From 3979d63b0c9b49512915d685fa4bb746fd3fefb0 Mon Sep 17 00:00:00 2001 From: "Christopher M. Karwin" Date: Tue, 12 Nov 2024 09:07:56 -0500 Subject: [PATCH 01/12] Now passing source coord to scattmap mathod in COSILike. --- cosipy/threeml/COSILike.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/cosipy/threeml/COSILike.py b/cosipy/threeml/COSILike.py index 5be8cadd..7aecd932 100644 --- a/cosipy/threeml/COSILike.py +++ b/cosipy/threeml/COSILike.py @@ -209,7 +209,7 @@ def set_model(self, model): dwell_time_map = self._get_dwell_time_map(coord) self._psr[name] = self._dr.get_point_source_response(exposure_map=dwell_time_map) elif self._coordsys == 'galactic': - scatt_map = self._get_scatt_map() + scatt_map = self._get_scatt_map(coord) self._psr[name] = self._dr.get_point_source_response(coord=coord, scatt_map=scatt_map) else: raise RuntimeError("Unknown coordinate system") @@ -340,16 +340,21 @@ def _get_dwell_time_map(self, coord): return dwell_time_map - def _get_scatt_map(self): + def _get_scatt_map(self, coord): """ Get the spacecraft attitude map of the source in the inertial (spacecraft) frame. + Parameters + ---------- + coord : astropy.coordinates.SkyCoord + The coordinates of the target object. + Returns ------- scatt_map : cosipy.spacecraftfile.scatt_map.SpacecraftAttitudeMap """ - scatt_map = self._sc_orientation.get_scatt_map(nside = self._dr.nside * 2, coordsys = 'galactic') + scatt_map = self._sc_orientation.get_scatt_map(coord, nside = self._dr.nside * 2, coordsys = 'galactic') return scatt_map From 8c70de41edd8a939dcefb07686fd49c5ab03c55a Mon Sep 17 00:00:00 2001 From: "Christopher M. Karwin" Date: Tue, 12 Nov 2024 10:09:20 -0500 Subject: [PATCH 02/12] added Earth occultation to likelihood fit --- cosipy/spacecraftfile/SpacecraftFile.py | 9 +++++++-- cosipy/threeml/COSILike.py | 6 ++++-- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/cosipy/spacecraftfile/SpacecraftFile.py b/cosipy/spacecraftfile/SpacecraftFile.py index 4615f149..b40a4887 100644 --- a/cosipy/spacecraftfile/SpacecraftFile.py +++ b/cosipy/spacecraftfile/SpacecraftFile.py @@ -518,6 +518,7 @@ def get_scatt_map(self, scheme = 'ring', coordsys = 'galactic', r_earth = 6378.0, + earth_occ = True ): """ @@ -537,7 +538,10 @@ def get_scatt_map(self, The coordinate system used in the scatt map (the default is "galactic). r_earth : float, optional Earth radius in km (default is 6378 km). - + earth_occ : bool, optional + Option to include Earth occultation in scatt map calculation. + Default is True. + Returns ------- h_ori : cosipy.spacecraftfile.scatt_map.SpacecraftAttitudeMap @@ -574,7 +578,8 @@ def get_scatt_map(self, # Define weights and set to 0 if blocked by Earth: weight = np.diff(timestamps.gps)*u.s - weight[earth_occ_index[:-1]] = 0 + if earth_occ == True: + weight[earth_occ_index[:-1]] = 0 # Fill histogram: h_ori.fill(x, y, weight = weight) diff --git a/cosipy/threeml/COSILike.py b/cosipy/threeml/COSILike.py index 7aecd932..b270a6ea 100644 --- a/cosipy/threeml/COSILike.py +++ b/cosipy/threeml/COSILike.py @@ -62,7 +62,7 @@ class COSILike(PluginPrototype): Full path to precomputed point source response in Galactic coordinates """ def __init__(self, name, dr, data, bkg, sc_orientation, - nuisance_param=None, coordsys=None, precomputed_psr_file=None, **kwargs): + nuisance_param=None, coordsys=None, precomputed_psr_file=None, earth_occ=True, **kwargs): # create the hash for the nuisance parameters. We have none for now. self._nuisance_parameters = collections.OrderedDict() @@ -77,6 +77,7 @@ def __init__(self, name, dr, data, bkg, sc_orientation, self._data = data self._bkg = bkg self._sc_orientation = sc_orientation + self.earth_occ = earth_occ try: if data.axes["PsiChi"].coordsys.name != bkg.axes["PsiChi"].coordsys.name: @@ -354,7 +355,8 @@ def _get_scatt_map(self, coord): scatt_map : cosipy.spacecraftfile.scatt_map.SpacecraftAttitudeMap """ - scatt_map = self._sc_orientation.get_scatt_map(coord, nside = self._dr.nside * 2, coordsys = 'galactic') + scatt_map = self._sc_orientation.get_scatt_map(coord, nside = self._dr.nside * 2, \ + coordsys = 'galactic', earth_occ = self.earth_occ) return scatt_map From ebdbfae0aa71905eb382c7eb3bb5a6e0b8190ae7 Mon Sep 17 00:00:00 2001 From: "Christopher M. Karwin" Date: Tue, 12 Nov 2024 11:38:22 -0500 Subject: [PATCH 03/12] fix bug --- cosipy/threeml/COSILike.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/cosipy/threeml/COSILike.py b/cosipy/threeml/COSILike.py index b270a6ea..c659ed39 100644 --- a/cosipy/threeml/COSILike.py +++ b/cosipy/threeml/COSILike.py @@ -60,6 +60,8 @@ class COSILike(PluginPrototype): attached to them precomputed_psr_file : str, optional Full path to precomputed point source response in Galactic coordinates + earth_occ : bool, optional + Option to include Earth occultation in fit (default is True). """ def __init__(self, name, dr, data, bkg, sc_orientation, nuisance_param=None, coordsys=None, precomputed_psr_file=None, earth_occ=True, **kwargs): From 222fff8206a96dbc101d9261ec829a2058061ddc Mon Sep 17 00:00:00 2001 From: "Christopher M. Karwin" Date: Tue, 12 Nov 2024 11:38:58 -0500 Subject: [PATCH 04/12] Updated Crab example notebook --- .../continuum_fit/crab/SpectralFit_Crab.ipynb | 382 ++++++++---------- 1 file changed, 161 insertions(+), 221 deletions(-) diff --git a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb index f4a2f72a..318c8e81 100644 --- a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb +++ b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb @@ -16,7 +16,7 @@ "metadata": {}, "source": [ "**To run this, you need the following files, which can be downloaded using the first few cells of this notebook:**\n", - "- orientation file (20280301_3_month.ori) \n", + "- orientation file (20280301_3_month_with_orbital_info.ori) \n", "- binned data (crab_bkg_binned_data.hdf5, crab_binned_data.hdf5, & bkg_binned_data.hdf5) \n", "- detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip) \n", "\n", @@ -71,12 +71,12 @@ { "data": { "text/html": [ - "
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        "                  available                                                                                        \n",
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+       "\u001b[38;5;46m11:14:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=257162;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=721248;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -328,12 +278,12 @@
     {
      "data": {
       "text/html": [
-       "
12:03:41 WARNING   Env. variable OMP_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
+       "
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        "                  performances in 3ML                                                                              \n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m12:03:41\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=828564;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=463928;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m11:14:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=712228;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=491076;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -343,12 +293,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
+       "
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        "                  performances in 3ML                                                                              \n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=215262;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=583062;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=575441;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=258749;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -358,12 +308,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal     __init__.py:387\n",
+       "
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        "                  performances in 3ML                                                                              \n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=752545;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=955651;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=898179;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=76702;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -391,7 +341,9 @@
     "\n",
     "from pathlib import Path\n",
     "\n",
-    "import os"
+    "import os\n",
+    "\n",
+    "%matplotlib inline"
    ]
   },
   {
@@ -417,7 +369,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_path = Path(\"/path/to/files\")"
+    "#data_path = Path(\"/path/to/files\")\n",
+    "data_path = Path(\"/discover/nobackup/ckarwin/COSI/COSIpy_Development/Crab_Notebook\")"
    ]
   },
   {
@@ -430,12 +383,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "36f96db4-640d-4233-8b18-a81bafcfd009",
    "metadata": {},
    "outputs": [],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month.ori', output=str(data_path / '20280301_3_month.ori'))"
+    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output=str(data_path / '20280301_3_month_modified.ori'))"
    ]
   },
   {
@@ -525,7 +478,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sc_orientation = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month.ori\")"
+    "sc_orientation = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month_with_orbital_info.ori\")"
    ]
   },
   {
@@ -623,7 +576,8 @@
     "                 data = crab_bkg.binned_data.project('Em', 'Phi', 'PsiChi'),   # data (source+background)\n",
     "                 bkg = bkg.binned_data.project('Em', 'Phi', 'PsiChi'),         # background model \n",
     "                 sc_orientation = sc_orientation,                              # spacecraft orientation\n",
-    "                 nuisance_param = bkg_par)                                     # background parameter"
+    "                 nuisance_param = bkg_par,                                     # background parameter\n",
+    "                 earth_occ = True)                                             # Option to account for Earth occultation"
    ]
   },
   {
@@ -643,39 +597,15 @@
     {
      "data": {
       "text/html": [
-       "
12:04:35 WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:794\n",
+       "
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        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m12:04:35\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=444891;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=999475;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m11:20:31\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=539156;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=631064;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "... Calculating point source responses ...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "WARNING ErfaWarning: ERFA function \"utctai\" yielded 7979956 of \"dubious year (Note 3)\"\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--> done (source name : source)\n",
-      "--> all done\n"
-     ]
     }
    ],
    "source": [
@@ -746,11 +676,11 @@
     {
      "data": {
       "text/html": [
-       "
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+       "
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        "
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       ],
       "text/plain": [
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+       "\u001b[38;5;46m11:22:07\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=375798;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=502880;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1045\u001b\\\u001b[2m1045\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -765,18 +695,28 @@
       "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
       "  the requested tolerance from being achieved.  The error may be \n",
       "  underestimated.\n",
+      "\n",
+      "\n",
+      "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
+      "  the requested tolerance from being achieved.  The error may be \n",
+      "  underestimated.\n",
+      "\n",
+      "\n",
+      "WARNING IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
+      "  the requested tolerance from being achieved.  The error may be \n",
+      "  underestimated.\n",
       "\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "
12:05:26 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "
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        "                  measurements such as AIC or BIC are unreliable                                                   \n",
        "
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       ],
       "text/plain": [
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+       "\u001b[38;5;46m11:23:04\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=430167;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=282327;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\u001b\\\u001b[2m130\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                       \u001b[0m\n"
       ]
      },
@@ -786,11 +726,11 @@
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        "
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@@ -844,27 +784,27 @@
        "  \n",
        "    \n",
        "      source.spectrum.main.Band.K\n",
-       "      (2.857 +/- 0.023) x 10^-5\n",
-       "      1 / (cm2 keV s)\n",
+       "      (2.831 +/- 0.024) x 10^-5\n",
+       "      1 / (keV s cm2)\n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.alpha\n",
-       "      -1.9886 +/- 0.0004\n",
+       "      -1.9884 +/- 0.0005\n",
        "      \n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.xp\n",
-       "      4.47 -0.17 +0.18\n",
+       "      4.39 -0.20 +0.21\n",
        "      keV\n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.beta\n",
-       "      -2.1964 +/- 0.0016\n",
+       "      -2.1674 +/- 0.0016\n",
        "      \n",
        "    \n",
        "    \n",
        "      background_cosi\n",
-       "      (9.9193 +/- 0.0020) x 10^-1\n",
+       "      (9.9414 +/- 0.0019) x 10^-1\n",
        "      \n",
        "    \n",
        "  \n",
@@ -874,11 +814,11 @@
       "text/plain": [
        "                                                      result             unit\n",
        "parameter                                                                    \n",
-       "source.spectrum.main.Band.K        (2.857 +/- 0.023) x 10^-5  1 / (cm2 keV s)\n",
-       "source.spectrum.main.Band.alpha           -1.9886 +/- 0.0004                 \n",
-       "source.spectrum.main.Band.xp                4.47 -0.17 +0.18              keV\n",
-       "source.spectrum.main.Band.beta            -2.1964 +/- 0.0016                 \n",
-       "background_cosi                  (9.9193 +/- 0.0020) x 10^-1                 "
+       "source.spectrum.main.Band.K        (2.831 +/- 0.024) x 10^-5  1 / (keV s cm2)\n",
+       "source.spectrum.main.Band.alpha           -1.9884 +/- 0.0005                 \n",
+       "source.spectrum.main.Band.xp                4.39 -0.20 +0.21              keV\n",
+       "source.spectrum.main.Band.beta            -2.1674 +/- 0.0016                 \n",
+       "background_cosi                  (9.9414 +/- 0.0019) x 10^-1                 "
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@@ -967,11 +907,11 @@
        "  \n",
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        "      cosi\n",
-       "      -2.612135e+08\n",
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        "    \n",
        "    \n",
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        "    \n",
        "  \n",
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@@ -979,8 +919,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi      -2.612135e+08\n",
-       "total     -2.612135e+08"
+       "cosi      -2.612141e+08\n",
+       "total     -2.612141e+08"
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      "metadata": {},
@@ -1030,11 +970,11 @@
        "  \n",
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        "      AIC\n",
-       "      -5.224270e+08\n",
+       "      -5.224283e+08\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      -5.224270e+08\n",
+       "      -5.224283e+08\n",
        "    \n",
        "  \n",
        "\n",
@@ -1042,8 +982,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC         -5.224270e+08\n",
-       "BIC         -5.224270e+08"
+       "AIC         -5.224283e+08\n",
+       "BIC         -5.224283e+08"
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      "metadata": {},
@@ -1053,21 +993,21 @@
      "data": {
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        "(                                    value  negative_error  positive_error  \\\n",
-       " source.spectrum.main.Band.K      0.000029   -2.272835e-07    2.278935e-07   \n",
-       " source.spectrum.main.Band.alpha -1.988617   -3.560944e-04    3.585775e-04   \n",
-       " source.spectrum.main.Band.xp     4.473464   -1.691279e-01    1.784522e-01   \n",
-       " source.spectrum.main.Band.beta  -2.196416   -1.564268e-03    1.565798e-03   \n",
-       " background_cosi                  0.991932   -1.915462e-04    1.937451e-04   \n",
+       " source.spectrum.main.Band.K      0.000028   -2.423793e-07    2.397817e-07   \n",
+       " source.spectrum.main.Band.alpha -1.988386   -5.021034e-04    4.499167e-04   \n",
+       " source.spectrum.main.Band.xp     4.385546   -2.065864e-01    2.077363e-01   \n",
+       " source.spectrum.main.Band.beta  -2.167420   -1.558193e-03    1.610435e-03   \n",
+       " background_cosi                  0.994138   -1.948180e-04    1.932436e-04   \n",
        " \n",
        "                                         error             unit  \n",
-       " source.spectrum.main.Band.K      2.275885e-07  1 / (cm2 keV s)  \n",
-       " source.spectrum.main.Band.alpha  3.573359e-04                   \n",
-       " source.spectrum.main.Band.xp     1.737900e-01              keV  \n",
-       " source.spectrum.main.Band.beta   1.565033e-03                   \n",
-       " background_cosi                  1.926456e-04                   ,\n",
+       " source.spectrum.main.Band.K      2.410805e-07  1 / (keV s cm2)  \n",
+       " source.spectrum.main.Band.alpha  4.760100e-04                   \n",
+       " source.spectrum.main.Band.xp     2.071613e-01              keV  \n",
+       " source.spectrum.main.Band.beta   1.584314e-03                   \n",
+       " background_cosi                  1.940308e-04                   ,\n",
        "        -log(likelihood)\n",
-       " cosi      -2.612135e+08\n",
-       " total     -2.612135e+08)"
+       " cosi      -2.612141e+08\n",
+       " total     -2.612141e+08)"
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      },
      "execution_count": 10,
@@ -1108,11 +1048,11 @@
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@@ -1211,27 +1151,27 @@
        "  \n",
        "    \n",
        "      source.spectrum.main.Band.K\n",
-       "      (2.857 +/- 0.023) x 10^-5\n",
-       "      1 / (cm2 keV s)\n",
+       "      (2.831 +/- 0.024) x 10^-5\n",
+       "      1 / (keV s cm2)\n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.alpha\n",
-       "      -1.9886 +/- 0.0004\n",
+       "      -1.9884 +/- 0.0005\n",
        "      \n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.xp\n",
-       "      4.47 -0.17 +0.18\n",
+       "      4.39 -0.20 +0.21\n",
        "      keV\n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.beta\n",
-       "      -2.1964 +/- 0.0016\n",
+       "      -2.1674 +/- 0.0016\n",
        "      \n",
        "    \n",
        "    \n",
        "      background_cosi\n",
-       "      (9.9193 +/- 0.0020) x 10^-1\n",
+       "      (9.9414 +/- 0.0019) x 10^-1\n",
        "      \n",
        "    \n",
        "  \n",
@@ -1241,11 +1181,11 @@
       "text/plain": [
        "                                                      result             unit\n",
        "parameter                                                                    \n",
-       "source.spectrum.main.Band.K        (2.857 +/- 0.023) x 10^-5  1 / (cm2 keV s)\n",
-       "source.spectrum.main.Band.alpha           -1.9886 +/- 0.0004                 \n",
-       "source.spectrum.main.Band.xp                4.47 -0.17 +0.18              keV\n",
-       "source.spectrum.main.Band.beta            -2.1964 +/- 0.0016                 \n",
-       "background_cosi                  (9.9193 +/- 0.0020) x 10^-1                 "
+       "source.spectrum.main.Band.K        (2.831 +/- 0.024) x 10^-5  1 / (keV s cm2)\n",
+       "source.spectrum.main.Band.alpha           -1.9884 +/- 0.0005                 \n",
+       "source.spectrum.main.Band.xp                4.39 -0.20 +0.21              keV\n",
+       "source.spectrum.main.Band.beta            -2.1674 +/- 0.0016                 \n",
+       "background_cosi                  (9.9414 +/- 0.0019) x 10^-1                 "
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-       "      -2.612135e+08\n",
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@@ -1346,8 +1286,8 @@
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@@ -1397,11 +1337,11 @@
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-       "      -5.224270e+08\n",
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@@ -1409,8 +1349,8 @@
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-       "BIC         -5.224270e+08"
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@@ -1426,11 +1366,11 @@
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-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=521892;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=431406;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=580430;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=907467;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -1452,11 +1392,11 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:794\n",
+       "
         WARNING   The current value of the parameter beta (-2.0) was above the new maximum -2.15. parameter.py:794\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=402878;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=82481;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The current value of the parameter beta \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;37m-2.0\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m was above the new maximum \u001b[0m\u001b[1;37m-2.15\u001b[0m\u001b[1;38;5;251m.\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=504220;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=99234;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/core/parameter.py#794\u001b\\\u001b[2m794\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -1487,28 +1427,28 @@
       "      * main:\n",
       "        * Band:\n",
       "          * K:\n",
-      "            * value: 2.8565585663971596e-05\n",
+      "            * value: 2.830532377573198e-05\n",
       "            * desc: Differential flux at the pivot energy\n",
-      "            * min_value: 1.0e-99\n",
+      "            * min_value: 1.0e-50\n",
       "            * max_value: null\n",
       "            * unit: keV-1 s-1 cm-2\n",
       "            * is_normalization: true\n",
       "          * alpha:\n",
-      "            * value: -1.9886166208617622\n",
+      "            * value: -1.9883862891924717\n",
       "            * desc: low-energy photon index\n",
       "            * min_value: -2.14\n",
       "            * max_value: 3.0\n",
       "            * unit: ''\n",
       "            * is_normalization: false\n",
       "          * xp:\n",
-      "            * value: 4.473463779563324\n",
+      "            * value: 4.385546491485944\n",
       "            * desc: peak in the x * x * N (nuFnu if x is a energy)\n",
       "            * min_value: 1.0\n",
       "            * max_value: null\n",
       "            * unit: keV\n",
       "            * is_normalization: false\n",
       "          * beta:\n",
-      "            * value: -2.196416422107725\n",
+      "            * value: -2.1674202553986954\n",
       "            * desc: high-energy photon index\n",
       "            * min_value: -5.0\n",
       "            * max_value: -2.15\n",
@@ -1598,7 +1538,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 14,
@@ -1607,7 +1547,7 @@
     },
     {
      "data": {
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\n",
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tLc34ynPnzp3ExcUBMHToUOMPRpMmTejZsyeffvopSUlJ1KtXj7Vr1xITE2P8a0YKPyj/TtqgsKT+xRdfMHLkSN59990iW4KJiEjp2LRpk/Gt1j333MO8efMYNmwYNWvWJDc3l6NHj9KqVSvCwsJo2rQpTZs2xdra2jjGq1q1aiQnJ9/Uva91bosWLQgPD6dr167G+ObPn0+XLl1wdHQkIyODM2fO0LRp02vey8/Pj4KCAjZs2EDfvn3ZtWsXMTExBAYGFln14bIrPS9AaGgoLVu2vIknvrYKk7hB4RRcNzc3fv/9d7Zv346bmxtPPvmk8d18SUaNGkXXrl1LXNy2RYsWfPzxx3h7e1/1vkuXLiUmJsb49bZt24wL+vbr169IRj9r1izc3NxYt24daWlpeHt78+abbxIYGHiDT1u5HDhwgC+++AIorKLOnz/fzBGJiJSOsrDW2uUxbnl5eTRo0ICPP/4YgAcffJDExETjW668vDweffRRWrVqxaxZswgPD8fW1hZHR0fjbkCPPfYYU6dOZf78+bz22msMHDjwuuO41rnDhg3j66+/ZsyYMQDMmDGD7Oxs2rdvb6yWzZgx47oSN1tbW37++WcmTpzI1KlTsbe3Z/ny5VSpUqXExO1Kz2swGAgKCmLmzJnX/Zw3wsKgDSQrjMvLgSxatMj411FFUVBQwBdffMHMmTN58cUXeeqpp8wdkojILcvKyjLujGNvb2/ucMqdgoIC2rVrxy+//IKHh4e5wwFg7dq1fPvtt3z77bcl9t/q/+YVZoybVGyWlpaMGTOG48ePM27cuCJ9l5O6kpZaERGRisvS0pJPPvmEs2fPmjsUo+Tk5FId36fETcqVmjVrFlv08IMPPuC///0vHTp04OjRo2aKTEREzKFNmzZlakvI+++/n7p165ba9ZW4SbmWnJzMc889BxSOgytpHIKIiEhFocRNyrXq1asTFBREkyZNePLJJ+nRo4e5QxIRESk1FWpWqVRObdu25cCBA+Tn5xfr+/333+nTp0+Je8SKiIiUN6q4SYVgZ2eHo6NjkbZNmzYxcOBAOnXqxKlTp8wUmYiIiOkocZMKKT8/n8cffxyAffv28dNPP5k5IhGR8uPyRu9X88ILL/Ddd9/d9D22bNli3JHhRh09ehQvL6+bvnd5plelUiFZWVnxww8/MGrUKBo0aMC0adPMHZKISIXy8ssv39L5W7ZsISkpiQEDBpgoospBFTepsO644w7279/P999/X2R/OcBk+8+KiJjaO++8g4eHBx4eHmzZsqVI35kzZ4x9JS1EPmTIEGP/vy1evJh33nnnhmLp0aMH06ZNo2vXrvj4+PDEE08Y+0aPHs27774LQG5uLjNnzqRdu3YEBgYyYsQILl26BBTO/h8zZgzNmjWjZcuWPProowQHB/Pxxx/z3XffERgYaEwC161bR5cuXWjTpg3t2rUrsj/17NmzadSoEW3atGHJkiU39BwViSpuUqFVrVqVqlWrFmmLiIigY8eOPPfcc4wbN67IJsIiIuaWkpLCuXPnAMjOzi7Sl5+fb+y7nBj9U3x8vLH/39LT00lJSbnheE6dOsXmzZvJzc2lSZMm7N69m44dOxY55u2338bJyYm9e/cCMGfOHJ577jk+/PBDJk+ejIODA4cPH8bS0pL4+HhcXV154oknSEpKMiZ/p0+fZvbs2axbt45q1apx8uRJunbtytmzZ9m4cSM//vgj+/fvp2rVqvzf//3fDT9HRaHETSqVvLw8Ro0axYULF5gwYQLZ2dk8/fTT5g5LRMSoWrVq1KtXDyicePVPVlZWxr4aNWoUO9fV1dXY/29OTk7FEsHrcf/992NtbY21tTWBgYGcOnWqWOL2yy+/kJycbBxPnJOTYxyD9ttvv/HHH38Y33y4urqWeJ+1a9dy8uRJunXrZmyztLQkMjKSoKAgRowYQbVq1QB4/PHH2bFjxw0/S0WgxE0qlfz8fO644w527dqFl5cXjzzyiLlDEhEpYsqUKUyZMqXEvoYNGxIdHX3Fc1etWnXFvtGjR99UPP/cT9PKyoq8vLxixxgMBt5//3369et3U/e4fI2+ffvy/fffX/PYyvymRGPcpFKxs7PjvffeY+XKlSxZsgRnZ2dzhyQiUu7dc889zJ8/n4yMDAAyMjI4duwYUDjubu7cuRQUFACFr3OhsLKYnJxsvEb//v3ZuHEjhw8fNrZdfvXap08ffvzxR1JTUzEYDHz66ae35bnKIiVuck3njiaw94cwUmLTzR2KyQwZMoT27dsXacvKymLmzJlF/iEREZEru1z5mjFjBm3btqV9+/a0aNGCDh06GJcTmT9/PtnZ2TRv3pzAwEBmzZoFwL333ktwcLBxcoKvry/ff/89jz/+OC1btiQgIMA4/m3gwIEMGzaM1q1bc8cdd1C/fn1zPG6ZYGEwGAzmDkJMIzw8nLFjx7Jo0SL8/PxMdt31c/cTeSAOgLrNXAjo7Un9Nm5YWVesvP+JJ57gk08+wdfXl59//pnmzZubOyQRqeCysrI4c+YMDRs2LPJKsjwYOHAgDz74IA8++KC5QylXbvV/c41xk6vKTs/l3JG/N24/fzSR80cTcahuS+PuHvj19KSam+NVrlA+nD9/nqVLlwIQHR1tLOmLiEhxffr0ITs7myFDhpg7lEpHiZtclZ2TDQ8s6MHxbecI3xRFSmzh+IXM5BwOrTrNoVWnqde8Fv69PWnQujaW5bQKV7duXQ4cOMCwYcN46qmnaNmypblDEhEpszZu3GjuECotJW5yTQ7V7Wh5lzctBjXk/LFEwjZFcXZfLIb8wrfs544kcO5IAg7OdjTu7oF/Tw+q1i5/VbiGDRuyZ8+eEjekj4uLo3bt2maISkQqA41aqjxu9X/r8lkeEbOwsLSgXvNa9J7UipEf9KTtA42p6upg7M9MyubQylMsfXora9/4k7N/xlCQV75eOZaUtH3xxRc0btyYX3755fYHJCIVmpWVFVC47plUDpdn3pb0++Z6qOImN8Wxuh0th/jQYrA3544mELYpioj9cYVVOANEH04g+nACjs52NO5ROBbun0leeXH48GEmTJhAVlYW9957LwcPHiQwMNDcYYlIBWFtbY2joyPx8fHY2NgU255PKg6DwUBGRgZxcXE4Ozsbk/YbpcRNbomFpQUeLVzxaOFKxqUsjm89R9jmKNLiC/cCzUjKJviXUwSvPIVHC1cCenvi2coVS6vy8Y9Tw4YNueuuu/jxxx8ZN26ckjYRMSkLCwvc3d05c+YMERER5g5HbgNnZ2fq1Klz0+drOZAKpLSWA7lRhgID544kELopisj9cRgKiv6IOda0w6+HJ349PKhSq+xX4QwGA9999x3Dhw8vtv2MiIgpFBQU6HVpJWBjY3PTlbbLlLhVIGUlcfun9EtZHN8STfjmKNISsor0WViAR6Ar/r088WxVG0vL8rWFyb59+wgKCuKZZ57R6w0REbkt9KpUSpVTDXta3etLy7t9OHc4gbCgSCIPxGEwgMEAUQfjiToYj1NNe/x6euDXwwMnl7Jfhbt48SLDhg0jIiKCnTt38u233xo3PxYRESktStzktrC0tMAz0BXPQFfSL2YRviWK8M3RpCcWVuHSL2Zx4KeTHPz5JJ6tauPf2xOPlq5ltgq3fv16IiMjAUhISCh3K56LiEj5pMRNbjunmva0vq8Rgff4En0onrCgKKIO/l2FizwQR+SBOJxcLlfhPHGqWbYSowceeABnZ2eefvppli5diq2trblDEhGRSkBj3CqQsjjG7XqlJ2YSviW6sAp38V9j4SwtqN/KFf/e9anXolaZqsLl5+cXG2h68eJF7O3tcXQsf4sQi4hI2aaKm5QJTi4OtB7aiMB7fIgKjidsUxTRwfGFVbgCAxH744jYH0eVWg749fSgcQ8PnGqYvwr376QtPz+fBx54gJiYGJYvX07jxo3NFJmIiFREStykTLG0sqRBGzcatHEjLeGvKtyWKDIuZgOQlpDJ/h9PcOCnk9Rv/ddYuOa1sCgjVbjXXnuNDRs2ADB48GBCQ0Nveeq3iIjIZUrcpMyqUsuBNsMa0erev6pwQVFEHYqHy1W4fbFE7IuliqsD/j09adzDA0dn866zNmzYMH744QfCw8P59NNPlbSJiIhJKXGTMu+fVbjU+EzCN0dxfEs0GUl/VeHiM9m37Dj7fzpBgza1C8fCNXUxSxUuICCAvXv3smXLFnr06HHb7y8iIhWbJidUIOV5csKNKsgrIPJgPGGbIok+nAD/+imuWtsB/16eNOrugWP1srHbwZtvvkmbNm3o06ePuUMREZFyShU3KZcsrS3xauuGV1s3UuMyCNsczfEtUWQmF24ZkxqXyZ9LjrP/xxM0uMMN/96e1G1iniocwG+//cbMmTOxsLDgtddeY+bMmWaJQ0REyjclblLuVa3tSNv7G9NmqC8RB+IIC4rk3JFEAAryDZz5I4Yzf8RQzc3xrypcPRyq3d4q3A8//AAU7nuqNd9ERORm6VVpBbBx40Y2btxIWloahw8frhSvSq8lJTadsE3RHN8aTVZK0Y2bLa0s8GpbB//enrg3qYmFRelX4QoKCnjttdc4fPgwS5cuvS33FBGRikeJWwVSmca4Xa/8vAIi9sUSFhTF+WOJxfqruzvh18uTxl3rYV+t9CthBoOhWNJ25swZvLy8lMyJiMg16VWpVGhW1pZ4d3DHu4M7yRfSCdscxYmt0WSl5gKQfCGdvd+FsW9pOA3bFVbh6viXXhXu39c9efIkbdq0YfDgwXzyySdUqVKlVO4rIiIVgxI3qTSquzvRfpQ/dwxvxNk/YwnbFMWFkIsAFOQZOLXrAqd2XaB6XafCsXBd62FftfSqcHl5eQwfPpyUlBS+//576tWrx1tvvVVq9xMRkfJPr0orEL0qvXHJF9IJ2xTF8a3RZKflFumzsrHEq10dAnp74uZXo1SqcMuXL+fRRx+lXr167N27l6pVq5r8HiIiUnGo4iaVWnV3J9o/6E+bf1ThYkILq3D5uQWc2nmeUzvP41yvCv69PPHtWhf7Kqarwg0bNowWLVqQn5+vpE1ERK7J0twBiJQF1rZW+Hauy+Dn2zPs7a40u9MLuyo2xv6kc2ns+SaUHyZsZstHh4gJv4SpitWNGzcmICCgSFtaWhoPP/wwkZGRJrmHiIhUDErcRP7FuV4VOvxfACM/6En38S2o41/D2JefW8DJHef57aU9/DxjB8fWnS32ivVWGQwGnnjiCb7++mtatWrF1q1bTXp9EREpv/SqVOQKrG2taNSlHo261ONSdCphm6I4se0cORl5AFyKTmP3V6Hs/SEc7w7u+PfypHYj51seC5eQkMCuXbsAyM3Nxd3d/ZafRUREKgYlbiLXoYZHVTo+1IS2D/hx5o8YwoKiiD1+CYD8nAJObDvHiW3nqOFZ9a8ZqXWxdbS5xlVL5urqyv79+xk9ejT/93//R+PGjU35KCIiUo5pVmkFolmlt9fFqMIq3Mntf1fhLrOytcSnozv+vevj6lP9pqpwJS3WazAYCA8Px9/f/5ZiFxGR8klj3ERuUk3PqnR6uAmjPuxFtyeaU7uRs7EvP6eA41vPseqF3ayYtZOQDRHkZNzYWLiSkr3333+f5s2bs2DBApNNjhARkfJDFbcKRBU387sYmUpYUCQndpwnN7NoFc7azgrvju4E9PaklveNV+GCg4Np27YteXmF192zZw/t27c3WewiIlL2qeImYkI161el0yNNGfVhT7o+1gxX3+rGvrzsfI5viWbl87v55X+7CA2KJOdfyd3VNGvWjKeffhqAZ555RkmbiEglpIpbBaKKW9mUeDalcCzcznPkZuYX6bOxt8KnU138exVW4a7H5s2b6dq1K9bWmlskIlLZKHGrQJS4lW25WXmc2n2B8KAo4k8nF+uv1bAa/r3r49PJHRv7G0vKtm/fzg8//MA777yDvb29qUIWEZEyRolbBaLErfxIOJNM2KYoTu08T25WCVW4znXx7+1JLa9rV+FiYmJo1aoVMTExtG7dmrVr1+Lq6lpaoYuIiBnpXYuIGdRqWJ0u/61Ou1H+nN51ntCgKBLPpgCQm5VPWFAUYUFRuHpXx7+3J94dr1yFCw4OJikpCQBnZ2dq1qx5ux5DRERuMyVuImZk62CNf+/6+PeuT8LpZEKDoji16zx52YVVuPjTycSfTmbPt6H4dq6Hf29PXBpUK3KNAQMG8McffzBp0iR++OEHrKyszPEoIiJyG+hVaQWiV6UVQ05GLqd2XSAsKJLEiNRi/a6+1QnoVR/vju5Y2109Sbtw4QKWlpa4ubmVVrgiInIbmaziduDAAfbv38/Ro0eJi4sjOTkZe3t7nJ2d8fb2JjAwkI4dO+Li4mKqW4pUSLaONgT0qY9/b09jFe707gt/V+FOJhN/8shfVbi6+PeuT836VYtdJzc3l+HDh3P69GmWLl1K165db/ejiIiIid1SxS0zM5OffvqJX3/9ldjYWONK7ra2tlSrVo3s7GzS09MpKCgAwNramk6dOjFixAiaN29umicQI1XcKq6cjFxO7jxPWFAUFyOLV+FqN3IuHAvXwR1r28Iq3OzZs3nppZcA8Pb2JiwsDBubm9s/VUREyoabTtxWrlzJl19+yaVLl/Dx8aFnz540bdoUf39/HB0djccZDAaio6MJCQnhzz//ZMeOHWRlZdG5c2cmTJhA3bp1TfYwlZ0St4rPYDAQfyqZsKBITu2+QH5OQZF+W0drfLvWI6C3Jzk2GYwaNYrt27ezY8cO2rVrZ6aoRUTEVG46cevZsyd9+vRh5MiReHt7X/d52dnZbNiwgW+//ZYBAwYwevTom7m9lECJW+WSnZ7LyR3nCdsUyaWotGL9bo1r0KhHXRKsoujStbMZIhQREVO76cQtKioKT0/Pm75xfn4+sbGxqriZkBK3yslgMBB3IomwTYVj4fJzi1bh7Jxs8O1aOBauRr0qAMyYMQN/f38eeeQRc4QsIiI36aYnJ9xK0gZgZWWlpE3EBCwsLHBrXAO3xjXo8J8ATu48R1hQFJeiC6tw2em5HFsbwbG1EdTxr8Epw0HeeustAPbt28eHH35ozvBFROQGaB03kQrErooNTft70aRfA2KPJxEWFMmZP2KMVbiYsEusObjReHyjBqrMioiUJyZN3FJTU9m5cycDBgww5WVF5AZZWFhQx68Gdfxq0OGhAE5uP0doUBTJ59MZ3moMns7enEwIwemgL6tf+QP/Xp54tXXDykaL94qIlGUmTdxiY2N54403lLiJlCH2VWxpdmdDmg7wIibsEmFBkVha96ZDw14AXAi5yIWQi9hXtSHPI4nBD/ehVn1n8wYtIiIluqHELTY29qr9CQkJtxSMiJQeCwsL3ANq4h5Qk44pOZzYfo6wTVEkX0gH4FTUCV77/GkavO/L84+8Sed7W9PgDjesrC3NHLmIiFx2Q4nbiBEjsLCwuGK/wWC4ar+IlA321WxpPqghzQZ6cSH0IiEbIpg9ezw5+dmciD/GNys/JzvKEvtqtjTu7oF/Lw+quTmZO2wRkUrvhhK3qlWr8t///pfAwMAS+yMiIpg9e7YJwhKR28HCwoK6TVyo28SF75p9xQMPPIC9hRP3tHgIgKyUHA7/eprDv56mbjMXAnp70qCNG5aqwomImMUNJW6NGzcmNTWVhg0bltifn5+P9qwXKZ+69+7GkdDDpKWmYZtRldCgSCL+jKUgv/Azff5oIuePJuJQvbAK59fTk2pujte4qoiImNINJW733HMPWVlZV+x3c3Nj5syZtxyUiJhHrVq1qFWrFgB1m7qQmZzNvtUhTHj2Me4O+D8a1GxEZnIOh1ad5tCq09RrXgv/3p40aF1bVTgRkdvghhK3bt26XbW/atWq3HnnnbcUkIiUHfbVbHnnx5c5ErmP8AuHmfWf1/DIbYrhryrcuSMJnDuSgIOzHX49PPDr6UFVV1XhRERKixbgFZErSk5O5sKFCwA4VXHi4ReGUtvZneNbownfFEVqfCYAmUnZBP9yiuCVp/BoUQv/XvWp39oVSytV4URETEmJm4hckbOzM9u2bWPatGn0798fLy8vAALv9qHlXd6cO5pAWFAUEfvjMBQYwADRhxKIPpSAY43CKlzjHp5UdXUw74OIiFQQt5y49ejRg2+++eaW9y4VkbLJ1taWBQsWFGs3YODUpRD6PN2djEtZHN9auC5cWkJhFS7jUjYHV5zi4C+n8Gzpin9vTzwDVYUTEbkVt/wvqGaRilROr7/+Oj169ODpp5/GpooVgff4MOLd7vSfcQcN7nDDwvKvNR0NEBUcz4Z5B1gyaQv7l58gLTHTvMGLiJRTelUqIjfsyJEjvPDCCwC89957DBs2jM6dO2NpaYFnS1c8W7qSfjGL41uiCd8SRVpC4Wz0jIvZHPz5JMErTuIR6EpA7/p4BLpiaamFu0VErocSNxG5Yc2aNeODDz5g8uTJ/O9//6Nz587FjnGqaU+r+3xpeY8P5w7FE7opiqgDcRgMYDBA1MF4og7G4+Rij19PD/x6eOJU094MTyMiUn4ocRORG2ZhYcG4cePo3r07/v7+xfr/uf2dpaUFnq1q49mqNumJmYRviSZ8czTpFwurcOmJWRxYfpKDP53Es3VtAnp5Uq+lqnAiIiXRKGERuWlNmjTB0rLoPyNr165l8ODBJCYmFjveycWB1kMbcf+CHvSb1gbPVq5c3t7YYIDI/XGse3s/yyZv5eCKk6RfuvKC3yIilZEqbiJiMpGRkfznP/8hMTGRVq1asWvXLjw8PIodZ2lpQf3WtanfujZpCZercFFkXMoGIC0hk/0/nuDATyep3+avKlzzWn9PeBARqaSUuJVRSUlJvPbaawQHB+Pq6sqUKVNo06aNucMSuapz584ZK3CBgYHUrVv3mudUqeVAm2GNaHWvD1EH4wkNiiL6cDwYwFBgIOLPWCL+jKWKqwP+PT1p3MMDR2e70n4UEZEy6ZYTt1GjRlGtWjVTxCL/MH/+fGrWrMmqVavYt28fL774It9//72+11KmdezYkYMHDzJ9+nQ++OCDYq9Rr8bSypIGd7jR4A43UuMzCN8czfEt0WQk/VWFi89k37Lj7P/pBA1a18a/tyf1mqkKJyKVi4VBC7GVORkZGQwePJglS5ZQu3ZtACZOnMiAAQMYOHDgFc8LDw9n7NixLFq0CD8/v9sVrsh1OXv2LBkZGTRp0uS6zynIKyDyYBxhQVFEH0mAf/1rVbW2A36Xq3DVVYUTkYqv1F+VGgwGoqOjsbW1xc3NrbRvZ/T111/z2Wef0bBhQ7766qtSu09GRgZLliwhJCSE0NBQUlNTefbZZ7nzzjtLPD4nJ4fPP/+c9evXk5qaio+PD2PGjKFt27bGY6Kjo3FwcDAmbQDe3t6cOXOm1J5DpDRlZWUxbNgwwsLCWLRoESNHjryu8yytLfFqWwevtnVIjcsgbHM0x7dGk/lXFS41LpN9S4+zf/kJvNq44d/bk7pNXVSFE5EKy2SzSrdu3cqrr75Kamqqse3ChQuMHj2a//u//+P+++9n9uzZ5Ofnm+qWVxQXF8e3336Lg0Pp74+YnJzM4sWLiYiIwNfX95rHv/766yxbtoy+ffsyceJELC0tmT59OocPHzYek5mZiZOTU5HznJycyMzUavNSPs2dO5f9+/eTnp7OCy+8QFbWjc8WrVrbkbb3N2bkgh70ntyKes1rGfsM+QbO7I3h99f/5Mep2zj062kyk7NN+QgiImWCyRK3lStXcuLECapWrWps++CDDzh79iytWrXCx8eHLVu2sGbNGlPd8oo++ugjmjRpcl2vC1NTU9m6desV+zdu3HjVhMnFxYUVK1bw448/Mm7cuKveKyQkhKCgIB577DHGjx/PkCFDePfdd6lTpw4LFy40Hufg4EB6enqRc9PT029LIipSGqZMmcLDDz+Mvb09y5cvx97+5hfatbS2pGG7Otz5bFtGzO9OyyHe2FezNfanxGbw5w/h/PDkZoIWHOT8sURtzSciFYbJErezZ88SEBBg/DojI4Pdu3fTq1cv5s+fzyeffEKDBg1KPXELDg5m69atPPXUU9d1/IoVK5g9ezbbtm0r1vfbb7/x8ssvs3bt2iueb2tri4uLy3Xda+vWrVhZWTFkyBBjm52dHYMGDeLYsWPExsYC4OHhQWZmJvHx8cbjzpw5Q8OGDa/rPiJljaOjI19++SWHDx+mZcuWRfpuJamq5uZI2wf8GPlBT3pPCqRus78/iwX5Bs7siWHNq3v5ceo2Dv92mswUVeFEpHwzWeKWkpJCzZo1jV8fPnyY/Px8evfuDYC1tTV33HEH586dM9Uti8nPz+e9995j0KBB+Pj4XNc5o0aNokOHDrz88svs37/f2L5161bmzZtHv379uOeee0wS34kTJ/Dw8Cj2GvRywnvy5Emg8Jdcly5d+OKLL8jOzmbXrl2cOnWKLl26mCQOEXOwsLCgUaNGxdrHjh3LCy+8cEvDKKysLWnY3p2Bs9ox/J1utLirYdEqXEwGe78vrMJtej+Y8yGqwolI+WSyyQlOTk6kpKQYvz548CCWlpZF/rq2tra+qbEt12vlypXExsYyf/786z7H2tqa2bNn88wzzzBr1izmz59PZmYmc+bMoX379sycOdO4dc+tSkxMLLE6d7ktISHB2DZlyhReffVVBg8ejKurK7Nnz77iUiAbN25k48aNpKWlmSROkdvl888/5/PPPwcKN67/+eefb/nzVr2OE+1G+tNmeGMi9sUSFhTF+WOFuzgU5Bk4vfsCp3dfoLq7E/69PGnUtV6RJE9EpCwzWeJWv359du3axZgxY7C0tGTjxo00bty4yJi3mJgYatSoYapbFpGcnMwXX3zBQw89hLOz8w2da2dnx+uvv86kSZOYPn06ubm5BAQE8NJLL2FtbbqJt9nZ2djY2BRrt7W1NfZf5uzszNtvv31d1+3Tpw99+vQxLgciUl6kpqZiZWVFfn4+w4cPN9kfSVBYhfPu4I53B3eSL6QTtjmKE1ujyUrNBSD5Qjp/fBfGn0vDadiuDv6961PHv4ZJYxARMTWTZSVDhw7lxRdfZOjQocbK2pgxY4ocExISQuPGjU11yyI+++wzqlatytChQ2/qfCcnJ8aPH8/kyZMBeOqpp7CzM+26UHZ2duTm5hZrz8nJMfaLVCaTJ0+mVatWBAUFMWrUqFK7T3V3J9qP8ueO4Y04+2csYZuiuBByESiswp3adYFTuy5Qve5fVbhu9bCvoiqciJQ9JkvcevTowdNPP83q1asB6NWrV5G1zIKDg0lPT6ddu3amuqVRVFQUv/76K0899VSR1405OTnk5eVx4cIFnJycrrrrwPnz55kzZw7169cnJyeH559/ng8//JBatWpd8Zwb5eLiUmTCwWWXN+M25b1Eyovu3bvTvXv3Yu3r16+nQ4cOJt0txMrGCp9OdfHpVLewCrcpiuNbo8lO+6sKdz6dP74NY9/S4zRsXwf/Xp64+akKJyJlh0kX4L3nnnuuOJA/MDCw1GaUJiQkUFBQwHvvvcd7771XrP/+++9n2LBhTJw48YrnT5kyBWtra9555x2ys7N58sknmTp1Ku+//77JfnH4+vpy8OBB0tPTi0xQCAkJMfaLCOzbt4+77roLLy8vli9fTvPmzU1+j+ruTrR/0J87RhRW4UKDoogJLazC5ecWcHLHeU7uOI9zvSr49/LEt2tdVeFExOwqxCbzDRs25NVXXy3W/tlnn5GRkcHEiROvuNl1amoq06ZNIzMzkw8++MC4W8HcuXOZNGkSM2bM4J133jHJGmo9evRgyZIlrFq1yrhyfE5ODmvWrKFJkya3dWcJkbLKYDAwduxYcnJyOH78OF9//fV1j/e8Gf+swiWdSyNsUxQntp8zVuGSzqWx55tQ/lwSXliF610ft8bOqsKJiFlUiMTN2dmZrl27Fmv/8ccfAUrsu2zFihXExcWxYMECPD09je2NGjXijTfeYOrUqaxdu5Z77733itf46aefSEtLM77y3LlzJ3FxcUDh2L8qVaoA0KRJE3r27Mmnn35KUlIS9erVY+3atcTExDBjxowbf3CRCsjCwoLly5czbNgwHBwcSvyjrLQ416tCh/8L4I77G3N2bwxhm6KICbsEFK3C1fCogn9vT3w718OuSvEJRyIipaVCbzI/ceJEkpOTr7pXaV5eHlFRUVdc3PbUqVN4e3tf9a/rESNGEBMTU2Lf0qVLcXd3N36dnZ1t3Ks0LS0Nb29vxowZY5Kxf9pkXiqSzMxMUlNTi+zZC1BQUIClpcmWoLymS9GphG+O5vi2aHLS84r0WdkWzlz17+VJ7UaqwolI6avQiVtlo8RNKrr4+Hj69OnDK6+8wl133XVb752Xk8+ZP2IIC4oi9vilYv01PKsS0NsT3y51sXVUFU5ESocStwpEiZtUZPn5+QwcOJD169cDhWNY//vf/5ollotRqYRtiuLk9nPkZBSvwvl0dMe/d31cfaqrCiciJlUhxriJSMWXnZ1tHC/q5ubGwIEDzRZLTc+qdHq4Ce0e8OP0HxcIC4oi7kQSAPk5BRzfeo7jW89Rs0HVwhmpnVWFExHTUMWtAlHFTSo6g8HA/PnzadWqFT179jR3OEVcjEwlLCiSEzvOk5tZtApnbWeFT6e/qnDe1c0UoYhUBDecuBUUFHD27FmqVatWbMHYvLw8jh49SmBgoCljlOukxE0qq7y8PJYtW8bIkSPN/moyNyuP03suELYpiviTycX6Xbyq4d/bE59OdbF10EsPEbkxNzQ1KyYmhtGjR/PII48wbNgwZs6cSXLy3/8wpaSkGLeMEhG5XZ5//nkefPBB7rvvPpKSkswai429NX49PLn75U7c+1pnAvrUx8beytifeDaFnZ8f4/vxm9jx2VESzhRP7kREruSGEreFCxdSq1YtlixZwqJFi8jOzmbChAlFtpnSm1cRuZ2OHz/OW2+9BcBvv/1GaGiomSP6m4tXNTo/2pRRH/Wiy9hm1PrHa9K87HzCNkXxy/928ctzOwnbHEVuVt5VriYicoOJ26FDhxg/fjzu7u40atSIefPm0aJFC5588kliY2MBzP6aQkQql8aNG/Prr79So0YN3nrrLTp27GjukIqxsbfGv6cn97zSiXte7YR/L0+s7f6uwiWcTmHHoqOFVbjPj5J4NsWM0YpIWXZDAyyysrKwsfl7ZpSlpSXTp09n3rx5PPXUUzz//PMmD1BE5FoGDhxISEhIidvG5eXlYW1ddsaS1WpYnS5jqtPuQX9O7zpPaFCUMVHLzconLCiKsKAoXH2q49/LE++O7tjYl534RcS8bqjiVr9+fcLDw4u1T506lQ4dOjBz5kyTBSYiciPq1KlTrOL/888/c8cdd3Dy5EkzRXVltg7W+Peuz72vdebuVzrh19OjSBUu/lQy2xcd5fsJm9n55TESI1WFE5EbrLh169aNDRs20K9fv2J9U6ZMAWDlypWmiUxE5BacOHGCRx55hJSUFNq0acPBgwfx9vY2d1glcvWujqt3c9o/6M+pXRcIDYrkYkQqALmZeYRuiCR0QySuvtUJ6FUf747uRZI8Eak8tI5bBaLlQET+FhYWxr333ktYWBgPPPAA33//fbkZg2swGIg/lUzYpihO7zpPXk5BkX4bB2sada2Hf29PanpWNVOUImIOGjghIhWSv78/e/fu5eWXX+aFF14oN0kbFE7yqu3rTG1fZzr8x5+TO88TFhTFxci/q3Ah6yMIWR9B7UbO+Pf2xLuDO9a2qsKJVHSquFUgqriJXJ+wsDDOnz9Pr169zB3KdTMYDMSfTCI0KIrTuy+Qn1u0Cmfr+HcVroaHqnAiFdUNTU4oSY8ePYiKijJFLCIipS4tLY2hQ4fSt29fXnvtNQoKCq59UhlgYWFB7UY16P5EC0Z91IuODzehhkcVY39ORh7H1kXw0/Qd/Dp7Nye2nyMvJ9+MEYtIabjlxE0FOxEpTz7++GNCQkIoKCjghx9+ICsry9wh3TA7Jxua9m/AfW924a7ZHWjUrR5WNn//cx57PImtCw/z/fhN7P46hEvn0swYrYiYksa4iUil8vTTT5ORkcE777zDTz/9hKOjo7lDumkWFha4Na6BW+MadPhPACd2nCMsKJKkc+nAX1W4tREcWxuBm18NAnp74tWujsbCiZRjStxEpFKxsrLihRdeYMKECbi4uBTpy8nJwcbGplxNZLjMrooNzQZ40bR/A2KPJxEWFMnpPTEU5BW+Co4Nv0Rs+CVsvwqlcbd6+PfyxLlelWtcVUTKGiVuIlIp/TtpMxgM/Oc//8He3p6FCxfi5ORkpshujYWFBXX8alDHrwYdHgrg5PbzhAZFknz+rypcei5Hfz/L0d/PUse/BgF96uPV1g0rG1XhRMoDJW4iIsCCBQv48ccfATh9+jTbt28vl5W3f7KvYkuzO71oOqABseGXCA2K4swff1fhYsIuERN2CTsnGxr38MC/lyfV3ctnwipSWShxExEB3N3dqVKlCmlpaTzzzDPlPmn7JwsLC+r416SOf006PhTAie3nCN0YSUpMBgDZ6bkcWX2GI6vP4N6kJv69/6rCWd/y/DURMTElbiIiwIgRI2jRogXr16/n7rvvNnc4pca+qi3NBzak2Z1exIRdJOxyFS6/cIWACyEXuRByEbuqNvj18MCvpyfV66gKJ1JW3HLiNmrUKKpVq2aKWEREzMrf3x9/f/9i7UuWLKFLly54eHiYIarSYWFhgXuAC+4BLnR8KIfj26IJC4oiJfavKlxqLod/PcPhX89Qt6kL/r09aXCHqnAi5qadEyoQ7ZwgYnrbtm2jV69e1KhRg++++45+/fqZO6RSYzAYuBBykdCgSCL+jDVW4S6zr2ZrrMJVcyu/y6iIlGd6VSoicgUGg4GZM2eSn59PQkIC27Ztq9CJm4WFBXWbulC3qQuZydkc31a4LlxqXCYAWSk5HFp1mkOrTlOvuQv+vevToHVtLFWFE7ltTF5xy8/PJz4+noSEBPLy8ko8JjAw0JS3lL+o4iZiehcvXuShhx4iMzOT9evXY2VVuZbNMBQYOH8ssbAKty8OQ0EJVbienvj38qCqq6pwIqXNZBW3goICvvnmG5YvX05qaupVj92yZYupbisiUqpq1qzJqlWrSEtLK5a0ZWZm4uDgYKbIbg8LSwvqNa9Fvea1yEjK5sS2aEI3RpGW8I8q3MpTHFp1inrNaxHQ25P6rWtjaaUqnEhpMFni9sknn7BkyRJq1KjBnXfeiYuLS6X7y1REKiZLS8tik7DOnTtHhw4dmDFjBhMmTKhQy4dciaOzHS2H+NBisDfnjiUStjGSiP1/VeEMcO5wAucOJ+BQvbAK59fTk6quFTuxFbndTJa4rVu3Dk9PTz799NNyvfefiMi15Obmcv/99xMdHc1TTz1FVlYW06ZNM3dYt42FpQUezWvh8VcV7vjWaEI3RpKemAVAZnIOwb+cInjlKTxa1CKgd308W7mqCidiAiZL3DIzM+nbt6+SNhGpFDp06MDOnTvx9PRk9OjR5g7HbByd7Qi824eWd3lz7kgCoUFRRB6IxVAAGCD6UALRhxJwcLbDv5cnfj09qOKiKpzIzTJZ4ubt7U1iYqKpLiciUmbZ2Ngwd+5cOnXqRL169ahVq5a5QzI7C0sLPFq64tHSlfRLWRzfEk1oUCQZF7MByEzK5uDPJzm44iSeLV3x7+2JZ6vaWFpW/FfMIqZksrr1Qw89xPbt2wkPDzfVJUVEyrT77ruP9u3bF2nLycnh7bffJjs720xRmZ9TDXta3evLAwt60n/6HdRvXRsu52cGiAqOZ8O8Ayx5cjMHfjpBemKmWeMVKU9MuhzIpk2beO+99+jcuTM+Pj44OZW8TcqAAQNMdUv5By0HImJ+EydO5P3336dt27b8+OOPNGjQwNwhlQnpF7MI3xJFWFAUGZf+ldRagGegK0361KdeS1dV4USuwmSvSnNycti1axfJycmsXr0aoNgsK4PBgIWFhRI3EamQIiIiWLRoEQCHDh0iISFBidtfnGra0/q+RgTe40t0cDyhGyOJOhQPBgqrcAfjiToYj2MNOwJ616dxTw+catibO2yRMsdkidsHH3zAhg0b8PHxoXv37loOREQqnQYNGrB7926GDh3KjBkzaNOmjblDKnMsLS2o37o29VvXJi0xk/DN0YRtiiIzqbAKl3Epm/3LT3Dg5xN4BtYmoG996jWvpSqcyF9Mlrht2bIFPz8/PvroI6yttZOWiFROgYGBHD58uMQZ9qmpqVStWtUMUZVNVVwcaDOsEa3u9SEqOJ7QDZFEH0kAAxgKIPJAHJEH4nCsaUdAn/r4dffAUVU4qeRMNjkhJyeHVq1aKWkTkUrPycmp2FCRr7/+moCAAHbu3GmmqMouSytLGrRxY8DMttz/bg9a3euDfTVbY3/GxWz2LzvBD09tYcO8/UQfji+29ZZIZWGyLMvPz4/o6GhTXU5EpMI4cuQITzzxBJmZmfTo0YPDhw8TEBBg7rDKpKquDrQZ3phW9/kSeSCe0I0RnDtSuNSUocBAxP44IvbH4eRiT0Cf+jTu7oGjs52Zoxa5fUxWcRs7dix79+5l165dprqkiEiF4Orqalw2ZPTo0UraroOllSVebd2489l23P9ed1re7Y191b+rcOmJWexbepwfntzMhncOcO5IgqpwUimYbDmQxYsXc+zYMf78809at259xeVALCwsePjhh01xS/kXLQciUnbl5eXx4Ycf8vjjj2Nvr3FaN6Mgr4CIA3GEbIjkwrHiC75XqfV3Fc6huqpwUjGZLHHr3r379d3QwoItW7aY4pbyL0rcRMqf4OBgDh8+zEMPPWTuUMqVlNgMwjZFEr4lmuzU3CJ9FpYWNGhTmyb9GuAeUBMLzUiVCsRkY9zee+89U11KRKRSSEpKYujQoZw+fZodO3awYMECVeOuUzU3R9qN9KfN8MZE7o8lZH0kF0IvAoVj4c7+GcvZP2OpUsuBgL71ady9Hg7VVIWT8s9kiVtgYKCpLiUiUiksXbqU06dPA4WVt3/PRJVrs7K2pGF7dxq2dyc5Jp3wTVGEb44mO72wCpeWkMmfP4Szf9lx6v+zCqfvtZRTWrtDRMRMLo93e+655/jxxx+xs1NF6FZUr+NEu1H+tBnRmIh9sYSsjyAm7BIABfkGzu6N5ezeWKrW/qsK182jyIQHkfLAZGPcjhw5wtatWxk5ciQuLi7F+hMSEliyZAk9e/akadOmpril/IvGuImUT1lZWcVekaampuLg4KC1MW9R8oV0wjZFEb4lipz0vCJ9llYWNGjrRpO+DajjX0NVOCkXTLYcyNKlS9m5c2eJSRtArVq12LVrF8uWLTPVLUVEKoR/J20FBQWMHDmSPn36EBMTY6aoKobq7k60f9CfBz/qRc8nW+LW2NnYV5Bv4MyeGFbP+YNlU7ZxZM0ZstJyzBesyHUw2Z9yYWFh19yXr2XLluzbt89UtxQRqZDefPNNVq9eDUC/fv0IDg7G0tJkf2dXSlY2Vvh0qotPp7oknU8jdGMkx7eeIzezsAqXGpvBH9+G8eeScLza1qFJvwa4NXZWFU7KHJMlbklJSdSqVeuqx9SsWZNLly6Z6pYiIhVS9+7dqVu3LhcuXGDevHlK2kzMuW4VOj7UhLYP+HF2bwzH1kcQfzIZgII8A6d3X+D07gtUq+NIk771adTVA7sqNmaOWqSQyRK3KlWqEBcXd9VjYmNjcXBwMNUtRUQqpE6dOnHw4EE2btxI3759zR1OhWVta4Vvl3r4dqnHpehUQoOiOLHt7ypcSkwGe74JY+8P4TRsV1iFq91IVTgxL5P9GdekSRO2bdtGbGxsif2xsbFs376dZs2ameqWIiIVVu3atRk1alSx9g8++ICDBw+aIaKKrYZHVTo93IQHF/ai+7gW1PKubuwryDNwatcFfp29h+XTtnFs7VnjciMit5vJErcRI0aQnZ3NhAkTWLt2LQkJCUDhbNLff/+d8ePHk5OTw/3332+qW4qIVCpr165l4sSJdOzYkS+++MLc4VRI1rZWNOpaj3te6cR9b3YhoE99rO2tjP3JFzLY/XUo343bxOYPg4k7mYSJFmcQuS4mXYB3woQJfPTRR7zxxhtA4fZWl3+gLSwseOqpp7RQr4jITVqwYAEGg4Hs7Gzi4+PNHU6FV9OzKp0fbUr7B/05/ccFjq2NIPFsClC4b+qpnRc4tfMC1d2daNKvAY261sXWUWPhpHSZbB23y06dOsXKlSsJCwsjLS2NKlWqEBAQwN133423t7cpb1WhJSUl8dprrxEcHIyrqytTpky55qxdreMmUrFlZ2czdepUoqKiWLFihSYtmMHFyFRC1kdwcud58rLzi/RZ2VjSsH0dmvZvQC3v6hoLJ6XC5ImbmMaLL76Ig4MDkydPZt++fbzxxht8//33VKtW7YrnKHETqRxyc3OxsSla2YmPj8fV1dVMEVU+uVl5nN5zgWPrIrgYkVqs37muE036N8C3Sz1sHbSIspiO/lwrgzIyMti+fTuPPvoo9vb2dOnSBW9vb3bs2GHu0ESkDPh30nbmzBn8/PyYNm0aubkaNH872Nhb49fDk/te78K9r3fGr6cH1rZ//0pNOp/Ori9D+O6JILYsPETC6WQzRisVSYX5M+DMmTN8+eWXhIeHc/HiRezt7WnQoAEjR46kc+fOpXbfjIwMlixZQkhICKGhoaSmpvLss89y5513lnh8Tk4On3/+OevXryc1NRUfHx/GjBlD27ZtjcdER0fj4OBA7dq1jW3e3t6cOXOm1J5DRMqnnJwchg0bxqVLl5g3bx5OTk689NJL5g6rUnFpUI2uY5vT4f8COLW7cCzcpajCKlx+bgEnt5/n5PbzONdzokl/L3w711UVTm5aham4xcTEkJGRwYABA5g4cSIPPfQQAM8++yyrVq0qtfsmJyezePFiIiIi8PX1vebxr7/+OsuWLaNv375MnDgRS0tLpk+fzuHDh43HZGZm4uTkVOQ8JycnMjMzTR6/iJRvNjY2PPLII9jY2ODr68uUKVPMHVKlZWNvjX9PT4a+2YV7XutE4+71sPpnFe5cOru+OMZ344LY+vFhEs6oCic3rsKk/B07dqRjx45F2u677z7Gjh3LsmXLGDJkSInnpaamcuDAAbp3715i/8aNG+ncufMVFw52cXFhxYoVuLi4EBYWxmOPPXbFGENCQggKCmLcuHGMHDkSgP79+zN69GgWLlzIwoULAXBwcCA9Pb3Iuenp6Vq8WESKsbCw4Mknn6Rt27Y4ODhQvXr1a58kpa6WV3W6Pd6Cjg834eTO8xxbF0FSdBoA+TkFnNh2jhPbzuHsUYWm/Rvg27kuNvYV5leylKIKU3EriZWVFbVr1yYtLe2Kx6xYsYLZs2ezbdu2Yn2//fYbL7/8MmvXrr3i+ba2tri4uFxXPFu3bsXKyqpIEmlnZ8egQYM4duyYcfFiDw8PMjMzi0z3P3PmDA0bNryu+4hI5dO+fXtatGhRpC0jI4PJkydz8eJFM0UlNvbWBPSuz7C3unLPK53w7VoXK5t/VOGi09j5+TG+fSKIbR8fJjEixYzRSnlwS4lbTk6OqeIwmczMTJKSkjh37hzLli3jjz/+oHXr1lc8ftSoUXTo0IGXX36Z/fv3G9u3bt3KvHnz6NevH/fcc49JYjtx4gQeHh7FXoMGBAQAcPLkSQAcHR3p0qULX3zxBdnZ2ezatYtTp07RpUsXk8QhIpXDk08+yXvvvUfr1q2120IZUMu7Oj3GteTBj3vT6ZEmVK/79++C/JwCjm87x4pnd/LTjO2EbYokNyvPjNFKWXVLddl7772XPn36MHDgwDKz/MSHH35oHNNmaWlJt27dePrpp694vLW1NbNnz+aZZ55h1qxZzJ8/n8zMTObMmUP79u2ZOXOmydbiSUxMLLE6d7nt8m4TAFOmTOHVV19l8ODBuLq6Mnv27CsuBbJx40Y2btx41cqiiFQu58+f59dffwUKlwqxtbU1c0Ryma2DNU36NiCgT30STidzdO1Zzu6NJT+3AIBLUWns+OwYe74Ow7tjHZre6YVL/SsvBSWVyy0lbjk5Ofzyyy+sXLkSHx8fBg0aRN++falataqp4rthw4cPp0ePHiQkJLB582by8/OvOT3ezs6O119/nUmTJjF9+nRyc3MJCAjgpZdewtradGMOsrOzi03jB4z/oGZnZxvbnJ2defvtt6/run369KFPnz7GddxEROrWrcuBAwcYMWIETz31FE2bNjV3SPIvFhYWuPo403NCIDmP5HJi+zlC1keSfKFwjHNeTj7Ht57j+NZz1PCsSrMBDfDpVBdrO6trXFkqslt6Vbpy5UqmTJmCn58fJ0+eZMGCBdx3333FXjveTg0aNOCOO+5gwIABvPnmm2RmZjJz5sxr7iXn5OTE+PHjSUlJITMzk6eeego7OzuTxmZnZ1diEnn5lbOp7ycilZunpyc7duwotlm9wWDg3LlzZopKSmLraEPT/l4Mm9uVIS93xLujO5bWf7/tuRSVyvZFR/n2iSC2f3qES9HFF/2VyuGWEjdHR0fuvvtuPvnkE7766iuGDx+Ok5MTQUFBTJ06lREjRvDVV18ZB92bQ48ePQgLCyMqKuqqx50/f545c+ZQv3596tSpw/PPP1/k1aUpuLi4kJiYWKz9clutWrVMej8RESur4tWZjz/+GH9/f5YtW2aGiORqLCwsqO3rTK+nAvnPx73p8FAA1dwcjf152fmEb4nmp+k7+HnmDsK3RJGXk3+VK0pFY7JZpV5eXkyYMIGffvqJV155hQ4dOpCQkMAXX3zBAw88wDPPPMOWLVvIy7u9gy0vv3682vivhIQEpkyZgrW1Ne+88w7z5s0jKyuLqVOnkpJiuhk+vr6+REdHF1vqIyQkxNgvIlKaDh48yOTJk0lLS+P+++8nODjY3CHJFdg62tBsgBfD3+nGXbM70LB9nSJVuIuRqWz/9K8q3KIjXDqncc6VgcmXA7GysqJr16688cYbLF++nCeeeAIPDw/27t3L7Nmzue+++0x9SwAuXbpUrC0vL49169ZhZ2eHl5dXieelpqYybdo0MjMzeeedd6hduzaenp7MnTuX+Ph4ZsyYYbKFb3v06EF+fn6RBYFzcnJYs2YNTZo0wc3NzST3ERG5ksaNGzNixAgAJk+eTGBgoHkDkmuysLDArXENek9qxYMf9ab9g/5Urf33up55WfmEb47mp2e2s2LWTo5vi1YVrgIr1dX+atasyciRI2nXrh3z58/nyJEjJq1g/dPcuXNJT0+nZcuWuLq6kpiYyIYNG4iMjGTChAk4OjqWeN6KFSuIi4tjwYIFeHp6GtsbNWrEG2+8wdSpU1m7di333nvvFe/9008/kZaWZnzluXPnTuLi4gAYOnQoVapUAaBJkyb07NmTTz/9lKSkJOrVq8fatWuJiYlhxowZpvpWiIhckZOTE19//TV33303d999t7nDkRtkV8WG5oMa0mygF7HHkzi65gyRB+IoyC8cx514NoVtHx9h9+IQfDrVpdlAL5zrVjFz1GJKFoZrjdq/SRkZGWzYsIHVq1dz/PhxDAYD9vb29OzZk5kzZ5r8fkFBQaxevZrTp0+TnJyMo6Mjfn5+3HfffVdd/ywvL4+oqKgrLm576tQpvL29r7okyIgRI4iJiSmxb+nSpbi7uxu/zs7ONu5VmpaWhre3N2PGjKFdu3bX+aRXdnlW6aJFi8rM8iwiUn7s3r2bDRs28Nxzz2FpWaHXZ69QstJyOL45mpANkaQlFH9DVKthNZoO8MK7Qx2sbDQjtbwzeeJ24MAB1qxZw/bt28nOzsZgMNCkSRMGDRpEr169rlj5klunxE1EblZ8fDytW7cmOjqa/v37s3TpUm2fVc4YDAZiwi5yZM1ZooPjjVW4y2wcrPHp5E7zgQ2p7u50hatIWWeSV6VxcXH8/vvv/P7778TExGAwGHB2dmbIkCEMGjToiuPLRESkbNi8eTPnz58HCneg+fcOL1L2WVhY4B7ggnuAC1mpOYRtjiJ0QyTpiVkA5GbmERYURVhQFLW8L1fh3LGyVnW1PLmliltQUBBr1qzhwIEDFBQUYGlpyR133MGgQYPo0qWLSRevlWtTxU1EbsWmTZt4+umnWbt2bZEhHlJ+GQwGLoRe5MjqM0QfSsBQULwK59vZnWYDG1K9jpL18uCWErfu3bsD4O7uzp133smdd95J7dq1TRac3BglbiJyqy7/Ef5P8fHx2Nvbm3VXHLl1WSk5hAZFEhoUScbF7GL9tbyr02xAAxqqClem3VJJrHfv3gwaNIg2bdqYKh4RETGjfydteXl5jBgxggsXLvDTTz9p66xyzL6aLa3u9SXwHh/OH0vkyOoznDuSaKzCJZxOZstHh9m1OASfznVpPrBhkcV/pWy4pcTthRdeMFUcIiJSBs2ZM4ctW7YAcN999xESElLibgxSflhYWFCvWS3qNatFZnI2oRsiCdscRcalwipcTkYeoRsiCd0QiatP9cKxcO3rYKkqXJlg0lmleXl5/Pzzz2zcuJHIyEiys7PZvHkzACdOnODXX39l+PDhRdZLE9PRq1IRMbWTJ08ybNgwjh07xpYtW+jcubO5Q5JSYCgwEH00gaNrznL+aAKGgqL9to7W+HSuS4tBDalaW1U4czLZ7IHs7GymTp3K0aNHqV69Ok5OTmRlZRn73d3dWbNmDVWrVmXs2LGmuq2IiJQiX19fdu/ezfbt25W0VWAWlhZ4tnDFs4UrGcnZhKyLIHxzFJnJOcC/qnC+1Wl2pxcN26oKZw4m+45/8803HDlyhMcee4xffvmFQYMGFemvUqUKgYGB/Pnnn6a6pYiI3AYODg7069evWPvLL7/M6tWrzRCRlCbH6nbcMaIxoz7sRf/pbajbzIV/rkEffzKZze8f4ttxQexafIzUeNNsCynXx2QVt02bNtGqVStGjRoFUOJOA3Xr1uXEiROmuqWIiJjJzz//zIsvvgjA7Nmzjf8tFYeFpQWegbXxDKxNxqUsjq2LIHxrNFmXq3DpeYSsjyRkfSS1fZ1peqcXDdu5YWmlKlxpMlniFhcXR9euXa96jIODA+np6aa6pYiImMnKlSuN/+3i4mLGSOR2cKxhT9sH/LhjRGOiguM5suYMF0Ivwl+j5ONOJhH3fjC7nGzw6exO80ENqeqqsXClwWSJm4ODA0lJSVc95vz589pCRUSkAli8eDHNmzfn0KFDTJgwwdzhyG1iYWlB/da1qd+6NumXsji29izHt54jK6WwCpednvt3Fa6RM00HNKBhuzqqwpmQyRK3pk2bsmvXLlJTU0tcpDE2NpY9e/ZcsyonIiJln4WFBdOmTcNgMBQbGhMeHk7jxo1LHDIjFYdTDXvajfTnjvv9iDwQy9E1Z4kJv/R3Fe5EEnEnkthV5a914QZ5UbWWqnC3ymQp8AMPPEBqaipPP/00R44cIT8/H4CsrCz279/PtGnTyM/P5/777zfVLUVExMz+nZyFhobSpk0bhg8fTkpKipmiktvJ0tICrzvqMPiFDox8vyfNBnphV9XG2J+dlkvIugiWTtrKqhd3c3rPBQoKTLYSWaVj0nXcfvnlFxYsWEBBQUGxPktLS6ZMmcLgwYNNdTv5F63jJiLmlJeXR2BgIMeOHQNg+vTpvPnmm2aOSsyhoMBAxL7CKlzsib+rcJfZVbHBt3Ndmg9uSBUXB/MEWU6ZNHEDOHv2LCtXriQ0NJSUlBScnJwICAjg3nvvpWHDhqa8lfyLEjcRMbdff/2Vhx56CA8PD/744w8cHfVqrLJLS8jkyOoznNx5nuy03KKdFuDWqAbNBnrR4A43LC31ev1aTJ64ifkocRORsuD06dPk5+fTqFEjc4ciZUhBfgFn/4zl6O9niTuZVKwKZ1/VpnAs3OCGVKmpKtyVmGxywvXKz8/XPnciIhWYt7d3sbaUlBTGjh3L66+/XmK/VHyWVpZ4d3DHu4M7qfEZHPntDCd3nScnPQ+ArNRcjq2N4Ni6CNwa16DZnV543eGGhapwRZhscsLPP/98zWPy8/N56aWXTHVLEREpBwwGA2PGjGHZsmW0bt2aoKAgc4ckZlbV1ZFOjzTlPx/3pseEFrj6/GOpMAPEhl8i6N2DfDduE3u+CSX9YtaVL1bJmKzitmDBAmrWrEmPHj1K7C8oKOCll15i27ZtprqliIiUA4mJiRw6dMj4tZeXl/mCkTLF0soS38718O1cj5S4DA7/eprTuy+Qk3G5CpfD0d/PcmztWWr71aD5nV40aFO5q3AmS9yaN2/OK6+8QrVq1WjdunWRvstJ29atW7nvvvtMdUsRESkHatWqxZ9//snYsWMZNWoUPj4+5g5JyqBqtR3p8t9mdHq4Cad2X+DY2rMknClcUsZggNiwS8SGXcK+mi2+nevSYnBDHGvYmznq289kkxPS09N58skniY2N5b333jMOSi0oKGDOnDls2rSJe+65h6efftoUt5MSaHKCiJQ3BoOBI0eO0KJFC3OHImVQckw6h389zZk/YoxVuMssLMDNr3AsXGWqwplsjJuTkxNz586lSpUqPPPMM5w/fx6DwcDLL7/Mpk2buPvuu5W0iYhIEfPmzaNVq1a8+eabaJED+bfqdZzoOrY5//m4N90eb46LVzVjn8EAMWGX2Dj/IN9P2MSeb0PJuFTxx8KZfDmQqKgoJkyYgJOTE40aNWLr1q0MHjyYZ555xpS3kRKo4iYi5cnBgwdp27atcaedPXv20L59ezNHJWVd0oU0Dv96hjN/xJCb+a8qnOXlKlxDGrSpXSG3XTP5rq+enp689dZbXLp0iW3btilpExGRErVo0YL//e9/WFhY8L///U9Jm1wXZ/cqdHusOf/5pDddxjajZoO/90c3FEBM6CU2vnOA78dv4o/vQslIzjZjtKZ30xW3xYsXX7X/0KFDnDx5knvvvRdLy7/zQwsLCx5++OGbuaVcgypuIlIe7dq1i/bt22uNT7lpl86lcnjVac7+GUtuVn6RPgtLC+r41aDpQC8atC7/VbibTty6d+9+cze0sGDLli03da5cnRI3EakogoKC+O677/jggw+0bZZct/y8Ao5viSZ0YyQXI1OL9Ts42xn3SHWsbmeGCG/dTS8H8t5775kyDhEREQDOnTvHyJEjiY+PZ9++fQQFBeHq6mrusKQcsLK2JKBPfQL61CcxMoXDv54mYl8cedmFVbjMpGyOrD7D0d/PUse/cEZq/XJWhbvpxC0wMNCEYYiIiBQ6duwYGRkZAHh4eODi4mLmiKQ8cqlfjZ4TAsnPzSdsUxShG6NIOpcGgKHAwIWQi1wIuYijsx2+XerSfJAXDtXL/rpwt32vUhERkavp168ff/75J1OmTOGbb74pMk5a5EZZ2VjRtL8XTft7kXg2hUO/nibywN9VuIykbA7/doYjv5/F3b8mze70wrOVa5mtwt104jZt2jT++9//EhAQcMPnZmZm8tNPP+Ho6KidFEREpJiAgAB+//33Yu1RUVFYWlpSr149M0Ql5Z2LVzV6PRVIbnYeYUFRhG2KIvl8OgCGfAPnjyVy/lgijjUuV+Ea4lCtbI2Fu+nELSkpiXHjxtGyZUv69+9Pt27dqFKlylXPOXbsGOvXr2fTpk1kZ2cza9asm729iIhUMjk5OQwbNowzZ87www8/0Lt3b3OHJOWUjZ01zQc2pPnAhsSfTuLwr2eIPBhHfk4BABmXsjn86xmOrDmLe0BNmg30wrNl2ajC3dICvL///juLFy8mJiYGS0tLPD098fPzo0aNGlSpUoWcnBxSUlKIiooiPDycjIwMLC0t6d27N2PGjMHNzc2Uz1LpaVapiFRkL7zwAnPmzAHA19eXkJAQbGxszByVVBS52XmEro8kfEsUyRcyivU71rTDv1d9mg1ogK2j+X7ubnnnBIPBwJ49e1izZg3BwcGkpKQUO8bS0hJvb2+6devGoEGDqFWr1q3cUq5AiZuIVGSJiYn85z//YfPmzezatYvWrVubOySpoOJOXOLwb2eIOhRvrMJdZmVryX2vd6G6u5NZYrvlyQkWFhZ07NiRjh07AnD27Fni4+NJSUnB1tYWZ2dnGjZseM3XqCIiIlfj4uLC6tWrCQ4OVtImpap2oxr0eboGuZl5HFsfwfGt0aTEFFbhqro6UK2O+dYWNPmsUi8vL7y8vEx9WRERESwtLUtM2iZNmoS/vz9PPPFEmRiHJBWDjYM1gXf7EHi3DzFhlwjfEoVb4xpm/RnTciAiIlKuffPNNyxYsACAAwcOsGjRIjNHJBVRHf8a1PGvYe4wTL/JvIiIyO0UGhpq/O/OnTubMRKR0qeKm4iIlGuvvfYarVu3ZufOnYwePdrc4YiUKlXcRESk3Bs2bBjz588v1r5r1y5ycnLMEJFI6VDiJiIiFdLBgwfp1asX3bp1IzIy0tzhiJiEEjcREalwCgoK+M9//kN2djZ//PEHH3zwgblDEjEJJW4iIlLhWFpa8vXXX9OwYUPatWtn3HFBpLwr9ckJPXr0YMuWLaV9GxERkSLatGnD/v37ycjIwM6u6EbhBoNB671JuVTqFbdb3FFLRETkptWoUYN69eoVaUtMTKRbt27s2bPHTFGJ3LwbStymTp3KJ598wubNm4mOjr6uc/75F823335Lbm7ujUUoIiJiIgUFBTz00EPs2LGDbt26sXz5cnOHJHJDbuhVab169Th06BA///wz2dnZODo64uPjQ+PGjWncuDGNGjXCy8sLS8u/88F/Vtw+++wzBg0aRI0ahSsPf/fdd9x33304ODiY6HFERESuLDU1ldTUVACqV69Ohw4dzByRyI25ocRtypQpQGEyFhkZSXh4OCdPnuTEiROsW7eOtLQ0bG1tWb9+fYnn//u16TfffEPPnj2NidulS5d4/PHHWbZs2c08i4iIyFVVr16dTZs2MWvWLPr27YuHh4e5QxK5ITc1OcHCwoIGDRrQoEED+vXrx4kTJ9i2bRurVq0iJSXluq/z70TOYDAQFxd3MyGJiIhcF2tra956661i7fn5+WzcuJH+/fubISqR63PTs0qPHj3Ktm3b2L59O/Hx8bRu3ZoxY8bQpUsXU8YnIiJyW7z88su8/PLLPP7447z77rvY29ubOySRYm4ocdu/fz9bt25l+/btZGZm0r59e8aMGUPHjh1xdHS8rmvs2rWLVq1aUbdu3ZsKWERExNSOHDliXOtt0aJFjB49WuPfpEy64TFutWrV4v/+7/+46667sLGxueY5/5xV2qhRI+bPn09eXh6Ojo7k5OTw3Xff0bx5cxo1akSVKlVu/AlERERuUfPmzfn8888ZP348s2fPVtImZdYNJW6NGjXi7NmzLFiwgC+//JJGjRrRqFEj46xST0/Pq57/2WefkZeXx9mzZzl+/DgnTpzg+PHjbNy4kezsbC2GKCIiZvPII4/QtWtXvL29i/VpwV4pK24ocSsp8Tp69Ci//PIL2dnZODg44Ovry/vvv3/lG1pb4+vri6+vr7HNYDAQFRVlnKUqIiJiDv/83XTZr7/+ynvvvcd3332Hm5ubGaIS+dsNT0642cTr7bffxsfHB19fX3x8fHBycjL2WVhYUL9+ferXr0/fvn1v4jFERERM7/Tp0zz00EMkJSXRqlUr9u7dqyVExKyuO3G71cTLYDCwbt06PvnkE7Kzs3FzczNe63IiqAkLIiJSliQkJODg4EBSUhKdOnUqtn2WyO12QxW3W0m8pk+fDhQmcAcPHuTFF1/k0qVLHDx4kJ9//pm0tDQcHBxo2LAhH3300a09lYiIiAm0a9eOgwcPMmvWLObPn69xbmJ21524PfPMM8CtJ14WFhZ8+eWXjB8/njvvvBOAvLw81q5dy8cff4yfn98tPpKIiIjpuLm58fnnnxdrP3nyJBkZGbRo0cIMUUlldcNj3EyReIWGhjJr1qy/g7C2ZvDgwdjZ2bFu3bobDUlEROS2ysjIYOjQoRw/fpyFCxcyevRoc4cklYTltQ8pLjQ0lMDAQOPXlxOvSZMmERUVdc3zfX192bJlS7H2pk2bcuTIkZsJSURE5LaZO3cuhw8fJisrizfffJOsrCxzhySVxE0lbreaeE2YMIHFixezYMECzp8/D0BBQQErV67UIrwiIlLmTZ8+nccffxwnJyd++uknbY8lt81N7VU6YcIEpk2bRnx8PMOGDaNu3bo3lHg1b96cjz76iPnz5zNy5EicnJzIy8sjLy+PadOm3UxIIiIit429vT0ff/wxzz77LA0aNCjSV1BQgKXlTdVFRK7pphI3UyRePj4+fPDBB5w/f54TJ05gYWGBn5+fFjcUEZFy499Jm8Fg4KGHHqJu3bq89tprWFvf1K9ZkSu66Z+o6028DAbDVa9Tt25drd8mIiIVwscff8x3330HFI4HX7VqlZYQEZO65T8FrpV4bd269VZvISIiUi4YDAasra3Jy8vj4YcfVtImJnfDiVtBQQFnz56lWrVq1KpVq0hfXl4eR48eLTLjVEREpLIYP348rVq1YvPmzQwbNszc4UgFdEOJW0xMDNOnTyciIgILCws6dOjAs88+S/Xq1QFISUlh8uTJJc44FRERqQw6duxIx44di7X/9ttvdO7cmRo1apghKqkobmjay8KFC6lVqxZLlixh0aJFZGdnM2HCBBISEozHXGtMm4iISGWze/du7r33Xtq0acOBAwfMHY6UYzeUuB06dIjx48fj7u5Oo0aNmDdvHi1atODJJ58kNjYWQO/zRURE/sFgMDB+/Hjy8vI4c+YMP/74o7lDknLshhK3rKwsbGxs/j7Z0pLp06fTtm1bnnrqKc6dO2fyAEVERMozCwsLVq5cSbt27ejWrRtz5swxd0hSjt1Q4la/fn3Cw8OLtU+dOpUOHTowc+ZMkwUmIiJSUdSvX5/t27fz888/F1vbLS8vz0xRSXl0Q4lbt27d2LBhQ4l9U6ZMoXfv3hrjJiIiUgJbW1tcXFyKtMXExNC0aVOWL19upqikvLEwKNOqMMLDwxk7diyLFi3Cz8/P3OGIiMhV5OXl0bdvX+NKDJ9++iljx441b1BS5t3SZmpnzpxRhU1EROQm5Obm4u7uDhQuZn/33XebOSIpD24pcXvkkUf4+uuvTRWLiIhIpeHg4MB3333HBx98wLJly6hdu7a5Q5Jy4JYSN4PBUKzitmrVKs2YERERuQ4WFhZMmDCBzp07F2nPzc3liy++oKCgwEyRSVl1S4lbSRITEwkKCiqx79tvv+Xxxx839S1FREQqlJkzZ/Lf//6XwYMHk5iYaO5wpAwxeeJ2Nbm5uSUuJyIiIiKFjh8/zrvvvgvAxo0bOX36tHkDkjLltiZuIiIicnWNGzdm3bp1uLq6Mn/+fNq2bWvukKQMuaFN5uX2SUpK4rXXXiM4OBhXV1emTJlCmzZtzB2WiIjcBn369CE0NJSaNWsW68vJycHW1tYMUUlZoIpbGTV//nxq1qzJqlWrGDduHC+++CIpKSnmDktERG4TFxeXYvt/L1myhFatWhEaGmqmqMTcbrnitmLFCk6cOIG/vz9+fn6kpaWZIq5KLSMjg+3bt7NkyRLs7e3p0qUL3t7e7Nixg4EDB5o7PBERMYPQ0FDGjBlDeno6bdu25dChQ/j4+Jg7LLnNbilxa9SoEWfPnmXHjh3s2LGjyF8GL774Ij4+Pvj4+ODr64ubm9stB3s1oaGhrF27loMHDxITE0O1atVo2rQpY8aMwdPTs9Tum5GRwZIlSwgJCSE0NJTU1FSeffZZ7rzzzhKPz8nJ4fPPP2f9+vWkpqbi4+PDmDFjioxhiI6OxsHBociaPt7e3pw5c6bUnkNERMo2a2trvL29OXLkCEOHDsXb29vcIYkZ3FLi9tlnn5GXl8epU6c4ceIE4eHhHD9+nFOnTrFlyxa2bNliTOYcHR2xt7c3SdAl+f777zly5Ag9e/bEx8eHxMREVqxYwZgxY1i4cGGp/YAnJyezePFi3Nzc8PX15eDBg1c9/vXXX2fLli0MHz4cDw8Pfv/9d6ZPn857771HixYtAMjMzMTJyanIeU5OTiQnJ5fKM4iISNnXqFEj9uzZw+uvv87MmTOLvUaVyuGWX5VaW1vj5+eHn58fgwcPBiA/P5+zZ88SHh5eJJlLTEwstR+0ESNG8MILL2BjY2Ns69WrF4888gjfffcdzz//fInnpaamcuDAAbp3715i/8aNG+ncuTMODg4l9ru4uLBixQpcXFwICwvjscceu2KMISEhBAUFMW7cOEaOHAlA//79GT16NAsXLmThwoVA4Wra6enpRc5NT0+/YgwiIlI5ODo6lrjI/ZEjRzh37hwDBgwwQ1RyO5XK5AQrKyt8fHwYOHAgTz/9NAsXLmTt2rUsXryYmTNnlsYtad68eZGkDcDT0xMvLy8iIiKueN6KFSuYPXs227ZtK9b322+/8fLLL7N27dornm9ra4uLi8t1xbh161asrKwYMmSIsc3Ozo5BgwZx7NgxYmNjAfDw8CAzM5P4+HjjcWfOnKFhw4bXdR8REak8UlJSGDp0KAMHDuTFF18kPz/f3CFJKbpts0otLS1p2LDhbf1rwGAwcOnSJapXr37FY0aNGkWHDh14+eWX2b9/v7F969atzJs3j379+nHPPfeYJJ4TJ07g4eFR7DVoQEAAACdPngQK/6Lq0qULX3zxBdnZ2ezatYtTp07RpUsXk8QhIiIVx6effsqJEycwGAz8+uuv5ObmmjskKUUVeh23DRs2EB8fz6OPPnrFY6ytrZk9ezbPPPMMs2bNYv78+WRmZjJnzhzat29v0nEEiYmJJVbnLrclJCQY26ZMmcKrr77K4MGDcXV1Zfbs2VSrVq3E627cuJGNGzdqRq+ISCU0ZcoU8vPzefvtt1m+fHmpjicX86uwiVtERATz58+nadOm16zy2dnZ8frrrzNp0iSmT59Obm4uAQEBvPTSS1hbm+5blJ2dXex1LmBcSDE7O9vY5uzszNtvv31d1+3Tpw99+vQhPDycsWPHmiZYEREpFywtLZkxYwZPPPFEsTdMWVlZ2NnZaSJDBVIhF+BNTExkxowZODk5MWfOHKysrK55jpOTE+PHjyclJYXMzEyeeuop7OzsTBqXnZ1diSXsnJwcY7+IiMjN+HfSZjAYeOCBB3jggQdITU01U1RiahUucUtLS2P69OmkpaUxd+5catWqdV3nnT9/njlz5lC/fn3q1KnD888/X+TVpSm4uLiQmJhYrP1y2/XGKiIici1z585l5cqVLFu2jAEDBmAwGMwdkphAhUrcsrOzmTlzJlFRUbzxxht4eXld13kJCQlMmTIFa2tr3nnnHebNm0dWVhZTp0416TZTvr6+REdHF1vqIyQkxNgvIiJiCo0bNzZW4Z577jm9Lq0gKkzilp+fz+zZszl27BgvvfQSzZo1u67zUlNTmTZtGpmZmbzzzjvUrl0bT09P5s6dS3x8PDNmzCAzM9MkMfbo0YP8/HxWrVplbMvJyWHNmjU0adKk1HeXEBGRyuPuu+9m//79fPzxx1fczUfKnwozOeHDDz9k586ddOrUidTUVNavX1+kv1+/fiWet2LFCuLi4liwYEGRrbEaNWrEG2+8wdSpU1m7di333nvvFe/9008/kZaWZnzluXPnTuLi4gAYOnQoVapUAaBJkyb07NmTTz/9lKSkJOrVq8fatWuJiYlhxowZt/T8IiIi/3Z568l/++qrr+jWrZvWBy2HLAwV5KX3xIkTCQ4OvmJ/SQvsAuTl5REVFXXFH95Tp07h7e191RLziBEjiImJKbFv6dKluLu7G7/Ozs427lWalpaGt7c3Y8aMoV27dle8/vW6PKt00aJF+Pn53fL1RESk4gkKCqJfv35Ur16dr7/+2rjrkZQPFSZxEyVuIiJydQaDga5du7Jz504AXnrpJV544QUzRyU3osK8KhUREZGrs7CwYPXq1Tz66KNkZGTw3HPPmTskuUFK3ERERCqR6tWrs3z5cjIyMrC0LDpHMS0tzTguW8qmCjOrVERERK6PhYVFsX2zIyMj8fX1Ze7cuVrzrQxT4iYiIlLJZWdnM3z4cGJjY3nmmWeue8tFuf2UuImIiFRy1tbW9O3bF4CGDRtq3+syTGPcREREKjkrKyteeeUVOnbsiLu7OzVq1DB3SHIFqriJiIgIAIMGDaJ169ZF2rKyspgzZ47JdhGSW6PETURERK5o0qRJvPDCC3Ts2JFTp06ZO5xKT4mbiIiIlOjs2bN8++23ABw/fpz09HQzRyRK3ERERKREXl5e/PHHH/j5+bFw4UJatGhh7pAqPU1OEBERkStq1qwZwcHB2NvbF2k3GAwkJSVpIsNtpoqbiIiIXNW/kzaAL774An9/fzZv3myGiCovJW4iIiJyQ4KDg5kwYQJxcXH06dOH0NBQc4dUaShxExERkRvi4eFB9+7dARg3bhwBAQFmjqjy0Bg3ERERuSG1atVizZo1fPbZZ4wePdrc4VQqqriJiIjIDbOysuLxxx/Hzs6uSPu+ffv49NNPtVF9KVHFTUREREwiMTGRYcOGERERwY4dO/jkk09wcHAwd1gViipuIiIiYhIrVqwgIiICgJMnT2JlZWXmiCoeJW4iIiJiEmPGjGHJkiU0bNiQZcuWYWtra+6QKhwlbiIiImIy999/P+Hh4Xh4eBRpT0pKIjc310xRVRxK3ERERMSkbGxsinydn5/P8OHD6dGjB9HR0WaKqmLQ5AQREREpVXPmzGHjxo0ADBo0iIMHD2JpqdrRzdB3TURERErVoEGDqF+/PpaWlixYsEBJ2y1QxU1ERERKVdu2bTlw4ABbtmwx7rggN0cpr4iIiJQ6FxcXhg4dWqx93rx57N271wwRlU9K3ERERMQsVq1axbRp0+jSpQsLFy40dzjlghI3ERERMYtFixYBkJubS1ZWlpmjKR80xk1ERETM4ueff2bmzJlERkYyefJkc4dTLihxExEREbOwsbFh3rx55OfnY2FhUaTv/Pnz1K1b10yRlV16VSoiIiJm9e89TU+cOEFAQABPPvkk2dnZZoqqbFLiJiIiImVGTk4Ow4YNIyUlhQ8//JDXXnvN3CGVKUrcREREpMywsbHhqaeews7OjoCAAJ555hlzh1SmaIybiIiIlBkWFhaMGTOGNm3aYGdnR5UqVcwdUpmiipuIiIiUOa1ataJJkyZF2tLS0njiiSeIj483U1Tmp8RNREREyjyDwcBjjz3GJ598QqtWrfjzzz/NHZJZKHETERGRMu/ChQts3rwZgJSUFKpXr27miMxDiZuIiIiUeXXr1uXAgQN0796dL774gsaNG5s7JLPQ5AQREREpF9zd3dm0aROWlkXrTgaDgYiICLy8vMwT2G2kipuIiIiUG/9O2gAWLFhAkyZN+Oabb8wQ0e2lxE1ERETKrX379jFt2jQyMzN56KGHCA4ONndIpUqJm4iIiJRbzZo145FHHgFg+vTpBAYGmjegUqYxbiIiIlJu2dvb8+mnnzJkyBAGDBhg7nBKnSpuIiIiUu4NHjwYa+ui9aht27Yxa9Ys8vLyzBSV6aniJiIiIhVOTEwM999/PzExMezevZsVK1bg7Oxs7rBumSpuIiIiUuHs2LGDhIQEAKysrKhataqZIzINJW4iIiJS4QwbNowtW7bQrl07vv/+e6ysrMwdkknoVamIiIhUSJ07d2bPnj1YWFgUaY+JicHe3r5cvjpVxU1EREQqrH8nbbm5uQwdOpQ77rijXK75psRNREREKo3Zs2eza9cuTp06xQMPPEB+fr65Q7ohStxERESk0hg7dix33HEHNjY2fP311+Vu7JvGuImIiEil4eXlxY4dO9izZw/t2rUzdzg3TBU3ERERqVTs7Ozo3r17sfZZs2axYsUKM0R0/ZS4iYiISKW3ZMkSXn/9de677z6ee+45c4dzRUrcREREpNLbuHGj8b8bNGhgxkiuTmPcREREpNJbtGgRgYGBHD58mDFjxpg7nCtS4iYiIiKVnoWFBU8++WSJfYcPH6ZZs2ZYWpr/RaX5IxAREREpo44cOUKHDh0YMmQIly5dMnc4StxERERESpKXl8eIESPIzMxk9erVvPXWW+YOSYmbiIiISEmsra159913cXFxoVWrVrz44ovmDklj3ERERESupH///hw8eJC8vDzs7e3NHY4SNxEREZGr8fT0NHcIRnpVKiIiIlJOKHETERERKSeUuImIiIiUE0rcRERERMoJJW4iIiIi5YQSNxEREZFyQombiIiISDmhxE1ERESknFDiJiIiIlJOKHETERERKSeUuImIiIiUE0rcRERERMoJJW4iIiIi5YQSNxEREZFywtrcAYjpZGdnAxAREWHmSERERORGNWjQAHt7+6seo8StAomJiQHglVdeMXMkIiIicqMWLVqEn5/fVY+xMBgMhtsUj5SypKQk9u7dyy+//MKkSZOu65z333+fp5566prHRURE8Morr/Dcc8/RoEGDWw213Lve75u53O74Sut+prrurVznZs690XOu53h9BovT5/D23M8U173Va5T257Cs/C5Uxa2ScXZ2pl+/fmzatOmaGftlVapUue5jofCH6kaOr6hu9Pt2u93u+Errfqa67q1c52bOvdFzbuR4fQb/ps/h7bmfKa57q9co7c9hefpdqMkJFVCfPn1K5Vj5W1n/vt3u+Errfqa67q1c52bOvdFzyvrPU1lV1r9v+hya7hql/Tks6z9L/6RXpXJdwsPDGTt27HW9fxcR09NnUMT8ysLnUBU3uS4uLi6MHj0aFxcXc4ciUinpMyhifmXhc6iKm4iIiEg5oYqbiIiISDmhxE1ERESknFDiJiaRk5PDG2+8wbBhwxgwYABPPPEER48eNXdYIpXK22+/zT333MOAAQN4+OGH2blzp7lDEqm0jh49Svfu3fnqq69Mel2NcROTyMzMZOnSpdx55524urqyefNm3n33XZYuXYqjo6O5wxOpFCIiInB3d8fW1pbQ0FCmTJnCkiVLqF69urlDE6lUCgoKGD9+PAaDgU6dOvHwww+b7NqquIlJODg4MHr0aNzc3LC0tKR3795YW1sTFRVl7tBEKo0GDRpga2sLgIWFBbm5uSQkJJg5KpHK59dffyUgIKBUdlfQzgmVVEZGBkuWLCEkJITQ0FBSU1N59tlnufPOO4sdm5OTw+eff8769etJTU3Fx8eHMWPG0LZt2ytePyoqitTUVOrVq1eajyFSbpXWZ/Cdd95hzZo15OTk0KFDB7y9vW/H44iUS6XxOUxOTubHH39k4cKFvP/++yaPWRW3Sio5OZnFixcTERGBr6/vVY99/fXXWbZsGX379mXixIlYWloyffp0Dh8+XOLx2dnZvPLKKzz44INUqVKlNMIXKfdK6zM4ZcoU1q1bx/z582nbti0WFhal9Qgi5V5pfA4XLVrE8OHDqVq1aqnErMStknJxcWHFihX8+OOPjBs37orHhYSEEBQUxGOPPcb48eMZMmQI7777LnXq1GHhwoXFjs/Ly+OFF16gXr16jB49uhSfQKR8K63PIICVlRVt2rRh//797N69u7QeQaTcM/Xn8Pjx44SFhTF48OBSi1mJWyVla2t7XSs/b926FSsrK4YMGWJss7OzY9CgQRw7dozY2Fhje0FBAa+88goWFhbMmjVLf+mLXEVpfAb/LT8/n3PnzpkkXpGKyNSfw+DgYKKiohg6dCj33HMPmzZt4vvvv+f11183Wcwa4yZXdeLECTw8PHBycirSHhAQAMDJkydxc3MDYO7cuSQmJjJ37lysrfWjJWIK1/sZTEtLY/fu3XTu3BlbW1u2b9/OwYMHeeyxx8wRtkiFcr2fwyFDhtC7d29j/4IFC3B3d+fBBx80WSz67SpXlZiYWOJfI5fbLs9Yi4mJ4bfffsPW1rbIXyRvvfUWLVu2vD3BilRA1/sZtLCw4LfffmP+/PkYDAbq1avH888/T6NGjW5rvCIV0fV+Du3t7bG3tzf229nZ4eDgYNLxbkrc5Kqys7OxsbEp1n55yYHs7GwA6tSpw7Zt225rbCKVwfV+Bp2cnHjvvfdua2wilcX1fg7/bdasWSaPRWPc5Krs7OzIzc0t1p6Tk2PsF5HSo8+giPmVpc+hEje5KhcXFxITE4u1X26rVavW7Q5JpFLRZ1DE/MrS51CJm1yVr68v0dHRpKenF2kPCQkx9otI6dFnUMT8ytLnUImbXFWPHj3Iz89n1apVxracnBzWrFlDkyZNjDNKRaR06DMoYn5l6XOoyQmV2E8//URaWpqx1Ltz507i4uIAGDp0KFWqVKFJkyb07NmTTz/9lKSkJOrVq8fatWuJiYlhxowZ5gxfpNzTZ1DE/Mrb59DCYDAYbusdpcwYMWIEMTExJfYtXboUd3d3oHC2zOX92dLS0vD29mbMmDG0a9fudoYrUuHoMyhifuXtc6jETURERKSc0Bg3ERERkXJCiZuIiIhIOaHETURERKScUOImIiIiUk4ocRMREREpJ5S4iYiIiJQTStxEREREygklbiIiIiLlhBI3ERERkXJCiZuIiIhIOaHETUSkEli2bBm9evXiwoULxrbff/+dbt268fvvv5sxsr/99ttv9OjRg1OnTpk7FJEyS4mbiJQ7Fy5coFu3blf9vxEjRpg7zDIjNTWVr7/+moEDBxo3zC4te/fupVu3bkydOvWax7788st069aNDRs2ADBgwADc3NxYuHBhqcYoUp5ZmzsAEZGbVa9ePfr27VtiX5UqVW5zNGXXsmXLSElJYeTIkaV+rzvuuAM3Nzf2799PbGwsbm5uJR6XlpbG9u3bqVKlCt26dQPA2tqaESNG8N5773HkyBGaN29e6vGKlDdK3ESk3KpXrx6PPvqoucMo0/Ly8vjtt99o3rw59erVK/X7WVpacuedd7J48WLWrl3Lww8/XOJxGzduJDs7m4EDB2JnZ2ds7927Nx988AErV65U4iZSAr0qFZFKoVu3bkycOJGLFy/y6quvctddd9GnTx+eeOIJDh48WOI5GRkZfPHFFzz00EP06dOHgQMHMnXqVA4fPlzs2IkTJ9KtWzeys7NZtGgRDzzwAD179uSLL74wHrN161bGjh1Lnz59uPvuu3nrrbdITU1lxIgRRV7tzpkzh27duhESElJiXJ9//jndunVj48aN13zuvXv3kpiYSI8ePa557GVxcXE8/PDD9OnThy1bthjbL126xPvvv8/IkSPp3bs3d911F8899xynT58ucv7AgQOxsLDg999/x2AwlHiPNWvWADBo0KAi7c7OzrRq1YotW7aQkZFx3TGLVBZK3ESk0khLS2PChAmcPXuWfv360a1bN8LDw5k2bVqx5CMlJYVx48axePFiqlatyt133023bt04fvw4kyZNYvv27SXe4/nnn2ft2rW0atWKYcOGGceUrV69mueff57o6Gj69+/PgAEDOHbsGFOmTCEvL6/INYYMGWI859/y8/NZs2YN1atXN75ivJr9+/cD0LRp02t/g4CzZ88yfvx44uLiePvtt40J37lz5xgzZgw//vgjdevW5b777qNDhw7s3buXcePGFUky69SpQ5s2bTh//nyJSfHp06cJCwujUaNGNG7cuFh/06ZNycnJ4ejRo9cVs0hlolelIlJunTt3rkhF65+aNm1K+/bti7SdPHmSe+65h8mTJ2NpWfh3a+vWrXnrrbf4+eefmTZtmvHYd999lzNnzjB9+nQGDx5sbL906RJjx47l7bffpl27dkVe8wEkJiby5ZdfUq1aNWNbamoqCxYswMHBgU8//RRPT08Axo4dy7Rp0wgPD6dOnTrG41u2bImXlxdBQUE8+eSTODg4GPv27t1LfHw8w4cPx9bW9prfoyNHjmBpaYmvr+81jz127BgzZszA2tqa999/v8g5r776KhcvXmTu3Lm0a9fO2P7QQw8xduxY3nrrLRYvXmxsHzRoEPv27WPNmjW0bt26yH2uVG27zM/PD4CjR48WuZeIqOImIuXYuXPnWLx4cYn/98cffxQ73sHBgSeeeMKYtEHhTEYrKyvCwsKMbUlJSWzevJnWrVsXSdoAatSowciRI0lKSjJWs/7pkUceKZK0AezYsYPMzEwGDhxoTNqgcDD+mDFjSny2IUOGkJGRQVBQUJH23377DYC77rrrSt+WIuLj46lSpco1k7zdu3fz9NNPU7VqVT766KMiSdvx48c5evQo/fv3L5ZIeXp6MnjwYE6fPl2katm1a1eqV6/O1q1bSU9PN7bn5eWxfv16bG1trzixpGbNmkDhK1sRKUoVNxEpt9q1a8fcuXOv+3gPDw8cHR2LtFlbW1OzZk3S0tKMbWFhYeTn55Obm1tiRS86OhqAiIgIOnXqVKQvICCg2PGX1yVr0aJFsb4mTZpgZWVVrL1///588skn/Pbbb8bk8eLFi+zatYtmzZrh5eV1jactlJKSgqur61WP2bx5M3/++Sc+Pj68/fbb1KhRo0j/5degly5dKvH7ERkZafz/3t7eAMbEbPny5WzcuJG7774bgJ07d5KUlESfPn2oWrVqifFcbk9OTr6uZxSpTJS4iUil4eTkVGK7lZUVBQUFxq9TUlKAwteMR44cueL1srKyirVdrhb90+WK078TIiichVm9evVi7VWrVqVnz56sXbuW06dP4+3tze+//05+fv51V9sA7OzsyMnJueoxx44dIz8/nxYtWpQY4+Xvx+7du9m9e/cVr5OZmVnk60GDBrF8+XLWrFljTNyu9ZoUMMZrb29/1bhFKiMlbiIi/3I5wbv//vuZMGHCDZ1rYWFxxetdunSpWF9BQQHJycklVsXuvvtu1q5dy6+//sqkSZNYvXo1Tk5O9OzZ87rjqV69OvHx8Vc95rHHHmPHjh0sX74cKyurYs98Of5JkyYxdOjQ6763j48P/v7+hIaGcubMGapWrcrevXtxd3cvNu7tny4nis7Oztd9L5HKQmPcRET+xd/fHwsLC44dO2aS6/n4+ACUWL0LDQ0lPz+/xPOaNm2Kj48PGzZsYO/evURHR9O3b98bqkR5e3uTk5NDbGzsFY+xtbXl1VdfpWPHjixdupQPPvigSP/l17838/24XFlbvXo169atIz8/37hcyJVcfvV6+bWriPxNiZuIyL+4uLjQs2dPjh49yg8//FDiWmQhISElviotSZcuXXBwcGD16tWcO3fO2J6Xl8fnn39+1XOHDBlCSkoKb7zxBkCxyRLXEhgYaIz3amxtbXnllVfo1KkTy5Yt4/333zf2NWnShCZNmhAUFFRssgQUVg2Dg4NLvG6fPn2wt7dn/fr1rFmzBktLSwYMGHDVWEJDQ4vELiJ/06tSESm3rrYcCMCDDz5YbLmO6zVlyhSioqJYuHAh69ato2nTplSpUoX4+HjCwsKIjo5mxYoV11X9qlq1Kk8++SRvv/02Y8eOpVevXjg5ObFnzx5sbW2pVavWFStQ/fr14+OPPyYhIQE/P78S1z27mi5duvDhhx+yb9++a75itbGxYc6cObzwwgv8+OOPGAwGJk6cCMALL7zA5MmTeemll1i+fDmNGjXCzs6OuLg4jh49SnJycokLAjs5OdG9e3fWrVtHUlIS7du3v+I2WAAGg4H9+/fToEGDIjNwRaSQEjcRKbcuLwdyJcOHD7/pxK1atWp89NFH/Pzzz2zatImNGzdSUFBAzZo18fX15eGHHy5xUsGV3HXXXVStWpVvvvmGtWvX4uTkROfOnXniiScYPnz4FbejcnJyomvXrqxfv/6Gq20A7u7utG3bli1btjBp0qRrLgtyOXl78cUXWb58OQaDgUmTJlG3bl0+//xzli5dyvbt2/n999+xtLTExcWFli1bXnVnhkGDBrFu3TqgcFeFqzl06BCxsbE89dRTN/ysIpWBheFK+5GIiEipi46OZtSoUfTs2ZOXXnqpxGMefvhhYmJi+Pnnn684M/Zq9u/fz9NPP81zzz1Hv379bjXkUjVnzhz++OMPfvjhhysuFyJSmWmMm4jIbZCamlpsWY7s7GzjRICuXbuWeN6ePXs4c+YMffr0uamkDaBNmza0b9+er7/+usiyJ2VNVFQUmzZt4qGHHlLSJnIFelUqInIbBAcH8+abb9K2bVtq165NcnIyBw4cICYmhtatW9OrV68ix//yyy/ExcXx22+/YWtry4MPPnhL9584cSIbNmwgPj7+qmPMzCkuLo7Ro0dz7733mjsUkTJLr0pFRG6DqKgoPv/8c44ePUpSUhIA9erVo1evXjzwwAPFxuKNGDGC+Ph4PD09eeKJJ4rt0CAilZMSNxEREZFyQmPcRERERMoJJW4iIiIi5YQSNxEREZFyQombiIiISDmhxE1ERESknFDiJiIiIlJOKHETERERKSeUuImIiIiUE/8PK2sTT3Ce/54AAAAASUVORK5CYII=",
       "text/plain": [
        "
"
       ]
@@ -1649,7 +1589,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 15,
@@ -1658,7 +1598,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1701,7 +1641,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 16,
@@ -1710,7 +1650,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1739,9 +1679,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:COSIPY]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-COSIPY-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1753,7 +1693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

From ae70e5068a4b3a9d26b33a70603a5e18454cee39 Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Tue, 12 Nov 2024 11:56:45 -0500
Subject: [PATCH 05/12] updated crab notebook

---
 .../spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb    | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
index 318c8e81..94906d20 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
@@ -369,8 +369,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#data_path = Path(\"/path/to/files\")\n",
-    "data_path = Path(\"/discover/nobackup/ckarwin/COSI/COSIpy_Development/Crab_Notebook\")"
+    "data_path = Path(\"/path/to/files\")"
    ]
   },
   {

From 52f3bb6d93bfe4cbbf3e031626f16172fbb8911b Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Tue, 12 Nov 2024 12:19:11 -0500
Subject: [PATCH 06/12] updated GRB spectral fit notebook with new ori file

---
 .../continuum_fit/grb/SpectralFit_GRB.ipynb   | 288 +++++++-----------
 1 file changed, 114 insertions(+), 174 deletions(-)

diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
index 8f0a2579..5626f64b 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
@@ -16,7 +16,7 @@
    "metadata": {},
    "source": [
     "**To run this, you need the following files, which can be downloaded using the first few cells of this notebook:**\n",
-    "- orientation file (20280301_3_month.ori)       \n",
+    "- orientation file (20280301_3_month_with_orbital_info.ori)       \n",
     "- binned data (grb_bkg_binned_data.hdf5, grb_binned_data.hdf5, & bkg_binned_data_1s_local.hdf5)     \n",
     "- detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.nonsparse_nside8.area.good_chunks_unzip.h5.zip)     \n",
     "\n",
@@ -72,12 +72,12 @@
     {
      "data": {
       "text/html": [
-       "
12:04:24 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48\n",
+       "
12:06:30 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48\n",
        "                  available                                                                                        \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m12:04:24\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=258198;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=478015;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m12:06:30\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=461027;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=498624;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -87,12 +87,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  functions.py:69\n",
+       "
         WARNING   The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  functions.py:69\n",
        "                  will not be available.                                                                           \n",
        "
\n"
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-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=559824;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=851427;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-      ]
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-     "output_type": "display_data"
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-      ]
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-     "output_type": "display_data"
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
         WARNING   The cthreeML package is not installed. You will not be able to use plugins which  __init__.py:94\n",
+       "
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        "                  require the C/C++ interface (currently HAWC)                                                     \n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=844985;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=163953;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m12:06:34\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=249297;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=177693;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m                                      \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m              \u001b[0m\n"
       ]
      },
@@ -286,12 +236,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   Could not import plugin HAWCLike.py. Do you have the relative instrument         __init__.py:144\n",
+       "
         WARNING   Could not import plugin FermiLATLike.py. Do you have the relative instrument     __init__.py:144\n",
        "                  software installed and configured?                                                               \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=871918;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=23540;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=358069;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=318978;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -301,12 +251,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   Could not import plugin FermiLATLike.py. Do you have the relative instrument     __init__.py:144\n",
+       "
         WARNING   Could not import plugin HAWCLike.py. Do you have the relative instrument         __init__.py:144\n",
        "                  software installed and configured?                                                               \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=354714;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=655302;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=114297;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=18607;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -316,11 +266,11 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   No fermitools installed                                              lat_transient_builder.py:44\n",
+       "
12:06:37 WARNING   No fermitools installed                                              lat_transient_builder.py:44\n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=194521;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=717765;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m12:06:37\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=366994;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=746721;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -329,12 +279,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   Env. variable OMP_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
+       "
12:06:37 WARNING   Env. variable OMP_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
        "                  performances in 3ML                                                                              \n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=22466;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=967970;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m12:06:37\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=750524;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=117728;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -344,12 +294,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
+       "
         WARNING   Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
        "                  performances in 3ML                                                                              \n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=971686;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=989174;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=372746;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=366281;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -359,12 +309,12 @@
     {
      "data": {
       "text/html": [
-       "
         WARNING   Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal     __init__.py:387\n",
+       "
         WARNING   Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal     __init__.py:387\n",
        "                  performances in 3ML                                                                              \n",
        "
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=980480;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=239627;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=525214;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=935569;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -395,7 +345,9 @@
     "\n",
     "from pathlib import Path\n",
     "\n",
-    "import os"
+    "import os\n",
+    "\n",
+    "%matplotlib inline"
    ]
   },
   {
@@ -439,7 +391,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month.ori', output=str(data_path / '20280301_3_month.ori'))"
+    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output=str(data_path / '20280301_3_month_with_orbital_info.ori'))"
    ]
   },
   {
@@ -532,12 +484,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "ed2c03a0-63e3-4044-9e16-50f0f17996af",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ori = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month.ori\")\n",
+    "ori = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month_with_orbital_info.ori\")\n",
     "tmin = Time(1842597410.0,format = 'unix')\n",
     "tmax = Time(1842597450.0,format = 'unix')\n",
     "sc_orientation = ori.source_interval(tmin, tmax)"
@@ -675,19 +627,7 @@
    "execution_count": 10,
    "id": "98b2d026-c24d-4cfe-8b7b-41415fce5d16",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "... Calculating point source responses ...\n",
-      "Now converting to the Spacecraft frame...\n",
-      "Conversion completed!\n",
-      "--> done (source name : source)\n",
-      "--> all done\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "l = 93.\n",
     "b = -53.\n",
@@ -748,11 +688,11 @@
     {
      "data": {
       "text/html": [
-       "
12:04:56 INFO      set the minimizer to minuit                                             joint_likelihood.py:1042\n",
+       "
12:15:15 INFO      set the minimizer to minuit                                             joint_likelihood.py:1045\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m12:04:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=963889;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=362192;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/classicMLE/joint_likelihood.py#1042\u001b\\\u001b[2m1042\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m12:15:15\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=758656;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=974578;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1045\u001b\\\u001b[2m1045\u001b[0m\u001b]8;;\u001b\\\n"
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      },
      "metadata": {},
@@ -768,12 +708,12 @@
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      "data": {
       "text/html": [
-       "
12:05:22 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:128\n",
+       "
12:16:19 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:130\n",
        "                  measurements such as AIC or BIC are unreliable                                                   \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m12:05:22\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=395184;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=957324;file:///Users/eneights/opt/anaconda3/envs/cosipy/lib/python3.9/site-packages/threeML/plugin_prototype.py#128\u001b\\\u001b[2m128\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m12:16:19\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=535176;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=502302;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\u001b\\\u001b[2m130\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                       \u001b[0m\n"
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@@ -829,7 +769,7 @@
        "    \n",
        "      source.spectrum.main.Band.K\n",
        "      (3.10 -0.20 +0.21) x 10^-2\n",
-       "      1 / (cm2 keV s)\n",
+       "      1 / (keV s cm2)\n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.alpha\n",
@@ -858,7 +798,7 @@
       "text/plain": [
        "                                                     result             unit\n",
        "parameter                                                                   \n",
-       "source.spectrum.main.Band.K      (3.10 -0.20 +0.21) x 10^-2  1 / (cm2 keV s)\n",
+       "source.spectrum.main.Band.K      (3.10 -0.20 +0.21) x 10^-2  1 / (keV s cm2)\n",
        "source.spectrum.main.Band.alpha      (-2.8 +/- 0.5) x 10^-1                 \n",
        "source.spectrum.main.Band.xp         (4.75 +/- 0.05) x 10^2              keV\n",
        "source.spectrum.main.Band.beta                 -6.8 +/- 1.2                 \n",
@@ -888,19 +828,19 @@
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-       "-0.37 -0.16  1.00 -0.18 -0.02\n",
-       " 0.20  0.18 -0.18  1.00  0.00\n",
+       " 1.00  0.97 -0.37  0.15 -0.00\n",
+       " 0.97  1.00 -0.15  0.12 -0.00\n",
+       "-0.37 -0.15  1.00 -0.17 -0.02\n",
+       " 0.15  0.12 -0.17  1.00  0.00\n",
        "-0.00 -0.00 -0.02  0.00  1.00"
       ]
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@@ -951,11 +891,11 @@
        "  \n",
        "    \n",
        "      cosi\n",
-       "      42920.049336\n",
+       "      42920.049339\n",
        "    \n",
        "    \n",
        "      total\n",
-       "      42920.049336\n",
+       "      42920.049339\n",
        "    \n",
        "  \n",
        "\n",
@@ -963,8 +903,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi       42920.049336\n",
-       "total      42920.049336"
+       "cosi       42920.049339\n",
+       "total      42920.049339"
       ]
      },
      "metadata": {},
@@ -1014,11 +954,11 @@
        "  \n",
        "    \n",
        "      AIC\n",
-       "      85838.098672\n",
+       "      85838.098678\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      85840.098672\n",
+       "      85840.098678\n",
        "    \n",
        "  \n",
        "\n",
@@ -1026,8 +966,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC          85838.098672\n",
-       "BIC          85840.098672"
+       "AIC          85838.098678\n",
+       "BIC          85840.098678"
       ]
      },
      "metadata": {},
@@ -1037,21 +977,21 @@
      "data": {
       "text/plain": [
        "(                                      value  negative_error  positive_error  \\\n",
-       " source.spectrum.main.Band.K        0.030995       -0.001939        0.002116   \n",
-       " source.spectrum.main.Band.alpha   -0.276632       -0.050195        0.049825   \n",
-       " source.spectrum.main.Band.xp     474.650732       -4.896060        4.873162   \n",
-       " source.spectrum.main.Band.beta    -6.756966       -1.215124        1.201150   \n",
-       " background_cosi                    0.164969       -0.012573        0.012434   \n",
+       " source.spectrum.main.Band.K        0.030988       -0.001991        0.002072   \n",
+       " source.spectrum.main.Band.alpha   -0.276759       -0.050823        0.049727   \n",
+       " source.spectrum.main.Band.xp     474.668373       -4.924520        5.001136   \n",
+       " source.spectrum.main.Band.beta    -6.757898       -1.224829        1.186228   \n",
+       " background_cosi                    0.164958       -0.012051        0.012140   \n",
        " \n",
        "                                     error             unit  \n",
-       " source.spectrum.main.Band.K      0.002027  1 / (cm2 keV s)  \n",
-       " source.spectrum.main.Band.alpha  0.050010                   \n",
-       " source.spectrum.main.Band.xp     4.884611              keV  \n",
-       " source.spectrum.main.Band.beta   1.208137                   \n",
-       " background_cosi                  0.012504                   ,\n",
+       " source.spectrum.main.Band.K      0.002032  1 / (keV s cm2)  \n",
+       " source.spectrum.main.Band.alpha  0.050275                   \n",
+       " source.spectrum.main.Band.xp     4.962828              keV  \n",
+       " source.spectrum.main.Band.beta   1.205529                   \n",
+       " background_cosi                  0.012096                   ,\n",
        "        -log(likelihood)\n",
-       " cosi       42920.049336\n",
-       " total      42920.049336)"
+       " cosi       42920.049339\n",
+       " total      42920.049339)"
       ]
      },
      "execution_count": 11,
@@ -1178,7 +1118,7 @@
        "    \n",
        "      source.spectrum.main.Band.K\n",
        "      (3.10 -0.20 +0.21) x 10^-2\n",
-       "      1 / (cm2 keV s)\n",
+       "      1 / (keV s cm2)\n",
        "    \n",
        "    \n",
        "      source.spectrum.main.Band.alpha\n",
@@ -1207,7 +1147,7 @@
       "text/plain": [
        "                                                     result             unit\n",
        "parameter                                                                   \n",
-       "source.spectrum.main.Band.K      (3.10 -0.20 +0.21) x 10^-2  1 / (cm2 keV s)\n",
+       "source.spectrum.main.Band.K      (3.10 -0.20 +0.21) x 10^-2  1 / (keV s cm2)\n",
        "source.spectrum.main.Band.alpha      (-2.8 +/- 0.5) x 10^-1                 \n",
        "source.spectrum.main.Band.xp         (4.75 +/- 0.05) x 10^2              keV\n",
        "source.spectrum.main.Band.beta                 -6.8 +/- 1.2                 \n",
@@ -1237,19 +1177,19 @@
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-       "-0.37 -0.16  1.00 -0.18 -0.02\n",
-       " 0.20  0.18 -0.18  1.00  0.00\n",
+       " 1.00  0.97 -0.37  0.15 -0.00\n",
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        "-0.00 -0.00 -0.02  0.00  1.00"
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@@ -1300,11 +1240,11 @@
        "  \n",
        "    \n",
        "      cosi\n",
-       "      42920.049336\n",
+       "      42920.049339\n",
        "    \n",
        "    \n",
        "      total\n",
-       "      42920.049336\n",
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        "\n",
@@ -1312,8 +1252,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi       42920.049336\n",
-       "total      42920.049336"
+       "cosi       42920.049339\n",
+       "total      42920.049339"
       ]
      },
      "metadata": {},
@@ -1363,11 +1303,11 @@
        "  \n",
        "    \n",
        "      AIC\n",
-       "      85838.098672\n",
+       "      85838.098678\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      85840.098672\n",
+       "      85840.098678\n",
        "    \n",
        "  \n",
        "\n",
@@ -1375,8 +1315,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC          85838.098672\n",
-       "BIC          85840.098672"
+       "AIC          85838.098678\n",
+       "BIC          85840.098678"
       ]
      },
      "metadata": {},
@@ -1408,28 +1348,28 @@
       "      * main:\n",
       "        * Band:\n",
       "          * K:\n",
-      "            * value: 0.030994516909178687\n",
+      "            * value: 0.03098812082120686\n",
       "            * desc: Differential flux at the pivot energy\n",
-      "            * min_value: 1.0e-99\n",
+      "            * min_value: 1.0e-50\n",
       "            * max_value: null\n",
       "            * unit: keV-1 s-1 cm-2\n",
       "            * is_normalization: true\n",
       "          * alpha:\n",
-      "            * value: -0.27663221293105034\n",
+      "            * value: -0.27675895447494075\n",
       "            * desc: low-energy photon index\n",
       "            * min_value: -1.5\n",
       "            * max_value: 3.0\n",
       "            * unit: ''\n",
       "            * is_normalization: false\n",
       "          * xp:\n",
-      "            * value: 474.6507320770641\n",
+      "            * value: 474.66837288907766\n",
       "            * desc: peak in the x * x * N (nuFnu if x is a energy)\n",
       "            * min_value: 10.0\n",
       "            * max_value: null\n",
       "            * unit: keV\n",
       "            * is_normalization: false\n",
       "          * beta:\n",
-      "            * value: -6.756965748051311\n",
+      "            * value: -6.757897666561581\n",
       "            * desc: high-energy photon index\n",
       "            * min_value: -15.0\n",
       "            * max_value: -1.6\n",
@@ -1519,7 +1459,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 15,
@@ -1528,7 +1468,7 @@
     },
     {
      "data": {
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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1570,7 +1510,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 16,
@@ -1579,7 +1519,7 @@
     },
     {
      "data": {
-      "image/png": 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enrz++uvn7dmZNm0ab7zxBvv27ePLL78ETu9AMWTIEJYtW8aTTz4JwJEjR6iurqZr166UlpaydetWhg4dyscff3ze9+zi4sIVV1zBkiVLeOONN+jVqxcJCQnk5uZa5mad/fmVlJRYZRL3772Hli1b4ujoyNVXX81VV13Fli1bSEhIoFevXpw6dYp3332X22+/nf/85z9kZ2fTq1cvWrduzd///neys7Px8/PjtddeY9SoURdcRyk5OZnOnTtzxx130L9/f6688krg9J/N5cuXM2DAANzd3SkpKSEtLY0ePXo06H2fTUWTFWhxy9oKfi3gH93+QUVJBW/yJq1pzUQmUkIJT/M0k5hEd7qzi12sYx0LWQjAu7yLI45MZjKVVLKEJdzADfSmN3vYwyd8whM8gQsufMiHVFGFH370ox/LWc44xtGPfiSSyPu8z6M8igcefMZnHOc4d3O6y/4pnmI4w7mSK0kllbd5m9nMxgUX/Bz9uPbNa4noEcHg5ME1eppuTr/Z0tN07dFra/Q0DUsdVqOn6fq06y09TRNyJlh6mg4lHiJlaQpT/ziVK668grLyMsYdHGfpaboh74YaPU0jD42s0dN0VcpVlp6miScmcseOO0icl0hJbomKJrGKm266CVdXVyoqKujUqZNlgvXtt99OXl4ew4cPx8HBgcrKSu6++24uu+wyHn/8cQ4cOICLiwvu7u68+uqrANx77708/PDDPPPMMzz11FOMHTu2zjl+77VOTk78+9//5oEHHmDx4sU4ODgwc+ZM7rvvPt59913uu+8+XnjhBRwcHFi1ahVBQUF1uuZdd93FY489Zil4AMaMGcOePXsYOHAgDg4O9OrVi1deeeWcr58wYQJ//OMfiYiIqLGkzbvvvsuf//xnoqKiAGjVqhWvvfYagYGBvPfee9x///2WzeWDg4PPm+9MUTJo0CAcHByIjo4mNTXVMnT4W+3atWPKlClERUXh4eHBzp076/QZnM/53kNVVRXTp0+noqKC6upqrrzySsaMGcPhw4fx9vYmJSWFK664gqKiIv7v//4PDw8PevTowd/+9jfLDVlBQUG8/vrrF8zw0Ucf8e677+Li4oJhGJY/m3PnzmXRokVcccUVlsJrzpw5Vi+aHAxDu0Ray5k5TStXrrT8ZNZcffPeN4y7dRzvLH2Hjt062qynKSMlg0/f/ZQHH36Qk8bJBvc0tXRpiUMrB/y6+Jn58V2U7z//nttvuJ23P3ubQdcPuvALpNEoKysjLS2N0NDQGsMRIk3BoUOH6NevH7m5uWZHsWjod049TWITfr5+jGIU3fp3IyomqsZzQf3/9xOfP/70pOf/Hp81nyOgX0CNc7vTvca5AxjAzX++udb1/fGnK13P2+5vH/vjTxfsdx0YJ0cnPPDAyVFfZxERW7LrdZqk8fJp58MABuDTThsa21pwQDA3cRPBAefv0hcRudQ6derUqHqZrEE/mlqB5jTVVlhUSAopFBYV4k/juRuoKaqqqqKMMqqqqsyOIiLSpKmnyQq0YW9taRlpvMM7pGWkmR2lydt3YB/LWMa+A/vMjiIi0qSpaBKb6BrelYd4iK7hXS98sjRIcEAwN3OzhudERGxMRZPYhIuzC1544eLsYnaUJq9N6zb0oAdtWrcxO4qISJOmoklsIjMrk3WsIzMr0+woTd7x/OP8xE8czz9udhRpAj799FOio6Pp06cP3bp1Y9SoUVRXV5sdy1QLFy7k1KlTZseQRkATwa1AE8FrKy0rJYssSstKzY7S5GVmZfJv/s0fs/5ID6y7kJs0L9nZ2cyYMYP//ve/ln2/4uPjL7hK88WwxurUl9qiRYt45JFHcHFRz3lzp54mK9BE8NoiQiO4l3uJCI0wO0qTF9U1iid5kqiuURc+WeR3ZGVl4eTkhLe3t+VY3759LUXTzp07GThwIL169aJ///58//33wOlFDH18/re8SFFRUa19xZ577jmGDx/O448/TkFBAffccw9RUVH07t3bspddRUUFc+fOpX///vTp04fJkyeTn59/zqwbNmzg8ssvp3fv3vTp04cff/wRgI0bN9K3b1969erFsGHDSEhIAGDz5s3069fP8vq9e/fSqVOnGvnnz59PdHQ04eHhfPHFFwDMmDEDgCuvvJI+ffpw7NgxVq1aRffu3enTpw9RUVGWa0vTZ1/lvojU4uDgQBll/LTlJ8tGmHsS9xDoH0i7Nu3IP5nPr4d/tayk/uvhX6msqrTs2bcncQ8dO3TEu603JwtPcijzkGUl9cysTMrKyywrqe87sI/23u3x9falqLiIg78epEvnLri2dOVw9mGKS4rp0vn0QqH7k/fTrk07Ovh2oKS0hJRDKZYtaLKOZeHi5ULPgT3P+76aq6ysLHJzcy1bVSQkJODp6UlQUBBlZWUkJCQQERGBp6cnR48eJTs7m969ewOndyVwdXUlJCSEiooK9uzZQ1hYGF5eXuTk5FBZWYm///mXAOnduzcDBw4kODiYYcOGceWVV3LrrbcSEBDAqVOnuPHGG1m5ciWjR4/mu+++46abbiIlJaVO76u8vJzNmzcDp7cqadWqFb/88gstWrQgJycHgGeeeYZWrVrxn//8B4C//vWvLFiwgBdffLFGWwcOHODuu+9m69atdOnShYqKCkpKSjh27Bi33XYbcXFxREVF8e677zJp0iT27t17wXx5eXlER0ezePFiNm7cyOzZsxk7diyvvfYa//znP/nhhx9o1aoVAA8//DD79++nY8eOVFRUUF5eXqfPQOyfiiaxiYTkBJ7maQYnD25Uu7Y3RRUuFaxzXMdnD3/GbdxGFVX8lb8ygQlcxmXsYx8f8RFzmYsrrnzMxxRTzJ3cCcBf+SujGU1/+nOAA/wf/8fDPIwnnvybf5NDDn/gD+xkJ7vZTT/6MYhBpJHGGtYwi1l4480XfMEhDnE/9wPwHM/Rl76MYASHOcxKVjKDGfjhxwY2kOqQyo7/7iAiWr2Rv/XPf/6TVatWkZl5ej7g5MmTGT58OC+99BKZmZlER0cTFxfH8OHD+de//sXf/vY3jh8/PZ9t6tSp9OjRg1WrVpGbm0t0dDTr169n3LhxfPjhh+Tk5LBw4cLzXrtFixZ88sknJCYmsmXLFr788kuWLl3Kzp07KS0txcXFhdGjRwMwePBg2rdvz+7du3+3EDvjTG8SwPr169m1axctWpwe7PD19QXg888/5+TJk3z88ccAnDp1irCwsFptffPNN4wdO5YuXU4X6M7Oznh5ebFu3TpL7w/AlClTmDlzJllZWRfM5+HhwYQJEwAYOHAgqamp5z135MiR3HHHHVx33XWMGTPGkkOaPhVNYhM+bX0YyEB82mpFcFsL7hnMR9s+ovBYIZ2COmEYBoMSB9Xoabr78N2WnqZrDl9To6dpYOLAGj1Nd2TeYelpGps1lrLyMpzKnDj83GHuufkeekX3svQ03fLrLZaepnHZ42r0NA1JHlKjp2nioYmWnqYrv7+Sbx78hlYOrcz86Bql++67j4kTJ1oev//++3h6egIQGBjIrl27iIg4XWjecccdlg1PAd566y3Lflo+Pj7s2rXLUnRMmjSJysrKOmXo2rUrXbt25b777uOaa65h7dq1xMTEnHNuk4ODA05OTjUWVy0rK6t13plemt9jGAavvPIKI0eOrFPOc72+vhl/uw+Zo6Pj7y4W++mnn7Jr1y42b97M2LFjWbJkCZMnT65XZrEzhlhNYmKiMWTIECMxMdHsKKY7suuIsZCFxpFdR8yOIo2Q/nycVlpaaiQkJBilpaVmRzEMwzAyMzON7777zvL4+PHjRpcuXYzPP//cKC8vN4KCgoxNmzYZhmEY33//veHn52cUFRUZFRUVRqtWrSx/9z333HPGb/95AYzCwkLL42nTphkPPPCAUVVVZRiGYRw7dswwDMNYvHixMW7cOKO4uNgwDMMoLi429u7dWytncnKy4efnZyQlJRmGYRinTp0y8vPzjWPHjhk+Pj5GQkKCYRiG8d577xk9evSwvDcvLy8jNzfXMAzDePDBB42QkBDDMAwjLS3N8Pb2trRfWFhYI7+np6eRmZlpGIZhVFRUGMnJyZbn5syZYzz00EN1/ITFbA39zqmnSWyiuKSYdNIpLik2O4o0QkmpSaxgBcNSh2n4thGprKxk8eLFpKWl4e7uTmVlJXfeeadl2OqTTz7hwQcfpLi4GFdXVz766CM8PDwAeOmllxgzZgyBgYGMGTPmd6/z/PPP89BDD9GzZ09cXFy4/PLLWblyJXPnzmXRokVcccUVlh6jOXPm0KNHzbtCw8PDWb16NbfccgsVFRU4Ojryz3/+k/79+/P2228zZcoUqqqqaNOmDR9++CEAAQEBPPLII/Tr149OnToxdOjQOn8uDz/8MCNHjsTNzY2vvvqKu+66ixMnTuDk5ISvry9vvvlmndsS++ZgGIZhdgh799slB3bv3s3KlSuJjIw0O5apvnr3K6657Ro2vrOR0VNGmx1HGpldX+7iwbEP8tIXLxE9JtrsOKYpKysjLS2N0NDQGsNDImIbDf3OqafJCmJiYoiJiSEpKYnp06ebHadR6NK5Cw/wgGV+i8hvdezQkau5mo4dOpodRUSkzrROk9iEa0tXfPDBtaV+epbaSstKOcYxLX4qInZFRZPYxJGjR9jIRo4cPWJ2FGmEktOSeYVXSE5LNjuKiEidqWgSmygqLiKVVIqKtbWM1BYWEsbd3E1YSO01eJojTS0VuTQa+l3TnCaxiS6duzCTmZrTJOfk4e5BEEF4uHuYHcVUzs7OODg4kJOTg6+vr1X3eBORmgzDICcnBwcHB5ydnevVhoomEbnkjuYcZQtbmJAzAX+a75IDjo6OBAYGkpmZyaFDh8yOI9LkOTg4EBgYiKOjY71er6LJThT8WsD+n/dTbVQTGhQKwO79uwnwC8C7rbdlf7HuEd1xcnLi18O/UlFZYRn+2Ju0Fz9fP3za+VBYVEhaRhpdw7vi4uxCZlYmpWWlls11E5IT8GnrQ3uf9hSXFJOanmpZ9fnI0SMUFRdZepASUxJp49UGP18/y/5i4Z3C+Xnbz7zIiwxNGap1eKSWvPw8/st/ycvPMzuK6Vq1akVERAQVFRVmRxFp8pydnetdMIGKJrtQ8GsB/+j2D94ueZsqqpjCFAAWspDruI5ookkggQ/5kMd4DHfc+YRPOMlJ7uIuAJaylFGMYgADSCGFd3iHh3gIL7xYxzpyyaUTnehHP17lVQYykCEMIZ103uRNHuABfPBhIxtJJZWZzATgeZ6nF70YxSiOcITXeZ17uRcvvOjt1BvfIF/TPjdpvLpHdOcRHqF7RHezozQKjo6ODfqLXEQuDRVNdqDoWBGlJaU88+wztAtvZ+lpGrB/QI2epmmHp1l6mq45fE2NnqYrk66s0dN0W8Ztlp6msVljSd2fyr/f+zcTH57IGMcxNXqa/pD+B0tP07VHr63R0zQ0ZWiNnqYbD91IeKdw3N3ccWrthG+4iiYREWkatCK4Fdh6RXCtri1NzdZPtvKHm/7ABx9/wNCJdd/OQkTETOppsgJbrwge1DGIG7iBoI5BVm9bxAxurm4EEYSbq5vZUURE6kzrNNmBtl5t6U1v2nq1NTuKiFUE+gcyjnEE+geaHUVEpM5UNNmBEwUn2MMeThScMDuKiFWUnyrnBCcoP1VudhQRkTpT0WQHMo5k8AmfkHEkw+woIlaRlJrEi7xIUmqS2VFEROpMRZMd6NGlB0/wBD269DA7iohVhAaFcju3W+4EFRGxByqa7ICjoyMuuGgdF2kyPFt5EkYYnq08zY4iIlJnKprsQHpmOh/yIemZ6WZHEbGKnLwcfuAHcvJyzI4iIlJnWnLADlRVV1FOOVXVVWZHEbGKo7lH2cxmEv+bSEFhAS1dWhLUMYiKigr2p+wnJDAEL08v8k7kcTj7ML269QLg4K8HcWzhSEhgCFVVVew7sI+gjkG09WrLiYITZBzJoGdkT1q0aEF6ZrrVtx1q498Gr2Av0z43ETGXiqbfWLhwIT/99BPl5eX4+flx7733cuWVV5odi87Bnbmd2+kc3NnsKCJW0X9Qfxa4L2D/X/bzCI/QjnbcwA0UU8wzPMNkJtOVruxkJxvYwAIWAPA2b9OSlkxiEqc4xVM8xUQmEkUUv/ALn/EZ85iHE058xEdUUcU4xuGJp1W2HfJx92Hm/pkqnESaKRVNv3HnnXfyl7/8BWdnZxISEnj44Yd5//338fLSX5Ai1uQV7MXM/TMpyS1h5KGRNXqaRqWMsvQ0TTwxkQeyH7D0NMX8GlOjp2nogaE1epruO3KfpacpamsUb696m4lPTKRT104N3naoIruChY8s5Ko9V3F58OVmfnwiYhIVTb8RGvq/O3kcHR2pqKggNzfX9KJp9/7dLGIRA/YPwL+vv6lZRKzFK9gLr2CvWn+mg68Itvy3P/70pOf/Hp91buDlgTXO7c7/NgC+se+N3PinG8/5Wn/86Ua387Z79rld6MLWT7aSRRalZaV1fo8i0rTYbdFUUlLCmjVrSE5OJjk5mYKCAqZOncq0adPOee6qVauIi4ujsLCQ4OBgpkyZwqhRo2qdu3jxYrZu3cqpU6cYMGAAnTubPyQW4BfAOMYR4BdgdhSRZisiNIJ7uZeI0Aizo4iISey2aCooKGDdunWEhYUxZMgQ1q9ff95z582bR2JiIvfddx9BQUHExsayaNEiqqurueqqq2qcO3/+fCorK4mPjyc9PR0HBwdbv5UL8m7rTT/64d3W2+woIiIizZbdFk1+fn5s2LABBwcH8vPzz1s0bd++nZ07dzJ//nxiYmIA6Nu3L9nZ2bz66quMHDmy1vpHTk5O9O/fn08++YTAwEAGDhxo8/fzewoKC0jk9F1G/mh4TsQMCckJPM3TDE4erGFykWbKbtdpcnBwqFMv0LZt23Bzc2P48OE1jo8dO5bc3FwSEhLO+9rq6moOHz583udzc3NJSkqy/EpPt806SumZ6bzP+1qnScREPm19GMhAfNr6mB1FRExitz1NdZWWlkZISAhOTjXfalhYmOX5qKgo8vLy2LNnD1dccQXOzs5s3bqVn376ifvuu++8ba9du5a33nrLlvEB6BbejUd5lG7h3S58sojYRHuf9gxhCO192psdRURM0uSLpoKCAjp27FjruKfn6e0bTp48aTn28ccf8/e//x0HBwcCAwNZuHAh4eHh5217/PjxDBo0yPI4PT2dJUuWWDH9ac7OznjggbOzs9XbFpG6KS4pJp10ikuKzY4iIiZp8kUTUKdhPG9vb15++eWLatfHxwcfHx9iY2OJjY2lqKiovhF/V8aRDD7jM8YcGaO5FCImSU1P5U3e5A/pfyB88Pl/mBKRpqvJF01eXl4UFBTUOl5YWAhA69atG3yNmJgYYmJiSEpKYvr06Q1u72zlp8o5znHKT5VbvW0RqZsunbvwAA/QpXMXs6OIiEnsdiJ4XXXu3Jn09HQqKytrHD948CBQc0HLxiq8Uzh3czfhnfTTrYhZXFu64oMPri1dzY4iIiZp8kXTkCFDKC0tZcuWLTWOb9y4ER8fH7p3736eV9ZdbGwsc+fOZcWKFQ1uS0QapyNHj7CRjRw5esTsKCJiErsentuxYwdlZWWUlJQApydib968GYABAwbg6urKgAED6NevH8uXL6ekpISAgAA2bdrEjz/+yLx582qt0VQfth6e25u0l6d4iiuTrtScJhGTFBUXkUoqRcW2mbsoIo2fXRdNy5cvJzs72/I4Li6OuLg4AD744AP8/U8XGEuWLGHlypWsXr3aso3KggULzrmNSmPUwacDwxlOB58OZkcRaba6dO7CTGZqTpNIM2bXRdOHH35Yp/Pc3d2ZPXs2s2fPtkkOW9895+vty5Vcia+3r03aFxERkQtr8nOaLoWYmBiWLVvGrFmzbNJ+YVEhqaRSWFRok/ZF5MISUxJ5nudJTEk0O4qImERFkx1Iy0jjbd4mLSPN7CgizVYbrzb0ohdtvNqYHUVETKKiyQ5EhkUym9lEhkWaHUWk2fLz9WMUo/Dz9TM7ioiYxK7nNDUWtp7T1NKlJW1pS0uXljZpX0QurKS0hCMcoaS0xOwoImIS9TRZga3nNGVmZbKBDWRmZdqkfRG5sJRDKbzO66QcSjE7ioiYREWTHSgtKyWDDErLSs2OItJshXcK517u1cr8Is2YiiY7EBEawQxmEBEaYXYUkWbL3c2djnTE3c3d7CgiYhLNabICW89pEhHzZedks4lNjM8Zjz9amV+kOVJPkxXYek5TQnICz/IsCckJNmlfRC4svyCf3ewmvyDf7CgiYhIVTXbAu403l3M53m28zY4i0mx1De/KQzxE1/CuZkcREZOoaLIDHXw7MIxhdPDV3nMiIiJmUdFkB4pLiskgg+KSYrOjiDRbBw4e4B/8gwMHD5gdRURMoongVmDrieCp6amsZjU3p99M+GDd7ixihlYerQgjjFYercyOIiImUdFkBTExMcTExJCUlMT06dOt3n5EaAT3c7+WHBAxUccOHbmGa+jYoaPZUUTEJBqeswNurm60pz1urm5mRxFptsrKy8gll7LyMrOjiIhJVDTZgSNHj/A1X3Pk6BGzo4g0WwcOHuBlXtacJpFmTEWTHSgsKiSJJAqLCs2OItJshYWEcRd3ERYSZnYUETGJiiY7EBkWySxmERkWaXYUkWbLw92DEELwcPcwO4qImERFk4hIHRzLPcbXfM2x3GMAJCYm8uuvvwJw6tQp4uPjOXny5Olzjx3j559/trz2wIEDpKWlAVBZWUl8fDwnTpwAIDc3l/j4eACysrJ48MEH2bFjBwDV1dXEx8dz/PhxAI4fP058fDxVVVUAHDx4kOTkZMt14uPjycnJASA/P5/4+HgqKips8nmINEcqmqwgNjaWuXPnsmLFCpu0n5SaxIu8SFJqkk3aF5ELO1Vxip/5GUdHRwBuv/12li5dCpwukqKjo9m+fTsA77//PoMGDbK89t577+XJJ58E4OTJk0RHRxMXFwfA559/TnR0NAApu1NYsWIFTz58+tyKigqio6P54osvAPjqq6+Ijo6mrOz0ZPQ5c+bwwAMPWK7Tv39/Pv30UwC2bt1KdHQ0+fn5Nvk8RJojB8MwDLNDNBVnlhxYuXIlkZHWG0qL3xjP7DGzefHLF+l7TV+rtSsidZcVn8Wz0c9y5zt34tvNl+S0ZNxc3Qj0D+RUxSkSUxIJDQrFs5Unucdzyc7JpmdkT+D0WmvOTs4EBwRTWVlJQnICwQHBtGndhrwTeRzOPkyvbr3Y+sVWJj85mZf//DI3TrmR6upq9ibtJahjEG292nKi4AQZRzLo0aUHjo6OpGemU1VdRefgzgDs3r+bAL8AvNt6U1BYQHpmOtEDovEJ8zHzoxNpMrROkx3wb+9PDDH4t9fO6iJmcfdxx9vdm89u++y85+xgR43HP/BDnc/dwQ4KKWQYw0hbnsbry1//3XPr4gQneNzxcd7a8ha9BvWq02tE5PxUNNmB0rJSssmmtKzU7CgizZZXsBcz98+kJLfEZtcoySkh4dMEut/YHXdf9wa3999N/+XTxz6l4GiBFdKJiIomO5CclsxrvMb1adfT+crOZscRaba8gr3wCvay6TXCRltvSYPLuZy7uZvwTtp+ScQaNBHcDoR3Cmc60/UXn4iIiIlUNNkBdzd3AgjA3a3h3fUi0nzsTdrLUzzF3qS9ZkcRaRJUNNmBozlHiSOOozlHzY4iInakg08HhjOcDj4dzI4i0iSoaLIDx/OPE088x/OPmx1FROyIr7cvV3Ilvt6+ZkcRaRI0EdwKYmNjiY2NpaioyCbtd4voxsM8TLeIbjZpX0SapsKiQlJJpbCoEH+0ZIlIQ6mnyQpiYmJYtmwZs2bNMjuKiIhFWkYab/M2aRlpZkcRaRJUNNmBAwcP8AqvcODgAbOjiIgdiQyLZDaztdm3iJWoaLIDHu4edKKTdlcXkYvS0qUlbWlLS5eWZkcRaRJUNNmBAL8AxjKWAL8As6OIiB3JzMpkAxvIzMo0O4pIk6CiyQ6UlZeRRx5l5WVmRxERO1JaVkoGGdqCScRKVDTZgQMHD7CCFZrTJCIXJSI0ghnMICI0wuwoIk2CiiY70Dm4M3dyJ52Dte+ciIiIWVQ02YFWHq0IJZRWHq3MjiIidiQhOYFneZaE5ASzo4g0CSqa7EBOXg7f8z05eTlmRxERO+LdxpvLuRzvNt5mRxFpErQi+P936tQpnnvuOXbu3ElxcTGdOnVi5syZREVFmR2NY3nH2MY2juUdMzuKiNiRDr4dGMYwOvhq7zkRa1BP0/9XVVWFv78/r7zyCl988QUTJkzgiSeeoKzM/DvWenTpwVzm0qNLD7OjiIgdKS4pJoMMikuKzY4i0iSoaPr/3NzcmDp1Kh06dKBFixaMGTOG6upqMjO1vomI2KfU9FRWs5rU9FSzo4g0CXY7PFdSUsKaNWtITk4mOTmZgoICpk6dyrRp08557qpVq4iLi6OwsJDg4GCmTJnCqFGjztt+eno65eXldOzY0ZZvo05SDqWwilWMPDQS/77adFNE6iYiNIL7uV9LDohYid32NBUUFLBu3ToqKioYMmTI7547b948Nm7cyNSpU3n66afp2rUrixYt4ptvvjnn+WVlZSxdupQ77rgDd3d3W8S/KK4tXfHFF9eWrmZHERE74ubqRnva4+bqZnYUkSbBbnua/Pz82LBhAw4ODuTn57N+/fpznrd9+3Z27tzJ/PnziYmJAaBv375kZ2fz6quvMnLkSBwdHS3nV1ZWsmDBAkJCQrj99tsvyXu5kED/QCYwgUD/QLOjiIgdOXL0CF/zNdcevRZ/1Est0lB229Pk4OCAg4PDBc/btm0bbm5uDB8+vMbxsWPHkpubS0LC/9Yvqa6uZunSpbRo0YI5c+ZcsP3c3FySkpIsv9LT0+v1Xi6koqKCQgqpqKiwSfsi0jQVFhWSRBKFRYVmRxFpEuy2p6mu0tLSCAkJwcmp5lsNCwuzPH9mWYFnn32WvLw8nnnmmVrnn8vatWt56623rJ75bPtT9vMcz3FVylUEXxFs8+uJSNMQGRbJLGYRGRZpdhSRJqHJF00FBQXnnMzt6ekJwMmTJwHIzs5m/fr1uLi4MH78eMt5Tz/9NL179z5n2+PHj2fQoEGWx+np6SxZssSa8QHoFNiJW7mVToGdrN62iIiI1E2TL5qAOg3j+fn5sXXr1otq18fHBx8fH2JjY4mNjaWoqKi+EX9Xa8/WdKELrT1b26R9EWmaklKTeJEXGZY6THfeiliB3c5pqisvLy8KCgpqHS8sPD3G37p1wwuRmJgYli1bxqxZsxrc1rnkncjjP/yHvBN5NmlfRJqm1p6t6UEP/cAlYiVNvmjq3Lkz6enpVFZW1jh+8OBBAEJDQ82IdVGOHD3CV3zFkaNHzI4iInbEv70/McTg3169TCLW0OSH54YMGcK6devYsmVLjcUsN27ciI+PD927d2/wNWw9PBfVNYoneZKorubvgyci9qO0rJRssiktKzU7ikiTYNdF044dOygrK6OkpAQ4PRF78+bNAAwYMABXV1cGDBhAv379WL58OSUlJQQEBLBp0yZ+/PFH5s2bV2ONpvqKiYkhJiaGpKQkpk+f3uD2RESsITktmdd4jevTrqfzlZ3NjiNi9+y6aFq+fDnZ2dmWx3FxccTFxQHwwQcf4O9/ukt6yZIlrFy5ktWrV1u2UVmwYMHvbqPSmBz89SBrWEPMrzGazCkidRbeKZzpTCe8U7jZUUSaBLsumj788MM6nefu7s7s2bOZPXu2TXLYenjOydEJDzxwcrTr/10icom5u7kTQADubuZvByXSFDT5ieCXgq3vngsOCOYmbiI4QAtbikjdHc05ShxxHM05anYUkSZBRZMdqKqqoowyqqqqzI4iInbkeP5x4onneP5xs6OINAka77ECWw/P7Tuwj2UsY/iB4QRerk17RaRuukV042EepltEN7OjiDQJKpqswNZ3zwUHBHMzN2t4TkRExEQanrMDbVq3oQc9aNO6jdlRRMSOHDh4gFd4hQMHD5gdRaRJUNFkB47nH+cnftK8BBG5KB7uHnSiEx7uHmZHEWkSVDTZgcysTP7Nv8nMyjQ7iojYkQC/AMYylgC/ALOjiDQJmtNkBdpGRUQao7LyMvLIo6y8zOwoIk2CepqswNbrNDk4OOCIIw4ODjZpX0SapgMHD7CCFZrTJGIlKprswKGMQ/wf/8ehjENmRxERO9I5uDN3ciedg7XvnIg1qGgSEWmiWnm0IpRQWnm0MjuKSJOgoskOdArqxK3cSqegTmZHERE7kpOXw/d8T05ejtlRRJoETQS3AltPBDcMgyqqMAzDJu2LSNN0LO8Y29jGsbxjZkcRaRLU02QFtp4IvidxD3/lr+xJ3GOT9kWkaerRpQdzmUuPLj3MjiLSJKhosgOB/oFMYAKB/tp3TkRExCwqmuxAuzbtuIzLaNemndlRRMSOpBxKYRWrSDmUYnYUkSZBRZMdyD+Zzz72kX8y3+woImJHXFu64osvri1dzY4i0iSoaLIDvx7+lY/4iF8P/2p2FBGxIxraF7EuFU12QJM5RaQ+KioqKKSQiooKm12jMKuQzQs3U5hVaLNriDQWWnLACmy95ICjoyOuuOLo6GiT9kWkadqfsp/neI6rUq4i+Ipgm1yjKKuILYu2EDk+Ek9/T5tcQ6SxUE+TFdh6yYFfD//Kx3ys4TkRuSidAv//wriBncyOItIkqKfJDlRWVVJMMZVVlWZHERE70tqzNV3oQnlmOVnxWTa5Ru7+3Bq/W4O7jztewV5Wa0/EWlQ02QFtuiki9VHiUMIu510U31aMBx42uUYhhexkJ4W3FeKJdYbnnN2dmbl/pgonaXRUNImINFEnOclXDl/xwLsPENU1yibX2PrFVp578jn++Nc/MnTs0Aa3l7M/h89u+4yS3BIVTdLoqGiyA2e2URmYOBD/vv5mxxERO3HZZZdRXl5u02u02d/m9O+hbfT3kzR5mghuBzp26MhoRtOxQ0ezo4iIiDRbKprsgHdbb/rTH++23mZHERE7kpyczIgRI0hOTqaqqor4+HhOnDgBQF5eHvHx8RiGAUBqaiqpqakAGIZBfHw8eXl5AJw4cYL4+HiqqqoASEtLIzk5uca1CgtPr9NUUFBAfHy8ZW2o9PR0kpKSLOf98ssvHD161PKa+Ph4ysrKAMjIyODAwQM2+SxErEFFkx04WXiSAxzgZOFJs6OIiB1p2bIlfn5+tGnThuLiYqKjo4mNjQVg3bp1REdHWwqhhx56iIceegiAqqoqoqOjWbduHXB6Lbro6GiKi4sBeOKJJ/jjH/8InL7TDeA/e/4DwHfffUd0dDS5uafvpvvrX//K1KlTLZlGjBjBv/71LwB27dpFdHQ0mZmZADzzzDP88fE/2uzzEGkozWmyAlsvbnko8xD/x/9xR+YdRBJpk2uISNMTHBzM66+/jqenJ1VVVezatYvQ0FAArrvuOnbt2mVZNPf555+3vM7R0ZFdu3YREhICnF6LbteuXXh4nL4D76mnnqKy8vQSKB6+p4+NvnE0AIMHD2bXrl34+PgA8OSTT1p6kgDi4uLw8/MDIDo6ml27dhEYeHqbl0cffZSJQyey+ebNNvk8RBrKwTjTNysNlpSUxPTp01m5ciWRkdYrbn798VdeGvASD+540Gar+oqI1Ed8fLyl+Onbt2+D2/v2g2+5fvL1fP7+54z8w0grJBSxHg3P2QFnZ2c88cTZ2dnsKCIiNtWuTTv60pd2bdqZHUWkFhVNdiAzK5N/828yszLNjiIiYlMdfDswghF08O1gdhSRWupdNKWlpbFx40bLxECA8vJynnvuOW688UZuueUWyyRCaZiy8jJyyKGsvOzCJ4uI2LGS0hIOc5iS0hKzo4jUUu+i6e233+af//wn7u7ulmOvv/46a9eupaSkhGPHjvHcc8+xa9cuqwRtzsI7hXMP9xDeKdzsKCIiNfj7+7NgwQL8/a2zsGXKoRRWspKUQylWaU/EmupdNO3fv5/LLrsMBwcHACorK/niiy/o1q0b//73v/nggw9o06YNH374odXCiohI4+Lv78/ChQutVjRFhEYwgxlEhEZYpT0Ra6p30XT8+HE6dPjfmHNCQgIlJSVMmDCBli1b4uPjw6BBg0hJ0U8LDbXvwD6WsYx9B/aZHUVExKbcXN3www83Vzezo4jUUu+iydHR0bLiK8Du3btxcHDgsssusxzz8vKioKCgYQmF9t7tGcIQ2nu3NzuKiIhNZR3LIpZYso5lmR1FpJZ6F01+fn789NNPlsebN2/G39/fsmgZQE5ODl5e2qW6oXy9fRnEIHy9fc2OIiJiUycLT7KPfdoBQRqlehdNV199NSkpKcyYMYMHHniAlJQURo0aVeOcAwcOWFZ6tQeff/45d999NyNGjOCNN94wO45FUXERaaRRVGybFcdFRBqLyLBIZjObyDDtfiCNT72LphtvvJHhw4eTmJjInj17uPzyy7n99tstz+/fv59Dhw5ZZYXYS8Xb25tp06YxZMgQs6PUcPDXg6xhDQd/PWh2FBERkWar3nvPubi4sGjRIoqLi3FwcKix9ACcvqNi9erVNYbrGrszxdJ3331ncpKaunTuwixm0aVzF7OjiIjYVFJqEitYwbDUYfj3tc4deSLWUu+i6eeff8bf37/GHXS/1aZNG06dOkVKSgp9+vSp72XOq6SkhDVr1pCcnExycjIFBQVMnTqVadOmnfPcVatWERcXR2FhIcHBwUyZMqXWcGJj5drSFW+8cW3panYUERGb8mzlSSSReLbyNDuKSC31Hp7705/+xJdffvm753zzzTf86U9/qu8lfldBQQHr1q2joqLigsNp8+bNY+PGjUydOpWnn36arl27smjRIr755hubZLO2w9mH+YIvOJx92OwoIiI21bFDR67majp26Gh2FJFa6t3TZBhGnc45s/iltfn5+bFhwwYcHBzIz89n/fr15zxv+/bt7Ny5k/nz5xMTEwNA3759yc7O5tVXX2XkyJE4OjrWK0Nubi55eXmWx+np6fVq50KKS4o5xCGKS4ovfLKIiB0rLSvlGMcoLSs1O4pILfUumuoiMzMTDw8Pm7Rd12Js27ZtuLm5MXz48BrHx44dy+LFi0lISCAqKqpeGdauXctbb71Vr9dejC6du3A/92tOk4g0eclpybzCK4xPG0/nKzubHUekhosqmpYtW1bj8bZt28jOzq51XlVVFTk5Ofzyyy9cccUVDUvYQGlpaYSEhODkVPOthoWFWZ4/UzRVVlZSVVVFdXU1VVVVlJeX4+TkdN6eqPHjxzNo0CDL4/T0dJYsWWKjdyIi0vSFhYRxN3cTFhJmdhSRWi6qaPrtHCYHBwdSUlLOu02Kg4MDXbt25YEHHmhYwgYqKCigY8faY+OenqcnGZ48+b8F1P71r3/V6Dl6++23efzxxxkzZsw52/bx8cHHx8e6gc9hf/J+nuM5hiQP0d0kItKkebh7EEQQHu62GaUQaYiLKpo++OAD4PRcpcmTJ3PzzTdz00031TqvRYsWeHp64ubWOPYOqutQ3rRp0855992FxMbGEhsbS1GRbRafbNemHX3pS7s27WzSvohIY3E05yhb2MKEnAn4ox8SpXG5qKLpt2suzZ07ly5dujT6dZjOt/9dYWEhAK1bt27wNWJiYoiJiSEpKYnp06c3uL2zdfDtwAhG0MH33Ms7iIg0FXn5efyX/5KXn3fhk0UusXpPBD/fkFVj07lzZ2JjY6msrKwxr+ngwdOra4eGhjb4GrbuaSopLeEwhykpLbFJ+yIijUX3iO48wiN0j+hudhSRWhp891xCQgKJiYkUFRVRXV1d63kHBwfuvPPOhl6m3oYMGcK6devYsmVLjcUsN27ciI+PD927N/yLaeueppRDKaxkJRMPTSRskCZHioiImKHeRdPJkyd54okn2Lt37++u2WTLomnHjh2UlZVRUnK6ByY9PZ3NmzcDMGDAAFxdXRkwYAD9+vVj+fLllJSUEBAQwKZNm/jxxx+ZN29evddoupQiQiOYwQwiQiPMjiIiYlPJacm8xmuMSBuhG1+k0al30fTyyy+zZ88e+vTpwzXXXEP79u0veQGyfPnyGksexMXFERcXB5yetO7vf/oLt2TJElauXMnq1ast26gsWLDAbrZRcXN1ww8/3Fwbx8R6ERFbcXN1I4gg/X0njVK9i6bt27fTrVs3XnjhBZut+n0hH374YZ3Oc3d3Z/bs2cyePdsmOWw9pynrWBaxxHLdset0N4mINGmB/oGMYxyB/oFmRxGppd57z506dYrevXubVjA1JjExMSxbtoxZs2bZpP2ThSfZxz5OFp688MkiInas/FQ5JzhB+alym10jKyuLhQsXkpWVZbNrSNNU76IpIiLinKuBi/VFhkUym9lEhkWaHUVExKaSUpN4kRdJSk2y2TWysrJYtGiRiia5aPUenrvrrruYM2cO+/bto0ePHtbMZHdsPTwnItJchAaFcju3ExrU8OVgRKyt3kVTTk4OAwcO5MEHH+Sqq64iIiLivJvzXnPNNfUOaA9sveRAUmoSK1jBsNRhuptERJo0z1aehBGGZytPs6OI1FLvoulvf/sbDg4OGIbBl19+yZdffllrfpNhGDg4ODT5osnWPFt5Ekmk/hIRkSYvJy+HH/iBG/Ju0I0v0ujUu2iaO3euNXPI7+jYoSNXczUdO9TeeFhEpCk5mnuUzWzmaO5Rs6OI1NLkt1G5FGw9p6m0rJRjHKO0rNQm7YuINBY9I3vyBE/QM7Knza5RnFNc43eRuqr33XPyP7ZeciA5LZlXeIXktGSbtC8i0pyU5JbU+F2krurd03T0aN27Tjt06FDfywgQFhLG3dxNWIj2nRORpi3lUAqrWc3IQyN144s0OvUumiZNmlSnhS0dHBwsW5tI/Xi4exBEEB7u5747UUSkqWjp0pJ2tKOlS0uzo4jUUu+iafTo0ecsmoqKikhNTSUrK4s+ffrg5+fXoIACR3OOsoUtTMiZoLtJRKRJC+oYxA3cQFDHILOjiNRS76LpiSeeOO9zhmHw/vvv89577zFnzpz6XkL+v7z8PP7Lf8nLzzM7ioiITVVUVFBMMRUVFWZHEanFJhPBHRwcuOWWWwgNDeWVV16xxSUaldjYWObOncuKFSts0n73iO48wiN0j+huk/ZFRBqL/Sn7eYZn2J+y3+woIrXY9O65yMhI4uPjbXmJRsHWd8+JiDQXIYEhTGYyIYEhZkcRqcWmRdPhw4epqqqy5SWaheS0ZF7jNS05ICJNnpenF13pipenl9lRRGqxetFUXV3N0aNHWbNmDd9//32z38zXGtxc3QgiCDdXN7OjiIjYVN6JPHayk7wTmsMpjU+9J4IPGzbsd5ccMAyDVq1acf/999f3EvL/BfoHMo5xBPoHmh1FRMSmDmcfZgMbeCD7AXpiu1XBReqj3kVT7969z1k0OTg44OnpSWRkJGPHjqVdu3YNCihQfqqcE5yg/FS52VFERGyqV7deLGABvbr1MjuKSC31Lppeeukla+awa7beey4pNYkXeZExqWPoNKCTTa4hIiIiv097z1mBre+eCw0K5XZuJzQo1Cbti4g0Fgd/PcjbvM3BXw+aHUWklnr3NP3Wnj17SElJobi4GHd3dyIiIoiKirJG0wJ4tvIkjDA8W3maHUVExKYcWzjSkpY4tnA0O4pILQ0qmhISEnjqqafIzMwETk/+PjPPKTAwkLlz59KzpybyNVROXg4/8AM35N2gbVREpEkLCQxhEpO0TpM0SvUumg4dOsRDDz1EWVkZ/fv3p0+fPrRr144TJ07w008/8Z///IdHHnmE1157jU6dOlkxcvNzNPcom9nM0dyjZkcREbGpqqoqTnFKa/xJo1Tvoumtt96isrKSZ599lssvv7zGc7feeis7d+5kzpw5vPXWWyxcuLChOZu1npE9eYIn6BmpXjsRadr2HdjHUzzF0ANDCbxcy6xI41LvieA//fQTw4YNq1UwndGvXz+GDRvGTz/9VO9wIiLSvAR1DGIiEwnqGGR2FJFa6l00FRcX4+//+/Nr/P39KS4uru8l5P9LOZTCalaTcijF7CgiIjbV1qstUUTR1qut2VFEaql30eTt7c2+fft+95yEhAS8vb3rewn5/1q6tKQd7Wjp0tLsKCIiNnWi4AS/8AsnCk6YHUWklnoXTYMHD+bnn39m1apVlJfXXKm6vLycN954g59++onBgwc3OGRzF9QxiBu4Qd3VItLkZRzJ4DM+I+NIBgcPHiQl5X897PHx8eTm5gJw4sQJ4uPjqaysBCAtLY0DBw5Yzv355585duwYACdPniQ+Pp5Tp04BWNo4Y/fu3WRnZwNQVFREfHw8ZWVlAGRmZpKQkGA5d+/evRw5cgSAkpIS4uPjKSkpsepncC6FWYVsXriZwqxCm19Lzq/eRdOdd96Jv78/77zzDjfffDNz5sxh2bJlzJkzh0mTJrFmzRr8/f258847rZm3UYqNjWXu3LmsWLHCJu1XVFRQTDEVFRU2aV9EpLHoGdmTecyjZ2RPHnnkEWbPnm15Ljo6ms8//xyAuLg4oqOjOXnyJABPPvkk9957r+XcQYMG8f777wOwfft2oqOjLUXU2n+vrXHNUaNG8eabbwKni63o6GjS09MBWL58OTfffLPl3GuvvZZXXnkFgMTERKKjo0lMTLTmR3BORVlFbFm0haIs2+w8IXVT77vnWrduzT//+U9eeeUVvv32W3bs2GF5zsXFhTFjxjBjxgxat25tlaCNWUxMDDExMSQlJTF9+nSrt78/ZT/P8AyjUkYRfEWw1dsXEWksWrRogRNOtGjRgmeffZbq6mrLc7t27SI4+PTfgSNGjGDXrl2Wf2P++te/1vjB8vvvv6djx44ADBw4kF27dtG+fXsAxk8Yz4dxH1rO3bRpk+W5Pn36sGvXLkJCTq8T9ec//5l77rnHcu769este6p27drVkmHevHncf//9lmtK09SgxS1bt27N3LlzeeSRR0hPT6ekpAR3d3dCQkJwcrLKYuPC6cXeJjNZi72JSLPSuXPnGo/79u1r+e+2bdvStu3/JouHhtbcZqpPnz6W/27dunWN1/r4+NQ4t1ev/20O3KpVqxrnBgbWXPbgtws2u7u707dvX/bu3cs777zD5MmTbVY0Hc05ShxxTMiZYDeLHCf/nMzSPy3lLy/8hYg+EWbHsYqLrmz+9a9/UVZWxrRp0yyFkZOTE2FhYZZzKioqWLlyJW5ubtx2223WS9tMeXl60ZWueHl6mR1FRETO0rNnTw4dOmTTaxzNPcoWttjVIscH9x1kzZY13LLvliZTNF3UnKadO3fyxhtv0Lp169/tSXJ2dqZ169asWrWKXbt2NThkc5d3Io+d7CTvRJ7ZUURERJqtiyqavvrqKzw9PbnxxhsveO4NN9yAp6cnX375Zb3DyWmHsw+zgQ0czj5sdhQRETlLQkICPXr0qHGXnTRNF1U07d27l+joaFxcXC54rouLC/369WPv3r31Dien9erWiwUsoFe3Xhc+WURELqnWrVszevToZnHjU3N3UUVTbm7uRU1y8/f3Jy9PQ0oiItJ0BQYGsnz58lqTxqXpuaiJ4C1atLAsJFYXlZWVtGhR76Wg5P87+OtB3uZtYn6Nwb+vfdw1ISLSEDn7c2zWdn5avuX3rPisBrdXVl5GXlkePQf2xNXVtcHtSeN1UUWTt7c3aWlpdT4/LS2t1q2djVl+fj5PPfUUP//8Mz4+Pjz00EPn3ZD4UnJs4UhLWuLYwtHsKCIiNuXu446zuzOf3faZza5xhNMren/75LckPtnwhSmPcITXeZ3N6zczbNywBrcnjddFFU29evXim2++ISsr64Kb9WZlZREfH8/o0aMbFPBSev7552nXrh1r165l586dLFiwgPfeew8vL3Nv9Q8JDGESk7ROk4g0eV7BXszcP5OSXNttTbL/h/0kzUriDyv+QLcruzW4vfSf0qm4p4IArwArpJPG7KKKphtuuIEvv/yS+fPn88wzz9CmTZtznldQUMCCBQuoqqpiwoQJ1shpcyUlJWzbto333nsPV1dXBg8eTHh4ON999x3jxo0zNVtVVRWnOEVVVZWpOURELgWvYC+8gm37w+oIRtDtym5Wm/IQQgge7h5WaUsar4sqmiIjI7n55pv56KOPuOOOO5gwYQKXXXYZvr6+wOmJ4rt27WLdunXk5+czadIkIiMjbRK8pKSENWvWkJycTHJyMgUFBUydOpVp06ad89xVq1YRFxdHYWEhwcHBTJkyhVGjRlnOyczMxM3NjQ4dOliOde7c+aKGI21l34F9PMVTDD0wlMDLNdFQRKQxOZZ7jG1s4/rc6+1mtW6pn4teEXzmzJm4uLjw3nvv8fbbb/P222/XeN4wDFq0aMFtt91WY78eaysoKGDdunWEhYUxZMgQ1q9ff95z582bR2JiIvfddx9BQUHExsayaNEiqqurueqqqwAoLS3Fw6PmTwkeHh7k5+fb7D3UVVDHICYykaCOQWZHERGRs+SeyGU728k9kWt2FLGxiy6aHBwcuPfeexk3bhxffPEFe/fu5fjx4wC0a9eOqKgoxowZQ0CAbcd2/fz82LBhAw4ODuTn55+3aNq+fTs7d+5k/vz5xMTEAKf3L8rOzubVV19l5MiRODo64ubmRnFxcY3XFhcX4+bmZtP3URdtvdoSRRRtvdpe+GQREbmkukd05zEeo3tEd7OjiI3Ve1fdgIAApk+fbs0sF8XBwaFO523btg03NzeGDx9e4/jYsWNZvHgxCQkJREVFERgYSGlpKceOHbPsdp2WlsbVV1993rZzc3NrrEOVnp5+8W+kDk4UnOAXfuFEwQl1/YqIiJikyS+ilJaWRkhISK298s5sMHxmzpK7uzuDBw/mzTffpLy8nB9++IHk5GQGDRp03rbXrl3L9OnTLb+WLFlik/eQcSSDz/iMjCMZNmlfRKQ5aeXfimELhtHKv5VV2ktOS+Z1Xic5Ldkq7UnjVe+eJntRUFBwzlXMPT09ATh58qTl2J///GeWLl3Ktddei4+PDwsXLjzvHYIA48ePr1FUpaen26Rw6hnZk3nMo2dkT6u3LSLS3Hj6ezJ84XCrtefm6oY//ri5mj+dQ2yryRdNUPehvDZt2vDMM8/UuV0fHx98fHyIjY0lNjaWoqKi+kb8XS1atMAJJ62uLiLSCAX6B3Id1xHor7ubm7om/6+wl5cXBQUFtY4XFhYCWGWDxZiYGJYtW8asWbMa3Na5pGem8z7vk55pmzlTIiJSf6cqTlFAAacqTpkdRWysyRdNnTt3Jj09vdaeeQcPHgQgNDTUjFgXpdqopooqqo1qs6OIiMhZElMSeZ7nSUxp+JYs0rg1+eG5IUOGsG7dOrZs2VJjMcuNGzfi4+ND9+4Nv0XU1sNzoUGhTGEKoUGNv8ATEWluQoNCuY3b9Hd0M2DXRdOOHTsoKyujpOT0HkXp6els3rwZgAEDBuDq6sqAAQPo168fy5cvp6SkhICAADZt2sSPP/7IvHnzcHRs+Ca4MTExxMTEkJSUZOoyDCIicul5tvIknHA8W3maHUVszK6LpuXLl5OdnW15HBcXR1xcHAAffPCBZVPhJUuWsHLlSlavXm3ZRmXBggU1ep4as937d7OQhQzYP8Bq+ySJiIh15B7PZQc7uPH4jVpLr4mz66Lpww8/rNN57u7uzJ49m9mzZ9skh62H5wL8AriO6wjw0w7aIiKNTXZONpvYRHZONlFEmR1HbMiui6bGwtbDc95tvYkmGu+23lZvW0REGqZnZE/+wl+0ll4z0OTvnmsK8k/mk0AC+SfzzY4iIiLSbKlosgO/Hv6VD/mQXw//anYUERE5S2p6Km/yJqnpqWZHERvT8JwV2HpOk3bQFhFpvJydnGlNa5ydnM2OIjamoskKbD2nycnJCXfca206LCIi5gsOCGYiEwkOCDY7itiYhufswK+Hf+UTPtHwnIhII1RZWUkJJbV2npCmR0WTHaiorOAkJ6morDA7ioiInCUhOYGneZqE5ASzo4iNabzHCmw9pyksJIy7uIuwkDCbtC8iIvUXHBDMJCZpeK4ZUNFkBdpGRUSk+WrTug3d6U6b1m3MjiI2puE5O7A3aS9LWcrepL1mRxERkbPknchjF7vIO5FndhSxMRVNdsDP149RjMLP18/sKCIicpbD2YdZxzoOZx82O4rYmIomO+DTzocBDMCnnY/ZUURE5Cy9uvViIQvp1a2X2VHExjSnyQpsPRG8sKiQFFIoLCrUDtoiIiImUU+TFcTExLBs2TJmzZplk/bTMtJ4h3dIy0izSfsiIlJ/aRlpvMu7+ju6GVDRZAe6hnflIR6ia3hXs6OIiMhZWji0wBFHWjjon9SmTv+H7YCLswteeOHi7GJ2FBEROUtIYAiTmUxIYIjZUcTGVDTZgcysTNaxjsysTLOjiIjIWaqrq6mkkurqarOjiI2paLIDpWWlZJFFaVmp2VFEROQse5P2soQlWkuvGVDRZAciQiO4l3uJCI0wO4qIiJwlqGMQN3ADQR2DzI4iNqYlB6zA1ksOiIhI49XWqy296U1br7ZmRxEbU0+TFdh6yQHtoC0i0nidKDjBHvZwouCE2VHExlQ02QGftj4MZCA+bbUiuIhIY5NxJINP+ISMIxlmRxEbU9FkB9r7tGcIQ2jv097sKCIicpYeXXrwBE/Qo0sPs6OIjalosgPFJcWkk05xSbHZUURE5CyOjo644IKjo6PZUcTGVDTZgdT0VN7kTVLTU82OIiIiZ0nPTOdDPiQ9M93sKGJjKprsQJfOXXiAB+jSuYvZUURE5CxV1VWUU05VdZXZUcTGVDTZAdeWrvjgg2tLV7OjiIjIWToHd+Z2bqdzcGezo4iNqWiyA0eOHmEjGzly9IjZUURERJotLW5pBbZe3LKouIhUUikq1uKZIiKNze79u1nEIgbsH4B/X3+z44gNqWiygpiYGGJiYkhKSmL69OlWb79L5y7MZKbmNImINEIBfgGMYxwBfgFmRxEb0/CciIhIA3i39aYf/fBu6212FLExFU12IDElked5nsSURLOjiIjIWQoKC0gkkYLCArOjiI2paLIDbbza0ItetPFqY3YUERE5S3pmOu/zvtZpagZUNNkBP18/RjEKP18/s6OIiMhZuoV341EepVt4N7OjiI2paLIDJaUlHOEIJaUlZkcREZGzODs744EHzs7OZkcRG1PRZAdSDqXwOq+TcijF7CgiInKWjCMZfMZnZBzJMDuK2JiKJjsQ3imce7mX8E7hZkcREZGzlJ8q5zjHKT9VbnYUsTEVTXbA3c2djnTE3c3d7CgiInKW8E7h3M3d+sG2GVDRdJbPP/+cu+++mxEjRvDGG2+YHQeA7JxsNrGJ7Jxss6OIiIg0WyqazuLt7c20adMYMmSI2VEs8gvy2c1u8gvyzY4iIiJn2Zu0l6d4ir1Je82OIjambVTOcqZY+u6770xO8j9dw7vyEA/RNbyr2VFEROQsHXw6MJzhdPDpYHYUsbFGWTSVlJSwZs0akpOTSU5OpqCggKlTpzJt2rRznrtq1Sri4uIoLCwkODiYKVOmMGrUKBOSi4hIc+Pr7cuVXImvt6/ZUcTGGuXwXEFBAevWraOiouKCw2Tz5s1j48aNTJ06laeffpquXbuyaNEivvnmm0uU1vYOHDzAP/gHBw4eMDuKiIicpbCokFRSKSwqNDuK2Fij7Gny8/Njw4YNODg4kJ+fz/r168953vbt29m5cyfz588nJiYGgL59+5Kdnc2rr77KyJEjcXR0BOBPf/oTe/eee7x58uTJ3HPPPbZ5M1bQyqMVYYTRyqOV2VFEROQsaRlpvM3bTMmYQhe6mB1HbKhRFk0ODg51Om/btm24ubkxfPjwGsfHjh3L4sWLSUhIICoqCoAXXnjByikhNzeXvLw8y+P0dNvsO9SxQ0eu4Ro6duhok/ZFRKT+IsMimc1sIsMizY4iNtYoi6a6SktLIyQkBCenmm8jLCzM8vyZoqmuKisrqaqqorq6mqqqKsrLy3FycrL0WP3W2rVreeutt+qdv67KysvIJZey8jKbX0tERC5OS5eWtKUtLV1amh1FbMyui6aCggI6dqzd++Lp6QnAyZMnL7rNf/3rXzUKobfffpvHH3+cMWPG1Dp3/PjxDBo0yPI4PT2dJUuWXPQ1L+TAwQO8zMtce/BaQgeGWr19ERGpv8ysTDawgbFZY/HH3+w4YkN2XTRB3Yfy6mratGnnvEvvXHx8fPDx8SE2NpbY2FiKioqsmuWMsJAw7uIuwkLCbNK+iIjUX2lZKRlkUFpWanYUsTG7Lpq8vLwoKCiodbyw8PQdDK1bt74kOWJiYoiJiSEpKYnp06dbvX0Pdw9CCMHD3cPqbYuISMNEhEYwgxlEhEaYHUVsrFEuOVBXnTt3Jj09ncrKyhrHDx48CEBoaNMYyjqWe4xtbONY7jGzo4iIiDRbdl00DRkyhNLSUrZs2VLj+MaNG/Hx8aF79+6XJEdsbCxz585lxYoVNmk/90Qu29lO7olcm7QvIiL1l5CcwLM8S0JygtlRxMYa7fDcjh07KCsro6SkBDg9yXrz5s0ADBgwAFdXVwYMGEC/fv1Yvnw5JSUlBAQEsGnTJn788UfmzZt3zjvebMHWw3PdI7rzGI/RPeLSFIEiIlJ33m28uZzLIQey4rNsco38tHzL79a6hruPO17BXlZpq7lotEXT8uXLyc7OtjyOi4sjLi4OgA8++AB//9N3KCxZsoSVK1eyevVqyzYqCxYs0DYqIiJySYR2CyXGPYbvZn3Hd9hm39IjHAHg2ye/JfHJRKu06ezuzMz9M1U4XYRGWzR9+OGHdTrP3d2d2bNnM3v2bBsnOj9b3z2XnJbM67zOiLQR+PfV7awiIo2JYztHLn/nckJ9Q212w87u/bt5/bbXufGdG+nVrVeD28vZn8Nnt31GSW6JiqaL0GiLJnti6+E5N1c3/PHHzdXN6m2LiEjDHDhwgKtvvJpdu3YR3jfcJtfI4vSQnG83X/3wbCIVTXYg0D+Q67iOQP9As6OIiMhZunXrxt69e+ncubPZUcTGVDTZgVMVpyiggFMVp8yOIiIiZ3Fzc6NHjx5mx5BLwK6XHGgsbL3kQGJKIs/zPIkp1pn8JyIi1pOZmcmjjz5KZmam2VHExlQ0WUFMTAzLli1j1qxZNmk/NCiU27iN0KCmsViniEhTUlBQwNq1a8+5Q4U0LRqeswOerTwJJxzPVp5mRxERkbP06NGDpKQks2PIJaCeJjuQezyXHewg97hWBBcRETGLiiYrsPWcpuycbDaxieyc7AufLCIil9S+ffsIDw9n3759ZkcRG9PwnBXYep2mnpE9+Qt/oWdkT6u3LSIiDdOmTRtuvvlm2rRpY3YUsTEVTSIiIg0QEBDA3/72N7NjyCWg4Tk7kJqeypu8SWp6qtlRRETkLKWlpezevZvS0lKzo4iNqWiyA85OzrSmNc5OzmZHERGRs+zfv5/evXuzf/9+s6OIjWl4zgpsvWFvcEAwE5lIcECwTdoXEZH6i4yM5D//+Q+RkZFmRxEbU9FkBbaeCF5ZWUkJJVRWVlq9bRERaRgPDw8uv/xys2PIJaDhOTuQkJzA0zxNQnKC2VFEROQsWVlZLFy4kKysLLOjiI2paLIDwQHBTGKShudERBqh3NxcVq1aRW6uFiBu6jQ8ZwfatG5Dd7rTpnUbs6OIiMhZoqKitFlvM6GeJjuQdyKPXewi70Se2VFERESaLRVNduBw9mHWsY7D2YfNjiIiImdJSEigV69eJCRo3mlTp+E5K7D1kgO9uvViIQvp1a2XTdoXEZH68/T0ZPjw4Xh6epodRWxMRZMV2HrJARERabyCgoJ46aWXzI4hl4CG5+xAWkYa7/IuaRlpZkcREZGzlJWVkZKSQllZmdlRxMZUNNmBFg4tcMSRFg763yUi0tgkJCQQERGhOU3NgP4VtgMhgSFMZjIhgSFmRxERkbNEREQQFxdHRESE2VHExjSnyQ5UV1dTSSXV1dVmRxERkbOcmQguTZ96muzA3qS9LGEJe5P2mh1FRETOcvToUZ555hmOHj1qdhSxMRVNdiCoYxA3cANBHYPMjiIiImfJzs7mb3/7G9nZ2WZHERvT8JwdaOvVlt70pq1XW7OjiIjIWXr37s3x48fNjiGXgIomK7D14pYnCk6whz2cKDiBP/42uYaIiIj8Pg3PWUFMTAzLli1j1qxZNmk/40gGn/AJGUcybNK+iIjUX1JSEgMHDiQpKcnsKGJjKprsQI8uPXiCJ+jRpYfZUURE5Cyurq706NEDV1dXs6OIjWl4zg44OjrigguOjo5mRxERkbOEhISwatUqs2PIJaCeJjuQnpnOh3xIema62VFEROQsFRUVZGVlUVFRYXYUsTEVTXagqrqKcsqpqq4yO4qIiJxlz549dOzYkT179pgdRWxMRZMd6Bzcmdu5nc7Bnc2OIiIiZwkLC2P9+vWEhYWZHUVsTHOaREREGsDLy4tx48aZHUMuAfU02YHd+3eziEXs3r/b7CgiInKWnJwc/vGPf5CTk2N2FLExFU12IMAvgHGMI8AvwOwoIiJylszMTP785z+TmZlpdhSxMQ3P/capU6d47rnn2LlzJ8XFxXTq1ImZM2cSFRVlai7vtt70ox/ebb1NzSEiIrVddtlllJeXmx1DLgH1NP1GVVUV/v7+vPLKK3zxxRdMmDCBJ554grKyMlNzFRQWkEgiBYUFpuYQERFpzlQ0/YabmxtTp06lQ4cOtGjRgjFjxlBdXW16l2t6Zjrv877WaRIRaYSSk5OJiYkhOTnZ7ChiY41yeK6kpIQ1a9aQnJxMcnIyBQUFTJ06lWnTpp3z3FWrVhEXF0dhYSHBwcFMmTKFUaNGNThHeno65eXldOzYscFtNUS38G48yqN0C+9mag4REanNyckJX19fnJwa5T+pYkWN8v9wQUEB69atIywsjCFDhrB+/frznjtv3jwSExO57777CAoKIjY2lkWLFlFdXc1VV11V7wxlZWUsXbqUO+64A3d393q3Yw3Ozs544IGzs7OpOUREpLbQ0FDee+89s2PIJdAoiyY/Pz82bNiAg4MD+fn55y2atm/fzs6dO5k/fz4xMTEA9O3bl+zsbF599VVGjhxp2a/tT3/6E3v37j1nO5MnT+aee+6xPK6srGTBggWEhIRw++23nzdnbm4ueXl5lsfp6bYZPss4ksFnfMaYI2Pw7+tvk2uIiEj9VFVVUVxcjIeHh/YIbeIaZdHk4OBQp/O2bduGm5sbw4cPr3F87NixLF68mISEBMudby+88EKd2qyurmbp0qW0aNGCOXPm/G6WtWvX8tZbb9Wp3YYoP1XOcY5Tfkp3Z4iINDa//PIL0dHR7Nq1i759+5odR2yoURZNdZWWlkZISEitceQzS9mnpaVd9HIBzz77LHl5eTzzzDMXHJ8eP348gwYNsjxOT09nyZIlF3W9ugjvFM7d3E14p3Crty0iIg0TGhrKhx9+SGhoqNlRxMbsumgqKCg45yRtT09PAE6ePHlR7WVnZ7N+/XpcXFwYP3685fjTTz9N7969a53v4+ODj4/PRaYWEZGmpG3bttx8881mx5BLwK6LJqj7UF5d+Pn5sXXr1ot+XWxsLLGxsRQVFVkty2/tTdrLUzzFlUlXak6TiEgjk5eXx7p167juuuvw9tYixE2ZXa/T5OXlRUFB7QUfCwsLAWjduvUlyRETE8OyZcuYNWuWTdrv4NOB4Qyng08Hm7QvIiL1l56ezl133WWzm4Gk8bDrnqbOnTsTGxtLZWVljflHBw8eBGgy48u+3r5cyZX4evuaHUVERM5y2WWXUVFRoTvnmgG77mkaMmQIpaWlbNmypcbxjRs34uPjQ/fu3S9JjtjYWObOncuKFSts0n5hUSGppFJYVGiT9kVEpP4cHBxwcnKy6nQRaZwabU/Tjh07KCsro6SkBDjd/bl582YABgwYgKurKwMGDKBfv34sX76ckpISAgIC2LRpEz/++CPz5s27ZFV/TEwMMTExJCUlMX36dKu3n5aRxtu8zZSMKXShi9XbFxGR+ktNTeWhhx7i+eeft9y9LU1Toy2ali9fTnZ2tuVxXFwccXFxAHzwwQf4+5+eEL1kyRJWrlzJ6tWrLduoLFiwwCrbqDQWkWGRzGY2kWGRZkcRERFpthpt0fThhx/W6Tx3d3dmz57N7NmzbZzo/Gx991xLl5a0pS0tXVrapH0REam/sLAw1q5da3YMuQTsek5TY2Hru+cyszLZwAYyszJt0r6IiNSfYRhUVlZiGIbZUcTGVDTZgdKyUjLIoLSs1OwoIiJylp9++glnZ2d++ukns6OIjTXa4Tl7YuvhuYjQCGYwg4jQCJu0LyIi9RcSEsKbb75JSEiI2VHExlQ0WYGt754TEZHGy9vbm6lTp5odQy4BDc/ZgYTkBJ7lWRKSE8yOIiIiZzlx4gQfffQRJ06cMDuK2JiKJjvg3caby7kc7zba00hEpLFJS0tj0qRJpKWlmR1FbEzDc3agg28HhjGMDr7ae05EpLHp3bs3BQUFeHh4mB1FbExFkxXYeiJ4cUkxGWRQXFJsk/ZFRKT+HB0dL9kG8WIuDc9Zga3XaUpNT2U1q0lNT7VJ+yIiUn9paWnccsstGp5rBlQ02YGI0Aju534tOSAi0ghVVlaSk5NDZWWl2VHExjQ8ZwfcXN1oT3vcXN3MjiIiImeJiIggNjbW7BhyCainyQ4cOXqEr/maI0ePmB1FRESk2VJPkxXYeiJ4YVEhSSRRWFRok/ZFRKT+fvrpJwYMGMCOHTu47LLLzI4jNqSiyQpsvSJ4ZFgks5hFZFik1dsWEZGGCQwMZPny5QQGBpodRWxMRZOIiEgD+Pr6MnPmTLNj1EvO/hybtZ2flm/5PSs+yyptuvu44xXsZZW26kNFkx1ISk3iRV5kWOow/Pv6mx1HRER+o6CggO+++47Bgwfj5WXeP+gXw93HHWd3Zz677TObXeMIp+fhfvvktyQ+mWiVNp3dnZm5f6ZphZOKJjvQ2rM1PehBa08tniYi0tikpqZy7bXXsmvXLvr27Wt2nDrxCvZi5v6ZlOSW2Owau/fv5vXbXufGd26kV7deDW4vZ38On932GSW5JSqa5Pz82/sTQwz+7dXLJCLS2ERFRXHkyBF8fHzMjnJRvIK9bFp8ZHF6SM63m2+TGSVR0WQFtr57rrSslGyyKS0rtUn7IiJSf87Ozvj7N42iQH6f1mmyAltvo5KclsxrvEZyWrJN2hcRkfpLT0/nnnvuIT093ewoYmMqmuxAeKdwpjOd8E7hZkcREZGzlJWVsW/fPsrKysyOIjam4Tk74O7mTgABuLu5mx1FRETOEhkZyfbt282OIZeAeprswNGco8QRx9Gco2ZHERERabZUNNmB4/nHiSee4/nHzY4iIiJn+eWXX2jXrh2//PKL2VHExlQ02YFuEd14mIfpFtHN7CgiInIWPz8/Hn/8cfz8/MyOIjamOU0iIiIN0KFDBx599FGzY8gloJ4mO3Dg4AFe4RUOHDxgdhQRETlLYWEhmzdvprCw0OwoYmMqmuyAh7sHneiEh7uH2VFEROQsycnJjBgxguRkraXX1Gl4zgpsvSJ4gF8AYxlLgF+ATdoXEZH66969O8nJyQQGBpodRWxMRZMVxMTEEBMTQ1JSEtOnT7d6+2XlZeSRR1m5Fk4TEWlsXF1dCQ/X4sPNgYbn7MCBgwdYwQrNaRIRaYQyMjJ48MEHycjIMDuK2Jh6mqyovLwcwOr7Dzk6OjLZYzKOjo4kJSVZtW0REWmYtLQ0tm3bxtixYykpKbHJNTIyMvDw8LD8bg+snTk3I5eTHic5mHGQkx4nrZCwtpCQEFxdXc/7vINhGIZNrtwMff311yxZssTsGCIiIlIPK1euJDIy8rzPq6fJivr37094eDgPPfQQLi4udXrNihUrmDVr1u+ek56ezpIlS5g3bx4hISHWiGr36vK5melS57PV9azVbkPaqc9rL+Y1dT1X38PaGvP30Ixstrhmc/gO1vX8S/EdvFC7KpqsqE2bNrRv356oqKg6v6ZVq1a/W9X+VkhISJ3Pbeou5nMzw6XOZ6vrWavdhrRTn9dezGsutn19D/+nMX8Pzchmi2s2h+/gxZ5v5ndQE8GtLCYmxqbny2mN/XO71PlsdT1rtduQdurz2ot5TWP/s9SYNebPzoxstrhmc/gO1vcaZtCcJjtwZimDC421iojt6HsoYq7G8B1UT5Md8Pb2ZurUqXh7e5sdRaTZ0vdQxFyN4TuoniYRERGROlBPk4iIiEgdqGgSERERqQMVTSIiIiJ1oKJJREREpA5UNDURp06d4m9/+xsTJ07kmmuuYcaMGezZs8fsWCLNysKFC5kwYQLXXHMNU6dO5YcffjA7kkiztHfvXoYNG8aaNWus2q7unmsiSktL+eCDDxgzZgy+vr589dVXvPLKK3z00Ue/u/mgiFhPWloagYGBODs7k5CQwMMPP8z777+Pl5eX2dFEmo3q6mr++Mc/4uDgwMCBA7nzzjut1rZ6mpoINzc3pk6dSocOHWjRogVjxoyhurqazMxMs6OJNBuhoaE4OzsD4OjoSEVFBbm5uSanEmle1q5dS1RUFMHBwVZvW3vPmaSkpIQ1a9aQnJxMcnIyBQUFTJ06lWnTpp3z3FWrVhEXF0dhYSHBwcFMmTKFUaNGnbf99PR0ysvL6dixoy3fhojdstV3cPHixWzdupVTp04xYMAAOnfufCnejojdscV3sKCggI8//pjXXnuNl156yeqZVTSZpKCggHXr1hEWFsaQIUNYv379ec+dN28eiYmJ3HfffQQFBREbG8uiRYuorq7mqquuqnV+WVkZS5cu5Y477sDd3d2Wb0PEbtnqOzh//nwqKyuJj48nPT0dBwcHW78VEbtki+/g66+/zqRJk2jVqpVNMqtoMomfnx8bNmzAwcGB/Pz88/5h2b59Ozt37mT+/PmWDQ379u1LdnY2r776KiNHjsTR0dFyfmVlJQsWLCAkJITbb7/9krwXEXtkq+8ggJOTE/379+eTTz4hMDCQgQMH2vz9iNgba38Hk5KSOHDgAA8//LDNMmtOk0kcHBzq9BPotm3bcHNzY/jw4TWOjx07ltzcXBISEizHqqurWbp0KS1atGDOnDn6CVfkd9jiO3i26upqDh8+3NCoIk2Stb+Dv/zyC4cOHWLChAlcd911fPvtt7z77rssXbrUapnV09TIpaWlERISgpNTzf9VYWFhluejoqIAePbZZ8nLy+OZZ56pdb6I1E9dv4N5eXns2bOHK664AmdnZ7Zu3cpPP/3EfffdZ0ZskSajrt/Ba6+9tkZh9Y9//AM/Pz9uvfVWq2XRv6yNXEFBwTknc3t6egJw8uRJALKzs1m/fj0uLi6MHz/ect7TTz9N7969L01YkSaort9BgI8//pi///3vODg4EBgYyMKFCwkPD79kWUWaorp+B93d3WvM423ZsiXu7u5WXfJDRZMdqEv3pZ+fH1u3br0EaUSan7p8B729vXn55ZcvQRqR5qc+002eeOIJq+fQnKZGzsvLi4KCglrHCwsLAWjduvWljiTSrOg7KGKuxvQdVNHUyHXu3Jn09HQqKytrHD948CBwejE9EbEdfQdFzNWYvoMqmhq5IUOGUFpaypYtW2oc37hxIz4+PnTv3t2kZCLNg76DIuZqTN9BzWky0Y4dOygrK6OkpAQ4vYr35s2bARgwYACurq4MGDCAfv36sXz5ckpKSggICGDTpk38+OOPzJs3r9b6MCJSd/oOipjL3r6D2rDXRJMmTSI7O/ucz33wwQf4+/sDp5ePX7lyZY3l42+77bbf3UZFRC5M30ERc9nbd1BFk4iIiEgdaE6TiIiISB2oaBIRERGpAxVNIiIiInWgoklERESkDlQ0iYiIiNSBiiYRERGROlDRJCIiIlIHKppERERE6kBFk4jIJfD8889z3XXXWbaLAHjjjTcYOnQoP/30k4nJ/mfp0qXcfPPNlJeXmx1FpFHS3nMictGysrL4wx/+8LvnhIeH88Ybb1yiRI1bRkYGa9eu5d5778Xd3d2m1/r3v//Nc889x/jx43nkkUd+99x77rmHAwcOsHLlSiIjI7nzzjuJjY3lo48+4rbbbrNpThF7pKJJROotICCAq6666pzPeXt7X+I0jdebb76Ji4sLEyZMsPm1YmJi+Mc//sG3337LrFmzaNmy5TnPS01N5cCBA0RERBAZGQlAYGAggwcP5v/+7/+YOHEibm5uNs8rYk9UNIlIvQUEBDBt2jSzYzRq+fn5bN26leHDh9u8lwnAw8ODYcOG8dVXX7Flyxauvvrqc563fv16AMaOHVvj+NVXX82WLVvYtGkT1157rc3zitgTzWkSkUti6NChPPjgg+Tn57Ns2TLGjx9PTEwMM2bMOO+cnpKSEt544w3uuOMOYmJiGDt2LI888gi7d++ude6DDz7I0KFDOXXqFKtXr+aWW25hxIgRNYYIt2zZwvTp04mJiWHChAk8/fTTFBYWMmnSJCZNmmQ5b+nSpQwdOpT9+/efM9err77K0KFD2bp16wXf96ZNmzh16hTDhw+/4LlnpKamcsMNN3DdddeRkJBgOX7kyBH+/ve/c9NNNzFq1Ciuv/56nnrqqVq7xI8bNw6AL7/88pztV1RUEBsbi4uLS62iasCAAbi5ufHFF1/UOa9Ic6GiSUQumaKiIu6//35SU1O56qqrGDp0KElJSTzyyCMcPHiwxrknT57kj3/8I2+99RatW7fm+uuvt5w/e/Zstm3bds5rzJs3jy+++ILevXtz880307FjRwA2bNjAk08+yeHDhxk9ejTXXHMN+/bt489//jOVlZU12hg/fjzwv96Y36qsrOSrr76iXbt2XHnllRd8z7t27QKgR48eF/6AgF9++YVZs2bh6OjIyy+/TPfu3QFISEjgnnvuYePGjURGRnLTTTfRu3dvvvnmG+677z6OHDliaaNPnz4EBgYSHx9PVlZWrWt8//33FBQUMHToUDw9PWs85+zsTJcuXdi/fz+lpaV1yizSXGh4TkTq7fDhw+ed7N2jRw+uuOKKGsdSUlK4/vrr+dOf/kSLFqd/Zuvbty9PP/00n376aY2Jyy+88AJpaWnMnTu3xhDS8ePHuffee3nmmWfo379/rTk7eXl5vPnmm7Ru3dpyrLCwkJdeegl3d3dWrVplKaSmT5/OY489RlJSEn5+fpbzo6KiCA0NZdOmTTzwwAM15vb88MMPHD9+nFtvvRUnpwv/Fbp37158fX1p27btBc/dtm0bixYtomPHjjz77LO0b98eOF2oLVy4kOrqalauXEl4eLjlNbt372b27Nm89NJLLFu2zHJ87NixvP7662zcuJG77rqrxnU2bNgA/K9H6myRkZH88ssv7N+/n759+14wt0hzoZ4mEam3w4cP89Zbb53z148//ljrfDc3N2bMmGEpmACuueYaHB0dSUxMtBzLz88nLi6O6OjoWnNu2rVrxy233EJ+fr6lF+e37rrrrhoFE8B3331HaWkp1157raVgAnBycuLuu+8+53sbP348JSUlfPvttzWOr1+/HgcHhzrN96moqCA/P79OBdP69euZP38+ERERvPzyy5aCCU4XatnZ2dxyyy01CiaAXr16MWjQIHbs2EFxcbHl+JnP9csvv8QwDMvx3Nxcdu7ciZ+f33kLojN5c3JyLphbpDlRT5OI1Fv//v159tln63x+YGBgrcnQTk5OtGvXjqKiIsuxxMREqqqqOHXq1Dl7sjIzMwFIT0+vNUTWrVu3WuenpqYC0LNnz1rPdevWDUdHx1rHr776al577TXWr19v6ZHJycnhv//9r2X460IKCgoAag2Bne3DDz/k+++/Z8CAASxevBhXV9caz+/btw+AX3/99Zyfx/Hjx6muriYjI4OuXbsC4OPjwxVXXMEPP/xAfHw80dHRwOl5TlVVVYwdOxYHB4dz5jlTdJ7JLyKnqWgSkUvGw8PjnMcdHR2prq62PD558iQAe/bsYc+ePedtr6ysrNaxdu3a1Tp2pgemTZs2tZ5r0aIFXl5etY57enoyYsQINm7cyKFDh+jUqRNffPEFVVVVdb6r7MzQ4YUWizwzsf2KK66oVTDB6eFFgG+++eZ32zn78xg3bhw//PADX375ZY2iqUWLFowZM+a87ZzJe77lCkSaKxVNItLonCmu/vCHPzBz5syLeu25ek/OtJefn1/ruerqagoKCvD19a313Pjx49m4cSPr169n5syZfPnll7Ru3ZqhQ4fWKYunpydOTk6WIvB85syZw7/+9S9eeuklWrRowQ033FDj+TO9c8uWLavT5PMzBg4cSLt27diyZQsPPfQQKSkpZGZm0r9/fzp06HDe153Je64iU6Q505wmEWl0unbtioODg2VYqqHCwsKA05Oyz7Z//36qqqrO+bqePXvSuXNnvvrqK3bs2MGRI0e46qqrLqoHJjQ0lOzs7Fp36P2Wp6cnzz//PF26dOH555/n008/rfH8mTvoLvbzcHJyYvTo0ZSXl7Np0ybLMgLnmwB+RkZGBgCdO3e+qOuJNHUqmkSk0fH29mbEiBHs3buX9957r8ZE5jMSEhLOOTx3LoMHD8bNzY3169fXuDW/srKS1atX/+5rr7vuOgoKCnjmmWcALnrBxz59+nDq1CnLvKrz8fT0ZPny5XTt2pUXXniBTz75pEb+Dh068MEHH/Dzzz/Xem1lZeU5166C/xVIn3/+OZs3b8bLy4vBgwf/bpaEhAS8vb0JCgq6wLsTaV40PCci9fZ7Sw4ADVot/M9//jMZGRm8+uqrfPXVV/To0YNWrVpx7NgxkpKSyMzM5LPPPjvnHKCzeXp68sADD/DMM89wzz33MHLkSDw8PNixYwcuLi74+Picd1L06NGj+ec//0lubi7du3e39FrV1ZAhQ/joo4/YtWuXZbuS38v53HPP8fDDD/Piiy9iGAY33XQTLi4uLF68mMcee4wHH3yQ6OhoQkNDATh69Ci7d+/Gy8uLd955p1abwcHBREVFWeaGXXvttTg7O583w+HDh8nKyuL666+/qPcp0hyoaBKRejuz5MD5NKRoat26Na+88gqffvop3377LbGxsVRXV9OuXTvCw8O58847zzmB+3yuu+46PD09efvtt9m4cSMeHh4MGjSIGTNmcPPNNxMQEHDO17Vq1YrBgwcTGxtbr21F+vTpQ3BwMF9//TW33nrrBc8/0+P08MMP89JLL2EYBjfffDPdunXjjTfe4L333mPHjh3s2bMHZ2dnfHx8GDJkCKNGjTpvm+PGjbMUTWcv4XC2r7/+GvjfAp8i8j8Oxrn6vUVEmonMzExuvfVWRowYwaJFi855zh133MHRo0f57LPP6rV/3Nq1a3n22Wd57bXXLPOTGqPKykqmTJmCn58fL774otlxRBodzWkSkWahsLCQU6dO1ThWXl7Oyy+/DJweRjuX7du3c+jQIUaPHl3vDXfHjRtHSEgIb775Zr1ef6l8/fXXZGdnc//995sdRaRR0vCciDQLP//8M3//+9+5/PLLad++PQUFBcTHx5OdnU3fvn0ZOXJkjfM///xzjh07xrp162jZsiW33HJLva/t6OjI448/zo8//khJSUm9iy9bc3Bw4NFHH73g3CuR5krDcyLSLGRkZLB69Wr27t1rWa8pICCAkSNHMnny5FrLCEyaNImcnByCgoKYMWPGRa2PJCJNk4omERERkTrQnCYRERGROlDRJCIiIlIHKppERERE6kBFk4iIiEgdqGgSERERqQMVTSIiIiJ1oKJJREREpA5UNImIiIjUgYomERERkTr4f+28zj5Jrf3oAAAAAElFTkSuQmCC\n",
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -1645,7 +1585,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 17,
@@ -1654,7 +1594,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1706,9 +1646,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:COSIPY]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-COSIPY-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1720,7 +1660,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

From c40a3c6dcf4f9ebefe9d09a496b3dffdfa160ace Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Tue, 12 Nov 2024 12:23:55 -0500
Subject: [PATCH 07/12] modified extended source example with new ori file

---
 .../diffuse_511_spectral_fit.ipynb                 | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb b/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb
index fca5e2f4..d1efe46b 100644
--- a/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb
+++ b/docs/tutorials/spectral_fits/extended_source_fit/diffuse_511_spectral_fit.ipynb
@@ -23,7 +23,7 @@
     "This tutotrial also walks through all the steps needed when performing a spectral fit, starting with the unbinned data, i.e. creating the combined data set, and binning the data. \n",
     "\n",
     "For the first two examples, you will need the following files (available on wasabi):
\n",
-    "**20280301_3_month.ori 
\n",
+    "**20280301_3_month_with_orbital_info.ori 
\n",
     "cosmic_photons_3months_unbinned_data.fits.gz 
\n",
     "511_Testing_3months.fits.gz 
\n",
     "SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5 
\n",
@@ -94,7 +94,7 @@
     "# ori file:\n",
     "# wasabi path: COSI-SMEX/DC2/Data/Orientation/20280301_3_month.ori\n",
     "# File size: 684 MB\n",
-    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month.ori')"
+    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori')"
    ]
   },
   {
@@ -770,7 +770,7 @@
    "source": [
     "response_file = \"SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5\"\n",
     "response = FullDetectorResponse.open(response_file)\n",
-    "ori = SpacecraftFile.parse_from_file(\"20280301_3_month.ori\")\n",
+    "ori = SpacecraftFile.parse_from_file(\"20280301_3_month_with_orbital_info.ori\")\n",
     "psr_file = \"psr_gal_511_DC2.h5\""
    ]
   },
@@ -3157,7 +3157,7 @@
     "# if not previously loaded in example 1, load the response, ori, and psr: \n",
     "response_file = \"SMEXv12.511keV.HEALPixO4.binnedimaging.imagingresponse.nonsparse_nside16.area.h5\"\n",
     "response = FullDetectorResponse.open(response_file)\n",
-    "ori = SpacecraftFile.parse_from_file(\"20280301_3_month.ori\")\n",
+    "ori = SpacecraftFile.parse_from_file(\"20280301_3_month_with_orbital_info.ori\")\n",
     "psr_file = \"psr_gal_511_DC2.h5\""
    ]
   },
@@ -4299,9 +4299,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:COSI]",
+   "display_name": "Python [conda env:analysis]",
    "language": "python",
-   "name": "conda-env-COSI-py"
+   "name": "conda-env-analysis-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -4313,7 +4313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

From 741083ab5d4e37eb716a215573bae1d9ee2b3b8d Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Tue, 12 Nov 2024 13:08:57 -0500
Subject: [PATCH 08/12] move spectral fit tests to proper place and increased
 coverage

---
 tests/spectral_fits/__init__.py                           | 0
 tests/{spectral_fits => threeml}/test_spectral_fitting.py | 6 ++++++
 2 files changed, 6 insertions(+)
 delete mode 100644 tests/spectral_fits/__init__.py
 rename tests/{spectral_fits => threeml}/test_spectral_fitting.py (94%)

diff --git a/tests/spectral_fits/__init__.py b/tests/spectral_fits/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/spectral_fits/test_spectral_fitting.py b/tests/threeml/test_spectral_fitting.py
similarity index 94%
rename from tests/spectral_fits/test_spectral_fitting.py
rename to tests/threeml/test_spectral_fitting.py
index d84a255f..fbeb7003 100644
--- a/tests/spectral_fits/test_spectral_fitting.py
+++ b/tests/threeml/test_spectral_fitting.py
@@ -70,3 +70,9 @@ def test_point_source_spectral_fit():
                        [1.0743623124061388, -1.1000643881813548, -2.299033632814098, 449.99790270666415, 1.0], atol=[0.1, 0.1, 0.1, 1.0, 0.1])
     
     assert np.allclose([cosi.get_log_like()], [337.17196587486285], atol=[1.0])
+
+    # Repeat fit in Galactic frame to test functionality: 
+    cosi._coordsys = "galactic"
+    plugins = DataList(cosi)
+    like = JointLikelihood(model, plugins, verbose = False)
+    like.fit()

From be82e455a394013ba0046a03b7ce6b6046138d16 Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Tue, 12 Nov 2024 14:04:43 -0500
Subject: [PATCH 09/12] updated threeml unit test

---
 tests/threeml/test_spectral_fitting.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/tests/threeml/test_spectral_fitting.py b/tests/threeml/test_spectral_fitting.py
index fbeb7003..781a21cf 100644
--- a/tests/threeml/test_spectral_fitting.py
+++ b/tests/threeml/test_spectral_fitting.py
@@ -4,6 +4,7 @@
 import numpy as np
 from threeML import Band, PointSource, Model, JointLikelihood, DataList
 from astromodels import Parameter
+from astropy.coordinates import SkyCoord
 
 data_path = test_data.path
 
@@ -70,9 +71,7 @@ def test_point_source_spectral_fit():
                        [1.0743623124061388, -1.1000643881813548, -2.299033632814098, 449.99790270666415, 1.0], atol=[0.1, 0.1, 0.1, 1.0, 0.1])
     
     assert np.allclose([cosi.get_log_like()], [337.17196587486285], atol=[1.0])
-
-    # Repeat fit in Galactic frame to test functionality: 
-    cosi._coordsys = "galactic"
-    plugins = DataList(cosi)
-    like = JointLikelihood(model, plugins, verbose = False)
-    like.fit()
+    
+    # Test scatt map method:
+    coord = SkyCoord(l=184.56*u.deg,b=-5.78*u.deg,frame="galactic")
+    cosi._get_scatt_map(coord)

From b2e8766df9be0cb0c4b3af7a2de2a59c404ec9c6 Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Wed, 13 Nov 2024 13:17:43 -0500
Subject: [PATCH 10/12] fix typo in crab notebook

---
 .../spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
index 94906d20..10f8219d 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
@@ -387,7 +387,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output=str(data_path / '20280301_3_month_modified.ori'))"
+    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output=str(data_path / '20280301_3_month_with_orbital_info.ori'))"
    ]
   },
   {

From 7854005e6607ef8ce27f2cba8a670178a187bc65 Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Thu, 14 Nov 2024 08:51:30 -0500
Subject: [PATCH 11/12] removed duplicate import

---
 .../continuum_fit/grb/SpectralFit_GRB.ipynb   | 191 +++++++++---------
 1 file changed, 95 insertions(+), 96 deletions(-)

diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
index 5626f64b..1bf90456 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
@@ -72,12 +72,12 @@
     {
      "data": {
       "text/html": [
-       "
12:06:30 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48\n",
+       "
08:46:50 WARNING   The naima package is not available. Models that depend on it will not be         functions.py:48\n",
        "                  available                                                                                        \n",
        "
\n"
       ],
       "text/plain": [
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+       "\u001b[38;5;46m08:46:50\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=713174;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=413436;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#48\u001b\\\u001b[2m48\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mavailable                                                                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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@@ -92,7 +92,7 @@
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=202856;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=855975;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#69\u001b\\\u001b[2m69\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mwill not be available.                                                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -102,12 +102,12 @@
     {
      "data": {
       "text/html": [
-       "
12:06:31 WARNING   The ebltable package is not available. Models that depend on it will not be     absorption.py:33\n",
+       "
08:46:53 WARNING   The ebltable package is not available. Models that depend on it will not be     absorption.py:33\n",
        "                  available                                                                                        \n",
        "
\n"
       ],
       "text/plain": [
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+       "\u001b[38;5;46m08:46:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The ebltable package is not available. Models that depend on it will not be    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=395347;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\u001b\\\u001b[2mabsorption.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=263203;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#33\u001b\\\u001b[2m33\u001b[0m\u001b]8;;\u001b\\\n",
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@@ -117,11 +117,11 @@
     {
      "data": {
       "text/html": [
-       "
12:06:32 INFO      Starting 3ML!                                                                     __init__.py:39\n",
+       "
08:46:54 INFO      Starting 3ML!                                                                     __init__.py:39\n",
        "
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       ],
       "text/plain": [
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+       "\u001b[38;5;46m08:46:54\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m Starting 3ML!                                                                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=638303;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=443065;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#39\u001b\\\u001b[2m39\u001b[0m\u001b]8;;\u001b\\\n"
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      "metadata": {},
@@ -134,7 +134,7 @@
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m WARNINGs here are \u001b[0m\u001b[1;31mNOT\u001b[0m\u001b[1;38;5;251m errors                                                     \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=185403;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=745790;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n"
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@@ -147,7 +147,7 @@
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m but are inform you about optional packages that can be installed                 \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=437664;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=479019;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#41\u001b\\\u001b[2m41\u001b[0m\u001b]8;;\u001b\\\n"
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=949768;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=157059;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m no display variable set. using backend for graphics without display \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251magg\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=566653;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=944073;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#50\u001b\\\u001b[2m50\u001b[0m\u001b]8;;\u001b\\\n"
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        "
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+       "\u001b[38;5;46m08:46:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=508106;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=126121;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1345\u001b\\\u001b[2m1345\u001b[0m\u001b]8;;\u001b\\\n"
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available                                          \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=438750;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=842981;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1357\u001b\\\u001b[2m1357\u001b[0m\u001b]8;;\u001b\\\n"
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available                                                     \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=709932;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=813304;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\u001b\\\u001b[2m1369\u001b[0m\u001b]8;;\u001b\\\n"
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@@ -221,12 +221,12 @@
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12:06:34 WARNING   The cthreeML package is not installed. You will not be able to use plugins which  __init__.py:94\n",
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08:46:56 WARNING   The cthreeML package is not installed. You will not be able to use plugins which  __init__.py:94\n",
        "                  require the C/C++ interface (currently HAWC)                                                     \n",
        "
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-       "\u001b[38;5;46m12:06:34\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=249297;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=177693;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m08:46:56\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=590709;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=892582;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m                                      \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m              \u001b[0m\n"
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=60242;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=580067;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=639772;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=980755;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251msoftware installed and configured?                                               \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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@@ -266,11 +266,11 @@
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12:06:37 WARNING   No fermitools installed                                              lat_transient_builder.py:44\n",
+       "
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        "
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+       "\u001b[38;5;46m08:47:00\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=192331;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=693460;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
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@@ -279,12 +279,12 @@
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      "data": {
       "text/html": [
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12:06:37 WARNING   Env. variable OMP_NUM_THREADS is not set. Please set it to 1 for optimal         __init__.py:387\n",
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        "                  performances in 3ML                                                                              \n",
        "
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+       "\u001b[38;5;46m08:47:00\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable OMP_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=722668;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=575708;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
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+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=592563;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=331679;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
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       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=525214;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=935569;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=535932;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=9315;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mperformances in 3ML                                                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m               \u001b[0m\n"
       ]
      },
@@ -345,9 +345,7 @@
     "\n",
     "from pathlib import Path\n",
     "\n",
-    "import os\n",
-    "\n",
-    "%matplotlib inline"
+    "import os"
    ]
   },
   {
@@ -368,12 +366,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "cdd53b2a-5176-42cf-bb2c-feb3387fc0a4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_path = Path(\"/path/to/files\")"
+    "#data_path = Path(\"/path/to/files\")\n",
+    "data_path = Path(\"/discover/nobackup/ckarwin/COSI/COSIpy_Development/GRB_Notebook\")"
    ]
   },
   {
@@ -484,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "ed2c03a0-63e3-4044-9e16-50f0f17996af",
    "metadata": {},
    "outputs": [],
@@ -505,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "3b5faaa1-1874-4d43-a6ae-7e1b0aaabb26",
    "metadata": {},
    "outputs": [],
@@ -525,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "620159d2-f01a-453e-9e4c-075c99740086",
    "metadata": {},
    "outputs": [],
@@ -545,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "acccab93-7f9c-4167-a8f9-eedcf74b8a05",
    "metadata": {},
    "outputs": [],
@@ -573,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "a29ec8c4-edea-40bf-8a3e-8038ba47bf8e",
    "metadata": {},
    "outputs": [],
@@ -594,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "a9f21e74-5f62-4030-9815-6c77ebaab16f",
    "metadata": {},
    "outputs": [],
@@ -624,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "98b2d026-c24d-4cfe-8b7b-41415fce5d16",
    "metadata": {},
    "outputs": [],
@@ -678,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "d56d3ad6-7226-437a-a037-57fbcd80d196",
    "metadata": {
     "scrolled": true,
@@ -688,11 +687,11 @@
     {
      "data": {
       "text/html": [
-       "
12:15:15 INFO      set the minimizer to minuit                                             joint_likelihood.py:1045\n",
+       "
08:49:04 INFO      set the minimizer to minuit                                             joint_likelihood.py:1045\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m12:15:15\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=758656;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=974578;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1045\u001b\\\u001b[2m1045\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m08:49:04\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m set the minimizer to minuit                                            \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=947353;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=786599;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1045\u001b\\\u001b[2m1045\u001b[0m\u001b]8;;\u001b\\\n"
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      },
      "metadata": {},
@@ -708,12 +707,12 @@
     {
      "data": {
       "text/html": [
-       "
12:16:19 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:130\n",
+       "
08:49:30 WARNING   get_number_of_data_points not implemented, values for statistical        plugin_prototype.py:130\n",
        "                  measurements such as AIC or BIC are unreliable                                                   \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m12:16:19\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=535176;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=502302;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\u001b\\\u001b[2m130\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m08:49:30\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m get_number_of_data_points not implemented, values for statistical       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=789908;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=222980;file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\u001b\\\u001b[2m130\u001b[0m\u001b]8;;\u001b\\\n",
        "\u001b[38;5;46m         \u001b[0m         \u001b[1;38;5;251mmeasurements such as AIC or BIC are unreliable                           \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m                       \u001b[0m\n"
       ]
      },
@@ -828,19 +827,19 @@
     {
      "data": {
       "text/html": [
-       "
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-       "\n",
-       "\n",
-       "\n",
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0.150.12-0.171.000.00
\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
        "
1.000.97-0.370.20-0.00
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       ],
       "text/plain": [
-       " 1.00  0.97 -0.37  0.15 -0.00\n",
-       " 0.97  1.00 -0.15  0.12 -0.00\n",
-       "-0.37 -0.15  1.00 -0.17 -0.02\n",
-       " 0.15  0.12 -0.17  1.00  0.00\n",
+       " 1.00  0.97 -0.37  0.20 -0.00\n",
+       " 0.97  1.00 -0.16  0.17 -0.00\n",
+       "-0.37 -0.16  1.00 -0.17 -0.02\n",
+       " 0.20  0.17 -0.17  1.00  0.00\n",
        "-0.00 -0.00 -0.02  0.00  1.00"
       ]
      },
@@ -891,11 +890,11 @@
        "  \n",
        "    \n",
        "      cosi\n",
-       "      42920.049339\n",
+       "      42920.049338\n",
        "    \n",
        "    \n",
        "      total\n",
-       "      42920.049339\n",
+       "      42920.049338\n",
        "    \n",
        "  \n",
        "\n",
@@ -903,8 +902,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi       42920.049339\n",
-       "total      42920.049339"
+       "cosi       42920.049338\n",
+       "total      42920.049338"
       ]
      },
      "metadata": {},
@@ -954,11 +953,11 @@
        "  \n",
        "    \n",
        "      AIC\n",
-       "      85838.098678\n",
+       "      85838.098676\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      85840.098678\n",
+       "      85840.098676\n",
        "    \n",
        "  \n",
        "\n",
@@ -966,8 +965,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC          85838.098678\n",
-       "BIC          85840.098678"
+       "AIC          85838.098676\n",
+       "BIC          85840.098676"
       ]
      },
      "metadata": {},
@@ -977,24 +976,24 @@
      "data": {
       "text/plain": [
        "(                                      value  negative_error  positive_error  \\\n",
-       " source.spectrum.main.Band.K        0.030988       -0.001991        0.002072   \n",
-       " source.spectrum.main.Band.alpha   -0.276759       -0.050823        0.049727   \n",
-       " source.spectrum.main.Band.xp     474.668373       -4.924520        5.001136   \n",
-       " source.spectrum.main.Band.beta    -6.757898       -1.224829        1.186228   \n",
-       " background_cosi                    0.164958       -0.012051        0.012140   \n",
+       " source.spectrum.main.Band.K        0.030997       -0.002034        0.002123   \n",
+       " source.spectrum.main.Band.alpha   -0.276547       -0.052063        0.049971   \n",
+       " source.spectrum.main.Band.xp     474.654036       -4.933778        4.828668   \n",
+       " source.spectrum.main.Band.beta    -6.755004       -1.205494        1.231109   \n",
+       " background_cosi                    0.164940       -0.012464        0.012279   \n",
        " \n",
        "                                     error             unit  \n",
-       " source.spectrum.main.Band.K      0.002032  1 / (keV s cm2)  \n",
-       " source.spectrum.main.Band.alpha  0.050275                   \n",
-       " source.spectrum.main.Band.xp     4.962828              keV  \n",
-       " source.spectrum.main.Band.beta   1.205529                   \n",
-       " background_cosi                  0.012096                   ,\n",
+       " source.spectrum.main.Band.K      0.002079  1 / (keV s cm2)  \n",
+       " source.spectrum.main.Band.alpha  0.051017                   \n",
+       " source.spectrum.main.Band.xp     4.881223              keV  \n",
+       " source.spectrum.main.Band.beta   1.218301                   \n",
+       " background_cosi                  0.012371                   ,\n",
        "        -log(likelihood)\n",
-       " cosi       42920.049339\n",
-       " total      42920.049339)"
+       " cosi       42920.049338\n",
+       " total      42920.049338)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1025,7 +1024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "02e57ec8-4459-4f99-b62f-73a63aba83f3",
    "metadata": {},
    "outputs": [],
@@ -1062,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "4e25c609-e754-43b8-8ea6-0a0a90811cda",
    "metadata": {
     "scrolled": true,
@@ -1177,19 +1176,19 @@
     {
      "data": {
       "text/html": [
-       "
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-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "
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-0.37-0.151.00-0.17-0.02
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\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
        "
1.000.97-0.370.20-0.00
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       ],
       "text/plain": [
-       " 1.00  0.97 -0.37  0.15 -0.00\n",
-       " 0.97  1.00 -0.15  0.12 -0.00\n",
-       "-0.37 -0.15  1.00 -0.17 -0.02\n",
-       " 0.15  0.12 -0.17  1.00  0.00\n",
+       " 1.00  0.97 -0.37  0.20 -0.00\n",
+       " 0.97  1.00 -0.16  0.17 -0.00\n",
+       "-0.37 -0.16  1.00 -0.17 -0.02\n",
+       " 0.20  0.17 -0.17  1.00  0.00\n",
        "-0.00 -0.00 -0.02  0.00  1.00"
       ]
      },
@@ -1240,11 +1239,11 @@
        "  \n",
        "    \n",
        "      cosi\n",
-       "      42920.049339\n",
+       "      42920.049338\n",
        "    \n",
        "    \n",
        "      total\n",
-       "      42920.049339\n",
+       "      42920.049338\n",
        "    \n",
        "  \n",
        "\n",
@@ -1252,8 +1251,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi       42920.049339\n",
-       "total      42920.049339"
+       "cosi       42920.049338\n",
+       "total      42920.049338"
       ]
      },
      "metadata": {},
@@ -1303,11 +1302,11 @@
        "  \n",
        "    \n",
        "      AIC\n",
-       "      85838.098678\n",
+       "      85838.098676\n",
        "    \n",
        "    \n",
        "      BIC\n",
-       "      85840.098678\n",
+       "      85840.098676\n",
        "    \n",
        "  \n",
        "\n",
@@ -1315,8 +1314,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC          85838.098678\n",
-       "BIC          85840.098678"
+       "AIC          85838.098676\n",
+       "BIC          85840.098676"
       ]
      },
      "metadata": {},
@@ -1348,28 +1347,28 @@
       "      * main:\n",
       "        * Band:\n",
       "          * K:\n",
-      "            * value: 0.03098812082120686\n",
+      "            * value: 0.03099749659547262\n",
       "            * desc: Differential flux at the pivot energy\n",
       "            * min_value: 1.0e-50\n",
       "            * max_value: null\n",
       "            * unit: keV-1 s-1 cm-2\n",
       "            * is_normalization: true\n",
       "          * alpha:\n",
-      "            * value: -0.27675895447494075\n",
+      "            * value: -0.2765469834147527\n",
       "            * desc: low-energy photon index\n",
       "            * min_value: -1.5\n",
       "            * max_value: 3.0\n",
       "            * unit: ''\n",
       "            * is_normalization: false\n",
       "          * xp:\n",
-      "            * value: 474.66837288907766\n",
+      "            * value: 474.6540362662719\n",
       "            * desc: peak in the x * x * N (nuFnu if x is a energy)\n",
       "            * min_value: 10.0\n",
       "            * max_value: null\n",
       "            * unit: keV\n",
       "            * is_normalization: false\n",
       "          * beta:\n",
-      "            * value: -6.757897666561581\n",
+      "            * value: -6.755004044507031\n",
       "            * desc: high-energy photon index\n",
       "            * min_value: -15.0\n",
       "            * max_value: -1.6\n",
@@ -1411,7 +1410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "cc7d6f50-06cd-450a-83d9-115b67d83b30",
    "metadata": {},
    "outputs": [],

From 5b693b5d43cd7ea5f0b1ee9f3c0a612f3a37b2a6 Mon Sep 17 00:00:00 2001
From: "Christopher M. Karwin" 
Date: Thu, 14 Nov 2024 08:52:34 -0500
Subject: [PATCH 12/12] removed duplicate import

---
 .../continuum_fit/grb/SpectralFit_GRB.ipynb   | 27 +++++++++----------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
index 1bf90456..23166ded 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
@@ -371,8 +371,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#data_path = Path(\"/path/to/files\")\n",
-    "data_path = Path(\"/discover/nobackup/ckarwin/COSI/COSIpy_Development/GRB_Notebook\")"
+    "data_path = Path(\"/path/to/files\")"
    ]
   },
   {
@@ -1410,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "cc7d6f50-06cd-450a-83d9-115b67d83b30",
    "metadata": {},
    "outputs": [],
@@ -1449,7 +1448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "f8dbd36f-4b16-4bec-8835-8f6f876ab169",
    "metadata": {
     "tags": []
@@ -1458,16 +1457,16 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1502,23 +1501,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "7d1dd8d1-f86d-4e63-8286-db1d5bc14b04",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1577,17 +1576,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "06df3b27-d2ed-4214-bda7-d4fda667e145",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },