diff --git a/doc/source/admin/install_devel.rst b/doc/source/admin/install_devel.rst
index 54337f3..0750a23 100644
--- a/doc/source/admin/install_devel.rst
+++ b/doc/source/admin/install_devel.rst
@@ -133,6 +133,6 @@ version as follows:
    $ source activate ctools-devel
    $ conda install --use-local ctools=1.6.0.dev1
 
-Note that you can chose between Python 2.7, 3.5, 3.6 and 3.7 for your
+Note that you can choose between Python 2.7, 3.5, 3.6 and 3.7 for your
 conda environment.
 
diff --git a/doc/source/users/tutorials/hess_dr1/bkgmodel.ipynb b/doc/source/users/tutorials/hess_dr1/bkgmodel.ipynb
index c974ad3..2fd4a01 100644
--- a/doc/source/users/tutorials/hess_dr1/bkgmodel.ipynb
+++ b/doc/source/users/tutorials/hess_dr1/bkgmodel.ipynb
@@ -49,22 +49,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obsfile = \"obs_crab_selected.xml\"\n",
+    "obsfile  = 'obs_crab_selected.xml'\n",
     "outmodel = 'bkgmodel.xml'\n",
-    "\n",
     "bkgmodel = cscripts.csbkgmodel()\n",
-    "bkgmodel['inobs'] = obsfile\n",
-    "bkgmodel['outmodel'] = outmodel\n",
+    "bkgmodel['inobs']      = obsfile\n",
+    "bkgmodel['outmodel']   = outmodel\n",
     "bkgmodel['instrument'] = 'HESS'\n",
-    "bkgmodel['spatial'] = 'GAUSS'\n",
-    "bkgmodel['gradient'] = True\n",
-    "bkgmodel['spectral'] = 'NODES'\n",
-    "bkgmodel['ebinalg'] = 'LOG'\n",
-    "bkgmodel['emin'] = 0.7\n",
-    "bkgmodel['emax'] = 30.\n",
-    "bkgmodel['enumbins'] = 8\n",
-    "bkgmodel['runwise'] = True\n",
-    "\n",
+    "bkgmodel['spatial']    = 'LOOKUP'\n",
+    "bkgmodel['slufile']    = '$CTOOLS/share/models/hess_bkg_lookup.fits'\n",
+    "bkgmodel['gradient']   = True\n",
+    "bkgmodel['spectral']   = 'NODES'\n",
+    "bkgmodel['ebinalg']    = 'LOG'\n",
+    "bkgmodel['emin']       = 0.7\n",
+    "bkgmodel['emax']       = 30.0\n",
+    "bkgmodel['enumbins']   = 8\n",
+    "bkgmodel['runwise']    = True\n",
     "bkgmodel.execute()"
    ]
   },
@@ -96,26 +95,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.19679080604377 +/- 0.148784168613255 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0882131417987659 +/- 0.0223563994341903 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.000692474001567828 +/- 0.0227254580377688 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0881748792818854 +/- 0.0222611841905954 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.000701218995455618 +/- 0.0227202811076142 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.00140372802004064 +/- 0.000174205101284546 [8.87696769787525e-14,infty[ ph/cm2/s/MeV (free,scale=0.000887696769787525,gradient)\n",
+      "  Intensity0 ...............: 0.00167503068442416 +/- 0.000204995398027476 [1.00182541969977e-13,infty[ ph/cm2/s/MeV (free,scale=0.00100182541969977,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.000257864797984792 +/- 1.41022572545599e-05 [2.91749105398033e-14,infty[ ph/cm2/s/MeV (free,scale=0.000291749105398033,gradient)\n",
+      "  Intensity1 ...............: 0.000275120588592054 +/- 1.41610992132859e-05 [3.21240099713853e-14,infty[ ph/cm2/s/MeV (free,scale=0.000321240099713853,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 9.08473780602549e-05 +/- 5.49730339459174e-06 [9.58858288072014e-15,infty[ ph/cm2/s/MeV (free,scale=9.58858288072014e-05,gradient)\n",
+      "  Intensity2 ...............: 0.000102369982797547 +/- 5.87189405970882e-06 [1.03007170346199e-14,infty[ ph/cm2/s/MeV (free,scale=0.000103007170346199,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 3.21049333853292e-05 +/- 2.44980628805133e-06 [3.15136944584645e-15,infty[ ph/cm2/s/MeV (free,scale=3.15136944584645e-05,gradient)\n",
+      "  Intensity3 ...............: 3.43310169853447e-05 +/- 2.52595458342912e-06 [3.30297405342054e-15,infty[ ph/cm2/s/MeV (free,scale=3.30297405342054e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 1.13774407454204e-05 +/- 1.12484264580111e-06 [1.03572441389469e-15,infty[ ph/cm2/s/MeV (free,scale=1.03572441389469e-05,gradient)\n",
+      "  Intensity4 ...............: 1.04184963555313e-05 +/- 1.01064863096553e-06 [1.05911438600856e-15,infty[ ph/cm2/s/MeV (free,scale=1.05911438600856e-05,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 4.50082783670747e-06 +/- 5.75883736185608e-07 [3.40399651634425e-16,infty[ ph/cm2/s/MeV (free,scale=3.40399651634425e-06,gradient)\n",
+      "  Intensity5 ...............: 4.1837003769505e-06 +/- 5.25942224806527e-07 [3.39610080039421e-16,infty[ ph/cm2/s/MeV (free,scale=3.39610080039421e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 8.01232298563249e-07 +/- 1.64363032163644e-07 [1.11875245266372e-16,infty[ ph/cm2/s/MeV (free,scale=1.11875245266372e-06,gradient)\n",
+      "  Intensity6 ...............: 8.6658971981008e-07 +/- 1.76584928413557e-07 [1.08897592165697e-16,infty[ ph/cm2/s/MeV (free,scale=1.08897592165697e-06,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.63561241594543e-07 +/- 9.61055896032478e-08 [3.67687523865408e-17,infty[ ph/cm2/s/MeV (free,scale=3.67687523865408e-07,gradient)\n",
+      "  Intensity7 ...............: 3.41087754372141e-07 +/- 1.23894301613722e-07 [3.49185323889972e-17,infty[ ph/cm2/s/MeV (free,scale=3.49185323889972e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -127,26 +126,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.30082209234815 +/- 0.156912961568416 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0153115021206004 +/- 0.0215680817043072 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.00906905504246978 +/- 0.0213184758960865 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0153166143265463 +/- 0.0215144304488992 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.00908019181469828 +/- 0.0213167666197536 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.00107656542145505 +/- 5.71767004891641e-05 [8.36211165467046e-14,infty[ ph/cm2/s/MeV (free,scale=0.000836211165467046,gradient)\n",
+      "  Intensity0 ...............: 0.00126074399446221 +/- 6.18948752992e-05 [9.62519069451052e-14,infty[ ph/cm2/s/MeV (free,scale=0.000962519069451052,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.00020191222774409 +/- 1.06852037575173e-05 [2.63699934530969e-14,infty[ ph/cm2/s/MeV (free,scale=0.000263699934530969,gradient)\n",
+      "  Intensity1 ...............: 0.000220109488746193 +/- 1.10099106908638e-05 [2.95441986174209e-14,infty[ ph/cm2/s/MeV (free,scale=0.000295441986174209,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 9.16681754173992e-05 +/- 5.73982387509952e-06 [8.31580088180217e-15,infty[ ph/cm2/s/MeV (free,scale=8.31580088180217e-05,gradient)\n",
+      "  Intensity2 ...............: 0.000104377151461511 +/- 6.26567390995899e-06 [9.068492247571e-15,infty[ ph/cm2/s/MeV (free,scale=9.068492247571e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 2.4697825754506e-05 +/- 2.13035134581624e-06 [2.62239520190935e-15,infty[ ph/cm2/s/MeV (free,scale=2.62239520190935e-05,gradient)\n",
+      "  Intensity3 ...............: 2.68164289254463e-05 +/- 2.2601490434718e-06 [2.78354314866283e-15,infty[ ph/cm2/s/MeV (free,scale=2.78354314866283e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 9.65793108295259e-06 +/- 1.0580848901101e-06 [8.26974658573933e-16,infty[ ph/cm2/s/MeV (free,scale=8.26974658573933e-06,gradient)\n",
+      "  Intensity4 ...............: 8.96128027251196e-06 +/- 9.61601396204211e-07 [8.54399193266454e-16,infty[ ph/cm2/s/MeV (free,scale=8.54399193266454e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 3.34440698323729e-06 +/- 4.70710485211692e-07 [2.60787193869763e-16,infty[ ph/cm2/s/MeV (free,scale=2.60787193869763e-06,gradient)\n",
+      "  Intensity5 ...............: 3.14829249162658e-06 +/- 4.37081753413707e-07 [2.62254954375342e-16,infty[ ph/cm2/s/MeV (free,scale=2.62254954375342e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 5.56094646218329e-07 +/- 1.31992669655457e-07 [8.22394734607041e-17,infty[ ph/cm2/s/MeV (free,scale=8.22394734607041e-07,gradient)\n",
+      "  Intensity6 ...............: 6.09624701513041e-07 +/- 1.43872314987655e-07 [8.04982748537824e-17,infty[ ph/cm2/s/MeV (free,scale=8.04982748537824e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.63249520342286e-07 +/- 9.70349092695409e-08 [2.59342910774655e-17,infty[ ph/cm2/s/MeV (free,scale=2.59342910774655e-07,gradient)\n",
+      "  Intensity7 ...............: 3.44982419809933e-07 +/- 1.26076763285202e-07 [2.4708674312253e-17,infty[ ph/cm2/s/MeV (free,scale=2.4708674312253e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -158,26 +157,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 4.32704664084324 +/- 0.368684845846136 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0187722831939023 +/- 0.0217106880517745 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.126627824474372 +/- 0.0204738099128812 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.018749400342 +/- 0.0217067733288596 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.126640342510319 +/- 0.020455126417247 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.000857178811233776 +/- 4.58638205127566e-05 [6.96233895968756e-14,infty[ ph/cm2/s/MeV (free,scale=0.000696233895968756,gradient)\n",
+      "  Intensity0 ...............: 0.00109294743570479 +/- 5.37668986080239e-05 [8.7418891142236e-14,infty[ ph/cm2/s/MeV (free,scale=0.00087418891142236,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.000184768403022439 +/- 1.008650496985e-05 [2.20204639078551e-14,infty[ ph/cm2/s/MeV (free,scale=0.000220204639078551,gradient)\n",
+      "  Intensity1 ...............: 0.000220103906688583 +/- 1.10283166290448e-05 [2.69181956755491e-14,infty[ ph/cm2/s/MeV (free,scale=0.000269181956755491,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 6.49318372405173e-05 +/- 4.46952677363829e-06 [6.96462544447719e-15,infty[ ph/cm2/s/MeV (free,scale=6.96462544447719e-05,gradient)\n",
+      "  Intensity2 ...............: 8.06623852687504e-05 +/- 5.23872750218462e-06 [8.28870337932099e-15,infty[ ph/cm2/s/MeV (free,scale=8.28870337932099e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 2.41177375305046e-05 +/- 2.06051735828825e-06 [2.20276955948036e-15,infty[ ph/cm2/s/MeV (free,scale=2.20276955948036e-05,gradient)\n",
+      "  Intensity3 ...............: 2.85428972580395e-05 +/- 2.37266748207121e-06 [2.55227373106486e-15,infty[ ph/cm2/s/MeV (free,scale=2.55227373106486e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 6.388150904145e-06 +/- 7.36345188624795e-07 [6.96691268016576e-16,infty[ ph/cm2/s/MeV (free,scale=6.96691268016576e-06,gradient)\n",
+      "  Intensity4 ...............: 6.46456658892385e-06 +/- 7.36558401693409e-07 [7.85901111449516e-16,infty[ ph/cm2/s/MeV (free,scale=7.85901111449516e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 3.11624385438516e-06 +/- 4.38829996211514e-07 [2.20349296566929e-16,infty[ ph/cm2/s/MeV (free,scale=2.20349296566929e-06,gradient)\n",
+      "  Intensity5 ...............: 3.20506991372933e-06 +/- 4.41036712274814e-07 [2.4199620497598e-16,infty[ ph/cm2/s/MeV (free,scale=2.4199620497598e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 7.66998023710854e-07 +/- 1.70693323247103e-07 [6.9692006669998e-17,infty[ ph/cm2/s/MeV (free,scale=6.9692006669998e-07,gradient)\n",
+      "  Intensity6 ...............: 9.17351299053318e-07 +/- 2.02856047440946e-07 [7.45159440158635e-17,infty[ ph/cm2/s/MeV (free,scale=7.45159440158635e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 1.35218346687107e-07 +/- 6.64294080047906e-08 [2.20421660943029e-17,infty[ ph/cm2/s/MeV (free,scale=2.20421660943029e-07,gradient)\n",
+      "  Intensity7 ...............: 1.93348643577948e-07 +/- 9.48901838749967e-08 [2.29450949990163e-17,infty[ ph/cm2/s/MeV (free,scale=2.29450949990163e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -189,26 +188,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.64795431824073 +/- 0.238749562945919 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0290086296998991 +/- 0.0231654281500121 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: 0.165462915333049 +/- 0.021293268413236 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0290076925442388 +/- 0.0231627042594752 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: 0.165459453396994 +/- 0.0212165299830029 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.00138280405680456 +/- 0.000179785336701836 [8.127607193775e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008127607193775,gradient)\n",
+      "  Intensity0 ...............: 0.00173543806725313 +/- 0.000221842434989178 [9.64800822522881e-14,infty[ ph/cm2/s/MeV (free,scale=0.000964800822522881,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.000220636852715846 +/- 1.29050674731524e-05 [2.64069813465664e-14,infty[ ph/cm2/s/MeV (free,scale=0.000264069813465664,gradient)\n",
+      "  Intensity1 ...............: 0.000247432426542881 +/- 1.35075275068334e-05 [3.05783271724063e-14,infty[ ph/cm2/s/MeV (free,scale=0.000305783271724063,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 8.63433571877371e-05 +/- 5.40186394640027e-06 [8.57975351431837e-15,infty[ ph/cm2/s/MeV (free,scale=8.57975351431837e-05,gradient)\n",
+      "  Intensity2 ...............: 0.000102349095137416 +/- 6.00669028431716e-06 [9.6914727976462e-15,infty[ ph/cm2/s/MeV (free,scale=9.6914727976462e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 3.02018682595367e-05 +/- 2.37672480959384e-06 [2.78760261918503e-15,infty[ ph/cm2/s/MeV (free,scale=2.78760261918503e-05,gradient)\n",
+      "  Intensity3 ...............: 3.39523135381865e-05 +/- 2.54888013407308e-06 [3.07160834724385e-15,infty[ ph/cm2/s/MeV (free,scale=3.07160834724385e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 9.44460701196062e-06 +/- 9.82873290475423e-07 [9.05705315370543e-16,infty[ ph/cm2/s/MeV (free,scale=9.05705315370543e-06,gradient)\n",
+      "  Intensity4 ...............: 9.09839000917866e-06 +/- 9.25509371718351e-07 [9.735133179293e-16,infty[ ph/cm2/s/MeV (free,scale=9.735133179293e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 3.08894388117525e-06 +/- 4.2969727730712e-07 [2.94267953633316e-16,infty[ ph/cm2/s/MeV (free,scale=2.94267953633316e-06,gradient)\n",
+      "  Intensity5 ...............: 3.01613230297962e-06 +/- 4.11382625961797e-07 [3.08544603688196e-16,infty[ ph/cm2/s/MeV (free,scale=3.08544603688196e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 8.17521009765721e-07 +/- 1.65391550069682e-07 [9.5609054143745e-17,infty[ ph/cm2/s/MeV (free,scale=9.5609054143745e-07,gradient)\n",
+      "  Intensity6 ...............: 9.30049222211062e-07 +/- 1.86843120695054e-07 [9.77899025229569e-17,infty[ ph/cm2/s/MeV (free,scale=9.77899025229569e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.92681614932954e-07 +/- 1.0444872894399e-07 [3.10638352610158e-17,infty[ ph/cm2/s/MeV (free,scale=3.10638352610158e-07,gradient)\n",
+      "  Intensity7 ...............: 3.98006363733031e-07 +/- 1.41569683967725e-07 [3.09934606573553e-17,infty[ ph/cm2/s/MeV (free,scale=3.09934606573553e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
      ]
@@ -217,6 +216,13 @@
    "source": [
     "print(gammalib.GModels(outmodel))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -235,7 +241,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/users/tutorials/hess_dr1/classical.ipynb b/doc/source/users/tutorials/hess_dr1/classical.ipynb
index 445c4be..14c437e 100644
--- a/doc/source/users/tutorials/hess_dr1/classical.ipynb
+++ b/doc/source/users/tutorials/hess_dr1/classical.ipynb
@@ -88,26 +88,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obsfile = \"obs_crab_selected.xml\"\n",
-    "\n",
-    "phagen = cscripts.csphagen()\n",
-    "phagen['inobs'] = obsfile\n",
-    "phagen['inmodel'] = 'NONE' # assume that the source is pointlike\n",
-    "phagen['ebinalg'] = 'LOG'\n",
-    "phagen['emin'] = 0.66\n",
-    "phagen['emax'] = 30.\n",
-    "phagen['enumbins'] = 20\n",
-    "phagen['coordsys'] = 'CEL'\n",
-    "phagen['ra'] = 83.63\n",
-    "phagen['dec'] = 22.01\n",
-    "phagen['rad'] = 0.2\n",
-    "phagen['bkgmethod'] = 'REFLECTED'\n",
+    "obsfile = 'obs_crab_selected.xml'\n",
+    "phagen  = cscripts.csphagen()\n",
+    "phagen['inobs']         = obsfile\n",
+    "phagen['inmodel']       = 'NONE' # assume that the source is pointlike\n",
+    "phagen['ebinalg']       = 'LOG'\n",
+    "phagen['emin']          = 0.66\n",
+    "phagen['emax']          = 30.0\n",
+    "phagen['enumbins']      = 20\n",
+    "phagen['coordsys']      = 'CEL'\n",
+    "phagen['ra']            = 83.63\n",
+    "phagen['dec']           = 22.01\n",
+    "phagen['rad']           = 0.2\n",
+    "phagen['bkgmethod']     = 'REFLECTED'\n",
     "phagen['use_model_bkg'] = False\n",
-    "phagen['maxoffset'] = 2.0\n",
-    "phagen['stack'] = True\n",
-    "phagen['outobs'] = 'obs_crab_onoff.xml'\n",
-    "phagen['outmodel'] = 'onoff_model.xml'\n",
-    "\n",
+    "phagen['maxoffset']     = 2.0\n",
+    "phagen['stack']         = True\n",
+    "phagen['outobs']        = 'obs_crab_onoff.xml'\n",
+    "phagen['outmodel']      = 'onoff_model.xml'\n",
     "phagen.execute()"
    ]
   },
@@ -245,7 +243,6 @@
    "outputs": [],
    "source": [
     "like = ctools.ctlike(phagen.obs())\n",
-    "\n",
     "like.run()"
    ]
   },
@@ -318,25 +315,24 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110d248d0>"
+       "<matplotlib.figure.Figure at 0x117111fd0>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "residuals = 'residuals_classical_nobkg.fits'\n",
     "res = cscripts.csresspec(like.obs())\n",
-    "\n",
     "res['components'] = True\n",
-    "res['algorithm'] = 'SIGNIFICANCE'\n",
-    "res['outfile'] = residuals\n",
-    "\n",
+    "res['algorithm']  = 'SIGNIFICANCE'\n",
+    "res['outfile']    = residuals\n",
     "res.execute()\n",
-    "\n",
     "from show_residuals import plot_residuals\n",
     "plot_residuals(residuals,'',0)"
    ]
@@ -358,14 +354,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "srcmodel = os.environ['CTOOLS']+'/share/models/crab_nobkg.xml'\n",
-    "bkgmodel = 'bkgmodel.xml'\n",
+    "srcmodel   = os.environ['CTOOLS']+'/share/models/crab_nobkg.xml'\n",
+    "bkgmodel   = 'bkgmodel.xml'\n",
     "startmodel = 'model_classical_bkg.xml'\n",
-    "\n",
     "merge = cscripts.csmodelmerge()\n",
     "merge['inmodels'] = srcmodel + ' ' + bkgmodel\n",
     "merge['outmodel'] = startmodel\n",
-    "\n",
     "merge.execute()"
    ]
   },
@@ -383,21 +377,21 @@
    "outputs": [],
    "source": [
     "phagen = cscripts.csphagen()\n",
-    "phagen['inobs'] = obsfile\n",
-    "phagen['inmodel'] = startmodel\n",
-    "phagen['ebinalg'] = 'LOG'\n",
-    "phagen['emin'] = 0.66\n",
-    "phagen['emax'] = 30.\n",
-    "phagen['enumbins'] = 20\n",
-    "phagen['coordsys'] = 'CEL'\n",
-    "phagen['ra'] = 83.63\n",
-    "phagen['dec'] = 22.01\n",
-    "phagen['rad'] = 0.2\n",
-    "phagen['bkgmethod'] = 'REFLECTED'\n",
+    "phagen['inobs']         = obsfile\n",
+    "phagen['inmodel']       = startmodel\n",
+    "phagen['srcname']       = 'Crab'\n",
+    "phagen['ebinalg']       = 'LOG'\n",
+    "phagen['emin']          = 0.66\n",
+    "phagen['emax']          = 30.0\n",
+    "phagen['enumbins']      = 20\n",
+    "phagen['coordsys']      = 'CEL'\n",
+    "phagen['ra']            = 83.63\n",
+    "phagen['dec']           = 22.01\n",
+    "phagen['rad']           = 0.2\n",
+    "phagen['bkgmethod']     = 'REFLECTED'\n",
     "phagen['use_model_bkg'] = True\n",
-    "phagen['maxoffset'] = 2.0\n",
-    "phagen['stack'] = False # treat observations separately in joint likelihood fit\n",
-    "\n",
+    "phagen['maxoffset']     = 2.0\n",
+    "phagen['stack']         = False # treat observations separately in joint likelihood fit\n",
     "phagen.run()"
    ]
   },
@@ -420,13 +414,13 @@
      "output_type": "stream",
      "text": [
       "=== GOptimizerLM ===\n",
-      " Optimized function value ..: -2417.569\n",
+      " Optimized function value ..: -2417.944\n",
       " Absolute precision ........: 0.005\n",
       " Acceptable value decrease .: 2\n",
       " Optimization status .......: converged\n",
       " Number of parameters ......: 86\n",
-      " Number of free parameters .: 46\n",
-      " Number of iterations ......: 28\n",
+      " Number of free parameters .: 42\n",
+      " Number of iterations ......: 29\n",
       " Lambda ....................: 0.0001\n",
       "=== GModels ===\n",
       " Number of models ..........: 5\n",
@@ -443,8 +437,8 @@
       "  RA .......................: 83.6331 [-360,360] deg (fixed,scale=1)\n",
       "  DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1)\n",
       " Number of spectral par's ..: 3\n",
-      "  Prefactor ................: 4.44948779297595e-17 +/- 2.98607771115441e-18 [1e-24,1e-14] ph/cm2/s/MeV (free,scale=1e-17,gradient)\n",
-      "  Index ....................: -2.64048429363838 +/- 0.084515805313649 [-0,-5]  (free,scale=-1,gradient)\n",
+      "  Prefactor ................: 4.44907776007907e-17 +/- 2.98566064292929e-18 [1e-24,1e-14] ph/cm2/s/MeV (free,scale=1e-17,gradient)\n",
+      "  Index ....................: -2.64019099792601 +/- 0.0844654299892131 [-0,-5]  (free,scale=-1,gradient)\n",
       "  PivotEnergy ..............: 1000000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
@@ -457,26 +451,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.19679080604377 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0882131417987659 +/- 0 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.000692474001567828 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0881748792818854 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.000701218995455618 +/- 0 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 1.95087973878045 +/- 3.18389824156818 [8.87696769787525e-14,infty[ ph/cm2/s/MeV (free,scale=0.000887696769787525,gradient)\n",
+      "  Intensity0 ...............: 1.89896487985208 +/- 3.09950981249023 [1.00182541969977e-13,infty[ ph/cm2/s/MeV (free,scale=0.00100182541969977,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.786206855260844 +/- 0.278691033333991 [2.91749105398033e-14,infty[ ph/cm2/s/MeV (free,scale=0.000291749105398033,gradient)\n",
+      "  Intensity1 ...............: 0.754995013333362 +/- 0.267674756209559 [3.21240099713853e-14,infty[ ph/cm2/s/MeV (free,scale=0.000321240099713853,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 1.16378010214047 +/- 0.324828629342433 [9.58858288072014e-15,infty[ ph/cm2/s/MeV (free,scale=9.58858288072014e-05,gradient)\n",
+      "  Intensity2 ...............: 1.17347310940125 +/- 0.327498259113055 [1.03007170346199e-14,infty[ ph/cm2/s/MeV (free,scale=0.000103007170346199,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 1.41647574287537 +/- 0.410958373186227 [3.15136944584645e-15,infty[ ph/cm2/s/MeV (free,scale=3.15136944584645e-05,gradient)\n",
+      "  Intensity3 ...............: 1.39420069744212 +/- 0.404449186550592 [3.30297405342054e-15,infty[ ph/cm2/s/MeV (free,scale=3.30297405342054e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 0.988046118826941 +/- 0.410766631433934 [1.03572441389469e-15,infty[ ph/cm2/s/MeV (free,scale=1.03572441389469e-05,gradient)\n",
+      "  Intensity4 ...............: 1.32748213047069 +/- 0.551998524503437 [1.05911438600856e-15,infty[ ph/cm2/s/MeV (free,scale=1.05911438600856e-05,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 0.767763395749799 +/- 0.431359851222795 [3.40399651634425e-16,infty[ ph/cm2/s/MeV (free,scale=3.40399651634425e-06,gradient)\n",
+      "  Intensity5 ...............: 1.53590487400556 +/- 0.862929853325373 [3.39610080039421e-16,infty[ ph/cm2/s/MeV (free,scale=3.39610080039421e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 0.50642281781849 +/- 0.539376809186644 [1.11875245266372e-16,infty[ ph/cm2/s/MeV (free,scale=1.11875245266372e-06,gradient)\n",
+      "  Intensity6 ...............: 1.26422194838399 +/- 1.34653218966622 [1.08897592165697e-16,infty[ ph/cm2/s/MeV (free,scale=1.08897592165697e-06,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 1.93817511343864 +/- 2.4495988155466 [3.67687523865408e-17,infty[ ph/cm2/s/MeV (free,scale=3.67687523865408e-07,gradient)\n",
+      "  Intensity7 ...............: 6.77476052324258 +/- 8.56245789647827 [3.49185323889972e-17,infty[ ph/cm2/s/MeV (free,scale=3.49185323889972e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -488,26 +482,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.30082209234815 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0153115021206004 +/- 0 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.00906905504246978 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0153166143265463 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.00908019181469828 +/- 0 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 1.41145187416786 +/- 0.66650710147696 [8.36211165467046e-14,infty[ ph/cm2/s/MeV (free,scale=0.000836211165467046,gradient)\n",
+      "  Intensity0 ...............: 1.30442623250239 +/- 0.615959779856916 [9.62519069451052e-14,infty[ ph/cm2/s/MeV (free,scale=0.000962519069451052,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.596849678261568 +/- 0.19986170208393 [2.63699934530969e-14,infty[ ph/cm2/s/MeV (free,scale=0.000263699934530969,gradient)\n",
+      "  Intensity1 ...............: 0.566084707224135 +/- 0.189571117408801 [2.95441986174209e-14,infty[ ph/cm2/s/MeV (free,scale=0.000295441986174209,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 0.955852776423173 +/- 0.27239261436471 [8.31580088180217e-15,infty[ ph/cm2/s/MeV (free,scale=8.31580088180217e-05,gradient)\n",
+      "  Intensity2 ...............: 0.955930666245828 +/- 0.272410595459555 [9.068492247571e-15,infty[ ph/cm2/s/MeV (free,scale=9.068492247571e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 1.96279835266255 +/- 0.605417301754042 [2.62239520190935e-15,infty[ ph/cm2/s/MeV (free,scale=2.62239520190935e-05,gradient)\n",
+      "  Intensity3 ...............: 1.91071527979174 +/- 0.589677088986724 [2.78354314866283e-15,infty[ ph/cm2/s/MeV (free,scale=2.78354314866283e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 0.61189370200522 +/- 0.392560122455627 [8.26974658573933e-16,infty[ ph/cm2/s/MeV (free,scale=8.26974658573933e-06,gradient)\n",
+      "  Intensity4 ...............: 0.796340097360303 +/- 0.511186601597472 [8.54399193266454e-16,infty[ ph/cm2/s/MeV (free,scale=8.54399193266454e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 0.670372977599336 +/- 0.49918330694888 [2.60787193869763e-16,infty[ ph/cm2/s/MeV (free,scale=2.60787193869763e-06,gradient)\n",
+      "  Intensity5 ...............: 1.31629202714156 +/- 0.980251656539197 [2.62254954375342e-16,infty[ ph/cm2/s/MeV (free,scale=2.62254954375342e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 0.91835054322145 +/- 1.23832365407144 [8.22394734607041e-17,infty[ ph/cm2/s/MeV (free,scale=8.22394734607041e-07,gradient)\n",
+      "  Intensity6 ...............: 2.27556128029012 +/- 3.06865755885414 [8.04982748537824e-17,infty[ ph/cm2/s/MeV (free,scale=8.04982748537824e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.59342910774655e-17 +/- 9.80203792605831e-14 [2.59342910774655e-17,infty[ ph/cm2/s/MeV (free,scale=2.59342910774655e-07,gradient)\n",
+      "  Intensity7 ...............: 2.4708674312253e-17 +/- 1.05696632580831e-13 [2.4708674312253e-17,infty[ ph/cm2/s/MeV (free,scale=2.4708674312253e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -519,26 +513,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 4.32704664084324 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0187722831939023 +/- 0 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.126627824474372 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.018749400342 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.126640342510319 +/- 0 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.808933010585171 +/- 0.200163172598279 [6.96233895968756e-14,infty[ ph/cm2/s/MeV (free,scale=0.000696233895968756,gradient)\n",
+      "  Intensity0 ...............: 0.885669706791242 +/- 0.219147786869672 [8.7418891142236e-14,infty[ ph/cm2/s/MeV (free,scale=0.00087418891142236,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 1.06066471644224 +/- 0.153500072375076 [2.20204639078551e-14,infty[ ph/cm2/s/MeV (free,scale=0.000220204639078551,gradient)\n",
+      "  Intensity1 ...............: 1.12011463147092 +/- 0.162106623373511 [2.69181956755491e-14,infty[ ph/cm2/s/MeV (free,scale=0.000269181956755491,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 0.710415433488969 +/- 0.137769419014371 [6.96462544447719e-15,infty[ ph/cm2/s/MeV (free,scale=6.96462544447719e-05,gradient)\n",
+      "  Intensity2 ...............: 0.747992399165037 +/- 0.145060618647741 [8.28870337932099e-15,infty[ ph/cm2/s/MeV (free,scale=8.28870337932099e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 0.729027655898775 +/- 0.178799879249574 [2.20276955948036e-15,infty[ ph/cm2/s/MeV (free,scale=2.20276955948036e-05,gradient)\n",
+      "  Intensity3 ...............: 0.750550500933036 +/- 0.184075541493125 [2.55227373106486e-15,infty[ ph/cm2/s/MeV (free,scale=2.55227373106486e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 0.803061536460788 +/- 0.258238882634414 [6.96691268016576e-16,infty[ ph/cm2/s/MeV (free,scale=6.96691268016576e-06,gradient)\n",
+      "  Intensity4 ...............: 0.804148948547538 +/- 0.258602777342412 [7.85901111449516e-16,infty[ ph/cm2/s/MeV (free,scale=7.85901111449516e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 1.00423153683732 +/- 0.350723666167284 [2.20349296566929e-16,infty[ ph/cm2/s/MeV (free,scale=2.20349296566929e-06,gradient)\n",
+      "  Intensity5 ...............: 0.947268446501844 +/- 0.33090320971996 [2.4199620497598e-16,infty[ ph/cm2/s/MeV (free,scale=2.4199620497598e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 0.568268794513719 +/- 0.367440704128657 [6.9692006669998e-17,infty[ ph/cm2/s/MeV (free,scale=6.9692006669998e-07,gradient)\n",
+      "  Intensity6 ...............: 0.510924531144191 +/- 0.330349647333246 [7.45159440158635e-17,infty[ ph/cm2/s/MeV (free,scale=7.45159440158635e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.20412624439493 +/- 2.17456371931644 [2.20421660943029e-17,infty[ ph/cm2/s/MeV (free,scale=2.20421660943029e-07,gradient)\n",
+      "  Intensity7 ...............: 1.98644760587573 +/- 1.95978734590705 [2.29450949990163e-17,infty[ ph/cm2/s/MeV (free,scale=2.29450949990163e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -550,26 +544,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.64795431824073 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0290086296998991 +/- 0 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: 0.165462915333049 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0290076925442388 +/- 0 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: 0.165459453396994 +/- 0 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 1.02971670368846 +/- 0.855827122441381 [8.127607193775e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008127607193775,gradient)\n",
+      "  Intensity0 ...............: 1.15673248048702 +/- 0.961419402688181 [9.64800822522881e-14,infty[ ph/cm2/s/MeV (free,scale=0.000964800822522881,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.947987059426059 +/- 0.159618808634336 [2.64069813465664e-14,infty[ ph/cm2/s/MeV (free,scale=0.000264069813465664,gradient)\n",
+      "  Intensity1 ...............: 0.995573238520915 +/- 0.167640057372438 [3.05783271724063e-14,infty[ ph/cm2/s/MeV (free,scale=0.000305783271724063,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 0.930329629369328 +/- 0.147573728952743 [8.57975351431837e-15,infty[ ph/cm2/s/MeV (free,scale=8.57975351431837e-05,gradient)\n",
+      "  Intensity2 ...............: 0.97430603880028 +/- 0.15455196754381 [9.6914727976462e-15,infty[ ph/cm2/s/MeV (free,scale=9.6914727976462e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 0.729974577227829 +/- 0.155604820302836 [2.78760261918503e-15,infty[ ph/cm2/s/MeV (free,scale=2.78760261918503e-05,gradient)\n",
+      "  Intensity3 ...............: 0.748497898296835 +/- 0.15953787473352 [3.07160834724385e-15,infty[ ph/cm2/s/MeV (free,scale=3.07160834724385e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 0.726106308630469 +/- 0.203524547813456 [9.05705315370543e-16,infty[ ph/cm2/s/MeV (free,scale=9.05705315370543e-06,gradient)\n",
+      "  Intensity4 ...............: 0.725418936580643 +/- 0.203356640612422 [9.735133179293e-16,infty[ ph/cm2/s/MeV (free,scale=9.735133179293e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 1.18037302547945 +/- 0.389214640445536 [2.94267953633316e-16,infty[ ph/cm2/s/MeV (free,scale=2.94267953633316e-06,gradient)\n",
+      "  Intensity5 ...............: 1.11101986414082 +/- 0.366485282801602 [3.08544603688196e-16,infty[ ph/cm2/s/MeV (free,scale=3.08544603688196e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 1.55090547056218 +/- 0.642354234762239 [9.5609054143745e-17,infty[ ph/cm2/s/MeV (free,scale=9.5609054143745e-07,gradient)\n",
+      "  Intensity6 ...............: 1.39697833190385 +/- 0.57861299733536 [9.77899025229569e-17,infty[ ph/cm2/s/MeV (free,scale=9.77899025229569e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 1.38574572560495 +/- 1.01260914296946 [3.10638352610158e-17,infty[ ph/cm2/s/MeV (free,scale=3.10638352610158e-07,gradient)\n",
+      "  Intensity7 ...............: 1.25607862208781 +/- 0.917860648895514 [3.09934606573553e-17,infty[ ph/cm2/s/MeV (free,scale=3.09934606573553e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
      ]
@@ -577,9 +571,7 @@
    ],
    "source": [
     "like = ctools.ctlike(phagen.obs())\n",
-    "\n",
     "like.run()\n",
-    "\n",
     "print(like.opt())\n",
     "print(like.obs().models())"
    ]
@@ -602,26 +594,25 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11132e5c0>"
+       "<matplotlib.figure.Figure at 0x1171e6b38>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "residuals = 'residuals_classical_bkgmod.fits'\n",
     "res = cscripts.csresspec(like.obs())\n",
-    "\n",
     "res['components'] = True\n",
-    "res['algorithm'] = 'SIGNIFICANCE'\n",
-    "res['outfile'] = residuals\n",
-    "res['stack'] = False\n",
-    "\n",
+    "res['algorithm']  = 'SIGNIFICANCE'\n",
+    "res['outfile']    = residuals\n",
+    "res['stack']      = False\n",
     "res.execute()\n",
-    "\n",
     "from show_residuals import plot_residuals\n",
     "plot_residuals(residuals,'',0)"
    ]
@@ -632,6 +623,13 @@
    "source": [
     "As expected the spectral residuals are not as good as in the first analysis in which the model was derived from the data during the fit itself, but the models still satisfactorily reproduce the data. Of course the total background coincides with the background model of the specific observation considered."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -650,7 +648,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/users/tutorials/hess_dr1/selecting_events.ipynb b/doc/source/users/tutorials/hess_dr1/selecting_events.ipynb
index 1e153a6..1b0ea1f 100644
--- a/doc/source/users/tutorials/hess_dr1/selecting_events.ipynb
+++ b/doc/source/users/tutorials/hess_dr1/selecting_events.ipynb
@@ -49,16 +49,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obsfile = \"obs_crab_selected.xml\"\n",
-    "\n",
-    "select = ctools.ctselect()\n",
+    "obsfile = 'obs_crab_selected.xml'\n",
+    "select  = ctools.ctselect()\n",
     "select['usethres'] = 'DEFAULT'\n",
-    "select['inobs'] = '$HESSDATA/obs/obs_crab.xml'\n",
-    "select['emin'] = 'INDEF' # no manual energy selection\n",
-    "select['rad'] = 2 # by default select around pointing direction of each observations\n",
-    "select['tmin'] = 'INDEF' # no temporal selection\n",
-    "select['outobs'] = obsfile\n",
-    "\n",
+    "select['inobs']    = '$HESSDATA/obs/obs_crab.xml'\n",
+    "select['emin']     = 'INDEF' # no manual energy selection\n",
+    "select['rad']      = 2 # by default select around pointing direction of each observations\n",
+    "select['tmin']     = 'INDEF' # no temporal selection\n",
+    "select['outobs']   = obsfile\n",
     "select.execute()"
    ]
   },
@@ -232,6 +230,13 @@
     "\n",
     "The overall energy range that you will from now on use for the data analysis is 0.66 - 100 TeV. "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -250,7 +255,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/users/tutorials/hess_dr1/skymap.ipynb b/doc/source/users/tutorials/hess_dr1/skymap.ipynb
index c6055f9..74859a9 100644
--- a/doc/source/users/tutorials/hess_dr1/skymap.ipynb
+++ b/doc/source/users/tutorials/hess_dr1/skymap.ipynb
@@ -66,11 +66,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obsfile = \"obs_crab_selected.xml\"\n",
-    "emin = 0.66\n",
-    "emax = 100.\n",
-    "\n",
-    "skymap = ctools.ctskymap()\n",
+    "obsfile = 'obs_crab_selected.xml'\n",
+    "emin    = 0.66\n",
+    "emax    = 100.0\n",
+    "skymap  = ctools.ctskymap()\n",
     "skymap['inobs']       = obsfile\n",
     "skymap['proj']        = 'CAR'\n",
     "skymap['coordsys']    = 'CEL'\n",
@@ -82,8 +81,6 @@
     "skymap['emin']        = emin\n",
     "skymap['emax']        = emax\n",
     "skymap['bkgsubtract'] = 'NONE'\n",
-    "\n",
-    "\n",
     "skymap.run()"
    ]
   },
@@ -103,10 +100,12 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x108cdb0f0>"
+       "<matplotlib.figure.Figure at 0x1211040b8>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -139,17 +138,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "srcmodel = 'crab.xml'\n",
-    "regfile = 'crab_sources.ds9'\n",
-    "\n",
+    "srcmodel  = 'crab.xml'\n",
+    "regfile   = 'crab_sources.ds9'\n",
     "srcdetect = cscripts.cssrcdetect(skymap.skymap().copy())\n",
-    "srcdetect['srcmodel'] = 'POINT'\n",
-    "srcdetect['bkgmodel'] = 'NONE' # we will determine the background model in a later step\n",
-    "srcdetect['threshold'] = 5\n",
-    "srcdetect['corr_rad'] =  0.1\n",
-    "srcdetect['outmodel'] = srcmodel\n",
+    "srcdetect['srcmodel']   = 'POINT'\n",
+    "srcdetect['bkgmodel']   = 'NONE' # we will determine the background model in a later step\n",
+    "srcdetect['threshold']  = 5\n",
+    "srcdetect['corr_rad']   =  0.1\n",
+    "srcdetect['outmodel']   = srcmodel\n",
     "srcdetect['outds9file'] = regfile\n",
-    "\n",
     "srcdetect.execute()"
    ]
   },
@@ -212,7 +209,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x112c4be80>"
+       "<matplotlib.collections.PathCollection at 0x1227d2dd8>"
       ]
      },
      "execution_count": 7,
@@ -223,10 +220,12 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1129e1358>"
+       "<matplotlib.figure.Figure at 0x12218d2e8>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -240,12 +239,17 @@
     "ax1.set_ylabel('Dec (deg)')\n",
     "cbar = plt.colorbar()\n",
     "cbar.set_label('Counts')\n",
-    "\n",
     "crab = models['Src001']\n",
-    "\n",
     "ax1.scatter(crab['RA'].value(),crab['DEC'].value(),\n",
     "           marker='x',c='white')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -264,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
diff --git a/doc/source/users/tutorials/hess_dr1/unbinned.ipynb b/doc/source/users/tutorials/hess_dr1/unbinned.ipynb
index 2712be2..de5cf12 100644
--- a/doc/source/users/tutorials/hess_dr1/unbinned.ipynb
+++ b/doc/source/users/tutorials/hess_dr1/unbinned.ipynb
@@ -118,26 +118,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.19679080604377 +/- 0.148784168613255 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0882131417987659 +/- 0.0223563994341903 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.000692474001567828 +/- 0.0227254580377688 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0881748792818854 +/- 0.0222611841905954 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.000701218995455618 +/- 0.0227202811076142 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.00140372802004064 +/- 0.000174205101284546 [8.87696769787525e-14,infty[ ph/cm2/s/MeV (free,scale=0.000887696769787525,gradient)\n",
+      "  Intensity0 ...............: 0.00167503068442416 +/- 0.000204995398027476 [1.00182541969977e-13,infty[ ph/cm2/s/MeV (free,scale=0.00100182541969977,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.000257864797984792 +/- 1.41022572545599e-05 [2.91749105398033e-14,infty[ ph/cm2/s/MeV (free,scale=0.000291749105398033,gradient)\n",
+      "  Intensity1 ...............: 0.000275120588592054 +/- 1.41610992132859e-05 [3.21240099713853e-14,infty[ ph/cm2/s/MeV (free,scale=0.000321240099713853,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 9.08473780602549e-05 +/- 5.49730339459174e-06 [9.58858288072014e-15,infty[ ph/cm2/s/MeV (free,scale=9.58858288072014e-05,gradient)\n",
+      "  Intensity2 ...............: 0.000102369982797547 +/- 5.87189405970882e-06 [1.03007170346199e-14,infty[ ph/cm2/s/MeV (free,scale=0.000103007170346199,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 3.21049333853292e-05 +/- 2.44980628805133e-06 [3.15136944584645e-15,infty[ ph/cm2/s/MeV (free,scale=3.15136944584645e-05,gradient)\n",
+      "  Intensity3 ...............: 3.43310169853447e-05 +/- 2.52595458342912e-06 [3.30297405342054e-15,infty[ ph/cm2/s/MeV (free,scale=3.30297405342054e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 1.13774407454204e-05 +/- 1.12484264580111e-06 [1.03572441389469e-15,infty[ ph/cm2/s/MeV (free,scale=1.03572441389469e-05,gradient)\n",
+      "  Intensity4 ...............: 1.04184963555313e-05 +/- 1.01064863096553e-06 [1.05911438600856e-15,infty[ ph/cm2/s/MeV (free,scale=1.05911438600856e-05,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 4.50082783670747e-06 +/- 5.75883736185608e-07 [3.40399651634425e-16,infty[ ph/cm2/s/MeV (free,scale=3.40399651634425e-06,gradient)\n",
+      "  Intensity5 ...............: 4.1837003769505e-06 +/- 5.25942224806527e-07 [3.39610080039421e-16,infty[ ph/cm2/s/MeV (free,scale=3.39610080039421e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 8.01232298563249e-07 +/- 1.64363032163644e-07 [1.11875245266372e-16,infty[ ph/cm2/s/MeV (free,scale=1.11875245266372e-06,gradient)\n",
+      "  Intensity6 ...............: 8.6658971981008e-07 +/- 1.76584928413557e-07 [1.08897592165697e-16,infty[ ph/cm2/s/MeV (free,scale=1.08897592165697e-06,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.63561241594543e-07 +/- 9.61055896032478e-08 [3.67687523865408e-17,infty[ ph/cm2/s/MeV (free,scale=3.67687523865408e-07,gradient)\n",
+      "  Intensity7 ...............: 3.41087754372141e-07 +/- 1.23894301613722e-07 [3.49185323889972e-17,infty[ ph/cm2/s/MeV (free,scale=3.49185323889972e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -149,26 +149,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.30082209234815 +/- 0.156912961568416 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0153115021206004 +/- 0.0215680817043072 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.00906905504246978 +/- 0.0213184758960865 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0153166143265463 +/- 0.0215144304488992 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.00908019181469828 +/- 0.0213167666197536 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.00107656542145505 +/- 5.71767004891641e-05 [8.36211165467046e-14,infty[ ph/cm2/s/MeV (free,scale=0.000836211165467046,gradient)\n",
+      "  Intensity0 ...............: 0.00126074399446221 +/- 6.18948752992e-05 [9.62519069451052e-14,infty[ ph/cm2/s/MeV (free,scale=0.000962519069451052,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.00020191222774409 +/- 1.06852037575173e-05 [2.63699934530969e-14,infty[ ph/cm2/s/MeV (free,scale=0.000263699934530969,gradient)\n",
+      "  Intensity1 ...............: 0.000220109488746193 +/- 1.10099106908638e-05 [2.95441986174209e-14,infty[ ph/cm2/s/MeV (free,scale=0.000295441986174209,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 9.16681754173992e-05 +/- 5.73982387509952e-06 [8.31580088180217e-15,infty[ ph/cm2/s/MeV (free,scale=8.31580088180217e-05,gradient)\n",
+      "  Intensity2 ...............: 0.000104377151461511 +/- 6.26567390995899e-06 [9.068492247571e-15,infty[ ph/cm2/s/MeV (free,scale=9.068492247571e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 2.4697825754506e-05 +/- 2.13035134581624e-06 [2.62239520190935e-15,infty[ ph/cm2/s/MeV (free,scale=2.62239520190935e-05,gradient)\n",
+      "  Intensity3 ...............: 2.68164289254463e-05 +/- 2.2601490434718e-06 [2.78354314866283e-15,infty[ ph/cm2/s/MeV (free,scale=2.78354314866283e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 9.65793108295259e-06 +/- 1.0580848901101e-06 [8.26974658573933e-16,infty[ ph/cm2/s/MeV (free,scale=8.26974658573933e-06,gradient)\n",
+      "  Intensity4 ...............: 8.96128027251196e-06 +/- 9.61601396204211e-07 [8.54399193266454e-16,infty[ ph/cm2/s/MeV (free,scale=8.54399193266454e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 3.34440698323729e-06 +/- 4.70710485211692e-07 [2.60787193869763e-16,infty[ ph/cm2/s/MeV (free,scale=2.60787193869763e-06,gradient)\n",
+      "  Intensity5 ...............: 3.14829249162658e-06 +/- 4.37081753413707e-07 [2.62254954375342e-16,infty[ ph/cm2/s/MeV (free,scale=2.62254954375342e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 5.56094646218329e-07 +/- 1.31992669655457e-07 [8.22394734607041e-17,infty[ ph/cm2/s/MeV (free,scale=8.22394734607041e-07,gradient)\n",
+      "  Intensity6 ...............: 6.09624701513041e-07 +/- 1.43872314987655e-07 [8.04982748537824e-17,infty[ ph/cm2/s/MeV (free,scale=8.04982748537824e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.63249520342286e-07 +/- 9.70349092695409e-08 [2.59342910774655e-17,infty[ ph/cm2/s/MeV (free,scale=2.59342910774655e-07,gradient)\n",
+      "  Intensity7 ...............: 3.44982419809933e-07 +/- 1.26076763285202e-07 [2.4708674312253e-17,infty[ ph/cm2/s/MeV (free,scale=2.4708674312253e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -180,26 +180,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 4.32704664084324 +/- 0.368684845846136 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0187722831939023 +/- 0.0217106880517745 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: -0.126627824474372 +/- 0.0204738099128812 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.018749400342 +/- 0.0217067733288596 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: -0.126640342510319 +/- 0.020455126417247 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.000857178811233776 +/- 4.58638205127566e-05 [6.96233895968756e-14,infty[ ph/cm2/s/MeV (free,scale=0.000696233895968756,gradient)\n",
+      "  Intensity0 ...............: 0.00109294743570479 +/- 5.37668986080239e-05 [8.7418891142236e-14,infty[ ph/cm2/s/MeV (free,scale=0.00087418891142236,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.000184768403022439 +/- 1.008650496985e-05 [2.20204639078551e-14,infty[ ph/cm2/s/MeV (free,scale=0.000220204639078551,gradient)\n",
+      "  Intensity1 ...............: 0.000220103906688583 +/- 1.10283166290448e-05 [2.69181956755491e-14,infty[ ph/cm2/s/MeV (free,scale=0.000269181956755491,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 6.49318372405173e-05 +/- 4.46952677363829e-06 [6.96462544447719e-15,infty[ ph/cm2/s/MeV (free,scale=6.96462544447719e-05,gradient)\n",
+      "  Intensity2 ...............: 8.06623852687504e-05 +/- 5.23872750218462e-06 [8.28870337932099e-15,infty[ ph/cm2/s/MeV (free,scale=8.28870337932099e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 2.41177375305046e-05 +/- 2.06051735828825e-06 [2.20276955948036e-15,infty[ ph/cm2/s/MeV (free,scale=2.20276955948036e-05,gradient)\n",
+      "  Intensity3 ...............: 2.85428972580395e-05 +/- 2.37266748207121e-06 [2.55227373106486e-15,infty[ ph/cm2/s/MeV (free,scale=2.55227373106486e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 6.388150904145e-06 +/- 7.36345188624795e-07 [6.96691268016576e-16,infty[ ph/cm2/s/MeV (free,scale=6.96691268016576e-06,gradient)\n",
+      "  Intensity4 ...............: 6.46456658892385e-06 +/- 7.36558401693409e-07 [7.85901111449516e-16,infty[ ph/cm2/s/MeV (free,scale=7.85901111449516e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 3.11624385438516e-06 +/- 4.38829996211514e-07 [2.20349296566929e-16,infty[ ph/cm2/s/MeV (free,scale=2.20349296566929e-06,gradient)\n",
+      "  Intensity5 ...............: 3.20506991372933e-06 +/- 4.41036712274814e-07 [2.4199620497598e-16,infty[ ph/cm2/s/MeV (free,scale=2.4199620497598e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 7.66998023710854e-07 +/- 1.70693323247103e-07 [6.9692006669998e-17,infty[ ph/cm2/s/MeV (free,scale=6.9692006669998e-07,gradient)\n",
+      "  Intensity6 ...............: 9.17351299053318e-07 +/- 2.02856047440946e-07 [7.45159440158635e-17,infty[ ph/cm2/s/MeV (free,scale=7.45159440158635e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 1.35218346687107e-07 +/- 6.64294080047906e-08 [2.20421660943029e-17,infty[ ph/cm2/s/MeV (free,scale=2.20421660943029e-07,gradient)\n",
+      "  Intensity7 ...............: 1.93348643577948e-07 +/- 9.48901838749967e-08 [2.29450949990163e-17,infty[ ph/cm2/s/MeV (free,scale=2.29450949990163e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
       "=== GCTAModelBackground ===\n",
@@ -211,26 +211,26 @@
       " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
       " Number of parameters ......: 20\n",
       " Number of spatial par's ...: 3\n",
-      "  1:Sigma ..................: 3.64795431824073 +/- 0.238749562945919 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
-      "  2:Grad_DETX ..............: 0.0290086296998991 +/- 0.0231654281500121 deg^-1 (free,scale=1,gradient)\n",
-      "  2:Grad_DETY ..............: 0.165462915333049 +/- 0.021293268413236 deg^-1 (free,scale=1,gradient)\n",
+      "  1:Normalization ..........: 1 [0.001,1000]  (fixed,scale=1,gradient)\n",
+      "  2:Grad_DETX ..............: 0.0290076925442388 +/- 0.0231627042594752 deg^-1 (free,scale=1,gradient)\n",
+      "  2:Grad_DETY ..............: 0.165459453396994 +/- 0.0212165299830029 deg^-1 (free,scale=1,gradient)\n",
       " Number of spectral par's ..: 16\n",
       "  Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
-      "  Intensity0 ...............: 0.00138280405680456 +/- 0.000179785336701836 [8.127607193775e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008127607193775,gradient)\n",
+      "  Intensity0 ...............: 0.00173543806725313 +/- 0.000221842434989178 [9.64800822522881e-14,infty[ ph/cm2/s/MeV (free,scale=0.000964800822522881,gradient)\n",
       "  Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
-      "  Intensity1 ...............: 0.000220636852715846 +/- 1.29050674731524e-05 [2.64069813465664e-14,infty[ ph/cm2/s/MeV (free,scale=0.000264069813465664,gradient)\n",
+      "  Intensity1 ...............: 0.000247432426542881 +/- 1.35075275068334e-05 [3.05783271724063e-14,infty[ ph/cm2/s/MeV (free,scale=0.000305783271724063,gradient)\n",
       "  Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
-      "  Intensity2 ...............: 8.63433571877371e-05 +/- 5.40186394640027e-06 [8.57975351431837e-15,infty[ ph/cm2/s/MeV (free,scale=8.57975351431837e-05,gradient)\n",
+      "  Intensity2 ...............: 0.000102349095137416 +/- 6.00669028431716e-06 [9.6914727976462e-15,infty[ ph/cm2/s/MeV (free,scale=9.6914727976462e-05,gradient)\n",
       "  Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
-      "  Intensity3 ...............: 3.02018682595367e-05 +/- 2.37672480959384e-06 [2.78760261918503e-15,infty[ ph/cm2/s/MeV (free,scale=2.78760261918503e-05,gradient)\n",
+      "  Intensity3 ...............: 3.39523135381865e-05 +/- 2.54888013407308e-06 [3.07160834724385e-15,infty[ ph/cm2/s/MeV (free,scale=3.07160834724385e-05,gradient)\n",
       "  Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
-      "  Intensity4 ...............: 9.44460701196062e-06 +/- 9.82873290475423e-07 [9.05705315370543e-16,infty[ ph/cm2/s/MeV (free,scale=9.05705315370543e-06,gradient)\n",
+      "  Intensity4 ...............: 9.09839000917866e-06 +/- 9.25509371718351e-07 [9.735133179293e-16,infty[ ph/cm2/s/MeV (free,scale=9.735133179293e-06,gradient)\n",
       "  Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
-      "  Intensity5 ...............: 3.08894388117525e-06 +/- 4.2969727730712e-07 [2.94267953633316e-16,infty[ ph/cm2/s/MeV (free,scale=2.94267953633316e-06,gradient)\n",
+      "  Intensity5 ...............: 3.01613230297962e-06 +/- 4.11382625961797e-07 [3.08544603688196e-16,infty[ ph/cm2/s/MeV (free,scale=3.08544603688196e-06,gradient)\n",
       "  Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
-      "  Intensity6 ...............: 8.17521009765721e-07 +/- 1.65391550069682e-07 [9.5609054143745e-17,infty[ ph/cm2/s/MeV (free,scale=9.5609054143745e-07,gradient)\n",
+      "  Intensity6 ...............: 9.30049222211062e-07 +/- 1.86843120695054e-07 [9.77899025229569e-17,infty[ ph/cm2/s/MeV (free,scale=9.77899025229569e-07,gradient)\n",
       "  Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
-      "  Intensity7 ...............: 2.92681614932954e-07 +/- 1.0444872894399e-07 [3.10638352610158e-17,infty[ ph/cm2/s/MeV (free,scale=3.10638352610158e-07,gradient)\n",
+      "  Intensity7 ...............: 3.98006363733031e-07 +/- 1.41569683967725e-07 [3.09934606573553e-17,infty[ ph/cm2/s/MeV (free,scale=3.09934606573553e-07,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
      ]
@@ -239,12 +239,10 @@
    "source": [
     "srcmodel = 'crab.xml'\n",
     "bkgmodel = 'bkgmodel.xml'\n",
-    "\n",
-    "models = gammalib.GModels()\n",
+    "models   = gammalib.GModels()\n",
     "for inmodels in [srcmodel,bkgmodel]:\n",
     "    for model in gammalib.GModels(inmodels):\n",
     "        models.append(model)\n",
-    "        \n",
     "print(models)"
    ]
   },
@@ -297,12 +295,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obsfile = \"obs_crab_selected.xml\"\n",
-    "\n",
-    "like = ctools.ctlike()\n",
-    "like['inobs'] = obsfile\n",
+    "obsfile = 'obs_crab_selected.xml'\n",
+    "like    = ctools.ctlike()\n",
+    "like['inobs']   = obsfile\n",
     "like['inmodel'] = modelfile\n",
-    "\n",
     "like.run()"
    ]
   },
@@ -323,12 +319,12 @@
      "output_type": "stream",
      "text": [
       "=== GOptimizerLM ===\n",
-      " Optimized function value ..: 98200.140\n",
+      " Optimized function value ..: 98167.993\n",
       " Absolute precision ........: 0.005\n",
       " Acceptable value decrease .: 2\n",
       " Optimization status .......: converged\n",
       " Number of parameters ......: 86\n",
-      " Number of free parameters .: 48\n",
+      " Number of free parameters .: 44\n",
       " Number of iterations ......: 28\n",
       " Lambda ....................: 0.1\n",
       "=== GModelSky ===\n",
@@ -340,11 +336,11 @@
       " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
       " Number of parameters ......: 6\n",
       " Number of spatial par's ...: 2\n",
-      "  RA .......................: 83.6230529061662 +/- 0.00239926887672786 deg (free,scale=1)\n",
-      "  DEC ......................: 22.0247811570052 +/- 0.00218925167816472 deg (free,scale=1)\n",
+      "  RA .......................: 83.6231161425766 +/- 0.00240718177620446 deg (free,scale=1)\n",
+      "  DEC ......................: 22.0247773403131 +/- 0.00218186675985154 deg (free,scale=1)\n",
       " Number of spectral par's ..: 3\n",
-      "  Prefactor ................: 4.8925181519509e-17 +/- 2.67898412185662e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
-      "  Index ....................: -2.70266618180016 +/- 0.0657789826195137 [10,-10]  (free,scale=-2.48,gradient)\n",
+      "  Prefactor ................: 4.80653958161532e-17 +/- 2.64950884427518e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
+      "  Index ....................: -2.68230589724908 +/- 0.0650730512302727 [10,-10]  (free,scale=-2.48,gradient)\n",
       "  PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
@@ -376,7 +372,6 @@
    "outputs": [],
    "source": [
     "like['edisp'] = True\n",
-    "\n",
     "like.run()"
    ]
   },
@@ -390,14 +385,14 @@
      "output_type": "stream",
      "text": [
       "=== GOptimizerLM ===\n",
-      " Optimized function value ..: 98201.032\n",
+      " Optimized function value ..: 98168.820\n",
       " Absolute precision ........: 0.005\n",
       " Acceptable value decrease .: 2\n",
       " Optimization status .......: converged\n",
       " Number of parameters ......: 86\n",
-      " Number of free parameters .: 48\n",
-      " Number of iterations ......: 3\n",
-      " Lambda ....................: 1e-06\n",
+      " Number of free parameters .: 44\n",
+      " Number of iterations ......: 13\n",
+      " Lambda ....................: 1\n",
       "=== GModelSky ===\n",
       " Name ......................: Crab\n",
       " Instruments ...............: all\n",
@@ -407,11 +402,11 @@
       " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
       " Number of parameters ......: 6\n",
       " Number of spatial par's ...: 2\n",
-      "  RA .......................: 83.6217041522328 +/- 0.00261598773627659 deg (free,scale=1)\n",
-      "  DEC ......................: 22.0245022862116 +/- 0.00239693484099142 deg (free,scale=1)\n",
+      "  RA .......................: 83.6217013908939 +/- 0.00262422672788241 deg (free,scale=1)\n",
+      "  DEC ......................: 22.0244582493434 +/- 0.00238660922515514 deg (free,scale=1)\n",
       " Number of spectral par's ..: 3\n",
-      "  Prefactor ................: 4.64521936275237e-17 +/- 2.43165266024221e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
-      "  Index ....................: -2.68313444140762 +/- 0.0679406521827419 [10,-10]  (free,scale=-2.48,gradient)\n",
+      "  Prefactor ................: 4.56725958702027e-17 +/- 2.41200684919881e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
+      "  Index ....................: -2.66140151299329 +/- 0.0671763204868478 [10,-10]  (free,scale=-2.48,gradient)\n",
       "  PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
       " Number of temporal par's ..: 1\n",
       "  Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
@@ -441,19 +436,20 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1105ee240>"
+       "<matplotlib.figure.Figure at 0x11ab0eef0>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "residuals = 'residuals.fits'\n",
-    "\n",
-    "resspec = cscripts.csresspec(like.obs())\n",
+    "resspec   = cscripts.csresspec(like.obs())\n",
     "resspec['stack']       = True\n",
     "resspec['components']  = True\n",
     "resspec['ebinalg']     = 'LOG'\n",
@@ -469,9 +465,7 @@
     "resspec['nypix']       = 200\n",
     "resspec['algorithm']   = 'SIGNIFICANCE'\n",
     "resspec['outfile']     = residuals\n",
-    "\n",
     "resspec.execute()\n",
-    "\n",
     "from show_residuals import plot_residuals\n",
     "plot_residuals(residuals,'',0)"
    ]
@@ -490,17 +484,16 @@
    "outputs": [],
    "source": [
     "resmap = cscripts.csresmap(like.obs())\n",
-    "resmap['emin']        = 0.66\n",
-    "resmap['emax']        = 100.\n",
-    "resmap['proj']        = 'CAR'\n",
-    "resmap['coordsys']    = 'CEL'\n",
-    "resmap['xref']        = 83.63\n",
-    "resmap['yref']        = 22.01\n",
-    "resmap['binsz']       = 0.02\n",
-    "resmap['nxpix']       = 200\n",
-    "resmap['nypix']       = 200\n",
-    "resmap['algorithm']   = 'SUBDIV'\n",
-    "\n",
+    "resmap['emin']      = 0.66\n",
+    "resmap['emax']      = 100.0\n",
+    "resmap['proj']      = 'CAR'\n",
+    "resmap['coordsys']  = 'CEL'\n",
+    "resmap['xref']      = 83.63\n",
+    "resmap['yref']      = 22.01\n",
+    "resmap['binsz']     = 0.02\n",
+    "resmap['nxpix']     = 200\n",
+    "resmap['nypix']     = 200\n",
+    "resmap['algorithm'] = 'SUBDIV'\n",
     "resmap.run()"
    ]
   },
@@ -520,18 +513,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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aQdOOrU54zPMBboz73Zlrf60tl3c+hzQ3zOezduDj5mnNtZpPkhbLdgOudAZVa1eHhx0vYq4i/MLMMmg0DRfaAHyVInIS2Rq5BnI2amNVJArfWo1/4uPc+Jik81wXp2nf1ivKsm+9ZQDn+9jQxUsaufOZJR1IwHrhQheiaXcXx8UER4f9PuY12DElfGDlR1/jZWDl1Wx2vY/wFPXDti95C5TVNw9Adg7oeQ5cvtSBqvc+iiYkzLL4MrMY5c0aOGuwfsLCDIbVC1MAxvsawM5mDbjacuBIzabyC4CdnVn4rixPJdeRH9FMfbYpP3sWAB0A28u29jeZ9L9PW7YabE/OBsAC/Z4S9fZVoMZzXp7mM8ByA/VpM3DmtFfOn+WFrSvcaXg6yLHwLlyAzndw5e5OS4vGhzFwtWL6ezyY9gLXXA7WwTciVYWy+fTqy2aiERh4SxqDR1P2o6X0bIfehsMGb5sBey8m00h5gGAPCh77vOZo4l89a+nmCMEeTUAaE3uhM21BiWXUASwAlLsdyHKUrrZpM3HMxlyru0h7tesODoZ9hPuDt+TxzGxToLjlYAdydgDWhDVVoJvSRyZiD2pAN9cuiq73+R4VoVfkzpVTnyK6gsHGnufnm/v7OMIEl+/sswo+CtPY4yKN17Sx2QyYVM2U9lKwMsg0HB83wEz33o3A0yr227RYz1EweFQVlgfdbTY95+m4nbPXZNS2DTie3RkT/0q8U0ek0XKTMObGAFYWh/3VehZ7wRIyKsm1kXIX2NubDZT7ogCwdJkw8txmAxHQmgYb0QPWiGaz4UzLRttNg+IWYHty9gB2K1vZyr1TthrsQM4OwHpfG3/OpmdsbPBr0oFOEzNjFDCc2nqi3qs/kQYbNaxItQT6pmkA2N2FTmetImQ0GyuFbLTi4954w9lhDXY2AyY4Ai65KF9+dRDn2xLipawUZ3dAs3A9eK2Wp79VBYX0psnGd/KqTT9lN6WNaXXzGPDltf+5YOXAMMh4TotlzbpEc5OvO+NgmY+OLGl1DZkvk9cAv9Bl0E58YZgrcd4mvYKYHcIq1Ba4sKV0k9QAyxZge3J2ADaKwWdiDYrJf98TGTQjTwEHsOoiLbZhDMeIvOgbyPbcozrNUQ8PgSt3DmN0A93U2X5z0hETwcXrUQSVJnA1zpBjtXqA9dypzZF54LKlrnxdrg68EbFIISR5gwHPSPD4YyDHoXV5uTAzF8DQTYurf4zZ4Vdk9c35qSpKzFNS3AYtEc/TG9l8cJBxJM5kxtqnzzjHwLX82Ghg7hBjBuIeAbwB2XoRDORs1AZrrmypBfpLWT1CWW/yWqmPpk+NWKvJoF+k0yWqqoQExgsAAzCJLO8tGC6AJQUb8XE8/LjgFSHmZf0KScuCX7LZszozwHK0L16Ly3XDCGgSOeByPftzTmNy1d6Lt20LK9ip3wzo7GnA19vExK7nhRk86WGcWsf9c6CQ+RdqCTGAWt35a9myP5sNByVPLOeOWSF42sJWzHW1yFUjzdXIVoPtydkAWGC4BIcR0DSHSAUE+uqQzUczZmTvfMBJJKwWFNWk2+6jud803uUSqImdyNm7GM/YVTaKS82KihXZgwcXZzoNFr0t3YM5ELj34fR0hmkm5gpk6TBFEGnp7cPR/91888SCsdwA1Zz6zU5jABu9Xt7Oho1Tpu3ao8d2cOD65Wt7rl2et7ApBi8uiaghq7ODg+GiEk6H69QbXiNAHNNALfO8VtgXcpOy5WAHcnYAlntFUfQt3lHMzBw9YGu6M9P8OvDAYiOufReFAEXZXuNnjpFHl/ckY4Bl5Zy3khp0cAyXxmof6+MZpUd4/ngXHqqPkOPzdZcjL+0/n0PnV2uHGMPZWG4Di2mnFsHPB5JirhToLxaw7EUrSz3I+sHQPlXVjSfT/fQQMcpkZ6eLkWEXse8Z0y523m9hzP+9Gh69izEimePxcmPLBbzdtGwBtidnB2DtxfGIDPS1scPDvvMo32e9KQeu6Hf83EzQn7P/fI11TL/jh//mVY48JvC4wH3MnuEVGn+PyLBvDtRoX/6I47ME7TO29Yif8zseu64BBHXKtxubY6+XFylNpx11aXXH2OKVP6ZJzCvPBqWIujHxYw97QXGa+3vzeMBip17fMID+Ahc2djInAsSrFv0Lr+v+5l+Rxmq/bRSO3tsmedMNAqyIPB7ASwGUAF6uqi9y538IwBc0f3cBfLyqXmzOHQO4pTn356r6hI1l7ARyagArIg8C8CoAn4jUtW5W1ZeKyHcD+AZ023Q/V1XfOJoYd3Sgr3ExcPCozVYQa2h8fYCkAoUP0BGJx24+xu3d7HKsWLMtzpQbvzgspzha2n5BjneUyPYX39ntQdyxTbjzeT9YO+bn9wQax7XkqnnwGBPPOPCAwo+0j7/G0vKUe10PI2cxOPsxh7Vdzj+P26qC/f3r0hDitXp+CE9tuNBcr9ygIrKYz/tnRaDrB9JohuLBfRPAuEEjl4iUAH4UwOOQtuJ+i4i8QVXfZdeo6rfS9d8M4JGUxBVV/cyNZOYa5DQ12CWAb1PVt4vIdQDeJiJvas79kKr+wNopFUVyxLeX5z0IIoOAvz8S3zgbiaiknC1i7BHc1j24eg3W+gGHIsxlN1fMiAptvyOt1TqEL5SPheg/ZqTh6axprY2RkKmRSPyjfP1yVnO2nmhnGKsHr/iZ9mkDVV33tVN+V0AXyc/G69msz83bjHt//7p+MHeuZ45iFanN5i7oC8yFZPH3jwEsa7OclltNh9msix1xrbJZDvbRAN6jqu9NSctrADwRwLsy1z8JaefZe5WcGsCq6gcAfKD5fZeI3ArgAVeVWFGktfr28tigYJKb93mCLgIU6l1VNeklwx3Yr+2PNNjI75Lbu19oBvQNYpZuZOFeZaDP9c92axyPWhY2kevJHsQ+wTa/jqa2QC+GYeTEEWn8/ArYx9eDKNDxoHWd8IDrKhp0mCrhMKpA3/DobXp8vKq6wXA67dgnXvpvs/ALF/YwudgUzAef9VP4SPwMwN5B1FZzo1a0FNa4XY6jTL7X2N8H7nOfFGxoU+5amwPYBwB4H/2/DcBjogtF5MEAHgrgN+nwXETeiqTovUhVX7+pjJ1E7hEOVkQegqS+/wGAxwJ4loh8HYC3Imm5Hw7ueQaAZwDAjfe/fwrKbL3MWjvQcVfWoNiXx1QXBgNuuF7qLgarl0hJtoAoRSHtsWgH5LHZG9AHDLs+wP4emPhwgp4C7QU68Q+0GxjVzLLNdTqbdddFPCt1Wp3OerZGY2VyUat4cUDkjJADY7s/N6hw/XBUMOZfTSv1EyKeRSwWQ0Xe4r4Cw11bLl5sQNZUZNVhQzCinevPL+DgeIh+SbLda/99+n6EsWfUdTfimD8cBzu/ePFjAbA3NABocrOq3kz/I64uaE0AgK8B8AuqyhVyo6q+X0Q+GcBvisgtqvon62ZuU3LqACsi+wBeC+DZqnqniPwYgBcgVdYLAPwggKf5+5rKvhkAbnrEIxQ33ND1QF6DbQE35vMUTJR9ebi3ciQnr50BLQAZD2udL5qm9ly0AEhRoKJNFTtPg648npaz3/wMDy45o5pd431deR9BdnMaeFb4AlXVMKDqfN7XVjljTts6RolFsCdfhOn2bVplNJj5Y14T9lH2xuqJ36OFC7SxxLhsNobZ1D+qJtNkgW6BFJfz+uv3MLnoMssRa3y0Latn2u22fQd+tZxXuy1t3qnAA6sFdLCRqKrSM4A0I7z+euCGG9L3JrjTk1EEt6vqTSPnbwPwIPr/QADvz1z7NQCeyQdU9f3N93tF5LeRFLx7HGA3ps9HIiITJHB9taq+DgBU9a9U9VhVawA/icS1bGUrWzkP4mc6uc9qeQuAh4nIQ0VkigSib/AXicjfBnBfAP+Zjt1XRGbN7xuQZs057vZU5TS9CATAKwDcqqovoeP3b/hZAPifAfyXlYlVFY4v3q+1B7QRoYCkwe7u9r+9GhXxizz9cmRqUfSnS72pp/GZzbV2QpqL0oqvdAlPZdeZgeXaXY66Y+XItFbbTmU6beoJSJGzbF7seQbef8W0Gx9voMmYd7uyavBBuSKKwHPYPMWPjNye3458i9nIxTxrXecNZ+bZwYq5XWuacRSIjXe0tSrihVvWzK6//jq0r5rbncUq4IC2XoPlWYTNIEys0B6k/KzCding6y2zHOT94kXgfvfrNNjIk+SkskEvAlVdisizAPwqkpvWK1X1nSLyfABvVVUD2ycBeI1qj4z6VAA/ISI1khL5IvY+uCflNCmCxwJ4CoBbROQdzbHnAniSiHwmEkXwZwC+cVVCx7Xggx/sQGsyEcznqaHsXtjB5MKFRBPwOvto3SkwBNgIZElCcPVzVksXQFkBVVW2U9HIHpf77ymCiMbz91v2rVi8G3Y7CLHjqKEP1wV3cqDt7EdLwXIRc6lcfHM5Y/cz7wZqtID9HqHBe6/LjrG7m4GrAZx/fVXVgaV337JjRkn6+jRcihgVv4EAexVwOi3IMsDawoSIIrD69zs3eu6Vf1va7NZVlqkBsM8rL3OeTpNhC0gAaxTBDTfcG41caNw33+iOfaf7/93Bfb8H4NM3lpFrkNP0IngzYqJ63Oc1kOUSuP32fltiY+ju7gT7+/fFbH+/A1nb+D5awXIVINvyrpHVCej9nkwLLCtpH2MLeAx0/Z5ZpsWVZWwUyuXJNC9Whizy/myqwJ1uqZgfEDy4Np3vuJrhyt19jtELc8jmHcELKSL+1StduTgkTBvafwNUnpxw3hiPTEP19cW4xMFg/Kv3GjKDrLXBVRzzhQvXYXJD1WmkvOMBk7oGqs330TJVSr3kvJUoK3TtzhfGKqkl3dFpk8zDzuchwB7Nrxusarsq2S6VHcg94kVwrXJ8nLZxtvbJXle2vdOlS8D+PgEtb+PhA8R4Ky4Zc7QoQ4+aopButMiZ9kmm01lvdaS5PLKGxoDAgOK1Vp7+9vPU1QWv4Z9O0SERMARYbwhpQPa4Sh3UxicfG8ELMyUMeryuw8TPQBlEmW7w26dZ+VmzZIU8CkhumqrXhhmXPIXgJTJI2mYYwHDc9hRjXQMXLuxgdj1pAl6Dbd6BVpOk+d89rDcbCIppmWgoX7icFEXnvGvP8xrsxYs43r0OH7ljg/sebgG2J2cGYD/60eGOHECHD/v7ndJ63XUT7O/fB+V8PozTaeL9OadTaFFmNba23TAijHjSl0WB6XTS0mHec8DcgCwrHnz5WuvY3HZNQbHIU+ZZ1dNe/UoGKwijcgOutk0N0A91muvHXoPNaX25/Jvw4LEkrc2eYd+eIuDpuQdKBvkcBnmNmimDqJyqwya0WAwB2zRpq7fd3TT1nk73MN3da137FNKV42BIN3B5rP5Kj+I5Nd0aFI901jgMYC9cAC5caJm1jQDsVoMdyJkA2LruduTwncowwms2yyWwvz/D7EI1DMVnCRDIHqPEctEHDhMLvtWyVF6DjaiCosBst0Jdy+CyxaLvJmTnmVf002Rut9yh2U/TIuBlwZ87IO/iN5+3GivQj5uT82Xl8nBfjvzd/f2+DzKQRscZUNnB39cNp+M1UJ++B1gOZmUfG8y5PfDvohjSP3fd1S+fNbvO6016eYzYpqiOepkeA1fjMfyIbfysAez+Pg6XZRuxckwZPpFsAbYnZwJgVbswpqYh8RbTXqvhxruzU2JvbydtiQz00atpqMe1rOW7WVbUk8d6B63BnM93BsYdBgmgn382GPHU2fOE3M94xWtlXJ0XQ2TbMpwMK0d12Wr/VkXsBRABXwSw/jhzrVH+WWP0Axs/l7Vj1pajPPn8MW1p57x4DZQnNnY+p1167dqC0ljZuAzsuWBiZY6WSGfNAgywnmdiDdYrE85L4eDOjgpal/sflW3A7YGcidqwlTW5WKDeGBPNVGaz1HKraQmBtpGzls7qHTEJrEm2MgawQKvaSFFgb2/Wi9LvLdAco8D80cuym4J6wPJbb/stU9qLWc3nkYI01+NqhgPnQZSb6luxufjAaICyMH82KHDIRWAYP5ef5bX7qMo5//59en99vt9wgXlyrsLlMjZA+nbGWra9RwZiDj3g26ePyWD5MVAuC0W7KaI1Ck7EuFk7Z//tegewR0vptcNclKI9AAAgAElEQVSNAKw9ayutnBmAZQ2PQdA0PQuqzNM11hg4uJZFzPJU6hj/WteJN5NcA2JVxW5q5ocCYGc+RVUR77bspo9HR90CNOM/Le+sXfDyWNMAfWeta3QVwTEL2Q+46WjGu/p425HrkaUdDjaBWL54dZllIQpmkwtODvSpBw/svASWl/2zCxdLRC3Y4Gd8tgG1HYuezZMgHrssH1FevXeD1Y/Xni1Pve3Vo2m/r3A/OrmZGseYPby7n+TGNNgtwPbkTAAs0AdD30kYPNmibhoAT0X9ViKcrneMt/RNg6lrdP6NUQOPMmyyTBvdTaoCmCdagikCCyZtebZt7nm2xx2ZQzDwI+sa0OkEMiUUZO3GLNfNJoscQtdr797ow1qcB1qvVJnmannk98AeBZyGB9dIs/X58VXO6RwdxfysH0iZlrb/pomaG6m3GfkFFNaurJy+OUSzDg+sflcGqY+HwOqFC+g5GA+wDR0G9KkW/96vSbYA25MzAbDmcM8dx4+4phFaxKP5vK8JsvhpnXVmb8AAujZtnTy05Hri0RN09iBCn7KqUDYZm+1WOJqXLX9nygbvR8jlZYOMf5xNS6fTWdo/jAtRFK2nxMGl4Q4BLD5ymAlzmqwBmm+v9enIaMTgy+nZN394sFtHY2Zh7dXCDlp+OX0TVvCKojNA8oIo3+68ls8hc21FHcfQtvT9oMOfSUWuCgeBUYEfGL0cPh+AK6qq1xw56a0GezpyJgB2K1vZyhmRrZGrJ2emNvxOATygW+g5785jGggbGw4Ph1PUyL3IZMC/TZ2ZOWdi9mqyF2f1mcznmOzPMZ12K8BMi7Vy+NtZG7LHFEW3l+FkYnPoZu8wx/9GPqU+e5EGmxPjRKPATvY/lxZrst57YYwX5/t5EsETBm+h965nbJD37cHTSEy18/OtrGZH2tnpL/33a1oGHOtiAVwmYn5Me/WfSFjLbSpfi7LnGujLdM2y1WAHciYANpoqRvyfWb+NKzMQiWgCjtvKnc1TWd5gcVxLmtqzD8+qUTuy3Ni9QDc6LBaYzeeoLsx66wC8w79nIyLKw6bHPhvcsbj/ssHIMyA+jUgY8IKZ6ShYrxOTOnpWLj1mZph69ivi+Le9zqiNsRGUp9iRHYlDu0axWzh+Tsux+nXG9gAPsP7FeAubL5x7EcwhWz/ZehGcrpwJgDWxdxcNlMwlccOxhmSKgYkH1QjA+LylVxRAMZ2kZbOsvUbIEAGrfxin03zK+RL7O3Ps7JShljmm2fn9uqysnuPMGfUsO9yHo50BckVlXtbzxMwVR3XstdcxzTXSYE1Yez0+7haKeMWPvTLGls56Ldae4bHPAJTjt3D0rZZjXTjE9h97gDc2sMtBXQ+J7WjEbf5rUeLwSt9zhfvHRmSrwQ7kTAEs0BlSfLsz4c4QOaaze2Ak0Tm29nf5mKCcB4l5IOXe7s/5tbDcyRYLlFWFnekU2O1iJNjtBpDs72n5jFYwRQBm4oHFwCICnjHQ4+Kw5uqt/f55J52i5vLhj1nV25Y0PGhYPoFhXr2Bj+vNK5i+zIMNCqZNQ10surCRPOjy7zEEN+GgCDyi8UjGnEfz3/Jqsxq/6nGrwZ6OnBmANX9K3uqDhd9rXQ/BldstEE99I80w15FTRxT42LFAmdq1aX/Lo/4y3UiDXS67Hfnqus9rNB1HqqrdXK+sKkyqCpimJb68EhgYOtnzogFfnqg/WL800PBAmKs77tcRQPP1kdbkB4RVMga0fuq+XPYHV150ZNZ9W2rMg4Jp7lZ/Hv844Ax7DuztAbI4TKAK9IMO8VQkN+hGFcPC/nJcIezKYceKAstFf0BgiuCkA9yobAG2J2cGYK2zW4Ag3+m5I0dUgQdYYAjKYyDL7Z+nvjkxf8jZbILJ7oqLWT0ysI0iRrO20pB55XyOnfkcVZWAneM1RGWw35wddqmyx/EiBs6Gpc0AFdGEUZp2jRUx4mXHNCnP80ZUAcsYlcJ5Y7/pAUdKD7cALZamn8G3rlg4Bi4f5COajSGaWWCtsVkBo1HfKpIzYJ2DrlfIYCbH2+NsTLZLZQdyJmpDZLgufAwwebZlymAUXJnbQm4K7XlDM5b0NhUksXNsWJnPBdP5TloFxh/ufN6VwVuoODNssm467mR3F5hPQiXZR/v3PCTnF4j5U77XruEYC/btNwr093N9W778/WMS5YXTjMQrdJY/3uPR9jCbFHnnfimKNHNoEprtuIHT+FXenMy/48ihORppOPMezXNqO6vp7ho/g/FJrKr3tWTLwQ7kTABsUXTTN7+OP5riGE6xgcv6BbtsseWclchV7TdnFTexFURF0a0Equvk/F/OqVAsi0V/HupJVr7HEnabGU729zF3IGtiyXnjjgGrTemBGFxXSQSuNrD5mANWn5wfP9O1a3LC0/0xLdbOrwLYnR2grI+AA+e/5snqiPNkLZNHdvOXs8Jbo2UOxv771RhcUZ6njaiESNtdQz21bGwEYC3BrbRyZgB2by+1LdOc2PAUgaK1QY7ZyemxNmrX59pvWfaVA9ZSc2Dg+53dt7MzQ7mbQS7ODLvteNcAQwgfmKEoMNvfRz3vq9Y+aAznkY0/nkP1FEpOIg8EpgkY3Ey4LieTvoaVs2r7dHL5y1EWXFZb3grQtJ4D4Xq+lBPzBfVaLIOs1wSiQhgvwbvI+jTrOh82jgvuG12ddkoWkcH4YLtrzGZbgD0tOTWAFZEHAXgVgE8EUCPte/5SOv8vAbwYwMep6u1jaRVFCmO5XHYRiWzmFWm1QNc3zChm19v7562K7PqcMcgrB1UVh5eza4F+EBIDEuOG5/MJJrtBQ2QrhAdZ39H9VNB6T1VhZ3cXvFtPUfTLZnXAAOg9fnyHY4DOga33o/XUA9/XLj0uu6IYfZibaXK+vfGMB0WLmeApDa+9sgbbaq4MsDkt1r7ZiscjrVV2zrfOruW1xLzRZJO+bTLZ7gXHIeV8u+D0ebrVXDOZTFoMB/rFigyRVyUbpghE5PEAXoq0Uublqvoid/7rkTDkL5pDL1PVlzfnngrgO5rj36OqP72xjJ1ATlODXQL4NlV9u4hcB+BtIvImVX1XA76PA/Dn6yTEAMuzLztXFJ2WZsoeA5tfydVmkGwEbHn3AMt4xseKIh8JfmWE+HmZeFMTHhE8cnifIKC/HM3Py4t+HFrLb5R/7+tq51gY/FbNOhkEW5qg2c/MvCCKaX93Wt6g0NL31WBpetCOtFhPkbCGzphmVdh6ehi4XrnSRcCJNubizLBazOIbkieNmUNqfLt0vtOjbLtHCqpqgul8gtKW91mj98/jCiBtdzqfYDYbDpJlmYq4qT0PNwWwIlIC+FEknLgNwFtE5A3B7rD/QVWf5e69HsB3AbgJaXPVtzX3fngjmTuBnJo+r6ofUNW3N7/vAnArgAc0p38IwL8CsCnvu61sZSsfa/GxKcc+q+XRAN6jqu9V1QWA1wB44po5+VIAb1LVOxpQfROAx19VmQCIyFc0SiJE5NtF5OeanbFXyj3CwYrIQwA8EsAfiMgTAPyFqv6/MkL8iMgzADwDAD7pk27Eddd12qgFNAbyfpb8m2dSRTG0NfCAn3PE9zNx1gDGZEyTLZr9mkrjPUw9572+PTncS6DoVBA3hZSqwmw26V0a8ZJsxM7ZV+r6ZBwd2296W503D5CiwHRaNoa/vvHQnmerr6wOeSYe+apyXUc0j6Uf0Z2t5Z/r3zRaP3vwPIibOWQ1OCY//Ye27eGd1nn3DttSaz6fYLafeRZrzc7gZu2BnRis6RhVsxFZX4O9QUTeSv9vVtWb6f8DALyP/t8G4DFBOv+LiHwugP8G4FtV9X2Zex8Q3LuufLeqvk5E/i6AfwTgJQB+HMDnrLrx1AFWRPYBvBbAs5Fog+cB+JJV9zWVfTMAPOIRN+l97tOnB3jLZS+RIZVBFeh3WqMScpH5eTrKdAHze5F4q7mnUQ209vZmEHZ3yPmVRajP3AbvM7NcoppP2tvYrcpv0NduSb7sp5+m9NKW34Ka+zqPjH5t1p3BxU5IBUynJY6PuyDXVhyjDjkd/uQwLUcRWBoeZMuCYg96jpO5WA4G4Y1cRubmtoCPaIEgM0dLwcFB2h8L6Ha4sEdWFe3csSsNzz5SaLYCN8+c7BZAYwA1ZZPpn2uWk3Gwt6vqTWOpBcf8jPeXAPx7VT0UkW8C8NMAvnDNe08i1pO/HMC/U9XXish3jN1gcqoAKyITJHB9dTMCfDqAhwIw7fWBAN4uIo9W1b/MpVOWaRNMxiCPOzwys7KX08qMP/WrdDwQsrDl2xsIoud4zpJ5YdPaLL8T9nI344mfSo1ZfvghjdgOpraDA5ejKChSfkRaNp8Us1Z6xif/SI85PY14WQ8rtbmhnBaYTKT1JGCPAleUNj320eWqYoniGTDl2c5S2cLvPwyy0f7llpnZrDtnFiQ/InMlRWr0dIrDuzutFUhAy49kjT4BrqQ2w9Zeb/Ay5GSQbfJY7U16M7t7GweLpHU+iP4/EMD7+QJV/RD9/UkA30f3fr6797evIS8fEJEfRaIZbhKRKdakV0/Ti0AAvALArar6EgBQ1VsAfDxd82cAbjqJF4Hx+znFDugCVBvI5bZj9rNwpgn884H88agfecXCztl/XvJ7eAhM9qo+ang/S68dsKbL6LOigffy6d19PBo1I0pRlG3ytq4/GoR8VlvN2E8n7Pdyiaqa9LZsYUOXxzNejjo2M4+UZq6ylh44WPZv8LOB5bK/YVqkEnsDWDTdMeGBk8D1aCntI3wwFm9cNKW512b4eTyzAboE2PUGgNR12mFjXvTaxTXJZr0I3gLgYSLyUCQvga8B8LX9x8n9VfUDzd8nINl5AOBXAXyviNy3+f8lAJ5zDXn5xwC+DMCPqOqHReSTAHz7Ojeepgb7WABPAXCLiLyjOfZcVX3jiVOqa5TLQ5RVhWK37DU43zeArj15vAD6HVIk3lLa+8BG4sGV+1GUP69Rc79UbcIg8tSREzZ0824B1ts82jTf5ubjgQbotNv2Au/jRigq0wLJkp1Os/Ztl3OWQpyPVHn0r/cA69+Dpwe8wm9i97HHGz+rqpplsNEoaL/NzYrjXnotvCWaab7twdY3vECDtTgBjIGG7byDsr1ui7PRhs60kzlhHiCnmfgGcrWyngFrpajqUkSehQSWJYBXquo7ReT5AN6qqm8A8C2NTWcJ4A4AX9/ce4eIvAAJpAHg+ap6xzVk52Wq+vWUt/eLyPcC+JVVN54awKrqmxFzIXzNQ9ZMrNUgJtMpMC97ndAaJ4Oln55zO/T+q9YRDVP8dIyBgyVar8+rpDgfEcj5Pl16rZVR3PMi3uco+PCM0WOzQrqXE3Uu5jiWy5YqsHqMttdhN6qicOl6NQz9U/yJMIPP8YcpA5/9qM47eiDQrP1o67XZgOZonbOjwljm7HgwOhyjHKwdsEfzLIzLEe32G9VrKJ4+4Iddq2zYD7ZRxt7ojn0n/X4OMpqpqr4SwCs3lJXP4D8iUgD47HVuPHUj10aEABYAJvMU+R/oKDAfuII9AryPJ2MXK2u52BoeALyGxjSEDzoD9NM1yVrlI3D1mod11hGA1aJETRSjx5CiQOuXOsiwieNRLHJYxIH77GfLFpSJcXeM5zVA5d+9XQEoI2WVErDt2TmdNtD1QK13hsWoIGOanh8lGPm9Btt8xhZmtVSLq5MQx3zd+vyMlWVTAMv5OAciIv8aiQq4TkRMAxYkg9kr1knjbAAs0CdJG+4O6Nx82NYAxJSZfUczJQNZphxYGMvs3mgq7Nu3B9lR8Rf4nhUVwi8yaDJoPC9XnYlp/cW07DZG9KSqH4WKAgJFWQBFISHtkStGFi2Loh3YcoOa/8/0dMvdsrHOPUeKohtITCKQ9Ilb3RuS+xfouaaRmUR7vdNebQuXCFyB7rGeo+7Fh8kR1jmVf9Wgei1y/oK9fD+AHwTwb0Gcq6quWkbUytkBWKA376uaWRnbDLiPiaw5jUUfVKPObvdH7dTOeQ2Zz/E0z7tKZSUCpUjViwxiDcD6amOx+AptdCi70D/TPvRsgcJ7Jtjto5qVS/e4lt5UOFd0X+TBIJnjAzhTY5XNYGR1Opt1S5zMj4zrwdRnC8XFljc/swCyFE6u7DbgW5b8Y6bTgEf2lReBfTT6b1LOEcCqqiLxu/+7iHwigBsBVOa/r6q/tyqNswOwTuU0I81kIoN+YJdHlBn3Ixbuo9xXPSCyJjXWb1e1s+x5TjS3FYDPEN9nAOs4ad+XurpJyzCFO1xO9c+UIafJZgtZVSm26gJZDS5S5jnKV0+59zixDlpbQ7DrGRwNXO13zm/Mti/Y2elvY+BdHZoyR+8op7Xz5Yzl83kHtDggw1tuCpADWLvG7t9UtJdzBLAmIvJCJIP9/4fOJ1aRPAtG5WwAbOR0T1IUnXWV/Sh9W7LvsUj7fiVXTqnj+6Nwe+u0sxHs6pfZHpBDMt85iC/15fPfprFXVYmiapzQQYASZI69E3zanD2FJOB2J0xzNWt4xD2y+IURPGMIJTPo9IKnsLsCA5A55M7n/fqeTvtRzRjxbAMuC9bCUbszDUMD+y9jPsccNo213YZmRvET2O3A8zZeM48a5iY12fMbcPsrATxcVQ9OeuPZqY1MA/HtiNuYn1EDQ010TJPlY36lZNR/RrI5eIZTyNPzKiqIJxsjf7Jc5hvJ+f8Cff9K1W5wSklL810OtcMgLf+7n00BGrDn98LT44j3zm3owIpYUTh3s8zL8PuZFUXa6kfY+u95Jr881nPUlhGOgmXfucZBkvLdhRDk1811xbRtqyAXzY4J0S60JmN0D1+zrjawjpw/DtbkT4Gri9tydgB2K1vZyr1fzifA3gXgj0Tk1wG08fhU9V+suvHsAKxXZ4LTbPgFYoOS115ZuzVZtaU33+v9MKM0vVYSSV27zLFbT1X1rdhjGW4SY4rAKy28k4NpsUXR56/HwheGeV9DWHs2TwY/W4iMhawQcrX08pXRXo9rablOP3uujBIxFbGuu29uPLwthT3HE6PkHZCdXXBAjLpGVZXtir7I2GePMc611V59nATAxzeMjQrRChG2Cm9CzifA/qfmc2I5OwALZF+eAavnX3O2GgbBsVgC9u2jNFk6bHhhgOXfdr89gz0K+FnHxw1nmXP1yVmBPJrTNRxlivPC208DnbE8Nwj5qo9WwPG5nLDhMLIb5eibqoojf7VLcX0Gi26DQuN6cytagcaTwgxdHImdl5b55ziXq9CIFHk20Ioqi8XAeWOa1/Ovs6l2gcHXAViqjzbPvhyWx3vhQoN7i6jqWj6vkZwtgAVCNGQ3RTYMR5pVDlx9p/YA6DVZr2wamDEIsMIThUtkEDfQm3h/yZwX+opRoRh5s1HdeJe2qH74fCR2PLfbwxhn68Gd69R7ovWMcJQAG9+YSmXlMcp/UTRLThloTRiU7L8lwhprTjOMpj6UkaqaDNoV59OawXSKIbCuC7BAN3BEI+Ym5RwCrIi8G0E0LlV9+Kp7zx7AmjQNiAORTKddo2QbhR+cPXj0Oq9b6mr3R6DEFEFP22reRVkki7mly+2e2zrP4CZzB7CLRX+fD3545HaD4V9+jjcs5Tr2OpSBvyfqv96gl7uen8saq9XpQGN1iUVbavtdKkwMMzlWQVm4DJhEI6ol4i1u/CIjbslX0HKJcl4lgC/j+ujFTvARv8zHDeieafyPPY/dNPxUa9Nge369CP4e/Z4D+CoA91nnxrNZGzwNrgoUU2k1Q9NmeWaXA0ffxsboM57eAm7JJi/TXPRBMGlG0uvQQTGGNAGDq2XA0N9URMvsmpsqRbPXKF/RsuKi6O+Wy1RALhYsc725/PCgw/jGY0dbv5E26OgAoAPXXKCwCKMHmbIMuGcNFg74aU80PeEK50LTqkQe/LheW35/WfcLw0S2FdoS8LsR2/OZ/vB531IEWVHVv3KHfkBE3rzOvWcPYH3vWCzSfvVFgcm8Wx1k7c/7WUaKBGuY0SKE6NFFQStpFsthx6LGW1STELh92gOawCgCVsVYIt6vEXMD4vxGz4x2cPD2NM/P+r4ZrZiLxJ+zZzNg8yaMLRUQLYN15TCt1dLNDa6Mlawht8HGvRrpLWwEsOb+1eJmVXZ+v/a++L/PcPO7qPpt0MTqvRf2MdIWTKJoMdFvroxNa5znEGBFhIO9FEh7fZ1TDTbXwJoXWzZa42Re4biWdiblt+f2t7IW1luCSNIqY16T8A3baUFpvf8J91zhDm3WO99ZbS7t1fF+lYRrFuy/33Uh+m2AGwXNsWysirWQowmiYg/eQYSSzbcFc/EYkrl8YDsE3PTbq7uZm46WyTvBQN3az9TiO9iU3HMwI5WUi0i5qs33/vs6s2nHctl/+T4K2NaLYEx+lH4vAfwZgK9e58azA7AMLjltwK5rOkU5naKcTlEUHT/HG3Ha5ay9DiItRc8DhlaUnGbQdLKc2xRnudfGuXNbR2CKwP7z+tEocfSNgD5oduRDn1OQDJCjRzEVwGAcYX8ObHvGKz/t8PVC1AAr+T5NP1Mxt6eqalZDAeNbdDt64LhO78DjMdfLdDpJw6lPi8F2xWKEHu2ydPXgp112gw3CXgtfBc6bAsXzSxH8/au992wArAELa40MdDw9Z42juX4yn8M0SNZkudO1U1I2IvAz+bfXYCOtx35T42ZDl2/frEX3DkYuWv5ZHsWa6w3UHUYMyu8NcP4xLJEGywOU4YZf3gq4VVf+QTWG9eyFwZWm5+z2xZf6arSPLI/Sbga8zJTfo6/LJiE2pEV+vHzLfD6BzOkgtymXIS6qr9/BrhAm7DzLYj50Vg5G6ojraStlAxrsOTVyNTvK/hsAn9sc+h0A39Pslj0qZ6c2DAF4Wg502of1NHYeNKkqWHhDH5i7N+hG6Ueg6oE10l5zaiCGg7y5JA00Fg+yXouP1MPgORGweuOTzSRN+HH8mEgrjfbIaoG2yMzb7Vj0O1cYB672CnKriFnBb0MaXqbdY70F3muYI2Dh93Gz2+q6a3rT6QTlnACW31uTvtFYEZOQ2kLGUudHTDseDRT2YrhMHJZrk1rnOdRgkQJ3/zcAX9f8fwqAn0KKUTAqZwNgmWe0xsP+f37XT3ZFaebCFvc0CrNnlw4stcBQu+FeFalOHlxdg4tmZmwtHmh5Vm4zeAH5aSxLXbcGl7T2fn0Fg6uZH8EKj22makX2m6q2lv9o5wDKY/ibUcZlgD0G7FX423rAiuMOUBcOXKNZCrcz+z8yYHIzYI7ajk8mFqS87AYbYFAO73FRFMRD5+gLD672gn2l8HKwDv3XjptwIjmfAPswVf0q+v9vaBusUTm12hCRB4nIb4nIrSLyThH5583xF4jIH4vIO0Tk15oNxLayla2cdYkC9uY+ayUnjxeR/yoi7xGRwSaDIvIvRORdDZ78hog8mM4dNxjzDhF5wzWW7EBE/g6l/TkA1oqsdZoa7BLAt6nq2xsO420i8iYAL1bVfwMAIvItAL4TwDeNpuTJc08R8HTPc145Pq+RrPuf5wM5LfY59HO74+Nu2kUfH5+Vp+mDaWw0xeN15L48a1AFES2QE9OoTCniPNpss41JiszWLQtX9+sQvFGm7Tdpr2bU8lQPRzqbVJpf+RR5fvBzPRHa1H0u0PhgA8vjvmZqdVSW0mN5eCGMj8hZFprqkPMbabG8CMU0b6/Z2hQjo8FGoROvWjakwYpIiWS9fxzSNtxvEZE3qOq76LI/QtqV+rKI/FOkHQjMun9FVT9zI5kB/hmAnxGRmaWNji4YlVMD2GY73Q80v+8SkVsBPMBV0B6CJWiheILQT+ENYA2Mnb+L32HVz07r2rlh+ZP8icCV5/tuTm2+uf7ygcEox+kC/WlqDmC9tA8se5eYF0FOmH+15Nk1l+k7y1o7ODCwnhRcPajS8QhcuT6NYgQacLWAKAcH3YcBlhHOxPOUlji1saJIvAgra94GyyDqmYacsFEQQL9dMwWWo594QYqvU//yAGA+hxZllse+KtmsF8GjAbxHVd+bkpbXAHgigBY/VPW36PrfB/DkTT2cRVXfDuDTROR6AKKqH1r33nuEgxWRhwB4JIA/aP6/EGkE+CiAL8jc8wwAzwCAG2+8cciNsTsA++h4Iqv5sD2DFQHz4azr5EN7Ij6KwcB6mfeZrLptmbmzeX/Ulm8b60jcc/n7xFOv/i3GpZpEFC/zrNZPSwvu7jWsMXDNSURO0/cYuPooaj1gvXwZuHIl7YHtAdY/nwvqjzfnynl6yGQiocua12Q5GT/ZMWHg7S2u8J+ovqJ689cFAKtFicWi8+Pd1J6HJ/AiuEFE3kr/b1bVm+n/AwC8j/7fBuAxI+k9Hf1ttOdN+ksAL1LV16+bMZOG1ryr2aEWtvV3s524qOqPrErj1AFWRPYBvBbAs1X1TgBQ1ecBeJ6IPAfAswB8l7+vqeybAeBRj7pJB40nahGsyvCcdjrFsmFMcjOt5RKo5s1KHK9J8f+67uaj3h8VGDTm41raHW9zHawokIxBY5TGGHjmOhn9ziXJireJ/23FMWqgN/0GYnA9SRlW5N8WEzC4sqbYutmxX6uB7OXLfS021wDsHfp8s2q5WLQAUlWTXrAt74UBDEHW8hxNfNri8sAUDVxcR/7mVQBL2rlfgLMROZkGe7uq3jSWWnAsHAZE5MlIq6s+jw7fqKrvF5FPBvCbInKLqv7Juplr5H9r0vXycgB/COBjC7AiMkEC11er6uuCS34WwH9EALAsqs0a/bGLrJP40Xo6xdFSQqcDbqcWH2Ogxfr/ZnX1vYTdX2gevTjo+0zarb0tZ7jdrOCM22dG83ifZ0pyLCkulj/nq1MWh8Alx3uvw3evMQjkMhsxNibt3o/GWQJ9gGWg9d4DnIeiiP33eJpuIAug3K3CUIOreO7cINu2hxz37/McJRId4/ZbFL2FEp473ohsjiK4DcCD6P8DActnaQEAACAASURBVLzfXyQiXwzgeQA+T1U5GPb7m+/3ishvI82gTwqw4DTp2IHIeo7DpwawTQZeAeBWVX0JHX+Yqr67+fsEpI3E1pcWlcgqYJqHJ/ObQJqHd/e9uhjsLIl2ZdO06ru65KbfHswC48HRUsIZXtgGV2l/VyER7xzFHuBi8H8G1xLHKRap5zKB4ZImk7GtdvnbH8+AbA4EWmDygM/uWUdHsc80F5ZVURsVc1P1xQLT6WygWJ701Tn8Q2/VlqdZ/KC6buL+N4bJbkQ2y8G+BcDDROShAP4CwNcA+Nr+4+SRAH4CwONV9YN0/L4ALqvqoYjcAOCxSAawE4uIfJyq/rU79vFYc+37SoAVkQLAIwB8EpL17J1BdJlIHovkkHsL+Yw9F8DTReRvA6gB/Hes8iBg8Son0Od8DA1oh8/DheDwMFFwQAywpvgeHQHTqXQ0gT0rBxD2m3m7BmC1mmCZmZGGDdoD7ElpAl8/RdHrQNEUO5esVW/Pl9QbihaLrlJz8Rx9EAMG0KgsI4NLTjHraX6+wMzPMzrbIMDxGO36KNQfB9shEJeqgu2KYMquPT6S3KBmuNQLxxjVRaTt8zH/Uj3y1zUQbIa5UdkQwKrqsuE6fxXJ/vxKVX2niDwfwFtV9Q0AXgxgH8DPNwrln6vqEwB8KoCfEJEaQIHEwb4rfNC4/CCA/ygi3wrg7c2xRwH4gebcSskCrIh8CoB/DeCLAbwbwF8jxUJ8uIhcRho5flpVw+akqm9GjPJvXCdjWfFTcu4kVdUDV53OsLjUxwJWvNjYs1x2htjSA6p/Pv923JZpr54+42wOhAFhDdeyXkL8nQEuez7bBceSHAXXMQ3WJ8Yf9kUyVMzNl90g0+526w2DvHX3MiiUIbAtBbWLbcYTDaJ+aSkwDKJLeTSAtfyNbZUeKaLZQYLrZUSzz4IqP4TyboHYrYgnVYpHZcNLZVX1jXB4oarfSb+/OHPf7wH49A08//8UkduRtN9PQ+KA3wXghar6S+ukMVYb3wPgxwB8o2p/ctaoyF+LpKH+9FXk/erFAywTmk57ZVzws0eg717K/WetMd7y4DXYqsIxShweDhd8edambdgR37aOBsPAEPQUvn2dSFN+B4ESx3lfUq7UiBowyiaXdw8ArGU5MBMoqipVns3gvVIc1ou9l+US2NlJN3Dk/+h6Xo7mVUwvdd36xnpq3iTyJDjxIDGGgGMDsb0D8pE1vr8opBeDYlPBtDZIEdwrRFV/GcAvX+39WYBV1SeNnPsggP/jah96NdLTLKOliw5gj+qy1VzNYgoM/ct5Fjj68HU6JdC6ZVls14g77GktAHpLdD3Xl9PyGAB4hAjEx0f14hW21q91seznx7hM++99fAw9OKp+1OG8duunuVwXzfUWU5cN/XabQPvpGLDyi2VfrmiU4QGTNzQ0jxQ+z1KPbzDJY0gU0nE0VnpUP+7ZAy3VZyTSIAAURRnuBn9Nck6jaV2LrMPBfkVw+KMAbmFi+R4RD2bAsFM11IAZtsxWEcUbNuWX/w/+RMjkaQLSqM0ti7VXu9VbmAfh+bwxZUyD5efyKp516AUnXkmrKsTGHRstomfkkJsfkNsqwYvVBYGEFAWqqhx9Fb1ZDfuymkpuWncUQ8Lyx7MSBlpetz94cP+Qpz75HD96VGv075rrhiX3rjMUgaddNi5bgO3JOoTJ0wH8HQC2auLzkVZNPFxEnq+qP3NKedvKVrZy1mQLsD1ZB2BrAJ9qngMi8glI3OxjAPwugHsEYOs67b/Vaqo9dQsdRTCd9jTXVVPjtR48Rly6qePSPTu3HqI1avhnMAfr1R1e2M75i8i/FeKVy3bvJ5A1O7dCa1Vl+uXCHMMQGNcEuTzO91OqCmWVNglsj0Vbd3ObsHZh2ivvhhiVwXPr0cee0VzHrzEnK2kofr7/HyW86n3zfZl3Z+9+Y3LOKIImVkpWVPWHV6WxDsA+xLllfRDAw1X1DhE5yt20aanrNP0uufPYCepIxyjbKTr3o6g/M+/Y48K4IUagEvB35sDNq7YYl/zze+0w6gB+GstWMu9etEIirs2qbBAzFeiDauTj6oHAG7gYnNglgQGQKyI37Y2c6+u670bH15uwkcooFL8FOqcdNZIxkKU6sDgTUTb4GL9OfpzxsnXdGFf94B2BbDTI5QaqNWRjmHj+Am5/3LUmsE5t/N8i8ssAfr75/5UAfldE9gB85FozsK60QFWVkAohwGpRthoka488Uhvfz7yjNxr3YpjaM/hZJtSJl81CBtNe/fJYlh6Q+2ewsN9PtI0rJ7aK18TwljAgNTCMOLVOh/Z58OAagFPvPj+osbDBb6yc0bux+3mwWAWyvhweaIHkLUJBfKLXx+NORF3Xdd/+VFbF8CXZhdEnklx7GGkXlr+NyDnSYC3q37XIOgD7TABfgbQ3uCC5Zb22cd0KA7WchvTdXcrONxLarlVfLjpgA4IgIBgqnwyu4Qwn6vTc+aoU7o29FHgZor+td8wDAqcdrYs/QefxIejYBdW+WxxsI2FRbIHcPlU+H1w3rl5WaX/tvbnBjNM1NPLlzQEtHzcUY2A163qUB59G8FzebJGV+5wfbISLVizTYgfLwSOtdJV3ic/7SDvhW7duWnlpwhR+PZIv7NyOq+ozVt27EmBVVZuoNB9V1V8XkV2k1RMr96PZpHDgjH476S8H5QZuYMLuMRFdZwbisszsgeQ7q9NsjI4A4lWVkdQ1gMqBhAGBdyWKtJoVAMDbdufwp6qa6/wa/TENdqxDW6VzhJgIYDkd5lPaisEQ8KxyxwYZn3bRhalsaQV+Kf4FrUG3eDGA5OXHURFySqd3ACmjd80JMOdgrnB2fa498H8nfifba5JzxsGSvArAewF8OYAXIq0BeOc6N67jpvUNSGEDrwfwKUhhxH4cwBddZWavSfxqJO6nflGX/WabkLcTDfp/BK727TW06RRalDi80scHVpY4GX+u7fyWnuceVwGsz5dr3Fw3/tYQXH10rDEN1nck9vznCmX3pog/9xXkQZArNdJc/XuJ6i3KO58bm3bzqpTeQNN3c/JJ5BTzaGsYa7stTeDFI7ZfvcC/eaGE/86A7EblfALsw1X1q0XkH6rqK0TkVUhLeFfKuhTBo9HEclXVdzcrue5R4SlMbsTl7an9LDQSu0ak28q5DRgdXWiNlaNlkdcAMFT8THgqybNUAJhYmvYsBlvOQ64QK8Q6ca/MtoMuZ8YXIjJweS7Y1xF3ar9RV84A4qkAHo18IXi0YND19dAYt4SfySMcDxwRJ+tBm48jxe8tmkDmPiRhpMHyNR5kAfrv3/sqrZpnVv6TA1n0q3xjcn41WDPmf0REPhXAXwF48Mj1rawDsIequrDoXCJSYd1dCDYo605lfMMde9/cNntbdkdELf+2WK8oe8GagBiTOH6I6hBgq3nZAUEOXFhynY7VqLq/wmhQbnftYIlbRDCa+OVHkXaf6+hRnu3eMTDxIOzVcr7Gr4U28avSPCXiqRA3W/H5K6cF6swmmrmi5NZahMplNMjWdRylLJo1OM3VeGOfJLBBDvZ8eRGYvKKJ0PVdSJrrLtJWVytlndr4HRF5LoAdEXkc0v40awU62JSsGhj9DJq/e6ulTPzvBYaaTcTBFkUvWtaComV5DpbjD7Arjsc0u6eqJokwGNPeOM8eDIMH8K6yduloJ44ANgeuuel60LGz4Ho14jVYlhywWt0wuEYBw3mQ4fdtM4pgp4OimGAy6QZOr4T7auOkrVpaGwDHtI0IfK9RO6+G3pYTZICta6AesQdsTOk8vxrsr6jqh5EWW90IACJy4zo3rgOw3460musWAN+IFN3m5VeXz6sTv2u3F+7LPSNVtA13jhzj3zmApTiz0R56div7PEZBXjg2ANAZOkrP87HwMU/4MsgGHTNpx9JL5sTb4wBDcGXEWMewkgPGXJnXldy02rtheXD1QYJtM0ug82EzH1o/p25eWjmvUFPgFKDvCsjC1cO2wKpqtgxaLPujrgd8/82A6zZLO0aJ2g3+QKz8FsXWi2CFvB7AZ61xbCDreBHUAH6y+XzMhBtthH09YOUpb45vG5uCR9Nfmn7ZDgk+jjPQ76NeLNkowEcqmwyd6LuT3UMiddjKE1nqXVJJJIEsgzoPMGPgu0qDzYF3TguPzmfyv5ZwOfhZ9qI49CIQu6RVVWyR8lPy5bLVYqOsexAL3eMOXFvlPDNSc314XpsG/+NasqyHtU1vb9yInDMNVkQejhRb9j4i8gQ6dQHkrjUmWYAVkVswwrWq6mesmc+tbGUrf1PkHAEskt/rVwC4COCr6PhdSLP5lTKmwX558/3M5ttiDvyvAC6vn8fNiEBRFsm1id/hSs01ckHy3KKf+kZO8TT1sg1KDw+76H0cuY81BtZaoo9dZ5KczYN1rZ57jMrM5wDntlQOlaKqRFm59DgNP6XPTflz3znJad/rytiMg/PLz2J6INJgDw/7L48jadkL9DOhZrFCWSnqQnqLOEzYHbAo3PbmuRVmvhxjlIzTYG2bomj7sYjC3bgGe46MXKr6iwB+UUT+XrOBwIklWxuq+t8BQEQeq6qPpVPfLiL/D4DnX80Dr1qaxibuf/s7B645g4anCLwzvAeS5tySaAFeFhuFQ+Tb/SP8o1pPhjXqoPedM84EwGWXA6kvJLtNidKMNxFIr1wtgRjgVoGef85J6YEI6Pm/ryPPvVo0dqC/E6blvarSSzVgNTIzQz/YzgaeoWnjO/C7YvTjeXxUB0xJeGB1Ri4G1whg7Z3nHrEROV8arMl7ReTnkVazAinI1bfaxopjss5ws8cILiJ/F8DeqptE5EFIKyA+EUCNtO/5S0XkxQD+EZLt/k8A/BNVXR3TINcZI20uAlcDWFM7fWeyPann86EfatOQmduy5A8Ph/2OG633vXebzqZrihWrx3J8ZfQxC7orgynt1p+LolPiW5D1wBdpsT6P0TlPlEfv0peD5VrAlY9HbSNqE7adt996IjNQDcpR10282saVkWdVvNNtDmCjILFsARsZobVxxfNN3tolv2/mYNcp3ollwxysiDwewEuR4uC8XFVf5M7PkDDmUQA+BOCrVfXPmnPPQTLOHwP4FlVda2FARn4KwC8AeHLz/ynNsS9ddeM6APt0AK8UkfsgcbIfBfC0Ne5bAvg2VX27iFwH4G0i8iYAbwLwnGZTs+8D8Bykvb/GZWxaydpr1JFYWzk46G8IT9P/VCNVZzm28801i4Ou8UaN2Av3C1492m6BvTTn2QDQIkNRBIBjH1d9Pq+MgwNNNkor0kK5Dg3Y7XvsXfo0OK0xGaMoOP2offiR0cdesHsyg1TW4NU8Q/jZ/Dygr1Z6FdPPPoDUQHiNN7dTGvAtyFBd95M8Pu4bX301sWfOvVGDFZESwI8CeBzSFt5vEZE3uM0Lnw7gw6r6t0TkawB8H4CvFpH/AWkX2k9D2qz110Xk4ap6tYuCP0FV2cj/8mZDxpWyjhfB2wA8QkQuABBV/eg6CavqBwB8oPl9l4jcCuABqvprdNnvI0XnWk88kNoxbtQeYA1cGWC55RXFEFxZmoZ9XHdbcDNFwDNLE+a2rAFzYPzZVPt5iDrZWOuPgGlMDSmKQb8HuimjPaacOt6Z0/buETzfNKQ2QPL89dhA4fKZy3/4339zfjwv7ZfQ+eV3uYHElyGikPy9Xp0EhpozT3043+yO4N3DmudrNekVA+iKE7kPchIsZbl6LDyRbFaDfTSA96jqe1PS8hoAT0TadNDkiQC+u/n9CwBeJmlF1BMBvEZVDwH8qYi8p0nvP19lXu5oAPw/NP//MYA71rlxzIvgyQB+1naNVdU73flPAXD/dchfEXkIgEeiWW5L8jTKtL/nGUgxEHDjjTemgx5IgSGHFVEEDLCXL/fBjf2/eB8nh5DR7C7agcRvJFcUfe21BVeblloZTPx0kCWnQdp9Po3mmELaDsh9muk927IcQLtAoVfnHgz4mZaAjSJ8ng1tEcjm8r/qXE579Wlz3fp24kOg+TxaHZrxhgeKaPCzNHI0RLRpJNMS3tcrQ1V5+60VzTMP7KqVq1bTKT4G4QpvaIJImdysqjfT/wcAeB/9vw0pyD+ia5oZ8UcB3K85/vvu3gesm7FAngbg3yFp1Nqk/fR1bhwbu+4H4I9E5G0A3oZu2+6/BeDzANyOtAhhVERkH8BrATybQVpEnodEI7w6uq+p7JsB4KabbupeP3cQYEgNsAZr00BvMT446GscbMiIOn5V4ejuoQ3NY71d7v+z9tqzYlvni6bb/H+MB/WS0RrNQG6bMeby26tnP1B5QPARc+whNlB5TXYsz2Pf617n64fVc/vvtVigj1S5F8icaFSm3ADP7e7KlW76E6GfGdWsTvkc0wIBeHrQ9Ta0qDqrKnURe3WbEAs8v4bcrqo3jZyPEvK5zF2zzr0rRUSepaova3jdLzvp/cC4F8FLReRlAL4QwGMBfAaAKwBuBfAUVf3zNTI4QQLXV6vq6+j4U5HcwL7IbwmelVVTMp7GeiIqmqp5LSennZkWSDEE/C6tXvGx2wbB/aNtsK0MdlOUUMQx+vv89XRfpPhGeW69GCKQyPn9AH0jofdIsOs8YPhv/3uF1qpuZVpRABbhSurjIbjyC7S2woM0c/JRPfKGiFbmMfCOOFgPrhE1tIKmyIEnH/Mb//IyXk5ysUjFsGZ4rcJebhuQ2wA8iP4/EIC32ts1tzUxUu6DNHVf59515GkAXnYV97Uyyr40pLAZpk4kDRfyCgC3qupL6PjjkYxan6eqa/nTqiLWGOw7AlduZdzQ/XQwAlcXMcYHr2YZgBQZD/zy8HCKyGVhIPJ5isocZSajKUYYzuA/mWBokLlypaNXmDf09WS7FuTGSpuH5kBz7AP0gpTUNdp19REGpcc1wXM8n848bMTBeu2VI7Z7v9MxrdxPc/jZ0SdKJ1MfnDTbar1ibo/2248ZK2JNf7GItxS6WtkgwL4FwMNE5KEA/gLJaPW17po3AHgqErf6lQB+s4lf/QYAPysiL0Eycj0MwB9uLGcnkNP0Cn4skjvDLSLyjubYcwH8MIAZgDc1Ebp+X1W/aSwhVUCLMr+M1AOPzYV9QwaGwUvYJSfTYQQKEWn7Fy8csD5oyTAdYJ/5nLTXSP1g9IvyHAFtRGeYBGXx+WWlrKVOLzka5fAwcdYMsgwKBjbHx53m6he1s8bnj3HGCMh4hwouvg9sbef4dpOJ14StTfDaZr/4xCoooz2GmjZnIpJ16ByrH15D7c38RdHSA76JWxG4WXETiXCcr492Mb8a2aQG23Cqz0KKXlUCeKWqvlNEng/grar6BiQF7mcaI9YdSCCM5rqfQzKILQE88yo9CD5DRO4Mjkt6jF5YlcCpAWxj/IpUvzeePK3UECbROnf7zYFgWfz1HPfQT/1GAHwySZsplmXnQWNJcONlwJrPiXu9fDDk3la1xlUabE7TdfcabphyzNTpfA7s7ACyOOwbAg8Oht+cb6s7IFED/LBcxC3OmwdZxzEaQKxTXD+7BpBCQEbTdza3s8Ze1107GHuILw8/wwtb1a2dmVrJz7E6YyPhbDZom1x+73LH2qrPfjTxsfvtcy/UYKGqb4TDC1X9Tvp9gP4SVr7uhUi7D1yL3KKqj7yWBM7PuratbGUrH3PZJMCeB1kJsCLyvQC+31ZbNYFnv01Vv+O0M2fSarBzWsUSTZ+Pjvo8ZlV1qhstKQTQRUriZVXecGHpL5eoqhKTSdL2TMyPkMW0Qvvs7gJycKXvOcAuQpZQNPVk8ZRBTgtmFabJnEynmEykt5zXqmk+B8r6KGmplxtK/MqVPjXARi6mM0wOD7t8m0rkOYnlsu/CxWV1U2DjFj1FGlEE9gj2cLIA5z0mneuEPSOAvpUnx8OOUQRePAdjzzcqxeqDxdrfvAnSNJ1222w0H+ae/dJsz4rxuUj42k1psN5D5RzIz4vI/VT1Q1ebwDoa7D9Q1efaH1X9sIh8GYB7HGCPa0kxUyOA5Q5uxyeT1Eh9r2TOjefzDMQOzMqqws5O12V5xucpAktufx8ol4dDoPJLMi3/HuT9FJcJt9x8OZpLLxaYTmdt/o6PabtuBlcGWJ/nsWVBVu9WIfzbc+FReZo0agLXaB1GRDtHs/YWLKL68+Z2e4AJT+NNmKf3D7f0ozphGsXa1XLZDeycPjtLA31+qWkTual/Tpi+ytHE6zBV68qGvQjuDVIjgewEwG8A+BUAf7i25xPWA9hSRGbNqgiIyA6SkeoeFesTvTXzwFBr8lqorwsGYVbj7OMd/K1TLhYop1Ps7JS9vhApIrNZ0nTL5WEfvHJWeK8hsTsQ58P/zwGs1QWVU4oCs2nVbbRoGx4eHACXLnUaK9AHVvPGYPLO78sT5SlnNY/KUnTbmbAjCNugvBYL9HGJi2vXDnLp8xKpgV6id2T/o/T5Wr9QJPIQ4ZGZ2x6T+FUVerKsUraZAh7L9qaMXMD5Atgm9sGLmqX+X4zktvXjzarU/wTgV1X1r8bSWAdg/y8AvyEiP4XkrPs0AD99TTk/oVinWyyAohBM5vOupXjzKQMtm/it4RpoAF3vZG3BpnCsnZBWXE6n2JlXmE4lVEYnk2a1lhmHeKod+T2asAbLAMvXR63Xe0WwpuYdHJfLZPixa3iVmwdYUyF9z4sMWNyz+aUx2Pr8B2WJFM0m261m62+z7LGy2OJXZLjMTev9OdtyIGe08+XIlc2uNw7DAysDLIOy2/7F7HORBm/ffsNPe2RU5I3tYEByDjVYAGmpP4BfbD5oYh38A6RAM6MBX1YCrKp+v4j8MRKCC4AXXGNkmqsS63CHSY9G1Ux5W39H7pXmPc08GEfT8tuCWEN2U7JWHMiiqhJlMB9qFrI8Ai4vhgDr4x947Ya1Hv98rybzvI/Fg60vgz2DAfjgIFECzEn6ubjly3Mh7AcbAc+YppjRwKNTzMlGyh9nkdNAlZkZ8ABsZQH6fL33fR3jXHlUGMugffvf0czFnt/EIPZjnV/2b4B6dBSzXF7GinMtch4BVkQeC+Adqnp3E0LgswC8VFV/cNW963oR3Apgqaq/LiK7InJdg+r3iKh2vJz972xWJYqq7EL+MchyBzEw8AE0zD2GVwXktB52lm860iA+rQGXNxKxV7jn5fg5vkNH5Jl1Yss/z/GirXf5evvPhp6jozTwGJB7/tGvwuIo4laOXI8dowgyCOAvZ9rAd2B7JXx963pkrloMmDaIGj9vN1g5uB2sAtkIWHP8eASkHvA5/ea/Gf6iqG2cFdMzJpN+tfq68ffa99bINSo/hhTw6hEA/hWS/+2rkEIGjMo6XgTfgBR05XoAn4IUNOHHAXzRNWT4xOL3nuc+kfq7oKpKVNNmFY9v0AP1BsPzfJ2forNwT/bHfMQkAzCLQWtipm7/jEibiTRYvpcrx0Yjf30wOPSMV9zDDLQ9VRFpYfzfq1VMDfjnO2QUKIqiv1tFpPBG44ennNnHc8JTbV75wXVqlBHTRZNJN/BGhkdGLh7Y/UgwRgMFAG5r+esardFvzDvAgyyPpVZfPHWPmtom5TxqsEjKpYrIE5E011c0y/1Xyjoa7DORQn39AQCo6rtF5OOvPq8nFx4Z/bpqplxNce3FNmXAshtMoml5Tkux33Yfi7+GLTL8zUjAAOvz0+SpNUhNR7RD02K9esNAy+5CJgwEvlx2rV9DycRdZOjyAO5BiAcmBt4GGYomlsCqTu+VdZ98D2DZrcP85tiLBOimRnztbNbFVohoI5MxgM1RBF6jNk110S+DtfsMmwKRPgfNrFE0aYjG4k1yseeVgwVwVxPA+8kAPreJVTtZcQ+A9QD2UFUXzbJWNEEVNhXcbG1h+pLbrs1W67qPIT2QtRuB/kU5gPWSa+Hcon2a/ExLw76Lot8ag7igCqEyC4pq0qcjrMBei/baOdCBLYOij1vo64VpAT7u6yX6zXlhh1auF3blaj5FUWaV5FUSjXGJJphAWHNlf157wGTS8U4Mxt5Hel2QzanaLE5rZUMeJxkNHiweIP2+YLkV42NpXoucU4D9aqQ4CE9X1b8UkRsBvHidG9cB2N8RkecC2BGRxwH4ZwB+6aqzepXiZ5mMl4Yfg+nPtEydi2/wF9EU7biWsIEURYmiyuyZxZ3L/rMPl/G/Hgw5L34O6JK2bJYd8dxVhtca+SaPVhHR5nlUX0d8jc8cR3uOBiFf11Z+Xnxg38slivkkjLTP/4+P+5obP4ajD/KK2JkBa26xBAPuZJIHWJrCF4VbB85lNfWT4wpnkMfc0+p6GFuAx6jo1fkJhe8bXqPlmOk5Tvta5TwCrKr+JYCX0P8/R+JgV8o6APvtSMFlb0HaqvaNAF5+8mxevVhbZSUsorYYhK1xlswf+BubTqNF2TY23h3WpNsmqWvRXTIlqqpEOa/8iT49YOeiFphTLdzposgExGaAM3RhbdXTHx65cr2CpwkmfgkQEHOPOXDl//b85j6pj9uYD0zn+gA1BrK+jthZgTeknO5NIKy9Wh2ZMMB6tz0yevkBuMzNVnydrSF+EsOAmJs8FcVwrQrrDHzOFpEZG2J1uqlQhcD5owhE5C7Es/XNBXtR1VpEXg/g9ar61yfP5la2spW/CXLevAhU9bprTSMLsE081+8C8CwkxBYROQbwI6r6/Gt98EmFpzY8Mtss3I6z9dS0vjDMYXPMtFe/VsHEzyZ9EoDNIFOmptMZyt3M8/zKhJyxrO6s6pyHAUXBc0tfCG/B8N4RQH9XAj4Xabkuf4OFDOzl7lUu3+t82pRv42F5ymsUqbEvrHRHr9Yr8603gXkP2M1WdoumZQ/28SartHsrG6GKAkAl3QyJZwfsKzVGKjfvGZBekDcrA8cIt2OcJBfBkveUMadnfcWKfOVKzFxdi5wnDdZLY9yf2/91Nh0Y02CfjRTT9bNV9U+bB3wygB8TkW9V1R+6ddfxDgAAIABJREFUxvyeSHIzTvYe4CniYLqS4WC9LcYb1lctI+TlmoDZSCaY7btOxwseos7HqND8rqqyT7VGBJ3dY4AXcbD2jIAe6X0YhCNKwYOrB4zDw5ju8L3Y7rPgPDQ4VPPJYHcWGzxzTE8OZO2dHh4Ck72q76LlG5T9Z5cuQiqjGwa7a0/JG8C/H0vPv2uXURtUclS3p7c9FcDFsKyU9RFw0OWjLAqU0ymqquzl356xKTmPACsiTwDwg0jBuz8I4MFIawM+bdW9YwD7dQAep6q32wFVfW+zkuHXANyjAAvEL8/bEfw1LQ/rO5QT5rrGrLlePDaZv/7RTLC3t9cZQooiLT7wfKTPLAGYcIeAAgviPKPdEexjz+POzZnPRepnI5rXzBg8bESLUC1yuvTf7HfqBguBoiylxyGaUmiGfg82/M1iiv10ChwtJWmxdsAQ2IT9/dzHtFdPp6fvJl2fqSiD3pDYXOO5Zj7GbdLHYPDltiwncHUR0JqTZROta29vp00/4rSvRs4bB0vyAgCfA+DXVfWRIvIFAJ60zo1jADthcDVR1b9uosvcYxIZNKxh5YwAgxtyCQSX+lB53Lg9VniANU3azu/t7fUpiqLolutyvlgTJbVVCnqg31cs+hho+CU9JqxNsy+mB1jyz+Rb2yAxOYD1mjg/nwcW8921b0cTmPOFZccwIjLMM1XD/72C34a79IDIN/m6qSqfvd7lyyVQmEtgBLCcvp8RNJmUomjpoEh43LKmy/sjmogAk0rTUm0fJN34gyZfAmBnZ6fX1jch5xRgj1T1QyJSiEihqr8lIt+3zo1jADtmX9yg7XE9iag9E14wFLlroUYMNnXn3G6neE0A0A8yktNkPcB62d+bD29mbdNGEEaDSJVZB2B9xvx/v97ex2AAoNUkTa2v9MeBdLtgNpthspsBWC+5HmeVGjhq2pSZKYKcMh4VN8rWctmEuzQtlhOt6849gVdvGcASTmUpiukEMh0B2PbCmHYpinLAw9ppbpOGlZEmX5ZNZXEAH+aXXSS6sqownU6wWGw12BXykWZ37N8F8GoR+SCAtYalMYB9xMh+NPPgeP8ikQch+Yp9IhLE3dzsVPtVAL4bwKcCeLSqvjWfiqU1PMYN60TrqB3AsvYQUQ28moa9e/x0jZO1+NOGZe30dLHoa0/ej8Y6Ju82mwPYw8N8vFY/redjrKE5Y85xE+BvQUna9BToDE9HR8B8XmK+u5fqIAKOohgP0j3yfoqqP132xhr+5uJF/70HW8iZAh3gOo1eIYO9EvkWq4+iaFaN2cPHOKVAourj72h89ovpigIdfcQAayQ2R4tvyjndnQzioF+L/P/tfWu0bclV1jfXWvtxzj3dXvIAOm1iR0WCQ2KjLT6CEkJ4KEgjhkjQmEhiRhBFQGKiQZ7iaJSHiAo04REggxBNINGAMYmJMUNE8oJEwkuMMaYlBHJz+957Xnvv8kfVt9ZXc9Xa55x7zzm39+01x9hjv9ajqlbVV7O+OWvO8/AiMLNHAPhJAHcBeB+Ap4cQPuKOuRsxhsDtAJYAvjWE8JPpvx9BjCPw0XT4s0MI78Kw3AtgD8BXA/iriNlrv/k4ZR0E2BDCQNDPY8sCMfPBO1I8xbeb2esBvAfAFwH4/pNczJPy2vG0P/ulfPvFL8eBdoRUVT04g+vgVIOXYsaQ9TejL5pqGGD0ol5F0soRYJmQ0GuvLJRXo3X0Dmivh6s6RSqLgMHVZcluxrmCxdnZ2Yo8d+meJTqEQL8GbPy7jzlzHCnxmE0jWmxpp59ro8WirJnpfNg+76aOlM5xJhIWLL1zolfeeV2fLCkWVYXI0ytHzwfpeYVk8Ku3l6jr4f5/EjlHDfZFAN4YQrjPzF6Uvr/QHXMNwF9PW/sfg4g/r2NmFgAvCCH82+PcLIRwVb6eKFTrcTYaXJeEEB4A8ED6/GAKUntnCOH1QFxqnkS4fBpY6Q/+lx3Ed08TNPXgeD/uqrd0buaJ5U8qzQbe0q4zibpFKSmo6cm5wcBrjN6oNQCuBFiOS8ap8caduo7KEDXb1QrY3p5hdrvTZPkqWe19eUR90xU7Dyel7AOSqazb8slzWy22lL6G70KZrA7Kz7uYWfgkO8i9lrCKXiO8F9kKJto8zs7brK+pZwkvAhRppslk69RiEpwTwN4L4Mnp80sBvBkOYEMIvyafP5iW9Y8GcAknFLfhYIoYh+DqTc0qq2JmdwH4FKSAMSc/P/9ewipV9Ia40uJFVrnP6RrFqhX9X0PKAjlmKJ3XahYKhqUZgapR5p+FnLPVTAMaUMYXkO8l384CuO7uxsO9cqxLYzIMahxpq7IzwWxnp68xqxrMcnm6woFsqTp6qv99tcopVT8BqxbbWv5V1JKWXsuVZUHQ/L39c+75+a3rgJ7CWSxg0yrrh9o0SstTiqCo99VlnfIZyMtanRIKnFCDfZSZKT14fwjh/mOe+3FJgUMI4YGjgk+Z2aciAuP/lJ+/1cy+HjEVzIuYsaUkfsOBmX0hYgCsI+XMATaRw68E8FUhhBKnO3Te8xDDJOLOOx+XTfYqHlDJTalWm21pLF0gyXFWdqX932qA13Cj/Fxj2XenUoRSbabE36lGwu+lCNQ8v1Qo1VrTXvvDVd1qqru7nQa7uxvHYSnLOC+t4WM1f+Nqe4KtnZ2uLHt7HUL4dDlDPqdCQat4gPPPy+NKCWvYbJWPuObc20IzweIgj1al7/RymM2S5V6f7XHBVd9TG5HHXa2s3d5KerjEIBWvXWqkIUPGarWWjjipnABgPxxCuGfoTzN7A6L9xsuLT1IeM7sDwI8BeFYIgaX7BwD+HyLo3o+o/R6LUwWAEMJPJ2riSDlTgE3uXK8E8LIQwqtOcm6aze4HgCc+8Z4wNJiAvusnBxHHdT2t+iPE0wQDzt76nUs1XR6qlgF0yUC3tlLUu2kAru31VUIFTF+xki9SCS1U1DFSgYuF4v56B65qJzuOBstbTaf9ELJdm9V9TbZEE3jQn05xuLCeQq7PQS/pQUGbTJvWNxv/m05joetplfHWGptCr13JYa1DP5bA3sHRAFsCvTWT6nQ6acvqN4YRk30iidUq5SHzHZMnq++bnHRcyvgoOc2tsiGEpw79Z2a/ZWZ3JO31DkTn/9JxtwN4LYCvCyH8N7n2A+njfkqF9bXrymJmXyRfKwD34JgRBc8MYNNW2x8E8N4Qwncedfwoo4yy+XJOHOxrADwLwH3p/dX+ADObIubQ+tEQwr9x/xGcDcAXIhre18lflM8LRM+Fe49T0LPUYJ8E4JkA3m1mdIH4h4gZab8HkXB+rZm9K4SwNnGYWlapDA0F7VeqqWnEcqwEHaUwdauG5H+j8aWkvVJZ9HGae6og195eRdKKKD/Hz0OcrTcoea0QyINNb29jf9HXXvmOQpE9ReCt1/75VBVQ7dQxOSX/UC8H/uaMbcw9NdQ8nvtWvtyvaLh60Wblf15iJoW6UyTFLU/ZG2YXAmKR7WDAk6P0XP1zKqnkUkmrKjRN3XqTKVVR4oRb8UZMDojVKvN19rEwNsyL4D4ArzCz5wB4P4AvBgAzuwfA80MIzwXwdAB/DsAjzezZ6Ty6Y73MzB6N6HL6LgDPX3ezEMLfuN6CnqUXwVuBQq7hKD910uv5jqSDXAcNt8hzgLUGgrQcND/y5PpHxSLVY0t4BhyTe1VCcF0lgdxQoSZmPcevWzUNOdCB63yO/UXdSxemGW6Abg7wmyxYnNIKt647upXv1fYE9VzKuQZgDxfWZjUfYk+GmkgnQDaD4oo2JdBfxXu6oeTqR3Dlc7aD/X4jel8+QGNd5oY838kKO1Qm8zlWU+uVVe2fOrmV2hWrVfdg1RpLfqGqTub9cIScB8CGEH4HhZRVyaf+uenzjyNmxC6d/5Tj3MfMvgdrqIAQwlcedY1z8SI4DfGDwA945WFz7bVzBAfEGdypN+qqNRSLVMviX5qgtA3+4q0tx7EuUxREPcr5Rmmazp2BwLq1FUEV6DTX1QTXrgFXr3ZeA4du2zqbpgSuXvQZaIBr+sk2TQx8Y9NVH+2qqtWoCK7e40xFU3KXnoUXfW46LymHvlYTFNFnaotk0aPa72cpj4a8MFGfz8l3oJKx8uAA0+ks+8vHK+il5talFQG2dIIaDk5RzkmDPS+hl8OTAPxhxM0NQNSY336cC2wEwPrlix981FTVY0m1V+X5A6yvVq+iq1bTWOt2o1v61T/baz0cHyW/zMHepnyD/qbnVVW+bUxHma4XlbOg9rq9DVy40AHszg4O0YHr7m6HCx7Y2J4eJ3zxvbCIqoHyNUlxDUoarIIrtebSvgQffGdIk6UwnZgHWaAfvN2f61cvbVNXEnBHHfkJsH5Thc68BFV21Lru802FCllVYTqdZOXRNtDNY75tW3WbjWrWZdJ1bnGnIbfaVtkQwksBIFEMnxFCOEzfvw8x4NWRshEAC/RBzNORSl2uUxpXK3S7jtxobaaz3i7KIU+oYxea7zqQhhBCKwL0sweUei8HSlQXW221fQFYNjNcu5KDK3dRaowYvyPNVwHor3h9e7DICtpVZbmrXDrpcJGnpCYOeHrAK2FDFKf/7ikfn2F1iEtm/Yq4px1JN3oov1HSYAlwrAwvzoIOaLAAUM+r1uNBV2tAzg5VFYAV+n1Nr9k74fTkVgu4LfIYALcB+N30fSf9dqRsDMAOiefYjppBqwrAQkBLLmJVhfl8ktmTOF50V5G+83TVsNjR2n3v3mWmZKzylSpVTG/CQqi24oxZBNhrVyKgMnwBwZW7KIc4T48D+pu2gV9tKmXcBj5v6ozrY/oVNaJp1UoTmo+EuFyKzUauXVXW/l6KOlVq4tK9/OPqXcBX2FemtCoh0NJZmw28LqBGFWO5zuddcHilOnirugpd39YK6aRemPTDoKnk5HIrabAi9wF4p5m9KX3/dMR4KkfKxgCs4suQeFrLv4A0EFWtVZBdrWBzYGtrUjR0qA3DJwPw4QSaxkVvUtVYs/NpBYc+UyUsBUJQgHUGrt29OHC4iqX2SnCl0lVSnpTWVSUZ6FM2utJVUaCN4/v4A3mdcsWyZjFRpQIG5mLrDETeUR8og4FqyxpvoHfsugJqMkh/YeWzlL/yHFShYNY0mDQNmnndK6utlpG+WOeCMQCwpym3IsCGEH7YzH4WwJ9MP70oJUI8UjYCYH0GUf2u/cUvCbmM1eVsprKp69Bi0XJW9TwaZ/SewPGCQyluV5Xse/cjXUVN3iWgLRGTLAArpwau+RzLaoK9vXiYxhXgJgLv0KD18e5ntKCvG4/8zxsij1pV6DPzBhtfriGDYzZpyh8xHF9XIL8IKDU1HwP7i+4KDLAu4SRnnNb65VIAeY2UBi7ejBUhyLJfkDf1YcDkOEszXkb763H6cjRYSU4LFG81DtbMnhBC+BUz+2Ppp/+T3h9jZo8JIbzjqGtsDMAqIHjxfZ3AQO1rMknLJ6AfE8CrNOkGE7oXzTsDg09bwt9LmhtfARYHRIkmKDk3+lHviUN/Y/oOqX/YdIrd3bJf61BqHE/PFdJStZoszxkK3Tg08WjR+a5aqHetWqeEZQoYwcWfsFqlNCnW0glsdn+fwqltHZUBmGgjKX9Cy6jODkNow+O4RGD5dcsWaQNO/ArEJQPVOoD1De8a0ystNyK3EsAC+BrE7frfUfgvAHjKURfYCIAFhrUh1Zy81qUAkZnISzM8b+Jksl0B89zAoKt1oJ/Cw/f7Wv847tKsVNESNcCZxTns0yqvVaY9pjTmlEfV7b7cOFHiWVX5H9LqOUd4Y5E2gz5bnU+GNEzFNUOB+3TIGTc8WGaA00fg78MykTLN+l7jNFi2/7qZ1t9EK08ydTLJ/dOY6UFdYfzLdzQPsuo9UJr5Uh0We6cDsLeakSuE8Lz0/hnXe42NANgQkEU1AvK+ok7g1LY0klU7CIFc2/EvP8On98l8jmq7zkBcx40/zfuTZxkVrkfUdK+VZoV169h0mu1rADpgLbk/aZm5L2EyiV5eul9BAVbHMrDetkPJ+EJ0BakB1BVa4AqwHNCQA69iRdOgP2HyBC0ocqd973qnc9eRMsRhDoEry1TagKAVIdDyeHXkpnMxwXw2699f71eKMantInUJsLZv3KjcahQBJSUJ+A8p7OrXAfhjAL4lhPDOo849XYZ7lFFGeVhLSW8pvTZM/lEC108D8DmIMWi/7zgnboQGC/TDnnoNVldqPbvDETRAb41IA5jcqJ5OceHCpLWLeWVJy6PWd1uJb6TXnEui61N/YT2mqopEKSNBqUZZWnK3QaKt0/79ztq0u7a9vCpCy2UeWnQoj6OnmrM/fCOi8wColRSuqtati03TM26VrqeVripMp7NWKXR/nUyL9eJ3mbAh/O4NLRvpAd5YaQAg9zIgTUAtVmkFFU8RKJ9baA9UVXvoyMGuFbqDfB6A7w0hvNrMvvE4J24EwHqA8A7hSoXxvSelpR07bml5xwEhiQRttcIsgdhRGFlXqdBDYQpLFjsPqiXOjO9KOAsKlqIhlug//UwA5cYvBVhysW190onLlfWC5Os91wYPKQFByXIopHDdNKibpvXZzLhXDyjecFlVwMEBrGkwm9UZ/vk20e+cgHoGNV5XieMSB+oBdoiH1QZTUOTuD70XPVJKvrN6f9VGeL9CfztNjfJWpQgA/F8z+34ATwXwbWY2wzFX/xsDsBQPnt6oqgOlDeQ+raMlHxjuAet+V6RaxfxJrZY1dM7BogNYasTq51VMqOTeh6xnHmATyC6rCQ73h213Hrf4WXfYAhFQudN2Ng19/7QEdtzC6cW7fxYHMdtE3eUU8VSbE+2NLkq9i/sKE1j5DBLINvOtnl+vLhi0naqqn2S23SrrJ0s/cfr9xwrGhZis2cYD/9A0ork3bOl3PiPlYLXPeMCtKqxGDfY48nQAnwvg20MIl1IM2hcc58SNAFgNpOIfYMmoqllAeS7BwNJAg75zIA7RBx5kPZKXji9pM36zfUmj8cFASrOH/kfPgWqSZZIpKcc00mnbqfbqYsNgUi27YOEetFYr1PMqXaduV7t+HLNJBtvU+5DRkqWhq0qVOc6anveQMttqiaqqs77kN8ixbfwrOvOLus74Az4yuVoUFfRL0dP40mhb+p/f0cLrlAxXCq7sBHpcAUWP2rxzErnVvAgoIYRrKafXpwH4dQCL9H6kbATAAmXejO8+clJVxX6vSgOVgNmsRjPfilGRaKEtuWxRvMbgtQb9T4/h4FYtVpd8/h7k1PyM4d95fDqGnOt+Ic2L8tXUxvSWfvOXAuwEh3kmBm14txRvpnVLF1IZY1V5uFa7hQsPsgoKvp0UjDy4rpvw/LNbxeSC6rXEwDB6uRLAtpkLgP7uDdXI6fLin7MP7sKHoM+bYOpdv/QcDS2m9WOja3/m+b5MnCTruh1DpyG3ogZrZt+AmMXgEwH8MGLSwx9HjLK1VjYCYM1iP9TkmD4SUmHcZ0GL1HYQPV0mmMxTh/U83jq6QG/kQVU/K8AOXVtHODU2VSsHHMvbffyOb9UxXsJwTUFFbx9qrwqw9UJinfrBWtD4NWkkRXf1UnlqAdYva7Xwvq39MURxD7i65tdOUJggKzGIAnnf4OPwLsa2kNkLyDVvDaKu2usQ76DPlU7bdDb2VJE3OPg6UTy4qg9sVeWxEtg+q1XPv/lG5BbmYP8SYtLWdwBtltrb1p8SZaMAdogz429Af4nix97+fuzLMaGcYTqdxWwHpAvWgWxJm/Wf9fyS94BeiwPG7cJqjVZN00b5X8nY8JdWxXjoVjpedfMXw8bO54DtpaRcPoi0aq+6lMgmmm7G0yU3V+gZ6DdVuS2VIuBJ+nBLFSpx66tVjhoFQCoxLv6/pol9JebdktivQB7z0Wvgek8lvLXxdSKdTIYBVutWevcGPaUJKENGt4MDNDuzXm6vG5FbFGAPQgjBzAIAmNmF4564EQBbVREEuMwfcgfyuKbns5/TOKuG1q2tlGFU+ap1PWXo5qXClPJPc7BpbE5NSjidIjSTwRyJJeNy6XYU79Ouhq35HNiaB7RpDoA+uA7RIiJDEx7HO/3mgZhs0Lz2eVLxQOSfV8krwd2nBLAlJbOlBlSDZWgyD67Ky6grlXIyfiLVFUupXn6i9l4TQO4alkfYybVatksqtx3sYzabnQrA3sIa7CuSF8FFM/ubAL4MwEuOc+LGACz7otfQtJ9p3/PKBDuQxnqlxGVgjbqRi5xEhnoVuUSfIkFTVrNQArCHqxr7V/upW7zHT2lbqcdw1s+vSgmuMevttVw7IzWg1mte9AQjSMvE1T1/rxXJ2D6ltL3+/umcUHUas5V6sZZV+csBJPFUZ5Ea0DbSPDva4UrZfYFitLNsUvWWWorSJ+p1oQ3Mz+wUOkh0oCyXfYpjbw+T7WqjNFgzewRidoG7EBMQPj2E8JHCcUsA705f3x9C+IL0++MBvBzAIxCX/c8MIRz48ykhhG83s88CcBmRh/36EMLrj1PWMwNYM3ssgB9FzG2+AnB/COG7j9s4KtRgSysdoHuomrbExwvwPBPHmlJ7NZeuJ+1tfn2p4p3BvRoJZAOPEf416pXaTljPkqau9VJ/YP1NFabZNORLXwWP0nK3VL8BGVLqgXjpWgGFgVJUU/Y0gFaiyv03m6bOQdZzEk5bXjcf8tUGCFLtlQ+FbaTRc/QifiYDclDVXRzzebtaOTxE59KOBPLbAxSF8i/rHgD/104DdLmC2Lan4Kd1jl4ELwLwxhDCfWb2ovT9hYXjdkMIdxd+/zYA3xVCeHnKTvAcAN+77oYJUF8PAGZWm9lfDSG87KiCnqUGuwDw90II70iE8NvN7PUAno3jNU4nqxUm1RLNvMbBQc77K02nkfHXRd46imo9Ukqa1VH/eSOHZkp0if9K4OpzZnHpXdrertZ8LQbHfesB5TOiKgdxHQ3jm0EvoZdcrQD4YORUGVkGtb5o4atu/3xOA6UViBZADV0KzgMTk79V1jZ7e53HAJBzNxStj088SefinZ0uR9qBYe/acLYZdpPZrMaFCxe6UInkmI+a7Eq8jddGCNin5Ah7ThTBvQCenD6/FMCbcRSGJEmpup8C4Evl/G9EAWDN7HYAXwHgTsRU4a9P31+AmI325gFsCOEBAA+kzw+a2XtTQa+7cUYZZZSHrpyQg32Umb1Nvt8fQrj/mOd+XMIXhBAeMLOPHThunu6xAHBfCOGnATwSwKUQAmfGDyDiUkl+DMBHAPwcYrbaFwCYArg3pf8+Us6FgzWzuxDdHH4ex2wcM3seYixGPO6xj42E/HSKpqlb7Y3iDT1DvOSR4i/kZUhT9XyfP8Zrr06DDc0k+rJKAkKl0koujGoo5kqQlO8Qdcnbt8tf7z7GGwwlq1rTkOR8SaX6VC2qTWfBq9kWq1VsAOWq1TIplfAc9HKZmJgm3qSeVt3yWRunaVoXt3XuyE2TluXKfapLFtDnZ5TuYJxHaqxA1Fp3doCdnZg6/VLMkaa+y3o52sfm8+73nQvznCNT/0MNxKE+cqx7afeY8runpHqe4DIfDiHcM/Snmb0BkV708uITFOdxyaXq9wP4T2b2bkQe1cuQ+v77QwifnMrzEgAfTtd88LgFOHOANbMdAK8E8FUhhMt2TI/mNJvdDwD33H134ICppv1AAzpgStioBh+32sytxRqMe90asiS6fPPH+xslUKGhRg1ZOkkMif6fLbsxEIcBOchWFfrAuo6YVKNTdpGuQEx7Tnoiy5flMhG0ZVTjU2kniVIEyqEOPKJWGoscb84hYLmyzEV0HcC26VdID3gapeS+5rfFJUAFAOzsIGxfwJUrwJWUI213t7ORqdsq0NE4eovJxDCbSk70Uvpjilke39Eb0bS/HtXhTiCnRRGEEJ469J+Z/ZaZ3ZEUtDsAfGjgGh9M779pZm9GVPJeiegN0CQt9vcC+ODArVo1LoSwNLP/dRJwBc4YYM1sglihl4UQXpV+PlbjHCVqMKHGx4FN0cmbHlHeBZHfM2txyc+rBC7HBaiC5rpcGRbJXuLBVbVBKkWqERJ3Sj6/ioVq5NLoWVWFaHbUhtR66fZM9e8qACvfmfZcgVXrU3Tx1ArpJgLPMUq7+3ixasdRWVWGqqpbJZa8q29rFIrSbolVYPUA68FVeVcF19tvBwAsp1u4fKkDVzpueCqX5VCbH920Dw6A2c6046q9C41WRhunxPv7rbmnIOdo5HoNgGchJiN8FoBX+wPM7GMAXAsh7JvZoxB3Xf3T5M/6JgBPQ/QkKJ6f5I+aGTVeA7CVvhuAEEK4/aiCnqUXgQH4QQDvDSF8p/x1ZOMULtZ2El21Abl7H10Agc77h7YTDnp1PeTnSbXMrcVDAOtBRkd5ySl+QItVTQroeztwXHCVq7jnZemszqV5oLRC7NVNT/B7ar3aX0S0VUvf8DTfFBngM9SgbyNfJvefXnMdyPoA/nxE+u6r2E4+2smYe8fvP9YTtUMpuO7sYDndAgBcvoxMe9W06QpK+oyrKv6veHq4sJihYcjZm5NVib7QgeB3Cp7CXtlz9IO9D9E39TkA3g/giwHAzO4B8PwQwnMBfBKA7zezFYAKkYP95XT+CwG83Mz+MYB3IuJUT0IIQ6Pm2HKWGuyTADwTwLvNjITwP8RA46wVsxgtCnUWWwPojLva9zlYdMeSarAKstFVyXn0e06KA0k1AtUShgBWl6luiVtannpA4Fgh0Crd5ulTd4uixlhUVvQEvbm/oF/OFyYaqypMp5Y1Tal+TYPhwh9jhA7RQfpZl9v8XqKbVeracdN0w9qXEGUa3NYH03Up08P2BVy7Eg+/erUDVmqvPquvPgY/IainWHNhAtve7jcqtVR1L/EUgfLa/nmegpwHwIYQfgfAZxZ+fxuiMQohhP8K4JMHzv9NAJ96lmWknKUXwVuBwYTrvcZZK1WF/UXdrtpKm44UE3VrP20OXBl5DbYR19PPAAAgAElEQVTnwD20mZ9gqZ1xHcAqqhVGtO/PJcVQl7QEWxZFN+cMuWp5CpNt07updxL2/w9p8L6wiwWsqjBpqjZuq4rRllCaHYYaZs1/6wBWD9dH5DFdpQV+3yfUIqaoXQJXcc9St9XSxi/vQlsS7WL0EqtrYD6fdbvhgFgOv/uOlVTttgSwQ8/0OuScNNiNkXPxIrhRWS7jEos+ojQOAOVgT0BnA9DEfUAOsLY4zNWEEmAC663oej6QF8R/Ti9dSlPagNZucMwuREpBtXYF2BJOedws4qIe5CeO0jHrBqJHMqSZ1avk+nnIg8Hfe0BKA3locHvOtlT89naL1frG5QylvKvfoZW2Oi/2un7qL1na0kyluFR+TqakPpbLFLBoRwj50vZmfYZiWG0PW1y323NPbuGtstctGwGwiwVw6VLXh6gJADmHxX7kU8doaNGWclqtcTvwQl8gFkYBU6MYAX1Qrap8dFUVrElbRXns0IBOFaqbBlvTKabTzutAGQ0Ww2uyFMUqDqZs11rJgs/G8mhdAk19X6dS6u/HafdSBW5AdB7Q722/qQo8QonL5OeWZ5r14gp4m8BQVbUM7LP63T837g3gM59OLRVngmZ70vVruQEDBh1cy50gqEWfpnFqBNhcNgJgl8sOYEtGLuWvdBwqNqjiUVVAa0UvLYOp0fHmKgomBFdVoWm5oqGIkU7UVOyBSZejBYDlYK7TrqCt+RRNY0VcLuFkiXoIsC7Lg5ZFG084Olrv9e9sZ5HW7TjgWSosH2Sp0GuKyXf/eQiTMy4YsnIemui0ndRI5DkoMRytCjQ+sN5X2K/gffm1ien7q7RRpMZqAJ33xOF+10VVMfHuaqcRePtWDbh9I7IxAHv5cr4a9+PZDxqKX3Lx+CzYiAovpGqx3kBFdzR4l4DDw24tx0Jr79Z378iurhDUlJQXXiwwmU7RzCcZJnhc8GDEahTbYIBj4LZUFlVjsTRNbNz2Ol6THULDIfGzY8kPdtUBkz4aBVT1IKFoYBxekn1jNgMmTQD2XCNqGZTcBvquKBJikpOR3kMnem1uH4NW66DftavxWWh9Fot4PLsdH4N2LU+taVVHDfZsZKMAFhjuELrK1Y7nZ+g29XxjqJpJNLzoCCUFoCBYQqp1PWm16nIs8TuvS1HUGoopWgJXXg+R52yaPJhMyZnBf24ZDTrk62QiDTrkmN9LK9VYPz+ZbyPPSfNdEb8E8myH9H3lnrtffPDwkgY4hPc1lt3GAm1rvyzSpRCjlQuw8l3vo0t+ugyqr7BvCsVzrZvPiei7k1lfDzg87GK6lOL56ILptDTYEWBz2QiAXa2imwuj2ZXEj1MqlermRFHsaxpDVU1Qz9Oo5C4ZDQl31PJXf9M0CqURrmq4d4VgmEAKt/KomsbKks+tKtChnoOyVDwKL9/l2LNuqS+NqQ79Jcd83/a1D6I9dGDpvaR2FxDTeyZ4DlWVy5LRsDcJLAStvJGIF/UxH334QedPGmCt4UjLSFtAu60X/WelHKxmD9Lu5KkPbS4yVkAEO4Iod43t7sbPQB5gBji93FwjwOayEQA7yiijPPRl1GD7shEAS21UfadVSrt2SBV4DXa1yrmqxYL0mmE6naDeFi0WKFuQlFP12tiQS4MWTN0hgDz1CCvqNyn4MpF0W8U4AL7ui0WulVCx5mVLQWFK7a4BZVSZ1uXoapWMZtoOpc9+yV+64dDxTdNjSjxf2e4CrUKfZCyNfM8Ze3pAk5j5m4gGywAyq4P8slpG7Q68hG8i7lvQOtGn22uv/p0SxNWYXUy1V2qwXCwplXYaMgJsLhsBsCqa/oSi/JUOejX+UDxu8Z1U5HRqaJpZTCEDrPeT5UnKY9LY5XO0DIEskAcV8UtlHQWl3lt1wafVEKJuW34QVdXRA4qJDNfRMqWyZJ/XcZnr1rvueqGqe3SFHsLAW3WVKuX92Er+rKX76UW1Uym4pn6xrKKB8fDq8TQ30jcak0VpLc/BEt/FkSPjYoH+9mcmzyDHrvGR9/dzDlYBtkQrXY+MXgR92RiA9VhVmslLDvWl3TJKq1FEIUxag6XrTtBMAVM+VEFS3at4IV9wD65eq+IoUL63afrI5i0i6bvnSX3AccU5GkOyAaX3les3zaRtj5JWphbyNrbAkPpW4FSp+bEIVeUQg/8VfH310u2Eq+2p/nzrQNa35xCoTqcxlU+ywiu/rwZUvYzeim5Z/K3El18PB9s2lQAl20i3B9Pg5f1gT1tGDTaXjQHYodxwQHkZNrT81aUtOxhnfdUmfFyDurYEANGgVDVAPZdEc94QVrqxqgxAvv72PkSKHCxECWCrqgMip8mWBpDXtgwuLqw0pAGYTjsvBR/nRI01g0vxwvI6VHXGhgxhXklK2msLOgvXEB5oSxMJ3xlyjeXVND6rGnsSPwDowErLo9pmiQVhdmQF4CNYkZ4Wy+tQfHp0An5pYeDbWqt/CrFeRg62IBsBsFWV54jTzlviY1UBUf6K19LzQuhbUkuahb7zc1UZzGrUdY1mOpPfZd+99nzNgcTC8qRS0FR1nNTcynIcrf0+yLiWv/S5A6VFH2Cl3ATZqupjEwd8G5xa6ZRygwE4WrEsaWqlZ8BmKoKxtruqm17lBDok4+TXNG1sgd09y2K37qbM5qoNanuoxxY1T63TatUxSqW5SOPBDvGwvpp8JztFbb/k6sVnxqYh73saAKvlGSXKxgAs42hQix3ScLQz+U4KlHkroANadrp14gMVeQAn+MYBVseMtdMq524VGVQdpIGLg3w2y2eM/CbtV4/PfpIoSdOgc673EcQiIR1BdrqKGxsaywDaVqnxSnynfyBVDHgN5BR0KRauPkOgPEkOPf+iBLfS8KR02xjIkHJ3z9oQg1ev5gDrqSd1Wda4F4VmaNugJL4YfGUUjDyE1vWsqVBVXcc9POz3/9ksX4WoZjwYzvKEMgJsLhsFsNvbfQ3Wd1jtyLpUawo1Vd5K+b3S7ljVFEqah6cpNJLdcglsbdV5JlXdFZSttZEDLF8eYFNh1BgVCmOwVM62jHDA4xFDG3a1gjVNF0NhITcpqaMeAasum2uJF3S4kVW31ERA7ny/WomfqyKTX/+WGsdzH02DZTVpI7f5INlaBy2vLs8nk7wZfJF4nl7Dv7f0zcEqv6FeWG4waRpUWxEpZ7PYthouQXl5zaBAZuRGZTRy9WVjAXZoxvXGAD/GaBUvbT2ko3ZpqU1AUJ6pt9xGrjkr5bBYIAKsR3+gU3mIIrwAw4Ap+cwLSuHrpmmDfvBnn1mAp3GZWFXIeWGveZa4Sp2RtJFK2ivXwtmxuTuZ32k85IbHW+o70MXgaX9P7mrmjVRqUtfyFyYBnqOhMRVk1fFDtw1rMCEPvnyk7Zbcts2VkpGG0UY6zowp59XTCpNJzCzBuNzKivBwNQco+3QjMnKwfdkogJ3Pu45amvH5rtvG2+Vm4kS5G8grbQQlitdYh9xh9d5AvKd2NGoRvQpRiwVyzqNE5qllRAso4Fg3DebzSXH5rIMrW2571dGPDj8DKZGov5fIVD1Oqg3k6Wu8eAznbwRSj/VsYw18MlFOlVQHg+7oZn2Kz29eVVgc5N5zu7u5m5MCFPuZB1b1ed3aAurVIXBlL9+jej2IVOJKiOxVhel00lZdkx9ocBkt/3Q6crBnJRsBsLS+MnCRarC6dMzAVLUEeeoMUjxpGjTzOtsRq1oVhYO7BK56nA58P8jaceBP0NQs3j/Hp/TwoOAL0DSwZoHZdAq6O/nBlA7r6N/FCQa5B2GvreproKwVjWJ1V/02fGLdgWgpQI9ioIIw66n7MADDxLvVLRbdw9XstRTH8/AUsh/0JfXxenTO4Xc+MkYxBAiuV/oR4n2f0HJppy6UsdTGWMSg501Tt8r7fN5Nsmxbz/WOGuzZyEYALGfZodVyGwOTwZK9yukvlgCMacCp1Jykk5XGAZAb/tXAkBmUvOpLisAvbVUF9ypdqX5pRE3mc1Tbk2wwqXttmxrFV8APZP+ieLA9JkDTKNY0dS/tl1fqhrCkdCtfjAgmKXcV28r3C837XnjwpZX6UCZaZX1o8Se4zpqEbJevdACr2Q79xbRj0w2hxL97cb8rHcTyqE3P0+2bpMGa2SMA/CSAuwC8D8DTQwgfccd8BoDvkp+eAOBLQgg/bWY/AuDTAXw0/ffsEMK7cAZyCvNWWczsh8zsQ2b2Hvntj5rZz5nZu83s35nZkVkZRxlllM2RoZXeUXTyCeVFAN4YQvgEAG9M3zMJIbwphHB3COFuAE8BcA3Af5RDXsD/zwpcgbPVYH8EwL8E8KPy20sAfG0I4T+b2ZcBeAGAf3Sci6ly1zSigSmZqlpKiRPku+wxV6NAaXlaVbkxY6hzqBuYhgqdzyOt0aYFL2mwqgK50HetlDhPb1iSFKT19jYuXJi1P2uIg6ZBX1XUdmGZvAuGNoBSAUPLbc+bpPJPptGlyO8mLu13UPFapf9NVxEhAJiLFssXd8zpyYUbNU3dU+DVj5T2Mj4qfdZ87ewAuJI2/6u1zIem9MsfIKeKtE39symtOpL4zB7anfgoeanT2mhwTl4E9wJ4cvr8UgBvRswUOyRPA/CzIYRrZ1usvpwZwIYQ3mJmd7mfPxHAW9Ln1wN4HY4BsOzYWZ9SsPF+mN7hHcg7MS+SfFIZ7k8P1e+6nPXi+7Wu8GczMW7osnAI9NWopSPC10UJQl1q0ucm3cO2431miQpRHhYLAVdPwvl1L3/jiKQv79CGglIjah1Wq5gGZx69HzgvKN85FCtB50z1YWb1gc6uZQZUW3WMK8H2UuLXi/Sfppm0bnY+M3HbhsizdbuM3bC93QioQJ6zm4EA9Jmq+wkrxfbWYK86I3mfr/QKVZ2FTCw9Dj3NbON2cn1cCOGBeM/wgJl97BHHfwmA73S/fauZfT2SBhxC2D+Dcp47B/seAF8A4NWI6bofO3SgmT0PwPMA4M47H9f+XlXivwkMr0GGfDLXrFE421PT4+H6f1sGiZg/BLDTKWAH+3m+jhLA+pNScrrusBpWOfWNgOGjdpSCEGxvo55Oo5tYqVKe3yvxr2rcEot1W/6h1YLXYinp/LqqsDWdIiSD41BiXx/rlJcoPUpPY27Np50VTK18ntM+PGzbdH5xC1tbnWFLOWyeUlWd4XVnB7j99u41w34HqkAXlJX7bVWT5oWD48UJrNqWJY4+/d/G8BXXLL775j8rIDzBdR9lZm+T7/eHEO7nFzN7A4CPL5z34pOUx8zuQEzf/Tr5+R8A+H8ApgDuR9R+v/kk1z2unDfAfhmAf5FmjtcAOBg6MDX2/QDwxCfeE0rL91ZKhhj97wjhtRVr/G5KXc7yVj4IB6/VJs/bEy2zhBoDALtEjYW0TNMkD1IPCArcHPH7+2XkWa1ydzBdH+qNpD01D1fV1Hna7VJ7l4BV76dlUeA4OIBNp62mzRWLenjwmWgAEzaDf1Z66aYBlivLN3loOXhhtsfeXjSAXruKnZ0LmaLpy8SFB7XX226Lr63pErgkRi2gA9YS4unqYEgUYGUSbhcFqU3Ux1tXBLrI84/jtHZx+eseIR8OIdwz9GcI4alD/5nZb5nZHUl7vQPAh9bc5+kAfiqE0Fo1qf0C2DezHwbwtccu9QnlXAE2hPArAD4bAMzsDwH4vOOeSysukGKPKjh5Ua1rSFt0nZmaK8XzVWoh5ulZZtpSoBHlgz3oKQUgGgkzgGrUw2JjDNEE02metEorwXsNcHe6lXW1yJsutoG1bm5r15x6Ei8I5LSOfzapjer5HFtbkxbIgG7+8JtDfNQwAq36/dJbK8tmoIGCWR7dZZLKPrvY4PbbZ9kjUoDlIyQ1cOECsLOV8htxf62mEPAe/76t/JKfTst8F2qAkzD7ibaDOkoQXH030fn1tGLBniNF8BoAzwJwX3p/9Zpjn4GosbYi4GwAvhBxZX0mcq4Aa2YfG0L4kJlVAL4OwPed5HwOrKpCvtxlTynxsJ4mKCx9q6ZLuVKiI0ltNg2NVQP30ILyvfS/DhagS5bXTNpB4zc9ZDjL0cMRo6lmWCaNvuUJbPmdWqoO1lJywCIPXdJgS+CrD87/xmcjbVTPY4AZtgEZCK6YCa4+opVWlyEWe8q8pwOA7p0UC48DsHXxIqqLs9av1Xt3KQc7qw6jO9blyxFYSQXwuQD9oBi6BPKTLn2snPteaCY42OvmVxbXz+2arNhrsEDuE7thAbfvA/AKM3sOgPcj0o0ws3sAPD+E8Nz0/S5EGvI/u/NfZmaPRkxr9y4Azz+rgp4ZwJrZTyBa+h5lZh8A8A0AdszsK9IhrwLww8e5lg8kvVgAqzawRY2qqlE1ErN1yMoOFAHAEBBzc/U7CPu9rZbA3kG/t5bWXDzRS4lHA+Kgmc6KxuXsMgrc3Ceqo4j/+TU2rUgK9E2e1FCDl5Rov6wMXgMt1S1RDEBquxK46s0IwK0m27QxeUvWaU3W6ykCXi4rZoW8b+zv59olG7ywlp7dfjtmj4iRtbwGOJ0Cs2mIk9xlCVrAfbUlS5PuzOMMphxUej5thBYXq5PFK82r+nhUg+Vnv4jS9xuV8/IiCCH8DoDPLPz+NgDPle/vA3Bn4binnGX5VM7Si+AZA3999/VcTwGWQSq6e/G7YTKp0TRiOfZgoNqs9Mi6qmLivtKNmXHUr7X8AKL4gAj6csYsABm4qsO9Mh3wQKtWDJ6o9dP1oDf4JXDVaug85PfYE69ttewO9B4aUrflyrCQ4FxVFSOKmQdZ/1kB8OAA0xQCkstxPn89rWT8KrJDqrly76vPn8LluDYI1eS9PWzN58B8mt/k4AC4JoELGNfQbyRQftt7AXiQ9ccJuC5RZ5fXnWUsMttEmrKoa2g1NkyD3Rg5byPXdYvfrkrx3/f32SctutrMm7xnqQpSAgq9sB7nAdbH2dNrqBtCSWulkSKl3ObmHg4CAgYjMq1WABpXNgUjH5KKQOHViVSOUNU4kB2bzC6q1QihU7Ank2S42zvIwcfTLtMpDhfWrox5+24pHes72ZbzPBAJnWCpDbnza7XqGxX949LmyR4raR3mTlGAvXo1V4Op6ilaHRx0Uc14UX0GugVW68SCang19QIoUTfueaFpgPk8TsR7fV4V6OsSfhHn+5bPUnEaMm6V7cvGAKyP0u+VHgr7cav1TGMywzZ2KZBrdnqxkmiPVbOsqghAH5wZzqoErvM5lqixezUeTo3Ea6+0VbVaWwlFVJP1FIKKlIOArqDula0eLejBwxPVSSv21+bfdd2BwfZ2ja2dnQhwBFlfN1HPJvM5AGuXoBoY66hU7i1uqaavcQiBPFVByU+WN2Z0MxUlO4caE8hXLRrYWHnXoUokft7P8ZwcgbwLlEDWa/9nJSPA5rIxAAvk+KEWY7+kVmmTzXkN0Pe+3prS3VjP8ekDvHDkOw1EcztpZKb9/b53FYvA2/UAXMk2VU8yRJZGSACv9/b+7sqcsN2mU9ko4X1udZdR02D3ah431R+q9qPl0rBzYTuvqJ81+d/eHibzOVZT6z0K7QteoWbzt1lmNfOfPgAapIA8epD3uCBlwP89mmlQYS752UbcdUKLWAJa9XfWOqgsFsBir5sbNJyBMkOqoZa0yULQsFOVUYPty0YB7CijjPLQlvMwcm2SbAzAeq1VV8Al+1XxAnzndO/XyCVzdGnpX9J01UdINEb/8hok0N/co9oFvc8y318ti89Gy7LpxnnZ88nkfTRyq+KoKao0pkJrxPFbmkRlJPd67VqkNGnr0aj5mjMq3s+i4Uj5cdVe+XuycM3mc2BeFxcgKl6DzbjqkgbLB6Db+OiFwZc+EH3efA7aIXXbKymFra1uR8L2NpbVJGt//9zViKu2zL29/N3bMbV9+bnUTZ3Dx6nIqMH2ZWMAFug8k7wohaWbAdSNsN0IoKbVg4OIBCWHytJ60yyPWsz/9V1dawiyiR5QcNX0IzpYaMFX+pZFG9xwU1oLakqaArhrtX2V+b31+9VRzRGrI3M6xf5uvObVq51/vVIE+txYtFistMuqZHws0Aez+RyreZ1hZglgOUn0+GP/4vXJsyqw64RaIvuBPtftwXVrK/4u4HqICfYKExzLru8sHrssn5sHWAXp0nVK9jOftPg0ZATYXDYKYCml3U2qsbDztJsD6F4E9MGVmezUctyqPugjtgdeIPfEV4PWAO+qeAX0wxTQCYDcJQdYXRotXnNSZCb/l0I98d5M4KdeZmq7yU73vr/LZXdwKkeo6pZHpharO3jpYqUJHFT7bLcBe3JVCyS/zbe7LawtRy3Sm1yvCUKVViwajVonVEUhLyW/JgVXRvthxG0aN6sJ9ndzjF8HsCVHBeVf/cJFx4BXthVIvYngNGTUYPuycQDr/bF1HBS3sTJhnDrdq6vV7m4HtLpVkmjAbHEEq/YGLqWsem07i/HhqvNdVDtLqUisE+uaaTm+0kCef0VVQ8bPA2IZMGkNUDqnsMh+kGfGIT+a9SDk89Xubr8+vEfJsSFa+FedNV49NfShSyGtqjCfb7X7Jzwe+4DnPQv/kPXHt50mnOSKZEjV047pqaH0DDCdtjvmtCjreEu/4PIGLk+P6WTmF1/ezbZdpZyS9spyjNLJRgGshi2kwphvYT3oABXo865Av8cq4ilpyBt4lx1FdO+GBXQDMQ0oOoYTYDX1CA3Xiiu6zOPKubX0TwXE2QiTSY6UHhwAYHs722DE+USr5UPBRuBzpnpfuFRnAgZdRpV60Oup9sTTeyDu/dV4ASJH+jy5fYrZrM5wX/2H23syfVBJK/b7f6lxKl/KdmRbe/FlLGwOYHsFWLZQ0iqW+FN99p46pj7gPcKUoVI6fij1ueoSpyEjwOayMQDL8aAYAqRwgJeu9UkpnuTJRR1sqkp473j1KdJBpNygDirZ9qpRsagpesWZ70DZN92sr3hhu6Ala1qUpukHJQVwWM1a4xMHJ+kBjdxHAGzjg3oU4J+uXb3XmteU6rrDLaDDr/kcfb+jUqxU1lnVtPkcs9lW5t6m2N/yr2pF0n6hszTVOHKmt93WtV8pV7zvF76v6UtWHUeBjzfkDoErm4oTNEXDyVZVl+dRq1yiC4CNC7i9MbIxANv6s07iwLRryUtfAxl7lFItjz2tBBrs2Yp6pbVXtu5Efh+lCJq4pZEgptsaPQ0I5BhAzlIHGM9dJjNXreCqjpCTSQzppIFJpYm4eWl392in86JW4y0jjptUxZ7YRXClQgh0nyc47AhbRQ4fJkutZHRvODjAZD5vA3YrzZHxr0Pgqio7T2JBCa5bW7E9dR1NnmNobV+YgErN6A9hEf3ltWm0qbwuoedy5eOryHuzmBrP+DRk5GD7shEAqz7bW1spUvzly/HPy5e76EV7ex1Acrk3n/cDGSv4lnp7CLlDuWq6umTW67ml80KAtHS6Xx4OfSfQavCnLQIBEVz3tN52G3DxInDxIq7sT9omUpBVd7AjBxnrRipiADh4LSbX0/lmPo94ddtt8bedHWBrHmLMVI8a3neJohcUg1XTTNpHqRjcznkHzjuE4MqZjL9rWKyS9soHo7MiVX9VnxUhB9qI+0F4WX8p/sZ+M+QAUfKeII2mfWgd8J0WuFJGgM1lIwAW6MZFjWW+zfHyZeDSpS648WIRe9nWVl+b5WcdaPN5XGupteTwMFfhdA3sB75eM31uI8uvcg+gUvpvSqljeoCl18HWxe0OiMy6yYBIdvEi9psLuPTb8edLl2Iz0XuAzeINT1rl5RJAI21FENFXKjRTRG9tdV5srA+b+MKFHGCzDKt8buou59UzRQpR66fzSRfz1dmqmgbAXoHL9aod+8ts1tED5DAY+cqXyS8BhohV+WwIqCrrGZ9KIKfPngyWuqWVFHOVo1LND7l636iMAJvLRgCs+u23ayUG6qAGe/ly/G+5jOoMRzpHOEUNU15tAHIw1u9HLPtuVPxSEegGiVqPAeDKVcNO4lfbiaGqWoDdr7bwu78LfCQlMr58GXjwwb7z/7qqhICu0VWj9O0jh9F7i4BXUgyBtAKh5krkUH8rb/HR5+SWBDWWmE7r3uHTqYv+pWVXrZQFJkl8220dh6HHqeaqMrT0GADbqqozDZN7G4aeRWk+Z5uXKGBNdKjnl5IoUJM+LRkpgr5sBMACYnFWQgrowJbqGdAFWlHLsbfy8zdvTmXYeq/1kNQj2qsKUFBF1ZjAd09fshhDOfio+VJ7ZZXjtSbYungxAkEycIXpDFeuAB/9aATXS5fi8VTwlbdTKzPF72NvU634WLJeg5129K86NehusO1toD7Y7Z4Z17lDXhrrRDMSLBaYz+tMaybA4tpe/iz57Djh8j4sIGcBftZJWJ16WTlNJ6DhqUqh39JnJp/UhYHStiXRvkNue2h/w3Sauyvy1t4QqRq/N3jdiIwAm8vGAOwoo4zy0JbRi6AvGwGw7bZ6qmD0MwJyTUg5V6+BqgY7n8ffaWpVVytqV6qGUSViClHVfCnKta2WqKo6M7ZQe1BHcLWdDHVO1WBVI1mtgL25YTKZAZghHAB7yZhFzpUa7IMP5k3GopeszLw+FcRaVV1tX6mzrZbY2urqqzwftSrb2+04Dm+lUZWe7mYl6sY3QOJODDE1eVaBPbflSVcyargDOr8x1V7n8zZHWVXFzQ0ZD+tXPkeJU+1Ua1XDl654tOuqTVYpGAoXV7rp0MfO9RqsDpPTklGDzWWzAHYhVqOSIQHoG2LYM3VHzfY2QpWypGpsTnodDPnTTqf5HlzvGynlieERT7buMutn3aYVGeiW8JxjiPX8jTupSElrxmhWqQSspSrwvlVVx3isnJAUFCmLBeoGmM/7ofdscRiDxejeYAKeemKoIY3/KSfrLULqv8bvUp6MuC5RRUoVON/hw1WNvSveUDeD6f2m086rgmVnfyhFBZfPPITeFjrhqh7gWQY/73hA1izH+v86xuU0zQojB5K7fEgAAApgSURBVNuXMwNYM/shAJ8P4EMhhD+SfrsbMdHhHMACwN8KIfz341yvqrDeaVxjbxIwuaVF94Rz6+ouEFPMzDC7KOC6vZ0DbEnL8iCuqgA1KwCTpkFVWbaLkp+V89LxWHLlWq3iWKYRabnswFTx5uCgc7CgazCQB7+mj233nLrPimd0CzMDMEsgW2p/ymIBq1ZdQBpVg9XXCOj7K/O9ZCwqPQPXztnmD20M3V/K+ylZScfcnZ3oM7yzgytXDbu7nddHXXenbpHv5j10U4pa9XTyVe8VJz5VvGaR8NUeMmrxsppDUZ+vT680JJvEwZrZFwP4RgCfBOBTUy6u0nGfi5iiqgbwkhDCfen3xwN4OYBHAHgHgGeGEA5K17hROUsN9kcA/EsAPyq//VMA3xRC+Fkz+wvp+5OPupBZMnBRtGdRQ53NOqQiUDLAsWz9WlZdJKPVKi2dp4b5fAuTi9P+TiKvMui7ii6jCRZNg7ppsDVvWod4NUJwharLf4ruH+AldUds03QAqytmGuc1WpdfjR8lXKWr5swUPA2t8x5odfmsrlb+xf/9CgHoUGLIm0CP1xD+jrJoG4NAe3iYe+HrpgKg1VwfvGKt95jatLoluWHGc7RBtRw8wW+Vdaqifw78TqDzFAE37PnFmQ/eQuVfk0KyuF7jVYA/rZ1c56TBvgfAFwH4/qEDzKwG8K8AfBaADwD4BTN7TQjhlwF8G4DvCiG83My+D8BzAHzvWRT0zAA2hPCWlDY3+xnA7enz7wHwwWNfUAe0bkOhZhVv2m15lPBwLQ2ATqFRdyWO9/m8xnR+IYK512BL717TAnLNLK6xgaqKQDudYp78Nsk2AH2lmN5mvLRqUxQ9h8dxt68PJqN0soof5KrkKeVITy1q4JNJSmK4WuYnDoGqB7kS/eKjkfhn7zMbasX4u05yvG9pgiQIytayK1ettymQh6rTQXU784ptdz8qwHqQFUpCsxf4pb3ffKBBuTwzo7ys4reudriD0OdbUyBXpuu0NNjzMHKFEN4LALa+0J8K4DdCCL+Zjn05gHvN7L0AngLgS9NxL0XUhjcLYAfkqwC8zsy+HUAF4M8MHWhmzwPwvPT1ijXNr55D+U5LHgXgwze7EGckt2rdHu71+n03fqu3vw6wRx3z4LmZ6dL+/hDC/TdehlbuBPB/5PsHAPxJAI8EcCmEsJDfe6m9T0vOG2C/HMBXhxBeaWZPB/CDAJ5aOjA19mk2+LmJmb0thHDPzS7HWcitWrexXjcuIYTPPa1rmdkbAHx84a8XhxBefZxLFH4La34/EzlvgH0WgL+bPv8bAC855/uPMsooGyAhhKLidQL5AIDHyvffi0hJfhjARTNrkhbL389ETslB49jyQQCfnj4/BcCvn/P9RxlllIeH/AKATzCzx5vZFMCXAHhNCCEAeBOAp6XjngXgOBrxdcmZAayZ/QSAnwPwiWb2ATN7DoC/CeA7zOwXAfwTdBzrrSYbSW0cU27Vuo312hAxs79kZh8A8KcBvNbMXpd+f4yZ/QwAJO30bwN4HYD3AnhFCOF/pEu8EMDXmNlvIHKyP3hmZQ2l3EKjjDLKKKPcsJw3RTDKKKOM8rCREWBHGWWUUc5IRoA9BTGzrzaz/2Fm7zGznzCzuUX5VjP7NTN7r5l95c0u50mlVC/573vM7MrNLN/1ysDzepmZ/Wr67YfMrJDh8KEvA3V7vJn9vJn9upn9ZDL6jHIOMgLsDYqZ3QngKwHck2Iu1IgWy2cjuok8IYTwSYh7nzdG1tQLZnYPgIs3sXjXLWvq9TIATwDwyQC2ADz3phXyOmVN3bg19BMAfARxa+go5yAjwJ6ONAC2zKwBsI3ojvblAL45hLACgBDCh25i+a5XevVKe7z/GYC/f1NLdmPSq1cI4WdCEgD/HdE/chPF1+0BRJfIf5v+fymAL7xJZXvYyQiwNyghhP8L4NsBvB+xM380hPAfAfwBAH/FzN5mZj9rZp9wM8t5UllTr7+N6E/4wM0s3/XKmnoBABI18EwA/+HmlPD6pVQ3AG/HOW4NHSWXEWBvUMzsYwDcC+DxAB4D4IKZ/TUAMwB7aZviDwD4oZtXypPLQL3+OoAvBvA9N7NsNyJrnhflXwN4Swjhv9yM8t2IlOoG4M8XDh19M89JRoC9cXkqgP8VQvjtEMIhgFchBrH5AIBXpmN+CsATb1L5rldK9fomAH8QwG+Y2fsAbCdn7U2SoecFM/sGAI8G8DU3sXw3IkN1u5goA+CMt4aOkssIsDcu7wfwp8xs22L8tM9E3Dny04jcFxC3B//aTSrf9UqpXt8ZQvj4EMJdIYS7AFwLIfzBm1rKk0vxeZnZcwF8DoBnkDffQCnV7ZdxjltDR8ll3Ml1CmJm3wTgryBmaXgnogV6C9Ey/TgAVwA8P4TwizetkNchpXqFEPbl/yshhJ2bVb7rlYHndRXA/wbwYDrsVSGEb745Jbx+Gajbnegi+L8TwF/T5zjK2ckIsKOMMsooZyQjRTDKKKOMckYyAuwoo4wyyhnJCLCjjDLKKGckI8COMsooo5yRjAA7yiijjHJGMgLsw1TMbGlm70pRl/6dmQ0GbzGzTzGzYGafc4Lr/3Mz+3OF359sZv/+Bsr9hrRjaZRRHvIyAuzDV3ZDCHenqEu/C+Ar1hz7DABvTe9Hipk9AsCfCiG85caL2ZMfA/C3zuC6o4xy6jIC7ChAzJ1WDACSdgQ9DTH84mdrTNg18jRIsBQz+1wz+xUzeyuAL5LfL6TYq79gZu80s3vT79tm9goz+6UUv/TnU4hEAHgNjgn0o4xys2UE2Ie5pPCDn4kIXCV5EuL+9v8J4M0A/sIxLvskxChOSID8AwD+IoA/izzX/YsB/KcQwp8A8BkA/pmZXUDUUD8SQngigG8B8Md5QgjhIwBmZvbI49ZxlFFulowA+/CVLTN7F4DfQdxC+fqB456BLlj4y3E87fEOAL+dPj8BEaB/PcVa/XE57rMBvCiV480A5ohbiz+N9wwhvAfAL7nrfwgxWtQoozykpTn6kFFuUdkNIdxtZr8HwL9H5GD/hR6QtNu/DOALzOzFAAzAI83sthDCg70ryrURwZIytB/bAPzlEMKvuvvaEWWfp3uMMspDWkYN9mEuIYSPIqYZ+dpCHqqnAvjFEMJjUwSt34cYgvGoiPjvRQxrCAC/AuDxZvYH0nfVgF8H4O8QUM3sU9LvbwXw9PTbH0ZM44L03RBphvcdu5KjjHKTZATYURBCeCeAX0SXc+td6a9nIMayVXklgC9Nx/2MmZWW6q8F8OR07T0AzwPw2mTk+t9y3LcAmAD4JTN7T/oOxKDXjzazXwLwQkSK4KPpvz8O4L9JhP5RRnnIyhhNa5QzkQSmnx9CuHQd59YAJiGEvaT5vhHAHwohHJjZdyOmrHnjKRd5lFFOXUYOdpSzkr+HaLA6McAiJut7U6IsDMCXhxAO0n/vGcF1lE2RUYMdZZRRRjkjGTnYUUYZZZQzkhFgRxlllFHOSEaAHWWUUUY5IxkBdpRRRhnljGQE2FFGGWWUM5L/D6u5zgXd23+MAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118483f60>"
+       "<matplotlib.figure.Figure at 0x11ba7b470>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "resmap._resmap.smooth('GAUSSIAN',0.1)\n",
-    "\n",
     "ax  = plt.subplot()\n",
     "plt.imshow(resmap._resmap.array(),origin='lower',\n",
     "            cmap='bwr',vmin=-1,vmax=1,\n",
@@ -549,6 +543,13 @@
    "source": [
     "The residuals look flat across the field analysed."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -567,7 +568,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,