdSUvtlxMTY5m957t/JICCGEkE0THp2zwbIYLQ9ui5ZvgjHc4OLZ8v3ULcc2PHm7yhtc5BIYzEbry6LypMVkQeayRKahCSGEkJEJD+WW+pl03WHBaxaVrZRrGVvJpYx2MzV3nDwKR8hH0Z+JcVcl3w0dXbA4NrlsmoaOV5Tr4ux49haW9P00NEVCCCFk04av+6uCWj37iO+GJoQQQki56VoWN4KyuNJuF72ppRSURUIIIYSUG8piX8gTzZU1xTZlkRBCCCFlh7I4UiiLhBBCCCk3lMWRQlkkhBBCSLmhLI4UyiIhhBBCyg1lcaS4hBBCCCFlp3/VhbIIAAAAI8oiAAAAjCiLAAAAMKIsAgAAwIiyWLVBX/867Bn4HwgAAMgoi1Vzt96jc06dOvW/i4WyCADAsKEsVm1rlsXzxUJZBABg2FAWq7Y1y+ILxUJZBABg2FAWq7Y1y+LzxUJZBABg2FAWq7Y1y+I3M7n44ouzCymLAAAMG8pi1bZmWXxGzcVRUsspiwAADBvKYtW2ZllsS7lYjfwSZREAgGFTRVn0HUsEsRr+oN9wV54thKh5pjdiN3VbtRxL2F6h/Rcri8uTY0KMTS73vchVkVOnTi1HuViX+FXKIgAAw6aikUXfsTZFU+z+LuKy6NWkd1R2WVyeHBsbG+u1Lc6OV9QvezzQqVOnniwWyiIAAMOGstjju6ikLAZlbHZciPHZ/nW49af3svi3xUJZBABg2AygLHq2sJxmPDVteyttvxH9Js3/JgvT89eenSz31KnhZMpbWI6fPg3PFtLKTVskDS8ugp4tLKeVf6xkoQh2GJTFVnTw6NB+w8qcRrYszo6nOmHUxZYnx7KvhEmXteQVEVXM5ckxkV092Gmwerhzea+z6jGlfYS70ByoMzue2x1PnTr1WLFQFgEAGDaDKYtxnQq7XfhSy7GSMuc7tahmKcs9W+qOXi2qa+mjtL2a5tJDeThQ3dazw+Iol8WcY2VHFuM3lWzVrSwGVUzT+8JFaltcnkwmpmcnNe1MHfBbnhyPflmeHIuL3fLkmBgbi1/qzI5LZ6B0SOWAHXmcMzuyGLwTwzjoqVOnvlQslEUAAIbNgMqiYXhPbWCSZHnTVuuXtLfUS7qpYam9ebawnYaV2a1UFnOOpSmLyXik37AMt8i01bKobVdyE1PaYnagMbcsGl5SGmB2G6kTpl6SDm860Oy4pvvy3dAAAGxmg5qGbkUvqYVMLYvSnLI8Vqf0sOQ6wmDkT03mOsK4QTZtUfPio0vHTU4v51jpU1WLqW5AMdblmsVUl1N+DeeEjYUx0+HkiWjzaKWyv2SBMt+cnnbOuWYxO8jId0MDALB5DW1ZbNpCnQIOfs5MLqtl0Tiel17fD8cUg9ln37Hkqefw55xjtcspi9nap9Q77fWJOY1R6XDqKJ9pajtzE41aFg291FgW1WlsqSzy3dAAAGxSw1oW1VG9ZPNMD0umhnMrWsJvWFbDi9uhVxN2Q6566shiD9PQ6yiLQbcyDSQaF2mXdXImsOUNzNdBxqeU3B9jGD80XbNoKJd8NzQAAJvXMJdF+Z5i9SYY800q0lhgy7G1D+tp2kIoNyxbllxMe7jBRT6Wtiz2eje0tp/Fd6fMjst3nmh6mTwWKO9KuY8mta369G91ZHB2XO5/0g0z6UHHzP3cmbIofyV0Nnw3NAAAQ2sA3+BS8JpFZSvNrcfBNYnN1LeqyA+1kZ+AI1MqoHqrdTt9ejnHil5KHp0THaLHsqiUPc1YXtQWs4+x0a4ZFbxk9eCZjfqyqKwoxmdTL8sXLqZGQHMvn0yXRe0XQ2e/HpqyCADAsNn03w2deizi8Bvy74bux4O9U98NLX89dGo5ZREAgGGzyctisZtahspQl8WevzSmUOTvhpa/JDq7kLIIAMCw2XxlUfn2lM3WFNvDVxbVJ+SU3xQ7fDc0AACb2eYri5vdsJXFCsJ3QwMAsHlRFqu2Ncsi3w0NAMAmRVms2tYsi3w3NAAAmxRlsWouyc3A/0AAAEBGWQQAAIARZREAAABGlEUAAAAYURYBAABgRFms2qBvIBn2DPwPBAAAZJTFqrlb8tE5/7tYKIsAAAwbymLVtmZZPF8slEUAAIYNZbFqW7MsvlAslEUAAIYNZbFqW7MsPl8slEUAAIYNZbFqW7MsfjOTiy++OLuQsggAwLChLFZta5bFZ9RcHCW1nLIIAMCwoSxWbWuWxbaUi9XIL1EWAQAYNn0pi75jiSBWwx/wO2w5lrC9HjbxbCFEzSt7t7FiZXF5ckyIscnlvhe5KnLq1KnlKBfrEr9KWQQAYNj0a2TRd6whaIorG2l169ytV8t/44XK4vLk2NjYWK9tcXa8on7Z44FOnTr1ZLFQFgEAGDaUxbJ3W0ZZDMrY7LgQ47P963DrT+9l8W+LhbIIAMCwqaIserawnGY8NW17K22/Ef0mTfgmC9Pz156dLPccS9hN5UDRa46fPZOg1bWitZR1tNt6trCcVrdD63ebrCyEsJttv2FlzipbFmfHU50w6mLLk2PZV8Kky1ryiogq5vLkmMiuHuw0WD3cubzXWfWY0j7CXWgO1Jkdz+2Op06deqxYKIsAAAybispiXKfCfha+1HIsETc/36lFvUpZ7tlSd/RqYQ/LHKXt1XTXGgZ9Tipz0fqmbeWyaD60cbfKyGK3shhUMU3vCxepbXF5MpmYnp3UtDN1wG95cjz6ZXlyLC52y5NjYmwsfqkzOy6dgdIhlQN25HHO7Mhi8E4M46CnTp36UrFQFgEAGDZVlcVkLLBpC2kC1zRpmyxv2mrfkvaWekk7NdxyLGmk0G9YYSk0biuVxZxDm3bb2zS0tl3JTUxpi9mBxtyyaHhJaYDZbaROmHpJOrzpQLPjmu7Ld0MDALCZVTYN3YpeUhuYWq2keeForE7uYfE6QWMLhvrUaMtisjAe6jNvm5xtzqFNu828o6wu1yymupzyazgnbCyMmQ4nT0SbRyuV/SULlPnm9LRzzjWL2UFGvhsaAIDNa3jKYtMWusnczOSyWhZ7fMaNUhb12yZnm3PokspitvYp9S57wWF+Y1Q6nDrKZ5raztxEo5ZFQy81lkV1Glsqi3w3NAAAm9TQlEV1GC/ZPHPZXzIXrLsiMMPQ6szbqiOLedPQGy+LQbcyDSQaF2mXdXImsOUNzNdBxqeU3B9jGD80XbNoKJd8NzQAAJvXUJXFaHlwW7R8E4zhBhf1Adotx862NGOrM20rnW3OoXPLYrzbXu+G1vaz+O6U2XH5zhNNL5PHAuVdKffRpLZVn/6tjgzOjsv9T7phJj3omLmfO1MW5a+EzobvhgYAYGhV8Q0uBa9ZVLZSxudayaWMdlOaC15pq0+rkR9505a21bc6w7bq2ZoOnbPbaJPCj85Ryp5mLC9qi9nH2GjXjApesnrwzEZ9WVRWFOOzqZflCxdTI6DmkURNWdR+MXT266EpiwAADJvN993QqecgbrpDD/l3Q/fjwd6p74aWvx46tZyyCADAsNlsZbHQTS1DfeihLos9f2lMocjfDS1/SXR2IWURAIBhswnKovK1KNU2xX4cetjKovqEnPKbYofvhgYAYDPbBGVxxAxbWawgfDc0AACbF2WxaluzLPLd0AAAbFKUxaptzbLId0MDALBJURar5pLcDPwPBAAAZJRFAAAAGFEWAQAAYERZBAAAgBFlEQAAAEaUxaoN+gYSQkhpGfj/ngBABSiLVXNd938RQjZ/KIsAtgjKYtVc171ACBmyzMzM9LoJZRHAFkFZrJrrui8SQoYsMzMzvW5CWQSwRVAWq+a67j8SQoYsMzMzvW5CWQSwRVAWq+a67jlCyJBlZmam100oiwC2CMpi1VzX/RYhZMgyMzPT6yaURQBbBGWxaq7r/n+EkCHLzMxMr5tQFgFsEYMsi75jiSQ1r9v6ni0spzXwj2yDXNc923s++3vSR/V7n13HHgghOZmZmel1E8oigC1iUGWx5VhqQfRqttdlq5Epi3/fW77w0UuFePv++Pf9H/3oF3rcRdXZ/3ZxaSUnWdmByKhnZmam100oiwC2iMGURd+xhNXwe9xqZMricz3lkY9cKi79yCO9bTTg/NXbKjrlyg5ERj0zMzO9bkJZBLBFDKQsNm0hcsYRlelpuxkvT5VFw2otxxK217S1U9teLdrAcvxkn8mu5ArrN5IDqNVWPnT8RqSF4c7bfsOKf464rrtaIPt2ip374h/jn9U87F4an8el7sPStpe6++LXdu6T11T2JO1A2r7AIVb37TRtmLwSH02/k4fdS8XOfcHq4VnJe93nXiqfbPZUNQda3bfT8D4Iyc/MzEz888XmyJtQFgFsEYMoi14t5wpF37GkV1uOlRRBuSyaV2s5lrCsWqqitdtqdfMacVkUIt6tcjjfiXeSWi4d2m84XrQwLpTxGzSUxdO5OeFaQgjLPZGzJFq+M1p2wrWE2HlX8MtdO5PVg22V36LVTrhWstO7diYvdD2EvOVdbua8Tp++a6d8uobzPOFawrLil07ftVOo51PgVNUDJW9Y81YIycvMzIz8q7YppjahLALYIoatLDbtVLvyG1a0slQWc1ZrOZZhtlraVcyzlcFLbb0Lzznsgtph0dT5BKOb+rfvuu43TDkeXslpfEkIyzmu39arxa8FH7D0gvpbuJq0QXQA7ZE1h+i6bmrf+peOO5a8Vnob6bzNp2o6kFfL+6wIyWRmZia1JNUUs5tQFgFsEUNWFjUvJT0sKYt5q+UUteCuGuXVzHWQSu1TZrqDsqg9+WR2O0lOWfw7Te60hRDbbvR1r6XWEvad8QL/xm3JIaPN77TlPRl+C3aVOudkx0l0hwiXadfPHjNnJ8ke1N+UBTmnmjlQ9syNJ0mIlJmZmezCuClqN6EsAtgiBlEWTaN37X6XxfjoQmhGK1P7adpCKDPLXcpi90f/BFzXfVqfh/50mxBCvHXa8Hqy1rY/fejpp5+efqsQ4Y/Bb9HP0o/m36bf2vVQOYfocr7KmqadPPSn26SNM+eTvJxzqqlTSp159zdISJiZmRnt8osvvti0CWURwBYxkBtclEsAFdkeqZ2GzlutW1mM1gl2lZ6GVi43TPpfckmitunm1N8M13W/lpupHUIIse2GB3Ne3zH1ta89eMM2sWMqWvrgDdvibaZ2yJsbfpM3MMV8iLxlqWMad9Jl79H7zD1V9c1FKwsh7ZiQIpmZmel1E8oigC1iUM9ZVMft2slzFj1bpO5ciUf+5FFA82rmspg8yjFZJ7jBJVretIWujwaDkdHZKoeObnBJn49tbJau6z5VILfvEDtuf+qpp556au76S8Kfwt9E8Mrc9ZeIS66fS5bGv92+I/4x77fbdwgh4l3PXb8jWUs6mO4Qt++INpu7/hIhnZ10FCGvoj3P1LZz11+SvIXg5OKXjacqH0j92AjpKTMzM71uQlkEsEUM8BtcWsoXuEjFUb5SUJ4jznl0jrTcXBalR+GoBbSZ7EkaZVSep5Pc4BJulezJzy40j4C2V1zXfbK33L5duVpv++3RC3PXXRIuu+S6udu3i0uum4s2iH7s/lscaXES/SGSpfqtkjW2324+z7nrLpHejHo0sf321MuGU1UORMi6MzMz0+smlEUAW8RW/27o6h/07bruE6RA/vJKcUl9dtBnQbZKZmZmet2Esghgi6AsDqAsniJd86krhbjyU4M+C7J1MjMz0+smlEUAWwRlcQBl8StEl09eIc+2X/HJQZ8P2VKZmZnpdRPKIoAtYquXxeq5rrtICBmyzMzM9LoJZRHAFkFZrJrrul8mhAxZZmZmet2Esghgi6AsVs113b8hhGz+UBYBbBGUxaq5hJBRycD/9wQAKkBZBAAAgBFlEQAAAEaURQAAABhRFgEAAGBEWQQAAIARZREAAABGlEUAAAAYURarxvPbAADAJkJZrJrrup1uoSwCAIAhQVmsWhllseVYQlgNX13u2cJyWt1OoOVYwvZKfEel77CoYu8XAABsCGWxaiWURb9hWf9/e3fXGjd2x3H8sAT2LeQ+r8CW30buNnY00+xVyWtonKx81vsadilDYo1xk+VclUDc1IEwmSQLC0spy1JqQqjbEEoppaWU0tteaKRzjnSORkczoxlb3z8fltHDedCwhB9H0jiKRJRMrP2ERQAAsHSExa4tHhazkJTGQsTj6v55EyAsAgCAAITFri0cFsdxtqY4OYjEIDUOmeEp+zxJIpGVvmedZbtpfsRYnpwc5Gc77nEXdJ8iy4ihHfrO9/djDar3ExYBAOgAYbFri4bFdJAHr/KSXiksClFsTpOoWIbM0tgscqWxjnGTZGDmttKy5cXsnEgUCXVykKQtOvSdX9NPpLNmOigmQFgEAKADhMWuLRgWzYQ0SSIz0pXDopn2Jgf5M47TJDIyVmV5ckZHUtM4FtU7zqEd+s737c9XUo3hsjkQFgEA6ABhsWsLhUWd+Ryb1dvQRtsictnrkXYP5i1mR1g0VvUMoR36zvfsTweiUoRFAAA6Q1js2iJh0cpe+iG+aXZ0sbA4js2A6FxZDAuLvg5bhEXXUiVhEQCAThAWu7ZAWLRv1F785cJ+nq/uNrSOXJ5MZt8+th4TvKicXJlVSIeBYdE9aPl6AQDAihAWu9Y+LDpj0+Qgct2WzV5wybPXOLZfdpmTybK3mF0vRKexcL7gEtJhYFgsDXoxTWJHOAYAACtCWOxa678Nbb4gbNAvGlduQ4/1XWu9yujNZNbv7LhfcMmnYf2KTWiHwWHRHrTmVjsAAFg+wuLVRJACAABLQVi8mgiLAABgKQiLVxNhEQAALAVhEQAAAF6ERQAAAHgRFgEAAOBFWAQAAIAXYREAAABehEUAAAB4ERa71vAvuFAUtfm19n9PAKADhMWuSSn/R1HU5S/CIoCeICx2TUr5X4qiNqyUUqFNCIsAeoKw2DUp5X8oitqwUkqFNiEsAugJwmLXpJT/pihqw0opFdqEsAigJwiLXZNS/ouiqA0rpVRoE8IigJ4gLHZNSvlPiqI2rJRSoU0IiwB6grDYNSnlPyiK2rBSSoU2ISwC6In2YTGNha54vOqJprGIkmlQk0kSCSGEGKTr/pZNUsq/h9e3nxvf9ufftuiBoqiaUkqFNiEsAuiJdmFxmkRWQEyTg8mKJxocFicHkYiSSeffaToQUd23IaX8W1h9f7gjxJ0nxfaTw8PvA7voup7cETudTLKzgairXkqp0CaERQA90SosriOHtQqL61hTbBAW/xpU3325I3a+/C6s0ZrrVz/raMqdDURd9VJKhTYhLALoiXYri+NYiDh1HZocRMXdUiMzpbGIknGSH4tT80wd6bJEOCnOK/cwLQbS57hiq3FUr4A6d2arpHE6jvOZNJyq80rLt+ZdqVpK+bFBnQzF8KT4WHy2643cKYbbkW+MtjvypDg2PDHPtHoyOjDaNxji48nQ11AfKUZzd/JG7ojhSXb6bFZmrydyx5xsdaqOgT6eDD3XQVH1pZQqPt/wl9mEsAigJ1o+s5gFr+pS3yQZ5NnIulWdxjrVzULbLGBVTyu6LR8qhpskkc6R6cD9VKK9sjhJIuM0s+dpEokoKqbddKq+K7VWFj1h8UNtvZaRECKSr2v25PuH+b7XMhJieJxtHA/16Vlbays/7bWMdKfHQ31g7hBmy2NZmdeHD8dDc7qeeb6WkYii4tCH46Gw59NgqvZA+oIdl0JRdaWUMjedSbHUhLAIoCcWeBs6X1rz3h02YlMam4t59sKk9zQrbBlhcRxbCSxbGnROr0iHpSbm0WkSWZfQcKq+K21yG/rPvnqVREKIQeo9JESUvHK3TQfFsSw/GwfsrdlpRoN8AOfIjiHmnlvq233oVRKZZ5XbGPP2T9U3UDqo+64oqlJKqdKeUlKsNiEsAuiJhX86Jx3Mbrnme6y7vUYKNAKZHd3ssGhHT32mPpSNaNecsOhYfSx6LmfNhlP1XWmTsPgnRx3FQojtLyauY6WzRHxU7Jh8sa3nkDc/is2ePFtHsaiU0bEu1xCzfc7zq2PWdKJ7sLesHTVTrQxUnbl3khRllFKqurNIis4mhEUAPbGM31nU63/j2BOblh0WG7y5stqw6L3SJmHxvbtePtgWQojbjzzH9VnbD16+f//+0W0hZh+zrfyz8dG/9ej23KFqhpgzX+tMXycvH2wbjSvz0YdrplqaUmnm8y+QomallHLuv3Hjhq8JYRFATyzlR7nze7XVxwTDw6J1G9oIebqHhu9im5OpNrFvQweHRf+VNgmL72rr4Z4QQmzff1FzfO/hu3cv7m+LvYf53hf3t4s2D/fM5p4ts4Gv/EPU7SuN6e1kTu/5ddZO1b64/GQhjI4pqkkppUKbEBYB9ETbn84p33cepBd2JsueaGwRFvU95XFsPBBp9pDGwnpbJXaFMzvPlZvo5xRbh0X3lVqrmJ4XXM4b1GhP7I3Oz8/Pz8/2t2afZlsiO3K2vyW29s/03mJrtFd8rNsa7Qkhiq7P9vf0WcZgriFGe3mzs/0tYczOGEWYpzjnWWp7tr+lLyGbXHHYO1VzIPtro6igUkqFNiEsAuiJ9j+dY1TppeP8Gb6Wt6H1z9ZYv/tt36E2f6TG/YZN5XcWzUcMjSYtn1n0Xens5WhR99M5fwyr0a71be+O8gNn97Zm+7bunY12xda9s7xB/nH+VlHGbl3uIfRedyt9xu7IP8+ze1vGxdijid1R6bBnqtZAFNW6lFKhTQiLAHpis/42dIu/6XfpSCn/QDWoX94SW7/4zbpnQfWllFKhTQiLAHqCsNg1KeVP1Nz65pYQt75Z9yyo/pRSKrQJYRFATxAWuyal/JFy1defmXfbP/t63fOhelVKqdAmhEUAPbFZYbEPpJS/pyhqw0opFdqEsAigJwiLXZNS/o6iqA0rpVRoE8IigJ4gLHZNSvkDRVGXvwiLAHqCsNg1SVHUVam1/3sCAB0gLAIAAMCLsAgAAAAvwiIAAAC8CIsAAADwIiwCAADAi7AIAAAAL8IiAAAAvAiLAAAA8BK7P//12+lvAQAAgCrCIgAAALwIiwAAAPAiLAIAAMCLsAgAAAAvwiIAAEAvfDJ49sngWegJdWGxvrugadVo3tXjp89vHp5ev3t6bfjs+t3Tm4enj58+X/v3DgAAUG8pcWiREFXqIejonLC4eF5cVlj86uj5p3fKbT+98+yrI/IiAADYaCsKiwvmxdJ/fR3OD4sL5sWlfDuPnzqSYpEXWV8EAACbbO56XotOFs+LpZjo66pRWFwkLy4lLN48PK3p4ebh6dr/JwAAAPBZaVhcfH2xvpOmYbF1XlxKWLx+ty4sXr9LWAQAAJtr1WExKKetamWxdV5cSli8Nqzr4dpwCS/iAAAArEgHYXGR+8D1wsLiIuucrCwCAIB+WkVYbNfJ21WvLLbIi0sJizyzCAAALq+NCotvV/fMYru8uJSwyNvQAADg8tqosNjFymLrCS3SD7+zCAAALqmlxKHldvJ2ub+zuPYLy/AXXAAAwGW0IZmq/kzfUf42NAAAQC/MzZSERQAAAIQhLAIAAMCLsAgAAAAvwiIAAAC8/g+QjiYhd9BaVAAAAABJRU5ErkJggg==[/IMG]

On va appliquer le process sur l'image couleur. Le process va ainsi créer un nouvelle image nommée HaRVB composée de la façon suivante :
- la couche Rouge de l'image finale vaut max(rvb_centree, ha_centree) : dans le rouge on garde les valeurs maximales entre les valeurs de l'image couleur et les valeurs de l'image H-Alpha
- les couches Vert et Bleu valent "$T" : ce symbole indique au process qu'on conserve les valeurs de l'image cible, sur laquelle on applique le process. Cela signifie qu'on conserve les composantes V et B de l'image couleur tels quels.

On applique le process sur l'image RVB et voilà le résultat :

[IMG]http://www.arnaudom.fr/Img/ngc6960006wa.jpg[/IMG]

En deux-trois essais (qui consistent à modifier la courbe appliquée sur l'image Ha), j'ai obtenu un résultat qui me semble satisfaisant. La nébuleuse a gagné beaucoup de rouge. Après ce n'est sans doute pas la meilleure méthode ni la méthode des puristes, mais le rapport effort fourni/résultat est intéressant !

[benjamindenantes]

Super, par contre il doit y avoir moyen de faire un mixage plus efficace car tu perds énormément du signal présent dans la couche Ha. Il doit y a voir moyen de mieux équilibrer tout ça. PAs mal de nuages faibles sur la Ha pas présent dans la finale, dommage.

 

Très beau résultat tout de même avec un 350D.

[Dominik]

Super, par contre il doit y avoir moyen de faire un mixage plus efficace car tu perds énormément du signal présent dans la couche Ha. Il doit y a voir moyen de mieux équilibrer tout ça. PAs mal de nuages faibles sur la Ha pas présent dans la finale, dommage.

 

Très beau résultat tout de même avec un 350D.

 

C'est pour ça que je pense que ça n'est pas la meilleure méthode, mais pour un premier test, ça permet déjà d'obtenir quelque chose.

[iserois]

Très belle Dentelle,trés bons résultats !

[Invité]

Elle est vraiment magnifique

[Dominik]

Très belle Dentelle,trés bons résultats !

 

Merci iserois du ... 34 ?

 

Elle est vraiment magnifique

 

Et merci natif d'Arrakis (revu Dune en DVD il y a 3 jours, de bons souvenirs...).

[VLe]

Je ne suis pas spécialiste, mais c'est une très belle image de ces dentelles !