From 6342bba911c9dae645e962048b52e0f2abfff92c Mon Sep 17 00:00:00 2001
From: ldigesu <150834542+ldigesu@users.noreply.github.com>
Date: Thu, 29 Jan 2026 15:02:36 +0100
Subject: [PATCH] This is to reopen the PR to merge the tutorial notebook that
 creates flux time series of a GRB by performing iterative fits with 3ml. I
 include also a python script and yaml file to perform data downloads and
 preparation.

---
 .../light_curves/get_lctutorial_data.py       |   90 +
 .../light_curves/prep_lctutorial_data.yaml    |   37 +
 .../light_curves/speclc_grbdc3.ipynb          | 2302 +++++++++++++++++
 3 files changed, 2429 insertions(+)
 create mode 100644 docs/tutorials/light_curves/get_lctutorial_data.py
 create mode 100644 docs/tutorials/light_curves/prep_lctutorial_data.yaml
 create mode 100644 docs/tutorials/light_curves/speclc_grbdc3.ipynb

diff --git a/docs/tutorials/light_curves/get_lctutorial_data.py b/docs/tutorials/light_curves/get_lctutorial_data.py
new file mode 100644
index 00000000..8027eabb
--- /dev/null
+++ b/docs/tutorials/light_curves/get_lctutorial_data.py
@@ -0,0 +1,90 @@
+#
+import os
+import subprocess
+from pathlib import Path
+from cosipy.util import fetch_wasabi_file
+from cosipy import BinnedData
+from cosipy.pipeline.task.task import cosi_bindata
+#
+indir=str("./")
+#
+
+def get_data (wasabipath,outpath,unzip):
+    if not os.path.exists(outpath) and not os.path.exists(outpath[:-3]):
+        print ("Downloading")
+        print (wasabipath)
+        fetch_wasabi_file(wasabipath,output=outpath)
+        if unzip==True:
+            if outpath[-2:] == 'gz':
+                print("Gunzipping")
+                subprocess.run(["gunzip", outpath])
+            elif outpath[-3:] == 'zip':
+                print("Unzipping")
+                subprocess.run(["unzip", outpath])
+    return()
+
+
+#
+#Get Response from the develop folder in wasabi (new version)
+#
+filename='ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5'
+wasabipath=os.path.join('COSI-SMEX/develop/Data/Responses',filename)
+outpath=os.path.join(indir,filename)
+get_data(wasabipath,outpath,False)
+
+#
+#Get Orientation files
+#
+filename="DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.ori"
+wasabipath=os.path.join('COSI-SMEX/DC3/Data/Orientation',filename)
+outpath=os.path.join(indir,filename)
+get_data(wasabipath,outpath,False)
+
+
+
+#
+#Get Galactic background
+#
+filename='GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits.gz'
+wasabipath=os.path.join('COSI-SMEX/DC3/Data/Backgrounds/Ge',filename)
+outpath=os.path.join(indir,filename)
+get_data(wasabipath,outpath,True)
+
+
+#
+#Get GRB source data
+#
+wasabirootpath="COSI-SMEX/DC3/Data/Sources"
+#
+#GRB
+#
+filename="GRB_bn081207680_3months_unbinned_data_filtered_with_SAAcut.fits.gz"
+wasabipath=os.path.join(wasabirootpath,filename)
+outpath=os.path.join(indir,filename)
+get_data(wasabipath,outpath,True)
+#
+#Bin GRB data using the app cosi-bindata
+#
+args=['--config','prep_lctutorial_data.yaml', '--overwrite','--config_group','bindata_grb','--suffix','grbdc3']
+cosi_bindata (argv=args)
+#
+#Bin and cut in time the bk data
+#
+args=['--config','prep_lctutorial_data.yaml', '--overwrite','--config_group','bindata_bk','--suffix','galbk']
+cosi_bindata (argv=args)
+#
+#==================================
+#
+#Combine grb and galactic background, to have a dataset for the fit
+#
+grb=BinnedData("bin_grbdc3.yaml")
+#
+grb_bk=os.path.join (indir,"galbk_grbdc3")
+#
+grb.combine_unbinned_data(["GRB_bn081207680_3months_unbinned_data_filtered_with_SAAcut.fits","GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits"], output_name=grb_bk)
+subprocess.run(["gunzip", "galbk_grbdc3.fits.gz"])
+#
+#Bin the grb+bk file:
+#
+args=['--config','prep_lctutorial_data.yaml', '--overwrite','--config_group','bindata_grbbk','--suffix','galbk_grbdc3']
+cosi_bindata(argv=args)
\ No newline at end of file
diff --git a/docs/tutorials/light_curves/prep_lctutorial_data.yaml b/docs/tutorials/light_curves/prep_lctutorial_data.yaml
new file mode 100644
index 00000000..95dee614
--- /dev/null
+++ b/docs/tutorials/light_curves/prep_lctutorial_data.yaml
@@ -0,0 +1,37 @@
+bindata_grb:
+  response:
+    class: ExpectationInterfaceTBD
+    args:
+      - ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5
+  unbinned_data_file: GRB_bn081207680_3months_unbinned_data_filtered_with_SAAcut.fits
+  sc_file: DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.ori
+  dt: 1
+  tmin: 1836496300.0
+  tmax: 1836496388.1
+  coo_sys: "local"
+
+
+bindata_bk:
+  response:
+    class: ExpectationInterfaceTBD
+    args:
+      - ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5
+  unbinned_data_file: GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits
+  sc_file: DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.ori
+  dt: 1
+  tmin: 1836496200.0
+  tmax: 1836496488.1
+  coo_sys: "local" 
+
+bindata_grbbk:
+  response:
+    class: ExpectationInterfaceTBD
+    args:
+      - ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5
+  unbinned_data_file: galbk_grbdc3.fits
+  sc_file: DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.ori
+  dt: 1
+  tmin: 1836496300.0
+  tmax: 1836496388.1
+  coo_sys: "local"
+   
diff --git a/docs/tutorials/light_curves/speclc_grbdc3.ipynb b/docs/tutorials/light_curves/speclc_grbdc3.ipynb
new file mode 100644
index 00000000..76bdd1e5
--- /dev/null
+++ b/docs/tutorials/light_curves/speclc_grbdc3.ipynb
@@ -0,0 +1,2302 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "raw",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "{\n",
+    " \"cells\": [\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"74a86fb5-4e54-4e3f-b349-3e60fbdd0279\",\n",
+    "   \"metadata\": {\n",
+    "    \"tags\": []\n",
+    "   },\n",
+    "   \"source\": [\n",
+    "    \"# FLUX Light-Curve example (GRB)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"e7df3443-3ce1-43f3-90b5-1bceb7bc9af0\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"**We provide a python script (get_lctutorial_data.py) that downloads the files and prepares them for analysis. The script creates in the working directory the following files:**\\n\",\n",
+    "    \"- orientation file (DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.ori)       \\n\",\n",
+    "    \"- binned data (tsel_binned_data_local_grbdc3.hdf5, tsel_binned_data_local_galbk.hdf5,tsel_binned_data_galbk_grbdc3.hdf5)\\n\",\n",
+    "    \"- corresponding yaml file with binning info (bin_grbdc3.yaml, bin_galbk.yaml, bin_galbk_grbdc3.yaml)     \\n\",\n",
+    "    \"- detector response \\n\",\n",
+    "    \"(ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5)     \\n\",\n",
+    "    \"\\n\",\n",
+    "    \"**The binned data are simulations of GRB 081207680 and Galactic photon background produced for Data Challenge 3. In the data preparation, the background file is cut in time 100s before and after the GRB time window (t_start=1836496300.0, t_stop=1836496388.1, duration=88s) to ensure enough statistics.**\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"This notebook slices a COSI dataset into time bins, ensuring that a minimum signal-to-noise of 10 is reached in each bin. Perform a fit with 3ML in each of them. It finally examines the time series of flux and fitted spectral parameters. It saves: a txt file of the raw lightcurves (lc.dat); a plot at each iteration of the fit (fit_i.pdf); a txt file (spec_lc.dat) including the mid-point of each time bin with errors, the total counts, the fluxes with asymmetric errors, the fitted parameters with symmetric errors; two plots (raw_flux_counts_lc.pdf, specpars_bk_lc.pdf) of the final time series.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 108,\n",
+    "   \"id\": \"ce42ab82-3bbd-4729-8f84-a4e32eb3bb24\",\n",
+    "   \"metadata\": {\n",
+    "    \"scrolled\": true\n",
+    "   },\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/plain\": [\n",
+    "       \"'/Users/lauradigesu'\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 108,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"from cosipy import COSILike, BinnedData\\n\",\n",
+    "    \"from cosipy.spacecraftfile import SpacecraftFile\\n\",\n",
+    "    \"from cosipy.response.FullDetectorResponse import FullDetectorResponse\\n\",\n",
+    "    \"from cosipy.util import fetch_wasabi_file\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"from scoords import SpacecraftFrame\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"from astropy.time import Time, TimeDelta\\n\",\n",
+    "    \"import astropy.units as u\\n\",\n",
+    "    \"from astropy.coordinates import SkyCoord\\n\",\n",
+    "    \"from astropy.stats import poisson_conf_interval\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"import numpy as np\\n\",\n",
+    "    \"import matplotlib.pyplot as plt\\n\",\n",
+    "    \"%matplotlib inline\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"from threeML import *\\n\",\n",
+    "    \"from threeML import Band, PointSource, Model, JointLikelihood, DataList\\n\",\n",
+    "    \"from cosipy import Band_Eflux\\n\",\n",
+    "    \"from astromodels import Parameter\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"from pathlib import Path\\n\",\n",
+    "    \"from cosipy.pipeline.src.plotting import *\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"import os\\n\",\n",
+    "    \"import subprocess\\n\",\n",
+    "    \"home=os. path. expanduser(\\\"~\\\")\\n\",\n",
+    "    \"home\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"bb816c15\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## A function to create time slices with a minimum signal-to-noise.\\n\",\n",
+    "    \"In this ways the binning in time is coarser in the weak tails of the light-curve and finer around the peak. The function returns the edges of the time slices.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 109,\n",
+    "   \"id\": \"ffa74cdc-ac8f-4d44-b1d6-78e0380a54a5\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"def make_minsn_tslices(yaml_path, hdf5_path, min_sn):\\n\",\n",
+    "    \"    #   \\n\",\n",
+    "    \"    data = BinnedData(yaml_path)\\n\",\n",
+    "    \"    data.load_binned_data_from_hdf5(hdf5_path)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    tstart=np.min(data.binned_data.axes['Time'].edges.value)\\n\",\n",
+    "    \"    tstop=np.max(data.binned_data.axes['Time'].edges.value)\\n\",\n",
+    "    \"    max_slices=data.binned_data.axes['Time'].nbins\\n\",\n",
+    "    \"    step=(tstop-tstart)/max_slices\\n\",\n",
+    "    \"    tmins = np.array([], dtype=float)\\n\",\n",
+    "    \"    tmaxs = np.array([], dtype=float)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    data = BinnedData(yaml_path)\\n\",\n",
+    "    \"    data.load_binned_data_from_hdf5(hdf5_path)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    tmax=tstart\\n\",\n",
+    "    \"    for i in range (max_slices):\\n\",\n",
+    "    \"        #\\n\",\n",
+    "    \"        tmin=tmax\\n\",\n",
+    "    \"        tmax_i=tstart+(i+1)*step\\n\",\n",
+    "    \"        #\\n\",\n",
+    "    \"        sou_min = np.where(data.binned_data.axes['Time'].edges.value >= tmin)[0][0]\\n\",\n",
+    "    \"        sou_max_all = np.where(data.binned_data.axes['Time'].edges.value <= tmax_i)\\n\",\n",
+    "    \"        y=len(sou_max_all[0])-1\\n\",\n",
+    "    \"        sou_max=np.where(data.binned_data.axes['Time'].edges.value <= tmax_i)[0][y]\\n\",\n",
+    "    \"        data_sliced=data.binned_data.slice[{'Time':slice(sou_min,sou_max)}].project('Em')\\n\",\n",
+    "    \"        #\\n\",\n",
+    "    \"        #\\n\",\n",
+    "    \"        signal=np.sum(data_sliced.todense().contents)\\n\",\n",
+    "    \"        noise=np.sqrt(signal)\\n\",\n",
+    "    \"        #\\n\",\n",
+    "    \"        sn=signal/noise\\n\",\n",
+    "    \"        if (sn >= min_sn and tmax_i<tstop):\\n\",\n",
+    "    \"            tmins = np.append(tmins, tmin)\\n\",\n",
+    "    \"            tmax=tmax_i\\n\",\n",
+    "    \"            tmaxs = np.append(tmaxs, tmax)\\n\",\n",
+    "    \"        elif(tmax_i>=tstop):\\n\",\n",
+    "    \"            tmaxs[-1] = tmax_i\\n\",\n",
+    "    \"    return(tmins, tmaxs)\\n\",\n",
+    "    \" \"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"8d1c0168-9823-4eb7-930e-5dc61d6448ca\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Download and read in binned data\\n\",\n",
+    "    \"Run the script to obtain the data to run this tutorial, if you haven't yet. Products will be saved in the current directory.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 110,\n",
+    "   \"id\": \"86174b78-7578-4d57-907c-fcac585723e2\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"/opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/io/package_data.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\\n\",\n",
+    "      \"  import pkg_resources\\n\",\n",
+    "      \"\\u001B[32m13:15:34\\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m The naima package is not available. Models   \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=212652;file:///opt/anaconda3/envs/cosipy_lctutorial/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=842588;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#43\\u001B\\\\\\u001B[2m43\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mthat depend on it will not be available       \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m The GSL library or the pygsl wrapper cannot  \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=851196;file:///opt/anaconda3/envs/cosipy_lctutorial/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=295525;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#65\\u001B\\\\\\u001B[2m65\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mbe loaded. Models that depend on it will not  \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mbe available.                                 \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m The ebltable package is not available.      \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=937149;file:///opt/anaconda3/envs/cosipy_lctutorial/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=919678;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#33\\u001B\\\\\\u001B[2m33\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mModels that depend on it will not be         \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m                \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mavailable                                    \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m                \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m13:15:35\\u001B[0m\\u001B[32m \\u001B[0m\\u001B[36mINFO    \\u001B[0m \\u001B[1;37m Starting 3ML!                                 \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=831003;file:///opt/anaconda3/envs/cosipy_lctutorial/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=415186;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#39\\u001B\\\\\\u001B[2m39\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m WARNINGs here are \\u001B[0m\\u001B[1;31mNOT\\u001B[0m\\u001B[1;37m errors                  \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=387400;file:///opt/anaconda3/envs/cosipy_lctutorial/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=124292;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#40\\u001B\\\\\\u001B[2m40\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m but are inform you about optional packages    \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=430652;file:///opt/anaconda3/envs/cosipy_lctutorial/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=367016;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#41\\u001B\\\\\\u001B[2m41\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mthat can be installed                          \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m              \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m \\u001B[0m\\u001B[1;31m to disable these messages, turn off \\u001B[0m\\u001B[1;37m         \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=333774;file:///opt/anaconda3/envs/cosipy_lctutorial/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=848607;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#44\\u001B\\\\\\u001B[2m44\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;31mstart_warning in your config file\\u001B[0m\\u001B[1;37m              \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m              \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m The cthreeML package is not installed. You    \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=261032;file:///opt/anaconda3/envs/cosipy_lctutorial/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=477057;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#94\\u001B\\\\\\u001B[2m94\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mwill not be able to use plugins which require  \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m              \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mthe C/C++ interface \\u001B[0m\\u001B[1;37m(\\u001B[0m\\u001B[1;37mcurrently HAWC\\u001B[0m\\u001B[1;37m)\\u001B[0m\\u001B[1;37m           \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m              \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m Could not import plugin HAWCLike.py. Do you  \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=826525;file:///opt/anaconda3/envs/cosipy_lctutorial/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=843018;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#144\\u001B\\\\\\u001B[2m144\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mhave the relative instrument software         \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37minstalled and configured?                     \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m Could not import plugin FermiLATLike.py. Do  \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=536784;file:///opt/anaconda3/envs/cosipy_lctutorial/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=876045;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#144\\u001B\\\\\\u001B[2m144\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37myou have the relative instrument software     \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37minstalled and configured?                     \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\u001B[32m13:15:35\\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m ROOT minimizer not available            \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=201321;file:///opt/anaconda3/envs/cosipy_lctutorial/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=724952;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1345\\u001B\\\\\\u001B[2m1345\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m Multinest minimizer not available       \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=17519;file:///opt/anaconda3/envs/cosipy_lctutorial/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=911465;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1357\\u001B\\\\\\u001B[2m1357\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m PyGMO is not available                  \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=776494;file:///opt/anaconda3/envs/cosipy_lctutorial/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=869820;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\\u001B\\\\\\u001B[2m1369\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m No fermitools installed          \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=212;file:///opt/anaconda3/envs/cosipy_lctutorial/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=529021;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\\u001B\\\\\\u001B[2m44\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m Env. variable OMP_NUM_THREADS is not set.    \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=595241;file:///opt/anaconda3/envs/cosipy_lctutorial/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=543339;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#387\\u001B\\\\\\u001B[2m387\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mPlease set it to \\u001B[0m\\u001B[1;37m1\\u001B[0m\\u001B[1;37m for optimal performances in\\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37m3ML                                           \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m Env. variable MKL_NUM_THREADS is not set.    \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=765409;file:///opt/anaconda3/envs/cosipy_lctutorial/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=447226;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#387\\u001B\\\\\\u001B[2m387\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mPlease set it to \\u001B[0m\\u001B[1;37m1\\u001B[0m\\u001B[1;37m for optimal performances in\\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37m3ML                                           \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m        \\u001B[0m\\u001B[32m \\u001B[0m\\u001B[95mWARNING \\u001B[0m \\u001B[1;37m Env. variable NUMEXPR_NUM_THREADS is not set.\\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B]8;id=922819;file:///opt/anaconda3/envs/cosipy_lctutorial/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=980744;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/__init__.py#387\\u001B\\\\\\u001B[2m387\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37mPlease set it to \\u001B[0m\\u001B[1;37m1\\u001B[0m\\u001B[1;37m for optimal performances in\\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"\\u001B[32m         \\u001B[0m         \\u001B[1;37m3ML                                           \\u001B[0m\\u001B[1;37m \\u001B[0m\\u001B[2m               \\u001B[0m\\n\",\n",
+    "      \"2026-01-29 13:15:35 - INFO - configurator.py:40 - Using configuration file at prep_lctutorial_data.yaml\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - configurator.py:40 - Using configuration file at /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_grbdc3.yaml\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - UnBinnedData.py:685 - Making data selections...\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - UnBinnedData.py:702 - Saving file...\\n\",\n",
+    "      \"gunzip: /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/tsel_unbinned_data_grbdc3.fits already exists -- skipping\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - configurator.py:40 - Using configuration file at /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_grbdc3.yaml\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - BinnedData.py:67 - binning data...\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - BinnedData.py:82 - Note: time bins must be equally spaced between min and max time.\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - BinnedData.py:83 - Using time bin size [s]: 1.0011363625526428\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - BinnedData.py:188 - Time unit: s\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - BinnedData.py:188 - Em unit: keV\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - BinnedData.py:188 - Phi unit: deg\\n\",\n",
+    "      \"2026-01-29 13:15:46 - INFO - BinnedData.py:188 - PsiChi unit: None\\n\",\n",
+    "      \"2026-01-29 13:15:47 - INFO - configurator.py:40 - Using configuration file at prep_lctutorial_data.yaml\\n\",\n",
+    "      \"2026-01-29 13:15:58 - INFO - configurator.py:40 - Using configuration file at /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_galbk.yaml\\n\",\n",
+    "      \"2026-01-29 13:15:58 - INFO - UnBinnedData.py:685 - Making data selections...\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - UnBinnedData.py:702 - Saving file...\\n\",\n",
+    "      \"gunzip: /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/tsel_unbinned_data_galbk.fits already exists -- skipping\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - configurator.py:40 - Using configuration file at /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_galbk.yaml\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - BinnedData.py:67 - binning data...\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - BinnedData.py:82 - Note: time bins must be equally spaced between min and max time.\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - BinnedData.py:83 - Using time bin size [s]: 1.0003472218910854\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - BinnedData.py:188 - Time unit: s\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - BinnedData.py:188 - Em unit: keV\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - BinnedData.py:188 - Phi unit: deg\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - BinnedData.py:188 - PsiChi unit: None\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - configurator.py:40 - Using configuration file at bin_grbdc3.yaml\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - UnBinnedData.py:802 - adding GRB_bn081207680_3months_unbinned_data_filtered_with_SAAcut.fits...\\n\",\n",
+    "      \"2026-01-29 13:15:59 - INFO - UnBinnedData.py:802 - adding GalTotal_SA100_F98_3months_unbinned_data_filtered_with_SAAcut.fits...\\n\",\n",
+    "      \"gunzip: galbk_grbdc3.fits already exists -- skipping\\n\",\n",
+    "      \"2026-01-29 13:16:18 - INFO - configurator.py:40 - Using configuration file at prep_lctutorial_data.yaml\\n\",\n",
+    "      \"2026-01-29 13:16:29 - INFO - configurator.py:40 - Using configuration file at /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_galbk_grbdc3.yaml\\n\",\n",
+    "      \"2026-01-29 13:16:29 - INFO - UnBinnedData.py:685 - Making data selections...\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - UnBinnedData.py:702 - Saving file...\\n\",\n",
+    "      \"gunzip: /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/tsel_unbinned_data_galbk_grbdc3.fits already exists -- skipping\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - configurator.py:40 - Using configuration file at /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_galbk_grbdc3.yaml\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - BinnedData.py:67 - binning data...\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - BinnedData.py:82 - Note: time bins must be equally spaced between min and max time.\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - BinnedData.py:83 - Using time bin size [s]: 1.0011363625526428\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - BinnedData.py:188 - Time unit: s\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - BinnedData.py:188 - Em unit: keV\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - BinnedData.py:188 - Phi unit: deg\\n\",\n",
+    "      \"2026-01-29 13:16:31 - INFO - BinnedData.py:188 - PsiChi unit: None\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stdout\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"Applying time selection 1836496300.000000-1836496388.100000 to the unbinned data\\n\",\n",
+    "      \" Binning configuration file /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_grbdc3.yaml is ready\\n\",\n",
+    "      \" Binned data file /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/tsel_binned_data_local_grbdc3 is ready for analysis\\n\",\n",
+    "      \"Applying time selection 1836496200.000000-1836496488.100000 to the unbinned data\\n\",\n",
+    "      \" Binning configuration file /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_galbk.yaml is ready\\n\",\n",
+    "      \" Binned data file /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/tsel_binned_data_local_galbk is ready for analysis\\n\",\n",
+    "      \"Applying time selection 1836496300.000000-1836496388.100000 to the unbinned data\\n\",\n",
+    "      \" Binning configuration file /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/bin_galbk_grbdc3.yaml is ready\\n\",\n",
+    "      \" Binned data file /Users/lauradigesu/Dropbox/work/cosi/GitHub/cosipy_lightcurves/docs/tutorials/light_curves/tsel_binned_data_local_galbk_grbdc3 is ready for analysis\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/plain\": [\n",
+    "       \"0\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 110,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"os.system (\\\"python get_lctutorial_data.py\\\")\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"dc364649-56e4-4bb1-8403-74e90cf3ed05\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"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. Define a directory where you want to save the outputs. Defaul is current directory.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 111,\n",
+    "   \"id\": \"cdd53b2a-5176-42cf-bb2c-feb3387fc0a4\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"indir=str(\\\".\\\")\\n\",\n",
+    "    \"data_path = Path(indir)\\n\",\n",
+    "    \"odir= str(\\\".\\\")\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"f579870f-c854-450d-84e8-f1d5ef0753d1\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"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\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 112,\n",
+    "   \"id\": \"3b5faaa1-1874-4d43-a6ae-7e1b0aaabb26\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"grb = BinnedData(data_path / \\\"bin_grbdc3.yaml\\\")\\n\",\n",
+    "    \"grb_bkg = BinnedData(data_path / \\\"bin_galbk_grbdc3.yaml\\\")\\n\",\n",
+    "    \"bkg = BinnedData(data_path / \\\"bin_galbk.yaml\\\")\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"cf8b5ab1-7452-493e-b516-73fa72e455e5\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"Load binned .hdf5 files\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 113,\n",
+    "   \"id\": \"620159d2-f01a-453e-9e4c-075c99740086\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"grb.load_binned_data_from_hdf5(binned_data=data_path / \\\"tsel_binned_data_local_grbdc3.hdf5\\\")\\n\",\n",
+    "    \"grb_bkg.load_binned_data_from_hdf5(binned_data=data_path / \\\"tsel_binned_data_local_galbk_grbdc3.hdf5\\\")\\n\",\n",
+    "    \"bkg.load_binned_data_from_hdf5(binned_data=data_path / \\\"tsel_binned_data_local_galbk.hdf5\\\")\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"a6bdaee8-45d7-41df-9835-413c1e397c12\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"Define the path to the detector response\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 114,\n",
+    "   \"id\": \"acccab93-7f9c-4167-a8f9-eedcf74b8a05\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"dr = str(data_path / \\\"ResponseContinuum.o3.e100_10000.b10log.s10396905069491.m2284.filtered.nonsparse.binnedimaging.imagingresponse.h5\\\") # path to detector response\\n\",\n",
+    "    \"ori = SpacecraftFile.parse_from_file(data_path/\\\"DC3_final_530km_3_month_with_slew_1sbins_GalacticEarth_SAA.ori\\\")\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"0ce73e23\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"Define the model to be fitted and initialize the parameters of the fits to something reasonable. In this example, for better results, we keep the indices and the pivot energy frozen. Hence, the free parameters are the normalization and the background parameter.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 115,\n",
+    "   \"id\": \"926aaf1c\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>description: Band model from Band et al., 1993, parametrized with the peak energy</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>formula: $K \\\\begin{cases} \\\\left(\\\\frac{x}{piv}\\\\right)^{\\\\alpha} \\\\exp \\\\left(- \\\\frac{(2+\\\\alpha)~x}{x_{p}}\\\\right) & x \\\\leq (\\\\alpha-\\\\beta) \\\\frac{x_{p}} {(\\\\alpha+2)} \\\\\\\\ \\\\left(\\\\frac{x}{piv}\\\\right)^{\\\\beta} \\\\exp (\\\\beta-\\\\alpha) \\\\left[\\\\frac{(\\\\alpha-\\\\beta)~x_{p}}{piv~(2+\\\\alpha)}\\\\right]^{\\\\alpha-\\\\beta} &x> (\\\\alpha-\\\\beta) \\\\frac{x_{p}}{(\\\\alpha+2)} \\\\end{cases} $</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>parameters: \\n\",\n",
+    "       \"<ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>K: \\n\",\n",
+    "       \"<ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>value: 0.1</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>desc: Differential flux at the pivot energy</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>min_value: 1e-50</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>max_value: None</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>unit: keV-1 s-1 cm-2</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>is_normalization: True</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>delta: 1e-05</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>free: True</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>alpha: \\n\",\n",
+    "       \"<ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>value: -0.58</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>desc: low-energy photon index</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>min_value: -1.5</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>max_value: 3.0</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>unit: </li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>is_normalization: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>delta: 0.1</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>free: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>xp: \\n\",\n",
+    "       \"<ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>value: 298.09999999999997</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>desc: peak in the x * x * N (nuFnu if x is a energy)</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>min_value: 10.0</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>max_value: None</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>unit: keV</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>is_normalization: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>delta: 50.0</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>free: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>beta: \\n\",\n",
+    "       \"<ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>value: -2.09</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>desc: high-energy photon index</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>min_value: -15.0</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>max_value: -1.6</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>unit: </li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>is_normalization: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>delta: 0.2</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>free: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>piv: \\n\",\n",
+    "       \"<ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>value: 500.0</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>desc: pivot energy</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>min_value: None</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>max_value: None</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>unit: keV</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>is_normalization: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>delta: 10.0</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"<li>free: False</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</ul>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</li>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</ul>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"  * description: Band model from Band et al., 1993, parametrized with the peak energy\\n\",\n",
+    "       \"  * formula: $K \\\\begin{cases} \\\\left(\\\\frac{x}{piv}\\\\right)^{\\\\alpha} \\\\exp \\\\left(- \\\\frac{(2+\\\\alpha)~x}{x_{p}}\\\\right)\\n\",\n",
+    "       \"    * & x \\\\leq (\\\\alpha-\\\\beta) \\\\frac{x_{p}} {(\\\\alpha+2)} \\\\\\\\ \\\\left(\\\\frac{x}{piv}\\\\right)^{\\\\beta}\\n\",\n",
+    "       \"    * \\\\exp (\\\\beta-\\\\alpha) \\\\left[\\\\frac{(\\\\alpha-\\\\beta)~x_{p}}{piv~(2+\\\\alpha)}\\\\right]^{\\\\alpha-\\\\beta}\\n\",\n",
+    "       \"    * &x> (\\\\alpha-\\\\beta) \\\\frac{x_{p}}{(\\\\alpha+2)} \\\\end{cases} $\\n\",\n",
+    "       \"  * parameters:\\n\",\n",
+    "       \"    * K:\\n\",\n",
+    "       \"      * value: 0.1\\n\",\n",
+    "       \"      * desc: Differential flux at the pivot energy\\n\",\n",
+    "       \"      * min_value: 1.0e-50\\n\",\n",
+    "       \"      * max_value: null\\n\",\n",
+    "       \"      * unit: keV-1 s-1 cm-2\\n\",\n",
+    "       \"      * is_normalization: true\\n\",\n",
+    "       \"      * delta: 1.0e-05\\n\",\n",
+    "       \"      * free: true\\n\",\n",
+    "       \"    * alpha:\\n\",\n",
+    "       \"      * value: -0.58\\n\",\n",
+    "       \"      * desc: low-energy photon index\\n\",\n",
+    "       \"      * min_value: -1.5\\n\",\n",
+    "       \"      * max_value: 3.0\\n\",\n",
+    "       \"      * unit: ''\\n\",\n",
+    "       \"      * is_normalization: false\\n\",\n",
+    "       \"      * delta: 0.1\\n\",\n",
+    "       \"      * free: false\\n\",\n",
+    "       \"    * xp:\\n\",\n",
+    "       \"      * value: 298.09999999999997\\n\",\n",
+    "       \"      * desc: peak in the x * x * N (nuFnu if x is a energy)\\n\",\n",
+    "       \"      * min_value: 10.0\\n\",\n",
+    "       \"      * max_value: null\\n\",\n",
+    "       \"      * unit: keV\\n\",\n",
+    "       \"      * is_normalization: false\\n\",\n",
+    "       \"      * delta: 50.0\\n\",\n",
+    "       \"      * free: false\\n\",\n",
+    "       \"    * beta:\\n\",\n",
+    "       \"      * value: -2.09\\n\",\n",
+    "       \"      * desc: high-energy photon index\\n\",\n",
+    "       \"      * min_value: -15.0\\n\",\n",
+    "       \"      * max_value: -1.6\\n\",\n",
+    "       \"      * unit: ''\\n\",\n",
+    "       \"      * is_normalization: false\\n\",\n",
+    "       \"      * delta: 0.2\\n\",\n",
+    "       \"      * free: false\\n\",\n",
+    "       \"    * piv:\\n\",\n",
+    "       \"      * value: 500.0\\n\",\n",
+    "       \"      * desc: pivot energy\\n\",\n",
+    "       \"      * min_value: null\\n\",\n",
+    "       \"      * max_value: null\\n\",\n",
+    "       \"      * unit: keV\\n\",\n",
+    "       \"      * is_normalization: false\\n\",\n",
+    "       \"      * delta: 10.0\\n\",\n",
+    "       \"      * free: false\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 115,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"#Model to be fitted\\n\",\n",
+    "    \"# # Setting parameters to something reasonable helps the fitting to converge\\\\n\\\",\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"beta = -2.09\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"l = 177.42\\n\",\n",
+    "    \"b = -9.41\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"alpha = -0.58                                     \\n\",\n",
+    "    \"xp = 298.1 * u.keV\\n\",\n",
+    "    \"piv = 500. * u.keV\\n\",\n",
+    "    \"K = 0.1 / u.cm / u.cm / u.s / u.keV\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"spectrum = Band()\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"spectrum.beta.min_value = -15.0\\n\",\n",
+    "    \"spectrum.alpha.value = alpha\\n\",\n",
+    "    \"spectrum.alpha.free=False\\n\",\n",
+    "    \"spectrum.beta.value = beta\\n\",\n",
+    "    \"spectrum.beta.free=False\\n\",\n",
+    "    \"spectrum.xp.value = xp.value\\n\",\n",
+    "    \"spectrum.xp.free=False\\n\",\n",
+    "    \"spectrum.K.value = K.value\\n\",\n",
+    "    \"spectrum.piv.value = piv.value\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"spectrum.xp.unit = xp.unit\\n\",\n",
+    "    \"spectrum.K.unit = K.unit\\n\",\n",
+    "    \"spectrum.piv.unit = piv.unit\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"spectrum\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"692e1656\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"Move to the product directory.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 116,\n",
+    "   \"id\": \"66517a53\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": [\n",
+    "    \"os.chdir(odir)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"393f276e\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"Find time window of the GRB data and make a raw lightcurve to be plotted as a comparison.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 117,\n",
+    "   \"id\": \"6c5c6c84\",\n",
+    "   \"metadata\": {\n",
+    "    \"scrolled\": true\n",
+    "   },\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/plain\": [\n",
+    "       \"(<Axes: xlabel='Time [s]'>, <ErrorbarContainer object of 3 artists>)\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 117,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 640x480 with 1 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"grb_bkg.get_raw_lightcurve(binned_data=data_path / \\\"tsel_binned_data_local_grbdc3.hdf5\\\", output_name=\\\"lc\\\")\\n\",\n",
+    "    \"time, rate = np.loadtxt(\\\"lc.dat\\\", skiprows=1, unpack=True)\\n\",\n",
+    "    \"grb_bkg.binned_data.project('Time').plot()\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"1451322e-a68f-4700-a025-0b93baf49d74\",\n",
+    "   \"metadata\": {\n",
+    "    \"scrolled\": true\n",
+    "   },\n",
+    "   \"source\": [\n",
+    "    \"Now we use the make_minsn_tslices function to create N time slices with a minimum signal to noise of 10.\\n\",\n",
+    "    \"You can customize the requirement of a minimum signal to noise to create less or more bins.\\n\",\n",
+    "    \"We used them to slice the data in time. \"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 118,\n",
+    "   \"id\": \"dcbe7732-a780-488e-ade7-9cc052300dfb\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/plain\": [\n",
+    "       \"4\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"execution_count\": 118,\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"execute_result\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"min_sn=10\\n\",\n",
+    "    \"yaml_path=data_path / \\\"bin_galbk_grbdc3.yaml\\\"\\n\",\n",
+    "    \"hdf5_path=data_path / \\\"tsel_binned_data_local_galbk_grbdc3.hdf5\\\"\\n\",\n",
+    "    \"tmins,tmaxs=make_minsn_tslices(yaml_path,hdf5_path,min_sn)\\n\",\n",
+    "    \"len(tmins)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"0e4dfa67\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"Now we used them to slice the data in time. We perform N spectral fits to determine fluxes and spectral parameters as a function of time. We save all the time series in a text file.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 119,\n",
+    "   \"id\": \"c08e2fdc\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">13:16:43 </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[38;5;46m13:16:43\\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=970563;file:///opt/anaconda3/envs/cosipy_lctutorial/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=634904;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1046\\u001B\\\\\\u001B[2m1046\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">         </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">130</span></a>\\n\",\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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\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=828388;file:///opt/anaconda3/envs/cosipy_lctutorial/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=170268;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\u001B\\\\\\u001B[2m130\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\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\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">         </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: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\\\">50.480000000000004</span><span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\"> percent of samples have been thrown away because     </span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">analysis_results.py</span></a><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">:</span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">1739</span></a>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">they failed the constraints on the parameters. This results might not be </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">suitable for error propagation. Enlarge the boundaries until you loose   </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">less than </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\\\"> percent of the samples.                                      </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\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;37m50.480000000000004\\u001B[0m\\u001B[1;38;5;251m percent of samples have been thrown away because    \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B]8;id=738177;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\u001B\\\\\\u001B[2manalysis_results.py\\u001B[0m\\u001B]8;;\\u001B\\\\\\u001B[2m:\\u001B[0m\\u001B]8;id=828186;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\u001B\\\\\\u001B[2m1739\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mthey failed the constraints on the parameters. This results might not be\\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251msuitable for error propagation. Enlarge the boundaries until you loose  \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mless than \\u001B[0m\\u001B[1;37m1\\u001B[0m\\u001B[1;38;5;251m percent of the samples.                                     \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Best fit values:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[1;4;38;5;49mBest fit values:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
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+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
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+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>result</th>\\n\",\n",
+    "       \"      <th>unit</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>parameter</th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>source.spectrum.main.Band.K</th>\\n\",\n",
+    "       \"      <td>(2.88 -0.28 +0.31) x 10^-4</td>\\n\",\n",
+    "       \"      <td>1 / (keV s cm2)</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>background_cosi</th>\\n\",\n",
+    "       \"      <td>(0.0 +/- 3.0) x 10^-6</td>\\n\",\n",
+    "       \"      <td></td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"                                                 result             unit\\n\",\n",
+    "       \"parameter                                                               \\n\",\n",
+    "       \"source.spectrum.main.Band.K  (2.88 -0.28 +0.31) x 10^-4  1 / (keV s cm2)\\n\",\n",
+    "       \"background_cosi                   (0.0 +/- 3.0) x 10^-6                 \"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Correlation matrix:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mCorrelation matrix:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div><table id=\\\"table13486082128\\\">\\n\",\n",
+    "       \"<tr><td>1.00</td><td>0.00</td></tr>\\n\",\n",
+    "       \"<tr><td>0.00</td><td>1.00</td></tr>\\n\",\n",
+    "       \"</table></div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"1.00 0.00\\n\",\n",
+    "       \"0.00 1.00\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\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",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\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",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>-log(likelihood)</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>cosi</th>\\n\",\n",
+    "       \"      <td>698.369046</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>total</th>\\n\",\n",
+    "       \"      <td>698.369046</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"       -log(likelihood)\\n\",\n",
+    "       \"cosi         698.369046\\n\",\n",
+    "       \"total        698.369046\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Values of statistical measures:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mValues of statistical measures:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>statistical measures</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>AIC</th>\\n\",\n",
+    "       \"      <td>1394.738091</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>BIC</th>\\n\",\n",
+    "       \"      <td>1396.738091</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"     statistical measures\\n\",\n",
+    "       \"AIC           1394.738091\\n\",\n",
+    "       \"BIC           1396.738091\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"application/vnd.jupyter.widget-view+json\": {\n",
+    "       \"model_id\": \"5a2591c2c63842b09efdb6e1788a21f5\",\n",
+    "       \"version_major\": 2,\n",
+    "       \"version_minor\": 0\n",
+    "      },\n",
+    "      \"text/plain\": [\n",
+    "       \"processing MLE analyses:   0%|                                                            | 0/1 [00:00<?, ?it/…\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: divide by zero encountered in divide\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: invalid value encountered in divide\\n\",\n",
+    "      \"\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">13:16:44 </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[38;5;46m13:16:44\\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=264375;file:///opt/anaconda3/envs/cosipy_lctutorial/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=616600;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1046\\u001B\\\\\\u001B[2m1046\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">         </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">130</span></a>\\n\",\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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\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=193094;file:///opt/anaconda3/envs/cosipy_lctutorial/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=690148;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\u001B\\\\\\u001B[2m130\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\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\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">         </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: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\\\">27.060000000000002</span><span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\"> percent of samples have been thrown away because     </span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">analysis_results.py</span></a><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">:</span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">1739</span></a>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">they failed the constraints on the parameters. This results might not be </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">suitable for error propagation. Enlarge the boundaries until you loose   </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">less than </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\\\"> percent of the samples.                                      </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\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;37m27.060000000000002\\u001B[0m\\u001B[1;38;5;251m percent of samples have been thrown away because    \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B]8;id=415591;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\u001B\\\\\\u001B[2manalysis_results.py\\u001B[0m\\u001B]8;;\\u001B\\\\\\u001B[2m:\\u001B[0m\\u001B]8;id=665770;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\u001B\\\\\\u001B[2m1739\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mthey failed the constraints on the parameters. This results might not be\\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251msuitable for error propagation. Enlarge the boundaries until you loose  \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mless than \\u001B[0m\\u001B[1;37m1\\u001B[0m\\u001B[1;38;5;251m percent of the samples.                                     \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Best fit values:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[1;4;38;5;49mBest fit values:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>result</th>\\n\",\n",
+    "       \"      <th>unit</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>parameter</th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>source.spectrum.main.Band.K</th>\\n\",\n",
+    "       \"      <td>(6.1 -0.6 +0.7) x 10^-4</td>\\n\",\n",
+    "       \"      <td>1 / (keV s cm2)</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>background_cosi</th>\\n\",\n",
+    "       \"      <td>(3 +/- 5) x 10^-3</td>\\n\",\n",
+    "       \"      <td></td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"                                              result             unit\\n\",\n",
+    "       \"parameter                                                            \\n\",\n",
+    "       \"source.spectrum.main.Band.K  (6.1 -0.6 +0.7) x 10^-4  1 / (keV s cm2)\\n\",\n",
+    "       \"background_cosi                    (3 +/- 5) x 10^-3                 \"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Correlation matrix:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mCorrelation matrix:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div><table id=\\\"table13157939472\\\">\\n\",\n",
+    "       \"<tr><td>1.00</td><td>-0.04</td></tr>\\n\",\n",
+    "       \"<tr><td>-0.04</td><td>1.00</td></tr>\\n\",\n",
+    "       \"</table></div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \" 1.00 -0.04\\n\",\n",
+    "       \"-0.04  1.00\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\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",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\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",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>-log(likelihood)</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>cosi</th>\\n\",\n",
+    "       \"      <td>724.874993</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>total</th>\\n\",\n",
+    "       \"      <td>724.874993</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"       -log(likelihood)\\n\",\n",
+    "       \"cosi         724.874993\\n\",\n",
+    "       \"total        724.874993\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Values of statistical measures:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mValues of statistical measures:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>statistical measures</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>AIC</th>\\n\",\n",
+    "       \"      <td>1447.749987</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>BIC</th>\\n\",\n",
+    "       \"      <td>1449.749987</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"     statistical measures\\n\",\n",
+    "       \"AIC           1447.749987\\n\",\n",
+    "       \"BIC           1449.749987\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"application/vnd.jupyter.widget-view+json\": {\n",
+    "       \"model_id\": \"bac7eb1ce4f341a69c9831ed11edaf67\",\n",
+    "       \"version_major\": 2,\n",
+    "       \"version_minor\": 0\n",
+    "      },\n",
+    "      \"text/plain\": [\n",
+    "       \"processing MLE analyses:   0%|                                                            | 0/1 [00:00<?, ?it/…\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: divide by zero encountered in divide\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: invalid value encountered in divide\\n\",\n",
+    "      \"\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">13:16:45 </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[38;5;46m13:16:45\\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=197029;file:///opt/anaconda3/envs/cosipy_lctutorial/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=625995;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1046\\u001B\\\\\\u001B[2m1046\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">         </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">130</span></a>\\n\",\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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\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=787157;file:///opt/anaconda3/envs/cosipy_lctutorial/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=472856;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\u001B\\\\\\u001B[2m130\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\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\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">         </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: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\\\">50.68</span><span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\"> percent of samples have been thrown away because they failed the  </span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">analysis_results.py</span></a><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">:</span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">1739</span></a>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">constraints on the parameters. This results might not be suitable for    </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">error propagation. Enlarge the boundaries until you loose less than </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: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">percent of the samples.                                                  </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\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;37m50.68\\u001B[0m\\u001B[1;38;5;251m percent of samples have been thrown away because they failed the \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B]8;id=413578;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\u001B\\\\\\u001B[2manalysis_results.py\\u001B[0m\\u001B]8;;\\u001B\\\\\\u001B[2m:\\u001B[0m\\u001B]8;id=930821;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\u001B\\\\\\u001B[2m1739\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mconstraints on the parameters. This results might not be suitable for   \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251merror propagation. Enlarge the boundaries until you loose less than \\u001B[0m\\u001B[1;37m1\\u001B[0m\\u001B[1;38;5;251m   \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mpercent of the samples.                                                 \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Best fit values:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[1;4;38;5;49mBest fit values:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
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+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
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+    "       \"    }\\n\",\n",
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+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>result</th>\\n\",\n",
+    "       \"      <th>unit</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>parameter</th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>source.spectrum.main.Band.K</th>\\n\",\n",
+    "       \"      <td>(7.5 -0.7 +0.8) x 10^-4</td>\\n\",\n",
+    "       \"      <td>1 / (keV s cm2)</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>background_cosi</th>\\n\",\n",
+    "       \"      <td>(0 +/- 9) x 10^-5</td>\\n\",\n",
+    "       \"      <td></td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"                                              result             unit\\n\",\n",
+    "       \"parameter                                                            \\n\",\n",
+    "       \"source.spectrum.main.Band.K  (7.5 -0.7 +0.8) x 10^-4  1 / (keV s cm2)\\n\",\n",
+    "       \"background_cosi                    (0 +/- 9) x 10^-5                 \"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Correlation matrix:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mCorrelation matrix:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div><table id=\\\"table6205687504\\\">\\n\",\n",
+    "       \"<tr><td>1.00</td><td>-0.00</td></tr>\\n\",\n",
+    "       \"<tr><td>-0.00</td><td>1.00</td></tr>\\n\",\n",
+    "       \"</table></div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \" 1.00 -0.00\\n\",\n",
+    "       \"-0.00  1.00\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\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",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\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",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>-log(likelihood)</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>cosi</th>\\n\",\n",
+    "       \"      <td>745.166611</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>total</th>\\n\",\n",
+    "       \"      <td>745.166611</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"       -log(likelihood)\\n\",\n",
+    "       \"cosi         745.166611\\n\",\n",
+    "       \"total        745.166611\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Values of statistical measures:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mValues of statistical measures:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>statistical measures</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>AIC</th>\\n\",\n",
+    "       \"      <td>1488.333223</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>BIC</th>\\n\",\n",
+    "       \"      <td>1490.333223</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"     statistical measures\\n\",\n",
+    "       \"AIC           1488.333223\\n\",\n",
+    "       \"BIC           1490.333223\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"application/vnd.jupyter.widget-view+json\": {\n",
+    "       \"model_id\": \"cc14c6e34f8a4070940f7dff1e16bb7f\",\n",
+    "       \"version_major\": 2,\n",
+    "       \"version_minor\": 0\n",
+    "      },\n",
+    "      \"text/plain\": [\n",
+    "       \"processing MLE analyses:   0%|                                                            | 0/1 [00:00<?, ?it/…\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: divide by zero encountered in divide\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: invalid value encountered in divide\\n\",\n",
+    "      \"\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">13:16:46 </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[38;5;46m13:16:46\\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=214555;file:///opt/anaconda3/envs/cosipy_lctutorial/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=680043;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/classicMLE/joint_likelihood.py#1046\\u001B\\\\\\u001B[2m1046\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"Adding 1e-12 to each bin of the expectation to avoid log-likelihood = -inf.\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">13:16:47 </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:///opt/anaconda3/envs/cosipy_lctutorial/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:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">130</span></a>\\n\",\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\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[38;5;46m13:16:47\\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=144906;file:///opt/anaconda3/envs/cosipy_lctutorial/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=525607;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/plugin_prototype.py#130\\u001B\\\\\\u001B[2m130\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\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\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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\\\">         </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: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\\\">50.22</span><span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\"> percent of samples have been thrown away because they failed the  </span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">analysis_results.py</span></a><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">:</span><a href=\\\"file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\\" target=\\\"_blank\\\"><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">1739</span></a>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">constraints on the parameters. This results might not be suitable for    </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">error propagation. Enlarge the boundaries until you loose less than </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: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"<span style=\\\"color: #00ff00; text-decoration-color: #00ff00\\\">         </span>         <span style=\\\"color: #c6c6c6; text-decoration-color: #c6c6c6; font-weight: bold\\\">percent of the samples.                                                  </span><span style=\\\"color: #7f7f7f; text-decoration-color: #7f7f7f\\\">                        </span>\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\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;37m50.22\\u001B[0m\\u001B[1;38;5;251m percent of samples have been thrown away because they failed the \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B]8;id=503570;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py\\u001B\\\\\\u001B[2manalysis_results.py\\u001B[0m\\u001B]8;;\\u001B\\\\\\u001B[2m:\\u001B[0m\\u001B]8;id=175915;file:///opt/anaconda3/envs/cosipy_lctutorial/lib/python3.10/site-packages/threeML/analysis_results.py#1739\\u001B\\\\\\u001B[2m1739\\u001B[0m\\u001B]8;;\\u001B\\\\\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mconstraints on the parameters. This results might not be suitable for   \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251merror propagation. Enlarge the boundaries until you loose less than \\u001B[0m\\u001B[1;37m1\\u001B[0m\\u001B[1;38;5;251m   \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\",\n",
+    "       \"\\u001B[38;5;46m         \\u001B[0m         \\u001B[1;38;5;251mpercent of the samples.                                                 \\u001B[0m\\u001B[1;38;5;251m \\u001B[0m\\u001B[2m                        \\u001B[0m\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\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: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Best fit values:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\u001B[1;4;38;5;49mBest fit values:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
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+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
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+    "       \"    }\\n\",\n",
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+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>result</th>\\n\",\n",
+    "       \"      <th>unit</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>parameter</th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>source.spectrum.main.Band.K</th>\\n\",\n",
+    "       \"      <td>(2.43 -0.20 +0.22) x 10^-4</td>\\n\",\n",
+    "       \"      <td>1 / (keV s cm2)</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>background_cosi</th>\\n\",\n",
+    "       \"      <td>(0 +/- 6) x 10^-7</td>\\n\",\n",
+    "       \"      <td></td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"                                                 result             unit\\n\",\n",
+    "       \"parameter                                                               \\n\",\n",
+    "       \"source.spectrum.main.Band.K  (2.43 -0.20 +0.22) x 10^-4  1 / (keV s cm2)\\n\",\n",
+    "       \"background_cosi                       (0 +/- 6) x 10^-7                 \"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Correlation matrix:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mCorrelation matrix:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div><table id=\\\"table13157931072\\\">\\n\",\n",
+    "       \"<tr><td>1.00</td><td>0.00</td></tr>\\n\",\n",
+    "       \"<tr><td>0.00</td><td>1.00</td></tr>\\n\",\n",
+    "       \"</table></div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"1.00 0.00\\n\",\n",
+    "       \"0.00 1.00\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\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",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\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",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>-log(likelihood)</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>cosi</th>\\n\",\n",
+    "       \"      <td>937.470237</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>total</th>\\n\",\n",
+    "       \"      <td>937.470237</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"       -log(likelihood)\\n\",\n",
+    "       \"cosi         937.470237\\n\",\n",
+    "       \"total        937.470237\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">\\n\",\n",
+    "       \"<span style=\\\"color: #00ffaf; text-decoration-color: #00ffaf; font-weight: bold; text-decoration: underline\\\">Values of statistical measures:</span>\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"</pre>\\n\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"\\n\",\n",
+    "       \"\\u001B[1;4;38;5;49mValues of statistical measures:\\u001B[0m\\n\",\n",
+    "       \"\\n\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"text/html\": [\n",
+    "       \"<div>\\n\",\n",
+    "       \"<style scoped>\\n\",\n",
+    "       \"    .dataframe tbody tr th:only-of-type {\\n\",\n",
+    "       \"        vertical-align: middle;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe tbody tr th {\\n\",\n",
+    "       \"        vertical-align: top;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"\\n\",\n",
+    "       \"    .dataframe thead th {\\n\",\n",
+    "       \"        text-align: right;\\n\",\n",
+    "       \"    }\\n\",\n",
+    "       \"</style>\\n\",\n",
+    "       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n",
+    "       \"  <thead>\\n\",\n",
+    "       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n",
+    "       \"      <th></th>\\n\",\n",
+    "       \"      <th>statistical measures</th>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </thead>\\n\",\n",
+    "       \"  <tbody>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>AIC</th>\\n\",\n",
+    "       \"      <td>1872.940474</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"    <tr>\\n\",\n",
+    "       \"      <th>BIC</th>\\n\",\n",
+    "       \"      <td>1874.940474</td>\\n\",\n",
+    "       \"    </tr>\\n\",\n",
+    "       \"  </tbody>\\n\",\n",
+    "       \"</table>\\n\",\n",
+    "       \"</div>\"\n",
+    "      ],\n",
+    "      \"text/plain\": [\n",
+    "       \"     statistical measures\\n\",\n",
+    "       \"AIC           1872.940474\\n\",\n",
+    "       \"BIC           1874.940474\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"application/vnd.jupyter.widget-view+json\": {\n",
+    "       \"model_id\": \"0807d6b1544045ecbd57aed7fe5c914e\",\n",
+    "       \"version_major\": 2,\n",
+    "       \"version_minor\": 0\n",
+    "      },\n",
+    "      \"text/plain\": [\n",
+    "       \"processing MLE analyses:   0%|                                                            | 0/1 [00:00<?, ?it/…\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"name\": \"stderr\",\n",
+    "     \"output_type\": \"stream\",\n",
+    "     \"text\": [\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: divide by zero encountered in divide\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"\\n\",\n",
+    "      \"WARNING RuntimeWarning: invalid value encountered in divide\\n\",\n",
+    "      \"\\n\"\n",
+    "     ]\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 640x480 with 2 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 640x480 with 2 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 640x480 with 2 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    },\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 640x480 with 2 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"#\\n\",\n",
+    "    \"#Initialize empty vectors that will be filled at each step.\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"tmins_lc=np.array([])\\n\",\n",
+    "    \"tmaxs_lc=np.array([])\\n\",\n",
+    "    \"tmeds_lc=np.array([])\\n\",\n",
+    "    \"e_tmeds_lc=np.array([])\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"cts_lc=np.array([])\\n\",\n",
+    "    \"fls=np.array([])\\n\",\n",
+    "    \"e_low_fls=np.array([])\\n\",\n",
+    "    \"e_hi_fls=np.array([])\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"pars_bk=np.array([])\\n\",\n",
+    "    \"epars_bk=np.array([])\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"par_epar=np.array([])\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"for i in range(len(tmaxs)):\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Slice the orientation file into the time interval\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    ori_min = Time(tmins[i],format = 'unix')\\n\",\n",
+    "    \"    ori_max = Time(tmaxs[i],format = 'unix')\\n\",\n",
+    "    \"    sc_orientation = ori.source_interval(ori_min, ori_max)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Find the index of time axes corresponding to the limits of the tmins[i],tmaxs[i].\\n\",\n",
+    "    \"    #These are used to slice the data in time in the fit setup.\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    sou_min = np.where(grb_bkg.binned_data.axes['Time'].edges.value >= tmins[i])[0][0]\\n\",\n",
+    "    \"    sou_max_all = np.where(grb_bkg.binned_data.axes['Time'].edges.value <= tmaxs[i])\\n\",\n",
+    "    \"    y=len(sou_max_all[0])-1\\n\",\n",
+    "    \"    sou_max=np.where(grb_bkg.binned_data.axes['Time'].edges.value <= tmaxs[i])[0][y]\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Save the time bin edges and middle points for plotting later.\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    tmin_lc=grb_bkg.binned_data.axes['Time'].edges.value[sou_min]\\n\",\n",
+    "    \"    tmins_lc=np.append(tmins_lc,tmin_lc)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    tmax_lc=grb_bkg.binned_data.axes['Time'].edges.value[sou_max]\\n\",\n",
+    "    \"    tmaxs_lc=np.append(tmaxs_lc,tmax_lc)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    dt=(tmax_lc-tmin_lc) #total duration of the time bin\\n\",\n",
+    "    \"    tmed_lc=tmin_lc+0.5*dt#midpoint of time bins\\n\",\n",
+    "    \"    tmeds_lc=np.append(tmeds_lc,tmed_lc)#midpoints of the time bin\\n\",\n",
+    "    \"    hdt=dt/2 #half duration of the time bin i.e symmetric error for t_med\\n\",\n",
+    "    \"    e_tmeds_lc=np.append(e_tmeds_lc,hdt)#half duration of the time bin i.e symmetric error for t_med\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Define the background parameters\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    bkg_par = Parameter(\\\"background_cosi\\\",                                                                          # background parameter\\n\",\n",
+    "    \"                    10,                                                                                        # initial value of parameter\\n\",\n",
+    "    \"                    min_value=0,                                                                                # minimum value of parameter\\n\",\n",
+    "    \"                    max_value=10,                                                                                # maximum value of parameter\\n\",\n",
+    "    \"                    delta=1e-3,                                                                                 # initial step used by fitting engine\\n\",\n",
+    "    \"                    desc=\\\"Background parameter for cosi\\\")\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #HERE's the key point. In the fit setup data, the grb_bkg data are sliced in Time in the interval tmins[i],tmaxs[i]\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    cosi = COSILike(\\\"cosi\\\",                                                                                         # COSI 3ML plugin\\n\",\n",
+    "    \"                    dr = dr,                                                                                        # detector response\\n\",\n",
+    "    \"                    data = grb_bkg.binned_data.slice[{'Time':slice(sou_min,sou_max)}].project('Em', 'Phi', 'PsiChi'), # data (source+background)\\n\",\n",
+    "    \"                    #bkg = bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em', 'Phi', 'PsiChi'), \\n\",\n",
+    "    \"                    bkg = bkg.binned_data.project('Em', 'Phi', 'PsiChi'),# background model \\n\",\n",
+    "    \"                    sc_orientation = sc_orientation,                                                                # spacecraft orientation\\n\",\n",
+    "    \"                    nuisance_param = bkg_par)  # background parameter\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    \\n\",\n",
+    "    \"    source = PointSource(\\\"source\\\",                 # Name of source (arbitrary, but needs to be unique)\\n\",\n",
+    "    \"                     l = l,                        # Longitude (deg)\\n\",\n",
+    "    \"                     b = b,                        # Latitude (deg)\\n\",\n",
+    "    \"                     spectral_shape = spectrum)    # Spectral model\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    model = Model(source)  # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"    # Optional: if you want to call get_log_like manually, then you also need to set the model manually\\n\",\n",
+    "    \"    # 3ML does this internally during the fit though\\n\",\n",
+    "    \"    cosi.set_model(model)    \\n\",\n",
+    "    \"    plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    like = JointLikelihood(model, plugins, verbose = False)\\n\",\n",
+    "    \"    like.fit()\\n\",\n",
+    "    \"    results = like.results\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Obtain counts in time slices:\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    cts=np.sum(grb_bkg.binned_data.slice[{'Time':slice(sou_min,sou_max)}])\\n\",\n",
+    "    \"    cts_lc=np.append(cts_lc,cts)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Obtain parameters:\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #bk\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    par_bk=results.get_variates(\\\"background_cosi\\\").median\\n\",\n",
+    "    \"    pars_bk=np.append(pars_bk,par_bk)\\n\",\n",
+    "    \"    epar_bk=results.get_variates(\\\"background_cosi\\\").std\\n\",\n",
+    "    \"    epars_bk=np.append(epars_bk,epar_bk)   \\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #These are dictionaries of parameters values and errors.\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    par_bf= {par.name:results.get_variates(par.path).median\\n\",\n",
+    "    \"                  for par in results.optimized_model[\\\"source\\\"].parameters.values()\\n\",\n",
+    "    \"                  if par.free}\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    epar_bf= {par.name:results.get_variates(par.path).std\\n\",\n",
+    "    \"                  for par in results.optimized_model[\\\"source\\\"].parameters.values()\\n\",\n",
+    "    \"                  if par.free}\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    par_list = list(par_bf.keys()) #This is a list of the parameter names.\\n\",\n",
+    "    \"    for j in range(len(par_list)):\\n\",\n",
+    "    \"        par_epar=np.append(par_epar,par_bf[par_list[j]])\\n\",\n",
+    "    \"        par_epar=np.append(par_epar,epar_bf[par_list[j]])         \\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Obtain fluxes:\\n\",\n",
+    "    \"    #Here I use the 3ML method to ge integrated flux in an energy range.\\n\",\n",
+    "    \"    #I use the energy range from the data 100--10000 keV\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    threeML_config.point_source.integrate_flux_method = \\\"trapz\\\"\\n\",\n",
+    "    \"    result_fl=results.get_flux(\\n\",\n",
+    "    \"        ene_min=100. * u.keV,\\n\",\n",
+    "    \"        ene_max= 10000.* u.keV,\\n\",\n",
+    "    \"        confidence_level=0.95,\\n\",\n",
+    "    \"        sum_sources=True,\\n\",\n",
+    "    \"        flux_unit=\\\"1/(cm2 s)\\\"\\n\",\n",
+    "    \"    )\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    fl=result_fl[\\\"flux\\\"].values[0].value\\n\",\n",
+    "    \"    fls=np.append(fls,fl)\\n\",\n",
+    "    \"    e_low_fl=np.abs(result_fl[\\\"low bound\\\"].values[0].value-fl)\\n\",\n",
+    "    \"    e_low_fls=np.append(e_low_fls, e_low_fl)\\n\",\n",
+    "    \"    e_hi_fl=result_fl[\\\"hi bound\\\"].values[0].value-fl\\n\",\n",
+    "    \"    e_hi_fls=np.append(e_hi_fls, e_hi_fl)\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    #Save a plot of the current fit.\\n\",\n",
+    "    \"    #\\n\",\n",
+    "    \"    sliced_data=grb_bkg.binned_data.slice[{'Time':slice(sou_min,sou_max)}]\\n\",\n",
+    "    \"    expectation = cosi._expected_counts['source']\\n\",\n",
+    "    \"    cts_exp=expectation.project('Em').todense().contents + (bkg_par.value * bkg.binned_data.project('Em').todense().contents)\\n\",\n",
+    "    \"    plot_filename=str(\\\"fit_\\\"+str(i)+\\\".pdf\\\")\\n\",\n",
+    "    \"    plot_fit(sliced_data, cts_exp, plot_filename)\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"#Save lc in in a text file:\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"lc=np.vstack((tmeds_lc,e_tmeds_lc,cts_lc,fls,e_low_fls,e_hi_fls,pars_bk,epars_bk)).T\\n\",\n",
+    "    \"nbins=len(tmeds_lc)\\n\",\n",
+    "    \"npars=2*len(par_list)\\n\",\n",
+    "    \"lc_par=par_epar.reshape(nbins,npars)\\n\",\n",
+    "    \"lc_all=np.hstack((lc,lc_par))\\n\",\n",
+    "    \"fl_list=['t','e_t','cts','fl','e+_fl','e-_fl','bk','e_bk']\\n\",\n",
+    "    \"par_list = list(par_bf.keys())\\n\",\n",
+    "    \"header=fl_list+par_list\\n\",\n",
+    "    \"np.savetxt(\\\"spec_lc.dat\\\", lc_all, delimiter=\\\" \\\",header=str(header))\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"\\n\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"markdown\",\n",
+    "   \"id\": \"e9088224\",\n",
+    "   \"metadata\": {},\n",
+    "   \"source\": [\n",
+    "    \"## Plotting the time series.\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"Now we plot the time-series of fluxes, counts and fitted parameters. We convert the time in mjd for plotting. \\n\",\n",
+    "    \"We use the raw lightcurve and the average flux injected as comparison. We plot the counts in each time bins to check that the fits had a reasonable statistics. In the future, we may be able to compute a goodness of fit in each time bin with 3ML.\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 120,\n",
+    "   \"id\": \"e45ac8c4\",\n",
+    "   \"metadata\": {\n",
+    "    \"scrolled\": true\n",
+    "   },\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 826.772x1169.29 with 3 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"time_mjd=Time(time,format='unix').mjd\\n\",\n",
+    "    \"tmeds_mjd=Time(tmeds_lc, format='unix').mjd\\n\",\n",
+    "    \"e_tmeds_mjd=TimeDelta(e_tmeds_lc, format='sec').to('day').value\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"cm = 1/2.54\\n\",\n",
+    "    \"fig, axs = plt.subplots(3, 1, sharex=True, figsize=(21*cm, 29.7*cm))\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"# Raw Lightcurve\\n\",\n",
+    "    \"axs[0].scatter (time_mjd, rate)\\n\",\n",
+    "    \"axs[0].set_title('Raw lightcurve')\\n\",\n",
+    "    \"axs[0].set_ylabel('Rate (cts/s)')\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"# Flux Lightcurve\\n\",\n",
+    "    \"axs[1].errorbar(tmeds_mjd, fls, xerr=e_tmeds_mjd, yerr=[e_low_fls,e_hi_fls],fmt='o', capsize=1)\\n\",\n",
+    "    \"axs[1].set_title('Flux[0.1-10 MeV] LightCurve')\\n\",\n",
+    "    \"axs[1].set_ylabel('Flux (Photons/s/cm2)')\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"#Counts in log scale. To check that the fits have reasonable statistics.\\n\",\n",
+    "    \"axs[2].set_yscale('log')\\n\",\n",
+    "    \"axs[2].step(tmeds_mjd, cts_lc, where='mid',color='purple')\\n\",\n",
+    "    \"axs[2].errorbar (tmeds_mjd, cts_lc,xerr=e_tmeds_mjd,fmt='o', capsize=1)\\n\",\n",
+    "    \"axs[2].set_title('Counts')\\n\",\n",
+    "    \"axs[2].set_xlabel('Time (MJD)')\\n\",\n",
+    "    \"axs[2].axhline(y=100, color='red', linestyle='--')\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"# Adjust spacing between subplots\\n\",\n",
+    "    \"plt.tight_layout()\\n\",\n",
+    "    \"plt.savefig(\\\"raw_flux_counts_lc.pdf\\\", dpi=300)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": 121,\n",
+    "   \"id\": \"1c99dbd5\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [\n",
+    "    {\n",
+    "     \"data\": {\n",
+    "      \"image/png\": 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\",\n",
+    "      \"text/plain\": [\n",
+    "       \"<Figure size 826.772x1169.29 with 2 Axes>\"\n",
+    "      ]\n",
+    "     },\n",
+    "     \"metadata\": {},\n",
+    "     \"output_type\": \"display_data\"\n",
+    "    }\n",
+    "   ],\n",
+    "   \"source\": [\n",
+    "    \"#\\n\",\n",
+    "    \"#Plot the free parameters as a function of time.\\n\",\n",
+    "    \"#\\n\",\n",
+    "    \"npar=len(par_list)\\n\",\n",
+    "    \"cm = 1/2.54 \\n\",\n",
+    "    \"fig, axs = plt.subplots(npar+1, 1, sharex=True, figsize=(21*cm, 29.7*cm))\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"h=np.arange(0, 2*npar, 2)\\n\",\n",
+    "    \"for i in range(npar):\\n\",\n",
+    "    \"    axs[i].errorbar(tmeds_mjd, lc_par[:,h[i]], xerr=e_tmeds_mjd, yerr=lc_par[:,h[i]+1], fmt='o', capsize=5)\\n\",\n",
+    "    \"    axs[i].set_ylabel(par_list[i])\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"axs[npar].errorbar(tmeds_mjd, pars_bk, xerr=e_tmeds_mjd, yerr=epars_bk, fmt='o')\\n\",\n",
+    "    \"axs[npar].set_title('Bakground COSI')\\n\",\n",
+    "    \"axs[npar].set_xlabel('Time (MJD)')\\n\",\n",
+    "    \"axs[npar].set_ylabel('Bk parameter')\\n\",\n",
+    "    \"\\n\",\n",
+    "    \"# Adjust spacing between subplots\\n\",\n",
+    "    \"plt.tight_layout()\\n\",\n",
+    "    \"plt.savefig(\\\"specpars_bk_lc.pdf\\\", dpi=300)\"\n",
+    "   ]\n",
+    "  },\n",
+    "  {\n",
+    "   \"cell_type\": \"code\",\n",
+    "   \"execution_count\": null,\n",
+    "   \"id\": \"2e620553-9b08-4c90-81e3-481111bf68e4\",\n",
+    "   \"metadata\": {},\n",
+    "   \"outputs\": [],\n",
+    "   \"source\": []\n",
+    "  }\n",
+    " ],\n",
+    " \"metadata\": {\n",
+    "  \"kernelspec\": {\n",
+    "   \"display_name\": \"Python 3 (ipykernel)\",\n",
+    "   \"language\": \"python\",\n",
+    "   \"name\": \"python3\"\n",
+    "  },\n",
+    "  \"language_info\": {\n",
+    "   \"codemirror_mode\": {\n",
+    "    \"name\": \"ipython\",\n",
+    "    \"version\": 3\n",
+    "   },\n",
+    "   \"file_extension\": \".py\",\n",
+    "   \"mimetype\": \"text/x-python\",\n",
+    "   \"name\": \"python\",\n",
+    "   \"nbconvert_exporter\": \"python\",\n",
+    "   \"pygments_lexer\": \"ipython3\",\n",
+    "   \"version\": \"3.10.19\"\n",
+    "  }\n",
+    " },\n",
+    " \"nbformat\": 4,\n",
+    " \"nbformat_minor\": 5\n",
+    "}\n"
+   ],
+   "id": "4a4ab34b596bd4d4"
+  }
+ ],
+ "metadata": {},
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+}