Domenico Tiziani / ctools

Commit fb39a2b9cf024ef23df00bffb63d7f87d58c7cf7

Authored by Jürgen Knödlseder
2 parents d83920dc e505d857

Merge branch 'integration' into devel

Inline Side-by-side
Showing 13 changed files with 757 additions and 181 deletions
ChangeLog
  View file @fb39a2b
1   -2018-01-15
  1 +2019-02-15
2 2  
3 3 * Version 1.6.0 released
4 4 ========================
... ...
1 1 New Features and Important Changes in ctools 1.6.0
2 2  
3   -15 January 2019
  3 +5 February 2019
4 4  
5 5  
6 6 Introduction
... ...
cscripts/csphagen.py
  View file @fb39a2b
... ... @@ -286,7 +286,8 @@ class csphagen(ctools.csobservation):
286 286 # If we have no model then create now a dummy model
287 287 if self._models.is_empty():
288 288 spatial = gammalib.GModelSpatialPointSource(self._src_dir)
289   - spectral = gammalib.GModelSpectralPlaw()
  289 + spectral = gammalib.GModelSpectralPlaw(1.0e-18, -2.0,
  290 + gammalib.GEnergy(1.0, 'TeV'))
290 291 model = gammalib.GModelSky(spatial, spectral)
291 292 model.name('Dummy')
292 293 self._models.append(model)
... ...
doc/source/admin/install_devel.rst
  View file @fb39a2b
... ... @@ -95,3 +95,44 @@ on how to install ctools.
95 95 pyext folder. However, you do not need `swig <http://www.swig.org/>`_
96 96 when fetching a release as the Python wrappers are bundled with the
97 97 release tarballs.
  98 +
  99 +
  100 +Making a conda package
  101 +----------------------
  102 +
  103 +To create a conda package for the development version, clone the gammalib and
  104 +ctools source codes using
  105 +
  106 +.. code-block:: bash
  107 +
  108 + $ git clone https://cta-gitlab.irap.omp.eu/gammalib/gammalib.git
  109 + $ git clone https://cta-gitlab.irap.omp.eu/ctools/ctools.git
  110 +
  111 +and type the following to create the conda packages (make sure that anaconda is
  112 +included in your ``$PATH`` environment):
  113 +
  114 +.. code-block:: bash
  115 +
  116 + $ cd gammalib
  117 + $ git checkout devel
  118 + $ ./autogen.sh
  119 + $ ./configure
  120 + $ conda-build dev/conda.recipe
  121 + $ cd ../ctools
  122 + $ git checkout devel
  123 + $ ./autogen.sh
  124 + $ ./configure
  125 + $ conda-build dev/conda.recipe
  126 +
  127 +Once this is done, you can create a conda environment using the development
  128 +version as follows:
  129 +
  130 +.. code-block:: bash
  131 +
  132 + $ conda create -n ctools-devel python=3.6
  133 + $ source activate ctools-devel
  134 + $ conda install --use-local ctools=1.6.0.dev1
  135 +
  136 +Note that you can chose between Python 2.7, 3.5, 3.6 and 3.7 for your
  137 +conda environment.
  138 +
... ...
doc/source/users/tutorials/hess_dr1/bkgmodel.ipynb
  View file @fb39a2b
... ... @@ -27,7 +27,7 @@
27 27 },
28 28 {
29 29 "cell_type": "code",
30   - "execution_count": 15,
  30 + "execution_count": 1,
31 31 "metadata": {},
32 32 "outputs": [],
33 33 "source": [
... ... @@ -45,7 +45,7 @@
45 45 },
46 46 {
47 47 "cell_type": "code",
48   - "execution_count": 16,
  48 + "execution_count": 2,
49 49 "metadata": {},
50 50 "outputs": [],
51 51 "source": [
... ... @@ -58,6 +58,7 @@
58 58 "bkgmodel['instrument'] = 'HESS'\n",
59 59 "bkgmodel['spatial'] = 'GAUSS'\n",
60 60 "bkgmodel['gradient'] = True\n",
  61 + "bkgmodel['spectral'] = 'NODES'\n",
61 62 "bkgmodel['ebinalg'] = 'LOG'\n",
62 63 "bkgmodel['emin'] = 0.7\n",
63 64 "bkgmodel['emax'] = 30.\n",
... ... @@ -76,7 +77,7 @@
76 77 },
77 78 {
78 79 "cell_type": "code",
79   - "execution_count": 17,
  80 + "execution_count": 3,
80 81 "metadata": {},
81 82 "outputs": [
82 83 {
... ... @@ -95,26 +96,26 @@
95 96 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
96 97 " Number of parameters ......: 20\n",
97 98 " Number of spatial par's ...: 3\n",
98   - " 1:Sigma ..................: 3.19679118852325 +/- 0.148784221714502 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
99   - " 2:Grad_DETX ..............: 0.0882131070561384 +/- 0.0223563997131535 deg^-1 (free,scale=1,gradient)\n",
100   - " 2:Grad_DETY ..............: -0.000692481271743188 +/- 0.0227254581791992 deg^-1 (free,scale=1,gradient)\n",
  99 + " 1:Sigma ..................: 3.19679080604377 +/- 0.148784168613255 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  100 + " 2:Grad_DETX ..............: 0.0882131417987659 +/- 0.0223563994341903 deg^-1 (free,scale=1,gradient)\n",
  101 + " 2:Grad_DETY ..............: -0.000692474001567828 +/- 0.0227254580377688 deg^-1 (free,scale=1,gradient)\n",
101 102 " Number of spectral par's ..: 16\n",
102 103 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
103   - " Intensity0 ...............: 0.00149714476600658 +/- 0.000185798282794166 [9.46696076103918e-14,infty[ ph/cm2/s/MeV (free,scale=0.000946696076103918,gradient)\n",
  104 + " Intensity0 ...............: 0.00140372802004064 +/- 0.000174205101284546 [8.87696769787525e-14,infty[ ph/cm2/s/MeV (free,scale=0.000887696769787525,gradient)\n",
104 105 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
105   - " Intensity1 ...............: 0.000275025454049597 +/- 1.50407490107521e-05 [3.11144874586726e-14,infty[ ph/cm2/s/MeV (free,scale=0.000311144874586726,gradient)\n",
  106 + " Intensity1 ...............: 0.000257864797984792 +/- 1.41022572545599e-05 [2.91749105398033e-14,infty[ ph/cm2/s/MeV (free,scale=0.000291749105398033,gradient)\n",
106 107 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
107   - " Intensity2 ...............: 9.68931866899334e-05 +/- 5.86314382972939e-06 [1.02262104412654e-14,infty[ ph/cm2/s/MeV (free,scale=0.000102262104412654,gradient)\n",
  108 + " Intensity2 ...............: 9.08473780602549e-05 +/- 5.49730339459174e-06 [9.58858288072014e-15,infty[ ph/cm2/s/MeV (free,scale=9.58858288072014e-05,gradient)\n",
108 109 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
109   - " Intensity3 ...............: 3.42414862721453e-05 +/- 2.61283857499118e-06 [3.36098674702412e-15,infty[ ph/cm2/s/MeV (free,scale=3.36098674702412e-05,gradient)\n",
  110 + " Intensity3 ...............: 3.21049333853292e-05 +/- 2.44980628805133e-06 [3.15136944584645e-15,infty[ ph/cm2/s/MeV (free,scale=3.15136944584645e-05,gradient)\n",
110 111 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
111   - " Intensity4 ...............: 1.2134598847599e-05 +/- 1.19969987797427e-06 [1.10463518999066e-15,infty[ ph/cm2/s/MeV (free,scale=1.10463518999066e-05,gradient)\n",
  112 + " Intensity4 ...............: 1.13774407454204e-05 +/- 1.12484264580111e-06 [1.03572441389469e-15,infty[ ph/cm2/s/MeV (free,scale=1.03572441389469e-05,gradient)\n",
112 113 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
113   - " Intensity5 ...............: 4.80035386464156e-06 +/- 6.14208278785316e-07 [3.63053768077518e-16,infty[ ph/cm2/s/MeV (free,scale=3.63053768077518e-06,gradient)\n",
  114 + " Intensity5 ...............: 4.50082783670747e-06 +/- 5.75883736185608e-07 [3.40399651634425e-16,infty[ ph/cm2/s/MeV (free,scale=3.40399651634425e-06,gradient)\n",
114 115 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
115   - " Intensity6 ...............: 8.5455367005893e-07 +/- 1.75301261074233e-07 [1.19322686539072e-16,infty[ ph/cm2/s/MeV (free,scale=1.19322686539072e-06,gradient)\n",
  116 + " Intensity6 ...............: 8.01232298563249e-07 +/- 1.64363032163644e-07 [1.11875245266372e-16,infty[ ph/cm2/s/MeV (free,scale=1.11875245266372e-06,gradient)\n",
116 117 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
117   - " Intensity7 ...............: 2.8110106615004e-07 +/- 1.02501352381156e-07 [3.92170658310361e-17,infty[ ph/cm2/s/MeV (free,scale=3.92170658310361e-07,gradient)\n",
  118 + " Intensity7 ...............: 2.63561241594543e-07 +/- 9.61055896032478e-08 [3.67687523865408e-17,infty[ ph/cm2/s/MeV (free,scale=3.67687523865408e-07,gradient)\n",
118 119 " Number of temporal par's ..: 1\n",
119 120 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
120 121 "=== GCTAModelBackground ===\n",
... ... @@ -126,26 +127,26 @@
126 127 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
127 128 " Number of parameters ......: 20\n",
128 129 " Number of spatial par's ...: 3\n",
129   - " 1:Sigma ..................: 3.30082205953218 +/- 0.156912956620767 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
130   - " 2:Grad_DETX ..............: 0.0153115422362867 +/- 0.0215680816698383 deg^-1 (free,scale=1,gradient)\n",
131   - " 2:Grad_DETY ..............: -0.00906905077562078 +/- 0.0213184759257543 deg^-1 (free,scale=1,gradient)\n",
  130 + " 1:Sigma ..................: 3.30082209234815 +/- 0.156912961568416 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  131 + " 2:Grad_DETX ..............: 0.0153115021206004 +/- 0.0215680817043072 deg^-1 (free,scale=1,gradient)\n",
  132 + " 2:Grad_DETY ..............: -0.00906905504246978 +/- 0.0213184758960865 deg^-1 (free,scale=1,gradient)\n",
132 133 " Number of spectral par's ..: 16\n",
133 134 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
134   - " Intensity0 ...............: 0.00115207913907334 +/- 6.11872558313686e-05 [8.950257713407e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008950257713407,gradient)\n",
  135 + " Intensity0 ...............: 0.00107656542145505 +/- 5.71767004891641e-05 [8.36211165467046e-14,infty[ ph/cm2/s/MeV (free,scale=0.000836211165467046,gradient)\n",
135 136 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
136   - " Intensity1 ...............: 0.000216074985117796 +/- 1.14346974881101e-05 [2.82228203274059e-14,infty[ ph/cm2/s/MeV (free,scale=0.000282228203274059,gradient)\n",
  137 + " Intensity1 ...............: 0.00020191222774409 +/- 1.06852037575173e-05 [2.63699934530969e-14,infty[ ph/cm2/s/MeV (free,scale=0.000263699934530969,gradient)\n",
137 138 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
138   - " Intensity2 ...............: 9.80980717622882e-05 +/- 6.14243331439364e-06 [8.8994933189452e-15,infty[ ph/cm2/s/MeV (free,scale=8.8994933189452e-05,gradient)\n",
  139 + " Intensity2 ...............: 9.16681754173992e-05 +/- 5.73982387509952e-06 [8.31580088180217e-15,infty[ ph/cm2/s/MeV (free,scale=8.31580088180217e-05,gradient)\n",
139 140 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
140   - " Intensity3 ...............: 2.64302082334315e-05 +/- 2.27978082923659e-06 [2.80627451173057e-15,infty[ ph/cm2/s/MeV (free,scale=2.80627451173057e-05,gradient)\n",
  141 + " Intensity3 ...............: 2.4697825754506e-05 +/- 2.13035134581624e-06 [2.62239520190935e-15,infty[ ph/cm2/s/MeV (free,scale=2.62239520190935e-05,gradient)\n",
141 142 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
142   - " Intensity4 ...............: 1.03353701064884e-05 +/- 1.13230244143927e-06 [8.84901685180661e-16,infty[ ph/cm2/s/MeV (free,scale=8.84901685180661e-06,gradient)\n",
  143 + " Intensity4 ...............: 9.65793108295259e-06 +/- 1.0580848901101e-06 [8.26974658573933e-16,infty[ ph/cm2/s/MeV (free,scale=8.26974658573933e-06,gradient)\n",
143 144 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
144   - " Intensity5 ...............: 3.57899467682822e-06 +/- 5.03727665165389e-07 [2.79035778275547e-16,infty[ ph/cm2/s/MeV (free,scale=2.79035778275547e-06,gradient)\n",
  145 + " Intensity5 ...............: 3.34440698323729e-06 +/- 4.70710485211692e-07 [2.60787193869763e-16,infty[ ph/cm2/s/MeV (free,scale=2.60787193869763e-06,gradient)\n",
145 146 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
146   - " Intensity6 ...............: 5.95101204005172e-07 +/- 1.41251129058325e-07 [8.79882667891458e-17,infty[ ph/cm2/s/MeV (free,scale=8.79882667891458e-07,gradient)\n",
  147 + " Intensity6 ...............: 5.56094646218329e-07 +/- 1.31992669655457e-07 [8.22394734607041e-17,infty[ ph/cm2/s/MeV (free,scale=8.22394734607041e-07,gradient)\n",
147 148 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
148   - " Intensity7 ...............: 2.81714577113613e-07 +/- 1.0384120888285e-07 [2.77453133085778e-17,infty[ ph/cm2/s/MeV (free,scale=2.77453133085778e-07,gradient)\n",
  149 + " Intensity7 ...............: 2.63249520342286e-07 +/- 9.70349092695409e-08 [2.59342910774655e-17,infty[ ph/cm2/s/MeV (free,scale=2.59342910774655e-07,gradient)\n",
149 150 " Number of temporal par's ..: 1\n",
150 151 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
151 152 "=== GCTAModelBackground ===\n",
... ... @@ -157,26 +158,26 @@
157 158 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
158 159 " Number of parameters ......: 20\n",
159 160 " Number of spatial par's ...: 3\n",
160   - " 1:Sigma ..................: 4.32704862685447 +/- 0.368685355107103 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
161   - " 2:Grad_DETX ..............: 0.018772378083067 +/- 0.0217106872366865 deg^-1 (free,scale=1,gradient)\n",
162   - " 2:Grad_DETY ..............: -0.126627961668458 +/- 0.0204738085196433 deg^-1 (free,scale=1,gradient)\n",
  161 + " 1:Sigma ..................: 4.32704664084324 +/- 0.368684845846136 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  162 + " 2:Grad_DETX ..............: 0.0187722831939023 +/- 0.0217106880517745 deg^-1 (free,scale=1,gradient)\n",
  163 + " 2:Grad_DETY ..............: -0.126627824474372 +/- 0.0204738099128812 deg^-1 (free,scale=1,gradient)\n",
163 164 " Number of spectral par's ..: 16\n",
164 165 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
165   - " Intensity0 ...............: 0.000915773345685034 +/- 4.89989530347851e-05 [7.43957604504927e-14,infty[ ph/cm2/s/MeV (free,scale=0.000743957604504927,gradient)\n",
  166 + " Intensity0 ...............: 0.000857178811233776 +/- 4.58638205127566e-05 [6.96233895968756e-14,infty[ ph/cm2/s/MeV (free,scale=0.000696233895968756,gradient)\n",
166 167 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
167   - " Intensity1 ...............: 0.000197398656687683 +/- 1.07759903733823e-05 [2.35277778278335e-14,infty[ ph/cm2/s/MeV (free,scale=0.000235277778278335,gradient)\n",
  168 + " Intensity1 ...............: 0.000184768403022439 +/- 1.008650496985e-05 [2.20204639078551e-14,infty[ ph/cm2/s/MeV (free,scale=0.000220204639078551,gradient)\n",
168 169 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
169   - " Intensity2 ...............: 6.93704083542102e-05 +/- 4.77505196699795e-06 [7.44069724086306e-15,infty[ ph/cm2/s/MeV (free,scale=7.44069724086306e-05,gradient)\n",
  170 + " Intensity2 ...............: 6.49318372405173e-05 +/- 4.46952677363829e-06 [6.96462544447719e-15,infty[ ph/cm2/s/MeV (free,scale=6.96462544447719e-05,gradient)\n",
170 171 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
171   - " Intensity3 ...............: 2.57663537500693e-05 +/- 2.20136814658691e-06 [2.35313236274662e-15,infty[ ph/cm2/s/MeV (free,scale=2.35313236274662e-05,gradient)\n",
  172 + " Intensity3 ...............: 2.41177375305046e-05 +/- 2.06051735828825e-06 [2.20276955948036e-15,infty[ ph/cm2/s/MeV (free,scale=2.20276955948036e-05,gradient)\n",
172 173 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
173   - " Intensity4 ...............: 6.82483279973139e-06 +/- 7.86680350899204e-07 [7.44181860564889e-16,infty[ ph/cm2/s/MeV (free,scale=7.44181860564889e-06,gradient)\n",
  174 + " Intensity4 ...............: 6.388150904145e-06 +/- 7.36345188624795e-07 [6.96691268016576e-16,infty[ ph/cm2/s/MeV (free,scale=6.96691268016576e-06,gradient)\n",
174 175 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
175   - " Intensity5 ...............: 3.3292540797357e-06 +/- 4.68826132796518e-07 [2.35348699614757e-16,infty[ ph/cm2/s/MeV (free,scale=2.35348699614757e-06,gradient)\n",
  176 + " Intensity5 ...............: 3.11624385438516e-06 +/- 4.38829996211514e-07 [2.20349296566929e-16,infty[ ph/cm2/s/MeV (free,scale=2.20349296566929e-06,gradient)\n",
176 177 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
177   - " Intensity6 ...............: 8.19437245391855e-07 +/- 1.82363529352901e-07 [7.44294013943215e-17,infty[ ph/cm2/s/MeV (free,scale=7.44294013943215e-07,gradient)\n",
  178 + " Intensity6 ...............: 7.66998023710854e-07 +/- 1.70693323247103e-07 [6.9692006669998e-17,infty[ ph/cm2/s/MeV (free,scale=6.9692006669998e-07,gradient)\n",
178 179 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
179   - " Intensity7 ...............: 1.44457348642334e-07 +/- 7.0968299684809e-08 [2.35384168299424e-17,infty[ ph/cm2/s/MeV (free,scale=2.35384168299424e-07,gradient)\n",
  180 + " Intensity7 ...............: 1.35218346687107e-07 +/- 6.64294080047906e-08 [2.20421660943029e-17,infty[ ph/cm2/s/MeV (free,scale=2.20421660943029e-07,gradient)\n",
180 181 " Number of temporal par's ..: 1\n",
181 182 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
182 183 "=== GCTAModelBackground ===\n",
... ... @@ -188,26 +189,26 @@
188 189 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
189 190 " Number of parameters ......: 20\n",
190 191 " Number of spatial par's ...: 3\n",
191   - " 1:Sigma ..................: 3.6479543205698 +/- 0.238749563358304 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
192   - " 2:Grad_DETX ..............: 0.0290086310507671 +/- 0.0231654281693484 deg^-1 (free,scale=1,gradient)\n",
193   - " 2:Grad_DETY ..............: 0.165462911909218 +/- 0.0212932684624286 deg^-1 (free,scale=1,gradient)\n",
  192 + " 1:Sigma ..................: 3.64795431824073 +/- 0.238749562945919 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  193 + " 2:Grad_DETX ..............: 0.0290086296998991 +/- 0.0231654281500121 deg^-1 (free,scale=1,gradient)\n",
  194 + " 2:Grad_DETY ..............: 0.165462915333049 +/- 0.021293268413236 deg^-1 (free,scale=1,gradient)\n",
194 195 " Number of spectral par's ..: 16\n",
195 196 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
196   - " Intensity0 ...............: 0.00147439433272687 +/- 0.000191693450881709 [8.6655214667889e-14,infty[ ph/cm2/s/MeV (free,scale=0.00086655214667889,gradient)\n",
  197 + " Intensity0 ...............: 0.00138280405680456 +/- 0.000179785336701836 [8.127607193775e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008127607193775,gradient)\n",
197 198 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
198   - " Intensity1 ...............: 0.000235250773930648 +/- 1.37598369145017e-05 [2.81551973223247e-14,infty[ ph/cm2/s/MeV (free,scale=0.000281551973223247,gradient)\n",
  199 + " Intensity1 ...............: 0.000220636852715846 +/- 1.29050674731524e-05 [2.64069813465664e-14,infty[ ph/cm2/s/MeV (free,scale=0.000264069813465664,gradient)\n",
199 200 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
200   - " Intensity2 ...............: 9.20623251759662e-05 +/- 5.75965738880592e-06 [9.1479219028787e-15,infty[ ph/cm2/s/MeV (free,scale=9.1479219028787e-05,gradient)\n",
  201 + " Intensity2 ...............: 8.63433571877371e-05 +/- 5.40186394640027e-06 [8.57975351431837e-15,infty[ ph/cm2/s/MeV (free,scale=8.57975351431837e-05,gradient)\n",
201 202 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
202   - " Intensity3 ...............: 3.22022945215284e-05 +/- 2.53414761162924e-06 [2.97225674475431e-15,infty[ ph/cm2/s/MeV (free,scale=2.97225674475431e-05,gradient)\n",
  203 + " Intensity3 ...............: 3.02018682595367e-05 +/- 2.37672480959384e-06 [2.78760261918503e-15,infty[ ph/cm2/s/MeV (free,scale=2.78760261918503e-05,gradient)\n",
203 204 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
204   - " Intensity4 ...............: 1.00701723302328e-05 +/- 1.04797408736245e-06 [9.65717706221071e-16,infty[ ph/cm2/s/MeV (free,scale=9.65717706221071e-06,gradient)\n",
  205 + " Intensity4 ...............: 9.44460701196062e-06 +/- 9.82873290475423e-07 [9.05705315370543e-16,infty[ ph/cm2/s/MeV (free,scale=9.05705315370543e-06,gradient)\n",
205 206 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
206   - " Intensity5 ...............: 3.29354066928594e-06 +/- 4.58158358563221e-07 [3.1377191413724e-16,infty[ ph/cm2/s/MeV (free,scale=3.1377191413724e-06,gradient)\n",
  207 + " Intensity5 ...............: 3.08894388117525e-06 +/- 4.2969727730712e-07 [2.94267953633316e-16,infty[ ph/cm2/s/MeV (free,scale=2.94267953633316e-06,gradient)\n",
207 208 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
208   - " Intensity6 ...............: 8.71669675678694e-07 +/- 1.76346292129154e-07 [1.01947819188903e-16,infty[ ph/cm2/s/MeV (free,scale=1.01947819188903e-06,gradient)\n",
  209 + " Intensity6 ...............: 8.17521009765721e-07 +/- 1.65391550069682e-07 [9.5609054143745e-17,infty[ ph/cm2/s/MeV (free,scale=9.5609054143745e-07,gradient)\n",
209 210 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
210   - " Intensity7 ...............: 3.12067435753287e-07 +/- 1.11366909796179e-07 [3.31239265501224e-17,infty[ ph/cm2/s/MeV (free,scale=3.31239265501224e-07,gradient)\n",
  211 + " Intensity7 ...............: 2.92681614932954e-07 +/- 1.0444872894399e-07 [3.10638352610158e-17,infty[ ph/cm2/s/MeV (free,scale=3.10638352610158e-07,gradient)\n",
211 212 " Number of temporal par's ..: 1\n",
212 213 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
213 214 ]
... ...
doc/source/users/tutorials/hess_dr1/classical.ipynb
  View file @fb39a2b
... ... @@ -25,7 +25,7 @@
25 25 },
26 26 {
27 27 "cell_type": "code",
28   - "execution_count": 4,
  28 + "execution_count": 1,
29 29 "metadata": {},
30 30 "outputs": [],
31 31 "source": [
... ... @@ -43,7 +43,7 @@
43 43 },
44 44 {
45 45 "cell_type": "code",
46   - "execution_count": 5,
  46 + "execution_count": 2,
47 47 "metadata": {},
48 48 "outputs": [],
49 49 "source": [
... ... @@ -60,7 +60,7 @@
60 60 },
61 61 {
62 62 "cell_type": "code",
63   - "execution_count": 6,
  63 + "execution_count": 3,
64 64 "metadata": {},
65 65 "outputs": [],
66 66 "source": [
... ... @@ -73,7 +73,9 @@
73 73 "cell_type": "markdown",
74 74 "metadata": {},
75 75 "source": [
76   - "## Preparing the binned observations\n",
  76 + "## Classical analysis without background model\n",
  77 + "\n",
  78 + "### Preparation of the On/Off binned data\n",
77 79 "\n",
78 80 "The first step for a classical analysis is to bin the events in bins of energy for an On (signal) region, and one or several Off (background regions).\n",
79 81 "\n",
... ... @@ -82,7 +84,7 @@
82 84 },
83 85 {
84 86 "cell_type": "code",
85   - "execution_count": 21,
  87 + "execution_count": 4,
86 88 "metadata": {},
87 89 "outputs": [],
88 90 "source": [
... ... @@ -102,25 +104,82 @@
102 104 "phagen['bkgmethod'] = 'REFLECTED'\n",
103 105 "phagen['use_model_bkg'] = False\n",
104 106 "phagen['maxoffset'] = 2.0\n",
105   - "phagen['stack'] = True # treat observations separately in joint likelihood fit\n",
  107 + "phagen['stack'] = True\n",
  108 + "phagen['outobs'] = 'obs_crab_onoff.xml'\n",
  109 + "phagen['outmodel'] = 'onoff_model.xml'\n",
106 110 "\n",
107   - "phagen.run()"
  111 + "phagen.execute()"
108 112 ]
109 113 },
110 114 {
111 115 "cell_type": "markdown",
112 116 "metadata": {},
113 117 "source": [
114   - "Note that we have used the hidden parameter *use_model_bkg* to specify that no background model should be used in the computation of the background normalisation factor, which is simply assumed to be the ratio of the solid angles of On/Off regions.\n",
  118 + "Note that we have used the hidden parameter `use_model_bkg` to specify that no background model should be used in the computation of the background normalisation factor, which is simply assumed to be the ratio of the solid angles of On/Off regions.\n",
115 119 "\n",
116   - "## Likelihood fit\n",
117   - "\n",
118   - "The next step will be to fit a model for the source to the On data (along with a model for the background derived directly from the On/Off data). The model for the source is"
  120 + "Let us peek at the resulting set of On/Off observations."
  121 + ]
  122 + },
  123 + {
  124 + "cell_type": "code",
  125 + "execution_count": 5,
  126 + "metadata": {},
  127 + "outputs": [
  128 + {
  129 + "name": "stdout",
  130 + "output_type": "stream",
  131 + "text": [
  132 + "=== GCTAOnOffObservation ===\n",
  133 + " Name ......................: \n",
  134 + " Identifier ................: \n",
  135 + " Instrument ................: HESSOnOff\n",
  136 + " Statistic .................: wstat\n",
  137 + " Ontime ....................: 6313.8117676 s\n",
  138 + " Livetime ..................: 6313.8117676 s\n",
  139 + " Deadtime correction .......: 1\n",
  140 + "=== GPha ===\n",
  141 + " Exposure ..................: 6313.8117676 s\n",
  142 + " Number of bins ............: 20\n",
  143 + " Energy range ..............: 660 GeV - 30 TeV\n",
  144 + " Observation energy range ..: 660.693448007596 GeV - 100 TeV\n",
  145 + " Total number of counts ....: 576\n",
  146 + " Underflow counts ..........: 0\n",
  147 + " Overflow counts ...........: 1\n",
  148 + " Outflow counts ............: 0\n",
  149 + "=== GPha ===\n",
  150 + " Exposure ..................: 6313.8117676 s\n",
  151 + " Number of bins ............: 20\n",
  152 + " Energy range ..............: 660 GeV - 30 TeV\n",
  153 + " Observation energy range ..: 660.693448007596 GeV - 100 TeV\n",
  154 + " Total number of counts ....: 772\n",
  155 + " Underflow counts ..........: 0\n",
  156 + " Overflow counts ...........: 4\n",
  157 + " Outflow counts ............: 0\n",
  158 + "=== GArf ===\n",
  159 + " Number of bins ............: 61\n",
  160 + " Energy range ..............: 330 GeV - 36.000002048 TeV\n",
  161 + "=== GRmf ===\n",
  162 + " Number of true energy bins : 61\n",
  163 + " Number of measured bins ...: 20\n",
  164 + " True energy range .........: 330 GeV - 36.000002048 TeV\n",
  165 + " Measured energy range .....: 660 GeV - 30 TeV\n"
  166 + ]
  167 + }
  168 + ],
  169 + "source": [
  170 + "print(phagen.obs()[0])"
  171 + ]
  172 + },
  173 + {
  174 + "cell_type": "markdown",
  175 + "metadata": {},
  176 + "source": [
  177 + "Note that the instrument is now `HESSOnOff`, to signify that we are dealing with On/Off observations. Since we have no background model the statistic has been set to `WSTAT`. The script has also created a model for the On region."
119 178 ]
120 179 },
121 180 {
122 181 "cell_type": "code",
123   - "execution_count": 22,
  182 + "execution_count": 6,
124 183 "metadata": {},
125 184 "outputs": [
126 185 {
... ... @@ -131,7 +190,7 @@
131 190 " Number of models ..........: 1\n",
132 191 " Number of parameters ......: 6\n",
133 192 "=== GModelSky ===\n",
134   - " Name ......................: Crab\n",
  193 + " Name ......................: Dummy\n",
135 194 " Instruments ...............: all\n",
136 195 " Instrument scale factors ..: unity\n",
137 196 " Observation identifiers ...: all\n",
... ... @@ -139,40 +198,53 @@
139 198 " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
140 199 " Number of parameters ......: 6\n",
141 200 " Number of spatial par's ...: 2\n",
142   - " RA .......................: 83.6331 [-360,360] deg (fixed,scale=1)\n",
143   - " DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1)\n",
  201 + " RA .......................: 83.63 deg (fixed,scale=1)\n",
  202 + " DEC ......................: 22.01 deg (fixed,scale=1)\n",
144 203 " Number of spectral par's ..: 3\n",
145   - " Prefactor ................: 5.7e-17 +/- 0 [1e-24,1e-14] ph/cm2/s/MeV (free,scale=1e-17,gradient)\n",
146   - " Index ....................: -2.48 +/- 0 [-0,-5] (free,scale=-1,gradient)\n",
147   - " PivotEnergy ..............: 1000000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)\n",
  204 + " Prefactor ................: 1e-18 +/- 0 [0,infty[ ph/cm2/s/MeV (free,scale=1e-18,gradient)\n",
  205 + " Index ....................: -2 +/- 0 [10,-10] (free,scale=-2,gradient)\n",
  206 + " PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)\n",
148 207 " Number of temporal par's ..: 1\n",
149 208 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
150 209 ]
151 210 }
152 211 ],
153 212 "source": [
154   - "srcmodel = os.environ['CTOOLS']+'/share/models/crab_nobkg.xml'\n",
155   - "\n",
156   - "print(gammalib.GModels(srcmodel))"
  213 + "print(phagen.obs().models())"
157 214 ]
158 215 },
159 216 {
160 217 "cell_type": "markdown",
161 218 "metadata": {},
162 219 "source": [
163   - "We can now fit this model to the data. Note that we have to specify WSTAT as statistic to derive the background model from the data without prior information."
  220 + "Since we do not have specified any input model the script has placed a point source named `Dummy`, at the center of the On region with a power law spectrum. We will rename this source as Crab. At this point you can also modify the spectral model, e.g., to use more appropriate parameter values or a different spectral shape."
164 221 ]
165 222 },
166 223 {
167 224 "cell_type": "code",
168   - "execution_count": 23,
  225 + "execution_count": 7,
169 226 "metadata": {},
170 227 "outputs": [],
171 228 "source": [
172   - "like = ctools.ctlike(phagen.obs())\n",
  229 + "phagen.obs().models()['Dummy'].name('Crab')"
  230 + ]
  231 + },
  232 + {
  233 + "cell_type": "markdown",
  234 + "metadata": {},
  235 + "source": [
  236 + "### Likelihood fit and residuals\n",
173 237 "\n",
174   - "like['statistic'] = 'WSTAT'\n",
175   - "like['inmodel'] = srcmodel\n",
  238 + "We can now fit this model to the data."
  239 + ]
  240 + },
  241 + {
  242 + "cell_type": "code",
  243 + "execution_count": 8,
  244 + "metadata": {},
  245 + "outputs": [],
  246 + "source": [
  247 + "like = ctools.ctlike(phagen.obs())\n",
176 248 "\n",
177 249 "like.run()"
178 250 ]
... ... @@ -181,12 +253,12 @@
181 253 "cell_type": "markdown",
182 254 "metadata": {},
183 255 "source": [
184   - "We can now look at the results from the optimisation and the fitted model."
  256 + "Let's look at the results from the optimisation and the fitted model."
185 257 ]
186 258 },
187 259 {
188 260 "cell_type": "code",
189   - "execution_count": 24,
  261 + "execution_count": 9,
190 262 "metadata": {},
191 263 "outputs": [
192 264 {
... ... @@ -194,14 +266,14 @@
194 266 "output_type": "stream",
195 267 "text": [
196 268 "=== GOptimizerLM ===\n",
197   - " Optimized function value ..: 16.457\n",
  269 + " Optimized function value ..: 15.746\n",
198 270 " Absolute precision ........: 0.005\n",
199 271 " Acceptable value decrease .: 2\n",
200 272 " Optimization status .......: converged\n",
201 273 " Number of parameters ......: 6\n",
202 274 " Number of free parameters .: 2\n",
203   - " Number of iterations ......: 4\n",
204   - " Lambda ....................: 1e-07\n",
  275 + " Number of iterations ......: 11\n",
  276 + " Lambda ....................: 0.0001\n",
205 277 "=== GModels ===\n",
206 278 " Number of models ..........: 1\n",
207 279 " Number of parameters ......: 6\n",
... ... @@ -214,18 +286,300 @@
214 286 " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
215 287 " Number of parameters ......: 6\n",
216 288 " Number of spatial par's ...: 2\n",
  289 + " RA .......................: 83.63 deg (fixed,scale=1)\n",
  290 + " DEC ......................: 22.01 deg (fixed,scale=1)\n",
  291 + " Number of spectral par's ..: 3\n",
  292 + " Prefactor ................: 4.61852950191309e-17 +/- 2.97687577951453e-18 [0,infty[ ph/cm2/s/MeV (free,scale=1e-18,gradient)\n",
  293 + " Index ....................: -2.63298373450669 +/- 0.0795120102122515 [10,-10] (free,scale=-2,gradient)\n",
  294 + " PivotEnergy ..............: 1000000 MeV (fixed,scale=1000000,gradient)\n",
  295 + " Number of temporal par's ..: 1\n",
  296 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
  297 + ]
  298 + }
  299 + ],
  300 + "source": [
  301 + "print(like.opt())\n",
  302 + "print(like.obs().models())"
  303 + ]
  304 + },
  305 + {
  306 + "cell_type": "markdown",
  307 + "metadata": {},
  308 + "source": [
  309 + "The fit has properly converged and the results are consistent with those obtained with the unbinned analysis.\n",
  310 + "\n",
  311 + "We will also check the spectral residuals."
  312 + ]
  313 + },
  314 + {
  315 + "cell_type": "code",
  316 + "execution_count": 10,
  317 + "metadata": {},
  318 + "outputs": [
  319 + {
  320 + "data": {
  321 + "image/png": 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\n",
  322 + "text/plain": [
  323 + "<matplotlib.figure.Figure at 0x110d248d0>"
  324 + ]
  325 + },
  326 + "metadata": {},
  327 + "output_type": "display_data"
  328 + }
  329 + ],
  330 + "source": [
  331 + "residuals = 'residuals_classical_nobkg.fits'\n",
  332 + "res = cscripts.csresspec(like.obs())\n",
  333 + "\n",
  334 + "res['components'] = True\n",
  335 + "res['algorithm'] = 'SIGNIFICANCE'\n",
  336 + "res['outfile'] = residuals\n",
  337 + "\n",
  338 + "res.execute()\n",
  339 + "\n",
  340 + "from show_residuals import plot_residuals\n",
  341 + "plot_residuals(residuals,'',0)"
  342 + ]
  343 + },
  344 + {
  345 + "cell_type": "markdown",
  346 + "metadata": {},
  347 + "source": [
  348 + "## Classical analysis with a background model\n",
  349 + "\n",
  350 + "### Preparation of the On/Off binned data\n",
  351 + "\n",
  352 + "If a background model is available this can be used to compute the expected background rates for different observations as a function of energy and normalise the number of background counts between On and Off regions. Here we will use the background model derived using csbkgmodel."
  353 + ]
  354 + },
  355 + {
  356 + "cell_type": "code",
  357 + "execution_count": 11,
  358 + "metadata": {},
  359 + "outputs": [],
  360 + "source": [
  361 + "srcmodel = os.environ['CTOOLS']+'/share/models/crab_nobkg.xml'\n",
  362 + "bkgmodel = 'bkgmodel.xml'\n",
  363 + "startmodel = 'model_classical_bkg.xml'\n",
  364 + "\n",
  365 + "merge = cscripts.csmodelmerge()\n",
  366 + "merge['inmodels'] = srcmodel + ' ' + bkgmodel\n",
  367 + "merge['outmodel'] = startmodel\n",
  368 + "\n",
  369 + "merge.execute()"
  370 + ]
  371 + },
  372 + {
  373 + "cell_type": "markdown",
  374 + "metadata": {},
  375 + "source": [
  376 + "Now we can run csphagen using this model including the background in input. Note that since the background model is defined per observation we will not stack the observations together."
  377 + ]
  378 + },
  379 + {
  380 + "cell_type": "code",
  381 + "execution_count": 12,
  382 + "metadata": {},
  383 + "outputs": [],
  384 + "source": [
  385 + "phagen = cscripts.csphagen()\n",
  386 + "phagen['inobs'] = obsfile\n",
  387 + "phagen['inmodel'] = startmodel\n",
  388 + "phagen['ebinalg'] = 'LOG'\n",
  389 + "phagen['emin'] = 0.66\n",
  390 + "phagen['emax'] = 30.\n",
  391 + "phagen['enumbins'] = 20\n",
  392 + "phagen['coordsys'] = 'CEL'\n",
  393 + "phagen['ra'] = 83.63\n",
  394 + "phagen['dec'] = 22.01\n",
  395 + "phagen['rad'] = 0.2\n",
  396 + "phagen['bkgmethod'] = 'REFLECTED'\n",
  397 + "phagen['use_model_bkg'] = True\n",
  398 + "phagen['maxoffset'] = 2.0\n",
  399 + "phagen['stack'] = False # treat observations separately in joint likelihood fit\n",
  400 + "\n",
  401 + "phagen.run()"
  402 + ]
  403 + },
  404 + {
  405 + "cell_type": "markdown",
  406 + "metadata": {},
  407 + "source": [
  408 + "### Likelihood fit and residuals\n",
  409 + "\n",
  410 + "We will now fit the model to the data and look at the results."
  411 + ]
  412 + },
  413 + {
  414 + "cell_type": "code",
  415 + "execution_count": 13,
  416 + "metadata": {},
  417 + "outputs": [
  418 + {
  419 + "name": "stdout",
  420 + "output_type": "stream",
  421 + "text": [
  422 + "=== GOptimizerLM ===\n",
  423 + " Optimized function value ..: -2417.569\n",
  424 + " Absolute precision ........: 0.005\n",
  425 + " Acceptable value decrease .: 2\n",
  426 + " Optimization status .......: converged\n",
  427 + " Number of parameters ......: 86\n",
  428 + " Number of free parameters .: 46\n",
  429 + " Number of iterations ......: 28\n",
  430 + " Lambda ....................: 0.0001\n",
  431 + "=== GModels ===\n",
  432 + " Number of models ..........: 5\n",
  433 + " Number of parameters ......: 86\n",
  434 + "=== GModelSky ===\n",
  435 + " Name ......................: Crab\n",
  436 + " Instruments ...............: all\n",
  437 + " Instrument scale factors ..: unity\n",
  438 + " Observation identifiers ...: all\n",
  439 + " Model type ................: PointSource\n",
  440 + " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
  441 + " Number of parameters ......: 6\n",
  442 + " Number of spatial par's ...: 2\n",
217 443 " RA .......................: 83.6331 [-360,360] deg (fixed,scale=1)\n",
218 444 " DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1)\n",
219 445 " Number of spectral par's ..: 3\n",
220   - " Prefactor ................: 4.58230817013557e-17 +/- 3.14037370499303e-18 [1e-24,1e-14] ph/cm2/s/MeV (free,scale=1e-17,gradient)\n",
221   - " Index ....................: -2.6252889331251 +/- 0.0832081186871608 [-0,-5] (free,scale=-1,gradient)\n",
  446 + " Prefactor ................: 4.44948779297595e-17 +/- 2.98607771115441e-18 [1e-24,1e-14] ph/cm2/s/MeV (free,scale=1e-17,gradient)\n",
  447 + " Index ....................: -2.64048429363838 +/- 0.084515805313649 [-0,-5] (free,scale=-1,gradient)\n",
222 448 " PivotEnergy ..............: 1000000 [10000,1000000000] MeV (fixed,scale=1000000,gradient)\n",
223 449 " Number of temporal par's ..: 1\n",
  450 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  451 + "=== GCTAModelBackground ===\n",
  452 + " Name ......................: Background_023523\n",
  453 + " Instruments ...............: HESSOnOff\n",
  454 + " Instrument scale factors ..: unity\n",
  455 + " Observation identifiers ...: 023523\n",
  456 + " Model type ................: CTABackground\n",
  457 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  458 + " Number of parameters ......: 20\n",
  459 + " Number of spatial par's ...: 3\n",
  460 + " 1:Sigma ..................: 3.19679080604377 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  461 + " 2:Grad_DETX ..............: 0.0882131417987659 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  462 + " 2:Grad_DETY ..............: -0.000692474001567828 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  463 + " Number of spectral par's ..: 16\n",
  464 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  465 + " Intensity0 ...............: 1.95087973878045 +/- 3.18389824156818 [8.87696769787525e-14,infty[ ph/cm2/s/MeV (free,scale=0.000887696769787525,gradient)\n",
  466 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  467 + " Intensity1 ...............: 0.786206855260844 +/- 0.278691033333991 [2.91749105398033e-14,infty[ ph/cm2/s/MeV (free,scale=0.000291749105398033,gradient)\n",
  468 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  469 + " Intensity2 ...............: 1.16378010214047 +/- 0.324828629342433 [9.58858288072014e-15,infty[ ph/cm2/s/MeV (free,scale=9.58858288072014e-05,gradient)\n",
  470 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  471 + " Intensity3 ...............: 1.41647574287537 +/- 0.410958373186227 [3.15136944584645e-15,infty[ ph/cm2/s/MeV (free,scale=3.15136944584645e-05,gradient)\n",
  472 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  473 + " Intensity4 ...............: 0.988046118826941 +/- 0.410766631433934 [1.03572441389469e-15,infty[ ph/cm2/s/MeV (free,scale=1.03572441389469e-05,gradient)\n",
  474 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  475 + " Intensity5 ...............: 0.767763395749799 +/- 0.431359851222795 [3.40399651634425e-16,infty[ ph/cm2/s/MeV (free,scale=3.40399651634425e-06,gradient)\n",
  476 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  477 + " Intensity6 ...............: 0.50642281781849 +/- 0.539376809186644 [1.11875245266372e-16,infty[ ph/cm2/s/MeV (free,scale=1.11875245266372e-06,gradient)\n",
  478 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  479 + " Intensity7 ...............: 1.93817511343864 +/- 2.4495988155466 [3.67687523865408e-17,infty[ ph/cm2/s/MeV (free,scale=3.67687523865408e-07,gradient)\n",
  480 + " Number of temporal par's ..: 1\n",
  481 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  482 + "=== GCTAModelBackground ===\n",
  483 + " Name ......................: Background_023526\n",
  484 + " Instruments ...............: HESSOnOff\n",
  485 + " Instrument scale factors ..: unity\n",
  486 + " Observation identifiers ...: 023526\n",
  487 + " Model type ................: CTABackground\n",
  488 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  489 + " Number of parameters ......: 20\n",
  490 + " Number of spatial par's ...: 3\n",
  491 + " 1:Sigma ..................: 3.30082209234815 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  492 + " 2:Grad_DETX ..............: 0.0153115021206004 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  493 + " 2:Grad_DETY ..............: -0.00906905504246978 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  494 + " Number of spectral par's ..: 16\n",
  495 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  496 + " Intensity0 ...............: 1.41145187416786 +/- 0.66650710147696 [8.36211165467046e-14,infty[ ph/cm2/s/MeV (free,scale=0.000836211165467046,gradient)\n",
  497 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  498 + " Intensity1 ...............: 0.596849678261568 +/- 0.19986170208393 [2.63699934530969e-14,infty[ ph/cm2/s/MeV (free,scale=0.000263699934530969,gradient)\n",
  499 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  500 + " Intensity2 ...............: 0.955852776423173 +/- 0.27239261436471 [8.31580088180217e-15,infty[ ph/cm2/s/MeV (free,scale=8.31580088180217e-05,gradient)\n",
  501 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  502 + " Intensity3 ...............: 1.96279835266255 +/- 0.605417301754042 [2.62239520190935e-15,infty[ ph/cm2/s/MeV (free,scale=2.62239520190935e-05,gradient)\n",
  503 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  504 + " Intensity4 ...............: 0.61189370200522 +/- 0.392560122455627 [8.26974658573933e-16,infty[ ph/cm2/s/MeV (free,scale=8.26974658573933e-06,gradient)\n",
  505 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  506 + " Intensity5 ...............: 0.670372977599336 +/- 0.49918330694888 [2.60787193869763e-16,infty[ ph/cm2/s/MeV (free,scale=2.60787193869763e-06,gradient)\n",
  507 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  508 + " Intensity6 ...............: 0.91835054322145 +/- 1.23832365407144 [8.22394734607041e-17,infty[ ph/cm2/s/MeV (free,scale=8.22394734607041e-07,gradient)\n",
  509 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  510 + " Intensity7 ...............: 2.59342910774655e-17 +/- 9.80203792605831e-14 [2.59342910774655e-17,infty[ ph/cm2/s/MeV (free,scale=2.59342910774655e-07,gradient)\n",
  511 + " Number of temporal par's ..: 1\n",
  512 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  513 + "=== GCTAModelBackground ===\n",
  514 + " Name ......................: Background_023559\n",
  515 + " Instruments ...............: HESSOnOff\n",
  516 + " Instrument scale factors ..: unity\n",
  517 + " Observation identifiers ...: 023559\n",
  518 + " Model type ................: CTABackground\n",
  519 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  520 + " Number of parameters ......: 20\n",
  521 + " Number of spatial par's ...: 3\n",
  522 + " 1:Sigma ..................: 4.32704664084324 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  523 + " 2:Grad_DETX ..............: 0.0187722831939023 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  524 + " 2:Grad_DETY ..............: -0.126627824474372 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  525 + " Number of spectral par's ..: 16\n",
  526 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  527 + " Intensity0 ...............: 0.808933010585171 +/- 0.200163172598279 [6.96233895968756e-14,infty[ ph/cm2/s/MeV (free,scale=0.000696233895968756,gradient)\n",
  528 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  529 + " Intensity1 ...............: 1.06066471644224 +/- 0.153500072375076 [2.20204639078551e-14,infty[ ph/cm2/s/MeV (free,scale=0.000220204639078551,gradient)\n",
  530 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  531 + " Intensity2 ...............: 0.710415433488969 +/- 0.137769419014371 [6.96462544447719e-15,infty[ ph/cm2/s/MeV (free,scale=6.96462544447719e-05,gradient)\n",
  532 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  533 + " Intensity3 ...............: 0.729027655898775 +/- 0.178799879249574 [2.20276955948036e-15,infty[ ph/cm2/s/MeV (free,scale=2.20276955948036e-05,gradient)\n",
  534 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  535 + " Intensity4 ...............: 0.803061536460788 +/- 0.258238882634414 [6.96691268016576e-16,infty[ ph/cm2/s/MeV (free,scale=6.96691268016576e-06,gradient)\n",
  536 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  537 + " Intensity5 ...............: 1.00423153683732 +/- 0.350723666167284 [2.20349296566929e-16,infty[ ph/cm2/s/MeV (free,scale=2.20349296566929e-06,gradient)\n",
  538 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  539 + " Intensity6 ...............: 0.568268794513719 +/- 0.367440704128657 [6.9692006669998e-17,infty[ ph/cm2/s/MeV (free,scale=6.9692006669998e-07,gradient)\n",
  540 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  541 + " Intensity7 ...............: 2.20412624439493 +/- 2.17456371931644 [2.20421660943029e-17,infty[ ph/cm2/s/MeV (free,scale=2.20421660943029e-07,gradient)\n",
  542 + " Number of temporal par's ..: 1\n",
  543 + " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
  544 + "=== GCTAModelBackground ===\n",
  545 + " Name ......................: Background_023592\n",
  546 + " Instruments ...............: HESSOnOff\n",
  547 + " Instrument scale factors ..: unity\n",
  548 + " Observation identifiers ...: 023592\n",
  549 + " Model type ................: CTABackground\n",
  550 + " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
  551 + " Number of parameters ......: 20\n",
  552 + " Number of spatial par's ...: 3\n",
  553 + " 1:Sigma ..................: 3.64795431824073 +/- 0 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  554 + " 2:Grad_DETX ..............: 0.0290086296998991 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  555 + " 2:Grad_DETY ..............: 0.165462915333049 +/- 0 deg^-1 (free,scale=1,gradient)\n",
  556 + " Number of spectral par's ..: 16\n",
  557 + " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
  558 + " Intensity0 ...............: 1.02971670368846 +/- 0.855827122441381 [8.127607193775e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008127607193775,gradient)\n",
  559 + " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
  560 + " Intensity1 ...............: 0.947987059426059 +/- 0.159618808634336 [2.64069813465664e-14,infty[ ph/cm2/s/MeV (free,scale=0.000264069813465664,gradient)\n",
  561 + " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
  562 + " Intensity2 ...............: 0.930329629369328 +/- 0.147573728952743 [8.57975351431837e-15,infty[ ph/cm2/s/MeV (free,scale=8.57975351431837e-05,gradient)\n",
  563 + " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
  564 + " Intensity3 ...............: 0.729974577227829 +/- 0.155604820302836 [2.78760261918503e-15,infty[ ph/cm2/s/MeV (free,scale=2.78760261918503e-05,gradient)\n",
  565 + " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
  566 + " Intensity4 ...............: 0.726106308630469 +/- 0.203524547813456 [9.05705315370543e-16,infty[ ph/cm2/s/MeV (free,scale=9.05705315370543e-06,gradient)\n",
  567 + " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
  568 + " Intensity5 ...............: 1.18037302547945 +/- 0.389214640445536 [2.94267953633316e-16,infty[ ph/cm2/s/MeV (free,scale=2.94267953633316e-06,gradient)\n",
  569 + " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
  570 + " Intensity6 ...............: 1.55090547056218 +/- 0.642354234762239 [9.5609054143745e-17,infty[ ph/cm2/s/MeV (free,scale=9.5609054143745e-07,gradient)\n",
  571 + " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
  572 + " Intensity7 ...............: 1.38574572560495 +/- 1.01260914296946 [3.10638352610158e-17,infty[ ph/cm2/s/MeV (free,scale=3.10638352610158e-07,gradient)\n",
  573 + " Number of temporal par's ..: 1\n",
224 574 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
225 575 ]
226 576 }
227 577 ],
228 578 "source": [
  579 + "like = ctools.ctlike(phagen.obs())\n",
  580 + "\n",
  581 + "like.run()\n",
  582 + "\n",
229 583 "print(like.opt())\n",
230 584 "print(like.obs().models())"
231 585 ]
... ... @@ -234,21 +588,23 @@
234 588 "cell_type": "markdown",
235 589 "metadata": {},
236 590 "source": [
237   - "The fit has properly converged and the results are consistent with those obtained with the unbinned analysis.\n",
  591 + "Results for the spectrum of the Crab nebula are once again consistent with those obtained before. Note how the spatial parameters of the background model, although formally free, have not been optimised (their errors are 0), since we are running a spectral analysis only.\n",
238 592 "\n",
239   - "We will also check the spectral residuals."
  593 + "Since we have provided a background model to csphagen the statistic was set by default to `CSTAT`, i.e. the background model was also used in the fit. You may also specify as statistic in ctlike `WSTAT`. In this case the spectral model for the background would be derived from the data during the fit, and the impact of the model used above would extend only to the computation of the background normalisation factor between On and Off regions. Using an a-priori background model on the other hand can be advantageous to avoid intrinsic biases of WSTAT when the counting statistics are limited.\n",
  594 + "\n",
  595 + "Let's check the residuals for the first of the 4 observations."
240 596 ]
241 597 },
242 598 {
243 599 "cell_type": "code",
244   - "execution_count": 26,
  600 + "execution_count": 14,
245 601 "metadata": {},
246 602 "outputs": [
247 603 {
248 604 "data": {
249   - "image/png": 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\n",
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++63Ffo6WE6zrjbepv+kKIZomUyeD6d4nswMLd+ZVDW57c7hd7bHHHuOGG24gJiaGrVu3mtf37duXZcuWMWvWLLZu3UpYWBjBwcHm9TNnzmTDhg0UFxcDMGDAAIYPH86UKVNo3bo1RUVFlJaW2vyaramJj4+3ua226iblZ/z5aHys1W3TfvNvbrh8psb6bt26mRvcU6ZMsXqstXKCQghhzbUfmiMiIjh27FiN/cLDw506K667T7KVkpJibnDL7MDCnXlVg7u2qd3dSdu2bXnqqadqrE9NTeXJJ5+kW7duBAYGkpmZCRhTYJKSkoiLi+O+++4zT4PcpUsX5s6dywMPPMDVq1fx9fVl4cKF0uB2ohOt4gD4aPxPa2xLTU0F7C8naK/qb3SuetMVQrjW9aRPyGBLIVzPqxrc7u78+Zr5wv369aNfv34AhISEsGbNmhr7hIaGsnHjRvPyH/7wB/Pjxx57jMcee6zGMdL48k6SsyhE0+Ro+oSM+xDCPXhVlRIhvF1KSgrp6enm5fDwcNLT0+WNUzhEKdVCKeU9dUS9XPX/53l5eVb/30utauEoqYDlXNLgFm7PNI27MLLnTVcIa5RSzZRSyUqpT5VSp4GDwHdKqX1Kqd8rpaJcHaMQjUEal5aaQtlJV/OqlBJPGTQpPNv+737gMRtlAwGr26yVExTCBbYAm4AZwF6t9VUApVQI0B/4jVJqtdb6ry6MUVwHdxls6c4kzUbKTrqCVzW4PWXQpElRQX69j2kedAOBwe47sY+3Gx7bztUhCHE9BmqtK65dqbUuAlYCK5VSvo0flnAWGfchjUvhHryqwe3tKi9fpgykwe1Cyb1uI7nXbVa3rZhg/NdaBZM5czZW/Vv/aiWO1u8W4lqmxrZS6lWttdUXo7UGufBcUqtaWCPfhDQ+aXC7UEjb9vXa35EeceEeSv3DrNbvtofU7xZO8KpSKhAIAf4NfKi1LnZxTMJJmnqtamlc1s0V34Q0tXKV0uBuRN9//z3PP/88//rXv/Dz8+P222/nrbfe4o477rDr+NuiOnH88H+dHKVwVG1fP9ZWv7s211O/W4haaKAM+AyIA75USj2utf6Pa8MSwvkkzaamxv4mpCnm0UuDu5ForXn44Yd54oknmDdvHgCnT5/m1KlT5gb3lStXMBikQpcQwukOaq1NMzR9rJTKABYD97suJCEah6TZWOfMb0Ikj97LGtz2VimZs3Yf+wt+aNBrd2kbzKvDutrcvmXLFnx9fZkwYQIFBQUAxMbGsnXrVvr370+bNm3Izc1l//79PPTQQ5w4cYKysjImT55s8Slw1pxf8c+vs2nVqhUffvghN910U4M+DyFEk3BGKdVDa50DoLU+pJSSm4loMpp6mo1ofF7V4HbnKiV79+6lR48eVrft2rWLvXv3EhkZCcDSpUsJCQnh0qVL9OzZk0ceeYTQ0FAuXLxIt5gYFr6Tzq9+9SvmzJnDggULGvNpiOtQVzlBa6ScoHCS54APlVI5wB6gG3DUtSEJ4d0as7fW1KPsLj3EkkfvZQ1ue9XWE+0Kd911l7mxDfD222+zevVqAE6cOMHhw4cJDQ2lWbNmPJw4DDB+Ffazn/3MJfGK+pNygsIdKKWUNvqPUioWGAjcibE+9wfV93FlnEII79YU8+ibZIPbFbp27crHH39sdVuLFi3Mj7du3cqmTZv45z//SWBgIP369aOsrMzqcTIdr+eorZxgbWyVE0xNTbW63kRKCQobtiilVgJrtNbHgU+BT5VSfsA9SqknMDa+M1wYoxDCyzXFPHqZ2r2R3H///Vy+fJklS5aY13399dds27bNYr+SkhJatWpFYGAgBw8e5F//+pd529WrV/nk008BWL58Offcc0/jBC+cSill88NTqX+YQ+d0l1KCtT034RIJwBXgA6VUgVJqv1LqCHAYSAL+oLXOcGWAQoimoXrjOi8vz6sb2yA93I1GKcXq1at5/vnnSUtLw9/fn9tvv52HHnrIYr+EhAQWL15Mt27d6NSpE7179zZvaxEYyMH/HqJHjx60bNmSjz76qLGfhmhkjpQTlFKCwhatdRmwCFhUNaNkGHBJa32uIa+jlEoA5gMG4M9a69805Plr42ht36ZWE1gI0bikwd2I2rZty4oVK8xVStq2bQvAL37x4xhPf39/NmzYYPV4Uw3uN96a7+RIhRDermpGye8a+rxKKQOwEBgE5ANfK6U+0Vrvb+hrXcvR2r7XUxPY3QanCSHckzS4hRBCNKS7gG+11kcAlFIfAsMBmw3us2fPkpGRYbGua9eu9OzZk4qKCoveZ5PY2Fi6d+9OYGAgjz76KAB/+9vfLAZhgbG27/jx49m0aZPF+i+//JJDhw4RGhpKWVmZ1eNGjRrFtGnTSEhI4L777rPYPmDAAG699VZuvfVWBgwYUCP+hIQEbrnlFo4cOcL27dtrxB8aGsrZs2f573//yz//WbN60cMPP0zLli3Zu3cv2dnZ5vVjx441xxcYGEhubi65ubk1jk9JScHX15evv/6affv2mY8zxWlaNv0eqvP19TV/0Ojbty8dOnSweH7Vf+ebNm0iP99yFuTg4GDzoP6srCy+//77Gs992DBjAYC1a9dy9uxZi5iysrJISEgAYNWqVfzwg2UZ3/bt2zNw4EAAVqxYUeO5RUZGmv9ey5Yto6KiwuL4O+64g7vvvtt8TUdee7GxsVy8eJEVK1bU2B4fH8+dd95JSUmJuQBCdT/96U/p1KkTZ86cYd26dTW2m37n33//PVlZWTW2m157J06c4Isvvqix3fS7u/bvZjJ06FDCwsKsvvbGjh3LqlWrAGq89kweffTRer32rlX9tXft3676a2/btm0cPWpZPMlZrz2TW265pV6vvWvvG7Wpd4NbKdUKuFVrvbu+xzqbvXW4PVnl5csOTfHePOgGAoNbOiEi4Wz1LScopQSFi7UDTlRbzgd6XbuTUmocMA6gXbuGqeJz4cKFeq23d7sQwjssWbKExMREl3wjpey5qFJqK5CIsYGeCxQC27TWU50anYPi4+P1tZ/KDhw4QOfOnV0UkaVrU0rsdfGHEsrOl9b7euWXLgHgFxBQ72ObYkO9sV8rtX0lvfyr46zJPVmv83X53ljZ5NVXX61jT+eTr9vrTymVo7WOd3UcjlJKjQQGa62fqloeDdyltX7W1jHW7tmOcLS27/XUBHb0Ne7txznCm59bY/OU34k3XM/ee7a9PdwttdY/KKWeAt7VWr+qlHK7Hm5vFxjc0qHG7/U01MsvXXLo2KbYUHcGR8oJ2iolaA8pJ9g0VDWKs7TWpUqpmUAcMFdr/e8GOH0+cGu15fZAQQOct06O1vZt7JrAMkBTiKbH3ga3j1KqDfAo8IoT4xFO0NgN9crLlymruq5ofKX+Ydxw+YxDx7pLOUHhdLO01n9TSt0DDAbeAP6EldQPB3wNRCmlIoGTwONAo3yKc7S2b2PWBL6eAZreTD6EiPrwxG8n7G1wzwE+A3Zorb9WSnXAWLdVeDFHG+pFBfkO5ZpLr3jDcKSUIEg5wSbmStW/Q4A/aa3XKKVSG+LEWutKpdQzGN8zDMBSrXXNkVNOkpKSYm4412eKaEePq4s9dehNAzRN1wfPakhcL/kQIpoCeye++U5r3U1r/TRA1ejzec4LyzsZDAZiY2MZOHAggwcP5ssvv3ToPGPHjrU5a6WrBQUF0TzoBnz8/a1u/2DF3+jZ51569rmXD1b8DYCLly7xWMpofhLXg65du/LSSy+Z91+8eDExMTHExsZyzz33sH+/sdBBXl4eAQEB5tHiEyZMMJ7r4kWGDBlCdHS03ecSogk6qZR6B+O3luuVUv404ERoWuv1Wus7tNYdtdbeO1ezsKm2Sa9M20w/o0aNslklpvp+tmitm9QHFOGZ7L3B/tHOdaIWAQEB5ObmsmnTJmbMmMGMGTMaPYbKykqnXyMwuCUhbdvX+KF5IG++/Ue+zskh+9//5s23/4gKaEFIm3Y88/REvtq+lW+++YadO3eaa5EnJyezZ88ecnNzmT59OlOn/jhOt2PHjuayRIsXLzavnzZtGgcPHqzXuYRoYh7F2AOdUDXpTQjwgmtD8k6mxqDpJzw83Op+4eHhFvu5g2vTPKyVyBNC2KfWlBKl1E+Bu4GblFLVWyfBGL8q9EwbXoLv9zTsOW+JgQftn0yttLSUVq1aAXD+/HmGDx9OcXExFRUVzJ07l+HDhwPw3nvv8cYbb6CUolu3brz//vsW55k1axYnTpxg6dKlZGVlMXXqVMLCwoiLi+PIkSOsW7eO1NRUCgoKyMvLIywsjKVLlzJx4kSys7Px8fFh3rx59O/fn4yMDLKzs1mwYAFgrNU5bdo0+vXrR1BQEJMnT2bdunUEBASwZs0abr75Zo4ePUpycjKVlZXm2pW2fPbZZwwaNIiQkBAABg0aRFZWFklJSdzb524qL1/m/JnTdOl0B//du4deP4kBoOi8sQ7mqfzjVJYbU1XOnfqOK5UVFBXkW6SiBAYG0r9/fwD8/PyIi4sz1+kMDg42x3LhwgWZclw0WVrri8Cqasvf4YRJcERNjT1A01HOTPO49gPF9VSJEcJT1JXD7QcEVe13Q7X1PwAjnBWUt7p06RKxsbGcP3+e06dPs2XLFgCaN2/O6tWrCQ4O5syZM/Tu3ZvExET2799PWloaO3fuJCwsjKKiIovzTZ8+nZKSEt59910uX77M+PHj2b59O5GRkSQlJVnsm5OTw44dOwgICODNN98EYM+ePRw8eJAHHnigxsQH17pw4QK9e/cmLS2N6dOns2TJEmbOnMnkyZOZOHEiY8aMYeHChbWe4+TJk9x664/FC9q3b8/Jk8aSd82DbqAMKCkp4bPPNzH+//3cvN+fMzL4U/oSyssr+PuKH6ezP378BPcNeoDg4GB+87vfc++991pc79y5c6xdu5bJkyeb1y1cuJB58+ZRXl7O5s2ba423MThroFB9a3dDw9fvlkFQ7kcpVQpY6z5VgNZaB1vZJhpQYw7QrA9X5pp7yocQIa5HrQ1urfU2YJtSKkNrXfPjp6eqR090QzKllBQUFJCdnc2YMWPYu3cvWmtefvlltm/fTrNmzTh58iSnTp1i8+bNjBgxgrCwMABzzzDAa6+9Rq9evUhPTwfg4MGDdOjQgcjISACSkpLM2wASExMJqKrDvWPHDp591lgSNzo6mvDw8Dob3H5+fgwdOhSAHj168PnnnwOwc+dOVq5cCcDo0aN58cUXbZ7D2o3ZdJMPDG6JX2ALUv7fL3h+yhTiftrHvM/0l2cy/eWZLF++nAVL/kxmZiYtQm/i+IkTqMuXyN29m+TkZPbt22fuxa6srCQpKYnnnnuODh06mM81adIkJk2axPLly5k7dy6ZmZm1Pm9nclYP0vDYhplE5HrIICj3pLW+oe69hLNdzwBNd0k3aUju+iHEFk+skCFcz94qJf5KqXQgovoxWuv7nRFUUxAfH8+ZM2coLCxk/fr1FBYWkpOTg6+vLxEREZSVlaG1ttnr0LNnT3JycigqKiIkJKTO//gtWrQwP7a1r4+PD1evXjUvl5WVmR/7+vqaYzEYDBa54PamZrRv356tW7eal/Pz8+nXr595edy4cURFRfH8889bPf7xxx9n4sSJAPj7++Pv709RQT53dupE+K3t+XrnP+j+k58A8OzUX3Jr2zaMeXSE1WopD/S9x3yuxtJYPUiO1O6G66vfnZqaWuc+Tb0Sg7upmjU4CmhuWqe1rjkHuWgSXJ3m4awqMUK4C3sHTf4N+AaYiXFgjenHrSilhiml0ktKSlwdSp2+/fZbrly5QmhoKCUlJbRu3RpfX1+2bNlivskNGDCAFStWcPbsWQCLlJKEhAReeuklhgwZQmlpKdHR0Rw5csR8o/roo49qXNOkb9++5q/7Dx06xPHjx+nUqRMRERHk5uZy9epVTpw4wa5du+p8Hn369OHDDz8EqHNAzeDBg9m4cSPFxcUUFxezceNGBg8eDMDMmTMpKSnhrbfesjimel3oTz/9lKioKAAKCwu5cuUKzYNuIP/77/nf0aNE3GZsZKb99nf8UFrKr+ekWpzrf0eOmh9vyMoiMsL64KWmqtQ/zOa21NRUuxrVrlZXNQPAasHpAAAgAElEQVRhVDWJ2XaMAydNZV9TXRmTcC9paWkEBgZarJM0DyEcZ28Pd6XW+k9OjaQBaK3XAmvj4+N/4epYrDHlcFdUVKC1JjMzE4PBQEpKCsOGDSM+Pp7Y2Fiio6MB6Nq1K6+88gr33XcfBoOB7t27k5GRYT7fyJEjKS0tJTExkfXr17No0SISEhIICwvjrrvushnH008/zYQJE4iJicHHx4eMjAz8/f3p06cPkZGRxMTEcOeddxIXF1fnc5o/fz7JycnMnz+fRx55pNZ9Q0JCmDVrFj179gRg9uzZhISEkJ+fT1paGtHR0eZrPvPMMzz11FMsWLCATZs24evrS6tWrcwpINu3b2f27Nn4+PhgMBhIT19Cx64x5OfnM+/tPxIdHc3AoYkW55rz29+bz3VDYCCL3vpDnc+vIbm6B6kutdXvNjW2rU0XP2fOHFJTUy22udtzEzVMBnoC/9Ja91dKRWNseAsBeF6ah3AtSbOpm7Lnl1M1IcJpYDVw2bRea11k6xhXio+P19nZ2RbrDhw4QOfOnV0UkaWCAuMsx23btm3Q854/f56goCC01kyaNImoqCimTJnSoNfwFqY0k5C27Wtsa6zXiinP+dqBQunp6S55UzMNsrTW4K7tZmpKQane4HbVc/OGm75SKkdrHe/ka3ytte6plMoFemmtLyulcrXWsc68ri3W7tnXw9HXgSPHNea1rkdjx9mYv0tHecrfzhGe8Pf2pOPqOKdd92x7e7ifqPq3ehqJBjpY2Ve4yJIlS8jMzKS8vJzu3bszfvx4V4ckauHNPUje/Ny8RL5S6kbg78DnSqlioMDFMQkncufGoRBNgV0Nbq11pLMDEddvypQpbtGjvWfPHkaPHm2xzt/fn6+++spFEbkvbx4o5M3PzdNprR+uepiqlNoCtAQ2uDAkIYQbkA9mzmNXg1spNcbaeq31ew0bjvAGMTEx5ObmujoM4YC66ndb29bQ9buF8ymlZltZHQv8qrFjEcJVpHEpGpO9KSU9qz1uDgwA/g1Ig1t4rMrLl62WDLxwrpiP5rxk87jOffrRbWDts2p6Ineo3y0azYVqj5sDQ4EDLopFCCG8nr0pJc9WX1ZKtQTet7G7EG7PNLNlfRXmGUsLemODu7b63SsmGP+1NqDSVL9beA6t9ZvVl5VSbwCfuCgcIYTwevb2cF/rIsYJE4TwSIHBLQkMbml126mSUh571fpspLX1fDd1tU2YY2tbVFQUycnJzgpJ2C8QGQQvhFeoPidGRESEDFh3E/bmcK/FWJUEwAB0BlY4KyghhOco9Q/jhstnHDq2+sRGovEopfZgeU+/CXjNdREJIRqCqSSrybFjx8zL0uh2LXt7uN+o9rgSOKa1rpn8KmplMBiIiYmhvLwcg8HAO++8w913313v84wdO5ahQ4cyYsQIJ0R5fYKCgjh//rzN7ZmZmcydOxcwzi75xBNPcPHiRUaOHMn//vc/DAYDw4YN4ze/MfYwL168mIULF2IwGAgKCiI9PZ0uXbqQl5dH586d6dSpEwC9e/dm8eLFtZ4LYMWKFaSmpqKU4ic/+QnLly934m+jabieCXOEywyt9rgSOKW1rnRVMEIIx9gzs+7FixcZNWqUuWoUuM+AUXeJozHYm8O9TSl1Mz8OnpRuKQcEBASQm5tLQUEBW7duZcaMGWzbtq1RY6isrMTHx9FMoutTVFTEnDlzyM7ORilFjx49SExMxN/fn2nTptG/f3/Ky8sZMGAAGzZs4MEHHyQ5OZkJE4wJxJ988glTp04lKysLgI4dO1qthmLrXIcPH+b1119n586dtGrVitOnTzfq8xfC1ZRSU2vZhtZ6XmPGI4QQTYW9KSWPAr8HtgIK+KNS6gWt9cdOjM1pfrvrtxwsOtig54wOiebFu160e//S0lJatWoFGGeIHD58OMXFxVRUVDB37lyGDx8OwHvvvccbb7yBUopu3brx/vuWY1VnzZrFiRMnWLp0KVlZWUydOpWwsDDi4uI4cuQI69atIzU1lYKCAvLy8ggLC2Pp0qVMnDiR7OxsfHx8mDdvHv379ycjI4Ps7GwWLFgAwNChQ5k2bRr9+vUjKCiIyZMns27dOgICAlizZg0333wzR48eJTk5mcrKShISah9I+NlnnzFo0CBCQkIAGDRoEFlZWSQlJdG/f38A/Pz8iIuLIz/f+AVKcHCw+fgLFy7U+Wk+MDDQ5rmWLFnCpEmTzL/31q1b1/FXEvaScoIe44aqfzth7EAxDZQcBmx3SURCCIdd20McERHBsWPHauwXHh4u8yG4WDM793sF6Km1fkJrPQa4C5jlvLC806VLl4iNjaVv37688MILzJpl/BU2b96c1atX8+9//5stW7bwy1/+Eq01+/btIy0tjc2bN/Of//yH+fPnW5xv+vTpnD59mnfffZfy8nLGjx/Phg0b2LFjB4WFhRb75uTksGbNGpYvX87ChQsB4wQ1H3zwAU888QRlZbXX7Lhw4QK9e/fmP//5D3379mXJkiUATJ48mYkTJ/L1119zyy231HqOkydPcuutt5qX27dvz8mTJy32OXfuHGvXrmXAgAHmdQsXLqRjx45Mnz6dt99+27z+6NGjdO/enfvuu49//OMfNa537bkOHTrEoUOH6NOnD7179zb3lIvrMzy2HV3aBNe9o3A5rfUcrfUcIAyI01r/Umv9S6AH0N610XmeawenVV8WwhXS0tIIDAy0WBcYGEhaWpqLIhIm9uYWNNNaV//+/Sz2N9bdTn16ohtS9ZSS7OxsxowZw969e9Fa8/LLL7N9+3aaNWvGyZMnOXXqFJs3b2bEiBGEhYUBmHuGAV577TV69epFeno6AAcPHqRDhw5ERhonBU1KSjJvA0hMTCQgIACAHTt28OyzxkqP0dHRhIeHc+jQoVpj9/PzY+hQY9pnjx49+PzzzwHYuXMnK1euBGD06NG8+KLt3621XK3qPdaVlZUkJSXx3HPP0aHDjwUTJk2axKRJk1i+fDlz584lMzOTNm3acPz4cUJDQ8nJyeGhhx5i37595h5xa+eqrKzk8OHDbN26lfz8fO6991727t3LjTfeWOtzv1Zh3lGHqpV4a/1uKSfokW4DyqstlwMR13tSpdRIIBXjwPq7tNbZ13tOdyWD04Q7Mr32TPna4eHhUqXETdjbaM5SSn2mlBqrlBoLfAqsd15YP1JKtVBKZSqlliilvOYVEx8fz5kzZygsLGTZsmUUFhaSk5NDbm4uN998M2VlZWitbaZQ9OzZk5ycHIqKioC6Bx60aNHC/NjWvj4+Ply9etW8XL3X29fX1xyLwWCgsvLH8VX2DNoAY4/2iRMnzMv5+fm0bdvWvDxu3DiioqJ4/vnnrR7/+OOP8/e//x0wThUfGhoKGD8AdOzY0eJDg7VztW/fnuHDh+Pr60tkZCSdOnWqd5WMzn36cVNEZL2OAWMj/cDOrfU+zh14Si+ep8TpJt4HdimlUpVSrwJfAZkNcN69wM/wwvQUpZTFz6hRo7h48aLFPqbBadX3E6KxVW9c5+XlSWO7iqvfI2rt4VZK3Q7crLV+QSn1M+AejDnc/wQcjlQptRTjKPnTWus7q61PAOZjLFP1Z631bzDevD/WWq9VSn10Pdd1J99++y1XrlwhNDSUkpISWrduja+vL1u2bDHnXw0YMICHH36YKVOmEBoaSlFRkbmXOyEhgcGDBzNkyBA2btxIdHQ0R44cIS8vj4iICD766COb1+7bty/Lli3j/vvv59ChQxw/fpxOnTrxww8/sGjRIq5evcrJkyfZtWtXnc+jT58+fPjhh4waNarOF+/gwYN5+eWXKS4uBmDjxo28/vrrgLFiSUlJCX/+858tjjl8+DBRUcaS759++qn5cWFhISEhIRgMBo4cOcLhw4fNPdm2zvXQQw/xwQcfMHbsWM6cOcOhQ4csetLt0W1ggkO91J5av9tTevE8JU53obVOU0plYbynAzyptf6mAc57AOz/EC6EEI3BHd4j6kopeQt4GUBrvQpYBaCUiq/aNszB62YAC6g2NbxSygAsBAYB+cDXSqlPMOYV7qna7YqD13MLphzuiooKtNZkZmZiMBhISUlh2LBhxMfHExsbS3R0NABdu3bllVde4b777sNgMNC9e3cyMjLM5xs5ciSlpaUkJiayfv16Fi1aREJCAmFhYdx1110243j66aeZMGECMTEx+Pj4kJGRgb+/P3369CEyMpKYmBjuvPNO4uLi6nxO8+fPJzk5mfnz5/PII4/Uum9ISAizZs2iZ09jsZvZs2cTEhJCfn4+aWlpREdHm6/5zDPP8NRTT7FgwQI2bdqEr68vrVq1IjPT2Am3fft2Zs+ejY+PDwaDgcWLF9d5rsGDB7Nx40a6dOmCwWDg97//vbmXXBi5osSUI+UBTeUGa+POpbDcgdY6B8hx1fWVUuOAcQC33WY9JclRjv6daztOBqcJ4R7smdjHkfcyZ78/qNouoJTaW70H+ppte7TWMQ5fWKkIYJ3p/EqpnwKpWuvBVcszqnbNB4q11uuUUh9qrR+3cb7qN+8e194IDxw4QOfOnR0Nt0EVFBQAWKRTNITz588TFBSE1ppJkyYRFRXFlClTGvQaTYEzXiumHm5rM1iabgzu0Bh0tGfSVuy1Pbdpv1no8IQ59jS4rXGH33FdlFI5Wut4J517h9b6HqVUKT9OfAPGby611rrO0a9KqU2AtRHSr2it11TtsxWYZm8Od3x8vM7O9qx0b1OPWfW0ksDAQNLT0+3qMXOn//e1cTROR47z9t9JY2rsGBvrevb+v3PkvczR2O29Z9fVw928lm0B9QupTu2AE9WW84FewNvAAqXUEGCtrYO11ulAOhhv3g0cm0dYsmQJmZmZlJeX0717d8aPH+/qkISHacxevNomzKnNnDlzSE1NtZhMR3ob7aO1vqfq3xvq2reWcwxsuIg8lwxOs02mFhcNpaG+dXWH94i6GtxfK6V+obVeUn2lUur/0fBfRVr7rWqt9QXgyQa+lleaMmWKW/Ro79mzh9GjR1us8/f356uvvnJRRMJRaWlpVnsTGqrEVF31u62xVr/b2XF6m6pqIlla61Kl1EwgDnitIfK4m5KUlBTzm7x8sDNyh1xZIa7lDu8RdTW4nwdWV1UHMTWw4wE/4OEGjiUfuLXacnugoD4nUEoNA4bdfvvtDRmXqKeYmBirM0AKz+PMXrzhse2u+xwm0ttYb7O01n9TSt0DDAbeABZj/FbRYUqph4E/AjcBnyqlck1pgsI7uWOurPAeDdVT7Q7vEbU2uLXWp4C7lVL9AVMu96da681OiOVrIEopFQmcBB4HkutzAq31WmBtfHz8L5wQnxBNkrN68Wqr310bW/W7pbexXkwD0IcAf9Jar1FKpV7vSbXWq4HV13uepkAanULU3/X0VLv6PcKuiW+01luALQ11UaXUB0A/IEwplQ+8qrX+i1LqGeAzjGUBl2qt9zXUNYVwtbomzLG1zVsnzBEudVIp9Q4wEPitUsofD57MTLhOQ+bKesKHEMlPdy136Kl2lEtusFrrJK11G621r9a6vdb6L1Xr12ut79Bad9Ra1zuxRik1TCmVXlJS0vBBC3EdmuKEOcKtPYqxcyNBa30OCAFecG1Iwht489TitvLTZZKtxuWpE/vYO7W7R5CUEuGuapsw5/HU3wLWSwZ66oQ5jaG2+t22tkVFRZGcXK9MNa+ktb5I1bwKVcvfAd+5LiLhLTy5B/JarpiXQHgv+QqxERkMBmJjYxk4cCCDBw/myy+/dOg8Y8eO5eOPP27g6BpGUFBQrdszMzOJiooiKirKPInNxYsXGTJkCNHR0XTt2pWXXvqxkbl48WJiYmKIjY3lnnvuYf/+/YDxU21AQACxsbHExsYyYcIE8zH9+vWjU6dO5m2nT58GjL0RAwYMoFu3bvTr14/8/PyGfvqiEZT6hzl87OHDhxswEs+ljEYppWZXLd+mlLI9W5YQ9eCpPZDuSinl1rO3unrKdE/hVT3c7i4gIIDc3FwKCgrYunUrM2bMYNu2bY0aQ2VlJT4+rvmzFxUVMWfOHLKzs1FK0aNHDxITE/H392fatGn079+f8vJyBgwYwIYNG3jwwQdJTk42N6Y/+eQTpk6dSlZWFgAdO3a0WQ1l2bJlxMdb1qGfNm0aY8aM4YknnmDz5s3MmDGD999/37lPWjS42up3mybEqV6j28SRGS292CLgKnA/8CugFFgJ9HRlUML9NOXeWnes5exupAyk/byqwW1vWcDvf/1rLh842KDX9u8czS0vv2z3/qWlpbRq1QowzhA5fPhwiouLqaioYO7cuQwfPhyA9957jzfeeAOlFN26davRQJw1axYnTpxg6dKlZGVlMXXqVMLCwoiLi+PIkSOsW7eO1NRUCgoKyMvLIywsjKVLlzJx4kSys7Px8fFh3rx59O/fn4yMDLKzs1mwYAEAQ4cOZdq0afTr14+goCAmT57MunXrCAgIYM2aNdx8880cPXqU5ORkKisrSUiofWDfZ599xqBBgwgJCQFg0KBBZGVlkZSURP/+/QHw8/MjLi7O3PscHPzjxHcXLly4rk/5+/fv5w9/+AMA/fv356GHHnL4XMK16qrfbW2btfrdTVgvrXWcUuobAK11sVLKz9VBCeHO3KGWs6tJmo3jvKrB7e453JcuXSI2Npbz589z+vRptmwxFn5p3rw5q1evJjg4mDNnztC7d28SExPZv38/aWlp7Ny5k7CwMIqKiizON336dEpKSnj33Xe5fPky48ePZ/v27URGRpKUlGSxb05ODjt27CAgIIA333wTME5Qc/DgQR544AEOHTpUa+wXLlygd+/epKWlMX36dJYsWcLMmTOZPHkyEydOZMyYMSxcuLDWc5w8eZJbb/2x1Hr79u05efKkxT7nzp1j7dq1TJ482bxu4cKFzJs3j/LycjZv/rEi5dGjR+nevTvBwcHMnTuXe++917ztySefxGAw8MgjjzBz5kyUUvzkJz9h5cqVTJ48mdWrV1NaWsrZs2cJDQ2tNW5Xq6u6iTXeXNmkIet3N2EVSikDVdO7K6VuwtjjLYSwwZvy00Xj86oGt73q0xPdkKqnlGRnZzNmzBj27t2L1pqXX36Z7du306xZM06ePMmpU6fYvHkzI0aMICzMmLNq6hkGeO211+jVqxfp6ekAHDx4kA4dOhAZaayEkZSUZN4GkJiYSEBAAAA7duzg2WefBSA6Oprw8PA6G9x+fn4MHToUgB49evD5558DsHPnTlauXAnA6NGjefHFF22ew9on3OqflisrK0lKSuK5556jQ4cO5vWTJk1i0qRJLF++nLlz55KZmUmbNm04fvw4oaGh5OTk8NBDD7Fv3z6Cg4NZtmwZ7dq1o7S0lEceeYT333+fMWPG8MYbb/DMM8+QkZFB3759adeuncvSa+zVuU+/eh9TmHcUwGsb3LXV715RlcpvLd3EVv3uJuptjPWyWyul0oARwEzXhiSE+3N1LWdXkzQbx7l3a8OLxcfHc+bMGQoLC1m/fj2FhYXk5OTg6+tLREQEZWVlaK1tfn3Ts2dPcnJyKCoqIiQkpM6va1q0aGF+bGtfHx8frl79sZOrrKzM/NjX19cci8FgoLKy0rzN3jSP9u3bs3XrVvNyfn4+/fr1My+PGzeOqKgonn/+eavHP/7440ycOBEwThXv7+8PGD8AdOzYkUOHDhEfH0+7dsYe0BtuuIHk5GR27drFmDFjaNu2LatWGQsznD9/npUrV9KyZUu7YneV2qqb2CKVTWpX31xub6xsorVeppTKAQZUrRoJxLgwJCGEB5I0G/t5VZUST6rD/e2333LlyhVCQ0MpKSmhdevW+Pr6smXLFvOnxQEDBrBixQrOnj0LYJFSkpCQwEsvvcSQIUMoLS0lOjqaI0eOmD9RfvTRRzav3bdvX/Mo4kOHDnH8+HE6depEREQEubm5XL16lRMnTrBr1646n0efPn348MMPAeocmTx48GA2btxIcXExxcXFbNy4kcGDjbM+z5w5k5KSEt566y2LY6pXlfj000+JiooCoLCwkCtXjJPlHTlyhMOHD9OhQwcqKys5c+YMABUVFaxbt4477zROknrmzBnzB4rXX3+dn//853U+P+E9HK1u4k2VTZRSwUqpGUqpBcBtGAdPNgPWYqzNLYQQdktJSbH4Nj08PJz09HRJs7HCq3q4PSWHu6KiAq01mZmZGAwGUlJSGDZsGPHx8cTGxhIdHQ1A165deeWVV7jvvvswGAx0796djIwM8/lGjhxJaWkpiYmJrF+/nkWLFpGQkEBYWBh33WW7wtfTTz/NhAkTiImJwcfHh4yMDPz9/enTpw+RkZHExMRw5513EhcXV+dzmj9/PsnJycyfP59HHnmk1n1DQkKYNWsWPXsaCyHMnj2bkJAQ8vPzSUtLIzo62nzNZ555hqeeeooFCxawadMmfH19adWqlbmU4Pbt25k9ezY+Pj4YDAYWL15MSEgIFy5cYPDgwVRUVHDlyhUGDhzIL35hfDmYKsMopejbt2+dOeeNxZsHk7jTc6utuoktXljZ5H2gGPgn8BTGyW78gOFaa+slf4QQohZNPc3GXsqd3hAbSnx8vM7OzrZYd+DAATp37uyiiCwVFBQA0LZt2wY97/nz5wkKCkJrzaRJk4iKimLKlCkNeo2mwJ1eK44wpZRYm0jHUaa0IXe/X9QWp6lyiSMNbmtlBp1FKZWjtY6ve0+Hzr1Hax1T9dgAnAFu01qXOuN69rJ2z/YUnvJ/o7F58++lsZ+bI9fzhBhdcT1nxGnvPdureribuiVLlpCZmUl5eTndu3dn/Pjxrg5JuIgjlU3Au6ubXA9He7obs6FupwrTA631FaXUUVc3toUQoimQBrcXmTJlilv0aO/Zs4fRo0dbrPP39+err75yUURNiyOVTcD7q5s4IioqyqtyuIGfKKV+qHqsgICqZQVorXWw7UOFEEI4yqsa3PZOfCOcKyYmxuYMkML5HKlsAk2jukldE+bUFMnwhHtsliH0NFprg6tjEEKIpsirqpRorddqrce5e6k3IUTjGx7bji5t6teBu/+7H1iTe7LuHYUQQohaeFUPtxBC2FLbhDm21K83XAgh3IM3Dlb1dF7Vwy2EEEIIIYS7kQa3EEIIIYQQTiQpJY3IYDAQExNDeXk5BoOBd955h7vvvrve5xk7dixDhw5lxIgRTojy+gQFBXH+/Hmb2zMzM5k7dy5gnF3yiSee4OLFi4wcOZL//e9/GAwGhg0bxm9+Y6whvXjxYhYuXIjBYCAoKIj09HS6dOlCXl4enTt3plOnTgD07t2bxYsXA8ZZNtPS0rhy5QpDhgzhd7/7HQDz5s3jz3/+Mz4+Ptx0000sXbqU8PBwZ/46PE5d5QRtbZNyggJAKfV7YBhQDvwPeFJrfc61UQkhhOt5VQ+3u0/tHhAQQG5uLps2bWLGjBnMmDGj0WOorKxs9GuaFBUVMWfOHL766it27drFnDlzKC4uBmDatGkcPHiQb775hp07d7JhwwYAkpOT2bNnD7m5uUyfPp2pU6eaz9exY0dyc3PJzc01N7bPnj3LCy+8wBdffMG+ffs4deoUX3zxBQDdu3cnOzub3bt3M2LECKZPn97IvwH31rlPP26KiKz3cYV5Rzmwc2vDByQ80efAnVrrbsAhoPFvckII4Ya8qofb3qnd/7HiEGdO2O6FdUTYrUHc++gddu9fWlpKq1atAOMMkcOHD6e4uJiKigrmzp3L8OHDAXjvvfd44403UErRrVs33n//fYvzzJo1ixMnTrB06VKysrKYOnUqYWFhxMXFceTIEdatW0dqaioFBQXk5eURFhbG0qVLmThxItnZ2fj4+DBv3jz69+9PRkYG2dnZLFiwAIChQ4cybdo0+vXrR1BQEJMnT2bdunUEBASwZs0abr75Zo4ePUpycjKVlZUkJNTew/nZZ58xaNAgQkJCABg0aBBZWVkkJSXRv39/APz8/IiLiyM/Px+A4OAfq0pcuHDBPEuULUeOHOGOO+7gpptuAmDgwIGsXLmSAQMGmK8Bxh7xv/71r7X/kZqY2soJPp76W8D67JVNoZygsI/WemO1xX8B7vc1XAOTwWlCCHt4VYPb3V26dInY2FjOnz/P6dOn2bJlCwDNmzdn9erVBAcHc+bMGXr37k1iYiL79+8nLS2NnTt3EhYWRlFRkcX5pk+fTklJCe+++y6XL19m/PjxbN++ncjISJKSkiz2zcnJYceOHQQEBPDmm28CxglqDh48yAMPPMChQ4dqjf3ChQv07t2btLQ0pk+fzpIlS5g5cyaTJ09m4sSJjBkzhoULF9Z6jpMnT3Lrrbeal9u3b8/Jk5Yl186dO8fatWuZPHmyed3ChQuZN28e5eXlbN682bz+6NGjdO/eneDgYObOncu9997L7bffzsGDB8nLy6N9+/b8/e9/p7y8vEYsf/nLX3jwwQdrjVcIcKR2t1GXtsG8OqyrEyLyGD8HPnJ1EEII4Q6aZIO7Pj3RDcmUUlJQUEB2djZjxoxh7969aK15+eWX2b59O82aNePkyZOcOnWKzZs3M2LECMLCwgDMPcMAr732Gr169SI9PR2AgwcP0qFDByIjjSkBSUlJ5m0AiYmJBAQEALBjxw6effZZAKKjowkPD6+zwe3n58fQoUMB6NGjB59//jkAO3fuZOXKlQCMHj2aF1980eY5rPUEVe+xrqysJCkpieeee44OHTqY10+aNIlJkyaxfPly5s6dS2ZmJm3atOH48eOEhoaSk5PDQw89xL59+2jVqhV/+tOfeOyxx2jWrBl33303R44csbjmX//6V7Kzs9m2bVutz1mI4bHtXB2C21FKbQJusbLpFa31mqp9XgEqgWW1nGccMA7gttu8Y2IhIYR7c+U3Uk2ywe0O4uPjOXPmDIWFhaxfv57CwkJycnLw9fUlIiKCsoPjFvAAACAASURBVLIytNY2Uyh69uxJTk4ORUVFhISE1PkiatGihfmxrX19fHy4evWqebmsrMz82NfX1xyLwWCwyAWvK83DpH379mzdutW8nJ+fT79+/czL48aNIyoqiueff97q8Y8//jgTJ04EjFPF+/v7A8YPAB07duTQoUPEx8czbNgwhg0bBkB6ejoGw4+T623atIm0tDS2bdtmPl4IWxyp3e3ttNYDa9uulHoCGAoM0LXcmLTW6UA6QHx8vORlCCG8mlcNmvQk3377LVeuXCE0NJSSkhJat26Nr68vW7Zs4dixYwAMGDCAFStWcPbsWQCLlJKEhAReeuklhgwZQmlpKdHR0Rw5coS8vDzAWKnDlr59+7JsmbHj6dChQxw/fpxOnToRERFBbm4uV69e5cSJE+zatavO59GnTx8+/PBDAPM5bRk8eDAbN26kuLiY4uJiNm7cyODBgwFjxZKSkhLeeusti2MOHz5sfvzpp58SFRUFQGFhIVeuXAGMeduHDx8294qfPn0agOLiYhYtWsRTTz0FwDfffMP48eP55JNPaN26dZ3PTQhRP0qpBOBFIFFrfdHV8QghhLuQHu5GZMrhrqioQGtNZmYmBoOBlJQUhg0bRnx8PLGxsURHRwPQtWtXXnnlFe677z4MBgPdu3cnIyPDfL6RI0dSWlpKYmIi69evZ9GiRSQkJBAWFsZdd91lM46nn36aCRMmEBMTg4+PDxkZGfj7+9OnTx8iIyOJiYnhzjvvJC4urs7nNH/+fJKTk5k/fz6PPPJIrfuGhIQwa9YsevbsCcDs2bMJCQkhPz+ftLQ0oqOjzdd85plneOqpp1iwYAGbNm3C19eXVq1akZmZCcD27duZPXs2Pj4+GAwGFi9ebE65mTx5Mv/5z3/M17jjDmMK0QsvvMD58+cZOXIkYPwa+5NPPqnzOQoh7LYA8Ac+r/rm619a6wmuDUkI4W08cbCy8sSg6xIfH6+zs7Mt1h04cIDOnTu7KCJLBQUFALRt27ZBz3v+/HmCgoLQWjNp0iSioqKYMmVKg16jKXCn14q7MKUNWbtfmKqUWKtg0thqi9NTKKVytNbxro6jMVm7ZwvP5g3/F21p7OfmCb9LT4jRWey9Z3tVSom71+F2tiVLlhAbG0vXrl0pKSlh/Pjxrg5JCCFEE6S1bpKNr4ZWPVUzIiKiztRN4b68KqXE3jrc3mrKlClu0aO9Z88eRo8ebbHO39+fr776ykURCSGEEJ5l2bJljBs3zrx87Ngx83JKSoqrwhIO8qoGt3APMTEx5ObmujoMIYQQwmPYU/Hr4sWLjBo1ilGjRgFNM4XDU3lVSokQQgghhBDuRhrcQgghhBB2clZ+uum8pp/w8HCr+4WHh0uOvAeSBrcQQgghhJtJS0sjMDDQYl1gYCBpaWkuikhcD2lwNyKDwUBsbCwDBw5k8ODBfPnllw6dZ+zYsXz88ccNHF3DCAoKcnUIQgghhMdLSUkhPT3dvBweHk56eroMmPRQMmiyEQUEBJCbm0tBQQFbt25lxowZbNu2rVFjqKysxMdH/uyiYRXmHTXX466Pzn360W1gghMiEkL8//buPUrOssz3/vdHCIZwyCgJHghUJzstKBACNMjBGcSgO24JDKAo6ZaDaIsO6qCOG0Umnc30q/Pq6OuMeGgHiToBBAwLg2xRFGGhjNLRmAEDhBW6JUYlCUMEQiTA9f5RVU11d3Wnurqqnqee+n3WqpV6zldVP3XnqrvugzW/zs7OoQ6SxZmk08jNW3bNNdwJefLJJ3npS18K5CesWbhwIUcddRSHH344N99889B+3/rWt5g/fz5HHHHEqKH2AC6//HLOP/98XnjhBW699VYOOeQQXv/61/OhD32IU089FYCenh66u7t585vfzLnnnsuOHTu44IILOPzwwznyyCO54447AFi+fDkXX3zx0LlPPfVUfvrTnwL5muvLLruMI444guOOO44//elPADzyyCMcf/zxHHPMMVx++eV1ea8s3V5z4huY1TZnwsdtHniEdT/7ae0DMjMzS5mWrOq8Y3kfjw1uqOk598/N5eTzu8fdpzi1+1NPPcVjjz02lOhOmzaNm266iX333ZctW7Zw3HHHcdppp/Hb3/6W3t5efvaznzFz5kwef/zxYef7+Mc/zrZt27j66qv5y1/+wvve9z7uuusu5syZwznnnDNs39WrV3P33Xez55578i//8i9AfrzsBx54gDe/+c089NBD48b+9NNPc9xxx9Hb28vHP/5xvv71r/OpT32KD3/4w7z//e/n3HPP5corr5zo22YZMP+URVXVUldTI25mZtaMXMPdQMUmJXfddRf/8R//wbnnnjvU0/iTn/wk8+fP55RTTuH3v/89f/rTn/jJT37C2972NmbOnAnAy172sqFzXXHFFTzxxBN87WtfQxIPPPAAc+fOZc6cfE3jyIT7tNNOY8899wTg7rvvHqotP+SQQ8jlcrtMuPfYY4+hGvOjjz566Ketn/3sZ0PXKlcDb2ZmZtbqMlXDLWkxsHjevHnj7rermuhG6OjoYMuWLWzevJlbb72VzZs3s3r1aqZOnUpbWxs7duwgIsYcCP+YY45h9erVPP7447zsZS/bZfupvfbaa+j5WPvuvvvuvPDCC0PLO3bsGHo+derUoVimTJnCc889N7StksH6zczMzFpVpmq4I2JVRHTPmDEj6VB26eGHH+b5559nv/32Y9u2bey///5MnTqVO+64g8HBQQAWLlzI9ddfz9atWwGGNSlZtGgRl156KW9961t58sknOeSQQ9iwYcNQzfN3vvOdMa/9N3/zN6xYsQKAhx56iN/97nccfPDBtLW1sWbNGl544QUeffRRfvnLX+7ydZx44olcd911AEPnNDMzM7MXZaqGO+2Kbbh37txJRPDNb36TKVOm0NnZyeLFi+no6GDBggUccsghABx66KFcdtllnHTSSUyZMoUjjzyS5cuXD53v7W9/O08++SSnnXYat956K1/+8pdZtGgRM2fO5Nhjjx0zjg984ANcdNFFHH744ey+++4sX76cl7zkJZx44onMmTOHww8/nMMOO4yjjjpql6/pi1/8IkuWLOGLX/wiZ5111qTfIzMzM7OsURaHcuno6Ij+/v5h69atW8drXvOahCIabtOmTQC86lWvqul5n3rqKfbee28igr/7u7+jvb2dSy65pKbXaAVpulfSothsqJblRbHT5DuWfqZm56xHnI0maXVEdCQdRyOVK7PNLC8L5VqWVVpmZ6pJSav7+te/zoIFCzj00EPZtm0b73vf+5IOyczMzKzluUlJhlxyySWu0TarobW3/6DqscIrGSrUzMxag2u4zczGsO5nP2XzwCNJh2FmZk3ONdxmZuOY1Tanpu3Mzcys9biG28zMzMysjpxwj0OSJ3UxM6uQpCskrZW0RtIPJdV2KCYzsyblhLuBpkyZwoIFCzj55JM55ZRT+PznPz9sZsdyBgYGuOaaaxoUoZnZpHw2IuZHxALgFuAfkw7IzCwN3IZ7DKWzJra1tdHb20tnZ+ekzrnnnnuyZs0aNm3axJYtW/jIRz7Ctm3bWLZs2ZjHFBPuJUuWTOraZmm0eeCRofG4J+I1J76B+acsqkNENhkR8eeSxb0ADxxsZoYT7rJWrFhBd/eLw3kNDg4OLU826S6aOXMmfX19HHPMMfT09DA4OMi73vUunn76aQC+9KUvccIJJ3DppZeybt06FixYwHnnnccZZ5xRdj+zZvOaE99Q1XHFUUMmmnBXM8Tf5oFHmNU2Z0LHtDpJvcC5wDbg5HH26wa6AQ466KDGBGdmlhDPNFmi0vba1b5ne++9N0899dSwmSZf+tKX8sADD7DPPvuw2267MW3aNNavX88555xDf38/P/3pT/nc5z7HLbfcAsD27dvL7me145kmR0vTTGfjzVA5XpzfWXZpVQl0o2vT0z7TpKTbgVeU2XRZRNxcst8ngGkRsXRX5/RMk2ZjS1P5a6NVWma7hjthxQ/Qzp07ufjii1mzZg1TpkzhoYceKrt/pfuZ2Wge4m/yIuKUCne9Bvg+sMuE28ws61LfaVLSXElXSbqx3teKCCKCXC5Xdnsul6vpN8wNGzYwZcoU9t9/f77whS/w8pe/nN/85jf09/fz7LPPlj2m0v3MzBpNUnvJ4mnAA0nFYmaWJnVNuCV9Q9Jjku4bsX6RpAclPSxp3B5TEbEhIi6sZ5wj9fb2Mn369GHrpk+fTm9vb82usXXrVi666CIuvvhiJLFt2zZe+cpXsttuu/Htb3+b559/HoB99tmHJ598cui4sfYzM0uBz0i6T9Ja4M3Ah5MOyMwsDerdpGQ58CXgW8UVkqYAVwJvAjYC90r6HjAF+PSI498dEY/VOcZRih0ju7q6gHzNdi1GKXnmmWdYsGABzzzzDFOmTOHd7343H/nIRwD4wAc+wFlnncUNN9zAySefzF577QXA/Pnz2X333TniiCM4//zzx9zPzCxpEXFW0jGYmaVRXRPuiLhLUtuI1ccCD0fEBgBJ1wGnR8SngVPrGc9EdHZ2DiXcAwMDNTlnsTa6tNNkUXt7O2vXrh1a/vSn8989pk6dyo9//ONh5ym3n5mZmZmlUxJtuA8AHi1Z3lhYV5ak/SR9FTiy0Ot9rP26JfVL6t+8eXNNAi226TYzMzMzq1YSo5SUG3tvzKw2IrYCF+3qpBHRB/RBfoipqqMzs9Tb1YQ55bZ5TG0zM0tKEgn3RuDAkuXZwKZGXDgiKh5r21qTf9FIv2onzJnVNqfqY83MkuL/l7IhiYT7XqBd0hzg98A7gZrMWy5pMbB43rx5o7ZNmzaNrVu3st9++znptrIigq1btzJt2rSkQ7FxzD9l0ZgT0byz55+B8pPimJmZJaWuCbeka4E3ADMlbQSWRsRVki4GbiM/Msk3IuL+WlwvIlYBqzo6Ot47ctvs2bPZuHEjtWrfPRlPPPEEkB/iz9Jl2rRpzJ49O+kwzMzMLEPqPUrJOWOsvxW4tZ7XHmnq1KnMmZOO9pvLli0DYOlST8BmZmZmlnWpn2lyIiQtltTnmmMzMzMzS4tMJdwRsSoiumfMmJF0KGZmZmZmQMYSbjMzMzOztFGWhpspjlICvANYP8ZuM4BK25xUsm8t9pkJbKkwpjSbyHub5mtO9pzVHu97s36a6d7MRcSsWgeTZpI2A4NjbK7156KS/fy5aK7r1uJ81ZzD92b9ZK/MLs6m2CoPoK+W+9ZiH6A/6fel0e9tmq852XNWe7zvzea6T9J4zSw+av25qGQ/fy6a67q1OF815/C92Tz3SBqu2YpNSlbVeN9a7ZMFSbzOelxzsues9njfm/WTlXuzFdX6c1HJfq3yt0vqddb6urU4XzXn8L1ZP5krszPVpKRZSeqPiI6k4zAbyfem2Wj+XFha+d5Mr1as4U6jvqQDMBuD702z0fy5sLTyvZlSruE2MzMzM6sj13CbmZmZmdWRE24zMzMzszpywm1mZmZmVkdOuM3MzMzM6sgJd8pI2kvSNyV9XVJn0vGYFUmaK+kqSTcmHYtZWrjMtrRymZ0uTrgbQNI3JD0m6b4R6xdJelDSw5IuLaw+E7gxIt4LnNbwYK2lTOTejIgNEXFhMpGaNY7LbEsrl9nNywl3YywHFpWukDQFuBJ4C/Ba4BxJrwVmA48Wdnu+gTFaa1pO5femWatYjstsS6fluMxuSk64GyAi7gIeH7H6WODhwjfQZ4HrgNOBjeQLcPDfx+psgvemWUtwmW1p5TK7eblwSM4BvFgrAvlC+wBgJXCWpK8Aq5IIzFpe2XtT0n6SvgocKekTyYRmlhiX2ZZWLrObwO5JB9DCVGZdRMTTwAWNDsasxFj35lbgokYHY5YSLrMtrVxmNwHXcCdnI3BgyfJsYFNCsZiV8r1pNpo/F5ZWvjebgBPu5NwLtEuaI2kP4J3A9xKOyQx8b5qV48+FpZXvzSbghLsBJF0L3AMcLGmjpAsj4jngYuA2YB1wfUTcn2Sc1np8b5qN5s+FpZXvzealiEg6BjMzMzOzzHINt5mZmZlZHTnhNish6XxJ/yVpu6Q/SvqKpL8qbOuRtFPSUyWPjxe2/VTSjhHbjk/21ZiZmVkaOOE2K5D0UeCfgX8AZgDHATngR4WOKADfiYi9Sx7/b8kpLh6x7Z7GvgIzMzNLI4/DbQZI2hdYBrw7In5QWD0g6WxgA9CVWHBmZmbW1FzDbZZ3AjCN/KxxQyLiKeD/Am9KIigzMzNrfk64zfJmAlsKwyuN9IfCdoCzJT1R8nhVyX7/WrL+V3WP2MzMzJqCE26zvC3ATEnlmlm9srAd8uOb/lXJo3Q2rw+VrD+q7hGbmTUJSc9LWlPyuDTpmIok3ShprqRfFGL7naTNJbG2jXHcP0m6YsS6DklrC89/LGlG/V+BNQMn3GZ59wB/Ac4sXSlpL+AtwI+TCMrMLCOeiYgFJY/PTPaEY1SQTPQchwJTImJDRLwuIhYA/0i+g3wx1oExDr8WeMeIde8srAe4BrhosjFaNjjhNgMiYhv5TpP/JmmRpKmFWo0bgI3AtxMMz8wskyQNSFom6VeFIVkPKazfS9I3JN0r6deSTi+sP1/SDZJWAT+UtJukL0u6X9Itkm6V9DZJCyXdVHKdN0laWSaETuDmCuJ8i6R7CnF+R9Jehdkcd0g6urCPgLcD1xUOuxlYMpn3x7LDCbdZQWGIv08CnwP+DPwCeBRYGBF/STI2M7Mmt+eIJiWlNcNbCs3wvgJ8rLDuMuAnEXEMcDLw2cIvjgDHA+dFxBvJ/yrZBhwOvKewDeAnwGskzSosXwBcXSauE4HV4wUuaX/gUvL/FxwFrAU+XNh8Lfla7eK5NkXEIwARsQXYpziXg7U2DwtoViIirgKuGmNbzzjHvaFOIZmZZcEzheYa5RRrnlfzYrO+NwOnSSom4NOAgwrPfxQRjxeevx64ISJeAP4o6Q6AiAhJ3wa6JF1NPhE/t8y1Xwls3kXsJwCvBX6er8RmD+DuwrZrgTsLk6CVNicp2ly4xhO7uIZlnBNuMzMzS1LxF8TneTEvEXBWRDxYuqOk1wFPl64a57xXA6uAHeST8nKjUD1DPpkfj4AfRMS7Rm6IiAFJm4C/Bs4Ajh6xy7TCNazFuUmJmZmZpc1twAcL7aKRdOQY+90NnFVoy/1y4A3FDYVRpDYBnwKWj3H8OmDeLmL5OXCSpLmFWPaS1F6y/VrgX4F1EfHH4kpJu5EfUvbRXZzfWoATbjMzM6u3kW24dzVKyRXAVGCtpPsKy+V8l3zH9vuAr5Hve7OtZPsK4NGI+O0Yx3+fkiS9nIj4E3Ah8B1JvyGfgL+6ZJfrgcN4sbNk0bHA3RHx/Hjnt9agiEg6BjMzM7OqSNo7Ip6StB/wS+DEYk2zpC8Bvy70zyl37J7AHYVjapoYS7qS/NwNd9byvNacMplwz5w5M9ra2pIOw8xswlavXr0lImbtes/scJltZs2q0jI7k50m29ra6O/vTzoMM7MJkzSYdAyN5jLbzJpVpWW223CbmZmZmdWRE24zMzMzszpywm1mZmZmVkdOuM3MzMzM6sgJt5mZmZlZHTnhNjMzMzOrIyfcKSOJwky2ZmZmVif+/9YayQm3mZmZmVkdOeE2MzMzM6sjJ9xmZmZmZnWUqYRb0mJJfdu2bUs6FDMzMzMzIGMJd0SsiojuGTNmJB1KVVasWDH0vK2tbdiymZmZmTWnTCXczWzFihV0d3cPLQ8ODtLd3e2k28zMzKzJ7Z50AK2qkqGItm/fTldXF11dXUPrIqKeYZmZmZlZjbmGu8V5HFIzMzOz+nINdwKuueYaenp6hq37whe+QLnOnjNmzOCSSy4ZduySJUvqHaKZmZmZ1YhruBOwfv36UesWLlzI1KlTh62bOnUqCxcu3OWxZmZmZpZeruFO0NKlS4ctr1ixYqi9di6Xo7e3l87OzqHty5Yta2h8ZmZmZjZ5ruFOkdLkemBgYNiymZmZmTUnJ9xmZmZmZnXkhLuFTWaiHY9uYmZmZlYZJ9wtyhPtmFk9SDpQ0h2S1km6X9KHk47JzCxp7jTZIjzRjpk1yHPARyPiV5L2AVZL+lFE/DbpwLKgWJa7bDZrLq7hNjOzmomIP0TErwrPnwTWAQckG5WZWbKccKdMRNSl5qJ43uIjl8uV3S+Xyw3bz8ysWpLagCOBXyQbiZlZspxwt6je3l6mT58+bN306dPp7e1NKCIzyxJJewPfBf4+Iv5cZnu3pH5J/Zs3b258gGZmDeSEu0V1dnbS19c3tJzL5ejr66to7O/JjG7SSB5JxSwZkqaST7ZXRMTKcvtERF9EdEREx6xZsxoboJlZgznhbmHVTLTj0U3MbDzKf8u9ClgXEZ9POh5rHq4ksSxzwm3jKhaAxUdXVxfbt28ftk9xdJPS/cysZZ0IvAt4o6Q1hcf/SjooM0ufVsoZPCygmZnVTETcDbTG/6BmZhVyDbeNy6ObmJmZmU2OE26bEI9uYmZmZjYxblLShJYtWzbhY9rb21myZMmkr13sWFmcjTKXy9Hb21tRh0szM7M0GDnalv8fs3pLfQ23pLmSrpJ0Y9KxJK29vX3MbT09PfT09Iy5ff369TWLo5rRTSajmk4VSQxd2EqdP8zMmpVH27IkKIn2tpK+AZwKPBYRh5WsXwR8EZgC/HtEfKZk240R8bZKzt/R0RH9/f01jrp2ijXUS5curdk5i4leub/neDXixSR9rGR9rJrx8a5XaxO9VrEwLR1NZfr06RWPM16tRr4nll2SVkdER9JxNFLay+w0yXI5U6/XVm1FSBbf47TJwv1caZmdVJOS5cCXgG8VV0iaAlwJvAnYCNwr6XsR8dtEIsyQ9vb2MWu4x6sVh3zN+HgJ+1jbatWEpRKVFKbFoQuLTWGguT/gZmYTkYXExqyZJZJwR8RdktpGrD4WeDgiNgBIug44Hago4ZbUDXQDHHTQQTWLNQuqTXyvueaaqpui1LIJi5mZWbVGfsloa2tjcHBw1H65XI6BgYEGRWWtJk2dJg8AHi1Z3gi8TtJ+QC9wpKRPRMSnyx0cEX1AH+R/nqx3sK1gvES9WDNerllMNZ06J8OFqZmZVaq3t7dss0OPtmX1lKaEu1y7gIiIrcBFjQ7GJm+iiXetmqG4MDUzs7F4tC1LQppGKdkIHFiyPBvYlFAsNgnjjaYynlo1Q+ns7KSvr29oOZfL1b3DpJmZNY9Gj7ZllqYa7nuBdklzgN8D7wQmVN0paTGweN68eXUIzypVTS11rZuhdHZ2DtVeuBmJmZmZJdl5OJEabknXAvcAB0vaKOnCiHgOuBi4DVgHXB8R90/kvBGxKiK6Z8yYUfugzczMzMyqkEjCHRHnRMQrI2JqRMyOiKsK62+NiFdHxP+ICDe4rVASk7xEREO+ISbx2qrRLHGamVn2eSK29ElTkxKrwlgzZgFN2SattGnJ2rVrWbVq1dDy4OAgF1xwAStXrmT+/PlD6xs55nc5WfsbmJmZWW054W4yWZ3kpb29vaLkdOfOnaxcuZKVK1cOrdvV5D21ltW/gZmZmdVHmkYpmTRJiyX1bdu2LelQbIKSrKE2MzMzq6dMJdyt0Gmy2Ha6+MjlcmX3y+Vyw/ZrBtW8tkbXbkO2/wZmZmZWe25S0uSyPMnLRF7beMMKjrXNE+2YmZlZI2SqhrsVNdMkLxPtNV3Ja6t2kh3wRDtmNnmNHA3CoyGZNS9l8afujo6O6O/vTzqMMRVrXJcuXVqzcyY5mHulqo2xHse16t/A0k/S6ojoSDqORkp7mT2eRn3ui6MhjfwlrdIv981QPjU6xmZ4T6rV6P9vq5WF61VaZmeqhtudJs3MkifpG5Iek3Rf0rE0q2LNefHR1dU1LNmGF0dDKt3PzNIpUwl3K3SatMq5s6JZYpYDi5IOwswsLTKVcJuZWfIi4i7g8aTjaGYeDcmK/OtFNjjhNjNrUZL2kjQl6Ths13p7e5k+ffqwdVkaDckdQi3rnHCbmbUISbtJWiLp+5IeAx4A/iDpfkmflVT9sD8Tj6VbUr+k/s2bN9f63FXVCKa5JjHLoyEVO4QWDQ4O0t3d7aQ741rtS1amEm53mjQzG9cdwP8APgG8IiIOjIj9gb8G/hP4jKSuRgQSEX0R0RERHbNmzWrEJZteaXI9MDDQtMm2O4RaK37JytTENxGxCljV0dHx3qRjseYw3oQ5Y6nVhDlmCTglInaOXBkRjwPfBb4raWrjw7KsyfKQezZxlXxhKn7J6up68Tt/lu6fTNVwm1UqDRPmmDVaMdmWNOYA9OUS8omSdC1wD3CwpI2SLpzsOS073CHUWlGmarjNKlVtDXU1NeJmKbRU0nTgZcCvgOsi4r9rdfKIOKdW57Ls6+3tLTupT1Y6hNromuq2tjYGBwdH7ZfL5RgYGGhQVI3lGm5riFbrHGGWcgHsAG4DDgR+LumIZEOyVpXlDqFWXtZH3SnHNdxWd2N1jgBcoJol44GIKDYruVHScuCrwBuTC8laWWdn51Db3azWcNqLiv/3F//muVyO3t7eTOcEruHOiLS1catlD/S0vbaxNEucZsAWSUcXFyLiIcBDhWSUf2G0NMrKqDuVylQNt6TFwOJ58+YlHYqZWZp9CLhO0mrgv4D5wCPJhmT14F8Y08Mjt7S2TNVwR8SqiOieMWNG0qG0PPdAN0sfFf7Hj4jfAAuAawub7gDOKd3HmpPHuDZLp0wl3JZOrdg5wiyl7pD0QUkHRcRfIuL7EfHPwLeA10n6JnBewjGaNYQre6yRnHBb3bkHullqLAKeB66VtEnSbyVtANaTr+H+QkQsTzJAmxyPcW2WTk64rSFarXOEWRpFxI6I+HJEnAjkgIXAURGRi4j3RsSahEO0GvMvjGZ5SXcedsJtZtaCImJnRPwhIp5IOharH//CaDZ25+FGJt2ZGqXErFGqmXGyvb296hkuzcyq4xl7swAAGbdJREFU5TGuW8vImtysj29dTiUdgYudh4ufjXo3rXINt9kEtLe3V33s+vXraxiJmZnZcGmoybXyalLDLemlwIERsbYW5zNLq2prqKupETczMxtPNTW5kP2xwEe+vra2NgYHB0ftl8vlGvarT9U13JJ+KmlfSS8DfgNcLenztQutqpgWS+rbtm1bkmGYmaWapLdL2qfw/FOSVko6Kum4zMzqIQ2dhyfTpGRGRPwZOBO4OiKOBk6pTVjV8cQ3ZmYVuTwinpT0euB/At8EvpJwTGY2QR4GsjJp6Dw8mYR7d0mvBM4GbqlRPGZmVn/PF/59K/CViLgZ2CPBeMyaRppn50xDTW5aJT088WQS7mXAbcDDEXGvpLnkJ08wM7N0+72kr5GvMLlV0ktwJ/oJS3pcX7OR0lCTa+VNpoD9Q0TMj4gPAETEBiDRNtxmZlaRs8lXmCwqjMP9MuAfkg2puXg0CEuramty/QWyviYzSsm/ASM72ZRbZ2ZmKRIR24GVJct/AP6QXETp59EgLMvG+gIJuHa8RiaccEs6HjgBmCXpIyWb9gWm1Cows6QU/2Nt1H+U1Vyv2hgbfZyli6QngXJ/RAEREfs2OCQzS4C/QDZeNU1K9gD2Jp+s71Py+DPwttqFZmZmtRQR+0TEvmUe+zjZHp9Hg7AkuJlHdkw44Y6IOyNiGXBcRCwreXw+Itxp0sysCUh6qaRjJf1N8VHDcy+S9KCkhyVdWqvzVqLaBGWix012NIg0j3Rh6VDPfgL+Atl4k2nD/RJJfUBb6Xki4o2TDcosq8abcXKsbe3t7VXPcGlWjqT3AB8GZgNrgOOAe4BJl9+SpgBXAm8CNgL3SvpeRPx2sufelWrboVZzXHF98ef2XC5Hb29v3du7OunJrmqaedTqfujt7aW7u5vt27cPrfNwgrU1mYT7BuCrwL/z4piuZlZGe3s769dX9wNQtceZjePDwDHAf0bEyZIOIT/Uay0cS3642A0Akq4DTgfGTLi3bt3K8uXLh6079NBDOeaYY9i5c2fZGr0FCxZw5JFHMn36dM4++2wAbrjhhmEJA+QTlPe9733cfvvtw9b//Oc/56GHHmK//fZjx44dZY/r6uriYx/7GIsWLeKkk04atn3hwoV0dnbyiU98goULF3LSSSexc+fOodexaNEiXvGKV7BhwwbuuuuuUfHvt99+bN26lQcffJB77rln1PYzzjiDGTNmcN9999Hf3z9q+9lnn8306dNZs2YNa9asGbW9s7OTqVOncu+993L//fdz/vnnAwzFV1wuvg+lpk6dOvTF4c477+SRRx4Ztr30Pb/99tvZuHHjsO377vti66Qf/OAH/PGPfxz12hcvXgzAqlWr2Lp167CYfvCDH7Bo0SIAVq5cyZ///Odhx8+ePZtTTsnPs3f99deP+tvNmTNn6O+1YsUKdu7cOWz7q1/9ak444YRh70epSu69NWvWsH37dq6//vpR2zs6OjjssMPYtm0bN91009D64ut78MEHOfjgg9myZQu33HLLqO133XUXGzZsGHXekQ488MCy8Rffu7lz55bdfuqppzJz5sxR915XVxcrVqzg6aefJpfL8f73v3/YPV000XtvpNJ7b+R9Wem9J4mFCxcOa18O+XvvzDPPBMrfe4sXL2bVqlXA8Huv6BWveMWk7r3xTCbhfi4iUjUzmaTFwOJ58+YlHYrZMOPVUPf09ACwdOnSUdvGqxE3m4QdEbGj0KzhJRHxgKSDa3TuA4BHS5Y3Aq8buZOkbqAb4IADDqjJhZ9++ukJra90u01M6ReM8847j7/927/l+OOPTzCi5lD8knD55Zczd+5cDjzwwFFfZiD/edm4cSOPPvooP/7xj2t2/eOPP55nn32WlStXMjAwMOaXvXLuvPPOYa/BRlO1P0dI6gEeA24C/lJcHxGP1ySySejo6IhKb5IkFJOocglWljXLSBdpGqVkrHvFo5Rkl6TVEdFR52vcBFwA/D35ZiT/DUyNiP9Vg3O/HfifEfGewvK7gGMj4oNjHVOrMrutrY3BwcFR63O5HAMDAzU/DprnM9Wo6xWb54xsmlDJ5CvNUs406m+exHvp4yau0jJ7MhPfnEd+ooSfA6sLj/RmuWZmBkBEnBERT0RED3A5cBX5Zh+1sBE4sGR5NrCpRuceV7UdGT0ddvWKnT+Lj66urjGb57ij6MR41shsqbpJSUTMqWUgln1pr7UwaxWS/rHM6gXA/6nB6e8F2iXNAX4PvBNoSK/fajsyJtUB0obz/xGjdXZ2Dt2Xu/q1pRm10t+86oRb0rnl1kfEt6oPx8zMGqC00fI04FRgXS1OHBHPSbqY/NTxU4BvRMTonlN1Um2CkvXEpl5GJkyTaZ5jlmWT6TR5TMnzacBC4FeAE24zsxSLiH8pXZb0OeB7NTz/rcCttTqfNQ8PL2dW3mSalAzrACNpBvDtSUdkZqOMNVqJx+62GpkOzE06CGt+bp5T3siJlfyetJ7J1HCPtB1or+H5zFpeteN3e+xuG4+k/wKKbQGmALOAK5KLyLLEzXOGq3ZCJsuWybThXsXwAvs1wOgR4M2samPVUnvsbpukU0uePwf8KSKeSyoYsyypZsZIaK0OhK1oMjXcnyt5/hwwGBGjR2g3M7NUkPSRcbYREZ9vZDxmZq2i6nG4I+JO4AFgH+ClwLO1CsrMzOpin8KjA3g/+VkhDwAuAl6bYFxmmRERwx65XK7sfrlcbth+lm1VJ9ySzgZ+CbwdOBv4haS31SowMzOrrYhYFhHLgJnAURHx0Yj4KHA0+QlqLKOc1CXHEysZTG6mycuAYyLivIg4FziW/IxlZk1rZE/y0uW0XK/aGBt9nKXaQQz/VfJZoC2ZUMyyzTNGGkyuDfduEfFYyfJWJpfAmyWq0T3Jq7letTE2+jhLvW8Dv5R0E/nO72cA30w2JLPs8sgtpmp/YpL0WWA+cG1h1TuAtRHxv2sUW9U6Ojqiv78/6TDGVBxFotwIE9Y4lfQkL2cSn5mqjmsG/qm6diStjoiOBlznaOD1hcW7IuLX9b7mWGpdZhc/axO9L6s5rpHXaiZZfn2N/ps3w33Z6L93muKstMyecA23pHnAyyPiHySdSb7AFnAPkOhvzZIWA4vnzZuXZBhmVtAshXDar1UPEbEaWJ10HJZuzX6fm6VFNU1K/j/gkwARsRJYCSCpo7Btcc2im6CIWAWs6ujoeG9SMVjzGPkfSFtbG4ODg6P2y+VyNfkJsBbXq+SYa665ZtTEN1/4whfYtm3bqONmzJjBJZdcMmxd6S8v9X5PrLEk3R0Rr5f0JC/OowD5SpOIiH0TCs3MrO6S/OJYTcLdFhFrR66MiH5JbZOOyCwhvb29dHd3s3379qF19exJXs31Kjmm3GQ58+bNK3vclVdeOdQWu9yEOY1+T6y+IuL1hX/3SToWs2bkKdqtWtV0cpw2zrY9qw3ELGmN7klezfWqjbHRx1m6SXq7pH0Kzz8laaWkI5OOyyzNxupEnsaRmzwMZPpMuNOkpGuBn0TE10esvxB4c0S8o4bxVcWdJm0ymqHdcT06jIx3X6apg0parlevazWi06SktRExX9LrgU+Tnzn4kxHxunpedyzuNJleWX9946l1x/pmeC+bIcYkrjeeunWaBP4euElSJy92uOkA9iA/tJSZmaXb84V/3wp8JSJultSTYDxmZpk24SYlEfGniDgBWAYMFB7LIuL4iPhjbcMzM7M6+L2kr5GfJfhWSS/B8yiYDeMp2q2Wqi5gI+KOiPi3wuMntQzKzMzq6mzgNmBRRDwBvAz4h2RDMks3T9Fuk+EaDTOzFhMR2yNiZUSsLyz/ISJ+mHRcrWLkSBdp7HTXTCQ1ZGIxdyK3yXDCbWbWYpTXJekfC8sHSTo26bia0USbETTTSBc2WmlyPTAwkLlk281i6qeaTpNmlmHlxuPe1bb29vay439ban0ZeAF4I/B/gCeB7wLHJBlUFlVS87p9+3a6urro6uoaWuekx6y8Zh0L3TXcZgbkk+ZqjZzZ0lLvdRHxd8AOgIj4b/IjTU1KYXzv+yW9UJh92MwyrpFNpJr5FyLXcJsZUH6GyqKenh6g/Bjd49WIW2rtlDSFwvTukmaRr/GerPuAM4Gv1eBcmTCyprqtrY3BwcFR++VyOQYGBhoUlVltjJUAAzWpdc7SL0Su4TYzaz3/CtwE7C+pF7gb+H8me9KIWBcRD072PFnmkS5sotLUrrrYQbX46OrqYvv27cP2KSbApfuZE24zs5YTESuAj5OfZXIT8HZq0KRkIiR1S+qX1L958+aanrvaBKURiY1HujCrXJbGQnfCbWbWIiTtK+kTkr4EHES+8+RuwCryY3NXco7bJd1X5nH6RGKJiL6I6IiIjlmzZk34tTSzrI90YdmVdALczL8QuQ23mVnr+Dbw38A9wHvIT3azB3B6RKyp5AQRcUr9wjOzZtLb20t3d/ewZiX1TICLX06L7bVzuVzTjFLihNvMrHXMjYjDAST9O7AFOCginkw2LMuatP6sb7WVRALc2dk5dL1m6mjsJiVmZq1jZ/FJRDwPPFLLZFvSGZI2AscD35d0W63ObWbp5CZSlXENt5lZ6zhC0p8LzwXsWVgWEBGx72ROHhE3kR/9xMzMSjjhNjNrERExJekYzMxakZuUmJmZmZnVkRNuMzMzM7M6Sn3CLWkvSd+U9HVJbolv1iRWrFgx9LytrW3YcrNfr9GvzcyaX5onZbH6SyThlvQNSY9Jum/E+kWSHpT0sKRLC6vPBG6MiPcCpzU8WDObsBUrVtDd3T20PDg4SHd3d90S00Zer9GvzczMml9SNdzLgUWlKyRNAa4E3gK8FjhH0muB2cCjhd2eb2CMZlahnp4eJA09urq6hk2EALB9+3a6urqG7Vet0nPU+3qNvJaZmWVTIgl3RNwFPD5i9bHAwxGxISKeBa4DTgc2kk+6YZx4JXVL6pfUv3nz5nqEbWZm1jLcdMqsdtLUhvsAXqzJhnyifQCwEjhL0leAVWMdHBF9EdERER2zZs2qb6RmNkxPT89Q+8SIIJfLld0vl8sN269apeeo9/UaeS2ztHDTKbPaSlPCXe432IiIpyPigoh4f0T4k27WBHp7e5k+ffqwddOnT6e3t7fpr9fo12bWCG46ZVZfaUq4NwIHlizPBjYlFIuZTUJnZyd9fX1Dy7lcjr6+vrpN+dvI6zX6tZmZWfNLU8J9L9AuaY6kPYB3At+byAkkLZbUt23btroEaGaVK01ABwYG6p6QNvJ6jX5tZvXmplNm9ZXUsIDXAvcAB0vaKOnCiHgOuBi4DVgHXB8R90/kvBGxKiK6Z8yYUfugzczMWoSbTpnV1u5JXDQizhlj/a3ArQ0Ox8xqYNmyZRPe1t7ezpIlS+oVkplVqfirTVdXF5Cv2e7t7fWvOWZVSlOTEjNrQu3t7VUfu379+hpGYma15KZTZrWTSA23mWXHeDXUPT09ACxdunTUtvFqxM3MKjFyrPBG1MK77bpVI1M13O40aWZm1ho8Vrg1k0wl3O40aWZmlk0eK9yaWaYSbjMzMzOztHHCbWZmZqnnscKtmTnhNjMzs6bjscKtmWQq4XanSTOz5Ej6rKQHJK2VdJOkv0o6prRy7evkdXZ20tfXN7Scy+Xo6+vz8IWWSplKuN1p0swsUT8CDouI+cBDwCcSjscyzmOFW7PIVMJtZmbJiYgfRsRzhcX/BGYnGY+ZZVMz/kLkhNvMzOrh3cD/HWujpG5J/ZL6N2/e3MCwzKzWmjEBbjTPNGlmZhWTdDvwijKbLouImwv7XAY8B4w5A0lE9AF9AB0dHf6f2swyzQm3mdkYrrnmGtavXz/uPuNNUV9uSvtmFxGnjLdd0nnAqcDCcJWXmRmQsSYlHqXEzGppV8m2DSdpEfC/gdMiYvuu9jczaxWZquGOiFXAqo6OjvcmHYs1r0ZXylVzvWpjbPRxWVGuprqnp2fMbS3sS8BLgB8VptT+z4i4KNmQzMySl6mE28zMkhMR85KOwcwsjTLVpMTMzMzMLG2ccJuZmZmZ1ZETbjMzMzOzOspUwu1RSszMzMwsbTKVcEfEqojonjFjRtKhmJmZmZkBHqXEzBI23sQxY2lvb2fJkiV1iMbMzKz2nHCbWSLa29urnlimmuMqmTXSzMysHpxwm1kiqq2hrqZGHKqfNbK9vb2q48zMzIqccJtZS/HMkGZm1mhOuM3MzKysiEg6BLNMyNQoJWZmZmZmaeOE28zMzMysjjKVcHviGzMzMzNLm0wl3J74xszMzMzSJlMJt5mZmZlZ2jjhNjMzMzOrIw8LaGZNabwJcKqdHMfMzKweXMNtZk1lMjM/etZIs+yJCI8XbqnnGm4zayrjTQnf09MDeDZJMzNLF9dwm5mZmZnVkRNuMzOrCUlXSForaY2kH0p6VdIxmZmlgRNuMzOrlc9GxPyIWADcAvxj0gGZmaWBE24zM6uJiPhzyeJegHuymZmRsU6TkhYDi+fNm5d0KGZmLUlSL3AusA04eZz9uoFugIMOOqgxwZmZJSRTNdye2t3MrL4k3S7pvjKP0wEi4rKIOBBYAVw81nkioi8iOiKiY9asWY0K38wsEcri2JWSNgODY2yeQb7mpRKV7FuLfWYCWyqMKc0m8t6m+ZqTPWe1x/verJ9mujdzEdH0GaikHPD9iDisgn0bWWZXsp8/F8113Vqcr5pz+N6sn+yV2cUB41vlAfTVct9a7AP0J/2+NPq9TfM1J3vOao/3vdlc90kar5n0A2gvef5B4MZGvo+V7lvBfe/PRRNdtxbnq+Ycvjeb5x5JwzUz1Ya7QqtqvG+t9smCJF5nPa452XNWe7zvzfrJyr2Zdp+RdDDwAvka64tqcM5afy4q2a9V/nZJvc5aX7cW56vmHL436ydzZXYmm5Q0G0n9EdGRdBxmI/neNBvNnwtLK9+b6ZWpTpNNrC/pAMzG4HvTbDR/LiytfG+mlGu4zczMzMzqyDXcZmZmZmZ15ITbzMzMzKyOnHCbmZmZmdWRE24zMzMzszpywp0ykvaS9E1JX5fUmXQ8ZkWS5kq6StKNScdilhYusy2tXGanixPuBpD0DUmPSbpvxPpFkh6U9LCkSwurzyQ/O9t7gdMaHqy1lIncmxGxISIuTCZSs8ZxmW1p5TK7eTnhbozlwKLSFZKmAFcCbwFeC5wj6bXAbODRwm7PNzBGa03LqfzeNGsVy3GZbem0HJfZTckJdwNExF3A4yNWHws8XPgG+ixwHXA6sJF8AQ7++1idTfDeNGsJLrMtrVxmNy8XDsk5gBdrRSBfaB8ArATOkvQVYFUSgVnLK3tvStpP0leBIyV9IpnQzBLjMtvSymV2E9g96QBamMqsi4h4Grig0cGYlRjr3twKXNToYMxSwmW2pZXL7CbgGu7kbAQOLFmeDWxKKBazUr43zUbz58LSyvdmE3DCnZx7gXZJcyTtAbwT+F7CMZmB702zcvy5sLTyvdkEnHA3gKRrgXuAgyVtlHRhRDwHXAzcBqwDro+I+5OM01qP702z0fy5sLTyvdm8FBFJx2BmZmZmllmu4TYzMzMzqyMn3GZmZmZmdeSE28zMzMysjpxwm5mZmZnVkRNuMzMzM7M6csJtZmZmZlZHTrit6Uh6XtKakselScdUJOlGSXMl/aIQ2+8kbS6JtW2M4/5J0hUj1nVIWlt4/mNJM+r/CszMas/ltrU6j8NtTUfSUxGxd43PuXth8oDJnONQ4J8i4oySdecDHRFxcQXH3hQRry5Z9zlga0R8WtKFwMyI+OfJxGhmlgSX29bqXMNtmSFpQNIySb+S9F+SDims30vSNyTdK+nXkk4vrD9f0g2SVgE/lLSbpC9Lul/SLZJulfQ2SQsl3VRynTdJWlkmhE7g5grifIukewpxfkfSXoVZwXZIOrqwj4C3A9cVDrsZWDKZ98fMLG1cblurcMJtzWjPET9NvqNk25aIOAr4CvCxwrrLgJ9ExDHAycBnJe1V2HY8cF5EvBE4E2gDDgfeU9gG8BPgNZJmFZYvAK4uE9eJwOrxApe0P3ApsLAQ51rgw4XN1wLvLDnXpoh4BCAitgD7SPqr8c5vZpZSLretpe2edABmVXgmIhaMsa1Yg7GafEEM8GbgNEnFgnwacFDh+Y8i4vHC89cDN0TEC8AfJd0BEBEh6dtAl6SryRfo55a59iuBzbuI/QTgtcDP85Uh7AHcXdh2LXCnpI+TL8CvHXHs5sI1ntjFNczM0sbltrU0J9yWNX8p/Ps8L97fAs6KiAdLd5T0OuDp0lXjnPdqYBWwg3zhXq7d4DPk/1MYj4AfRMS7Rm6IiAFJm4C/Bs4Ajh6xy7TCNczMssTltmWem5RYK7gN+GChfR2Sjhxjv7uBswptAl8OvKG4ISI2AZuATwHLxzh+HTBvF7H8HDhJ0txCLHtJai/Zfi3wr8C6iPhjcaWk3YCZwKO7OL+ZWRa43LZMccJtzWhkW8DP7GL/K4CpwFpJ9xWWy/kusBG4D/ga8AtgW8n2FcCjEfHbMY7/PiWFfTkR8SfgQuA7kn5DviB/dcku1wOH8WKnm6Jjgbsj4vnxzm9mllIut62leVhAsxKS9o6IpyTtB/wSOLFYYyHpS8CvI+KqMY7dE7ijcExNC1hJVwLXR8SdtTyvmVmzc7ltzcBtuM2Gu6XQo3wP4IqSQns1+XaDHx3rwIh4RtJS4ADgdzWO69cutM3MynK5bannGm4zMzMzszpyG24zMzMzszpywm1mZmZmVkdOuM3MzMzM6sgJt5mZmZlZHTnhNjMzMzOro/8f2iYCENtFaZMAAAAASUVORK5CYII=\n",
250 606 "text/plain": [
251   - "<matplotlib.figure.Figure at 0x110a33908>"
  607 + "<matplotlib.figure.Figure at 0x11132e5c0>"
252 608 ]
253 609 },
254 610 "metadata": {},
... ... @@ -256,19 +612,26 @@
256 612 }
257 613 ],
258 614 "source": [
259   - "residuals = 'residuals_classical.fits'\n",
  615 + "residuals = 'residuals_classical_bkgmod.fits'\n",
260 616 "res = cscripts.csresspec(like.obs())\n",
261 617 "\n",
262   - "res['statistic'] = 'WSTAT'\n",
263 618 "res['components'] = True\n",
264 619 "res['algorithm'] = 'SIGNIFICANCE'\n",
265 620 "res['outfile'] = residuals\n",
  621 + "res['stack'] = False\n",
266 622 "\n",
267 623 "res.execute()\n",
268 624 "\n",
269 625 "from show_residuals import plot_residuals\n",
270 626 "plot_residuals(residuals,'',0)"
271 627 ]
  628 + },
  629 + {
  630 + "cell_type": "markdown",
  631 + "metadata": {},
  632 + "source": [
  633 + "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."
  634 + ]
272 635 }
273 636 ],
274 637 "metadata": {
... ...
doc/source/users/tutorials/hess_dr1/index.rst
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... ... @@ -12,3 +12,4 @@ Analysing public H.E.S.S. data
12 12 bkgmodel
13 13 unbinned
14 14 classical
  15 + jointfermi
... ...
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doc/source/users/tutorials/hess_dr1/jointfermi.rst 0 → 100644
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  1 +.. _hess_dr1_data:
  2 +
  3 +Joint analysis of H.E.S.S. and Fermi data
  4 +=========================
  5 +
  6 +
  7 + .. admonition:: What you will learn
  8 +
  9 + You will learn how to **jointly analyse data from H.E.S.S. and Fermi**.
  10 +
  11 +In this tutorial you will learn how to jointly analyse data observed with H.E.S.S. and the
  12 +Fermi-LAT. This follows the same procedure illustrated in `this section <../howto/howto_combine_instruments>`_.
  13 +
  14 +Prepare Fermi-LAT data
  15 +^^^^^^^^^^^^^^^^^^^^^^
  16 +
  17 +For this tutorial you need to gather data for a source available in
  18 +the H.E.S.S. public data release, the Crab nebula.
  19 +The preparation of Fermi-LAT data can be adapted from
  20 +`this section <..howto/howto_fermi_prepare>`_. Get Source Class LAT data from the
  21 +Crab in the energy range 50-800 GeV. Apply a zenith angle cut at 105
  22 +deg. Then bin them on a 60x60 pixel
  23 +grid with 0.05 deg step, and on a logarithmic energy grid with 15 bins
  24 +from 50 GeV to 800 GeV, and calculate livetime cube, exposure map, and
  25 +diffuse model source maps. The results shown here correspond to P8R3 LAT
  26 +data covering the Fermi Mission Elapsed Time (MET) between 239557417 s
  27 +and 565315205 s, analysed using the fermitools 1.0.3. To run the tutorial faster you can select a shorter dataset.
  28 +
  29 +Prepare H.E.S.S. data
  30 +^^^^^^^^^^^^^^^^
  31 +
  32 +We will use the On/Off observations generated in `this section <classical>`_.
  33 +
  34 +Combining the observations
  35 +^^^^^^^^^^^^^^^^^^^^^^^^^^
  36 +
  37 +Now you have all the data and hand. You have to create an
  38 +:ref:`observation definition file <glossary_obsdef>`
  39 +to combine the data for an analysis:
  40 +
  41 +.. code-block:: xml
  42 +
  43 + <?xml version="1.0" standalone="no"?>
  44 + <observation_list title="observation library">
  45 + <observation name="Crab" id="000001" instrument="LAT">
  46 + <parameter name="CountsMap" file="srcmaps.fits"/>
  47 + <parameter name="ExposureMap" file="expmap.fits"/>
  48 + <parameter name="LiveTimeCube" file="ltcube.fits"/>
  49 + <parameter name="IRF" value="P8R2_SOURCE_V6"/>
  50 + </observation>
  51 + <observation name="Crab" id="000002" instrument="HESSOnOff" statistic="wstat">
  52 + <parameter name="Pha_on" file="onoff_stacked_pha_on.fits" />
  53 + <parameter name="Pha_off" file="onoff_stacked_pha_off.fits" />
  54 + <parameter name="Arf" file="onoff_stacked_arf.fits" />
  55 + <parameter name="Rmf" file="onoff_stacked_rmf.fits" />
  56 + </observation>
  57 + </observation_list>
  58 +
  59 +The ``instrument`` attribute distinguishes between the two
  60 +instruments. Note how we have specified thet ``statistic`` attribute
  61 +so that WSTAT will be used by all likelihood tools. Note how we are
  62 +using different analysis methods for the LAT and H.E.S.S. data, that
  63 +is binned and On/Off, respectively. Those are the standard analysis
  64 +methods for each instrument, but you can use any other method for
  65 +H.E.S.S. data, such as binned or unbinned. For H.E.S.S. you may choose
  66 +another analysis method, such as unbinned or binned.
  67 +
  68 +Fit a spectral model to the data
  69 +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  70 +
  71 +You have to define a
  72 +:ref:`model definition file <glossary_moddef>`
  73 +that will be used to model the events for both observations.
  74 +The model that will be used in this analysis is shown below:
  75 +
  76 +.. code-block:: xml
  77 +
  78 + <?xml version="1.0" standalone="no"?>
  79 + <source_library title="source library">
  80 + <source type="PointSource" name="Crab">
  81 + <spectrum type="LogParabola">
  82 + <parameter name="Prefactor" scale="1e-17" value="3.23" min="1e-07" max="10000.0" free="1"/>
  83 + <parameter name="Index" scale="-1" value="2.47" min="0.5" max="+5.0" free="1"/>
  84 + <parameter name="Curvature" scale="-1" value="0.24" min="-5.0" max="+5.0" free="1"/>
  85 + <parameter name="PivotEnergy" scale="1e6" value="1.0" min="0.01" max="1000.0" free="0"/>
  86 + </spectrum>
  87 + <spatialModel type="PointSource">
  88 + <parameter name="RA" scale="1.0" value="83.633" min="-360" max="360" free="0"/>
  89 + <parameter name="DEC" scale="1.0" value="22.015" min="-90" max="90" free="0"/>
  90 + </spatialModel>
  91 + </source>
  92 + <source type="DiffuseSource" name="Galactic_diffuse" instrument="LAT">
  93 + <spectrum type="Constant">
  94 + <parameter name="Normalization" scale="1.0" value="1.0" min="0.1" max="10.0" free="1"/>
  95 + </spectrum>
  96 + <spatialModel type="DiffuseMapCube" file="gll_iem_v06.fits">
  97 + <parameter name="Normalization" scale="1.0" value="1.0" min="0.1" max="10.0" free="0"/>
  98 + </spatialModel>
  99 + </source>
  100 + <source type="DiffuseSource" name="Isotropic_diffuse" instrument="LAT">
  101 + <spectrum type="FileFunction" file="iso_P8R2_SOURCE_V6_v06.txt">
  102 + <parameter name="Normalization" scale="1.0" value="1.0" min="0.0" max="1000.0" free="0"/>
  103 + </spectrum>
  104 + <spatialModel type="DiffuseIsotropic">
  105 + <parameter name="Value" scale="1.0" value="1.0" min="0.0" max="10.0" free="0"/>
  106 + </spatialModel>
  107 + </source>
  108 + </source_library>
  109 +
  110 +The model contains a point source located at the position of the Crab
  111 +with a log-parabola spectrum. It does not have any ``instrument`` attribute, which means that it applies to all instruments. In addition, the model contains two ``DiffuseSource``
  112 +components that are only applied for Fermi-LAT observations, which is indicated
  113 +by their ``instrument="LAT"`` attribute. Both components model the diffuse
  114 +background and are the same that were included in the generation of the source maps (the names need to coincide).
  115 +
  116 +Now you can fit the model to the data using :ref:`ctlike`:
  117 +
  118 +.. code-block:: bash
  119 +
  120 + $ ctlike
  121 + Input event list, counts cube or observation definition XML file [] joint_observations.xml
  122 + Input model definition XML file [] joint_models.xml
  123 + Output model definition XML file [] joint_results.xml
  124 +
  125 +The results are: prefactor (for 1 TeV pivot energy) (4.4±0.2)×10−11
  126 +photons cm−2 s−1 TeV−1, spectral index 2.42±0.04, and curvature
  127 +-0.106±0.018. They are broadly consistent with results from published
  128 +studies, e.g., `MAGIC collaboration (2015)
  129 +<http://adsabs.harvard.edu/abs/2015JHEAp...5...30A>`_.
  130 +We have used an energy threshold of 50 GeV to avoid contamination from
  131 +the Crab pulsar. The Fermi analysis can be extended to lower energies
  132 +for example by selecting photons based on the pulsar phase.
  133 +
  134 +Butterfly and SED
  135 +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  136 +
  137 +We can now generate the butterfly
  138 +
  139 +.. code-block:: bash
  140 +
  141 + $ ctbutterfly
  142 + Input event list, counts cube or observation definition XML file [events.fits] joint_observations.xml
  143 + Source of interest [Crab]
  144 + Input model definition XML file [$CTOOLS/share/models/crab.xml] joint_results.xml
  145 + Lower energy limit (TeV) [0.1] 0.05
  146 + Upper energy limit (TeV) [100.0]
  147 + Output ASCII file [butterfly.txt]
  148 +
  149 +and spectral energy distribution (SED)
  150 +
  151 +.. code-block:: bash
  152 +
  153 + $ csspec
  154 + Input event list, counts cube, or observation definition XML file [joint_observations.xml]
  155 + Input model definition XML file [joint_results.xml]
  156 + Source name [Crab]
  157 + Spectrum generation method (SLICE|NODES|AUTO) [AUTO]
  158 + Algorithm for defining energy bins (FILE|LIN|LOG) [LOG]
  159 + Start value for first energy bin in TeV [0.1] 0.05
  160 + Stop value for last energy bin in TeV [100.0]
  161 + Number of energy bins (1-200) [10] 15
  162 + Output spectrum file [spectrum.fits]
  163 +
  164 +Below you can see the resulting butterfly and SED.
  165 +
  166 +.. figure:: joint_fermi_butterfly.png
  167 + :width: 600px
  168 + :align: center
  169 +
  170 +.. figure:: joint_fermi_sed.png
  171 + :width: 600px
  172 + :align: center
  173 +
  174 + *Spectral energy distribution of the Crab nebula from joint analysis of H.E.S.S. and Fermi data*
  175 +
  176 +.. note::
  177 + These figures were created by typing:
  178 +
  179 + .. code-block:: bash
  180 +
  181 + $ $CTOOLS/share/examples/python/show_butterfly.py butterfly.txt
  182 +
  183 + $ $CTOOLS/share/examples/python/show_spectrum.py spectrum.fits
  184 +
  185 +The SED is not shown above 40 TeV because the low counting statistics
  186 +make the results uninteresting.
... ...
doc/source/users/tutorials/hess_dr1/selecting_events.ipynb
  View file @fb39a2b
... ... @@ -27,7 +27,7 @@
27 27 },
28 28 {
29 29 "cell_type": "code",
30   - "execution_count": 7,
  30 + "execution_count": 1,
31 31 "metadata": {},
32 32 "outputs": [],
33 33 "source": [
... ... @@ -45,7 +45,7 @@
45 45 },
46 46 {
47 47 "cell_type": "code",
48   - "execution_count": 9,
  48 + "execution_count": 2,
49 49 "metadata": {},
50 50 "outputs": [],
51 51 "source": [
... ... @@ -71,7 +71,7 @@
71 71 },
72 72 {
73 73 "cell_type": "code",
74   - "execution_count": 10,
  74 + "execution_count": 3,
75 75 "metadata": {},
76 76 "outputs": [
77 77 {
... ... @@ -100,7 +100,7 @@
100 100 },
101 101 {
102 102 "cell_type": "code",
103   - "execution_count": 11,
  103 + "execution_count": 4,
104 104 "metadata": {},
105 105 "outputs": [
106 106 {
... ...
doc/source/users/tutorials/hess_dr1/skymap.ipynb
  View file @fb39a2b
... ... @@ -27,7 +27,7 @@
27 27 },
28 28 {
29 29 "cell_type": "code",
30   - "execution_count": 2,
  30 + "execution_count": 1,
31 31 "metadata": {},
32 32 "outputs": [],
33 33 "source": [
... ... @@ -45,7 +45,7 @@
45 45 },
46 46 {
47 47 "cell_type": "code",
48   - "execution_count": 3,
  48 + "execution_count": 2,
49 49 "metadata": {},
50 50 "outputs": [],
51 51 "source": [
... ... @@ -62,7 +62,7 @@
62 62 },
63 63 {
64 64 "cell_type": "code",
65   - "execution_count": 29,
  65 + "execution_count": 3,
66 66 "metadata": {},
67 67 "outputs": [],
68 68 "source": [
... ... @@ -96,14 +96,14 @@
96 96 },
97 97 {
98 98 "cell_type": "code",
99   - "execution_count": 30,
  99 + "execution_count": 4,
100 100 "metadata": {},
101 101 "outputs": [
102 102 {
103 103 "data": {
104 104 "image/png": 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\n",
105 105 "text/plain": [
106   - "<matplotlib.figure.Figure at 0x11f6265f8>"
  106 + "<matplotlib.figure.Figure at 0x108cdb0f0>"
107 107 ]
108 108 },
109 109 "metadata": {},
... ... @@ -135,7 +135,7 @@
135 135 },
136 136 {
137 137 "cell_type": "code",
138   - "execution_count": 31,
  138 + "execution_count": 5,
139 139 "metadata": {},
140 140 "outputs": [],
141 141 "source": [
... ... @@ -162,7 +162,7 @@
162 162 },
163 163 {
164 164 "cell_type": "code",
165   - "execution_count": 32,
  165 + "execution_count": 6,
166 166 "metadata": {},
167 167 "outputs": [
168 168 {
... ... @@ -201,45 +201,29 @@
201 201 "cell_type": "markdown",
202 202 "metadata": {},
203 203 "source": [
204   - "We can overlay the position of the source we found to the smoothed skymap to make sure everything worked smoothly."
  204 + "We can overlay the position of the source we found to the smoothed skymap to make sure everything worked well."
205 205 ]
206 206 },
207 207 {
208 208 "cell_type": "code",
209   - "execution_count": 45,
  209 + "execution_count": 7,
210 210 "metadata": {},
211 211 "outputs": [
212 212 {
213   - "name": "stderr",
214   - "output_type": "stream",
215   - "text": [
216   - "/Users/ltibaldo/anaconda3/envs/ctools/lib/python3.5/site-packages/matplotlib/colors.py:1238: UserWarning: Power-law scaling on negative values is ill-defined, clamping to 0.\n",
217   - " warnings.warn(\"Power-law scaling on negative values is \"\n"
218   - ]
219   - },
220   - {
221 213 "data": {
222 214 "text/plain": [
223   - "<matplotlib.collections.PathCollection at 0x121307c88>"
  215 + "<matplotlib.collections.PathCollection at 0x112c4be80>"
224 216 ]
225 217 },
226   - "execution_count": 45,
  218 + "execution_count": 7,
227 219 "metadata": {},
228 220 "output_type": "execute_result"
229 221 },
230 222 {
231   - "name": "stderr",
232   - "output_type": "stream",
233   - "text": [
234   - "/Users/ltibaldo/anaconda3/envs/ctools/lib/python3.5/site-packages/matplotlib/colors.py:1199: RuntimeWarning: invalid value encountered in power\n",
235   - " np.power(resdat, gamma, resdat)\n"
236   - ]
237   - },
238   - {
239 223 "data": {
240   - "image/png": 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\n",
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\n",
241 225 "text/plain": [
242   - "<matplotlib.figure.Figure at 0x120d52278>"
  226 + "<matplotlib.figure.Figure at 0x1129e1358>"
243 227 ]
244 228 },
245 229 "metadata": {},
... ... @@ -251,7 +235,6 @@
251 235 "plt.imshow(srcdetect._map.array(),origin='lower',\n",
252 236 " extent=[83.63+0.02*200,83.63-0.02*200,22.01-0.02*200,22.01+0.02*200],\n",
253 237 " # boundaries of the coord grid\n",
254   - " norm=colors.PowerNorm(gamma=0.5),\n",
255 238 " cmap=plt.get_cmap('jet')) # square root scale \n",
256 239 "ax1.set_xlabel('R.A. (deg)')\n",
257 240 "ax1.set_ylabel('Dec (deg)')\n",
... ...
doc/source/users/tutorials/hess_dr1/unbinned.ipynb
  View file @fb39a2b
... ... @@ -118,26 +118,26 @@
118 118 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
119 119 " Number of parameters ......: 20\n",
120 120 " Number of spatial par's ...: 3\n",
121   - " 1:Sigma ..................: 3.19679118852325 +/- 0.148784221714502 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
122   - " 2:Grad_DETX ..............: 0.0882131070561384 +/- 0.0223563997131535 deg^-1 (free,scale=1,gradient)\n",
123   - " 2:Grad_DETY ..............: -0.000692481271743188 +/- 0.0227254581791992 deg^-1 (free,scale=1,gradient)\n",
  121 + " 1:Sigma ..................: 3.19679080604377 +/- 0.148784168613255 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  122 + " 2:Grad_DETX ..............: 0.0882131417987659 +/- 0.0223563994341903 deg^-1 (free,scale=1,gradient)\n",
  123 + " 2:Grad_DETY ..............: -0.000692474001567828 +/- 0.0227254580377688 deg^-1 (free,scale=1,gradient)\n",
124 124 " Number of spectral par's ..: 16\n",
125 125 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
126   - " Intensity0 ...............: 0.00149714476600658 +/- 0.000185798282794166 [9.46696076103918e-14,infty[ ph/cm2/s/MeV (free,scale=0.000946696076103918,gradient)\n",
  126 + " Intensity0 ...............: 0.00140372802004064 +/- 0.000174205101284546 [8.87696769787525e-14,infty[ ph/cm2/s/MeV (free,scale=0.000887696769787525,gradient)\n",
127 127 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
128   - " Intensity1 ...............: 0.000275025454049597 +/- 1.50407490107521e-05 [3.11144874586726e-14,infty[ ph/cm2/s/MeV (free,scale=0.000311144874586726,gradient)\n",
  128 + " Intensity1 ...............: 0.000257864797984792 +/- 1.41022572545599e-05 [2.91749105398033e-14,infty[ ph/cm2/s/MeV (free,scale=0.000291749105398033,gradient)\n",
129 129 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
130   - " Intensity2 ...............: 9.68931866899334e-05 +/- 5.86314382972939e-06 [1.02262104412654e-14,infty[ ph/cm2/s/MeV (free,scale=0.000102262104412654,gradient)\n",
  130 + " Intensity2 ...............: 9.08473780602549e-05 +/- 5.49730339459174e-06 [9.58858288072014e-15,infty[ ph/cm2/s/MeV (free,scale=9.58858288072014e-05,gradient)\n",
131 131 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
132   - " Intensity3 ...............: 3.42414862721453e-05 +/- 2.61283857499118e-06 [3.36098674702412e-15,infty[ ph/cm2/s/MeV (free,scale=3.36098674702412e-05,gradient)\n",
  132 + " Intensity3 ...............: 3.21049333853292e-05 +/- 2.44980628805133e-06 [3.15136944584645e-15,infty[ ph/cm2/s/MeV (free,scale=3.15136944584645e-05,gradient)\n",
133 133 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
134   - " Intensity4 ...............: 1.2134598847599e-05 +/- 1.19969987797427e-06 [1.10463518999066e-15,infty[ ph/cm2/s/MeV (free,scale=1.10463518999066e-05,gradient)\n",
  134 + " Intensity4 ...............: 1.13774407454204e-05 +/- 1.12484264580111e-06 [1.03572441389469e-15,infty[ ph/cm2/s/MeV (free,scale=1.03572441389469e-05,gradient)\n",
135 135 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
136   - " Intensity5 ...............: 4.80035386464156e-06 +/- 6.14208278785316e-07 [3.63053768077518e-16,infty[ ph/cm2/s/MeV (free,scale=3.63053768077518e-06,gradient)\n",
  136 + " Intensity5 ...............: 4.50082783670747e-06 +/- 5.75883736185608e-07 [3.40399651634425e-16,infty[ ph/cm2/s/MeV (free,scale=3.40399651634425e-06,gradient)\n",
137 137 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
138   - " Intensity6 ...............: 8.5455367005893e-07 +/- 1.75301261074233e-07 [1.19322686539072e-16,infty[ ph/cm2/s/MeV (free,scale=1.19322686539072e-06,gradient)\n",
  138 + " Intensity6 ...............: 8.01232298563249e-07 +/- 1.64363032163644e-07 [1.11875245266372e-16,infty[ ph/cm2/s/MeV (free,scale=1.11875245266372e-06,gradient)\n",
139 139 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
140   - " Intensity7 ...............: 2.8110106615004e-07 +/- 1.02501352381156e-07 [3.92170658310361e-17,infty[ ph/cm2/s/MeV (free,scale=3.92170658310361e-07,gradient)\n",
  140 + " Intensity7 ...............: 2.63561241594543e-07 +/- 9.61055896032478e-08 [3.67687523865408e-17,infty[ ph/cm2/s/MeV (free,scale=3.67687523865408e-07,gradient)\n",
141 141 " Number of temporal par's ..: 1\n",
142 142 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
143 143 "=== GCTAModelBackground ===\n",
... ... @@ -149,26 +149,26 @@
149 149 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
150 150 " Number of parameters ......: 20\n",
151 151 " Number of spatial par's ...: 3\n",
152   - " 1:Sigma ..................: 3.30082205953218 +/- 0.156912956620767 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
153   - " 2:Grad_DETX ..............: 0.0153115422362867 +/- 0.0215680816698383 deg^-1 (free,scale=1,gradient)\n",
154   - " 2:Grad_DETY ..............: -0.00906905077562078 +/- 0.0213184759257543 deg^-1 (free,scale=1,gradient)\n",
  152 + " 1:Sigma ..................: 3.30082209234815 +/- 0.156912961568416 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  153 + " 2:Grad_DETX ..............: 0.0153115021206004 +/- 0.0215680817043072 deg^-1 (free,scale=1,gradient)\n",
  154 + " 2:Grad_DETY ..............: -0.00906905504246978 +/- 0.0213184758960865 deg^-1 (free,scale=1,gradient)\n",
155 155 " Number of spectral par's ..: 16\n",
156 156 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
157   - " Intensity0 ...............: 0.00115207913907334 +/- 6.11872558313686e-05 [8.950257713407e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008950257713407,gradient)\n",
  157 + " Intensity0 ...............: 0.00107656542145505 +/- 5.71767004891641e-05 [8.36211165467046e-14,infty[ ph/cm2/s/MeV (free,scale=0.000836211165467046,gradient)\n",
158 158 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
159   - " Intensity1 ...............: 0.000216074985117796 +/- 1.14346974881101e-05 [2.82228203274059e-14,infty[ ph/cm2/s/MeV (free,scale=0.000282228203274059,gradient)\n",
  159 + " Intensity1 ...............: 0.00020191222774409 +/- 1.06852037575173e-05 [2.63699934530969e-14,infty[ ph/cm2/s/MeV (free,scale=0.000263699934530969,gradient)\n",
160 160 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
161   - " Intensity2 ...............: 9.80980717622882e-05 +/- 6.14243331439364e-06 [8.8994933189452e-15,infty[ ph/cm2/s/MeV (free,scale=8.8994933189452e-05,gradient)\n",
  161 + " Intensity2 ...............: 9.16681754173992e-05 +/- 5.73982387509952e-06 [8.31580088180217e-15,infty[ ph/cm2/s/MeV (free,scale=8.31580088180217e-05,gradient)\n",
162 162 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
163   - " Intensity3 ...............: 2.64302082334315e-05 +/- 2.27978082923659e-06 [2.80627451173057e-15,infty[ ph/cm2/s/MeV (free,scale=2.80627451173057e-05,gradient)\n",
  163 + " Intensity3 ...............: 2.4697825754506e-05 +/- 2.13035134581624e-06 [2.62239520190935e-15,infty[ ph/cm2/s/MeV (free,scale=2.62239520190935e-05,gradient)\n",
164 164 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
165   - " Intensity4 ...............: 1.03353701064884e-05 +/- 1.13230244143927e-06 [8.84901685180661e-16,infty[ ph/cm2/s/MeV (free,scale=8.84901685180661e-06,gradient)\n",
  165 + " Intensity4 ...............: 9.65793108295259e-06 +/- 1.0580848901101e-06 [8.26974658573933e-16,infty[ ph/cm2/s/MeV (free,scale=8.26974658573933e-06,gradient)\n",
166 166 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
167   - " Intensity5 ...............: 3.57899467682822e-06 +/- 5.03727665165389e-07 [2.79035778275547e-16,infty[ ph/cm2/s/MeV (free,scale=2.79035778275547e-06,gradient)\n",
  167 + " Intensity5 ...............: 3.34440698323729e-06 +/- 4.70710485211692e-07 [2.60787193869763e-16,infty[ ph/cm2/s/MeV (free,scale=2.60787193869763e-06,gradient)\n",
168 168 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
169   - " Intensity6 ...............: 5.95101204005172e-07 +/- 1.41251129058325e-07 [8.79882667891458e-17,infty[ ph/cm2/s/MeV (free,scale=8.79882667891458e-07,gradient)\n",
  169 + " Intensity6 ...............: 5.56094646218329e-07 +/- 1.31992669655457e-07 [8.22394734607041e-17,infty[ ph/cm2/s/MeV (free,scale=8.22394734607041e-07,gradient)\n",
170 170 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
171   - " Intensity7 ...............: 2.81714577113613e-07 +/- 1.0384120888285e-07 [2.77453133085778e-17,infty[ ph/cm2/s/MeV (free,scale=2.77453133085778e-07,gradient)\n",
  171 + " Intensity7 ...............: 2.63249520342286e-07 +/- 9.70349092695409e-08 [2.59342910774655e-17,infty[ ph/cm2/s/MeV (free,scale=2.59342910774655e-07,gradient)\n",
172 172 " Number of temporal par's ..: 1\n",
173 173 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
174 174 "=== GCTAModelBackground ===\n",
... ... @@ -180,26 +180,26 @@
180 180 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
181 181 " Number of parameters ......: 20\n",
182 182 " Number of spatial par's ...: 3\n",
183   - " 1:Sigma ..................: 4.32704862685447 +/- 0.368685355107103 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
184   - " 2:Grad_DETX ..............: 0.018772378083067 +/- 0.0217106872366865 deg^-1 (free,scale=1,gradient)\n",
185   - " 2:Grad_DETY ..............: -0.126627961668458 +/- 0.0204738085196433 deg^-1 (free,scale=1,gradient)\n",
  183 + " 1:Sigma ..................: 4.32704664084324 +/- 0.368684845846136 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  184 + " 2:Grad_DETX ..............: 0.0187722831939023 +/- 0.0217106880517745 deg^-1 (free,scale=1,gradient)\n",
  185 + " 2:Grad_DETY ..............: -0.126627824474372 +/- 0.0204738099128812 deg^-1 (free,scale=1,gradient)\n",
186 186 " Number of spectral par's ..: 16\n",
187 187 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
188   - " Intensity0 ...............: 0.000915773345685034 +/- 4.89989530347851e-05 [7.43957604504927e-14,infty[ ph/cm2/s/MeV (free,scale=0.000743957604504927,gradient)\n",
  188 + " Intensity0 ...............: 0.000857178811233776 +/- 4.58638205127566e-05 [6.96233895968756e-14,infty[ ph/cm2/s/MeV (free,scale=0.000696233895968756,gradient)\n",
189 189 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
190   - " Intensity1 ...............: 0.000197398656687683 +/- 1.07759903733823e-05 [2.35277778278335e-14,infty[ ph/cm2/s/MeV (free,scale=0.000235277778278335,gradient)\n",
  190 + " Intensity1 ...............: 0.000184768403022439 +/- 1.008650496985e-05 [2.20204639078551e-14,infty[ ph/cm2/s/MeV (free,scale=0.000220204639078551,gradient)\n",
191 191 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
192   - " Intensity2 ...............: 6.93704083542102e-05 +/- 4.77505196699795e-06 [7.44069724086306e-15,infty[ ph/cm2/s/MeV (free,scale=7.44069724086306e-05,gradient)\n",
  192 + " Intensity2 ...............: 6.49318372405173e-05 +/- 4.46952677363829e-06 [6.96462544447719e-15,infty[ ph/cm2/s/MeV (free,scale=6.96462544447719e-05,gradient)\n",
193 193 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
194   - " Intensity3 ...............: 2.57663537500693e-05 +/- 2.20136814658691e-06 [2.35313236274662e-15,infty[ ph/cm2/s/MeV (free,scale=2.35313236274662e-05,gradient)\n",
  194 + " Intensity3 ...............: 2.41177375305046e-05 +/- 2.06051735828825e-06 [2.20276955948036e-15,infty[ ph/cm2/s/MeV (free,scale=2.20276955948036e-05,gradient)\n",
195 195 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
196   - " Intensity4 ...............: 6.82483279973139e-06 +/- 7.86680350899204e-07 [7.44181860564889e-16,infty[ ph/cm2/s/MeV (free,scale=7.44181860564889e-06,gradient)\n",
  196 + " Intensity4 ...............: 6.388150904145e-06 +/- 7.36345188624795e-07 [6.96691268016576e-16,infty[ ph/cm2/s/MeV (free,scale=6.96691268016576e-06,gradient)\n",
197 197 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
198   - " Intensity5 ...............: 3.3292540797357e-06 +/- 4.68826132796518e-07 [2.35348699614757e-16,infty[ ph/cm2/s/MeV (free,scale=2.35348699614757e-06,gradient)\n",
  198 + " Intensity5 ...............: 3.11624385438516e-06 +/- 4.38829996211514e-07 [2.20349296566929e-16,infty[ ph/cm2/s/MeV (free,scale=2.20349296566929e-06,gradient)\n",
199 199 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
200   - " Intensity6 ...............: 8.19437245391855e-07 +/- 1.82363529352901e-07 [7.44294013943215e-17,infty[ ph/cm2/s/MeV (free,scale=7.44294013943215e-07,gradient)\n",
  200 + " Intensity6 ...............: 7.66998023710854e-07 +/- 1.70693323247103e-07 [6.9692006669998e-17,infty[ ph/cm2/s/MeV (free,scale=6.9692006669998e-07,gradient)\n",
201 201 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
202   - " Intensity7 ...............: 1.44457348642334e-07 +/- 7.0968299684809e-08 [2.35384168299424e-17,infty[ ph/cm2/s/MeV (free,scale=2.35384168299424e-07,gradient)\n",
  202 + " Intensity7 ...............: 1.35218346687107e-07 +/- 6.64294080047906e-08 [2.20421660943029e-17,infty[ ph/cm2/s/MeV (free,scale=2.20421660943029e-07,gradient)\n",
203 203 " Number of temporal par's ..: 1\n",
204 204 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n",
205 205 "=== GCTAModelBackground ===\n",
... ... @@ -211,26 +211,26 @@
211 211 " Model components ..........: \"Multiplicative\" * \"NodeFunction\" * \"Constant\"\n",
212 212 " Number of parameters ......: 20\n",
213 213 " Number of spatial par's ...: 3\n",
214   - " 1:Sigma ..................: 3.6479543205698 +/- 0.238749563358304 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
215   - " 2:Grad_DETX ..............: 0.0290086310507671 +/- 0.0231654281693484 deg^-1 (free,scale=1,gradient)\n",
216   - " 2:Grad_DETY ..............: 0.165462911909218 +/- 0.0212932684624286 deg^-1 (free,scale=1,gradient)\n",
  214 + " 1:Sigma ..................: 3.64795431824073 +/- 0.238749562945919 [7.71728e-08,infty[ deg2 (free,scale=1,gradient)\n",
  215 + " 2:Grad_DETX ..............: 0.0290086296998991 +/- 0.0231654281500121 deg^-1 (free,scale=1,gradient)\n",
  216 + " 2:Grad_DETY ..............: 0.165462915333049 +/- 0.021293268413236 deg^-1 (free,scale=1,gradient)\n",
217 217 " Number of spectral par's ..: 16\n",
218 218 " Energy0 ..................: 700000 [7e-05,infty[ MeV (fixed,scale=700000)\n",
219   - " Intensity0 ...............: 0.00147439433272687 +/- 0.000191693450881709 [8.6655214667889e-14,infty[ ph/cm2/s/MeV (free,scale=0.00086655214667889,gradient)\n",
  219 + " Intensity0 ...............: 0.00138280405680456 +/- 0.000179785336701836 [8.127607193775e-14,infty[ ph/cm2/s/MeV (free,scale=0.0008127607193775,gradient)\n",
220 220 " Energy1 ..................: 1197413.67463685 [0.000119741367463685,infty[ MeV (fixed,scale=1197413.67463685)\n",
221   - " Intensity1 ...............: 0.000235250773930648 +/- 1.37598369145017e-05 [2.81551973223247e-14,infty[ ph/cm2/s/MeV (free,scale=0.000281551973223247,gradient)\n",
  221 + " Intensity1 ...............: 0.000220636852715846 +/- 1.29050674731524e-05 [2.64069813465664e-14,infty[ ph/cm2/s/MeV (free,scale=0.000264069813465664,gradient)\n",
222 222 " Energy2 ..................: 2048285.01172474 [0.000204828501172474,infty[ MeV (fixed,scale=2048285.01172474)\n",
223   - " Intensity2 ...............: 9.20623251759662e-05 +/- 5.75965738880592e-06 [9.1479219028787e-15,infty[ ph/cm2/s/MeV (free,scale=9.1479219028787e-05,gradient)\n",
  223 + " Intensity2 ...............: 8.63433571877371e-05 +/- 5.40186394640027e-06 [8.57975351431837e-15,infty[ ph/cm2/s/MeV (free,scale=8.57975351431837e-05,gradient)\n",
224 224 " Energy3 ..................: 3503777.83227556 [0.000350377783227556,infty[ MeV (fixed,scale=3503777.83227556)\n",
225   - " Intensity3 ...............: 3.22022945215284e-05 +/- 2.53414761162924e-06 [2.97225674475431e-15,infty[ ph/cm2/s/MeV (free,scale=2.97225674475431e-05,gradient)\n",
  225 + " Intensity3 ...............: 3.02018682595367e-05 +/- 2.37672480959384e-06 [2.78760261918503e-15,infty[ ph/cm2/s/MeV (free,scale=2.78760261918503e-05,gradient)\n",
226 226 " Energy4 ..................: 5993530.69893743 [0.000599353069893743,infty[ MeV (fixed,scale=5993530.69893743)\n",
227   - " Intensity4 ...............: 1.00701723302328e-05 +/- 1.04797408736245e-06 [9.65717706221071e-16,infty[ ph/cm2/s/MeV (free,scale=9.65717706221071e-06,gradient)\n",
  227 + " Intensity4 ...............: 9.44460701196062e-06 +/- 9.82873290475423e-07 [9.05705315370543e-16,infty[ ph/cm2/s/MeV (free,scale=9.05705315370543e-06,gradient)\n",
228 228 " Energy5 ..................: 10252479.454662 [0.0010252479454662,infty[ MeV (fixed,scale=10252479.454662)\n",
229   - " Intensity5 ...............: 3.29354066928594e-06 +/- 4.58158358563221e-07 [3.1377191413724e-16,infty[ ph/cm2/s/MeV (free,scale=3.1377191413724e-06,gradient)\n",
  229 + " Intensity5 ...............: 3.08894388117525e-06 +/- 4.2969727730712e-07 [2.94267953633316e-16,infty[ ph/cm2/s/MeV (free,scale=2.94267953633316e-06,gradient)\n",
230 230 " Energy6 ..................: 17537798.7113509 [0.00175377987113509,infty[ MeV (fixed,scale=17537798.7113509)\n",
231   - " Intensity6 ...............: 8.71669675678694e-07 +/- 1.76346292129154e-07 [1.01947819188903e-16,infty[ ph/cm2/s/MeV (free,scale=1.01947819188903e-06,gradient)\n",
  231 + " Intensity6 ...............: 8.17521009765721e-07 +/- 1.65391550069682e-07 [9.5609054143745e-17,infty[ ph/cm2/s/MeV (free,scale=9.5609054143745e-07,gradient)\n",
232 232 " Energy7 ..................: 30000000 [0.003,infty[ MeV (fixed,scale=30000000)\n",
233   - " Intensity7 ...............: 3.12067435753287e-07 +/- 1.11366909796179e-07 [3.31239265501224e-17,infty[ ph/cm2/s/MeV (free,scale=3.31239265501224e-07,gradient)\n",
  233 + " Intensity7 ...............: 2.92681614932954e-07 +/- 1.0444872894399e-07 [3.10638352610158e-17,infty[ ph/cm2/s/MeV (free,scale=3.10638352610158e-07,gradient)\n",
234 234 " Number of temporal par's ..: 1\n",
235 235 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
236 236 ]
... ... @@ -340,11 +340,11 @@
340 340 " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
341 341 " Number of parameters ......: 6\n",
342 342 " Number of spatial par's ...: 2\n",
343   - " RA .......................: 83.6230528971337 +/- 0.00239926913139826 deg (free,scale=1)\n",
344   - " DEC ......................: 22.0247811584824 +/- 0.0021892515628272 deg (free,scale=1)\n",
  343 + " RA .......................: 83.6230529061662 +/- 0.00239926887672786 deg (free,scale=1)\n",
  344 + " DEC ......................: 22.0247811570052 +/- 0.00218925167816472 deg (free,scale=1)\n",
345 345 " Number of spectral par's ..: 3\n",
346   - " Prefactor ................: 4.89251809524732e-17 +/- 2.67898412101055e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
347   - " Index ....................: -2.70266617269294 +/- 0.0657789822882349 [10,-10] (free,scale=-2.48,gradient)\n",
  346 + " Prefactor ................: 4.8925181519509e-17 +/- 2.67898412185662e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
  347 + " Index ....................: -2.70266618180016 +/- 0.0657789826195137 [10,-10] (free,scale=-2.48,gradient)\n",
348 348 " PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
349 349 " Number of temporal par's ..: 1\n",
350 350 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
... ... @@ -390,7 +390,7 @@
390 390 "output_type": "stream",
391 391 "text": [
392 392 "=== GOptimizerLM ===\n",
393   - " Optimized function value ..: 98197.286\n",
  393 + " Optimized function value ..: 98201.032\n",
394 394 " Absolute precision ........: 0.005\n",
395 395 " Acceptable value decrease .: 2\n",
396 396 " Optimization status .......: converged\n",
... ... @@ -407,11 +407,11 @@
407 407 " Model components ..........: \"PointSource\" * \"PowerLaw\" * \"Constant\"\n",
408 408 " Number of parameters ......: 6\n",
409 409 " Number of spatial par's ...: 2\n",
410   - " RA .......................: 83.6216748574258 +/- 0.00272066304506809 deg (free,scale=1)\n",
411   - " DEC ......................: 22.0244889319664 +/- 0.0024860621013803 deg (free,scale=1)\n",
  410 + " RA .......................: 83.6217041522328 +/- 0.00261598773627659 deg (free,scale=1)\n",
  411 + " DEC ......................: 22.0245022862116 +/- 0.00239693484099142 deg (free,scale=1)\n",
412 412 " Number of spectral par's ..: 3\n",
413   - " Prefactor ................: 4.14814740728779e-17 +/- 2.00490449017781e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
414   - " Index ....................: -2.73454025222651 +/- 0.0699765314511747 [10,-10] (free,scale=-2.48,gradient)\n",
  413 + " Prefactor ................: 4.64521936275237e-17 +/- 2.43165266024221e-18 [0,infty[ ph/cm2/s/MeV (free,scale=5.7e-18,gradient)\n",
  414 + " Index ....................: -2.68313444140762 +/- 0.0679406521827419 [10,-10] (free,scale=-2.48,gradient)\n",
415 415 " PivotEnergy ..............: 999999.999999999 MeV (fixed,scale=300000,gradient)\n",
416 416 " Number of temporal par's ..: 1\n",
417 417 " Normalization ............: 1 (relative value) (fixed,scale=1,gradient)\n"
... ... @@ -441,9 +441,9 @@
441 441 "outputs": [
442 442 {
443 443 "data": {
444   - "image/png": 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\n",
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\n",
445 445 "text/plain": [
446   - "<matplotlib.figure.Figure at 0x10fee3c88>"
  446 + "<matplotlib.figure.Figure at 0x1105ee240>"
447 447 ]
448 448 },
449 449 "metadata": {},
... ... @@ -520,9 +520,9 @@
520 520 "outputs": [
521 521 {
522 522 "data": {
523   - "image/png": 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\n",
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\n",
524 524 "text/plain": [
525   - "<matplotlib.figure.Figure at 0x103e6f978>"
  525 + "<matplotlib.figure.Figure at 0x118483f60>"
526 526 ]
527 527 },
528 528 "metadata": {},
... ...