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‎ReleaseNotes‎

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Release v2.3.1, Mon 17 Jul 2023
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=======================================================
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* fixed bug for reading QNUMBERS from SLHA files
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* small fixes in how pythia6 and pythia8 are built
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* small fix in truncated Gaussian llhd experimental feature
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* small fix in computation of combined upper limits
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* combinationmatrices are now forced to be symmetric
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* added isCombinableWith method also for CombinedDataSets
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* added a recipe for how to use a combinations matrix
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* runtime.nCPUs() now returns number of available CPUs, not all CPUs
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* xseccomputer now has --tempdir option
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* StatsComputer now has CLs method
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* changed default prompt width from 1e-8 to 1e-11 GeV in code
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Release v2.3.0, Tue 23 May 2023
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=======================================================
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‎docs/manual/source/ConfrontPredictions.rst‎

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The individual likelihoods can correspond to the best signal region, if the combination of signal regions is turned off, or to the combined signal region likelihood otherwise.
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For the determination of the maximum likelihood, `scipy.optimize.minimize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html>`_ is used
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with the method `BFGS
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<https://docs.scipy.org/doc/scipy/reference/optimize.minimize-bfgs.html#optimize-minimize-bfgs>`_ in order to compute :math:`\mu_{max}` and :math:`\mathcal{L}_{max} = \mathcal{L}_{C}(\mu_{max})`.
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<https://docs.scipy.org/doc/scipy/reference/optimize.minimize-bfgs.html#optimize-minimize-bfgs>`_ in order to compute :math:`\hat{\mu}` and :math:`\mathcal{L}_{max} = \mathcal{L}_{C}(\hat{\mu})`.
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The resulting likelihood and :math:`r`-values for the combination are displayed in the :ref:`output <outputDescription>` along with the individual results for each analysis.
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‎docs/manual/source/Examples.rst‎

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* `How to plot likelihoods from analysis combinations <plotCombinedLikelihood.html>`_ (download the Python code :download:`here <recipes/plotCombinedLikelihood.py>`, IPython notebook :download:`here <recipes/plotCombinedLikelihood.ipynb>`)
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* `How to use a combinations matrix <combinationsmatrix.html>`_ (download the Python code :download:`here <recipes/combinationsmatrix.py>`, IPython notebook :download:`here <recipes/combinationsmatrix.ipynb>`)
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Examples using the cross-section computer:
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------------------------------------------

‎docs/manual/source/ReleaseUpdate.rst‎

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==========
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The major novelties of all releases since v1.0 are as follows:
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New in Version 2.3.1:
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^^^^^^^^^^^^^^^^^^^^^
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* fixed bug for reading :ref:`QNUMBERS blocks <qnumberSLHA>` from SLHA files
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* small fixes in how pythia6 and pythia8 are built
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* small fix in truncated Gaussian llhd experimental feature
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* small fix in computation of combined upper limits
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* combinationmatrices are now forced to be symmetric
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* added isCombinableWith method also for CombinedDataSets
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* added a recipe for how to use a `combinations matrix <combinationsmatrix.html>`_
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* `runtime.nCPUs() <tools.html#tools.runtime.nCPUs>`_ now returns number of available CPUs, not all CPUs
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* :ref:`xsecComputer <xsecCalc>` now has --tempdir option
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* StatsComputer now has `CLs <tools.html#tools.statsTools.StatsComputer.CLs>`_ method
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* changed default prompt width from 1e-8 to 1e-11 GeV in code
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New in Version 2.3.0:
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^^^^^^^^^^^^^^^^^^^^^

‎docs/manual/source/recipes/combinationsmatrix.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# How To: Use a combinations matrix to keep track of combinabilities"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Preparations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set up the path to SModelS installation folder\n",
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"import sys; sys.path.append(\".\"); import smodels_paths"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import those parts of smodels that are needed for this exercise\n",
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"from smodels.tools.physicsUnits import GeV\n",
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"from smodels.experiment.databaseObj import Database\n",
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"from smodels.theory.theoryPrediction import theoryPredictionsFor, TheoryPredictionsCombiner\n",
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"from smodels.theory import decomposer\n",
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"from smodels.tools.physicsUnits import fb\n",
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"from smodels.particlesLoader import BSMList\n",
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"from smodels.share.models.SMparticles import SMList\n",
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"from smodels.theory.model import Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# a combinations matrix is just a dictionary with combinable analyses as both keys and values.\n",
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"# we assume symmetricity! (if A is combinable with B, then B is combinable with A)\n",
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"# Also, CMS results are automatically assumed to be combinable with ATLAS results,\n",
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"# and 8 TeV with 13 TeV results.\n",
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"combinationsmatrix = { \"ATLAS-SUSY-2018-41\": [ \"ATLAS-SUSY-2019-08\"]} \n",
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"# also define a list of analyses we are interested in\n",
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"analyses = [ \"CMS-SUS-21-002\", \"ATLAS-SUSY-2018-41\", \"ATLAS-SUSY-2019-08\", \"CMS-SUS-20-004\"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"here is a visualisation of the combinations matrix for the analyses under consideration:"
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]
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},
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{
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"attachments": {
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"matrix-2.png": {
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"image/png": 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"
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}
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![matrix-2.png](attachment:matrix-2.png)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"we have selected 4 analyses\n"
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]
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}
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],
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"source": [
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"## Load the official database:\n",
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"db = Database(\"official\", combinationsmatrix=combinationsmatrix)\n",
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"results = db.getExpResults(analysisIDs=analyses, dataTypes = [ \"efficiencyMap\"])\n",
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"print ( f\"we have selected {len(results)} analyses\" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['ATLAS-SUSY-2018-41',\n",
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" 'ATLAS-SUSY-2019-08',\n",
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" 'CMS-SUS-20-004',\n",
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" 'CMS-SUS-21-002']"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"[ x.globalInfo.id for x in results ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# the theory we will be using\n",
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"inputFile = \"inputFiles/slha/ew_ymi2l51r.slha\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# prepare the model\n",
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"model = Model(BSMparticles=BSMList, SMparticles=SMList)\n",
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"model.updateParticles(inputFile=inputFile)\n",
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"# obtain the decomposed model\n",
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"toplist = decomposer.decompose(model,sigmacut=0.*fb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## run SModelS"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get the predictions\n",
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"predictions = list ( theoryPredictionsFor(results, toplist, combinedResults=True ) )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('ATLAS-SUSY-2018-41', 'r_exp=', 0.869),\n",
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" ('ATLAS-SUSY-2019-08', 'r_exp=', 0.961),\n",
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" ('CMS-SUS-21-002', 'r_exp=', 1.158)]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# let's have a quick look at the predictions, and the (expected) r-values\n",
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"[ (x.dataset.globalInfo.id, \"r_exp=\", round(x.getRValue(expected=True),3)) for x in predictions ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"can I combine ATLAS-SUSY-2018-41 with ATLAS-SUSY-2019-08? yes\n",
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"can I combine ATLAS-SUSY-2018-41 with CMS-SUS-21-002? yes\n",
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"can I combine ATLAS-SUSY-2019-08 with CMS-SUS-21-002? yes\n"
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]
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}
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],
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"source": [
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"for i,pr1 in enumerate(predictions[:-1]):\n",
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" id1 = pr1.dataset.globalInfo.id\n",
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" for pr2 in predictions[i+1:]:\n",
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" id2 = pr2.dataset.globalInfo.id\n",
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" combinable = pr1.dataset.isCombinableWith(pr2.dataset)\n",
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" print ( f\"can I combine {id1} with {id2}? {'yes' if combinable else 'no'}\" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## combine!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# given [10] from above, we are allowed to combine the three analyses listed below\n",
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"analyses = [ \"CMS-SUS-21-002\", \"ATLAS-SUSY-2018-41\", \"ATLAS-SUSY-2019-08\"]\n",
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"combiner = TheoryPredictionsCombiner.selectResultsFrom ( predictions, analyses )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.674"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# the combined expected r value must be above the individual expected r-values\n",
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"round(combiner.getRValue(expected=True),3)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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