diff --git a/cosipy/event_selection/good_time_interval.py b/cosipy/event_selection/good_time_interval.py
index ec1aa08f..45779197 100644
--- a/cosipy/event_selection/good_time_interval.py
+++ b/cosipy/event_selection/good_time_interval.py
@@ -6,11 +6,11 @@
 from astropy.io import fits
 
 class GoodTimeInterval():
-    
+
     def __init__(self, tstart_list, tstop_list):
         """
         Initialize GTI object.
-        
+
         Parameters
         ----------
         tstart_list : astropy.time.Time (array)
@@ -24,48 +24,48 @@ def __init__(self, tstart_list, tstop_list):
         if tstop_list.isscalar == True:
             tstop_list = Time([tstop_list])
 
-        # Check that starts and stops have the same length 
+        # Check that starts and stops have the same length
         if len(tstart_list) != len(tstop_list):
             raise ValueError(f"Length mismatch between starts ({len(tstart_list)}) and stops ({len(tstop_list)})")
-        
+
         self._tstart_list = tstart_list
         self._tstop_list = tstop_list
-        
+
         # Sort by start time
         self._sort()
 
     @property
     def tstart_list(self):
         return self._tstart_list
-    
+
     @property
     def tstop_list(self):
         return self._tstop_list
-    
+
     def __len__(self):
         """Return the number of GTI intervals."""
         return len(self._tstart_list)
-    
+
     def __getitem__(self, index):
         """
         Get GTI interval(s) by index.
-        
+
         Parameters
         ----------
         index : int, slice, or array-like
             Index, slice, or boolean/integer array to retrieve
-        
+
         Returns
         -------
         tuple of (Time, Time)
             (tstart_list, tstop_list) for the indexed interval(s)
         """
         return self._tstart_list[index], self._tstop_list[index]
-    
+
     def __iter__(self):
         """
         Iterate over GTI intervals.
-        
+
         Yields
         ------
         tuple of (Time, Time)
@@ -73,22 +73,22 @@ def __iter__(self):
         """
         for start, stop in zip(self._tstart_list, self._tstop_list):
             yield start, stop
-        
+
     def _sort(self):
         """
         Sort GTI by start time in ascending order.
-        
+
         Modifies the GTI in place.
         Stops are sorted according to the start time order.
         """
         sort_idx = np.argsort(self._tstart_list)
         self._tstart_list = self._tstart_list[sort_idx]
         self._tstop_list = self._tstop_list[sort_idx]
-    
+
     def save_as_fits(self, filename, overwrite=False, output_format='unix'):
         """
         Save GTI data to a FITS file.
-        
+
         Parameters
         ----------
         filename : str
@@ -101,73 +101,73 @@ def save_as_fits(self, filename, overwrite=False, output_format='unix'):
         # Get values in the specified output format using getattr
         if not hasattr(self._tstart_list, output_format):
             raise ValueError(f"Unsupported output format: {output_format}")
-        
+
         start_times = getattr(self._tstart_list, output_format)
         stop_times = getattr(self._tstop_list, output_format)
-        
+
         # Use the scale from the stored Time objects
         output_scale = self._tstart_list.scale
 
         output_unit = 's'
         if output_format in ['jd', 'mjd']:
             output_unit = 'd'
-        
+
         # Create primary HDU
         primary_hdu = fits.PrimaryHDU()
-        
+
         # Define table columns
         col1 = fits.Column(name='TSTART', format='D', unit=output_unit, array=start_times)
         col2 = fits.Column(name='TSTOP', format='D', unit=output_unit, array=stop_times)
-        
+
         # Create table HDU
         table_hdu = fits.BinTableHDU.from_columns([col1, col2])
         table_hdu.header['EXTNAME'] = 'GTI'
         table_hdu.header['TIMESYS'] = output_scale.upper()
         table_hdu.header['TIMEUNIT'] = output_unit
-        table_hdu.header['TIMEFORMAT'] = output_format
-        
+        table_hdu.header['HIERARCH TIMEFORMAT'] = output_format
+
         # Create HDUList and write to FITS file
         hdul = fits.HDUList([primary_hdu, table_hdu])
         hdul.writeto(filename, overwrite=overwrite)
-    
+
     @classmethod
     def from_fits(cls, filename):
         """
         Load GTI from a FITS file.
-        
+
         Reads time format and scale from FITS header.
-        
+
         Parameters
         ----------
         filename : str
             Input FITS filename
-            
+
         Returns
         -------
         GoodTimeIntervals
             GTI object
         """
         infile = fits.open(filename)
-        
+
         # Search for GTI extension
         gti_hdu = None
         for hdu in infile:
             if isinstance(hdu, fits.BinTableHDU) and hdu.name in ['GTI']:
                 gti_hdu = hdu
                 break
-        
+
         if gti_hdu is None:
             infile.close()
             logger.error("GTI table not found in FITS file")
-        
+
         # Read time system/format from header
         time_scale = gti_hdu.header.get('TIMESYS').lower()
         time_format = gti_hdu.header.get('TIMEFORMAT').lower()
-        
+
         # Read start and stop times as arrays
         tstart_list = Time(gti_hdu.data['TSTART'], format=time_format, scale=time_scale)
         tstop_list = Time(gti_hdu.data['TSTOP'], format=time_format, scale=time_scale)
-        
+
         infile.close()
         return cls(tstart_list, tstop_list)
 
@@ -175,22 +175,22 @@ def from_fits(cls, filename):
     def intersection(cls, *gti_list):
         """
         Find the intersection of multiple GTI objects.
-        
+
         Returns a new GTI object containing only time intervals that overlap
         in all input GTI objects.
-        
+
         Assumes all GTI objects are sorted by start time (guaranteed by _sort() in __init__).
-        
+
         Parameters
         ----------
         *gti_list : GoodTimeInterval
             Variable number of GTI objects to intersect
-            
+
         Returns
         -------
         GoodTimeInterval
             New GTI object with intersected intervals
-            
+
         Examples
         --------
         >>> gti1 = GoodTimeInterval(tstart1, tstop1)
@@ -200,58 +200,58 @@ def intersection(cls, *gti_list):
         """
         if len(gti_list) == 0:
             raise ValueError("At least one GTI object is required")
-        
+
         if len(gti_list) == 1:
             # Return a copy of the single GTI
             gti = gti_list[0]
             return cls(gti.tstart_list.copy(), gti.tstop_list.copy())
-        
+
         # Start with intervals from the first GTI
         current_starts = list(gti_list[0].tstart_list)
         current_stops = list(gti_list[0].tstop_list)
-        
+
         # Iteratively intersect with each subsequent GTI
         for gti in gti_list[1:]:
             new_starts = []
             new_stops = []
-            
+
             i = 0  # Index for current intervals
             j = 0  # Index for gti intervals
-            
+
             # Two-pointer approach for sorted intervals
             while i < len(current_starts) and j < len(gti):
                 start1, stop1 = current_starts[i], current_stops[i]
                 start2, stop2 = gti[j]
-                
+
                 # Check if intervals overlap
                 if start1 < stop2 and start2 < stop1:
                     # Calculate intersection
                     max_start = max(start1, start2)
                     min_stop = min(stop1, stop2)
-                    
+
                     if max_start < min_stop:
                         new_starts.append(max_start)
                         new_stops.append(min_stop)
-                
+
                 # Move the pointer for the interval that ends first
                 if stop1 <= stop2:
                     i += 1
                 else:
                     j += 1
-            
+
             # Update current intervals for next iteration
             current_starts = new_starts
             current_stops = new_stops
-            
+
             # If no overlaps found, we can stop early
             if len(current_starts) == 0:
                 break
-        
+
         # Handle case with no overlapping intervals
         if len(current_starts) == 0:
             # Return empty GTI with appropriate time format
-            empty_time = Time([], format=gti_list[0].tstart_list.format, 
+            empty_time = Time([], format=gti_list[0].tstart_list.format,
                              scale=gti_list[0].tstart_list.scale)
             return cls(empty_time, empty_time.copy())
-        
+
         return cls(Time(current_starts), Time(current_stops))
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 1a56fe4a..bfc3a053 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab.ipynb
@@ -64,31 +64,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "53bc23a7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from cosipy import COSILike, test_data, BinnedData\n",
-    "from cosipy.spacecraftfile import SpacecraftFile\n",
-    "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n",
-    "from cosipy.util import fetch_wasabi_file\n",
-    "\n",
-    "from scoords import SpacecraftFrame\n",
-    "\n",
-    "from astropy.time import Time\n",
-    "import astropy.units as u\n",
-    "from astropy.coordinates import SkyCoord, Galactic\n",
+    "%%capture\n",
+    "from pathlib import Path\n",
     "\n",
     "import numpy as np\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n",
-    "from astromodels import Parameter\n",
+    "import astropy.units as u\n",
     "\n",
-    "from pathlib import Path\n",
+    "from astromodels import Model, Parameter, PointSource, Band\n",
+    "from threeML import JointLikelihood, DataList\n",
     "\n",
-    "import os\n",
+    "from cosipy import BinnedData, COSILike\n",
+    "from cosipy.spacecraftfile import SpacecraftFile\n",
+    "from cosipy.util import fetch_wasabi_file\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -111,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "724affc6",
    "metadata": {},
    "outputs": [],
@@ -129,12 +124,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "67f61e08",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named 20280301_3_month_with_orbital_info.ori already exists with the specified checksum (416fcc296fc37a056a069378a2d30cb2). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "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'), checksum = '416fcc296fc37a056a069378a2d30cb2')"
+    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output = data_path / '20280301_3_month_with_orbital_info.ori', checksum = '416fcc296fc37a056a069378a2d30cb2')"
    ]
   },
   {
@@ -147,12 +150,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "3f6f7006",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named crab_bkg_binned_data.hdf5 already exists with the specified checksum (85658e102414c4f746e64a7d29c607a4). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_bkg_binned_data.hdf5', output=str(data_path / 'crab_bkg_binned_data.hdf5'), checksum = '85658e102414c4f746e64a7d29c607a4')"
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_bkg_binned_data.hdf5', output = data_path / 'crab_bkg_binned_data.hdf5', checksum = '85658e102414c4f746e64a7d29c607a4')"
    ]
   },
   {
@@ -165,12 +176,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "900ae6e0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named crab_binned_data.hdf5 already exists with the specified checksum (6e5bccb48556bdbd259519c52dec9dcb). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_binned_data.hdf5', output=str(data_path / 'crab_binned_data.hdf5'), checksum = '6e5bccb48556bdbd259519c52dec9dcb')"
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_binned_data.hdf5', output = data_path / 'crab_binned_data.hdf5', checksum = '6e5bccb48556bdbd259519c52dec9dcb')"
    ]
   },
   {
@@ -183,12 +202,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "ae494207",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named bkg_binned_data.hdf5 already exists with the specified checksum (54221d8556eb4ef520ef61da8083e7f4). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/bkg_binned_data.hdf5', output=str(data_path / 'bkg_binned_data.hdf5'), checksum = '54221d8556eb4ef520ef61da8083e7f4')"
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/bkg_binned_data.hdf5', output = data_path / 'bkg_binned_data.hdf5', checksum = '54221d8556eb4ef520ef61da8083e7f4')"
    ]
   },
   {
@@ -201,12 +228,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "1ee43be1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5 already exists with the specified checksum (eb72400a1279325e9404110f909c7785). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', output=str(data_path / 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5'), checksum = 'eb72400a1279325e9404110f909c7785')"
+    "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', output = data_path / 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', checksum = 'eb72400a1279325e9404110f909c7785')"
    ]
   },
   {
@@ -219,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "4944ec3a",
    "metadata": {},
    "outputs": [],
@@ -237,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "901211ca",
    "metadata": {},
    "outputs": [],
@@ -257,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "ac0604e5",
    "metadata": {},
    "outputs": [],
@@ -277,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "e9062c51",
    "metadata": {},
    "outputs": [],
@@ -305,11 +340,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "a8305077",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 19 s, sys: 4.94 s, total: 23.9 s\n",
+      "Wall time: 26.4 s\n"
+     ]
+    }
+   ],
    "source": [
+    "%%time\n",
+    "\n",
     "bkg_par = Parameter(\"background_cosi\",                                         # background parameter\n",
     "                     1,                                                        # initial value of parameter\n",
     "                     min_value=0,                                              # minimum value of parameter\n",
@@ -336,10 +382,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "1b02ed80",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:32:58 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:32:58\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=585611;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=657389;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "l = 184.56\n",
     "b = -5.78\n",
@@ -353,13 +413,12 @@
     "\n",
     "spectrum = Band()\n",
     "\n",
-    "spectrum.alpha.min_value = -2.14\n",
+    "spectrum.alpha.min_value = -2.0\n",
     "spectrum.alpha.max_value = 3.0\n",
     "spectrum.beta.min_value = -5.0\n",
     "spectrum.beta.max_value = -2.15\n",
     "spectrum.xp.min_value = 1.0\n",
-    "spectrum.alpha.delta = 0.01\n",
-    "spectrum.beta.delta = 0.01\n",
+    "spectrum.K.max_value = 1e+10 # set max to prevent overflow\n",
     "\n",
     "spectrum.alpha.value = alpha\n",
     "spectrum.beta.value = beta\n",
@@ -371,6 +430,9 @@
     "spectrum.K.unit = K.unit\n",
     "spectrum.piv.unit = piv.unit\n",
     "\n",
+    "spectrum.alpha.delta = 0.01\n",
+    "spectrum.beta.delta = 0.01\n",
+    "\n",
     "source = PointSource(\"source\",                     # Name of source (arbitrary, but needs to be unique)\n",
     "                     l = l,                        # Longitude (deg)\n",
     "                     b = b,                        # Latitude (deg)\n",
@@ -393,13 +455,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "e0056610",
    "metadata": {
     "scrolled": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:32:58 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:32:58\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=118865;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=880708;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:33:16 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">measurements such as AIC or BIC are unreliable                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                       </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:33:16\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=283617;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=158166;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\u001b\\\u001b[2m119\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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:33:16 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m14:33:16\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=31972;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=835038;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 44.2 s, sys: 1.81 s, total: 46 s\n",
+      "Wall time: 17.7 s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "\n",
@@ -407,7 +526,7 @@
     "\n",
     "like = JointLikelihood(model, plugins, verbose = False)\n",
     "\n",
-    "_ = like.fit()"
+    "like.fit(quiet=True);"
    ]
   },
   {
@@ -428,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "3d2c61a3",
    "metadata": {},
    "outputs": [],
@@ -442,11 +561,9 @@
     "\n",
     "spectrum_inj = Band()\n",
     "\n",
-    "spectrum_inj.alpha.min_value = -2.14\n",
-    "spectrum_inj.alpha.max_value = 3.0\n",
-    "spectrum_inj.beta.min_value = -5.0\n",
-    "spectrum_inj.beta.max_value = -2.15\n",
-    "spectrum_inj.xp.min_value = 1.0\n",
+    "# tell Astromodels to allow our parameter values\n",
+    "spectrum_inj.alpha.min_value = -2.0 # default is -1.5\n",
+    "spectrum_inj.xp.min_value = 1. #default is 10\n",
     "\n",
     "spectrum_inj.alpha.value = alpha_inj\n",
     "spectrum_inj.beta.value = beta_inj\n",
@@ -464,18 +581,381 @@
    "id": "7b61657c",
    "metadata": {},
    "source": [
-    "The summary of the results above tell you the optimal values of the parameters, as well as the errors. Propogate the errors to the \"evaluate_at\" method of the spectrum"
+    "The summary of the results tells you the optimal values of the parameters, as well as the errors. Propagate the errors to the \"evaluate_at\" method of the spectrum"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "753ae553",
    "metadata": {
     "scrolled": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Best fit values:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "      <th>unit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>parameter</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>source.spectrum.main.Band.K</th>\n",
+       "      <td>(2.835 +/- 0.024) x 10^-5</td>\n",
+       "      <td>1 / (keV s cm2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source.spectrum.main.Band.alpha</th>\n",
+       "      <td>-1.9882 +/- 0.0005</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source.spectrum.main.Band.xp</th>\n",
+       "      <td>4.39 -0.20 +0.21</td>\n",
+       "      <td>keV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source.spectrum.main.Band.beta</th>\n",
+       "      <td>-2.1654 +/- 0.0016</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>background_cosi</th>\n",
+       "      <td>(9.9401 +/- 0.0019) x 10^-1</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                      result             unit\n",
+       "parameter                                                                    \n",
+       "source.spectrum.main.Band.K        (2.835 +/- 0.024) x 10^-5  1 / (keV s cm2)\n",
+       "source.spectrum.main.Band.alpha           -1.9882 +/- 0.0005                 \n",
+       "source.spectrum.main.Band.xp                4.39 -0.20 +0.21              keV\n",
+       "source.spectrum.main.Band.beta            -2.1654 +/- 0.0016                 \n",
+       "background_cosi                  (9.9401 +/- 0.0019) x 10^-1                 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Correlation matrix:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><table id=\"table139834309118720\">\n",
+       "<tr><td>1.00</td><td>0.48</td><td>-0.51</td><td>-0.08</td><td>0.03</td></tr>\n",
+       "<tr><td>0.48</td><td>1.00</td><td>0.49</td><td>0.08</td><td>-0.03</td></tr>\n",
+       "<tr><td>-0.51</td><td>0.49</td><td>1.00</td><td>-0.06</td><td>0.02</td></tr>\n",
+       "<tr><td>-0.08</td><td>0.08</td><td>-0.06</td><td>1.00</td><td>-0.50</td></tr>\n",
+       "<tr><td>0.03</td><td>-0.03</td><td>0.02</td><td>-0.50</td><td>1.00</td></tr>\n",
+       "</table></div>"
+      ],
+      "text/plain": [
+       " 1.00  0.48 -0.51 -0.08  0.03\n",
+       " 0.48  1.00  0.49  0.08 -0.03\n",
+       "-0.51  0.49  1.00 -0.06  0.02\n",
+       "-0.08  0.08 -0.06  1.00 -0.50\n",
+       " 0.03 -0.03  0.02 -0.50  1.00"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of -log(likelihood) at the minimum:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>-log(likelihood)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cosi</th>\n",
+       "      <td>-2.612137e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>-2.612137e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       -log(likelihood)\n",
+       "cosi      -2.612137e+08\n",
+       "total     -2.612137e+08"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of statistical measures:</span>\n",
+       "\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n",
+       "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>statistical measures</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AIC</th>\n",
+       "      <td>-5.224274e+08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BIC</th>\n",
+       "      <td>-5.224274e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     statistical measures\n",
+       "AIC         -5.224274e+08\n",
+       "BIC         -5.224274e+08"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\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=1273;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=373661;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\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=141001;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=529197;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The current value of the parameter beta (</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.0</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">) was above the new maximum </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">-2.15</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">. </span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">parameter.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">810</span></a>\n",
+       "</pre>\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=621537;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py\u001b\\\u001b[2mparameter.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=916016;file:///project/cassini/miniconda3/envs/cosipy-312/lib/python3.12/site-packages/astromodels/core/parameter.py#810\u001b\\\u001b[2m810\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  * source (point source):\n",
+      "    * position:\n",
+      "      * l:\n",
+      "        * value: 184.56\n",
+      "        * desc: Galactic longitude\n",
+      "        * min_value: 0.0\n",
+      "        * max_value: 360.0\n",
+      "        * unit: deg\n",
+      "        * is_normalization: false\n",
+      "      * b:\n",
+      "        * value: -5.78\n",
+      "        * desc: Galactic latitude\n",
+      "        * min_value: -90.0\n",
+      "        * max_value: 90.0\n",
+      "        * unit: deg\n",
+      "        * is_normalization: false\n",
+      "      * equinox: J2000\n",
+      "    * spectrum:\n",
+      "      * main:\n",
+      "        * Band:\n",
+      "          * K:\n",
+      "            * value: 2.8349368874851148e-05\n",
+      "            * desc: Differential flux at the pivot energy\n",
+      "            * min_value: 1.0e-50\n",
+      "            * max_value: 10000000000.0\n",
+      "            * unit: keV-1 s-1 cm-2\n",
+      "            * is_normalization: true\n",
+      "          * alpha:\n",
+      "            * value: -1.9881559898425003\n",
+      "            * desc: low-energy photon index\n",
+      "            * min_value: -2.0\n",
+      "            * max_value: 3.0\n",
+      "            * unit: ''\n",
+      "            * is_normalization: false\n",
+      "          * xp:\n",
+      "            * value: 4.389329834768734\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: -2.1654141844909893\n",
+      "            * desc: high-energy photon index\n",
+      "            * min_value: -5.0\n",
+      "            * max_value: -2.15\n",
+      "            * unit: ''\n",
+      "            * is_normalization: false\n",
+      "          * piv:\n",
+      "            * value: 500.0\n",
+      "            * desc: pivot energy\n",
+      "            * min_value: null\n",
+      "            * max_value: null\n",
+      "            * unit: keV\n",
+      "            * is_normalization: false\n",
+      "        * polarization: {}\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "results = like.results\n",
     "\n",
@@ -500,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "26b1ba71",
    "metadata": {},
    "outputs": [],
@@ -519,12 +999,7 @@
     "    flux_inj[i] = spectrum_inj.evaluate_at(e)\n",
     "    \n",
     "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n",
-    "binned_energy = np.array([])\n",
-    "bin_sizes = np.array([])\n",
-    "\n",
-    "for i in range(len(binned_energy_edges)-1):\n",
-    "    binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n",
-    "    bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n",
+    "binned_energy = crab.binned_data.axes['Em'].centers.value\n",
     "\n",
     "expectation = cosi._expected_counts['source']"
    ]
@@ -539,12 +1014,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "57db086e",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+448/yM7O1nZ5HjhwgOvXrwMwYMAA7TdGYGAgISEhfPfdd6Snp+Pk5MTWrVtJSkrS/jUjSj4otydtUFJSX7ZsGUOGDOGLL74otSWYEEKIyhEWFqbt1XrqqaeYN28eAwcOpH79+hQWFnL27FmCg4OJioqiUaNGNGrUCENDQ+0Yrzp16pCRkfFAz77XtU2bNiU6OpqOHTtq41uwYAEdOnTA3Nyc3NxcYmNjadSo0T2f5efnR3FxMTt27KBHjx4cPHiQpKQkgoKCSq36cMud3hcgMjKSZs2aPcAb31uNSdygZAquvb09W7ZsYd++fdjb2zNmzBht33x5hg4dSseOHctd3LZp06Z8++23eHp63vW5q1atIikpSfvvvXv3ahf07dmzZ6mMfsaMGdjb27Nt2zays7Px9PTks88+Iygo6D7ftnY5ceIEy5YtA0qqqAsWLNBzREIIUTmqwlprt8a4FRUV4ebmxrfffgvA888/T1pamraXq6ioiFdffZXg4GBmzJhBdHQ0xsbGmJuba3cDev3115k8eTILFizg448/pk+fPhWO417XDhw4kJ9//pnhw4cDMG3aNFQqFa1bt9ZWy6ZNm1ahxM3Y2Jg///yTcePGMXnyZExNTVm7di2WlpblJm53el+NRsPOnTuZPn16hd/zfig0soFkjXFrOZClS5dq/zqqKYqLi1m2bBnTp0/n/fffZ+zYsfoOSQghHlp+fr52ZxxTU1N9h1PtFBcX06pVK/766y+cnZ31HQ4AW7du5ddff+XXX38tt/1h/5vXmDFuomZTKpUMHz6c8+fPM3LkyFJtt5K68pZaEUIIUXMplUqWLFlCXFycvkPRysjIqNTxfZK4iWqlfv36ZRY9XLRoEa+99hpt2rTh7NmzeopMCCGEPrRo0aJKbQn57LPP4ujoWGn3l8RNVGsZGRm88847QMk4uPLGIQghhBA1hSRuolqrW7cuO3fuJDAwkDFjxtClSxd9hySEEEJUmho1q1TUTi1btuTEiROo1eoybVu2bKF79+7l7hErhBBCVDdScRM1gomJCebm5qWOhYWF0adPH9q1a8fFixf1FJkQQgihO5K4iRpJrVbzxhtvAHDs2DH++OMPPUckhBDVx62N3u/mvffe47fffnvgZ+zevVu7I8P9Onv2LO7u7g/87OpMukpFjWRgYMDvv//O0KFDcXNzY8qUKfoOSQghapRZs2Y91PW7d+8mPT2d3r176yii2kEqbqLGeuyxxzh+/DgrVqwotb8coLP9Z4UQQtfmz5+Ps7Mzzs7O7N69u1RbbGystq28hcj79++vbb/d8uXLmT9//n3F0qVLF6ZMmULHjh3x8vLizTff1LYNGzaML774AoDCwkKmT59Oq1atCAoKYvDgwdy8eRMomf0/fPhwGjduTLNmzXj11VcJDw/n22+/5bfffiMoKEibBG7bto0OHTrQokULWrVqVWp/6pkzZ+Lj40OLFi1YuXLlfb1HTSIVN1GjWVlZYWVlVepYfHw8bdu25Z133mHkyJGlNhEWQgh9y8zM5MqVKwCoVKpSbWq1Wtt2KzH6r5SUFG377XJycsjMzLzveC5evMiuXbsoLCwkMDCQQ4cO0bZt21LnfP7551hYWHDkyBEAZs+ezTvvvMPXX3/NhAkTMDMz4/Tp0yiVSlJSUrCzs+PNN98kPT1dm/xdunSJmTNnsm3bNurUqUNMTAwdO3YkLi6O0NBQ1qxZw/Hjx7GysuLFF1+87/eoKSRxE7VKUVERQ4cO5dq1a4wePRqVSsXEiRP1HZYQQmjVqVMHJycnoGTi1X8ZGBho2+rVq1fmWjs7O2377SwsLMokghXx7LPPYmhoiKGhIUFBQVy8eLFM4vbXX3+RkZGhHU9cUFCgHYO2ceNG/vnnH23Ph52dXbnP2bp1KzExMXTq1El7TKlUcvnyZXbu3MngwYOpU6cOAG+88Qb79++/73epCSRxE7WKWq3mscce4+DBg7i7u/PKK6/oOyQhhChl0qRJTJo0qdw2Dw8PEhMT73jthg0b7tg2bNiwB4rnv/tpGhgYUFRUVOYcjUbDV199Rc+ePR/oGbfu0aNHD1asWHHPc2tzT4mMcRO1iomJCV9++SXr169n5cqVWFtb6zskIYSo9p566ikWLFhAbm4uALm5uZw7dw4oGXc3d+5ciouLgZLuXCipLGZkZGjv0atXL0JDQzl9+rT22K2u1+7du7NmzRqysrLQaDR89913j+S9qiJJ3MQ9ZaXkknIxHY1Go+9QdKZ///60bt261LH8/HymT59e6geJEEKIO7tV+Zo2bRotW7akdevWNG3alDZt2miXE1mwYAEqlYomTZoQFBTEjBkzAHj66acJDw/XTk7w9vZmxYoVvPHGGzRr1oyAgADt+Lc+ffowcOBAmjdvzmOPPYarq6s+XrdKUGhq0m/jWi46OpoRI0awdOlS/Pz8dHbfQz9FcG5bPDZuVvh1dcG7vSPG5jVvJ4I333yTJUuW4O3tzZ9//kmTJk30HZIQoobLz88nNjYWDw+PUl2S1UGfPn14/vnnef755/UdSrXysP/NZYybuKuiAjUX9pXMUEqLz+LgjxEcWRGNZ5uG+IW40MDHukaMNbh69SqrVq0CIDExUVvSF0IIUVb37t1RqVT0799f36HUOpK4iXtq/bw/UWEJpFws6UIsUqk5v+cK5/dcoZ6LJX4hLvh0cMLEsvpW4RwdHTlx4gQDBw5k7NixNGvWTN8hCSFElRUaGqrvEGotSdzEXRkaG+AX4oJfiAtp8ZlEhyVwYf9VCvNKZhXdTMjm8M+RHP09Go/WDfHv6oK9X71qWYXz8PDg8OHD5W5If/36dRo0aKCHqIQQtYGMWqo9Hva/tUxOEBVm41aHdq804vlvutLpzSY08LXWtqkLi4nZf5WNs/5h7Vv7OLMplvzMAv0F+4DKS9qWLVuGr68v69ev10NEQoiazMDAAChZ90zUDrdm3pb3+6YiZHJCDVJZkxPu5mZiFlFhCcTsu4oqp7BUm9JQgXvLkiqcQ2D9almFO336NK1btyY/Px+AkydPEhQUpN+ghBA1hkaj4fLlyxQWFuLo6Fhmez5Rc2g0GnJzc7l+/TrW1tY4ODg80H2kq1Q8lHrOVrR9KZCWz/kRdySJqF2JJEXeAKC4SMOlQ9e4dOgadRqa4xfigm8nJ8zqmtzjrlWHh4cHTzzxBGvWrGHkyJGStAkhdEqhUODg4EBsbCzx8fH6Dkc8AtbW1jRs2PCBr5eKWw2ij4pbedKvZBO9O5ELexPJz7qtCmegwK2FPX7dXHBqZINCWfWrcBqNht9++41BgwaV2X5GCCF0obi4WLpLawEjIyNt9/iDksStBqkqidst6kI1cceuEx2WwNVzaWXarezMSqpwnZ0wr1e91i8COHbsGDt37uStt96S7g0hhBCPhHSVikpjYGSAV1sHvNo6kJGUQ/SuRM7vSdROWshKyePY6vMcX3sB1+YN8O/qglNTW5TVoAp348YNBg4cSHx8PAcOHODXX3/Vbn4shBBCVBZJ3MQjUbehBa2G+NFikA+XT1wnamcCV86kAqAp1hB/LJn4Y8lY2pri28UZv87OWNiY6TnqO9u+fTuXL18GIDU1tdqteC6EEKJ6ksRNPFIGhko8WjXEo1VDsq7nEr07kfO7E8lNVwGQnZrPibUxnPwjBucgO/y7uuASZIfSoGp1RT733HNYW1szceJEVq1ahbGxsb5DEkIIUQvIGLcapKqNcauoYnUxCSdTiApLIOFUCtz2HWle3wS/zs74dnHByq5qVeHUanWZgaY3btzA1NQUc3NzPUUlhBCippKKm9A7pYESt8fscXvMnuzUPG0VLudGydppuTdUnFx3kZN/XcS5qR3+XZ1xDW6A0lD/Vbjbkza1Ws1zzz1HUlISa9euxdfXV0+RCSGEqIkkcRNViqWtGS0G+hD8jDeJp/6/CncyBU2xBjSQeCqFxFMpmFmb4NvJCb8QF+rYV53K1scff8yOHTsA6NevH5GRkQ899VsIIYS4RRI3USUplQpcgxvgGtyAnBv5nN+TSPSuRLJT8wDIS1dxasMlTm24hFMTG/y7uuDawh4DPVfhBg4cyO+//050dDTfffedJG1CCCF0ShI3UeVZ1Dcl+Glvmj3pxdUzqUSFJRB/4joadclguCtn0rhyJg3TOsb4dnbCr4sLdR0s9BJrQEAAR44cYffu3XTp0kUvMQghhKi5JHET1YZSqcC5mR3OzezITVdxYW8iUWEJZF0vqcLlZxZw+u9YTv8di0Ngffy7uuDe0h4Do0db9bK0tKRfv35ljn/22We0aNGC7t27P9J4hBBC1BySuIlqydzahGb9vWjaz5OrEWklVbijyRT/fxXuWsQNrkXcwMTSCJ+OTvh1daGek6Xe4t24cSPTp09HoVDw8ccfM336dL3FIoQQovqSxE1UawqlAqfGtjg1tiUvU8WFvVeICksgMykXAFV2IWe3xHF2Sxz2fvXw7+qCR+uGGBo/2irc77//DpTseyprvgkhhHhQso5bDRAaGkpoaCjZ2dmcPn262q3jpmsajYakqBtE7Uwg7mgy6sLiUu3G5oZ4d3TCP8SF+q5WjySm4uJiPv74Y06fPs2qVatQKKr+tl5CCCGqHkncapDqugBvZcrPKiBm/xWiwhJJv5Jdpr2BtzV+XZ3xbOOAkWnlF6A1Gk2ZpC02NhZ3d3dJ5oQQQtyTdJWKGs3UypjGj3vQqLc7yefTid6VwKVD17RVuOsx6VyPSefwL1F4t3fEr6sztu51Ky2e25OzmJgYWrRoQb9+/ViyZAmWlvobhyeEEKLqk8RN1AoKhYKGfvVo6FePNi8GcPHAVaLCErhxOQuAwrwiIkMvExl6GVvPuviHOOPZzhFjs8r7iBQVFTFo0CAyMzNZsWIFTk5OzJkzp9KeJ4QQovqTrtIaRLpK749GoyHlYgZRYSVVuCKVulS7oYkBXu0c8e/qjK1n3Urpyly7di2vvvoqTk5OHDlyBCurRzPmTgghRPUkFTdRaykUChp4W9PA25o2L/hz8dA1osMSSI3NBKBIpSZ6VwLRuxKwcbPCr6sL3u0dMTY30lkMAwcOpGnTpqjVaknahBBC3JP+d+kWogowNjcioJsrT33Unqc+bId/NxeMTP9dMiQtPouDP0bw26gw9nx7muTzN9FVsdrX15eAgIBSx7Kzs3n55Ze5fPmyTp4hhBCiZpCKmxC3sfWsSwfPurR+3p9Lh64RFZZAysUMANQFxVzYe4ULe69Qz9mypArXwRFTS92tzabRaHjzzTf57bff2LhxI3/++SedO3fW2f2FEEJUX5K4CXEHRqaG+IW44BfiQlp8JtFhCcQcuEpBbhEANxOzOfxzJEd/j8ajdUP8u7pg71fvocfCpaamcvDgQQAKCwtxcHB46HcRQghRM0jiJkQF2LjVod0rjWg11J9L/5RU4a6fTwdAXVhMzP6rxOy/Sl1HC/xDXPDp6IRpnQerwtnZ2XH8+HGGDRvGiy++iK+vrw7fRAghRHUms0prEJlV+mjdTMwiKiyBmH1XUeUUlmpTGipwb1lShXMIrP9AVbjyFuvVaDRER0fj7+//ULELIYSonmRyghAPqJ6zFW1fCmTI1yF0Gd2MhgH1tW3FRRouHbrG5o+OsGbSXk79fYm8DNV93b+8ZO+rr76iSZMmLFy4UGeTI4QQQlQfUnGrQaTipn/pV7OJ3pXIhb2J5GeVrsIpDBS4t7DHr6szTo1tUSjvrwoXHh5Oy5YtKSoqGWN3+PBhWrdurbPYhRBCVH1ScRNCh6wdLWn9vD9DFoXQdVwQjo1stG0atYbYI0ls/fQYqyfuIfyvi+TezK/wvRs3bszEiRMBeOuttyRpE0KIWkgqbjWIVNyqpszkHKJ3JXJ+TyJ5GQWl2hRKBa7NG+Af4oxTMzuUFajC7dq1i44dO2JoKHOLhBCitpGf/EJUsjr2FrR8zo8WA32IP3Gd6LAEEs+kggY0xRrijyUTfywZCxtT/Lo449vFGUsbszveLyQkpMyxffv28fvvvzN//nxMTU0r83WEEELokSRuQjwiSkMlHq0a4tGqIVkpuSVVuN2J5KaXTFrIScvnxB8xnPwzBucgO/y7uuASZIfS4O4jGpKSkhg8eDBJSUn8888/bN26FTs7u0fxSkIIIR4xSdyE0AMrO3MeG+xL8wHeJISnEBWWQGJ4ChoNaDSQcDKFhJMpmNcz+f8qnAtWduVX4cLDw0lPTwfA2tqa+vXrl3ueEEKI6k8SNyH0SGmgxK2FPW4t7MlOy+P87kSidyWSc6Nk0kLuTRUn113k5F8XcW5qi1+IC27NG6A0/LcK17t3b/755x/Gjx/P77//joGBwZ0eJ4QQopqTyQk1iExOqBmKizUknkohOiyByydT0BSX/oia1TXGt7MzfiEu1LE3v+u9rl27hlKpxN7evjJDFkII8YjorOJ24sQJjh8/ztmzZ7l+/ToZGRmYmppibW2Np6cnQUFBtG3bFhsbm3vfTIhaTKlU4BrcANfgBuTczOf8nkSiwxLJTs0DIC+jgFMbLnFqwyUcG9vg39UFt8fsMTAsPRausLCQQYMGcenSJVatWkXHjh318TpCCCF06KEqbnl5efzxxx/8/fffJCcna1dyNzY2pk6dOqhUKnJyciguLgbA0NCQdu3aMXjwYJo0aaKbNxBaUnGruTTFGq6cTSUqLIH449fRqEt/bE3rGOPTyQn/EBfqOlgAMHPmTD744AMAPD09iYqKwsjI6JHHLoQQQnceOHFbv349P/74Izdv3sTLy4uQkBAaNWqEv78/5ub/dt9oNBoSExOJiIjg6NGj7N+/n/z8fNq3b8/o0aNxdHTU2cvUdpK41Q65GSou7CkZC5eZnFum3SGwPv5dXTBzhRdffpF9+/axf/9+WrVqpYdohRBC6NIDJ24hISF0796dIUOG4OnpWeHrVCoVO3bs4Ndff6V3794MGzbsQR4vyiGJW+2iKdZwNSKN6LBE4o4mUXxbFc7E0gjPDg3Jtb5Oj/5d9RSlEEIIXXrgxC0hIQEXF5cHfrBarSY5OVkqbjokiVvtlZep4sLeK0TvSiTjWk6Z9ob+9fDr6oJHq4YYGhswbdo0/P39eeWVV/QQrRBCiAf1wJMTHiZpAzAwMJCkTQgdMatjQtN+njTp60FS1A2idiYQdzQZdWHJ+NKkqJskRd3k0PII4o1PM2fxHACOHTvG119/rc/QhRBC3AdZx02IGkShUOAQYINDgA35WQVc2HeF6F0JpF8pqcIV5Bax/8BB7fkNzVwpUqkxNJG134QQojrQaeKWlZXFgQMH6N27ty5vK4R4AKZWxjTp40Hjx91Jjr5J9K5ELh2+xqDg4bhYexKTGkHDK034bVQY3h0c8Q9xwca9jr7DFkIIcRc6TdySk5P59NNPJXETogpRKBQ09K9PQ//6tHkxgJgDV6kXZsXNhJIJC4V5RUTuuEzkjstkmF6l55AQ/Du6YmQqBXkhhKhq7usnc3Jy8l3bU1NTHyoYIUTlMrE0olEvNwJ7upISk07UrkQuHbpGkUrNlfQ4Pt4+kSUbvRnV9X881qMJ/iEu2HrW1XfYQggh/t99JW6DBw9GoVDcsV2j0dy1XQhRNSgUChr41KOBTz3avODPhf1X6PPCGArUKi6knGNz+BosldZE7UzAxr0O/t1c8GrrgLG5LOArhBD6dF+Jm5WVFa+99hpBQUHltsfHxzNz5kwdhCWEeFSMzY1o1NOdVRt+Y+AzgzA3tGRQq1egqKQ9LS6TAz+c459fo/Bs64B/VxfsvOrKH2lCCKEH95W4+fr6kpWVhYeHR7ntarUa2bNeiOqpTZs2hJ8+SU5ODo72Tlw8dI3onQmkXMoAoEil5vzuRM7vTqS+qxX+XV3wau+IiYVU4YQQ4lG5r8TtqaeeIj8//47t9vb2TJ8+/aGDEkLoh62tLba2tgD4h7jgH+LCpdOXeW7oc/T1eh4ni5JdUm5czuLg8gj+WRGFZxsH/EJcsPe1liqcEEJUsofaZF5ULbJzgtA1jUbDM888w19//YWxsTHz312ES2Fjrl9IL3OutZMl/l2d8e7ohKml8aMPVgghagGZ7y+EuKOMjAyuXbsGgIWFBX1f6IG7uzs3LmcRtSuBmH1XKMgtGQyXfiWbw79EcXTledxbNcS/qzMN/etLFU4IIXRIEjchxB1ZW1uzd+9epkyZQq9evXB3dwegvqsV7V4OpNUQP2L/SSIqLIHk6JsAqAuLuXjgKhcPXKWugwV+XV3w7eiEaR2pwgkhxMN66K7SLl268Msvvzz03qXi4UlXqXiUiouL2bdvH507dwbgZmIW0bsSubDvCqrswlLnKg0VuD/WEP9uLjgE1EehlCqcEEI8COXD3kCGyAlRO33yySd06dKFiRMnUlhYSD1nK9q8GMCQRSGEjGmGQ2B97bnFRRouHb7G5o+OsGbyXk5tuEhuhkqP0QshRPUkXaVCiPt25swZ3nvvPQC+/PJLBg4cSPv27QEwNDbAq50jXu0cybiWQ9SuBC7sSSQ/q6QKl5mcy9GV5zm25gJuLRrg39UFp8a2UoUTQogKkMRNCHHfGjduzKJFi5gwYQL/+9//tEnb7eo6WNB6qD+PDfYl/lgyUWEJXD2bBoBGrSHuSDJxR5KxtDPDL8QZ387OWNQzfZSvIoQQ1YokbkKI+6ZQKBg5ciSdO3fG39+/TPvt298ZGCrxbOOAZxsHMpNziN6VyPk9ieRlFACQnZLH8dUXOLE2Btdgu5IqXDM7lFKFE0KIUh56jJsQovYKDAxEqSz9Y2Tr1q3069ePtLS0cq+pY29By+f8GPJVCN0nBuPczBb+Pz/TFGuIP36dbZ8fZ9X43Zz44wLZaXmV/RpCCFFtSMVNCKEzly9f5oUXXiAtLY3g4GAOHjyIs7NzuecqDZW4t2yIe8uGZKXkcn53ItG7E8m9WTJpISctnxN/xHDyzxicg0qqcC5BdigN5O9NIUTtJYlbFZWens7HH39MeHg4dnZ2TJo0iRYtWug7LCHu6sqVK9oKXFBQEI6OjhW6zsrOnBaDfAl+xpuE8BSiwhJIDE9BowGNBhJOppBwMgXzeib4dnbGL8QZKzvzynwVIYSokh46cRs6dCh16tTRRSziPxYsWED9+vXZsGEDx44d4/3332fFihXytRZVWtu2bTl58iRTp05l0aJFZbpR70VpoMSthT1uLezJScsjek8i0bsSyUkr2SM596aK8L8uEr7+Is5NbPHr6oJb8wYoDaUKJ4SoHWSv0iooNzeXfv36sXLlSho0aADAuHHj6N27N3369LnjdbIAr6jK4uLiyM3NJTAw8L6uKy7WcOVUClG7Erl84jqa4tI/sszqGmurcHXsLXQZshBCVDmV3lWq0WhITEzE2NgYe3v7yn6c1s8//8z333+Ph4cHP/30U6U9Jzc3l5UrVxIREUFkZCRZWVm8/fbbPP744+WeX1BQwA8//MD27dvJysrCy8uL4cOH07JlS+05iYmJmJmZaZM2AE9PT2JjYyvtPYSoTPn5+QwcOJCoqCiWLl3KkCFDKnytUqnAJbgBLsENyLmZz4U9V4jalUB2SsmkhbyMAk5tuMSpDZdwbGSDfzcX3Fo0wMDIoLJeRwgh9EZn/Qt79uzho48+IisrS3vs2rVrDBs2jBdffJFnn32WmTNnolardfXIO7p+/Tq//vorZmZmlf6sjIwMli9fTnx8PN7e3vc8/5NPPmH16tX06NGDcePGoVQqmTp1KqdPn9aek5eXh4VF6cqBhYUFeXkyu05UT3PnzuX48ePk5OTw3nvvkZ+f/0D3sahnStBTXjy7oDO9pz+GR6uGKAz+XTLk6rk0whaG8/uYXfzzWxQZ13J09QpCCFEl6Kzitn79em7cuIGVlZX22KJFi4iLi6N58+ZkZmaye/duWrRowRNPPKGrx5brm2++ITAwkOLiYjIyMu56blZWFidOnNDut3i70NBQ2rdvf8ck0MbGhnXr1mFjY0NUVBSvv/76HZ8VERHBzp07GTlypLbi0KtXL4YNG8bixYtZvHgxAGZmZuTklP6Fk5OT80gSUSEqw6RJk4iJiWHVqlWsXbsWU9OHW2RXoVTg3NQO56Z25GaouLD3CtFhCWQm5wKQn1XImU2xnNkUS8OA+vh3dcG9pT2GxlKFE0JUbzqruMXFxREQEKD9d25uLocOHaJr164sWLCAJUuW4ObmxubNm3X1yHKFh4ezZ88exo4dW6Hz161bx8yZM9m7d2+Zto0bNzJr1iy2bt16x+uNjY2xsbGp0LP27NmDgYEB/fv31x4zMTGhb9++nDt3juTkZACcnZ3Jy8sjJSVFe15sbCweHh4Veo4QVY25uTk//vgjp0+fplmzZqXaHnaYrXldE5o94cmgeZ3o879WeLZ1QGn4bxUuKfIGu78+xe9jdnH4l0huJmbd5W5CCFG16Sxxy8zMpH79fzeVPn36NGq1mm7dugFgaGjIY489xpUrV3T1yDLUajVffvklffv2xcvLq0LXDB06lDZt2jBr1iyOHz+uPb5nzx7mzZtHz549eeqpp3QS34ULF3B2di7TDXor4Y2JiQFKfsl16NCBZcuWoVKpOHjwIBcvXqRDhw46iUMIfVAoFPj4+JQ5PmLECN57772HHkahUCpwbGRD17FBDFkUQqvn/anr8O9nTZVdyNktcfwxdT9/zzzMhb1XKFJV/tANIYTQJZ11lVpYWJCZman998mTJ1EqlaX+ujY0NHzgsS0VsX79epKTk1mwYEGFrzE0NGTmzJm89dZbzJgxgwULFpCXl8fs2bNp3bo106dPL7V1z8NIS0srtzp361hqaqr22KRJk/joo4/o168fdnZ2zJw5845LgYSGhhIaGkp2drZO4hTiUfnhhx/44YcfgJKN6//880+dfN7M6pjQtK8HTfq4kxR1k+hdCcT+k4S6sBiA5PM3ST5/k0M/R+DdwQm/rs7YuMpSO0KIqk9niZurqysHDx5k+PDhKJVKQkND8fX1LTXmLSkpiXr16unqkaVkZGSwbNkyXnrpJaytre/rWhMTEz755BPGjx/P1KlTKSwsJCAggA8++ABDQ91NvFWpVBgZGZU5bmxsrG2/xdrams8//7xC9+3evTvdu3fXLgciRHWRlZWFgYEBarWaQYMG6eyPpFsUCgUOAfVxCKhPm5cCiNl3lehdCdxMLPkjpyC3iIjt8URsj8fOuy7+XV3wbOOAkamsTS6EqJp09tNpwIABvP/++wwYMEBbWRs+fHipcyIiIvD19dXVI0v5/vvvsbKyYsCAAQ90vYWFBaNGjWLChAkAjB07FhMTEx1GWJIgFhYWljleUFCgbReiNpkwYQLBwcHs3LmToUOHVuqzTC2Nafy4O416u3H9QjpRYQlcOnwNdUFJFS4lJoOUmAwO/xyJV3tH/Lu6YOtRt1JjEkKI+6WzxK1Lly5MnDiRTZs2AdC1a9dSa5mFh4eTk5NDq1atdPVIrYSEBP7++2/Gjh1bqruxoKCAoqIirl27hoWFxV13Hbh69SqzZ8/G1dWVgoIC3n33Xb7++mtsbW11FqeNjU2pCQe33NqMW5fPEqK66Ny5c7mzurdv306bNm10vluIQqHA3rce9r71aPNiABcPXiVqZwI3LpdMWijMVxO1M4GonQnYetQpqcK1c8TYTKpwQgj90+lPoqeeeuqOA/mDgoIqbUZpamoqxcXFfPnll3z55Zdl2p999lkGDhzIuHHj7nj9pEmTMDQ0ZP78+ahUKsaMGcPkyZP56quvdPaLw9vbm5MnT5KTk1NqgkJERIS2XQgBx44d44knnsDd3Z21a9fSpEmTSnmOiYURgT3cCOjuSsrFDKJ3JXDx4DXtpIXU2Ez2/3COw79G4dXOoaQK51lX5126QghRUTXiT0gPDw8++uijMse///57cnNzGTdu3B03u87KymLKlCnk5eWxaNEi7W4Fc+fOZfz48UybNo358+frZA21Ll26sHLlSjZs2KBdx62goIDNmzcTGBj4SHeWEKKq0mg0jBgxgoKCAs6fP8/PP/9c4fGeD0qhUNDA25oG3ta0ft6fS4euERWWQGpsyYSrIpWa6F0l+6bauFnh19UF7/aOGJuXHbMqhBCVqUYkbtbW1nTs2LHM8TVr1gCU23bLunXruH79OgsXLsTFxUV73MfHh08//ZTJkyezdetWnn766Tve448//iA7O1vb5XngwAGuX78OlIz9s7S0BCAwMJCQkBC+++470tPTcXJyYuvWrSQlJTFt2rT7f3EhaiCFQsEff/zBwIEDMTU1LfePsspkbG6EfzdX/Lu5khqbQVRYAhcPXKUwv6QKlxafxcEfI/jntyg825RU4Rr4WEsVTgjxSNToTebHjRtHRkbGXfcqLSoqIiEh4Y6L2168eBFPT8+7/lAePHgwSUlJ5batWrUKBwcH7b9VKpV2r9Ls7Gw8PT0ZPny4Tsb+ySbzoibJz88nMzOz1J69AMXFxSiVOluCskIK84tKqnC7EkiJKbsbSz1ny5IqXAdHTC2NH2lsQojapUYnbrWNJG6ipktJSaF79+58+OGHlb513p2kXc4kOiyRmP1XKMgtKtWmNFTi2aYh/l1dsPerJ1U4IYTOSeJWg0jiJmoytVpNnz592L59O1AyhvW1117TWzxFKjWX/rlGdFgiyedvlmmv62iBf4gLPh2dMK0jVTghhG7UiDFuQoiaT6VSaceL2tvb06dPH73GY2higG8nZ3w7OXMzMYvoXYmc35tIQU5JFS7jag7//BbF0VXRuLcsqcI5BNaXKpwQ4qFI4iaEqBbMzc1Zu3YtCxYsIDg4uNTYUX2r52xFmxcDeOxZX+KOJBG1K5GkyBsAFBdpuHToGpcOXaOOvTl+XV3w7eSEWV1ZcFsIcf/uu6u0uLiYuLg46tSpU2bB2KKiIs6ePUtQUJAuYxQVJF2lorYqKipi9erVDBkypMpUtNKvZhO9K5ELexPJzyq9Y4pCqcD9MXv8urng1MgGhbJqxCyEqPrua2pWUlISw4YN45VXXmHgwIFMnz6djIx/Z1hlZmZqt4wSQohH5d133+X555/nmWeeIT09Xd/hAGDtaEnr5/0ZsiiEkLFBODay0bZpijXEHkli6ydHWTVxD+F/XST3Zr4eoxVCVBf3lbgtXrwYW1tbVq5cydKlS1GpVIwePbrUNlMy10EI8SidP3+eOXPmALBx40YiIyP1HFFpBkYGeLV1oM//WjFofieaPuFZarJCdkoex1af5/exu9kx/wQJ4SkUF8vPUSFE+e4rcTt16hSjRo3CwcEBHx8f5s2bR9OmTRkzZgzJyckAVaabQghRO/j6+vL3339Tr1495syZQ9u2bfUd0h3VbWhBqyF+DFkUQrcJwTg3tYX//5GpKdYQfyyZbXOOsWr8bk78eYGctDz9BiyEqHLua3JCfn4+Rkb/bvGiVCqZOnUq8+bNY+zYsbz77rs6D1AIIe6lT58+RERElLttXFFREYaGVWseloGhEo9WDfFo1ZCslNyS7bR2J5KXrgIgJy2fE2tjOPlHDM5Bdvh3dcElyA6lwaNdeFgIUfXc108BV1dXoqOjyxyfPHkybdq0Yfr06ToLTAgh7kfDhg3LVPz//PNPHnvsMWJiYvQU1b1Z2Znz2GBfhnzVhR6TmuMSbMet19BoIOFkCjvmnWDl2N0cX3OerBSpwglRm93Xn6GdOnVix44d9OzZs0zbpEmTAFi/fr1uIhNCiIdw4cIFXnnlFTIzM2nRogUnT57E09NT32HdkdJAidtj9rg9Zk92ah7n95Rsap9zo2TSQm66ipPrLnLyr4s4N7XFv6sLrsENUBpKFU6I2kR2TqhBZDkQIf4VFRXF008/TVRUFM899xwrVqyodmNwi4s1JJ5KITosgcsnU9DcNmnBrK4xvp2d8QtxoY69uZ6iFEI8SlVr4IcQQuiIv78/R44cYdasWbz33nvVLmkDUCoVuAY3wDW4ATk38v+/CpdAdmpJFS4vo4BTGy5xasMlHBvb4N/VBbfH7DGQKpwQNZZU3GoQqbgJUTFRUVFcvXqVrl276juU+6Yp1nDlTCpRuxKIP3a9TBXO1MoIn87O+Ie4UNfBQk9RCiEqy0P/WdalSxcSEhJ0EYsQQlS67OxsBgwYQI8ePfj4448pLi7Wd0j3RaFU4NzMju4TmjNkUQgtn/PFqoGZtj0/q5AzG2NZM3kvm2b/w8WDVykqUOsxYiGELj10V6kU7IQQ1cm3335LREQEAL///jsTJkzA3Lx6jg8ztzahWX8vmvbz5GpEGtFhicQdTaJYXfJz+VrkDa5F3sDEwgifTk74dXWhnpOlnqMWQjwMGeMmhKhVJk6cSG5uLvPnz+ePP/6otknbfymUCpwa2+LU2Ja8TBUX9l4hKiyBzKRcAFQ5hZzdEsfZLXHY+9XDv5sLHq0aYmhsoOfIhRD3SxI3IUStYmBgwHvvvcfo0aOxsbEp1VZQUICRkVG1nMhwi1kdE5r286RJXw+Som4QtTOB2CPJFBeVdAknR98kOfomh36M0Fbh6rtY6TlqIURFSeImhKiVbk/aNBoNL7zwAqampixevBgLi+o9sF+hUOAQYINDgA1tswqI2V9ShUu/kgNAQV4R57bFc25bPHbe1gR0c8GzjQOGJlKFE6Iqk8RNCCGAhQsXsmbNGgAuXbrEvn37qnXl7b9MrYxp/LgHjXq7k3w+nehdCVw6dA11YUkVLiUmnZSYdA79HIl3B0f8u7pg41ZHz1ELIcojiZsQQgAODg5YWlqSnZ3NW2+9VWOStv9SKBQ09KtHQ796tHkhgJgDV4naeZmbidkAFOYVEbnjMpE7LmPrUQf/7q54tXXAyFR+VQhRVcinUQghgMGDB9O0aVO2b9/Ok08+qe9wKp2JpRGNerkR2NOVlIsZRIUlcPHgVdQFJVW41NhM9i89y+GfI/FuX1KFs/Wsq+eohRAPnbgNHTqUOnWkpC6EqP78/f3x9/cvc3zlypV06NABZ2dnPURVuRQKBQ28rWngbU2bF/y5ePAakTsvcyM+C4AilZqosASiwhKo72pFQHdXvNo5YGxupOfIhaidZOeEGkR2ThBC9/bu3UvXrl2pV68eK1asoEePHvoO6ZFIvVRShYs5cJUiVekFfA2MlXi1dcC/myt2XnVrZLeyEFWVdJUKIcQdaDQapk+fjlqtJjU1lT179tSaxM3Wsy4dPOvS6nl/Lh26RtTOy6TGZgKgLijm/J4rnN9zBWsnSwK6u+Dd3gkTS6nCCVHZdF5xU6vVpKSkkJqaSlFRUbnnBAUF6fKR4v9JxU0I3btx4wYvvfQSeXl5bN++HQOD2rtcRmpcBtFhicTsv0JhfukqnNJQiWebhvh3dcHer55U4YSoJDpL3IqLi/nll19Yu3YtWVlZdz139+7dunikuI0kbkJUjuLiYrKzs8uM583Ly8PMzOwOV9VchflFxP6TROTOy6TEZJRpr+tggX83F3w6OmFqZayHCIWouXTWVbpkyRJWrlxJvXr1ePzxx7GxsanVf5kKIWoOpVJZJmm7cuUKbdq0Ydq0aYwePbpWVZiMTA3x7eyMb2dnbiRkER2WwPm9VyjMK+llybiWwz+/RnH092jcW5dU4RwC6teqr5EQlUVnidu2bdtwcXHhu+++qxF7/wkhxJ0UFhby7LPPkpiYyNixY8nPz2fKlCn6Dksv6rtY0fblQFoO8SP2SBJROxNIjr4JQLFaw6WD17h08BpW9uYEdHXBp5MTZnVN9By1ENWXzhK3vLw8evToIUmbEKJWaNOmDQcOHMDFxYVhw4bpOxy9MzQ2wKeDEz4dnEi/kk3UrgTO77lCQU4hAFnJuRz5PZqjq8/j1qIBAd1ccWxkg0IpVTgh7ofOEjdPT0/S0tJ0dTshhKiyjIyMmDt3Lu3atcPJyQlbW1t9h1SlWDtZ0uaFAFo+60vc0WSiwhK4FnEDAI1aQ9yRZOKOJGNpa4p/N1d8Oztjbi1VOCEqQqmrG7300kvs27eP6OhoXd1SCCGqtGeeeYbWrVuXOlZQUMDnn3+OSqXSU1RVh4GRAV7tHOn7TmsGzetE0yc8MLH6d8mQ7NR8jq06z+9jdrF93nESTqVQXCxLiwpxNzpdDiQsLIwvv/yS9u3b4+XlhYWFRbnn9e7dW1ePFP8hs0qF0L9x48bx1Vdf0bJlS9asWYObm5u+Q6pS1EXFXD6eTOTOBK6eLdtLY17PhIBurvh2ccaivqkeIhSiatNZV2lBQQEHDx4kIyODTZs2AZSZQaTRaFAoFJK4CSFqpPj4eJYuXQrAqVOnSE1NlcTtNgaGSjxaO+DR2oHM5FyidycSvSuB/MwCAHJvqji+9gLH/7iASzM7Arq74tzMFqWBzjqIhKjWdJa4LVq0iB07duDl5UXnzp1lORAhRK3j5ubGoUOHGDBgANOmTaNFixb6DqlKq2NvTstnfWkxwJvLJ1OI2nmZxDOpoAE0kBCeQkJ4CmZ1jfHv6oJfiAuWtrVv3Twh/ktnidvu3bvx8/Pjm2++wdBQdtISQtROQUFBnD59utwZ9llZWVhZWekhqqpNaajEvaU97i3tyUrJ4/yeRKLCLpOXXlKFy8so4OS6i5xcdxGnJjYEdHfDNdgOpaFU4UTto7Pv+oKCAoKDgyVpE0LUehYWFmWGivz8888EBARw4MABPUVVPVjZmdFioA9Dvgqh55QWuATZwX++lFfOpBG64AQrRu/i6Mposq7n6i9YIfRAZ1mWn58fiYmJurqdEELUGGfOnOHNN98kLy+PLl26cPr0aQICAvQdVpWmNFDi2rwBrs0bkJOWx/k9V4jceZncmyWzdfOzCji14RKnNlzCIbA+gT1ccW1hj4FU4UQNp7Pv8BEjRnDkyBEOHjyoq1sKIUSNYGdnp102ZNiwYZK03ScLGzOCn/Hmua9C6DXtMVxbNEDxn99e1yJusPPLcFaMCuOfFVFkJOXoL1ghKpnOlgNZvnw5586d4+jRozRv3vyOy4EoFApefvllXTxS3EaWAxGi6ioqKuLrr7/mjTfewNRUlrl4WLk38zm/t6QKl5OaX6a9oV89Anq64f5YAwyMZKKcqDl0lrh17ty5Yg9UKNi9e7cuHiluI4mbENVPeHg4p0+f5qWXXtJ3KNWSpljD1XNpRIZeJv7EdTTq0r/SjM0N8e3sjH83F6wdLfUUpRC6o7Mxbl9++aWubiWEELVCeno6AwYM4NKlS+zfv5+FCxdKNe4+KZQKnJrY4tTElrwMFRf2XiEi9DLZKXkAFOQWcXZLHGe3xNHAuy6BPd1wb9UQQ2OpwonqSWeJW1BQkK5uJYQQtcKqVau4dOkSUFJ5u30mqrg/ZnVNaPqEJ036eXAt4kZJFe5YMsX/X4W7HpPB9ZjTGP0YgU9HRwK6u1LPWZZnEdWLrN0hhBB6cmu82zvvvMOaNWswMZGN1nVBoVDg2MgGx0Y25GcWcGHfFSJDL5OZXLJ0SGFeERHbLxOx/TK2nnUI7OmGZ2sHDE2kCieqPp2NcTtz5gx79uxhyJAh2NjYlGlPTU1l5cqVhISE0KhRI108UtxGxrgJUT3l5+eX6SLNysrCzMxM1sbUEY1GQ3L0TSJ2XCbuaBLFRaV/9RmaGODd3pGAnq7YuNbRU5RC3JvOfiKsWrWKixcvMmbMmHLbbW1tOXjwICkpKXzwwQe6eqwQQlR7tydtxcXFDBkyhJycHH7//XcaNmyop8hqDoVCQUP/+jT0r09+dgEx+68SuSOejGslVbgilZqosASiwhKo72ZFYE83vNo6YGQqibOoWnT2HRkVFXXPffmaNWvGsWPHdPVIIYSokT777DM2bdoEQM+ePQkPD0eplIVldcXU0pjGvd1p1MuN6xfSiQy9TOw/SagLiwG4EZ/F/qVnOfxTBJ5tHQjs6YatR109Ry1ECZ0lbunp6dja2t71nPr163Pz5k1dPVIIIWqkzp074+joyLVr15g3b54kbZVEoVBg71sPe996tH05kIsHrxKxPZ70KyUL+BYVFHN+zxXO77lCPWdLAnu44tXeEWNzIz1HLmoznSVulpaWXL9+/a7nJCcnY2ZmpqtHCiFEjdSuXTtOnjxJaGgoPXr00Hc4tYKJhRGBPdwI6O5K6qUMIkMvc/HQNdQFJVW4m4nZHPgxgsO/RuHRpiGBPdyw86orM4HFI6ezP+MCAwPZu3cvycnJ5bYnJyezb98+GjdurKtHCiFEjdWgQQOGDh1a5viiRYs4efKkHiKqHRQKBXZe1nR6oynPL+5Gh9caUd/13yVD1IXFxOy7yob3DrF2yj7ObotDlVOox4hFbaOzxG3w4MGoVCpGjx7N1q1bSU1NBUpmk27ZsoVRo0ZRUFDAs88+q6tHCiFErbJ161bGjRtH27ZtWbZsmb7DqfGMzQzx7+bKM5924KmP2+EX4lxqyZCMazkc/imS30aGEfZVOMnnb6KjhRqEuCOdLsA7evRovvnmGz799FOg5C+XW9/ECoWCsWPHykK9QgjxgBYuXIhGo0GlUpGSkqLvcGoVW/e6dBzRhDYvBnDp8DUitl8mLS4TgOKiYi4dusalQ9ewsjcnsLsrPp2dMLU01nPUoibS2Tput1y8eJH169cTFRVFdnY2lpaWBAQE8OSTT+Lp6anLR9Vo6enpfPzxx4SHh2NnZ8ekSZPuOWtX1nETomZTqVRMnjyZhIQE1q1bJ5MW9CztciaROy4Ts/8qRSp1qTalgQLXFg1o1Mudhv71ZCyc0BmdJ25CN95//33MzMyYMGECx44d49NPP2XFihXUqXPnhSElcROidigsLMTIqPTMxpSUFOzs7PQUUe1WpFIT+08S57bHk3opo0y7pa0ZAd1d8OvigmkdqcKJhyN/rlVBubm57Nu3j1dffRVTU1M6dOiAp6cn+/fv13doQogq4PakLTY2Fj8/P6ZMmUJhoQyUf9QMTQzw6eTEUx+2Y8CcDgT2dMPI7N+RSNmpeRxdeZ7fRoexfe4xrp5LQ1MsNRPxYGrMktCxsbH8+OOPREdHc+PGDUxNTXFzc2PIkCG0b9++0p6bm5vLypUriYiIIDIykqysLN5++20ef/zxcs8vKCjghx9+YPv27WRlZeHl5cXw4cNp2bKl9pzExETMzMxo0KCB9pinpyexsbGV9h5CiOqpoKCAgQMHcvPmTebNm4eFhYXsTqNH9ZytaDcskFZD/Yg7mkzEtjiux5RU4TRqDZdPpHD5RArm9U0I6OaKX1cXzOvKHrWi4mpMxS0pKYnc3Fx69+7NuHHjeOmllwB4++232bBhQ6U9NyMjg+XLlxMfH4+3t/c9z//kk09YvXo1PXr0YNy4cSiVSqZOncrp06e15+Tl5WFhYVHqOgsLC/Ly8nQevxCiejMyMuKVV17ByMgIb29vJk2apO+QBGBoXLL3af9Z7Rg4tyONerthbP5vrST3horjay7w++gwts45SuKZVKnCiQqpMRW3tm3b0rZt21LHnnnmGUaMGMHq1avp379/uddlZWVx4sQJOnfuXG57aGgo7du3v+PCwTY2Nqxbtw4bGxuioqJ4/fXX7xhjREQEO3fuZOTIkQwZMgSAXr16MWzYMBYvXszixYsBMDMzIycnp9S1OTk5snixEKIMhULBmDFjaNmyJWZmZtStK1szVTXWjpa0fSmQVkP8iD9+nXNb40g+nw6AphgSw1NJDE/F3NoEv64uBHRzwbye6d1vKmqtGlNxK4+BgQENGjQgOzv7juesW7eOmTNnsnfv3jJtGzduZNasWWzduvWO1xsbG2NjY1OhePbs2YOBgUGpJNLExIS+ffty7tw57eLFzs7O5OXllZruHxsbi4eHR4WeI4SofVq3bk3Tpk1LHcvNzWXChAncuHFDT1GJ/zIwMsCzjQNPzGzL4AWdaNLHA2OLf8cr5qarOPlnDL+P2cWWT45w+eR1iqUKJ27zUIlbQUGBruLQmby8PNLT07ly5QqrV6/mn3/+oXnz5nc8f+jQobRp04ZZs2Zx/Phx7fE9e/Ywb948evbsyVNPPaWT2C5cuICzs3OZbtCAgAAAYmJiADA3N6dDhw4sW7YMlUrFwYMHuXjxIh06dNBJHEKI2mHMmDF8+eWXNG/eXHZbqGLq2FvQ+gV/nl/clW4TgmnoX0/bptHAlTNpbP/8OCtGhXF0VTQ5aTJURpR4qK7Sp59+mu7du9OnT58qs/zE119/rR3TplQq6dSpExMnTrzj+YaGhsycOZO33nqLGTNmsGDBAvLy8pg9ezatW7dm+vTpOlt/Jy0trdzq3K1jt3abAJg0aRIfffQR/fr1w87OjpkzZ95xKZDQ0FBCQ0PvWlkUQtQuV69e5e+//wZKlgoxNpZlKKoiA0MlHq0a4tGqIVnXc4kIvcz53YmosktmB+dnFnBq/SVObbhEQ//6NH7cHddgO5QGNbrDTNzFQyVuBQUF/PXXX6xfvx4vLy/69u1Ljx49sLKyuvfFlWTQoEF06dKF1NRUdu3ahVqtvuf0eBMTEz755BPGjx/P1KlTKSwsJCAggA8++ABDQ90NA1SpVGWm8QPaH6gqlUp7zNrams8//7xC9+3evTvdu3fXruMmhBCOjo6cOHGCwYMHM3bsWBo1aqTvkMQ9WDUwp/VQf1o+60v8ietEbIvnWsT/d3NrICnyBkmRNzCxNMK3sxOBPd2xspOxz7XNQ2Ul69evZ8eOHWzevJmoqCgWLlzI4sWL6dixI3379r3nSv+Vwc3NDTc3NwB69+7NpEmTmD59OkuWLLlr5czCwoJRo0YxYcIEAMaOHYuJiW6naJuYmJSbRN7qctb184QQtZuLiwv79+/HwMCg1HGNRsPVq1dxcnLSU2TibpQGSjxaNsSjZUOyU/OIDL1M9O5E8jNLfleosgs5symOM5visPe1plFvd9wfs0dpKFW42uCh/iubm5vz5JNPsmTJEn766ScGDRqEhYUFO3fuZPLkyQwePJiffvpJO+heH7p06UJUVBQJCQl3Pe/q1avMnj0bV1dXGjZsyLvvvluq61IXbGxsSEtLK3P81jFbW1udPk8IIW5P2gC+/fZb/P39Wb16tR4iEvfD0taMls/5MfSbrvSY0hzHxjb8twaRfD6dsIXh/DpyJwd/iiAzOVd/wYpHQmfpubu7O6NHj+aPP/7gww8/pE2bNqSmprJs2TKee+453nrrLXbv3k1RUZGuHlkht7of7zb+KzU1lUmTJmFoaMj8+fOZN28e+fn5TJ48mczMTJ3F4u3tTWJiYpmlPiIiIrTtQghRmU6ePMmECRPIzs7m2WefJTw8XN8hiQpQKhW4Nbenz4xWPLcohOCnvTCz/reXpiCniIht8ayeuIf17x4k5uBV1EXFeoxYVBad11UNDAzo2LEjn376KWvXruXNN9/E2dmZI0eOMHPmTJ555hldPxKAmzdvljlWVFTEtm3bMDExwd3dvdzrsrKymDJlCnl5ecyfP58GDRrg4uLC3LlzSUlJYdq0aTpb+LZLly6o1epSCwIXFBSwefNmAgMDsbe318lzhBDiTnx9fRk8eDAAEyZMICgoSL8BiftmUc+UFoN8GboohF7THsO5qS2K//w2T7mYwe5Fp/j1jZ3sX3aWjGs5d76ZqHYqdQHe+vXrM2TIEFq1asWCBQs4c+aMTitY/zV37lxycnJo1qwZdnZ2pKWlsWPHDi5fvszo0aMxNzcv97p169Zx/fp1Fi5ciIuLi/a4j48Pn376KZMnT2br1q08/fTTd3z2H3/8QXZ2trbL88CBA1y/fh2AAQMGYGlpCUBgYCAhISF89913pKen4+TkxNatW0lKSmLatGm6+lIIIcQdWVhY8PPPP/Pkk0/y5JNP6jsc8RAUSgUuzexwaWZHboaKqJ2XidqZQO7Nkp6mwrwiokITiApNwNajDoG93PBq64CBUdnuc1F9KDQaTaWs7pebm8uOHTvYtGkT58+fR6PRYGpqSkhICNOnT9f583bu3MmmTZu4dOkSGRkZmJub4+fnxzPPPHPX9c+KiopISEi44+K2Fy9exNPT864TGwYPHkxSUlK5batWrcLBwUH7b5VKpd2rNDs7G09PT4YPH06rVq0q+KZ3dmtW6dKlS6vM8ixCiOrj0KFD7Nixg3feeQelUga6V0eaYg1Xz6VxdmsciafKbqNlZGqAR5uGNOnrST0nSz1FKR6GzhO3EydOsHnzZvbt24dKpUKj0RAYGEjfvn3p2rXrHStf4uFJ4iaEeFApKSk0b96cxMREevXqxapVq2T7rGouL1NFVFgCUTsTyEnLL9Ne39WKwB6ueHd0wtBYqnDVhU66Sq9fv86WLVvYsmULSUlJaDQarK2t6d+/P3379r3j+DIhhBBVw65du7h69SpQsgPN7Tu8iOrHrI4JwU95E/SkF9cib3B2SxyJ4SkUq0vqNTcuZ7H/h3Mc/jUK95b2NOnrgY1b+Qu9i6rjoRK3nTt3snnzZk6cOEFxcTFKpZKWLVvSt29fOnTooNPFa4UQQlSewYMHY2try8SJE1m5cqX8/K5BFAoFjoE2OAbakJ9VQPSuBCJ3JpCdUjLxrkilJmb/VWL2X8XayYKAHq74dnLGyFS+B6qih+oq7dy5MwAODg48/vjjPP744zRo0EBnwYn7I12lQoiHdeuP8P9KSUnB1NRUr7viCN3SaDQkR9/kzJY4Ek5c11bhbjEwUuLWogFN+nli5yld5lXJQ6XT3bp109sOCUIIIXTv9qStqKiIwYMHc+3aNf744w/ZOquGUCgUNPSvT0P/+qhyCrVVuKz/X8BXXVjMpcNJXDqcRJ2G5gR0d8UvxAVjM6nC6VulzSoVj55U3IQQuvb+++8za9YsoGQNuIiIiHJ3YxDVn0ajIeViOmc2xRF//DrFty3gqzRU4hpsR5N+HjTwtr7ragui8ug0dS4qKuLPP/8kNDSUy5cvo1Kp2LVrFwAXLlzg77//ZtCgQaXWSxNCCFF1vfjii6xfv55z586xbNkySdpqMIVCQQPvenQbX4+C3ELO77lCxI54MpNKqnDFRcXEHU0m7mgyVg3M8O/qQkB3V4zNjfQcee2is8RNpVIxefJkzp49S926dbGwsCA//9/pxw4ODmzevBkrKytGjBihq8cKIYSoRN7e3hw6dIh9+/bRvn17fYcjHhFjcyMaP+5O48fdSbn0/1W4Y8moC0uqcFnX8zi68jzH117AuZkdTft4YO9fT6pwj4DOVlj85ZdfOHPmDK+//jp//fUXffv2LdVuaWlJUFAQR48e1dUjhRBCPAJmZmb07NmzzPFZs2axadMmPUQkHiU7T2u6jg3ihSXdaDssEGvHf5eKKS7ScPn4dTbO/odV43dzcl0MquxCPUZb8+ms4hYWFkZwcDBDhw4FKDfrdnR05MKFC7p6pBBCCD35888/ef/99wGYOXOm9n+LmsvI1JBGPd1o1NON1LiMkirc0SSKCkqqcNmp+Rxfc4GTf8bg2NiGJn09cGxkI1U4HdNZ4nb9+nU6dux413PMzMzIyZHNboUQorpbv3699n/b2NjoMRKhD7budQkZ3YwiVWMu7E0kYsdlbiZmA1Cs1pB4KpXEU6lY1DfFt7MTjXq7Y2plrOeoawadJW5mZmakp6ff9ZyrV6/KFipCCFEDLF++nCZNmnDq1ClGjx6t73CEnhiaGBDQw42AHm7cSMjkzMY4Yo8kUaRSA5BzI5+T6y4Svv4SjoH1adzXA+emtlKFewg6S9waNWrEwYMHycrKKneRxuTkZA4fPnzPqpwQQoiqT6FQMGXKFDQaTZlfwtHR0fj6+sov51qmvksdOo9sSvvXGhGz/woR2y9z43IWAJpiDVfOpnHlbBrm1iZ4d3SkcR8PzOua6Dnq6kdnkxOee+45srKymDhxImfOnEGtLsm28/PzOX78OFOmTEGtVvPss8/q6pFCCCH07PbkLDIykhYtWjBo0CAyMzP1FJXQJ0NjA/y7uvLMpx0YMKcDvp2dMTL9dxmZ3HQVp/+O5ffRYWya/Q9xx5LQFMuSshWl0wV4//rrLxYuXEhxcXGZNqVSyaRJk+jXr5+uHiduIwvwCiH0qaioiKCgIM6dOwfA1KlT+eyzz/QclagK1IVqYg5cJWL7ZdLiyib0pnWM8W7vSJO+HljUN9VDhNWHzndOiIuLY/369URGRpKZmYmFhQUBAQE8/fTTeHh46PJR4jaSuAkh9O3vv//mpZdewtnZmX/++Qdzc3N9hySqmPSr2ZzZFMulw0kU5hWValMooIGPNY16uePeuiFKpXS33062vKpBJHETQlQFly5dQq1W4+Pjo+9QRBWmLirm0sGrnNseT+qlslU4E0sjPNs60LSfB1Z28gfALY98t1i1Wi1bpgghRA3m6elZ5lhmZiYjRozgk08+Kbdd1D4Ghkp8Ojnj08mZzKQcTm+K5dKhaxTkllThVNmFRO64TGToZew86xLYyw2vtg4oDXQ2PL9a0tnb//nnn/c8R61W88EHH+jqkUIIIaoBjUbD8OHDWb16Nc2bN2fnzp36DklUMXUaWtDhtca8sKQbXUY3xc7rP0uHaSDlYgZ7vjnNr2/uZN/SM2Qm1d41YXVWcVu4cCH169enS5cu5bYXFxfzwQcfsHfvXl09UgghRDWQlpbGqVOntP92d3fXXzCiSlMaKPFu74R3eyeyrueWVOEOXkOVU7KNVkFOEdG7EonelYitRx0Curvi09EJpWHtqcLp7E2bNGnChx9+yIkTJ8q03Ura9uzZw9NPP62rRwohhKgGbG1tOXr0KIMHD+ann37Cy8tL3yGJasCqgTntX2nE80u60XVcEA18rOE/cxVSYzPZt/Qsv7yxkz3fnib9apbeYn2UdDY5IScnhzFjxpCcnMyXX36pHZRaXFzM7NmzCQsL46mnnmLixIm6eJwoh0xOEEJUNxqNhjNnztC0aVN9hyKqgezUPM5siiXm4FVUWWU3s6/vakVANxd8Q1wwqKFVOJ29lYWFBXPnzsXS0pK33nqLq1evotFomDVrFmFhYTz55JOStAkhhChl3rx5BAcH89lnnyGLHIh7sbQ1o+3LgTy/uBvdxgdj71eP/64BfeNyFgd+jODX10PZ9XW4dueGmkTny4EkJCQwevRoLCws8PHxYc+ePfTr14+33npLl48R5ZCKmxCiOjl58iQtW7bU7rRz+PBhWrdureeoRHWTcyO/pAp34Cr5mQVl2us5W+LX1QX/ri4YGlf/VS10Xkd0cXFhzpw53Lx5k71790rSJoQQolxNmzblf//7HwqFgv/973+StIkHYlHflDYvBvD8N13pPjGYhgH1Ufwnu7mZmM3hnyP59Y2d7Fx4kpTYDP0FqwMPXHFbvnz5XdtPnTpFTEwMTz/9NErlv19BhULByy+//CCPFPcgFTchRHV08OBBWrduLWt8Cp3JvZnP6c1xXNx/hbyMslW4uo4W+HZ2JrCHK0amj3xJ24fywIlb586dH+yBCgW7d+9+oGvF3UniJoSoKXbu3Mlvv/3GokWLZNss8cA0Gg2XT1zn7JY4kqJultnM3sBYiXMzO5r29cDet56eorw/D5xmfvnll7qMQwghhADgypUrDBkyhJSUFI4dO8bOnTuxs7PTd1iiGlIoFLi1sMethT15GSrObI4jZt8VctNVAKgLiok/mkz80WTq2Jvj08mJRr3dMTarulW4B44sKChIh2EIIYQQJc6dO0dubi4Azs7O2NjY6DkiUROY1TWh1RA/Wj7nS2J4Kme2xHIt8gYadUkVLjM5l+NrLhD+10WcmtjQuI87joG2eo66rKqbUgohhKiVevbsyZEjR5g0aRK//PJLqXHSQjwshUKBS7AdLsF25GUVcHZzLDH7rpJzIx8AdWExl0+kcPlEClZ2Znh3cKTx4+6YWBrrOfISDzzGbcqUKbz22msEBATc97V5eXn88ccfmJub88wzzzzI40U5ZIybEKKmS0hIQKlU4uTkpO9QRA2i0Wi4ciaNM5tjuXYujWJ16dRIaajAsZENTfp64NRYv1W4B664paenM3LkSJo1a0avXr3o1KkTlpaWd73m3LlzbN++nbCwMFQqFTNmzHjQxwshhKhlCgoKGDhwILGxsfz+++9069ZN3yGJGkKhUODc1BbnprbkZxdwdkvJWLjs1JIqXHGRhsRTqSSeSsXGvQ7dJwZjZaefSTMPtQDvli1bWL58OUlJSSiVSlxcXPDz86NevXpYWlpSUFBAZmYmCQkJREdHk5ubi1KppFu3bgwfPhx7e3tdvkutJxU3IURN9t577zF79mwAvL29iYiIwMjISM9RiZpKo9Fw7dwNzmyO5crZVIqLStIls7rGDPkqRG8b2z/UGLfHH3+c3r17c/jwYTZv3kx4eDjbt28vc55SqcTT05NOnTrRt29fbG2r3mA/IYQQVdv48eM5evQou3btYtWqVZK0iUqlUChwbGyDY2MbVDmFnNsaR+w/Sbg2b6C3pA0qYcuruLg4UlJSyMzMxNjYGGtrazw8PO7ZjSoenlTchBA1XXFxMeHh4TRv3lzfoYhaqrioWK+Jm85nlbq7u+Pu7q7r2wohhBAolcpyk7bx48fj7+/Pm2++ieK/u44LoWP6TNpAlgMRQghRzf3yyy8sXLgQgBMnTrB06VI9RyRE5ZHFcYQQQlRrkZGR2v/dvn17PUYiROWTipsQQohq7eOPP6Z58+YcOHCAYcOG6TscISqVVNyEEEJUewMHDmTBggVljh88eJCCggI9RCRE5ZDETQghRI108uRJunbtSqdOnbh8+bK+wxFCJyRxE0IIUeMUFxfzwgsvoFKp+Oeff1i0aJG+QxJCJyRxE0IIUeMolUp+/vlnPDw8aNWqlXbHBSGqu0qfnNClSxd2795d2Y8RQgghSmnRogXHjx8nNzcXExOTUm0ajUbWexPVUqVX3HS8MYMQQghRYfXq1cPJyanUsbS0NDp16sThw4f1FJUQD+6+ErfJkyezZMkSdu3aRWJiYoWu+e9fNL/++iuFhYX3F6EQQgihI8XFxbz00kvs37+fTp06sXbtWn2HJMR9ua+uUicnJ06dOsWff/6JSqXC3NwcLy8vfH198fX1xcfHB3d3d5TKf/PB/1bcvv/+e/r27Uu9evUA+O2333jmmWcwMzPT0esIIYQQd5aVlUVWVhYAdevWpU2bNnqOSIj7c1+J26RJk4CSZOzy5ctER0cTExPDhQsX2LZtG9nZ2RgbG7N9+/Zyr7+92/SXX34hJCREm7jdvHmTN954g9WrVz/IuwghhBB3VbduXcLCwpgxYwY9evTA2dlZ3yEJcV8eaHKCQqHAzc0NNzc3evbsyYULF9i7dy8bNmwgMzOzwve5PZHTaDRcv379QUISQgghKsTQ0JA5c+aUOa5WqwkNDaVXr156iEqIinngWaVnz55l79697Nu3j5SUFJo3b87w4cPp0KGDLuMTQgghHolZs2Yxa9Ys3njjDb744gtMTU31HZIQZdxX4nb8+HH27NnDvn37yMvLo3Xr1gwfPpy2bdtibm5eoXscPHiQ4OBgHB0dHyhgIYQQQtfOnDmjXett6dKlDBs2TMa/iSrpvse42dra8uKLL/LEE09gZGR0z2v+O6vUx8eHBQsWUFRUhLm5OQUFBfz22280adIEHx8fLC0t7/8NhBBCiIfUpEkTfvjhB0aNGsXMmTMlaRNV1n0lbj4+PsTFxbFw4UJ+/PFHfHx88PHx0c4qdXFxuev133//PUVFRcTFxXH+/HkuXLjA+fPnCQ0NRaVSyWKIQggh9OaVV16hY8eOeHp6lmmTBXtFVXFfiVt5idfZs2f566+/UKlUmJmZ4e3tzVdffXXnBxoa4u3tjbe3t/aYRqMhISFBO0tVCCGE0If//m665e+//+bLL7/kt99+w97eXg9RCfGv+56c8KCJ1+eff46Xlxfe3t54eXlhYWGhbVMoFLi6uuLq6kqPHj0e4DWEEEII3bt06RIvvfQS6enpBAcHc+TIEVlCROhVhRO3h028NBoN27ZtY8mSJahUKuzt7bX3upUIyoQFIYQQVUlqaipmZmakp6fTrl27MttnCfGo3VfF7WESr6lTpwIlCdzJkyd5//33uXnzJidPnuTPP/8kOzsbMzMzPDw8+Oabbx7urYQQQggdaNWqFSdPnmTGjBksWLBAxrkJvatw4vbWW28BD594KRQKfvzxR0aNGsXjjz8OQFFREVu3buXbb7/Fz8/vIV9JCCGE0B17e3t++OGHMsdjYmLIzc2ladOmeohK1Fb3PcZNF4lXZGQkM2bM+DcIQ0P69euHiYkJ27Ztu9+QhBBCiEcqNzeXAQMGcP78eRYvXsywYcP0HZKoJZT3PqWsyMhIgoKCtP++lXiNHz+ehISEe17v7e3N7t27yxxv1KgRZ86ceZCQhBBCiEdm7ty5nD59mvz8fD777DPy8/P1HZKoJR4ocXvYxGv06NEsX76chQsXcvXqVQCKi4tZv369LMIrhBCiyps6dSpvvPEGFhYW/PHHH7I9lnhkHmiv0tGjRzNlyhRSUlIYOHAgjo6O95V4NWnShG+++YYFCxYwZMgQLCwsKCoqoqioiClTpjxISEIIIcQjY2pqyrfffsvbb7+Nm5tbqbbi4mKUygeqiwhxTw+UuOki8fLy8mLRokVcvXqVCxcuoFAo8PPzk8UNhRBCVBu3J20ajYaXXnoJR0dHPv74YwwNH+jXrBB39MDfURVNvDQazV3v4+joKOu3CSGEqBG+/fZbfvvtN6BkPPiGDRtkCRGhUw/9p8C9Eq89e/Y87COEEEKIakGj0WBoaEhRUREvv/yyJG1C5+47cSsuLiYuLo46depga2tbqq2oqIizZ8+WmnEqhBBC1BajRo0iODiYXbt2MXDgQH2HI2qg+0rckpKSmDp1KvHx8SgUCtq0acPbb79N3bp1AcjMzGTChAnlzjgVQgghaoO2bdvStm3bMsc3btxI+/btqVevnh6iEjXFfU17Wbx4Mba2tqxcuZKlS5eiUqkYPXo0qamp2nPuNaZNCCGEqG0OHTrE008/TYsWLThx4oS+wxHV2H0lbqdOnWLUqFE4ODjg4+PDvHnzaNq0KWPGjCE5ORlA+vOFEEKI/9BoNIwaNYqioiJiY2NZs2aNvkMS1dh9JW75+fkYGRn9e7FSydSpU2nZsiVjx47lypUrOg9QCCGEqM4UCgXr16+nVatWdOrUidmzZ+s7JFGN3Vfi5urqSnR0dJnjkydPpk2bNkyfPl1ngQkhhBA1haurK/v27ePPP/8ss7ZbUVGRnqIS1dF9JW6dOnVix44d5bZNmjSJbt26yRg3IYQQohzGxsbY2NiUOpaUlESjRo1Yu3atnqIS1Y1CI5lWjREdHc2IESNYunQpfn5++g5HCCHEXRQVFdGjRw/tSgzfffcdI0aM0G9Qosp7qM3UYmNjpcImhBBCPIDCwkIcHByAksXsn3zyST1HJKqDh0rcXnnlFX7++WddxSKEEELUGmZmZvz2228sWrSI1atX06BBA32HJKqBh0rcNBpNmYrbhg0bZMaMEEIIUQEKhYLRo0fTvn37UscLCwtZtmwZxcXFeopMVFUPlbiVJy0tjZ07d5bb9uuvv/LGG2/o+pFCCCFEjTJ9+nRee+01+vXrR1pamr7DEVWIzhO3uyksLCx3OREhhBBClDh//jxffPEFAKGhoVy6dEm/AYkq5ZEmbkIIIYS4O19fX7Zv346dnR0LFiygZcuW+g5JVCH3tcm8eHTS09P5+OOPCQ8Px87OjkmTJtGiRQt9hyWEEOIR6NatG5GRkdSvX79MW0FBAcbGxnqISlQFUnGrohYsWED9+vXZsGEDI0eO5P333yczM1PfYQkhhHhEbGxsyuz/vXLlSoKDg4mMjNRTVELfHrritm7dOi5cuIC/vz9+fn5kZ2frIq5aLTc3l3379rFy5UpMTU3p0KEDnp6e7N+/nz59+ug7PCGEEHoQGRnJ8OHDycnJoWXLlpw6dQovLy99hyUesYdK3Hx8fIiLi2P//v3s37+/1F8G77//Pl5eXnh5eeHt7Y29vf1DB3s3kZGRbN26lZMnT5KUlESdOnVo1KgRw4cPx8XFpdKem5uby8qVK4mIiCAyMpKsrCzefvttHn/88XLPLygo4IcffmD79u1kZWXh5eXF8OHDS41hSExMxMzMrNSaPp6ensTGxlbaewghhKjaDA0N8fT05MyZMwwYMABPT099hyT04KESt++//56ioiIuXrzIhQsXiI6O5vz581y8eJHdu3eze/dubTJnbm6OqampToIuz4oVKzhz5gwhISF4eXmRlpbGunXrGD58OIsXL660b/CMjAyWL1+Ovb093t7enDx58q7nf/LJJ+zevZtBgwbh7OzMli1bmDp1Kl9++SVNmzYFIC8vDwsLi1LXWVhYkJGRUSnvIIQQourz8fHh8OHDfPLJJ0yfPr1MN6qoHR66q9TQ0BA/Pz/8/Pzo168fAGq1mri4OKKjo0slc2lpaZX2jTZ48GDee+89jIyMtMe6du3KK6+8wm+//ca7775b7nVZWVmcOHGCzp07l9seGhpK+/btMTMzK7fdxsaGdevWYWNjQ1RUFK+//vodY4yIiGDnzp2MHDmSIUOGANCrVy+GDRvG4sWLWbx4MVCymnZOTk6pa3Nycu4YgxBCiNrB3Ny83EXuz5w5w5UrV+jdu7ceohKPUqVMTjAwMMDLy4s+ffowceJEFi9ezNatW1m+fDnTp0+vjEfSpEmTUkkbgIuLC+7u7sTHx9/xunXr1jFz5kz27t1bpm3jxo3MmjWLrVu33vF6Y2NjbGxsKhTjnj17MDAwoH///tpjJiYm9O3bl3PnzpGcnAyAs7MzeXl5pKSkaM+LjY3Fw8OjQs8RQghRe2RmZjJgwAD69OnD+++/j1qt1ndIohI9slmlSqUSDw+PR/rXgEaj4ebNm9StW/eO5wwdOpQ2bdowa9Ysjh8/rj2+Z88e5s2bR8+ePXnqqad0Es+FCxdwdnYu0w0aEBAAQExMDFDyF1WHDh1YtmwZKpWKgwcPcvHiRTp06KCTOIQQQtQc3333HRcuXECj0fD3339TWFio75BEJarR67jt2LGDlJQUXn311TueY2hoyMyZM3nrrbeYMWMGCxYsIC8vj9mzZ9O6dWudjiNIS0srtzp361hqaqr22KRJk/joo4/o168fdnZ2zJw5kzp16pR739DQUEJDQ2VGrxBC1EKTJk1CrVbz+eefs3bt2kodTy70r8YmbvHx8SxYsIBGjRrds8pnYmLCJ598wvjx45k6dSqFhYUEBATwwQcfYGiouy+RSqUq050LaBdSVKlU2mPW1tZ8/vnnFbpv9+7d6d69O9HR0YwYMUI3wQohhKgWlEol06ZN48033yzTw5Sfn4+JiYlMZKhBauQCvGlpaUybNg0LCwtmz56NgYHBPa+xsLBg1KhRZGZmkpeXx9ixYzExMdFpXCYmJuWWsAsKCrTtQgghxIO4PWnTaDQ899xzPPfcc2RlZekpKqFrNS5xy87OZurUqWRnZzN37lxsbW0rdN3Vq1eZPXs2rq6uNGzYkHfffbdU16Uu2NjYkJaWVub4rWMVjVUIIYS4l7lz57J+/XpWr15N79690Wg0+g5J6ECNStxUKhXTp08nISGBTz/9FHd39wpdl5qayqRJkzA0NGT+/PnMmzeP/Px8Jk+erNNtpry9vUlMTCyz1EdERIS2XQghhNAFX19fbRXunXfeke7SGqLGJG5qtZqZM2dy7tw5PvjgAxo3blyh67KyspgyZQp5eXnMnz+fBg0a4OLiwty5c0lJSWHatGnk5eXpJMYuXbqgVqvZsGGD9lhBQQGbN28mMDCw0neXEEIIUXs8+eSTHD9+nG+//faOu/mI6qfGTE74+uuvOXDgAO3atSMrK4vt27eXau/Zs2e5161bt47r16+zcOHCUltj+fj48OmnnzJ58mS2bt3K008/fcdn//HHH2RnZ2u7PA8cOMD169cBGDBgAJaWlgAEBgYSEhLCd999R3p6Ok5OTmzdupWkpCSmTZv2UO8vhBBC3O7W1pO3++mnn+jUqZOsD1oNKTQ1pNN73LhxhIeH37G9vAV2AYqKikhISLjjN+/Fixfx9PS8a4l58ODBJCUlldu2atUqHBwctP9WqVTavUqzs7Px9PRk+PDhtGrV6o73r6hbs0qXLl2Kn5/fQ99PCCFEzbNz50569uxJ3bp1+fnnn7W7HonqocYkbkISNyGEEHen0Wjo2LEjBw4cAOCDDz7gvffe03NU4n7UmK5SIYQQQtydQqFg06ZNvPrqq+Tm5vLOO+/oOyRxnyRxE0IIIWqRunXrsnbtWnJzc1EqS89RzM7O1o7LFlVTjZlVKoQQQoiKUSgUZfbNvnz5Mt7e3sydO1fWfKvCJHETQgghajmVSsWgQYNITk7mrbfeqvCWi+LRk8RNCCGEqOUMDQ3p0aMHAB4eHrLvdRUmY9yEEEKIWs7AwIAPP/yQtm3b4uDgQL169fQdkrgDqbgJIYQQAoC+ffvSvHnzUsfy8/OZPXu2znYREg9HEjchhBBC3NH48eN57733aNu2LRcvXtR3OLWeJG5CCCGEKFdcXBy//vorAOfPnycnJ0fPEQlJ3IQQQghRLnd3d/755x/8/PxYvHgxTZs21XdItZ5MThBCCCHEHTVu3Jjw8HBMTU1LHddoNKSnp8tEhkdMKm5CCCGEuKvbkzaAZcuW4e/vz65du/QQUe0liZsQQggh7kt4eDijR4/m+vXrdO/encjISH2HVGtI4iaEEEKI++Ls7Eznzp0BGDlyJAEBAXqOqPaQMW5CCCGEuC+2trZs3ryZ77//nmHDhuk7nFpFKm5CCCGEuG8GBga88cYbmJiYlDp+7NgxvvvuO9movpJIxU0IIYQQOpGWlsbAgQOJj49n//79LFmyBDMzM32HVaNIxU0IIYQQOrFu3Tri4+MBiImJwcDAQM8R1TySuAkhhBBCJ4YPH87KlSvx8PBg9erVGBsb6zukGkcSNyGEEELozLPPPkt0dDTOzs6ljqenp1NYWKinqGoOSdyEEEIIoVNGRkal/q1Wqxk0aBBdunQhMTFRT1HVDDI5QQghhBCVavbs2YSGhgLQt29fTp48iVIptaMHIV81IYQQQlSqvn374urqilKpZOHChZK0PQSpuAkhhBCiUrVs2ZITJ06we/du7Y4L4sFIyiuEEEKISmdjY8OAAQPKHJ83bx5HjhzRQ0TVkyRuQgghhNCLDRs2MGXKFDp06MDixYv1HU61IImbEEIIIfRi6dKlABQWFpKfn6/naKoHGeMmhBBCCL34888/mT59OpcvX2bChAn6DqdakMRNCCGEEHphZGTEvHnzUKvVKBSKUm1Xr17F0dFRT5FVXdJVKoQQQgi9un1P0wsXLhAQEMCYMWNQqVR6iqpqksRNCCGEEFVGQUEBAwcOJDMzk6+//pqPP/5Y3yFVKZK4CSGEEKLKMDIyYuzYsZiYmBAQEMBbb72l75CqFBnjJoQQQogqQ6FQMHz4cFq0aIGJiQmWlpb6DqlKkYqbEEIIIaqc4OBgAgMDSx3Lzs7mzTffJCUlRU9R6Z8kbkIIIYSo8jQaDa+//jpLliwhODiYo0eP6jskvZDETQghhBBV3rVr19i1axcAmZmZ1K1bV88R6YckbkIIIYSo8hwdHTlx4gSdO3dm2bJl+Pr66jskvZDJCUIIIYSoFhwcHAgLC0OpLF130mg0xMfH4+7urp/AHiGpuAkhhBCi2rg9aQNYuHAhgYGB/PLLL3qI6NGSxE0IIYQQ1daxY8eYMmUKeXl5vPTSS4SHh+s7pEoliZsQQgghqq3GjRvzyiuvADB16lSCgoL0G1AlkzFuQgghhKi2TE1N+e677+jfvz+9e/fWdziVTipuQgghhKj2+vXrh6Fh6XrU3r17mTFjBkVFRXqKSvek4iaEEEKIGicpKYlnn32WpKQkDh06xLp167C2ttZ3WA9NKm5CCCGEqHH2799PamoqAAYGBlhZWek5It2QxE0IIYQQNc7AgQPZvXs3rVq1YsWKFRgYGOg7JJ2QrlIhhBBC1Ejt27fn8OHDKBSKUseTkpIwNTWtll2nUnETQgghRI11e9JWWFjIgAEDeOyxx6rlmm+SuAkhhBCi1pg5cyYHDx7k4sWLPPfcc6jVan2HdF8kcRNCCCFErTFixAgee+wxjIyM+Pnnn6vd2DcZ4yaEEEKIWsPd3Z39+/dz+PBhWrVqpe9w7ptU3IQQQghRq5iYmNC5c+cyx2fMmMG6dev0EFHFSeImhBBCiFpv5cqVfPLJJzzzzDO88847+g7njiRxE0IIIUStFxoaqv3fbm5ueozk7mSMmxBCCCFqvaVLlxIUFMTp06cZPny4vsO5I0nchBBCCFHrKRQKxowZU27b6dOnady4MUql/jsq9R+BEEIIIUQVdebMGdq0aUP//v25efOmvsORxE0IIYQQojxFRUUMHjyYvLw8Nm3axJw5c/QdkiRuQgghhBDlMTQ05IsvvsDGxobg4GDef/99fYckY9yEEEIIIe6kV69enDx5kqKiIkxNTfUdjiRuQgghhBB34+Liou8QtKSrVAghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimpDETQghhBCimjDUdwBCd1QqFQDx8fF6jkQIIYQQ98vNzQ1TU9O7niOJWw2SlJQEwIcffqjnSIQQQghxv5YuXYqfn99dz1FoNBrNI4pHVLL09HSOHDnCX3/9xfjx4yt0zVdffcXYsWPveV58fDwffvgh77zzDm5ubg8barVX0a+bvjzq+Crrebq678Pc50Guvd9rKnK+fAbLks/ho3meLu77sPeo7M9hVfldKBW3Wsba2pqePXsSFhZ2z4z9FktLywqfCyXfVPdzfk11v1+3R+1Rx1dZz9PVfR/mPg9y7f1ecz/ny2fwX/I5fDTP08V9H/Yelf05rE6/C2VyQg3UvXv3SjlX/Kuqf90edXyV9Txd3fdh7vMg197vNVX9+6mqqupfN/kc6u4elf05rOrfS/8lXaWiQqKjoxkxYkSF+t+FELonn0Eh9K8qfA6l4iYqxMbGhmHDhmFjY6PvUISoleQzKIT+VYXPoVTchBBCCCGqCam4CSGEEEJUE5K4CSGEEEJUE5K4CZ0oKCjg008/ZeDAgfTu3Zs333yTs2fP6jssIWqVzz//nKeeeorevXvz8ssvc+DAAX2HJEStdfbsWTp37sxPP/2k0/vKGDehE3l5eaxatYrHH38cOzs7du3axRdffMGqVaswNzfXd3hC1Arx8fE4ODhgbGxMZGQkkyZNYuXKldStW1ffoQlRqxQXFzNq1Cg0Gg3t2rXj5Zdf1tm9peImdMLMzIxhw4Zhb2+PUqmkW7duGBoakpCQoO/QhKg13NzcMDY2BkChUFBYWEhqaqqeoxKi9vn7778JCAiolN0VZOeEWio3N5eVK1cSERFBZGQkWVlZvP322zz++ONlzi0oKOCHH35g+/btZGVl4eXlxfDhw2nZsuUd75+QkEBWVhZOTk6V+RpCVFuV9RmcP38+mzdvpqCggDZt2uDp6fkoXkeIaqkyPocZGRmsWbOGxYsX89VXX+k8Zqm41VIZGRksX76c+Ph4vL2973ruJ598wurVq+nRowfjxo1DqVQydepUTp8+Xe75KpWKDz/8kOeffx5LS8vKCF+Iaq+yPoOTJk1i27ZtLFiwgJYtW6JQKCrrFYSo9irjc7h06VIGDRqElZVVpcQsiVstZWNjw7p161izZg0jR46843kRERHs3LmT119/nVGjRtG/f3+++OILGjZsyOLFi8ucX1RUxHvvvYeTkxPDhg2rxDcQonqrrM8ggIGBAS1atOD48eMcOnSosl5BiGpP15/D8+fPExUVRb9+/SotZkncailjY+MKrfy8Z88eDAwM6N+/v/aYiYkJffv25dy5cyQnJ2uPFxcX8+GHH6JQKJgxY4b8pS/EXVTGZ/B2arWaK1eu6CReIWoiXX8Ow8PDSUhIYMCAATz11FOEhYWxYsUKPvnkE53FLGPcxF1duHABZ2dnLCwsSh0PCAgAICYmBnt7ewDmzp1LWloac+fOxdBQvrWE0IWKfgazs7M5dOgQ7du3x9jYmH379nHy5Elef/11fYQtRI1S0c9h//796datm7Z94cKFODg48Pzzz+ssFvntKu4qLS2t3L9Gbh27NWMtKSmJjRs3YmxsXOovkjlz5tCsWbNHE6wQNVBFP4MKhYKNGzeyYMECNBoNTk5OvPvuu/j4+DzSeIWoiSr6OTQ1NcXU1FTbbmJigpmZmU7Hu0niJu5KpVJhZGRU5vitJQdUKhUADRs2ZO/evY80NiFqg4p+Bi0sLPjyyy8faWxC1BYV/RzebsaMGTqPRca4ibsyMTGhsLCwzPGCggJtuxCi8shnUAj9q0qfQ0ncxF3Z2NiQlpZW5vitY7a2to86JCFqFfkMCqF/VelzKImbuCtvb28SExPJyckpdTwiIkLbLoSoPPIZFEL/qtLnUBI3cVddunRBrVazYcMG7bGCggI2b95MYGCgdkapEKJyyGdQCP2rSp9DmZxQi/3xxx9kZ2drS70HDhzg+vXrAAwYMABLS0sCAwMJCQnhu+++Iz09HScnJ7Zu3UpSUhLTpk3TZ/hCVHvyGRRC/6rb51Ch0Wg0j/SJosoYPHgwSUlJ5batWrUKBwcHoGS2zK392bKzs/H09GT48OG0atXqUYYrRI0jn0Eh9K+6fQ4lcRNCCCGEqCZkjJsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQgghRDUhiZsQQtQCq1evpmvXrly7dk17bMuWLXTq1IktW7boMbJ/bdy4kS5dunDx4kV9hyJElSWJmxCi2rl27RqdOnW66/8NHjxY32FWGVlZWfz888/06dNHu2F2ZTly5AidOnVi8uTJ9zx31qxZdOrUiR07dgDQu3dv7O3tWbx4caXGKER1ZqjvAIQQ4kE5OTnRo0ePctssLS0fcTRV1+rVq8nMzGTIkCGV/qzHHnsMe3t7jh8/TnJyMvb29uWel52dzb59+7C0tKRTp04AGBoaMnjwYL788kvOnDlDkyZNKj1eIaobSdyEENWWk5MTr776qr7DqNKKiorYuHEjTZo0wcnJqdKfp1Qqefzxx1m+fDlbt27l5ZdfLve80NBQVCoVffr0wcTERHu8W7duLFq0iPXr10viJkQ5pKtUCFErdOrUiXHjxnHjxg0++ugjnnjiCbp3786bb77JyZMny70mNzeXZcuW8dJLL9G9e3f69OnD5MmTOX36dJlzx40bR6dOnVCpVCxdupTnnnuOkJAQli1bpj1nz549jBgxgu7du/Pkk08yZ84csrKyGDx4cKmu3dmzZ9OpUyciIiLKjeuHH36gU6dOhIaG3vO9jxw5QlpaGl26dLnnubdcv36dl19+me7du7N7927t8Zs3b/LVV18xZMgQunXrxhNPPME777zDpUuXSl3fp08fFAoFW7ZsQaPRlPuMzZs3A9C3b99Sx62trQkODmb37t3k5uZWOGYhagtJ3IQQtUZ2djajR48mLi6Onj170qlTJ6Kjo5kyZUqZ5CMzM5ORI0eyfPlyrKysePLJJ+nUqRPnz59n/Pjx7Nu3r9xnvPvuu2zdupXg4GAGDhyoHVO2adMm3n33XRITE+nVqxe9e/fm3LlzTJo0iaKiolL36N+/v/aa26nVajZv3kzdunW1XYx3c/z4cQAaNWp07y8QEBcXx6hRo7h+/Tqff/65NuG7cuUKw4cPZ82aNTg6OvLMM8/Qpk0bjhw5wsiRI0slmQ0bNqRFixZcvXq13KT40qVLREVF4ePjg6+vb5n2Ro0aUVBQwNmzZysUsxC1iXSVCiGqrStXrpSqaP1Xo0aNaN26daljMTExPPXUU0yYMAGlsuTv1ubNmzNnzhz+/PNPpkyZoj33iy++IDY2lqlTp9KvXz/t8Zs3bzJixAg+//xzWrVqVaqbDyAtLY0ff/yROnXqaI9lZWWxcOFCzMzM+O6773BxcQFgxIgRTJkyhejoaBo2bKg9v1mzZri7u7Nz507GjBmDmZmZtu3IkSOkpKQwaNAgjI2N7/k1OnPmDEqlEm9v73uee+7cOaZNm4ahoSFfffVVqWs++ugjbty4wdy5c2nVqpX2+EsvvcSIESOYM2cOy5cv1x7v27cvx44dY/PmzTRv3rzUc+5UbbvFz88PgLNnz5Z6lhBCKm5CiGrsypUrLF++vNz/++eff8qcb2ZmxptvvqlN2qBkJqOBgQFRUVHaY+np6ezatYvmzZuXStoA6tWrx5AhQ0hPT9dWs/7rlVdeKZW0Aezfv5+8vDz69OmjTdqgZDD+8OHDy323/v37k5uby86dO0sd37hxIwBPPPHEnb4spaSkpGBpaXnPJO/QoUNMnDgRKysrvvnmm1JJ2/nz5zl79iy9evUqk0i5uLjQr18/Ll26VKpq2bFjR+rWrcuePXvIycnRHi8qKmL79u0YGxvfcWJJ/fr1gZIuWyFEaVJxE0JUW61atWLu3LkVPt/Z2Rlzc/NSxwwNDalfvz7Z2dnaY1FRUajVagoLC8ut6CUmJgIQHx9Pu3btSrUFBASUOf/WumRNmzYt0xYYGIiBgUGZ47169WLJkiVs3LhRmzzeuHGDgwcP0rhxY9zd3e/xtiUyMzOxs7O76zm7du3i6NGjeHl58fnnn1OvXr1S7be6QW/evFnu1+Py5cva/+/p6QmgTczWrl1LaGgoTz75JAAHDhwgPT2d7t27Y2VlVW48t45nZGRU6B2FqE0kcRNC1BoWFhblHjcwMKC4uFj778zMTKCkm/HMmTN3vF9+fn6ZY7eqRf91q+J0e0IEJbMw69atW+a4lZUVISEhbN26lUuXLuHp6cmWLVtQq9UVrrYBmJiYUFBQcNdzzp07h1qtpmnTpuXGeOvrcejQIQ4dOnTH++Tl5ZX6d9++fVm7di2bN2/WJm736iYFtPGampreNW4haiNJ3IQQ4ja3Erxnn32W0aNH39e1CoXijve7efNmmbbi4mIyMjLKrYo9+eSTbN26lb///pvx48ezadMmLCwsCAkJqXA8devWJSUl5a7nvP766+zfv5+1a9diYGBQ5p1vxT9+/HgGDBhQ4Wd7eXnh7+9PZGQksbGxWFlZceTIERwcHMqMe/uvW4mitbV1hZ8lRG0hY9yEEOI2/v7+KBQKzp07p5P7eXl5AZRbvYuMjEStVpd7XaNGjfDy8mLHjh0cOXKExMREevTocV+VKE9PTwoKCkhOTr7jOcbGxnz00Ue0bduWVatWsWjRolLtt7p/H+TrcauytmnTJrZt24ZardYuF3Int7peb3W7CiH+JYmbEELcxsbGhpCQEM6ePcvvv/9e7lpkERER5XaVlqdDhw6YmZmxadMmrly5oj1eVFTEDz/8cNdr+/fvT2ZmJp9++ilAmckS9xIUFKSN926MjY358MMPadeuHatXr+arr77StgUGBhIYGMjOnTvLTJaAkqpheHh4ufft3r07pqambN++nc2bN6NUKundu/ddY4mMjCwVuxDiX9JVKoSotu62HAjA888/X2a5joqaNGkSCQkJLF68mG3bttGoUSMsLS1JSUkhKiqKxMRE1q1bV6Hql5WVFWPGjOHzzz9nxIgRdO3aFQsLCw4fPoyxsTG2trZ3rED17NmTb7/9ltTUVPz8/Mpd9+xuOnTowNdff82xY8fu2cVqZGTE7Nmzee+991izZg0ajYZx48YB8N577zFhwgQ++OAD1q5di4+PDyYmJly/fp2zZ8+SkZFR7oLAFhYWdO7cmW3btpGenk7r1q3vuA0WgEaj4fjx47i5uZWagSuEKCGJmxCi2rq1HMidDBo06IETtzp16vDNN9/w559/EhYWRmhoKMXFxdSvXx9vb29efvnlcicV3MkTTzyBlZUVv/zyC1u3bsXCwoL27dvz5ptv8n/t3T1rIlEUxvFnU9hYCoKKn0FQJNUUithYpTCIgqa0iWIviFj5IWxsJBCChSBGEMFGC0UhvYUWvjQGq1SmWHYhGF2yISaD/199h7nTPcw995xoNHpwHJXVapVhGHp8fPzw3zZJcjgc8vv96na7ymaz/2wL8ie8FQoF3d/fa7fbKZvNyul0qlKp6O7uTr1eT81mUxcXF7LZbPJ4PEcnM0QiEbVaLUm/pyocM5lMtFwudXt7++FvBc7Br92heSQAgC83n88Vj8cVCARULBbfXZNKpbRYLPTw8HDwZuwxw+FQuVxO+Xxe4XD4s1v+UqVSSYPBQLVa7WC7EOCcUeMGACew3W732nK8vLz8vQhgGMa7z/X7fU2nU4VCof8KbZLk8/l0eXmparX6pu3JTzObzdTpdJRMJgltwAEclQLACYzHY5XLZfn9ftntdj0/P2s0GmmxWMjr9SoYDL5ZX6/XtVqt1Gg0ZLFYlEgkPvX+TCajdrut9Xp9tMbsO61WK93c3Ojq6uq7twL8WByVAsAJzGYzVSoVPT09abPZSJJcLpeCwaBisdheLd719bXW67XcbrfS6fTehAYA54ngBgAAYBLUuAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJN4BUjuQZJo69RUAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig,ax = plt.subplots()\n",
     "\n",
@@ -558,7 +1044,7 @@
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n",
     "\n",
-    "_ = ax.legend()"
+    "ax.legend();"
    ]
   },
   {
@@ -571,17 +1057,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "19c43f92",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "em_inj = crab.binned_data.project('Em').todense().contents\n",
+    "em_fit = expectation.project('Em').todense().contents\n",
+    "\n",
     "fig,ax = plt.subplots()\n",
     "\n",
-    "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
-    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
-    "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_fit, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
+    "ax.errorbar(binned_energy, em_fit, yerr=np.sqrt(em_fit), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_inj, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
+    "ax.errorbar(binned_energy, em_inj, yerr=np.sqrt(em_inj), color='black', linewidth=0, elinewidth=1)\n",
     "\n",
     "ax.set_xscale(\"log\")\n",
     "ax.set_yscale(\"log\")\n",
@@ -589,7 +1089,7 @@
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(\"Counts\")\n",
     "\n",
-    "_ = ax.legend()"
+    "ax.legend();"
    ]
   },
   {
@@ -602,17 +1102,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "0ed27ca4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "em_inj_bkg = crab_bkg.binned_data.project('Em').todense().contents\n",
+    "em_fit_bkg = bkg_par.value * bkg.binned_data.project('Em').todense().contents\n",
+    "em_fit_total = em_fit + em_fit_bkg\n",
+    "\n",
     "fig,ax = plt.subplots()\n",
     "\n",
-    "ax.stairs(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n",
-    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), yerr=np.sqrt(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents)), color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.stairs(crab_bkg.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n",
-    "ax.errorbar(binned_energy, crab_bkg.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab_bkg.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_fit_total, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n",
+    "ax.errorbar(binned_energy, em_fit_total, yerr=np.sqrt(em_fit_total), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_inj_bkg, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n",
+    "ax.errorbar(binned_energy, em_inj_bkg, yerr=np.sqrt(em_inj_bkg), color='black', linewidth=0, elinewidth=1)\n",
     "\n",
     "ax.set_xscale(\"log\")\n",
     "ax.set_yscale(\"log\")\n",
@@ -620,15 +1135,15 @@
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(\"Counts\")\n",
     "\n",
-    "_ = ax.legend()"
+    "ax.legend();"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cosipy",
+   "display_name": "cosipy-312",
    "language": "python",
-   "name": "cosipy"
+   "name": "cosipy-312"
   },
   "language_info": {
    "codemirror_mode": {
@@ -640,7 +1155,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.19"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,
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 e944a89e..4c49f16a 100644
--- a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
+++ b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "8cb3f07c",
    "metadata": {
     "tags": []
    },
@@ -11,6 +12,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8f37244c",
    "metadata": {},
    "source": [
     "**To run this, you need the following files, which can be downloaded using the first few cells of this notebook:**\n",
@@ -23,6 +25,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b39db505",
    "metadata": {},
    "source": [
     "This notebook fits the spectrum of a GRB simulated using MEGAlib and combined with background.\n",
@@ -63,289 +66,33 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "b94ecea9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">08:46:50 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The naima package is not available. Models that depend on it will not be         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#48\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">48</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                         </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
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-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">will not be available.                                                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
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-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                        </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
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-       "\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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-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">require the C/C++ interface (currently HAWC)                                       </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
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-       "\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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-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
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-       "\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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-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">08:47:00 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable OMP_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable MKL_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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 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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from cosipy import COSILike, BinnedData\n",
-    "from cosipy.spacecraftfile import SpacecraftFile\n",
-    "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n",
-    "from cosipy.util import fetch_wasabi_file\n",
-    "\n",
-    "from scoords import SpacecraftFrame\n",
-    "\n",
-    "from astropy.time import Time\n",
-    "import astropy.units as u\n",
-    "from astropy.coordinates import SkyCoord\n",
-    "from astropy.stats import poisson_conf_interval\n",
+    "%%capture\n",
+    "from pathlib import Path\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
     "\n",
-    "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n",
-    "from cosipy import Band_Eflux\n",
-    "from astromodels import Parameter\n",
+    "import astropy.units as u\n",
+    "from astropy.time import Time\n",
+    "from astropy.stats import poisson_conf_interval\n",
     "\n",
-    "from pathlib import Path\n",
+    "from astromodels import Model, Parameter, PointSource, Band\n",
+    "from threeML import JointLikelihood, DataList\n",
+    "    \n",
+    "from cosipy import COSILike, BinnedData\n",
+    "from cosipy.spacecraftfile import SpacecraftFile\n",
+    "from cosipy.util import fetch_wasabi_file\n",
     "\n",
-    "import os"
+    "%matplotlib inline"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f19d1cd6",
    "metadata": {},
    "source": [
     "## Download and read in binned data"
@@ -353,6 +100,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "13c7a807",
    "metadata": {},
    "source": [
     "Define the path to the directory containing the data, detector response, orientation file, and yaml files if they have already been downloaded, or the directory to download the files into"
@@ -360,7 +108,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
+   "id": "349a3796",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -369,6 +118,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "efda3f7e",
    "metadata": {},
    "source": [
     "Download the orientation file (684.38 MB)"
@@ -376,15 +126,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
+   "id": "7ae10147",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named 20280301_3_month_with_orbital_info.ori already exists with the specified checksum (416fcc296fc37a056a069378a2d30cb2). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "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'), checksum = '416fcc296fc37a056a069378a2d30cb2')"
+    "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output = data_path / '20280301_3_month_with_orbital_info.ori', checksum = '416fcc296fc37a056a069378a2d30cb2')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ed2b7fc0",
    "metadata": {},
    "source": [
     "Download the binned GRB+background data (75.73 KB)"
@@ -392,15 +152,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
+   "id": "b6e45f6d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named grb_bkg_binned_data.hdf5 already exists with the specified checksum (fce391a4b45624b25552c7d111945f60). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/grb_bkg_binned_data.hdf5', output=str(data_path / 'grb_bkg_binned_data.hdf5'), checksum = 'fce391a4b45624b25552c7d111945f60')"
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/grb_bkg_binned_data.hdf5', output = data_path / 'grb_bkg_binned_data.hdf5', checksum = 'fce391a4b45624b25552c7d111945f60')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7aa9d355",
    "metadata": {},
    "source": [
     "Download the binned GRB data (76.90 KB)"
@@ -408,15 +178,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
+   "id": "273059a9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named grb_binned_data.hdf5 already exists with the specified checksum (fcf7022369b6fb378d67b780fc4b5db8). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/grb_binned_data.hdf5', output=str(data_path / 'grb_binned_data.hdf5'), checksum = 'fcf7022369b6fb378d67b780fc4b5db8')"
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/grb_binned_data.hdf5', output = data_path / 'grb_binned_data.hdf5', checksum = 'fcf7022369b6fb378d67b780fc4b5db8')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d658d5ad",
    "metadata": {},
    "source": [
     "Download the binned background data (255.97 MB)"
@@ -424,15 +204,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
+   "id": "dd2aec67",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A file named bkg_binned_data_1s_local.hdf5 already exists with the specified checksum (b842a7444e6fc1a5dd567b395c36ae7f). Skipping.\n"
+     ]
+    }
+   ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/bkg_binned_data_1s_local.hdf5', output=str(data_path / 'bkg_binned_data_1s_local.hdf5'), checksum = 'b842a7444e6fc1a5dd567b395c36ae7f')"
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/bkg_binned_data_1s_local.hdf5', output = data_path / 'bkg_binned_data_1s_local.hdf5', checksum = 'b842a7444e6fc1a5dd567b395c36ae7f')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "04fe3d75",
    "metadata": {},
    "source": [
     "Download the response file (596.06 MB)"
@@ -440,23 +230,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
+   "id": "6465b90c",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "A file named SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5 already exists with the specified checksum (eb72400a1279325e9404110f909c7785). Skipping.\n"
      ]
     }
    ],
    "source": [
-    "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', output=str(data_path / 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5'), checksum = 'eb72400a1279325e9404110f909c7785')"
+    "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', output= data_path / 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', checksum = 'eb72400a1279325e9404110f909c7785')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "3bf22e57",
    "metadata": {},
    "source": [
     "Read in the spacecraft orientation file & select the beginning and end times of the GRB"
@@ -464,7 +256,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
+   "id": "b0a9e882",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,6 +269,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9a7fbcc9",
    "metadata": {},
    "source": [
     "Create BinnedData objects for the GRB only, GRB+background, and background only. The GRB only simulation is not used for the spectral fit, but can be used to compare the fitted spectrum to the source simulation"
@@ -483,7 +277,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
+   "id": "4a72609b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,6 +289,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "44bc64e6",
    "metadata": {},
    "source": [
     "Load binned .hdf5 files"
@@ -501,7 +297,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
+   "id": "4492b7f6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,6 +309,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7155d7c0",
    "metadata": {},
    "source": [
     "Define the path to the detector response"
@@ -519,15 +317,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
+   "id": "2c9dfcb1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "dr = str(data_path / \"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\") # path to detector response"
+    "dr = data_path / \"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\" # path to detector response"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "224bb49c",
    "metadata": {
     "tags": []
    },
@@ -537,6 +337,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e7f94a21",
    "metadata": {},
    "source": [
     "Define time window of binned background simulation to use for background model"
@@ -544,435 +345,187 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
+   "id": "cde9d769",
    "metadata": {},
    "outputs": [],
    "source": [
     "bkg_tmin = 1842597310.0\n",
     "bkg_tmax = 1842597550.0\n",
-    "bkg_min = np.where(bkg.binned_data.axes['Time'].edges.value == bkg_tmin)[0][0]\n",
-    "bkg_max = np.where(bkg.binned_data.axes['Time'].edges.value == bkg_tmax)[0][0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set background parameter, which is used to fit the amplitude of the background, and instantiate the COSI 3ML plugin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bkg_par = Parameter(\"background_cosi\",                                                                          # background parameter\n",
-    "                    0.1,                                                                                        # initial value of parameter\n",
-    "                    min_value=0,                                                                                # minimum value of parameter\n",
-    "                    max_value=5,                                                                                # maximum value of parameter\n",
-    "                    delta=1e-3,                                                                                 # initial step used by fitting engine\n",
-    "                    desc=\"Background parameter for cosi\")\n",
-    "\n",
-    "cosi = COSILike(\"cosi\",                                                                                         # COSI 3ML plugin\n",
-    "                dr = dr,                                                                                        # detector response\n",
-    "                data = grb_bkg.binned_data.project('Em', 'Phi', 'PsiChi'),                                      # data (source+background)\n",
-    "                bkg = bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em', 'Phi', 'PsiChi'),    # background model \n",
-    "                sc_orientation = sc_orientation,                                                                # spacecraft orientation\n",
-    "                nuisance_param = bkg_par)                                                                       # background parameter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define a point source at the known location with a Band function spectrum and add it to the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "l = 93.\n",
-    "b = -53.\n",
-    "\n",
-    "alpha = -1                                       # Setting parameters to something reasonable helps the fitting to converge\\n\",\n",
-    "beta = -3\n",
-    "xp = 450. * u.keV\n",
-    "piv = 500. * u.keV\n",
-    "K = 1 / u.cm / u.cm / u.s / u.keV\n",
-    "\n",
-    "spectrum = Band()\n",
-    "\n",
-    "spectrum.beta.min_value = -15.0\n",
     "\n",
-    "spectrum.alpha.value = alpha\n",
-    "spectrum.beta.value = beta\n",
-    "spectrum.xp.value = xp.value\n",
-    "spectrum.K.value = K.value\n",
-    "spectrum.piv.value = piv.value\n",
-    "\n",
-    "spectrum.xp.unit = xp.unit\n",
-    "spectrum.K.unit = K.unit\n",
-    "spectrum.piv.unit = piv.unit\n",
-    "\n",
-    "source = PointSource(\"source\",                     # Name of source (arbitrary, but needs to be unique)\n",
-    "                     l = l,                        # Longitude (deg)\n",
-    "                     b = b,                        # Latitude (deg)\n",
-    "                     spectral_shape = spectrum)    # Spectral model\n",
-    "\n",
-    "# Optional: free the position parameters\n",
-    "#source.position.l.free = True\n",
-    "#source.position.b.free = True\n",
-    "\n",
-    "model = Model(source)                              # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)\n",
-    "\n",
-    "# Optional: if you want to call get_log_like manually, then you also need to set the model manually\n",
-    "# 3ML does this internally during the fit though\n",
-    "cosi.set_model(model)"
+    "bkg_min, bkg_max = np.searchsorted(bkg.binned_data.axes['Time'].edges.value,\n",
+    "                                   (bkg_tmin, bkg_tmax), side='left')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Gather all plugins and combine with the model in a JointLikelihood object, then perform maximum likelihood fit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">08:49:04 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                             </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1045\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1045</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">08:49:30 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">130</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">measurements such as AIC or BIC are unreliable                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                       </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Best fit values:</span>\n",
-       "\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1;4;38;5;49mBest fit values:\u001b[0m\n",
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>result</th>\n",
-       "      <th>unit</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>parameter</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>source.spectrum.main.Band.K</th>\n",
-       "      <td>(3.10 -0.20 +0.21) x 10^-2</td>\n",
-       "      <td>1 / (keV s cm2)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>source.spectrum.main.Band.alpha</th>\n",
-       "      <td>(-2.8 +/- 0.5) x 10^-1</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>source.spectrum.main.Band.xp</th>\n",
-       "      <td>(4.75 +/- 0.05) x 10^2</td>\n",
-       "      <td>keV</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>source.spectrum.main.Band.beta</th>\n",
-       "      <td>-6.8 +/- 1.2</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>background_cosi</th>\n",
-       "      <td>(1.65 +/- 0.13) x 10^-1</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                     result             unit\n",
-       "parameter                                                                   \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",
-       "background_cosi                     (1.65 +/- 0.13) x 10^-1                 "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Correlation matrix:</span>\n",
-       "\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\n",
-       "\u001b[1;4;38;5;49mCorrelation matrix:\u001b[0m\n",
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><table id=\"table47975352332640\">\n",
-       "<tr><td>1.00</td><td>0.97</td><td>-0.37</td><td>0.20</td><td>-0.00</td></tr>\n",
-       "<tr><td>0.97</td><td>1.00</td><td>-0.16</td><td>0.17</td><td>-0.00</td></tr>\n",
-       "<tr><td>-0.37</td><td>-0.16</td><td>1.00</td><td>-0.17</td><td>-0.02</td></tr>\n",
-       "<tr><td>0.20</td><td>0.17</td><td>-0.17</td><td>1.00</td><td>0.00</td></tr>\n",
-       "<tr><td>-0.00</td><td>-0.00</td><td>-0.02</td><td>0.00</td><td>1.00</td></tr>\n",
-       "</table></div>"
-      ],
-      "text/plain": [
-       " 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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+  {
+   "cell_type": "markdown",
+   "id": "c70c507b",
+   "metadata": {},
+   "source": [
+    "Set background parameter, which is used to fit the amplitude of the background, and instantiate the COSI 3ML plugin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "99251ab5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.37 s, sys: 119 ms, total: 2.49 s\n",
+      "Wall time: 2.52 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "bkg_par = Parameter(\"background_cosi\",                                                                          # background parameter\n",
+    "                    0.1,                                                                                        # initial value of parameter\n",
+    "                    min_value=0,                                                                                # minimum value of parameter\n",
+    "                    max_value=5,                                                                                # maximum value of parameter\n",
+    "                    delta=1e-3,                                                                                 # initial step used by fitting engine\n",
+    "                    desc=\"Background parameter for cosi\")\n",
+    "\n",
+    "cosi = COSILike(\"cosi\",                                                                                         # COSI 3ML plugin\n",
+    "                dr = dr,                                                                                        # detector response\n",
+    "                data = grb_bkg.binned_data.project('Em', 'Phi', 'PsiChi'),                                      # data (source+background)\n",
+    "                bkg = bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em', 'Phi', 'PsiChi'),    # background model \n",
+    "                sc_orientation = sc_orientation,                                                                # spacecraft orientation\n",
+    "                nuisance_param = bkg_par)                                                                       # background parameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccef9f70",
+   "metadata": {},
+   "source": [
+    "Define a point source at the known location with a Band function spectrum and add it to the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "78339955",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = 93.\n",
+    "b = -53.\n",
+    "\n",
+    "alpha = -1                                       # Setting parameters to something reasonable helps the fitting to converge\\n\",\n",
+    "beta = -3\n",
+    "xp = 450. * u.keV\n",
+    "piv = 500. * u.keV\n",
+    "K = 1 / u.cm / u.cm / u.s / u.keV\n",
+    "\n",
+    "spectrum = Band()\n",
+    "\n",
+    "spectrum.beta.min_value = -15.0\n",
+    "\n",
+    "spectrum.alpha.value = alpha\n",
+    "spectrum.beta.value = beta\n",
+    "spectrum.xp.value = xp.value\n",
+    "spectrum.K.value = K.value\n",
+    "spectrum.piv.value = piv.value\n",
+    "\n",
+    "spectrum.xp.unit = xp.unit\n",
+    "spectrum.K.unit = K.unit\n",
+    "spectrum.piv.unit = piv.unit\n",
+    "\n",
+    "source = PointSource(\"source\",                     # Name of source (arbitrary, but needs to be unique)\n",
+    "                     l = l,                        # Longitude (deg)\n",
+    "                     b = b,                        # Latitude (deg)\n",
+    "                     spectral_shape = spectrum)    # Spectral model\n",
+    "\n",
+    "# Optional: free the position parameters\n",
+    "#source.position.l.free = True\n",
+    "#source.position.b.free = True\n",
+    "\n",
+    "model = Model(source)                              # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)\n",
+    "\n",
+    "# Optional: if you want to call get_log_like manually, then you also need to set the model manually\n",
+    "# 3ML does this internally during the fit though\n",
+    "cosi.set_model(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "335b57bf",
+   "metadata": {},
+   "source": [
+    "Gather all plugins and combine with the model in a JointLikelihood object, then perform maximum likelihood fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f67d1c61",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of -log(likelihood) at the minimum:</span>\n",
-       "\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:13:48 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\n",
-       "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mlikelihood\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n",
-       "\n"
+       "\u001b[38;5;46m15:13:48\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=148410;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=275418;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>-log(likelihood)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>cosi</th>\n",
-       "      <td>42920.049338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>total</th>\n",
-       "      <td>42920.049338</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       -log(likelihood)\n",
-       "cosi       42920.049338\n",
-       "total      42920.049338"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
+     ]
     },
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-       "<span style=\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\">Values of statistical measures:</span>\n",
-       "\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:13:59 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">measurements such as AIC or BIC are unreliable                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                       </span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\n",
-       "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n",
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>statistical measures</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>AIC</th>\n",
-       "      <td>85838.098676</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BIC</th>\n",
-       "      <td>85840.098676</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     statistical measures\n",
-       "AIC          85838.098676\n",
-       "BIC          85840.098676"
+       "\u001b[38;5;46m15:13:59\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=300052;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=486504;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\u001b\\\u001b[2m119\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"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "data": {
-      "text/plain": [
-       "(                                      value  negative_error  positive_error  \\\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      0.002079  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       42920.049338\n",
-       " total      42920.049338)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 13s, sys: 78.5 ms, total: 1min 13s\n",
+      "Wall time: 10.5 s\n"
+     ]
     }
    ],
    "source": [
+    "%%time\n",
+    "\n",
     "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n",
     "\n",
     "like = JointLikelihood(model, plugins, verbose = False)\n",
     "\n",
-    "like.fit()"
+    "like.fit(quiet=True);"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "bcdff7dc",
    "metadata": {},
    "source": [
     "## Error propagation and plotting"
@@ -980,6 +533,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b52939bb",
    "metadata": {},
    "source": [
     "Define Band function spectrum injected into MEGAlib"
@@ -987,7 +541,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
+   "id": "bcabdfd4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,6 +570,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4bf3adae",
    "metadata": {},
    "source": [
     "The summary of the results above tell you the optimal values of the parameters, as well as the errors. Propogate the errors to the \"evaluate_at\" method of the spectrum"
@@ -1022,7 +578,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
+   "id": "de3ccfc2",
    "metadata": {
     "scrolled": true,
     "tags": []
@@ -1136,19 +693,19 @@
     {
      "data": {
       "text/html": [
-       "<div><table id=\"table47975317042368\">\n",
+       "<div><table id=\"table139776929291472\">\n",
        "<tr><td>1.00</td><td>0.97</td><td>-0.37</td><td>0.20</td><td>-0.00</td></tr>\n",
-       "<tr><td>0.97</td><td>1.00</td><td>-0.16</td><td>0.17</td><td>-0.00</td></tr>\n",
+       "<tr><td>0.97</td><td>1.00</td><td>-0.16</td><td>0.18</td><td>-0.00</td></tr>\n",
        "<tr><td>-0.37</td><td>-0.16</td><td>1.00</td><td>-0.17</td><td>-0.02</td></tr>\n",
-       "<tr><td>0.20</td><td>0.17</td><td>-0.17</td><td>1.00</td><td>0.00</td></tr>\n",
+       "<tr><td>0.20</td><td>0.18</td><td>-0.17</td><td>1.00</td><td>0.00</td></tr>\n",
        "<tr><td>-0.00</td><td>-0.00</td><td>-0.02</td><td>0.00</td><td>1.00</td></tr>\n",
        "</table></div>"
       ],
       "text/plain": [
        " 1.00  0.97 -0.37  0.20 -0.00\n",
-       " 0.97  1.00 -0.16  0.17 -0.00\n",
+       " 0.97  1.00 -0.16  0.18 -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",
+       " 0.20  0.18 -0.17  1.00  0.00\n",
        "-0.00 -0.00 -0.02  0.00  1.00"
       ]
      },
@@ -1199,11 +756,11 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>cosi</th>\n",
-       "      <td>42920.049338</td>\n",
+       "      <td>42920.049341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total</th>\n",
-       "      <td>42920.049338</td>\n",
+       "      <td>42920.049341</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1211,8 +768,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi       42920.049338\n",
-       "total      42920.049338"
+       "cosi       42920.049341\n",
+       "total      42920.049341"
       ]
      },
      "metadata": {},
@@ -1262,11 +819,11 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AIC</th>\n",
-       "      <td>85838.098676</td>\n",
+       "      <td>85838.098681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BIC</th>\n",
-       "      <td>85840.098676</td>\n",
+       "      <td>85840.098681</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1274,8 +831,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC          85838.098676\n",
-       "BIC          85840.098676"
+       "AIC          85838.098681\n",
+       "BIC          85840.098681"
       ]
      },
      "metadata": {},
@@ -1307,28 +864,28 @@
       "      * main:\n",
       "        * Band:\n",
       "          * K:\n",
-      "            * value: 0.03099749659547262\n",
+      "            * value: 0.030993394366579514\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: -0.2765469834147527\n",
+      "            * value: -0.276652358047131\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: 474.6540362662719\n",
+      "            * value: 474.6491443889869\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: -6.755004044507031\n",
+      "            * value: -6.754169602442122\n",
       "            * desc: high-energy photon index\n",
       "            * min_value: -15.0\n",
       "            * max_value: -1.6\n",
@@ -1362,6 +919,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a1355b6e",
    "metadata": {},
    "source": [
     "Evaluate the flux and errors at a range of energies for the fitted and injected spectra, and the simulated source flux"
@@ -1369,7 +927,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
+   "id": "9cbe7c20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1387,18 +946,14 @@
     "    flux_inj[i] = spectrum_inj.evaluate_at(e)\n",
     "    \n",
     "binned_energy_edges = grb.binned_data.axes['Em'].edges.value\n",
-    "binned_energy = np.array([])\n",
-    "bin_sizes = np.array([])\n",
-    "\n",
-    "for i in range(len(binned_energy_edges)-1):\n",
-    "    binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n",
-    "    bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n",
+    "binned_energy = grb.binned_data.axes['Em'].centers.value\n",
     "\n",
     "expectation = cosi._expected_counts['source']"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "184bcd78",
    "metadata": {},
    "source": [
     "Plot the fitted and injected spectra"
@@ -1406,24 +961,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
+   "id": "bc6b8ced",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x2ba21fa42d40>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1445,11 +991,30 @@
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n",
     "\n",
-    "ax.legend()"
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "c0910ecd-f7b9-4713-a24a-b5ef89ee8866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_errors(counts):\n",
+    "    gaussian_error = np.zeros(len(counts))\n",
+    "    poisson_error  = np.zeros((2, len(counts)))\n",
+    "\n",
+    "    hi_mask = (counts > 5)\n",
+    "    gaussian_error[hi_mask] = np.sqrt(counts[hi_mask])\n",
+    "    poisson_error[:,~hi_mask] = poisson_conf_interval(counts[~hi_mask], interval=\"frequentist-confidence\", sigma=1)\n",
+    "\n",
+    "    return gaussian_error, poisson_error"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8984de35",
    "metadata": {},
    "source": [
     "Plot the fitted spectrum convolved with the response, as well as the simulated source counts"
@@ -1457,22 +1022,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
+   "id": "fea4749b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x2ba222185e40>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1482,35 +1038,20 @@
     }
    ],
    "source": [
-    "fit_poisson_error = np.zeros((2,len(expectation.project('Em').todense().contents)))\n",
-    "fit_gaussian_error = np.zeros(len(expectation.project('Em').todense().contents))\n",
-    "inj_poisson_error = np.zeros((2,len(grb.binned_data.project('Em').todense().contents)))\n",
-    "inj_gaussian_error = np.zeros(len(grb.binned_data.project('Em').todense().contents))\n",
+    "em_inj = grb.binned_data.project('Em').todense().contents\n",
+    "em_fit = expectation.project('Em').todense().contents\n",
     "\n",
-    "for i, counts in enumerate(expectation.project('Em').todense().contents):\n",
-    "    if counts > 5:\n",
-    "        fit_gaussian_error[i] = np.sqrt(counts)\n",
-    "    else:\n",
-    "        poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n",
-    "        fit_poisson_error[0][i] = poisson_error[0]\n",
-    "        fit_poisson_error[1][i] = poisson_error[1]\n",
+    "fit_gaussian_error, fit_poisson_error = compute_errors(em_fit)\n",
+    "inj_gaussian_error, inj_poisson_error = compute_errors(em_inj)\n",
     "\n",
-    "for i, counts in enumerate(grb.binned_data.project('Em').todense().contents):\n",
-    "    if counts > 5:\n",
-    "        inj_gaussian_error[i] = np.sqrt(counts)\n",
-    "    else:\n",
-    "        poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n",
-    "        inj_poisson_error[0][i] = poisson_error[0]\n",
-    "        inj_poisson_error[1][i] = poisson_error[1]\n",
-    "        \n",
     "fig,ax = plt.subplots()\n",
     "\n",
-    "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
-    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=fit_poisson_error, color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=fit_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.stairs(grb.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
-    "ax.errorbar(binned_energy, grb.binned_data.project('Em').todense().contents, yerr=inj_poisson_error, color='black', linewidth=0, elinewidth=1)\n",
-    "ax.errorbar(binned_energy, grb.binned_data.project('Em').todense().contents, yerr=inj_gaussian_error, color='black', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_fit, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
+    "ax.errorbar(binned_energy, em_fit, yerr=fit_poisson_error, color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.errorbar(binned_energy, em_fit, yerr=fit_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_inj, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n",
+    "ax.errorbar(binned_energy, em_inj, yerr=inj_poisson_error, color='black', linewidth=0, elinewidth=1)\n",
+    "ax.errorbar(binned_energy, em_inj, yerr=inj_gaussian_error, color='black', linewidth=0, elinewidth=1)\n",
     "\n",
     "ax.set_xscale(\"log\")\n",
     "ax.set_yscale(\"log\")\n",
@@ -1518,11 +1059,12 @@
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(\"Counts\")\n",
     "\n",
-    "ax.legend()"
+    "ax.legend();"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "eaa331ed",
    "metadata": {},
    "source": [
     "Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts"
@@ -1530,22 +1072,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
+   "id": "e6788e91",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x2ba221d57460>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1555,35 +1088,21 @@
     }
    ],
    "source": [
-    "fit_bkg_poisson_error = np.zeros((2,len(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents))))\n",
-    "fit_bkg_gaussian_error = np.zeros(len(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents)))\n",
-    "inj_bkg_poisson_error = np.zeros((2,len(grb_bkg.binned_data.project('Em').todense().contents)))\n",
-    "inj_bkg_gaussian_error = np.zeros(len(grb_bkg.binned_data.project('Em').todense().contents))\n",
+    "em_inj_bkg = grb_bkg.binned_data.project('Em').todense().contents\n",
+    "em_fit_bkg = bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents\n",
+    "em_fit_total = em_fit + em_fit_bkg\n",
     "\n",
-    "for i, counts in enumerate(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents)):\n",
-    "    if counts > 5:\n",
-    "        fit_bkg_gaussian_error[i] = np.sqrt(counts)\n",
-    "    else:\n",
-    "        poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n",
-    "        fit_bkg_poisson_error[0][i] = poisson_error[0]\n",
-    "        fit_bkg_poisson_error[1][i] = poisson_error[1]\n",
+    "fit_bkg_gaussian_error, fit_bkg_poisson_error = compute_errors(em_fit_total)\n",
+    "inj_bkg_gaussian_error, inj_bkg_poisson_error = compute_errors(em_inj_bkg)\n",
     "\n",
-    "for i, counts in enumerate(grb_bkg.binned_data.project('Em').todense().contents):\n",
-    "    if counts > 5:\n",
-    "        inj_bkg_gaussian_error[i] = np.sqrt(counts)\n",
-    "    else:\n",
-    "        poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n",
-    "        inj_bkg_poisson_error[0][i] = poisson_error[0]\n",
-    "        inj_bkg_poisson_error[1][i] = poisson_error[1]\n",
-    "        \n",
     "fig,ax = plt.subplots()\n",
     "\n",
-    "ax.stairs(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n",
-    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), yerr=fit_bkg_poisson_error, color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), yerr=fit_bkg_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.stairs(grb_bkg.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n",
-    "ax.errorbar(binned_energy, grb_bkg.binned_data.project('Em').todense().contents, yerr=inj_bkg_poisson_error, color='black', linewidth=0, elinewidth=1)\n",
-    "ax.errorbar(binned_energy, grb_bkg.binned_data.project('Em').todense().contents, yerr=inj_bkg_gaussian_error, color='black', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_fit_total, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n",
+    "ax.errorbar(binned_energy, em_fit_total, yerr=fit_bkg_poisson_error, color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.errorbar(binned_energy, em_fit_total, yerr=fit_bkg_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(em_inj_bkg, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n",
+    "ax.errorbar(binned_energy, em_inj_bkg, yerr=inj_bkg_poisson_error, color='black', linewidth=0, elinewidth=1)\n",
+    "ax.errorbar(binned_energy, em_inj_bkg, yerr=inj_bkg_gaussian_error, color='black', linewidth=0, elinewidth=1)\n",
     "\n",
     "ax.set_xscale(\"log\")\n",
     "ax.set_yscale(\"log\")\n",
@@ -1591,15 +1110,15 @@
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(\"Counts\")\n",
     "\n",
-    "ax.legend()"
+    "ax.legend();"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:cosipy]",
+   "display_name": "cosipy-312",
    "language": "python",
-   "name": "conda-env-cosipy-py"
+   "name": "cosipy-312"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1611,7 +1130,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,
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 21b5fbf7..dfd01ee3 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
@@ -42,261 +42,9 @@
    "execution_count": 1,
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> PyGMO is not available                                                      </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/minimizer/minimization.py#1228\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1228</span></a>\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=909722;file:///project/cassini/cosi/threeML_git/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=80253;file:///project/cassini/cosi/threeML_git/threeML/minimizer/minimization.py#1228\u001b\\\u001b[2m1228\u001b[0m\u001b]8;;\u001b\\\n"
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:25:57 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The cthreeML package is not installed. You will not be able to use plugins which  </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py#95\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">95</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">require the C/C++ interface (currently HAWC)                                       </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
-       "</pre>\n"
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-       "\u001b[38;5;46m14:25:57\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=712;file:///project/cassini/cosi/threeML_git/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=559491;file:///project/cassini/cosi/threeML_git/threeML/__init__.py#95\u001b\\\u001b[2m95\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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin FermiLATLike.py. Do you have the relative instrument     </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py#136\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">136</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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=890007;file:///project/cassini/cosi/threeML_git/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=219605;file:///project/cassini/cosi/threeML_git/threeML/__init__.py#136\u001b\\\u001b[2m136\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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin HAWCLike.py. Do you have the relative instrument         </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py#136\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">136</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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=879300;file:///project/cassini/cosi/threeML_git/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=640253;file:///project/cassini/cosi/threeML_git/threeML/__init__.py#136\u001b\\\u001b[2m136\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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> No fermitools installed                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/utils/data_builders/fermi/lat_transient_builder.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lat_transient_builder.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\n",
-       "</pre>\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 fermitools installed                                             \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=368308;file:///project/cassini/cosi/threeML_git/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=269027;file:///project/cassini/cosi/threeML_git/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable OMP_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py#345\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">345</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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 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=207854;file:///project/cassini/cosi/threeML_git/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=777029;file:///project/cassini/cosi/threeML_git/threeML/__init__.py#345\u001b\\\u001b[2m345\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"
-      ]
-     },
-     "metadata": {},
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable MKL_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py#345\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">345</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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=657672;file:///project/cassini/cosi/threeML_git/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=805287;file:///project/cassini/cosi/threeML_git/threeML/__init__.py#345\u001b\\\u001b[2m345\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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal     </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/__init__.py#345\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">345</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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 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=253092;file:///project/cassini/cosi/threeML_git/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=173544;file:///project/cassini/cosi/threeML_git/threeML/__init__.py#345\u001b\\\u001b[2m345\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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "%%capture\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -1018,8 +766,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 46s, sys: 10.3 s, total: 1min 56s\n",
-      "Wall time: 1min 57s\n"
+      "CPU times: user 1min 47s, sys: 10.6 s, total: 1min 57s\n",
+      "Wall time: 1min 58s\n"
      ]
     }
    ],
@@ -1073,11 +821,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:29:22 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:10:17 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:29:22\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=1705;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=761608;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m15:10:17\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=127778;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=898211;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -1093,12 +841,12 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">14:29:27 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119</span></a>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:10:22 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119</span></a>\n",
        "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">measurements such as AIC or BIC are unreliable                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                       </span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m14:29:27\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=809656;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=259356;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\u001b\\\u001b[2m119\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m15:10:22\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=614847;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=843583;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\u001b\\\u001b[2m119\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"
       ]
      },
@@ -1109,8 +857,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 42 s, sys: 37.2 ms, total: 42 s\n",
-      "Wall time: 5.28 s\n"
+      "CPU times: user 41.8 s, sys: 13 ms, total: 41.9 s\n",
+      "Wall time: 5.26 s\n"
      ]
     }
    ],
@@ -1237,7 +985,7 @@
     {
      "data": {
       "text/html": [
-       "<div><table id=\"table140658778567984\">\n",
+       "<div><table id=\"table140662664934704\">\n",
        "<tr><td>1.00</td><td>-0.40</td></tr>\n",
        "<tr><td>-0.40</td><td>1.00</td></tr>\n",
        "</table></div>"
@@ -1518,17 +1266,18 @@
    "source": [
     "# Get expected counts from likelihood scan (i.e. best-fit convolved with response):\n",
     "total_expectation = cosi._expected_counts['gaussian']\n",
+    "total_expectation_em = total_expectation.project('Em').todense().contents\n",
+    "gal_511_em = gal_511.binned_data.project('Em').todense().contents\n",
     "\n",
-    "# Plot:       \n",
     "fig,ax = plt.subplots()\n",
     "\n",
     "binned_energy_edges = gal_511.binned_data.axes['Em'].edges.value\n",
     "binned_energy = gal_511.binned_data.axes['Em'].centers.value\n",
     "\n",
-    "ax.stairs(total_expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
-    "ax.errorbar(binned_energy, total_expectation.project('Em').todense().contents, yerr=np.sqrt(total_expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.stairs(gal_511.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Injected source counts\")\n",
-    "ax.errorbar(binned_energy, gal_511.binned_data.project('Em').todense().contents, yerr=np.sqrt(gal_511.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(total_expectation_em, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n",
+    "ax.errorbar(binned_energy, total_expectation_em, yerr=np.sqrt(total_expectation_em), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(gal_511_em, binned_energy_edges, color = 'black', ls = \":\", label = \"Injected source counts\")\n",
+    "ax.errorbar(binned_energy, gal_511_em, yerr=np.sqrt(gal_511_em), color='black', linewidth=0, elinewidth=1)\n",
     "\n",
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(\"Counts\")\n",
@@ -1536,7 +1285,7 @@
     "ax.legend()\n",
     "\n",
     "# Note: We are plotting the error, but it's very small:\n",
-    "print(f\"Error: {np.sqrt(total_expectation.project('Em').todense().contents)}\")"
+    "print(f\"Error: {np.sqrt(total_expectation_em)}\")"
    ]
   },
   {
@@ -1663,10 +1412,6 @@
     }
    ],
    "source": [
-    "# Note: Astromodels only takes ra,dec for point source input:\n",
-    "c = SkyCoord(l=0*u.deg, b=0*u.deg, frame='galactic')\n",
-    "c_icrs = c.transform_to('icrs')\n",
-    "\n",
     "# Define spectrum:\n",
     "# Note that the units of the Gaussian function below are [F/sigma]=[ph/cm2/s/keV]\n",
     "F = 1e-2 / u.cm / u.cm / u.s  \n",
@@ -1690,7 +1435,7 @@
     "spectrum2.sigma.free = False\n",
     "\n",
     "# Define source:\n",
-    "src2 = PointSource('point_source', ra = c_icrs.ra.deg, dec = c_icrs.dec.deg, spectral_shape=spectrum2)\n",
+    "src2 = PointSource('point_source', l=0, b=0, spectral_shape=spectrum2)\n",
     "\n",
     "# Print some info about the source just as a sanity check.\n",
     "# This will also show you which parameters are free. \n",
@@ -1987,8 +1732,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 44s, sys: 10.2 s, total: 1min 54s\n",
-      "Wall time: 1min 54s\n"
+      "CPU times: user 1min 47s, sys: 12.7 s, total: 1min 59s\n",
+      "Wall time: 2min\n"
      ]
     }
    ],
@@ -2162,26 +1907,6 @@
     "model.display()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "0b3e274f",
-   "metadata": {},
-   "source": [
-    "Before we perform the fit, let's first change the 3ML console logging level, in order to mimimize the amount of console output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "d7254296",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is a simple workaround for now to prevent a lot of output. \n",
-    "from threeML import update_logging_level\n",
-    "update_logging_level(\"CRITICAL\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "4f2fd3f4",
@@ -2192,13 +1917,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "106b5f41",
    "metadata": {
     "scrolled": true,
     "tags": []
    },
    "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:12:26 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m15:12:26\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=661602;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=241456;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -2206,6 +1944,21 @@
       "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:12:42 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">measurements such as AIC or BIC are unreliable                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                       </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m15:12:42\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=553179;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=71426;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\u001b\\\u001b[2m119\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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
@@ -2254,17 +2007,17 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>gaussian.spectrum.main.Gaussian.F</th>\n",
-       "      <td>(4.6951 +/- 0.0025) x 10^-2</td>\n",
+       "      <td>(3.199 +/- 0.008) x 10^-2</td>\n",
        "      <td>1 / (s cm2)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>point_source.spectrum.main.Gaussian.F</th>\n",
-       "      <td>(0.0 +/- 2.1) x 10^-9</td>\n",
+       "      <td>(1.480 +/- 0.007) x 10^-2</td>\n",
        "      <td>1 / (s cm2)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>background_cosi</th>\n",
-       "      <td>(9.32 +/- 0.05) x 10^-1</td>\n",
+       "      <td>1.005 +/- 0.005</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2272,17 +2025,11 @@
        "</div>"
       ],
       "text/plain": [
-       "                                                            result  \\\n",
-       "parameter                                                            \n",
-       "gaussian.spectrum.main.Gaussian.F      (4.6951 +/- 0.0025) x 10^-2   \n",
-       "point_source.spectrum.main.Gaussian.F        (0.0 +/- 2.1) x 10^-9   \n",
-       "background_cosi                            (9.32 +/- 0.05) x 10^-1   \n",
-       "\n",
-       "                                              unit  \n",
-       "parameter                                           \n",
-       "gaussian.spectrum.main.Gaussian.F      1 / (s cm2)  \n",
-       "point_source.spectrum.main.Gaussian.F  1 / (s cm2)  \n",
-       "background_cosi                                     "
+       "                                                          result         unit\n",
+       "parameter                                                                    \n",
+       "gaussian.spectrum.main.Gaussian.F      (3.199 +/- 0.008) x 10^-2  1 / (s cm2)\n",
+       "point_source.spectrum.main.Gaussian.F  (1.480 +/- 0.007) x 10^-2  1 / (s cm2)\n",
+       "background_cosi                                  1.005 +/- 0.005             "
       ]
      },
      "metadata": {},
@@ -2308,16 +2055,16 @@
     {
      "data": {
       "text/html": [
-       "<div><table id=\"table140658702127552\">\n",
-       "<tr><td>1.00</td><td>0.01</td><td>-0.40</td></tr>\n",
-       "<tr><td>0.01</td><td>1.00</td><td>0.03</td></tr>\n",
-       "<tr><td>-0.40</td><td>0.03</td><td>1.00</td></tr>\n",
+       "<div><table id=\"table140662575831120\">\n",
+       "<tr><td>1.00</td><td>-0.95</td><td>-0.18</td></tr>\n",
+       "<tr><td>-0.95</td><td>1.00</td><td>0.05</td></tr>\n",
+       "<tr><td>-0.18</td><td>0.05</td><td>1.00</td></tr>\n",
        "</table></div>"
       ],
       "text/plain": [
-       " 1.00 0.01 -0.40\n",
-       " 0.01 1.00  0.03\n",
-       "-0.40 0.03  1.00"
+       " 1.00 -0.95 -0.18\n",
+       "-0.95  1.00  0.05\n",
+       "-0.18  0.05  1.00"
       ]
      },
      "metadata": {},
@@ -2367,11 +2114,11 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>cosi</th>\n",
-       "      <td>-1.527559e+07</td>\n",
+       "      <td>-1.529533e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total</th>\n",
-       "      <td>-1.527559e+07</td>\n",
+       "      <td>-1.529533e+07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2379,8 +2126,8 @@
       ],
       "text/plain": [
        "       -log(likelihood)\n",
-       "cosi      -1.527559e+07\n",
-       "total     -1.527559e+07"
+       "cosi      -1.529533e+07\n",
+       "total     -1.529533e+07"
       ]
      },
      "metadata": {},
@@ -2430,11 +2177,11 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AIC</th>\n",
-       "      <td>-3.055119e+07</td>\n",
+       "      <td>-3.059066e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BIC</th>\n",
-       "      <td>-3.055119e+07</td>\n",
+       "      <td>-3.059066e+07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2442,8 +2189,8 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC         -3.055119e+07\n",
-       "BIC         -3.055119e+07"
+       "AIC         -3.059066e+07\n",
+       "BIC         -3.059066e+07"
       ]
      },
      "metadata": {},
@@ -2453,8 +2200,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 53s, sys: 946 ms, total: 1min 54s\n",
-      "Wall time: 20.6 s\n"
+      "CPU times: user 1min 28s, sys: 700 ms, total: 1min 29s\n",
+      "Wall time: 16 s\n"
      ]
     }
    ],
@@ -2497,7 +2244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "4b5c4ea0",
    "metadata": {},
    "outputs": [],
@@ -2522,7 +2269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "6f06701e",
    "metadata": {},
    "outputs": [],
@@ -2570,7 +2317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "a3bed0d3",
    "metadata": {},
    "outputs": [],
@@ -2605,7 +2352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "id": "229770ad",
    "metadata": {},
    "outputs": [],
@@ -2625,7 +2372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "id": "3613f683",
    "metadata": {},
    "outputs": [
@@ -2918,7 +2665,7 @@
        "        * polarization: {}"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2937,7 +2684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "id": "5e046a06",
    "metadata": {},
    "outputs": [
@@ -2965,7 +2712,7 @@
     "axs.set_ylabel(r\"dN/dE [$\\mathrm{ph \\ cm^{-2} \\ s^{-1} \\ keV^{-1}}$]\", fontsize=14)\n",
     "axs.set_xlabel(\"Energy [keV]\", fontsize=14);\n",
     "plt.ylim(0,);\n",
-    "#axs[0].set_yscale(\"log\")"
+    "#axs[0].set_yscale(\"log\");"
    ]
   },
   {
@@ -2978,7 +2725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "id": "9c25d07b",
    "metadata": {},
    "outputs": [],
@@ -3007,7 +2754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "id": "3866405b",
    "metadata": {},
    "outputs": [
@@ -3077,7 +2824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "id": "1c3caa29",
    "metadata": {},
    "outputs": [],
@@ -3091,7 +2838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "id": "91c210b6",
    "metadata": {},
    "outputs": [],
@@ -3115,7 +2862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "id": "3f24160f",
    "metadata": {},
    "outputs": [
@@ -3123,8 +2870,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1min 42s, sys: 32 s, total: 2min 14s\n",
-      "Wall time: 2min 15s\n"
+      "CPU times: user 1min 44s, sys: 36.1 s, total: 2min 20s\n",
+      "Wall time: 2min 22s\n"
      ]
     }
    ],
@@ -3144,7 +2891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "id": "a64a965f",
    "metadata": {},
    "outputs": [
@@ -3613,12 +3360,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "id": "77794760",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:16:14 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m15:16:14\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=924377;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=585270;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -3626,17 +3386,33 @@
       "Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:16:49 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119</span></a>\n",
+       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">measurements such as AIC or BIC are unreliable                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                       </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;5;46m15:16:49\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=409704;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=10962;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\u001b\\\u001b[2m119\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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3min 46s, sys: 1.03 s, total: 3min 47s\n",
-      "Wall time: 34.8 s\n"
+      "CPU times: user 3min 44s, sys: 994 ms, total: 3min 45s\n",
+      "Wall time: 34.5 s\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
+    "\n",
     "# likelihood of data + model\n",
     "like = JointLikelihood(totalModel, plugins, verbose = False)\n",
     "like.fit(quiet=True);"
@@ -3652,7 +3428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "id": "9cd0b813",
    "metadata": {
     "scrolled": true
@@ -3754,7 +3530,7 @@
     {
      "data": {
       "text/html": [
-       "<div><table id=\"table140658701074256\">\n",
+       "<div><table id=\"table140662574802960\">\n",
        "<tr><td>1.00</td><td>-0.32</td><td>0.09</td></tr>\n",
        "<tr><td>-0.32</td><td>1.00</td><td>-0.60</td></tr>\n",
        "<tr><td>0.09</td><td>-0.60</td><td>1.00</td></tr>\n",
@@ -3904,7 +3680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "id": "2268995d",
    "metadata": {},
    "outputs": [
diff --git a/docs/tutorials/spectral_fits/galactic_diffuse_continuum/galdiff_continuum.ipynb b/docs/tutorials/spectral_fits/galactic_diffuse_continuum/galdiff_continuum.ipynb
index ebe66745..200cc47b 100644
--- a/docs/tutorials/spectral_fits/galactic_diffuse_continuum/galdiff_continuum.ipynb
+++ b/docs/tutorials/spectral_fits/galactic_diffuse_continuum/galdiff_continuum.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "a8435339",
    "metadata": {},
    "source": [
     "# Galactic Diffuse Continuum\n",
@@ -12,284 +13,39 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "3343b830",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:21:36 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The naima package is not available. Models that depend on it will not be         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#47\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">47</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                         </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:21:36\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=371084;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=206508;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#47\u001b\\\u001b[2m47\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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it  </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">functions.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#68\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">68</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">will not be available.                                                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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 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=558785;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=117758;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#68\u001b\\\u001b[2m68\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"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:21:37 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The ebltable package is not available. Models that depend on it will not be     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">absorption.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#33\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">33</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">available                                                                        </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "</pre>\n"
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-       "\u001b[38;5;46m17:21:37\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=495304;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=146465;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#33\u001b\\\u001b[2m33\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"
-      ]
-     },
-     "metadata": {},
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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=93509;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=75756;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#41\u001b\\\u001b[2m41\u001b[0m\u001b]8;;\u001b\\\n"
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\"> to disable these messages, turn off start_warning in your config file</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">            </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\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=900142;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=528346;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n"
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> no display variable set. using backend for graphics without display (agg)         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#50\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">50</span></a>\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=773071;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=948519;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#50\u001b\\\u001b[2m50\u001b[0m\u001b]8;;\u001b\\\n"
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:21:40 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> ROOT minimizer not available                                                </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1345\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1345</span></a>\n",
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Multinest minimizer not available                                           </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1357\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1357</span></a>\n",
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> PyGMO is not available                                                      </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/minimizer/minimization.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">minimization.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1369</span></a>\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=142620;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=870920;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\u001b\\\u001b[2m1369\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:21:41 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> The cthreeML package is not installed. You will not be able to use plugins which  </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#94\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">94</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">require the C/C++ interface (currently HAWC)                                       </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
-       "</pre>\n"
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-       "\u001b[38;5;46m17:21:41\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=746553;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=543941;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin FermiLATLike.py. Do you have the relative instrument     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#144\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">144</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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=999964;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=759955;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:21:42 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Could not import plugin HAWCLike.py. Do you have the relative instrument         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#144\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">144</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">software installed and configured?                                                </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:21:42\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=598117;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=734450;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:21:45 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> No fermitools installed                                              </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lat_transient_builder.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">44</span></a>\n",
-       "</pre>\n"
-      ],
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-       "\u001b[38;5;46m17:21:45\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=835321;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=762097;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:21:45 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable OMP_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:21:45\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=230016;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=161736;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable MKL_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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 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=416184;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=612783;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> for optimal     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/__init__.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
-       "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">performances in 3ML                                                               </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
-       "</pre>\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 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=882432;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=613886;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# imports:\n",
-    "from cosipy import COSILike, test_data, BinnedData\n",
+    "%%capture\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import astropy.units as u\n",
+    "from astropy.io import fits\n",
+    "\n",
+    "from astromodels import Model, Parameter, ExtendedSource, Constant\n",
+    "from astromodels.functions import GalPropTemplate_3D\n",
+    "\n",
+    "from threeML import JointLikelihood, DataList\n",
+    "\n",
+    "from cosipy import COSILike, BinnedData\n",
     "from cosipy.spacecraftfile import SpacecraftFile\n",
     "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n",
     "from cosipy.threeml.custom_functions import GalpropHealpixModel\n",
     "from cosipy.util import fetch_wasabi_file\n",
-    "from threeML import PointSource, Model, JointLikelihood, DataList, update_logging_level\n",
-    "from threeML.analysis_results import *\n",
-    "from astromodels import *\n",
-    "from astromodels.functions import GalPropTemplate_3D\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import sys\n",
-    "import logging\n",
     "\n",
     "# Set logging output:\n",
+    "import logging\n",
     "logging.basicConfig()\n",
-    "logging.getLogger().setLevel(logging.INFO)"
+    "logging.getLogger().setLevel(logging.INFO)\n",
+    "\n",
+    "%matplotlib inline"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ef3e1c12",
    "metadata": {},
    "source": [
     "### Get the data"
@@ -297,7 +53,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "id": "060877bf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,7 +64,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "id": "4fb00900",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +75,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "id": "a85388b0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,997 +86,256 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# source file\n",
-    "fetch_wasabi_file('COSI-SMEX/DC3/Data/Sources/GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits.gz', checksum = '9fda5a7b15a90358abc2b886979f9fef')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# precomputed point source response\n",
-    "fetch_wasabi_file('COSI-SMEX/DC3/Data/Responses/extended_source_response/extended_source_response_continuum_merged.h5.gz', unzip = True, checksum = '92ed7e22b1dafce6b57611d5cdb6cf70')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# GALPROP input model\n",
-    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/galactic_diffuse_continuum/total_healpix_57_SA100_F98_example.gz', checksum = '82cbeb9a86d86637f19f31c762f379fc')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Input files:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
+   "id": "ce3c28c8",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "rsp_file = \"ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\"\n",
-    "ori_file = \"DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori\"\n",
-    "BG_file = \"AlbedoPhotons_3months_unbinned_data_filtered_with_SAAcut.fits.gz\"\n",
-    "src_file = \"GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits.gz\"\n",
-    "psr_file = \"extended_source_response_continuum_merged.h5\"\n",
-    "galprop_model_file = \"total_healpix_57_SA100_F98_example.gz\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make the dataset and bin\n",
-    "This step only needs to be run once. Afterwards, the files can be loaded directly using the cell below. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Make combined dataset:\n",
-    "analysis = BinnedData(\"galdiff.yaml\")\n",
-    "input_files = [BG_file,src_file]\n",
-    "analysis.combine_unbinned_data(input_files, output_name=\"combined_data\")\n",
-    "\n",
-    "# Bin galdiff:\n",
-    "galdiff = BinnedData(\"galdiff.yaml\")\n",
-    "galdiff.get_binned_data(unbinned_data=src_file, output_name=\"galdiff_binned_data\")\n",
-    "\n",
-    "# Bin background:\n",
-    "bg_tot = BinnedData(\"galdiff.yaml\")\n",
-    "bg_tot.get_binned_data(unbinned_data=BG_file, output_name=\"albedo_photons_binned_data\")\n",
-    "\n",
-    "# Bin combined data:\n",
-    "data_combined = BinnedData(\"galdiff.yaml\")\n",
-    "data_combined.get_binned_data(unbinned_data=\"combined_data.fits.gz\", output_name=\"combined_binned_data\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load binned files:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:yayc.configurator:Using configuration file at galdiff.yaml\n",
-      "INFO:yayc.configurator:Using configuration file at galdiff.yaml\n",
-      "INFO:yayc.configurator:Using configuration file at galdiff.yaml\n"
-     ]
-    }
-   ],
-   "source": [
-    "galdiff = BinnedData(\"galdiff.yaml\")\n",
-    "galdiff.load_binned_data_from_hdf5(binned_data=\"galdiff_binned_data.hdf5\")\n",
-    "\n",
-    "# Load background:\n",
-    "bg_tot = BinnedData(\"galdiff.yaml\")\n",
-    "bg_tot.load_binned_data_from_hdf5(binned_data=\"albedo_photons_binned_data.hdf5\")\n",
-    "\n",
-    "# Load combined data:\n",
-    "data_combined = BinnedData(\"galdiff.yaml\")\n",
-    "data_combined.load_binned_data_from_hdf5(binned_data=\"combined_binned_data.hdf5\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define GALPROP model\n",
-    "Below is how to define the custom GALPROP model. We will save the model to a yaml file so that it can be directly uploaded in the future (as shown at the bottom). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:cosipy.threeml.custom_functions:loading GALPROP model: GALPROP_DC3/total_healpix_57_SA100_F98_example.gz\n"
-     ]
-    }
-   ],
-   "source": [
-    "# defining the model:\n",
-    "galprop_model = GalpropHealpixModel()\n",
-    "galprop_model.load_file(galprop_model_file)\n",
-    "\n",
-    "# The spectrum is defined in the data cube, \n",
-    "# and so we use a dummy model for defining an extended source in astromodels. \n",
-    "# NB: This has no impact on the results - just make sure the parameter is fixed!\n",
-    "spectrum = Constant()\n",
-    "spectrum.k.value = 0.0\n",
-    "spectrum.k.free = False\n",
-    "\n",
-    "src = ExtendedSource(\"galprop_source\", spatial_shape=galprop_model, spectral_shape=spectrum)\n",
-    "model = Model(src)\n",
-    "model.save(\"galprop_model.yaml\", overwrite=True)\n",
-    "\n",
-    "# uncomment below to load saved model:\n",
-    "#model = load_model('galprop_model.yaml')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup and perform fit\n",
-    "Set background parameter, which is used to fit the amplitude of the background:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bkg_par = Parameter(\"background_cosi\",                                        # background parameter\n",
-    "                    1,                                                        # initial value of parameter\n",
-    "                    min_value=0,                                              # minimum value of parameter\n",
-    "                    max_value=5,                                              # maximum value of parameter\n",
-    "                    delta=0.05,                                               # initial step used by fitting engine\n",
-    "                    desc=\"Background parameter for cosi\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify orientation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ori = SpacecraftFile.parse_from_file(ori_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instantiate the COSI 3ML plugin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:cosipy.threeml.COSILike:... loading the pre-computed image response ...\n",
-      "INFO:cosipy.threeml.COSILike:--> done\n"
-     ]
-    }
-   ],
-   "source": [
-    "cosi = COSILike(\"cosi\",                                                       # COSI 3ML plugin\n",
-    "    dr = rsp_file,                                                            # detector response\n",
-    "    data = data_combined.binned_data.project('Em', 'Phi', 'PsiChi'),          # data (source+background)\n",
-    "    bkg = bg_tot.binned_data.project('Em', 'Phi', 'PsiChi'),                  # background model\n",
-    "    sc_orientation = ori,                                                     # spacecraft orientation\n",
-    "    nuisance_param = bkg_par,                                                 # background parameter                        \n",
-    "    precomputed_psr_file = psr_file)                                          # precomputed extended source response "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Perform fit:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:cosipy.threeml.custom_functions:Interpolating GALPROP map...\n",
-      "INFO:cosipy.response.functions_3d:Integrating intensity over energy bins...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:40 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                             </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1046\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1046</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:40\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=727888;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=514129;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1046\u001b\\\u001b[2m1046\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:cosipy.threeml.COSILike:Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:41 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171587296.292</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                               </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:41\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171587296.292\u001b[0m\u001b[1;38;5;251m                              \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=774961;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=620531;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0201</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171574244.963</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                          </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m1.0201\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171574244.963\u001b[0m\u001b[1;38;5;251m                         \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=859194;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=638806;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.9801</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171599150.352</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                          </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.9801\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171599150.352\u001b[0m\u001b[1;38;5;251m                         \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=648167;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=872776;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0052</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171584000.669</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                          </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m1.0052\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171584000.669\u001b[0m\u001b[1;38;5;251m                         \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=838620;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=778796;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:42 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.99478</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171590510.094</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:42\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.99478\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171590510.094\u001b[0m\u001b[1;38;5;251m                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=19433;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=478804;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0201</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171558692.899</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                          </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0201\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171558692.899\u001b[0m\u001b[1;38;5;251m                         \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=111688;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=468944;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.98007</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171606499.522</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                         </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.98007\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171606499.522\u001b[0m\u001b[1;38;5;251m                        \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=995657;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=790689;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.002</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171584857.330</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                           </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.002\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171584857.330\u001b[0m\u001b[1;38;5;251m                          \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=23752;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=194515;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:43 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.998</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171589641.252</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                           </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:43\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.998\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171589641.252\u001b[0m\u001b[1;38;5;251m                          \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=33104;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=382905;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.80282</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.9496</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171622941.959</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.80282\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.9496\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171622941.959\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=163446;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=881364;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.87613</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.96887</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171645734.801</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                   </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.87613\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.96887\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171645734.801\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=960916;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=223339;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.88103</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.96887</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171645314.168</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                   </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.88103\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.96887\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171645314.168\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=438679;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=47562;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:44 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.87124</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.96887</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171646080.383</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                   </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:44\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.87124\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.96887\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171646080.383\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=254843;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=242311;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.87613</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.97072</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171646253.912</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                   </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.87613\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.97072\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171646253.912\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=234518;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=170638;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.87613</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.96703</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171645129.664</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                   </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.87613\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.96703\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171645129.664\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=262387;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=275541;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:45 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.84847</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.97973</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171650454.928</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                   </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:45\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.84847\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m0.97973\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171650454.928\u001b[0m\u001b[1;38;5;251m                  \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=761282;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=309624;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77906</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.008</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655633.154</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77906\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.008\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655633.154\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=890795;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=491452;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.78389</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.008</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655478.642</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.78389\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.008\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655478.642\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=571805;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=830907;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77425</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.008</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655713.030</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77425\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.008\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655713.030\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=505209;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=221778;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:46 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77906</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0098</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655496.731</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:46\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77906\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0098\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655496.731\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=79029;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=254900;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77906</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0062</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655687.244</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77906\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0062\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655687.244\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=739349;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=837625;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77284</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0076</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.151</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77284\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0076\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.151\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=399897;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=326218;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77233</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0076</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.710</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77233\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0076\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.710\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=398433;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=298601;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:47 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77736</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0076</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.436</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:47\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77736\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0076\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.436\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=907384;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=558082;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.76731</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0076</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655685.700</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.76731\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0076\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655685.700\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=820987;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=989066;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77233</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0094</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655686.800</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77233\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0094\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655686.800\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=128547;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=336033;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77233</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0058</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655682.589</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77233\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0058\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655682.589\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=421759;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=702190;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:48 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.792</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:48\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.792\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=79644;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=32979;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.7771</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.857</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.7771\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.857\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=885037;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=148595;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.76702</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.898</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.76702\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.898\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=548579;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=155791;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:49 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0095</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.882</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:49\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0095\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.882\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=935155;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=725383;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0059</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.849</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0059\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.849\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=751236;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=453550;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.792</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.792\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=483680;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=315313;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.7771</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.857</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.7771\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.857\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=915872;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=585384;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:50 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.76702</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.898</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:50\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.76702\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.898\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=683832;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=229915;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0095</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.882</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0095\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.882\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=681003;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=967754;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0059</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655684.849</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0059\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655684.849\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=788100;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=835596;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77306</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655724.165</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77306\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655724.165\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=750062;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=506532;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:51 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77104</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655724.145</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:51\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77104\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655724.145\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=638277;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=367765;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77225</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.728</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77225\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.728\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=646722;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=971822;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77185</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.724</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77185\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.724\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=163647;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=220314;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0081</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655724.155</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0081\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655724.155\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=539764;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=180688;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:52 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0073</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655724.154</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:52\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0073\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655724.154\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=663232;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=161827;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0078</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.726</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0078\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.726\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=443351;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=66517;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0076</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.726</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0076\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.726\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=997236;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=972484;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:53 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.7771</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0095</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655584.801</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                     </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:53\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.7771\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0095\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655584.801\u001b[0m\u001b[1;38;5;251m                    \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=448005;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=669174;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.792</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.792\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=273039;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=52610;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77225</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.728</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77225\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.728\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=665354;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=640057;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77185</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.724</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77185\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.724\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=334934;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=746418;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:54 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0078</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.726</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:54\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0078\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.726\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=170191;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=747041;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0076</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.726</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0076\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.726\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=668908;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=284348;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77209</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.789</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77209\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.789\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=986832;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=174298;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77201</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.789</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77201\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.789\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=613117;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=51515;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">17:24:55 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.789</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m17:24:55\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.789\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=727040;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=783089;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+   "outputs": [],
+   "source": [
+    "# source file\n",
+    "fetch_wasabi_file('COSI-SMEX/DC3/Data/Sources/GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits.gz', checksum = '9fda5a7b15a90358abc2b886979f9fef')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "31cf8e8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# precomputed point source response\n",
+    "fetch_wasabi_file('COSI-SMEX/DC3/Data/Responses/extended_source_response/extended_source_response_continuum_merged.h5.gz', unzip = True, checksum = '92ed7e22b1dafce6b57611d5cdb6cf70')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5fea5692",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GALPROP input model\n",
+    "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/galactic_diffuse_continuum/total_healpix_57_SA100_F98_example.gz', checksum = '82cbeb9a86d86637f19f31c762f379fc')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d175ffc",
+   "metadata": {},
+   "source": [
+    "Input files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8cb17273",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rsp_file = \"ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\"\n",
+    "ori_file = \"DC3_final_530km_3_month_with_slew_15sbins_GalacticEarth_SAA.ori\"\n",
+    "BG_file = \"AlbedoPhotons_3months_unbinned_data_filtered_with_SAAcut.fits.gz\"\n",
+    "src_file = \"GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits.gz\"\n",
+    "psr_file = \"extended_source_response_continuum_merged.h5\"\n",
+    "galprop_model_file = \"total_healpix_57_SA100_F98_example.gz\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb67cdc5",
+   "metadata": {},
+   "source": [
+    "### Make the dataset and bin\n",
+    "This step only needs to be run once. Afterwards, the files can be loaded directly using the cell below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "855ceed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make combined dataset:\n",
+    "analysis = BinnedData(\"galdiff.yaml\")\n",
+    "input_files = [BG_file,src_file]\n",
+    "analysis.combine_unbinned_data(input_files, output_name=\"combined_data\")\n",
+    "\n",
+    "# Bin galdiff:\n",
+    "galdiff = BinnedData(\"galdiff.yaml\")\n",
+    "galdiff.get_binned_data(unbinned_data=src_file, output_name=\"galdiff_binned_data\")\n",
+    "\n",
+    "# Bin background:\n",
+    "bg_tot = BinnedData(\"galdiff.yaml\")\n",
+    "bg_tot.get_binned_data(unbinned_data=BG_file, output_name=\"albedo_photons_binned_data\")\n",
+    "\n",
+    "# Bin combined data:\n",
+    "data_combined = BinnedData(\"galdiff.yaml\")\n",
+    "data_combined.get_binned_data(unbinned_data=\"combined_data.fits.gz\", output_name=\"combined_binned_data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80b52c22",
+   "metadata": {},
+   "source": [
+    "Load binned files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e3dff193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "galdiff = BinnedData(\"galdiff.yaml\")\n",
+    "galdiff.load_binned_data_from_hdf5(binned_data=\"galdiff_binned_data.hdf5\")\n",
+    "\n",
+    "# Load background:\n",
+    "bg_tot = BinnedData(\"galdiff.yaml\")\n",
+    "bg_tot.load_binned_data_from_hdf5(binned_data=\"albedo_photons_binned_data.hdf5\")\n",
+    "\n",
+    "# Load combined data:\n",
+    "data_combined = BinnedData(\"galdiff.yaml\")\n",
+    "data_combined.load_binned_data_from_hdf5(binned_data=\"combined_binned_data.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1b77f11",
+   "metadata": {},
+   "source": [
+    "## Define GALPROP model\n",
+    "Below is how to define the custom GALPROP model. We will save the model to a yaml file so that it can be directly uploaded in the future (as shown at the bottom). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8dc1070d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining the model:\n",
+    "galprop_model = GalpropHealpixModel()\n",
+    "galprop_model.load_file(galprop_model_file)\n",
+    "\n",
+    "# The spectrum is defined in the data cube, \n",
+    "# and so we use a dummy model for defining an extended source in astromodels. \n",
+    "# NB: This has no impact on the results - just make sure the parameter is fixed!\n",
+    "spectrum = Constant()\n",
+    "spectrum.k.value = 0.0\n",
+    "spectrum.k.free = False\n",
+    "\n",
+    "src = ExtendedSource(\"galprop_source\", spatial_shape=galprop_model, spectral_shape=spectrum)\n",
+    "model = Model(src)\n",
+    "model.save(\"galprop_model.yaml\", overwrite=True)\n",
+    "\n",
+    "# uncomment below to load saved model:\n",
+    "#model = load_model('galprop_model.yaml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b7630b2",
+   "metadata": {},
+   "source": [
+    "## Setup and perform fit\n",
+    "Set background parameter, which is used to fit the amplitude of the background:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "82e2c007",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bkg_par = Parameter(\"background_cosi\",                                        # background parameter\n",
+    "                    1,                                                        # initial value of parameter\n",
+    "                    min_value=0,                                              # minimum value of parameter\n",
+    "                    max_value=5,                                              # maximum value of parameter\n",
+    "                    delta=0.05,                                               # initial step used by fitting engine\n",
+    "                    desc=\"Background parameter for cosi\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82d11113",
+   "metadata": {},
+   "source": [
+    "Specify orientation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7518106a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori = SpacecraftFile.parse_from_file(ori_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "897976b5",
+   "metadata": {},
+   "source": [
+    "Instantiate the COSI 3ML plugin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6c1574f0",
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77205</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0077</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.789</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77205\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0077\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.789\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=264463;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=709856;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 71.1 ms, sys: 4.83 s, total: 4.9 s\n",
+      "Wall time: 4.92 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "cosi = COSILike(\"cosi\",                                                       # COSI 3ML plugin\n",
+    "    dr = rsp_file,                                                            # detector response\n",
+    "    data = data_combined.binned_data.project('Em', 'Phi', 'PsiChi'),          # data (source+background)\n",
+    "    bkg = bg_tot.binned_data.project('Em', 'Phi', 'PsiChi'),                  # background model\n",
+    "    sc_orientation = ori,                                                     # spacecraft orientation\n",
+    "    nuisance_param = bkg_par,                                                 # background parameter                        \n",
+    "    precomputed_psr_file = psr_file)                                          # precomputed extended source response "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "239587b5",
+   "metadata": {},
+   "source": [
+    "Perform fit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e9b441d6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> trial values: </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">0.77225</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">,</span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">1.0078</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> -&gt; logL = </span><span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">171655725.568</span><span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">                    </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014</span></a>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:00:20 </span><span style=\"color: #00ffaf; text-decoration-color: #00ffaf\">INFO    </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> set the minimizer to minuit                                              </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">joint_likelihood.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">994</span></a>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[38;5;46m        \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO    \u001b[0m \u001b[1;38;5;251m trial values: \u001b[0m\u001b[1;37m0.77225\u001b[0m\u001b[1;38;5;251m,\u001b[0m\u001b[1;37m1.0078\u001b[0m\u001b[1;38;5;251m -> logL = \u001b[0m\u001b[1;37m171655725.568\u001b[0m\u001b[1;38;5;251m                   \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=42846;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=987545;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1014\u001b\\\u001b[2m1014\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[38;5;46m15:00:20\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=959197;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py\u001b\\\u001b[2mjoint_likelihood.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=80216;file:///project/cassini/cosi/threeML_git/threeML/classicMLE/joint_likelihood.py#994\u001b\\\u001b[2m994\u001b[0m\u001b]8;;\u001b\\\n"
       ]
      },
      "metadata": {},
@@ -1326,12 +344,12 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">130</span></a>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">15:00:40 </span><span style=\"color: #af5fd7; text-decoration-color: #af5fd7\">WARNING </span> <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\"> get_number_of_data_points not implemented, values for statistical        </span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">plugin_prototype.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">119</span></a>\n",
        "<span style=\"color: #00ff00; text-decoration-color: #00ff00\">         </span>         <span style=\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\">measurements such as AIC or BIC are unreliable                            </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                       </span>\n",
        "</pre>\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 get_number_of_data_points not implemented, values for statistical       \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=371417;file:///discover/nobackup/ckarwin/Software/COSIPY_new/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=874141;file:///discover/nobackup/ckarwin/Software/COSIPY_new/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\u001b\\\u001b[2m130\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[38;5;46m15:00:40\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=271288;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py\u001b\\\u001b[2mplugin_prototype.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=35756;file:///project/cassini/cosi/threeML_git/threeML/plugin_prototype.py#119\u001b\\\u001b[2m119\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"
       ]
      },
@@ -1428,7 +446,7 @@
     {
      "data": {
       "text/html": [
-       "<div><table id=\"table23337880777008\">\n",
+       "<div><table id=\"table139693362740256\">\n",
        "<tr><td>1.00</td><td>-0.72</td></tr>\n",
        "<tr><td>-0.72</td><td>1.00</td></tr>\n",
        "</table></div>"
@@ -1548,11 +566,11 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AIC</th>\n",
-       "      <td>-3.433115e+08</td>\n",
+       "      <td>-3.433114e+08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BIC</th>\n",
-       "      <td>-3.433115e+08</td>\n",
+       "      <td>-3.433114e+08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1560,26 +578,32 @@
       ],
       "text/plain": [
        "     statistical measures\n",
-       "AIC         -3.433115e+08\n",
-       "BIC         -3.433115e+08"
+       "AIC         -3.433114e+08\n",
+       "BIC         -3.433114e+08"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 12s, sys: 63.5 ms, total: 2min 12s\n",
+      "Wall time: 19.3 s\n"
+     ]
     }
    ],
    "source": [
+    "%%time\n",
     "plugins = DataList(cosi)\n",
-    "like = JointLikelihood(model, plugins, verbose = True)\n",
-    "like.fit()\n",
-    "\n",
-    "# Save results to file:\n",
-    "results = like.results\n",
-    "results.write_to(\"fit_results.fits\", overwrite=True)"
+    "like = JointLikelihood(model, plugins, verbose = False)\n",
+    "like.fit();"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "b8b6adde",
    "metadata": {},
    "source": [
     "The best-fit normalizations are: <br>\n",
@@ -1592,6 +616,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7fd588b9",
    "metadata": {},
    "source": [
     "Compare best-fit to injected source:"
@@ -1599,24 +624,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
+   "id": "383a7752",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
       "galdiff expected counts:\n",
-      "[7.75334220e+05 1.89566418e+06 1.76323635e+06 1.10662652e+06\n",
-      " 6.56071259e+05 3.87738657e+05 2.35773990e+05 9.77339502e+04\n",
-      " 2.13603984e+04 1.03920660e+03]\n",
-      "\n"
+      "[7.75334204e+05 1.89566414e+06 1.76323631e+06 1.10662650e+06\n",
+      " 6.56071246e+05 3.87738649e+05 2.35773985e+05 9.77339482e+04\n",
+      " 2.13603980e+04 1.03920658e+03]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1626,37 +650,36 @@
     }
    ],
    "source": [
-    "%matplotlib inline\n",
     "# Get expected counts from likelihood scan (i.e. best-fit convolved with response):\n",
     "total_expectation = cosi._expected_counts['galprop_source']\n",
-    "print()\n",
+    "total_expectation_em = total_expectation.project('Em').todense().contents\n",
+    "galdiff_em = galdiff.binned_data.project('Em').todense().contents\n",
+    "\n",
     "print(\"galdiff expected counts:\")\n",
-    "print(total_expectation.project('Em').todense().contents)\n",
-    "print()\n",
+    "print(total_expectation_em)\n",
     "\n",
-    "# Plot:\n",
     "fig,ax = plt.subplots()\n",
     "\n",
     "binned_energy_edges = galdiff.binned_data.axes['Em'].edges.value\n",
     "binned_energy = galdiff.binned_data.axes['Em'].centers.value\n",
     "\n",
-    "ax.stairs(total_expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best-fit convolved with response\")\n",
-    "ax.errorbar(binned_energy, total_expectation.project('Em').todense().contents, yerr=np.sqrt(total_expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.stairs(galdiff.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Simulated source counts\")\n",
-    "ax.errorbar(binned_energy, galdiff.binned_data.project('Em').todense().contents, yerr=np.sqrt(galdiff.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(total_expectation_em, binned_energy_edges, color='purple', label = \"Best-fit convolved with response\")\n",
+    "ax.errorbar(binned_energy, total_expectation_em, yerr=np.sqrt(total_expectation_em), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(galdiff_em, binned_energy_edges, color = 'black', ls = \":\", label = \"Simulated source counts\")\n",
+    "ax.errorbar(binned_energy, galdiff_em, yerr=np.sqrt(galdiff_em), color='black', linewidth=0, elinewidth=1)\n",
     "\n",
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(\"Counts\")\n",
     "plt.yscale('log')\n",
     "plt.xscale('log')\n",
     "ax.legend()\n",
-    "plt.savefig(\"injected_model_comparison.pdf\")\n",
-    "plt.show()\n",
-    "plt.close()"
+    "\n",
+    "plt.savefig(\"injected_model_comparison.pdf\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "eead6217",
    "metadata": {},
    "source": [
     "percent difference: "
@@ -1664,12 +687,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
+   "id": "cf7c2df5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1679,18 +703,18 @@
     }
    ],
    "source": [
-    "diff = (galdiff.binned_data.project('Em').todense().contents - total_expectation.project('Em').todense().contents)/total_expectation.project('Em').todense().contents\n",
+    "diff = (galdiff_em - total_expectation_em)/total_expectation_em\n",
     "\n",
     "plt.semilogx(binned_energy,diff,ls=\"--\",marker=\"o\")\n",
     "plt.xlabel(\"Energy [keV]\")\n",
     "plt.ylabel(\"(data - model) / model\")\n",
-    "plt.savefig(\"percent_diff.pdf\")\n",
-    "plt.show()\n",
-    "plt.close()"
+    "\n",
+    "plt.savefig(\"percent_diff.pdf\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ea6bd284",
    "metadata": {},
    "source": [
     "Compare best-fit to injected for total counts:"
@@ -1698,12 +722,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
+   "id": "85f2057e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1713,21 +738,15 @@
     }
    ],
    "source": [
-    "# Plot:\n",
+    "fit_em = total_expectation_em + bkg_par.value * bg_tot.binned_data.project('Em').todense().contents\n",
+    "inj_em = data_combined.binned_data.project('Em').todense().contents\n",
+    "\n",
     "fig,ax = plt.subplots()\n",
     "\n",
-    "ax.stairs(total_expectation.project('Em').todense().contents \\\n",
-    "        +(bkg_par.value * bg_tot.binned_data.project('Em').todense().contents) \\\n",
-    "        , binned_energy_edges, color='purple', label = \"Best fit convolved with response plus BG\")\n",
-    "ax.errorbar(binned_energy, total_expectation.project('Em').todense().contents \\\n",
-    "        +(bkg_par.value * bg_tot.binned_data.project('Em').todense().contents) \\\n",
-    "        , yerr=np.sqrt(total_expectation.project('Em').todense().contents \\\n",
-    "        +(bkg_par.value * bg_tot.binned_data.project('Em').todense().contents)), \\\n",
-    "        color='purple', linewidth=0, elinewidth=1)\n",
-    "ax.stairs(data_combined.binned_data.project('Em').todense().contents, binned_energy_edges, \\\n",
-    "        color = 'black', ls = \":\", label = \"Simulated total counts\")\n",
-    "ax.errorbar(binned_energy, data_combined.binned_data.project('Em').todense().contents, \\\n",
-    "        yerr=np.sqrt(data_combined.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(fit_em, binned_energy_edges, color='purple', label = \"Best fit convolved with response plus BG\")\n",
+    "ax.errorbar(binned_energy, fit_em, yerr=np.sqrt(fit_em), color='purple', linewidth=0, elinewidth=1)\n",
+    "ax.stairs(inj_em, binned_energy_edges, color = 'black', ls = \":\", label = \"Simulated total counts\")\n",
+    "ax.errorbar(binned_energy, inj_em, yerr=np.sqrt(inj_em), color='black', linewidth=0, elinewidth=1)\n",
     "\n",
     "ax.set_xlabel(\"Energy (keV)\")\n",
     "ax.set_ylabel(\"Counts\")\n",
@@ -1735,19 +754,19 @@
     "ax.legend()\n",
     "plt.yscale('log')\n",
     "plt.xscale('log')\n",
-    "plt.savefig(\"injected_total_comparison.pdf\")\n",
-    "plt.show()\n",
-    "plt.close()"
+    "\n",
+    "plt.savefig(\"injected_total_comparison.pdf\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
+   "id": "6821ba30",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1757,20 +776,18 @@
     }
    ],
    "source": [
-    "mod_tot = total_expectation.project('Em').todense().contents \\\n",
-    "        +(bkg_par.value * bg_tot.binned_data.project('Em').todense().contents)\n",
-    "diff = (data_combined.binned_data.project('Em').todense().contents - mod_tot)/mod_tot\n",
+    "diff = (inj_em - fit_em)/fit_em\n",
     "\n",
     "plt.semilogx(binned_energy,diff,ls=\"--\",marker=\"o\")\n",
     "plt.xlabel(\"Energy [keV]\")\n",
     "plt.ylabel(\"(data - model) / model\")\n",
-    "plt.savefig(\"percent_diff.pdf\")\n",
-    "plt.show()\n",
-    "plt.close()"
+    "\n",
+    "plt.savefig(\"percent_diff.pdf\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "9f1513ee",
    "metadata": {},
    "source": [
     "Plot average intensity (averaged over full sky):"
@@ -1778,31 +795,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
+   "id": "a41a47a6",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:cosipy.threeml.custom_functions:using nside=8 from user input in evaluate method\n",
-      "INFO:cosipy.threeml.custom_functions:loading GALPROP model: GALPROP_DC3/total_healpix_57_SA100_F98_example.gz\n",
-      "INFO:cosipy.threeml.custom_functions:Interpolating GALPROP map...\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "intensity error:\n",
-      "[6.56078943e-07 6.93643149e-07 7.23477880e-07 7.63933238e-07\n",
-      " 8.07484834e-07 8.81433266e-07 9.58088343e-07 1.07182018e-06\n",
-      " 1.18119475e-06 1.33628090e-06]\n"
+      "[6.29596129e-07 6.65644045e-07 6.94274489e-07 7.33096855e-07\n",
+      " 7.74890478e-07 8.45853961e-07 9.19414834e-07 1.02855586e-06\n",
+      " 1.13351549e-06 1.28234154e-06]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1812,31 +821,30 @@
     }
    ],
    "source": [
-    "# Get parameter error manually:\n",
-    "hdu = fits.open(\"fit_results.fits\")\n",
-    "data = hdu[1].data\n",
-    "K = data['VALUE'][0]\n",
-    "Kerr = data[\"ERROR\"][0]\n",
+    "results = like.results\n",
+    "mle_data = results.get_data_frame()\n",
+    "K    = mle_data[\"value\"][\"galprop_source.GalpropHealpixModel.K\"]\n",
+    "Kerr = mle_data[\"error\"][\"galprop_source.GalpropHealpixModel.K\"]\n",
     "\n",
     "# We will pass the response nside in order to use the same spatial sampling as the fit:\n",
     "with FullDetectorResponse.open(rsp_file) as response:\n",
     "    nside = response.nside\n",
     "\n",
     "# We will also use the same energy values as was used in the fit:\n",
-    "binned_energy = galdiff.binned_data.axes['Em'].centers.to(u.MeV).value\n",
     "binned_energy_edges = galdiff.binned_data.axes['Em'].edges.to(u.MeV).value\n",
-    "energy_err = np.diff(binned_energy_edges)/2.0\n",
+    "binned_energy = galdiff.binned_data.axes['Em'].centers.to(u.MeV).value\n",
+    "energy_err = 0.5*np.diff(binned_energy_edges)\n",
     "\n",
     "# Below we will pass avg_int=True in order to get the average intensity. Otherwise, the function returns the total intensity by default.\n",
     "intensity = results.optimized_model[\"galprop_source\"].spatial_shape.get_total_spatial_integral(binned_energy, avg_int=True, nside=nside)\n",
     "intensity = intensity.value\n",
     "\n",
-    "yerr = (Kerr/K)*(intensity)\n",
-    "yerr *= (binned_energy**2)\n",
+    "yerr = (Kerr/K)*intensity\n",
+    "yerr *= binned_energy**2\n",
     "print(\"intensity error:\")\n",
     "print(yerr)\n",
     "\n",
-    "intensity *= (binned_energy**2)\n",
+    "intensity *= binned_energy**2\n",
     "\n",
     "fig,ax = plt.subplots()\n",
     "\n",
@@ -1854,19 +862,19 @@
     "#gal_spec = instance.spectra_list\n",
     "#ax.loglog(gal_energy, gal_spec, ls=\"-\", marker=\"\", color=\"red\", label = \"GALPROP model\")\n",
     "\n",
-    "plt.ylabel(\"dN/dE [$\\mathrm{MeV \\ cm^{-2} \\ s^{-1} \\ sr^{-1}}$]\")\n",
+    "plt.ylabel(r\"dN/dE [$\\mathrm{MeV \\ cm^{-2} \\ s^{-1} \\ sr^{-1}}$]\")\n",
     "plt.xlabel(\"Energy [MeV]\")\n",
     "plt.title(\"Average Intensity\")\n",
     "ax.legend()\n",
     "plt.xlim(5e-2,20)\n",
     "plt.ylim(1e-4,5e-3)\n",
-    "plt.savefig(\"intensity.pdf\")\n",
-    "plt.show()\n",
-    "plt.close()"
+    "\n",
+    "plt.savefig(\"intensity.pdf\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a1472763",
    "metadata": {},
    "source": [
     "Below we plot the best-fit spectrum just for demonstration. Again, this is just a dummy model since the spectrum is contained in the 3D data cube. This has no impact on the fit.    "
@@ -1874,12 +882,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
+   "id": "8af9ae13",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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6AAAAh2HIoy0CAgI0adIkzZs3T+PHj7eW9+zZU5Lk5OSkyZMn3/K+MgAAgLLGsIe+9uvXTwEBAVq3bp1OnjyplJQUeXh4yN/fX/3791fDhg2NmgoAAMBhGBbGJKlBgwaaOHFivvXZ2dlydnY2ckoAAIBSzZB7xr7++utbtsnOztZbb71lxHQAAAAOw5AwNm/ePIWHh+dbbzab9dZbb2n37t1GTAcAAOAwDAljLVu21IwZM/TTTz/lqssJYrt27VL//v2NmA4AAMBhGBLGZs2apbp16+q1115TTEyMtdxsNuvtt99WeHi4+vXrV+D9ZAAAAGWRITfwe3h46P3339eYMWP00ksvacGCBapZs6amT5+unTt3qm/fvnr++eeNmKpYMjMztXTpUm3btk2pqany9fXV6NGj1a5du1v2TUxM1Pz583Xw4EGZzWYFBgZq/PjxqlWrlrVNQkKCwsLCtH//fsXHx8vZ2VkNGzbUE088obZt25bkoQEAgFLOkDNjkuTt7a05c+bIbDbrhRde0JtvvqmdO3fq0Ucf1eTJk42apljeeecdrV69Wg8//LAmTJggJycnvfzyyzp27FiB/a5fv66JEyfq6NGjGjFihEaNGqWYmBiNHz9eycnJ1nZ79+7VF198oTp16mj06NF64okndP36dU2ePFlhYWElfXgAAKAUM1ksFouRA0ZFRWnSpElKT09XcHCwXnrpJSOHL7LIyEg9++yzGjNmjIYNGyZJysjI0MiRI1WlShUtXLgw375ffPGFFi1apMWLF8vf31+SFBsbq5EjR2rYsGF6+umnJUlnzpxR1apVVaVKFWvfzMxMjRo1Sjdu3NBXX31VpDVHR0crNDRUS5Ys4UG5AAA4uGJdply+fHmB9f7+/jp16pS8vb1t2ppMJj355JPFmbLYdu3aJWdnZ4WEhFjL3NzcFBwcrI8++kgJCQny8fHJs294eLiaNWtmDWKSVL9+fbVp00Y7d+60hrG8Hmjr6uqqv/3tb1q9erWuX7+uChUqGHxkAADAERQrjC1btqxQ7T799FObz/YIYzExMapTp448PDxsynMC1qlTp/IMY2azWadPn1bv3r1z1fn7++vgwYO3DFmXL19W+fLl5ebmVuAak5KSdOnSJevn2NjYAtsDAADHUawwNnfuXKPXUWIuXbokb2/vXOU5ZUlJSXn2S0lJUWZm5i371qtXL8/+8fHx2r17t4KCgm751oH169ff8mwjAABwTMUKYwEBAQYvo+RkZGSoXLlyucpdXV2t9fn1k1Ssvunp6XrzzTfl5uamZ5555pZrDAkJUceOHa2fY2NjNWPGjFv2AwAApZ+h76a8G7m5uSkrKytXeWZmprU+v36Sitw3Oztb06ZN09mzZ/Xee++pWrVqt1xjtWrVCtUOAAA4HsMebXG38vb2trkfK0dOWX4hyNPTU66urkXuO3v2bO3fv1+vvPKK7rvvvttZOgAAKAMcPow1btxY8fHxunbtmk15ZGSktT4vTk5OatSokaKionLVRUZGqlatWrlu3l+wYIHCwsI0btw4de/e3aAjAAAAjszhw1jXrl2VnZ2t9evXW8syMzMVFham5s2bW79JmZCQkOtbjF26dFFUVJRNIPvtt9905MgRde3a1abtl19+qZUrV+p//ud/NGjQoJI7IAAA4FAc/p6x5s2bKygoSB999JGuXr2q2rVra8uWLbpw4YKmTJlibTdz5kwdPXpUu3fvtpb1799fGzdu1JQpUzR06FA5Oztr9erVqlq1qoYOHWptt3v3bi1cuFB16tRR/fr1tW3bNps1tG3bVl5eXiV/sAAAoNRx+DAmSa+++qp8fHy0detWpaWlqVGjRnr33Xdv+a3QChUqaO7cuZo/f74+++wz67spx40bZ/O0/VOnTkn643EWeX0Lcu7cuYQxAACQJ8Nfh4Tbx+uQAAAoOxz+njEAAIC7maFhLDU1VVu2bDFySAAAAIdmaBhLSEjQrFmzjBwSAADAoRXpBv6EhIQC6/N7zyMAAADyVqQwNnjwYJlMpnzrLRZLgfUAAACwVaQwVqlSJT311FP5PhIiNjZW06ZNM2BZAAAAZUORwljTpk2Vmpqqhg0b5lmfnZ0tnpQBAABQeEUKY/369VN6enq+9T4+Ppo6deptLwoAAKCsKFIY69y5c4H1lSpVUq9evW5rQQAAAGUJD30FAACwo9sOY127dlVcXJwRawEAAChzbjuMccM+AABA8XGZEgAAwI4IYwAAAHZEGAMAALAjwhgAAIAdEcYAAADsiDAGAABgR7cdxoYPHy5PT08j1gIAAFDmFOl1SHl55plnjFgHAABAmVSilyktFovi4uKUkJBQktMAAACUWrd9ZkySdu3apb1792rChAmqVKmSJOn8+fOaOnWqYmNjJf3x2qTXX39dzs7ORkwJAADgEAw5M7Zu3TrFxMRYg5gkzZ8/X2fPnlVgYKB8fX0VHh6usLAwI6YDAABwGIaEsbNnz8rf39/6+fr169q/f7+6deumDz74QIsXL1b9+vUJYwAAAH9hSBhLSUmRl5eX9fOxY8eUnZ2thx56SJLk4uKitm3b6ty5c0ZMBwAA4DAMCWMeHh5KSUmxfj5y5IicnJzUunVra5mLi4vS09ONmA4AAMBhGBLG6tWrp3379ik5OVmpqanavn27mjZtanMP2YULF1S1alUjpgMAAHAYhoSxAQMGKCkpSQMGDNCgQYN06dIl9evXz6ZNZGSkGjdubMR0AAAADsOQR1t07dpVzz//vDZt2iRJ6tatm3r16mWtP3r0qK5du6b777/fiOkAAAAchslisVjsvQjYio6OVmhoqJYsWSI/Pz97LwcAAJQgXhQOAABgR4QxAAAAOyKMAQAA2BFhDAAAwI4IYwAAAHZEGAMAALAjwhgAAIAdEcYAAADsqMhhzGw26/Tp00pKSspVd/PmTR09etSIdQEAAJQJRXod0oULF/Tyyy8rNjZWJpNJf/vb3/TKK6+ocuXKkqSUlBRNmjRJ4eHhJbFWAAAAh1OkM2MLFy5UtWrVtHLlSi1ZskQZGRkaO3aszVky3q4EAABQeEUKYz///LOee+451axZU02aNNGcOXPUqlUrjRs3TgkJCZIkk8lUIgsFAABwREUKY+np6SpXrtx/Ozs56eWXX1a7du00fvx4nTt3zvAFAgAAOLIihbF69eopOjo6V/kLL7ygv/3tb5o6daphCwMAACgLihTGOnfurG+//TbPusmTJ+uhhx7injEAAIAiMFlIT3ed6OhohYaGasmSJfLz87P3cgAAQAnioa8AAAB2dNthrGvXroqLizNiLQAAAGXObYcxrnICAAAUH5cpAQAA7IgwBgAAYEeEMQAAADsijAEAANiRi70XcKdkZmZq6dKl2rZtm1JTU+Xr66vRo0erXbt2t+ybmJio+fPn6+DBgzKbzQoMDNT48eNVq1atXG03btyolStX6sKFC6pevboGDhyoAQMGlMQhAQAAB1Bmzoy98847Wr16tR5++GFNmDDB+l7NY8eOFdjv+vXrmjhxoo4ePaoRI0Zo1KhRiomJ0fjx45WcnGzTdt26dXrvvffUsGFDTZw4Uffee6/mzp2rf//73yV5aAAAoBS77TNjw4cPl6enpxFrKTGRkZHasWOHxowZo2HDhkmSevTooZEjR2rhwoVauHBhvn3Xrl2r+Ph4LV68WP7+/pKkBx54QCNHjtSqVav09NNPS5IyMjL08ccfq3379nr77bclSX369JHZbNZnn32mkJAQVapUqYSPFAAAlDa3fWbsmWeeUeXKlY1YS4nZtWuXnJ2dFRISYi1zc3NTcHCwIiIilJCQkG/f8PBwNWvWzBrEJKl+/fpq06aNdu7caS376aeflJycrH79+tn079+/v27cuKH9+/cbd0AAAMBhlIl7xmJiYlSnTh15eHjYlOcErFOnTsnHxydXP7PZrNOnT6t379656vz9/XXw4EFdv35dFSpUUExMjCSpWbNmNu38/Pzk5OSkX375RY888kie60tKStKlS5esn2NjY4t2gIW09h/f63pyRomMDQBAaVKhspv6zexo72VIKiNh7NKlS/L29s5VnlOWlJSUZ7+UlBRlZmbesm+9evV06dIlOTs7q2rVqjbtypUrJ09PT5uw9Vfr16/X8uXLC3s4xXY9OUPXLxPGAAC4mxgaxrKzs5WYmKikpCTdvHkzzzYBAQFGTlkoGRkZKleuXK5yV1dXa31+/SQVqm9GRoZcXPLeTldX13znkKSQkBB17PjfdB4bG6sZM2bk2764KlR2M3xMAABKo7vp34mGhDGz2awVK1ZozZo1Sk1NLbBteHi4EVMWiZubm7KysnKVZ2ZmWuvz6yepUH3d3NzyDaCZmZn5ziFJ1apVU7Vq1Qo4AmPcLadjAQDAfxkSxhYvXqyVK1eqatWq6tWrl7y9veXs7GzE0Ibw9vZWYmJirvKcS4f5BSFPT0+5urrmeYnxr329vb2VnZ2tK1eu2FyqzMrKUkpKSp6XOgEAAAwJY1u3blXdunX10UcfqUKFCkYMaajGjRvryJEjunbtms1N/JGRkdb6vDg5OalRo0aKiorKVRcZGalatWpZj7dJkyaSpKioKLVv397aLioqSmaz2VoPAADwZ4Y89PXGjRtq3779XRnEJKlr167Kzs7W+vXrrWWZmZkKCwtT8+bNrd+kTEhIyPVNxi5duigqKsomkP322286cuSIunbtai1r06aNPD09tW7dOpv+69atU/ny5W0CGgAAQA5Dzow1atSowG8L2lvz5s0VFBSkjz76SFevXlXt2rW1ZcsWXbhwQVOmTLG2mzlzpo4ePardu3dby/r376+NGzdqypQpGjp0qJydnbV69WpVrVpVQ4cOtbZzc3PTU089pQ8++EBvvPGG7r//fv3888/atm2bQkND7/oH4wIAAPswJIw98cQTeuONNxQdHS0/Pz8jhjTcq6++Kh8fH23dulVpaWlq1KiR3n333Vt+u7NChQqaO3eu5s+fr88++8z6bspx48apSpUqNm379+8vFxcXrVq1St9//73uuecejRs3ToMGDSq5AwMAAKWayWKxWIwY6LvvvtPcuXPVsWNH+fr65nrAao6ePXsaMZ1Di46OVmhoqJYsWXLXhlsAAGAMQ86MZWZmat++fUpOTtamTZskSSaTyaaNxWKRyWQijAEAAPyJIWFs/vz5+vbbb+Xr66suXbrcdY+2AAAAuFsZEsbCw8Pl5+enBQsW5PsUegAAAORmyKMtMjMzFRgYSBADAAAoIkPCmJ+fn+Lj440YCgAAoEwxJIyFhobqwIED2rdvnxHDAQAAlBmGXFc8dOiQAgIC9Oqrr6pNmzb5PtrCZDLpySefNGJKAAAAh2BIGFu2bJn174cPH9bhw4fzbEcYAwAAsGVIGJs7d64RwwAAAJQ5hoSxW71SCAAAAHkz5Ab+48ePa/78+fm+LDwpKUnz589XRESEEdMBAAA4DEPCWM6Lsb29vfOsr1atmvbt26fVq1cbMR0AAIDDMCSMRUVFqVWrVgW2ad26tSIjI42YDgAAwGEYEsauXr2qatWqFdjGy8tLV65cMWI6AAAAh2FIGKtYsaIuXrxYYJuEhAS5u7sbMR0AAIDDMCSMNW/eXLt371ZCQkKe9QkJCdqzZ4/uvfdeI6YDAABwGIaEscGDBysjI0Njx47Vli1blJSUJOmPb1Fu3rxZzz33nDIzMzVkyBAjpgMAAHAYhj1nbOzYsVqwYIFmzZol6Y+n7VssFuvfx48fz/PIAAAA/sKQMCZJgwYNUps2bbRu3TpFRUUpLS1NFStWlL+/v/r27atGjRoZNRUAAIDDMCyMSZKvr68mT55s5JAAAAAOzZB7xgAAAFA8hDEAAAA7IowBAADYEWEMAADAjghjAAAAdkQYAwAAsCPCGAAAgB0RxgAAAOzIsIe+ZmVlac+ePdan75vN5jzbTZ061agpAQAASj1DwtiFCxc0efJk/f7779b3UebFZDIRxgAAAP7EkDD24Ycf6ty5c3rkkUcUHBys6tWry9nZ2YihAQAAHJohYezIkSO677779I9//MOI4QAAAMoMQ27gN5vNatKkiRFDAQAAlCmGhLHmzZsrNjbWiKEAAADKFEPC2DPPPKOffvpJ4eHhRgwHAABQZhTrnrHly5fnKgsMDNS0adPUunVrNW3aVB4eHrnamEwmPfnkk8WZEgAAwCEVK4wtW7Ys37qjR4/q6NGjedYRxgAAAGwVK4zNnTvX6HUAAACUScUKYwEBAQYvAwAAoGwy7HVIOZKTk3Xq1Cldu3ZNHh4eaty4sSpXrmz0NAAAAA7BsDB2/vx5zZs3Tz/88IPNK5FMJpPat2+v8ePHq2bNmkZNBwAA4BAMCWPnzp3T2LFjdeXKFdWpU0ctW7ZU1apVdeXKFZ04cULff/+9IiMjtWDBAtWqVcuIKQEAAByCIWFs0aJFunr1ql544QX16dNHJpPJWmexWLR+/Xp98MEHWrRokaZPn27ElAAAAA7BkDB2+PBhdezYUSEhIbnqTCaT+vbtqx9++EGHDh0yYjoAAACHYdi7KRs0aFBgm0aNGslsNhsxHQAAgMMwJIw1bdpUZ8+eLbDNmTNn5OfnZ8R0AAAADsOQMBYaGqoff/xRGzduzLN+/fr1OnDggEaPHm3EdAAAAA7DsHvGAgMD9f7772vlypVq2bKlvLy8dPnyZR0/flzx8fFq166dDh8+rMOHD1v78XokAABQ1hkSxv78rsq4uDjFxcXlanPgwAEdOHDApowwBgAAyjpDwhjvqgQAACgeQ8IY76oEAAAoHkNu4AcAAEDxEMYAAADsqFiXKadPn64uXbqoS5cuxZr0dvsXVWpqqhYtWqTdu3crIyND/v7+eu655wr93LOzZ89q/vz5On78uFxcXNS+fXuNGzdOVapUsbaJjY1VWFiYDh48qHPnzsnd3V1NmzbVqFGj1KxZsxI6MgAAUNoV68zYjh07dObMmWJPerv9i8JsNmvKlCnavn27HnvsMT377LO6cuWKJk6cmOe3Pv/q4sWLGj9+vM6dO6fQ0FANHTpU+/fv1+TJk5WVlWVtt3HjRm3YsEF+fn4aO3ashgwZori4OI0ZM4bXQAEAgHwV+wb+mJgYbdmyxci1lIjw8HCdOHFC06dPV9euXSVJ3bp10/Dhw7Vs2TK98cYbBfb//PPPlZ6ero8//lg+Pj6SJH9/f02ePFmbN2+2vo+ze/fu+vvf/64KFSpY+/bu3Vv/8z//o2XLlqlt27Ylc4AAAKBUK3YY27t3r77//vsi97NYLMWdslh27dolLy8vde7c2VpWpUoVBQUF6dtvv1VmZqZcXV0L7N+hQwdrEJOktm3bqm7dutq5c6c1jOV1ybNy5cpq1aqVjh49atwBAQAAh1KsMDZ16tTbnrhJkya3PUZh/PLLL2rSpImcnGyvyPr7+2vDhg2Ki4uTr69vnn0TExN15cqVPIOWv7+/fvjhh1vOf/nyZVWuXLnANklJSbp06ZL1c2xs7C3HBQAAjqFYYaxXr15Gr6PEXL58Wa1bt85V7u3tLUm6dOlSvmEsJyDltP1r/5SUlALPrP3888+KiIjQE088UeAa169fr+XLlxfYBgAAOCZDHvp6p5jNZpub5gvi6uoqk8mkjIyMPMNSTllGRka+Y+TUlStXrsD+eY1/5coVTZ8+XTVr1tSwYcMKXGtISIg6duxo/RwbG6sZM2YU2AcAADiGUhXGfv75Z02cOLFQbVesWKH69evLzc1NmZmZuepzytzc3PIdI6curwBYUP8bN25oypQpunHjhmbPnm1zU39eqlWrpmrVqhXYBgAAOKZSFcbq1aunV155pVBtcy4tenl52dyPlaOgS5B/HSO//p6enrnOimVlZem1117T6dOn9f7776tRo0aFWi8AACibSlUY8/b2LvL9ak2aNNGxY8dkNpttbuI/efKkypcvr7p16+bbt3r16qpSpYqio6Nz1Z08eVKNGze2KTObzZo5c6Z++uknTZs2jXd2AgCAW3L41yF16dJFly9f1u7du61lV69e1c6dO9WhQwebM1vnzp3TuXPncvXft2+fEhISrGWHDx9WXFycgoKCbNr+61//0nfffafnn3/+jr1dAAAAlG6l6sxYcXTt2lVr1qzRO++8o7Nnz6py5cpau3atzGazRo0aZdP2+eeflyStXr3aWjZixAiFh4dr0qRJGjhwoG7cuKEvv/xSjRo1sjlLt3r1aq1du1YtWrRQ+fLltW3bNpuxO3XqJHd39xI8UgAAUBo5fBhzdnbWe++9pwULFuirr75SRkaGmjVrpldeeUX16tW7ZX8fHx/NmzdP8+fP1+LFi63vphw7dqzNWbVTp05JkiIiIhQREZFrnFWrVhHGAABALibLHXokflZWljIzM+Xh4XEnpivVoqOjFRoaqiVLlhT6ZeYAAKB0KvY9Y0OGDNGaNWtsyg4cOKD58+fn2f7zzz9XcHBwcacDAABwSMUOYxcuXFBaWppNWURERK6ABgAAgPw5/LcpAQAA7maEMQAAADsijAEAANgRYQwAAMCOCGMAAAB2dFsPfd22bZvNA05zXiX00ksv5Wr719cMAQAA4DbDWF7vcpT+eN5YXkwm0+1MBwAA4HCKHcZWrVpl5DoAAADKpGKHsRo1ahi5DgAAgDKJG/gBAADsqFhnxmbNmlXsCadOnVrsvgAAAI6mWGFs8+bNeZabTCZZLJZ8y00mE2EMAADgT4oVxv56877ZbNa8efMUGRmpgQMHqlWrVvLy8tLly5f1888/66uvvlKLFi00fvx4QxYNAADgKIoVxv568/7nn3+ukydP6pNPPlG1atWs5fXq1VNAQIB69+6tp556SuHh4Ro+fPjtrRgAAMCBGHID/6ZNmxQUFGQTxP6sevXqCgoK0oYNG4yYDgAAwGEYEsYSExPl6upaYBtXV1clJiYaMR0AAIDDMCSMVa9eXXv27FFGRkae9enp6dqzZ4+qV69uxHQAAAAOw5Aw9uijj+r333/X2LFjtWfPHiUnJ0uSkpOTtWfPHo0dO1YXLlxQnz59jJgOAADAYdzWuylzDBs2THFxcdq8ebNef/11SbaPubBYLOrVq5eGDRtmxHQAAAAOw5Aw5uTkpKlTp6pnz57asmWLfv31V6WlpalixYry9fVVjx49FBgYaMRUAAAADsWQMJYjICBAAQEBRg4JAADg0Ip9z9g333zDtyMBAABuU7HPjP3rX//S3Llz1bRpUz344IPq2LGjfH19jVwbAACAwyt2GPvoo4+0Z88eff/991q6dKk++eQT1ahRQw8++KAefPBBtWrVSk5OhnxZEwAAwGGZLHm92buIzp8/bw1mx44dk8ViUaVKldShQwc9+OCDateuncqXL2/EesuE6OhohYaGasmSJfLz87P3cgAAQAky5Ab+mjVravDgwRo8eLBSUlK0b98+7dmzR+Hh4dqyZYtcXV113333qVOnTurQoYOqVq1qxLQAAAClnqHfppQkT09P9ezZUz179lRmZqYOHTqkvXv3at++fdq/f7+cnJzUvHlz/d///Z/RUwMAAJQ6hoexP3N1dVWHDh3UoUMHWSwWnThxQnv37tX3339fktMCAACUGiUaxv7MZDKpZcuWatmypcaMGXOnpgUAALirFTuMzZo1q1j9pk6dWtwpAQAAHE6xw9jmzZsL3TbnPZUmk4kwBgAA8CfFDmMLFy4sVLv4+HgtW7ZMv//+e3GnAgAAcFjFDmPNmzcvsP7q1atavny5Nm7cqKysLLVs2VLPPvtscacDAABwSIbfwJ+enq6VK1dq1apVun79uho0aKCnn35aHTt2NHoqAACAUs+wMJadna3169fr008/1ZUrV1S9enWNHz9ePXv25LVIAAAA+TAkjO3cuVMff/yxzp07Jw8PDz3zzDMaMGCA3NzcjBgeAADAYd1WGDty5IgWLVqk6Ohoubi4aMiQIRoxYoQqVapk1PoAAAAcWrHD2EsvvaSDBw/KZDKpZ8+eGjVqlO655x4j1wYAAODwih3GDhw4IJPJJB8fH126dEmzZ8++ZR+TyaT33nuvuFMCAAA4nNu6TGmxWHT+/HmdP3++UO1NJtPtTAcAAOBwih3GVq1aZeQ6AAAAyqRih7EaNWoYuQ4AAIAyiQeAAQAA2FGxzozNmjWr2BPyonAAAID/KlYY27x5c57lJpNJFosl33KTyUQYAwAA+JNihbG/3rxvNps1b948RUZGauDAgWrVqpW8vLx0+fJl/fzzz/rqq6/UokULjR8/3pBFAwAAOIpihbG/3rz/+eef6+TJk/rkk09UrVo1a3m9evUUEBCg3r1766mnnlJ4eLiGDx9+eysGAABwIIbcwL9p0yYFBQXZBLE/q169uoKCgrRhwwYjpgMAAHAYhoSxxMREubq6FtjG1dVViYmJRkwHAADgMAwJY9WrV9eePXuUkZGRZ316err27Nmj6tWrGzEdAACAwzAkjD366KP6/fffNXbsWO3Zs0fJycmSpOTkZO3Zs0djx47VhQsX1KdPHyOmAwAAcBi39W7KHMOGDVNcXJw2b96s119/XZLtYy4sFot69eqlYcOGGTFdkaWmpmrRokXavXu3MjIy5O/vr+eee05+fn6F6n/27FnNnz9fx48fl4uLi9q3b69x48apSpUq+fbZtm2bZsyYIXd3d23dutWgIwEAAI7GkDDm5OSkqVOnqmfPntqyZYt+/fVXpaWlqWLFivL19VWPHj0UGBhoxFRFZjabNWXKFP36668aOnSoKleurLVr12rixIlasmSJ6tatW2D/ixcvavz48apYsaJCQ0N148YNrVy5UqdPn9bixYtVrly5XH2uX7+uRYsWyd3dvaQOCwAAOAhDwliOgIAABQQEGDnkbQsPD9eJEyc0ffp0de3aVZLUrVs3DR8+XMuWLdMbb7xRYP/PP/9c6enp+vjjj+Xj4yNJ8vf31+TJk7V582aFhITk6vPZZ5+pQoUKCgwM1N69ew0/JgAA4Dgc/t2Uu3btkpeXlzp37mwtq1KlioKCgrR3715lZmbesn+HDh2sQUyS2rZtq7p162rnzp252sfFxek///mPxo4dK2dnZ+MOBAAAOCSHD2O//PKLmjRpIicn20P19/dXenq64uLi8u2bmJioK1eu5Hlvmb+/v2JiYnKVf/jhhwoMDFT79u0LvcakpCRFR0db/8TGxha6LwAAKN0MvUx5N7p8+bJat26dq9zb21uSdOnSJfn6+ubZ99KlSzZt/9o/JSVFmZmZ1mes7d+/XwcPHtSyZcuKtMb169dr+fLlReoDAAAcQ6kKY2azWVlZWYVq6+rqKpPJpIyMjDwfSJtTlt+z0f5cl9dN+n/u7+rqqqysLH344Yfq27evGjRoUKg15ggJCVHHjh2tn2NjYzVjxowijQEAAEqnUhXGfv75Z02cOLFQbVesWKH69evLzc0tz/vCcsrc3NzyHSOnLq8A+Nf+q1evVnJyskaNGlWo9f1ZtWrV8n2VFAAAcGylKozVq1dPr7zySqHa5lxa9PLysl5u/LOCLkH+dYz8+nt6esrV1VVpaWn67LPP1K9fP127dk3Xrl2TJN24cUMWi0Xnz59X+fLlVbVq1UKtHQAAlB2lKox5e3urV69eRerTpEkTHTt2TGaz2eYm/pMnT6p8+fIFPmesevXqqlKliqKjo3PVnTx5Uo0bN5b0x0Nlb9y4oS+//FJffvllrrZDhgzRgw8+qH/+859FWjsAAHB8pSqMFUeXLl0UHh6u3bt3W58zdvXqVe3cuVMdOnSwuZ/s3LlzkqTatWvb9N+yZYsSEhKsj7c4fPiw4uLiNHjwYElS1apVNXPmzFxzr1mzRhEREXrzzTcLPAMHAADKLocPY127dtWaNWv0zjvv6OzZs9Yn8JvN5lz3dz3//POS/rj/K8eIESMUHh6uSZMmaeDAgdYzYI0aNbKepStfvrw6deqUa+49e/YoKioqzzoAAACpDIQxZ2dnvffee1qwYIG++uorZWRkqFmzZnrllVdUr169W/b38fHRvHnzNH/+fC1evNj6bsqxY8fm+S1NAACAojBZct7mjbtGdHS0QkNDtWTJkkK/zBwAAJRODv8EfgAAgLsZYQwAAMCOCGMAAAB2RBgDAACwI8IYAACAHRHGAAAA7IgwBgAAYEeEMQAAADsijAEAANgRYQwAAMCOCGMAAAB2RBgDAACwI8IYAACAHRHGAAAA7IgwBgAAYEeEMQAAADsijAEAANgRYQwAAMCOCGMAAAB2RBgDAACwI8IYAACAHRHGAAAA7IgwBgAAYEeEMQAAADsijAEAANgRYQwAAMCOCGMAAAB2RBgDAACwI8IYAACAHRHGAAAA7IgwBgAAYEeEMQAAADsijAEAANgRYQwAAMCOCGMAAAB2RBgDAACwI8IYAACAHRHGAAAA7IgwBgAAYEeEMQAAADsijAEAANiRi70XgNwyMjIkSbGxsXZeCQAAKKr69eurfPnyhW5PGLsLXbhwQZI0Y8YMO68EAAAU1ZIlS+Tn51fo9iaLxWIpwfWgGK5evaoDBw6oZs2acnV1LfY4sbGxmjFjhl577TXVr1/fwBXir9jrO4e9vnPY6zuHvb5z7sRec2bMAVSpUkWPPPKIYePVr1+/SAkdxcde3zns9Z3DXt857PWdczftNTfwAwAA2BFhDAAAwI4IYw7M29tbI0eOlLe3t72X4vDY6zuHvb5z2Os7h72+c+7GveYGfgAAADvizBgAAIAdEcYAAADsiDAGAABgR4QxAAAAO+Khrw4oMzNTS5cu1bZt25SamipfX1+NHj1a7dq1s/fS7ionT57Uli1bdOTIEV24cEGenp5q0aKFRo8erbp169q0PXv2rObPn6/jx4/LxcVF7du317hx41SlShWbdmazWStXrtTatWt1+fJl1alTRyNGjFD37t1zzV/YMR3VZ599po8//lgNGzbUp59+alN3/PhxLVq0SL/88os8PDwUFBSk0NBQVahQwaZdUX7WCzumo4iOjtayZct0/PhxZWZmqlatWurTp48GDhxobcM+3764uDgtXbpUx48fV0pKinx8fNS9e3cNHTrU5gns7HXRXL9+XStXrlRkZKROnjyp1NRUvfLKK+rVq1eutvb8/VyUMQvCtykd0FtvvaXw8HANGjRIderU0ebNmxUVFaW5c+eqVatW9l7eXeP111/X8ePHFRQUJF9fX126dEnffPONbty4oYULF6pRo0aSpIsXL+qpp55SxYoVNWDAAN24cUMrV66Uj4+PFi9erHLlylnHXLx4sf7973+rT58+atasmfbu3av9+/frzTff1EMPPWRtV5QxHdHFixc1YsQImUwm1ahRwyaMxcTEaMyYMapfv75CQkJ08eJFrVq1SoGBgZo9e7bNOIX9WS/KmI7gwIEDeuWVV9SkSRN169ZN7u7uOnfunCwWi8aMGSOJfTZCQkKC/v73v6tixYoKCQmRp6enIiIitHnzZnXs2FHvvPOOJPa6OM6fP68hQ4bIx8dHtWrV0pEjR/IMY/b+/VzYMW/JAocSERFh6dSpk+WLL76wlqWnp1uGDh1qefbZZ+24srvPsWPHLJmZmTZlv/32m+Whhx6yTJ8+3Vo2Z84cS/fu3S0XLlywlh08eNDSqVMny7p166xlFy9etAQFBVn+93//11pmNpstY8eOtTz22GOWmzdvFnlMR/Xmm29aJk6caBk/frzliSeesKl78cUXLf369bOkpaVZyzZs2GDp1KmT5ccff7SWFeVnvbBjOoK0tDRL3759La+++qolOzs733bs8+377LPPLJ06dbKcPn3apnzGjBmWTp06WVJSUiwWC3tdHBkZGZakpCSLxWKxnDx50tKpUydLWFhYrnb2/P1clDFvhXvGHMyuXbvk7OyskJAQa5mbm5uCg4MVERGhhIQEO67u7tKyZctcZ6Dq1q2rBg0aKDY21lq2a9cudejQQT4+Ptaytm3bqm7dutq5c6e1bO/evbp586b69+9vLTOZTOrXr58SExMVERFR5DEd0dGjR7Vr1y6NHz8+V921a9d06NAhPfLII/Lw8LCW9+jRQ+7u7jZ7U9if9aKM6Qi2b9+uy5cvKzQ0VE5OTrpx44bMZrNNG/bZGNeuXZMkVa1a1abc29tbTk5OcnFxYa+LydXVtVAPZbXn7+eijHkrhDEHExMTozp16tj8AypJ/v7+kqRTp07ZY1mlhsVi0ZUrV1S5cmVJUmJioq5cuZLny2T9/f0VExNj/RwTEyN3d3fVr18/V7uc+qKO6Wiys7M1d+5cBQcHy9fXN1f96dOnlZ2dnWtvypUrpyZNmuTa78L8rBdlTEdw6NAheXh4KCkpSY8//rh69OihXr16ac6cOcrIyJDEPhslMDBQkvTuu+8qJiZGCQkJ2rFjh9atW6cBAwbI3d2dvS5B9v79XNgxC4Mw5mAuXbqU539N5JQlJSXd6SWVKt9++60SExPVrVs3SX/sp6R89zQlJUWZmZnWtlWrVpXJZMrVTvrv3hdlTEezbt06JSQkaPTo0XnW32pv/vzzW9if9aKM6Qji4+OVnZ2tV199Vffff7/efvtt9e7dW+vWrdOsWbMksc9GeeCBB/TUU0/p0KFDeuqppzRo0CC99dZbeuyxx6xnftnrkmPv38+FHbMw+Dalg8nIyMjz5m9XV1drPfIWGxurDz74QC1atFDPnj0l/Xe/brWnrq6uhd77oozpSJKTk/XJJ5/oiSeeyPcbo7famz+HVKP229GC740bN5Senq6+fftq4sSJkqQuXbooKytL69ev16hRo9hnA9WsWVOtW7dWly5d5Onpqf379+vzzz+Xl5eXBgwYwF6XIHv/fjby37eEMQfj5uamrKysXOU5/3C6ubnd6SWVCpcuXdKUKVPk4eGht99+W87OzpL+u1+F2dPC7n1RxnQkH3/8sSpVqqQBAwbk2+ZWe/PngGrUfjta6M053r9+k6t79+5av369IiIirI9cYJ9vz44dOzR79mz9+9//1j333CPpj+BrsVi0ePFide/enZ/pEmTv389G/vuWy5QOxtvb23qa9c9yyqpVq3anl3TXS0tL08svv6y0tDS9//77NnuUc7o5vz319PS0/uLz9vbW5cuXZfnL02L+uvdFGdNRxMXFacOGDRo4cKCSkpJ0/vx5nT9/XpmZmbp586bOnz+vlJSUW+7NX/+/KczPelHGdAQ5x+vl5WVTnnOTeWpqKvtskG+++UZNmjSxBrEcHTt2VHp6umJiYtjrEmTv38+FHbMwCGMOpnHjxoqPj7d+yydHZGSktR7/lZGRoalTpyouLk6zZs1SgwYNbOqrV6+uKlWqKDo6OlffkydP2uxn48aNlZ6ebvNNTCn33hdlTEeRlJQks9msuXPnasiQIdY/kZGRiouL05AhQ7R8+XI1bNhQzs7OufYmKytLMTExufa7MD/rRRnTEeTceJyYmGhTnnP/SpUqVdhng1y5ciXXN1Ul6ebNm5L++MIKe11y7P37ubBjFgZhzMF07dpV2dnZWr9+vbUsMzNTYWFhat68uc1Xdcu67OxsTZs2TREREXrrrbd077335tmuS5cu2rdvn81jQQ4fPqy4uDgFBQVZyx588EG5uLjom2++sZZZLBatW7dO1atXtxm/sGM6ioYNG2rmzJm5/jRs2FA+Pj6aOXOmgoODVbFiRbVt21bbtm3T9evXrf23bt2qGzdu2OxNYX/WizKmI8g5nk2bNtmUb9q0Sc7OzgoMDGSfDVK3bl3FxMQoLi7OpnzHjh1ycnKSr68ve13C7Pn7uShj3gr3jDmY5s2bKygoSB999JGuXr2q2rVra8uWLbpw4YKmTJli7+XdVf7v//5P33//vTp06KDU1FRt27bNpv6RRx6RJI0YMULh4eGaNGmSBg4cqBs3bujLL79Uo0aNbJ4Gfc8992jQoEH68ssvdfPmTfn7+2vPnj06duyYXn/9det9aEUZ01FUqVJFnTp1ylX+n//8R5Js6kaPHq2xY8dq/PjxNk8Wb9eunR544AFru6L8rBd2TEfQtGlT9e7dW2FhYcrOzlZAQICOHj2qnTt3asSIEdZLJ+zz7Rs6dKh+/PFHjRs3To899pg8PT21b98+/fjjj3r00UfZ69v01VdfKS0tzXrZ7/vvv9fFixclSQMGDFDFihXt+vu5KGPeCq9DckAZGRnWd5ulpaWpUaNGGj16tO6//357L+2uMmHCBB09ejTf+t27d1v/fubMmVzvKRs7dmyu+3LMZrO++OILrV+/XpcuXVKdOnX0+OOPW4PdnxV2TEc2YcIEJScn53o35bFjx6zv3KtQoYKCgoL0zDPP5HrnXlF+1gs7piO4efOmVqxYoc2bNyspKUk+Pj7q37+/Bg8ebNOOfb59kZGRWrZsmWJiYpSSkqKaNWuqZ8+eGjZsmFxc/nu+g70uusGDB+vChQt51q1atUo1a9aUZN/fz0UZsyCEMQAAADvinjEAAAA7IowBAADYEWEMAADAjghjAAAAdkQYAwAAsCPCGAAAgB0RxgAAAOyIMAYAAGBHhDEAAAA7IowBQCnQuXNnmz8ZGRnWus2bN6tz587avHmzHVf4X+vWrbNZ6z//+U97Lwm4q/GicAB2c/78eQ0ZMqTANjVq1NDq1avv0IrubjVq1FDPnj0lqUgvIS6OAwcO6MUXX1S7du00Z86cAttOnz5d27dv1+uvv66HH35Yfn5+GjlypNLS0rRmzZoSXSfgCAhjAOyudu3aevjhh/Osq1ix4h1ezd2rRo0aGjVq1B2Zq23btvLx8dHhw4eVkJAgHx+fPNulpaVpz549qlixojp37ixJatasmZo1a6bz588TxoBCIIwBsLvatWvfsZCBwnFyclKvXr20fPlybdmyRU8++WSe7bZv366MjAz17t1bbm5ud3iVgGPgnjEApUrnzp01YcIEXb58WTNnzlSfPn3UvXt3Pfvsszpy5Eiefa5fv65PPvlETzzxhLp3767evXvrhRde0LFjx3K1nTBhgvWerCVLlmjo0KEKCgrSJ598Ym2za9cuhYaGqnv37urbt6/ee+89paamavDgwRo8eLC13dtvv63OnTsrMjIyz3UtXbpUnTt31vbt229zV/J28eJFPfnkk+revbvCw8Ot5VeuXNGHH36oYcOG6aGHHlKfPn302muv6fTp0zb9e/fuLZPJpM2bN8tiseQ5R1hYmCQpODi4RI4BKAsIYwBKnbS0NI0dO1Znz57VI488os6dOys6OlovvvhirkCRkpKiMWPGaPny5apUqZL69u2rzp0765dfftHEiRO1Z8+ePOd4/fXXtWXLFgUGBmrgwIGqWbOmJGnTpk16/fXXFR8frx49eqhnz56KiIjQ5MmTdfPmTZsxQkJCrH3+Kjs7W2FhYapcubL18p6Rzp49q+eee04XL17U7Nmz1bVrV0nSuXPnNHr0aP3nP/9RrVq19Nhjj+lvf/ubDhw4oDFjxtgExxo1aui+++7T77//nmfQPX36tKKiotSkSRM1bdrU8GMAygouUwKwu3PnztmcefqzFi1a6IEHHrApO3XqlPr166dJkybJyemP/6Zs06aN3nvvPX399dd68cUXrW3/9a9/6cyZM3r55Zf16KOPWsuvXLmi0NBQzZ49W/fff3+uS2yXLl3SsmXL5OnpaS1LTU3VvHnz5O7uro8++kh169aVJIWGhurFF19UdHS0atSoYW3funVrNWjQQDt27NC4cePk7u5urTtw4IASExM1aNAgubq6FnXLChQREaEpU6bIxcVFH374oRo3bmytmzlzpi5fvqz3339f999/v7X8iSeeUGhoqN577z0tX77cWh4cHKxDhw4pLCxMbdq0sZmHs2KAMTgzBsDuzp07p+XLl+f558cff8zV3t3dXc8++6w1iElSz5495ezsrKioKGvZ1atXtXPnTrVp08YmiElS1apVNWzYMF29elWHDx/ONcff//53myAmSXv37tWNGzfUu3dvaxCTJBcXF40ePTrPYwsJCdH169e1Y8cOm/KNGzdKkvr06ZPfthTL/v379fzzz6tSpUpasGCBTRD75ZdfdOLECfXo0cMmiElS3bp19eijj+r06dM2Zxc7deqkypUra9euXbp27Zq1/ObNm9q2bZtcXV3z/fIFgMLhzBgAu7v//vv1/vvvF7p9nTp1VKFCBZsyFxcXeXl5KS0tzVoWFRWl7OxsZWVl5XnmLT4+XpIUGxurDh062NT5+/vnav/rr79Kklq1apWrrnnz5nk+bqJHjx5avHixNm7caA2Ely9f1r59+3TvvfeqQYMGtzjawtu5c6cOHjwoX19fzZ49W1WrVrWpz7kEeeXKlTz347fffrP+b6NGjSTJGrbWrFmj7du3q2/fvpKk77//XlevXlX37t1VqVIlw44BKIsIYwBKHQ8PjzzLnZ2dZTabrZ9TUlIkScePH9fx48fzHS89PT1XmZeXV66ynDNDfw050h/fPqxcuXKu8kqVKikoKEhbtmzR6dOn1ahRI23evFnZ2dmGnxWLiIhQdna2WrVqlecac/Zj//792r9/f77j3Lhxw+ZzcHCw1qxZo7CwMGsY4xIlYBzCGACHlRPahgwZorFjxxapr8lkyne8K1eu5Kozm81KTk5W9erVc9X17dtXW7Zs0YYNGzRx4kRt2rRJHh4eCgoKKtKabuXpp5/W3r17tWbNGjk7O+c65pz1T5w4UQMGDCj0uL6+vmrWrJlOnjypM2fOqFKlSjpw4IBq1qyZ6z4yAEXHPWMAHFazZs1kMpkUERFhyHi+vr6SlOdZtpMnTyo7OzvPfi1atJCvr6++/fZbHThwQPHx8Xr44YdVvnx5Q9aVw9XVVTNnzlT79u21atUqzZ8/36Y+59JrcfYj5wzYpk2btHXrVmVnZ1sffQHg9hDGADgsb29vBQUF6cSJE/ryyy/zfFZWZGRknpcp8/Lggw/K3d1dmzZt0rlz56zlN2/e1NKlSwvsGxISopSUFM2aNUuScn2hwCiurq6aMWOGOnTooNWrV+vDDz+01jVv3lzNmzfXjh07cn2hQPrj7N7Ro0fzHLd79+4qX768tm3bprCwMDk5OVlfzQTg9nCZEoDdFfRoC0l6/PHHi/1098mTJysuLk4LFy7U1q1b1aJFC1WsWFGJiYmKiopSfHy8vvnmm0KdpapUqZLGjRun2bNnKzQ0VN26dZOHh4d++OEHubq6qlq1avmeKXrkkUe0aNEiJSUlyc/Pr0Sfy1WuXDm9/fbbeuONN/Sf//xHFotFEyZMkCS98cYbmjRpkt566y2tWbNGTZo0kZubmy5evKgTJ04oOTk5z4fQenh4qEuXLtq6dauuXr2qBx54IN9XJAEoGsIYALvLebRFfgYNGlTsMObp6akFCxbo66+/1nfffaft27fLbDbLy8tLjRs31pNPPpnnjff56dOnjypVqqQVK1Zoy5Yt8vDwUMeOHfXss89q0KBBql27dp79PDw81KlTJ23btq3Ezor9WU4ge/PNN7VmzRpZLBZNnDhRtWrV0tKlS7Vq1Srt2bNHmzdvlpOTk7y9vdW6dWvrw2HzEhwcrK1bt0r64+n8AIxhsuT3jgsAQKHFx8dr+PDhCgoK0ltvvZVnmyeffFIXLlzQ119/ne83QvPTuXNnBQQEaN68eUYs9444f/68hgwZop49e+rVV1+193KAuxZnxgCgCFJTU+Xm5mbz1PyMjAzrzfKdOnXKs98PP/ygM2fOqE+fPkUOYjmOHj1qfXXSt99+e9e+mHvdunWaM2eOvZcBlBqEMQAogqNHj+rdd99Vu3btdM899yg5OVk//fSTLly4oDZt2qhbt2427deuXauLFy9q48aNcnV11eOPP16seUeOHGnzOa8HzN4t/Pz8bNbbpEkT+y0GKAW4TAkARRAXF6elS5fqxIkTunr1qiSpdu3a6tatm4YOHZrrbNXgwYOVmJiounXr6tlnn831pH8AIIwBAADYEc8ZAwAAsCPCGAAAgB0RxgAAAOyIMAYAAGBHhDEAAAA7IowBAADYEWEMAADAjghjAAAAdvT/OPERQYEr/J4AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1896,20 +905,19 @@
     "\n",
     "ax.plot(energy, flux, label = \"Best fit\")\n",
     "\n",
-    "plt.ylabel(\"dN/dE [$\\mathrm{ph \\ cm^{-2} \\ s^{-1} \\ keV^{-1}}$]\", fontsize=14)\n",
+    "plt.ylabel(r\"dN/dE [$\\mathrm{ph \\ cm^{-2} \\ s^{-1} \\ keV^{-1}}$]\", fontsize=14)\n",
     "plt.xlabel(\"Energy [keV]\", fontsize=14)\n",
     "ax.legend()\n",
-    "plt.savefig(\"best_fit_model.pdf\")\n",
-    "plt.show()\n",
-    "plt.close()"
+    "\n",
+    "plt.savefig(\"best_fit_model.pdf\")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:COSIPY_new]",
+   "display_name": "cosipy-312",
    "language": "python",
-   "name": "conda-env-COSIPY_new-py"
+   "name": "cosipy-312"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1921,7 +929,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,
diff --git a/tests/background_estimation/test_line_background_estimation.py b/tests/background_estimation/test_line_background_estimation.py
index 56cb9020..a81ec214 100644
--- a/tests/background_estimation/test_line_background_estimation.py
+++ b/tests/background_estimation/test_line_background_estimation.py
@@ -27,7 +27,7 @@ def bkg_model(x, a, b):
     # set mask
     instance.set_mask((0.0, 1000.0) * u.keV, (3000.0, 5000.0) * u.keV)
 
-    # run fitting w/ par limint
+    # run fitting w/ par limit
     m = instance.fit_energy_spectrum(param_limits = {1: (None, 100)})
     m = instance.fit_energy_spectrum(param_limits = {1: (-100, None)})
     m = instance.fit_energy_spectrum(param_limits = {1: (-100, 100)})
@@ -35,8 +35,9 @@ def bkg_model(x, a, b):
     # run fitting w/ par fixed
     m = instance.fit_energy_spectrum(fixed_params = {1: 0})
 
-    # run fitting w/ stepsize
-    m = instance.fit_energy_spectrum(stepsize_params = {1: 0.1})
+    # run fitting w/ stepsize; set limit to prevent overflow
+    m = instance.fit_energy_spectrum(stepsize_params = {1: 0.1},
+                                     param_limits={1: (None, 100)})
 
     # run fitting from scratch
     instance.set_bkg_energy_spectrum_model(bkg_model, [1.0, -3.0])
diff --git a/tests/threeml/test_spectral_fitting.py b/tests/threeml/test_spectral_fitting.py
index bb4cc9ee..be455f8a 100644
--- a/tests/threeml/test_spectral_fitting.py
+++ b/tests/threeml/test_spectral_fitting.py
@@ -65,7 +65,8 @@ def test_point_source_spectral_fit():
 
     like = JointLikelihood(model, plugins, verbose = False)
 
-    like.fit(compute_covariance = False) # avoid sampling-related threeML crashes
+    # avoid output- and sampling-related threeML crashes
+    like.fit(quiet=True, compute_covariance = False)
 
     sp = source.spectrum.main.Band